- g1(double, double) - Method in class weka.classifiers.functions.pace.PaceMatrix
-
Constructs the Givens rotation
- g2(double[], int, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
-
Performs the Givens rotation
- GABitSet() - Constructor for class weka.attributeSelection.GeneticSearch.GABitSet
-
Constructor
- gain(double[][], double) - Method in class weka.classifiers.trees.RandomTree
-
Computes value of splitting criterion after split.
- gain(double[][], double) - Method in class weka.classifiers.trees.REPTree.Tree
-
Computes value of splitting criterion after split.
- gainRatio() - Method in class weka.classifiers.trees.j48.BinC45Split
-
Returns (C4.5-type) gain ratio for the generated split.
- gainRatio() - Method in class weka.classifiers.trees.j48.C45Split
-
Returns (C4.5-type) gain ratio for the generated split.
- gainRatio(double[][]) - Static method in class weka.core.ContingencyTables
-
Computes gain ratio for contingency table (split on rows).
- GainRatioAttributeEval - Class in weka.attributeSelection
-
GainRatioAttributeEval :
Evaluates the worth of an attribute by measuring the gain ratio with respect to the class.
GainR(Class, Attribute) = (H(Class) - H(Class | Attribute)) / H(Attribute).
Valid options are:
- GainRatioAttributeEval() - Constructor for class weka.attributeSelection.GainRatioAttributeEval
-
Constructor
- GainRatioSplitCrit - Class in weka.classifiers.trees.j48
-
Class for computing the gain ratio for a given distribution.
- GainRatioSplitCrit() - Constructor for class weka.classifiers.trees.j48.GainRatioSplitCrit
-
- gamma(double) - Static method in class weka.core.Statistics
-
Returns the Gamma function of the argument.
- gammaTipText() - Method in class weka.classifiers.functions.LibSVM
-
Returns the tip text for this property
- gammaTipText() - Method in class weka.classifiers.functions.supportVector.RBFKernel
-
Returns the tip text for this property
- GAUSS - Static variable in class weka.classifiers.lazy.LWL
-
- GAUSSIAN - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
-
Distributions available
- GAUSSIAN - Static variable in class weka.datagenerators.clusterers.SubspaceCluster
-
cluster type: gaussian
- GAUSSIAN - Static variable in class weka.filters.unsupervised.attribute.RandomProjection
-
distribution type: gaussian
- GaussianPriorImpl - Class in weka.classifiers.bayes.blr
-
Implementation of the Gaussian Prior update function based on
CLG Algorithm with a certain Trust Region Update.
- GaussianPriorImpl() - Constructor for class weka.classifiers.bayes.blr.GaussianPriorImpl
-
- GaussianProcesses - Class in weka.classifiers.functions
-
Implements Gaussian Processes for regression without hyperparameter-tuning.
- GaussianProcesses() - Constructor for class weka.classifiers.functions.GaussianProcesses
-
the default constructor
- GE - Static variable in interface weka.core.mathematicalexpression.sym
-
- GE - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
-
- GeneralizedSequentialPatterns - Class in weka.associations
-
Class implementing a GSP algorithm for discovering sequential patterns in a sequential data set.
The attribute identifying the distinct data sequences contained in the set can be determined by the respective option.
- GeneralizedSequentialPatterns() - Constructor for class weka.associations.GeneralizedSequentialPatterns
-
Constructor.
- GeneralRegression - Class in weka.classifiers.pmml.consumer
-
Class implementing import of PMML General Regression model.
- GeneralRegression(Element, Instances, MiningSchema) - Constructor for class weka.classifiers.pmml.consumer.GeneralRegression
-
Constructs a GeneralRegression classifier.
- generate() - Method in class weka.classifiers.meta.GridSearch.PerformanceTable
-
generates the table
- generate() - Method in class weka.core.Javadoc
-
generates either the plain Javadoc (if no filename specified) or the
updated file (if a filename is specified).
- generate() - Method in class weka.core.ListOptions
-
generates the options string.
- generate() - Method in class weka.core.TestInstances
-
Generates a new dataset
- generate(String) - Method in class weka.core.TestInstances
-
generates a new dataset.
- generateArtificialData(int, Instances) - Method in class weka.classifiers.meta.Decorate
-
Generate artificial training examples.
- generateAttribute(int, int, String) - Method in class weka.core.TestInstances
-
creates a new Attribute of the given type
- generateAttributeValue(Instances, int, double) - Method in class weka.core.TestInstances
-
Generates a new value for the specified attribute.
- generateClassValue(Instances) - Method in class weka.core.TestInstances
-
Generates the class value
- generateDistribution() - Method in class weka.associations.PriorEstimation
-
Calculates the prior distribution.
- generateExample() - Method in class weka.datagenerators.classifiers.classification.Agrawal
-
Generates one example of the dataset.
- generateExample() - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
Generates one example of the dataset.
- generateExample() - Method in class weka.datagenerators.classifiers.classification.LED24
-
Generates one example of the dataset.
- generateExample() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
-
Generates one example of the dataset.
- generateExample() - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Generate an example of the dataset dataset.
- generateExample() - Method in class weka.datagenerators.classifiers.regression.Expression
-
Generates one example of the dataset.
- generateExample() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Generates one example of the dataset.
- generateExample() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Generate an example of the dataset.
- generateExample() - Method in class weka.datagenerators.clusterers.SubspaceCluster
-
Generate an example of the dataset.
- generateExample() - Method in class weka.datagenerators.DataGenerator
-
Generates one example of the dataset.
- generateExamples() - Method in class weka.datagenerators.classifiers.classification.Agrawal
-
Generates all examples of the dataset.
- generateExamples() - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
Generates all examples of the dataset.
- generateExamples() - Method in class weka.datagenerators.classifiers.classification.LED24
-
Generates all examples of the dataset.
- generateExamples() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
-
Generates all examples of the dataset.
- generateExamples() - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Generate all examples of the dataset.
- generateExamples(int, Random, Instances) - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Generate all examples of the dataset.
- generateExamples() - Method in class weka.datagenerators.classifiers.regression.Expression
-
Generates all examples of the dataset.
- generateExamples() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Generates all examples of the dataset.
- generateExamples() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Generate all examples of the dataset.
- generateExamples(Random, Instances) - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Generate all examples of the dataset.
- generateExamples() - Method in class weka.datagenerators.clusterers.SubspaceCluster
-
Generate all examples of the dataset.
- generateExamples() - Method in class weka.datagenerators.DataGenerator
-
Generates all examples of the dataset.
- generateFinished() - Method in class weka.datagenerators.classifiers.classification.Agrawal
-
Generates a comment string that documentats the data generator.
- generateFinished() - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
Generates a comment string that documentats the data generator.
- generateFinished() - Method in class weka.datagenerators.classifiers.classification.LED24
-
Generates a comment string that documentats the data generator.
- generateFinished() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
-
Generates a comment string that documentats the data generator.
- generateFinished() - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Compiles documentation about the data generation.
- generateFinished() - Method in class weka.datagenerators.classifiers.regression.Expression
-
Generates a comment string that documentats the data generator.
- generateFinished() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Generates a comment string that documentats the data generator.
- generateFinished() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Compiles documentation about the data generation after
the generation process
- generateFinished() - Method in class weka.datagenerators.clusterers.SubspaceCluster
-
Compiles documentation about the data generation after
the generation process
- generateFinished() - Method in class weka.datagenerators.DataGenerator
-
Generates a comment string that documentates the data generator.
- generateGroupsFromNumbers(Instances, Random) - Method in class weka.classifiers.meta.RotationForest
-
generates the groups of attributes, given their minimum and maximum
numbers.
- generateGroupsFromSizes(Instances, Random) - Method in class weka.classifiers.meta.RotationForest
-
generates the groups of attributes, given their minimum and maximum
sizes.
- generateHelp() - Method in class weka.core.Javadoc
-
generates a string to print as help on the console
- generateHelp() - Method in class weka.core.ListOptions
-
generates a string to print as help on the console
- generateID() - Method in class weka.core.TechnicalInformation
-
Generates an ID based on the current settings and returns it.
- generateInstances() - Method in class weka.classifiers.bayes.net.BayesNetGenerator
-
GenerateInstances generates random instances sampling from the
distribution represented by the Bayes network structure.
- generateInstances(int[]) - Method in interface weka.gui.boundaryvisualizer.DataGenerator
-
Generate an instance.
- generateInstances(int[]) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
-
Generates a new instance using one kernel estimator.
- generateInstances() - Method in class weka.gui.explorer.PreprocessPanel
-
sets Instances generated via DataGenerators (pops up a Dialog)
- generateJavadoc(int) - Method in class weka.core.AllJavadoc
-
generates and returns the Javadoc for the specified start/end tag pair.
- generateJavadoc(int) - Method in class weka.core.GlobalInfoJavadoc
-
generates and returns the Javadoc for the specified start/end tag pair.
- generateJavadoc(int) - Method in class weka.core.Javadoc
-
generates and returns the Javadoc for the specified start/end tag pair.
- generateJavadoc() - Method in class weka.core.Javadoc
-
generates and returns the Javadoc
- generateJavadoc(int) - Method in class weka.core.OptionHandlerJavadoc
-
generates and returns the Javadoc for the specified start/end tag pair.
- generateJavadoc(int) - Method in class weka.core.TechnicalInformationHandlerJavadoc
-
generates and returns the Javadoc for the specified start/end tag pair.
- generateKCandidates(FastVector) - Static method in class weka.associations.gsp.Sequence
-
Generates candidate k-Sequences on the basis of a given (k-1)-Sequence set.
- generateMetaLevel(Instances, Random) - Method in class weka.classifiers.meta.Grading
-
Generates the meta data
- generateMetaLevel(Instances, Random) - Method in class weka.classifiers.meta.Stacking
-
Generates the meta data
- generateMetaLevel(Instances, Random) - Method in class weka.classifiers.meta.StackingC
-
Method that builds meta level.
- generateOutput() - Method in class weka.gui.visualize.BMPWriter
-
generates the actual output
- generateOutput() - Method in class weka.gui.visualize.JComponentWriter
-
generates the actual output
- generateOutput() - Method in class weka.gui.visualize.JPEGWriter
-
generates the actual output.
- generateOutput() - Method in class weka.gui.visualize.PNGWriter
-
generates the actual output
- generateOutput() - Method in class weka.gui.visualize.PostscriptWriter
-
generates the actual output
- generateOutputProperties() - Method in class weka.gui.GenericPropertiesCreator
-
fills in all the classes (based on the packages in the input properties
file) into the output properties file
- generateRandomNetwork() - Method in class weka.classifiers.bayes.net.BayesNetGenerator
-
Generate random connected Bayesian network with discrete nodes
having all the same cardinality.
- generateRandomNetworkStructure(int, int) - Method in class weka.classifiers.bayes.net.BayesNetGenerator
-
GenerateRandomNetworkStructure generate random connected Bayesian network
- generateRandomNumber(int) - Method in class weka.attributeSelection.ScatterSearchV1
-
- generateRankingTipText() - Method in class weka.attributeSelection.GreedyStepwise
-
Returns the tip text for this property
- generateRankingTipText() - Method in class weka.attributeSelection.RaceSearch
-
Returns the tip text for this property
- generateRankingTipText() - Method in class weka.attributeSelection.Ranker
-
Returns the tip text for this property
- GenerateReferenceSet(List<ScatterSearchV1.Subset>, int, int) - Method in class weka.attributeSelection.ScatterSearchV1
-
Generate the a ReferenceSet containing the n best solutions and the m most diverse solutions of
the initial Population.
- generateRuleItem(ItemSet, ItemSet, Instances, int, int, double[], Hashtable) - Method in class weka.associations.RuleItem
-
Constructs a new RuleItem if the support of the given rule is above the support threshold.
- generateRules(double, FastVector, int) - Method in class weka.associations.AprioriItemSet
-
Generates all rules for an item set.
- generateRules(int, double[], Hashtable, double, Instances, TreeSet, int) - Method in class weka.associations.CaRuleGeneration
-
Generates all rules for an item set.
- generateRules(double, boolean) - Method in class weka.associations.LabeledItemSet
-
Generates rules out of item sets
- generateRules(int, double[], Hashtable, double, Instances, TreeSet, int) - Method in class weka.associations.RuleGeneration
-
Generates all rules for an item set.
- generateRulesBruteForce(double, int, FastVector, int, int, double) - Method in class weka.associations.AprioriItemSet
-
Generates all significant rules for an item set.
- generateRulesBruteForce(FPGrowth.FrequentItemSets, FPGrowth.AssociationRule.METRIC_TYPE, double, int, int, int) - Static method in class weka.associations.FPGrowth.AssociationRule
-
Generate all association rules, from the supplied frequet item sets,
that meet a given minimum metric threshold.
- generateRulesTipText() - Method in class weka.classifiers.trees.m5.M5Base
-
Returns the tip text for this property
- generateStart() - Method in class weka.datagenerators.classifiers.classification.Agrawal
-
Generates a comment string that documentates the data generator.
- generateStart() - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
Generates a comment string that documentates the data generator.
- generateStart() - Method in class weka.datagenerators.classifiers.classification.LED24
-
Generates a comment string that documentates the data generator.
- generateStart() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
-
Generates a comment string that documentates the data generator.
- generateStart() - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Generates a comment string that documentates the data generator.
- generateStart() - Method in class weka.datagenerators.classifiers.regression.Expression
-
Generates a comment string that documentates the data generator.
- generateStart() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Generates a comment string that documentates the data generator.
- generateStart() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Compiles documentation about the data generation before
the generation process
- generateStart() - Method in class weka.datagenerators.clusterers.SubspaceCluster
-
Compiles documentation about the data generation before
the generation process
- generateStart() - Method in class weka.datagenerators.DataGenerator
-
Generates a comment string that documentates the data generator.
- generateSubset(Instances, Range) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
-
generates a subset of the dataset with only the attributes from the range
(class is always added if present).
- GeneratorPropertyIteratorPanel - Class in weka.gui.experiment
-
This panel controls setting a list of values for an arbitrary
resultgenerator property for an experiment to iterate over.
- GeneratorPropertyIteratorPanel() - Constructor for class weka.gui.experiment.GeneratorPropertyIteratorPanel
-
Creates the property iterator panel initially disabled.
- GeneratorPropertyIteratorPanel(Experiment) - Constructor for class weka.gui.experiment.GeneratorPropertyIteratorPanel
-
Creates the property iterator panel and sets the experiment.
- GenericArrayEditor - Class in weka.gui
-
A PropertyEditor for arrays of objects that themselves have
property editors.
- GenericArrayEditor() - Constructor for class weka.gui.GenericArrayEditor
-
Sets up the array editor.
- GenericObjectEditor - Class in weka.gui
-
A PropertyEditor for objects.
- GenericObjectEditor() - Constructor for class weka.gui.GenericObjectEditor
-
Default constructor.
- GenericObjectEditor(boolean) - Constructor for class weka.gui.GenericObjectEditor
-
Constructor that allows specifying whether it is possible
to change the class within the editor dialog.
- GenericObjectEditor.CapabilitiesFilterDialog - Class in weka.gui
-
A dialog for selecting Capabilities to look for in the GOE tree.
- GenericObjectEditor.GOEPanel - Class in weka.gui
-
Handles the GUI side of editing values.
- GenericObjectEditor.GOETreeNode - Class in weka.gui
-
A specialized TreeNode for supporting filtering via Capabilities.
- GenericObjectEditor.JTreePopupMenu - Class in weka.gui
-
Creates a popup menu containing a tree that is aware
of the screen dimensions.
- GenericPropertiesCreator - Class in weka.gui
-
This class can generate the properties object that is normally loaded from
the GenericObjectEditor.props
file (= PROPERTY_FILE).
- GenericPropertiesCreator() - Constructor for class weka.gui.GenericPropertiesCreator
-
initializes the creator, locates the props file with the Utils class.
- GenericPropertiesCreator(String) - Constructor for class weka.gui.GenericPropertiesCreator
-
initializes the creator, the given file overrides the props-file search
of the Utils class
- GeneticSearch - Class in weka.attributeSelection
-
GeneticSearch:
Performs a search using the simple genetic algorithm described in Goldberg (1989).
For more information see:
David E.
- GeneticSearch() - Constructor for class weka.attributeSelection.GeneticSearch
-
Constructor.
- GeneticSearch - Class in weka.classifiers.bayes.net.search.global
-
This Bayes Network learning algorithm uses genetic search for finding a well scoring Bayes network structure.
- GeneticSearch() - Constructor for class weka.classifiers.bayes.net.search.global.GeneticSearch
-
- GeneticSearch - Class in weka.classifiers.bayes.net.search.local
-
This Bayes Network learning algorithm uses genetic search for finding a well scoring Bayes network structure.
- GeneticSearch() - Constructor for class weka.classifiers.bayes.net.search.local.GeneticSearch
-
- GeneticSearch.GABitSet - Class in weka.attributeSelection
-
A bitset for the genetic algorithm
- get(int) - Method in class weka.attributeSelection.GeneticSearch.GABitSet
-
get the value of a bit in the chromosome
- get(int, GridSearch.PointDouble) - Method in class weka.classifiers.meta.GridSearch.PerformanceCache
-
returns a cached performance object, null if not yet in the cache
- get(int) - Method in class weka.core.matrix.DoubleVector
-
Gets a single element.
- get(int) - Method in class weka.core.matrix.IntVector
-
Gets the value of an element.
- get(int, int) - Method in class weka.core.matrix.Matrix
-
Get a single element.
- get(int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList
-
Returns an element at the specified index in
the list.
- get() - Method in class weka.core.neighboursearch.CoverTree.MyHeap
-
returns the first element and removes it from the heap.
- get() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.MyHeap
-
returns the first element and removes it from the heap.
- get(int) - Method in class weka.core.PropertyPath.Path
-
returns the element at the given index
- get(int) - Method in class weka.core.Tee
-
returns the specified PrintStream from the list.
- get(String) - Method in class weka.core.xml.MethodHandler
-
returns the stored method for the given property
- get(Class) - Method in class weka.core.xml.MethodHandler
-
returns the stored method for the given class
- get(int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
-
Returns the element at the specified position in this list.
- get(String, String) - Static method in class weka.gui.experiment.ExperimenterDefaults
-
returns the value for the specified property, if non-existent then the
default value.
- get(String, String) - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns the value for the specified property, if non-existent then the
default value.
- get_scale(double) - Method in class weka.core.neighboursearch.CoverTree
-
Finds the scale/level of a given value.
- getAboutPanel() - Method in class weka.gui.PropertySheetPanel
-
Return the panel containing global info and help for
the object being edited.
- getAccu() - Method in class weka.classifiers.rules.JRip.Antd
-
- getAccuRate() - Method in class weka.classifiers.rules.JRip.Antd
-
- getActionListener(JFrame) - Method in interface weka.gui.MainMenuExtension
-
If the extension has a custom ActionListener for the menu item, then it
must be returned here.
- getActualClassifier() - Method in class weka.filters.supervised.attribute.AddClassification
-
Returns the actual classifier to use, either from the serialized model
or the one specified by the user.
- getActualIndex(int) - Method in class weka.core.AttributeLocator
-
returns actual index in the Instances object.
- getActualRow(int) - Method in class weka.gui.SortedTableModel
-
Returns the actual underlying row the given visible one represents.
- getAcuity() - Method in class weka.clusterers.Cobweb
-
get the acuity value
- getAddress() - Static method in class weka.core.Copyright
-
returns the address of the owner
- getAdjustWeights() - Method in class weka.filters.supervised.instance.SpreadSubsample
-
Returns true if instance weights will be adjusted to maintain
total weight per class.
- getADTree() - Method in class weka.classifiers.bayes.BayesNet
-
get ADTree strucrture containing efficient representation of counts.
- getAdvanceDataSetFirst() - Method in class weka.experiment.Experiment
-
Get the value of m_DataSetFirstFirst.
- getAlgorithm() - Method in class weka.filters.supervised.attribute.PLSFilter
-
Gets the type of algorithm to use
- getAlgorithm() - Method in class weka.filters.unsupervised.attribute.Wavelet
-
Gets the type of algorithm to use
- getAlgorithmStart() - Method in class weka.associations.GeneralizedSequentialPatterns
-
Returns the time/date string the algorithm was started
- getAlgorithmType() - Method in class weka.classifiers.mi.MILR
-
Gets the type of algorithm.
- getAllBits(List<ScatterSearchV1.Subset>) - Method in class weka.attributeSelection.ScatterSearchV1
-
Save in Bitset all the gens that are in many others subsets.
- getAllowedIndices() - Method in class weka.core.AttributeLocator
-
returns the indices that are allowed to check for the attribute type
- getAllowUnclassifiedInstances() - Method in class weka.classifiers.trees.RandomTree
-
Get the value of NumFolds.
- getAllTheRules() - Method in class weka.associations.Apriori
-
returns all the rules
- getAllTheRules() - Method in class weka.associations.PredictiveApriori
-
returns all the rules
- getAlpha() - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
-
Get prior used in probability table estimation
- getAlpha() - Method in class weka.classifiers.functions.Winnow
-
Get the value of Alpha.
- getAmplitude() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Gets the amplitude multiplier.
- getAnimatedIcon() - Method in class weka.gui.beans.BeanVisual
-
Returns the animated icon
- getAnimatedIconPath() - Method in class weka.gui.beans.BeanVisual
-
returns the path for the animated icon
- getAntds() - Method in class weka.classifiers.rules.JRip.RipperRule
-
Return the antecedents
- getAntiOperation(HillClimber.Operation) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
-
getAntiOperation determines the Operation, which is needed to cancel oOperation
- getAppendPredictedProbabilities() - Method in class weka.gui.beans.PredictionAppender
-
Return true if predicted probabilities are to be appended rather
than class value
- getArffFile() - Method in class weka.gui.streams.InstanceLoader
-
- getArffFile() - Method in class weka.gui.streams.InstanceSavePanel
-
- getArray() - Method in class weka.core.matrix.DoubleVector
-
Access the internal one-dimensional array.
- getArray() - Method in class weka.core.matrix.IntVector
-
Access the internal one-dimensional array.
- getArray() - Method in class weka.core.matrix.Matrix
-
Access the internal two-dimensional array.
- getArrayClass(Class) - Static method in class weka.core.Utils
-
Returns the basic class of an array class (handles multi-dimensional
arrays).
- getArrayCopy() - Method in class weka.core.matrix.DoubleVector
-
Returns a copy of the DoubleVector usng a double array.
- getArrayCopy() - Method in class weka.core.matrix.IntVector
-
Returns a copy of the internal one-dimensional array.
- getArrayCopy() - Method in class weka.core.matrix.Matrix
-
Copy the internal two-dimensional array.
- getArrayDimensions(Class) - Static method in class weka.core.Utils
-
Returns the dimensions of the given array.
- getArrayDimensions(Object) - Static method in class weka.core.Utils
-
Returns the dimensions of the given array.
- getArrayDimensions(Element) - Method in class weka.core.xml.XMLSerialization
-
returns an array with the dimensions of the array stored in XML
- getArtificialSize() - Method in class weka.classifiers.meta.Decorate
-
Factor that determines number of artificial examples to generate.
- getASCrossvalidationFolds() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns the default number of folds of the CV in the attribute selection
panel.
- getASEvaluator() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns the default attribute evalautor (fully configured) for the
attribute selection panel.
- getAsInstance(Instances, Random) - Method in class weka.core.AlgVector
-
Gets the elements of the vector as an instance.
- getASRandomSeed() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns the default random seed value in the attribute selection panel.
- getASSearch() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns the default attribute selection search scheme (fully configured)
for the attribute selection panel.
- getAssignments() - Method in class weka.clusterers.SimpleKMeans
-
Gets the assignments for each instance
- getAssociatedConnections() - Method in class weka.gui.beans.MetaBean
-
- getAssociationRules() - Method in class weka.associations.FPGrowth
-
Gets the list of mined association rules.
- getAssociator() - Method in class weka.associations.CheckAssociator
-
Get the associator being tested
- getAssociator() - Method in class weka.associations.SingleAssociatorEnhancer
-
Get the associator used as the base associator.
- getAssociator() - Method in class weka.gui.beans.Associator
-
Get the associator currently set for this wrapper
- getAssociator() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns the default associator (fully configured) for the associations panel.
- getAssociatorSpec() - Method in class weka.associations.SingleAssociatorEnhancer
-
Gets the associator specification string, which contains the class name of
the associator and any options to the associator
- getASTestMode() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns the default attribute selection test mode for the attribute
selection panel.
- getAsText() - Method in class weka.gui.CostMatrixEditor
-
Some objects can be represented as text, but a cost matrix cannot.
- getAsText() - Method in class weka.gui.GenericArrayEditor
-
Returns null as we don't support getting/setting values as text.
- getAsText() - Method in class weka.gui.GenericObjectEditor
-
Returns null as we don't support getting/setting values as text.
- getAsText() - Method in class weka.gui.SelectedTagEditor
-
Gets the current value as text.
- getAsText() - Method in class weka.gui.SimpleDateFormatEditor
-
Returns the date format string.
- getAttIndex(int) - Method in class weka.classifiers.lazy.LBR.Indexes
-
Returns the boolean value at the specified index in the Attribute Indexes array
- getAttList_Irr() - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Gets the array that defines which of the attributes
are seen to be irrelevant.
- getAttr() - Method in class weka.classifiers.rules.JRip.Antd
-
- getAttribute() - Method in class weka.associations.FPGrowth.BinaryItem
-
Get the attribute that this item corresponds to.
- getAttribute1() - Method in class weka.gui.visualize.VisualizePanelEvent
-
- getAttribute2() - Method in class weka.gui.visualize.VisualizePanelEvent
-
- getAttributeAt(int) - Method in class weka.gui.arffviewer.ArffSortedTableModel
-
returns the attribute at the given index, can be NULL if not an attribute
column
- getAttributeAt(int) - Method in class weka.gui.arffviewer.ArffTableModel
-
returns the attribute at the given index, can be NULL if not an attribute
column
- getAttributeCapabilities() - Method in class weka.core.Capabilities
-
returns all attribute capabilities
- getAttributeColumn(String) - Method in class weka.gui.arffviewer.ArffSortedTableModel
-
returns the column of the given attribute name, -1 if not found
- getAttributeColumn(String) - Method in class weka.gui.arffviewer.ArffTableModel
-
returns the column of the given attribute name, -1 if not found
- getAttributeEvaluator() - Method in class weka.attributeSelection.FilteredAttributeEval
-
Get the attribute evaluator to use
- getAttributeEvaluator() - Method in class weka.attributeSelection.RaceSearch
-
Get the attribute evaluator used to generate the ranking.
- getAttributeEvaluator() - Method in class weka.attributeSelection.RankSearch
-
Get the attribute evaluator used to generate the ranking.
- getAttributeID() - Method in class weka.experiment.ClassifierSplitEvaluator
-
Get the index of Attibute Identifying the instances
- getAttributeIndex() - Method in class weka.classifiers.functions.SimpleLinearRegression
-
Returns the index of the attribute used in the regression.
- getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.Add
-
Get the index of the attribute used.
- getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.AddNoise
-
Get the index of the attribute used.
- getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.AddValues
-
Get the index of the attribute used.
- getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
-
Gets the index of the attribute converted.
- getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
-
Get the index of the attribute used.
- getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
-
Get the index of the attribute used.
- getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.SwapValues
-
Get the index of the attribute used.
- getAttributeIndex() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
Get the index of the attribute used.
- getAttributeIndex() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Get the index of the attribute used.
- getAttributeIndexes() - Method in class weka.filters.unsupervised.attribute.NominalToString
-
Get the index of the attribute used.
- getAttributeIndices() - Method in class weka.core.AttributeLocator
-
Returns the indices of the attributes.
- getAttributeIndices() - Method in interface weka.core.DistanceFunction
-
Gets the range of attributes used in the calculation of the distance.
- getAttributeIndices() - Method in class weka.core.NormalizableDistance
-
Gets the range of attributes used in the calculation of the distance.
- getAttributeIndices() - Method in class weka.filters.supervised.attribute.Discretize
-
Gets the current range selection
- getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
-
Get the current range selection
- getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.Copy
-
Get the current range selection
- getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.Discretize
-
Gets the current range selection
- getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.FirstOrder
-
Get the current range selection
- getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
-
Gets the current range selection
- getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
-
Gets the current range selection
- getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Gets the selection of the columns, e.g., first-last or first-3,5-last
- getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.NumericToNominal
-
Gets the current range selection
- getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.NumericTransform
-
Get the current range selection
- getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.Remove
-
Get the current range selection.
- getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.Reorder
-
Get the current range selection
- getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Gets the current range selection.
- getAttributeMaxValues() - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database
-
Returns the array of maximum-values for each attribute
- getAttributeMaxValues() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
-
Returns the array of maximum-values for each attribute
- getAttributeMinValues() - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database
-
Returns the array of minimum-values for each attribute
- getAttributeMinValues() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
-
Returns the array of minimum-values for each attribute
- getAttributeName() - Method in class weka.filters.unsupervised.attribute.Add
-
Get the name of the attribute to be created.
- getAttributeName() - Method in class weka.filters.unsupervised.attribute.AddID
-
Get the name of the attribute to be created
- getAttributeNamePrefix() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Get the attribute name prefix.
- getAttributeRange() - Method in class weka.filters.unsupervised.attribute.StringToNominal
-
Get the range of indices of the attributes used.
- getAttributes() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
- getAttributes() - Method in class weka.gui.arffviewer.ArffPanel
-
returns a list with the attributes
- getAttributeSelectionMethod() - Method in class weka.classifiers.functions.LinearRegression
-
Gets the method used to select attributes for use in the
linear regression.
- getAttributeType() - Method in class weka.filters.unsupervised.attribute.Add
-
Gets the type of attribute to generate.
- getAttributeType() - Method in class weka.filters.unsupervised.attribute.RemoveType
-
Gets the attribute type to be deleted by the filter.
- getAttributeTypes() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
-
Returns the current attribute - attribute-type relation in use.
- getAttributeTypeString() - Method in class weka.filters.unsupervised.attribute.RemoveType
-
Gets the attribute type to be deleted by the filter as a string.
- getAttrIndexRange() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
returns the attribute range(s).
- getAttrValue() - Method in class weka.classifiers.rules.JRip.Antd
-
- getAttsToEliminatePerIteration() - Method in class weka.attributeSelection.SVMAttributeEval
-
Get the constant rate of attribute elimination per iteration
- getAuthors() - Method in class weka.core.TechnicalInformation
-
splits the authors on the " and " and returns a vector with the names
- getAutoBuild() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- getAutoKeyGeneration() - Method in class weka.core.converters.DatabaseSaver
-
Gets whether or not a primary key will be generated automatically.
- getAverage(int) - Method in class weka.experiment.ResultMatrix
-
returns the average of the mean at the given position, if the position is
valid, otherwise 0
- getBackground() - Method in class weka.gui.visualize.BMPWriter
-
returns the current background color
- getBackground() - Method in class weka.gui.visualize.JPEGWriter
-
returns the current background color.
- getBackground() - Method in class weka.gui.visualize.PNGWriter
-
returns the current background color
- getBackground() - Method in class weka.gui.visualize.PostscriptGraphics
-
- getBackup() - Method in class weka.gui.GenericObjectEditor
-
Returns the backup object (may be null if there is no
backup.
- getBagSizePercent() - Method in class weka.classifiers.meta.Bagging
-
Gets the size of each bag, as a percentage of the training set size.
- getBagSizePercent() - Method in class weka.classifiers.meta.MetaCost
-
Gets the size of each bag, as a percentage of the training set size.
- getBalanceClass() - Method in class weka.datagenerators.classifiers.classification.Agrawal
-
Gets whether the class is balanced.
- getBalanced() - Method in class weka.classifiers.functions.Winnow
-
Get the value of Balanced.
- getBallSplitter() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
-
Returns the BallSplitter algorithm set that would be
used by the TopDown BallTree constructor.
- getBallTreeConstructor() - Method in class weka.core.neighboursearch.BallTree
-
Returns the BallTreeConstructor currently in use.
- getBase() - Method in class weka.core.neighboursearch.CoverTree
-
Returns the base in use for expansion constant.
- getBaseExperiment() - Method in class weka.experiment.RemoteExperiment
-
Get the base experiment used by this remote experiment
- getBean() - Method in class weka.gui.beans.BeanInstance
-
Gets the bean encapsulated in this instance
- getBeanConnectionRelation(MetaBean) - Method in class weka.gui.beans.xml.XMLBeans
-
returns the relation for the given MetaBean, for the regular connections,
null has to be used
- getBeanContext() - Method in class weka.gui.beans.AbstractDataSource
-
Return the bean context (if any) that this bean is embedded in
- getBeanContext() - Method in class weka.gui.beans.CostBenefitAnalysis
-
- getBeanContext() - Method in class weka.gui.beans.DataVisualizer
-
Return the bean context (if any) that this bean is embedded in
- getBeanContext() - Method in class weka.gui.beans.GraphViewer
-
Return the bean context (if any) that this bean is embedded in
- getBeanContext() - Method in class weka.gui.beans.ModelPerformanceChart
-
Return the bean context (if any) that this bean is embedded in
- getBeanContext() - Method in class weka.gui.beans.TextViewer
-
Return the bean context (if any) that this bean is embedded in
- getBeanDescriptor() - Method in class weka.gui.beans.AssociatorBeanInfo
-
Get the bean descriptor for this bean
- getBeanDescriptor() - Method in class weka.gui.beans.ClassAssignerBeanInfo
-
- getBeanDescriptor() - Method in class weka.gui.beans.ClassifierBeanInfo
-
Get the bean descriptor for this bean
- getBeanDescriptor() - Method in class weka.gui.beans.ClassValuePickerBeanInfo
-
- getBeanDescriptor() - Method in class weka.gui.beans.ClustererBeanInfo
-
Get the bean descriptor for this bean
- getBeanDescriptor() - Method in class weka.gui.beans.CrossValidationFoldMakerBeanInfo
-
Return the bean descriptor for this bean
- getBeanDescriptor() - Method in class weka.gui.beans.FilterBeanInfo
-
Get the bean descriptor for this bean
- getBeanDescriptor() - Method in class weka.gui.beans.IncrementalClassifierEvaluatorBeanInfo
-
Return the bean descriptor for this bean
- getBeanDescriptor() - Method in class weka.gui.beans.LoaderBeanInfo
-
Get the bean descriptor for this bean
- getBeanDescriptor() - Method in class weka.gui.beans.PredictionAppenderBeanInfo
-
Return the bean descriptor for this bean
- getBeanDescriptor() - Method in class weka.gui.beans.SaverBeanInfo
-
Get the bean descriptor for this bean
- getBeanDescriptor() - Method in class weka.gui.beans.SerializedModelSaverBeanInfo
-
Get the bean descriptor for this bean
- getBeanDescriptor() - Method in class weka.gui.beans.StripChartBeanInfo
-
Get the bean descriptor for this bean
- getBeanDescriptor() - Method in class weka.gui.beans.TrainTestSplitMakerBeanInfo
-
Get the bean descriptor for this bean
- getBeanInfoInputs() - Method in class weka.gui.beans.MetaBean
-
- getBeanInfoOutputs() - Method in class weka.gui.beans.MetaBean
-
- getBeanInfoSubFlow() - Method in class weka.gui.beans.MetaBean
-
- getBeanInstances() - Static method in class weka.gui.beans.BeanInstance
-
Return the list of displayed beans
- getBeanInstancesForIDs(Vector) - Method in class weka.gui.beans.xml.XMLBeans
-
returns a vector with references to BeanInstances according to the IDs
in the given Vector.
- getBeansInInputs() - Method in class weka.gui.beans.MetaBean
-
Return all the beans in the inputs
- getBeansInOutputs() - Method in class weka.gui.beans.MetaBean
-
Return all the beans in the outputs
- getBeansInSubFlow() - Method in class weka.gui.beans.MetaBean
-
Return all the beans in the sub flow
- getBestClassifier() - Method in class weka.classifiers.meta.GridSearch
-
returns the best Classifier setup
- getBestClassifierIndex() - Method in class weka.classifiers.meta.MultiScheme
-
Get the index of the classifier that was determined as best during
cross-validation.
- getBestClassifierOptions() - Method in class weka.classifiers.meta.CVParameterSelection
-
Returns (a copy of) the best options found for the classifier.
- getBestCommitteeChunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Get the best committee chunk size
- getBestCommitteeErrorEstimate() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Get the best committee's error on the validation data
- getBestCommitteeLLEstimate() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Get the best committee's log likelihood on the validation data
- getBestCommitteeSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Get the number of members in the best committee
- getBestFilter() - Method in class weka.classifiers.meta.GridSearch
-
returns the best filter setup
- getBestgen(ScatterSearchV1.Subset, BitSet) - Method in class weka.attributeSelection.ScatterSearchV1
-
Evaluate each gen of a BitSet inserted in a Subset and get the most significant for that Subset
- getBestGroup() - Method in class weka.attributeSelection.LFSMethods
-
- getBestGroupOfSize(int) - Method in class weka.attributeSelection.LFSMethods
-
- getBestIteration(double[], int) - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Helper function to find the minimum in an array of error values.
- getBestMerit() - Method in class weka.attributeSelection.LFSMethods
-
- getBeta() - Method in class weka.classifiers.functions.Winnow
-
Get the value of Beta.
- getBias() - Method in class weka.classifiers.BVDecompose
-
Get the calculated bias squared
- getBias() - Method in class weka.classifiers.functions.LibLINEAR
-
Returns bias term value (default 1)
No bias term is added if value < 0
- getBias() - Method in class weka.classifiers.misc.VFI
-
Get the value of the bias parameter
- getBiasToUniformClass() - Method in class weka.filters.supervised.instance.Resample
-
Gets the bias towards a uniform class.
- getBIFFile() - Method in class weka.classifiers.bayes.BayesNet
-
Get name of network in BIF file to compare with
- getBIFFile() - Method in class weka.classifiers.bayes.net.search.fixed.FromFile
-
Get name of network in BIF file to read structure from
- getBIFHeader() - Method in class weka.classifiers.bayes.BayesNet
-
- getBinarizeNumericAttributes() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
-
get whether numeric attributes are just being binarized.
- getBinarizeNumericAttributes() - Method in class weka.attributeSelection.InfoGainAttributeEval
-
get whether numeric attributes are just being binarized.
- getBinaryAttributesNominal() - Method in class weka.filters.supervised.attribute.NominalToBinary
-
Gets if binary attributes are to be treated as nominal ones.
- getBinaryAttributesNominal() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
-
Gets if binary attributes are to be treated as nominal ones.
- getBinarySplits() - Method in class weka.classifiers.rules.PART
-
Get the value of binarySplits.
- getBinarySplits() - Method in class weka.classifiers.trees.J48
-
Get the value of binarySplits.
- getBinarySplits() - Method in class weka.classifiers.trees.J48graft
-
Get the value of binarySplits.
- getBins() - Method in class weka.filters.unsupervised.attribute.Discretize
-
Gets the number of bins numeric attributes will be divided into
- getBins() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
-
Ignored
- getBinSplit() - Method in class weka.classifiers.trees.FT
-
Get the value of binarySplits.
- getBinValue() - Method in class weka.clusterers.XMeans
-
Gets value that represents true in a new numeric attribute.
- getBinValue() - Method in class weka.core.pmml.Discretize.DiscretizeBin
-
Get the bin value for this DiscretizeBin
- getBooleanCols() - Method in class weka.datagenerators.ClusterGenerator
-
returns the range of boolean attributes.
- getBuilder() - Method in class weka.core.xml.XMLDocument
-
returns the DocumentBuilder.
- getBuildLogisticModels() - Method in class weka.classifiers.functions.SMO
-
Get the value of buildLogisticModels.
- getBuildLogisticModels() - Method in class weka.classifiers.mi.MISMO
-
Get the value of buildLogisticModels.
- getBuildRegressionTree() - Method in class weka.classifiers.trees.m5.M5Base
-
Get the value of regressionTree.
- getC() - Method in class weka.classifiers.functions.SMO
-
Get the value of C.
- getC() - Method in class weka.classifiers.functions.SMOreg
-
Get the value of C.
- getC() - Method in class weka.classifiers.mi.MISMO
-
Get the value of C.
- getC() - Method in class weka.classifiers.mi.MISVM
-
Get the value of C.
- getCache(Class, String) - Static method in class weka.core.ClassDiscovery
-
returns the list of classnames associated with this class and package, if
available, otherwise null.
- getCacheHits() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
-
return the number of kernel cache hits
- getCacheKeyName() - Method in class weka.experiment.DatabaseResultListener
-
Get the value of CacheKeyName.
- getCacheSize() - Method in class weka.classifiers.functions.LibSVM
-
Gets cache memory size in MB
- getCacheSize() - Method in class weka.classifiers.functions.supportVector.CachedKernel
-
Gets the size of the cache
- getCacheSize() - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Gets the size of the cache
- getCacheValues(double) - Method in class weka.classifiers.lazy.kstar.KStarCache
-
Returns the values in the cache mapped by the specified key
- getCalcOutOfBag() - Method in class weka.classifiers.meta.Bagging
-
Get whether the out of bag error is calculated.
- getCalculatedNumToSelect() - Method in class weka.attributeSelection.GreedyStepwise
-
Gets the calculated number of attributes to retain.
- getCalculatedNumToSelect() - Method in class weka.attributeSelection.RaceSearch
-
Gets the calculated number of attributes to retain.
- getCalculatedNumToSelect() - Method in interface weka.attributeSelection.RankedOutputSearch
-
Gets the calculated number of attributes to retain.
- getCalculatedNumToSelect() - Method in class weka.attributeSelection.Ranker
-
Gets the calculated number to select.
- getCalculateStdDevs() - Method in class weka.experiment.AveragingResultProducer
-
Get the value of CalculateStdDevs.
- getCanChangeClassInDialog() - Method in class weka.gui.GenericObjectEditor
-
Returns whether the user can change the class in the dialog.
- getCapabilities() - Method in class weka.associations.AbstractAssociator
-
Returns the Capabilities of this associator.
- getCapabilities() - Method in class weka.associations.Apriori
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in interface weka.associations.Associator
-
Returns the Capabilities of this associator.
- getCapabilities() - Method in class weka.associations.FilteredAssociator
-
Returns default capabilities of the associator.
- getCapabilities() - Method in class weka.associations.FPGrowth
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.associations.GeneralizedSequentialPatterns
-
Returns the Capabilities of the algorithm.
- getCapabilities() - Method in class weka.associations.PredictiveApriori
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.associations.SingleAssociatorEnhancer
-
Returns default capabilities of the base associator.
- getCapabilities() - Method in class weka.associations.Tertius
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.attributeSelection.ASEvaluation
-
Returns the capabilities of this evaluator.
- getCapabilities() - Method in class weka.attributeSelection.CfsSubsetEval
-
Returns the capabilities of this evaluator.
- getCapabilities() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
-
Returns the capabilities of this evaluator.
- getCapabilities() - Method in class weka.attributeSelection.ClassifierSubsetEval
-
Returns the capabilities of this evaluator.
- getCapabilities() - Method in class weka.attributeSelection.ConsistencySubsetEval
-
Returns the capabilities of this evaluator.
- getCapabilities() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.attributeSelection.FilteredAttributeEval
-
Returns default capabilities of the evaluator.
- getCapabilities() - Method in class weka.attributeSelection.FilteredSubsetEval
-
Returns default capabilities of the evaluator.
- getCapabilities() - Method in class weka.attributeSelection.GainRatioAttributeEval
-
Returns the capabilities of this evaluator.
- getCapabilities() - Method in class weka.attributeSelection.InfoGainAttributeEval
-
Returns the capabilities of this evaluator.
- getCapabilities() - Method in class weka.attributeSelection.LatentSemanticAnalysis
-
Returns the capabilities of this evaluator.
- getCapabilities() - Method in class weka.attributeSelection.OneRAttributeEval
-
Returns the capabilities of this evaluator.
- getCapabilities() - Method in class weka.attributeSelection.PrincipalComponents
-
Returns the capabilities of this evaluator.
- getCapabilities() - Method in class weka.attributeSelection.ReliefFAttributeEval
-
Returns the capabilities of this evaluator.
- getCapabilities() - Method in class weka.attributeSelection.SVMAttributeEval
-
Returns the capabilities of this evaluator.
- getCapabilities() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
-
Returns the capabilities of this evaluator.
- getCapabilities() - Method in class weka.attributeSelection.WrapperSubsetEval
-
Returns the capabilities of this evaluator.
- getCapabilities() - Method in class weka.classifiers.bayes.AODE
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.bayes.AODEsr
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
This method tests what kind of data this classifier can handle.
- getCapabilities() - Method in class weka.classifiers.bayes.BayesNet
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.bayes.ComplementNaiveBayes
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.bayes.DMNBtext
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.bayes.HNB
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.bayes.NaiveBayes
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.bayes.NaiveBayesMultinomial
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.bayes.NaiveBayesSimple
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.bayes.WAODE
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.Classifier
-
Returns the Capabilities of this classifier.
- getCapabilities() - Method in class weka.classifiers.functions.GaussianProcesses
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.functions.IsotonicRegression
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.functions.LeastMedSq
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.functions.LibLINEAR
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.functions.LibSVM
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.functions.LinearRegression
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.functions.Logistic
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.functions.PaceRegression
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.functions.PLSClassifier
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.functions.RBFNetwork
-
Returns default capabilities of the classifier, i.e., and "or" of
Logistic and LinearRegression.
- getCapabilities() - Method in class weka.classifiers.functions.SimpleLinearRegression
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.functions.SimpleLogistic
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.functions.SMO
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.functions.SMOreg
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.functions.SPegasos
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.functions.supportVector.Kernel
-
Returns the Capabilities of this kernel.
- getCapabilities() - Method in class weka.classifiers.functions.supportVector.PolyKernel
-
Returns the Capabilities of this kernel.
- getCapabilities() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
-
Returns the Capabilities of this kernel.
- getCapabilities() - Method in class weka.classifiers.functions.supportVector.Puk
-
Returns the Capabilities of this kernel.
- getCapabilities() - Method in class weka.classifiers.functions.supportVector.RBFKernel
-
Returns the Capabilities of this kernel.
- getCapabilities() - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Returns the Capabilities of this kernel.
- getCapabilities() - Method in class weka.classifiers.functions.VotedPerceptron
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.functions.Winnow
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.lazy.IB1
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.lazy.IBk
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.lazy.KStar
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.lazy.LBR
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.lazy.LWL
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.AdaBoostM1
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.AdditiveRegression
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.ClassificationViaClustering
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.ClassificationViaRegression
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.CostSensitiveClassifier
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.CVParameterSelection
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.Decorate
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.END
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.FilteredClassifier
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.GridSearch
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.LogitBoost
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.MetaCost
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.MultiClassClassifier
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.nestedDichotomies.ND
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.OrdinalClassClassifier
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.RegressionByDiscretization
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.Stacking
-
Returns combined capabilities of the base classifiers, i.e., the
capabilities all of them have in common.
- getCapabilities() - Method in class weka.classifiers.meta.ThresholdSelector
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.meta.Vote
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.CitationKNN
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.MDD
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.MIBoost
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.MIDD
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.MIEMDD
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.MILR
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.MINND
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.MIOptimalBall
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.MISMO
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.MISVM
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.MIWrapper
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.SimpleMI
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.supportVector.MIPolyKernel
-
Returns the Capabilities of this kernel.
- getCapabilities() - Method in class weka.classifiers.mi.supportVector.MIRBFKernel
-
Returns the Capabilities of this kernel.
- getCapabilities() - Method in class weka.classifiers.misc.HyperPipes
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.misc.SerializedClassifier
-
Returns default capabilities of the base classifier.
- getCapabilities() - Method in class weka.classifiers.misc.VFI
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.MultipleClassifiersCombiner
-
Returns combined capabilities of the base classifiers, i.e., the
capabilities all of them have in common.
- getCapabilities() - Method in class weka.classifiers.rules.ConjunctiveRule
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.rules.DecisionTable
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.rules.DTNB
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.rules.JRip
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.rules.NNge
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.rules.OneR
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.rules.PART
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.rules.part.MakeDecList
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.rules.Prism
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.rules.Ridor
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.rules.ZeroR
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.SingleClassifierEnhancer
-
Returns default capabilities of the base classifier.
- getCapabilities() - Method in class weka.classifiers.trees.ADTree
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.trees.BFTree
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.trees.DecisionStump
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.trees.FT
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.trees.Id3
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTree
-
Returns default capabilities of the classifier tree.
- getCapabilities() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG
-
Returns default capabilities of the classifier tree.
- getCapabilities() - Method in class weka.classifiers.trees.j48.ClassifierTree
-
Returns default capabilities of the classifier tree.
- getCapabilities() - Method in class weka.classifiers.trees.J48
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.trees.j48.NBTreeClassifierTree
-
Returns default capabilities of the classifier tree.
- getCapabilities() - Method in class weka.classifiers.trees.j48.PruneableClassifierTree
-
Returns default capabilities of the classifier tree.
- getCapabilities() - Method in class weka.classifiers.trees.J48graft
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.trees.LADTree
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.trees.LMT
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.trees.m5.M5Base
-
Returns default capabilities of the classifier, i.e., of LinearRegression.
- getCapabilities() - Method in class weka.classifiers.trees.NBTree
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.trees.RandomForest
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.trees.RandomTree
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.trees.REPTree
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.trees.SimpleCart
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.trees.UserClassifier
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.clusterers.AbstractClusterer
-
Returns the Capabilities of this clusterer.
- getCapabilities() - Method in class weka.clusterers.CLOPE
-
Returns default capabilities of the clusterer.
- getCapabilities() - Method in interface weka.clusterers.Clusterer
-
Returns the Capabilities of this clusterer.
- getCapabilities() - Method in class weka.clusterers.Cobweb
-
Returns default capabilities of the clusterer.
- getCapabilities() - Method in class weka.clusterers.DBScan
-
Returns default capabilities of the clusterer.
- getCapabilities() - Method in class weka.clusterers.EM
-
Returns default capabilities of the clusterer (i.e., the ones of
SimpleKMeans).
- getCapabilities() - Method in class weka.clusterers.FarthestFirst
-
Returns default capabilities of the clusterer.
- getCapabilities() - Method in class weka.clusterers.FilteredClusterer
-
Returns default capabilities of the clusterer.
- getCapabilities() - Method in class weka.clusterers.HierarchicalClusterer
-
- getCapabilities() - Method in class weka.clusterers.MakeDensityBasedClusterer
-
Returns default capabilities of the clusterer (i.e., of the wrapper
clusterer).
- getCapabilities() - Method in class weka.clusterers.OPTICS
-
Returns default capabilities of the clusterer.
- getCapabilities() - Method in class weka.clusterers.sIB
-
Returns default capabilities of the clusterer.
- getCapabilities() - Method in class weka.clusterers.SimpleKMeans
-
Returns default capabilities of the clusterer.
- getCapabilities() - Method in class weka.clusterers.SingleClustererEnhancer
-
Returns default capabilities of the clusterer.
- getCapabilities() - Method in class weka.clusterers.XMeans
-
Returns default capabilities of the clusterer.
- getCapabilities() - Method in interface weka.core.CapabilitiesHandler
-
Returns the capabilities of this object.
- getCapabilities() - Method in class weka.core.converters.AbstractSaver
-
Returns the Capabilities of this saver.
- getCapabilities() - Method in class weka.core.converters.ArffSaver
-
Returns the Capabilities of this saver.
- getCapabilities() - Method in class weka.core.converters.C45Saver
-
Returns the Capabilities of this saver.
- getCapabilities() - Method in class weka.core.converters.CSVSaver
-
Returns the Capabilities of this saver.
- getCapabilities() - Method in class weka.core.converters.DatabaseSaver
-
Returns the Capabilities of this saver.
- getCapabilities() - Method in class weka.core.converters.LibSVMSaver
-
Returns the Capabilities of this saver.
- getCapabilities() - Method in class weka.core.converters.SerializedInstancesSaver
-
Returns the Capabilities of this saver.
- getCapabilities() - Method in class weka.core.converters.SVMLightSaver
-
Returns the Capabilities of this saver.
- getCapabilities() - Method in class weka.core.converters.XRFFSaver
-
Returns the Capabilities of this saver.
- getCapabilities() - Method in class weka.core.FindWithCapabilities
-
The capabilities to search for.
- getCapabilities() - Method in class weka.estimators.DiscreteEstimator
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.estimators.Estimator
-
Returns the Capabilities of this Estimator.
- getCapabilities() - Method in class weka.estimators.KernelEstimator
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.estimators.MahalanobisEstimator
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.estimators.NormalEstimator
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.estimators.PoissonEstimator
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.filters.AllFilter
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.Filter
-
Returns the Capabilities of this filter.
- getCapabilities(Instances) - Method in class weka.filters.Filter
-
Returns the Capabilities of this filter, customized based on the data.
- getCapabilities() - Method in class weka.filters.MultiFilter
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.supervised.attribute.AddClassification
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.supervised.attribute.AttributeSelection
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.supervised.attribute.ClassOrder
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.supervised.attribute.Discretize
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.supervised.attribute.NominalToBinary
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.supervised.attribute.PLSFilter
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.supervised.instance.Resample
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.supervised.instance.SMOTE
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.supervised.instance.SpreadSubsample
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.Add
-
Returns the Capabilities of this filter.
- getCapabilities(Instances) - Method in class weka.filters.unsupervised.attribute.AddCluster
-
Returns the Capabilities of this filter, makes sure that the class is
never set (for the clusterer).
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.AddCluster
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.AddExpression
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.AddID
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.AddNoise
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.AddValues
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.Center
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.ClassAssigner
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.ClusterMembership
-
Returns the Capabilities of this filter.
- getCapabilities(Instances) - Method in class weka.filters.unsupervised.attribute.ClusterMembership
-
Returns the Capabilities of this filter, makes sure that the class is
never set (for the clusterer).
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.Copy
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.Discretize
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.FirstOrder
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.KernelFilter
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.MathExpression
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.NominalToString
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.Normalize
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.NumericToBinary
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.NumericToNominal
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.NumericTransform
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.Obfuscate
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Returns the capabilities of this evaluator.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.RandomProjection
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.RandomSubset
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.RELAGGS
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.Remove
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.RemoveType
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.RemoveUseless
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.Reorder
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.Standardize
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.StringToNominal
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.SwapValues
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.TimeSeriesDelta
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.Wavelet
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.instance.NonSparseToSparse
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.instance.Normalize
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.instance.Randomize
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.instance.RemoveFolds
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.instance.RemovePercentage
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.instance.RemoveRange
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.instance.Resample
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.instance.ReservoirSample
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.instance.SparseToNonSparse
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.filters.unsupervised.instance.SubsetByExpression
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
-
returns the currently selected capabilities.
- getCapabilitiesFilter() - Method in class weka.gui.ConverterFileChooser
-
returns the capabilities filter for the savers, can be null if all are
listed.
- getCapabilitiesFilter() - Method in class weka.gui.GenericObjectEditor
-
Returns the current Capabilities filter, can be null.
- getCar() - Method in class weka.associations.Apriori
-
Gets whether class association ruels are mined
- getCar() - Method in class weka.associations.PredictiveApriori
-
Gets whether class association ruels are mined
- getCardinality(int) - Method in class weka.classifiers.bayes.BayesNet
-
get number of values a node can take
- getCardinality() - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
Gets the cardinality of the attributes (incl class attribute)
- getCardinalityOfParents() - Method in class weka.classifiers.bayes.net.ParentSet
-
returns cardinality of parents
- getCell(int, int) - Method in class weka.classifiers.CostMatrix
-
Return the contents of a particular cell.
- getCellEditor(int, int) - Method in class weka.gui.arffviewer.ArffTable
-
returns the cell editor for the given cell
- getCellEditorValue() - Method in class weka.gui.arffviewer.ArffTable.RelationalCellEditor
-
Returns the value contained in the editor.
- getCells() - Method in class weka.gui.sql.ResultSetHelper
-
returns an 2-dimensional array with the content of the resultset, the first
dimension is the row, the second the column (i.e., getCells()[y][x]).
- getCenter() - Method in class weka.gui.treevisualizer.Node
-
Get the value of center.
- getCenterData() - Method in class weka.attributeSelection.PrincipalComponents
-
Get whether to center (rather than standardize)
the data.
- getCenterData() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Get whether to center (rather than standardize)
the data.
- getCenteredLeft() - Method in class weka.gui.arffviewer.ArffViewer
-
returns the left coordinate if the frame would be centered
- getCenteredTop() - Method in class weka.gui.arffviewer.ArffViewer
-
returns the top coordinate if the frame would be centered
- getChangeInWeights() - Method in class weka.classifiers.functions.neural.NeuralNode
-
call this function to get the chnage in weights array.
- getChar() - Method in class weka.core.Trie.TrieNode
-
returns the stored character
- getCharSet() - Method in class weka.core.converters.TextDirectoryLoader
-
Get the character set to use when reading text files.
- getChecked() - Method in class weka.gui.CheckBoxList.CheckBoxListItem
-
returns the checked state of the item
- getChecked(int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
-
returns the checked state of the element at the given index
- getChecked(int) - Method in class weka.gui.CheckBoxList
-
returns the checked state of the element at the given index
- getCheckedIndices() - Method in class weka.gui.CheckBoxList
-
returns an array with the indices of all checked items
- getCheckErrorRate() - Method in class weka.classifiers.rules.JRip
-
Gets whether to check for error rate is in stopping criterion
- getChecksTurnedOff() - Method in class weka.classifiers.functions.SMO
-
Returns whether the checks are turned off or not.
- getChecksTurnedOff() - Method in class weka.classifiers.functions.supportVector.Kernel
-
Returns whether the checks are turned off or not.
- getChecksTurnedOff() - Method in class weka.classifiers.mi.MISMO
-
Returns whether the checks are turned off or not.
- getChecksTurnedOff() - Method in class weka.filters.unsupervised.attribute.KernelFilter
-
Returns whether the checks are turned off or not.
- getChild(int) - Method in class weka.gui.treevisualizer.Node
-
Get the Edge for the child number 'i'.
- getChildForBranch(int) - Method in class weka.classifiers.trees.adtree.Splitter
-
Gets the child for a branch of the split.
- getChildForBranch(int) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
-
Gets the child for a branch of the split.
- getChildForBranch(int) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
-
Gets the child for a branch of the split.
- getChildForBranch(int) - Method in class weka.classifiers.trees.LADTree.Splitter
-
- getChildForBranch(int) - Method in class weka.classifiers.trees.LADTree.TwoWayNominalSplit
-
- getChildForBranch(int) - Method in class weka.classifiers.trees.LADTree.TwoWayNumericSplit
-
- getChildren(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
return list of children of a node
- getChildren() - Method in class weka.classifiers.trees.adtree.PredictionNode
-
Gets the children of this node.
- getChildren() - Method in class weka.classifiers.trees.LADTree.PredictionNode
-
- getChildTags(Node) - Static method in class weka.core.xml.XMLDocument
-
returns all non tag-children from the given node.
- getChildTags(Node, String) - Static method in class weka.core.xml.XMLDocument
-
returns all non tag-children from the given node.
- getChooseClassPopupMenu() - Method in class weka.gui.GenericObjectEditor
-
Returns a popup menu that allows the user to change
the class of object.
- getChromosome() - Method in class weka.attributeSelection.GeneticSearch.GABitSet
-
get the chromosome
- getCindex() - Method in class weka.gui.visualize.PlotData2D
-
Get the currently set colouring index of the data
- getCIndex() - Method in class weka.gui.visualize.VisualizePanel
-
Get the index of the attribute selected for coloring
- getClassAttribute() - Method in class weka.gui.beans.ThresholdDataEvent
-
Return the class attribute for which the threshold data was generated
for.
- getClassCapabilities() - Method in class weka.core.Capabilities
-
returns all class capabilities
- getClassColumn() - Method in class weka.gui.beans.ClassAssigner
-
- getClassCounts() - Method in class weka.filters.supervised.attribute.ClassOrder
-
Get the class distribution of the sorted class values.
- getClassesFromProperties() - Method in class weka.gui.GenericObjectEditor
-
Called when the class of object being edited changes.
- getClassesToClusters() - Method in class weka.clusterers.ClusterEvaluation
-
Return the array (ordered by cluster number) of minimum error class to
cluster mappings
- getClassFlag() - Method in class weka.datagenerators.ClusterGenerator
-
Gets the class flag.
- getClassForIRStatistics() - Method in class weka.experiment.ClassifierSplitEvaluator
-
Get the value of ClassForIRStatistics.
- getClassification() - Method in class weka.associations.Tertius
-
Get the value of classification.
- getClassifier() - Method in class weka.attributeSelection.ClassifierSubsetEval
-
Get the classifier used as the base learner.
- getClassifier() - Method in class weka.attributeSelection.WrapperSubsetEval
-
Get the classifier used as the base learner.
- getClassifier() - Method in class weka.classifiers.BVDecompose
-
Gets the name of the classifier being analysed
- getClassifier() - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Gets the name of the classifier being analysed
- getClassifier() - Method in class weka.classifiers.CheckClassifier
-
Get the classifier used as the classifier
- getClassifier() - Method in class weka.classifiers.CheckSource
-
Gets the classifier being used for the tests, can be null.
- getClassifier(int) - Method in class weka.classifiers.meta.MultiScheme
-
Gets a single classifier from the set of available classifiers.
- getClassifier(int) - Method in class weka.classifiers.MultipleClassifiersCombiner
-
Gets a single classifier from the set of available classifiers.
- getClassifier() - Method in class weka.classifiers.SingleClassifierEnhancer
-
Get the classifier used as the base learner.
- getClassifier() - Method in class weka.experiment.ClassifierSplitEvaluator
-
Get the value of Classifier.
- getClassifier() - Method in class weka.experiment.RegressionSplitEvaluator
-
Get the value of Classifier.
- getClassifier() - Method in class weka.filters.supervised.attribute.AddClassification
-
Gets the classifier used by the filter.
- getClassifier() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Gets the classifier used by the filter.
- getClassifier() - Method in class weka.gui.beans.BatchClassifierEvent
-
Get the classifier
- getClassifier() - Method in class weka.gui.beans.Classifier
-
Get the classifier currently set for this wrapper
- getClassifier() - Method in class weka.gui.beans.IncrementalClassifierEvent
-
Get the classifier
- getClassifier() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns the default classifier (fully configured) for the classify panel.
- getClassifierCostSensitiveEval() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns whether the evaluation of the classifier is done cost-sensitively.
- getClassifierCrossvalidationFolds() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns the default number of folds of the CV in the classify panel.
- getClassifierOutputAdditionalAttributes() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns the string with the additional indices to output alongside the
predictions.
- getClassifierOutputConfusionMatrix() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns whether the confusion matrix for the classifier is output.
- getClassifierOutputEntropyEvalMeasures() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns whether entropy-based evaluation meastures of the classifier
are output.
- getClassifierOutputModel() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns whether the built model is output.
- getClassifierOutputPerClassStats() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns whether additional per-class stats of the classifier are output.
- getClassifierOutputPredictions() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns whether the predictions of the classifier are output as well.
- getClassifierOutputSourceCode() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns whether the source of a sourcable Classifier is output
in the classify tab.
- getClassifierPercentageSplit() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns the default classifier test mode for the classify panel (0-99).
- getClassifierPreserveOrder() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns whether the order is preserved in case of the percentage split
in the classify tab.
- getClassifierRandomSeed() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns the default random seed value for the classifier for the
classify panel.
- getClassifiers() - Method in class weka.classifiers.meta.MultiScheme
-
Gets the list of possible classifers to choose from.
- getClassifiers() - Method in class weka.classifiers.MultipleClassifiersCombiner
-
Gets the list of possible classifers to choose from.
- getClassifierSourceCodeClass() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns the default classname for a sourcable Classifier in the classify tab.
- getClassifierSpec() - Method in class weka.classifiers.meta.CostSensitiveClassifier
-
Gets the classifier specification string, which contains the class name of
the classifier and any options to the classifier
- getClassifierSpec() - Method in class weka.classifiers.meta.MetaCost
-
Gets the classifier specification string, which contains the
class name of the classifier and any options to the classifier
- getClassifierSpec(int) - Method in class weka.classifiers.meta.MultiScheme
-
Gets the classifier specification string, which contains the class name of
the classifier and any options to the classifier
- getClassifierSpec(int) - Method in class weka.classifiers.MultipleClassifiersCombiner
-
Gets the classifier specification string, which contains the class name of
the classifier and any options to the classifier
- getClassifierSpec() - Method in class weka.classifiers.SingleClassifierEnhancer
-
Gets the classifier specification string, which contains the class name of
the classifier and any options to the classifier
- getClassifierSpec() - Method in class weka.filters.supervised.attribute.AddClassification
-
Gets the classifier specification string, which contains the class name of
the classifier and any options to the classifier.
- getClassifierSpec() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Gets the classifier specification string, which contains the class name of
the classifier and any options to the classifier.
- getClassifierStorePredictionsForVis() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns whether the predictions of the classifier are stored for
visualization.
- getClassifierTemplate() - Method in class weka.gui.beans.Classifier
-
Return the classifier template currently in use.
- getClassifierTestMode() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns the default classifier test mode for the classify panel.
- getClassifyIterations() - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Gets the number of times an instance is classified
- getClassIndex() - Method in class weka.associations.Apriori
-
Gets the class index
- getClassIndex() - Method in class weka.associations.FilteredAssociator
-
Gets the class index
- getClassIndex() - Method in class weka.associations.PredictiveApriori
-
Gets the index of the class attribute
- getClassIndex() - Method in class weka.associations.Tertius
-
Get the value of classIndex.
- getClassIndex() - Method in class weka.classifiers.BVDecompose
-
Get the index (starting from 1) of the attribute used as the class.
- getClassIndex() - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Get the index (starting from 1) of the attribute used as the class.
- getClassIndex() - Method in class weka.classifiers.CheckSource
-
Gets the class index of the dataset.
- getClassIndex() - Method in class weka.core.converters.LibSVMSaver
-
Get the index of the class attribute.
- getClassIndex() - Method in class weka.core.converters.SVMLightSaver
-
Get the index of the class attribute.
- getClassIndex() - Method in class weka.core.converters.XRFFSaver
-
Get the index of the class attribute.
- getClassIndex() - Method in class weka.core.FindWithCapabilities
-
returns the current current class index, -1 if no class attribute.
- getClassIndex() - Method in class weka.core.TestInstances
-
returns the current class index (0-based), -1 is last attribute
- getClassIndex() - Method in class weka.filters.CheckSource
-
Gets the class index of the dataset.
- getClassIndex() - Method in class weka.filters.unsupervised.attribute.ClassAssigner
-
returns the class index.
- getClassIndex() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Gets the attribute on which misclassifications are based.
- getClassMatches(String) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion
-
returns all the class/package matches with the partial search string.
- getClassname() - Method in class weka.core.Javadoc
-
returns the current classname
- getClassname() - Method in class weka.core.ListOptions
-
returns the current classname
- getClassName() - Method in class weka.filters.unsupervised.attribute.NumericTransform
-
Get the class containing the transformation method.
- getClassname(String) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion
-
returns the classname part of the partial classname.
- getClassnameFromPath(TreePath) - Method in class weka.gui.GenericObjectEditor
-
creates a classname from the given path.
- getClassnames(String) - Static method in class weka.gui.GenericObjectEditor
-
Returns the available classnames for a certain property in the
props file.
- getClassOrder() - Method in class weka.filters.supervised.attribute.ClassOrder
-
Get the wanted class order
- getClassPriors() - Method in class weka.classifiers.Evaluation
-
Get the current weighted class counts
- getClassType() - Method in class weka.core.TestInstances
-
returns the current class type
- getClassValue() - Method in class weka.filters.supervised.instance.SMOTE
-
Gets the index of the class value to which SMOTE should be applied.
- getClassValue() - Method in class weka.gui.beans.ClassValuePicker
-
Gets the class value considered to be the "positive"
class value.
- getClearEachDataset() - Method in class weka.gui.streams.InstanceViewer
-
- getClip() - Method in class weka.gui.visualize.PostscriptGraphics
-
Not implemented
- getClipBounds() - Method in class weka.gui.visualize.PostscriptGraphics
-
This returns the full current drawing area
- getClipBounds(Rectangle) - Method in class weka.gui.visualize.PostscriptGraphics
-
This returns the full current drawing area
- getClipRect() - Method in class weka.gui.visualize.PostscriptGraphics
-
Not implemented
- getClock() - Method in class weka.core.Debug
-
returns the instance of the Clock that is internally used
- getClosestConnections(Point, int) - Static method in class weka.gui.beans.BeanConnection
-
Return a list of connections within some delta of a point
- getClosestConnectorPoint(Point) - Method in class weka.gui.beans.BeanVisual
-
Returns the coordinates of the closest "connector" point to the
supplied point.
- getCloseTo() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Get the "close to" number.
- getCloseToDefault() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Get the "close to" default.
- getCloseToTolerance() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Get the "close to" Tolerance.
- getClusterAssignments() - Method in class weka.clusterers.ClusterEvaluation
-
Return an array of cluster assignments corresponding to the most
recent set of instances clustered.
- getClusterCenters() - Method in class weka.clusterers.XMeans
-
Return the centers of the clusters as an Instances object
- getClusterCentroids() - Method in class weka.clusterers.SimpleKMeans
-
Gets the the cluster centroids
- getClusterDefinitions() - Method in class weka.datagenerators.clusterers.SubspaceCluster
-
returns the currently set clusters
- getClusterer() - Method in class weka.attributeSelection.UnsupervisedSubsetEvaluator
-
Get the clusterer
- getClusterer() - Method in class weka.classifiers.meta.ClassificationViaClustering
-
Get the clusterer used as the base learner.
- getClusterer() - Method in class weka.clusterers.CheckClusterer
-
Get the clusterer used as the clusterer
- getClusterer() - Method in class weka.clusterers.MakeDensityBasedClusterer
-
Gets the clusterer being wrapped.
- getClusterer() - Method in class weka.clusterers.SingleClustererEnhancer
-
Get the clusterer used as the base clusterer.
- getClusterer() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
-
Get the value of clusterer
- getClusterer() - Method in class weka.filters.unsupervised.attribute.AddCluster
-
Gets the clusterer used by the filter.
- getClusterer() - Method in class weka.gui.beans.BatchClustererEvent
-
Get the clusterer
- getClusterer() - Method in class weka.gui.beans.Clusterer
-
Get the clusterer currently set for this wrapper
- getClusterer() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns the default clusterer (fully configured) for the clusterer panel.
- getClustererSpec() - Method in class weka.clusterers.SingleClustererEnhancer
-
Gets the clusterer specification string, which contains the class name of
the clusterer and any options to the clusterer
- getClustererSpec() - Method in class weka.filters.unsupervised.attribute.AddCluster
-
Gets the clusterer specification string, which contains the class name of
the clusterer and any options to the clusterer.
- getClustererStoreClustersForVis() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns whether the clusters are storeed for visualization purposes
in the cluster panel.
- getClustererTestMode() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns the default cluster test mode for the cluster panel.
- getClusteringSeed() - Method in class weka.classifiers.functions.RBFNetwork
-
Get the random seed used by K-means.
- getClusterLabel() - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
-
Returns the clusterID, to which this DataObject belongs to
- getClusterLabel() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
-
Returns the clusterID, to which this DataObject belongs to
- getClusterLabel() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
-
Returns the clusterID, to which this DataObject belongs to
- getClusterModelsNumericAtts() - Method in class weka.clusterers.EM
-
Return the normal distributions for the cluster models
- getClusterNominalCounts() - Method in class weka.clusterers.SimpleKMeans
-
Returns for each cluster the frequency counts for the values of each
nominal attribute
- getClusterPriors() - Method in class weka.clusterers.EM
-
Return the priors for the clusters
- getClusters() - Method in class weka.datagenerators.clusterers.SubspaceCluster
-
returns the current cluster definitions, if necessary initializes them
- getClusterSizes() - Method in class weka.clusterers.SimpleKMeans
-
Gets the number of instances in each cluster
- getClusterStandardDevs() - Method in class weka.clusterers.SimpleKMeans
-
Gets the standard deviations of the numeric attributes in each cluster
- getClusterSubType() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Gets the cluster sub type.
- getClusterType() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Gets the cluster type.
- getCoef0() - Method in class weka.classifiers.functions.LibSVM
-
Gets coef
- getCoefficients() - Method in class weka.classifiers.trees.ft.FTtree
-
Returns an array containing the coefficients of the logistic regression function at this node.
- getCoefficients() - Method in class weka.classifiers.trees.lmt.LMTNode
-
Returns an array containing the coefficients of the logistic regression function at this node.
- getCoefficients() - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Returns an array holding the coefficients of the logistic model.
- getCoefficients() - Method in class weka.core.matrix.LinearRegression
-
returns the calculated coefficients
- getColCount() - Method in class weka.experiment.ResultMatrix
-
returns the number of columns
- getColHidden(int) - Method in class weka.experiment.ResultMatrix
-
returns the hidden status of the column, if the index is valid, otherwise
false
- getColName(int) - Method in class weka.experiment.ResultMatrix
-
returns the name of the row, if the index is valid, otherwise null.
- getColNameWidth() - Method in class weka.experiment.ResultMatrix
-
returns the current width for the column names
- getColor() - Method in class weka.gui.treevisualizer.Node
-
Get the value of color.
- getColor(String) - Method in class weka.gui.treevisualizer.TreeVisualizer
-
Processes the color string.
- getColor() - Method in class weka.gui.visualize.PostscriptGraphics
-
Get current pen color.
- getColorBox() - Method in class weka.gui.AttributeVisualizationPanel
-
Returns the class selection combo box if the parent component wants to
place it in itself or in some component other than this component.
- getColOrder() - Method in class weka.experiment.ResultMatrix
-
returns the current order of the columns, null means the default order
- getColoringIndex() - Method in class weka.gui.AttributeVisualizationPanel
-
Get the coloring (class) index for the plot
- getColoringIndex() - Method in class weka.gui.beans.AttributeSummarizer
-
Return the coloring index for the attribute summary plots
- getColors() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Get the current vector of Color objects used for the classes
- getColSize(String[][], int) - Method in class weka.experiment.ResultMatrix
-
returns the length of the longest cell in the given column
- getColSize(String[][], int, boolean, boolean) - Method in class weka.experiment.ResultMatrix
-
returns the length of the longest cell in the given column
- getColumn(int) - Method in class weka.classifiers.functions.pace.PaceMatrix
-
Return a DoubleVector that stores a column of the matrix
- getColumn(int, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
-
Return a DoubleVector that stores some elements of a column of the
matrix
- getColumn(int) - Method in class weka.core.Matrix
-
Deprecated.
Gets a column of the matrix and returns it as a double array.
- getColumn() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
the comma-separated list of attribute names that identify a column
- getColumnClass(int) - Method in class weka.gui.arffviewer.ArffTableModel
-
returns the most specific superclass for all the cell values in the
column (always String)
- getColumnClass(int) - Method in class weka.gui.SortedTableModel
-
Returns the most specific superclass for all the cell values in the
column.
- getColumnClass(int) - Method in class weka.gui.sql.ResultSetTableModel
-
returns the most specific superclass for all the cell values in the
column (always String).
- getColumnClasses() - Method in class weka.gui.sql.ResultSetHelper
-
returns the classes for the columns.
- getColumnCount() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.ResultVectorTableModel
-
Returns the number of columns of this model.
- getColumnCount() - Method in class weka.gui.arffviewer.ArffTableModel
-
returns the number of columns in the model
- getColumnCount() - Method in class weka.gui.SortedTableModel
-
Returns the number of columns in the model
- getColumnCount() - Method in class weka.gui.sql.ResultSetHelper
-
returns the number of columns in the resultset.
- getColumnCount() - Method in class weka.gui.sql.ResultSetTableModel
-
returns the number of columns in the model.
- getColumnDimension() - Method in class weka.core.matrix.Matrix
-
Get column dimension.
- getColumnName(int) - Method in class weka.gui.arffviewer.ArffTableModel
-
returns the name of the column at columnIndex
- getColumnName(int) - Method in class weka.gui.SortedTableModel
-
Returns the name of the column at columnIndex
- getColumnName(int) - Method in class weka.gui.sql.ResultSetTableModel
-
returns the name of the column at columnIndex.
- getColumnNames() - Method in class weka.gui.sql.ResultSetHelper
-
returns an array with the names of the columns in the resultset.
- getColumnPackedCopy() - Method in class weka.core.matrix.Matrix
-
Make a one-dimensional column packed copy of the internal array.
- getCombination() - Method in class weka.attributeSelection.ScatterSearchV1
-
Get the combination
- getCombinationRule() - Method in class weka.classifiers.meta.Vote
-
Gets the combination rule used
- getCommand() - Method in class weka.gui.treevisualizer.TreeDisplayEvent
-
- getComment() - Method in enum weka.core.TechnicalInformation.Field
-
returns the comment string
- getComment() - Method in enum weka.core.TechnicalInformation.Type
-
returns the comment string
- getCommonPrefix() - Method in class weka.core.Trie
-
returns the common prefix for all the nodes
- getCommonPrefix() - Method in class weka.core.Trie.TrieNode
-
returns the common prefix for all the nodes starting with this node.
- getCommonPrefix(String) - Method in class weka.core.Trie.TrieNode
-
returns the common prefix for all the nodes starting with the node
for the specified prefix.
- getCommonPrefix(Vector<String>) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion
-
returns the common prefix for all the items in the list.
- getComparisonField() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
returns the name of the field used for comparison
- getCompatibilityState() - Method in class weka.experiment.AveragingResultProducer
-
Gets a description of the internal settings of the result
producer, sufficient for distinguishing a ResultProducer
instance from another with different settings (ignoring
those settings set through this interface).
- getCompatibilityState() - Method in class weka.experiment.CrossValidationResultProducer
-
Gets a description of the internal settings of the result
producer, sufficient for distinguishing a ResultProducer
instance from another with different settings (ignoring
those settings set through this interface).
- getCompatibilityState() - Method in class weka.experiment.DatabaseResultProducer
-
Gets a description of the internal settings of the result
producer, sufficient for distinguishing a ResultProducer
instance from another with different settings (ignoring
those settings set through this interface).
- getCompatibilityState() - Method in class weka.experiment.LearningRateResultProducer
-
Gets a description of the internal settings of the result
producer, sufficient for distinguishing a ResultProducer
instance from another with different settings (ignoring
those settings set through this interface).
- getCompatibilityState() - Method in class weka.experiment.RandomSplitResultProducer
-
Gets a description of the internal settings of the result
producer, sufficient for distinguishing a ResultProducer
instance from another with different settings (ignoring
those settings set through this interface).
- getCompatibilityState() - Method in interface weka.experiment.ResultProducer
-
Gets a description of the internal settings of the result
producer, sufficient for distinguishing a ResultProducer
instance from another with different settings (ignoring
those settings set through this interface).
- getComplexityParameter() - Method in class weka.attributeSelection.SVMAttributeEval
-
Get the value of C used with SMO
- getComponent() - Method in class weka.gui.visualize.JComponentWriter
-
returns the component that is stored in the output format
- getComponent() - Method in class weka.gui.visualize.PrintableComponent
-
returns the GUI component this print dialog is part of.
- getComposite() - Method in class weka.gui.visualize.PostscriptGraphics
-
- getCompressOutput() - Method in class weka.core.converters.ArffSaver
-
Gets whether the output data is compressed.
- getCompressOutput() - Method in class weka.core.converters.XRFFSaver
-
Gets whether the output data is compressed.
- getConfidenceFactor() - Method in class weka.classifiers.rules.PART
-
Get the value of CF.
- getConfidenceFactor() - Method in class weka.classifiers.trees.J48
-
Get the value of CF.
- getConfidenceFactor() - Method in class weka.classifiers.trees.J48graft
-
Get the value of CF.
- getConfirmation() - Method in class weka.associations.tertius.Rule
-
Get the confirmation value of this rule.
- getConfirmationThreshold() - Method in class weka.associations.Tertius
-
Get the value of confirmationThreshold.
- getConfirmationValues() - Method in class weka.associations.Tertius
-
Get the value of confirmationValues.
- getConfirmExit() - Method in class weka.gui.arffviewer.ArffViewer
-
returns the setting of whether to display a confirm messagebox or not
on exit
- getConfirmExit() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
returns the setting of whether to display a confirm messagebox or not
on exit
- getConfusionMatrix() - Method in class weka.classifiers.evaluation.TwoClassStats
-
Generates a ConfusionMatrix
representing the current
two-class statistics, using class names "negative" and "positive".
- getConnectedFormat() - Method in class weka.gui.beans.ClassAssigner
-
Returns the structure of the incoming instances (if any)
- getConnectedFormat() - Method in class weka.gui.beans.ClassValuePicker
-
Returns the structure of the incoming instances (if any)
- getConnection() - Method in class weka.gui.sql.DbUtils
-
returns the current database connection.
- getConnections() - Static method in class weka.gui.beans.BeanConnection
-
Returns the list of connections
- getConnectorPoint(int) - Method in class weka.gui.beans.BeanVisual
-
Returns the coordinates of the connector point given a compass point
- getConsequence() - Method in class weka.associations.FPGrowth.AssociationRule
-
Get the consequence of this rule.
- getConsequenceSupport() - Method in class weka.associations.FPGrowth.AssociationRule
-
Get the support for the consequence.
- getConsequent() - Method in class weka.classifiers.rules.JRip.RipperRule
-
Gets the internal representation of the class label to be predicted
- getConsequent() - Method in class weka.classifiers.rules.Rule
-
Get the consequent of this rule, i.e.
- getConservativeForwardSelection() - Method in class weka.attributeSelection.GreedyStepwise
-
Gets whether conservative selection has been enabled
- getConstError(double[]) - Method in class weka.classifiers.trees.ft.FTtree
-
- getContainChildBalls() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
Gets whether if a parent ball should completely enclose
its two child balls.
- getContent(Element) - Method in class weka.classifiers.bayes.net.BIFReader
-
Returns all TEXT children of the given node in one string.
- getContent(Element) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
XML helper function.
- getContent(Element) - Static method in class weka.core.xml.XMLDocument
-
returns the text between the opening and closing tag of a node
(performs a trim()
on the result).
- getContent() - Method in class weka.gui.CheckBoxList.CheckBoxListItem
-
returns the content object
- getControlPanel() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
-
This method returns a handle to the extra
controls panel, so that the visualizing
class can add it to some of it's own
gui panel.
- getControlPanel() - Method in interface weka.gui.graphvisualizer.LayoutEngine
-
This method returns the extra controls panel
for the LayoutEngine, if there is any.
- getConverterForExtension(String, Hashtable<String, String>) - Static method in class weka.core.converters.ConverterUtils
-
tries to determine the loader to use for this kind of extension, returns
null if none can be found.
- getConverterForFile(String, Hashtable<String, String>) - Static method in class weka.core.converters.ConverterUtils
-
tries to determine the converter to use for this kind of file, returns
null if none can be found in the given hashtable.
- getConverters(Hashtable<String, String>) - Static method in class weka.core.converters.ConverterUtils
-
returns a vector with the classnames of all the loaders from the
given hashtable.
- getConvertNominal() - Method in class weka.classifiers.trees.LMT
-
Get the value of convertNominal.
- getConvertNominalToBinary() - Method in class weka.classifiers.functions.LibLINEAR
-
Gets whether conversion of nominal to binary is
turned on.
- getCopy(String[]) - Method in class weka.core.CheckOptionHandler
-
creates a copy of the given options
- getCoreConvertersOnly() - Method in class weka.gui.ConverterFileChooser
-
Returns whether only the hardcoded core converters are displayed.
- getCoreDistance() - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
-
Returns the coreDistance for this dataObject
- getCoreDistance() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
-
Returns the coreDistance for this dataObject
- getCoreDistance() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
-
Returns the coreDistance for this dataObject
- getCost() - Method in class weka.classifiers.functions.LibLINEAR
-
Returns the cost parameter C
- getCost() - Method in class weka.classifiers.functions.LibSVM
-
Sets the parameter C of C-SVC, epsilon-SVR, and nu-SVR
- getCostMatrix() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
-
Gets the misclassification cost matrix.
- getCostMatrix() - Method in class weka.classifiers.meta.CostSensitiveClassifier
-
Gets the misclassification cost matrix.
- getCostMatrix() - Method in class weka.classifiers.meta.MetaCost
-
Gets the misclassification cost matrix.
- getCostMatrixSource() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
-
Gets the source location method of the cost matrix.
- getCostMatrixSource() - Method in class weka.classifiers.meta.CostSensitiveClassifier
-
Gets the source location method of the cost matrix.
- getCostMatrixSource() - Method in class weka.classifiers.meta.MetaCost
-
Gets the source location method of the cost matrix.
- getCount(int) - Method in class weka.associations.FPGrowth.ShadowCounts
-
Get the count at the specified recursion depth.
- getCount(double) - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
-
Get a counts for a value
- getCount(double) - Method in class weka.estimators.DiscreteEstimator
-
Get the count for a value
- getCount(int) - Method in class weka.experiment.ResultMatrix
-
returns the count for the row.
- getCount(Node, int) - Static method in class weka.gui.treevisualizer.Node
-
Recursively finds the number of visible nodes there are (this may
accidentally count some of the invis nodes).
- getCounterInstancesFrequency() - Method in class weka.associations.tertius.LiteralSet
-
Get the frequency of counter-instances of this LiteralSet in the data.
- getCounterInstancesNumber() - Method in class weka.associations.tertius.LiteralSet
-
Get the number of counter-instances of this LiteralSet.
- getCounts(int[], int[], int[], int, int, boolean) - Method in class weka.classifiers.bayes.net.ADNode
-
get counts for specific instantiation of a set of nodes
- getCounts(int[], int[], int[], int, int, ADNode, boolean) - Method in class weka.classifiers.bayes.net.VaryNode
-
get counts for specific instantiation of a set of nodes
- getCountWidth() - Method in class weka.experiment.ResultMatrix
-
returns the current width for the counts
- getCover() - Method in class weka.classifiers.rules.JRip.Antd
-
- getCoverSet(int, Stack<Stack<CoverTree.d_node>>) - Method in class weka.core.neighboursearch.CoverTree
-
Returns a cover set for a given level/scale.
- getCreatorApplication() - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
-
Get the name of the application that created this model
- getCreatorApplication() - Method in interface weka.core.pmml.PMMLModel
-
Get the name of the application that created this model.
- getCriticalValue() - Method in class weka.classifiers.bayes.AODEsr
-
Gets the critical value.
- getCrossoverProb() - Method in class weka.attributeSelection.GeneticSearch
-
get the probability of crossover
- getCrossVal() - Method in class weka.classifiers.rules.DecisionTable
-
Gets the number of folds for cross validation
- getCrossValidate() - Method in class weka.classifiers.lazy.IBk
-
Gets whether hold-one-out cross-validation will be used
to select the best k value.
- getCurrent() - Method in class weka.core.Memory
-
returns the current memory consumption
- getCurrentDatasetNumber() - Method in class weka.experiment.Experiment
-
When an experiment is running, this returns the current dataset number.
- getCurrentDir() - Static method in class weka.core.Debug
-
returns the current working directory of the user
- getCurrentFilename() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
returns the filename of the current tab
- getCurrentIndex() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
returns the currently selected tab index
- getCurrentInstance() - Method in class weka.gui.beans.IncrementalClassifierEvent
-
Get the current instance
- getCurrentModel() - Method in class weka.classifiers.misc.SerializedClassifier
-
Gets the currently loaded model (can be null).
- getCurrentPanel() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
returns the currently selected panel
- getCurrentPropertyNumber() - Method in class weka.experiment.Experiment
-
When an experiment is running, this returns the index of the
current custom property value.
- getCurrentRunNumber() - Method in class weka.experiment.Experiment
-
When an experiment is running, this returns the current run number.
- getCurrentTable() - Method in class weka.gui.sql.ResultPanel
-
returns the table of the current tab, can be NULL
- getCurrentTime() - Method in class weka.core.Debug.Clock
-
returns the current time in msec
- getCurve(FastVector) - Method in class weka.classifiers.evaluation.CostCurve
-
Calculates the performance stats for the default class and return
results as a set of Instances.
- getCurve(FastVector, int) - Method in class weka.classifiers.evaluation.CostCurve
-
Calculates the performance stats for the desired class and return
results as a set of Instances.
- getCurve(FastVector) - Method in class weka.classifiers.evaluation.MarginCurve
-
Calculates the cumulative margin distribution for the set of
predictions, returning the result as a set of Instances.
- getCurve(FastVector) - Method in class weka.classifiers.evaluation.ThresholdCurve
-
Calculates the performance stats for the default class and return
results as a set of Instances.
- getCurve(FastVector, int) - Method in class weka.classifiers.evaluation.ThresholdCurve
-
Calculates the performance stats for the desired class and return
results as a set of Instances.
- getCustomEditor() - Method in class weka.gui.CostMatrixEditor
-
Gets a GUI component with which the user can edit the cost matrix.
- getCustomEditor() - Method in class weka.gui.FileEditor
-
Gets the custom editor component.
- getCustomEditor() - Method in class weka.gui.GenericArrayEditor
-
Returns the array editing component.
- getCustomEditor() - Method in class weka.gui.GenericObjectEditor
-
Returns the array editing component.
- getCustomEditor() - Method in class weka.gui.SimpleDateFormatEditor
-
Gets a GUI component with which the user can edit the date format.
- getCustomHeight() - Method in class weka.gui.visualize.JComponentWriter
-
gets the custom height currently used
- getCustomName() - Method in class weka.gui.beans.Associator
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in interface weka.gui.beans.BeanCommon
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.ClassAssigner
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.Classifier
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.ClassValuePicker
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.Clusterer
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.ClustererPerformanceEvaluator
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.CostBenefitAnalysis
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.CrossValidationFoldMaker
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.Filter
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.IncrementalClassifierEvaluator
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.InstanceStreamToBatchMaker
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.Loader
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.MetaBean
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.PredictionAppender
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.Saver
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.SerializedModelSaver
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.StripChart
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.TestSetMaker
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.TextViewer
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.TrainingSetMaker
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomName() - Method in class weka.gui.beans.TrainTestSplitMaker
-
Get the custom (descriptive) name for this bean (if one has been set)
- getCustomPanel() - Method in interface weka.gui.CustomPanelSupplier
-
Gets the custom panel for the object.
- getCustomPanel() - Method in class weka.gui.GenericObjectEditor
-
Gets the custom panel used for editing the object.
- getCustomWidth() - Method in class weka.gui.visualize.JComponentWriter
-
gets the custom width currently used
- getCutoff() - Method in class weka.clusterers.Cobweb
-
get the cutoff
- getCutOffFactor() - Method in class weka.clusterers.XMeans
-
Gets the cutoff factor.
- getCutPoints(int) - Method in class weka.filters.supervised.attribute.Discretize
-
Gets the cut points for an attribute
- getCutPoints(int) - Method in class weka.filters.unsupervised.attribute.Discretize
-
Gets the cut points for an attribute
- getCVisible() - Method in class weka.gui.treevisualizer.Node
-
Get If this node's childs are visible.
- getCVParameter(int) - Method in class weka.classifiers.meta.CVParameterSelection
-
Gets the scheme paramter with the given index.
- getCVParameters() - Method in class weka.classifiers.meta.CVParameterSelection
-
Get method for CVParameters.
- getCVPredictions(Classifier, Instances, int) - Method in class weka.classifiers.evaluation.EvaluationUtils
-
Generate a bunch of predictions ready for processing, by performing a
cross-validation on the supplied dataset.
- getCVType() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
-
get cross validation strategy to be used in searching for networks.
- getCycleEnd() - Method in class weka.associations.GeneralizedSequentialPatterns
-
Returns the time/date string the cycle ended
- getCycleStart() - Method in class weka.associations.GeneralizedSequentialPatterns
-
Returns the time/date string the cycle was started
- getD() - Method in class weka.core.matrix.EigenvalueDecomposition
-
Return the block diagonal eigenvalue matrix
- getData() - Method in class weka.attributeSelection.BestFirst.Link2
-
Get a group
- getData() - Method in class weka.attributeSelection.LFSMethods.Link2
-
Get a group
- getData() - Method in class weka.classifiers.rules.RuleStats
-
Get the data of the stats
- getData() - Method in class weka.core.AttributeLocator
-
returns the underlying data
- getData() - Method in class weka.core.converters.ArffLoader.ArffReader
-
Returns the data that was read
- getData() - Method in class weka.core.TestInstances
-
returns the current dataset, can be null
- getDatabase_distanceType() - Method in class weka.clusterers.DBScan
-
Returns the distance-type
- getDatabase_distanceType() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
-
Returns the distance-type
- getDatabase_distanceType() - Method in class weka.clusterers.OPTICS
-
Returns the distance-type
- getDatabase_Type() - Method in class weka.clusterers.DBScan
-
Returns the type of the used index (database)
- getDatabase_Type() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
-
Returns the type of the used index (database)
- getDatabase_Type() - Method in class weka.clusterers.OPTICS
-
Returns the type of the used index (database)
- getDatabaseOutput() - Method in class weka.clusterers.OPTICS
-
Returns the file to save the database to - if directory, database is not
saved.
- getDatabaseSize() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
-
Returns the database's size
- getDatabaseURL() - Method in class weka.experiment.DatabaseUtils
-
Get the value of DatabaseURL.
- getDataDictionary() - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
-
Get the data dictionary.
- getDataDictionaryAsInstances(Document) - Static method in class weka.core.pmml.PMMLFactory
-
Get the data dictionary as an Instances object
- getDataFileName() - Method in class weka.classifiers.BVDecompose
-
Get the name of the data file used for the decomposition
- getDataFileName() - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Get the name of the data file used for the decomposition
- getDataObject(String) - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database
-
Select a dataObject from the database
- getDataObject(String) - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
-
Select a dataObject from the database
- getDataObject() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.EpsilonRange_ListElement
-
Returns this dataObject
- getDataPoint() - Method in class weka.gui.beans.ChartEvent
-
Get the data point
- getDataSeqID() - Method in class weka.associations.GeneralizedSequentialPatterns
-
Returns the attribute representing the data sequence ID.
- getDataset() - Method in class weka.classifiers.CheckSource
-
Gets the dataset to use for testing, can be null.
- getDataSet() - Method in class weka.core.converters.AbstractLoader
-
- getDataSet() - Method in class weka.core.converters.ArffLoader
-
Return the full data set.
- getDataSet() - Method in class weka.core.converters.C45Loader
-
Return the full data set.
- getDataSet() - Method in class weka.core.converters.ConverterUtils.DataSource
-
returns the full dataset, can be null in case of an error.
- getDataSet(int) - Method in class weka.core.converters.ConverterUtils.DataSource
-
returns the full dataset with the specified class index set,
can be null in case of an error.
- getDataSet() - Method in class weka.core.converters.CSVLoader
-
Return the full data set.
- getDataSet() - Method in class weka.core.converters.DatabaseLoader
-
Return the full data set in batch mode (header and all intances at once).
- getDataSet() - Method in class weka.core.converters.LibSVMLoader
-
Return the full data set.
- getDataSet() - Method in interface weka.core.converters.Loader
-
Return the full data set.
- getDataSet() - Method in class weka.core.converters.SerializedInstancesLoader
-
Return the full data set.
- getDataSet() - Method in class weka.core.converters.SVMLightLoader
-
Return the full data set.
- getDataSet() - Method in class weka.core.converters.TextDirectoryLoader
-
Return the full data set.
- getDataSet() - Method in class weka.core.converters.XRFFLoader
-
Return the full data set.
- getDataset() - Method in class weka.filters.CheckSource
-
Gets the dataset to use for testing, can be null.
- getDataSet() - Method in class weka.gui.beans.DataSetEvent
-
Return the instances of the data set
- getDataSet() - Method in class weka.gui.beans.ThresholdDataEvent
-
Return the instances of the data set
- getDataSet() - Method in class weka.gui.beans.VisualizableErrorEvent
-
Return the instances of the data set
- getDatasetFormat() - Method in class weka.datagenerators.DataGenerator
-
Gets the format of the dataset that is to be generated.
- getDatasetKeyColumns() - Method in class weka.experiment.PairedTTester
-
Get the value of DatasetKeyColumns.
- getDatasetKeyColumns() - Method in interface weka.experiment.Tester
-
Get the value of DatasetKeyColumns.
- getDatasets() - Method in class weka.experiment.Experiment
-
Gets the datasets in the experiment.
- getDatasetsFirst() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
whether datasets or algorithms are iterated first
- getDataType() - Method in class weka.gui.beans.xml.XMLBeans
-
returns the type of data that is to be read/written
- getDateAttributes() - Method in class weka.core.converters.CSVLoader
-
Returns the current attribute range to be forced to type date.
- getDateFormat() - Method in class weka.core.Attribute
-
Returns the Date format pattern in case this attribute is of type DATE,
otherwise an empty string.
- getDateFormat() - Method in class weka.core.converters.CSVLoader
-
Get the format to use for parsing date values.
- getDateFormat() - Method in class weka.filters.unsupervised.attribute.Add
-
Get the date format, complying to ISO-8601.
- getDateFormat() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
-
Get the date format used in output.
- getDbUtils() - Method in class weka.gui.sql.event.ConnectionEvent
-
returns the DbUtils instance that is responsible for the
connect/disconnect.
- getDbUtils() - Method in class weka.gui.sql.event.QueryExecuteEvent
-
returns the DbUtils instance that was executed the query
- getDebug() - Method in class weka.associations.GeneralizedSequentialPatterns
-
Get whether debugging is turned on.
- getDebug() - Method in class weka.attributeSelection.RaceSearch
-
Get whether output is to be verbose
- getDebug() - Method in class weka.attributeSelection.ScatterSearchV1
-
Get whether output is to be verbose
- getDebug() - Method in class weka.classifiers.BVDecompose
-
Gets whether debugging is turned on
- getDebug() - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Gets whether debugging is turned on
- getDebug() - Method in class weka.classifiers.Classifier
-
Get whether debugging is turned on.
- getDebug() - Method in class weka.classifiers.functions.LeastMedSq
-
Returns whether or not debugging output shouild be printed
- getDebug() - Method in class weka.classifiers.functions.LinearRegression
-
Controls whether debugging output will be printed
- getDebug() - Method in class weka.classifiers.functions.Logistic
-
Gets whether debugging output will be printed.
- getDebug() - Method in class weka.classifiers.functions.PaceRegression
-
Controls whether debugging output will be printed
- getDebug() - Method in class weka.classifiers.functions.supportVector.Kernel
-
Gets whether debugging output is turned on or not.
- getDebug() - Method in class weka.classifiers.meta.MultiScheme
-
Get whether debugging is turned on
- getDebug() - Method in class weka.classifiers.rules.JRip
-
Gets whether debug information is output to the console
- getDebug() - Method in class weka.clusterers.EM
-
Get debug mode
- getDebug() - Method in class weka.clusterers.HierarchicalClusterer
-
Get whether debugging is turned on.
- getDebug() - Method in class weka.clusterers.sIB
-
Get debug mode
- getDebug() - Method in class weka.core.Check
-
Get whether debugging is turned on
- getDebug() - Method in class weka.core.converters.TextDirectoryLoader
-
Gets whether additional debug information is printed.
- getDebug() - Method in class weka.core.Debug.Random
-
returns whether to print the generated random values or not
- getDebug() - Method in class weka.datagenerators.DataGenerator
-
Gets the debug flag.
- getDebug() - Method in class weka.estimators.CheckEstimator
-
Get whether debugging is turned on
- getDebug() - Method in class weka.estimators.Estimator
-
Get whether debugging is turned on.
- getDebug() - Method in class weka.experiment.DatabaseUtils
-
Gets whether there should be printed some debugging output to stderr or not.
- getDebug() - Method in class weka.filters.SimpleFilter
-
Returns the current debugging mode state.
- getDebug() - Method in class weka.filters.unsupervised.attribute.AddExpression
-
Gets whether debug is set
- getDebug() - Method in class weka.gui.DatabaseConnectionDialog
-
Returns the debug flag
- getDebug() - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion
-
returns whether debug mode is on.
- getDebug() - Method in class weka.gui.streams.InstanceCounter
-
- getDebug() - Method in class weka.gui.streams.InstanceJoiner
-
- getDebug() - Method in class weka.gui.streams.InstanceLoader
-
- getDebug() - Method in class weka.gui.streams.InstanceSavePanel
-
- getDebug() - Method in class weka.gui.streams.InstanceTable
-
- getDebug() - Method in class weka.gui.streams.InstanceViewer
-
- getDebugLevel() - Method in class weka.clusterers.XMeans
-
Gets the debug level.
- getDebugVectorsFile() - Method in class weka.clusterers.XMeans
-
Gets the file name for a file that has the random vectors stored.
- getDecay() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- getDecimals() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Get the number of decimals to round to.
- getDefault() - Method in class weka.core.Tee
-
returns the default printstrean, can be NULL.
- getDefaultHandler() - Method in class weka.core.CheckOptionHandler
-
returns a new instance of the OptionHandler's class
- getDefaultOptions() - Method in class weka.core.CheckOptionHandler
-
returns the default options the default OptionHandler will return
- getDefaultValue() - Method in class weka.core.pmml.TargetMetaInfo
-
Get the default value (numeric target)
- getDefaultWeight() - Method in class weka.classifiers.functions.Winnow
-
Get the value of defaultWeight.
- getDegree() - Method in class weka.classifiers.functions.LibSVM
-
Gets the degree of the kernel
- getDegreesOfFreedom() - Method in class weka.experiment.PairedStats
-
Gets the degrees of freedom.
- getDeletedList() - Method in class weka.classifiers.rules.DTNB.EvalWithDelete
-
- getDeleteEmptyBins() - Method in class weka.classifiers.meta.RegressionByDiscretization
-
Gets the number of bins numeric attributes will be divided into
- getDelimiters() - Method in class weka.core.tokenizers.CharacterDelimitedTokenizer
-
Get the value of delimiters (not backquoted).
- getDelta() - Method in class weka.associations.Apriori
-
Get the value of delta.
- getDelta() - Method in class weka.associations.FPGrowth
-
Get the value of delta.
- getDelta() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
-
- getDelta() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
-
- getDensityBasedClusterer() - Method in class weka.filters.unsupervised.attribute.ClusterMembership
-
Get the clusterer used by this filter
- getDerivedFields() - Method in class weka.core.pmml.MiningSchema
-
- getDerivedValue(double[]) - Method in class weka.core.pmml.DerivedFieldMetaInfo
-
Get the derived field value for the given incoming vector of
values.
- getDescendantPopulationSize() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
-
- getDescendantPopulationSize() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
-
- getDescription() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERFileFilter
-
The description of this filter.
- getDescription() - Method in class weka.gui.ExtensionFileFilter
-
Gets the description of accepted files.
- getDescription() - Method in class weka.gui.visualize.BMPWriter
-
returns the name of the writer, to display in the FileChooser.
- getDescription() - Method in class weka.gui.visualize.JComponentWriter
-
returns the name of the writer, to display in the FileChooser.
- getDescription() - Method in class weka.gui.visualize.JPEGWriter
-
returns the name of the writer, to display in the FileChooser.
- getDescription() - Method in class weka.gui.visualize.PNGWriter
-
returns the name of the writer, to display in the FileChooser.
- getDescription() - Method in class weka.gui.visualize.PostscriptWriter
-
returns the name of the writer, to display in the FileChooser.
- getDescriptor() - Method in class weka.core.PropertyPath.PropertyContainer
-
returns the stored descriptor
- getDescriptorByName(Object, String) - Method in class weka.core.xml.XMLSerialization
-
returns a descriptor for a given objet by providing the name
- getDescriptors(Object) - Method in class weka.core.xml.XMLSerialization
-
returns a hashtable with PropertyDescriptors that have "get" and "set"
methods indexed by the property name.
- getDesignatedClass() - Method in class weka.classifiers.meta.ThresholdSelector
-
Gets the method to determine which class value to optimize.
- getDesignVersion() - Method in interface weka.gui.visualize.plugins.ErrorVisualizePlugin
-
Get the specific version of Weka the class is designed for.
- getDesignVersion() - Method in interface weka.gui.visualize.plugins.GraphVisualizePlugin
-
Get the specific version of Weka the class is designed for.
- getDesignVersion() - Method in interface weka.gui.visualize.plugins.TreeVisualizePlugin
-
Get the specific version of Weka the class is designed for.
- getDesignVersion() - Method in interface weka.gui.visualize.plugins.VisualizePlugin
-
Get the specific version of Weka the class is designed for.
- getDesiredSize() - Method in class weka.classifiers.meta.Decorate
-
Gets the desired size of the committee.
- getDesiredWeightOfInstancesPerInterval() - Method in class weka.filters.unsupervised.attribute.Discretize
-
Get the DesiredWeightOfInstancesPerInterval value.
- getDestination() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
returns the default destination
- getDetectionPerAttribute() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
-
Gets whether an Outlier/ExtremeValue attribute pair is generated for
each numeric attribute ("true") or just one pair for all numeric
attributes together ("false").
- getDeviceConfiguration() - Method in class weka.gui.visualize.PostscriptGraphics
-
- getDir() - Method in class weka.core.Javadoc
-
returns the current dir containing the class to update.
- getDir() - Method in class weka.gui.Loader
-
returns the dir prefix
- getDirection() - Method in class weka.attributeSelection.BestFirst
-
Get the search direction
- getDirectory() - Method in class weka.core.converters.TextDirectoryLoader
-
get the Dir specified as the source
- getDirectory() - Method in class weka.gui.beans.SerializedModelSaver
-
Get the directory that the model(s) will be saved into
- getDiscretizeBin() - Method in class weka.classifiers.mi.MIBoost
-
Get the number of bins in discretization
- getDiscretizer() - Method in class weka.classifiers.trees.j48.NBTreeNoSplit
-
Return the discretizer used at this node
- getDisplay() - Method in enum weka.core.TechnicalInformation.Field
-
returns the display string
- getDisplay() - Method in enum weka.core.TechnicalInformation.Type
-
returns the display string
- getDisplayCol(int) - Method in class weka.experiment.ResultMatrix
-
returns the displayed index of the given col, depending on the order of
columns, returns -1 if index out of bounds
- getDisplayedResultsets() - Method in class weka.experiment.PairedTTester
-
Gets the indices of the the datasets that are displayed (if null
then all are displayed).
- getDisplayedResultsets() - Method in interface weka.experiment.Tester
-
Gets the indices of the the datasets that are displayed (if null
then all are displayed).
- getDisplayModelInOldFormat() - Method in class weka.classifiers.bayes.NaiveBayes
-
Get whether to display model output in the old, original
format.
- getDisplayModelInOldFormat() - Method in class weka.clusterers.EM
-
Get whether to display model output in the old, original
format.
- getDisplayName() - Method in class weka.experiment.PairedCorrectedTTester
-
returns the name of the tester
- getDisplayName() - Method in class weka.experiment.PairedTTester
-
returns the name of the tester
- getDisplayName() - Method in class weka.experiment.ResultMatrix
-
returns the name of the output format
- getDisplayName() - Method in class weka.experiment.ResultMatrixCSV
-
returns the name of the output format
- getDisplayName() - Method in class weka.experiment.ResultMatrixGnuPlot
-
returns the name of the output format
- getDisplayName() - Method in class weka.experiment.ResultMatrixHTML
-
returns the name of the output format
- getDisplayName() - Method in class weka.experiment.ResultMatrixLatex
-
returns the name of the output format
- getDisplayName() - Method in class weka.experiment.ResultMatrixPlainText
-
returns the name of the output format
- getDisplayName() - Method in class weka.experiment.ResultMatrixSignificance
-
returns the name of the output format
- getDisplayName() - Method in interface weka.experiment.Tester
-
returns the name of the testing algorithm
- getDisplayRow(int) - Method in class weka.experiment.ResultMatrix
-
returns the displayed index of the given row, depending on the order of
rows, returns -1 if index out of bounds
- getDisplayRules() - Method in class weka.classifiers.rules.DecisionTable
-
Gets whether rules are being printed
- getDisplayStdDevs() - Method in class weka.clusterers.SimpleKMeans
-
Gets whether standard deviations and nominal count
Should be displayed in the clustering output
- getDisplayValue() - Method in class weka.core.pmml.FieldMetaInfo.Value
-
- getDistance() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.EpsilonRange_ListElement
-
Returns the distance that was calulcated for this dataObject
(The distance between this dataObject and the dataObject for which an epsilon-range-query
was performed.)
- getDistanceF() - Method in class weka.clusterers.XMeans
-
Gets the distance function.
- getDistanceFSpec() - Method in class weka.clusterers.XMeans
-
Gets the distance function specification string, which contains the
class name of the distance function class and any options to it.
- getDistanceFunction() - Method in class weka.clusterers.HierarchicalClusterer
-
- getDistanceFunction() - Method in class weka.clusterers.SimpleKMeans
-
returns the distance function currently in use.
- getDistanceFunction() - Method in class weka.core.neighboursearch.KDTree
-
returns the distance function currently in use.
- getDistanceFunction() - Method in class weka.core.neighboursearch.NearestNeighbourSearch
-
returns the distance function currently in use.
- getDistanceIsBranchLength() - Method in class weka.clusterers.HierarchicalClusterer
-
- getDistances() - Method in class weka.core.neighboursearch.BallTree
-
Returns the distances of the k nearest neighbours.
- getDistances() - Method in class weka.core.neighboursearch.CoverTree
-
Returns the distances of the (k)-NN(s) found earlier
by kNearestNeighbours()/nearestNeighbour().
- getDistances() - Method in class weka.core.neighboursearch.KDTree
-
Returns the distances to the kNearest or 1 nearest neighbour currently
found with either the kNearestNeighbours or the nearestNeighbour method.
- getDistances() - Method in class weka.core.neighboursearch.LinearNNSearch
-
Returns the distances of the k nearest neighbours.
- getDistances() - Method in class weka.core.neighboursearch.NearestNeighbourSearch
-
Returns the distances of the k nearest neighbours.
- getDistanceWeighting() - Method in class weka.classifiers.lazy.IBk
-
Gets the distance weighting method used.
- getDistMult() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Gets the distance multiplier.
- getDistribution(String) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
returns distribution of a node in matrix form with matrix representing distribution
with P[i][j] = P(node = j | parent configuration = i)
- getDistribution(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
returns distribution of a node in matrix form with matrix representing distribution
with P[i][j] = P(node = j | parent configuration = i)
- getDistribution() - Method in class weka.filters.unsupervised.attribute.RandomProjection
-
Returns the current distribution that'll be used for calculating the
random matrix
- getDistributions() - Method in class weka.classifiers.bayes.BayesNet
-
Get full set of estimators.
- getDistributions(int) - Method in class weka.classifiers.rules.RuleStats
-
Get the class distribution predicted by the rule in
given position
- getDistributionSpread() - Method in class weka.filters.supervised.instance.SpreadSubsample
-
Gets the value for the distribution spread
- getDocType() - Method in class weka.core.xml.XMLDocument
-
returns the current DOCTYPE, can be null
.
- getDocument() - Method in class weka.core.xml.XMLDocument
-
returns the parsed DOM document.
- getDocument() - Method in class weka.core.xml.XMLOptions
-
returns the parsed DOM document.
- getDominantEigenVector(Matrix) - Method in class weka.filters.supervised.attribute.PLSFilter
-
determines the dominant eigenvector for the given matrix and returns it
- getDoNotOperateOnPerClassBasis() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Get the DoNotOperateOnPerClassBasis value.
- getDoNotReplaceMissingValues() - Method in class weka.classifiers.functions.LibLINEAR
-
Gets whether automatic replacement of missing values is
disabled.
- getDoNotReplaceMissingValues() - Method in class weka.classifiers.functions.LibSVM
-
Gets whether automatic replacement of missing values is
disabled.
- getDontFilterAfterFirstBatch() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Get whether to apply the filter to instances that arrive once
the first (training) batch has been seen.
- getDontNormalize() - Method in class weka.classifiers.functions.SPegasos
-
Get whether normalization has been turned off.
- getDontNormalize() - Method in class weka.core.NormalizableDistance
-
Gets whether if the attribute values are to be normazlied in distance
calculation.
- getDontReplaceMissing() - Method in class weka.classifiers.functions.SPegasos
-
Get whether global replacement of missing values has been
disabled.
- getDontReplaceMissingValues() - Method in class weka.clusterers.SimpleKMeans
-
Gets whether missing values are to be replaced
- getDoublePivot() - Method in class weka.core.matrix.LUDecomposition
-
Return pivot permutation vector as a one-dimensional double array
- getEditor() - Method in class weka.gui.PropertyDialog
-
Gets the current property editor.
- getEditorActive() - Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
-
Returns true if the editor is currently in an active status---that
is the array is active and able to be edited.
- getElapsedTime() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
-
Returns the elapsed-time
- getElement(int, int) - Method in class weka.classifiers.CostMatrix
-
Return the value of a cell as a double (for legacy code)
- getElement(int, int, Instance) - Method in class weka.classifiers.CostMatrix
-
Return the value of a cell as a double.
- getElement(int) - Method in class weka.core.AlgVector
-
Returns the value of a cell in the matrix.
- getElement(int, int) - Method in class weka.core.Matrix
-
Deprecated.
Returns the value of a cell in the matrix.
- getElementAt(int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
-
Returns the component at the specified index.
- getElements() - Method in class weka.associations.gsp.Sequence
-
Returns the Elements of the Sequence.
- getElements() - Method in class weka.core.AlgVector
-
Gets the elements of the vector and returns them as double array.
- getEliminateColinearAttributes() - Method in class weka.classifiers.functions.LinearRegression
-
Get the value of EliminateColinearAttributes.
- getEnabled() - Method in class weka.core.Debug
-
returns whether the logging is enabled
- getEnclosureCharacters() - Method in class weka.core.converters.CSVLoader
-
Get the character(s) to use/recognize as string enclosures
- getEntropicAutoBlend() - Method in class weka.classifiers.lazy.KStar
-
Get whether entropic blending being used
- getEntry(double) - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable
-
Returns the table entry to which the specified key is mapped in
this hashtable.
- getEnumerateColNames() - Method in class weka.experiment.ResultMatrix
-
returns whether column names or numbers instead are enumerateed
- getEnumerateRowNames() - Method in class weka.experiment.ResultMatrix
-
returns whether row names or numbers instead are enumerateed
- getEnvironment() - Method in class weka.gui.beans.FlowRunner
-
Get the environment variables that are in use.
- getEpochs() - Method in class weka.classifiers.functions.SPegasos
-
Get current number of epochs
- getEps() - Method in class weka.classifiers.functions.LibLINEAR
-
Gets tolerance of termination criterion
- getEps() - Method in class weka.classifiers.functions.LibSVM
-
Gets tolerance of termination criterion
- getEpsilon() - Method in class weka.classifiers.functions.SMO
-
Get the value of epsilon.
- getEpsilon() - Method in class weka.classifiers.functions.supportVector.RegSMO
-
Get the value of epsilon.
- getEpsilon() - Method in class weka.classifiers.mi.MISMO
-
Get the value of epsilon.
- getEpsilon() - Method in class weka.clusterers.DBScan
-
Returns the value of epsilon
- getEpsilon() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
-
Returns the value of epsilon
- getEpsilon() - Method in class weka.clusterers.OPTICS
-
Returns the value of epsilon
- getEpsilonParameter() - Method in class weka.attributeSelection.SVMAttributeEval
-
Get the value of P used with SMO
- getEpsilonParameter() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
-
Get the value of epsilon parameter of the epsilon insensitive loss function.
- getError() - Method in class weka.classifiers.BVDecompose
-
Get the calculated error rate
- getError() - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Get the calculated error rate
- getErrorOnProbabilities() - Method in class weka.classifiers.functions.SimpleLogistic
-
Get the value of errorOnProbabilities.
- getErrorOnProbabilities() - Method in class weka.classifiers.trees.FT
-
Get the value of errorOnProbabilities.
- getErrorOnProbabilities() - Method in class weka.classifiers.trees.LMT
-
Get the value of errorOnProbabilities.
- getErrorRate(Instances) - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Returns the misclassification error of the current model on a set of instances.
- getErrors() - Method in class weka.classifiers.trees.j48.NBTreeNoSplit
-
Return the errors made by the naive bayes model at this node
- getErrors() - Method in class weka.classifiers.trees.j48.NBTreeSplit
-
Return the errors made by the naive bayes models arising
from this split.
- getEstimatedErrors() - Method in class weka.classifiers.trees.ft.FTtree
-
Computes estimated errors for tree.
- getEstimatedErrorsForBranch(Instances) - Method in class weka.classifiers.trees.ft.FTtree
-
Computes estimated errors for one branch.
- getEstimatedErrorsForDistribution(Distribution) - Method in class weka.classifiers.trees.ft.FTtree
-
Computes estimated errors for leaf.
- getEstimatedErrorsForLeaf() - Method in class weka.classifiers.rules.part.C45PruneableDecList
-
Computes estimated errors for leaf.
- getEstimator() - Method in class weka.classifiers.bayes.BayesNet
-
Get the BayesNetEstimator used for calculating the CPTs
- getEstimator() - Method in class weka.classifiers.functions.PaceRegression
-
Gets the estimator
- getEstimator() - Method in class weka.estimators.CheckEstimator
-
Get the estimator used as the estimator
- getEstimator(double) - Method in interface weka.estimators.ConditionalEstimator
-
Get a probability estimator for a value
- getEstimator(double) - Method in class weka.estimators.DDConditionalEstimator
-
Get a probability estimator for a value
- getEstimator(double) - Method in class weka.estimators.DKConditionalEstimator
-
Get a probability estimator for a value
- getEstimator(double) - Method in class weka.estimators.DNConditionalEstimator
-
Get a probability estimator for a value
- getEstimator(double) - Method in class weka.estimators.KDConditionalEstimator
-
Get a probability estimator for a value
- getEstimator(double) - Method in class weka.estimators.KKConditionalEstimator
-
Get a probability estimator for a value
- getEstimator(double) - Method in class weka.estimators.NDConditionalEstimator
-
Get a probability estimator for a value
- getEstimator(double) - Method in class weka.estimators.NNConditionalEstimator
-
Get a probability estimator for a value
- getEtimateConstModel(Distribution) - Method in class weka.classifiers.trees.ft.FTtree
-
Computes estimated errors for Constructor Model.
- getEvaluation() - Method in class weka.classifiers.meta.GridSearch
-
Gets the criterion used for evaluating the classifier performance.
- getEvaluation() - Method in class weka.classifiers.meta.GridSearch.PerformanceComparator
-
returns the performance measure that's used to compare the objects
- getEvaluationMeasure() - Method in class weka.classifiers.rules.DecisionTable
-
Gets the currently set performance evaluation measure used for selecting
attributes for the decision table
- getEvaluationMode() - Method in class weka.classifiers.meta.ThresholdSelector
-
Gets the evaluation mode used.
- getEvaluator() - Method in class weka.attributeSelection.CheckAttributeSelection
-
Get the current evaluator
- getEvaluator() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
-
Get the evaluator used as the base evaluator.
- getEvaluator() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
-
Gets the attribute evaluator used
- getEvaluator() - Method in class weka.filters.supervised.attribute.AttributeSelection
-
Get the name of the attribute/subset evaluator
- getEvaluatorSpec() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
-
Gets the evaluator specification string, which contains the class name of
the evaluator and any options to the evaluator
- getEvaluatorSpec() - Method in class weka.attributeSelection.FilteredAttributeEval
-
Get the evaluator + options as a string
- getEvaluatorSpec() - Method in class weka.attributeSelection.FilteredSubsetEval
-
Get the evaluator + options as a string
- getEvaluatorSpec() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
-
Gets the evaluator specification string, which contains the class name of
the attribute evaluator and any options to it
- getEvalUsingTrainingData() - Method in class weka.attributeSelection.OneRAttributeEval
-
Returns true if the training data is to be used for evaluation
- getEventName() - Method in class weka.gui.beans.BeanConnection
-
Returns the name of the event for this conncetion
- getEvents() - Method in class weka.associations.gsp.Element
-
Returns the events Array of an Element.
- getEventSetDescriptors() - Method in class weka.gui.beans.AbstractDataSinkBeanInfo
-
Get the event set descriptors for this bean
- getEventSetDescriptors() - Method in class weka.gui.beans.AbstractDataSourceBeanInfo
-
Get the event set descriptors pertinent to data sources
- getEventSetDescriptors() - Method in class weka.gui.beans.AbstractTestSetProducerBeanInfo
-
- getEventSetDescriptors() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducerBeanInfo
-
- getEventSetDescriptors() - Method in class weka.gui.beans.AbstractTrainingSetProducerBeanInfo
-
Returns event set descriptors for this type of bean
- getEventSetDescriptors() - Method in class weka.gui.beans.AssociatorBeanInfo
-
- getEventSetDescriptors() - Method in class weka.gui.beans.AttributeSummarizerBeanInfo
-
Get the event set descriptors for this bean
- getEventSetDescriptors() - Method in class weka.gui.beans.ClassAssignerBeanInfo
-
Returns the event set descriptors
- getEventSetDescriptors() - Method in class weka.gui.beans.ClassifierBeanInfo
-
- getEventSetDescriptors() - Method in class weka.gui.beans.ClassifierPerformanceEvaluatorBeanInfo
-
- getEventSetDescriptors() - Method in class weka.gui.beans.ClassValuePickerBeanInfo
-
Returns the event set descriptors
- getEventSetDescriptors() - Method in class weka.gui.beans.ClustererBeanInfo
-
- getEventSetDescriptors() - Method in class weka.gui.beans.ClustererPerformanceEvaluatorBeanInfo
-
- getEventSetDescriptors() - Method in class weka.gui.beans.CostBenefitAnalysisBeanInfo
-
Get the event set descriptors for this bean
- getEventSetDescriptors() - Method in class weka.gui.beans.DataVisualizerBeanInfo
-
Get the event set descriptors for this bean
- getEventSetDescriptors() - Method in class weka.gui.beans.FilterBeanInfo
-
Get the event set descriptors for this bean
- getEventSetDescriptors() - Method in class weka.gui.beans.GraphViewerBeanInfo
-
Get the event set descriptors for this bean
- getEventSetDescriptors() - Method in class weka.gui.beans.IncrementalClassifierEvaluatorBeanInfo
-
Get the event set descriptors for this bean
- getEventSetDescriptors() - Method in class weka.gui.beans.InstanceStreamToBatchMakerBeanInfo
-
Returns the event set descriptors
- getEventSetDescriptors() - Method in class weka.gui.beans.ModelPerformanceChartBeanInfo
-
Get the event set descriptors for this bean
- getEventSetDescriptors() - Method in class weka.gui.beans.PredictionAppenderBeanInfo
-
Get the event set descriptors pertinent to data sources
- getEventSetDescriptors() - Method in class weka.gui.beans.ScatterPlotMatrixBeanInfo
-
Get the event set descriptors for this bean
- getEventSetDescriptors() - Method in class weka.gui.beans.SerializedModelSaverBeanInfo
-
Get the event set descriptors for this bean
- getEventSetDescriptors() - Method in class weka.gui.beans.StripChartBeanInfo
-
Get the event set descriptors for this bean
- getEventSetDescriptors() - Method in class weka.gui.beans.TextViewerBeanInfo
-
Get the event set descriptors for this bean
- getEvidence(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
get evidence state of a node.
- getException() - Method in class weka.gui.sql.event.ConnectionEvent
-
returns the stored exception, if any (can be NULL)
- getException() - Method in class weka.gui.sql.event.QueryExecuteEvent
-
returns the exception, if one happened, otherwise NULL
- getExclusive() - Method in class weka.classifiers.rules.ConjunctiveRule
-
Returns whether exclusive expressions for nominal attributes splits are
considered
- getExecutionSlots() - Method in class weka.gui.beans.Classifier
-
Get the number of execution slots (threads) used
to train models.
- getExecutionStatus() - Method in class weka.experiment.TaskStatusInfo
-
Get the execution status of this Task.
- getExitIfNoWindowsOpen() - Static method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
Gets whether System.exit gets called after the last window gets closed
- getExitOnClose() - Method in class weka.gui.arffviewer.ArffViewer
-
returns TRUE if a System.exit(0) is done on a close
- getExitOnClose() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
returns TRUE if a System.exit(0) is done on a close
- getExpectedFrequency() - Method in class weka.associations.tertius.Rule
-
Get the expected frequency of counter-instances of this rule.
- getExpectedNumber() - Method in class weka.associations.tertius.Rule
-
- getExpectedResultsPerAverage() - Method in class weka.experiment.AveragingResultProducer
-
Get the value of ExpectedResultsPerAverage.
- getExperiment() - Method in class weka.experiment.RemoteExperimentSubTask
-
Get the experiment for this sub task
- getExperiment() - Method in class weka.gui.experiment.SetupModePanel
-
Gets the currently configured experiment.
- getExperiment() - Method in class weka.gui.experiment.SetupPanel
-
Gets the currently configured experiment.
- getExperiment() - Method in class weka.gui.experiment.SimpleSetupPanel
-
Gets the currently configured experiment.
- getExperimentType() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
returns the default experiment type
- getExplicitPropsFile() - Method in class weka.gui.GenericPropertiesCreator
-
returns TRUE, if a file is loaded and not the Utils class used for
locating the props file.
- getExplorer() - Method in class weka.gui.explorer.AssociationsPanel
-
returns the parent Explorer frame
- getExplorer() - Method in class weka.gui.explorer.AttributeSelectionPanel
-
returns the parent Explorer frame
- getExplorer() - Method in class weka.gui.explorer.ClassifierPanel
-
returns the parent Explorer frame
- getExplorer() - Method in class weka.gui.explorer.ClustererPanel
-
returns the parent Explorer frame
- getExplorer() - Method in interface weka.gui.explorer.Explorer.ExplorerPanel
-
returns the parent Explorer frame
- getExplorer() - Method in class weka.gui.explorer.PreprocessPanel
-
returns the parent Explorer frame
- getExplorer() - Method in class weka.gui.explorer.VisualizePanel
-
returns the parent Explorer frame
- getExponent() - Method in class weka.classifiers.functions.supportVector.PolyKernel
-
Gets the exponent value.
- getExponent() - Method in class weka.classifiers.functions.VotedPerceptron
-
Get the value of exponent.
- getExpression(Node, FieldMetaInfo.Optype, ArrayList<Attribute>, TransformationDictionary) - Static method in class weka.core.pmml.Expression
-
Static factory method that returns a subclass of Expression that
encapsulates the type of expression contained in the Element
supplied.
- getExpression(String, Node, FieldMetaInfo.Optype, ArrayList<Attribute>, TransformationDictionary) - Static method in class weka.core.pmml.Expression
-
Static factory method that returns a subclass of Expression that
encapsulates the type of expression supplied as an argument.
- getExpression() - Method in class weka.datagenerators.classifiers.regression.Expression
-
Gets the mathematical expression for generating y out of x
- getExpression() - Method in class weka.filters.unsupervised.attribute.AddExpression
-
Get the expression
- getExpression() - Method in class weka.filters.unsupervised.attribute.MathExpression
-
Get the expression
- getExpression() - Method in class weka.filters.unsupervised.instance.SubsetByExpression
-
Returns the expression used for filtering.
- getExtension() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
returns the default experiment extension
- getExtension() - Method in class weka.gui.visualize.BMPWriter
-
returns the extension (incl.
- getExtension() - Method in class weka.gui.visualize.JComponentWriter
-
returns the extension (incl.
- getExtension() - Method in class weka.gui.visualize.JPEGWriter
-
returns the extension (incl.
- getExtension() - Method in class weka.gui.visualize.PNGWriter
-
returns the extension (incl.
- getExtension() - Method in class weka.gui.visualize.PostscriptWriter
-
returns the extension (incl.
- getExtensions() - Method in class weka.gui.ExtensionFileFilter
-
Returns a copy of the acceptable extensions.
- getExtremeValuesAsOutliers() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
-
Get whether extreme values are also tagged as outliers.
- getExtremeValuesFactor() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
-
Gets the factor for determining the thresholds for extreme values.
- getFactory() - Method in class weka.core.xml.XMLDocument
-
returns the DocumentBuilderFactory.
- getFailReason() - Method in class weka.core.Capabilities
-
returns the reason why the tests failed, is null if tests succeeded
- getFallout() - Method in class weka.classifiers.evaluation.TwoClassStats
-
Calculate the fallout.
- getFalseNegative() - Method in class weka.classifiers.evaluation.TwoClassStats
-
Gets the number of positive instances predicted as negative
- getFalsePositive() - Method in class weka.classifiers.evaluation.TwoClassStats
-
Gets the number of negative instances predicted as positive
- getFalsePositiveRate() - Method in class weka.classifiers.evaluation.TwoClassStats
-
Calculate the false positive rate.
- getFastRegression() - Method in class weka.classifiers.trees.LMT
-
Get the value of fastRegression.
- getField(Object, String) - Method in class weka.classifiers.functions.LibLINEAR
-
returns the current value of the specified field
- getField(Object, String) - Method in class weka.classifiers.functions.LibSVM
-
returns the current value of the specified field
- getFieldAsAttribute() - Method in class weka.core.pmml.DefineFunction.ParameterField
-
- getFieldAsAttribute() - Method in class weka.core.pmml.DerivedFieldMetaInfo
-
Get this derived field as an Attribute.
- getFieldAsAttribute() - Method in class weka.core.pmml.FieldMetaInfo
-
Return this field as an Attribute.
- getFieldAsAttribute() - Method in class weka.core.pmml.MiningFieldMetaInfo
-
Return this mining field as an Attribute.
- getFieldAsAttribute() - Method in class weka.core.pmml.TargetMetaInfo
-
Return this field as an Attribute.
- getFieldDef(String) - Method in class weka.core.pmml.Expression
-
Return the named attribute from the list of reference fields.
- getFieldDefIndex(String) - Method in class weka.core.pmml.Expression
-
- getFieldName() - Method in class weka.core.pmml.FieldMetaInfo
-
Get the name of this field.
- getFieldsAsInstances() - Method in class weka.core.pmml.MiningSchema
-
Get the all the fields (both mining schema and derived) as Instances.
- getFieldsMappingString() - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
-
Get a textual description of the mapping between mining schema
fields and incoming data fields.
- getFieldsMappingString() - Method in class weka.core.pmml.MappingInfo
-
Get a textual description of them mapping between mining schema
fields and incoming data fields.
- getFile() - Method in class weka.gui.visualize.JComponentWriter
-
returns the file being used for storing the output
- getFileConverters(String, String[]) - Static method in class weka.core.converters.ConverterUtils
-
returns a hashtable with the association
"file extension <-> converter classname" for the comma-separated list
of converter classnames.
- getFileConverters(Vector, String[]) - Static method in class weka.core.converters.ConverterUtils
-
returns a hashtable with the association
"file extension <-> converter classname" for the list of converter
classnames.
- getFileDescription() - Method in class weka.core.converters.AbstractFileSaver
-
to be pverridden
- getFileDescription() - Method in class weka.core.converters.ArffLoader
-
Returns a description of the file type.
- getFileDescription() - Method in class weka.core.converters.ArffSaver
-
Returns a description of the file type.
- getFileDescription() - Method in class weka.core.converters.C45Loader
-
Returns a description of the file type.
- getFileDescription() - Method in class weka.core.converters.C45Saver
-
Returns a description of the file type.
- getFileDescription() - Method in class weka.core.converters.CSVLoader
-
Returns a description of the file type.
- getFileDescription() - Method in class weka.core.converters.CSVSaver
-
Returns a description of the file type.
- getFileDescription() - Method in interface weka.core.converters.FileSourcedConverter
-
Get a one line description of the type of file
- getFileDescription() - Method in class weka.core.converters.LibSVMLoader
-
Returns a description of the file type.
- getFileDescription() - Method in class weka.core.converters.LibSVMSaver
-
Returns a description of the file type.
- getFileDescription() - Method in class weka.core.converters.SerializedInstancesLoader
-
Returns a description of the file type.
- getFileDescription() - Method in class weka.core.converters.SerializedInstancesSaver
-
Returns a description of the file type.
- getFileDescription() - Method in class weka.core.converters.SVMLightLoader
-
Returns a description of the file type.
- getFileDescription() - Method in class weka.core.converters.SVMLightSaver
-
Returns a description of the file type.
- getFileDescription() - Method in class weka.core.converters.TextDirectoryLoader
-
Returns a description of the file type, actually it's directories.
- getFileDescription() - Method in class weka.core.converters.XRFFLoader
-
Returns a description of the file type.
- getFileDescription() - Method in class weka.core.converters.XRFFSaver
-
Returns a description of the file type.
- getFileExtension() - Method in class weka.core.converters.AbstractFileSaver
-
Gets ihe file extension.
- getFileExtension() - Method in class weka.core.converters.AbstractSaver
-
Default implementation throws an IOException.
- getFileExtension() - Method in class weka.core.converters.ArffLoader
-
Get the file extension used for arff files
- getFileExtension() - Method in class weka.core.converters.C45Loader
-
Get the file extension used for arff files
- getFileExtension() - Method in class weka.core.converters.CSVLoader
-
Get the file extension used for arff files.
- getFileExtension() - Method in interface weka.core.converters.FileSourcedConverter
-
Get the file extension used for this type of file
- getFileExtension() - Method in class weka.core.converters.LibSVMLoader
-
Get the file extension used for libsvm files.
- getFileExtension() - Method in interface weka.core.converters.Saver
-
Gets the file extension
- getFileExtension() - Method in class weka.core.converters.SerializedInstancesLoader
-
Get the file extension used for arff files
- getFileExtension() - Method in class weka.core.converters.SVMLightLoader
-
Get the file extension used for svm light files.
- getFileExtension() - Method in class weka.core.converters.XRFFLoader
-
Get the file extension used for libsvm files
- getFileExtensions() - Method in class weka.core.converters.AbstractFileSaver
-
Gets all the file extensions used for this type of file
- getFileExtensions() - Method in class weka.core.converters.ArffLoader
-
Gets all the file extensions used for this type of file
- getFileExtensions() - Method in class weka.core.converters.ArffSaver
-
Gets all the file extensions used for this type of file
- getFileExtensions() - Method in class weka.core.converters.C45Loader
-
Gets all the file extensions used for this type of file
- getFileExtensions() - Method in class weka.core.converters.CSVLoader
-
Gets all the file extensions used for this type of file.
- getFileExtensions() - Method in interface weka.core.converters.FileSourcedConverter
-
Gets all the file extensions used for this type of file
- getFileExtensions() - Method in class weka.core.converters.LibSVMLoader
-
Gets all the file extensions used for this type of file.
- getFileExtensions() - Method in class weka.core.converters.SerializedInstancesLoader
-
Gets all the file extensions used for this type of file
- getFileExtensions() - Method in class weka.core.converters.SVMLightLoader
-
Gets all the file extensions used for this type of file.
- getFileExtensions() - Method in class weka.core.converters.XRFFLoader
-
Gets all the file extensions used for this type of file
- getFileExtensions() - Method in class weka.core.converters.XRFFSaver
-
Gets all the file extensions used for this type of file
- getFileFormat() - Method in class weka.gui.beans.SerializedModelSaver
-
Get the file format to use for saving.
- getFileLoaders() - Static method in class weka.core.converters.ConverterUtils
-
returns a vector with the classnames of all the file loaders.
- getFileMatches(String) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion
-
returns all the file/dir matches with the partial search string.
- getFileMustExist() - Method in class weka.gui.ConverterFileChooser
-
Returns whether the selected file must exist (only open dialog).
- getFileName() - Method in class weka.classifiers.bayes.net.BIFReader
-
returns the current filename
- getFilename() - Method in class weka.core.Debug.Log
-
returns the filename of the log, can be null
- getFilename() - Method in class weka.core.Debug.SimpleLog
-
returns the filename of the log, can be null
- getFilename() - Method in class weka.core.FindWithCapabilities
-
returns the current filename for the dataset to base the capabilities on.
- getFilename() - Method in class weka.gui.arffviewer.ArffPanel
-
returns the filename
- getFilename(int) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
returns the filename of the specified panel
- getFileSavers() - Static method in class weka.core.converters.ConverterUtils
-
returns a vector with the classnames of all the file savers.
- getFilesRecursively(File, Vector) - Method in class weka.gui.experiment.DatasetListPanel
-
Gets all the files in the given directory
that match the currently selected extension.
- getFillWithMissing() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
-
Gets whether missing values should be used rather than removing instances
where the translated value is not known (due to border effects).
- getFilter() - Method in class weka.associations.FilteredAssociator
-
Gets the filter used.
- getFilter() - Method in class weka.attributeSelection.FilteredAttributeEval
-
Get the filter to use
- getFilter() - Method in class weka.attributeSelection.FilteredSubsetEval
-
Get the filter to use
- getFilter() - Method in class weka.classifiers.functions.PLSClassifier
-
Get the PLS filter.
- getFilter() - Method in class weka.classifiers.meta.FilteredClassifier
-
Gets the filter used.
- getFilter() - Method in class weka.classifiers.meta.GridSearch
-
Get the kernel filter.
- getFilter() - Method in class weka.clusterers.FilteredClusterer
-
Gets the filter used.
- getFilter() - Method in class weka.filters.CheckSource
-
Gets the filter being used for the tests, can be null.
- getFilter(int) - Method in class weka.filters.MultiFilter
-
Gets a single filter from the set of available filters.
- getFilter(int) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
-
Gets a single filter from the set of available filters.
- getFilter() - Method in class weka.filters.unsupervised.attribute.Wavelet
-
Get the preprocessing filter.
- getFilter() - Method in class weka.gui.beans.Filter
-
- getFilter() - Method in class weka.gui.explorer.Explorer.CapabilitiesFilterChangeEvent
-
returns the associated Capabilities filter
- getFilter() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns the default filter (fully configured) for the preprocess panel.
- getFilterAfterFirstBatch() - Method in class weka.filters.unsupervised.instance.SubsetByExpression
-
Get whether to apply the filter to instances that arrive once
the first (training) batch has been seen.
- getFilterAttributes() - Method in class weka.associations.GeneralizedSequentialPatterns
-
Returns the String containing the attributes which are used for output
filtering.
- getFiltered(int) - Method in class weka.classifiers.rules.RuleStats
-
Get the data after filtering the given rule
- getFilters() - Method in class weka.filters.MultiFilter
-
Gets the list of possible filters to choose from.
- getFilters() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
-
Gets the list of possible filters to choose from.
- getFilterSpec() - Method in class weka.associations.FilteredAssociator
-
Gets the filter specification string, which contains the class name of
the filter and any options to the filter
- getFilterSpec() - Method in class weka.attributeSelection.FilteredAttributeEval
-
Get the filter + options as a string
- getFilterSpec() - Method in class weka.attributeSelection.FilteredSubsetEval
-
Get the filter + options as a string
- getFilterSpec() - Method in class weka.classifiers.meta.FilteredClassifier
-
Gets the filter specification string, which contains the class name of
the filter and any options to the filter
- getFilterSpec() - Method in class weka.clusterers.FilteredClusterer
-
Gets the filter specification string, which contains the class name of
the filter and any options to the filter.
- getFilterSpec(Filter) - Method in class weka.filters.MultiFilter
-
returns the filter classname and the options as one string
- getFilterSpec(Filter) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
-
returns the filter classname and the options as one string.
- getFilterType() - Method in class weka.attributeSelection.SVMAttributeEval
-
Get the filtering mode passed to SMO
- getFilterType() - Method in class weka.classifiers.functions.GaussianProcesses
-
Gets how the training data will be transformed.
- getFilterType() - Method in class weka.classifiers.functions.SMO
-
Gets how the training data will be transformed.
- getFilterType() - Method in class weka.classifiers.functions.SMOreg
-
Gets how the training data will be transformed.
- getFilterType() - Method in class weka.classifiers.mi.MDD
-
Gets how the training data will be transformed.
- getFilterType() - Method in class weka.classifiers.mi.MIDD
-
Gets how the training data will be transformed.
- getFilterType() - Method in class weka.classifiers.mi.MIEMDD
-
Gets how the training data will be transformed.
- getFilterType() - Method in class weka.classifiers.mi.MIOptimalBall
-
Gets how the training data will be transformed.
- getFilterType() - Method in class weka.classifiers.mi.MISMO
-
Gets how the training data will be transformed.
- getFilterType() - Method in class weka.classifiers.mi.MISVM
-
Gets how the training data will be transformed.
- getFindAllRulesForSupportLevel() - Method in class weka.associations.FPGrowth
-
Get whether all rules meeting the lower bound on min support
and the minimum metric threshold are to be found.
- getFindNumBins() - Method in class weka.filters.unsupervised.attribute.Discretize
-
Get the value of FindNumBins.
- getFindNumBins() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
-
Get the value of FindNumBins.
- getFirst() - Method in class weka.associations.tertius.SimpleLinkedList
-
- getFirst() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList
-
Returns the first element in the list.
- getFirst() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborList
-
returns the first element in the list.
- getFirstToken() - Method in class weka.core.converters.ArffLoader.ArffReader
-
Gets next token, skipping empty lines.
- getFirstToken(StreamTokenizer) - Static method in class weka.core.converters.ConverterUtils
-
Gets token, skipping empty lines.
- getFirstValueIndex() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
-
Get the index of the first value used.
- getFirstValueIndex() - Method in class weka.filters.unsupervised.attribute.SwapValues
-
Get the index of the first value used.
- getFitness() - Method in class weka.attributeSelection.GeneticSearch.GABitSet
-
gets the scaled fitness
- getFlag(char, String[]) - Static method in class weka.core.Utils
-
Checks if the given array contains the flag "-Char".
- getFlag(String, String[]) - Static method in class weka.core.Utils
-
Checks if the given array contains the flag "-String".
- getFlow() - Method in class weka.gui.beans.KnowledgeFlowApp
-
Gets the current flow being edited.
- getFlows() - Method in class weka.gui.beans.FlowRunner
-
Get the vector holding the flow(s)
- getFMeasure() - Method in class weka.classifiers.evaluation.TwoClassStats
-
Calculate the F-Measure.
- getFold() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
-
Gets the fold which is selected.
- getFold() - Method in class weka.filters.unsupervised.instance.RemoveFolds
-
Gets the fold which is selected.
- getFoldColumn() - Method in class weka.experiment.PairedTTester
-
Get the value of FoldColumn.
- getFoldColumn() - Method in interface weka.experiment.Tester
-
Get the value of FoldColumn.
- getFolds() - Method in class weka.attributeSelection.OneRAttributeEval
-
Get the number of folds used for cross validation
- getFolds() - Method in class weka.attributeSelection.WrapperSubsetEval
-
Get the number of folds used for accuracy estimation
- getFolds() - Method in class weka.classifiers.rules.ConjunctiveRule
-
returns the current number of folds
- getFolds() - Method in class weka.classifiers.rules.JRip
-
Gets the number of folds
- getFolds() - Method in class weka.classifiers.rules.Ridor
-
- getFolds() - Method in class weka.gui.beans.CrossValidationFoldMaker
-
Get the currently set number of folds
- getFolds() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
returns the number of folds used for cross-validation
- getFoldsType() - Method in class weka.attributeSelection.RaceSearch
-
Get the xfold type
- getFont() - Method in class weka.gui.visualize.PostscriptGraphics
-
Get current font.
- getFontMetrics(Font) - Method in class weka.gui.visualize.PostscriptGraphics
-
Get Font metrics
- getFontRenderContext() - Method in class weka.gui.visualize.PostscriptGraphics
-
START overridden Graphics2D methods
- getFormat() - Method in class weka.core.Debug.Timestamp
-
returns the current timestamp format
- getForwardSelectionMethod() - Method in class weka.attributeSelection.LinearForwardSelection
-
Get the search direction
- getFPRate() - Method in class weka.associations.tertius.Rule
-
Get the rate of False Positive instances of this rule.
- getFrameLocation() - Method in class weka.gui.MemoryUsagePanel
-
Returns the default position for the dialog.
- getFrameTitle() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
returns the title (incl.
- getFrequency() - Method in class weka.associations.FPGrowth.BinaryItem
-
Get the frequency of this item.
- getFrequencyLimit() - Method in class weka.classifiers.bayes.AODE
-
Gets the frequency limit.
- getFrequencyLimit() - Method in class weka.classifiers.bayes.AODEsr
-
Gets the frequency limit.
- getFrequencyThreshold() - Method in class weka.associations.Tertius
-
Get the value of frequencyThreshold.
- getFreshCardinalityOfParents(Instances) - Method in class weka.classifiers.bayes.net.ParentSet
-
returns cardinality of parents after recalculation
- getFromYear() - Static method in class weka.core.Copyright
-
returns the start year of the copyright
- getFs(Instance) - Method in class weka.classifiers.trees.ft.FTtree
-
Computes the F-values of LogitBoost for an instance from the current logistic model at the node
Note that this also takes into account the (partial) logistic model fit at higher levels in
the tree.
- getFs(Instance) - Method in class weka.classifiers.trees.lmt.LMTNode
-
Computes the F-values of LogitBoost for an instance from the current logistic model at the node
Note that this also takes into account the (partial) logistic model fit at higher levels in
the tree.
- getFs(Instance) - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Computes the F-values for a single instance.
- getFs(Instances) - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Computes the F-values for a set of instances.
- getFunction(String) - Static method in class weka.core.pmml.Function
-
Get a built-in PMML Function.
- getFunction(String, TransformationDictionary) - Static method in class weka.core.pmml.Function
-
Get either a function.
- getFunction() - Method in class weka.datagenerators.classifiers.classification.Agrawal
-
Gets the function for generating the data.
- getFunctionValue(int) - Method in class weka.classifiers.functions.pace.DiscreteFunction
-
Gets a particular function value
- getFunctionValues() - Method in class weka.classifiers.functions.pace.DiscreteFunction
-
Gets all function values
- getFurthestFromMeanAnchor(int, int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Returns an anchor point which is furthest from the
mean point for a given set of points (instances)
(The anchor instance is chosen from the given
set of points).
- getGamma() - Method in class weka.classifiers.functions.LibSVM
-
Gets gamma
- getGamma() - Method in class weka.classifiers.functions.supportVector.RBFKernel
-
Gets the gamma value.
- getGCount(Node, int) - Static method in class weka.gui.treevisualizer.Node
-
Recursively finds the number of visible groups of siblings there are.
- getGenerateRanking() - Method in class weka.attributeSelection.GreedyStepwise
-
Gets whether ranking has been requested.
- getGenerateRanking() - Method in class weka.attributeSelection.RaceSearch
-
Gets whether ranking has been requested.
- getGenerateRanking() - Method in interface weka.attributeSelection.RankedOutputSearch
-
Gets whether the user has opted to see a ranked list of
attributes rather than the normal result of the search
- getGenerateRanking() - Method in class weka.attributeSelection.Ranker
-
This is a dummy method.
- getGenerateRules() - Method in class weka.classifiers.trees.m5.M5Base
-
get whether rules are being generated rather than a tree
- getGenerator() - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
returns the actual datagenerator
- getGenerator() - Method in class weka.gui.explorer.DataGeneratorPanel
-
returns the currently selected DataGenerator
- getGeneratorSamplesBase() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Get the base used for computing the number of samples to obtain from
each generator
- getGlobalBlend() - Method in class weka.classifiers.lazy.KStar
-
Get the value of the global blend parameter
- getGlobalInfo(Classifier) - Static method in class weka.classifiers.Evaluation
-
Return the global info (if it exists) for the supplied classifier
- getGlobalInfo(Clusterer) - Static method in class weka.clusterers.ClusterEvaluation
-
Return the global info (if it exists) for the supplied clusterer
- getGlobalInfo(Object) - Static method in class weka.gui.beans.KnowledgeFlowApp
-
Utility method for grabbing the global info help (if it exists) from
an arbitrary object
- getGlobalModel() - Method in class weka.classifiers.trees.j48.NBTreeSplit
-
Return the global naive bayes model for this node
- getGoodOperations(BayesNet, Instances, int) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
-
getGoodOperations determines the nrOfGoodOperations best Operations, which are considered for
the calculation of an optimal operationsequence
- getGraphString() - Method in class weka.gui.beans.GraphEvent
-
Return the dot string for the graph
- getGraphTitle() - Method in class weka.gui.beans.GraphEvent
-
Return the graph title
- getGraphType() - Method in class weka.gui.beans.GraphEvent
-
Return the graph type
- getGrid() - Method in class weka.classifiers.meta.GridSearch.PerformanceTable
-
returns the corresponding grid
- getGridExtensionsPerformed() - Method in class weka.classifiers.meta.GridSearch
-
returns the number of grid extensions that took place during the search
(only applicable if the grid was extendable).
- getGridIsExtendable() - Method in class weka.classifiers.meta.GridSearch
-
Get whether the grid can be extended dynamically.
- getGridWidth() - Method in class weka.gui.beans.AttributeSummarizer
-
Get the width of the grid of plots
- getGroupIdentifier() - Method in class weka.gui.beans.BatchClassifierEvent
-
- getGUI() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- getGUIType() - Method in class weka.gui.Main
-
Gets the currently set type of GUI to display.
- getH() - Method in class weka.core.matrix.QRDecomposition
-
Return the Householder vectors
- getHandler() - Method in class weka.core.FindWithCapabilities
-
returns the current set CapabilitiesHandler to generate the dataset
for, can be null.
- getHandler() - Method in class weka.core.TestInstances
-
returns the current set CapabilitiesHandler to generate the dataset
for, can be null
- getHashtable(FastVector, int) - Static method in class weka.associations.ItemSet
-
Return a hashtable filled with the given item sets.
- getHashtable(FastVector, int) - Static method in class weka.associations.LabeledItemSet
-
Return a hashtable filled with the given item sets.
- getHDRank() - Method in class weka.classifiers.mi.CitationKNN
-
Returns the rank associated to the Hausdorff distance
- getHeader(String) - Method in class weka.experiment.ResultMatrix
-
returns the value associated with the given key, null if if cannot be
found
- getHeight() - Method in class weka.gui.beans.BeanInstance
-
Gets the height of this bean
- getHeight(Node, int) - Static method in class weka.gui.treevisualizer.Node
-
Recursively finds the number of visible levels there are.
- getHeuristic() - Method in class weka.classifiers.trees.BFTree
-
Get if use heuristic search for nominal attributes in multi-class problems.
- getHeuristic() - Method in class weka.classifiers.trees.SimpleCart
-
Get if use heuristic search for nominal attributes in multi-class problems.
- getHeuristicStop() - Method in class weka.classifiers.functions.SimpleLogistic
-
Get the value of heuristicStop.
- getHiddenLayers() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- getHistory() - Method in class weka.gui.sql.ConnectionPanel
-
returns the history.
- getHistory() - Method in class weka.gui.sql.event.HistoryChangedEvent
-
returns the history model
- getHistory() - Method in class weka.gui.sql.QueryPanel
-
returns the history.
- getHistoryFilename() - Method in class weka.gui.sql.SqlViewer
-
returns the filename of the history file.
- getHistoryName() - Method in class weka.gui.sql.event.HistoryChangedEvent
-
returns the name of the history
- getHoldOutFile() - Method in class weka.attributeSelection.ClassifierSubsetEval
-
Gets the file that holds hold out/test instances.
- getHomeDir() - Static method in class weka.core.Debug
-
returns the home directory of the user
- getHornClauses() - Method in class weka.associations.Tertius
-
Get the value of hornClauses.
- getHyperparameterRange() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Get the range of hyperparameter values to consider
during CV-based selection.
- getHyperparameterSelection() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Get the method used to select the hyperparameter
- getHyperparameterValue() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Get the hyperparameter value.
- getIconPath() - Method in class weka.gui.beans.BeanVisual
-
returns the path for the icon
- getId() - Method in class weka.classifiers.functions.neural.NeuralConnection
-
- getID(int, GridSearch.PointDouble) - Method in class weka.classifiers.meta.GridSearch.PerformanceCache
-
returns the ID string for a cache item
- getID() - Method in class weka.core.Debug.Random
-
returns the unique ID of this number generator
- getID() - Method in class weka.core.Tag
-
Gets the numeric ID of the Tag.
- getID() - Method in class weka.core.TechnicalInformation
-
returns the unique ID (either the one used in creating this instance
or the automatically generated one)
- getID() - Method in class weka.gui.streams.InstanceEvent
-
Get the event type
- getID() - Method in class weka.gui.treevisualizer.TreeDisplayEvent
-
- getIDFTransform() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Sets whether if the word frequencies in a document should be transformed
into:
fij*log(num of Docs/num of Docs with word i)
where fij is the frequency of word i in document(instance) j.
- getIDIndex() - Method in class weka.filters.unsupervised.attribute.AddID
-
Get the index of the attribute used.
- getIDsForBeanInstances(Vector) - Method in class weka.gui.beans.xml.XMLBeans
-
returns the IDs for the given BeanInstances, i.e., the stored IDs
in m_BeanInstancesID, based on m_BeanInstances
- getIDStr() - Method in class weka.core.Tag
-
Gets the string ID of the Tag.
- getIgnoreClass() - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer
-
Gets the IgnoreClass value.
- getIgnoredAttributeIndices() - Method in class weka.filters.unsupervised.attribute.AddCluster
-
Gets ranges of attributes to be ignored.
- getIgnoredAttributeIndices() - Method in class weka.filters.unsupervised.attribute.ClusterMembership
-
Gets ranges of attributes to be ignored.
- getIgnoredProperties() - Method in class weka.core.CheckGOE
-
Get the ignored properties used in checkToolTips() as comma-separated
list (sorted).
- getIgnoreRange() - Method in class weka.filters.unsupervised.attribute.MathExpression
-
Get the current range selection.
- getImage(String, String) - Static method in class weka.gui.ComponentHelper
-
returns the Image for a given directory and filename, NULL if not successful
- getImage(String) - Static method in class weka.gui.ComponentHelper
-
returns the Image for a given filename, NULL if not successful
- getImageIcon(String, String) - Static method in class weka.gui.ComponentHelper
-
returns the ImageIcon for a given filename and directory, NULL if not successful
- getImageIcon(String) - Static method in class weka.gui.ComponentHelper
-
returns the ImageIcon for a given filename, NULL if not successful
- getImagEigenvalues() - Method in class weka.core.matrix.EigenvalueDecomposition
-
Return the imaginary parts of the eigenvalues
- getIncludeClass() - Method in class weka.core.InstanceComparator
-
returns TRUE if the class is included in the comparison
- getIncludeClass() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Gets whether the class is included in the cleaning process or always
skipped.
- getIndentionLength(String) - Method in class weka.core.Javadoc
-
determines the number of indention strings that have to be inserted to
generated the given indention string.
- getIndentionString(String) - Method in class weka.core.Javadoc
-
determines the base string of the given indention string, whether it's
either only spaces (one space will be retured) or mixed mode (tabs and
spaces, in that case the same string will be returned)
- getIndex() - Method in class weka.associations.tertius.Predicate
-
- getIndex() - Method in class weka.core.converters.ArffLoader.ArffReader
-
Gets index, checking for a premature and of line.
- getIndex() - Method in class weka.core.PropertyPath.PathElement
-
returns the index of the property, -1 if the property is not an
index-based one
- getIndex() - Method in class weka.core.SingleIndex
-
Gets the selected index
- getIndex() - Method in class weka.gui.SortedTableModel.SortContainer
-
Returns the original index of the item.
- getIndexofBiggest(List<Integer>) - Method in class weka.attributeSelection.ScatterSearchV1
-
get the index in a List where this have the biggest number
- getIndices() - Method in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
-
Gets the indices in an array of ints.
- getInitAsNaiveBayes() - Method in class weka.classifiers.bayes.net.search.global.HillClimber
-
Gets whether to init as naive bayes
- getInitAsNaiveBayes() - Method in class weka.classifiers.bayes.net.search.global.K2
-
Gets whether to init as naive bayes
- getInitAsNaiveBayes() - Method in class weka.classifiers.bayes.net.search.local.HillClimber
-
Gets whether to init as naive bayes
- getInitAsNaiveBayes() - Method in class weka.classifiers.bayes.net.search.local.K2
-
Gets whether to init as naive bayes
- getInitFile() - Method in class weka.filters.unsupervised.attribute.KernelFilter
-
Gets the file to initialize the filter with, can be null.
- getInitFileClassIndex() - Method in class weka.filters.unsupervised.attribute.KernelFilter
-
Gets the class index of the file to initialize the filter with.
- getInitGenericObjectEditorFilter() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns if the GOEs in the Explorer will be initialized based on the
data that is loaded into the Explorer.
- getInitial() - Method in class weka.core.Memory
-
returns the initial size of the JVM
- getInitialDatasetsDirectory() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
returns the initial directory for the datasets (if empty, it returns
the user's home directory)
- getInitialDirectory() - Static method in class weka.gui.explorer.ExplorerDefaults
-
Returns the initial directory for the file chooser used for opening
datasets.
- getInnerNodes() - Method in class weka.classifiers.trees.SimpleCart
-
Return a list of all inner nodes in the tree.
- getInputCenterFile() - Method in class weka.clusterers.XMeans
-
Gets the file to read the list of centers from.
- getInputFilename() - Method in class weka.gui.GenericPropertiesCreator
-
returns the name of the input file
- getInputFormat() - Method in class weka.filters.Filter
-
Gets the currently set inputformat instances.
- getInputNums() - Method in class weka.classifiers.functions.neural.NeuralConnection
-
Use this to get easy access to the input numbers.
- getInputOrder() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Gets the input order.
- getInputProperties() - Method in class weka.gui.GenericPropertiesCreator
-
returns the input properties object (template containing the packages)
- getInputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
-
Use this to get easy access to the inputs.
- getInputs() - Method in class weka.gui.beans.MetaBean
-
- getInputStream(String, String) - Static method in class weka.gui.Loader
-
returns an InputStream for the given dir and filename, can be NULL if it
fails
- getInputStream(String) - Method in class weka.gui.Loader
-
returns an InputStream for the given filename, can be NULL if it fails
- getInstalledLookAndFeels() - Static method in class weka.gui.LookAndFeel
-
returns an array with the classnames of all the installed LnFs
- getInstance() - Static method in class weka.associations.gsp.Messages
-
getInstance.
- getInstance() - Static method in class weka.associations.Messages
-
getInstance.
- getInstance() - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
-
Returns the original instance
- getInstance() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
-
Returns the original instance
- getInstance() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
-
Returns the original instance
- getInstance(Instances, boolean) - Method in class weka.core.converters.ArffLoader.ArffReader
-
Reads a single instance using the tokenizer and returns it.
- getInstance() - Method in class weka.core.Javadoc
-
Returns a new instance of the class
- getInstance() - Static method in class weka.gui.arffviewer.Messages
-
getInstance.
- getInstance() - Method in class weka.gui.beans.InstanceEvent
-
Get the instance
- getInstance() - Static method in class weka.gui.beans.Messages
-
getInstance.
- getInstance() - Static method in class weka.gui.beans.xml.Messages
-
getInstance.
- getInstance() - Static method in class weka.gui.boundaryvisualizer.Messages
-
getInstance.
- getInstance() - Static method in class weka.gui.experiment.Messages
-
getInstance.
- getInstance() - Static method in class weka.gui.explorer.Messages
-
getInstance.
- getInstance() - Static method in class weka.gui.graphvisualizer.Messages
-
getInstance.
- getInstance() - Static method in class weka.gui.hierarchyvisualizer.Messages
-
getInstance.
- getInstance() - Static method in class weka.gui.Messages
-
getInstance.
- getInstance() - Static method in class weka.gui.sql.event.Messages
-
getInstance.
- getInstance() - Static method in class weka.gui.sql.Messages
-
getInstance.
- getInstance() - Static method in class weka.gui.streams.Messages
-
getInstance.
- getInstance() - Static method in class weka.gui.treevisualizer.Messages
-
getInstance.
- getInstance() - Static method in class weka.gui.visualize.Messages
-
getInstance.
- getInstanceFull(boolean) - Method in class weka.core.converters.ArffLoader.ArffReader
-
Reads a single instance using the tokenizer and returns it.
- getInstanceIndex(int) - Method in class weka.classifiers.lazy.LBR.Indexes
-
Returns the boolean value at the specified index in the Instance Index array
- getInstanceRange() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
-
Gets the number of instances forward to translate values between.
- getInstances() - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database
-
Returns the original instances delivered from WEKA
- getInstances() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
-
Returns the original instances delivered from WEKA
- getInstances() - Method in class weka.core.converters.AbstractSaver
-
Gets instances that should be stored.
- getInstances() - Method in interface weka.core.DistanceFunction
-
returns the instances currently set.
- getInstances() - Method in class weka.core.neighboursearch.NearestNeighbourSearch
-
returns the instances currently set.
- getInstances() - Method in class weka.core.NormalizableDistance
-
returns the instances currently set.
- getInstances() - Method in class weka.core.xml.XMLInstances
-
returns the current instances, either the ones that were set or the ones
that were generated from the XML structure.
- getInstances() - Method in class weka.experiment.PairedTTester
-
Get the value of Instances.
- getInstances() - Method in interface weka.experiment.Tester
-
Get the value of Instances.
- getInstances() - Method in class weka.gui.arffviewer.ArffPanel
-
returns the instances of the panel, if none then NULL
- getInstances() - Method in class weka.gui.arffviewer.ArffSortedTableModel
-
returns the data
- getInstances() - Method in class weka.gui.arffviewer.ArffTableModel
-
returns the data
- getInstances() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
Get the training instances
- getInstances() - Method in class weka.gui.explorer.DataGeneratorPanel
-
returns the generated instances, null if the process was cancelled.
- getInstances() - Method in class weka.gui.explorer.PreprocessPanel
-
Gets the working set of instances.
- getInstances() - Method in class weka.gui.SetInstancesPanel
-
Gets the set of instances currently held by the panel
- getInstances() - Method in class weka.gui.treevisualizer.Node
-
This will return the Instances object related to this node.
- getInstances() - Method in class weka.gui.ViewerDialog
-
returns the currently displayed instances
- getInstances() - Method in class weka.gui.visualize.VisualizePanel
-
Get the master plot's instances
- getInstances1() - Method in class weka.gui.visualize.VisualizePanelEvent
-
- getInstances2() - Method in class weka.gui.visualize.VisualizePanelEvent
-
- getInstancesAt(int, int) - Method in class weka.gui.arffviewer.ArffTable.RelationalCellEditor
-
returns the underlying instances at the given position
- getInstancesFromClass(Instances, int, int, double, Instances) - Static method in class weka.estimators.EstimatorUtils
-
Returns a dataset that contains all instances of a certain class value.
- getInstancesFromClass(Instances, int, double) - Static method in class weka.estimators.EstimatorUtils
-
Returns a dataset that contains of all instances of a certain class value.
- getInstancesFromValue(Instances, int, double) - Static method in class weka.estimators.EstimatorUtils
-
Returns a dataset that contains of all instances of a certain value
for the given attribute.
- getInstancesIndices() - Method in class weka.filters.unsupervised.instance.RemoveRange
-
Gets ranges of instances selected.
- getInstancesNoClass() - Method in class weka.associations.Apriori
-
Gets the instances without the class atrribute.
- getInstancesNoClass() - Method in interface weka.associations.CARuleMiner
-
Gets the instances without the class attribute
- getInstancesNoClass() - Method in class weka.associations.PredictiveApriori
-
Gets the instances without the class attribute
- getInstancesOnlyClass() - Method in class weka.associations.Apriori
-
Gets only the class attribute of the instances.
- getInstancesOnlyClass() - Method in interface weka.associations.CARuleMiner
-
Gets the class attribute and its values for all instances
- getInstancesOnlyClass() - Method in class weka.associations.PredictiveApriori
-
Gets the class attribute of all instances
- getInstanceSparse(boolean) - Method in class weka.core.converters.ArffLoader.ArffReader
-
Reads a single instance using the tokenizer and returns it.
- getInstancesValueAt(int, int) - Method in class weka.gui.arffviewer.ArffSortedTableModel
-
returns the double value of the underlying Instances object at the
given position, -1 if out of bounds
- getInstancesValueAt(int, int) - Method in class weka.gui.arffviewer.ArffTableModel
-
returns the double value of the underlying Instances object at the
given position, -1 if out of bounds
- getInstanceWeight() - Method in class weka.core.converters.ArffLoader.ArffReader
-
Gets the value of an instance's weight (if one exists)
- getInstNums() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Gets the upper and lower boundary for instances per cluster.
- getInstNums() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Get a string with the upper and lower boundary for the
number of instances for this cluster.
- getIntercept() - Method in class weka.classifiers.functions.SimpleLinearRegression
-
Returns the intercept of the function.
- getInternalCacheSize() - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Gets the size of the internal cache
- getInternals() - Method in class weka.classifiers.bayes.WAODE
-
Gets whether more internals of the classifier are printed.
- getInterpreter() - Method in class weka.core.Jython
-
returns the currently used Python Interpreter
- getInterval() - Method in class weka.gui.MemoryUsagePanel.MemoryMonitor
-
Returns the refresh interval in msecs.
- getInvert() - Method in class weka.core.Range
-
Gets whether the range sense is inverted, i.e.
- getInvert() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Get whether selection is inverted.
- getInvertSelection() - Method in interface weka.core.DistanceFunction
-
Gets whether the matching sense of attribute indices is inverted or not.
- getInvertSelection() - Method in class weka.core.NormalizableDistance
-
Gets whether the matching sense of attribute indices is inverted or not.
- getInvertSelection() - Method in class weka.filters.supervised.attribute.Discretize
-
Gets whether the supplied columns are to be removed or kept
- getInvertSelection() - Method in class weka.filters.supervised.instance.Resample
-
Gets whether selection is inverted (only if instances are drawn WIHTOUT
replacement).
- getInvertSelection() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
-
Gets if selection is to be inverted.
- getInvertSelection() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
-
Get whether the supplied columns are to be removed or kept
- getInvertSelection() - Method in class weka.filters.unsupervised.attribute.Copy
-
Get whether the supplied columns are to be removed or kept
- getInvertSelection() - Method in class weka.filters.unsupervised.attribute.Discretize
-
Gets whether the supplied columns are to be removed or kept
- getInvertSelection() - Method in class weka.filters.unsupervised.attribute.MathExpression
-
Get whether the supplied columns are to be select or unselect
- getInvertSelection() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
-
Gets whether the supplied columns are to be removed or kept
- getInvertSelection() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Gets whether the selection of the columns is inverted
- getInvertSelection() - Method in class weka.filters.unsupervised.attribute.NumericToNominal
-
Gets whether the supplied columns are to be worked on or the others.
- getInvertSelection() - Method in class weka.filters.unsupervised.attribute.NumericTransform
-
Get whether the supplied columns are to be transformed or not
- getInvertSelection() - Method in class weka.filters.unsupervised.attribute.RELAGGS
-
Gets whether the supplied columns are to be processed or skipped
- getInvertSelection() - Method in class weka.filters.unsupervised.attribute.Remove
-
Get whether the supplied columns are to be removed or kept
- getInvertSelection() - Method in class weka.filters.unsupervised.attribute.RemoveType
-
Get whether the supplied columns are to be removed or kept
- getInvertSelection() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Gets whether the supplied columns are to be processed or skipped.
- getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemoveFolds
-
Gets if selection is to be inverted.
- getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
Get whether the supplied columns are to be removed or kept
- getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemovePercentage
-
Gets if selection is to be inverted.
- getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemoveRange
-
Gets if selection is to be inverted.
- getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Get whether the supplied columns are to be removed or kept
- getInvertSelection() - Method in class weka.filters.unsupervised.instance.Resample
-
Gets whether selection is inverted (only if instances are drawn WIHTOUT
replacement).
- getItem() - Method in class weka.associations.FPGrowth.FPTreeNode
-
Get the item at this node.
- getItem(int) - Method in class weka.associations.FPGrowth.FrequentBinaryItemSet
-
Get a particular item from this item set.
- getItems() - Method in class weka.associations.FPGrowth.FrequentBinaryItemSet
-
Get the items in this item set.
- getItemSet(int) - Method in class weka.associations.FPGrowth.FrequentItemSets
-
Get an item set.
- getJavaInitializationString() - Method in class weka.gui.CostMatrixEditor
-
Returns the Java code that generates an object the same as the one being edited.
- getJavaInitializationString() - Method in class weka.gui.FileEditor
-
Returns a representation of the current property value as java source.
- getJavaInitializationString() - Method in class weka.gui.GenericArrayEditor
-
Supposedly returns an initialization string to create a classifier
identical to the current one, including it's state, but this doesn't
appear possible given that the initialization string isn't supposed to
contain multiple statements.
- getJavaInitializationString() - Method in class weka.gui.GenericObjectEditor
-
Supposedly returns an initialization string to create a Object
identical to the current one, including it's state, but this doesn't
appear possible given that the initialization string isn't supposed to
contain multiple statements.
- getJavaInitializationString() - Method in class weka.gui.SelectedTagEditor
-
Returns a description of the property value as java source.
- getJavaInitializationString() - Method in class weka.gui.SimpleDateFormatEditor
-
Returns the Java code that generates an object the same as the one being edited.
- getJTable() - Method in class weka.gui.JTableHelper
-
returns the JTable
- getKDTree() - Method in class weka.clusterers.XMeans
-
Gets the KDTree class.
- getKDTreeSpec() - Method in class weka.clusterers.XMeans
-
Gets the KDTree specification string, which contains the class name of
the KDTree class and any options to the KDTree.
- getKernel() - Method in class weka.classifiers.functions.GaussianProcesses
-
Gets the kernel to use.
- getKernel() - Method in class weka.classifiers.functions.SMO.BinarySMO
-
Returns the kernel to use
- getKernel() - Method in class weka.classifiers.functions.SMO
-
Returns the kernel to use
- getKernel() - Method in class weka.classifiers.functions.SMOreg
-
Returns the kernel to use
- getKernel() - Method in class weka.classifiers.functions.supportVector.CheckKernel
-
Get the kernel being tested
- getKernel() - Method in class weka.classifiers.mi.MISMO.BinaryMISMO
-
Returns the kernel to use
- getKernel() - Method in class weka.classifiers.mi.MISMO
-
Gets the kernel to use.
- getKernel() - Method in class weka.classifiers.mi.MISVM
-
Gets the kernel to use.
- getKernel() - Method in class weka.filters.unsupervised.attribute.KernelFilter
-
Gets the kernel to use.
- getKernelBandwidth() - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
-
Get the kernel bandwidth
- getKernelEvaluations() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
-
returns the number of kernel evaluations
- getKernelFactorExpression() - Method in class weka.filters.unsupervised.attribute.KernelFilter
-
Gets the expression for the kernel.
- getKernelMatrixFile() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
-
Gets the file containing the kernel matrix.
- getKernelType() - Method in class weka.classifiers.functions.LibSVM
-
Gets type of kernel function
- getKey() - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
-
Returns the key for this DataObject
- getKey() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
-
Returns the key for this DataObject
- getKey() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
-
Returns the key for this DataObject
- getKey() - Method in class weka.experiment.ClassifierSplitEvaluator
-
Gets the key describing the current SplitEvaluator.
- getKey() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
-
Gets the key describing the current SplitEvaluator.
- getKey() - Method in class weka.experiment.RegressionSplitEvaluator
-
Gets the key describing the current SplitEvaluator.
- getKey() - Method in interface weka.experiment.SplitEvaluator
-
Gets the key describing the current SplitEvaluator.
- getKeyFieldName() - Method in class weka.experiment.AveragingResultProducer
-
Get the value of KeyFieldName.
- getKeyNames() - Method in class weka.experiment.AveragingResultProducer
-
Gets the names of each of the columns produced for a single run.
- getKeyNames() - Method in class weka.experiment.ClassifierSplitEvaluator
-
Gets the names of each of the key columns produced for a single run.
- getKeyNames() - Method in class weka.experiment.CrossValidationResultProducer
-
Gets the names of each of the columns produced for a single run.
- getKeyNames() - Method in class weka.experiment.DatabaseResultProducer
-
Gets the names of each of the columns produced for a single run.
- getKeyNames() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
-
Gets the names of each of the key columns produced for a single run.
- getKeyNames() - Method in class weka.experiment.LearningRateResultProducer
-
Gets the names of each of the columns produced for a single run.
- getKeyNames() - Method in class weka.experiment.RandomSplitResultProducer
-
Gets the names of each of the columns produced for a single run.
- getKeyNames() - Method in class weka.experiment.RegressionSplitEvaluator
-
Gets the names of each of the key columns produced for a single run.
- getKeyNames() - Method in interface weka.experiment.ResultProducer
-
Gets the names of each of the key columns produced for a single run.
- getKeyNames() - Method in interface weka.experiment.SplitEvaluator
-
Gets the names of each of the key columns produced for a single run.
- getKeys() - Method in class weka.core.converters.DatabaseLoader
-
Gets the key columns' name
- getKeyTypes() - Method in class weka.experiment.AveragingResultProducer
-
Gets the data types of each of the columns produced for a single run.
- getKeyTypes() - Method in class weka.experiment.ClassifierSplitEvaluator
-
Gets the data types of each of the key columns produced for a single run.
- getKeyTypes() - Method in class weka.experiment.CrossValidationResultProducer
-
Gets the data types of each of the columns produced for a single run.
- getKeyTypes() - Method in class weka.experiment.DatabaseResultProducer
-
Gets the data types of each of the columns produced for a single run.
- getKeyTypes() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
-
Gets the data types of each of the key columns produced for a single run.
- getKeyTypes() - Method in class weka.experiment.LearningRateResultProducer
-
Gets the data types of each of the columns produced for a single run.
- getKeyTypes() - Method in class weka.experiment.RandomSplitResultProducer
-
Gets the data types of each of the columns produced for a single run.
- getKeyTypes() - Method in class weka.experiment.RegressionSplitEvaluator
-
Gets the data types of each of the key columns produced for a single run.
- getKeyTypes() - Method in interface weka.experiment.ResultProducer
-
Gets the data types of each of the key columns produced for a single run.
- getKeyTypes() - Method in interface weka.experiment.SplitEvaluator
-
Gets the data types of each of the key columns produced for a single run.
- getKeywords() - Method in class weka.experiment.DatabaseUtils
-
Returns the currently stored keywords (as comma-separated list).
- getKeywordsMaskChar() - Method in class weka.experiment.DatabaseUtils
-
Returns the currently set mask character.
- getKNN() - Method in class weka.classifiers.lazy.IBk
-
Gets the number of neighbours the learner will use.
- getKNN() - Method in class weka.classifiers.lazy.LWL
-
Gets the number of neighbours used for kernel bandwidth setting.
- getKthNearest() - Method in class weka.core.neighboursearch.CoverTree.MyHeap
-
returns the kth nearest element or null if none there.
- getKthNearest() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.MyHeap
-
returns the kth nearest element or null if none there.
- getKValue() - Method in class weka.classifiers.trees.RandomTree
-
Get the value of K.
- getKWBias() - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Get the calculated bias squared according to the Kohavi and Wolpert definition
- getKWSigma() - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Get the calculated sigma according to the Kohavi and Wolpert definition
- getKWVariance() - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Get the calculated variance according to the Kohavi and Wolpert definition
- getL() - Method in class weka.core.matrix.CholeskyDecomposition
-
Return triangular factor.
- getL() - Method in class weka.core.Matrix
-
Deprecated.
Returns the L part of the matrix.
- getL() - Method in class weka.core.matrix.LUDecomposition
-
Return lower triangular factor
- getLabel() - Method in class weka.gui.treevisualizer.Edge
-
Get the value of label.
- getLabel() - Method in class weka.gui.treevisualizer.Node
-
Get the value of label.
- getLabels() - Method in class weka.filters.unsupervised.attribute.AddValues
-
Get the comma-separated list of labels that are added.
- getLabelX() - Method in class weka.classifiers.meta.GridSearch.Grid
-
returns the label for the X axis
- getLabelY() - Method in class weka.classifiers.meta.GridSearch.Grid
-
returns the label for the Y axis
- getLambda() - Method in class weka.classifiers.functions.SPegasos
-
Get the current value of lambda
- getLambda() - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Gets the lambda constant used in the string kernel
- getLast() - Method in class weka.associations.tertius.SimpleLinkedList
-
- getLast() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborList
-
returns the last element in the list.
- getLastLiteral() - Method in class weka.associations.tertius.LiteralSet
-
Give the last literal added to this set.
- getLastToken(boolean) - Method in class weka.core.converters.ArffLoader.ArffReader
-
Gets token and checks if its end of line.
- getLeaf() - Method in class weka.classifiers.trees.j48.GraftSplit
-
- getLearningRate() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- getLegendText() - Method in class weka.gui.beans.ChartEvent
-
Get the legend text vector
- getLevel() - Method in class weka.gui.HierarchyPropertyParser
-
Get the level of current node.
- getLikelihoodThreshold() - Method in class weka.classifiers.meta.LogitBoost
-
Get the value of Precision.
- getLine(int) - Method in class weka.gui.treevisualizer.Edge
-
Returns line number n
- getLine(int) - Method in class weka.gui.treevisualizer.Node
-
Returns the text String for the specfied line.
- getLineNo() - Method in class weka.core.converters.ArffLoader.ArffReader
-
returns the current line number
- getLink() - Method in class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd
-
- getLinkAt(int) - Method in class weka.attributeSelection.BestFirst.LinkedList2
-
returns the element (Link) at a specific index from the list.
- getLinkAt(int) - Method in class weka.attributeSelection.LFSMethods.LinkedList2
-
returns the element (Link) at a specific index from the list.
- getLinkType() - Method in class weka.clusterers.HierarchicalClusterer
-
- getList() - Method in class weka.gui.ResultHistoryPanel
-
Gets the JList used by the results list
- getListCellRendererComponent(JList, Object, int, boolean, boolean) - Method in class weka.gui.CheckBoxList.CheckBoxListRenderer
-
Return a component that has been configured to display the specified
value.
- getListCellRendererComponent(JList, Object, int, boolean, boolean) - Method in class weka.gui.experiment.AlgorithmListPanel.ObjectCellRenderer
-
Return a component that has been configured to display the specified
value.
- getListCellRendererComponent(JList, Object, int, boolean, boolean) - Method in class weka.gui.sql.InfoPanelCellRenderer
-
Return a component that has been configured to display the specified value.
- getLiteral(int) - Method in class weka.associations.tertius.Predicate
-
- getLNorm() - Method in class weka.filters.unsupervised.instance.Normalize
-
Get the L Norm used.
- getLoader() - Method in class weka.core.converters.ConverterUtils.DataSource
-
returns the determined loader, null if the DataSource was initialized
with data alone and not a file/URL.
- getLoader() - Method in class weka.gui.beans.Loader
-
Get the loader
- getLoader() - Method in class weka.gui.ConverterFileChooser
-
returns the loader that was chosen by the user, can be null in case the
user aborted the dialog or the save dialog was shown
- getLoader() - Method in class weka.gui.SetInstancesPanel
-
Gets the currently used Loader
- getLoaderForExtension(String) - Static method in class weka.core.converters.ConverterUtils
-
tries to determine the loader to use for this kind of extension, returns
null if none can be found.
- getLoaderForFile(String) - Static method in class weka.core.converters.ConverterUtils
-
tries to determine the loader to use for this kind of file, returns
null if none can be found.
- getLoaderForFile(File) - Static method in class weka.core.converters.ConverterUtils
-
tries to determine the loader to use for this kind of file, returns
null if none can be found.
- getLocallyPredictive() - Method in class weka.attributeSelection.CfsSubsetEval
-
Return true if including locally predictive attributes
- getLocation(GridSearch.PointDouble) - Method in class weka.classifiers.meta.GridSearch.Grid
-
returns the closest index pair for the given value pair in the grid.
- getLocation() - Static method in class weka.core.logging.Logger
-
Returns the location the logging happened.
- getLocator(int) - Method in class weka.core.AttributeLocator
-
Returns the AttributeLocator at the given index.
- getLocatorIndices() - Method in class weka.core.AttributeLocator
-
Returns the indices of the AttributeLocator objects.
- getLog() - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
-
Get the logger.
- getLog() - Method in class weka.core.Debug.Random
-
the currently used log, if null then stdout is used for outputting
the debugging information
- getLog() - Method in interface weka.core.pmml.PMMLModel
-
Get the logger.
- getLogFile() - Method in class weka.classifiers.meta.GridSearch
-
Gets current log file.
- getLogFile() - Method in class weka.core.logging.FileLogger
-
Returns the log file to use.
- getLogger() - Method in class weka.core.Debug.Log
-
initializes and returns the logger if necessary (e.g., due to
serialization).
- getLoglikeliHood(double[], Instances) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
- getLoglikelihood() - Method in class weka.classifiers.bayes.blr.Prior
-
- getLogLikelihood() - Method in class weka.clusterers.ClusterEvaluation
-
Return the log likelihood corresponding to the most recent
set of instances clustered.
- getLogPosterior() - Method in class weka.classifiers.bayes.blr.Prior
-
- getLogProbForTargetClass(Instance) - Method in class weka.classifiers.bayes.DMNBtext.DNBBinary
-
Calculates the class membership probabilities for the given test
instance.
- getLookupCacheSize() - Method in class weka.attributeSelection.BestFirst
-
Return the maximum size of the evaluated subset cache (expressed as a multiplier
for the number of attributes in a data set.
- getLookupCacheSize() - Method in class weka.attributeSelection.LinearForwardSelection
-
Return the maximum size of the evaluated subset cache (expressed as a
multiplier for the number of attributes in a data set.
- getLookupCacheSize() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Return the maximum size of the evaluated subset cache (expressed as a
multiplier for the number of attributes in a data set.
- getLoss() - Method in class weka.classifiers.functions.LibSVM
-
Gets the epsilon in loss function of epsilon-SVR
- getLossFunction() - Method in class weka.classifiers.functions.SPegasos
-
Get the current loss function.
- getLower() - Method in class weka.gui.experiment.RunNumberPanel
-
Gets the current lower run number.
- getLowerBoundMinSupport() - Method in class weka.associations.Apriori
-
Get the value of lowerBoundMinSupport.
- getLowerBoundMinSupport() - Method in class weka.associations.FPGrowth
-
Get the value of lowerBoundMinSupport.
- getLowerCaseTokens() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Gets whether if the tokens are to be downcased or not.
- getLowerNumericBound() - Method in class weka.core.Attribute
-
Returns the lower bound of a numeric attribute.
- getLowerSize() - Method in class weka.experiment.LearningRateResultProducer
-
Get the value of LowerSize.
- getM5RootNode() - Method in class weka.classifiers.trees.m5.M5Base
-
- getM5RootNode() - Method in class weka.classifiers.trees.m5.Rule
-
- getMainPanel() - Method in class weka.gui.arffviewer.ArffViewer
-
returns the main panel
- getMajorityClass() - Method in class weka.classifiers.rules.Ridor
-
- getMakeBinary() - Method in class weka.filters.supervised.attribute.Discretize
-
Gets whether binary attributes should be made for discretized ones.
- getMakeBinary() - Method in class weka.filters.unsupervised.attribute.Discretize
-
Gets whether binary attributes should be made for discretized ones.
- getManualThresholdValue() - Method in class weka.classifiers.meta.ThresholdSelector
-
Returns the value of the manual threshold.
- getMargin(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
return marginal distibution for a node
- getMargin(int) - Method in class weka.classifiers.bayes.net.MarginCalculator
-
- getMarkovBlanketClassifier() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
-
- getMarkovBlanketClassifier() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
-
- getMarkovBlanketClassifier() - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
-
- getMasterPlot() - Method in class weka.gui.visualize.Plot2D
-
Get the master plot
- getMatches() - Method in class weka.core.FindWithCapabilities
-
returns the matches from the last find call.
- getMatches(String) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion
-
returns all the matches with the partial search string, files or
classes.
- getMatchMissingValues() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Gets whether missing values are counted as a match.
- getMatrix() - Method in class weka.core.Matrix
-
Deprecated.
returns the internal matrix
- getMatrix(int, int, int, int) - Method in class weka.core.matrix.Matrix
-
Get a submatrix.
- getMatrix(int[], int[]) - Method in class weka.core.matrix.Matrix
-
Get a submatrix.
- getMatrix(int, int, int[]) - Method in class weka.core.matrix.Matrix
-
Get a submatrix.
- getMatrix(int[], int, int) - Method in class weka.core.matrix.Matrix
-
Get a submatrix.
- getMax() - Method in class weka.classifiers.meta.GridSearch.PerformanceTable
-
the maximum performance
- getMax() - Method in class weka.core.Memory
-
returns the maximum amount of memory that can be assigned
- getMax() - Method in class weka.gui.beans.ChartEvent
-
Get the max y value
- getMaxArray() - Method in class weka.filters.unsupervised.attribute.Normalize
-
Returns the calculated maximum values for the attributes in the data.
- getMaxBoostingIterations() - Method in class weka.classifiers.functions.SimpleLogistic
-
Get the value of maxBoostingIterations.
- getMaxC() - Method in class weka.gui.visualize.Plot2D
-
Return the current max value of the colouring attribute
- getMaxCardinality() - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
-
returns the max cardinality
- getMaxCardinality() - Method in class weka.filters.unsupervised.attribute.RELAGGS
-
Gets the maximum number of values allowed for nominal attributes, before
they're skipped.
- getMaxChunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Get the maximum chunk size
- getMaxCoordsPerPoint() - Method in class weka.core.neighboursearch.PerformanceStats
-
Returns the maximum of coords per point.
- getMaxCost(int) - Method in class weka.classifiers.CostMatrix
-
Gets the maximum cost for a particular class value.
- getMaxCost(int, Instance) - Method in class weka.classifiers.CostMatrix
-
Gets the maximum cost for a particular class value.
- getMaxCount() - Method in class weka.filters.supervised.instance.SpreadSubsample
-
Gets the value for the max count
- getMaxDefault() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Get the maximum default.
- getMaxDepth() - Method in class weka.classifiers.trees.RandomForest
-
Get the maximum depth of trh tree, 0 for unlimited.
- getMaxDepth() - Method in class weka.classifiers.trees.RandomTree
-
Get the maximum depth of trh tree, 0 for unlimited.
- getMaxDepth() - Method in class weka.classifiers.trees.REPTree
-
Get the value of MaxDepth.
- getMaxDepth() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
Returns the depth of the built tree.
- getMaxGenerations() - Method in class weka.attributeSelection.GeneticSearch
-
get the number of generations
- getMaxGridExtensions() - Method in class weka.classifiers.meta.GridSearch
-
Gets the maximum number of grid extensions, -1 for unlimited.
- getMaxGroup() - Method in class weka.classifiers.meta.RotationForest
-
Gets the maximum size of a group.
- getMaximumAttributeNames() - Method in class weka.attributeSelection.LatentSemanticAnalysis
-
Gets maximum number of attributes to include in
transformed attribute names.
- getMaximumAttributeNames() - Method in class weka.attributeSelection.PrincipalComponents
-
Gets maximum number of attributes to include in
transformed attribute names.
- getMaximumAttributeNames() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Gets maximum number of attributes to include in
transformed attribute names.
- getMaximumAttributes() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Gets maximum number of PC attributes to retain.
- getMaximumVariancePercentageAllowed() - Method in class weka.filters.unsupervised.attribute.RemoveUseless
-
Gets the maximum variance attributes are allowed to have before they are
deleted by the filter.
- getMaxInfoGain() - Method in class weka.classifiers.rules.JRip.Antd
-
- getMaxInstancesInLeaf() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
Returns the maximum number of instances allowed in a leaf.
- getMaxInstInLeaf() - Method in class weka.core.neighboursearch.KDTree
-
Get the maximum number of instances in a leaf.
- getMaxInstNum() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Gets the upper boundary for instances per cluster.
- getMaxInstNum() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Gets the upper boundary for instances per cluster.
- getMaxIntNodesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats
-
returns the maximum of internal nodes visited.
- getMaxIterations() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Get the maximum number of iterations to perform
- getMaxIterations() - Method in class weka.classifiers.mi.MIBoost
-
Get the maximum number of boost iterations
- getMaxIterations() - Method in class weka.classifiers.mi.MISVM
-
Gets the maximum number of iterations.
- getMaxIterations() - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Returns the maxIterations parameter.
- getMaxIterations() - Method in class weka.clusterers.EM
-
Get the maximum number of iterations
- getMaxIterations() - Method in class weka.clusterers.sIB
-
Get the max number of iterations
- getMaxIterations() - Method in class weka.clusterers.SimpleKMeans
-
gets the number of maximum iterations to be executed
- getMaxIterations() - Method in class weka.clusterers.XMeans
-
Gets the maximum number of iterations.
- getMaxIterations() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Gets the maximum number of cleansing iterations performed
- getMaxIts() - Method in class weka.classifiers.functions.Logistic
-
Get the value of MaxIts.
- getMaxIts() - Method in class weka.classifiers.functions.RBFNetwork
-
Get the value of MaxIts.
- getMaxK() - Method in class weka.classifiers.functions.VotedPerceptron
-
Get the value of maxK.
- getMaxKMeans() - Method in class weka.clusterers.XMeans
-
Gets the maximum number of iterations in KMeans.
- getMaxKMeansForChildren() - Method in class weka.clusterers.XMeans
-
Gets the maximum number of iterations in KMeans.
- getMaxLeavesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats
-
Returns the maximum number of leaves visited.
- getMaxNrOfParents() - Method in class weka.classifiers.bayes.net.search.global.HillClimber
-
Gets the max number of parents.
- getMaxNrOfParents() - Method in class weka.classifiers.bayes.net.search.global.K2
-
Gets the max number of parents.
- getMaxNrOfParents() - Method in class weka.classifiers.bayes.net.search.local.HillClimber
-
Gets the max number of parents.
- getMaxNrOfParents() - Method in class weka.classifiers.bayes.net.search.local.K2
-
Gets the max number of parents.
- getMaxNrOfParents() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
-
Gets the max number of parents.
- getMaxNumberOfItems() - Method in class weka.associations.FPGrowth
-
Gets the maximum number of items to be included in large item sets.
- getMaxNumClusters() - Method in class weka.clusterers.XMeans
-
Gets the maximum number of clusters to generate.
- getMaxPlots() - Method in class weka.gui.beans.AttributeSummarizer
-
Get the number of plots to display
- getMaxPointsVisited() - Method in class weka.core.neighboursearch.PerformanceStats
-
Returns the maximum of points visited.
- getMaxRadius() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Gets the upper boundary for the radiuses of the clusters.
- getMaxRange() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Gets the upper boundary for the range of x
- getMaxRelativeLeafRadius() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
Returns the maximum relative radius of a leaf node.
- getMaxRelativeNodeWidth(double[][], double[][]) - Method in class weka.core.neighboursearch.KDTree
-
Returns the maximum attribute width of instances/points
in a KDTreeNode relative to the whole dataset.
- getMaxRows() - Method in class weka.gui.sql.event.QueryExecuteEvent
-
returns the maximum number of rows to retrieve.
- getMaxRows() - Method in class weka.gui.sql.QueryPanel
-
returns the current value for the maximum number of rows.
- getMaxRows() - Method in class weka.gui.sql.ResultSetHelper
-
the maximum number of rows to retrieve, less than 1 means unlimited.
- getMaxRuleSize() - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Gets the maximum number of tests in rules.
- getMaxRunNumber() - Method in class weka.gui.beans.BatchClassifierEvent
-
Get the maximum run number
- getMaxRunNumber() - Method in class weka.gui.beans.TestSetEvent
-
Get the maximum number of runs.
- getMaxRunNumber() - Method in class weka.gui.beans.TrainingSetEvent
-
Get the maximum number of runs.
- getMaxSetNumber() - Method in class weka.gui.beans.BatchClassifierEvent
-
Get the maximum set number (ie the total number of training
and testing sets in the series).
- getMaxSetNumber() - Method in class weka.gui.beans.BatchClustererEvent
-
Get the maximum set number (ie the total number of training
and testing sets in the series).
- getMaxSetNumber() - Method in class weka.gui.beans.TestSetEvent
-
Get the maximum set number
- getMaxSetNumber() - Method in class weka.gui.beans.TrainingSetEvent
-
Get the maximum set number
- getMaxSubsequenceLength() - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Returns the maximum length of the subsequence
- getMaxThreshold() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Get the maximum threshold.
- getMaxValue() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
- getMaxVersion() - Method in interface weka.gui.visualize.plugins.ErrorVisualizePlugin
-
Get the maximum version of Weka, exclusive, the class
is designed to work with.
- getMaxVersion() - Method in interface weka.gui.visualize.plugins.GraphVisualizePlugin
-
Get the maximum version of Weka, exclusive, the class
is designed to work with.
- getMaxVersion() - Method in interface weka.gui.visualize.plugins.TreeVisualizePlugin
-
Get the maximum version of Weka, exclusive, the class
is designed to work with.
- getMaxVersion() - Method in interface weka.gui.visualize.plugins.VisualizePlugin
-
Get the maximum version of Weka, exclusive, the class
is designed to work with.
- getMaxX() - Method in class weka.classifiers.meta.GridSearch.Grid
-
returns the right border
- getMaxX() - Method in class weka.gui.visualize.Plot2D
-
Return the current max value of the attribute plotted on the x axis
- getMaxXBound() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Gets the maximum x-coordinate bound, in training-instance units (not mouse coordinates).
- getMaxY() - Method in class weka.classifiers.meta.GridSearch.Grid
-
returns the top border
- getMaxY() - Method in class weka.gui.visualize.Plot2D
-
Return the current max value of the attribute plotted on the y axis
- getMaxYBound() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Gets the maximum x-coordinate bound, in training-instance units (not mouse coordinates).
- getMean() - Method in class weka.estimators.NormalEstimator
-
Return the value of the mean of this normal estimator.
- getMean(int, int) - Method in class weka.experiment.ResultMatrix
-
returns the mean at the given position, if the position is valid,
otherwise 0
- getMeanAbsoluteError(Instances) - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Returns the error of the probability estimates for the current model on a set of instances.
- getMeanCoordsPerPoint() - Method in class weka.core.neighboursearch.PerformanceStats
-
Returns the mean of coords per point.
- getMeanIntNodesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats
-
Returns the mean of internal nodes visited.
- getMeanLeavesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats
-
Returns the mean of number of leaves visited.
- getMeanPointsVisited() - Method in class weka.core.neighboursearch.PerformanceStats
-
Returns the mean of points visited.
- getMeanPrec() - Method in class weka.experiment.ResultMatrix
-
returns the current precision for the means
- getMeanPrec() - Method in class weka.gui.experiment.OutputFormatDialog
-
Gets the precision used for printing the mean.
- getMeanPrecision() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
returns the default precision for the mean
- getMeans() - Method in class weka.estimators.KernelEstimator
-
Return the means of the kernels.
- getMeanSquared() - Method in class weka.classifiers.lazy.IBk
-
Gets whether the mean squared error is used rather than mean
absolute error when doing cross-validation.
- getMeanStddev() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
returns the current mean/stddev setup
- getMeanValue() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
- getMeanWidth() - Method in class weka.experiment.ResultMatrix
-
returns the current width for the mean
- getMeasure(String) - Method in class weka.classifiers.bayes.BayesNet
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.functions.SimpleLogistic
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.functions.SMOreg
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.lazy.IBk
-
Returns the value of the named measure from the
neighbour search algorithm, plus the chosen K in case
cross-validation is enabled.
- getMeasure(String) - Method in class weka.classifiers.lazy.LWL
-
Returns the value of the named measure from the
neighbour search algorithm.
- getMeasure(String) - Method in class weka.classifiers.meta.AdditiveRegression
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.meta.AttributeSelectedClassifier
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.meta.Bagging
-
Returns the value of the named measure.
- getMeasure(String) - Method in class weka.classifiers.meta.GridSearch
-
Returns the value of the named measure
- getMeasure() - Method in class weka.classifiers.meta.ThresholdSelector
-
get measure used for determining threshold
- getMeasure(String) - Method in class weka.classifiers.rules.DecisionTable
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.rules.DTNB
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.rules.JRip
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.rules.PART
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.rules.Ridor
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.trees.ADTree
-
Returns the value of the named measure.
- getMeasure(String) - Method in class weka.classifiers.trees.BFTree
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.trees.FT
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.trees.J48
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.trees.J48graft
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.trees.LADTree
-
Returns the value of the named measure.
- getMeasure(String) - Method in class weka.classifiers.trees.LMT
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.trees.m5.M5Base
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.trees.NBTree
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.trees.RandomForest
-
Returns the value of the named measure.
- getMeasure(String) - Method in class weka.classifiers.trees.REPTree
-
Returns the value of the named measure.
- getMeasure(String) - Method in class weka.classifiers.trees.SimpleCart
-
Returns the value of the named measure.
- getMeasure(String) - Method in interface weka.core.AdditionalMeasureProducer
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.core.neighboursearch.BallTree
-
Returns the value of the named measure.
- getMeasure(String) - Method in class weka.core.neighboursearch.CoverTree
-
Returns the value of the named measure.
- getMeasure(String) - Method in class weka.core.neighboursearch.KDTree
-
Returns the value of the named measure.
- getMeasure(String) - Method in class weka.core.neighboursearch.NearestNeighbourSearch
-
Returns the value of the named measure.
- getMeasure(String) - Method in class weka.core.neighboursearch.PerformanceStats
-
Returns the value of the named measure.
- getMeasure(String) - Method in class weka.core.neighboursearch.TreePerformanceStats
-
Returns the value of the named measure.
- getMeasure(String) - Method in class weka.experiment.AveragingResultProducer
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.experiment.ClassifierSplitEvaluator
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.experiment.CrossValidationResultProducer
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.experiment.DatabaseResultProducer
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.experiment.LearningRateResultProducer
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.experiment.RandomSplitResultProducer
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.experiment.RegressionSplitEvaluator
-
Returns the value of the named measure
- getMeasurePerformance() - Method in class weka.core.neighboursearch.NearestNeighbourSearch
-
Gets whether performance statistics are being calculated or not.
- getMenu() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
returns the menu bar to be added in a frame
- getMenuBar() - Method in class weka.classifiers.bayes.net.GUI
-
Get the menu bar for this application.
- getMenuTitle() - Method in interface weka.gui.MainMenuExtension
-
Returns the name of the menu item.
- getMestWeight() - Method in class weka.classifiers.bayes.AODEsr
-
Gets the weight used in m-estimate
- getMetaClassifier() - Method in class weka.classifiers.meta.Stacking
-
Gets the meta classifier.
- getMetadata() - Method in class weka.core.Attribute
-
Returns the properties supplied for this attribute.
- getMetaData() - Method in class weka.core.converters.DatabaseConnection
-
Gets meta data for the database connection object.
- getMethod() - Method in class weka.classifiers.functions.neural.NeuralNode
-
- getMethod() - Method in class weka.classifiers.meta.MultiClassClassifier
-
Gets the method used.
- getMethod() - Method in class weka.classifiers.mi.MIWrapper
-
Get the method used in testing.
- getMethodName() - Method in class weka.filters.unsupervised.attribute.NumericTransform
-
Get the transformation method.
- getMetricType() - Method in class weka.associations.Apriori
-
Get the metric type
- getMetricType() - Method in class weka.associations.FPGrowth.AssociationRule
-
Get the metric type of this rule (e.g.
- getMetricType() - Method in class weka.associations.FPGrowth
-
Get the metric type to use.
- getMetricValue() - Method in class weka.associations.FPGrowth.AssociationRule
-
Get the value of the metric for this rule.
- getMiddle(double[]) - Method in class weka.core.EuclideanDistance
-
Returns value in the middle of the two parameter values.
- getMidPoints() - Method in class weka.associations.PriorEstimation
-
returns an ordered array of all mid points
- getMin() - Method in class weka.classifiers.meta.GridSearch.PerformanceTable
-
the minimum performance
- getMin() - Method in class weka.gui.beans.ChartEvent
-
Get the min y value
- getMinArray() - Method in class weka.filters.unsupervised.attribute.Normalize
-
Returns the calculated minimum values for the attributes in the data.
- getMinBoxRelWidth() - Method in class weka.core.neighboursearch.KDTree
-
Gets the minimum relative box width.
- getMinBucketSize() - Method in class weka.classifiers.rules.OneR
-
Get the value of minBucketSize.
- getMinC() - Method in class weka.gui.visualize.Plot2D
-
Return the current min value of the colouring attribute
- getMinChange() - Method in class weka.clusterers.sIB
-
get the minimum number of changes
- getMinChunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Get the minimum chunk size
- getMinCoordsPerPoint() - Method in class weka.core.neighboursearch.PerformanceStats
-
Returns the minimum of coords per point.
- getMinDefault() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Get the minimum default.
- getMinFunction() - Method in class weka.core.Optimization
-
Get the minimal function value
- getMinGroup() - Method in class weka.classifiers.meta.RotationForest
-
Gets the minimum size of a group.
- getMinimax() - Method in class weka.classifiers.mi.MISMO
-
Check if the MIMinimax feature space is to be used.
- getMinimizeExpectedCost() - Method in class weka.classifiers.meta.CostSensitiveClassifier
-
Gets the value of MinimizeExpectedCost.
- getMinimumBucketSize() - Method in class weka.attributeSelection.OneRAttributeEval
-
Get the minimum bucket size used by oneR
- getMinimumMaximum(Instances, int) - Method in class weka.estimators.CheckEstimator
-
Gets the minimum and maximum of the values a the first attribute
of the given data set
- getMinimumNumberInstances() - Method in class weka.core.Capabilities
-
returns the minimum number of instances that have to be in the dataset
- getMiningFields() - Method in class weka.core.pmml.MiningSchema
-
- getMiningSchema() - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
-
Get the mining schema for this model.
- getMiningSchema() - Method in interface weka.core.pmml.PMMLModel
-
Get the mining schema.
- getMiningSchemaAsInstances() - Method in class weka.core.pmml.MiningSchema
-
Get the mining schema fields as an Instances object.
- getMiningSchemaAsInstances(Element, Instances) - Static method in class weka.core.pmml.PMMLFactory
-
- getMinInstNum() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Gets the lower boundary for instances per cluster.
- getMinInstNum() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Gets the lower boundary for instances per cluster.
- getMinIntNodesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats
-
Returns the minimum of internal nodes visited.
- getMinLeavesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats
-
Returns the minimum number of leaves visited.
- getMinLevel() - Method in class weka.core.logging.Logger
-
Returns the minimum level log messages must have in order to appear in
the log.
- getMinMax(Instances, int, double[]) - Static method in class weka.estimators.CheckEstimator
-
Find the minimum and the maximum of the attribute and return it in
the last parameter..
- getMinMax(Instances, int, double[]) - Static method in class weka.estimators.EstimatorUtils
-
Find the minimum and the maximum of the attribute and return it in
the last parameter..
- getMinMetric() - Method in class weka.associations.Apriori
-
Get the value of minConfidence.
- getMinMetric() - Method in class weka.associations.FPGrowth
-
Get the value of minConfidence.
- getMinNo() - Method in class weka.classifiers.rules.ConjunctiveRule
-
Gets the minimum total weight of the instances in a rule
- getMinNo() - Method in class weka.classifiers.rules.JRip
-
Gets the minimum total weight of the instances in a rule
- getMinNo() - Method in class weka.classifiers.rules.Ridor
-
- getMinNum() - Method in class weka.classifiers.trees.RandomTree
-
Get the value of MinNum.
- getMinNum() - Method in class weka.classifiers.trees.REPTree
-
Get the value of MinNum.
- getMinNumClusters() - Method in class weka.clusterers.XMeans
-
Gets the minimum number of clusters to generate.
- getMinNumInstances() - Method in class weka.classifiers.trees.FT
-
Get the value of minNumInstances.
- getMinNumInstances() - Method in class weka.classifiers.trees.LMT
-
Get the value of minNumInstances.
- getMinNumInstances() - Method in class weka.classifiers.trees.m5.M5Base
-
Get the minimum number of instances to allow at a leaf node
- getMinNumInstances() - Method in class weka.classifiers.trees.m5.Rule
-
Get the minimum number of instances to allow at a leaf node
- getMinNumInstances() - Method in class weka.classifiers.trees.m5.RuleNode
-
Get the minimum number of instances to allow at a leaf node
- getMinNumObj() - Method in class weka.classifiers.rules.PART
-
Get the value of minNumObj.
- getMinNumObj() - Method in class weka.classifiers.trees.BFTree
-
Get minimal number of instances at the terminal nodes.
- getMinNumObj() - Method in class weka.classifiers.trees.J48
-
Get the value of minNumObj.
- getMinNumObj() - Method in class weka.classifiers.trees.J48graft
-
Get the value of minNumObj.
- getMinNumObj() - Method in class weka.classifiers.trees.SimpleCart
-
Get minimal number of instances at the terminal nodes.
- getMinPoints() - Method in class weka.clusterers.DBScan
-
Returns the value of minPoints
- getMinPoints() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
-
Returns the number of minPoints
- getMinPoints() - Method in class weka.clusterers.OPTICS
-
Returns the value of minPoints
- getMinPointsVisited() - Method in class weka.core.neighboursearch.PerformanceStats
-
Returns the minimum of points visited.
- getMinRadius() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Gets the lower boundary for the radiuses of the clusters.
- getMinRange() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Gets the lower boundary for the range of x
- getMinRuleSize() - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Gets the minimum number of tests in rules.
- getMinStdDev() - Method in class weka.classifiers.functions.RBFNetwork
-
Get the MinStdDev value.
- getMinStdDev() - Method in class weka.clusterers.EM
-
Get the minimum allowable standard deviation.
- getMinStdDev() - Method in class weka.clusterers.MakeDensityBasedClusterer
-
Get the minimum allowable standard deviation.
- getMinSupport() - Method in class weka.associations.GeneralizedSequentialPatterns
-
Returns the minimum support threshold.
- getMinTermFreq() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Get the MinTermFreq value.
- getMinThreshold() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Get the minimum threshold.
- getMinValue() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
- getMinVarianceProp() - Method in class weka.classifiers.trees.REPTree
-
Get the value of MinVarianceProp.
- getMinVersion() - Method in interface weka.gui.visualize.plugins.ErrorVisualizePlugin
-
Get the minimum version of Weka, inclusive, the class
is designed to work with.
- getMinVersion() - Method in interface weka.gui.visualize.plugins.GraphVisualizePlugin
-
Get the minimum version of Weka, inclusive, the class
is designed to work with.
- getMinVersion() - Method in interface weka.gui.visualize.plugins.TreeVisualizePlugin
-
Get the minimum version of Weka, inclusive, the class
is designed to work with.
- getMinVersion() - Method in interface weka.gui.visualize.plugins.VisualizePlugin
-
Get the minimum version of Weka, inclusive, the class
is designed to work with.
- getMinX() - Method in class weka.classifiers.meta.GridSearch.Grid
-
returns the left border
- getMinX() - Method in class weka.gui.visualize.Plot2D
-
Return the current min value of the attribute plotted on the x axis
- getMinXBound() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Gets the minimum x-coordinate bound, in training-instance units (not mouse coordinates).
- getMinY() - Method in class weka.classifiers.meta.GridSearch.Grid
-
returns the bottom border
- getMinY() - Method in class weka.gui.visualize.Plot2D
-
Return the current min value of the attribute plotted on the y axis
- getMinYBound() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Gets the minimum y-coordinate bound, in training-instance units (not mouse coordinates).
- getMisses() - Method in class weka.core.FindWithCapabilities
-
returns the misses from the last find call.
- getMissingMerge() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
-
get whether missing values are being distributed or not
- getMissingMerge() - Method in class weka.attributeSelection.GainRatioAttributeEval
-
get whether missing values are being distributed or not
- getMissingMerge() - Method in class weka.attributeSelection.InfoGainAttributeEval
-
get whether missing values are being distributed or not
- getMissingMerge() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
-
get whether missing values are being distributed or not
- getMissingMode() - Method in class weka.classifiers.lazy.KStar
-
Gets the method to use for handling missing values.
- getMissingSeparate() - Method in class weka.attributeSelection.CfsSubsetEval
-
Return true is missing is treated as a separate value
- getMissingValue() - Method in class weka.core.converters.CSVLoader
-
Returns the current placeholder for missing values.
- getMissingValues() - Method in class weka.associations.Tertius
-
Get the value of missingValues.
- getMissingValueTreatmentMethod() - Method in class weka.core.pmml.MiningFieldMetaInfo
-
Get the missing value treatment method for this field.
- getMixingDistribution() - Method in class weka.classifiers.functions.pace.MixtureDistribution
-
Gets the mixing distribution
- getModel() - Method in class weka.classifiers.functions.LibLINEAR
-
- getModel() - Method in class weka.classifiers.trees.m5.RuleNode
-
Get the linear model at this node
- getModel() - Method in class weka.gui.SortedTableModel
-
returns the current model, can be null
- getModelElement(Document, PMMLFactory.ModelType) - Static method in class weka.core.pmml.PMMLFactory
-
Get the Element that contains the pmml model
- getModelFile() - Method in class weka.classifiers.misc.SerializedClassifier
-
Gets the file containing the serialized model.
- getModelInstance(Document, PMMLFactory.ModelType, Element, Instances, MiningSchema) - Static method in class weka.core.pmml.PMMLFactory
-
Get an instance of a PMMLModel from the supplied Document
- getModelParameters() - Method in class weka.classifiers.trees.ft.FTtree
-
Returns a string describing the number of LogitBoost iterations performed at this node, the total number
of LogitBoost iterations performed (including iterations at higher levels in the tree), and the number
of training examples at this node.
- getModelParameters() - Method in class weka.classifiers.trees.lmt.LMTNode
-
Returns a string describing the number of LogitBoost iterations performed at this node, the total number
of LogitBoost iterations performed (including iterations at higher levels in the tree), and the number
of training examples at this node.
- getModelType() - Method in class weka.classifiers.trees.FT
-
Get the type of functional tree model being used.
- getModelType(Document) - Static method in class weka.core.pmml.PMMLFactory
-
Get the type of model
- getModelValueAt(int, int) - Method in class weka.gui.arffviewer.ArffSortedTableModel
-
returns the value at the given position
- getModifyHeader() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
Gets whether the header will be modified when selecting on nominal
attributes.
- getModifyHeader() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Gets whether the header will be modified when selecting on nominal
attributes.
- getMomentum() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- getMultiInstance() - Method in class weka.core.TestInstances
-
Gets whether multi-instance data (with a fixed structure) is generated
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.CitationKNN
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MDD
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MIBoost
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MIDD
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MIEMDD
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MILR
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MINND
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MIOptimalBall
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MISMO
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MISVM
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MIWrapper
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.SimpleMI
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.supportVector.MIPolyKernel
-
Returns the capabilities of this multi-instance kernel for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.supportVector.MIRBFKernel
-
Returns the capabilities of this multi-instance kernel for the
relational data.
- getMultiInstanceCapabilities() - Method in interface weka.core.MultiInstanceCapabilitiesHandler
-
Returns the capabilities of this multi-instance classifier for the
relational data (i.e., the bags).
- getMultiInstanceCapabilities() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
-
Returns the capabilities of this multi-instance filter for the
relational data (i.e., the bags).
- getMultinomialWord() - Method in class weka.classifiers.bayes.DMNBtext
-
Gets whether use binary text representation
- getMutationProb() - Method in class weka.attributeSelection.GeneticSearch
-
get the probability of mutation
- getNaiveBayesModel() - Method in class weka.classifiers.trees.j48.NBTreeNoSplit
-
Get the naive bayes model at this node
- getName() - Method in class weka.classifiers.bayes.BayesNet
-
get name of the Bayes network
- getName() - Method in class weka.core.pmml.Function
-
- getName() - Method in class weka.core.pmml.MiningFieldMetaInfo
-
Get the name of this field.
- getName() - Method in class weka.core.PropertyPath.PathElement
-
returns the name of the property
- getName() - Method in class weka.filters.unsupervised.attribute.AddExpression
-
Returns the name of the new attribute
- getName() - Method in class weka.gui.visualize.VisualizePanel
-
Returns the name associated with this plot.
- getNameAtIndex(int) - Method in class weka.gui.ResultHistoryPanel
-
Gets the name of theitem in the list at the specified index
- getNamedBuffer(String) - Method in class weka.gui.ResultHistoryPanel
-
Gets the named buffer
- getNamedObject(String) - Method in class weka.gui.ResultHistoryPanel
-
Get the named object from the list
- getNearestNeighbors() - Method in class weka.filters.supervised.instance.SMOTE
-
Gets the number of nearest neighbors to use.
- getNearestNeighbourSearchAlgorithm() - Method in class weka.classifiers.lazy.IBk
-
Returns the current nearestNeighbourSearch algorithm in use.
- getNearestNeighbourSearchAlgorithm() - Method in class weka.classifiers.lazy.LWL
-
Returns the current nearestNeighbourSearch algorithm in use.
- getNegation() - Method in class weka.associations.Tertius
-
Get the value of negation.
- getNegation() - Method in class weka.associations.tertius.Literal
-
- getNewDecList(Instances, boolean) - Method in class weka.classifiers.rules.part.C45PruneableDecList
-
Returns a newly created tree.
- getNewDecList(Instances, boolean) - Method in class weka.classifiers.rules.part.ClassifierDecList
-
Returns a newly created tree.
- getNewDecList(Instances, Instances, boolean) - Method in class weka.classifiers.rules.part.PruneableDecList
-
Returns a newly created tree.
- getNewTree(Instances) - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTree
-
Returns a newly created tree.
- getNewTree(Instances) - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG
-
Returns a newly created tree.
- getNewTree(Instances) - Method in class weka.classifiers.trees.j48.ClassifierTree
-
Returns a newly created tree.
- getNewTree(Instances, Instances) - Method in class weka.classifiers.trees.j48.ClassifierTree
-
Returns a newly created tree.
- getNewTree(Instances) - Method in class weka.classifiers.trees.j48.NBTreeClassifierTree
-
Returns a newly created tree.
- getNewTree(Instances, Instances) - Method in class weka.classifiers.trees.j48.NBTreeClassifierTree
-
Returns a newly created tree.
- getNewTree(Instances, Instances) - Method in class weka.classifiers.trees.j48.PruneableClassifierTree
-
Returns a newly created tree.
- getNext(int) - Method in class weka.classifiers.functions.supportVector.SMOset
-
Gets the next element in the set.
- getNextDebugVectorsInstance(Instances) - Method in class weka.clusterers.XMeans
-
Read an instance from debug vectors file.
- getNextInstance(Instances) - Method in class weka.core.converters.AbstractLoader
-
- getNextInstance(Instances) - Method in class weka.core.converters.ArffLoader
-
Read the data set incrementally---get the next instance in the data
set or returns null if there are no
more instances to get.
- getNextInstance(Instances) - Method in class weka.core.converters.C45Loader
-
Read the data set incrementally---get the next instance in the data
set or returns null if there are no
more instances to get.
- getNextInstance(Instances) - Method in class weka.core.converters.CSVLoader
-
CSVLoader is unable to process a data set incrementally.
- getNextInstance(Instances) - Method in class weka.core.converters.DatabaseLoader
-
Read the data set incrementally---get the next instance in the data
set or returns null if there are no
more instances to get.
- getNextInstance(Instances) - Method in class weka.core.converters.LibSVMLoader
-
LibSVmLoader is unable to process a data set incrementally.
- getNextInstance(Instances) - Method in interface weka.core.converters.Loader
-
Read the data set incrementally---get the next instance in the data
set or returns null if there are no
more instances to get.
- getNextInstance(Instances) - Method in class weka.core.converters.SerializedInstancesLoader
-
Read the data set incrementally---get the next instance in the data
set or returns null if there are no
more instances to get.
- getNextInstance(Instances) - Method in class weka.core.converters.SVMLightLoader
-
SVMLightLoader is unable to process a data set incrementally.
- getNextInstance(Instances) - Method in class weka.core.converters.TextDirectoryLoader
-
TextDirectoryLoader is unable to process a data set incrementally.
- getNextInstance(Instances) - Method in class weka.core.converters.XRFFLoader
-
XRFFLoader is unable to process a data set incrementally.
- getNextTabName() - Method in class weka.gui.sql.ResultPanel
-
returns the next name for a tab "QueryXYZ'
- getNextToken() - Method in class weka.core.converters.ArffLoader.ArffReader
-
Gets next token, checking for a premature and of line.
- getNGramMaxSize() - Method in class weka.core.tokenizers.NGramTokenizer
-
Gets the max N of the NGram.
- getNGramMinSize() - Method in class weka.core.tokenizers.NGramTokenizer
-
Gets the min N of the NGram.
- getNoClass() - Method in class weka.core.TestInstances
-
whether no class attribute is generated
- getNode(String) - Method in class weka.classifiers.bayes.net.BIFReader
-
getNode finds the index of the node with name sNodeName
and throws an exception if no such node can be found.
- getNode(String) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
returns index of node with given name.
- getNode(String) - Method in class weka.classifiers.bayes.net.MarginCalculator
-
- getNode(String) - Method in class weka.core.xml.XMLDocument
-
Returns the node represented by the XPath expression.
- getNode2(String) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
returns index of node with given name, or -1 if no such node exists
- getNodeName(int) - Method in class weka.classifiers.bayes.BayesNet
-
get name of a node in the Bayes network
- getNodes() - Method in class weka.classifiers.trees.ft.FTtree
-
Return a list of all inner nodes in the tree
- getNodes(Vector) - Method in class weka.classifiers.trees.ft.FTtree
-
Fills a list with all inner nodes in the tree
- getNodes() - Method in class weka.classifiers.trees.lmt.LMTNode
-
Return a list of all inner nodes in the tree
- getNodes(Vector) - Method in class weka.classifiers.trees.lmt.LMTNode
-
Fills a list with all inner nodes in the tree
- getNodes() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
-
give access to set of graph nodes
- getNodes() - Method in interface weka.gui.graphvisualizer.LayoutEngine
-
give access to set of graph nodes
- getNodeSplitter() - Method in class weka.core.neighboursearch.KDTree
-
Returns the splitting method currently in use to split the nodes of the
KDTree.
- getNodeValue(int, int) - Method in class weka.classifiers.bayes.BayesNet
-
get name of a particular value of a node
- getNoise() - Method in class weka.classifiers.functions.GaussianProcesses
-
Get the value of noise.
- getNoisePercent() - Method in class weka.datagenerators.classifiers.classification.LED24
-
Gets the noise percentage.
- getNoiseRate() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Gets the gaussian noise rate.
- getNoiseRate() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Gets the percentage of noise set.
- getNoiseRate() - Method in class weka.datagenerators.clusterers.SubspaceCluster
-
Gets the percentage of noise set.
- getNoiseThreshold() - Method in class weka.associations.Tertius
-
Get the value of noiseThreshold.
- getNoiseVariance() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Gets the noise variance
- getNominalAttributes() - Method in class weka.core.converters.CSVLoader
-
Returns the current attribute range to be forced to type nominal.
- getNominalCols() - Method in class weka.datagenerators.ClusterGenerator
-
returns the range of nominal attributes
- getNominalIndices() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Get the set of nominal value indices that will be used for selection
- getNominalLabels() - Method in class weka.filters.unsupervised.attribute.Add
-
Get the list of labels for nominal attribute creation.
- getNominalToBinaryFilter() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- getNoPruning() - Method in class weka.classifiers.trees.REPTree
-
Get the value of NoPruning.
- getNoReplacement() - Method in class weka.filters.supervised.instance.Resample
-
Gets whether instances are drawn with or without replacement.
- getNoReplacement() - Method in class weka.filters.unsupervised.instance.Resample
-
Gets whether instances are drawn with or without replacement.
- getNorm() - Method in class weka.filters.unsupervised.instance.Normalize
-
Get the instance's Norm.
- getNormalize() - Method in class weka.attributeSelection.LatentSemanticAnalysis
-
Gets whether or not input data is to be normalized
- getNormalize() - Method in class weka.classifiers.functions.LibLINEAR
-
whether to normalize input data
- getNormalize() - Method in class weka.classifiers.functions.LibSVM
-
whether to normalize input data
- getNormalizeAttributes() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- getNormalizeDimWidths() - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
-
Whether we are normalizing the widths(ranges) of the dimensions (attributes)
or not.
- getNormalizeDocLength() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Gets whether if the word frequencies for a document (instance) should
be normalized or not.
- getNormalizeNodeWidth() - Method in class weka.core.neighboursearch.KDTree
-
Gets the normalize flag.
- getNormalizeNumericClass() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- getNormalizeWordWeights() - Method in class weka.classifiers.bayes.ComplementNaiveBayes
-
Returns true if the word weights for each class are to be normalized
- getNot() - Method in class weka.datagenerators.Test
-
Negates the test.
- getNotCapabilities() - Method in class weka.core.FindWithCapabilities
-
The "not to have" capabilities to search for.
- getNotes() - Method in class weka.experiment.Experiment
-
Get the user notes.
- getNotUnifyNorm() - Method in class weka.clusterers.sIB
-
Get whether to normalize instances to unify prior probability
before building the clusterer
- getNPointPrecision(Instances, int) - Static method in class weka.classifiers.evaluation.ThresholdCurve
-
Calculates the n point precision result, which is the precision averaged
over n evenly spaced (w.r.t recall) samples of the curve.
- getNrOfGoodOperations() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
-
Gets the number of "good operations"
- getNrOfLookAheadSteps() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
-
Gets the number of look-ahead steps
- getNrOfNodes() - Method in class weka.classifiers.bayes.BayesNet
-
get number of nodes in the Bayes network
- getNrOfParents(int) - Method in class weka.classifiers.bayes.BayesNet
-
get number of parents of a node in the network structure
- getNrOfParents() - Method in class weka.classifiers.bayes.net.ParentSet
-
returns number of parents
- getNu() - Method in class weka.classifiers.functions.LibSVM
-
Gets nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
- getNumAntds() - Method in class weka.classifiers.rules.ConjunctiveRule
-
Gets the number of antecedants
- getNumArcs() - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
Gets the number of arcs for the bayesian net
- getNumAttemptsOfGeneOption() - Method in class weka.classifiers.rules.NNge
-
Gets the number of attempts for generalisation.
- getNumAttributes() - Method in class weka.classifiers.lazy.LBR.Indexes
-
Returns the number of attributes in the dataset
- getNumAttributes() - Method in class weka.core.TestInstances
-
returns the overall number of attributes (incl.
- getNumAttributes() - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
Gets the number of attributes that should be produced.
- getNumAttributes() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
-
Gets the number of attributes that should be produced.
- getNumAttributes() - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Gets the number of attributes that should be produced.
- getNumAttributes() - Method in class weka.datagenerators.ClusterGenerator
-
Gets the number of attributes that should be produced.
- getNumAttributes() - Method in class weka.filters.unsupervised.attribute.RandomSubset
-
Get the number of attributes (< 1 percentage, >= 1 absolute number).
- getNumAttributesSet() - Method in class weka.classifiers.lazy.LBR.Indexes
-
Returns the number of attributes "in use"
- getNumberLiterals() - Method in class weka.associations.Tertius
-
Get the value of numberLiterals.
- getNumberOfAttributes() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
-
Returns the number of Attributes of the specified database
- getNumberOfAttributes() - Method in class weka.filters.unsupervised.attribute.RandomProjection
-
Gets the current number of attributes (dimensionality) to which the data
will be reduced to.
- getNumberOfGeneratedClusters() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
-
Returns the number of generated clusters
- getNumberOfGroups() - Method in class weka.classifiers.meta.RotationForest
-
Get whether minGroup and maxGroup refer to the number of groups or their
size
- getNumberOfTransactions() - Method in class weka.associations.FPGrowth.FrequentItemSets
-
Get the total number of transactions in the data that the item
sets were derived from.
- getNumBins() - Method in class weka.classifiers.meta.RegressionByDiscretization
-
Gets the number of bins numeric attributes will be divided into
- getNumBoostingIterations() - Method in class weka.classifiers.functions.SimpleLogistic
-
Get the value of numBoostingIterations.
- getNumBoostingIterations() - Method in class weka.classifiers.trees.FT
-
Get the value of numBoostingIterations.
- getNumBoostingIterations() - Method in class weka.classifiers.trees.LMT
-
Get the value of numBoostingIterations.
- getNumCentroids() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
-
Gets the number of centroids.
- getNumCiters() - Method in class weka.classifiers.mi.CitationKNN
-
Returns the number of citers considered to estimate
the class prediction of tests bags
- getNumClasses() - Method in class weka.core.TestInstances
-
returns the current number of classes
- getNumClasses() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
-
Gets the number of classes the dataset should have.
- getNumClasses() - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Gets the number of classes the dataset should have.
- getNumClusters() - Method in class weka.attributeSelection.UnsupervisedSubsetEvaluator
-
Return the number of clusters used by the subset evaluator
- getNumClusters() - Method in class weka.classifiers.functions.RBFNetwork
-
Return the number of clusters to generate.
- getNumClusters() - Method in class weka.clusterers.ClusterEvaluation
-
Return the number of clusters found for the most recent call to
evaluateClusterer
- getNumClusters() - Method in class weka.clusterers.EM
-
Get the number of clusters
- getNumClusters() - Method in class weka.clusterers.FarthestFirst
-
gets the number of clusters to generate
- getNumClusters() - Method in class weka.clusterers.HierarchicalClusterer
-
- getNumClusters() - Method in class weka.clusterers.sIB
-
Get the number of clusters
- getNumClusters() - Method in class weka.clusterers.SimpleKMeans
-
gets the number of clusters to generate
- getNumClusters() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Gets the number of clusters the dataset should have.
- getNumComponents() - Method in class weka.filters.supervised.attribute.PLSFilter
-
returns the maximum number of attributes to use.
- getNumCycles() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Gets the number of cycles.
- getNumDatasets() - Method in class weka.experiment.PairedTTester
-
Gets the number of datasets in the resultsets
- getNumDatasets() - Method in interface weka.experiment.Tester
-
Gets the number of datasets in the resultsets
- getNumDate() - Method in class weka.core.CheckScheme
-
returns the current number of date attributes
- getNumDate() - Method in class weka.core.TestInstances
-
returns the current number of date attributes
- getNumeric() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
-
Check if new attribute is to be numeric.
- getNumericColumns() - Method in class weka.gui.sql.ResultSetHelper
-
returns an array that indicates whether a column is numeric or nor.
- getNumericData(Instances) - Method in class weka.classifiers.trees.ft.FTtree
-
Returns a numeric version of a set of instances.
- getNumericData(Instances) - Method in class weka.classifiers.trees.lmt.LMTNode
-
Returns a numeric version of a set of instances.
- getNumericData(Instances) - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Converts training data to numeric version.
- getNumEvalsCached() - Method in class weka.attributeSelection.LFSMethods
-
- getNumEvalsTotal() - Method in class weka.attributeSelection.LFSMethods
-
- getNumExamples() - Method in class weka.datagenerators.ClassificationGenerator
-
Gets the number of examples, given by option.
- getNumExamples() - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
Gets the number of examples, given by option.
- getNumExamples() - Method in class weka.datagenerators.RegressionGenerator
-
Gets the number of examples, given by option.
- getNumExamplesAct() - Method in class weka.datagenerators.DataGenerator
-
Gets the number of examples the dataset should have.
- getNumFeatures() - Method in class weka.classifiers.trees.RandomForest
-
Get the number of features used in random selection.
- getNumFiles() - Method in class weka.core.Debug.Log
-
returns the number of files being used
- getNumFoldersMIOption() - Method in class weka.classifiers.rules.NNge
-
Gets the number of folder for mutual information.
- getNumFolds() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Return the number of folds for CV-based hyperparameter selection
- getNumFolds() - Method in class weka.classifiers.functions.SMO
-
Get the value of numFolds.
- getNumFolds() - Method in class weka.classifiers.meta.CVParameterSelection
-
Gets the number of folds for the cross-validation.
- getNumFolds() - Method in class weka.classifiers.meta.Dagging
-
Gets the number of folds to use for splitting the training set.
- getNumFolds() - Method in class weka.classifiers.meta.LogitBoost
-
Get the value of NumFolds.
- getNumFolds() - Method in class weka.classifiers.meta.MultiScheme
-
Gets the number of folds for cross-validation.
- getNumFolds() - Method in class weka.classifiers.meta.Stacking
-
Gets the number of folds for the cross-validation.
- getNumFolds() - Method in class weka.classifiers.mi.MISMO
-
Get the value of numFolds.
- getNumFolds() - Method in class weka.classifiers.rules.PART
-
Get the value of numFolds.
- getNumFolds() - Method in class weka.classifiers.trees.J48
-
Get the value of numFolds.
- getNumFolds() - Method in class weka.classifiers.trees.RandomTree
-
Get the value of NumFolds.
- getNumFolds() - Method in class weka.classifiers.trees.REPTree
-
Get the value of NumFolds.
- getNumFolds() - Method in class weka.experiment.CrossValidationResultProducer
-
Get the value of NumFolds.
- getNumFolds() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
-
Gets the number of folds in which dataset is to be split into.
- getNumFolds() - Method in class weka.filters.unsupervised.instance.RemoveFolds
-
Gets the number of folds in which dataset is to be split into.
- getNumFolds() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Gets the number of cross-validation folds used by the filter.
- getNumFoldsPruning() - Method in class weka.classifiers.trees.BFTree
-
Set number of folds in internal cross-validation.
- getNumFoldsPruning() - Method in class weka.classifiers.trees.SimpleCart
-
Set number of folds in internal cross-validation.
- getNumGeneratingModels() - Method in interface weka.gui.boundaryvisualizer.DataGenerator
-
Returns the number of generating models used by this DataGenerator
- getNumGeneratingModels() - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
-
Return the number of kernels (there is one per training instance)
- getNumInnerNodes() - Method in class weka.classifiers.trees.ft.FTtree
-
Method to count the number of inner nodes in the tree
- getNumInnerNodes() - Method in class weka.classifiers.trees.lmt.LMTNode
-
Method to count the number of inner nodes in the tree
- getNumInputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
-
- getNumInstances() - Method in class weka.classifiers.lazy.LBR.Indexes
-
Returns the number of instances in the dataset
- getNumInstances() - Method in class weka.classifiers.trees.m5.RuleNode
-
Return the number of instances that reach this node.
- getNumInstances() - Method in class weka.core.CheckScheme
-
Gets the current number of instances to use for the datasets.
- getNumInstances() - Method in class weka.core.TestInstances
-
returns the current number of instances to produce
- getNumInstances() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
- getNumInstances() - Method in class weka.estimators.CheckEstimator
-
Gets the current number of instances to use for the datasets.
- getNumInstancesRelational() - Method in class weka.core.CheckScheme
-
returns the current number of instances in relational/bag attributes to produce
- getNumInstancesRelational() - Method in class weka.core.TestInstances
-
returns the current number of instances in relational/bag attributes to produce
- getNumInstancesSet() - Method in class weka.classifiers.lazy.LBR.Indexes
-
Returns the number of instances "in use"
- getNumIrrelevant() - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Gets the number of irrelevant attributes.
- getNumIterations() - Method in class weka.classifiers.bayes.DMNBtext
-
Gets the number of iterations to be performed
- getNumIterations() - Method in class weka.classifiers.functions.VotedPerceptron
-
Get the value of NumIterations.
- getNumIterations() - Method in class weka.classifiers.functions.Winnow
-
Get the value of numIterations.
- getNumIterations() - Method in class weka.classifiers.IteratedSingleClassifierEnhancer
-
Gets the number of bagging iterations
- getNumIterations() - Method in class weka.classifiers.meta.MetaCost
-
Gets the number of bagging iterations
- getNumKernels() - Method in class weka.estimators.KernelEstimator
-
Return the number of kernels in this kernel estimator
- getNumLeaves() - Method in class weka.classifiers.trees.ft.FTtree
-
Returns the number of leaves in the tree.
- getNumLeaves() - Method in class weka.classifiers.trees.lmt.LMTNode
-
Returns the number of leaves in the tree.
- getNumLeaves() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
Returns the number of leaves in the built tree.
- getNumNeighbours() - Method in class weka.attributeSelection.ReliefFAttributeEval
-
Get the number of nearest neighbours
- getNumNeighbours() - Method in class weka.classifiers.mi.MINND
-
Returns the number of nearest neighbours to estimate
the class prediction of tests bags
- getNumNodes() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
Returns the number of nodes (internal + leaf)
in the built tree.
- getNumNominal() - Method in class weka.core.CheckScheme
-
returns the current number of nominal attributes
- getNumNominal() - Method in class weka.core.TestInstances
-
returns the current number of nominal attributes
- getNumNominalValues() - Method in class weka.core.TestInstances
-
returns the current number of values for nominal attributes
- getNumNumeric() - Method in class weka.core.CheckScheme
-
returns the current number of numeric attributes
- getNumNumeric() - Method in class weka.core.TestInstances
-
returns the current number of numeric attributes
- getNumNumeric() - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Gets the number of numerical attributes.
- getNumOfBoostingIterations() - Method in class weka.classifiers.trees.ADTree
-
Gets the number of boosting iterations.
- getNumOfBoostingIterations() - Method in class weka.classifiers.trees.LADTree
-
Gets the number of boosting iterations.
- getNumOfBranches() - Method in class weka.classifiers.trees.adtree.Splitter
-
Gets the number of branches of the split.
- getNumOfBranches() - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
-
Gets the number of branches of the split.
- getNumOfBranches() - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
-
Gets the number of branches of the split.
- getNumOfBranches() - Method in class weka.classifiers.trees.LADTree.Splitter
-
- getNumOfBranches() - Method in class weka.classifiers.trees.LADTree.TwoWayNominalSplit
-
- getNumOfBranches() - Method in class weka.classifiers.trees.LADTree.TwoWayNumericSplit
-
- getNumOutputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
-
- getNumQueries() - Method in class weka.core.neighboursearch.PerformanceStats
-
Returns the number of queries.
- getNumReferences() - Method in class weka.classifiers.mi.CitationKNN
-
Returns the number of references considered to estimate
the class prediction of tests bags
- getNumRegressions() - Method in class weka.classifiers.functions.SimpleLogistic
-
Get the number of LogitBoost iterations performed (= the number of
regression functions fit by LogitBoost).
- getNumRegressions() - Method in class weka.classifiers.trees.lmt.LogisticBase
-
The number of LogitBoost iterations performed (= the number of simple
regression functions fit).
- getNumRelational() - Method in class weka.core.CheckScheme
-
returns the current number of relational attributes
- getNumRelational() - Method in class weka.core.TestInstances
-
returns the current number of relational attributes
- getNumRelationalDate() - Method in class weka.core.TestInstances
-
returns the current number of date attributes in a relational attribute
- getNumRelationalNominal() - Method in class weka.core.TestInstances
-
returns the current number of nominal attributes in a relational attribute
- getNumRelationalNominalValues() - Method in class weka.core.TestInstances
-
returns the current number of values for nominal attributes in a relational attribute
- getNumRelationalNumeric() - Method in class weka.core.TestInstances
-
returns the current number of numeric attributes in a relational attribute
- getNumRelationalString() - Method in class weka.core.TestInstances
-
returns the current number of string attributes in a relational attribute
- getNumRestarts() - Method in class weka.clusterers.sIB
-
Get the number of restarts
- getNumResultsets() - Method in class weka.experiment.PairedTTester
-
Gets the number of resultsets in the data.
- getNumResultsets() - Method in interface weka.experiment.Tester
-
Gets the number of resultsets in the data.
- getNumRules() - Method in class weka.associations.Apriori
-
Get the value of numRules.
- getNumRules() - Method in class weka.associations.PredictiveApriori
-
Get the value of the number of required rules.
- getNumRulesToFind() - Method in class weka.associations.FPGrowth
-
Get the number of rules to find.
- getNumRuns() - Method in class weka.classifiers.meta.LogitBoost
-
Get the value of NumRuns.
- getNumSamplesPerRegion() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Get the number of points to sample from a region (fixed dimensions).
- getNumString() - Method in class weka.core.CheckScheme
-
returns the current number of string attributes
- getNumString() - Method in class weka.core.TestInstances
-
returns the current number of string attributes
- getNumSubCmtys() - Method in class weka.classifiers.meta.MultiBoostAB
-
Get the number of sub committees to use
- getNumSubsetSizeCVFolds() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Get the number of cross validation folds for subset size determination
(default = 5).
- getNumSymbols() - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
-
Gets the number of symbols this estimator operates with
- getNumSymbols() - Method in class weka.estimators.DiscreteEstimator
-
Gets the number of symbols this estimator operates with
- getNumTestingNoises() - Method in class weka.classifiers.mi.MINND
-
Returns The number of nearest neighbour instances in the
selection of noises in the test data
- getNumToSelect() - Method in class weka.attributeSelection.GreedyStepwise
-
Gets the number of attributes to be retained.
- getNumToSelect() - Method in class weka.attributeSelection.RaceSearch
-
Gets the number of attributes to be retained.
- getNumToSelect() - Method in interface weka.attributeSelection.RankedOutputSearch
-
Gets the user specified number of attributes to be retained.
- getNumToSelect() - Method in class weka.attributeSelection.Ranker
-
Gets the number of attributes to be retained.
- getNumTraining() - Method in class weka.classifiers.lazy.IBk
-
Get the number of training instances the classifier is currently using.
- getNumTrainingNoises() - Method in class weka.classifiers.mi.MINND
-
Returns the number of nearest neighbour instances in the
selection of noises in the training data
- getNumTrees() - Method in class weka.classifiers.trees.RandomForest
-
Get the value of numTrees.
- getNumUsedAttributes() - Method in class weka.attributeSelection.LinearForwardSelection
-
Get the number of top-ranked attributes that taken into account by the
search process.
- getNumUsedAttributes() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Get the number of top-ranked attributes that taken into account by the
search process.
- getNumValues() - Method in class weka.datagenerators.clusterers.SubspaceCluster
-
returns array that stores the number of values for a nominal attribute.
- getNumValues() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
Gets how many values are retained
- getNumXValFolds() - Method in class weka.classifiers.meta.ThresholdSelector
-
Get the number of folds used for cross-validation.
- getObject() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueueElement
-
Returns the object
- getObject() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueueElement
-
Returns the object
- getObject() - Method in class weka.core.CheckGOE
-
Get the object used in the tests.
- getObject() - Method in class weka.core.PropertyPath.PropertyContainer
-
returns the stored object
- getObject() - Method in class weka.core.SerializedObject
-
Returns a serialized object.
- getObject(String, String) - Static method in class weka.gui.explorer.ExplorerDefaults
-
Tries to instantiate the class stored for this property, optional
options will be set as well.
- getObject(String, String, Class) - Static method in class weka.gui.explorer.ExplorerDefaults
-
Tries to instantiate the class stored for this property, optional
options will be set as well.
- getObjective() - Method in class weka.attributeSelection.GeneticSearch.GABitSet
-
gets the objective merit
- getObjectKey() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueueElement
-
Returns the key
- getObservedFrequency() - Method in class weka.associations.tertius.Rule
-
Get the observed frequency of counter-instances of this rule in the dataset.
- getObservedNumber() - Method in class weka.associations.tertius.Rule
-
Get the observed number of counter-instances of this rule in the dataset.
- getOmega() - Method in class weka.classifiers.functions.supportVector.Puk
-
Gets the omega value.
- getOnDemandDirectory() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
-
Returns the directory that will be searched for cost files when
loading on demand.
- getOnDemandDirectory() - Method in class weka.classifiers.meta.CostSensitiveClassifier
-
Returns the directory that will be searched for cost files when
loading on demand.
- getOnDemandDirectory() - Method in class weka.classifiers.meta.MetaCost
-
Returns the directory that will be searched for cost files when
loading on demand.
- getOnDemandDirectory() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
-
Returns the directory that will be searched for cost files when
loading on demand.
- getOneElements(Instances) - Static method in class weka.associations.gsp.Element
-
Returns all events of the given data set as Elements containing a single
event.
- getOptimalOperations(BayesNet, Instances, int, int) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
-
getOptimalOperations determines an optimal operationsequence in respect of the parameters
nrOfLookAheadSteps and nrOfGoodOperations
- getOptimistic() - Method in class weka.associations.tertius.Rule
-
Get the optimistic estimate of the confirmation obtained by refining
this rule.
- getOptimizations() - Method in class weka.classifiers.rules.JRip
-
Gets the the number of optimization runs
- getOption(char, String[]) - Static method in class weka.core.Utils
-
Gets an option indicated by a flag "-Char" from the given array
of strings.
- getOption(String, String[]) - Static method in class weka.core.Utils
-
Gets an option indicated by a flag "-String" from the given array
of strings.
- getOptionHandler() - Method in class weka.core.CheckOptionHandler
-
Get the OptionHandler used in the tests.
- getOptionPos(char, String[]) - Static method in class weka.core.Utils
-
Gets the index of an option or flag indicated by a flag "-Char" from
the given array of strings.
- getOptionPos(String, String[]) - Static method in class weka.core.Utils
-
Gets the index of an option or flag indicated by a flag "-String" from
the given array of strings.
- getOptions() - Method in class weka.associations.Apriori
-
Gets the current settings of the Apriori object.
- getOptions() - Method in class weka.associations.CheckAssociator
-
Gets the current settings of the CheckAssociator.
- getOptions() - Method in class weka.associations.FilteredAssociator
-
Gets the current settings of the Associator.
- getOptions() - Method in class weka.associations.FPGrowth
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.associations.GeneralizedSequentialPatterns
-
Returns an Array containing the current options settings.
- getOptions() - Method in class weka.associations.PredictiveApriori
-
Gets the current settings of the PredictiveApriori object.
- getOptions() - Method in class weka.associations.SingleAssociatorEnhancer
-
Gets the current settings of the associator.
- getOptions() - Method in class weka.associations.Tertius
-
Gets the current settings of the Tertius object.
- getOptions() - Method in class weka.attributeSelection.BestFirst
-
Gets the current settings of BestFirst.
- getOptions() - Method in class weka.attributeSelection.CfsSubsetEval
-
Gets the current settings of CfsSubsetEval
- getOptions() - Method in class weka.attributeSelection.CheckAttributeSelection
-
Gets the current settings of the CheckAttributeSelection.
- getOptions() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
-
Gets the current settings.
- getOptions() - Method in class weka.attributeSelection.ClassifierSubsetEval
-
Gets the current settings of ClassifierSubsetEval
- getOptions() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
-
Gets the current settings of the subset evaluator.
- getOptions() - Method in class weka.attributeSelection.ExhaustiveSearch
-
Gets the current settings of RandomSearch.
- getOptions() - Method in class weka.attributeSelection.FilteredAttributeEval
-
Gets the current settings of the subset evaluator.
- getOptions() - Method in class weka.attributeSelection.FilteredSubsetEval
-
Gets the current settings of the subset evaluator.
- getOptions() - Method in class weka.attributeSelection.GainRatioAttributeEval
-
Gets the current settings of WrapperSubsetEval.
- getOptions() - Method in class weka.attributeSelection.GeneticSearch
-
Gets the current settings of ReliefFAttributeEval.
- getOptions() - Method in class weka.attributeSelection.GreedyStepwise
-
Gets the current settings of ReliefFAttributeEval.
- getOptions() - Method in class weka.attributeSelection.InfoGainAttributeEval
-
Gets the current settings of WrapperSubsetEval.
- getOptions() - Method in class weka.attributeSelection.LatentSemanticAnalysis
-
Gets the current settings of LatentSemanticAnalysis
- getOptions() - Method in class weka.attributeSelection.LinearForwardSelection
-
Gets the current settings of LinearForwardSelection.
- getOptions() - Method in class weka.attributeSelection.OneRAttributeEval
-
returns the current setup.
- getOptions() - Method in class weka.attributeSelection.PrincipalComponents
-
Gets the current settings of PrincipalComponents
- getOptions() - Method in class weka.attributeSelection.RaceSearch
-
Gets the current settings of BestFirst.
- getOptions() - Method in class weka.attributeSelection.RandomSearch
-
Gets the current settings of RandomSearch.
- getOptions() - Method in class weka.attributeSelection.Ranker
-
Gets the current settings of ReliefFAttributeEval.
- getOptions() - Method in class weka.attributeSelection.RankSearch
-
Gets the current settings of WrapperSubsetEval.
- getOptions() - Method in class weka.attributeSelection.ReliefFAttributeEval
-
Gets the current settings of ReliefFAttributeEval.
- getOptions() - Method in class weka.attributeSelection.ScatterSearchV1
-
Gets the current settings of ScatterSearchV1.
- getOptions() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Gets the current settings of LinearForwardSelection.
- getOptions() - Method in class weka.attributeSelection.SVMAttributeEval
-
Gets the current settings of SVMAttributeEval
- getOptions() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
-
Gets the current settings of WrapperSubsetEval.
- getOptions() - Method in class weka.attributeSelection.WrapperSubsetEval
-
Gets the current settings of WrapperSubsetEval.
- getOptions() - Method in class weka.classifiers.bayes.AODE
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.bayes.AODEsr
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
- getOptions() - Method in class weka.classifiers.bayes.BayesNet
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.bayes.ComplementNaiveBayes
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.bayes.DMNBtext
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.bayes.NaiveBayes
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.bayes.net.BayesNetGenerator
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.bayes.net.search.fixed.FromFile
-
Gets the current settings of the search algorithm.
- getOptions() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
-
Gets the current settings of the search algorithm.
- getOptions() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
-
Gets the current settings of the search algorithm.
- getOptions() - Method in class weka.classifiers.bayes.net.search.global.HillClimber
-
Gets the current settings of the search algorithm.
- getOptions() - Method in class weka.classifiers.bayes.net.search.global.K2
-
Gets the current settings of the search algorithm.
- getOptions() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
-
Gets the current settings of the search algorithm.
- getOptions() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
-
Gets the current settings of the search algorithm.
- getOptions() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
-
Gets the current settings of the search algorithm.
- getOptions() - Method in class weka.classifiers.bayes.net.search.global.TAN
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
-
Gets the current settings of the search algorithm.
- getOptions() - Method in class weka.classifiers.bayes.net.search.local.HillClimber
-
Gets the current settings of the search algorithm.
- getOptions() - Method in class weka.classifiers.bayes.net.search.local.K2
-
Gets the current settings of the search algorithm.
- getOptions() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
-
Gets the current settings of the search algorithm.
- getOptions() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
-
Gets the current settings of the search algorithm.
- getOptions() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
-
Gets the current settings of the search algorithm.
- getOptions() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
-
Gets the current settings of the search algorithm.
- getOptions() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
-
Gets the current settings of the search algorithm.
- getOptions() - Method in class weka.classifiers.bayes.net.search.local.TAN
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.bayes.WAODE
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.classifiers.BVDecompose
-
Gets the current settings of the CheckClassifier.
- getOptions() - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Gets the current settings of the CheckClassifier.
- getOptions() - Method in class weka.classifiers.CheckClassifier
-
Gets the current settings of the CheckClassifier.
- getOptions() - Method in class weka.classifiers.CheckSource
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.Classifier
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.functions.GaussianProcesses
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.functions.LeastMedSq
-
Gets the current option settings for the OptionHandler.
- getOptions() - Method in class weka.classifiers.functions.LibLINEAR
-
Returns the current options
- getOptions() - Method in class weka.classifiers.functions.LibSVM
-
Returns the current options
- getOptions() - Method in class weka.classifiers.functions.LinearRegression
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.functions.Logistic
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
Gets the current settings of NeuralNet.
- getOptions() - Method in class weka.classifiers.functions.PaceRegression
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.functions.PLSClassifier
-
returns the options of the current setup
- getOptions() - Method in class weka.classifiers.functions.RBFNetwork
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.functions.SimpleLogistic
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.functions.SMO
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.functions.SMOreg
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.functions.SPegasos
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.functions.supportVector.CachedKernel
-
Gets the current settings of the Kernel.
- getOptions() - Method in class weka.classifiers.functions.supportVector.CheckKernel
-
Gets the current settings of the CheckKernel.
- getOptions() - Method in class weka.classifiers.functions.supportVector.Kernel
-
Gets the current settings of the Kernel.
- getOptions() - Method in class weka.classifiers.functions.supportVector.PolyKernel
-
Gets the current settings of the Kernel.
- getOptions() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
-
Gets the current settings of the Kernel.
- getOptions() - Method in class weka.classifiers.functions.supportVector.Puk
-
Gets the current settings of the Kernel.
- getOptions() - Method in class weka.classifiers.functions.supportVector.RBFKernel
-
Gets the current settings of the Kernel.
- getOptions() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.functions.supportVector.RegSMO
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
-
Gets the current settings of the object.
- getOptions() - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Gets the current settings of the Kernel.
- getOptions() - Method in class weka.classifiers.functions.VotedPerceptron
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.functions.Winnow
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.IteratedSingleClassifierEnhancer
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.lazy.IBk
-
Gets the current settings of IBk.
- getOptions() - Method in class weka.classifiers.lazy.KStar
-
Gets the current settings of K*.
- getOptions() - Method in class weka.classifiers.lazy.LWL
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.meta.AdaBoostM1
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.AdditiveRegression
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.Bagging
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.ClassificationViaClustering
-
returns the options of the current setup
- getOptions() - Method in class weka.classifiers.meta.CostSensitiveClassifier
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.CVParameterSelection
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.Dagging
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.Decorate
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.FilteredClassifier
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.GridSearch
-
returns the options of the current setup
- getOptions() - Method in class weka.classifiers.meta.LogitBoost
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.MetaCost
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.MultiBoostAB
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.MultiClassClassifier
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.MultiScheme
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.OrdinalClassClassifier
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.RandomSubSpace
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.RegressionByDiscretization
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.RotationForest
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.Stacking
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.ThresholdSelector
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.meta.Vote
-
Gets the current settings of Vote.
- getOptions() - Method in class weka.classifiers.mi.CitationKNN
-
Gets the current option settings for the OptionHandler.
- getOptions() - Method in class weka.classifiers.mi.MDD
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.mi.MIBoost
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.mi.MIDD
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.mi.MIEMDD
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.mi.MILR
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.mi.MINND
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.mi.MIOptimalBall
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.mi.MISMO
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.mi.MISVM
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.mi.MIWrapper
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.mi.SimpleMI
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.misc.SerializedClassifier
-
returns the options of the current setup
- getOptions() - Method in class weka.classifiers.misc.VFI
-
Gets the current settings of VFI
- getOptions() - Method in class weka.classifiers.MultipleClassifiersCombiner
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.RandomizableClassifier
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.rules.ConjunctiveRule
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.rules.DecisionTable
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.rules.DTNB
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.rules.JRip
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.rules.NNge
-
Gets the current option settings for the OptionHandler.
- getOptions() - Method in class weka.classifiers.rules.OneR
-
Gets the current settings of the OneR classifier.
- getOptions() - Method in class weka.classifiers.rules.PART
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.rules.Ridor
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.SingleClassifierEnhancer
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.trees.ADTree
-
Gets the current settings of ADTree.
- getOptions() - Method in class weka.classifiers.trees.BFTree
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.trees.FT
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.trees.J48
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.trees.J48graft
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.trees.LADTree
-
Gets the current settings of ADTree.
- getOptions() - Method in class weka.classifiers.trees.LMT
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.trees.m5.M5Base
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.trees.M5P
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.trees.RandomForest
-
Gets the current settings of the forest.
- getOptions() - Method in class weka.classifiers.trees.RandomTree
-
Gets options from this classifier.
- getOptions() - Method in class weka.classifiers.trees.REPTree
-
Gets options from this classifier.
- getOptions() - Method in class weka.classifiers.trees.SimpleCart
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.clusterers.CheckClusterer
-
Gets the current settings of the CheckClusterer.
- getOptions() - Method in class weka.clusterers.CLOPE
-
Gets the current settings of CLOPE
- getOptions() - Method in class weka.clusterers.Cobweb
-
Gets the current settings of Cobweb.
- getOptions() - Method in class weka.clusterers.DBScan
-
Gets the current option settings for the OptionHandler.
- getOptions() - Method in class weka.clusterers.EM
-
Gets the current settings of EM.
- getOptions() - Method in class weka.clusterers.FarthestFirst
-
Gets the current settings of FarthestFirst
- getOptions() - Method in class weka.clusterers.FilteredClusterer
-
Gets the current settings of the clusterer.
- getOptions() - Method in class weka.clusterers.HierarchicalClusterer
-
Gets the current settings of the clusterer.
- getOptions() - Method in class weka.clusterers.MakeDensityBasedClusterer
-
Gets the current settings of the clusterer.
- getOptions() - Method in class weka.clusterers.OPTICS
-
Gets the current option settings for the OptionHandler.
- getOptions() - Method in class weka.clusterers.RandomizableClusterer
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.clusterers.RandomizableDensityBasedClusterer
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.clusterers.RandomizableSingleClustererEnhancer
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.clusterers.sIB
-
Gets the current settings.
- getOptions() - Method in class weka.clusterers.SimpleKMeans
-
Gets the current settings of SimpleKMeans
- getOptions() - Method in class weka.clusterers.SingleClustererEnhancer
-
Gets the current settings of the clusterer.
- getOptions() - Method in class weka.clusterers.XMeans
-
Gets the current settings of SimpleKMeans.
- getOptions() - Method in class weka.core.Check
-
Gets the current settings of the CheckClassifier.
- getOptions() - Method in class weka.core.CheckGOE
-
Gets the current settings of the object.
- getOptions() - Method in class weka.core.CheckOptionHandler
-
Gets the current settings of the CheckClassifier.
- getOptions() - Method in class weka.core.CheckScheme
-
Gets the current settings of the CheckClassifier.
- getOptions() - Method in class weka.core.converters.AbstractFileSaver
-
Gets the current settings of the Saver object.
- getOptions() - Method in class weka.core.converters.ArffSaver
-
returns the options of the current setup
- getOptions() - Method in class weka.core.converters.C45Saver
-
Gets the current settings of the C45Saver object.
- getOptions() - Method in class weka.core.converters.CSVLoader
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.core.converters.DatabaseLoader
-
Gets the setting
- getOptions() - Method in class weka.core.converters.DatabaseSaver
-
Gets the setting.
- getOptions() - Method in class weka.core.converters.LibSVMSaver
-
returns the options of the current setup
- getOptions() - Method in class weka.core.converters.SVMLightSaver
-
returns the options of the current setup.
- getOptions() - Method in class weka.core.converters.TextDirectoryLoader
-
Gets the setting
- getOptions() - Method in class weka.core.converters.XRFFSaver
-
returns the options of the current setup
- getOptions() - Method in class weka.core.FindWithCapabilities
-
Gets the current settings of this object.
- getOptions() - Method in class weka.core.Javadoc
-
Gets the current settings of this object.
- getOptions() - Method in class weka.core.ListOptions
-
Gets the current settings of this object.
- getOptions() - Method in class weka.core.neighboursearch.BallTree
-
Gets the current settings of KDtree.
- getOptions() - Method in class weka.core.neighboursearch.balltrees.BallSplitter
-
Gets the current settings of the object.
- getOptions() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
Gets the current settings.
- getOptions() - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
-
Gets the current settings of the object.
- getOptions() - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
-
Gets the current settings.
- getOptions() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Gets the current settings of this BallTree MiddleOutConstructor.
- getOptions() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
-
Gets the current settings of KDtree.
- getOptions() - Method in class weka.core.neighboursearch.CoverTree
-
Gets the current settings of KDtree.
- getOptions() - Method in class weka.core.neighboursearch.KDTree
-
Gets the current settings of KDtree.
- getOptions() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
-
Gets the current settings of the object.
- getOptions() - Method in class weka.core.neighboursearch.LinearNNSearch
-
Gets the current settings.
- getOptions() - Method in class weka.core.neighboursearch.NearestNeighbourSearch
-
Gets the current settings.
- getOptions() - Method in class weka.core.NormalizableDistance
-
Gets the current settings.
- getOptions() - Method in interface weka.core.OptionHandler
-
Gets the current option settings for the OptionHandler.
- getOptions() - Method in class weka.core.OptionHandlerJavadoc
-
Gets the current settings of this object.
- getOptions() - Method in class weka.core.stemmers.SnowballStemmer
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.core.TechnicalInformationHandlerJavadoc
-
Gets the current settings of this object.
- getOptions() - Method in class weka.core.TestInstances
-
Gets the current settings of this object.
- getOptions() - Method in class weka.core.tokenizers.CharacterDelimitedTokenizer
-
Gets the current option settings for the OptionHandler.
- getOptions() - Method in class weka.core.tokenizers.NGramTokenizer
-
Gets the current option settings for the OptionHandler.
- getOptions() - Method in class weka.core.tokenizers.Tokenizer
-
Gets the current option settings for the OptionHandler.
- getOptions() - Method in class weka.datagenerators.ClassificationGenerator
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.datagenerators.classifiers.classification.Agrawal
-
Gets the current settings of the datagenerator.
- getOptions() - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
Gets the current settings of the datagenerator.
- getOptions() - Method in class weka.datagenerators.classifiers.classification.LED24
-
Gets the current settings of the datagenerator.
- getOptions() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
-
Gets the current settings of the datagenerator.
- getOptions() - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Gets the current settings of the datagenerator RDG1.
- getOptions() - Method in class weka.datagenerators.classifiers.regression.Expression
-
Gets the current settings of the datagenerator BIRCHCluster.
- getOptions() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Gets the current settings of the datagenerator BIRCHCluster.
- getOptions() - Method in class weka.datagenerators.ClusterDefinition
-
Gets the current settings of the datagenerator BIRCHCluster.
- getOptions() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Gets the current settings of the datagenerator BIRCHCluster.
- getOptions() - Method in class weka.datagenerators.clusterers.SubspaceCluster
-
Gets the current settings of the datagenerator.
- getOptions() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Gets the current settings of the datagenerator BIRCHCluster.
- getOptions() - Method in class weka.datagenerators.ClusterGenerator
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.datagenerators.DataGenerator
-
Gets the current settings of the datagenerator RDG1.
- getOptions() - Method in class weka.datagenerators.RegressionGenerator
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.estimators.CheckEstimator
-
Gets the current settings of the CheckEstimator.
- getOptions() - Method in class weka.estimators.Estimator
-
Gets the current settings of the Estimator.
- getOptions() - Method in class weka.experiment.AveragingResultProducer
-
Gets the current settings of the result producer.
- getOptions() - Method in class weka.experiment.ClassifierSplitEvaluator
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.experiment.CrossValidationResultProducer
-
Gets the current settings of the result producer.
- getOptions() - Method in class weka.experiment.CSVResultListener
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.experiment.DatabaseResultProducer
-
Gets the current settings of the result producer.
- getOptions() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.experiment.Experiment
-
Gets the current settings of the experiment iterator.
- getOptions() - Method in class weka.experiment.InstanceQuery
-
Gets the current settings of InstanceQuery
- getOptions() - Method in class weka.experiment.LearningRateResultProducer
-
Gets the current settings of the result producer.
- getOptions() - Method in class weka.experiment.PairedTTester
-
Gets current settings of the PairedTTester.
- getOptions() - Method in class weka.experiment.RandomSplitResultProducer
-
Gets the current settings of the result producer.
- getOptions() - Method in class weka.experiment.RegressionSplitEvaluator
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.filters.CheckSource
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.MultiFilter
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.SimpleFilter
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.supervised.attribute.AddClassification
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.filters.supervised.attribute.AttributeSelection
-
Gets the current settings for the attribute selection (search, evaluator)
etc.
- getOptions() - Method in class weka.filters.supervised.attribute.ClassOrder
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.supervised.attribute.Discretize
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.supervised.attribute.NominalToBinary
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.supervised.attribute.PLSFilter
-
returns the options of the current setup
- getOptions() - Method in class weka.filters.supervised.instance.Resample
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.supervised.instance.SMOTE
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.supervised.instance.SpreadSubsample
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.Add
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.AddCluster
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.AddExpression
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.AddID
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.AddNoise
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.AddValues
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.ClassAssigner
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.ClusterMembership
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.Copy
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.Discretize
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.FirstOrder
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.KernelFilter
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.MathExpression
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.NominalToString
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.Normalize
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.NumericToNominal
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.NumericTransform
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.filters.unsupervised.attribute.RandomProjection
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.RandomSubset
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.RELAGGS
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.filters.unsupervised.attribute.Remove
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.RemoveType
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.RemoveUseless
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.Reorder
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.StringToNominal
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.SwapValues
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.attribute.Wavelet
-
returns the options of the current setup
- getOptions() - Method in class weka.filters.unsupervised.instance.Normalize
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.instance.Randomize
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.instance.RemoveFolds
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.instance.RemovePercentage
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.instance.RemoveRange
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.instance.Resample
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.instance.ReservoirSample
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.filters.unsupervised.instance.SubsetByExpression
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.gui.Main
-
returns the options of the current setup.
- getOptype() - Method in class weka.core.pmml.Expression
-
Get the optype of the result of applying this Expression.
- getOptype() - Method in class weka.core.pmml.FieldMetaInfo
-
Get the optype.
- getOrder() - Method in enum weka.core.logging.Logger.Level
-
Returns the order of this level.
- getOrderedFlag() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Gets the ordered flag (option O).
- getOriginalCoords() - Method in class weka.gui.beans.MetaBean
-
returns the vector containing the original coordinates (instances of class
Point) for the inputs
- getOtherCapabilities() - Method in class weka.core.Capabilities
-
returns all other capabilities, besides class and attribute related ones
- getOtherLeaf() - Method in class weka.classifiers.trees.j48.GraftSplit
-
- getOutlierFactor() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
-
Gets the factor for determining the thresholds for outliers.
- getOutlierTreatmentMethod() - Method in class weka.core.pmml.MiningFieldMetaInfo
-
Get the outlier treatment method used for this field.
- getOutput() - Method in class weka.datagenerators.DataGenerator
-
Gets the print writer.
- getOutput() - Method in class weka.gui.explorer.DataGeneratorPanel
-
returns the generated output as text
- getOutputCenterFile() - Method in class weka.clusterers.XMeans
-
Gets the file to write the list of centers to.
- getOutputClassification() - Method in class weka.filters.supervised.attribute.AddClassification
-
Get whether the classifiction of the classifier is output.
- getOutputDef() - Method in class weka.core.pmml.BuiltInArithmetic
-
Get the structure of the result produced by this function.
- getOutputDef() - Method in class weka.core.pmml.BuiltInMath
-
Get the structure of the result produced by this function.
- getOutputDef() - Method in class weka.core.pmml.BuiltInString
-
Get the structure of the result produced by this function.
- getOutputDef() - Method in class weka.core.pmml.Constant
-
Return the structure of the result of applying this Expression
as an Attribute.
- getOutputDef() - Method in class weka.core.pmml.DefineFunction
-
Get the structure of the result produced by this function.
- getOutputDef() - Method in class weka.core.pmml.Discretize
-
Return the structure of the result of applying this Expression
as an Attribute.
- getOutputDef() - Method in class weka.core.pmml.Expression
-
Return the structure of the result of applying this Expression
as an Attribute.
- getOutputDef() - Method in class weka.core.pmml.FieldRef
-
Return the structure of the result of applying this Expression
as an Attribute.
- getOutputDef() - Method in class weka.core.pmml.Function
-
Get the structure of the result produced by this function.
- getOutputDef() - Method in class weka.core.pmml.NormContinuous
-
Return the structure of the result of applying this Expression
as an Attribute.
- getOutputDef() - Method in class weka.core.pmml.NormDiscrete
-
Return the structure of the result of applying this Expression
as an Attribute.
- getOutputDistribution() - Method in class weka.filters.supervised.attribute.AddClassification
-
Get whether the classifiction of the classifier is output.
- getOutputErrorFlag() - Method in class weka.filters.supervised.attribute.AddClassification
-
Get whether the classifiction of the classifier is output.
- getOutputFile() - Method in class weka.experiment.CrossValidationResultProducer
-
Get the value of OutputFile.
- getOutputFile() - Method in class weka.experiment.CSVResultListener
-
Get the value of OutputFile.
- getOutputFile() - Method in class weka.experiment.RandomSplitResultProducer
-
Get the value of OutputFile.
- getOutputFilename() - Method in class weka.core.converters.TextDirectoryLoader
-
Gets whether the filename will be stored as an extra attribute.
- getOutputFilename() - Method in class weka.gui.GenericPropertiesCreator
-
returns the name of the output file
- getOutputFormat() - Method in class weka.core.Debug.Clock
-
returns the output format
- getOutputFormat() - Method in class weka.filters.Filter
-
Gets the format of the output instances.
- getOutputFormat() - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer
-
Gets the format of the output instances.
- getOutputFormat() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
returns the classname of the ResultMatrix class, responsible for the
output format
- getOutputItemSets() - Method in class weka.associations.Apriori
-
Gets whether itemsets are output as well
- getOutputNums() - Method in class weka.classifiers.functions.neural.NeuralConnection
-
Use this to get easy access to the output numbers.
- getOutputOffsetMultiplier() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
-
Gets whether an additional attribute "Offset" is generated per
Outlier/ExtremeValue attribute pair that lists the multiplier the value
is off the median: value = median + 'multiplier' * IQR.
- getOutputPerClassInfoRetrievalStats() - Method in class weka.gui.beans.IncrementalClassifierEvaluator
-
Get whether per-class information retrieval stats are to be output.
- getOutputProperties() - Method in class weka.gui.GenericPropertiesCreator
-
returns the output properties object (structure like the template, but
filled with classes instead of packages)
- getOutputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
-
Use this to get easy access to the outputs.
- getOutputs() - Method in class weka.gui.beans.MetaBean
-
- getOutputTypes() - Method in class weka.core.Debug.DBO
-
Gets the current output type selection
- getOutputWordCounts() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Gets whether output instances contain 0 or 1 indicating word
presence, or word counts.
- getOverwriteWarning() - Method in class weka.gui.ConverterFileChooser
-
Returns whether a popup appears with a warning that the file already
exists (only save dialog).
- getOwner() - Method in class weka.core.Capabilities
-
returns the owner of this capabilities object
- getOwner() - Static method in class weka.core.Copyright
-
returns the entity owning the copyright
- getP() - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Get the proportion of instances that are common between two training sets.
- getPackage(String) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion
-
returns the packages part of the partial classname.
- getPadding() - Method in class weka.filters.unsupervised.attribute.Wavelet
-
Gets the type of Padding to use
- getPaint() - Method in class weka.gui.visualize.PostscriptGraphics
-
- getPanel(int) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
returns the specified panel, null
if index is out of bounds
- getPanelCount() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
returns the number of panels currently open
- getPanels() - Method in class weka.gui.explorer.Explorer
-
returns all the panels, apart from the PreprocessPanel
- getParameterNames() - Method in class weka.core.pmml.BuiltInArithmetic
-
Returns an array of the names of the parameters expected
as input by this function
- getParameterNames() - Method in class weka.core.pmml.BuiltInMath
-
Returns an array of the names of the parameters expected
as input by this function.
- getParameterNames() - Method in class weka.core.pmml.BuiltInString
-
Returns an array of the names of the parameters expected
as input by this function.
- getParameterNames() - Method in class weka.core.pmml.DefineFunction
-
Returns an array of the names of the parameters expected
as input by this function.
- getParameterNames() - Method in class weka.core.pmml.Function
-
Returns an array of the names of the parameters expected
as input by this function.
- getParameters() - Method in class weka.classifiers.functions.LibLINEAR
-
transfers the local variables into a svm_parameter object
- getParameters() - Method in class weka.classifiers.functions.LibSVM
-
transfers the local variables into a svm_parameter object
- getParent() - Method in class weka.associations.FPGrowth.FPTreeNode
-
Get the parent node.
- getParent(int, int) - Method in class weka.classifiers.bayes.BayesNet
-
get node index of a parent of a node in the network structure
- getParent(int) - Method in class weka.classifiers.bayes.net.ParentSet
-
returns index parent of parent specified by index
- getParent() - Method in class weka.datagenerators.ClusterDefinition
-
returns the parent datagenerator this cluster belongs to
- getParent(int) - Method in class weka.gui.treevisualizer.Node
-
Get the parent edge.
- getParentCardinality(int) - Method in class weka.classifiers.bayes.BayesNet
-
get number of values the collection of parents of a node can take
- getParentDialog(Container) - Static method in class weka.gui.PropertyDialog
-
Tries to determine the dialog this panel is part of.
- getParentFrame() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
returns the parent frame, if it's a JFrame, otherwise null
- getParentFrame() - Method in class weka.gui.GUIChooser.ChildFrameSDI
-
returns the parent frame, can be null.
- getParentFrame() - Method in class weka.gui.Main.ChildFrameMDI
-
returns the parent frame, can be null.
- getParentFrame() - Method in class weka.gui.Main.ChildFrameSDI
-
returns the parent frame, can be null.
- getParentFrame(Container) - Static method in class weka.gui.PropertyDialog
-
Tries to determine the frame this panel is part of.
- getParentFrame() - Method in class weka.gui.SetInstancesPanel
-
Returns the current frame the panel knows of, that it resides in.
- getParentInternalFrame() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
returns the parent frame, if it's a JInternalFrame, otherwise null
- getParents() - Method in class weka.classifiers.bayes.net.ParentSet
-
- getParentSet(int) - Method in class weka.classifiers.bayes.BayesNet
-
get the parent set of a node
- getParentSets() - Method in class weka.classifiers.bayes.BayesNet
-
Get full set of parent sets.
- getParts() - Method in class weka.associations.tertius.IndividualInstance
-
- getPassword() - Method in class weka.core.converters.DatabaseLoader
-
Returns the database password
- getPassword() - Method in class weka.core.converters.DatabaseSaver
-
Returns the database password.
- getPassword() - Method in class weka.experiment.DatabaseUtils
-
Get the database password.
- getPassword() - Method in class weka.gui.DatabaseConnectionDialog
-
Returns password from dialog
- getPassword() - Method in class weka.gui.sql.ConnectionPanel
-
returns the current Password.
- getPassword() - Method in class weka.gui.sql.event.ResultChangedEvent
-
returns the password that produced the table model
- getPassword() - Method in class weka.gui.sql.ResultSetTable
-
returns the password that produced the table model
- getPassword() - Method in class weka.gui.sql.SqlViewer
-
returns the password from the currently active tab in the ResultPanel,
otherwise an empty string.
- getPassword() - Method in class weka.gui.sql.SqlViewerDialog
-
returns the chosen password, if any
- getPath(Element) - Method in class weka.core.xml.XMLSerialization
-
returns the path of the "name" attribute from the root down to this node
(including it).
- getPath() - Method in class weka.gui.PropertySelectorDialog
-
Gets the path of property nodes to the selected property.
- getPattern() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Gets the pattern type.
- getPenalty() - Method in class weka.classifiers.bayes.blr.Prior
-
- getPercent() - Method in class weka.filters.unsupervised.attribute.AddNoise
-
Gets the size of noise data as a percentage of the original set.
- getPercent() - Method in class weka.filters.unsupervised.attribute.RandomProjection
-
Gets the percent the attributes (dimensions) of the data will be reduced to
- getPercentage() - Method in class weka.filters.supervised.instance.SMOTE
-
Gets the percentage of SMOTE instances to create.
- getPercentage() - Method in class weka.filters.unsupervised.instance.RemovePercentage
-
Gets the percentage of instances to select.
- getPercentCompleted() - Method in class weka.gui.boundaryvisualizer.RemoteResult
-
Return the progress for this row
- getPercentThreshold() - Method in class weka.attributeSelection.SVMAttributeEval
-
Get the threshold below which percentage elimination reverts to
constant elimination.
- getPercentToEliminatePerIteration() - Method in class weka.attributeSelection.SVMAttributeEval
-
Get the percentage rate of attribute elimination per iteration
- getPerformance(int) - Method in class weka.classifiers.meta.GridSearch.Performance
-
returns the performance measure
- getPerformances() - Method in class weka.classifiers.meta.GridSearch.PerformanceTable
-
returns the underlying performances
- getPerformanceStats() - Method in class weka.core.neighboursearch.NearestNeighbourSearch
-
Gets the class object that contains the performance statistics of
the search method.
- getPerformPrediction() - Method in class weka.filters.supervised.attribute.PLSFilter
-
Gets whether the class attribute is updated with the predicted value.
- getPerformRanking() - Method in class weka.attributeSelection.LinearForwardSelection
-
Get boolean if initial ranking should be performed to select the
top-ranked attributes
- getPerformRanking() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Get boolean if initial ranking should be performed to select the
top-ranked attributes
- getPeriodicPruning() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Gets the rate at which the dictionary is periodically pruned, as a
percentage of the dataset size.
- getPerturbationFraction() - Method in class weka.datagenerators.classifiers.classification.Agrawal
-
Gets the perturbation fraction.
- getPivot() - Method in class weka.core.matrix.LUDecomposition
-
Return pivot permutation vector
- getPivot() - Method in class weka.core.neighboursearch.balltrees.BallNode
-
Returns the pivot/centre of the
node's ball.
- getPlainColumnName(int) - Method in class weka.gui.arffviewer.ArffTable
-
returns the basically the attribute name of the column and not the
HTML column name via getColumnName(int)
- getPlotInstances() - Method in class weka.gui.visualize.PlotData2D
-
Returns the instances for this plot
- getPlotName() - Method in class weka.gui.visualize.PlotData2D
-
Get the name of this plot
- getPlotNameHTML() - Method in class weka.gui.visualize.PlotData2D
-
Get the name of the plot for use in a tool tip text.
- getPlotPanel() - Method in class weka.gui.visualize.VisualizePanel
-
Returns the underlying plot panel.
- getPlots() - Method in class weka.gui.visualize.Plot2D
-
Return the list of plots
- getPlotTrainingData() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Returns true if training data is to be superimposed
- getPMMLModel(String) - Static method in class weka.core.pmml.PMMLFactory
-
Read and return a PMML model.
- getPMMLModel(File) - Static method in class weka.core.pmml.PMMLFactory
-
Read and return a PMML model.
- getPMMLModel(InputStream) - Static method in class weka.core.pmml.PMMLFactory
-
Read and return a PMML model.
- getPMMLModel(String, Logger) - Static method in class weka.core.pmml.PMMLFactory
-
Read and return a PMML model.
- getPMMLModel(File, Logger) - Static method in class weka.core.pmml.PMMLFactory
-
Read and return a PMML model.
- getPMMLModel(InputStream, Logger) - Static method in class weka.core.pmml.PMMLFactory
-
Read and return a PMML model.
- getPMMLVersion() - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
-
Get the PMML version used for this model.
- getPMMLVersion() - Method in interface weka.core.pmml.PMMLModel
-
Get the version of PMML used to encode this model.
- getPointValue(int) - Method in class weka.classifiers.functions.pace.DiscreteFunction
-
Gets a particular point value
- getPointValues() - Method in class weka.classifiers.functions.pace.DiscreteFunction
-
Gets all point values
- getPopulationSize() - Method in class weka.attributeSelection.GeneticSearch
-
get the size of the population
- getPopulationSize() - Method in class weka.attributeSelection.ScatterSearchV1
-
Get the population size
- getPopulationSize() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
-
- getPopulationSize() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
-
- getPopup() - Method in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
-
returns the currently set JPopupMenu.
- getPositionX(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
get x position of a node
- getPositionY(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
get y position of a node
- getPositiveIndex() - Method in class weka.associations.FPGrowth
-
Get the index of the attribute value to consider as positive
for binary attributes in normal dense instances.
- getPostFixExpression() - Method in class weka.core.AttributeExpression
-
Return the postfix expression
- getPostProcessor() - Method in class weka.core.CheckScheme
-
returns the current PostProcessor, can be null
- getPostProcessor() - Method in class weka.estimators.CheckEstimator
-
returns the current PostProcessor, can be null
- getPrecision() - Method in class weka.classifiers.evaluation.TwoClassStats
-
Calculate the precision.
- getPrecision() - Method in class weka.estimators.KernelEstimator
-
Return the precision of this kernel estimator.
- getPrecision() - Method in class weka.estimators.NormalEstimator
-
Return the value of the precision of this normal estimator.
- getPredicate() - Method in class weka.associations.tertius.Literal
-
- getPrediction(Classifier, Instance) - Method in class weka.classifiers.evaluation.EvaluationUtils
-
Generate a single prediction for a test instance given the pre-trained
classifier.
- getPredictions(Instances, int, int) - Method in class weka.classifiers.meta.ThresholdSelector
-
Collects the classifier predictions using the specified evaluation method.
- getPredTargetColumn() - Method in class weka.experiment.ClassifierSplitEvaluator
-
- getPreferredScrollableViewportSize() - Method in class weka.gui.AttributeSelectionPanel
-
- getPrefix() - Method in class weka.gui.beans.SerializedModelSaver
-
Get the prefix to prepend to the model file names.
- getPremise() - Method in class weka.associations.FPGrowth.AssociationRule
-
Get the premise of this rule.
- getPremiseSupport() - Method in class weka.associations.FPGrowth.AssociationRule
-
Get the support for the premise.
- getPreprocessing() - Method in class weka.filters.supervised.attribute.PLSFilter
-
Gets the type of preprocessing to use
- getPreprocessing() - Method in class weka.filters.unsupervised.attribute.KernelFilter
-
Gets the filter used for preprocessing
- getPreprocessPanel() - Method in class weka.gui.explorer.Explorer
-
returns the instance of the PreprocessPanel being used in this instance
of the Explorer
- getPreserveInstancesOrder() - Method in class weka.clusterers.SimpleKMeans
-
Gets whether order of instances must be preserved
- getPrimitive(Element) - Method in class weka.core.xml.XMLSerialization
-
returns an Object representing the primitive described by the given node.
- getPrintColNames() - Method in class weka.experiment.ResultMatrix
-
returns whether column names or numbers instead are printed
- getPrintNewick() - Method in class weka.clusterers.HierarchicalClusterer
-
- getPrintRowNames() - Method in class weka.experiment.ResultMatrix
-
returns whether row names or numbers instead are printed
- getPriorClass() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Get the type of prior to use.
- getPriority(int) - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueue
-
Returns the priority for the object at the specified index
- getPriority() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueueElement
-
Returns the priority for this object
- getPriority(int) - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueue
-
Returns the priority for the object at the specified index
- getPriority() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueueElement
-
Returns the priority for this object
- getPriorProbability(String) - Method in class weka.core.pmml.TargetMetaInfo
-
Get the prior probability for the supplied value.
- getProbabilities() - Method in class weka.gui.boundaryvisualizer.RemoteResult
-
Return the probability distributions for this row in the visualization
- getProbability(int, int, int) - Method in class weka.classifiers.bayes.BayesNet
-
get particular probability of the conditional probability distribtion
of a node given its parents.
- getProbability(double) - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
-
Get a probability estimate for a value
- getProbability(double, double) - Method in interface weka.estimators.ConditionalEstimator
-
Get a probability for a value conditional on another value
- getProbability(double, double) - Method in class weka.estimators.DDConditionalEstimator
-
Get a probability estimate for a value
- getProbability(double) - Method in class weka.estimators.DiscreteEstimator
-
Get a probability estimate for a value
- getProbability(double, double) - Method in class weka.estimators.DKConditionalEstimator
-
Get a probability estimate for a value
- getProbability(double, double) - Method in class weka.estimators.DNConditionalEstimator
-
Get a probability estimate for a value
- getProbability(double) - Method in class weka.estimators.Estimator
-
Get a probability estimate for a value.
- getProbability(double, double) - Method in class weka.estimators.KDConditionalEstimator
-
Get a probability estimate for a value
- getProbability(double) - Method in class weka.estimators.KernelEstimator
-
Get a probability estimate for a value.
- getProbability(double, double) - Method in class weka.estimators.KKConditionalEstimator
-
Get a probability estimate for a value
- getProbability(double) - Method in class weka.estimators.MahalanobisEstimator
-
Get a probability estimate for a value
- getProbability(double, double) - Method in class weka.estimators.NDConditionalEstimator
-
Get a probability estimate for a value
- getProbability(double, double) - Method in class weka.estimators.NNConditionalEstimator
-
Get a probability estimate for a value
- getProbability(double) - Method in class weka.estimators.NormalEstimator
-
Get a probability estimate for a value
- getProbability(double) - Method in class weka.estimators.PoissonEstimator
-
Get a probability estimate for a value
- getProbabilityEstimates() - Method in class weka.classifiers.functions.LibLINEAR
-
Sets whether to generate probability estimates instead of -1/+1 for
classification problems.
- getProbabilityEstimates() - Method in class weka.classifiers.functions.LibSVM
-
Sets whether to generate probability estimates instead of -1/+1 for
classification problems.
- getProblem(List<Object>, List<Integer>, int) - Method in class weka.classifiers.functions.LibLINEAR
-
returns the svm_problem
- getProblem(Vector, Vector) - Method in class weka.classifiers.functions.LibSVM
-
returns the svm_problem
- getProbs(double[][]) - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Computes the p-values (probabilities for the different classes) from
the F-values for a set of instances.
- getProgressBar() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
-
Returns a handle to the progressBar
of this LayoutEngine.
- getProgressBar() - Method in interface weka.gui.graphvisualizer.LayoutEngine
-
This method returns the progress bar
for the LayoutEngine, which shows
the progress of the layout process,
if it takes a while to layout the
graph
- getProjectedCount(int) - Method in class weka.associations.FPGrowth.FPTreeNode
-
Get the projected count at the given recursion level for this node.
- getProjectionFilter() - Method in class weka.classifiers.meta.RotationForest
-
Gets the filter used to project the data.
- getProjectionFilterSpec() - Method in class weka.classifiers.meta.RotationForest
-
Gets the filter specification string, which contains the class name of
the filter and any options to the filter
- getProlog() - Method in class weka.core.OptionHandlerJavadoc
-
whether "Valid options are..." prolog is included in the Javadoc
- getProlog() - Method in class weka.core.TechnicalInformationHandlerJavadoc
-
whether "Valid options are..." prolog is included in the Javadoc
- getProperties() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
returns the associated properties file
- getProperties() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns the associated properties file.
- getProperty() - Method in class weka.core.pmml.FieldMetaInfo.Value
-
- getPropertyArray() - Method in class weka.experiment.Experiment
-
Gets the array of values to set the custom property to.
- getPropertyArrayLength() - Method in class weka.experiment.Experiment
-
Gets the number of custom iterator values that have been defined
for the experiment.
- getPropertyArrayValue(int) - Method in class weka.experiment.Experiment
-
Gets a specified value from the custom property iterator array.
- getPropertyDescriptor(Object, PropertyPath.Path) - Static method in class weka.core.PropertyPath
-
returns the property associated with the given path, null if a problem
occurred.
- getPropertyDescriptor(Object, String) - Static method in class weka.core.PropertyPath
-
returns the property associated with the given path
- getPropertyDescriptors() - Method in class weka.gui.beans.ClassAssignerBeanInfo
-
Returns the property descriptors
- getPropertyDescriptors() - Method in class weka.gui.beans.ClassValuePickerBeanInfo
-
Returns the property descriptors
- getPropertyDescriptors() - Method in class weka.gui.beans.CrossValidationFoldMakerBeanInfo
-
Return the property descriptors for this bean
- getPropertyDescriptors() - Method in class weka.gui.beans.IncrementalClassifierEvaluatorBeanInfo
-
Return the property descriptors for this bean
- getPropertyDescriptors() - Method in class weka.gui.beans.PredictionAppenderBeanInfo
-
Return the property descriptors for this bean
- getPropertyDescriptors() - Method in class weka.gui.beans.StripChartBeanInfo
-
Get the property descriptors for this bean
- getPropertyDescriptors() - Method in class weka.gui.beans.TrainTestSplitMakerBeanInfo
-
Get the property descriptors for this bean
- getPropertyPath() - Method in class weka.experiment.Experiment
-
Gets the path of properties taken to get to the custom property
to iterate over.
- getPruningMethod() - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Gets the method used for pruning.
- getPruningStrategy() - Method in class weka.classifiers.trees.BFTree
-
Gets the pruning strategy.
- getPruningType() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Get the pruning type
- getQ() - Method in class weka.core.matrix.QRDecomposition
-
Generate and return the (economy-sized) orthogonal factor
- getQuality() - Method in class weka.gui.visualize.JPEGWriter
-
returns the quality the JPEG will be stored in.
- getQuery() - Method in class weka.core.converters.DatabaseLoader
-
Gets the query to execute against the database
- getQuery() - Method in class weka.experiment.InstanceQuery
-
Get the query to execute against the database
- getQuery() - Method in class weka.gui.sql.event.QueryExecuteEvent
-
returns the query that was executed
- getQuery() - Method in class weka.gui.sql.event.ResultChangedEvent
-
returns the query that was executed
- getQuery() - Method in class weka.gui.sql.QueryPanel
-
returns the currently displayed query.
- getQuery() - Method in class weka.gui.sql.ResultSetTable
-
returns the query that produced the table model
- getQuery() - Method in class weka.gui.sql.SqlViewer
-
returns the query from the currently active tab in the ResultPanel,
otherwise an empty string.
- getQuery() - Method in class weka.gui.sql.SqlViewerDialog
-
returns the chosen query, if any
- getQueryPanel() - Method in class weka.gui.sql.ResultPanel
-
returns the currently set QueryPanel, can be NULL
- getR() - Method in class weka.core.matrix.QRDecomposition
-
Return the upper triangular factor
- getRaceType() - Method in class weka.attributeSelection.RaceSearch
-
Get the race type
- getRadius() - Method in class weka.core.neighboursearch.balltrees.BallNode
-
Returns the radius of the node's ball.
- getRadiuses() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Gets the upper and lower boundary for the radius of the clusters.
- getRandom(int) - Method in class weka.classifiers.trees.ADTree
-
Gets the next random value.
- getRandom() - Method in class weka.datagenerators.DataGenerator
-
Gets the random generator.
- getRandomAnchor(int, int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Returns a random anchor point/instance from a
given set of points/instances.
- getRandomize() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
-
Gets whether the order of the generated is randomized
- getRandomizeData() - Method in class weka.experiment.RandomSplitResultProducer
-
Get if dataset is to be randomized
- getRandomNumberGenerator(long) - Method in class weka.core.Instances
-
Returns a random number generator.
- getRandomOrder() - Method in class weka.classifiers.bayes.net.search.global.K2
-
Get random order flag
- getRandomOrder() - Method in class weka.classifiers.bayes.net.search.local.K2
-
Get random order flag
- getRandomSeed() - Method in class weka.classifiers.functions.LeastMedSq
-
get the seed for the random number generator
- getRandomSeed() - Method in class weka.classifiers.functions.SMO
-
Get the value of randomSeed.
- getRandomSeed() - Method in class weka.classifiers.mi.MISMO
-
Get the value of randomSeed.
- getRandomSeed() - Method in class weka.classifiers.trees.ADTree
-
Gets random seed for a random walk.
- getRandomSeed() - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
-
Returns the seed value of random
number generator.
- getRandomSeed() - Method in class weka.filters.supervised.instance.Resample
-
Gets the random number seed.
- getRandomSeed() - Method in class weka.filters.supervised.instance.SMOTE
-
Gets the random number seed.
- getRandomSeed() - Method in class weka.filters.supervised.instance.SpreadSubsample
-
Gets the random number seed.
- getRandomSeed() - Method in class weka.filters.unsupervised.attribute.AddNoise
-
Gets the random number seed.
- getRandomSeed() - Method in class weka.filters.unsupervised.attribute.RandomProjection
-
Gets the random seed of the random number generator
- getRandomSeed() - Method in class weka.filters.unsupervised.instance.Randomize
-
Get the random number generator seed value.
- getRandomSeed() - Method in class weka.filters.unsupervised.instance.Resample
-
Gets the random number seed.
- getRandomSeed() - Method in class weka.filters.unsupervised.instance.ReservoirSample
-
Gets the random number seed.
- getRandomWidthFactor() - Method in class weka.classifiers.meta.MultiClassClassifier
-
Gets the multiplier when generating random codes.
- getRange() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Gets the upper and lower boundary for the range of x
- getRange(int) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
-
Gets a single Range from the set of available Ranges.
- getRangeCorrection() - Method in class weka.classifiers.meta.ThresholdSelector
-
Gets the confidence range correction mode used.
- getRanges() - Method in class weka.core.NormalizableDistance
-
Method to get the ranges.
- getRanges() - Method in class weka.core.Range
-
Gets the string representing the selected range of values
- getRanges() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
-
Gets the list of possible Ranges to choose from.
- getRank() - Method in class weka.attributeSelection.LatentSemanticAnalysis
-
Gets the desired matrix rank (or coverage proportion) for feature-space reduction
- getRawOutput() - Method in class weka.experiment.CrossValidationResultProducer
-
Get if raw split evaluator output is to be saved
- getRawOutput() - Method in class weka.experiment.RandomSplitResultProducer
-
Get if raw split evaluator output is to be saved
- getRawResultOutput() - Method in class weka.experiment.ClassifierSplitEvaluator
-
Gets the raw output from the classifier
- getRawResultOutput() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
-
Gets the raw output from the classifier
- getRawResultOutput() - Method in class weka.experiment.RegressionSplitEvaluator
-
Gets the raw output from the classifier
- getRawResultOutput() - Method in interface weka.experiment.SplitEvaluator
-
Returns the raw output for the most recent call to getResult.
- getReachabilityDistance() - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
-
Returns the reachabilityDistance for this dataObject
- getReachabilityDistance() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
-
Returns the reachabilityDistance for this dataObject
- getReachabilityDistance() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
-
Returns the reachabilityDistance for this dataObject
- getReadable() - Method in class weka.core.Tag
-
Gets the string description of the Tag.
- getReader(String, String) - Static method in class weka.gui.Loader
-
returns a Reader for the given filename and dir, can be NULL if it fails
- getReader(String) - Method in class weka.gui.Loader
-
returns a Reader for the given filename, can be NULL if it fails
- getReadIncrementally() - Method in class weka.gui.SetInstancesPanel
-
Gets whether instances are to be read incrementally or not
- getRealEigenvalues() - Method in class weka.core.matrix.EigenvalueDecomposition
-
Return the real parts of the eigenvalues
- getRecall() - Method in class weka.classifiers.evaluation.TwoClassStats
-
Calculate the recall.
- getReducedErrorPruning() - Method in class weka.classifiers.rules.PART
-
Get the value of reducedErrorPruning.
- getReducedErrorPruning() - Method in class weka.classifiers.trees.J48
-
Get the value of reducedErrorPruning.
- getRefer() - Method in class weka.gui.treevisualizer.Node
-
Get the value of refer.
- getRefreshFreq() - Method in class weka.gui.beans.StripChart
-
Get the refresh frequency
- getRegOptimizer() - Method in class weka.classifiers.functions.SMOreg
-
returns the learning algorithm
- getRegressionTree() - Method in class weka.classifiers.trees.m5.Rule
-
Get the value of regressionTree.
- getRegressionTree() - Method in class weka.classifiers.trees.m5.RuleNode
-
Get the value of regressionTree.
- getRelabel() - Method in class weka.classifiers.trees.J48graft
-
Get the value of relabelling
- getRelation() - Method in class weka.core.TestInstances
-
returns the current name of the relation
- getRelationalClassFormat() - Method in class weka.core.TestInstances
-
returns the current strcuture of the relational class attribute, can
be null
- getRelationalFormat(int) - Method in class weka.core.TestInstances
-
returns the format for the specified relational attribute, can be null
- getRelationForTableName() - Method in class weka.core.converters.DatabaseSaver
-
Gets whether or not the relation name is used as name of the table.
- getRelationName() - Method in class weka.datagenerators.DataGenerator
-
Gets the relation name the dataset should have.
- getRelationNameForFilename() - Method in class weka.gui.beans.Saver
-
Get whether the relation name is the primary part of the filename.
- getRelationNameToUse() - Method in class weka.datagenerators.DataGenerator
-
returns the relation name to use, i.e., in case the currently set
relation name is empty, a generic one is returned.
- getRemoteHosts() - Method in class weka.experiment.RemoteExperiment
-
Get the list of remote host names
- getRemoveAllMissingCols() - Method in class weka.associations.Apriori
-
Returns whether columns containing all missing values are to be removed
- getRemoveClassColumn() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
-
Get whether the class column is to be removed.
- getRemovedPercentage() - Method in class weka.classifiers.meta.RotationForest
-
Gets the percentage of instances to be removed
- getRemoveFilterClassnames() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
whether the filter classnames in the dataset names are removed by default
- getRemoveFilterName() - Method in class weka.experiment.ResultMatrix
-
returns whether the filter classname is removed from the dataset name
- getRemoveFilterName() - Method in class weka.gui.experiment.OutputFormatDialog
-
returns whether the filter classname is removed from the dataset name.
- getRemoveOldClass() - Method in class weka.filters.supervised.attribute.AddClassification
-
Get whether the old class attribute is removed.
- getRemoveUnused() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
-
Gets whether unused attributes (ones that are not covered by any of the
ranges) are removed from the output.
- getRenderingHint(RenderingHints.Key) - Method in class weka.gui.visualize.PostscriptGraphics
-
- getRenderingHints() - Method in class weka.gui.visualize.PostscriptGraphics
-
- getRepeatLiterals() - Method in class weka.associations.Tertius
-
Get the value of repeatLiterals.
- getRepetitions() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
returns the number of repetitions to use
- getReplaceMissing() - Method in class weka.filters.supervised.attribute.PLSFilter
-
Gets whether missing values are replace.
- getReplaceMissingValues() - Method in class weka.filters.unsupervised.attribute.RandomProjection
-
Gets the current setting for using ReplaceMissingValues filter
- getReportFrequency() - Method in class weka.attributeSelection.GeneticSearch
-
get how often repports are generated
- getRepulsion() - Method in class weka.clusterers.CLOPE
-
gets the repulsion
- getReset() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- getReset() - Method in class weka.gui.beans.ChartEvent
-
get the value of the reset flag
- getResult() - Method in class weka.core.mathematicalexpression.Parser
-
Returns the result of the evaluation.
- getResult(double[]) - Method in class weka.core.pmml.BuiltInArithmetic
-
Get the result of applying this function.
- getResult(double[]) - Method in class weka.core.pmml.BuiltInMath
-
Get the result of applying this function.
- getResult(double[]) - Method in class weka.core.pmml.BuiltInString
-
Get the result of applying this function.
- getResult(double[]) - Method in class weka.core.pmml.Constant
-
Get the result of evaluating the expression.
- getResult(double[]) - Method in class weka.core.pmml.DefineFunction
-
Get the result of applying this function.
- getResult(double[]) - Method in class weka.core.pmml.Discretize
-
Get the result of evaluating the expression.
- getResult(double[]) - Method in class weka.core.pmml.Expression
-
Get the result of evaluating the expression.
- getResult(double[]) - Method in class weka.core.pmml.FieldRef
-
- getResult(double[]) - Method in class weka.core.pmml.Function
-
Get the result of applying this function.
- getResult(double[]) - Method in class weka.core.pmml.NormContinuous
-
Get the result of evaluating the expression.
- getResult(double[]) - Method in class weka.core.pmml.NormDiscrete
-
Get the result of evaluating the expression.
- getResult(Instances, Instances) - Method in class weka.experiment.ClassifierSplitEvaluator
-
Gets the results for the supplied train and test datasets.
- getResult(Instances, Instances) - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
-
Gets the results for the supplied train and test datasets.
- getResult(Instances, Instances) - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
-
Gets the results for the supplied train and test datasets.
- getResult(Instances, Instances) - Method in class weka.experiment.RegressionSplitEvaluator
-
Gets the results for the supplied train and test datasets.
- getResult(Instances, Instances) - Method in interface weka.experiment.SplitEvaluator
-
Gets the results for the supplied train and test datasets.
- getResult() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
-
Returns the result of the evaluation.
- getResult() - Method in class weka.gui.experiment.OutputFormatDialog
-
the result from the last display of the dialog, the same is returned
from showDialog
.
- getResultCategorical(double[]) - Method in class weka.core.pmml.Constant
-
Gets the result of evaluating the expression when the
optype is categorical or ordinal as the actual String
value.
- getResultCategorical(double[]) - Method in class weka.core.pmml.Discretize
-
Gets the result of evaluating the expression when the
optype is categorical or ordinal as the actual String
value.
- getResultCategorical(double[]) - Method in class weka.core.pmml.Expression
-
Gets the result of evaluating the expression when the
optype is categorical or ordinal as the actual String
value.
- getResultCategorical(double[]) - Method in class weka.core.pmml.FieldRef
-
- getResultCategorical(double[]) - Method in class weka.core.pmml.NormContinuous
-
Always throws an Exception since the result of NormContinuous must
be continuous.
- getResultCategorical(double[]) - Method in class weka.core.pmml.NormDiscrete
-
Always throws an Exception since the result of NormDiscrete must
be continuous.
- getResultContinuous(double[]) - Method in class weka.core.pmml.Expression
-
Get the result of evaluating the expression for continuous
optype.
- getResultFromTable(String, ResultProducer, Object[]) - Method in class weka.experiment.DatabaseUtils
-
Executes a database query to extract a result for the supplied key
from the database.
- getResultInverse(double[]) - Method in class weka.core.pmml.NormContinuous
-
Compute the inverse of the normalization (i.e.
- getResultListener() - Method in class weka.experiment.Experiment
-
Gets the result listener where results will be sent.
- getResultMatrix() - Method in class weka.experiment.PairedTTester
-
Gets the instance that produces the output.
- getResultMatrix() - Method in interface weka.experiment.Tester
-
Gets the instance that produces the output.
- getResultMatrix() - Method in class weka.gui.experiment.OutputFormatDialog
-
Gets the currently selected output format result matrix.
- getResultNames() - Method in class weka.experiment.AveragingResultProducer
-
Gets the names of each of the columns produced for a single run.
- getResultNames() - Method in class weka.experiment.ClassifierSplitEvaluator
-
Gets the names of each of the result columns produced for a single run.
- getResultNames() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
-
Gets the names of each of the result columns produced for a single run.
- getResultNames() - Method in class weka.experiment.CrossValidationResultProducer
-
Gets the names of each of the columns produced for a single run.
- getResultNames() - Method in class weka.experiment.DatabaseResultProducer
-
Gets the names of each of the columns produced for a single run.
- getResultNames() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
-
Gets the names of each of the result columns produced for a single run.
- getResultNames() - Method in class weka.experiment.LearningRateResultProducer
-
Gets the names of each of the columns produced for a single run.
- getResultNames() - Method in class weka.experiment.RandomSplitResultProducer
-
Gets the names of each of the columns produced for a single run.
- getResultNames() - Method in class weka.experiment.RegressionSplitEvaluator
-
Gets the names of each of the result columns produced for a single run.
- getResultNames() - Method in interface weka.experiment.ResultProducer
-
Gets the names of each of the result columns produced for a single run.
- getResultNames() - Method in interface weka.experiment.SplitEvaluator
-
Gets the names of each of the result columns produced for a single run.
- getResultProducer() - Method in class weka.experiment.AveragingResultProducer
-
Get the ResultProducer.
- getResultProducer() - Method in class weka.experiment.DatabaseResultProducer
-
Get the ResultProducer.
- getResultProducer() - Method in class weka.experiment.Experiment
-
Get the result producer used for the current experiment.
- getResultProducer() - Method in class weka.experiment.LearningRateResultProducer
-
Get the ResultProducer.
- getResults() - Method in class weka.associations.Tertius
-
returns the results
- getResultSet() - Method in class weka.experiment.DatabaseUtils
-
Gets the results generated by a previous query.
- getResultSet() - Method in class weka.gui.sql.event.QueryExecuteEvent
-
returns the resultset that was produced, can be null in case the query
failed
- getResultSet() - Method in class weka.gui.sql.ResultSetHelper
-
the underlying resultset.
- getResultsetKeyColumns() - Method in class weka.experiment.PairedTTester
-
Get the value of ResultsetKeyColumns.
- getResultsetKeyColumns() - Method in interface weka.experiment.Tester
-
Get the value of ResultsetKeyColumns.
- getResultsetName(int) - Method in class weka.experiment.PairedTTester
-
Gets a string descriptive of the specified resultset.
- getResultsetName(int) - Method in interface weka.experiment.Tester
-
Gets a string descriptive of the specified resultset.
- getResultsTableName(ResultProducer) - Method in class weka.experiment.DatabaseUtils
-
Gets the name of the experiment table that stores results from a
particular ResultProducer.
- getResultTypes() - Method in class weka.experiment.AveragingResultProducer
-
Gets the data types of each of the columns produced for a single run.
- getResultTypes() - Method in class weka.experiment.ClassifierSplitEvaluator
-
Gets the data types of each of the result columns produced for a
single run.
- getResultTypes() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
-
Gets the data types of each of the result columns produced for a
single run.
- getResultTypes() - Method in class weka.experiment.CrossValidationResultProducer
-
Gets the data types of each of the columns produced for a single run.
- getResultTypes() - Method in class weka.experiment.DatabaseResultProducer
-
Gets the data types of each of the columns produced for a single run.
- getResultTypes() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
-
Gets the data types of each of the result columns produced for a
single run.
- getResultTypes() - Method in class weka.experiment.LearningRateResultProducer
-
Gets the data types of each of the columns produced for a single run.
- getResultTypes() - Method in class weka.experiment.RandomSplitResultProducer
-
Gets the data types of each of the columns produced for a single run.
- getResultTypes() - Method in class weka.experiment.RegressionSplitEvaluator
-
Gets the data types of each of the result columns produced for a
single run.
- getResultTypes() - Method in interface weka.experiment.ResultProducer
-
Gets the data types of each of the result columns produced for a
single run.
- getResultTypes() - Method in interface weka.experiment.SplitEvaluator
-
Gets the data types of each of the result columns produced for a
single run.
- getResultVector() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
-
Returns the resultVector
- getResultVector() - Method in class weka.clusterers.OPTICS
-
Returns the resultVector
- getRetrieval() - Method in class weka.core.converters.AbstractLoader
-
Gets the retrieval mode.
- getRetrieval() - Method in class weka.core.converters.AbstractSaver
-
Gets the retrieval mode.
- getReturnValue(int) - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Recursion-ending function that is called at the end of each
recursion branch.
- getReturnValue() - Method in class weka.gui.DatabaseConnectionDialog
-
Returns which of OK or cancel was clicked from dialog
- getReturnValue() - Method in class weka.gui.sql.SqlViewerDialog
-
returns whether the user clicked OK (JOptionPane.OK_OPTION) or whether he
cancelled the dialog (JOptionPane.CANCEL_OPTION)
- getRevision() - Method in class weka.associations.AbstractAssociator
-
Returns the revision string.
- getRevision() - Method in class weka.associations.Apriori
-
Returns the revision string.
- getRevision() - Method in class weka.associations.AprioriItemSet
-
Returns the revision string.
- getRevision() - Method in class weka.associations.AssociatorEvaluation
-
Returns the revision string.
- getRevision() - Method in class weka.associations.CaRuleGeneration
-
Returns the revision string.
- getRevision() - Method in class weka.associations.CheckAssociator
-
Returns the revision string.
- getRevision() - Method in class weka.associations.FilteredAssociator
-
Returns the revision string.
- getRevision() - Method in class weka.associations.FPGrowth
-
Returns the revision string.
- getRevision() - Method in class weka.associations.GeneralizedSequentialPatterns
-
Returns the revision string.
- getRevision() - Method in class weka.associations.gsp.Element
-
Returns the revision string.
- getRevision() - Method in class weka.associations.gsp.Sequence
-
Returns the revision string.
- getRevision() - Method in class weka.associations.ItemSet
-
Returns the revision string.
- getRevision() - Method in class weka.associations.LabeledItemSet
-
Returns the revision string.
- getRevision() - Method in class weka.associations.PredictiveApriori
-
Returns the revision string.
- getRevision() - Method in class weka.associations.PriorEstimation
-
Returns the revision string.
- getRevision() - Method in class weka.associations.RuleGeneration
-
Returns the revision string.
- getRevision() - Method in class weka.associations.RuleItem
-
Returns the revision string.
- getRevision() - Method in class weka.associations.tertius.AttributeValueLiteral
-
Returns the revision string.
- getRevision() - Method in class weka.associations.tertius.Body
-
Returns the revision string.
- getRevision() - Method in class weka.associations.Tertius
-
Returns the revision string.
- getRevision() - Method in class weka.associations.tertius.Head
-
Returns the revision string.
- getRevision() - Method in class weka.associations.tertius.IndividualInstance
-
Returns the revision string.
- getRevision() - Method in class weka.associations.tertius.IndividualInstances
-
Returns the revision string.
- getRevision() - Method in class weka.associations.tertius.IndividualLiteral
-
Returns the revision string.
- getRevision() - Method in class weka.associations.tertius.Predicate
-
Returns the revision string.
- getRevision() - Method in class weka.associations.tertius.Rule
-
Returns the revision string.
- getRevision() - Method in class weka.associations.tertius.SimpleLinkedList
-
Returns the revision string.
- getRevision() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator
-
Returns the revision string.
- getRevision() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListIterator
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.ASEvaluation
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.ASSearch
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.AttributeSelection
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.BestFirst
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.BestFirst.Link2
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.BestFirst.LinkedList2
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.CfsSubsetEval
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.CheckAttributeSelection
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.ClassifierSubsetEval
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.ConsistencySubsetEval
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.CostSensitiveAttributeEval
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.CostSensitiveSubsetEval
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.ExhaustiveSearch
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.FilteredAttributeEval
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.FilteredSubsetEval
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.GainRatioAttributeEval
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.GeneticSearch.GABitSet
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.GeneticSearch
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.GreedyStepwise
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.InfoGainAttributeEval
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.LatentSemanticAnalysis
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.LFSMethods
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.LFSMethods.Link2
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.LFSMethods.LinkedList2
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.LinearForwardSelection
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.OneRAttributeEval
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.PrincipalComponents
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.RaceSearch
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.RandomSearch
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.Ranker
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.RankSearch
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.ReliefFAttributeEval
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.ScatterSearchV1
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.SVMAttributeEval
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
-
Returns the revision string.
- getRevision() - Method in class weka.attributeSelection.WrapperSubsetEval
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.AODE
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.AODEsr
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.BayesNet
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.blr.GaussianPriorImpl
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.blr.LaplacePriorImpl
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.ComplementNaiveBayes
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.DMNBtext
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.HNB
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.NaiveBayes
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.NaiveBayesMultinomial
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.NaiveBayesSimple
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.NaiveBayesUpdateable
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.ADNode
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.BayesNetGenerator
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.BIFReader
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorFullBayes
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.estimate.SimpleEstimator
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.MarginCalculator
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeSeparator
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.ParentSet
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.ci.CISearchAlgorithm
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.fixed.FromFile
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.fixed.NaiveBayes
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.global.HillClimber
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.global.K2
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.global.TAN
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.local.HillClimber
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.local.K2
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.local.TAN
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.net.VaryNode
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.bayes.WAODE
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.BVDecompose
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.CheckClassifier
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.CheckSource
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.Classifier
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.CostMatrix
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.evaluation.ConfusionMatrix
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.evaluation.CostCurve
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.evaluation.EvaluationUtils
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.Evaluation
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.evaluation.MarginCurve
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.evaluation.NominalPrediction
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.evaluation.NumericPrediction
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.evaluation.ThresholdCurve
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.evaluation.TwoClassStats
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.GaussianProcesses
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.IsotonicRegression
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.LeastMedSq
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.LibLINEAR
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.LibSVM
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.LinearRegression
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.Logistic
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.neural.LinearUnit
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.neural.NeuralNode
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.neural.SigmoidUnit
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.pace.ChisqMixture
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.pace.DiscreteFunction
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.pace.NormalMixture
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.pace.PaceMatrix
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.PaceRegression
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.PLSClassifier
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.RBFNetwork
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.SimpleLinearRegression
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.SimpleLogistic
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.SMO.BinarySMO
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.SMO
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.SMOreg
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.SPegasos
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.supportVector.CheckKernel
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.supportVector.Kernel
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.supportVector.KernelEvaluation
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.supportVector.NormalizedPolyKernel
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.supportVector.PolyKernel
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.supportVector.Puk
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.supportVector.RBFKernel
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.supportVector.RegSMO
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.supportVector.SMOset
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.VotedPerceptron
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.functions.Winnow
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.lazy.IB1
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.lazy.IBk
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.lazy.KStar
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.lazy.kstar.KStarCache
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.lazy.kstar.KStarCache.TableEntry
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.lazy.kstar.KStarWrapper
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.lazy.LBR
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.lazy.LBR.Indexes
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.lazy.LWL
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.AdaBoostM1
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.AdditiveRegression
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.Bagging
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.ClassificationViaClustering
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.ClassificationViaRegression
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.CostSensitiveClassifier
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.CVParameterSelection.CVParameter
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.CVParameterSelection
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.Dagging
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.Decorate
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.END
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.FilteredClassifier
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.Grading
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.GridSearch
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.GridSearch.Grid
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.GridSearch.Performance
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.GridSearch.PerformanceCache
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.GridSearch.PerformanceComparator
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.GridSearch.PerformanceTable
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.GridSearch.PointDouble
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.GridSearch.PointInt
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.LogitBoost
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.MetaCost
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.MultiBoostAB
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.MultiClassClassifier
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.MultiScheme
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.nestedDichotomies.ND
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.OrdinalClassClassifier
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.RandomCommittee
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.RandomSubSpace
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.RegressionByDiscretization
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.RotationForest
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.Stacking
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.StackingC
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.ThresholdSelector
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.meta.Vote
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.CitationKNN
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.MDD
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.MIBoost
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.MIDD
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.MIEMDD
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.MILR
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.MINND
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.MIOptimalBall
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.MISMO.BinaryMISMO
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.MISMO
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.MISVM
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.MIWrapper
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.SimpleMI
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.supportVector.MIPolyKernel
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.supportVector.MIRBFKernel
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.misc.HyperPipes
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.misc.SerializedClassifier
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.misc.VFI
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.pmml.consumer.GeneralRegression
-
- getRevision() - Method in class weka.classifiers.pmml.consumer.NeuralNetwork
-
- getRevision() - Method in class weka.classifiers.pmml.consumer.Regression
-
- getRevision() - Method in class weka.classifiers.rules.ConjunctiveRule
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.DecisionTable
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.DecisionTableHashKey
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.DTNB.BackwardsWithDelete
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.DTNB.EvalWithDelete
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.DTNB
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.JRip.Antd
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.JRip
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.JRip.NominalAntd
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.JRip.NumericAntd
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.JRip.RipperRule
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.M5Rules
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.NNge
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.OneR
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.part.C45PruneableDecList
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.part.ClassifierDecList
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.PART
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.part.MakeDecList
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.part.PruneableDecList
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.Prism
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.Ridor
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.RuleStats
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.rules.ZeroR
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.ADTree
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.adtree.PredictionNode
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.adtree.ReferenceInstances
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.BFTree
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.DecisionStump
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.ft.FTInnerNode
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.ft.FTLeavesNode
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.ft.FTNode
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.ft.FTtree
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.FT
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.Id3
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.j48.BinC45ModelSelection
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.j48.BinC45Split
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.j48.C45ModelSelection
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTree
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.j48.C45Split
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.j48.ClassifierTree
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.j48.Distribution
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.j48.EntropySplitCrit
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.j48.GainRatioSplitCrit
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.J48
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.j48.GraftSplit
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.j48.InfoGainSplitCrit
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.j48.NBTreeClassifierTree
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.j48.NBTreeModelSelection
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.j48.NBTreeNoSplit
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.j48.NBTreeSplit
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.j48.NoSplit
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.j48.PruneableClassifierTree
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.j48.Stats
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.J48graft
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.LADTree
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.LMT
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.lmt.LMTNode
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.lmt.ResidualModelSelection
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.lmt.ResidualSplit
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.m5.Impurity
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.m5.PreConstructedLinearModel
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.m5.Rule
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.m5.RuleNode
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.m5.Values
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.m5.YongSplitInfo
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.M5P
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.NBTree
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.RandomForest
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.RandomTree
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.REPTree
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.REPTree.Tree
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.SimpleCart
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.trees.UserClassifier
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.xml.XMLClassifier
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.AbstractClusterer
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.CheckClusterer
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.CLOPE
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.ClusterEvaluation
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.Cobweb
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.DBScan
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.EM
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.FarthestFirst
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.FilteredClusterer
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.OPTICS_Visualizer
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.ResultVectorTableModel
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERFileFilter
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.EpsilonRange_ListElement
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueue
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueueElement
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueue
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueueElement
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.HierarchicalClusterer
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.MakeDensityBasedClusterer
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.OPTICS
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.sIB
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.SimpleKMeans
-
Returns the revision string.
- getRevision() - Method in class weka.clusterers.XMeans
-
Returns the revision string.
- getRevision() - Method in class weka.core.AlgVector
-
Returns the revision string.
- getRevision() - Method in class weka.core.AllJavadoc
-
Returns the revision string.
- getRevision() - Method in class weka.core.Attribute
-
Returns the revision string.
- getRevision() - Method in class weka.core.AttributeExpression
-
Returns the revision string.
- getRevision() - Method in class weka.core.AttributeLocator
-
Returns the revision string.
- getRevision() - Method in class weka.core.AttributeStats
-
Returns the revision string.
- getRevision() - Method in class weka.core.BinarySparseInstance
-
Returns the revision string.
- getRevision() - Method in class weka.core.Capabilities
-
Returns the revision string.
- getRevision() - Method in class weka.core.ChebyshevDistance
-
Returns the revision string.
- getRevision() - Method in class weka.core.CheckGOE
-
Returns the revision string.
- getRevision() - Method in class weka.core.CheckOptionHandler
-
Returns the revision string.
- getRevision() - Method in class weka.core.CheckScheme.PostProcessor
-
Returns the revision string.
- getRevision() - Method in class weka.core.ClassDiscovery
-
Returns the revision string.
- getRevision() - Method in class weka.core.ClassDiscovery.StringCompare
-
Returns the revision string.
- getRevision() - Method in class weka.core.ClassloaderUtil
-
Returns the revision string.
- getRevision() - Method in class weka.core.ContingencyTables
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.ArffLoader.ArffReader
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.ArffLoader
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.ArffSaver
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.C45Loader
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.C45Saver
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.ConverterUtils.DataSink
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.ConverterUtils.DataSource
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.ConverterUtils
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.CSVLoader
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.CSVSaver
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.DatabaseConnection
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.DatabaseLoader
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.DatabaseSaver
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.LibSVMLoader
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.LibSVMSaver
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.SerializedInstancesLoader
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.SerializedInstancesSaver
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.SVMLightLoader
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.SVMLightSaver
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.TextDirectoryLoader
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.XRFFLoader
-
Returns the revision string.
- getRevision() - Method in class weka.core.converters.XRFFSaver
-
Returns the revision string.
- getRevision() - Method in class weka.core.Debug.Clock
-
Returns the revision string.
- getRevision() - Method in class weka.core.Debug.DBO
-
Returns the revision string.
- getRevision() - Method in class weka.core.Debug
-
Returns the revision string.
- getRevision() - Method in class weka.core.Debug.Log
-
Returns the revision string.
- getRevision() - Method in class weka.core.Debug.Random
-
Returns the revision string.
- getRevision() - Method in class weka.core.Debug.SimpleLog
-
Returns the revision string.
- getRevision() - Method in class weka.core.Debug.Timestamp
-
Returns the revision string.
- getRevision() - Method in class weka.core.EditDistance
-
Returns the revision string.
- getRevision() - Method in class weka.core.Environment
-
Returns the revision string.
- getRevision() - Method in class weka.core.EuclideanDistance
-
Returns the revision string.
- getRevision() - Method in class weka.core.FastVector.FastVectorEnumeration
-
Returns the revision string.
- getRevision() - Method in class weka.core.FastVector
-
Returns the revision string.
- getRevision() - Method in class weka.core.FindWithCapabilities
-
Returns the revision string.
- getRevision() - Method in class weka.core.GlobalInfoJavadoc
-
Returns the revision string.
- getRevision() - Method in class weka.core.Instance
-
Returns the revision string.
- getRevision() - Method in class weka.core.InstanceComparator
-
Returns the revision string.
- getRevision() - Method in class weka.core.Instances
-
Returns the revision string.
- getRevision() - Method in class weka.core.Jython
-
Returns the revision string.
- getRevision() - Method in class weka.core.ListOptions
-
Returns the revision string.
- getRevision() - Method in class weka.core.logging.ConsoleLogger
-
Returns the revision string.
- getRevision() - Method in class weka.core.logging.FileLogger
-
Returns the revision string.
- getRevision() - Method in class weka.core.logging.OutputLogger
-
Returns the revision string.
- getRevision() - Method in class weka.core.ManhattanDistance
-
Returns the revision string.
- getRevision() - Method in class weka.core.MathematicalExpression
-
Returns the revision string.
- getRevision() - Method in class weka.core.matrix.CholeskyDecomposition
-
Returns the revision string.
- getRevision() - Method in class weka.core.matrix.DoubleVector
-
Returns the revision string.
- getRevision() - Method in class weka.core.matrix.EigenvalueDecomposition
-
Returns the revision string.
- getRevision() - Method in class weka.core.matrix.ExponentialFormat
-
Returns the revision string.
- getRevision() - Method in class weka.core.matrix.FlexibleDecimalFormat
-
Returns the revision string.
- getRevision() - Method in class weka.core.matrix.FloatingPointFormat
-
Returns the revision string.
- getRevision() - Method in class weka.core.Matrix
-
Deprecated.
Returns the revision string.
- getRevision() - Method in class weka.core.matrix.IntVector
-
Returns the revision string.
- getRevision() - Method in class weka.core.matrix.LinearRegression
-
Returns the revision string.
- getRevision() - Method in class weka.core.matrix.LUDecomposition
-
Returns the revision string.
- getRevision() - Method in class weka.core.matrix.Maths
-
Returns the revision string.
- getRevision() - Method in class weka.core.matrix.Matrix
-
Returns the revision string.
- getRevision() - Method in class weka.core.matrix.QRDecomposition
-
Returns the revision string.
- getRevision() - Method in class weka.core.matrix.SingularValueDecomposition
-
Returns the revision string.
- getRevision() - Method in class weka.core.Memory
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.BallTree
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.balltrees.BallNode
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.balltrees.BallSplitter
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor.TempNode
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor.ListNode
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor.TempNode
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.CoverTree.CoverTreeNode
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.CoverTree
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.CoverTree.MyHeap
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.CoverTree.MyHeapElement
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.covertrees.Stack
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.KDTree
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNode
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.kdtrees.KMeansInpiredMethod
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.LinearNNSearch
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.MyHeap
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.MyHeapElement
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborList
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborNode
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.PerformanceStats
-
Returns the revision string.
- getRevision() - Method in class weka.core.neighboursearch.TreePerformanceStats
-
Returns the revision string.
- getRevision() - Method in class weka.core.Option
-
Returns the revision string.
- getRevision() - Method in class weka.core.OptionHandlerJavadoc
-
Returns the revision string.
- getRevision() - Method in class weka.core.PropertyPath
-
Returns the revision string.
- getRevision() - Method in class weka.core.PropertyPath.Path
-
Returns the revision string.
- getRevision() - Method in class weka.core.PropertyPath.PathElement
-
Returns the revision string.
- getRevision() - Method in class weka.core.PropertyPath.PropertyContainer
-
Returns the revision string.
- getRevision() - Method in class weka.core.ProtectedProperties
-
Returns the revision string.
- getRevision() - Method in class weka.core.Queue
-
Returns the revision string.
- getRevision() - Method in class weka.core.Queue.QueueNode
-
Returns the revision string.
- getRevision() - Method in class weka.core.RandomVariates
-
Returns the revision string.
- getRevision() - Method in class weka.core.Range
-
Returns the revision string.
- getRevision() - Method in class weka.core.RelationalLocator
-
Returns the revision string.
- getRevision() - Method in interface weka.core.RevisionHandler
-
Returns the revision string.
- getRevision() - Method in class weka.core.SelectedTag
-
Returns the revision string.
- getRevision() - Method in class weka.core.SerializationHelper
-
Returns the revision string.
- getRevision() - Method in class weka.core.SerializedObject
-
Returns the revision string.
- getRevision() - Method in class weka.core.SingleIndex
-
Returns the revision string.
- getRevision() - Method in class weka.core.SparseInstance
-
Returns the revision string.
- getRevision() - Method in class weka.core.SpecialFunctions
-
Returns the revision string.
- getRevision() - Method in class weka.core.Statistics
-
Returns the revision string.
- getRevision() - Method in class weka.core.stemmers.IteratedLovinsStemmer
-
Returns the revision string.
- getRevision() - Method in class weka.core.stemmers.LovinsStemmer
-
Returns the revision string.
- getRevision() - Method in class weka.core.stemmers.NullStemmer
-
Returns the revision string.
- getRevision() - Method in class weka.core.stemmers.SnowballStemmer
-
Returns the revision string.
- getRevision() - Method in class weka.core.stemmers.Stemming
-
Returns the revision string.
- getRevision() - Method in class weka.core.Stopwords
-
Returns the revision string.
- getRevision() - Method in class weka.core.StringLocator
-
Returns the revision string.
- getRevision() - Method in class weka.core.SystemInfo
-
Returns the revision string.
- getRevision() - Method in class weka.core.Tag
-
Returns the revision string.
- getRevision() - Method in class weka.core.TechnicalInformation
-
Returns the revision string.
- getRevision() - Method in class weka.core.TechnicalInformationHandlerJavadoc
-
Returns the revision string.
- getRevision() - Method in class weka.core.Tee
-
Returns the revision string.
- getRevision() - Method in class weka.core.TestInstances
-
Returns the revision string.
- getRevision() - Method in class weka.core.tokenizers.AlphabeticTokenizer
-
Returns the revision string.
- getRevision() - Method in class weka.core.tokenizers.NGramTokenizer
-
Returns the revision string.
- getRevision() - Method in class weka.core.tokenizers.WordTokenizer
-
Returns the revision string.
- getRevision() - Method in class weka.core.Trie
-
Returns the revision string.
- getRevision() - Method in class weka.core.Trie.TrieIterator
-
Returns the revision string.
- getRevision() - Method in class weka.core.Trie.TrieNode
-
Returns the revision string.
- getRevision() - Method in class weka.core.Utils
-
Returns the revision string.
- getRevision() - Method in class weka.core.Version
-
Returns the revision string.
- getRevision() - Method in class weka.core.xml.KOML
-
Returns the revision string.
- getRevision() - Method in class weka.core.xml.MethodHandler
-
Returns the revision string.
- getRevision() - Method in class weka.core.xml.PropertyHandler
-
Returns the revision string.
- getRevision() - Method in class weka.core.xml.SerialUIDChanger
-
Returns the revision string.
- getRevision() - Method in class weka.core.xml.XMLBasicSerialization
-
Returns the revision string.
- getRevision() - Method in class weka.core.xml.XMLDocument
-
Returns the revision string.
- getRevision() - Method in class weka.core.xml.XMLInstances
-
Returns the revision string.
- getRevision() - Method in class weka.core.xml.XMLOptions
-
Returns the revision string.
- getRevision() - Method in class weka.core.xml.XMLSerialization
-
Returns the revision string.
- getRevision() - Method in class weka.core.xml.XMLSerializationMethodHandler
-
Returns the revision string.
- getRevision() - Method in class weka.core.xml.XStream
-
Returns the revision string.
- getRevision() - Method in class weka.datagenerators.classifiers.classification.Agrawal
-
Returns the revision string.
- getRevision() - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
Returns the revision string.
- getRevision() - Method in class weka.datagenerators.classifiers.classification.LED24
-
Returns the revision string.
- getRevision() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
-
Returns the revision string.
- getRevision() - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Returns the revision string.
- getRevision() - Method in class weka.datagenerators.classifiers.regression.Expression
-
Returns the revision string.
- getRevision() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Returns the revision string.
- getRevision() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Returns the revision string.
- getRevision() - Method in class weka.datagenerators.clusterers.SubspaceCluster
-
Returns the revision string.
- getRevision() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Returns the revision string.
- getRevision() - Method in class weka.datagenerators.Test
-
Returns the revision string.
- getRevision() - Method in class weka.estimators.CheckEstimator.AttrTypes
-
Returns the revision string.
- getRevision() - Method in class weka.estimators.CheckEstimator.EstTypes
-
Returns the revision string.
- getRevision() - Method in class weka.estimators.CheckEstimator
-
Returns the revision string.
- getRevision() - Method in class weka.estimators.CheckEstimator.PostProcessor
-
Returns the revision string.
- getRevision() - Method in class weka.estimators.DDConditionalEstimator
-
Returns the revision string.
- getRevision() - Method in class weka.estimators.DiscreteEstimator
-
Returns the revision string.
- getRevision() - Method in class weka.estimators.DKConditionalEstimator
-
Returns the revision string.
- getRevision() - Method in class weka.estimators.DNConditionalEstimator
-
Returns the revision string.
- getRevision() - Method in class weka.estimators.EstimatorUtils
-
Returns the revision string.
- getRevision() - Method in class weka.estimators.KDConditionalEstimator
-
Returns the revision string.
- getRevision() - Method in class weka.estimators.KernelEstimator
-
Returns the revision string.
- getRevision() - Method in class weka.estimators.KKConditionalEstimator
-
Returns the revision string.
- getRevision() - Method in class weka.estimators.MahalanobisEstimator
-
Returns the revision string.
- getRevision() - Method in class weka.estimators.NDConditionalEstimator
-
Returns the revision string.
- getRevision() - Method in class weka.estimators.NNConditionalEstimator
-
Returns the revision string.
- getRevision() - Method in class weka.estimators.NormalEstimator
-
Returns the revision string.
- getRevision() - Method in class weka.estimators.PoissonEstimator
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.AveragingResultProducer
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.ClassifierSplitEvaluator
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.CrossValidationResultProducer
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.CSVResultListener
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.DatabaseResultListener
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.DatabaseResultProducer
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.DatabaseUtils
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.Experiment
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.InstanceQuery
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.InstancesResultListener
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.LearningRateResultProducer
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.OutputZipper
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.PairedCorrectedTTester
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.PairedStats
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.PairedStatsCorrected
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.PairedTTester.Dataset
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.PairedTTester.DatasetSpecifiers
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.PairedTTester
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.PairedTTester.Resultset
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.PropertyNode
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.RandomSplitResultProducer
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.RegressionSplitEvaluator
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.RemoteEngine
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.RemoteExperiment
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.RemoteExperimentSubTask
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.ResultMatrixCSV
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.ResultMatrixGnuPlot
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.ResultMatrixHTML
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.ResultMatrixLatex
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.ResultMatrixPlainText
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.ResultMatrixSignificance
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.Stats
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.TaskStatusInfo
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.xml.XMLExperiment
-
Returns the revision string.
- getRevision() - Method in class weka.filters.AllFilter
-
Returns the revision string.
- getRevision() - Method in class weka.filters.CheckSource
-
Returns the revision string.
- getRevision() - Method in class weka.filters.Filter
-
Returns the revision string.
- getRevision() - Method in class weka.filters.MultiFilter
-
Returns the revision string.
- getRevision() - Method in class weka.filters.supervised.attribute.AddClassification
-
Returns the revision string.
- getRevision() - Method in class weka.filters.supervised.attribute.AttributeSelection
-
Returns the revision string.
- getRevision() - Method in class weka.filters.supervised.attribute.ClassOrder
-
Returns the revision string.
- getRevision() - Method in class weka.filters.supervised.attribute.Discretize
-
Returns the revision string.
- getRevision() - Method in class weka.filters.supervised.attribute.NominalToBinary
-
Returns the revision string.
- getRevision() - Method in class weka.filters.supervised.attribute.PLSFilter
-
Returns the revision string.
- getRevision() - Method in class weka.filters.supervised.instance.Resample
-
Returns the revision string.
- getRevision() - Method in class weka.filters.supervised.instance.SMOTE
-
Returns the revision string.
- getRevision() - Method in class weka.filters.supervised.instance.SpreadSubsample
-
Returns the revision string.
- getRevision() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.Add
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.AddCluster
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.AddExpression
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.AddID
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.AddNoise
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.AddValues
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.Center
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.ClassAssigner
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.ClusterMembership
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.Copy
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.Discretize
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.FirstOrder
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.KernelFilter
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.MathExpression
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.NominalToString
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.Normalize
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.NumericToBinary
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.NumericToNominal
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.NumericTransform
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.Obfuscate
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.RandomProjection
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.RandomSubset
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.RELAGGS
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.Remove
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.RemoveType
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.RemoveUseless
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.Reorder
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.Standardize
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.StringToNominal
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.SwapValues
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.TimeSeriesDelta
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.attribute.Wavelet
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.instance.NonSparseToSparse
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.instance.Normalize
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.instance.Randomize
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.instance.RemoveFolds
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.instance.RemovePercentage
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.instance.RemoveRange
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.instance.Resample
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.instance.ReservoirSample
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.instance.SparseToNonSparse
-
Returns the revision string.
- getRevision() - Method in class weka.filters.unsupervised.instance.SubsetByExpression
-
Returns the revision string.
- getRevision() - Method in class weka.gui.beans.FlowRunner
-
- getRevision() - Method in class weka.gui.sql.DbUtils
-
Returns the revision string.
- getRidge() - Method in class weka.classifiers.functions.LinearRegression
-
Get the value of Ridge.
- getRidge() - Method in class weka.classifiers.functions.Logistic
-
Gets the ridge in the log-likelihood.
- getRidge() - Method in class weka.classifiers.functions.RBFNetwork
-
Gets the ridge value.
- getRidge() - Method in class weka.classifiers.mi.MILR
-
Gets the ridge in the log-likelihood.
- getRocAnalysis() - Method in class weka.associations.Tertius
-
Get the value of rocAnalysis.
- getROCArea(Instances) - Static method in class weka.classifiers.evaluation.ThresholdCurve
-
Calculates the area under the ROC curve as the Wilcoxon-Mann-Whitney statistic.
- getROCString() - Method in class weka.gui.visualize.ThresholdVisualizePanel
-
This extracts the ROC area string
- getRoot() - Method in class weka.core.Trie
-
returns the root node of the trie
- getRoot() - Method in class weka.gui.treevisualizer.Node
-
Get the value of root.
- getRootFromClass(String, String) - Static method in class weka.gui.GenericObjectEditor
-
returns the name of the root element of the given class name,
null
if it doesn't contain the separator.
- getRootNode() - Method in class weka.core.xml.XMLDocument
-
returns the current root node.
- getRow(int) - Method in class weka.core.Matrix
-
Deprecated.
Gets a row of the matrix and returns it as double array.
- getRow() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
the comma-separated list of attribute names that identify a row
- getRowCount() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.ResultVectorTableModel
-
Returns the number of rows of this model.
- getRowCount() - Method in class weka.experiment.ResultMatrix
-
returns the number of rows
- getRowCount() - Method in class weka.gui.arffviewer.ArffTableModel
-
returns the number of rows in the model
- getRowCount() - Method in class weka.gui.SortedTableModel
-
Returns the number of rows in the model.
- getRowCount() - Method in class weka.gui.sql.ResultSetHelper
-
returns the number of rows in the resultset.
- getRowCount() - Method in class weka.gui.sql.ResultSetTableModel
-
returns the number of rows in the model.
- getRowDimension() - Method in class weka.core.matrix.Matrix
-
Get row dimension.
- getRowHidden(int) - Method in class weka.experiment.ResultMatrix
-
returns the hidden status of the row, if the index is valid, otherwise
false
- getRowName(int) - Method in class weka.experiment.ResultMatrix
-
returns the name of the row, if the index is valid, otherwise null.
- getRowNameWidth() - Method in class weka.experiment.ResultMatrix
-
returns the current width for the row names
- getRowOrder() - Method in class weka.experiment.ResultMatrix
-
returns the current order of the rows, null means the default order
- getRowPackedCopy() - Method in class weka.core.matrix.Matrix
-
Make a one-dimensional row packed copy of the internal array.
- getRsource() - Method in class weka.gui.treevisualizer.Edge
-
Get the value of rsource.
- getRtarget() - Method in class weka.gui.treevisualizer.Edge
-
Get the value of rtarget.
- getRuleset() - Method in class weka.classifiers.rules.JRip
-
Get the ruleset generated by Ripper
- getRuleset() - Method in class weka.classifiers.rules.RuleStats
-
Get the ruleset of the stats
- getRulesetSize() - Method in class weka.classifiers.rules.RuleStats
-
Get the size of the ruleset in the stats
- getRulesMustContain() - Method in class weka.associations.FPGrowth
-
Get the comma separated list of items that
rules must contain in order to be output.
- getRuleStats(int) - Method in class weka.classifiers.rules.JRip
-
Get the statistics of the ruleset in the given position
- getRunColumn() - Method in class weka.experiment.PairedTTester
-
Get the value of RunColumn.
- getRunColumn() - Method in interface weka.experiment.Tester
-
Get the value of RunColumn.
- getRunLower() - Method in class weka.experiment.Experiment
-
Get the lower run number for the experiment.
- getRunNumber() - Method in class weka.gui.beans.BatchClassifierEvent
-
Get the run number.
- getRunNumber() - Method in class weka.gui.beans.TestSetEvent
-
Get the run number that this training set belongs to.
- getRunNumber() - Method in class weka.gui.beans.TrainingSetEvent
-
Get the run number that this training set belongs to.
- getRuns() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
-
- getRuns() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
-
Returns the number of runs
- getRuns() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
-
- getRuns() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
-
- getRuns() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
-
- getRuns() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
-
- getRuns() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
-
- getRuns() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
-
- getRunUpper() - Method in class weka.experiment.Experiment
-
Get the upper run number for the experiment.
- getS() - Method in class weka.core.matrix.SingularValueDecomposition
-
Return the diagonal matrix of singular values
- getSampleSize() - Method in class weka.attributeSelection.ReliefFAttributeEval
-
Get the number of instances used for estimating attributes
- getSampleSize() - Method in class weka.classifiers.functions.LeastMedSq
-
gets number of samples
- getSampleSize() - Method in class weka.filters.unsupervised.instance.ReservoirSample
-
Gets the subsample size.
- getSampleSizePercent() - Method in class weka.classifiers.meta.GridSearch
-
Gets the sample size for the initial grid search.
- getSampleSizePercent() - Method in class weka.filters.supervised.instance.Resample
-
Gets the subsample size as a percentage of the original set.
- getSampleSizePercent() - Method in class weka.filters.unsupervised.instance.Resample
-
Gets the subsample size as a percentage of the original set.
- getSaveDialogTitle() - Method in class weka.gui.visualize.PrintableComponent
-
returns the title for the save dialog.
- getSaveDialogTitle() - Method in interface weka.gui.visualize.PrintableHandler
-
returns the title for the save dialog
- getSaveDialogTitle() - Method in class weka.gui.visualize.PrintablePanel
-
returns the title for the save dialog
- getSaveInstanceData() - Method in class weka.classifiers.trees.ADTree
-
Gets whether the tree is to save instance data.
- getSaveInstanceData() - Method in class weka.classifiers.trees.J48
-
Check whether instance data is to be saved.
- getSaveInstanceData() - Method in class weka.classifiers.trees.J48graft
-
Check whether instance data is to be saved.
- getSaveInstanceData() - Method in class weka.clusterers.Cobweb
-
Get the value of saveInstances.
- getSaveInstances() - Method in class weka.classifiers.trees.M5P
-
Get whether instance data is being save.
- getSaver() - Method in class weka.gui.ConverterFileChooser
-
returns the saver that was chosen by the user, can be null in case the
user aborted the dialog or the open dialog was shown
- getSaverForExtension(String) - Static method in class weka.core.converters.ConverterUtils
-
tries to determine the saver to use for this kind of extension, returns
null if none can be found.
- getSaverForFile(String) - Static method in class weka.core.converters.ConverterUtils
-
tries to determine the saver to use for this kind of file, returns
null if none can be found.
- getSaverForFile(File) - Static method in class weka.core.converters.ConverterUtils
-
tries to determine the saver to use for this kind of file, returns
null if none can be found.
- getSaverTemplate() - Method in class weka.gui.beans.Saver
-
Get the saver
- getScale() - Method in class weka.filters.unsupervised.attribute.Normalize
-
Get the scaling factor.
- getScalingEnabled() - Method in class weka.gui.visualize.JComponentWriter
-
whether scaling is enabled or ignored
- getScore() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
-
Compute the value of the objective function.
- getScoreType() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
-
get quality measure to be used in searching for networks.
- getSearch() - Method in class weka.attributeSelection.CheckAttributeSelection
-
Get the current search method
- getSearch() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
-
Gets the search method used
- getSearch() - Method in class weka.classifiers.rules.DecisionTable
-
Gets the current search method
- getSearch() - Method in class weka.classifiers.rules.DTNB
-
Gets the current search method
- getSearch() - Method in class weka.filters.supervised.attribute.AttributeSelection
-
Get the name of the search method
- getSearchAlgorithm() - Method in class weka.classifiers.bayes.BayesNet
-
Get the SearchAlgorithm used as the search algorithm
- getSearchBackwards() - Method in class weka.attributeSelection.GreedyStepwise
-
Get whether to search backwards
- getSearchPath() - Method in class weka.classifiers.trees.ADTree
-
Gets the method of searching the tree for a new insertion.
- getSearchPercent() - Method in class weka.attributeSelection.RandomSearch
-
get the percentage of the search space to consider
- getSearchSpec() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
-
Gets the search specification string, which contains the class name of
the search method and any options to it
- getSearchSpec() - Method in class weka.classifiers.rules.DecisionTable
-
Gets the search specification string, which contains the class name of
the search method and any options to it
- getSearchString() - Method in class weka.gui.arffviewer.ArffTable
-
returns the search string, can be NULL if no search string is set
- getSearchTermination() - Method in class weka.attributeSelection.BestFirst
-
Get the termination criterion (number of non-improving nodes).
- getSearchTermination() - Method in class weka.attributeSelection.LinearForwardSelection
-
Get the termination criterion (number of non-improving nodes).
- getSecondValueIndex() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
-
Get the index of the second value used.
- getSecondValueIndex() - Method in class weka.filters.unsupervised.attribute.SwapValues
-
Get the index of the second value used.
- getSeed() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
-
Gets the seed for the random number generations.
- getSeed() - Method in class weka.attributeSelection.GeneticSearch
-
get the value of the random number generator's seed
- getSeed() - Method in class weka.attributeSelection.OneRAttributeEval
-
Get the random number seed
- getSeed() - Method in class weka.attributeSelection.RandomSearch
-
Get the random seed to use
- getSeed() - Method in class weka.attributeSelection.ReliefFAttributeEval
-
Get the seed used for randomly sampling instances.
- getSeed() - Method in class weka.attributeSelection.ScatterSearchV1
-
get the value of the random number generator's seed
- getSeed() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Seed for cross validation subset size determination.
- getSeed() - Method in class weka.attributeSelection.WrapperSubsetEval
-
Get the random number seed used for cross validation
- getSeed() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Get the seed for randomizing the instances for CV-based
hyperparameter selection
- getSeed() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
-
- getSeed() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
-
Returns the random seed
- getSeed() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
-
- getSeed() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
-
- getSeed() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
-
- getSeed() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
-
- getSeed() - Method in class weka.classifiers.BVDecompose
-
Gets the random number seed
- getSeed() - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Gets the random number seed
- getSeed() - Method in class weka.classifiers.evaluation.EvaluationUtils
-
Gets the seed for randomization during cross-validation
- getSeed() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- getSeed() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
-
Gets the current seed value for the random number generator
- getSeed() - Method in class weka.classifiers.functions.VotedPerceptron
-
Get the value of Seed.
- getSeed() - Method in class weka.classifiers.functions.Winnow
-
Get the value of Seed.
- getSeed() - Method in class weka.classifiers.meta.MultiScheme
-
Gets the random number seed.
- getSeed() - Method in class weka.classifiers.RandomizableClassifier
-
Gets the seed for the random number generations
- getSeed() - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
-
Gets the seed for the random number generations
- getSeed() - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner
-
Gets the seed for the random number generations
- getSeed() - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer
-
Gets the seed for the random number generations
- getSeed() - Method in class weka.classifiers.rules.ConjunctiveRule
-
returns the current seed value for randomizing the data
- getSeed() - Method in class weka.classifiers.rules.JRip
-
Gets the current seed value to use in randomizing the data
- getSeed() - Method in class weka.classifiers.rules.PART
-
Get the value of Seed.
- getSeed() - Method in class weka.classifiers.rules.Ridor
-
- getSeed() - Method in class weka.classifiers.trees.J48
-
Get the value of Seed.
- getSeed() - Method in class weka.classifiers.trees.RandomForest
-
Gets the seed for the random number generations
- getSeed() - Method in class weka.classifiers.trees.RandomTree
-
Gets the seed for the random number generations
- getSeed() - Method in class weka.classifiers.trees.REPTree
-
Get the value of Seed.
- getSeed() - Method in class weka.clusterers.RandomizableClusterer
-
Gets the seed for the random number generations
- getSeed() - Method in class weka.clusterers.RandomizableDensityBasedClusterer
-
Gets the seed for the random number generations
- getSeed() - Method in class weka.clusterers.RandomizableSingleClustererEnhancer
-
Gets the seed for the random number generations
- getSeed() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Returns the seed for random number generator.
- getSeed() - Method in interface weka.core.Randomizable
-
Gets the seed for the random number generations
- getSeed() - Method in class weka.core.TestInstances
-
returns the current seed value
- getSeed() - Method in class weka.datagenerators.DataGenerator
-
Gets the random number seed.
- getSeed() - Method in class weka.filters.supervised.attribute.ClassOrder
-
Get the current randomization seed
- getSeed() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
-
Gets the random number seed used for shuffling the dataset.
- getSeed() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
-
Returns the current seed value for randomizing the order of the generated
data
- getSeed() - Method in class weka.filters.unsupervised.attribute.RandomSubset
-
Get the seed value for the random number generator.
- getSeed() - Method in class weka.filters.unsupervised.instance.RemoveFolds
-
Gets the random number seed used for shuffling the dataset.
- getSeed() - Method in class weka.gui.beans.CrossValidationFoldMaker
-
Get the currently set seed
- getSeed() - Method in class weka.gui.beans.TrainTestSplitMaker
-
Get the value of the random seed
- getSelectedAttributes() - Method in class weka.gui.AttributeSelectionPanel
-
Gets an array containing the indices of all selected attributes.
- getSelectedBuffer() - Method in class weka.gui.ResultHistoryPanel
-
Gets the buffer associated with the currently
selected item in the list.
- getSelectedName() - Method in class weka.gui.ResultHistoryPanel
-
Get the name of the currently selected item in the list
- getSelectedObject() - Method in class weka.gui.ResultHistoryPanel
-
Gets the object associated with the currently
selected item in the list.
- getSelectedRange() - Method in class weka.filters.unsupervised.attribute.RELAGGS
-
Gets the current range selection.
- getSelectedRange() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Get the value of m_SelectedRange.
- getSelectedTag() - Method in class weka.core.SelectedTag
-
Gets the selected Tag.
- getSelection() - Method in class weka.core.Range
-
Gets an array containing all the selected values, in the order
that they were selected (or ascending order if range inversion is on)
- getSelectionModel() - Method in class weka.gui.AttributeListPanel
-
Gets the selection model used by the table.
- getSelectionModel() - Method in class weka.gui.AttributeSelectionPanel
-
Gets the selection model used by the table.
- getSelectionModel() - Method in class weka.gui.ResultHistoryPanel
-
Gets the selection model used by the results list.
- getSelectionThreshold() - Method in class weka.attributeSelection.RaceSearch
-
Returns the threshold so that the AttributeSelection module can
discard attributes from the ranking.
- getSeparatingThreshold() - Method in class weka.classifiers.functions.pace.ChisqMixture
-
Gets the separating threshold value.
- getSeparatingThreshold() - Method in class weka.classifiers.functions.pace.NormalMixture
-
Gets the separating threshold value.
- getSeperator() - Method in class weka.gui.HierarchyPropertyParser
-
Get the seperator between levels.
- getSequentialAttIndex(int) - Method in class weka.classifiers.lazy.LBR.Indexes
-
Returns the boolean value at the specified index in the Sequential Attribute Indexes array
- getSequentialInstanceIndex(int) - Method in class weka.classifiers.lazy.LBR.Indexes
-
Returns the boolean value at the specified index in the Sequential Instance Indexes array
- getSequentialNumAttributes() - Method in class weka.classifiers.lazy.LBR.Indexes
-
Returns the number of attributes in the Sequential array
- getSequentialNumInstances() - Method in class weka.classifiers.lazy.LBR.Indexes
-
Returns the number of instances in the Sequential array
- getSerializedClassifierFile() - Method in class weka.filters.supervised.attribute.AddClassification
-
Gets the file pointing to a serialized, trained classifier.
- getSERObject() - Method in class weka.clusterers.OPTICS
-
Returns the internal database
- getSetNumber() - Method in class weka.gui.beans.BatchClassifierEvent
-
Get the set number (ie which fold this is)
- getSetNumber() - Method in class weka.gui.beans.BatchClustererEvent
-
Get the set number (ie which fold this is)
- getSetNumber() - Method in class weka.gui.beans.TestSetEvent
-
Get the test set number (eg.
- getSetNumber() - Method in class weka.gui.beans.TrainingSetEvent
-
Get the set number (eg.
- getShape() - Method in class weka.gui.treevisualizer.Node
-
Get the value of shape.
- getShapes() - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
-
- getShowAttBars() - Method in class weka.gui.visualize.VisualizePanel
-
Gets whether or not attribute bars are being displayed.
- getShowAverage() - Method in class weka.experiment.ResultMatrix
-
returns whether average per column is displayed or not
- getShowAverage() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
returns whether the Average is shown by default
- getShowAverage() - Method in class weka.gui.experiment.OutputFormatDialog
-
returns whether the average for each column is displayed.
- getShowClassPanel() - Method in class weka.gui.visualize.VisualizePanel
-
Gets whether or not the class panel is being displayed.
- getShowGUI() - Method in class weka.clusterers.OPTICS
-
Returns the flag for showing the OPTICS visualizer GUI.
- getShowStdDev() - Method in class weka.experiment.ResultMatrix
-
returns whether std deviations are displayed or not
- getShowStdDevs() - Method in class weka.experiment.PairedTTester
-
Returns true if standard deviations have been requested.
- getShowStdDevs() - Method in interface weka.experiment.Tester
-
Returns true if standard deviations have been requested.
- getShowStdDevs() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
returns whether StdDevs are shown by default
- getShrinkage() - Method in class weka.classifiers.meta.AdditiveRegression
-
Get the shrinkage rate.
- getShrinkage() - Method in class weka.classifiers.meta.LogitBoost
-
Get the value of Shrinkage.
- getShrinking() - Method in class weka.classifiers.functions.LibSVM
-
whether to use the shrinking heuristics
- getShuffle() - Method in class weka.classifiers.rules.Ridor
-
- getSigma() - Method in class weka.attributeSelection.ReliefFAttributeEval
-
Get the value of sigma.
- getSigma() - Method in class weka.classifiers.BVDecompose
-
Get the calculated sigma squared
- getSigma() - Method in class weka.classifiers.functions.supportVector.Puk
-
Gets the sigma value.
- getSignificance(int, int) - Method in class weka.experiment.ResultMatrix
-
returns the significance at the given position, if the position is valid,
otherwise SIGNIFICANCE_ATIE
- getSignificance() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
returns the default significance
- getSignificanceCount(int, int) - Method in class weka.experiment.ResultMatrix
-
counts the occurrences of the given significance type in the given
column.
- getSignificanceLevel() - Method in class weka.associations.Apriori
-
Get the value of significanceLevel.
- getSignificanceLevel() - Method in class weka.attributeSelection.RaceSearch
-
Get the significance level
- getSignificanceLevel() - Method in class weka.experiment.PairedTTester
-
Get the value of SignificanceLevel.
- getSignificanceLevel() - Method in interface weka.experiment.Tester
-
Get the value of SignificanceLevel.
- getSignificanceWidth() - Method in class weka.experiment.ResultMatrix
-
returns the current width for the significance
- getSilent() - Method in class weka.core.Check
-
Get whether silent mode is turned on
- getSilent() - Method in class weka.core.Javadoc
-
whether output in the console is suppressed
- getSilent() - Method in class weka.estimators.CheckEstimator
-
Get whether silent mode is turned on
- getSimpleStats(int) - Method in class weka.classifiers.rules.RuleStats
-
Get the simple stats of one rule, including 6 parameters:
0: coverage; 1:uncoverage; 2: true positive; 3: true negatives;
4: false positives; 5: false negatives
- getSIndex() - Method in class weka.gui.visualize.VisualizePanel
-
Get the index of the shape selected for creating splits.
- getSingleIndex() - Method in class weka.core.SingleIndex
-
Gets the string representing the selected range of values
- getSingleModeFlag() - Method in class weka.datagenerators.classifiers.classification.Agrawal
-
Return if single mode is set for the given data generator
mode depends on option setting and or generator type.
- getSingleModeFlag() - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
Return if single mode is set for the given data generator
mode depends on option setting and or generator type.
- getSingleModeFlag() - Method in class weka.datagenerators.classifiers.classification.LED24
-
Return if single mode is set for the given data generator
mode depends on option setting and or generator type.
- getSingleModeFlag() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
-
Return if single mode is set for the given data generator
mode depends on option setting and or generator type.
- getSingleModeFlag() - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Gets the single mode flag.
- getSingleModeFlag() - Method in class weka.datagenerators.classifiers.regression.Expression
-
Return if single mode is set for the given data generator
mode depends on option setting and or generator type.
- getSingleModeFlag() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Return if single mode is set for the given data generator
mode depends on option setting and or generator type.
- getSingleModeFlag() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Gets the single mode flag.
- getSingleModeFlag() - Method in class weka.datagenerators.clusterers.SubspaceCluster
-
Gets the single mode flag.
- getSingleModeFlag() - Method in class weka.datagenerators.DataGenerator
-
Return if single mode is set for the given data generator
mode depends on option setting and or generator type.
- getSingleton() - Static method in class weka.core.logging.Logger
-
Returns the singleton instance of the logger.
- getSingleton() - Static method in class weka.gui.beans.KnowledgeFlowApp
-
Return the singleton instance of the KnowledgeFlow
- getSingleton() - Static method in class weka.gui.GUIChooser
-
Get the singleton instance of the GUIChooser
- getSingleton() - Static method in class weka.gui.Main
-
Return the singleton instance of the Main GUI.
- getSingletons(Instances) - Method in class weka.associations.FPGrowth
-
Get the singleton items in the data
- getSingularValues() - Method in class weka.core.matrix.SingularValueDecomposition
-
Return the one-dimensional array of singular values
- getSize() - Method in class weka.core.Debug.Log
-
returns the size of the files
- getSizeOfBranch() - Method in class weka.classifiers.rules.part.ClassifierDecList
-
Returns the number of instances covered by a branch
- getSizePer() - Method in class weka.classifiers.trees.BFTree
-
Get training set size.
- getSizePer() - Method in class weka.classifiers.trees.SimpleCart
-
Get training set size.
- getSkipIdentical() - Method in class weka.core.neighboursearch.LinearNNSearch
-
Gets whether if identical instances are skipped from the neighbourhood.
- getSlope() - Method in class weka.classifiers.functions.SimpleLinearRegression
-
Returns the slope of the function.
- getSmoothing() - Method in class weka.classifiers.trees.m5.Rule
-
Get whether or not smoothing has been turned on
- getSmoothingParameter() - Method in class weka.classifiers.bayes.ComplementNaiveBayes
-
Gets the smoothing value to be used to avoid zero WordGivenClass
probabilities.
- getSort() - Method in class weka.filters.unsupervised.attribute.AddValues
-
Gets whether the labels are sorted or not.
- getSortColumn() - Method in class weka.experiment.PairedTTester
-
Returns the column to sort on, -1 means the default sorting.
- getSortColumn() - Method in interface weka.experiment.Tester
-
Returns the column to sort on, -1 means the default sorting.
- getSortColumnName() - Method in class weka.experiment.PairedTTester
-
Returns the name of the column to sort on.
- getSortColumnName() - Method in interface weka.experiment.Tester
-
Returns the name of the column to sort on.
- getSorting() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
returns the default sorting (empty string means none)
- getSource() - Method in class weka.gui.beans.BeanConnection
-
returns the source BeanInstance for this connection
- getSource() - Method in class weka.gui.treevisualizer.Edge
-
Get the value of source.
- getSourceCode() - Method in class weka.classifiers.CheckSource
-
Gets the class to test.
- getSourceCode() - Method in class weka.filters.CheckSource
-
Gets the class to test.
- getSourceEventSetDescriptor() - Method in class weka.gui.beans.BeanConnection
-
Returns the event set descriptor for the event generated by the source
for this connection
- getSparseData() - Method in class weka.experiment.InstanceQuery
-
Gets whether data is to be returned as a set of sparse instances
- getSplitByDataSet() - Method in class weka.experiment.RemoteExperiment
-
Returns true if sub experiments are to be created on the basis of
data set..
- getSplitDim() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNode
-
Gets the splitting dimension.
- getSplitEvaluator() - Method in class weka.experiment.CrossValidationResultProducer
-
Get the SplitEvaluator.
- getSplitEvaluator() - Method in class weka.experiment.RandomSplitResultProducer
-
Get the SplitEvaluator.
- getSplitOnResiduals() - Method in class weka.classifiers.trees.LMT
-
Get the value of splitOnResiduals.
- getSplitPoint() - Method in class weka.classifiers.rules.JRip.NumericAntd
-
Get split point of this numeric antecedent
- getSplitPoint() - Method in class weka.classifiers.trees.lmt.ResidualSplit
-
Selects split point for numeric attribute.
- getSplitPoint() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Get the split point used for numeric selection
- getSplitValue() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNode
-
Gets the splitting value.
- getSquaredError() - Method in class weka.clusterers.SimpleKMeans
-
Gets the squared error for all clusters
- getStamp() - Method in class weka.core.Debug.Timestamp
-
returns the associated date/time
- getStandardDeviation(Instance) - Method in class weka.classifiers.functions.GaussianProcesses
-
Gives the variance of the prediction at the given instance
- getStart() - Method in class weka.core.Debug.Clock
-
returns the start time
- getStartMessage() - Method in class weka.gui.beans.Loader
-
Gets a string that describes the start action.
- getStartMessage() - Method in interface weka.gui.beans.Startable
-
Gets a string that describes the start action.
- getStartPoint() - Method in class weka.attributeSelection.RankSearch
-
Get the point at which to start evaluating the ranking
- getStartSequentially() - Method in class weka.gui.beans.FlowRunner
-
Gets whether Startable beans will be launched sequentially
or all in parallel.
- getStartSet() - Method in class weka.attributeSelection.BestFirst
-
Returns a list of attributes (and or attribute ranges) as a String
- getStartSet() - Method in class weka.attributeSelection.GeneticSearch
-
Returns a list of attributes (and or attribute ranges) as a String
- getStartSet() - Method in class weka.attributeSelection.GreedyStepwise
-
Returns a list of attributes (and or attribute ranges) as a String
- getStartSet() - Method in class weka.attributeSelection.LinearForwardSelection
-
Returns a list of attributes (and or attribute ranges) as a String
- getStartSet() - Method in class weka.attributeSelection.RandomSearch
-
Returns a list of attributes (and or attribute ranges) as a String
- getStartSet() - Method in class weka.attributeSelection.Ranker
-
Returns a list of attributes (and or attribute ranges) as a String
- getStartSet() - Method in interface weka.attributeSelection.StartSetHandler
-
Returns a list of attributes (and or attribute ranges) as a String
- getStaticIcon() - Method in class weka.gui.beans.BeanVisual
-
Returns the static icon
- getStats() - Method in class weka.core.neighboursearch.PerformanceStats
-
Returns a string representation of the statistics.
- getStats() - Method in class weka.core.neighboursearch.TreePerformanceStats
-
Returns a string representation of the statistics.
- getStatus() - Method in class weka.gui.beans.IncrementalClassifierEvent
-
Get the status
- getStatus() - Method in class weka.gui.beans.InstanceEvent
-
Get the status
- getStatusFrequency() - Method in class weka.gui.beans.IncrementalClassifierEvaluator
-
Get how often progress is reported to the status bar.
- getStatusMessage() - Method in class weka.experiment.TaskStatusInfo
-
Get the status message.
- getStatusTable() - Method in class weka.gui.beans.LogPanel
-
The JTable used for the status messages (in case clients
want to attach listeners etc.)
- getStdDev() - Method in class weka.estimators.KernelEstimator
-
Return the standard deviation of this kernel estimator.
- getStdDev() - Method in class weka.estimators.NormalEstimator
-
Return the value of the standard deviation of this normal estimator.
- getStdDev(int, int) - Method in class weka.experiment.ResultMatrix
-
returns the std deviation at the given position, if the position is valid,
otherwise 0
- getStdDevCoordsPerPoint() - Method in class weka.core.neighboursearch.PerformanceStats
-
Returns the standard deviation of coords per point.
- getStdDevIntNodesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats
-
Returns the standard deviation of internal nodes visited.
- getStdDevLeavesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats
-
Returns the standard deviation of leaves visited.
- getStdDevPointsVisited() - Method in class weka.core.neighboursearch.PerformanceStats
-
Returns the standard deviation of points visited.
- getStdDevPrec() - Method in class weka.experiment.ResultMatrix
-
returns the current standard deviation precision
- getStdDevPrec() - Method in class weka.gui.experiment.OutputFormatDialog
-
Gets the precision used for printing the std.
- getStdDevPrecision() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
returns the default precision for the stddevs
- getStddevValue() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
- getStdDevWidth() - Method in class weka.experiment.ResultMatrix
-
returns the current width for the std dev
- getStemmer() - Method in class weka.core.stemmers.SnowballStemmer
-
returns the name of the current stemmer, null if none is set.
- getStemmer() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Returns the current stemming algorithm, null if none is used.
- getStepSize() - Method in class weka.attributeSelection.RankSearch
-
Get the number of attributes to add from the rankining
in each iteration
- getStepSize() - Method in class weka.experiment.LearningRateResultProducer
-
Get the value of StepSize.
- getStepX() - Method in class weka.classifiers.meta.GridSearch.Grid
-
returns the step size on the X axis
- getStepY() - Method in class weka.classifiers.meta.GridSearch.Grid
-
returns the step size on the Y axis
- getStop() - Method in class weka.core.Debug.Clock
-
returns the stop time or, if still running, the current time
- getStopwords() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
returns the file used for obtaining the stopwords, if the file represents
a directory then the default ones are used.
- getString(String) - Static method in class weka.associations.gsp.Messages
-
getString.
- getString(String, Locale) - Static method in class weka.associations.gsp.Messages
-
getString.
- getString(String) - Static method in class weka.associations.Messages
-
getString.
- getString(String, Locale) - Static method in class weka.associations.Messages
-
getString.
- getString(int[]) - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
-
Returns the list of indices as a string.
- getString(int[]) - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
-
Returns the list of indices as a string.
- getString() - Method in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
-
Returns the list of indices as a string.
- getString() - Method in class weka.core.Trie.TrieNode
-
returns the full string up to the root
- getString(String) - Static method in class weka.gui.arffviewer.Messages
-
getString.
- getString(String, Locale) - Static method in class weka.gui.arffviewer.Messages
-
getString.
- getString(String) - Static method in class weka.gui.beans.Messages
-
getString.
- getString(String, Locale) - Static method in class weka.gui.beans.Messages
-
getString.
- getString(String) - Static method in class weka.gui.beans.xml.Messages
-
getString.
- getString(String, Locale) - Static method in class weka.gui.beans.xml.Messages
-
getString.
- getString(String) - Static method in class weka.gui.boundaryvisualizer.Messages
-
getString.
- getString(String, Locale) - Static method in class weka.gui.boundaryvisualizer.Messages
-
getString.
- getString(String) - Static method in class weka.gui.experiment.Messages
-
getString.
- getString(String, Locale) - Static method in class weka.gui.experiment.Messages
-
getString.
- getString(String) - Static method in class weka.gui.explorer.Messages
-
getString.
- getString(String, Locale) - Static method in class weka.gui.explorer.Messages
-
getString.
- getString(String) - Static method in class weka.gui.graphvisualizer.Messages
-
getString.
- getString(String, Locale) - Static method in class weka.gui.graphvisualizer.Messages
-
getString.
- getString(String) - Static method in class weka.gui.hierarchyvisualizer.Messages
-
getString.
- getString(String, Locale) - Static method in class weka.gui.hierarchyvisualizer.Messages
-
getString.
- getString(String) - Static method in class weka.gui.Messages
-
getString.
- getString(String, Locale) - Static method in class weka.gui.Messages
-
getString.
- getString(String) - Static method in class weka.gui.sql.event.Messages
-
getString.
- getString(String, Locale) - Static method in class weka.gui.sql.event.Messages
-
getString.
- getString(String) - Static method in class weka.gui.sql.Messages
-
getString.
- getString(String, Locale) - Static method in class weka.gui.sql.Messages
-
getString.
- getString(String) - Static method in class weka.gui.streams.Messages
-
getString.
- getString(String, Locale) - Static method in class weka.gui.streams.Messages
-
getString.
- getString(String) - Static method in class weka.gui.treevisualizer.Messages
-
getString.
- getString(String, Locale) - Static method in class weka.gui.treevisualizer.Messages
-
getString.
- getString(String) - Static method in class weka.gui.visualize.Messages
-
getString.
- getString(String, Locale) - Static method in class weka.gui.visualize.Messages
-
getString.
- getStringAttributes() - Method in class weka.core.converters.CSVLoader
-
Returns the current attribute range to be forced to type string.
- getStringSelection() - Method in class weka.gui.arffviewer.ArffTable
-
returns the selected content in a StringSelection that can be copied to
the clipboard and used in Excel, if nothing is selected the whole table
is copied to the clipboard
- getStroke() - Method in class weka.gui.visualize.PostscriptGraphics
-
- getStructure() - Method in class weka.core.converters.AbstractLoader
-
- getStructure() - Method in class weka.core.converters.ArffLoader.ArffReader
-
Returns the header format
- getStructure() - Method in class weka.core.converters.ArffLoader
-
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
- getStructure() - Method in class weka.core.converters.C45Loader
-
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
- getStructure() - Method in class weka.core.converters.ConverterUtils.DataSource
-
returns the structure of the data.
- getStructure(int) - Method in class weka.core.converters.ConverterUtils.DataSource
-
returns the structure of the data, with the defined class index.
- getStructure() - Method in class weka.core.converters.CSVLoader
-
Determines and returns (if possible) the structure (internally the header)
of the data set as an empty set of instances.
- getStructure() - Method in class weka.core.converters.DatabaseLoader
-
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
- getStructure() - Method in class weka.core.converters.LibSVMLoader
-
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
- getStructure() - Method in interface weka.core.converters.Loader
-
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
- getStructure() - Method in class weka.core.converters.SerializedInstancesLoader
-
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
- getStructure() - Method in class weka.core.converters.SVMLightLoader
-
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
- getStructure() - Method in class weka.core.converters.TextDirectoryLoader
-
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
- getStructure() - Method in class weka.core.converters.XRFFLoader
-
Determines and returns (if possible) the structure (internally the
header) of the data set as an empty set of instances.
- getStructure(String) - Method in class weka.gui.beans.ClassAssigner
-
Get the structure of the output encapsulated in the named
event.
- getStructure(String) - Method in class weka.gui.beans.ClassValuePicker
-
- getStructure() - Method in class weka.gui.beans.ClassValuePicker
-
- getStructure() - Method in class weka.gui.beans.IncrementalClassifierEvent
-
Get the instances structure (may be null if this is not
a NEW_BATCH event)
- getStructure() - Method in class weka.gui.beans.InstanceEvent
-
Get the instances structure (may be null if this is not
a FORMAT_AVAILABLE event)
- getStructure(String) - Method in class weka.gui.beans.Loader
-
Get the structure of the output encapsulated in the named
event.
- getStructure(String) - Method in interface weka.gui.beans.StructureProducer
-
Get the structure of the output encapsulated in the named
event.
- getSubDirectories(String, File, HashSet) - Static method in class weka.core.ClassDiscovery
-
adds all the sub-directories recursively to the list.
- getSubFlow() - Method in class weka.gui.beans.MetaBean
-
- getSubmenuTitle() - Method in interface weka.gui.MainMenuExtension
-
Returns the name of the submenu.
- getSubsequenceLength() - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Returns the length of the subsequence
- getSubsetEvaluator() - Method in class weka.attributeSelection.FilteredSubsetEval
-
Get the subset evaluator to use
- getSubsetSizeEvaluator() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Get the subset evaluator used for subset size determination.
- getSubSpaceSize() - Method in class weka.classifiers.meta.RandomSubSpace
-
Gets the size of each subSpace, as a percentage of the training set size.
- getSubtreeRaising() - Method in class weka.classifiers.trees.J48
-
Get the value of subtreeRaising.
- getSubtreeRaising() - Method in class weka.classifiers.trees.J48graft
-
Get the value of subtreeRaising.
- getSuccess() - Method in class weka.core.CheckGOE
-
returns the success of the tests
- getSuccess() - Method in class weka.core.CheckOptionHandler
-
returns the success of the tests
- getSuitableTargets(EventSetDescriptor) - Method in class weka.gui.beans.MetaBean
-
Return a list of input beans capable of receiving the
supplied event
- getSummary() - Method in class weka.gui.SetInstancesPanel
-
Gets the instances summary panel associated with
this panel
- getSummaryTitle(int) - Method in class weka.experiment.ResultMatrix
-
returns the character representation of the given column
- getSumOfCounts() - Method in class weka.estimators.DiscreteEstimator
-
Get the sum of all the counts
- getSumOfWeights() - Method in class weka.estimators.NormalEstimator
-
Return the sum of the weights for this normal estimator.
- getSupport() - Method in class weka.associations.FPGrowth.FrequentBinaryItemSet
-
Get the support of this item set.
- getSupportCount() - Method in class weka.associations.gsp.Sequence
-
Returns the support count of the Sequence.
- getSupportedCursorScrollType() - Method in class weka.experiment.DatabaseUtils
-
Returns the type of scrolling that the cursor supports, -1 if not
supported or not connected.
- getSVMType() - Method in class weka.classifiers.functions.LibLINEAR
-
Gets type of SVM
- getSVMType() - Method in class weka.classifiers.functions.LibSVM
-
Gets type of SVM
- getSymbols() - Method in class weka.core.mathematicalexpression.Parser
-
Returns the current variable - value relation in use.
- getSymbols() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
-
Returns the current variable - value relation in use.
- getSystemInfo() - Method in class weka.core.SystemInfo
-
returns a copy of the system info.
- getSystemLookAndFeel() - Static method in class weka.gui.LookAndFeel
-
returns the system LnF classname
- getSystemWide() - Static method in class weka.core.Environment
-
Get the singleton system-wide (visible to every
class in the running VM) set of environment
variables.
- getTabbedPane() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
returns the tabbedpane instance
- getTabbedPane() - Method in class weka.gui.explorer.Explorer
-
returns the tabbed pane of the Explorer
- getTable() - Method in class weka.classifiers.meta.GridSearch.PerformanceTable
-
returns the generated table
- getTable() - Method in class weka.gui.arffviewer.ArffPanel
-
returns the table component
- getTableCellEditorComponent(JTable, Object, boolean, int, int) - Method in class weka.gui.arffviewer.ArffTable.RelationalCellEditor
-
Sets an initial value for the editor.
- getTableCellRendererComponent(JTable, Object, boolean, boolean, int, int) - Method in class weka.gui.arffviewer.ArffTableCellRenderer
-
Returns the default table cell renderer.
- getTableCellRendererComponent(JTable, Object, boolean, boolean, int, int) - Method in class weka.gui.sql.ResultSetTableCellRenderer
-
Returns the default table cell renderer.
- getTableModel() - Method in class weka.gui.AttributeSelectionPanel
-
Get the table model in use (or null if no instances
have been set yet).
- getTableName() - Method in class weka.core.converters.DatabaseSaver
-
Gets the table's name.
- getTabs() - Static method in class weka.gui.explorer.ExplorerDefaults
-
returns an array with the classnames of all the additional panels to
display as tabs in the Explorer.
- getTabTitle() - Method in class weka.gui.explorer.AssociationsPanel
-
Returns the title for the tab in the Explorer
- getTabTitle() - Method in class weka.gui.explorer.AttributeSelectionPanel
-
Returns the title for the tab in the Explorer
- getTabTitle() - Method in class weka.gui.explorer.ClassifierPanel
-
Returns the title for the tab in the Explorer
- getTabTitle() - Method in class weka.gui.explorer.ClustererPanel
-
Returns the title for the tab in the Explorer
- getTabTitle() - Method in interface weka.gui.explorer.Explorer.ExplorerPanel
-
Returns the title for the tab in the Explorer
- getTabTitle() - Method in class weka.gui.explorer.PreprocessPanel
-
Returns the title for the tab in the Explorer
- getTabTitle() - Method in class weka.gui.explorer.VisualizePanel
-
Returns the title for the tab in the Explorer
- getTabTitleToolTip() - Method in class weka.gui.explorer.AssociationsPanel
-
Returns the tooltip for the tab in the Explorer
- getTabTitleToolTip() - Method in class weka.gui.explorer.AttributeSelectionPanel
-
Returns the tooltip for the tab in the Explorer
- getTabTitleToolTip() - Method in class weka.gui.explorer.ClassifierPanel
-
Returns the tooltip for the tab in the Explorer
- getTabTitleToolTip() - Method in class weka.gui.explorer.ClustererPanel
-
Returns the tooltip for the tab in the Explorer
- getTabTitleToolTip() - Method in interface weka.gui.explorer.Explorer.ExplorerPanel
-
Returns the tooltip for the tab in the Explorer
- getTabTitleToolTip() - Method in class weka.gui.explorer.PreprocessPanel
-
Returns the tooltip for the tab in the Explorer
- getTabTitleToolTip() - Method in class weka.gui.explorer.VisualizePanel
-
Returns the tooltip for the tab in the Explorer
- getTabuList() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
-
- getTabuList() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
-
- getTags() - Method in class weka.core.SelectedTag
-
Gets the set of all valid Tags.
- getTags() - Method in class weka.gui.CostMatrixEditor
-
Some objects can return tags, but a cost matrix cannot.
- getTags() - Method in class weka.gui.GenericArrayEditor
-
Returns null as we don't support getting values as tags.
- getTags() - Method in class weka.gui.GenericObjectEditor
-
Returns null as we don't support getting values as tags.
- getTags() - Method in class weka.gui.SelectedTagEditor
-
Gets the list of tags that can be selected from.
- getTags() - Method in class weka.gui.SimpleDateFormatEditor
-
Some objects can return tags, but a date format cannot.
- getTarget() - Method in class weka.gui.beans.BeanConnection
-
Returns the target BeanInstance for this connection
- getTarget() - Method in class weka.gui.treevisualizer.Edge
-
Get the value of target.
- getTargetClass() - Method in class weka.classifiers.bayes.DMNBtext.DNBBinary
-
Gets the Target Class
- getTargetMetaData() - Method in class weka.core.pmml.MiningSchema
-
Get the Target meta data.
- getTaskResult() - Method in class weka.experiment.TaskStatusInfo
-
Get the returnable result of this task.
- getTaskStatus() - Method in class weka.experiment.RemoteExperimentSubTask
-
- getTaskStatus() - Method in interface weka.experiment.Task
-
Clients should be able to call this method at any time to obtain
information on a current task.
- getTaskStatus() - Method in class weka.gui.beans.Classifier.TrainingTask
-
- getTaskStatus() - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
-
Return status information for this sub task
- getTechnicalInformation() - Method in class weka.associations.Apriori
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.associations.FPGrowth
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.associations.GeneralizedSequentialPatterns
-
Returns TechnicalInformation about the paper related to the algorithm.
- getTechnicalInformation() - Method in class weka.associations.PredictiveApriori
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.associations.Tertius
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.attributeSelection.CfsSubsetEval
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.attributeSelection.ConsistencySubsetEval
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.attributeSelection.GeneticSearch
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.attributeSelection.LinearForwardSelection
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.attributeSelection.RaceSearch
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.attributeSelection.RandomSearch
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.attributeSelection.RankSearch
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.attributeSelection.ReliefFAttributeEval
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.attributeSelection.ScatterSearchV1
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.attributeSelection.SVMAttributeEval
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.attributeSelection.WrapperSubsetEval
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.AODE
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.AODEsr
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.ComplementNaiveBayes
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.DMNBtext
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.HNB
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.NaiveBayes
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.NaiveBayesMultinomial
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.NaiveBayesSimple
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.NaiveBayesUpdateable
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.net.ADNode
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.net.BIFReader
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.global.K2
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.global.TAN
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.local.K2
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.local.TAN
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.bayes.WAODE
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.BVDecompose
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.functions.GaussianProcesses
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.functions.LeastMedSq
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.functions.LibLINEAR
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.functions.LibSVM
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.functions.Logistic
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.functions.pace.MixtureDistribution
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.functions.PaceRegression
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.functions.SimpleLogistic
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.functions.SMO
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.functions.SMOreg
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.functions.SPegasos
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.functions.supportVector.Puk
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.functions.supportVector.RegSMO
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.functions.VotedPerceptron
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.functions.Winnow
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.lazy.IB1
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.lazy.IBk
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.lazy.KStar
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.lazy.LBR
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.lazy.LWL
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.AdaBoostM1
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.AdditiveRegression
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.Bagging
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.ClassificationViaRegression
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.CVParameterSelection
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.Dagging
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.Decorate
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.END
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.Grading
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.LogitBoost
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.MetaCost
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.MultiBoostAB
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.nestedDichotomies.ND
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.OrdinalClassClassifier
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.RandomSubSpace
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.RotationForest
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.Stacking
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.StackingC
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.meta.Vote
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.mi.CitationKNN
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.mi.MDD
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.mi.MIBoost
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.mi.MIDD
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.mi.MIEMDD
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.mi.MINND
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.mi.MIOptimalBall
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.mi.MISMO
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.mi.MISVM
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.mi.MIWrapper
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.misc.VFI
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.rules.DecisionTable
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.rules.DTNB
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.rules.JRip
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.rules.M5Rules
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.rules.NNge
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.rules.OneR
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.rules.PART
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.rules.Prism
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.trees.ADTree
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.trees.BFTree
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.trees.FT
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.trees.Id3
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.trees.J48
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.trees.J48graft
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.trees.LADTree
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.trees.LMT
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.trees.m5.M5Base
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.trees.NBTree
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.trees.RandomForest
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.trees.SimpleCart
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.trees.UserClassifier
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.clusterers.CLOPE
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.clusterers.Cobweb
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.clusterers.DBScan
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.clusterers.FarthestFirst
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.clusterers.OPTICS
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.clusterers.sIB
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.clusterers.XMeans
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.core.ChebyshevDistance
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.core.EuclideanDistance
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.core.ManhattanDistance
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.core.neighboursearch.BallTree
-
Returns an instance of a TechnicalInformation object, containing detailed
information about the technical background of this class, e.g., paper
reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor
-
Returns an instance of a TechnicalInformation object, containing detailed
information about the technical background of this class, e.g., paper
reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
-
Returns an instance of a TechnicalInformation object, containing detailed
information about the technical background of this class, e.g., paper
reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
-
Returns an instance of a TechnicalInformation object, containing detailed
information about the technical background of this class, e.g., paper
reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Returns an instance of a TechnicalInformation object, containing detailed
information about the technical background of this class, e.g., paper
reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren
-
Returns an instance of a TechnicalInformation object, containing detailed
information about the technical background of this class, e.g., paper
reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
-
Returns an instance of a TechnicalInformation object, containing detailed
information about the technical background of this class, e.g., paper
reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.core.neighboursearch.CoverTree
-
Returns an instance of a TechnicalInformation object, containing detailed
information about the technical background of this class, e.g., paper
reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.core.neighboursearch.KDTree
-
Returns an instance of a TechnicalInformation object, containing detailed
information about the technical background of this class, e.g., paper
reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.core.neighboursearch.kdtrees.KMeansInpiredMethod
-
Returns an instance of a TechnicalInformation object, containing detailed
information about the technical background of this class, e.g., paper
reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension
-
Returns an instance of a TechnicalInformation object, containing detailed
information about the technical background of this class, e.g., paper
reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension
-
Returns an instance of a TechnicalInformation object, containing detailed
information about the technical background of this class, e.g., paper
reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide
-
Returns an instance of a TechnicalInformation object, containing detailed
information about the technical background of this class, e.g., paper
reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.core.Optimization
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.core.stemmers.LovinsStemmer
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in interface weka.core.TechnicalInformationHandler
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.datagenerators.classifiers.classification.Agrawal
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.datagenerators.classifiers.classification.LED24
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.experiment.PairedCorrectedTTester
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.filters.supervised.attribute.Discretize
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.filters.supervised.attribute.NominalToBinary
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.filters.supervised.attribute.PLSFilter
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.filters.supervised.instance.SMOTE
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.filters.unsupervised.attribute.KernelFilter
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.filters.unsupervised.attribute.RandomProjection
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.filters.unsupervised.attribute.RELAGGS
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.filters.unsupervised.attribute.Wavelet
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTempDir() - Static method in class weka.core.Debug
-
returns the system temp directory
- getTester() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
returns the display name of the preferred Tester algorithm
- getTestEvaluator() - Method in class weka.attributeSelection.CheckAttributeSelection
-
Gets whether the evaluator is being tested or the search method.
- getTestObject() - Method in class weka.attributeSelection.CheckAttributeSelection
-
returns either the evaluator or the search method.
- getTestOrTrain() - Method in class weka.gui.beans.BatchClustererEvent
-
Get whether the set of instances is a test or a training set
- getTestPredictions(Classifier, Instances) - Method in class weka.classifiers.evaluation.EvaluationUtils
-
Generate a bunch of predictions ready for processing, by performing a
evaluation on a test set assuming the classifier is already trained.
- getTestSet() - Method in class weka.gui.beans.BatchClassifierEvent
-
Get the test set
- getTestSet() - Method in class weka.gui.beans.BatchClustererEvent
-
Get the training/test set
- getTestSet() - Method in class weka.gui.beans.TestSetEvent
-
Get the test set instances
- getText() - Method in class weka.gui.beans.BeanVisual
-
Get the visual's label
- getText() - Method in class weka.gui.beans.TextEvent
-
Describe getText
method here.
- getTextTitle() - Method in class weka.gui.beans.TextEvent
-
Describe getTextTitle
method here.
- getTFTransform() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Gets whether if the word frequencies should be transformed into
log(1+fij) where fij is the frequency of word i in document(instance) j.
- getThreadMonitor() - Method in class weka.core.Debug.Clock
-
Returns a new thread monitor if the current one is null (e.g., due to
serialization) or the currently set one.
- getThreshold() - Method in class weka.attributeSelection.GreedyStepwise
-
Returns the threshold so that the AttributeSelection module can
discard attributes from the ranking.
- getThreshold() - Method in class weka.attributeSelection.RaceSearch
-
Get the threshold
- getThreshold() - Method in interface weka.attributeSelection.RankedOutputSearch
-
Gets the threshold by which attributes can be discarded.
- getThreshold() - Method in class weka.attributeSelection.Ranker
-
Returns the threshold so that the AttributeSelection module can
discard attributes from the ranking.
- getThreshold() - Method in class weka.attributeSelection.ScatterSearchV1
-
Get the treshold
- getThreshold() - Method in class weka.attributeSelection.WrapperSubsetEval
-
Get the value of the threshold
- getThreshold() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Return the threshold being used.
- getThreshold() - Method in class weka.classifiers.functions.PaceRegression
-
Gets the threshold for olsc estimator
- getThreshold() - Method in class weka.classifiers.functions.Winnow
-
Get the value of Threshold.
- getThreshold() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Gets the threshold for the max error when predicting a numeric class.
- getThresholdInstance(Instances, double) - Static method in class weka.classifiers.evaluation.ThresholdCurve
-
Gets the index of the instance with the closest threshold value to the
desired target
- getTimeAndDate() - Method in class weka.associations.GeneralizedSequentialPatterns
-
Returns the current time and date.
- getTimestamp() - Static method in class weka.experiment.CrossValidationResultProducer
-
Gets a Double representing the current date and time.
- getTimestamp() - Static method in class weka.experiment.RandomSplitResultProducer
-
Gets a Double representing the current date and time.
- getTimestamp() - Static method in class weka.gui.LogPanel
-
Gets a string containing current date and time.
- getTimestamp() - Static method in class weka.gui.SysErrLog
-
Gets a string containing current date and time.
- getTitle() - Method in class weka.gui.arffviewer.ArffPanel
-
returns the title for the Tab, i.e.
- getToken(StreamTokenizer) - Static method in class weka.core.converters.ConverterUtils
-
Gets token.
- getTokenizer() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Returns the current tokenizer algorithm.
- getTolerance() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Get the tolerance value
- getTolerance() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
-
returns the current tolerance
- getToleranceParameter() - Method in class weka.attributeSelection.SVMAttributeEval
-
Get the value of T used with SMO
- getToleranceParameter() - Method in class weka.classifiers.functions.SMO
-
Get the value of tolerance parameter.
- getToleranceParameter() - Method in class weka.classifiers.mi.MISMO
-
Get the value of tolerance parameter.
- getToolTipText() - Method in class weka.experiment.PairedCorrectedTTester
-
returns a string that is displayed as tooltip on the "perform test"
button in the experimenter
- getToolTipText() - Method in class weka.experiment.PairedTTester
-
returns a string that is displayed as tooltip on the "perform test"
button in the experimenter
- getToolTipText() - Method in interface weka.experiment.Tester
-
returns a string that is displayed as tooltip on the "perform test"
button in the experimenter
- getToolTipText(MouseEvent) - Method in class weka.gui.AttributeVisualizationPanel
-
Returns "<nominal value> [<nominal value count>]"
if displaying a bar plot and mouse is on some bar.
- getToolTipText(PrintableComponent) - Static method in class weka.gui.visualize.PrintableComponent
-
Returns a tooltip only if the user wants it.
- getTop() - Method in class weka.gui.treevisualizer.Node
-
Get the value of top.
- getTotalCoordsPerPoint() - Method in class weka.core.neighboursearch.PerformanceStats
-
Returns the total sum of coords per point.
- getTotalCount(Node, int) - Static method in class weka.gui.treevisualizer.Node
-
Recursively finds the total number of nodes there are.
- getTotalGCount(Node, int) - Static method in class weka.gui.treevisualizer.Node
-
Recursively finds the total number of groups of siblings there are.
- getTotalHeight(Node, int) - Static method in class weka.gui.treevisualizer.Node
-
Recursively finds the total number of levels there are.
- getTotalIntNodesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats
-
Returns the total number of internal nodes visited.
- getTotalLeavesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats
-
Returns the total number of leaves visited.
- getTotalPointsVisited() - Method in class weka.core.neighboursearch.PerformanceStats
-
Returns the total number of points visited.
- getTotalSupport() - Method in class weka.associations.FPGrowth.AssociationRule
-
Get the total support for this rule (premise + consequence).
- getTotalTransactions() - Method in class weka.associations.FPGrowth.AssociationRule
-
Get the total number of transactions in the data.
- getToYear() - Static method in class weka.core.Copyright
-
returns the end year of the copyright (i.e., current year)
- getTPRate() - Method in class weka.associations.tertius.Rule
-
Get the rate of True Positive instances of this rule.
- getTrainingSet() - Method in class weka.gui.beans.TrainingSetEvent
-
Get the training instances
- getTrainingTime() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- getTrainIterations() - Method in class weka.classifiers.BVDecompose
-
Gets the maximum number of boost iterations
- getTrainPercent() - Method in class weka.experiment.RandomSplitResultProducer
-
Get the value of TrainPercent.
- getTrainPercent() - Method in class weka.gui.beans.TrainTestSplitMaker
-
Get the percentage of the data that will be in the training portion of
the split
- getTrainPercentage() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
returns the training percentage in case of splits
- getTrainPoolSize() - Method in class weka.classifiers.BVDecompose
-
Get the number of instances in the training pool.
- getTrainSet() - Method in class weka.gui.beans.BatchClassifierEvent
-
Get the train set
- getTrainSize() - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Get the training size
- getTrainTestPredictions(Classifier, Instances, Instances) - Method in class weka.classifiers.evaluation.EvaluationUtils
-
Generate a bunch of predictions ready for processing, by performing a
evaluation on a test set after training on the given training set.
- getTransactionsMustContain() - Method in class weka.associations.FPGrowth
-
Gets the comma separated list of items that
transactions must contain in order to be considered
for large item sets and rules.
- getTransform() - Method in class weka.gui.visualize.PostscriptGraphics
-
- getTransformAllValues() - Method in class weka.filters.supervised.attribute.NominalToBinary
-
Gets if all nominal values are turned into new attributes, not only if
there are more than 2.
- getTransformAllValues() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
-
Gets if all nominal values are turned into new attributes, not only if
there are more than 2.
- getTransformationDictionary() - Method in class weka.core.pmml.MiningSchema
-
Get the transformation dictionary .
- getTransformationDictionary(Document, Instances) - Static method in class weka.core.pmml.PMMLFactory
-
Get the transformation dictionary (if there is one).
- getTransformBackToOriginal() - Method in class weka.attributeSelection.PrincipalComponents
-
Gets whether the data is to be transformed back to the original
space.
- getTransformMethod() - Method in class weka.classifiers.mi.SimpleMI
-
Get the method used in transformation.
- getTranslation() - Method in class weka.filters.unsupervised.attribute.Normalize
-
Get the translation.
- getTraversal() - Method in class weka.classifiers.meta.GridSearch
-
Gets the type of traversal for the grid.
- getTrimingThreshold() - Method in class weka.classifiers.functions.pace.ChisqMixture
-
Gets the triming thresholding value.
- getTrimingThreshold() - Method in class weka.classifiers.functions.pace.NormalMixture
-
Gets the triming thresholding value.
- getTrueNegative() - Method in class weka.classifiers.evaluation.TwoClassStats
-
Gets the number of negative instances predicted as negative
- getTruePositive() - Method in class weka.classifiers.evaluation.TwoClassStats
-
Gets the number of positive instances predicted as positive
- getTruePositiveRate() - Method in class weka.classifiers.evaluation.TwoClassStats
-
Calculate the true positive rate.
- getTStart() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
-
- getTStart() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
-
- getTwoClassStats(int) - Method in class weka.classifiers.evaluation.ConfusionMatrix
-
Gets the performance with respect to one of the classes
as a TwoClassStats object.
- getType() - Method in class weka.associations.tertius.IndividualLiteral
-
- getType() - Method in class weka.associations.tertius.LiteralSet
-
Give the type of properties in this set (individual or part properties).
- getType() - Method in class weka.attributeSelection.LinearForwardSelection
-
Get the type
- getType() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Get the type
- getType() - Method in class weka.classifiers.functions.neural.NeuralConnection
-
- getType() - Method in class weka.classifiers.meta.GridSearch.PerformanceTable
-
returns the type of performance
- getType() - Method in class weka.core.AttributeLocator
-
returns the type of attribute that is located
- getType(RevisionHandler) - Static method in class weka.core.RevisionUtils
-
Determines the type of a (sanitized) revision string returned by the
RevisionHandler.
- getType(String) - Static method in class weka.core.RevisionUtils
-
Determines the type of a (sanitized) revision string.
- getType() - Method in class weka.core.TechnicalInformation
-
returns the type of this technical information
- getType(int) - Method in class weka.gui.arffviewer.ArffSortedTableModel
-
returns the TYPE of the attribute at the given position
- getType(int, int) - Method in class weka.gui.arffviewer.ArffSortedTableModel
-
returns the TYPE of the attribute at the given position
- getType(int) - Method in class weka.gui.arffviewer.ArffTableModel
-
returns the TYPE of the attribute at the given position
- getType(int, int) - Method in class weka.gui.arffviewer.ArffTableModel
-
returns the TYPE of the attribute at the given position
- getType() - Method in class weka.gui.sql.event.ConnectionEvent
-
returns the type of this event, CONNECT or DISCONNECT
- getU() - Method in class weka.core.Matrix
-
Deprecated.
Returns the U part of the matrix.
- getU() - Method in class weka.core.matrix.LUDecomposition
-
Return upper triangular factor
- getU() - Method in class weka.core.matrix.SingularValueDecomposition
-
Return the left singular vectors
- getUID(String) - Static method in class weka.core.SerializationHelper
-
reads or creates the serialVersionUID for the given class.
- getUID(Class) - Static method in class weka.core.SerializationHelper
-
reads or creates the serialVersionUID for the given class.
- getUnpruned() - Method in class weka.classifiers.rules.PART
-
Get the value of unpruned.
- getUnpruned() - Method in class weka.classifiers.trees.J48
-
Get the value of unpruned.
- getUnpruned() - Method in class weka.classifiers.trees.J48graft
-
Get the value of unpruned.
- getUnpruned() - Method in class weka.classifiers.trees.m5.M5Base
-
Get whether unpruned tree/rules are being generated
- getUnpruned() - Method in class weka.classifiers.trees.m5.Rule
-
Get whether unpruned tree/rules are being generated
- getUpdateCount() - Method in class weka.core.converters.DatabaseConnection
-
Dewtermines if the current query retrieves a result set or updates a table
- getUpdateIncrementalClassifier() - Method in class weka.gui.beans.Classifier
-
Get whether an incremental classifier will be updated on the
incoming instance stream.
- getUpper() - Method in class weka.gui.experiment.RunNumberPanel
-
Gets the current upper run number.
- getUpperBoundMinSupport() - Method in class weka.associations.Apriori
-
Get the value of upperBoundMinSupport.
- getUpperBoundMinSupport() - Method in class weka.associations.FPGrowth
-
Get the value of upperBoundMinSupport.
- getUpperCase() - Method in class weka.core.converters.DatabaseConnection
-
Check if the property checkUpperCaseNames in the DatabaseUtils file is
set to true or false.
- getUpperNumericBound() - Method in class weka.core.Attribute
-
Returns the upper bound of a numeric attribute.
- getUpperSize() - Method in class weka.experiment.LearningRateResultProducer
-
Get the value of UpperSize.
- getURL(String, String) - Static method in class weka.core.ClassDiscovery
-
If the given package can be found in this part of the classpath then
an URL object is returned, otherwise null
.
- getUrl() - Method in interface weka.core.converters.DatabaseConverter
-
- getUrl() - Method in class weka.core.converters.DatabaseLoader
-
Gets the URL
- getUrl() - Method in class weka.core.converters.DatabaseSaver
-
Gets the database URL.
- getURL() - Static method in class weka.core.Copyright
-
returns the URL of the owner
- getURL() - Method in class weka.gui.DatabaseConnectionDialog
-
Returns URL from dialog
- getURL(String, String) - Static method in class weka.gui.Loader
-
returns a URL for the given filename, can be NULL if it fails
- getURL(String) - Method in class weka.gui.Loader
-
returns a URL for the given filename, can be NULL if it fails
- getURL() - Method in class weka.gui.sql.ConnectionPanel
-
returns the current URL.
- getURL() - Method in class weka.gui.sql.event.ResultChangedEvent
-
returns the database URL that produced the table model
- getURL() - Method in class weka.gui.sql.ResultSetTable
-
returns the database URL that produced the table model
- getURL() - Method in class weka.gui.sql.SqlViewer
-
returns the database URL from the currently active tab in the ResultPanel,
otherwise an empty string.
- getURL() - Method in class weka.gui.sql.SqlViewerDialog
-
returns the chosen URL, if any
- getURLFileLoaders() - Static method in class weka.core.converters.ConverterUtils
-
returns a vector with the classnames of all the URL file loaders.
- getURLLoaderForExtension(String) - Static method in class weka.core.converters.ConverterUtils
-
tries to determine the URL loader to use for this kind of extension, returns
null if none can be found.
- getURLLoaderForFile(String) - Static method in class weka.core.converters.ConverterUtils
-
tries to determine the URL loader to use for this kind of file, returns
null if none can be found.
- getURLLoaderForFile(File) - Static method in class weka.core.converters.ConverterUtils
-
tries to determine the URL loader to use for this kind of file, returns
null if none can be found.
- getUsageType() - Method in class weka.core.pmml.MiningFieldMetaInfo
-
Get the usage type of this field.
- getUseADTree() - Method in class weka.classifiers.bayes.BayesNet
-
Method declaration
- getUseAIC() - Method in class weka.classifiers.functions.SimpleLogistic
-
Get the value of useAIC.
- getUseAIC() - Method in class weka.classifiers.trees.FT
-
Get the value of useAIC.
- getUseAIC() - Method in class weka.classifiers.trees.LMT
-
Get the value of useAIC.
- getUseAIC() - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Get the value of useAIC.
- getUseArcReversal() - Method in class weka.classifiers.bayes.net.search.global.HillClimber
-
get use the arc reversal operation
- getUseArcReversal() - Method in class weka.classifiers.bayes.net.search.local.HillClimber
-
get use the arc reversal operation
- getUseBetterEncoding() - Method in class weka.filters.supervised.attribute.Discretize
-
Gets whether better encoding is to be used for MDL.
- getUseClassification() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
whether classification or regression is used
- getUseCpuTime() - Method in class weka.core.Debug.Clock
-
returns whether the use of CPU is time is enabled/disabled (regardless
whether the system supports it or not)
- getUseCrossOver() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
-
- getUseCrossOver() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
-
- getUseCrossValidation() - Method in class weka.classifiers.functions.SimpleLogistic
-
Get the value of useCrossValidation.
- getUseCustomDimensions() - Method in class weka.gui.visualize.JComponentWriter
-
whether custom dimensions are to used for the size of the image
- getUsedAttributes() - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Returns an array of the indices of the attributes used in the logistic model.
- getUseEqualFrequency() - Method in class weka.classifiers.meta.RegressionByDiscretization
-
Get the value of UseEqualFrequency.
- getUseEqualFrequency() - Method in class weka.filters.unsupervised.attribute.Discretize
-
Get the value of UseEqualFrequency.
- getUseEqualFrequency() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
-
Get the value of UseEqualFrequency.
- getUseErrorRate() - Method in class weka.classifiers.trees.BFTree
-
Get if use error rate in internal cross-validation.
- getUseGini() - Method in class weka.classifiers.trees.BFTree
-
Get if use Gini index as splitting criterion.
- getUseGUI() - Method in class weka.core.Memory
-
whether to display a dialog in case of a problem (= TRUE) or just print
on stderr (= FALSE)
- getUseIBk() - Method in class weka.classifiers.rules.DecisionTable
-
Gets whether IBk is being used instead of the majority class
- getUseKDTree() - Method in class weka.clusterers.XMeans
-
Gets whether the KDTree is used or not.
- getUseKernelEstimator() - Method in class weka.classifiers.bayes.NaiveBayes
-
Gets if kernel estimator is being used.
- getUseKononenko() - Method in class weka.filters.supervised.attribute.Discretize
-
Gets whether Kononenko's MDL criterion is to be used.
- getUseLaplace() - Method in class weka.classifiers.bayes.AODEsr
-
Gets if laplace correction is being used.
- getUseLaplace() - Method in class weka.classifiers.trees.J48
-
Get the value of useLaplace.
- getUseLaplace() - Method in class weka.classifiers.trees.J48graft
-
Get the value of useLaplace.
- getUseLeastValues() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
Gets whether to use values with least or most instances
- getUseLowerOrder() - Method in class weka.classifiers.functions.supportVector.PolyKernel
-
Gets whether lower-order terms are used.
- getUseMEstimates() - Method in class weka.classifiers.bayes.AODE
-
Gets if m-estimaces is being used.
- getUseMissing() - Method in class weka.filters.unsupervised.attribute.AddNoise
-
Gets the flag if missing values are treated as extra values.
- getUseMutation() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
-
- getUseMutation() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
-
- getUseNormalization() - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Returns whether normalization is used.
- getUseOneSE() - Method in class weka.classifiers.trees.BFTree
-
Get if use the 1SE rule to choose final model.
- getUseOneSE() - Method in class weka.classifiers.trees.SimpleCart
-
Get if use the 1SE rule to choose final model.
- getUseORForMustContainList() - Method in class weka.associations.FPGrowth
-
Gets whether OR is to be used rather than AND when
considering must contain lists.
- getUsePairwiseCoupling() - Method in class weka.classifiers.meta.MultiClassClassifier
-
Gets whether to use pairwise coupling with 1-vs-1
classification to improve probability estimates.
- getUseProb() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
-
- getUsePropertyIterator() - Method in class weka.experiment.Experiment
-
Gets whether the custom property iterator should be used.
- getUsePrune() - Method in class weka.classifiers.trees.SimpleCart
-
Get if use minimal cost-complexity pruning.
- getUsePruning() - Method in class weka.classifiers.rules.JRip
-
Gets whether pruning is performed
- getUser() - Method in interface weka.core.converters.DatabaseConverter
-
- getUser() - Method in class weka.core.converters.DatabaseLoader
-
Gets the user name
- getUser() - Method in class weka.core.converters.DatabaseSaver
-
Gets the database user.
- getUser() - Method in class weka.gui.sql.ConnectionPanel
-
returns the current User.
- getUser() - Method in class weka.gui.sql.event.ResultChangedEvent
-
returns the user that produced the table model
- getUser() - Method in class weka.gui.sql.ResultSetTable
-
returns the user that produced the table model
- getUser() - Method in class weka.gui.sql.SqlViewer
-
returns the user from the currently active tab in the ResultPanel,
otherwise an empty string.
- getUser() - Method in class weka.gui.sql.SqlViewerDialog
-
returns the chosen user, if any
- getUseRelativePath() - Method in class weka.core.converters.AbstractFileLoader
-
Gets whether relative paths are to be used
- getUseRelativePath() - Method in class weka.core.converters.AbstractFileSaver
-
Gets whether relative paths are to be used
- getUseRelativePath() - Method in interface weka.core.converters.FileSourcedConverter
-
Gets whether relative paths are to be used
- getUseRelativePath() - Method in class weka.gui.beans.SerializedModelSaver
-
Get whether to use relative paths for the directory.
- getUseRelativePaths() - Static method in class weka.gui.experiment.ExperimenterDefaults
-
whether relative paths are used by default
- getUseResampling() - Method in class weka.classifiers.meta.AdaBoostM1
-
Get whether resampling is turned on
- getUseResampling() - Method in class weka.classifiers.meta.LogitBoost
-
Get whether resampling is turned on
- getUseResampling() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Get whether resampling is turned on
- getUsername() - Method in class weka.experiment.DatabaseUtils
-
Get the database username.
- getUsername() - Method in class weka.gui.DatabaseConnectionDialog
-
Returns Username from dialog
- getUserOptions() - Method in class weka.classifiers.functions.supportVector.KernelEvaluation
-
returns the options the user supplied for the kernel
- getUserOptions() - Method in class weka.core.CheckOptionHandler
-
Gets the current user-supplied options (creates a copy)
- getUseStars() - Method in class weka.core.Javadoc
-
whether the Javadoc is prefixed with "*"
- getUseStoplist() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Gets whether if the words on the stoplist are to be ignored (The stoplist
is in weka.core.StopWords).
- getUseSupervisedDiscretization() - Method in class weka.classifiers.bayes.NaiveBayes
-
Get whether supervised discretization is to be used.
- getUseTournamentSelection() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
-
- getUseTournamentSelection() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
-
- getUseTraining() - Method in class weka.attributeSelection.ClassifierSubsetEval
-
Get if training data is to be used instead of hold out/test data
- getUseTree() - Method in class weka.classifiers.trees.m5.Rule
-
get whether an m5 tree is being used rather than rules
- getUseUnsmoothed() - Method in class weka.classifiers.trees.m5.M5Base
-
Get whether or not smoothing is being used
- getV() - Method in class weka.core.matrix.EigenvalueDecomposition
-
Return the eigenvector matrix
- getV() - Method in class weka.core.matrix.SingularValueDecomposition
-
Return the right singular vectors
- getValidating() - Method in class weka.core.xml.XMLDocument
-
returns whether a validating parser is used.
- getValidating() - Method in class weka.core.xml.XMLOptions
-
returns whether a validating parser is used.
- getValidationChunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Get the validation chunk size
- getValidationSetSize() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- getValidationThreshold() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- getValue() - Method in class weka.classifiers.trees.adtree.PredictionNode
-
Gets the prediction value of the node.
- getValue() - Method in class weka.core.pmml.FieldMetaInfo.Value
-
- getValue(Object, PropertyPath.Path) - Static method in class weka.core.PropertyPath
-
returns the value specified by the given path from the object
- getValue(Object, String) - Static method in class weka.core.PropertyPath
-
returns the value specified by the given path from the object
- getValue(TechnicalInformation.Field) - Method in class weka.core.TechnicalInformation
-
returns the value associated with the given field, or empty if field is
not currently stored.
- getValue(Instance, int) - Static method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
-
Returns either a String object for nominal attributes or a Double for numeric
ones.
- getValue() - Method in class weka.gui.CostMatrixEditor
-
Gets the cost matrix that is being edited.
- getValue() - Method in class weka.gui.GenericArrayEditor
-
Gets the current object array.
- getValue() - Method in class weka.gui.GenericObjectEditor
-
Gets the current Object.
- getValue() - Method in class weka.gui.HierarchyPropertyParser
-
Get the value of current node
- getValue() - Method in class weka.gui.SimpleDateFormatEditor
-
Gets the date format that is being edited.
- getValue() - Method in class weka.gui.SortedTableModel.SortContainer
-
Returns the value to sort on.
- getValueAt(int, int) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.ResultVectorTableModel
-
Returns the value for the JTable for a given position.
- getValueAt(int, int) - Method in class weka.gui.arffviewer.ArffTableModel
-
returns the value for the cell at columnindex and rowIndex
- getValueAt(int, int) - Method in class weka.gui.SortedTableModel
-
Returns the value for the cell at columnIndex and rowIndex.
- getValueAt(int, int) - Method in class weka.gui.sql.ResultSetTableModel
-
returns the value for the cell at columnindex and rowIndex.
- getValueIndex() - Method in class weka.associations.FPGrowth.BinaryItem
-
Get the value index for this item.
- getValueIndices() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
-
Get the indices of the indicator values.
- getValueName(int, int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
returns value of a node
- getValueRange() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
-
Get the range containing the indicator values.
- getValues(String) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
returns array of values of a node
- getValues(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
returns array of values of a node
- getValues() - Method in class weka.classifiers.meta.GridSearch
-
returns the parameter pair that was found to work best
- getValues(int, int) - Method in class weka.classifiers.meta.GridSearch.Grid
-
returns the values at the given point in the grid
- getValues() - Method in class weka.classifiers.meta.GridSearch.Performance
-
returns the values-pair for this performance
- getValues() - Method in class weka.classifiers.trees.LADTree.PredictionNode
-
- getValues() - Method in class weka.core.pmml.TargetMetaInfo
-
Get the values (discrete case only) for this Target.
- getValues() - Method in class weka.gui.visualize.VisualizePanelEvent
-
- getValuesList() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
returns the range for each attribute as string
- getValuesOutput() - Method in class weka.associations.Tertius
-
Get the value of valuesOutput.
- getVarbValues() - Method in class weka.core.Optimization
-
Get the variable values.
- getVariableNames() - Method in class weka.core.Environment
-
Get the names of the variables (keys) stored in the
internal map.
- getVariableValue(String) - Method in class weka.core.Environment
-
Get the value for a particular variable.
- getVariance() - Method in class weka.classifiers.BVDecompose
-
Get the calculated variance
- getVarianceCovered() - Method in class weka.attributeSelection.PrincipalComponents
-
Gets the proportion of total variance to account for when
retaining principal components
- getVarianceCovered() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Gets the proportion of total variance to account for when
retaining principal components.
- getVector(Matrix, int) - Method in class weka.filters.supervised.attribute.PLSFilter
-
returns the (column) vector of the matrix at the specified index
- getVectorOfAttrTypes() - Method in class weka.estimators.CheckEstimator.AttrTypes
-
- getVerbose() - Method in class weka.associations.Apriori
-
Gets whether algorithm is run in verbose mode
- getVerbose() - Method in class weka.attributeSelection.ExhaustiveSearch
-
get whether or not output is verbose
- getVerbose() - Method in class weka.attributeSelection.LinearForwardSelection
-
Get whether output is to be verbose
- getVerbose() - Method in class weka.attributeSelection.RandomSearch
-
get whether or not output is verbose
- getVerbose() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Get whether output is to be verbose
- getVerbose() - Method in class weka.classifiers.meta.Dagging
-
Gets the verbose state
- getVersion() - Method in class weka.core.xml.XMLSerialization
-
returns the WEKA version with which the serialized object was created
- getVisible() - Method in class weka.gui.treevisualizer.Node
-
Get the value of visible.
- getVisibleColCount() - Method in class weka.experiment.ResultMatrix
-
returns the number of visible columns
- getVisibleRowCount() - Method in class weka.experiment.ResultMatrix
-
returns the number of visible rows
- getVisual() - Method in class weka.gui.beans.AbstractDataSink
-
Get the visual being used by this data source.
- getVisual() - Method in class weka.gui.beans.AbstractDataSource
-
Get the visual being used by this data source.
- getVisual() - Method in class weka.gui.beans.AbstractEvaluator
-
Get the visual
- getVisual() - Method in class weka.gui.beans.AbstractTestSetProducer
-
Get the visual for this bean
- getVisual() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
-
Get the visual for this bean
- getVisual() - Method in class weka.gui.beans.AbstractTrainingSetProducer
-
Get the visual for this bean
- getVisual() - Method in class weka.gui.beans.Associator
-
Gets the visual appearance of this wrapper bean
- getVisual() - Method in class weka.gui.beans.ClassAssigner
-
- getVisual() - Method in class weka.gui.beans.Classifier
-
Gets the visual appearance of this wrapper bean
- getVisual() - Method in class weka.gui.beans.ClassValuePicker
-
- getVisual() - Method in class weka.gui.beans.Clusterer
-
Gets the visual appearance of this wrapper bean
- getVisual() - Method in class weka.gui.beans.CostBenefitAnalysis
-
- getVisual() - Method in class weka.gui.beans.DataVisualizer
-
Return the visual appearance of this bean
- getVisual() - Method in class weka.gui.beans.Filter
-
Get the visual appearance of this bean
- getVisual() - Method in class weka.gui.beans.GraphViewer
-
Get the visual appearance of this bean
- getVisual() - Method in class weka.gui.beans.InstanceStreamToBatchMaker
-
Gets the visual appearance of this wrapper bean
- getVisual() - Method in class weka.gui.beans.MetaBean
-
Gets the visual appearance of this wrapper bean
- getVisual() - Method in class weka.gui.beans.ModelPerformanceChart
-
Return the visual appearance of this bean
- getVisual() - Method in class weka.gui.beans.PredictionAppender
-
Get the visual being used by this data source.
- getVisual() - Method in class weka.gui.beans.SerializedModelSaver
-
Get the visual being used by this data source.
- getVisual() - Method in class weka.gui.beans.StripChart
-
Get the visual appearance of this bean
- getVisual() - Method in class weka.gui.beans.TextViewer
-
Get the visual appearance of this bean
- getVisual() - Method in interface weka.gui.beans.Visible
-
Get the visual representation
- getVisualizeMenuItem(Instances) - Method in interface weka.gui.visualize.plugins.ErrorVisualizePlugin
-
Get a JMenu or JMenuItem which contain action listeners
that perform the visualization of the classifier errors.
- getVisualizeMenuItem(String, String) - Method in interface weka.gui.visualize.plugins.GraphVisualizePlugin
-
Get a JMenu or JMenuItem which contain action listeners
that perform the visualization of the graph in XML BIF format.
- getVisualizeMenuItem(String, String) - Method in interface weka.gui.visualize.plugins.TreeVisualizePlugin
-
Get a JMenu or JMenuItem which contain action listeners
that perform the visualization of the tree in GraphViz's dotty format.
- getVisualizeMenuItem(FastVector, Attribute) - Method in interface weka.gui.visualize.plugins.VisualizePlugin
-
Get a JMenu or JMenuItem which contain action listeners
that perform the visualization, using some but not
necessarily all of the data.
- getVoteFlag() - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Gets the vote flag.
- getWBias() - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Get the calculated bias according to the Webb definition
- getWeight() - Method in class weka.classifiers.bayes.AODE
-
Gets the weight used in m-estimate
- getWeightByConfidence() - Method in class weka.classifiers.misc.VFI
-
Get whether feature intervals are being weighted by confidence
- getWeightByDistance() - Method in class weka.attributeSelection.ReliefFAttributeEval
-
Get whether nearest neighbours are being weighted by distance
- getWeightingKernel() - Method in class weka.classifiers.lazy.LWL
-
Gets the kernel weighting method to use.
- getWeightMethod() - Method in class weka.classifiers.mi.MIWrapper
-
Returns the current weighting method for instances.
- getWeightMethod() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
-
Returns the current weighting method for instances.
- getWeights() - Method in class weka.classifiers.functions.LibLINEAR
-
Gets the parameters C of class i to weight[i]*C (default 1).
- getWeights() - Method in class weka.classifiers.functions.LibSVM
-
Gets the parameters C of class i to weight[i]*C, for C-SVC (default 1).
- getWeights() - Method in class weka.classifiers.functions.neural.NeuralNode
-
call this function to get the weights array.
- getWeights() - Method in class weka.estimators.KernelEstimator
-
Return the weights of the kernels.
- getWeights() - Method in interface weka.gui.boundaryvisualizer.DataGenerator
-
Get weights
- getWeights() - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
-
- getWeightThreshold() - Method in class weka.classifiers.meta.AdaBoostM1
-
Get the degree of weight thresholding
- getWeightThreshold() - Method in class weka.classifiers.meta.LogitBoost
-
Get the degree of weight thresholding
- getWeightTrimBeta() - Method in class weka.classifiers.functions.SimpleLogistic
-
Get the value of weightTrimBeta.
- getWeightTrimBeta() - Method in class weka.classifiers.trees.FT
-
Get the value of weightTrimBeta.
- getWeightTrimBeta() - Method in class weka.classifiers.trees.LMT
-
Get the value of weightTrimBeta.
- getWeightTrimBeta() - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Get the value of weightTrimBeta.
- getWholeDataErr() - Method in class weka.classifiers.rules.Ridor
-
- getWidth() - Method in class weka.gui.beans.BeanInstance
-
Gets the width of this bean
- getWindow(Class) - Method in class weka.gui.Main
-
returns the first instance of the given window class, null if none can be
found.
- getWindow(String) - Method in class weka.gui.Main
-
returns the first window with the given title, null if none can be
found.
- getWindowList() - Method in class weka.gui.Main
-
returns all currently open frames.
- getWindowSize() - Method in class weka.classifiers.lazy.IBk
-
Gets the maximum number of instances allowed in the training
pool.
- getWithPrefix(String) - Method in class weka.core.Trie
-
returns all stored strings that match the given prefix
- getWords() - Method in class weka.core.CheckScheme
-
returns the words used for assembling strings in a comma-separated list.
- getWords() - Method in class weka.core.TestInstances
-
returns the words used for assembling strings in a comma-separated list.
- getWordSeparators() - Method in class weka.core.CheckScheme
-
returns the word separators (chars) to use for assembling strings.
- getWordSeparators() - Method in class weka.core.TestInstances
-
returns the word separators (chars) to use for assembling strings.
- getWordsToKeep() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Gets the number of words (per class if there is a class attribute
assigned) to attempt to keep.
- getWrappedAlgorithm() - Method in class weka.gui.beans.Associator
-
Returns the wrapped associator
- getWrappedAlgorithm() - Method in class weka.gui.beans.Classifier
-
Returns the wrapped classifier
- getWrappedAlgorithm() - Method in class weka.gui.beans.Clusterer
-
Returns the wrapped clusterer
- getWrappedAlgorithm() - Method in class weka.gui.beans.Filter
-
Get the filter wrapped by this bean
- getWrappedAlgorithm() - Method in class weka.gui.beans.Loader
-
Get the loader
- getWrappedAlgorithm() - Method in class weka.gui.beans.Saver
-
Get the saver
- getWrappedAlgorithm() - Method in interface weka.gui.beans.WekaWrapper
-
Get the algorithm
- getWriteMode() - Method in class weka.core.converters.AbstractSaver
-
Gets the write mode.
- getWriteMode() - Method in interface weka.core.converters.Saver
-
Gets the write mode
- getWriteOPTICSresults() - Method in class weka.clusterers.OPTICS
-
Returns the flag for writing actions
- getWriter() - Method in class weka.core.converters.AbstractFileSaver
-
Gets the writer
- getWriter(String) - Method in class weka.gui.visualize.PrintableComponent
-
returns the JComponentWriter associated with the given name, is
null
if not found.
- getWriter() - Method in class weka.gui.visualize.PrintableComponent.JComponentWriterFileFilter
-
returns the associated writer.
- getWriter(String) - Method in interface weka.gui.visualize.PrintableHandler
-
returns the JComponentWriter associated with the given name, is
null
if not found
- getWriter(String) - Method in class weka.gui.visualize.PrintablePanel
-
returns the JComponentWriter associated with the given name, is
null
if not found
- getWriters() - Method in class weka.gui.visualize.PrintableComponent
-
returns a Hashtable with the current available JComponentWriters in the
save dialog.
- getWriters() - Method in interface weka.gui.visualize.PrintableHandler
-
returns a Hashtable with the current available JComponentWriters in the
save dialog.
- getWriters() - Method in class weka.gui.visualize.PrintablePanel
-
returns a Hashtable with the current available JComponentWriters in the
save dialog.
- getWs(double[][], double[][]) - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Computes the LogitBoost weights from an array of y/p values
(actual/estimated class probabilities).
- getWVariance() - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Get the calculated variance according to the Webb definition
- getX() - Method in class weka.classifiers.functions.neural.NeuralConnection
-
- getX(Instances) - Method in class weka.filters.supervised.attribute.PLSFilter
-
returns the data minus the class column as matrix
- getX(Instance) - Method in class weka.filters.supervised.attribute.PLSFilter
-
returns the data minus the class column as matrix
- getX() - Method in class weka.gui.beans.BeanInstance
-
Gets the x coordinate of this bean
- getXBase() - Method in class weka.classifiers.meta.GridSearch
-
Get the value of the base for X.
- getXExpression() - Method in class weka.classifiers.meta.GridSearch
-
Get the expression for the X value.
- getXindex() - Method in class weka.gui.visualize.PlotData2D
-
Get the currently set x index of the data
- getXIndex() - Method in class weka.gui.visualize.VisualizePanel
-
Get the index of the attribute on the x axis
- getXLabelFreq() - Method in class weka.gui.beans.StripChart
-
Get the frequency by which x axis values are printed
- getXMax() - Method in class weka.classifiers.meta.GridSearch
-
Get the value of the Maximum of X.
- getXMin() - Method in class weka.classifiers.meta.GridSearch
-
Get the value of the minimum of X.
- getXMLDocument() - Method in class weka.core.xml.XMLOptions
-
returns the handler of the XML document.
- getXProperty() - Method in class weka.classifiers.meta.GridSearch
-
Get the X property to test (normally the filter).
- getXScale() - Method in class weka.gui.visualize.JComponentWriter
-
returns the scale factor for the x-axis
- getXScale() - Method in class weka.gui.visualize.PrintableComponent
-
returns the scale factor for the x-axis.
- getXScale() - Method in interface weka.gui.visualize.PrintableHandler
-
returns the scale factor for the x-axis
- getXScale() - Method in class weka.gui.visualize.PrintablePanel
-
returns the scale factor for the x-axis
- getXStep() - Method in class weka.classifiers.meta.GridSearch
-
Get the value of the step size for X.
- getY() - Method in class weka.classifiers.functions.neural.NeuralConnection
-
- getY(Instances) - Method in class weka.filters.supervised.attribute.PLSFilter
-
returns the data class column as matrix
- getY(Instance) - Method in class weka.filters.supervised.attribute.PLSFilter
-
returns the data class column as matrix
- getY() - Method in class weka.gui.beans.BeanInstance
-
Gets the y coordinate of this bean
- getYBase() - Method in class weka.classifiers.meta.GridSearch
-
Get the value of the base for Y.
- getYExpression() - Method in class weka.classifiers.meta.GridSearch
-
Get the expression for the Y value.
- getYindex() - Method in class weka.gui.visualize.PlotData2D
-
Get the currently set y index of the data
- getYIndex() - Method in class weka.gui.visualize.VisualizePanel
-
Get the index of the attribute on the y axis
- getYMax() - Method in class weka.classifiers.meta.GridSearch
-
Get the value of the Maximum of Y.
- getYMin() - Method in class weka.classifiers.meta.GridSearch
-
Get the value of the minimum of Y.
- getYProperty() - Method in class weka.classifiers.meta.GridSearch
-
Get the Y property (normally the classifier).
- getYs(Instances) - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Computes the Y-values (actual class probabilities) for a set of instances.
- getYScale() - Method in class weka.gui.visualize.JComponentWriter
-
returns the scale factor for the y-axis
- getYScale() - Method in class weka.gui.visualize.PrintableComponent
-
returns the scale factor for the y-axis.
- getYScale() - Method in interface weka.gui.visualize.PrintableHandler
-
returns the scale factor for the y-axis
- getYScale() - Method in class weka.gui.visualize.PrintablePanel
-
returns the scale factor for the y-axis
- getYStep() - Method in class weka.classifiers.meta.GridSearch
-
Get the value of the step size for Y.
- getZ(double, double) - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Computes the LogitBoost response variable from y/p values
(actual/estimated class probabilities).
- getZs(double[][], double[][]) - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Computes the LogitBoost response for an array of y/p values
(actual/estimated class probabilities).
- globalBlendTipText() - Method in class weka.classifiers.lazy.KStar
-
Returns the tip text for this property
- globalInfo() - Method in class weka.associations.Apriori
-
Returns a string describing this associator
- globalInfo() - Method in class weka.associations.FilteredAssociator
-
Returns a string describing this Associator
- globalInfo() - Method in class weka.associations.FPGrowth
-
Returns a string describing this associator
- globalInfo() - Method in class weka.associations.GeneralizedSequentialPatterns
-
Returns global information about the algorithm.
- globalInfo() - Method in class weka.associations.PredictiveApriori
-
Returns a string describing this associator
- globalInfo() - Method in class weka.associations.Tertius
-
Returns a string describing this associator.
- globalInfo() - Method in class weka.attributeSelection.BestFirst
-
Returns a string describing this search method
- globalInfo() - Method in class weka.attributeSelection.CfsSubsetEval
-
Returns a string describing this attribute evaluator
- globalInfo() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
-
Returns a string describing this attribute evaluator
- globalInfo() - Method in class weka.attributeSelection.ClassifierSubsetEval
-
Returns a string describing this attribute evaluator
- globalInfo() - Method in class weka.attributeSelection.ConsistencySubsetEval
-
Returns a string describing this search method
- globalInfo() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
-
- globalInfo() - Method in class weka.attributeSelection.ExhaustiveSearch
-
Returns a string describing this search method
- globalInfo() - Method in class weka.attributeSelection.FilteredAttributeEval
-
- globalInfo() - Method in class weka.attributeSelection.FilteredSubsetEval
-
- globalInfo() - Method in class weka.attributeSelection.GainRatioAttributeEval
-
Returns a string describing this attribute evaluator
- globalInfo() - Method in class weka.attributeSelection.GeneticSearch
-
Returns a string describing this search method
- globalInfo() - Method in class weka.attributeSelection.GreedyStepwise
-
Returns a string describing this search method
- globalInfo() - Method in class weka.attributeSelection.InfoGainAttributeEval
-
Returns a string describing this attribute evaluator
- globalInfo() - Method in class weka.attributeSelection.LatentSemanticAnalysis
-
Returns a string describing this attribute transformer
- globalInfo() - Method in class weka.attributeSelection.LinearForwardSelection
-
Returns a string describing this search method
- globalInfo() - Method in class weka.attributeSelection.OneRAttributeEval
-
Returns a string describing this attribute evaluator
- globalInfo() - Method in class weka.attributeSelection.PrincipalComponents
-
Returns a string describing this attribute transformer
- globalInfo() - Method in class weka.attributeSelection.RaceSearch
-
Returns a string describing this search method
- globalInfo() - Method in class weka.attributeSelection.RandomSearch
-
Returns a string describing this search method
- globalInfo() - Method in class weka.attributeSelection.Ranker
-
Returns a string describing this search method
- globalInfo() - Method in class weka.attributeSelection.RankSearch
-
Returns a string describing this search method
- globalInfo() - Method in class weka.attributeSelection.ReliefFAttributeEval
-
Returns a string describing this attribute evaluator
- globalInfo() - Method in class weka.attributeSelection.ScatterSearchV1
-
Returns a string describing this search method
- globalInfo() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Returns a string describing this search method
- globalInfo() - Method in class weka.attributeSelection.SVMAttributeEval
-
Returns a string describing this attribute evaluator
- globalInfo() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
-
Returns a string describing this attribute evaluator
- globalInfo() - Method in class weka.attributeSelection.WrapperSubsetEval
-
Returns a string describing this attribute evaluator
- globalInfo() - Method in class weka.classifiers.bayes.AODE
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.classifiers.bayes.AODEsr
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
- globalInfo() - Method in class weka.classifiers.bayes.BayesNet
-
This will return a string describing the classifier.
- globalInfo() - Method in class weka.classifiers.bayes.ComplementNaiveBayes
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.classifiers.bayes.DMNBtext
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.classifiers.bayes.HNB
-
Returns a string describing this classifier.
- globalInfo() - Method in class weka.classifiers.bayes.NaiveBayes
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.classifiers.bayes.NaiveBayesMultinomial
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.classifiers.bayes.NaiveBayesSimple
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.classifiers.bayes.NaiveBayesUpdateable
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.classifiers.bayes.net.BIFReader
-
This will return a string describing the classifier.
- globalInfo() - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
-
This will return a string describing the class.
- globalInfo() - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator
-
Returns a string describing this object
- globalInfo() - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
-
Returns a string describing this object
- globalInfo() - Method in class weka.classifiers.bayes.net.estimate.SimpleEstimator
-
Returns a string describing this object
- globalInfo() - Method in class weka.classifiers.bayes.net.search.ci.CISearchAlgorithm
-
Returns a string describing this object
- globalInfo() - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
-
This will return a string describing the search algorithm.
- globalInfo() - Method in class weka.classifiers.bayes.net.search.fixed.FromFile
-
Returns a string describing this object
- globalInfo() - Method in class weka.classifiers.bayes.net.search.fixed.NaiveBayes
-
Returns a string describing this object
- globalInfo() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
-
This will return a string describing the classifier.
- globalInfo() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
-
This will return a string describing the search algorithm.
- globalInfo() - Method in class weka.classifiers.bayes.net.search.global.HillClimber
-
This will return a string describing the search algorithm.
- globalInfo() - Method in class weka.classifiers.bayes.net.search.global.K2
-
This will return a string describing the search algorithm.
- globalInfo() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
-
This will return a string describing the classifier.
- globalInfo() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
-
This will return a string describing the classifier.
- globalInfo() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
-
This will return a string describing the classifier.
- globalInfo() - Method in class weka.classifiers.bayes.net.search.global.TAN
-
This will return a string describing the classifier.
- globalInfo() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
-
This will return a string describing the classifier.
- globalInfo() - Method in class weka.classifiers.bayes.net.search.local.HillClimber
-
This will return a string describing the search algorithm.
- globalInfo() - Method in class weka.classifiers.bayes.net.search.local.K2
-
This will return a string describing the search algorithm.
- globalInfo() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
-
This will return a string describing the search algorithm.
- globalInfo() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
-
This will return a string describing the search algorithm.
- globalInfo() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
-
This will return a string describing the classifier.
- globalInfo() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
-
This will return a string describing the classifier.
- globalInfo() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
-
This will return a string describing the classifier.
- globalInfo() - Method in class weka.classifiers.bayes.net.search.local.TAN
-
This will return a string describing the classifier.
- globalInfo() - Method in class weka.classifiers.bayes.WAODE
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.classifiers.BVDecompose
-
Returns a string describing this object
- globalInfo() - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Returns a string describing this object
- globalInfo() - Method in class weka.classifiers.functions.GaussianProcesses
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.functions.IsotonicRegression
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.classifiers.functions.LeastMedSq
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.classifiers.functions.LibLINEAR
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.functions.LibSVM
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.functions.LinearRegression
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.classifiers.functions.Logistic
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
This will return a string describing the classifier.
- globalInfo() - Method in class weka.classifiers.functions.PaceRegression
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.classifiers.functions.PLSClassifier
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.functions.RBFNetwork
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.classifiers.functions.SimpleLinearRegression
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.classifiers.functions.SimpleLogistic
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.functions.SMO
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.functions.SMOreg
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.functions.SPegasos
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.functions.supportVector.Kernel
-
Returns a string describing the kernel
- globalInfo() - Method in class weka.classifiers.functions.supportVector.NormalizedPolyKernel
-
Returns a string describing the kernel
- globalInfo() - Method in class weka.classifiers.functions.supportVector.PolyKernel
-
Returns a string describing the kernel
- globalInfo() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
-
Returns a string describing the kernel
- globalInfo() - Method in class weka.classifiers.functions.supportVector.Puk
-
Returns a string describing the kernel
- globalInfo() - Method in class weka.classifiers.functions.supportVector.RBFKernel
-
Returns a string describing the kernel
- globalInfo() - Method in class weka.classifiers.functions.supportVector.RegSMO
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
-
Returns a string describing the object
- globalInfo() - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Returns a string describing the kernel
- globalInfo() - Method in class weka.classifiers.functions.VotedPerceptron
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.classifiers.functions.Winnow
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.lazy.IB1
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.lazy.IBk
-
Returns a string describing classifier.
- globalInfo() - Method in class weka.classifiers.lazy.KStar
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.lazy.LBR
-
- globalInfo() - Method in class weka.classifiers.lazy.LWL
-
Returns a string describing classifier.
- globalInfo() - Method in class weka.classifiers.meta.AdaBoostM1
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.meta.AdditiveRegression
-
Returns a string describing this attribute evaluator
- globalInfo() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
-
Returns a string describing this search method
- globalInfo() - Method in class weka.classifiers.meta.Bagging
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.meta.ClassificationViaClustering
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.meta.ClassificationViaRegression
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.meta.CostSensitiveClassifier
-
- globalInfo() - Method in class weka.classifiers.meta.CVParameterSelection
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.classifiers.meta.Dagging
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.meta.Decorate
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.meta.END
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.meta.FilteredClassifier
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.classifiers.meta.Grading
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.meta.GridSearch
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.meta.LogitBoost
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.meta.MetaCost
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.meta.MultiBoostAB
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.meta.MultiClassClassifier
-
- globalInfo() - Method in class weka.classifiers.meta.MultiScheme
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
-
- globalInfo() - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
-
- globalInfo() - Method in class weka.classifiers.meta.nestedDichotomies.ND
-
- globalInfo() - Method in class weka.classifiers.meta.OrdinalClassClassifier
-
Returns a string describing this attribute evaluator
- globalInfo() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
- globalInfo() - Method in class weka.classifiers.meta.RandomCommittee
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.meta.RandomSubSpace
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.meta.RegressionByDiscretization
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.meta.RotationForest
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.meta.Stacking
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.meta.StackingC
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.meta.ThresholdSelector
-
- globalInfo() - Method in class weka.classifiers.meta.Vote
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.mi.CitationKNN
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.MDD
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.MIBoost
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.MIDD
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.MIEMDD
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.MILR
-
Returns the tip text for this property
- globalInfo() - Method in class weka.classifiers.mi.MINND
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.MIOptimalBall
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.MISMO
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.mi.MISVM
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.MIWrapper
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.SimpleMI
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.misc.HyperPipes
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.misc.SerializedClassifier
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.misc.VFI
-
Returns a string describing this search method
- globalInfo() - Method in class weka.classifiers.rules.ConjunctiveRule
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.rules.DecisionTable
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.rules.DTNB.BackwardsWithDelete
-
- globalInfo() - Method in class weka.classifiers.rules.DTNB
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.rules.JRip
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.rules.M5Rules
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.rules.NNge
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.rules.OneR
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.rules.PART
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.rules.Prism
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.rules.Ridor
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.rules.ZeroR
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.trees.ADTree
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.trees.BFTree
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.trees.DecisionStump
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.trees.FT
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.trees.Id3
-
Returns a string describing the classifier.
- globalInfo() - Method in class weka.classifiers.trees.J48
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.trees.J48graft
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.trees.LADTree
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.trees.LMT
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.trees.m5.M5Base
-
returns information about the classifier
- globalInfo() - Method in class weka.classifiers.trees.NBTree
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.trees.RandomForest
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.trees.RandomTree
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.trees.REPTree
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.trees.SimpleCart
-
Return a description suitable for displaying in the explorer/experimenter.
- globalInfo() - Method in class weka.classifiers.trees.UserClassifier
-
This will return a string describing the classifier.
- globalInfo() - Method in class weka.clusterers.CLOPE
-
Returns a string describing this DataMining-Algorithm
- globalInfo() - Method in class weka.clusterers.Cobweb
-
Returns a string describing this clusterer
- globalInfo() - Method in class weka.clusterers.DBScan
-
Returns a string describing this DataMining-Algorithm
- globalInfo() - Method in class weka.clusterers.EM
-
Returns a string describing this clusterer
- globalInfo() - Method in class weka.clusterers.FarthestFirst
-
Returns a string describing this clusterer
- globalInfo() - Method in class weka.clusterers.FilteredClusterer
-
Returns a string describing this clusterer.
- globalInfo() - Method in class weka.clusterers.HierarchicalClusterer
-
This will return a string describing the clusterer.
- globalInfo() - Method in class weka.clusterers.MakeDensityBasedClusterer
-
Returns a string describing classifier
- globalInfo() - Method in class weka.clusterers.OPTICS
-
Returns a string describing this DataMining-Algorithm
- globalInfo() - Method in class weka.clusterers.sIB
-
Returns a string describing this clusterer
- globalInfo() - Method in class weka.clusterers.SimpleKMeans
-
Returns a string describing this clusterer
- globalInfo() - Method in class weka.clusterers.XMeans
-
Returns a string describing this clusterer.
- globalInfo() - Method in class weka.core.ChebyshevDistance
-
Returns a string describing this object.
- globalInfo() - Method in class weka.core.converters.ArffLoader
-
Returns a string describing this Loader
- globalInfo() - Method in class weka.core.converters.ArffSaver
-
Returns a string describing this Saver
- globalInfo() - Method in class weka.core.converters.C45Loader
-
Returns a string describing this attribute evaluator
- globalInfo() - Method in class weka.core.converters.C45Saver
-
Returns a string describing this Saver
- globalInfo() - Method in class weka.core.converters.CSVLoader
-
Returns a string describing this attribute evaluator.
- globalInfo() - Method in class weka.core.converters.CSVSaver
-
Returns a string describing this Saver
- globalInfo() - Method in class weka.core.converters.DatabaseLoader
-
Returns a string describing this Loader
- globalInfo() - Method in class weka.core.converters.DatabaseSaver
-
Returns a string describing this Saver.
- globalInfo() - Method in class weka.core.converters.LibSVMLoader
-
Returns a string describing this Loader.
- globalInfo() - Method in class weka.core.converters.LibSVMSaver
-
Returns a string describing this Saver
- globalInfo() - Method in class weka.core.converters.SerializedInstancesLoader
-
Returns a string describing this object
- globalInfo() - Method in class weka.core.converters.SerializedInstancesSaver
-
Returns a string describing this Saver.
- globalInfo() - Method in class weka.core.converters.SVMLightLoader
-
Returns a string describing this Loader.
- globalInfo() - Method in class weka.core.converters.SVMLightSaver
-
Returns a string describing this Saver.
- globalInfo() - Method in class weka.core.converters.TextDirectoryLoader
-
Returns a string describing this loader
- globalInfo() - Method in class weka.core.converters.XRFFLoader
-
Returns a string describing this Loader
- globalInfo() - Method in class weka.core.converters.XRFFSaver
-
Returns a string describing this Saver
- globalInfo() - Method in class weka.core.EditDistance
-
Returns a string describing this object.
- globalInfo() - Method in class weka.core.EuclideanDistance
-
Returns a string describing this object.
- globalInfo() - Method in class weka.core.ManhattanDistance
-
Returns a string describing this object.
- globalInfo() - Method in class weka.core.neighboursearch.BallTree
-
Returns a string describing this nearest neighbour search algorithm.
- globalInfo() - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor
-
Returns a string describing this nearest neighbour search algorithm.
- globalInfo() - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
-
Returns a string describing this nearest neighbour search algorithm.
- globalInfo() - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
-
Returns a string describing this nearest neighbour search algorithm.
- globalInfo() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Returns a string describing this nearest neighbour search algorithm.
- globalInfo() - Method in class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren
-
Returns a string describing this object.
- globalInfo() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
-
Returns a string describing this nearest neighbour search algorithm.
- globalInfo() - Method in class weka.core.neighboursearch.CoverTree
-
Returns a string describing this nearest neighbour search algorithm.
- globalInfo() - Method in class weka.core.neighboursearch.KDTree
-
Returns a string describing this nearest neighbour search algorithm.
- globalInfo() - Method in class weka.core.neighboursearch.kdtrees.KMeansInpiredMethod
-
Returns a string describing this nearest neighbour search algorithm.
- globalInfo() - Method in class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension
-
Returns a string describing this nearest neighbour search algorithm.
- globalInfo() - Method in class weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension
-
Returns a string describing this nearest neighbour search algorithm.
- globalInfo() - Method in class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide
-
Returns a string describing this nearest neighbour search algorithm.
- globalInfo() - Method in class weka.core.neighboursearch.LinearNNSearch
-
Returns a string describing this nearest neighbour search algorithm.
- globalInfo() - Method in class weka.core.neighboursearch.NearestNeighbourSearch
-
Returns a string describing this nearest neighbour search algorithm.
- globalInfo() - Method in class weka.core.NormalizableDistance
-
Returns a string describing this object.
- globalInfo() - Method in class weka.core.stemmers.IteratedLovinsStemmer
-
Returns a string describing the stemmer
- globalInfo() - Method in class weka.core.stemmers.LovinsStemmer
-
Returns a string describing the stemmer
- globalInfo() - Method in class weka.core.stemmers.NullStemmer
-
Returns a string describing the stemmer
- globalInfo() - Method in class weka.core.stemmers.SnowballStemmer
-
Returns a string describing the stemmer.
- globalInfo() - Method in class weka.core.tokenizers.AlphabeticTokenizer
-
Returns a string describing the stemmer
- globalInfo() - Method in class weka.core.tokenizers.NGramTokenizer
-
Returns a string describing the stemmer
- globalInfo() - Method in class weka.core.tokenizers.Tokenizer
-
Returns a string describing the stemmer
- globalInfo() - Method in class weka.core.tokenizers.WordTokenizer
-
Returns a string describing the stemmer
- globalInfo() - Method in class weka.datagenerators.classifiers.classification.Agrawal
-
Returns a string describing this data generator.
- globalInfo() - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
Returns a string describing this data generator.
- globalInfo() - Method in class weka.datagenerators.classifiers.classification.LED24
-
Returns a string describing this data generator.
- globalInfo() - Method in class weka.datagenerators.classifiers.classification.RandomRBF
-
Returns a string describing this data generator.
- globalInfo() - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Returns a string describing this data generator.
- globalInfo() - Method in class weka.datagenerators.classifiers.regression.Expression
-
Returns a string describing this data generator.
- globalInfo() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Returns a string describing this data generator.
- globalInfo() - Method in class weka.datagenerators.ClusterDefinition
-
Returns a string describing this data generator.
- globalInfo() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Returns a string describing this data generator.
- globalInfo() - Method in class weka.datagenerators.clusterers.SubspaceCluster
-
Returns a string describing this data generator.
- globalInfo() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Returns a string describing this data generator.
- globalInfo() - Method in class weka.experiment.AveragingResultProducer
-
Returns a string describing this result producer
- globalInfo() - Method in class weka.experiment.ClassifierSplitEvaluator
-
Returns a string describing this split evaluator
- globalInfo() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
-
Returns a string describing this split evaluator
- globalInfo() - Method in class weka.experiment.CrossValidationResultProducer
-
Returns a string describing this result producer
- globalInfo() - Method in class weka.experiment.CSVResultListener
-
Returns a string describing this result listener
- globalInfo() - Method in class weka.experiment.DatabaseResultListener
-
Returns a string describing this result listener
- globalInfo() - Method in class weka.experiment.DatabaseResultProducer
-
Returns a string describing this result producer
- globalInfo() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
-
Returns a string describing this split evaluator
- globalInfo() - Method in class weka.experiment.InstancesResultListener
-
Returns a string describing this result listener
- globalInfo() - Method in class weka.experiment.LearningRateResultProducer
-
Returns a string describing this result producer
- globalInfo() - Method in class weka.experiment.RandomSplitResultProducer
-
Returns a string describing this result producer
- globalInfo() - Method in class weka.experiment.RegressionSplitEvaluator
-
Returns a string describing this split evaluator
- globalInfo() - Method in class weka.filters.AllFilter
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.MultiFilter
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.SimpleFilter
-
Returns a string describing this classifier.
- globalInfo() - Method in class weka.filters.supervised.attribute.AddClassification
-
Returns a string describing this filter.
- globalInfo() - Method in class weka.filters.supervised.attribute.AttributeSelection
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.supervised.attribute.ClassOrder
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.supervised.attribute.Discretize
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.supervised.attribute.NominalToBinary
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.supervised.attribute.PLSFilter
-
Returns a string describing this classifier.
- globalInfo() - Method in class weka.filters.supervised.instance.Resample
-
Returns a string describing this filter.
- globalInfo() - Method in class weka.filters.supervised.instance.SMOTE
-
Returns a string describing this classifier.
- globalInfo() - Method in class weka.filters.supervised.instance.SpreadSubsample
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.Add
-
Returns a string describing this filter.
- globalInfo() - Method in class weka.filters.unsupervised.attribute.AddCluster
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.AddExpression
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.AddID
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.AddNoise
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.AddValues
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.Center
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.ClassAssigner
-
Returns a string describing this classifier.
- globalInfo() - Method in class weka.filters.unsupervised.attribute.ClusterMembership
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.Copy
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.Discretize
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.FirstOrder
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.InterquartileRange
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.KernelFilter
-
Returns a string describing this filter.
- globalInfo() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
-
- globalInfo() - Method in class weka.filters.unsupervised.attribute.MathExpression
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.NominalToString
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.Normalize
-
Returns a string describing this filter.
- globalInfo() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Returns a string describing this filter.
- globalInfo() - Method in class weka.filters.unsupervised.attribute.NumericToBinary
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.NumericToNominal
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.NumericTransform
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.Obfuscate
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
-
Returns a string describing this filter.
- globalInfo() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Returns a string describing this filter.
- globalInfo() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.RandomProjection
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.RandomSubset
-
Returns a string describing this filter.
- globalInfo() - Method in class weka.filters.unsupervised.attribute.RELAGGS
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.Remove
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.RemoveType
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.RemoveUseless
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.Reorder
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.Standardize
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.StringToNominal
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Returns a string describing this filter.
- globalInfo() - Method in class weka.filters.unsupervised.attribute.SwapValues
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.attribute.TimeSeriesDelta
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.filters.unsupervised.attribute.Wavelet
-
Returns a string describing this classifier.
- globalInfo() - Method in class weka.filters.unsupervised.instance.NonSparseToSparse
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.instance.Normalize
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.instance.Randomize
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveFolds
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.instance.RemovePercentage
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveRange
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.filters.unsupervised.instance.Resample
-
Returns a string describing this classifier
- globalInfo() - Method in class weka.filters.unsupervised.instance.ReservoirSample
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.instance.SparseToNonSparse
-
Returns a string describing this filter
- globalInfo() - Method in class weka.filters.unsupervised.instance.SubsetByExpression
-
Returns a string describing this filter.
- globalInfo() - Method in class weka.gui.beans.Associator
-
Global info (if it exists) for the wrapped classifier
- globalInfo() - Method in class weka.gui.beans.AttributeSummarizer
-
Global info for this bean
- globalInfo() - Method in class weka.gui.beans.ClassAssigner
-
Global info for this bean
- globalInfo() - Method in class weka.gui.beans.Classifier
-
Global info (if it exists) for the wrapped classifier
- globalInfo() - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
-
Global info for this bean
- globalInfo() - Method in class weka.gui.beans.ClassValuePicker
-
Global info for this bean
- globalInfo() - Method in class weka.gui.beans.Clusterer
-
Global info (if it exists) for the wrapped classifier
- globalInfo() - Method in class weka.gui.beans.ClustererPerformanceEvaluator
-
Global info for this bean
- globalInfo() - Method in class weka.gui.beans.CostBenefitAnalysis
-
Global info for this bean
- globalInfo() - Method in class weka.gui.beans.CrossValidationFoldMaker
-
Global info for this bean
- globalInfo() - Method in class weka.gui.beans.DataVisualizer
-
Global info for this bean
- globalInfo() - Method in class weka.gui.beans.Filter
-
Global info (if it exists) for the wrapped filter
- globalInfo() - Method in class weka.gui.beans.GraphViewer
-
Global info for this bean
- globalInfo() - Method in class weka.gui.beans.IncrementalClassifierEvaluator
-
Global info for this bean
- globalInfo() - Method in class weka.gui.beans.Loader
-
Global info (if it exists) for the wrapped loader
- globalInfo() - Method in class weka.gui.beans.ModelPerformanceChart
-
Global info for this bean
- globalInfo() - Method in class weka.gui.beans.PredictionAppender
-
Global description of this bean
- globalInfo() - Method in class weka.gui.beans.Saver
-
Global info (if it exists) for the wrapped loader
- globalInfo() - Method in class weka.gui.beans.ScatterPlotMatrix
-
Global info for this bean
- globalInfo() - Method in class weka.gui.beans.SerializedModelSaver
-
Global info for this bean.
- globalInfo() - Method in class weka.gui.beans.StripChart
-
Global info for this bean
- globalInfo() - Method in class weka.gui.beans.TestSetMaker
-
Global info for this bean
- globalInfo() - Method in class weka.gui.beans.TextViewer
-
Global info for this bean
- globalInfo() - Method in class weka.gui.beans.TrainingSetMaker
-
Global info for this bean
- globalInfo() - Method in class weka.gui.beans.TrainTestSplitMaker
-
Global info for this bean
- globalInfo() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
Returns a string describing this tool
- GLOBALINFO_ENDTAG - Static variable in class weka.core.GlobalInfoJavadoc
-
the end comment tag for inserting the generated Javadoc
- GLOBALINFO_METHOD - Static variable in class weka.core.GlobalInfoJavadoc
-
the globalInfo method name
- GLOBALINFO_STARTTAG - Static variable in class weka.core.GlobalInfoJavadoc
-
the start comment tag for inserting the generated Javadoc
- GlobalInfoJavadoc - Class in weka.core
-
Generates Javadoc comments from the class's globalInfo method.
- GlobalInfoJavadoc() - Constructor for class weka.core.GlobalInfoJavadoc
-
default constructor
- GlobalScoreSearchAlgorithm - Class in weka.classifiers.bayes.net.search.global
-
This Bayes Network learning algorithm uses cross validation to estimate classification accuracy.
- GlobalScoreSearchAlgorithm() - Constructor for class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
-
- goDown(String) - Method in class weka.gui.HierarchyPropertyParser
-
Go to a certain node of the tree down from the current node
according to the specified relative path.
- GOEPanel() - Constructor for class weka.gui.GenericObjectEditor.GOEPanel
-
Creates the GUI editor component.
- GOETreeNode() - Constructor for class weka.gui.GenericObjectEditor.GOETreeNode
-
Creates a tree node that has no parent and no children, but which
allows children.
- GOETreeNode(Object) - Constructor for class weka.gui.GenericObjectEditor.GOETreeNode
-
Creates a tree node with no parent, no children, but which allows
children, and initializes it with the specified user object.
- GOETreeNode(Object, boolean) - Constructor for class weka.gui.GenericObjectEditor.GOETreeNode
-
Creates a tree node with no parent, no children, initialized with the
specified user object, and that allows children only if specified.
- goTo(String) - Method in class weka.gui.HierarchyPropertyParser
-
Go to a certain node of the tree according to the specified path
Note that the path must be absolute path from the root.
- goToChild(String) - Method in class weka.gui.HierarchyPropertyParser
-
Go to one child node from the current position in the tree
according to the given value
If the child node with the given value cannot be found it
returns false, true otherwise.
- goToChild(int) - Method in class weka.gui.HierarchyPropertyParser
-
Go to one child node from the current position in the tree
according to the given position
- goToParent() - Method in class weka.gui.HierarchyPropertyParser
-
Go to the parent from the current position in the tree
If the current position is the root, it stays there and does
not move
- goToRoot() - Method in class weka.gui.HierarchyPropertyParser
-
Go to the root of the tree
- gr(double, double) - Static method in class weka.core.Utils
-
Tests if a is greater than b.
- Grading - Class in weka.classifiers.meta
-
Implements Grading.
- Grading() - Constructor for class weka.classifiers.meta.Grading
-
- GraftSplit - Class in weka.classifiers.trees.j48
-
Class implementing a split for nodes added to a tree during grafting.
- GraftSplit(int, double, int, double, double) - Constructor for class weka.classifiers.trees.j48.GraftSplit
-
constructor
- GraftSplit(int, double, int, double, double[][]) - Constructor for class weka.classifiers.trees.j48.GraftSplit
-
constructor
- graph(FPGrowth.FPTreeRoot) - Method in class weka.associations.FPGrowth
-
Assemble a dot graph representation of the FP-tree.
- graph() - Method in class weka.classifiers.bayes.BayesNet
-
Returns a BayesNet graph in XMLBIF ver 0.3 format.
- graph() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
-
Returns graph describing the classifier (if possible).
- graph() - Method in class weka.classifiers.meta.CostSensitiveClassifier
-
Returns graph describing the classifier (if possible).
- graph() - Method in class weka.classifiers.meta.CVParameterSelection
-
Returns graph describing the classifier (if possible).
- graph() - Method in class weka.classifiers.meta.FilteredClassifier
-
Returns graph describing the classifier (if possible).
- graph() - Method in class weka.classifiers.meta.ThresholdSelector
-
Returns graph describing the classifier (if possible).
- graph() - Method in class weka.classifiers.trees.ADTree
-
Returns graph describing the tree.
- graph() - Method in class weka.classifiers.trees.ft.FTtree
-
Returns graph describing the tree.
- graph() - Method in class weka.classifiers.trees.FT
-
Returns graph describing the tree.
- graph() - Method in class weka.classifiers.trees.j48.ClassifierTree
-
Returns graph describing the tree.
- graph() - Method in class weka.classifiers.trees.J48
-
Returns graph describing the tree.
- graph() - Method in class weka.classifiers.trees.j48.NBTreeClassifierTree
-
Returns graph describing the tree.
- graph() - Method in class weka.classifiers.trees.J48graft
-
Returns graph describing the tree.
- graph() - Method in class weka.classifiers.trees.LADTree
-
Returns graph describing the tree.
- graph() - Method in class weka.classifiers.trees.LMT
-
Returns graph describing the tree.
- graph() - Method in class weka.classifiers.trees.lmt.LMTNode
-
Returns graph describing the tree.
- graph(StringBuffer) - Method in class weka.classifiers.trees.m5.RuleNode
-
Assign a unique identifier to each node in the tree and then
calls graphTree
- graph() - Method in class weka.classifiers.trees.M5P
-
Return a dot style String describing the tree.
- graph() - Method in class weka.classifiers.trees.NBTree
-
Returns graph describing the tree.
- graph() - Method in class weka.classifiers.trees.RandomTree
-
Returns graph describing the tree.
- graph() - Method in class weka.classifiers.trees.REPTree
-
Outputs the decision tree as a graph
- graph() - Method in class weka.classifiers.trees.UserClassifier
-
- graph() - Method in class weka.clusterers.Cobweb
-
Generates the graph string of the Cobweb tree
- graph() - Method in class weka.clusterers.HierarchicalClusterer
-
- graph() - Method in interface weka.core.Drawable
-
Returns a string that describes a graph representing
the object.
- graph(StreamTokenizer) - Method in class weka.gui.graphvisualizer.DotParser
-
Following methods parse the DOT input and mimic the DOT
language's grammar in their structure
- GraphConstants - Interface in weka.gui.graphvisualizer
-
GraphConstants.java
- GraphEdge - Class in weka.gui.graphvisualizer
-
This class represents an edge in the graph
- GraphEdge(int, int, int) - Constructor for class weka.gui.graphvisualizer.GraphEdge
-
- GraphEdge(int, int, int, String, String) - Constructor for class weka.gui.graphvisualizer.GraphEdge
-
- GraphEvent - Class in weka.gui.beans
-
Event for graphs
- GraphEvent(Object, String, String, int) - Constructor for class weka.gui.beans.GraphEvent
-
Creates a new GraphEvent
instance.
- graphFPTree(StringBuffer) - Method in class weka.associations.FPGrowth.FPTreeNode
-
Generate a dot graph description string for the tree.
- graphID - Variable in class weka.gui.graphvisualizer.GraphVisualizer
-
String containing graph's name
- GraphListener - Interface in weka.gui.beans
-
Describe interface TextListener
here.
- graphMatrix - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
-
Interconnection matrix for the graph
- graphName - Variable in class weka.gui.graphvisualizer.BIFParser
-
This holds the name of the graph (i.e.
- GraphNode - Class in weka.gui.graphvisualizer
-
This class represents a node in the Graph.
- GraphNode(String, String) - Constructor for class weka.gui.graphvisualizer.GraphNode
-
Constructor
- GraphNode(String, String, int) - Constructor for class weka.gui.graphvisualizer.GraphNode
-
Constructor
- GraphPanel - Class in weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI
-
GraphPanel.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht
Date: Sep 16, 2004
Time: 10:28:19 AM
$ Revision 1.4 $
- GraphPanel(FastVector, int, boolean, boolean) - Constructor for class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
-
- graphTraverse(PredictionNode, StringBuffer, int, int, Instances) - Method in class weka.classifiers.trees.ADTree
-
Traverses the tree, graphing each node.
- graphTraverse(LADTree.PredictionNode, StringBuffer, int, int) - Method in class weka.classifiers.trees.LADTree
-
Traverses the tree, graphing each node.
- graphTree(StringBuffer) - Method in class weka.classifiers.trees.ft.FTtree
-
Helper function for graph description of tree
- graphTree(StringBuffer) - Method in class weka.classifiers.trees.m5.RuleNode
-
Return a dotty style string describing the tree
- graphType() - Method in class weka.classifiers.bayes.BayesNet
-
Returns the type of graph this classifier
represents.
- graphType() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
-
Returns the type of graph this classifier
represents.
- graphType() - Method in class weka.classifiers.meta.CostSensitiveClassifier
-
Returns the type of graph this classifier
represents.
- graphType() - Method in class weka.classifiers.meta.CVParameterSelection
-
Returns the type of graph this classifier
represents.
- graphType() - Method in class weka.classifiers.meta.FilteredClassifier
-
Returns the type of graph this classifier
represents.
- graphType() - Method in class weka.classifiers.meta.ThresholdSelector
-
Returns the type of graph this classifier
represents.
- graphType() - Method in class weka.classifiers.trees.ADTree
-
Returns the type of graph this classifier
represents.
- graphType() - Method in class weka.classifiers.trees.FT
-
Returns the type of graph this classifier
represents.
- graphType() - Method in class weka.classifiers.trees.j48.ClassifierTree
-
Returns the type of graph this classifier
represents.
- graphType() - Method in class weka.classifiers.trees.J48
-
Returns the type of graph this classifier
represents.
- graphType() - Method in class weka.classifiers.trees.J48graft
-
Returns the type of graph this classifier
represents.
- graphType() - Method in class weka.classifiers.trees.LADTree
-
Returns the type of graph this classifier
represents.
- graphType() - Method in class weka.classifiers.trees.LMT
-
Returns the type of graph this classifier
represents.
- graphType() - Method in class weka.classifiers.trees.M5P
-
Returns the type of graph this classifier
represents.
- graphType() - Method in class weka.classifiers.trees.NBTree
-
Returns the type of graph this classifier
represents.
- graphType() - Method in class weka.classifiers.trees.RandomTree
-
Returns the type of graph this classifier represents.
- graphType() - Method in class weka.classifiers.trees.REPTree
-
Returns the type of graph this classifier
represents.
- graphType() - Method in class weka.classifiers.trees.UserClassifier
-
Returns the type of graph this classifier
represents.
- graphType() - Method in class weka.clusterers.Cobweb
-
Returns the type of graphs this class
represents
- graphType() - Method in class weka.clusterers.HierarchicalClusterer
-
- graphType() - Method in interface weka.core.Drawable
-
Returns the type of graph representing
the object.
- GraphViewer - Class in weka.gui.beans
-
A bean encapsulating weka.gui.treevisualize.TreeVisualizer
- GraphViewer() - Constructor for class weka.gui.beans.GraphViewer
-
- GraphViewerBeanInfo - Class in weka.gui.beans
-
Bean info class for the graph viewer
- GraphViewerBeanInfo() - Constructor for class weka.gui.beans.GraphViewerBeanInfo
-
- GraphVisualizePlugin - Interface in weka.gui.visualize.plugins
-
Interface implemented by classes loaded dynamically to
visualize graphs in the explorer.
- GraphVisualizer - Class in weka.gui.graphvisualizer
-
This class displays the graph we want to visualize.
- GraphVisualizer() - Constructor for class weka.gui.graphvisualizer.GraphVisualizer
-
Constructor
Sets up the gui and initializes all the other previously
uninitialized variables.
- GreedyStepwise - Class in weka.attributeSelection
-
GreedyStepwise :
Performs a greedy forward or backward search through the space of attribute subsets.
- GreedyStepwise() - Constructor for class weka.attributeSelection.GreedyStepwise
-
Constructor
- Grid(double, double, double, double, double, double) - Constructor for class weka.classifiers.meta.GridSearch.Grid
-
initializes the grid
- Grid(double, double, double, String, double, double, double, String) - Constructor for class weka.classifiers.meta.GridSearch.Grid
-
initializes the grid
- GRID - Static variable in class weka.datagenerators.clusterers.BIRCHCluster
-
Constant set for choice of pattern.
- gridIsExtendableTipText() - Method in class weka.classifiers.meta.GridSearch
-
Returns the tip text for this property
- GridSearch - Class in weka.classifiers.meta
-
Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting.
The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy).
- GridSearch() - Constructor for class weka.classifiers.meta.GridSearch
-
the default constructor
- GridSearch.Grid - Class in weka.classifiers.meta
-
for generating the parameter pairs in a grid
- GridSearch.Performance - Class in weka.classifiers.meta
-
A helper class for storing the performance of a values-pair.
- GridSearch.PerformanceCache - Class in weka.classifiers.meta
-
Represents a simple cache for performance objects.
- GridSearch.PerformanceComparator - Class in weka.classifiers.meta
-
A concrete Comparator for the Performance class.
- GridSearch.PerformanceTable - Class in weka.classifiers.meta
-
Generates a 2-dim array for the performances from a grid for a certain
type.
- GridSearch.PointDouble - Class in weka.classifiers.meta
-
a serializable version of Point2D.Double
- GridSearch.PointInt - Class in weka.classifiers.meta
-
a serializable version of Point
- grOrEq(double, double) - Static method in class weka.core.Utils
-
Tests if a is greater or equal to b.
- grouping(boolean) - Method in class weka.core.matrix.FlexibleDecimalFormat
-
- grow(Instances) - Method in class weka.classifiers.rules.JRip.RipperRule
-
Build one rule using the growing data
- grow(Instances) - Method in class weka.classifiers.rules.Rule
-
Build this rule
- GT - Static variable in interface weka.core.mathematicalexpression.sym
-
- GT - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
-
- GUI - Class in weka.classifiers.bayes.net
-
GUI interface to Bayesian Networks.
- GUI() - Constructor for class weka.classifiers.bayes.net.GUI
-
Constructor
Sets up the gui and initializes all the other previously uninitialized
variables.
- GUI_MDI - Static variable in class weka.gui.Main
-
displays the GUI as MDI.
- GUI_SDI - Static variable in class weka.gui.Main
-
displays the GUI as SDI.
- GUIChooser - Class in weka.gui
-
The main class for the Weka GUIChooser.
- GUIChooser() - Constructor for class weka.gui.GUIChooser
-
Creates the experiment environment gui with no initial experiment
- GUIChooser.ChildFrameSDI - Class in weka.gui
-
Specialized JFrame class.
- GUIEDITORS_PROPERTY_FILE - Static variable in class weka.gui.GenericObjectEditor
-
the properties files containing the class/editor mappings.
- GUITipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- m - Variable in class weka.core.matrix.Matrix
-
Row and column dimensions.
- M5Base - Class in weka.classifiers.trees.m5
-
M5Base.
- M5Base() - Constructor for class weka.classifiers.trees.m5.M5Base
-
Constructor
- M5P - Class in weka.classifiers.trees
-
M5Base.
- M5P() - Constructor for class weka.classifiers.trees.M5P
-
Creates a new M5P
instance.
- M5Rules - Class in weka.classifiers.rules
-
Generates a decision list for regression problems using separate-and-conquer.
- M5Rules() - Constructor for class weka.classifiers.rules.M5Rules
-
Constructor
- m_ACC - Variable in class weka.classifiers.meta.GridSearch.Performance
-
the Accuracy
- m_accuracy - Variable in class weka.associations.RuleItem
-
The expected predictive accuracy of a rule.
- m_activationFunction - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
-
The activation function to use
- m_ActiveIndices - Variable in class weka.core.NormalizableDistance
-
The boolean flags, whether an attribute will be used or not.
- m_ActualClassifier - Variable in class weka.filters.supervised.attribute.AddClassification
-
The actual classifier used to do the classification.
- m_ActualClusterer - Variable in class weka.classifiers.meta.ClassificationViaClustering
-
the actual cluster algorithm being used
- m_ActualCount - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
The number of train instances with no missing attribute values
- m_ActualFilter - Variable in class weka.classifiers.functions.PLSClassifier
-
the actual filter to use
- m_ActualFilter - Variable in class weka.filters.unsupervised.attribute.KernelFilter
-
for centering/standardizing the data (the actual filter to use)
- m_ActualKernel - Variable in class weka.filters.unsupervised.attribute.KernelFilter
-
the Kernel which is actually used for computation
- m_acuity - Variable in class weka.clusterers.Cobweb
-
Acuity (minimum standard deviation).
- m_add - Variable in class weka.attributeSelection.RankSearch
-
add this many attributes in each iteration from the ranking
- m_AddBut - Variable in class weka.gui.experiment.AlgorithmListPanel
-
Click to add an algorithm
- m_AddBut - Variable in class weka.gui.experiment.DatasetListPanel
-
Click to add a dataset.
- m_Additional - Variable in class weka.core.TechnicalInformation
-
additional technical informations
- m_AdditionalMeasures - Variable in class weka.experiment.AveragingResultProducer
-
The names of any additional measures to look for in SplitEvaluators
- m_AdditionalMeasures - Variable in class weka.experiment.ClassifierSplitEvaluator
-
The names of any additional measures to look for in SplitEvaluators
- m_AdditionalMeasures - Variable in class weka.experiment.CrossValidationResultProducer
-
The names of any additional measures to look for in SplitEvaluators
- m_AdditionalMeasures - Variable in class weka.experiment.DatabaseResultProducer
-
The names of any additional measures to look for in SplitEvaluators
- m_additionalMeasures - Variable in class weka.experiment.DensityBasedClustererSplitEvaluator
-
The names of any additional measures to look for in SplitEvaluators
- m_AdditionalMeasures - Variable in class weka.experiment.Experiment
-
Method names of additional measures of objects contained in the
custom property iterator.
- m_AdditionalMeasures - Variable in class weka.experiment.LearningRateResultProducer
-
The names of any additional measures to look for in SplitEvaluators
- m_AdditionalMeasures - Variable in class weka.experiment.RandomSplitResultProducer
-
The names of any additional measures to look for in SplitEvaluators
- m_AdditionalMeasures - Variable in class weka.experiment.RegressionSplitEvaluator
-
The names of any additional measures to look for in SplitEvaluators
- m_addPointsButton - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
- m_addRemovePointsButtonGroup - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
- m_addRemovePointsPanel - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
- m_ADNodes - Variable in class weka.classifiers.bayes.net.VaryNode
-
list of ADNode children
- m_AdvanceDataSetFirst - Variable in class weka.experiment.Experiment
-
If true an experiment will advance the current data set befor
any custom itererator
- m_advanceDataSetFirst - Variable in class weka.gui.experiment.SetupPanel
-
Click to advacne data set before custom generator
- m_advancedPanel - Variable in class weka.gui.experiment.SetupModePanel
-
The advanced setup panel
- m_AdvancedSetupRBut - Variable in class weka.gui.experiment.SetupModePanel
-
The button for choosing advanced setup mode
- m_advanceIteratorFirst - Variable in class weka.gui.experiment.SetupPanel
-
Click to advance custom generator before data set
- m_AEEPanel - Variable in class weka.gui.explorer.AttributeSelectionPanel
-
The panel showing the current attribute evaluation method
- m_ALF - Variable in class weka.core.Optimization
-
- m_Algorithm - Variable in class weka.filters.supervised.attribute.PLSFilter
-
the type of algorithm
- m_Algorithm - Variable in class weka.filters.unsupervised.attribute.Wavelet
-
the type of algorithm
- m_AlgorithmListModel - Variable in class weka.gui.experiment.AlgorithmListPanel
-
The list model used
- m_AlgorithmListPanel - Variable in class weka.gui.experiment.SimpleSetupPanel
-
The panel for configuring selected algorithms
- m_algorithmName - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
-
- m_algorithmName - Variable in class weka.classifiers.pmml.consumer.Regression
-
Description of the algorithm
- m_AlgorithmStart - Variable in class weka.associations.GeneralizedSequentialPatterns
-
String indicating the starting time of the algorithm.
- m_AlgorithmType - Variable in class weka.classifiers.mi.MILR
-
the type of processing
- m_Alin - Variable in class weka.classifiers.functions.GaussianProcesses
-
The parameters of the linear transforamtion realized
by the filter on the class attribute
- m_Allowed - Variable in class weka.core.xml.PropertyHandler
-
lists for a class the properties allowed to use for setting and getting.
- m_AllowedIndices - Variable in class weka.core.AttributeLocator
-
the attribute indices that may be inspected
- m_AllowUnclassifiedInstances - Variable in class weka.classifiers.trees.RandomTree
-
Whether unclassified instances are allowed
- m_AllSequentialPatterns - Variable in class weka.associations.GeneralizedSequentialPatterns
-
all generated frequent sequences, i.e.
- m_allTheRules - Variable in class weka.associations.Apriori
-
The list of all generated rules.
- m_allTheRules - Variable in class weka.associations.PredictiveApriori
-
The list of all generated rules.
- m_alpha - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
The Lagrange multipliers.
- m_alpha - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
-
alpha and alpha* arrays containing weights for solving dual problem
- m_Alpha - Variable in class weka.classifiers.functions.Winnow
-
The promotion coefficient
- m_alpha - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
The Lagrange multipliers.
- m_alpha - Variable in class weka.classifiers.trees.lmt.LMTNode
-
Alpha-value (for pruning) at the node
- m_Alpha - Variable in class weka.classifiers.trees.SimpleCart
-
Alpha-value (for pruning) at the node.
- m_alpha1 - Variable in class weka.classifiers.functions.supportVector.RegSMO
-
alpha value for first candidate
- m_alpha1Star - Variable in class weka.classifiers.functions.supportVector.RegSMO
-
alpha* value for first candidate
- m_alpha2 - Variable in class weka.classifiers.functions.supportVector.RegSMO
-
alpha value for second candidate
- m_alpha2Star - Variable in class weka.classifiers.functions.supportVector.RegSMO
-
alpha* value for second candidate
- m_alphaStar - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
-
- m_altitude - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
-
Altitude for radial basis
- m_alwaysDisplayPointsOfThisSize - Variable in class weka.gui.visualize.PlotData2D
-
If the shape size of a point equals this size then always plot
it (i.e.
- m_Amplitude - Variable in class weka.datagenerators.classifiers.regression.MexicanHat
-
the amplitude of y
- m_AnalysisResults - Variable in class weka.estimators.CheckEstimator
-
The results of the analysis as a string
- m_animatedIcon - Variable in class weka.gui.beans.BeanVisual
-
- m_animatedIconPath - Variable in class weka.gui.beans.BeanVisual
-
Holds name (including path) of the animated icon
- m_Antds - Variable in class weka.classifiers.rules.ConjunctiveRule
-
The vector of antecedents of this rule
- m_Antds - Variable in class weka.classifiers.rules.JRip.RipperRule
-
The vector of antecedents of this rule
- m_appendProbabilities - Variable in class weka.gui.beans.PredictionAppender
-
Append classifier's predicted probabilities (if the class is discrete
and the classifier is a distribution classifier)
- m_ApplyFilterBut - Variable in class weka.gui.explorer.PreprocessPanel
-
Click to apply filters and save the results
- m_arffFileFilter - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
- m_arffFileFilter - Variable in class weka.gui.experiment.ResultsPanel
-
ARFF file filter.
- m_arffFileFilter - Variable in class weka.gui.experiment.SimpleSetupPanel
-
FIlter for choosing ARFF files
- m_ArffFilter - Variable in class weka.gui.visualize.VisualizePanel
-
Filter to ensure only arff files are selected
- m_ArffPanel - Variable in class weka.gui.ViewerDialog
-
the panel to display the Instances-object
- m_ArffReader - Variable in class weka.core.converters.ArffLoader
-
The parser for the ARFF file
- m_ArffViewers - Variable in class weka.gui.GUIChooser
-
keeps track of the opened ArffViewer instancs
- m_ArrayEditor - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel
-
Allows editing of the custom property values
- m_ArtSize - Variable in class weka.classifiers.meta.Decorate
-
Amount of artificial/random instances to use - specified as a
fraction of the training data size.
- m_as - Variable in class weka.gui.AttributeVisualizationPanel
-
This holds the attribute stats of the current attribute on display.
- m_asCache - Variable in class weka.gui.AttributeVisualizationPanel
-
Cache of attribute stats info for the current data set
- m_ASEPanel - Variable in class weka.gui.explorer.AttributeSelectionPanel
-
The panel showing the current search method
- m_ASEval - Variable in class weka.attributeSelection.GreedyStepwise
-
- m_AspectRatio - Variable in class weka.gui.visualize.PrintableComponent
-
the aspect ratio.
- m_AspectRatioCheckBox - Static variable in class weka.gui.visualize.PrintableComponent
-
the checkbox for keeping the aspect ration.
- m_Assignments - Variable in class weka.clusterers.SimpleKMeans
-
Assignments obtained
- m_associatedConnections - Variable in class weka.gui.beans.MetaBean
-
- m_Associator - Variable in class weka.associations.CheckAssociator
-
The associator to be examined
- m_Associator - Variable in class weka.associations.SingleAssociatorEnhancer
-
The base associator to use
- m_AssociatorEditor - Variable in class weka.gui.explorer.AssociationsPanel
-
Lets the user configure the associator
- m_attIndex - Variable in class weka.classifiers.trees.lmt.ResidualSplit
-
The index of the attribute selected for the split
- m_AttIndex - Variable in class weka.filters.unsupervised.attribute.AddValues
-
The attribute's index setting.
- m_AttIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes
-
the array attribute indexes
- m_AttPanel - Variable in class weka.gui.explorer.PreprocessPanel
-
Panel to let the user toggle attributes
- m_attrFilter - Variable in class weka.classifiers.meta.StackingC
-
Filter to transform metaData - Remove
- m_attrib - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
-
The attribute itself.
- m_attrib - Variable in class weka.gui.visualize.VisualizePanel
-
The panel that displays the attributes , using color to represent
another attribute.
- m_attribIndex - Variable in class weka.gui.AttributeVisualizationPanel
-
This holds the index of the current attribute on display and should be
set through setAttribute(int idx).
- m_attribIndex - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
-
The index for this attribute.
- m_attribList - Variable in class weka.gui.visualize.MatrixPanel
-
The list for selecting the attributes to display the plot matrix
- m_attribute - Variable in class weka.associations.FPGrowth.BinaryItem
-
The attribute that the item corresponds to
- m_Attribute - Variable in class weka.classifiers.trees.BFTree
-
Attribute used for splitting.
- m_attribute - Variable in class weka.classifiers.trees.lmt.ResidualSplit
-
The attribute selected for the split
- m_Attribute - Variable in class weka.classifiers.trees.RandomTree
-
The attribute to split on.
- m_Attribute - Variable in class weka.classifiers.trees.REPTree.Tree
-
The attribute to split on.
- m_Attribute - Variable in class weka.classifiers.trees.SimpleCart
-
Attribute used to split data.
- m_AttributeEvaluatorEditor - Variable in class weka.gui.explorer.AttributeSelectionPanel
-
Lets the user configure the attribute evaluator
- m_AttributeFilter - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Filter for removing class attribute, nominal attributes with 0 or 1 value.
- m_attributeFilter - Variable in class weka.filters.unsupervised.attribute.RemoveType
-
The attribute filter used to do the filtering
- m_AttributeIndices - Variable in class weka.core.NormalizableDistance
-
The range of attributes to use for calculating the distance.
- m_AttributeIndices - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
-
the generated indices (only for performance reasons)
- m_AttributeNameLab - Variable in class weka.gui.AttributeSummaryPanel
-
Displays the name of the relation
- m_Attributes - Variable in class weka.classifiers.mi.CitationKNN
-
attribute name structure of the relational attribute
- m_Attributes - Variable in class weka.classifiers.mi.MDD
-
All attribute names
- m_Attributes - Variable in class weka.classifiers.mi.MIBoost
-
attributes name for the new dataset used to build the model
- m_Attributes - Variable in class weka.classifiers.mi.MIDD
-
All attribute names
- m_Attributes - Variable in class weka.classifiers.mi.MIEMDD
-
All attribute names
- m_Attributes - Variable in class weka.classifiers.mi.MILR
-
All attribute names
- m_Attributes - Variable in class weka.classifiers.mi.MINND
-
header info of the data
- m_Attributes - Variable in class weka.core.AttributeLocator
-
contains the attribute locations, either true or false Boolean objects
- m_Attributes - Variable in class weka.core.Instances
-
The attribute information.
- m_attributes - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
attributes of this cluster
- m_Attributes - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
-
the attribute range to work on
- m_AttributeSearchEditor - Variable in class weka.gui.explorer.AttributeSelectionPanel
-
Lets the user configure the search method
- m_AttributeSelection - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
-
The attribute selection object
- m_AttributeStats - Variable in class weka.classifiers.meta.Decorate
-
Attribute statistics - used for generating artificial examples.
- m_AttributeStats - Variable in class weka.gui.AttributeSummaryPanel
-
Cached stats on the attributes we've summarized so far
- m_AttributeTest - Variable in class weka.core.Capabilities
-
whether to perform attribute based tests
- m_AttributeType - Variable in class weka.filters.unsupervised.attribute.Add
-
Record the type of attribute to insert.
- m_AttributeTypeLab - Variable in class weka.gui.AttributeSummaryPanel
-
Displays the type of attribute
- m_AttributeTypes - Variable in class weka.experiment.InstancesResultListener
-
Stores the attribute types for each column
- m_AttributeTypes - Variable in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
-
attribute - attribute-type (constants from weka.core.Attribute) relation.
- m_AttrIndex - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
The index of the nominal attribute in the test and train instances
- m_AttrIndex - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
The index of the attribute in the test and train instances
- m_AttrIndexRange - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
range of atttributes
- m_attrIndices - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
global indices of the attributes of the cluster
- m_AttStats - Variable in class weka.filters.unsupervised.attribute.RELAGGS
-
stores the attribute statistics
att_index-att_index_in_rel_att <-> AttributeStats
- m_AttSummaryPanel - Variable in class weka.gui.explorer.PreprocessPanel
-
Displays summary stats on the selected attribute
- m_attTypeToDelete - Variable in class weka.filters.unsupervised.attribute.RemoveType
-
The type of attribute to delete
- m_AttValues - Variable in class weka.core.Instance
-
The instance's attribute values.
- m_AttVisualizePanel - Variable in class weka.gui.explorer.PreprocessPanel
-
The visualization of the attribute values
- m_auxLocalModel - Variable in class weka.classifiers.trees.ft.FTtree
-
Auxiliary copy ClassifierSplitModel (for splitting)
- M_AVERAGE - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
-
- m_AverageProb - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
Average probability of test attribute transforming into train
attribute
- m_AverageProb - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
Average probability of test attribute transforming into train
attribute
- m_avg_target - Variable in class weka.classifiers.functions.GaussianProcesses
-
The training data.
- m_axisChanged - Variable in class weka.gui.visualize.Plot2D
-
if the user changes attribute assigned to an axis
- m_axisColour - Variable in class weka.gui.visualize.Plot2D
-
Default colour for the axis
- m_axisPad - Variable in class weka.gui.visualize.Plot2D
-
Axis padding
- m_b - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
The thresholds.
- m_b - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
-
offset
- m_b - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
The thresholds.
- m_Background - Variable in class weka.gui.Main.BackgroundDesktopPane
-
the actual background image.
- m_Background - Variable in class weka.gui.visualize.BMPWriter
-
the background color
- m_Background - Variable in class weka.gui.visualize.JPEGWriter
-
the background color.
- m_Background - Variable in class weka.gui.visualize.PNGWriter
-
the background color
- m_BackgroundColor - Variable in class weka.gui.beans.StripChart
-
the background color.
- m_BackgroundColor - Variable in class weka.gui.MemoryUsagePanel
-
the background color.
- m_BackgroundColor - Variable in class weka.gui.treevisualizer.TreeVisualizer
-
the background color.
- m_backgroundColor - Variable in class weka.gui.visualize.AttributePanel
-
If set, it allows this panel to avoid setting a color in
the color list that is equal to the background color
- m_backgroundColor - Variable in class weka.gui.visualize.ClassPanel
-
if set, it allows this panel to steer away from setting up
a color in the color list that is equal to the background color
- m_backgroundColour - Variable in class weka.gui.visualize.Plot2D
-
Default colour for the plot background
- m_backup - Variable in class weka.gui.beans.ClassifierCustomizer
-
Copy of the current classifier in case cancel is selected
- m_Backup - Variable in class weka.gui.GenericObjectEditor
-
Holds a copy of the current object that can be reverted to
if the user decides to cancel.
- m_backward - Variable in class weka.attributeSelection.GreedyStepwise
-
Use a backwards search instead of a forwards one
- m_backwardWithDelete - Variable in class weka.classifiers.rules.DTNB
-
- m_bagger - Variable in class weka.classifiers.trees.RandomForest
-
The bagger.
- m_BagRelAtts - Variable in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
-
Indices of relational attributes in the bag
- m_BagRelAtts - Variable in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
-
Indices of relational attributes in the bag
- m_BagSizePercent - Variable in class weka.classifiers.meta.Bagging
-
The size of each bag sample, as a percentage of the training size
- m_BagSizePercent - Variable in class weka.classifiers.meta.MetaCost
-
The size of each bag sample, as a percentage of the training size
- m_BagStringAtts - Variable in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
-
Indices of string attributes in the bag
- m_BagStringAtts - Variable in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
-
Indices of string attributes in the bag
- m_BalanceClass - Variable in class weka.datagenerators.classifiers.classification.Agrawal
-
whether to balance the class
- m_Balanced - Variable in class weka.classifiers.functions.Winnow
-
Use the balanced variant?
- m_barColour - Variable in class weka.gui.visualize.AttributePanel
-
The default colour to use for the background of the bars if
a colour is not defined in Visualize.props
- m_barRange - Variable in class weka.gui.AttributeVisualizationPanel
-
Contains the range of each bar in a histogram.
- m_Base - Variable in class weka.core.neighboursearch.CoverTree
-
The base of our expansion constant.
- m_BaseFormat - Variable in class weka.classifiers.meta.Stacking
-
Format for base data
- m_BatchBuffer - Variable in class weka.core.converters.ConverterUtils.DataSource
-
the batch buffer.
- m_BatchCounter - Variable in class weka.core.converters.ConverterUtils.DataSource
-
the instance counter for the batch case.
- m_BayesNetGUIFrame - Variable in class weka.gui.GUIChooser
-
The frame containing the Bayes net GUI
- m_bcSupport - Variable in class weka.gui.beans.AbstractDataSource
-
BeanContextChild support
- m_bcSupport - Variable in class weka.gui.beans.CostBenefitAnalysis
-
BeanContextChild support
- m_bcSupport - Variable in class weka.gui.beans.DataVisualizer
-
BeanContextChild support
- m_bcSupport - Variable in class weka.gui.beans.GraphViewer
-
BeanContextChild support
- m_bcSupport - Variable in class weka.gui.beans.KnowledgeFlowApp
-
- m_bcSupport - Variable in class weka.gui.beans.ModelPerformanceChart
-
BeanContextChild support
- m_bcSupport - Variable in class weka.gui.beans.TextViewer
-
BeanContextChild support
- m_bDebug - Variable in class weka.clusterers.HierarchicalClusterer
-
Whether the classifier is run in debug mode.
- m_bDistanceIsBranchLength - Variable in class weka.clusterers.HierarchicalClusterer
-
Whether the distance represent node height (if false) or branch length (if true).
- m_BeanConnectionRelation - Variable in class weka.gui.beans.xml.XMLBeans
-
the relation between Bean and connection, MetaBean BeanConnections
are stored under the reference of the MetaBean, regular connections
are stored under REGULAR_CONNECTION.
- m_beanContext - Variable in class weka.gui.beans.AbstractDataSource
-
BeanContex that this bean might be contained within
- m_beanContext - Variable in class weka.gui.beans.CostBenefitAnalysis
-
BeanContex that this bean might be contained within
- m_beanContext - Variable in class weka.gui.beans.DataVisualizer
-
BeanContex that this bean might be contained within
- m_beanContext - Variable in class weka.gui.beans.GraphViewer
-
BeanContex that this bean might be contained within
- m_beanContext - Variable in class weka.gui.beans.ModelPerformanceChart
-
BeanContex that this bean might be contained within
- m_beanContext - Variable in class weka.gui.beans.TextViewer
-
BeanContex that this bean might be contained within
- m_BeanContextSupport - Variable in class weka.gui.beans.xml.XMLBeans
-
the beancontext to use for loading from XML and the beancontext is
null in the bean
- m_BeanInstances - Variable in class weka.gui.beans.xml.XMLBeans
-
keeps track of the BeanInstances read so far, used for the BeanConnections
- m_BeanInstancesID - Variable in class weka.gui.beans.xml.XMLBeans
-
keeps track of the BeanInstances read so far, used for the BeanConnections
- m_BeanLayout - Variable in class weka.gui.beans.xml.XMLBeans
-
the component that manages the layout of the beans
- m_beans - Variable in class weka.gui.beans.FlowRunner
-
The potential flow(s) to execute
- m_benefitR - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_best - Variable in class weka.associations.PredictiveApriori
-
The n best rules.
- m_best - Variable in class weka.associations.RuleGeneration
-
The list of the actual n best rules.
- m_best_group - Variable in class weka.attributeSelection.GreedyStepwise
-
the best subset found
- m_bestChanged - Variable in class weka.associations.PredictiveApriori
-
Flag keeping track if the list of the n best rules has changed.
- m_BestClassifier - Variable in class weka.classifiers.meta.GridSearch
-
the Classifier with the best setup
- m_BestClassifierOptions - Variable in class weka.classifiers.meta.CVParameterSelection
-
The set of all classifier options as determined by cross-validation
- m_bestCommittee - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
The current best committee
- m_BestFilter - Variable in class weka.classifiers.meta.GridSearch
-
the Filter with the best setup
- m_bestMerit - Variable in class weka.attributeSelection.BestFirst
-
holds the merit of the best subset found
- m_bestMerit - Variable in class weka.attributeSelection.GreedyStepwise
-
the merit of the best subset found
- m_bestMerit - Variable in class weka.attributeSelection.LinearForwardSelection
-
holds the merit of the best subset found
- m_bestMerit - Variable in class weka.attributeSelection.ScatterSearchV1
-
holds the merit of the best subset found
- m_bestMerit - Variable in class weka.attributeSelection.SubsetSizeForwardSelection
-
holds the merit of the best subset found
- m_BestPerformance - Variable in class weka.classifiers.meta.CVParameterSelection
-
The cross-validated performance of the best options
- m_BestThreshold - Variable in class weka.classifiers.meta.ThresholdSelector
-
The threshold that lead to the best performance
- m_BestValue - Variable in class weka.classifiers.meta.ThresholdSelector
-
The best value that has been observed
- m_Beta - Variable in class weka.classifiers.functions.Winnow
-
The demotion coefficient
- m_Beta - Variable in class weka.classifiers.mi.MIBoost
-
Voting weights of models
- m_BETA - Variable in class weka.core.Optimization
-
- m_Betas - Variable in class weka.classifiers.meta.AdaBoostM1
-
Array for storing the weights for the votes.
- m_Bias - Variable in class weka.classifiers.BVDecompose
-
The calculated bias (squared)
- m_Bias - Variable in class weka.classifiers.functions.LibLINEAR
-
bias term value
- m_bias - Variable in class weka.classifiers.misc.VFI
-
Bias towards more confident intervals
- m_BiasToUniformClass - Variable in class weka.filters.supervised.instance.Resample
-
The degree of bias towards uniform (nominal) class distribution.
- m_Bic - Variable in class weka.clusterers.XMeans
-
BIC-Score of the current model.
- m_binaryFilter - Variable in class weka.gui.beans.Classifier
-
- m_bInitAsNaiveBayes - Variable in class weka.classifiers.bayes.net.search.SearchAlgorithm
-
determines whether initial structure is an empty graph or a Naive Bayes network
- m_bins - Variable in class weka.core.pmml.Discretize
-
The bins for this discretization
- m_BinValue - Variable in class weka.clusterers.XMeans
-
Distance value between true and false of binary attributes and
"same" and "different" of nominal attributes (default = 1.0).
- m_BlendFactor - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
default sphere of influence blend setting
- m_BlendFactor - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
default sphere of influence blend setting
- m_BlendMethod - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
B_SPHERE = use specified blend, B_ENTROPY = entropic blend setting
- m_BlendMethod - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
0 = use specified blend, 1 = entropic blend setting
- m_BlendMethod - Variable in class weka.classifiers.lazy.KStar
-
0 = use specified blend, 1 = entropic blend setting
- m_Blin - Variable in class weka.classifiers.functions.GaussianProcesses
-
- m_block - Variable in class weka.gui.beans.Classifier
-
true if we should block any further training data sets.
- m_bLow - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
The thresholds.
- m_bLow - Variable in class weka.classifiers.functions.supportVector.RegSMOImproved
-
b.up and b.low boundaries used to determine stopping criterion
- m_bLow - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
The thresholds.
- m_bMarkovBlanketClassifier - Variable in class weka.classifiers.bayes.net.search.SearchAlgorithm
-
Determines whether after structure is found a MarkovBlanketClassifier correction should be applied
If this is true, m_bInitAsNaiveBayes is overridden and interpreted as false.
- m_bModelBuilt - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
-
flag to indicate whether the model is built yet
- m_booleanCols - Variable in class weka.datagenerators.ClusterGenerator
-
Stores which columns are boolean (default numeric)
- m_boostedModel - Variable in class weka.classifiers.functions.SimpleLogistic
-
The actual logistic regression model
- m_boostingIterations - Variable in class weka.classifiers.trees.ADTree
-
Option - the number of boosting iterations o perform
- m_boostingIterations - Variable in class weka.classifiers.trees.LADTree
-
- m_boundaryPanel - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
the plotting panel
- m_BoundaryVisualizerFrame - Variable in class weka.gui.GUIChooser
-
The frame containing the boundary visualizer
- m_BrowseDestinationButton - Variable in class weka.gui.experiment.SimpleSetupPanel
-
Button for browsing destination files
- m_Buffer - Variable in class weka.core.converters.LibSVMLoader
-
the buffer of the rows read so far.
- m_Buffer - Variable in class weka.core.converters.SVMLightLoader
-
the buffer of the rows read so far.
- m_Builder - Variable in class weka.core.xml.XMLDocument
-
the instance of a DocumentBuilder.
- m_bUp - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
The thresholds.
- m_bUp - Variable in class weka.classifiers.functions.supportVector.RegSMOImproved
-
b.up and b.low boundaries used to determine stopping criterion
- m_bUp - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
The thresholds.
- m_bUseK2Prior - Variable in class weka.classifiers.bayes.net.estimate.BMAEstimator
-
whether to use K2 prior
- m_bUseK2Prior - Variable in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
-
whether to use K2 prior
- m_Button - Variable in class weka.gui.arffviewer.ArffTable.RelationalCellEditor
-
the button for opening the dialog
- m_ButtonCancel - Variable in class weka.gui.sql.SqlViewerDialog
-
the Cancel button
- m_ButtonClear - Variable in class weka.gui.LogWindow
-
the clear button
- m_ButtonClear - Variable in class weka.gui.sql.InfoPanel
-
the button to clear the area
- m_ButtonClear - Variable in class weka.gui.sql.QueryPanel
-
the clear button.
- m_ButtonClose - Variable in class weka.gui.LogWindow
-
the close button
- m_ButtonClose - Variable in class weka.gui.sql.ResultPanel
-
the close button
- m_ButtonCloseAll - Variable in class weka.gui.sql.ResultPanel
-
the close all button
- m_ButtonConnect - Variable in class weka.gui.sql.ConnectionPanel
-
the button for connecting to the database.
- m_ButtonCopy - Variable in class weka.gui.sql.InfoPanel
-
the button to copy the selected message
- m_ButtonCopyQuery - Variable in class weka.gui.sql.ResultPanel
-
the button that copies the query into the QueryPanel
- m_ButtonDatabase - Variable in class weka.gui.sql.ConnectionPanel
-
the button for the DB-Dialog.
- m_ButtonExecute - Variable in class weka.gui.sql.QueryPanel
-
the execute button.
- m_ButtonGC - Variable in class weka.gui.MemoryUsagePanel
-
the button for running the garbage collector.
- m_ButtonHistory - Variable in class weka.gui.sql.ConnectionPanel
-
the button for the history.
- m_ButtonHistory - Variable in class weka.gui.sql.QueryPanel
-
the history button.
- m_ButtonOK - Variable in class weka.gui.sql.SqlViewerDialog
-
the OK button
- m_ButtonOptWidth - Variable in class weka.gui.sql.ResultPanel
-
the button for the optimal column width of the current table
- m_C - Variable in class weka.classifiers.functions.GaussianProcesses
-
The covariance matrix.
- m_C - Variable in class weka.classifiers.functions.SMO
-
The complexity parameter.
- m_C - Variable in class weka.classifiers.functions.SMOreg
-
capacity parameter
- m_C - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
-
capacity parameter, copied from SMOreg
- m_C - Variable in class weka.classifiers.mi.MISMO
-
The complexity parameter.
- m_C - Variable in class weka.classifiers.mi.MISVM
-
The complexity parameter.
- m_Cache - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
A cache for storing attribute values and their corresponding
stop parameters
- m_Cache - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
A cache for storing attribute values and their corresponding scale
parameters
- m_Cache - Variable in class weka.classifiers.lazy.KStar
-
A custom data structure for caching distinct attribute values
and their scale factor or stop parameter.
- m_Cache - Variable in class weka.classifiers.meta.GridSearch
-
the cache for points in the grid that got calculated
- m_Cache - Variable in class weka.classifiers.meta.GridSearch.PerformanceCache
-
the cache for points in the grid that got calculated
- m_Cache - Static variable in class weka.core.ClassDiscovery
-
for caching queries (classname+packagename <-> Vector with classnames).
- m_Cache - Variable in class weka.experiment.DatabaseResultListener
-
Stores the cached values
- m_cached - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
-
The x position of each point.
- m_cacheHits - Variable in class weka.classifiers.functions.supportVector.CachedKernel
-
Counts the number of kernel cache hits.
- m_CacheKey - Variable in class weka.experiment.DatabaseResultListener
-
Stores the key for which the cache is valid
- m_CacheKeyIndex - Variable in class weka.experiment.DatabaseResultListener
-
Stores the index of the key column holding the cache key data
- m_CacheKeyName - Variable in class weka.experiment.DatabaseResultListener
-
Holds the name of the key field to cache upon, or null if no caching
- m_cacheSize - Variable in class weka.attributeSelection.BestFirst
-
holds the maximum size of the lookup cache for evaluated subsets
- m_cacheSize - Variable in class weka.attributeSelection.LinearForwardSelection
-
holds the maximum size of the lookup cache for evaluated subsets
- m_cacheSize - Variable in class weka.attributeSelection.SubsetSizeForwardSelection
-
holds the maximum size of the lookup cache for evaluated subsets
- m_CacheSize - Variable in class weka.classifiers.functions.LibSVM
-
in MB
- m_cacheSize - Variable in class weka.classifiers.functions.supportVector.CachedKernel
-
The size of the cache (a prime number)
- m_cacheSlots - Variable in class weka.classifiers.functions.supportVector.CachedKernel
-
number of cache slots in an entry
- m_CalcOutOfBag - Variable in class weka.classifiers.meta.Bagging
-
Whether to calculate the out of bag error
- m_calculatedNumToSelect - Variable in class weka.attributeSelection.GreedyStepwise
-
- m_CalculateStdDevs - Variable in class weka.experiment.AveragingResultProducer
-
True if standard deviation fields should be produced
- m_cancel - Variable in class weka.gui.visualize.VisualizePanel
-
Button for the user to remove all splits.
- m_cancelBut - Variable in class weka.gui.GenericObjectEditor.GOEPanel
-
cancel button.
- m_CancelBut - Variable in class weka.gui.ListSelectorDialog
-
Click to cancel the property selection
- m_CancelBut - Variable in class weka.gui.PropertySelectorDialog
-
Click to cancel the property selection
- m_CancelButton - Variable in class weka.gui.experiment.OutputFormatDialog
-
Click to cancel the dialog.
- m_CancelButton - Variable in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
-
the Cancel button.
- m_CancelButton - Variable in class weka.gui.ViewerDialog
-
Click to cancel the dialog
- m_canChangeClassInDialog - Variable in class weka.gui.GenericObjectEditor
-
whether the class can be changed.
- m_CanMeasureCpuTime - Variable in class weka.core.Debug.Clock
-
whether the system can measure the CPU time
- m_Capabilities - Variable in class weka.core.Capabilities
-
the hashset for storing the active capabilities
- m_Capabilities - Variable in class weka.core.FindWithCapabilities
-
the capabilities to look for.
- m_Capabilities - Variable in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
-
the capabilities used for initializing the dialog.
- m_Capabilities - Variable in class weka.gui.GenericObjectEditor.GOETreeNode
-
the Capabilities object to use for filtering.
- m_CapabilitiesFilter - Variable in class weka.gui.ConverterFileChooser
-
the Capabilities filter for the savers
- m_CapabilitiesFilter - Variable in class weka.gui.GenericObjectEditor
-
for filtering the tree based on the Capabilities of the leaves.
- m_CapabilitiesFilterChangeListeners - Variable in class weka.gui.explorer.Explorer
-
the listeners that listen to filter changes
- m_car - Variable in class weka.associations.Apriori
-
Flag indicating whether class association rules are mined.
- m_car - Variable in class weka.associations.PredictiveApriori
-
Flag indicating whether class association rules are mined.
- m_CARs - Variable in class weka.associations.PriorEstimation
-
Flag indicating whether standard association rules or class association rules are mined.
- m_castInteger - Variable in class weka.core.pmml.TargetMetaInfo
-
cast integers (default no casting)
- m_categoricalConst - Variable in class weka.core.pmml.Constant
-
- m_CC - Variable in class weka.classifiers.meta.GridSearch.Performance
-
the Correlation coefficient
- m_Center - Variable in class weka.classifiers.mi.MIOptimalBall
-
center of the optimal ball
- m_centerFilter - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Filter for centering the data
- m_CenterInput - Variable in class weka.clusterers.XMeans
-
input file for the cluster centers.
- m_CenterOutput - Variable in class weka.clusterers.XMeans
-
output file for the cluster centers.
- m_centroidClasses - Variable in class weka.datagenerators.classifiers.classification.RandomRBF
-
the classes of the centroids
- m_centroids - Variable in class weka.datagenerators.classifiers.classification.RandomRBF
-
the centroids
- m_centroidStdDevs - Variable in class weka.datagenerators.classifiers.classification.RandomRBF
-
the stddevs of the centroids
- m_centroidWeights - Variable in class weka.datagenerators.classifiers.classification.RandomRBF
-
the weights of the centroids
- m_CEPanel - Variable in class weka.gui.explorer.AssociationsPanel
-
The panel showing the current associator selection
- m_CEPanel - Variable in class weka.gui.explorer.ClassifierPanel
-
The panel showing the current classifier selection
- m_CF - Variable in class weka.classifiers.trees.ft.FTtree
-
Confidence level
- m_change - Variable in class weka.associations.RuleGeneration
-
Flag indicating whether the list fo the best rules has changed.
- m_charSet - Variable in class weka.core.converters.TextDirectoryLoader
-
The charset to use when loading text files (default is to just use the
default charset).
- m_CheckBoxWordwrap - Variable in class weka.gui.LogWindow
-
whether to allow wordwrap or not
- m_checkForLowerCaseNames - Variable in class weka.experiment.DatabaseUtils
-
For databases where Tables and Columns are created in lower case.
- m_checkForUpperCaseNames - Variable in class weka.experiment.DatabaseUtils
-
For databases where Tables and Columns are created in upper case.
- m_checksTurnedOff - Variable in class weka.classifiers.functions.GaussianProcesses
-
Turn off all checks and conversions? Turning them off assumes
that data is purely numeric, doesn't contain any missing values,
and has a numeric class.
- m_checksTurnedOff - Variable in class weka.classifiers.functions.SMO
-
Turn off all checks and conversions? Turning them off assumes
that data is purely numeric, doesn't contain any missing values,
and has a nominal class.
- m_ChecksTurnedOff - Variable in class weka.classifiers.functions.supportVector.Kernel
-
Turns off all checks
- m_checksTurnedOff - Variable in class weka.classifiers.mi.MISMO
-
Turn off all checks and conversions? Turning them off assumes
that data is purely numeric, doesn't contain any missing values,
and has a nominal class.
- m_checksTurnedOff - Variable in class weka.filters.unsupervised.attribute.KernelFilter
-
Turn off all checks and conversions? Turning them off assumes
that data is purely numeric, doesn't contain any missing values,
and has a nominal class.
- m_ChildFrames - Variable in class weka.gui.GUIChooser
-
contains the child frames (title <-> object).
- m_ChildFrames - Variable in class weka.gui.Main
-
contains the child frames (title <-> object).
- m_ChildPropertySheet - Variable in class weka.gui.GenericObjectEditor.GOEPanel
-
The component that performs classifier customization.
- m_children - Variable in class weka.associations.FPGrowth.FPTreeNode
-
the children of this node
- m_children - Variable in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
-
- m_Children - Variable in class weka.core.Trie.TrieNode
-
for fast access to the children
- m_chunkSize - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
-
- m_cIndex - Variable in class weka.gui.visualize.AttributePanel
-
- m_cIndex - Variable in class weka.gui.visualize.Plot2D
-
- m_cIndex - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
-
- m_Citers - Variable in class weka.classifiers.mi.CitationKNN
-
C nearest citers
- m_CitersDebug - Variable in class weka.classifiers.mi.CitationKNN
-
- m_class - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
The transformed class values.
- m_Class - Variable in class weka.classifiers.mi.MINND
-
The class label of each exemplar
- m_class - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
The transformed class values.
- m_classAttBox - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
- m_classAttrib - Variable in class weka.gui.visualize.MatrixPanel
-
The combo box to allow user to select the colouring attribute
- m_classAttribute - Variable in class weka.classifiers.functions.SMO
-
The class attribute
- m_ClassAttribute - Variable in class weka.classifiers.meta.LogitBoost
-
The actual class attribute (for getting class names)
- m_ClassAttribute - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
The actual class attribute (for getting class names)
- m_classAttribute - Variable in class weka.classifiers.mi.MISMO
-
The class attribute
- m_ClassAttribute - Variable in class weka.classifiers.trees.BFTree
-
Class attribute of a dataset.
- m_ClassAttribute - Variable in class weka.classifiers.trees.SimpleCart
-
Class attriubte of data.
- m_classAttribute - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
The class attribute from the data that was used to generate
the threshold curve
- m_ClassCombo - Variable in class weka.gui.explorer.AttributeSelectionPanel
-
Lets the user select the class column
- m_ClassCombo - Variable in class weka.gui.explorer.ClassifierPanel
-
Lets the user select the class column
- m_ClassCombo - Variable in class weka.gui.explorer.ClustererPanel
-
Lets the user select the class column for classes to clusters based
evaluation
- m_ClassDistribution - Variable in class weka.classifiers.bayes.NaiveBayes
-
The class estimator.
- m_ClassDistribution - Variable in class weka.classifiers.trees.RandomTree
-
Class probabilities from the training data.
- m_Classes - Variable in class weka.classifiers.mi.CitationKNN
-
Class labels for each bag
- m_Classes - Variable in class weka.classifiers.mi.MDD
-
Class labels for each bag
- m_Classes - Variable in class weka.classifiers.mi.MIBoost
-
Class labels for each bag
- m_Classes - Variable in class weka.classifiers.mi.MIDD
-
Class labels for each bag
- m_Classes - Variable in class weka.classifiers.mi.MIEMDD
-
Class labels for each bag
- m_Classes - Variable in class weka.classifiers.mi.MILR
-
Class labels for each bag
- m_ClassesToClustersBut - Variable in class weka.gui.explorer.ClustererPanel
-
Click to set test mode to classes to clusters based evaluation
- m_ClassFirst - Variable in class weka.experiment.Experiment
-
True if the class attribute is the first attribute for all
datasets involved in this experiment.
- m_ClassFirst - Variable in class weka.gui.experiment.Experimenter
-
True if the class attribute is the first attribute for all
datasets involved in this experiment.
- m_ClassFlag - Variable in class weka.datagenerators.ClusterGenerator
-
class flag
- m_classificationAccV - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
Classification accuracy
- m_Classifier - Variable in class weka.classifiers.BVDecompose
-
An instantiated base classifier used for getting and testing options.
- m_Classifier - Variable in class weka.classifiers.BVDecomposeSegCVSub
-
An instantiated base classifier used for getting and testing options.
- m_Classifier - Variable in class weka.classifiers.CheckClassifier
-
The classifier to be examined
- m_Classifier - Variable in class weka.classifiers.CheckSource
-
the classifier used for generating the source code
- m_Classifier - Variable in class weka.classifiers.meta.MultiScheme
-
The classifier that had the best performance on training data.
- m_Classifier - Variable in class weka.classifiers.SingleClassifierEnhancer
-
The base classifier to use
- m_Classifier - Variable in class weka.experiment.ClassifierSplitEvaluator
-
The classifier used for evaluation
- m_Classifier - Variable in class weka.experiment.RegressionSplitEvaluator
-
The classifier used for evaluation
- m_Classifier - Variable in class weka.filters.supervised.attribute.AddClassification
-
The classifier template used to do the classification.
- m_classifier - Variable in class weka.gui.beans.BatchClassifierEvent
-
The classifier
- m_classifier - Variable in class weka.gui.beans.IncrementalClassifierEvent
-
- m_classifier - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
distribution classifier to use
- m_classifierEditor - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
- m_ClassifierEditor - Variable in class weka.gui.experiment.AlgorithmListPanel
-
Lets the user configure the classifier
- m_ClassifierEditor - Variable in class weka.gui.explorer.ClassifierPanel
-
Lets the user configure the classifier
- m_ClassifierIndex - Variable in class weka.classifiers.meta.MultiScheme
-
The index into the vector for the selected scheme
- m_ClassifierOptions - Variable in class weka.classifiers.BVDecompose
-
The options to be passed to the base classifier.
- m_ClassifierOptions - Variable in class weka.classifiers.BVDecomposeSegCVSub
-
The options to be passed to the base classifier.
- m_ClassifierOptions - Variable in class weka.classifiers.meta.CVParameterSelection
-
The base classifier options (not including those being set
by cross-validation)
- m_ClassifierOptions - Variable in class weka.experiment.ClassifierSplitEvaluator
-
The classifier options (if any)
- m_ClassifierOptions - Variable in class weka.experiment.RegressionSplitEvaluator
-
The classifier options (if any)
- m_ClassifierPanel - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
- m_classifiers - Variable in class weka.classifiers.functions.SMO
-
The binary classifier(s)
- m_Classifiers - Variable in class weka.classifiers.IteratedSingleClassifierEnhancer
-
Array for storing the generated base classifiers.
- m_Classifiers - Variable in class weka.classifiers.meta.LogitBoost
-
Array for storing the generated base classifiers.
- m_classifiers - Variable in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
-
The hashtable for this node.
- m_classifiers - Variable in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
-
The hashtable for this node.
- m_classifiers - Variable in class weka.classifiers.meta.nestedDichotomies.ND
-
The hashtable containing all the classifiers
- m_classifiers - Variable in class weka.classifiers.mi.MISMO
-
The binary classifier(s)
- m_Classifiers - Variable in class weka.classifiers.MultipleClassifiersCombiner
-
Array for storing the generated base classifiers.
- m_ClassifierVersion - Variable in class weka.experiment.ClassifierSplitEvaluator
-
The classifier version
- m_ClassifierVersion - Variable in class weka.experiment.RegressionSplitEvaluator
-
The classifier version
- m_ClassifyIterations - Variable in class weka.classifiers.BVDecomposeSegCVSub
-
The number of times an instance is classified
- m_classIndex - Variable in class weka.associations.Apriori
-
The class index.
- m_ClassIndex - Variable in class weka.associations.FilteredAssociator
-
The class index.
- m_classIndex - Variable in class weka.associations.PredictiveApriori
-
The class index.
- m_classIndex - Variable in class weka.attributeSelection.BestFirst
-
holds the class index
- m_classIndex - Variable in class weka.attributeSelection.GreedyStepwise
-
holds the class index
- m_classIndex - Variable in class weka.attributeSelection.LinearForwardSelection
-
holds the class index
- m_ClassIndex - Variable in class weka.classifiers.BVDecompose
-
The index of the class attribute
- m_ClassIndex - Variable in class weka.classifiers.BVDecomposeSegCVSub
-
The index of the class attribute
- m_ClassIndex - Variable in class weka.classifiers.CheckSource
-
the class index
- m_classIndex - Variable in class weka.classifiers.functions.GaussianProcesses
-
The class index from the training data
- m_ClassIndex - Variable in class weka.classifiers.functions.Logistic
-
The index of the class attribute
- m_classIndex - Variable in class weka.classifiers.functions.SMO
-
The class index from the training data
- m_classIndex - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
-
index of class variable in data set
- m_ClassIndex - Variable in class weka.classifiers.lazy.LBR.Indexes
-
the Class Index for the data set
- m_ClassIndex - Variable in class weka.classifiers.mi.CitationKNN
-
The index of the class attribute
- m_ClassIndex - Variable in class weka.classifiers.mi.MDD
-
The index of the class attribute
- m_ClassIndex - Variable in class weka.classifiers.mi.MIDD
-
The index of the class attribute
- m_ClassIndex - Variable in class weka.classifiers.mi.MIEMDD
-
The index of the class attribute
- m_classIndex - Variable in class weka.classifiers.mi.MISMO
-
The class index from the training data
- m_ClassIndex - Variable in class weka.classifiers.misc.HyperPipes
-
The index of the class attribute
- m_ClassIndex - Variable in class weka.classifiers.misc.VFI
-
The index of the class attribute
- m_ClassIndex - Variable in class weka.core.converters.LibSVMSaver
-
the class index
- m_ClassIndex - Variable in class weka.core.converters.SVMLightSaver
-
the class index.
- m_ClassIndex - Variable in class weka.core.converters.XRFFSaver
-
the class index
- m_ClassIndex - Variable in class weka.core.FindWithCapabilities
-
the class index, in case the capabilities are based on a file.
- m_ClassIndex - Variable in class weka.core.Instances
-
The class attribute's index
- m_ClassIndex - Variable in class weka.core.TestInstances
-
the class index (-1 is LAST, -2 means no class)
- m_ClassIndex - Variable in class weka.filters.CheckSource
-
the class index
- m_ClassIndex - Variable in class weka.filters.unsupervised.attribute.ClassAssigner
-
the class index.
- m_ClassIndex - Variable in class weka.filters.unsupervised.attribute.PotentialClassIgnorer
-
Storing the class index
- m_ClassIndex - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Class index.
- m_classIndex - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
-
The attribute to treat as the class for purposes of cleansing.
- m_classIndex - Variable in class weka.gui.AttributeVisualizationPanel
-
Contains the current class index.
- m_ClassIsNominal - Variable in class weka.classifiers.Evaluation
-
Is the class nominal or numeric?
- m_classIsNominal - Variable in class weka.classifiers.rules.DecisionTable
-
Class is nominal
- m_classLabel - Variable in class weka.associations.LabeledItemSet
-
The class label.
- m_ClassMean - Variable in class weka.filters.supervised.attribute.PLSFilter
-
the mean of the class
- m_ClassMeans - Variable in class weka.classifiers.meta.RegressionByDiscretization
-
The mean values for each Discretized class interval.
- m_ClassMode - Variable in class weka.classifiers.meta.ThresholdSelector
-
Method to determine which class to optimize for
- m_Classname - Variable in class weka.core.Javadoc
-
the classname
- m_Classname - Variable in class weka.core.ListOptions
-
the classname
- m_ClassNameLabel - Variable in class weka.gui.GenericObjectEditor.GOEPanel
-
The name of the current class.
- m_ClassnameOverride - Variable in class weka.core.xml.XMLSerialization
-
for overriding class names (Class <-> Classname (String))
- m_ClassNames - Variable in class weka.classifiers.evaluation.ConfusionMatrix
-
Stores the names of the classes
- m_ClassNames - Variable in class weka.classifiers.Evaluation
-
The names of the classes.
- m_classPanel - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
- m_classPanel - Variable in class weka.gui.visualize.VisualizePanel
-
The panel that displays the legend for the colouring attribute
- m_ClasspathProblems - Variable in class weka.core.CheckScheme
-
whether classpath problems occurred
- m_ClasspathProblems - Variable in class weka.estimators.CheckEstimator
-
whether classpath problems occurred
- m_classPriorCounts - Variable in class weka.classifiers.rules.DecisionTable
-
The class priors to use when there is no match in the table
- m_ClassPriors - Variable in class weka.classifiers.Evaluation
-
The prior probabilities of the classes
- m_classPriors - Variable in class weka.classifiers.rules.DecisionTable
-
- m_ClassPriorsSum - Variable in class weka.classifiers.Evaluation
-
The sum of counts for priors
- m_ClassProbs - Variable in class weka.classifiers.trees.BFTree
-
Class probabilities.
- m_ClassProbs - Variable in class weka.classifiers.trees.REPTree.Tree
-
Class probabilities from the training data in the nominal case.
- m_ClassProbs - Variable in class weka.classifiers.trees.SimpleCart
-
Class probabilities.
- m_ClassStdDev - Variable in class weka.filters.supervised.attribute.PLSFilter
-
the standard deviation of the class
- m_classSurround - Variable in class weka.gui.visualize.VisualizePanel
-
Panel that surrounds the class panel with a titled border
- m_ClassType - Variable in class weka.classifiers.lazy.IBk
-
The class attribute type.
- m_ClassType - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
The class attribute type
- m_ClassType - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
The class attribute type
- m_ClassType - Variable in class weka.classifiers.lazy.KStar
-
The class attribute type
- m_ClassType - Variable in class weka.core.TestInstances
-
the class type
- m_ClassType - Variable in class weka.gui.GenericObjectEditor
-
The Class of objects being edited.
- m_ClassValue - Variable in class weka.classifiers.trees.BFTree
-
Class value for a node.
- m_ClassValue - Variable in class weka.classifiers.trees.SimpleCart
-
Class value if the node is leaf.
- m_classValueIndex - Variable in class weka.estimators.Estimator
-
The class value index is > -1 if subset is taken with specific class value only
- m_ClassValueIndex - Variable in class weka.filters.supervised.instance.SMOTE
-
the index of the class value.
- m_classValueSelector - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
- m_cleansingClassifier - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
-
The classifier used to do the cleansing
- m_Clock - Variable in class weka.core.Debug
-
for clocking
- m_CloseBut - Variable in class weka.gui.SetInstancesPanel
-
Click to close the dialog
- m_CloseButPanel - Variable in class weka.gui.SetInstancesPanel
-
the panel the Close-Button is located in
- m_CloseTo - Variable in class weka.filters.unsupervised.attribute.NumericCleaner
-
the number the values are checked for closeness to
- m_CloseToDefault - Variable in class weka.filters.unsupervised.attribute.NumericCleaner
-
the default replacement value for numbers "close-to"
- m_CloseToTolerance - Variable in class weka.filters.unsupervised.attribute.NumericCleaner
-
the tolerance distance, below which numbers are considered being "close-to"
- m_closure - Variable in class weka.core.pmml.FieldMetaInfo.Interval
-
- m_CLPanel - Variable in class weka.gui.explorer.ClustererPanel
-
The panel showing the current clusterer selection
- m_clusterAssignments - Variable in class weka.clusterers.CLOPE
-
- m_ClusterAssignments - Variable in class weka.clusterers.XMeans
-
temporary variable holding cluster assignments while iterating.
- m_ClusterCenters - Variable in class weka.clusterers.XMeans
-
cluster centers.
- m_ClusterCentroids - Variable in class weka.clusterers.FarthestFirst
-
holds the cluster centroids
- m_Clusterer - Variable in class weka.classifiers.meta.ClassificationViaClustering
-
the cluster algorithm used (template)
- m_Clusterer - Variable in class weka.clusterers.CheckClusterer
-
The clusterer to be examined
- m_Clusterer - Variable in class weka.clusterers.SingleClustererEnhancer
-
the clusterer
- m_clusterer - Variable in class weka.experiment.DensityBasedClustererSplitEvaluator
-
The clusterer used for evaluation
- m_Clusterer - Variable in class weka.filters.unsupervised.attribute.AddCluster
-
The clusterer used to do the cleansing
- m_clusterer - Variable in class weka.filters.unsupervised.attribute.ClusterMembership
-
The clusterer
- m_clusterer - Variable in class weka.gui.beans.BatchClustererEvent
-
The clusterer
- m_ClustererEditor - Variable in class weka.gui.explorer.ClustererPanel
-
Lets the user configure the clusterer
- m_clustererOptions - Variable in class weka.experiment.DensityBasedClustererSplitEvaluator
-
The clusterer options (if any)
- m_clusterers - Variable in class weka.filters.unsupervised.attribute.ClusterMembership
-
Array for storing the clusterers
- m_clustererVersion - Variable in class weka.experiment.DensityBasedClustererSplitEvaluator
-
The clusterer version
- m_ClusteringHeader - Variable in class weka.classifiers.meta.ClassificationViaClustering
-
the modified training data header
- m_Clusters - Variable in class weka.datagenerators.clusterers.SubspaceCluster
-
cluster list
- m_ClustersToClasses - Variable in class weka.classifiers.meta.ClassificationViaClustering
-
the mapping between clusters and classes
- m_clustersubtype - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
cluster subtypes
- m_clustertype - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
cluster type
- m_CNN - Variable in class weka.classifiers.mi.CitationKNN
-
C nearest neighbors considering all the bags
- m_CNNDebug - Variable in class weka.classifiers.mi.CitationKNN
-
Different debugging output
- m_Cnsqt - Variable in class weka.classifiers.rules.ConjunctiveRule
-
The consequent of this rule
- m_cobwebTree - Variable in class weka.clusterers.Cobweb
-
Holds the root of the Cobweb tree.
- m_Coef0 - Variable in class weka.classifiers.functions.LibSVM
-
for poly/sigmoid
- m_Coefficients - Variable in class weka.core.matrix.LinearRegression
-
the coefficients
- m_col - Variable in class weka.gui.treevisualizer.NamedColor
-
The actual color object
- m_ColHidden - Variable in class weka.experiment.ResultMatrix
-
whether a column is hidden
- m_ColNames - Variable in class weka.experiment.ResultMatrix
-
the column names
- m_ColNameWidth - Variable in class weka.experiment.ResultMatrix
-
the size of the names of the columns
- m_colorAttrib - Variable in class weka.gui.AttributeVisualizationPanel
-
This stores and lets the user select a class attribute.
- m_ColOrder - Variable in class weka.experiment.PairedTTester
-
The sorting of the columns (test base is always first)
- m_ColOrder - Variable in class weka.experiment.ResultMatrix
-
the ordering of the columns
- m_coloringIndex - Variable in class weka.gui.beans.AttributeSummarizer
-
Index on which to color the plots.
- m_colorList - Variable in class weka.gui.beans.StripChart
-
default colours for colouring lines
- m_colorList - Variable in class weka.gui.visualize.AttributePanel
-
The colour map to use for colouring points
- m_colorList - Variable in class weka.gui.visualize.Plot2D
-
The list of the colors used
- m_colorList - Variable in class weka.gui.visualize.VisualizePanel
-
The list of the colors used
- m_Colors - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
- m_Colors - Variable in class weka.gui.MemoryUsagePanel
-
the corresponding colors for the thresholds.
- m_ColourCombo - Variable in class weka.gui.visualize.VisualizePanel
-
Lets the user select the attribute to use for colouring
- m_Cols - Variable in class weka.filters.unsupervised.attribute.NumericCleaner
-
Stores which columns to cleanse
- m_Cols - Variable in class weka.filters.unsupervised.attribute.NumericToNominal
-
Stores which columns to turn into nominals
- m_cols - Variable in class weka.gui.treevisualizer.Colors
-
The array with all the colors input
- m_ColumnClasses - Variable in class weka.gui.sql.ResultSetHelper
-
the class for each column.
- m_ColumnCount - Variable in class weka.gui.sql.ResultSetHelper
-
the number of columns.
- m_ColumnIndex - Variable in class weka.gui.arffviewer.ArffTable.RelationalCellEditor
-
the column index this editor is for
- m_ColumnNames - Variable in class weka.gui.sql.ResultSetHelper
-
the column names.
- m_Columns - Variable in class weka.filters.unsupervised.attribute.NominalToBinary
-
Stores which columns to act on
- m_CombinationRule - Variable in class weka.classifiers.meta.Vote
-
Combination Rule variable
- m_CommandHistory - Variable in class weka.gui.SimpleCLIPanel
-
The history of commands entered interactively.
- m_Comment - Variable in enum weka.core.TechnicalInformation.Field
-
the comment about this type
- m_Comment - Variable in enum weka.core.TechnicalInformation.Type
-
the comment about this type
- m_Committee - Variable in class weka.classifiers.meta.Decorate
-
Vector of classifiers that make up the committee/ensemble.
- m_committees - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
The committees
- m_CompareCombo - Variable in class weka.gui.experiment.ResultsPanel
-
Lets the user select which performance measure to analyze.
- m_CompareModel - Variable in class weka.gui.experiment.ResultsPanel
-
The model embedded in m_CompareCombo.
- m_completedSets - Variable in class weka.gui.beans.Classifier
-
Stores which sets from which runs have been completed.
- m_completeReLayout - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
-
This tells the the LayoutGraph method if
a completeReLayout should be performed
when it is called.
- m_Completion - Variable in class weka.gui.SimpleCLIPanel
-
The commandline completion.
- m_Component - Variable in class weka.gui.visualize.PrintableComponent
-
the parent component of this print dialog.
- m_CompressOutput - Variable in class weka.core.converters.ArffSaver
-
whether to compress the output
- m_CompressOutput - Variable in class weka.core.converters.XRFFSaver
-
whether to compress the output
- m_ComputeRandomCols - Variable in class weka.classifiers.lazy.KStar
-
Flag turning on and off the computation of random class colomns
- m_conf_aa - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_conf_ab - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_conf_actualA - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_conf_actualB - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_conf_ba - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_conf_bb - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_conf_predictedA - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_conf_predictedB - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_ConfigureBut - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel
-
Click to select the property to iterate over
- m_ConfigureButton - Variable in class weka.gui.ConverterFileChooser
-
the configure button
- m_configureHostNames - Variable in class weka.gui.experiment.DistributeExperimentPanel
-
Popup the HostListPanel
- m_ConfusionMatrix - Variable in class weka.classifiers.Evaluation
-
Array for storing the confusion matrix.
- m_Connected - Variable in class weka.gui.sql.QueryPanel
-
whether we have a connection to a database or not.
- m_Connection - Variable in class weka.experiment.DatabaseUtils
-
The database connection.
- m_ConnectionListeners - Variable in class weka.gui.sql.ConnectionPanel
-
the connection listeners.
- m_ConnectionPanel - Variable in class weka.gui.sql.SqlViewer
-
the connection panel.
- m_connectPoints - Variable in class weka.gui.visualize.PlotData2D
-
Additional optional information to control the drawing of lines
between consecutive points.
- m_consequence - Variable in class weka.associations.FPGrowth.AssociationRule
-
The consequence of the rule
- m_consequence - Variable in class weka.associations.RuleItem
-
The consequence of a rule.
- m_consequenceSupport - Variable in class weka.associations.FPGrowth.AssociationRule
-
The support for the consequence
- m_conservativeSelection - Variable in class weka.attributeSelection.GreedyStepwise
-
If set then attributes will continue to be added during a forward
search as long as the merit does not degrade
- m_constError - Variable in class weka.classifiers.trees.ft.FTtree
-
Constructor error
- m_Contents - Variable in class weka.core.Queue.QueueNode
-
The nodes contents
- m_continuousConst - Variable in class weka.core.pmml.Constant
-
- m_controlPanel - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
- m_controlsPanel - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
-
The panel containing extra options,
specific to this LayoutEngine, for
greater control over layout of the graph
- m_convertNominal - Variable in class weka.classifiers.trees.FT
-
convert nominal attributes to binary ?
- m_convertNominal - Variable in class weka.classifiers.trees.LMT
-
convert nominal attributes to binary ?
- m_ConvertToMI - Variable in class weka.classifiers.mi.MIOptimalBall
-
filter used to convert the single-instance dataset into MI dataset
- m_ConvertToProp - Variable in class weka.classifiers.mi.MISVM
-
filter used to convert the MI dataset into single-instance dataset
- m_ConvertToProp - Variable in class weka.classifiers.mi.MIWrapper
-
Filter used to convert MI dataset into single-instance dataset
- m_ConvertToSI - Variable in class weka.classifiers.mi.MIBoost
-
filter used to convert the MI dataset into single-instance dataset
- m_ConvertToSI - Variable in class weka.classifiers.mi.MIOptimalBall
-
filter used to convert the MI dataset into single-instance dataset
- m_CoordCount - Variable in class weka.core.neighboursearch.PerformanceStats
-
The number of coordinates looked at for
the current/last query.
- m_CopyCols - Variable in class weka.filters.unsupervised.attribute.Copy
-
Stores which columns to copy
- m_CoreConvertersOnly - Variable in class weka.gui.ConverterFileChooser
-
whether to display only core converters (hardcoded in ConverterUtils).
- m_Correct - Variable in class weka.classifiers.Evaluation
-
The weight of all correctly classified instances.
- m_Correlation - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Correlation matrix for the original data.
- m_Cost - Variable in class weka.classifiers.functions.LibLINEAR
-
cost Parameter C
- m_Cost - Variable in class weka.classifiers.functions.LibSVM
-
cost, for C_SVC, EPSILON_SVR and NU_SVR
- m_cost_aa - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_cost_ab - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_cost_actualA - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_cost_actualB - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_cost_ba - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_cost_bb - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_cost_predictedA - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_cost_predictedB - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_costBenefit - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
Data for the cost/benefit curve
- m_costBenefitL - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_costBenefitPanel - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
Displays the cost/benefit (profit/loss) graph
- m_costBenefitV - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_CostFile - Variable in class weka.attributeSelection.CostSensitiveASEvaluation
-
The name of the cost file, for command line options
- m_CostFile - Variable in class weka.classifiers.meta.CostSensitiveClassifier
-
The name of the cost file, for command line options
- m_CostFile - Variable in class weka.classifiers.meta.MetaCost
-
The name of the cost file, for command line options
- m_CostMatrix - Variable in class weka.attributeSelection.CostSensitiveASEvaluation
-
The cost matrix
- m_CostMatrix - Variable in class weka.classifiers.Evaluation
-
The cost matrix (if given).
- m_CostMatrix - Variable in class weka.classifiers.meta.CostSensitiveClassifier
-
The cost matrix
- m_CostMatrix - Variable in class weka.classifiers.meta.MetaCost
-
The cost matrix
- m_CostMatrixEditor - Variable in class weka.gui.explorer.ClassifierPanel
-
The cost matrix editor for evaluation costs
- m_costR - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_count - Variable in class weka.associations.PredictiveApriori
-
Counter for the time of generation for an association rule.
- m_count - Variable in class weka.associations.RuleGeneration
-
Integer indicating the generation time of a rule.
- m_counter - Variable in class weka.associations.ItemSet
-
Counter for how many transactions contain this item set.
- m_counter - Variable in class weka.associations.RuleGeneration
-
Counter for how many transactions contain this item set.
- m_Counter - Variable in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
-
A classifier counter.
- m_Counter - Variable in class weka.filters.unsupervised.attribute.AddID
-
the counter for the ID
- m_CountFieldName - Variable in class weka.experiment.AveragingResultProducer
-
The name of the field that will contain the number of results
averaged over.
- m_Counts - Variable in class weka.classifiers.bayes.NaiveBayesSimple
-
All the counts for nominal attributes.
- m_Counts - Variable in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
-
Hold the counts
- m_Counts - Variable in class weka.classifiers.lazy.LBR
-
All the counts for nominal attributes.
- m_counts - Variable in class weka.classifiers.misc.VFI
-
The class counts for each interval of each attribute
- m_Counts - Variable in class weka.experiment.ResultMatrix
-
the counts for the different datasets
- m_CountWidth - Variable in class weka.experiment.ResultMatrix
-
the size of the counts
- m_covariateList - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
-
- m_CoverVariance - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
the amount of varaince to cover in the original data when
retaining the best n PC's.
- m_cp - Variable in class weka.gui.visualize.MatrixPanel
-
The panel that displays the legend of the colouring attribute
- m_createIndex - Variable in class weka.experiment.DatabaseUtils
-
create index on the database?
- m_CreatingRelationName - Variable in class weka.datagenerators.DataGenerator
-
flag, that indicates whether the relationname is currently assembled
- m_creatorApplication - Variable in class weka.classifiers.pmml.consumer.PMMLClassifier
-
Creator application
- m_CrossValidate - Variable in class weka.classifiers.lazy.IBk
-
Whether to select k by cross validation.
- m_csvFileFilter - Variable in class weka.gui.experiment.ResultsPanel
-
CSV file filter.
- m_csvFileFilter - Variable in class weka.gui.experiment.SimpleSetupPanel
-
Filter for choosing CSV files
- m_cumulativeInstances - Variable in class weka.core.converters.CSVLoader
-
Holds instances accumulated so far.
- m_cumulativeLinkFunction - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
-
- m_cumulativeStructure - Variable in class weka.core.converters.CSVLoader
-
A list of hash tables for accumulating nominal values during parsing.
- m_CurrDebugFlag - Variable in class weka.clusterers.XMeans
-
Flag: I'm debugging.
- m_currentBatchIdentifier - Variable in class weka.gui.beans.Classifier
-
Identifier for the current batch.
- m_CurrentConverter - Variable in class weka.gui.ConverterFileChooser
-
the converter that was chosen by the user
- m_CurrentID - Static variable in class weka.core.Debug.Random
-
for keeping track of unique IDs
- m_currentInst - Variable in class weka.filters.unsupervised.instance.ReservoirSample
-
The current instance being processed
- m_CurrentInst - Variable in class weka.gui.arffviewer.ArffTable.RelationalCellEditor
-
the current instances
- m_currentInstance - Variable in class weka.gui.beans.IncrementalClassifierEvent
-
- m_CurrentInstances - Variable in class weka.experiment.Experiment
-
The dataset currently being used
- m_CurrentLeaf - Variable in class weka.core.Trie.TrieIterator
-
the current leaf node
- m_CurrentMetaBean - Variable in class weka.gui.beans.xml.XMLBeans
-
the current MetaBean (for the BeanConnections)
- m_CurrentNode - Variable in class weka.core.xml.XMLSerialization
-
the node that is currently processed, in case of writing the parent node
(something might go wrong writing the new child) and in case of reading
the actual node that is tried to process
- m_CurrentPos - Variable in class weka.core.tokenizers.AlphabeticTokenizer
-
the current position
- m_CurrentPosition - Variable in class weka.core.tokenizers.NGramTokenizer
-
the current position for returning elements
- m_CurrentProperty - Variable in class weka.experiment.Experiment
-
The custom property value that has actually been set
- m_currentSet - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
The instances currently in memory for training
- m_CurrentSize - Variable in class weka.experiment.LearningRateResultProducer
-
The current dataset size during stepping
- m_CurrentVis - Variable in class weka.gui.explorer.ClassifierPanel
-
The current visualization object
- m_CurrentVis - Variable in class weka.gui.explorer.ClustererPanel
-
The current visualization object
- m_customColour - Variable in class weka.gui.visualize.PlotData2D
-
- m_CustomDimensionsCheckBox - Static variable in class weka.gui.visualize.PrintableComponent
-
the checkbox for the custom dimensions.
- m_CustomHeight - Variable in class weka.gui.visualize.JComponentWriter
-
the custom height
- m_CustomHeightText - Static variable in class weka.gui.visualize.PrintableComponent
-
the edit field for the custom height.
- m_CustomMethods - Variable in class weka.core.xml.XMLSerialization
-
for handling custom read/write methods
- m_CustomWidth - Variable in class weka.gui.visualize.JComponentWriter
-
the custom width
- m_CustomWidthText - Static variable in class weka.gui.visualize.PrintableComponent
-
the edit field for the custom width.
- m_cutoff - Variable in class weka.clusterers.Cobweb
-
Cutoff (minimum category utility).
- m_CutOffFactor - Variable in class weka.clusterers.XMeans
-
cutoff factor - percentage of splits done in Improve-Structure part
only relevant, if all children lost.
- m_CutPoints - Variable in class weka.filters.supervised.attribute.Discretize
-
Store the current cutpoints
- m_CutPoints - Variable in class weka.filters.unsupervised.attribute.Discretize
-
Store the current cutpoints
- m_CVBut - Variable in class weka.gui.explorer.AttributeSelectionPanel
-
Click to set evaluation mode to cross-validation
- m_CVBut - Variable in class weka.gui.explorer.ClassifierPanel
-
Click to set test mode to cross-validation
- m_CVLab - Variable in class weka.gui.explorer.AttributeSelectionPanel
-
Label by where the cv folds are entered
- m_CVLab - Variable in class weka.gui.explorer.ClassifierPanel
-
Label by where the cv folds are entered
- m_CVParams - Variable in class weka.classifiers.meta.CVParameterSelection
-
The set of parameters to cross-validate over
- m_CVText - Variable in class weka.gui.explorer.AttributeSelectionPanel
-
The field where the cv folds are entered
- m_CVText - Variable in class weka.gui.explorer.ClassifierPanel
-
The field where the cv folds are entered
- m_CycleEnd - Variable in class weka.associations.GeneralizedSequentialPatterns
-
String indicating the ending time of an cycle.
- m_cycles - Variable in class weka.associations.Apriori
-
Number of cycles used before required number of rules was one.
- m_Cycles - Variable in class weka.associations.GeneralizedSequentialPatterns
-
number of cycles performed until termination
- m_CycleStart - Variable in class weka.associations.GeneralizedSequentialPatterns
-
String indicating the starting time of an cycle.
- m_Data - Variable in class weka.classifiers.functions.Logistic
-
The data saved as a matrix
- m_data - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
The training data.
- m_data - Variable in class weka.classifiers.functions.SPegasos
-
Holds the header of the training data
- m_data - Variable in class weka.classifiers.functions.supportVector.Kernel
-
The dataset
- m_data - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
-
points to data set
- m_Data - Variable in class weka.classifiers.meta.GridSearch
-
the training data
- m_Data - Variable in class weka.classifiers.mi.MDD
-
MI data
- m_Data - Variable in class weka.classifiers.mi.MIDD
-
MI data
- m_Data - Variable in class weka.classifiers.mi.MIEMDD
-
MI data
- m_Data - Variable in class weka.classifiers.mi.MILR
-
MI data
- m_data - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
The training data.
- m_data - Variable in class weka.classifiers.trees.lmt.ResidualSplit
-
The set of instances
- m_Data - Variable in class weka.core.AttributeLocator
-
the referenced data
- m_Data - Variable in class weka.core.converters.ArffLoader.ArffReader
-
the actual data
- m_Data - Variable in class weka.core.NormalizableDistance
-
the instances used internally.
- m_Data - Variable in class weka.core.TestInstances
-
the generated data
- m_data - Variable in class weka.gui.AttributeVisualizationPanel
-
This holds the current set of instances
- m_Data - Variable in class weka.gui.sql.ResultSetTableModel
-
the data.
- m_data - Variable in class weka.gui.visualize.MatrixPanel
-
The dataset for which this panel will display the plot matrix for
- m_DatabaseURL - Variable in class weka.experiment.DatabaseUtils
-
Database URL.
- m_dataDictionary - Variable in class weka.classifiers.pmml.consumer.PMMLClassifier
-
The data dictionary
- m_DataFileName - Variable in class weka.classifiers.BVDecompose
-
The name of the data file used for the decomposition
- m_DataFileName - Variable in class weka.classifiers.BVDecomposeSegCVSub
-
The name of the data file used for the decomposition
- m_dataGenerator - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
data generator to use
- m_DataGenerator - Variable in class weka.gui.explorer.PreprocessPanel
-
The last generator that was selected
- m_DataSeqID - Variable in class weka.associations.GeneralizedSequentialPatterns
-
number indicating the attribute holding the data sequence ID
- m_Dataset - Variable in class weka.classifiers.CheckSource
-
the dataset to use for testing
- m_Dataset - Variable in class weka.core.converters.SerializedInstancesLoader
-
Holds the structure (header) of the data set.
- m_Dataset - Variable in class weka.core.Instance
-
The dataset the instance has access to.
- m_Dataset - Variable in class weka.filters.CheckSource
-
the dataset to use for testing
- m_DatasetFormat - Variable in class weka.datagenerators.DataGenerator
-
The format for the generated dataset
- m_DatasetKeyBut - Variable in class weka.gui.experiment.ResultsPanel
-
Click to edit the columns used to determine the scheme.
- m_DatasetKeyColumns - Variable in class weka.experiment.PairedTTester
-
An array containing the indexes of just the selected columns
- m_DatasetKeyColumnsRange - Variable in class weka.experiment.PairedTTester
-
The range of columns that specify a unique "dataset"
(eg: scheme plus configuration)
- m_DatasetKeyLabel - Variable in class weka.gui.experiment.ResultsPanel
-
Displays the currently selected column names for the scheme & options.
- m_DatasetKeyList - Variable in class weka.gui.experiment.ResultsPanel
-
Displays the list of selected columns determining the scheme.
- m_DatasetKeyModel - Variable in class weka.gui.experiment.ResultsPanel
-
Stores the list of attributes for selecting the scheme columns.
- m_DatasetListPanel - Variable in class weka.gui.experiment.SetupPanel
-
The panel for configuring selected datasets
- m_DatasetListPanel - Variable in class weka.gui.experiment.SimpleSetupPanel
-
The panel for configuring selected datasets
- m_DatasetModel - Variable in class weka.gui.experiment.ResultsPanel
-
The model embedded in m_DatasetCombo.
- m_DatasetNumber - Variable in class weka.experiment.Experiment
-
The current dataset number when the experiment is running
- m_Datasets - Variable in class weka.experiment.Experiment
-
An array of dataset files
- m_DatasetSpecifiers - Variable in class weka.experiment.PairedTTester
-
The list of dataset specifiers
- m_dataSourceListeners - Variable in class weka.gui.beans.PredictionAppender
-
Objects listenening for dataset events
- m_DataType - Variable in class weka.gui.beans.xml.XMLBeans
-
the type of data that is be read/written
- m_dataWs - Variable in class weka.classifiers.trees.lmt.ResidualSplit
-
The LogitBoost-weights for the set of instances
- m_dataZs - Variable in class weka.classifiers.trees.lmt.ResidualSplit
-
The Z-values (LogitBoost response) for the set of instances
- m_dateAttributes - Variable in class weka.core.converters.CSVLoader
-
The range of attributes to force to type date
- m_dateFormat - Variable in class weka.core.converters.CSVLoader
-
The formatting string to use to parse dates
- m_DateFormat - Static variable in class weka.core.logging.Logger
-
for formatting the dates.
- m_DateFormat - Variable in class weka.filters.unsupervised.attribute.Add
-
The date format.
- m_DbaseURLLab - Variable in class weka.gui.DatabaseConnectionDialog
-
- m_DbaseURLText - Variable in class weka.gui.DatabaseConnectionDialog
-
- m_DbDialog - Variable in class weka.gui.sql.ConnectionPanel
-
the databae connection dialog.
- m_DbUtils - Variable in class weka.gui.sql.ConnectionPanel
-
for connecting to the database.
- m_DbUtils - Variable in class weka.gui.sql.event.ConnectionEvent
-
the databaseutils instance reponsible for the connection
- m_DbUtils - Variable in class weka.gui.sql.event.QueryExecuteEvent
-
the Db utils instance for the current DB connection
- m_DbUtils - Variable in class weka.gui.sql.QueryPanel
-
for working on the database.
- m_Debug - Variable in class weka.associations.GeneralizedSequentialPatterns
-
Whether the classifier is run in debug mode.
- m_debug - Variable in class weka.attributeSelection.BestFirst
-
for debugging
- m_Debug - Variable in class weka.classifiers.BVDecompose
-
Debugging mode, gives extra output if true
- m_Debug - Variable in class weka.classifiers.BVDecomposeSegCVSub
-
Debugging mode, gives extra output if true.
- m_Debug - Variable in class weka.classifiers.Classifier
-
Whether the classifier is run in debug mode.
- m_Debug - Variable in class weka.classifiers.functions.Logistic
-
Debugging output
- m_Debug - Variable in class weka.classifiers.functions.supportVector.Kernel
-
enables debugging output
- m_Debug - Variable in class weka.classifiers.mi.CitationKNN
-
Debugging output
- m_Debug - Variable in class weka.core.Check
-
Debugging mode, gives extra output if true
- m_Debug - Variable in class weka.core.converters.TextDirectoryLoader
-
whether to print some debug information
- m_Debug - Variable in class weka.core.Debug.Random
-
whether to output debug information
- m_Debug - Static variable in class weka.core.Optimization
-
- m_Debug - Variable in class weka.datagenerators.DataGenerator
-
Debugging mode
- m_Debug - Variable in class weka.estimators.CheckEstimator
-
Debugging mode, gives extra output if true
- m_Debug - Variable in class weka.experiment.DatabaseResultListener
-
True if debugging output should be printed
- m_Debug - Variable in class weka.experiment.DatabaseUtils
-
True if debugging output should be printed.
- m_Debug - Variable in class weka.filters.SimpleFilter
-
Whether debugging is on
- m_Debug - Variable in class weka.gui.SimpleCLIPanel.CommandlineCompletion
-
debug mode on/off.
- m_DebugCheckBox - Variable in class weka.gui.DatabaseConnectionDialog
-
- m_DebugLab - Variable in class weka.gui.DatabaseConnectionDialog
-
- m_DebugLevel - Variable in class weka.clusterers.XMeans
-
level of debug output, 0 is no output.
- m_debugOutput - Variable in class weka.experiment.CrossValidationResultProducer
-
Save raw output of split evaluators --- for debugging purposes
- m_debugOutput - Variable in class weka.experiment.RandomSplitResultProducer
-
Save raw output of split evaluators --- for debugging purposes
- m_DebugVectors - Variable in class weka.clusterers.XMeans
-
all the debug vectors.
- m_DebugVectorsFile - Variable in class weka.clusterers.XMeans
-
file name of the input file for the random vectors.
- m_DebugVectorsIndex - Variable in class weka.clusterers.XMeans
-
the index for the current debug vector.
- m_DebugVectorsInput - Variable in class weka.clusterers.XMeans
-
input file for the random vectors --> USED FOR DEBUGGING.
- m_Decimals - Variable in class weka.filters.unsupervised.attribute.NumericCleaner
-
the number of decimals to round to (-1 means no rounding)
- m_decisionFeatures - Variable in class weka.classifiers.rules.DecisionTable
-
Holds the final feature set
- m_Default - Variable in class weka.core.Tee
-
the default printstream.
- m_DefaultColor - Variable in class weka.gui.MemoryUsagePanel
-
the default color.
- m_DefaultColors - Variable in class weka.gui.visualize.AttributePanel
-
default colours for colouring discrete class
- m_DefaultColors - Variable in class weka.gui.visualize.ClassPanel
-
default colours for colouring discrete class
- m_DefaultColors - Variable in class weka.gui.visualize.Plot2D
-
default colours for colouring discrete class
- m_DefaultColors - Variable in class weka.gui.visualize.VisualizePanel
-
default colours for colouring discrete class
- m_DefaultCols - Variable in class weka.filters.unsupervised.attribute.Discretize
-
The default columns to discretize
- m_DefaultCols - Variable in class weka.filters.unsupervised.attribute.NumericToNominal
-
The default columns to turn into nominals
- m_defaultExpression - Static variable in class weka.filters.unsupervised.attribute.MathExpression
-
The default modification expression
- m_defaultModel - Variable in class weka.classifiers.lazy.IBk
-
Default ZeroR model to use when there are no training instances
- m_DefaultOutput - Variable in class weka.datagenerators.DataGenerator
-
default output (is printed to stdout after generation)
- m_defaultValue - Variable in class weka.core.pmml.Discretize
-
The default value (if defined)
- m_defaultValueDefined - Variable in class weka.core.pmml.Discretize
-
True if a default value has been specified
- m_defaultValueOrPriorProbs - Variable in class weka.core.pmml.TargetMetaInfo
-
default value (numeric) or prior distribution (categorical)
- m_defaultWeight - Variable in class weka.classifiers.functions.Winnow
-
Starting weights for the prediction vector(s)
- m_DefDstr - Variable in class weka.classifiers.rules.ConjunctiveRule
-
The default rule distribution of the data not covered
- m_Degree - Variable in class weka.classifiers.functions.LibSVM
-
for poly - in older versions of libsvm declared as a double.
- m_degreesOfFreedom - Variable in class weka.experiment.PairedStats
-
The degrees of freedom (if set programmatically)
- m_Del - Static variable in class weka.classifiers.functions.SMO
-
Precision constant for updating sets
- m_Del - Static variable in class weka.classifiers.functions.supportVector.RegSMO
-
Precision constant for updating sets
- m_Del - Static variable in class weka.classifiers.mi.MISMO
-
Precision constant for updating sets
- M_DELETE - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
-
Missing value handling mode
- m_DeleteBut - Variable in class weka.gui.experiment.AlgorithmListPanel
-
Click to remove the selected dataset from the list
- m_DeleteBut - Variable in class weka.gui.experiment.DatasetListPanel
-
Click to remove the selected dataset from the list.
- m_DeleteBut - Variable in class weka.gui.experiment.HostListPanel
-
Click to remove the selected host from the list
- m_DeleteEmptyBins - Variable in class weka.classifiers.meta.RegressionByDiscretization
-
Whether to delete empty intervals.
- m_Delimiters - Variable in class weka.core.tokenizers.CharacterDelimitedTokenizer
-
Delimiters used in tokenization
- m_delta - Variable in class weka.associations.Apriori
-
Delta by which m_minSupport is decreased in each iteration.
- m_delta - Variable in class weka.associations.FPGrowth
-
The amount by which to decrease the support in each iteration
- m_delta - Variable in class weka.classifiers.functions.GaussianProcesses
-
Gaussian Noise Value.
- m_DeltaCols - Variable in class weka.filters.unsupervised.attribute.FirstOrder
-
Stores which columns to take differences between
- m_delTransform - Variable in class weka.classifiers.rules.DecisionTable
-
Filter used to remove columns discarded by feature selection
- m_Dependencies - Variable in class weka.core.Capabilities
-
the hashset for storing dependent capabilities, eg for meta-classifiers
- m_derivedMeta - Variable in class weka.core.pmml.MiningSchema
-
Meta information about derived fields (those defined in
the TransformationDictionary followed by those defined in
LocalTransformations)
- m_Description - Variable in class weka.gui.ExtensionFileFilter
-
The text description of the types of files accepted
- m_Descriptor - Variable in class weka.core.PropertyPath.PropertyContainer
-
the descriptor
- m_design - Variable in class weka.gui.beans.AbstractDataSource
-
True if this bean's appearance is the design mode appearance
- m_design - Variable in class weka.gui.beans.CostBenefitAnalysis
-
True if this bean's appearance is the design mode appearance
- m_design - Variable in class weka.gui.beans.DataVisualizer
-
True if this bean's appearance is the design mode appearance
- m_design - Variable in class weka.gui.beans.GraphViewer
-
True if this bean's appearance is the design mode appearance
- m_design - Variable in class weka.gui.beans.ModelPerformanceChart
-
True if this bean's appearance is the design mode appearance
- m_design - Variable in class weka.gui.beans.TextViewer
-
True if this bean's appearance is the design mode appearance
- m_DesignatedClass - Variable in class weka.classifiers.meta.ThresholdSelector
-
Designated class value, determined during building
- m_DesiredSize - Variable in class weka.classifiers.meta.Decorate
-
The desired ensemble size.
- m_DesiredWeightOfInstancesPerInterval - Variable in class weka.filters.unsupervised.attribute.Discretize
-
The desired weight of instances per bin
- m_DestFileChooser - Variable in class weka.gui.experiment.SimpleSetupPanel
-
The file chooser for selecting result destinations
- m_destinationDatabaseURL - Variable in class weka.gui.experiment.SimpleSetupPanel
-
The database destination URL to store results into
- m_destinationFilename - Variable in class weka.gui.experiment.SimpleSetupPanel
-
The filename to store results into
- m_DetectionPerAttribute - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
-
whether to generate Outlier/ExtremeValue attributes for each attribute
instead of a general one
- m_DetectMinorityClass - Variable in class weka.filters.supervised.instance.SMOTE
-
whether to detect the minority class automatically.
- m_Devs - Variable in class weka.classifiers.bayes.NaiveBayesSimple
-
The standard deviations for numeric attributes.
- m_DialogType - Variable in class weka.gui.ConverterFileChooser
-
the type of dialog to display
- m_Dimension - Variable in class weka.classifiers.mi.MINND
-
The dimension of each exemplar, i.e.
- m_Dir - Variable in class weka.core.Javadoc
-
the directory above the class to update
- m_Disc - Variable in class weka.classifiers.bayes.NaiveBayes
-
The discretization filter.
- m_DiscretizeBin - Variable in class weka.classifiers.mi.MIBoost
-
the number of discretization bins
- m_DiscretizeCols - Variable in class weka.filters.supervised.attribute.Discretize
-
Stores which columns to Discretize
- m_DiscretizeCols - Variable in class weka.filters.unsupervised.attribute.Discretize
-
Stores which columns to Discretize
- m_DiscretizedHeader - Variable in class weka.classifiers.meta.RegressionByDiscretization
-
Header of discretized data.
- m_DiscretizeFilter - Variable in class weka.classifiers.bayes.BayesNet
-
filter used to quantize continuous variables, if any
- m_Discretizer - Variable in class weka.classifiers.meta.RegressionByDiscretization
-
The discretization filter.
- m_Display - Variable in enum weka.core.TechnicalInformation.Field
-
the string used in toString()
- m_Display - Variable in enum weka.core.TechnicalInformation.Type
-
the string used in toString()
- m_displayAllPoints - Variable in class weka.gui.visualize.PlotData2D
-
Display all points (ie.
- m_DisplayedButton - Variable in class weka.gui.experiment.ResultsPanel
-
Lets the user select which schemes are compared to base.
- m_DisplayedList - Variable in class weka.gui.experiment.ResultsPanel
-
Holds the list of schemes to display.
- m_DisplayedModel - Variable in class weka.gui.experiment.ResultsPanel
-
The model embedded in m_DisplayedList.
- m_DisplayedResultsets - Variable in class weka.experiment.PairedTTester
-
An array containing the indexes of the datasets to display
- m_displayModelInOldFormat - Variable in class weka.classifiers.bayes.NaiveBayes
-
- m_displayName - Variable in class weka.core.pmml.DerivedFieldMetaInfo
-
display name
- m_displayRules - Variable in class weka.classifiers.rules.DecisionTable
-
Display Rules
- m_displayValue - Variable in class weka.core.pmml.FieldMetaInfo.Value
-
The display value (might hold a human readable value - e.g.
- m_displayValues - Variable in class weka.core.pmml.TargetMetaInfo
-
corresponding display values
- m_Distance - Variable in class weka.classifiers.mi.MIOptimalBall
-
the distances from each instance in a positive bag to each bag
- m_Distance - Variable in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborNode
-
The distance from the current instance to this neighbor.
- m_DistanceF - Variable in class weka.clusterers.XMeans
-
the distance function used.
- m_DistanceFunction - Variable in class weka.clusterers.HierarchicalClusterer
-
distance function used for comparing members of a cluster
- m_DistanceFunction - Variable in class weka.clusterers.SimpleKMeans
-
the distance function used.
- m_DistanceFunction - Variable in class weka.core.neighboursearch.balltrees.BallSplitter
-
The distance function (metric) from which
the tree is (OR is to be) built.
- m_DistanceFunction - Variable in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
The distance function to use to build the tree.
- m_DistanceFunction - Variable in class weka.core.neighboursearch.NearestNeighbourSearch
-
the distance function used.
- m_DistanceList - Variable in class weka.core.neighboursearch.CoverTree
-
Array holding the distances of the nearest neighbours.
- m_DistanceList - Variable in class weka.core.neighboursearch.KDTree
-
Array holding the distances of the nearest neighbours.
- m_Distances - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
The set of disctances from the test attribute to the set of train
attributes
- m_Distances - Variable in class weka.core.neighboursearch.BallTree
-
Array holding the distances of the nearest neighbours.
- m_Distances - Variable in class weka.core.neighboursearch.LinearNNSearch
-
Array holding the distances of the nearest neighbours.
- m_DistanceWeighting - Variable in class weka.classifiers.lazy.IBk
-
Whether the neighbours should be distance-weighted.
- m_DistinctLab - Variable in class weka.gui.AttributeSummaryPanel
-
Displays the number of distinct values
- m_distParameter - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
-
- m_disTransform - Variable in class weka.classifiers.rules.DecisionTable
-
Discretization filter
- m_DistributeExperimentPanel - Variable in class weka.gui.experiment.SetupPanel
-
The panel for enabling a distributed experiment
- m_distribution - Variable in class weka.associations.PriorEstimation
-
Hashtable to store the confidence values of randomly generated rules.
- m_Distribution - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
Distribution of the attribute value in the train dataset
- m_distribution - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
-
- m_Distribution - Variable in class weka.classifiers.trees.BFTree
-
Class distributions.
- m_distribution - Variable in class weka.classifiers.trees.j48.ClassifierSplitModel
-
Distribution of class values.
- m_Distribution - Variable in class weka.classifiers.trees.REPTree.Tree
-
The (unnormalized) class distribution in the nominal
case.
- m_Distribution - Variable in class weka.classifiers.trees.SimpleCart
-
Distributions of leaf node (or temporary leaf node in minimal cost-complexity pruning)
- m_distribution - Variable in class weka.filters.unsupervised.attribute.RandomProjection
-
Stores the distribution to use for calculating the
random matrix
- m_Distributions - Variable in class weka.classifiers.bayes.BayesNet
-
The attribute estimators containing CPTs.
- m_Distributions - Variable in class weka.classifiers.bayes.NaiveBayes
-
The attribute estimators.
- m_Dists - Variable in class weka.classifiers.trees.BFTree
-
Distributions of each attribute for two successor nodes.
- m_DocType - Variable in class weka.core.xml.XMLDocument
-
the DOCTYPE node as String.
- m_Document - Variable in class weka.core.xml.XMLDocument
-
the DOM document.
- m_Document - Variable in class weka.core.xml.XMLSerialization
-
the XMLDocument that performs the transformation to and fro XML
- m_doesProduce - Variable in class weka.experiment.ClassifierSplitEvaluator
-
Array of booleans corresponding to the measures in m_AdditionalMeasures
indicating which of the AdditionalMeasures the current classifier
can produce
- m_doesProduce - Variable in class weka.experiment.DensityBasedClustererSplitEvaluator
-
Array of booleans corresponding to the measures in m_AdditionalMeasures
indicating which of the AdditionalMeasures the current clusterer
can produce
- m_doesProduce - Variable in class weka.experiment.RegressionSplitEvaluator
-
Array of booleans corresponding to the measures in m_AdditionalMeasures
indicating which of the AdditionalMeasures the current classifier
can produce
- m_doneRanking - Variable in class weka.attributeSelection.GreedyStepwise
-
used to indicate whether or not ranking has been performed
- m_dontFilterAfterFirstBatch - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
-
Whether to filter instances after the first batch has been processed
- m_dontNormalize - Variable in class weka.classifiers.functions.SPegasos
-
Turn off normalization of the input data.
- m_DontNormalize - Variable in class weka.core.NormalizableDistance
-
True if normalization is turned off (default false).
- m_dontReplaceMissing - Variable in class weka.classifiers.functions.SPegasos
-
Turn off global replacement of missing values.
- m_doRank - Variable in class weka.attributeSelection.GreedyStepwise
-
go from one side of the search space to the other in order to generate
a ranking
- m_doubleType - Variable in class weka.experiment.DatabaseUtils
-
double type for the create table statement.
- m_DownBut - Variable in class weka.gui.experiment.AlgorithmListPanel
-
Click to move the selected algorithm(s) one down
- m_DownBut - Variable in class weka.gui.experiment.DatasetListPanel
-
Click to move the selected dataset(s) one down.
- m_drawnPoints - Variable in class weka.gui.visualize.Plot2D
-
An array used to show if a point is hidden or not.
- m_dtInstances - Variable in class weka.classifiers.rules.DecisionTable
-
Holds the final feature selected set of instances
- m_edges - Variable in class weka.gui.graphvisualizer.BIFParser
-
These holds the nodes and edges of the graph
- m_edges - Variable in class weka.gui.graphvisualizer.DotParser
-
These holds the nodes and edges of the graph
- m_edges - Variable in class weka.gui.graphvisualizer.GraphVisualizer
-
Vector containing edges
- m_edges - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
-
FastVector containing nodes and edges
- m_EditBut - Variable in class weka.gui.experiment.AlgorithmListPanel
-
Click to edit the selected algorithm
- m_EditBut - Variable in class weka.gui.experiment.DatasetListPanel
-
Click to edit the selected algorithm.
- m_EditBut - Variable in class weka.gui.explorer.PreprocessPanel
-
Click to open the current instances in a viewer
- m_Editing - Variable in class weka.gui.experiment.AlgorithmListPanel
-
Whether an algorithm is added or only edited
- m_EditorComponent - Variable in class weka.gui.GenericObjectEditor
-
The GUI component for editing values, created when needed.
- m_EditorsRegistered - Static variable in class weka.gui.GenericObjectEditor
-
whether the Weka Editors were already registered.
- m_Eigenvalues - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Eigenvalues for the corresponding eigenvectors.
- m_Eigenvectors - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Will hold the unordered linear transformations of the (normalized)
original data.
- m_Elements - Variable in class weka.associations.gsp.Sequence
-
ordered list of the comprised elements/itemsets
- m_Elements - Variable in class weka.core.AlgVector
-
The values of the matrix
- m_Elements - Variable in class weka.core.PropertyPath.Path
-
the structure
- m_emData - Variable in class weka.classifiers.mi.MIEMDD
-
MI data
- m_Enabled - Variable in class weka.core.Debug
-
whether logging is enabled
- m_Enabled - Static variable in class weka.core.Memory
-
whether memory management is enabled
- m_Enabled - Variable in class weka.gui.GenericObjectEditor
-
True if the GUI component is needed.
- m_enableDistributedExperiment - Variable in class weka.gui.experiment.DistributeExperimentPanel
-
Distribute the current experiment to remote hosts
- m_Enclosures - Variable in class weka.core.converters.CSVLoader
-
enclosure character(s) to use for strings
- m_End - Variable in class weka.core.neighboursearch.balltrees.BallNode
-
The end index of the portion of the master index array,
which stores indices of the instances/points the node
contains.
- m_End - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode
-
The end index of the portion of the master index array,
which stores indices of the instances/points the node
contains.
- m_EndTag - Variable in class weka.core.Javadoc
-
the end tag
- m_EnsembleLibraryFrame - Variable in class weka.gui.GUIChooser
-
The frame containing the ensemble library interface
- m_entries - Variable in class weka.classifiers.rules.DecisionTable
-
The hashtable used to hold training instances
- m_EnumerateColNames - Variable in class weka.experiment.ResultMatrix
-
whether a "(x)" is printed before each column name with "x" as the
index
- m_EnumerateRowNames - Variable in class weka.experiment.ResultMatrix
-
whether a "(x)" is printed before each row name with "x" as the index
- m_env - Variable in class weka.core.converters.AbstractFileLoader
-
Environment variables
- m_env - Variable in class weka.core.converters.AbstractFileSaver
-
Environment variables
- m_env - Variable in class weka.gui.beans.FlowRunner
-
- m_env - Variable in class weka.gui.beans.Loader
-
The environment variables.
- m_env - Variable in class weka.gui.beans.Saver
-
The environment variables.
- m_env - Variable in class weka.gui.beans.SerializedModelSaver
-
The environment variables.
- m_epochs - Variable in class weka.classifiers.functions.SPegasos
-
The number of epochs to perform (batch learning).
- m_eps - Variable in class weka.classifiers.functions.LibLINEAR
-
stopping criteria
- m_eps - Variable in class weka.classifiers.functions.LibSVM
-
stopping criteria
- m_eps - Variable in class weka.classifiers.functions.SMO
-
Epsilon for rounding.
- m_eps - Variable in class weka.classifiers.functions.supportVector.RegSMO
-
tolerance parameter, smaller changes on alpha in inner loop will be ignored
- m_eps - Variable in class weka.classifiers.mi.MISMO
-
Epsilon for rounding.
- m_epsilon - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
-
epsilon of epsilon-insensitive cost function
- m_Epsilon - Static variable in class weka.core.Optimization
-
Compute machine precision
- m_Error - Variable in class weka.classifiers.BVDecompose
-
The error rate
- m_Error - Variable in class weka.classifiers.BVDecomposeSegCVSub
-
The error rate
- m_ErrorEstimator - Variable in class weka.classifiers.Evaluation
-
Numeric class error estimator for scheme
- m_ErrorFlags - Variable in class weka.classifiers.lazy.LBR
-
leave-one-out error flags on the training dataaet.
- m_errorOnProbabilities - Variable in class weka.classifiers.functions.SimpleLogistic
-
If true, use minimize error on probabilities instead of misclassification error
- m_errorOnProbabilities - Variable in class weka.classifiers.trees.FT
-
use error on probabilties instead of misclassification for stopping criterion of LogitBoost?
- m_errorOnProbabilities - Variable in class weka.classifiers.trees.lmt.LogisticBase
-
Use error on probabilities for stopping criterion of LogitBoost?
- m_errorOnProbabilities - Variable in class weka.classifiers.trees.LMT
-
use error on probabilties instead of misclassification for stopping criterion of LogitBoost?
- m_errors - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
The current set of errors for all non-bound examples.
- m_Errors - Variable in class weka.classifiers.lazy.LBR
-
leave-one-out errors on the training dataset.
- m_errors - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
The current set of errors for all non-bound examples.
- m_ErrRedirector - Variable in class weka.gui.SimpleCLIPanel
-
The thread that sends output from m_POE to the output box.
- m_Estimator - Variable in class weka.estimators.CheckEstimator
-
The estimator to be examined
- m_EstimatorOptions - Variable in class weka.estimators.CheckEstimator
-
The options to be passed to the base estimator.
- m_EuclideanDistance - Variable in class weka.core.neighboursearch.CoverTree
-
The euclidean distance function to use.
- m_EuclideanDistance - Variable in class weka.core.neighboursearch.KDTree
-
The euclidean distance function to use.
- m_EuclideanDistance - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
-
The distance function used for building the tree.
- m_EvalMode - Variable in class weka.classifiers.meta.ThresholdSelector
-
The evaluation mode
- m_Evaluation - Variable in class weka.classifiers.meta.GridSearch
-
the type of evaluation
- m_Evaluation - Variable in class weka.classifiers.meta.GridSearch.PerformanceComparator
-
the performance measure to use for comparison
- m_evaluation - Variable in class weka.classifiers.rules.DecisionTable
-
The evaluation object used to evaluate subsets
- m_evaluationMeasure - Variable in class weka.classifiers.rules.DecisionTable
-
- m_Evaluations - Variable in class weka.classifiers.functions.supportVector.KernelEvaluation
-
the kernel evaluation results
- m_Evaluator - Variable in class weka.attributeSelection.CheckAttributeSelection
-
The evaluator to be examined
- m_evaluator - Variable in class weka.attributeSelection.CostSensitiveASEvaluation
-
The base evaluator to use
- m_evaluator - Variable in class weka.attributeSelection.FilteredAttributeEval
-
Base evaluator
- m_evaluator - Variable in class weka.attributeSelection.FilteredSubsetEval
-
Base evaluator
- m_Evaluator - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
-
The attribute evaluator to use
- m_evaluator - Variable in class weka.classifiers.rules.DecisionTable
-
Our own internal evaluator
- m_EvalWRTCostsBut - Variable in class weka.gui.explorer.ClassifierPanel
-
Check to evaluate w.r.t a cost matrix
- m_Events - Variable in class weka.associations.gsp.Element
-
events/items stored as an array of ints
- m_examplesCounted - Variable in class weka.classifiers.trees.ADTree
-
Statistics - the number of instances processed during search
- m_examplesCounted - Variable in class weka.classifiers.trees.LADTree
-
- m_Exception - Variable in class weka.gui.sql.event.ConnectionEvent
-
a possible exception that occurred if not successful
- m_Exception - Variable in class weka.gui.sql.event.QueryExecuteEvent
-
a possible exception, if the query failed
- m_Excludes - Variable in class weka.gui.GenericPropertiesCreator
-
the hashtable that stores the excludes:
key -> Hashtable(prefix -> Vector of classnames)
- m_executionSlots - Variable in class weka.gui.beans.Classifier
-
Number of threads to use to train models with
- m_executorPool - Variable in class weka.gui.beans.Classifier
-
Pool of threads to train models on incoming data
- m_ExitIfNoWindowsOpen - Static variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
whether the exit if there are no more windows open
- m_Exp - Variable in class weka.gui.experiment.AlgorithmListPanel
-
The experiment to set the algorithm list of
- m_Exp - Variable in class weka.gui.experiment.DatasetListPanel
-
The experiment to set the dataset list of.
- m_Exp - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel
-
The experiment this all applies to
- m_Exp - Variable in class weka.gui.experiment.HostListPanel
-
The remote experiment to set the host list of
- m_Exp - Variable in class weka.gui.experiment.ResultsPanel
-
An experiment (used for identifying a result source) -- optional.
- m_Exp - Variable in class weka.gui.experiment.RunNumberPanel
-
The experiment being configured
- m_Exp - Variable in class weka.gui.experiment.RunPanel
-
The experiment to run
- m_Exp - Variable in class weka.gui.experiment.SetupPanel
-
The experiment being configured
- m_Exp - Variable in class weka.gui.experiment.SimpleSetupPanel
-
The experiment being configured
- m_Expansion - Static variable in class weka.classifiers.trees.BFTree
-
Number of expansions.
- m_ExpClassificationRBut - Variable in class weka.gui.experiment.SimpleSetupPanel
-
Radio button for choosing classification experiment
- m_expectation - Variable in class weka.associations.PredictiveApriori
-
The expected predictive accuracy a rule needs to be a candidate for the output.
- m_expectation - Variable in class weka.associations.RuleGeneration
-
The minimum expected predictive accuracy that is needed to be a candidate for the list of the best rules.
- m_ExpectedResultsPerAverage - Variable in class weka.experiment.AveragingResultProducer
-
The number of results expected to average over for each run
- m_ExperimenterBut - Variable in class weka.gui.GUIChooser
-
Click to open the Explorer
- m_ExperimenterFrame - Variable in class weka.gui.GUIChooser
-
The frame containing the experiment interface
- m_experimentFinished - Variable in class weka.experiment.RemoteExperimentEvent
-
True if a remote experiment has finished
- m_ExperimentParameterLabel - Variable in class weka.gui.experiment.SimpleSetupPanel
-
Label for parameter field
- m_ExperimentParameterTField - Variable in class weka.gui.experiment.SimpleSetupPanel
-
Input field for experiment parameter
- m_ExperimentTypeCBox - Variable in class weka.gui.experiment.SimpleSetupPanel
-
Combo box for choosing experiment type
- m_ExpFilter - Variable in class weka.gui.experiment.SetupPanel
-
A filter to ensure only experiment files get shown in the chooser
- m_ExpFilter - Variable in class weka.gui.experiment.SimpleSetupPanel
-
A filter to ensure only experiment files get shown in the chooser
- m_ExplicitPropsFile - Variable in class weka.gui.GenericPropertiesCreator
-
whether an explicit input file was given - if false, the Utils class
is used to locate the props-file
- m_Explorer - Variable in class weka.gui.explorer.AssociationsPanel
-
the parent frame
- m_Explorer - Variable in class weka.gui.explorer.AttributeSelectionPanel
-
the parent frame
- m_Explorer - Variable in class weka.gui.explorer.ClassifierPanel
-
the parent frame
- m_Explorer - Variable in class weka.gui.explorer.ClustererPanel
-
the parent frame
- m_Explorer - Variable in class weka.gui.explorer.PreprocessPanel
-
the parent frame
- m_Explorer - Variable in class weka.gui.explorer.VisualizePanel
-
the parent frame
- m_ExplorerBut - Variable in class weka.gui.GUIChooser
-
Click to open the Explorer
- m_ExplorerFrame - Variable in class weka.gui.GUIChooser
-
The frame containing the explorer interface
- m_exponent - Variable in class weka.classifiers.functions.supportVector.PolyKernel
-
The exponent for the polynomial kernel.
- m_ExpRegressionRBut - Variable in class weka.gui.experiment.SimpleSetupPanel
-
Radio button for choosing regression experiment
- m_expression - Variable in class weka.core.pmml.DefineFunction
-
The Expression for this function to use
- m_expression - Variable in class weka.core.pmml.DerivedFieldMetaInfo
-
the single expression that defines the value of this field
- m_Expression - Variable in class weka.datagenerators.classifiers.regression.Expression
-
the expression for computing y
- m_Expression - Variable in class weka.filters.unsupervised.instance.SubsetByExpression
-
the expresion to use for filtering.
- m_Extension - Variable in class weka.gui.ExtensionFileFilter
-
The filename extensions of accepted files
- m_extent - Variable in class weka.gui.visualize.PostscriptGraphics
-
The bounding box of the output
- m_ExtremeValuesAsOutliers - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
-
whether extreme values are also tagged as outliers
- m_ExtremeValuesFactor - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
-
the factor for detecting extreme values, by default 2*m_OutlierFactor
- m_f - Variable in class weka.core.Optimization
-
function value
- m_factor - Variable in class weka.classifiers.functions.supportVector.Puk
-
Cached factor for the Puk kernel.
- m_factorList - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
-
- m_Factory - Variable in class weka.core.xml.XMLDocument
-
the factory for DocumentBuilder.
- m_FailReason - Variable in class weka.core.Capabilities
-
the reason why the test failed, used to throw an exception
- m_fAlpha - Variable in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
-
Holds prior on count
- m_fastRegression - Variable in class weka.classifiers.trees.lmt.LMTNode
-
Use heuristic that determines the number of LogitBoost iterations only once in the beginning?
- m_fastRegression - Variable in class weka.classifiers.trees.LMT
-
use heuristic that determines the number of LogitBoost iterations only once in the beginning?
- m_field - Variable in class weka.core.pmml.NormDiscrete
-
The actual attribute itself
- m_fieldDefs - Variable in class weka.core.pmml.Expression
-
The field defs
- m_fieldIndex - Variable in class weka.core.pmml.Discretize
-
The index of the field
- m_fieldIndex - Variable in class weka.core.pmml.NormContinuous
-
The index of the field
- m_fieldIndex - Variable in class weka.core.pmml.NormDiscrete
-
The index of the attribute
- m_fieldInstancesStructure - Variable in class weka.core.pmml.MiningSchema
-
The structure of all the fields (both mining schema and derived) as Instances
- m_fieldName - Variable in class weka.core.pmml.Discretize
-
The name of the field to be discretized
- m_fieldName - Variable in class weka.core.pmml.FieldMetaInfo
-
the name of the field
- m_fieldName - Variable in class weka.core.pmml.FieldRef
-
The name of the field to reference
- m_fieldName - Variable in class weka.core.pmml.NormContinuous
-
The name of the field to use
- m_fieldName - Variable in class weka.core.pmml.NormDiscrete
-
The name of the field to lookup our value in
- m_fieldsMap - Variable in class weka.classifiers.pmml.consumer.PMMLClassifier
-
The mapping between mining schema fields and incoming instance
attributes
- m_fieldValue - Variable in class weka.core.pmml.NormDiscrete
-
The actual value (as a String) that will correspond to an output of 1
- m_fieldValueIndex - Variable in class weka.core.pmml.NormDiscrete
-
If we are referring to a nominal (rather than String) attribute
then this holds the index of the value in question.
- m_File - Variable in class weka.core.converters.AbstractFileLoader
-
the file
- m_File - Variable in class weka.core.converters.ConverterUtils.DataSource
-
the file to load.
- m_FileChooser - Variable in class weka.gui.beans.KnowledgeFlowApp
-
The file chooser for selecting layout files
- m_FileChooser - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
The file chooser for selecting arff files
- m_FileChooser - Variable in class weka.gui.experiment.AlgorithmListPanel
-
The file chooser for selecting experiments
- m_FileChooser - Variable in class weka.gui.experiment.DatasetListPanel
-
The file chooser component.
- m_FileChooser - Variable in class weka.gui.experiment.ResultsPanel
-
The file chooser for selecting result files.
- m_FileChooser - Variable in class weka.gui.experiment.SetupPanel
-
The file chooser for selecting experiments
- m_FileChooser - Variable in class weka.gui.experiment.SimpleSetupPanel
-
The file chooser for selecting experiments
- m_FileChooser - Variable in class weka.gui.explorer.ClassifierPanel
-
The file chooser for selecting model files
- m_FileChooser - Variable in class weka.gui.explorer.ClustererPanel
-
The file chooser for selecting model files
- m_FileChooser - Variable in class weka.gui.explorer.PreprocessPanel
-
The file chooser for selecting data files
- m_FileChooser - Variable in class weka.gui.FileEditor
-
The file chooser used for selecting files
- m_FileChooser - Variable in class weka.gui.GenericObjectEditor.GOEPanel
-
The filechooser for opening and saving object files.
- m_FileChooser - Variable in class weka.gui.SetInstancesPanel
-
The file chooser for selecting arff files
- m_FileChooser - Variable in class weka.gui.visualize.VisualizePanel
-
file chooser for saving instances
- m_FileChooserGraphVisualizer - Variable in class weka.gui.GUIChooser
-
filechooser for the GraphVisualizer
- m_FileChooserGraphVisualizer - Variable in class weka.gui.Main
-
filechooser for the GraphVisualizer.
- m_FileChooserPanel - Static variable in class weka.gui.visualize.PrintableComponent
-
the filechooser for saving the panel.
- m_FileChooserPlot - Variable in class weka.gui.GUIChooser
-
filechooser for Plots
- m_FileChooserPlot - Variable in class weka.gui.Main
-
filechooser for Plots.
- m_FileChooserROC - Variable in class weka.gui.GUIChooser
-
filechooser for ROC curves
- m_FileChooserROC - Variable in class weka.gui.Main
-
filechooser for ROC curves.
- m_FileChooserTreeVisualizer - Variable in class weka.gui.GUIChooser
-
filechooser for the TreeVisualizer
- m_FileChooserTreeVisualizer - Variable in class weka.gui.Main
-
filechooser for the TreeVisualizer.
- m_FileLoaders - Static variable in class weka.core.converters.ConverterUtils
-
all available loaders (extension <-> classname).
- m_FileMustExist - Variable in class weka.gui.ConverterFileChooser
-
whether the file to be opened must exist (only open dialog)
- m_Filename - Variable in class weka.core.Debug.Log
-
the filename, if any
- m_Filename - Variable in class weka.core.Debug.SimpleLog
-
the file to write to (if null then only stdout is used)
- m_Filename - Variable in class weka.core.FindWithCapabilities
-
a file the capabilities can be based on.
- m_FileSavers - Static variable in class weka.core.converters.ConverterUtils
-
all available savers (extension <-> classname).
- m_FillWithMissing - Variable in class weka.filters.unsupervised.attribute.AbstractTimeSeries
-
True if missing values should be used rather than removing instances
where the translated value is not known (due to border effects).
- m_Filter - Variable in class weka.associations.FilteredAssociator
-
The filter
- m_filter - Variable in class weka.attributeSelection.FilteredAttributeEval
-
Filter
- m_filter - Variable in class weka.attributeSelection.FilteredSubsetEval
-
Filter
- m_Filter - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
-
Filter interface used to point to weka.filters.unsupervised.attribute.Normalize object
- m_Filter - Variable in class weka.classifiers.functions.GaussianProcesses
-
The filter used to standardize/normalize all values.
- m_Filter - Variable in class weka.classifiers.functions.LibLINEAR
-
for normalizing the data
- m_Filter - Variable in class weka.classifiers.functions.LibSVM
-
for normalizing the data
- m_Filter - Variable in class weka.classifiers.functions.PLSClassifier
-
the PLS filter
- m_Filter - Variable in class weka.classifiers.functions.SMO
-
The filter used to standardize/normalize all values.
- m_Filter - Variable in class weka.classifiers.functions.SMOreg
-
The filter used to standardize/normalize all values.
- m_Filter - Variable in class weka.classifiers.meta.FilteredClassifier
-
The filter
- m_Filter - Variable in class weka.classifiers.meta.GridSearch
-
the Filter
- m_Filter - Variable in class weka.classifiers.mi.MDD
-
The filter used to standardize/normalize all values.
- m_Filter - Variable in class weka.classifiers.mi.MIBoost
-
filter used for discretization
- m_Filter - Variable in class weka.classifiers.mi.MIDD
-
The filter used to standardize/normalize all values.
- m_Filter - Variable in class weka.classifiers.mi.MIEMDD
-
The filter used to standardize/normalize all values.
- m_Filter - Variable in class weka.classifiers.mi.MIOptimalBall
-
The filter used to standardize/normalize all values.
- m_Filter - Variable in class weka.classifiers.mi.MISMO
-
The filter used to standardize/normalize all values.
- m_Filter - Variable in class weka.classifiers.mi.MISVM
-
The filter used to standardize/normalize all values.
- m_Filter - Variable in class weka.clusterers.FilteredClusterer
-
The filter.
- m_Filter - Variable in class weka.datagenerators.classifiers.regression.Expression
-
the filter for generating y out of x
- m_Filter - Variable in class weka.filters.CheckSource
-
the classifier used for generating the source code
- m_Filter - Variable in class weka.filters.supervised.attribute.PLSFilter
-
for centering the data
- m_Filter - Variable in class weka.filters.unsupervised.attribute.KernelFilter
-
for centering/standardizing the data
- m_Filter - Variable in class weka.filters.unsupervised.attribute.Wavelet
-
an optional filter for preprocessing of the data
- m_Filter - Variable in class weka.gui.explorer.Explorer.CapabilitiesFilterChangeEvent
-
the capabilities filter
- m_filterAfterFirstBatch - Variable in class weka.filters.unsupervised.instance.SubsetByExpression
-
Whether to filter instances after the first batch has been processed
- m_FilterAttributes - Variable in class weka.associations.GeneralizedSequentialPatterns
-
String containing the attribute numbers that are used for result
filtering; -1 means no filtering
- m_FilterAttrVector - Variable in class weka.associations.GeneralizedSequentialPatterns
-
Vector containing the attribute numbers that are used for result
filtering; -1 means no filtering
- m_FilteredClassifier - Variable in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
-
The filtered classifier in which the base classifier is wrapped.
- m_FilteredClassifier - Variable in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
-
The filtered classifier in which the base classifier is wrapped.
- m_FilteredInstances - Variable in class weka.associations.FilteredAssociator
-
The instance structure of the filtered instances
- m_filteredInstances - Variable in class weka.attributeSelection.FilteredAttributeEval
-
Filtered instances structure
- m_filteredInstances - Variable in class weka.attributeSelection.FilteredSubsetEval
-
Filtered instances structure
- m_FilteredInstances - Variable in class weka.classifiers.meta.FilteredClassifier
-
The instance structure of the filtered instances
- m_FilteredInstances - Variable in class weka.clusterers.FilteredClusterer
-
The instance structure of the filtered instances.
- m_FilterEditor - Variable in class weka.gui.explorer.PreprocessPanel
-
Lets the user configure the filter
- m_FilterPanel - Variable in class weka.gui.explorer.PreprocessPanel
-
Filter configuration
- m_Filters - Variable in class weka.filters.MultiFilter
-
The filters
- m_Filters - Variable in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
-
The filters.
- m_filterThread - Variable in class weka.gui.beans.Filter
-
- m_filterType - Variable in class weka.classifiers.functions.GaussianProcesses
-
Whether to normalize/standardize/neither
- m_filterType - Variable in class weka.classifiers.functions.SMO
-
Whether to normalize/standardize/neither
- m_filterType - Variable in class weka.classifiers.functions.SMOreg
-
Whether to normalize/standardize/neither
- m_filterType - Variable in class weka.classifiers.mi.MDD
-
Whether to normalize/standardize/neither, default:standardize
- m_filterType - Variable in class weka.classifiers.mi.MIDD
-
Whether to normalize/standardize/neither, default:standardize
- m_filterType - Variable in class weka.classifiers.mi.MIEMDD
-
Whether to normalize/standardize/neither, default:standardize
- m_filterType - Variable in class weka.classifiers.mi.MIOptimalBall
-
Whether to normalize/standardize/neither
- m_filterType - Variable in class weka.classifiers.mi.MISMO
-
Whether to normalize/standardize/neither
- m_filterType - Variable in class weka.classifiers.mi.MISVM
-
Whether to normalize/standardize/neither
- m_filterType - Variable in class weka.filters.unsupervised.attribute.StringToWordVector
-
The normalization to apply.
- m_findAllRulesForSupportLevel - Variable in class weka.associations.FPGrowth
-
If true, just all rules meeting the lower bound on the minimum
support will be found.
- m_FindNumBins - Variable in class weka.filters.unsupervised.attribute.Discretize
-
Find the number of bins using cross-validated entropy.
- m_Finished - Variable in class weka.experiment.Experiment
-
True if the experiment has finished running
- m_First - Variable in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborList
-
The first node in the list.
- m_First - Variable in class weka.gui.LogPanel
-
An indicator for whether text has been output yet
- m_FirstBatchDone - Variable in class weka.filters.Filter
-
True if the first batch has been done
- m_firstBatchFinished - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Have we processed the first batch (i.e.
- m_FirstCheck - Variable in class weka.core.converters.CSVLoader
-
whether the first row has been read.
- m_FirstSuccessor - Variable in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
-
The first successor
- m_FirstSuccessor - Variable in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
-
The first successor
- m_fitLogisticModels - Variable in class weka.classifiers.functions.SMO
-
Whether logistic models are to be fit
- m_fitLogisticModels - Variable in class weka.classifiers.mi.MISMO
-
Whether logistic models are to be fit
- m_FixedExpansion - Variable in class weka.classifiers.trees.BFTree
-
Fixed number of expansions (if no pruning method is used, its value is -1.
- m_fixedNumIterations - Variable in class weka.classifiers.trees.lmt.LogisticBase
-
Use fixed number of iterations for LogitBoost? (if negative, cross-validate number of iterations)
- m_flowEnvironment - Variable in class weka.gui.beans.KnowledgeFlowApp
-
Environment variables for the current flow
- m_FlowHeight - Variable in class weka.gui.beans.KnowledgeFlowApp
-
the flow layout height
- m_FlowWidth - Variable in class weka.gui.beans.KnowledgeFlowApp
-
the flow layout width
- m_fMarginP - Variable in class weka.classifiers.bayes.net.EditableBayesNet
-
marginal distributions *
- m_fnPrevious - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_FoldColumn - Variable in class weka.experiment.PairedTTester
-
The option setting for the fold number column (-1 means none)
- m_FontColor - Variable in class weka.gui.treevisualizer.TreeVisualizer
-
the font color.
- m_Format - Variable in class weka.core.Debug.Timestamp
-
the format of the timestamp
- m_format - Variable in class weka.gui.beans.PredictionAppender
-
Format of instances to be produced.
- m_formatter - Variable in class weka.core.converters.CSVLoader
-
The formatter to use on dates
- m_Formatter - Variable in class weka.core.Debug.Timestamp
-
handles the format of the output
- m_forwardSearchMethod - Variable in class weka.attributeSelection.LinearForwardSelection
-
0 == forward selection, 1 == floating forward search
- m_fpPrevious - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_fPrior - Variable in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
-
Holds the prior probability
- m_FramedOutput - Variable in class weka.gui.ResultHistoryPanel
-
A Hashtable mapping names to output text components
- m_FrameLocation - Variable in class weka.gui.MemoryUsagePanel
-
the position for the dialog.
- m_framePoppedUp - Variable in class weka.gui.beans.CostBenefitAnalysis
-
- m_framePoppedUp - Variable in class weka.gui.beans.DataVisualizer
-
- m_framePoppedUp - Variable in class weka.gui.beans.ModelPerformanceChart
-
- m_frequency - Variable in class weka.associations.FPGrowth.BinaryItem
-
The frequency of the item
- m_FromDBaseBut - Variable in class weka.gui.experiment.ResultsPanel
-
Click to load results from a database.
- m_FromExpBut - Variable in class weka.gui.experiment.ResultsPanel
-
Click to get results from the destination given in the experiment.
- m_FromFileBut - Variable in class weka.gui.experiment.ResultsPanel
-
Click to load results from a file.
- m_FromLab - Variable in class weka.gui.experiment.ResultsPanel
-
Displays a message about the current result set.
- m_FullyContainChildBalls - Variable in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
Should a parent ball completely enclose the balls
of its two children, or only the points inside
its children.
- m_func - Variable in class weka.core.pmml.BuiltInMath
-
The function to apply
- m_func - Variable in class weka.core.pmml.BuiltInString
-
The function to apply
- m_Function - Variable in class weka.datagenerators.classifiers.classification.Agrawal
-
the function to use for generating the data
- m_functionName - Variable in class weka.core.pmml.Function
-
The name of this function
- m_functionType - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
-
- m_functionType - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
-
The mining function
- m_gainV - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_Gamma - Variable in class weka.classifiers.functions.LibSVM
-
for poly/rbf/sigmoid
- m_gamma - Variable in class weka.classifiers.functions.supportVector.RBFKernel
-
Gamma for the RBF kernel.
- m_GammaActual - Variable in class weka.classifiers.functions.LibSVM
-
for poly/rbf/sigmoid (the actual gamma)
- m_GenerateBut - Variable in class weka.gui.explorer.PreprocessPanel
-
Click to generate artificial data
- m_Generator - Variable in class weka.datagenerators.classifiers.classification.BayesNet
-
the bayesian net generator, that produces the actual data
- m_GeneratorEditor - Variable in class weka.gui.explorer.DataGeneratorPanel
-
the GOE for the generators
- m_GeneratorPropertyPanel - Variable in class weka.gui.experiment.SetupPanel
-
The panel that configures iteration on custom resultproducer property
- m_GenericPropertiesCreator - Variable in class weka.core.FindWithCapabilities
-
whether to use the GenericPropertiesCreator with the superclass.
- m_genTime - Variable in class weka.associations.RuleItem
-
The generation time of a rule.
- m_GetCurrentMethod - Variable in class weka.core.stemmers.SnowballStemmer
-
the getCurrent method.
- m_GlobalBlend - Variable in class weka.classifiers.lazy.KStar
-
default sphere of influence blend setting
- m_globalCounts - Variable in class weka.classifiers.misc.VFI
-
The global class counts
- m_globalInfo - Variable in class weka.gui.beans.Associator
-
Global info for the wrapped associator (if it exists).
- m_globalInfo - Variable in class weka.gui.beans.Classifier
-
Global info for the wrapped classifier (if it exists).
- m_globalInfo - Variable in class weka.gui.beans.Clusterer
-
Global info for the wrapped classifier (if it exists).
- m_globalInfo - Variable in class weka.gui.beans.Filter
-
Global info for the wrapped filter (if it exists).
- m_globalInfo - Variable in class weka.gui.beans.Loader
-
Global info for the wrapped loader (if it exists).
- m_globalInfo - Variable in class weka.gui.beans.Saver
-
Global info for the wrapped loader (if it exists).
- m_globalMaxValue - Variable in class weka.datagenerators.clusterers.SubspaceCluster
-
store global max values
- m_globalMinValue - Variable in class weka.datagenerators.clusterers.SubspaceCluster
-
store global min values
- m_gp - Variable in class weka.gui.graphvisualizer.GraphVisualizer
-
Panel actually displaying the graph
- m_graphName - Variable in class weka.gui.graphvisualizer.DotParser
-
This holds the name of the graph if there is any otherwise it is null
- m_GraphPanel - Variable in class weka.classifiers.bayes.net.GUI
-
Panel actually displaying the graph
- m_graphString - Variable in class weka.gui.beans.GraphEvent
-
- m_graphTitle - Variable in class weka.gui.beans.GraphEvent
-
- m_graphType - Variable in class weka.gui.beans.GraphEvent
-
- m_GraphVisualizers - Variable in class weka.gui.GUIChooser
-
keeps track of the opened graph visualizer instancs
- m_Grid - Variable in class weka.classifiers.meta.GridSearch
-
the value-pairs grid
- m_Grid - Variable in class weka.classifiers.meta.GridSearch.PerformanceTable
-
the corresponding grid
- m_GridExtensionsPerformed - Variable in class weka.classifiers.meta.GridSearch
-
the number of extensions performed
- m_GridIsExtendable - Variable in class weka.classifiers.meta.GridSearch
-
whether the grid can be extended
- m_gridWidth - Variable in class weka.gui.beans.AttributeSummarizer
-
The number of plots horizontally in the display
- m_groupIdentifier - Variable in class weka.gui.beans.BatchClassifierEvent
-
An identifier that can be used to group all related runs/sets
together.
- m_Groups - Variable in class weka.classifiers.meta.RotationForest
-
The attributes of each group
- m_GUIType - Variable in class weka.gui.Main
-
the type of GUI to display.
- m_Handler - Variable in class weka.core.FindWithCapabilities
-
a capabilities handler to retrieve the capabilities from.
- m_Handler - Variable in class weka.core.TestInstances
-
the CapabilitiesHandler to get the Capabilities from
- m_HandleRightClicks - Variable in class weka.gui.ResultHistoryPanel
-
Let the result history list handle right clicks in the default
manner---ie, pop up a window displaying the buffer
- m_hasClass - Variable in class weka.attributeSelection.BestFirst
-
does the data have a class
- m_hasClass - Variable in class weka.attributeSelection.GreedyStepwise
-
does the data have a class
- m_hasClass - Variable in class weka.attributeSelection.LinearForwardSelection
-
does the data have a class
- m_HasClass - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Data has a class set.
- m_hasConstr - Variable in class weka.classifiers.trees.ft.FTtree
-
True if node has or splits on constructor
- m_HashCode - Variable in class weka.core.Trie
-
the hash code
- m_hashtable - Variable in class weka.classifiers.meta.END
-
The hashtable containing the classifiers for the END.
- m_hashtablegiven - Variable in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
-
Is Hashtable given from END?
- m_hashtablegiven - Variable in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
-
Is Hashtable given from END?
- m_hashtablegiven - Variable in class weka.classifiers.meta.nestedDichotomies.ND
-
Is Hashtable given from END?
- m_hashtables - Variable in class weka.associations.Apriori
-
The same information stored in hash tables.
- m_hashtables - Variable in class weka.associations.PredictiveApriori
-
The same information stored in hash tables.
- m_HDistanceDebug - Variable in class weka.classifiers.mi.CitationKNN
-
- m_HDRank - Variable in class weka.classifiers.mi.CitationKNN
-
Rank associated to the Hausdorff distance
- m_Head - Variable in class weka.core.Queue
-
Store a reference to the head of the queue
- m_headerInfo - Variable in class weka.classifiers.bayes.DMNBtext
-
- m_headerInfo - Variable in class weka.classifiers.bayes.NaiveBayesMultinomial
-
copy of header information for use in toString method
- m_HeaderKeys - Variable in class weka.experiment.ResultMatrix
-
contains the keys for the header
- m_Headers - Variable in class weka.classifiers.meta.RotationForest
-
Headers of the transformed dataset
- m_HeaderValues - Variable in class weka.experiment.ResultMatrix
-
contains the values for the header
- m_Height - Variable in class weka.classifiers.meta.GridSearch.Grid
-
the number of points on the Y axis
- m_heights - Variable in class weka.gui.visualize.AttributePanel
-
Holds the random height for each instance.
- m_Helper - Variable in class weka.gui.sql.ResultSetTableModel
-
for retrieving the data etc.
- m_Heuristic - Variable in class weka.classifiers.trees.BFTree
-
If use huristic search for binary split (default true).
- m_Heuristic - Variable in class weka.classifiers.trees.SimpleCart
-
If use huristic search for nominal attributes in multi-class problems (default true).
- m_heuristicStop - Variable in class weka.classifiers.functions.SimpleLogistic
-
Parameter for the heuristic for early stopping of LogitBoost
- m_heuristicStop - Variable in class weka.classifiers.trees.lmt.LogisticBase
-
Use heuristic to stop performing LogitBoost iterations earlier?
If enabled, LogitBoost is stopped if the current (local) minimum of the error on a test set as
a function of the number of iterations has not changed for m_heuristicStop iterations.
- m_higherRegressions - Variable in class weka.classifiers.trees.ft.FTtree
-
Simple regression functions fit by LogitBoost at higher levels in the tree
- m_higherRegressions - Variable in class weka.classifiers.trees.lmt.LMTNode
-
Simple regression functions fit by LogitBoost at higher levels in the tree
- m_HighThreshold - Variable in class weka.classifiers.meta.ThresholdSelector
-
The upper threshold used as the basis of correction
- m_highValue - Variable in class weka.core.pmml.MiningFieldMetaInfo
-
outlier high value
- m_histBarCounts - Variable in class weka.gui.AttributeVisualizationPanel
-
This array holds the count (or height) for the each of the bars in a
barplot or a histogram.
- m_History - Variable in class weka.filters.unsupervised.attribute.AbstractTimeSeries
-
Stores the historical instances to copy values between
- m_history - Variable in class weka.gui.beans.GraphViewer
-
- m_history - Variable in class weka.gui.beans.TextViewer
-
List of text revieved so far
- m_History - Variable in class weka.gui.experiment.ResultsPanel
-
A panel controlling results viewing.
- m_History - Variable in class weka.gui.explorer.AssociationsPanel
-
A panel controlling results viewing
- m_History - Variable in class weka.gui.explorer.AttributeSelectionPanel
-
A panel controlling results viewing
- m_History - Variable in class weka.gui.explorer.ClassifierPanel
-
A panel controlling results viewing
- m_History - Variable in class weka.gui.explorer.ClustererPanel
-
A panel controlling results viewing
- m_History - Variable in class weka.gui.MemoryUsagePanel
-
the memory usage over time.
- m_History - Variable in class weka.gui.sql.ConnectionPanel
-
the history of connections.
- m_History - Variable in class weka.gui.sql.event.HistoryChangedEvent
-
the history model
- m_History - Variable in class weka.gui.sql.QueryPanel
-
the query history.
- m_History - Variable in class weka.gui.sql.SqlViewer
-
stores the history.
- m_HistoryChangedListeners - Variable in class weka.gui.sql.ConnectionPanel
-
the history listeners.
- m_HistoryChangedListeners - Variable in class weka.gui.sql.QueryPanel
-
the history listeners.
- m_HistoryName - Variable in class weka.gui.sql.event.HistoryChangedEvent
-
the name of the history
- m_HistoryPos - Variable in class weka.gui.SimpleCLIPanel
-
The current position in the command history.
- m_HoldOutDist - Variable in class weka.classifiers.trees.REPTree.Tree
-
Class distribution of hold-out set at node in the nominal
case.
- m_HoldOutError - Variable in class weka.classifiers.trees.REPTree.Tree
-
The hold-out error of the node.
- m_HostField - Variable in class weka.gui.experiment.HostListPanel
-
The field with which to enter host names
- m_hostList - Variable in class weka.gui.experiment.DistributeExperimentPanel
-
The host list panel
- m_HyperPipes - Variable in class weka.classifiers.misc.HyperPipes
-
Stores the HyperPipe for each class
- m_I0 - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
{i: 0 < m_alpha[i] < C}
- m_I0 - Variable in class weka.classifiers.functions.supportVector.RegSMOImproved
-
The different sets used by the algorithm.
- m_I0 - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
{i: 0 < m_alpha[i] < C}
- m_I1 - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
{i: m_class[i] = 1, m_alpha[i] = 0}
- m_I1 - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
{i: m_class[i] = 1, m_alpha[i] = 0}
- m_I2 - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
{i: m_class[i] = -1, m_alpha[i] =C}
- m_I2 - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
{i: m_class[i] = -1, m_alpha[i] = C}
- m_I3 - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
{i: m_class[i] = 1, m_alpha[i] = C}
- m_I3 - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
{i: m_class[i] = 1, m_alpha[i] = C}
- m_I4 - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
{i: m_class[i] = -1, m_alpha[i] = 0}
- m_I4 - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
{i: m_class[i] = -1, m_alpha[i] = 0}
- m_ibk - Variable in class weka.classifiers.rules.DecisionTable
-
IB1 used to classify non matching instances rather than majority class
- m_icon - Variable in class weka.gui.beans.BeanVisual
-
ImageIcons for the icons.
- m_Icon - Variable in class weka.gui.GUIChooser
-
the icon for the frames
- m_iconPath - Variable in class weka.gui.beans.BeanVisual
-
Holds name (including path) of the static icon
- m_ID - Variable in class weka.associations.FPGrowth.FPTreeNode
-
ID (for graphing the tree)
- m_id - Variable in class weka.classifiers.functions.neural.NeuralConnection
-
The string that uniquely (provided naming is done properly) identifies
this unit.
- m_id - Variable in class weka.classifiers.trees.ft.FTtree
-
Node id
- m_id - Variable in class weka.classifiers.trees.j48.ClassifierTree
-
The id for the node.
- m_id - Variable in class weka.classifiers.trees.lmt.LMTNode
-
Node id
- m_ID - Variable in class weka.core.Debug.Random
-
the unique ID for this number generator
- m_ID - Variable in class weka.core.Tag
-
The ID
- m_ID - Variable in class weka.core.TechnicalInformation
-
the unique identifier of this information, will be generated
automatically if left empty
- m_IdIndex - Variable in class weka.classifiers.mi.CitationKNN
-
- m_IDStr - Variable in class weka.core.Tag
-
The unique string for this tag, doesn't have to be numeric
- m_IgnoreAttributesRange - Variable in class weka.filters.unsupervised.attribute.AddCluster
-
Range of attributes to ignore
- m_ignoreAttributesRange - Variable in class weka.filters.unsupervised.attribute.ClusterMembership
-
Range of attributes to ignore
- m_IgnoreBeanConnections - Variable in class weka.gui.beans.xml.XMLBeans
-
whether to ignore the BeanConnection
- m_ignoreBut - Variable in class weka.gui.explorer.ClustererPanel
-
The button used to popup a list for choosing attributes to ignore while
clustering
- m_IgnoreChange - Variable in class weka.gui.visualize.PrintableComponent
-
whether to ignore the update of the text field (in case of "keep ratio").
- m_IgnoreClass - Variable in class weka.filters.unsupervised.attribute.PotentialClassIgnorer
-
True if the class is to be unset
- m_Ignored - Variable in class weka.core.xml.PropertyHandler
-
contains display names of properties to ignore in the serialization
process
- m_IgnoredProperties - Variable in class weka.core.CheckGOE
-
properties that are skipped in the checkToolTips method
- m_ignoreKeyList - Variable in class weka.gui.explorer.ClustererPanel
-
- m_ignoreKeyModel - Variable in class weka.gui.explorer.ClustererPanel
-
- m_iLow - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
The indices for m_bLow and m_bUp
- m_iLow - Variable in class weka.classifiers.functions.supportVector.RegSMOImproved
-
index of the instance that gave us b.up and b.low
- m_iLow - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
The indices for m_bLow and m_bUp
- m_importance - Variable in class weka.core.pmml.MiningFieldMetaInfo
-
importance (if defined)
- m_IncludeAll - Variable in class weka.gui.AttributeSelectionPanel
-
Press to select all attributes
- m_IncludeClass - Variable in class weka.core.InstanceComparator
-
whether to include the class in the comparison
- m_IncludeClass - Variable in class weka.filters.unsupervised.attribute.NumericCleaner
-
whether to include the class attribute
- m_Incorrect - Variable in class weka.classifiers.Evaluation
-
The weight of all incorrectly classified instances.
- m_Incremental - Variable in class weka.core.converters.ConverterUtils.DataSource
-
whether the loader is incremental.
- m_IncrementalBuffer - Variable in class weka.core.converters.ConverterUtils.DataSource
-
the last internally read instance.
- m_incrementalCounter - Variable in class weka.core.converters.AbstractFileSaver
-
Counter.
- m_IncrementalIndex - Variable in class weka.core.converters.SerializedInstancesLoader
-
The current index position for incremental reading
- m_index - Variable in class weka.core.pmml.MiningFieldMetaInfo
-
the index of the field in the mining schema Instances
- m_Index - Variable in class weka.core.PropertyPath.PathElement
-
the index of the array (-1 for none)
- m_Index - Variable in class weka.filters.unsupervised.attribute.AddID
-
the index of the attribute
- m_Index - Variable in class weka.gui.SortedTableModel.SortContainer
-
the index of the value.
- m_IndexString - Variable in class weka.core.SingleIndex
-
Record the string representation of the number
- m_indexVal - Variable in class weka.gui.visualize.AttributePanelEvent
-
The index for the new attribute
- m_indices - Variable in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
-
The indices associated with this node
- m_Indices - Variable in class weka.core.AttributeLocator
-
the indices
- m_Indices - Variable in class weka.core.SparseInstance
-
The index of the attribute associated with each stored value.
- m_Indices - Variable in class weka.filters.unsupervised.attribute.RandomSubset
-
The indices of the attributes that got selected.
- m_IndicesBuffer - Variable in class weka.core.converters.ArffLoader.ArffReader
-
Buffer of indices for sparse instance
- m_IndicesUnused - Variable in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
-
the indices of the unused attributes.
- m_Info - Variable in class weka.classifiers.trees.RandomTree
-
The header information.
- m_Info - Variable in class weka.classifiers.trees.REPTree.Tree
-
The header information (for printing the tree).
- m_Info - Variable in class weka.gui.sql.InfoPanel
-
the list the contains the messages
- m_InfoLabel - Variable in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
-
the label, listing the name of the superclass.
- m_InfoPanel - Variable in class weka.gui.sql.SqlViewer
-
the info panel.
- m_InitFile - Variable in class weka.filters.unsupervised.attribute.KernelFilter
-
The dataset to initialize the filter with
- m_InitFileClassIndex - Variable in class weka.filters.unsupervised.attribute.KernelFilter
-
the class index for the file to initialized with
- m_InitFlag - Variable in class weka.classifiers.lazy.KStar
-
Flag turning on and off the initialisation of config variables
- m_Initial - Static variable in class weka.core.Memory
-
the initial size of the JVM
- m_initialized - Variable in class weka.classifiers.pmml.consumer.PMMLClassifier
-
Has the classifier been initialized (i.e.
- m_Initialized - Variable in class weka.filters.unsupervised.attribute.KernelFilter
-
whether the filter was initialized
- m_Initialized - Variable in class weka.gui.sql.ResultSetHelper
-
whether we initialized.
- m_InitOptions - Variable in class weka.classifiers.meta.CVParameterSelection
-
The set of all options at initialization time.
- m_iNode - Variable in class weka.classifiers.bayes.net.VaryNode
-
index of the node varied
- m_input - Variable in class weka.gui.graphvisualizer.DotParser
-
This is the input containing DOT stream to be parsed
- m_Input - Variable in class weka.gui.SimpleCLIPanel
-
The command input area.
- m_InputCenterFile - Variable in class weka.clusterers.XMeans
-
file name of the output file for the cluster centers.
- m_InputFilename - Variable in class weka.gui.GenericPropertiesCreator
-
the input file with the packages
- m_InputFormat - Variable in class weka.gui.streams.InstanceJoiner
-
The input format for instances
- m_inputList - Variable in class weka.classifiers.functions.neural.NeuralConnection
-
The list of inputs to this unit.
- m_inputMap - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
-
A map for storing network input values (computed from an incoming instance)
- m_inputNums - Variable in class weka.classifiers.functions.neural.NeuralConnection
-
The numbering for the connections at the other end of the input lines.
- m_InputProperties - Variable in class weka.gui.GenericPropertiesCreator
-
the "template" properties file with the layout and the packages
- m_InputRelAtts - Variable in class weka.filters.Filter
-
Indices of relational attributes in the input format
- m_inputs - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
-
The inputs to the network
- m_inputs - Variable in class weka.gui.beans.MetaBean
-
- m_InputStringAtts - Variable in class weka.filters.Filter
-
Indices of string attributes in the input format
- m_InputStringIndex - Variable in class weka.filters.unsupervised.attribute.Reorder
-
Contains an index of string attributes in the input format
that survive the filtering process -- some entries may be duplicated
- m_Instance - Variable in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborNode
-
The neighbor instance.
- m_instanceEvent - Variable in class weka.gui.beans.PredictionAppender
-
- m_InstanceInfo - Variable in class weka.gui.visualize.Plot2D
-
For popping up text info on data points
- m_InstanceInfoText - Variable in class weka.gui.visualize.Plot2D
-
- m_instanceListeners - Variable in class weka.gui.beans.PredictionAppender
-
Objects listening for instances events
- m_InstanceQuery - Variable in class weka.gui.experiment.ResultsPanel
-
Does any database querying for us.
- m_InstanceRange - Variable in class weka.filters.unsupervised.attribute.AbstractTimeSeries
-
The number of instances forward to translate values between.
- m_instances - Variable in class weka.associations.Apriori
-
The instances (transactions) to be used for generating
the association rules.
- m_instances - Variable in class weka.associations.PredictiveApriori
-
The instances (transactions) to be used for generating
the association rules.
- m_instances - Variable in class weka.associations.PriorEstimation
-
The instances for which association rules are mined.
- m_instances - Variable in class weka.associations.RuleGeneration
-
The instances.
- m_Instances - Variable in class weka.attributeSelection.GreedyStepwise
-
- m_Instances - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
-
Dataset provided to do Training/Test set.
- m_Instances - Variable in class weka.classifiers.bayes.BayesNet
-
The dataset header for the purposes of printing out a semi-intelligible
model
- m_Instances - Variable in class weka.classifiers.bayes.blr.Prior
-
- m_Instances - Variable in class weka.classifiers.bayes.NaiveBayes
-
The dataset header for the purposes of printing out a semi-intelligible
model
- m_Instances - Variable in class weka.classifiers.bayes.NaiveBayesSimple
-
The instances used for training.
- m_Instances - Variable in class weka.classifiers.bayes.net.ADNode
-
list of Instance children (either m_Instances or m_VaryNodes is instantiated)
- m_Instances - Variable in class weka.classifiers.lazy.LBR
-
The set of instances used for current training.
- m_Instances - Variable in class weka.classifiers.misc.HyperPipes
-
The structure of the training data
- m_Instances - Variable in class weka.classifiers.misc.VFI
-
The training data
- m_instances - Variable in class weka.clusterers.FarthestFirst
-
training instances, not necessary to keep,
could be replaced by m_ClusterCentroids where needed for header info
- m_Instances - Variable in class weka.clusterers.XMeans
-
training instances.
- m_Instances - Variable in class weka.core.Instances
-
The instances.
- m_Instances - Variable in class weka.core.neighboursearch.balltrees.BallSplitter
-
The instance on which the tree is built.
- m_Instances - Variable in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
The instances on which to build the tree.
- m_Instances - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
-
The instances that'll be used for tree construction.
- m_Instances - Variable in class weka.core.neighboursearch.NearestNeighbourSearch
-
The neighbourhood of instances to find neighbours in.
- m_Instances - Variable in class weka.core.xml.XMLInstances
-
the underlying Instances
- m_Instances - Variable in class weka.experiment.AveragingResultProducer
-
The dataset of interest
- m_Instances - Variable in class weka.experiment.CrossValidationResultProducer
-
The dataset of interest
- m_Instances - Variable in class weka.experiment.DatabaseResultProducer
-
The dataset of interest
- m_Instances - Variable in class weka.experiment.InstancesResultListener
-
Stores the instances created so far, before assigning to a header
- m_Instances - Variable in class weka.experiment.LearningRateResultProducer
-
The dataset of interest
- m_Instances - Variable in class weka.experiment.PairedTTester
-
The set of instances we will analyse
- m_Instances - Variable in class weka.experiment.RandomSplitResultProducer
-
The dataset of interest
- m_Instances - Variable in class weka.gui.AttributeSummaryPanel
-
The instances we're playing with
- m_Instances - Variable in class weka.gui.experiment.ResultsPanel
-
The instances we're extracting results from.
- m_Instances - Variable in class weka.gui.explorer.AssociationsPanel
-
The main set of instances we're playing with
- m_Instances - Variable in class weka.gui.explorer.AttributeSelectionPanel
-
The main set of instances we're playing with
- m_Instances - Variable in class weka.gui.explorer.ClassifierPanel
-
The main set of instances we're playing with
- m_Instances - Variable in class weka.gui.explorer.ClustererPanel
-
The main set of instances we're playing with
- m_Instances - Variable in class weka.gui.explorer.DataGeneratorPanel
-
the generated Instances
- m_Instances - Variable in class weka.gui.explorer.PreprocessPanel
-
The working instances
- m_Instances - Variable in class weka.gui.InstancesSummaryPanel
-
The instances we're playing with
- m_Instances - Variable in class weka.gui.SetInstancesPanel
-
The current set of instances loaded
- m_instancesConsumed - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
-
number eaten from m_currentSet
- m_InstancesTest - Variable in class weka.core.Capabilities
-
whether to perform data based tests
- m_InstIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes
-
the array instance indexes
- m_InstList - Variable in class weka.core.neighboursearch.BallTree
-
The instances indices sorted inorder of appearence in the tree from left
most leaf node to the right most leaf node.
- m_Instlist - Variable in class weka.core.neighboursearch.balltrees.BallSplitter
-
The master index array that'll be reshuffled as nodes
are split (and the tree is constructed).
- m_InstList - Variable in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
The master index array.
- m_InstList - Variable in class weka.core.neighboursearch.KDTree
-
Indexlist of the instances of this kdtree.
- m_InstList - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
-
The master index array that'll be reshuffled as nodes
are split and the tree is constructed.
- m_InstPerClass - Variable in class weka.classifiers.meta.Grading
-
InstPerClass
- m_InstSummaryPanel - Variable in class weka.gui.explorer.PreprocessPanel
-
Displays simple stats on the working instances
- m_Interpreter - Variable in class weka.core.Jython
-
the interpreter
- m_Interval - Variable in class weka.gui.MemoryUsagePanel.MemoryMonitor
-
the refresh interval in msecs.
- m_intervalBounds - Variable in class weka.classifiers.misc.VFI
-
The lower bounds for each attribute
- m_IntNodeCount - Variable in class weka.core.neighboursearch.TreePerformanceStats
-
The number of internal nodes looked at
for the current/last query.
- m_intType - Variable in class weka.experiment.DatabaseUtils
-
integer type for the create table statement.
- m_invert - Variable in class weka.filters.unsupervised.attribute.RemoveType
-
Whether to invert selection
- m_Invert - Variable in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
whether to invert the matching sense.
- m_Invert - Variable in class weka.gui.AttributeSelectionPanel
-
Press to invert the current selection
- m_invertMatching - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Whether to invert the match so the correctly classified instances are discarded
- m_InvertSelection - Variable in class weka.filters.supervised.instance.Resample
-
Whether to invert the selection (only if instances are drawn WITHOUT
replacement).
- m_InvertSelection - Variable in class weka.filters.unsupervised.instance.Resample
-
Whether to invert the selection (only if instances are drawn WITHOUT
replacement)
- m_IOThread - Variable in class weka.gui.explorer.PreprocessPanel
-
A thread for loading/saving instances from a file or URL
- m_IOThread - Variable in class weka.gui.SetInstancesPanel
-
The thread we do loading in
- m_IQR - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
-
the interquartile range
- m_isEmpty - Variable in class weka.classifiers.rules.part.ClassifierDecList
-
True if node is empty.
- m_isEmpty - Variable in class weka.classifiers.trees.j48.ClassifierTree
-
True if node is empty.
- m_iSet - Variable in class weka.classifiers.functions.supportVector.RegSMOImproved
-
Index set {i: 0 < m_alpha[i] < C || 0 < m_alphaStar[i] < C}}
- m_isLeaf - Variable in class weka.classifiers.rules.part.ClassifierDecList
-
True if node is leaf.
- m_isLeaf - Variable in class weka.classifiers.trees.BFTree
-
If the ndoe is leaf node.
- m_isLeaf - Variable in class weka.classifiers.trees.ft.FTtree
-
True if node is leaf
- m_isLeaf - Variable in class weka.classifiers.trees.j48.ClassifierTree
-
True if node is leaf.
- m_isLeaf - Variable in class weka.classifiers.trees.lmt.LMTNode
-
True if node is leaf
- m_isLeaf - Variable in class weka.classifiers.trees.SimpleCart
-
Indicate if the node is a leaf node.
- m_item - Variable in class weka.associations.FPGrowth.FPTreeNode
-
item at this node
- m_items - Variable in class weka.associations.FPGrowth.FrequentBinaryItemSet
-
The list of items in the item set
- m_items - Variable in class weka.associations.ItemSet
-
The items stored as an array of of ints.
- m_items - Variable in class weka.associations.RuleGeneration
-
The items stored as an array of of integer.
- m_IterationCount - Variable in class weka.clusterers.XMeans
-
counts iterations done in main loop.
- m_iUp - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
The indices for m_bLow and m_bUp
- m_iUp - Variable in class weka.classifiers.functions.supportVector.RegSMOImproved
-
index of the instance that gave us b.up and b.low
- m_iUp - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
The indices for m_bLow and m_bUp
- m_Javadocs - Static variable in class weka.core.AllJavadoc
-
contains all the
- m_jBtSave - Variable in class weka.gui.graphvisualizer.GraphVisualizer
-
Save button to save the current graph in DOT or XMLBIF format.
- m_jCbEdgeConcentration - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
-
controls edge concentration by concentrating multilple singular dummy
child nodes into one plural dummy child node
- m_jitter - Variable in class weka.gui.visualize.MatrixPanel
-
The slider to add jitter to the plots
- m_Jitter - Variable in class weka.gui.visualize.VisualizePanel
-
The jitter slider
- m_JitterLab - Variable in class weka.gui.visualize.VisualizePanel
-
Label for the jitter slider
- m_JitterVal - Variable in class weka.gui.visualize.Plot2D
-
the level of jitter
- m_JRand - Variable in class weka.gui.visualize.Plot2D
-
random values for perterbing the data points
- m_jRbBottomup - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
-
- m_jRbNaiveLayout - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
-
- m_jRbPriorityLayout - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
-
- m_jRbTopdown - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
-
- m_js - Variable in class weka.gui.visualize.MatrixPanel
-
The scroll pane to scrolling the matrix
- m_k - Variable in class weka.filters.unsupervised.attribute.RandomProjection
-
Stores the number of dimensions to reduce the data to
- m_Kappa - Variable in class weka.classifiers.meta.GridSearch.Performance
-
the kappa value
- m_KDTree - Variable in class weka.clusterers.XMeans
-
KDTrees class if KDTrees are used.
- m_kernel - Variable in class weka.classifiers.functions.GaussianProcesses
-
Kernel to use
- m_kernel - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
Kernel to use
- m_kernel - Variable in class weka.classifiers.functions.SMO
-
the kernel to use
- m_kernel - Variable in class weka.classifiers.functions.SMOreg
-
the configured kernel
- m_Kernel - Variable in class weka.classifiers.functions.supportVector.CheckKernel
-
The kernel to be examined
- m_kernel - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
-
the kernel
- m_kernel - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
Kernel to use
- m_kernel - Variable in class weka.classifiers.mi.MISMO
-
Kernel to use
- m_kernel - Variable in class weka.classifiers.mi.MISVM
-
the kernel to use
- m_Kernel - Variable in class weka.filters.unsupervised.attribute.KernelFilter
-
Kernel to use
- m_kernelEvals - Variable in class weka.classifiers.functions.supportVector.CachedKernel
-
Counts the number of kernel evaluations.
- m_KernelFactor - Variable in class weka.filters.unsupervised.attribute.KernelFilter
-
the calculated kernel factor
- m_KernelFactorExpression - Variable in class weka.filters.unsupervised.attribute.KernelFilter
-
optimizes the kernel with this formula
(A = # of attributes, N = # of instances)
- m_KernelIsLinear - Variable in class weka.classifiers.functions.GaussianProcesses
-
whether the kernel is a linear one
- m_KernelIsLinear - Variable in class weka.classifiers.functions.SMO
-
whether the kernel is a linear one
- m_kernelMatrix - Variable in class weka.classifiers.functions.supportVector.CachedKernel
-
The kernel matrix if full cache is used (i.e.
- m_KernelMatrix - Variable in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
-
The kernel matrix.
- m_KernelMatrixFile - Variable in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
-
The file holding the kernel matrix.
- m_kernelPrecalc - Variable in class weka.classifiers.functions.supportVector.Puk
-
The precalculated dotproducts of <inst_i,inst_i>
- m_kernelPrecalc - Variable in class weka.classifiers.functions.supportVector.RBFKernel
-
The precalculated dotproducts of <inst_i,inst_i>
- m_kernelPrecalc - Variable in class weka.classifiers.mi.supportVector.MIRBFKernel
-
The precalculated dotproducts of <inst_i,inst_i>
- m_KernelType - Variable in class weka.classifiers.functions.LibSVM
-
the kernel type
- m_KeyFieldName - Variable in class weka.experiment.AveragingResultProducer
-
The name of the key field to average over
- m_KeyIndex - Variable in class weka.experiment.AveragingResultProducer
-
The index of the field to average over in the resultproducers key
- m_keys - Variable in class weka.classifiers.functions.supportVector.CachedKernel
-
- m_Keys - Variable in class weka.core.converters.DatabaseLoader
-
the keys for unique ordering
- m_Keys - Variable in class weka.experiment.AveragingResultProducer
-
Collects the keys from a single run
- m_Keywords - Variable in class weka.experiment.DatabaseUtils
-
the keywords for the current database type.
- m_KeywordsMaskChar - Variable in class weka.experiment.DatabaseUtils
-
the character to mask SQL keywords (by appending this character).
- m_KfFilter - Variable in class weka.gui.beans.KnowledgeFlowApp
-
A filter to ensure only KnowledgeFlow files in binary format get shown in
the chooser
- m_KMeansStopped - Variable in class weka.clusterers.XMeans
-
counter to say how often kMeans was stopped by loop counter.
- m_kNN - Variable in class weka.classifiers.lazy.IBk
-
The number of neighbours to use for classification (currently).
- m_kNN - Variable in class weka.classifiers.lazy.LWL
-
The number of neighbours used to select the kernel bandwidth.
- m_kNN - Variable in class weka.core.neighboursearch.NearestNeighbourSearch
-
The number of neighbours to find.
- m_kNNUpper - Variable in class weka.classifiers.lazy.IBk
-
The value of kNN provided by the user.
- m_kNNValid - Variable in class weka.classifiers.lazy.IBk
-
Whether the value of k selected by cross validation has
been invalidated by a change in the training instances.
- m_KnowledgeFlowBut - Variable in class weka.gui.GUIChooser
-
Click to open the KnowledgeFlow
- m_KnowledgeFlowFrame - Variable in class weka.gui.GUIChooser
-
The frame containing the knowledge flow interface
- m_KOMLFilter - Variable in class weka.gui.beans.Classifier
-
- m_KOMLFilter - Variable in class weka.gui.beans.KnowledgeFlowApp
-
A filter to ensure only KnowledgeFlow files in KOML format
get shown in the chooser
- m_KOMLFilter - Variable in class weka.gui.experiment.SetupPanel
-
A filter to ensure only experiment (in KOML format) files get shown in the chooser
- m_KOMLFilter - Variable in class weka.gui.experiment.SimpleSetupPanel
-
A filter to ensure only experiment (in KOML format) files get shown in the chooser
- m_KValue - Variable in class weka.classifiers.trees.RandomForest
-
Final number of features that were considered in last build.
- m_KValue - Variable in class weka.classifiers.trees.RandomTree
-
The number of attributes considered for a split.
- m_KWBias - Variable in class weka.classifiers.BVDecomposeSegCVSub
-
The calculated Kohavi & Wolpert bias (squared)
- m_KWSigma - Variable in class weka.classifiers.BVDecomposeSegCVSub
-
The calculated Kohavi & Wolpert sigma
- m_KWVariance - Variable in class weka.classifiers.BVDecomposeSegCVSub
-
The calculated Kohavi & Wolpert variance
- m_LabelCurrentSize - Variable in class weka.gui.LogWindow
-
the current size
- m_labelFont - Variable in class weka.gui.visualize.Plot2D
-
Font for labels
- m_labelMetrics - Variable in class weka.gui.visualize.Plot2D
-
- m_LabelQuery - Variable in class weka.gui.sql.SqlViewerDialog
-
displays the current query
- m_Labels - Variable in class weka.filters.unsupervised.attribute.Add
-
The list of labels for nominal attribute.
- m_Labels - Variable in class weka.filters.unsupervised.attribute.AddValues
-
The values to add.
- m_LabelURL - Variable in class weka.gui.sql.ConnectionPanel
-
the label for the URL.
- m_LabelX - Variable in class weka.classifiers.meta.GridSearch.Grid
-
the label for the X axis
- m_LabelY - Variable in class weka.classifiers.meta.GridSearch.Grid
-
the label for the Y axis
- m_lambda - Variable in class weka.classifiers.functions.SPegasos
-
The regularization parameter
- m_lambda - Variable in class weka.classifiers.functions.supportVector.StringKernel
-
the decay factor that penalizes non-continuous substring matches.
- m_largeItemSets - Variable in class weka.associations.FPGrowth
-
Holds the large item sets found
- m_Last - Variable in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborList
-
The last node in the list.
- m_lastAddedSplitNum - Variable in class weka.classifiers.trees.ADTree
-
The number of the last splitter added to the tree
- m_lastAddedSplitNum - Variable in class weka.classifiers.trees.LADTree
-
- m_LastFilter - Variable in class weka.gui.ConverterFileChooser
-
the last filter that was used for opening/saving
- m_lastLabel - Variable in class weka.datagenerators.classifiers.classification.Agrawal
-
the last class label that was generated
- m_LastLeaf - Variable in class weka.core.Trie.TrieIterator
-
the last leaf for this root node
- m_lastLogLikelihood - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
-
- m_LastURL - Variable in class weka.gui.explorer.PreprocessPanel
-
Stores the last URL that instances were loaded from
- m_LastURL - Variable in class weka.gui.SetInstancesPanel
-
Stores the last URL that instances were loaded from
- m_lastValidationError - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
-
- m_layers - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
-
The hidden layers in the network
- m_layoutEngine - Variable in class weka.classifiers.bayes.net.GUI
-
The current LayoutEngine
- m_le - Variable in class weka.gui.graphvisualizer.GraphVisualizer
-
The current LayoutEngine
- m_leafclass - Variable in class weka.classifiers.trees.ft.FTtree
-
Stores leaf class value
- m_LeafCount - Variable in class weka.core.neighboursearch.TreePerformanceStats
-
The number of leaf nodes looked at
for the current/last query.
- m_leafModelNum - Variable in class weka.classifiers.trees.ft.FTtree
-
ID of logistic model at leaf
- m_leafModelNum - Variable in class weka.classifiers.trees.lmt.LMTNode
-
ID of logistic model at leaf
- m_LeastValues - Variable in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
whether to retain values with least instances instead of most.
- m_left - Variable in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
-
The left successor
- m_left - Variable in class weka.classifiers.trees.m5.RuleNode
-
left child node
- m_Left - Variable in class weka.core.neighboursearch.balltrees.BallNode
-
The left child of the node.
- m_Left - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode
-
left subtree; contains instances with smaller or equal to split value.
- m_leftMargin - Variable in class weka.core.pmml.FieldMetaInfo.Interval
-
The left boundary value
- m_legendPanel - Variable in class weka.gui.visualize.VisualizePanel
-
The panel that displays legend info if there is more than one plot
- m_LegendPanelBorderColor - Variable in class weka.gui.beans.StripChart
-
the color of the legend panel's border.
- m_Length - Variable in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborList
-
The number of nodes to attempt to maintain in the list.
- m_levelSibling - Variable in class weka.associations.FPGrowth.FPTreeNode
-
link to another sibling at this level in the tree
- m_linearNormNorm - Variable in class weka.core.pmml.NormContinuous
-
norm values for the LinearNorm entries
- m_linearNormOrig - Variable in class weka.core.pmml.NormContinuous
-
original values for the LinearNorm entries
- m_linearSelectionType - Variable in class weka.attributeSelection.LinearForwardSelection
-
0 == fixed-set, 1 == fixed-width
- m_linearSelectionType - Variable in class weka.attributeSelection.SubsetSizeForwardSelection
-
0 == fixed-set, 1 == fixed-width
- m_LineColor - Variable in class weka.gui.treevisualizer.TreeVisualizer
-
the line color.
- m_LineFeed - Variable in class weka.core.logging.FileLogger
-
the line feed.
- m_LineFeed - Variable in class weka.core.logging.OutputLogger.OutputPrintStream
-
the line feed.
- m_Lines - Variable in class weka.core.converters.ArffLoader.ArffReader
-
the number of lines read so far
- m_Lines - Variable in class weka.core.Instances
-
The lines read so far in case of incremental loading.
- m_linkFunction - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
-
- m_linkParameter - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
-
- m_List - Variable in class weka.gui.experiment.AlgorithmListPanel
-
The component displaying the algorithm list
- m_List - Variable in class weka.gui.experiment.DatasetListPanel
-
The component displaying the dataset list.
- m_List - Variable in class weka.gui.experiment.HostListPanel
-
The component displaying the host list
- m_List - Variable in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
-
the list with all the capabilities.
- m_List - Variable in class weka.gui.ListSelectorDialog
-
The list component
- m_List - Variable in class weka.gui.ResultHistoryPanel
-
The list component
- m_listenee - Variable in class weka.gui.beans.AbstractDataSink
-
Non null if this object is a target for any events.
- m_listenee - Variable in class weka.gui.beans.AbstractEvaluator
-
- m_listenee - Variable in class weka.gui.beans.AbstractTestSetProducer
-
non null if this object is a target for any events.
- m_listenee - Variable in class weka.gui.beans.AbstractTrainAndTestSetProducer
-
non null if this object is a target for any events.
- m_listenee - Variable in class weka.gui.beans.AbstractTrainingSetProducer
-
non null if this object is a target for any events.
- m_listenee - Variable in class weka.gui.beans.CostBenefitAnalysis
-
The object sending us data (we allow only one connection at any one time)
- m_listenee - Variable in class weka.gui.beans.PredictionAppender
-
Non null if this object is a target for any events.
- m_listenee - Variable in class weka.gui.beans.SerializedModelSaver
-
Non null if this object is a target for any events.
- m_Listener - Variable in class weka.gui.ConverterFileChooser
-
the propertychangelistener
- m_listeners - Variable in class weka.gui.beans.AbstractDataSource
-
Objects listening for events from data sources
- m_listeners - Variable in class weka.gui.beans.AbstractTestSetProducer
-
Objects listening to us
- m_listeners - Variable in class weka.gui.beans.AbstractTrainingSetProducer
-
Objects listening for training set events
- m_listeners - Variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
-
a list of RemoteExperimentListeners
- m_Listeners - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel
-
Listeners who want to be notified about editing status of this
panel
- m_Listeners - Variable in class weka.gui.sql.ResultPanel
-
the result change listeners
- m_Listeners - Variable in class weka.gui.sql.ResultSetTableModel
-
the listeners.
- m_Listeners - Variable in class weka.gui.visualize.AttributePanel
-
The list of things listening to this panel
- m_LL - Variable in class weka.classifiers.functions.Logistic
-
Log-likelihood of the searched model
- m_lnFactorialCache - Variable in class weka.classifiers.bayes.NaiveBayesMultinomial
-
cache lnFactorial computations
- m_LNorm - Variable in class weka.filters.unsupervised.instance.Normalize
-
The L-norm to use
- m_Loader - Variable in class weka.core.converters.ConverterUtils.DataSource
-
the loader.
- m_Loader - Variable in class weka.gui.SetInstancesPanel
-
The current loader used to obtain the current instances
- m_LoaderFileFilters - Static variable in class weka.gui.ConverterFileChooser
-
the file filters for the loaders
- m_LoadOptionsBut - Variable in class weka.gui.experiment.AlgorithmListPanel
-
Click to edit the load the options for athe selected algorithm
- m_LoadThread - Variable in class weka.gui.experiment.ResultsPanel
-
A thread to load results instances from a file or database.
- m_localGraphicsState - Variable in class weka.gui.visualize.PostscriptGraphics
-
The current local graphics state for this PostscriptGraphics object
- m_localModel - Variable in class weka.classifiers.rules.part.ClassifierDecList
-
Local model at node.
- m_localModel - Variable in class weka.classifiers.trees.ft.FTtree
-
The ClassifierSplitModel (for splitting)
- m_localModel - Variable in class weka.classifiers.trees.j48.ClassifierTree
-
Local model at node.
- m_localModel - Variable in class weka.classifiers.trees.lmt.LMTNode
-
The ClassifierSplitModel (for splitting)
- m_LocatorIndices - Variable in class weka.core.AttributeLocator
-
the indices of locator objects
- m_Locators - Variable in class weka.core.AttributeLocator
-
contains the locator locations, either null or a AttributeLocator reference
- m_log - Variable in class weka.classifiers.pmml.consumer.PMMLClassifier
-
Logger
- m_Log - Variable in class weka.core.Debug
-
for logging
- m_Log - Variable in class weka.core.Debug.Random
-
the log to use for outputting the data, otherwise just stdout
- m_log - Variable in class weka.gui.beans.FlowRunner
-
- m_log - Variable in class weka.gui.beans.Loader
-
Logging
- m_Log - Variable in class weka.gui.experiment.RunPanel
-
- m_Log - Variable in class weka.gui.explorer.AssociationsPanel
-
The destination for log/status messages
- m_Log - Variable in class weka.gui.explorer.AttributeSelectionPanel
-
The destination for log/status messages
- m_Log - Variable in class weka.gui.explorer.ClassifierPanel
-
The destination for log/status messages
- m_Log - Variable in class weka.gui.explorer.ClustererPanel
-
The destination for log/status messages
- m_Log - Variable in class weka.gui.explorer.DataGeneratorPanel
-
The destination for log/status messages
- m_Log - Variable in class weka.gui.explorer.PreprocessPanel
-
The message logger
- m_Log - Variable in class weka.gui.visualize.VisualizePanel
-
the logger
- m_logButton - Variable in class weka.gui.LogPanel
-
The button for viewing the log
- m_LogFile - Variable in class weka.classifiers.meta.GridSearch
-
the log file to use
- m_LogFile - Variable in class weka.core.logging.FileLogger
-
the log file.
- m_Logger - Variable in class weka.core.Debug.Log
-
the actual logger, if null only stdout is used
- m_logger - Variable in class weka.gui.beans.AbstractDataSink
-
- m_logger - Variable in class weka.gui.beans.AbstractEvaluator
-
- m_logger - Variable in class weka.gui.beans.AbstractTestSetProducer
-
Logger
- m_logger - Variable in class weka.gui.beans.AbstractTrainAndTestSetProducer
-
- m_logger - Variable in class weka.gui.beans.AbstractTrainingSetProducer
-
- m_logger - Variable in class weka.gui.beans.ClassAssigner
-
- m_logger - Variable in class weka.gui.beans.ClassValuePicker
-
- m_logger - Variable in class weka.gui.beans.PredictionAppender
-
- m_logger - Variable in class weka.gui.beans.SerializedModelSaver
-
The log for this bean
- m_LoggerInitFailed - Variable in class weka.core.Debug.Log
-
whether the initialization of the logger failed
- m_logistic - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
Stores logistic regression model for probability estimate
- m_logistic - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
Stores logistic regression model for probability estimate
- m_logMessage - Variable in class weka.experiment.RemoteExperimentEvent
-
A log type message
- m_logPanel - Variable in class weka.gui.beans.KnowledgeFlowApp
-
- m_LogPanel - Variable in class weka.gui.explorer.Explorer
-
The panel for log and status messages
- m_LogText - Variable in class weka.gui.LogPanel
-
Displays the log messages
- m_LogWindow - Static variable in class weka.gui.GUIChooser
-
The frame of the LogWindow
- m_LogWindow - Static variable in class weka.gui.Main
-
The frame of the LogWindow.
- m_Loss - Variable in class weka.classifiers.functions.LibSVM
-
loss, for EPSILON_SVR
- m_loss - Variable in class weka.classifiers.functions.SPegasos
-
The current loss function to minimize
- m_lowerBoundMinSupport - Variable in class weka.associations.Apriori
-
The lower bound for the minimum support.
- m_lowerBoundMinSupport - Variable in class weka.associations.FPGrowth
-
The lower bound on minimum support
- m_LowerExtremeValue - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
-
the lower extreme value threshold (= Q1 - EVF*IQR)
- m_lowerOrder - Variable in class weka.classifiers.functions.supportVector.PolyKernel
-
Use lower-order terms?
- m_LowerOutlier - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
-
the lower outlier threshold (= Q1 - OF*IQR)
- m_LowerSize - Variable in class weka.experiment.LearningRateResultProducer
-
The minimum number of instances to use.
- m_LowerText - Variable in class weka.gui.experiment.RunNumberPanel
-
Configures the lower run number
- m_LowThreshold - Variable in class weka.classifiers.meta.ThresholdSelector
-
The lower threshold used as the basis of correction
- m_lowValue - Variable in class weka.core.pmml.MiningFieldMetaInfo
-
outlier low value
- m_Ls - Variable in class weka.associations.Apriori
-
The set of all sets of itemsets L.
- m_Ls - Variable in class weka.associations.PredictiveApriori
-
The set of all sets of itemsets.
- m_MAE - Variable in class weka.classifiers.meta.GridSearch.Performance
-
the Mean absolute error
- m_MainCommandline - Static variable in class weka.gui.Main
-
variable for the Main class which would be set to null by the memory
monitoring thread to free up some memory if we running out of memory.
- m_MainSingleton - Static variable in class weka.gui.Main
-
singleton instance of the GUI.
- m_majority - Variable in class weka.classifiers.rules.DecisionTable
-
Holds the majority class
- m_MakeBinary - Variable in class weka.filters.supervised.attribute.Discretize
-
Output binary attributes for discretized attributes.
- m_MakeBinary - Variable in class weka.filters.unsupervised.attribute.Discretize
-
Output binary attributes for discretized attributes.
- m_makeIndicatorFilter - Variable in class weka.classifiers.meta.StackingC
-
Filter to transform metaData - MakeIndicator
- m_manualThreshold - Variable in class weka.classifiers.meta.ThresholdSelector
-
True if a manually set threshold is being used
- m_manualThresholdValue - Variable in class weka.classifiers.meta.ThresholdSelector
-
-1 = not used by default
- m_mapMissingDefined - Variable in class weka.core.pmml.Discretize
-
True if a replacement for missing values has been specified
- m_mapMissingDefined - Variable in class weka.core.pmml.NormContinuous
-
True if a replacement for missing values has been specified
- m_mapMissingDefined - Variable in class weka.core.pmml.NormDiscrete
-
True if a replacement for missing values has been specified
- m_mapMissingTo - Variable in class weka.core.pmml.Discretize
-
The value of the missing value replacement (if defined)
- m_mapMissingTo - Variable in class weka.core.pmml.NormContinuous
-
The value of the missing value replacement (if defined)
- m_mapMissingTo - Variable in class weka.core.pmml.NormDiscrete
-
The value of the missing value replacement (if defined)
- m_MarginCounts - Variable in class weka.classifiers.Evaluation
-
Cumulative margin distribution
- m_masterName - Variable in class weka.gui.visualize.Plot2D
-
The name of the master plot
- m_masterPlot - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
Data for the threshold curve
- m_masterPlot - Variable in class weka.gui.beans.ModelPerformanceChart
-
- m_masterPlot - Variable in class weka.gui.visualize.Plot2D
-
The master plot
- m_Matches - Variable in class weka.core.FindWithCapabilities
-
the classes that matched.
- m_MatchMissingValues - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
-
True if missing values should count as a match
- m_matrix - Variable in class weka.classifiers.CostMatrix
-
[rows][columns]
- m_Matrix - Variable in class weka.core.Matrix
-
Deprecated.
The actual matrix
- m_matrixPanel - Variable in class weka.gui.beans.ScatterPlotMatrix
-
- m_MatrixSource - Variable in class weka.attributeSelection.CostSensitiveASEvaluation
-
Indicates the current cost matrix source
- m_MatrixSource - Variable in class weka.classifiers.meta.CostSensitiveClassifier
-
Indicates the current cost matrix source
- m_MatrixSource - Variable in class weka.classifiers.meta.MetaCost
-
Indicates the current cost matrix source
- m_Max - Variable in class weka.classifiers.meta.GridSearch.PerformanceTable
-
the maximum performance
- m_Max - Variable in class weka.core.Memory
-
the maximum amount of memory that can be used
- m_max - Variable in class weka.core.pmml.TargetMetaInfo
-
- m_Max - Variable in class weka.gui.boundaryvisualizer.KDDataGenerator
-
The maximum values for numeric attributes.
- m_MaxArray - Variable in class weka.filters.unsupervised.attribute.Normalize
-
The maximum values for numeric attributes.
- m_MaxAttributes - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
maximum number of attributes in the transformed data (-1 for all).
- m_MaxAttrsInName - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
maximum number of attributes in the transformed attribute name.
- m_maxBatchSizeRequired - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
The maximum number of instances required for processing
- m_maxBoostingIterations - Variable in class weka.classifiers.functions.SimpleLogistic
-
Maximum number of iterations for LogitBoost
- m_MaxC - Variable in class weka.core.neighboursearch.PerformanceStats
-
The min and max coordinates(attributes) looked
at per query.
- m_maxC - Variable in class weka.gui.visualize.AttributePanel
-
Holds the min and max values of the colouring attributes
- m_maxC - Variable in class weka.gui.visualize.Plot2D
-
- m_maxC - Variable in class weka.gui.visualize.PlotData2D
-
- m_MaxCardinality - Variable in class weka.filters.unsupervised.attribute.RELAGGS
-
the max.
- m_maxChunkSize - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
The maimum chunk size used for training
- m_MaxDefault - Variable in class weka.filters.unsupervised.attribute.NumericCleaner
-
the maximum default replacement value
- m_MaxDepth - Variable in class weka.classifiers.trees.RandomForest
-
The maximum depth of the trees (0 = unlimited)
- m_MaxDepth - Variable in class weka.classifiers.trees.RandomTree
-
The maximum depth of the tree (0 = unlimited)
- m_MaxDepth - Variable in class weka.classifiers.trees.REPTree
-
Upper bound on the tree depth
- m_MaxDepth - Variable in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
The depth of the built tree.
- m_MaxDepth - Variable in class weka.core.neighboursearch.CoverTree
-
Number of nodes in the tree.
- m_MaxDepth - Variable in class weka.core.neighboursearch.KDTree
-
Tree stats.
- M_MAXDIFF - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
-
- m_maxEntrop - Variable in class weka.classifiers.misc.VFI
-
The maximum entropy for the class
- m_MaxGridExtensions - Variable in class weka.classifiers.meta.GridSearch
-
maximum number of grid extensions (-1 means unlimited)
- m_MaxGroup - Variable in class weka.classifiers.meta.RotationForest
-
The maximum size of a group
- m_maximizeCB - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_MaxInstancesInLeaf - Variable in class weka.core.neighboursearch.BallTree
-
The maximum number of instances in a leaf.
- m_MaxInstancesInLeaf - Variable in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
The maximum number of instances allowed in a leaf.
- m_MaxInstInLeaf - Variable in class weka.core.neighboursearch.KDTree
-
maximal number of instances in a leaf.
- m_MaxInstNum - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
maximal number of instances for this cluster
- m_MaxIntNodes - Variable in class weka.core.neighboursearch.TreePerformanceStats
-
The min and max number internal nodes looked
for a query by the tree based NNS algorithm.
- m_maxItems - Variable in class weka.associations.FPGrowth
-
- m_MaxIterations - Variable in class weka.classifiers.mi.MIBoost
-
the maximum number of boost iterations
- m_MaxIterations - Variable in class weka.classifiers.mi.MISVM
-
the maximum number of iterations to perform
- m_maxIterations - Variable in class weka.classifiers.trees.lmt.LogisticBase
-
The maximum number of LogitBoost iterations
- m_MaxIterations - Variable in class weka.clusterers.XMeans
-
maximum overall iterations.
- m_MAXITS - Variable in class weka.core.Optimization
-
- m_MaxKMeans - Variable in class weka.clusterers.XMeans
-
maximum iterations to perform Kmeans part
if negative, iterations are not checked.
- m_MaxKMeansForChildren - Variable in class weka.clusterers.XMeans
-
see above, but for kMeans of splitted clusters.
- m_MaxLeaves - Variable in class weka.core.neighboursearch.TreePerformanceStats
-
The min and max number leaf nodes looked
for a query by the tree based NNS algorithm.
- m_MaxNumClusters - Variable in class weka.clusterers.XMeans
-
max number of clusters to generate.
- m_MaxP - Variable in class weka.core.neighboursearch.PerformanceStats
-
The min and max data points looked for a query by
the NNS algorithm.
- m_maxPlots - Variable in class weka.gui.beans.AttributeSummarizer
-
The maximum number of plots to show
- m_MaxPosition - Variable in class weka.core.tokenizers.NGramTokenizer
-
the number of strings available
- m_MaxRange - Variable in class weka.datagenerators.classifiers.regression.MexicanHat
-
the upper boundary of the range, x is drawn from
- m_MaxRelLeafRadius - Variable in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
The maximum relative radius of a leaf node
(relative to the smallest ball enclosing all the
data (training) points).
- m_MaxRows - Variable in class weka.gui.sql.event.QueryExecuteEvent
-
the maximum number of rows to retrieve
- m_MaxRows - Variable in class weka.gui.sql.ResultSetHelper
-
the maximum number of rows to retrieve.
- m_maxRunNumber - Variable in class weka.gui.beans.BatchClassifierEvent
-
The maximum number of runs
- m_maxRunNumber - Variable in class weka.gui.beans.TestSetEvent
-
Maximum number of runs.
- m_maxRunNumber - Variable in class weka.gui.beans.TrainingSetEvent
-
Maximum number of runs.
- m_maxSetNumber - Variable in class weka.gui.beans.BatchClassifierEvent
-
The last set number for this series
- m_maxSetNumber - Variable in class weka.gui.beans.BatchClustererEvent
-
The last set number for this series
- m_maxSetNumber - Variable in class weka.gui.beans.TestSetEvent
-
Maximum number of sets (ie 10 in a 10 fold)
- m_maxSetNumber - Variable in class weka.gui.beans.TrainingSetEvent
-
Maximum number of sets (ie 10 in a 10 fold)
- m_maxStale - Variable in class weka.attributeSelection.BestFirst
-
maximum number of stale nodes before terminating search
- m_maxStale - Variable in class weka.attributeSelection.LinearForwardSelection
-
maximum number of stale nodes before terminating search
- m_MaxThreshold - Variable in class weka.filters.unsupervised.attribute.NumericCleaner
-
the maximum threshold
- m_maxVal - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
-
The min and max values for this attribute.
- m_maxValue - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
ranges of each attribute (max); not used if gaussian
- m_maxValue - Variable in class weka.gui.AttributeVisualizationPanel
-
This holds the max value of the current attribute.
- m_maxVariancePercentage - Variable in class weka.filters.unsupervised.attribute.RemoveUseless
-
The type of attribute to delete
- m_MaxX - Variable in class weka.classifiers.meta.GridSearch.Grid
-
the maximum on the X axis
- m_maxX - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
- m_maxX - Variable in class weka.gui.visualize.Plot2D
-
Holds the min and max values of the x, y and colouring attributes
over all plots
- m_maxX - Variable in class weka.gui.visualize.PlotData2D
-
Holds the min and max values of the x, y and colouring attributes
for this plot
- m_MaxY - Variable in class weka.classifiers.meta.GridSearch.Grid
-
the maximum on the Y axis
- m_maxY - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
- m_maxY - Variable in class weka.gui.visualize.Plot2D
-
- m_maxY - Variable in class weka.gui.visualize.PlotData2D
-
- m_Mean - Variable in class weka.classifiers.mi.MINND
-
The mean for each attribute of each exemplar
- m_Mean - Variable in class weka.experiment.ResultMatrix
-
the values
- m_MeanPrec - Variable in class weka.experiment.ResultMatrix
-
the standard mean precision
- m_MeanPrec - Variable in class weka.gui.experiment.OutputFormatDialog
-
the number of digits after the period (= precision) for printing the mean.
- m_MeanPrecSpinner - Variable in class weka.gui.experiment.OutputFormatDialog
-
the spinner to choose the precision for the mean from.
- m_Means - Variable in class weka.classifiers.bayes.NaiveBayesSimple
-
The means for numeric attributes.
- m_MeanSquared - Variable in class weka.classifiers.lazy.IBk
-
Whether to minimise mean squared error rather than mean absolute
error when cross-validating on numeric prediction tasks.
- m_meanValue - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
mean ; only used if gaussian
- m_MeanWidth - Variable in class weka.experiment.ResultMatrix
-
the size of the mean columns
- m_MeasurePerformance - Variable in class weka.core.neighboursearch.NearestNeighbourSearch
-
Should we measure Performance.
- m_Median - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
-
the median
- m_Memory - Static variable in class weka.gui.Main
-
for monitoring the Memory consumption.
- m_Memory - Variable in class weka.gui.MemoryUsagePanel
-
for monitoring the memory usage.
- m_MemoryUsageFrame - Variable in class weka.gui.GUIChooser
-
The frame containing the memory usage
- m_messageString - Variable in class weka.experiment.RemoteExperimentEvent
-
The message
- m_MetaClassifier - Variable in class weka.classifiers.meta.Stacking
-
The meta classifier
- m_MetaClassifiers - Variable in class weka.classifiers.meta.Grading
-
The meta classifiers, one for each base classifier.
- m_MetaClassifiers - Variable in class weka.classifiers.meta.StackingC
-
The meta classifiers (one for each class, like in ClassificationViaRegression)
- m_MetaFormat - Variable in class weka.classifiers.meta.Stacking
-
Format for meta data
- m_Method - Variable in class weka.classifiers.mi.MIWrapper
-
the test method
- m_Methods - Variable in class weka.core.xml.MethodHandler
-
stores the properties/class - Method relationship
- m_metric - Variable in class weka.associations.FPGrowth
-
- m_metricThreshold - Variable in class weka.associations.FPGrowth
-
- m_metricType - Variable in class weka.associations.Apriori
-
The selected metric type.
- m_metricType - Variable in class weka.associations.FPGrowth.AssociationRule
-
The metric type for this rule
- m_midPoints - Variable in class weka.associations.PredictiveApriori
-
The mid points of the intervals used for the prior estimation.
- m_midPoints - Variable in class weka.associations.PriorEstimation
-
The mid points of the discrete intervals in which the interval [0,1] is divided.
- m_midPoints - Variable in class weka.associations.RuleGeneration
-
Sorted array of the mied points of the intervals used for prior estimation.
- m_Min - Variable in class weka.classifiers.meta.GridSearch.PerformanceTable
-
the minimum performance
- m_min - Variable in class weka.core.pmml.TargetMetaInfo
-
min and max
- m_Min - Variable in class weka.gui.boundaryvisualizer.KDDataGenerator
-
The minimum values for numeric attributes.
- m_MinArray - Variable in class weka.filters.unsupervised.attribute.Normalize
-
The minimum values for numeric attributes.
- m_MinBoxRelWidth - Variable in class weka.core.neighboursearch.KDTree
-
minimal relative width of a KDTree rectangle.
- m_MinC - Variable in class weka.core.neighboursearch.PerformanceStats
-
The min and max coordinates(attributes) looked
at per query.
- m_minC - Variable in class weka.gui.visualize.AttributePanel
-
- m_minC - Variable in class weka.gui.visualize.Plot2D
-
- m_minC - Variable in class weka.gui.visualize.PlotData2D
-
- m_minChunkSize - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
The minimum chunk size used for training
- m_MinDefault - Variable in class weka.filters.unsupervised.attribute.NumericCleaner
-
the minimum default replacement value
- m_MinGroup - Variable in class weka.classifiers.meta.RotationForest
-
The minimum size of a group
- m_minimax - Variable in class weka.classifiers.mi.MISMO
-
Use MIMinimax feature space?
- m_minimizeCB - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_MinimizeExpectedCost - Variable in class weka.classifiers.meta.CostSensitiveClassifier
-
True if the costs should be used by selecting the minimum expected
cost (false means weight training data by the costs)
- m_MinimumNumberInstances - Variable in class weka.core.Capabilities
-
the minimum number of instances in a dataset
- m_MinimumNumberInstancesTest - Variable in class weka.core.Capabilities
-
whether to test for minimum number of instances
- m_minInfoGain - Variable in class weka.classifiers.trees.lmt.ResidualModelSelection
-
Minimum information gain for split
- m_miningMeta - Variable in class weka.core.pmml.MiningSchema
-
Meta information about the mining schema fields
- m_miningSchema - Variable in class weka.classifiers.pmml.consumer.PMMLClassifier
-
The fields and meta data used by the model
- m_miningSchemaInstancesStructure - Variable in class weka.core.pmml.MiningSchema
-
Just the mining schema fields as Instances
- m_MinInstNum - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
minimal number of instances for this cluster
- m_MinIntNodes - Variable in class weka.core.neighboursearch.TreePerformanceStats
-
The min and max number internal nodes looked
for a query by the tree based NNS algorithm.
- m_MinLeaves - Variable in class weka.core.neighboursearch.TreePerformanceStats
-
The min and max number leaf nodes looked
for a query by the tree based NNS algorithm.
- m_MinLevel - Variable in class weka.core.logging.Logger
-
the minimum level of log events to have in order to end up in the log.
- m_minMetric - Variable in class weka.associations.Apriori
-
The minimum metric score.
- m_MinNum - Variable in class weka.classifiers.trees.RandomTree
-
Minimum number of instances for leaf.
- m_MinNum - Variable in class weka.classifiers.trees.REPTree
-
The minimum number of instances per leaf.
- m_MinNumClusters - Variable in class weka.clusterers.XMeans
-
min number of clusters to generate.
- m_minNumInstances - Variable in class weka.classifiers.trees.ft.FTtree
-
minimum number of instances at which a node is considered for splitting
- m_minNumInstances - Variable in class weka.classifiers.trees.FT
-
minimum number of instances at which a node is considered for splitting
- m_minNumInstances - Variable in class weka.classifiers.trees.lmt.LMTNode
-
minimum number of instances at which a node is considered for splitting
- m_minNumInstances - Variable in class weka.classifiers.trees.LMT
-
minimum number of instances at which a node is considered for splitting
- m_minNumInstances - Variable in class weka.classifiers.trees.lmt.ResidualModelSelection
-
Minimum number of instances for leaves
- m_minNumInstances - Variable in class weka.classifiers.trees.m5.M5Base
-
The minimum number of instances to allow at a leaf node
- m_minNumObj - Variable in class weka.classifiers.rules.part.ClassifierDecList
-
Minimum number of objects
- m_minNumObj - Variable in class weka.classifiers.trees.BFTree
-
Minimum number of instances at leaf nodes.
- m_minNumObj - Variable in class weka.classifiers.trees.SimpleCart
-
Minimum number of instances in at the terminal nodes.
- m_MinP - Variable in class weka.core.neighboursearch.PerformanceStats
-
The min and max data points looked for a query by
the NNS algorithm.
- m_MinRange - Variable in class weka.datagenerators.classifiers.regression.MexicanHat
-
the lower boundary of the range, x is drawn from
- m_minRuleCount - Variable in class weka.associations.RuleGeneration
-
The minimum support a rule needs to be a candidate for the list of the best rules.
- m_minSupport - Variable in class weka.associations.Apriori
-
The minimum support.
- m_MinSupport - Variable in class weka.associations.GeneralizedSequentialPatterns
-
the minimum support threshold
- m_MinThreshold - Variable in class weka.filters.unsupervised.attribute.NumericCleaner
-
the minimum threshold
- m_minVal - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
-
- m_minValue - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
ranges of each attribute (min); not used if gaussian
- m_MinVarianceProp - Variable in class weka.classifiers.trees.REPTree
-
The minimum proportion of the total variance (over all the data)
required for split.
- m_MinX - Variable in class weka.classifiers.meta.GridSearch.Grid
-
the minimum on the X axis
- m_minX - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
- m_minX - Variable in class weka.gui.visualize.Plot2D
-
- m_minX - Variable in class weka.gui.visualize.PlotData2D
-
- m_MinY - Variable in class weka.classifiers.meta.GridSearch.Grid
-
the minimum on the Y axis
- m_minY - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
- m_minY - Variable in class weka.gui.visualize.Plot2D
-
- m_minY - Variable in class weka.gui.visualize.PlotData2D
-
- m_Misses - Variable in class weka.core.FindWithCapabilities
-
the class that didn't match.
- m_missing - Variable in class weka.associations.tertius.Literal
-
- m_Missing - Variable in class weka.classifiers.functions.GaussianProcesses
-
The filter used to get rid of missing values.
- m_Missing - Variable in class weka.classifiers.functions.SMO
-
The filter used to get rid of missing values.
- m_Missing - Variable in class weka.classifiers.functions.SMOreg
-
The filter used to get rid of missing values.
- m_Missing - Variable in class weka.classifiers.mi.MDD
-
The filter used to get rid of missing values.
- m_Missing - Variable in class weka.classifiers.mi.MIDD
-
The filter used to get rid of missing values.
- m_Missing - Variable in class weka.classifiers.mi.MIEMDD
-
The filter used to get rid of missing values.
- m_Missing - Variable in class weka.classifiers.mi.MISMO
-
The filter used to get rid of missing values.
- m_Missing - Variable in class weka.filters.supervised.attribute.PLSFilter
-
for replacing missing values
- m_Missing - Variable in class weka.filters.unsupervised.attribute.KernelFilter
-
The filter used to get rid of missing values.
- m_MissingClass - Variable in class weka.classifiers.Evaluation
-
The weight of all instances that had no class assigned to them.
- m_MissingClassValuesTest - Variable in class weka.core.Capabilities
-
whether to test for missing class values
- m_MissingLab - Variable in class weka.gui.AttributeSummaryPanel
-
Displays the number of missing values
- m_MissingMode - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
missing value treatment
- m_MissingMode - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
missing value treatment
- m_MissingMode - Variable in class weka.classifiers.lazy.KStar
-
missing value treatment
- m_MissingProb - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
Probability of test attribute transforming into train attribute
with missing value
- m_MissingProb - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
Probability of test attribute transforming into train attribute
with missing value
- m_MissingValue - Variable in class weka.core.converters.CSVLoader
-
The placeholder for missing values.
- m_missingValueReplacementNominal - Variable in class weka.core.pmml.MiningFieldMetaInfo
-
actual missing value replacements (if specified)
- m_missingValueReplacementNumeric - Variable in class weka.core.pmml.MiningFieldMetaInfo
-
- m_MissingValuesFilter - Variable in class weka.classifiers.bayes.BayesNet
-
filter used to fill in missing values, if any
- m_MissingValuesTest - Variable in class weka.core.Capabilities
-
whether to test for missing values
- m_missingValueTreatmentMethod - Variable in class weka.core.pmml.MiningFieldMetaInfo
-
missing values treatment method
- m_Mistakes - Variable in class weka.classifiers.functions.Winnow
-
Accumulated mistake count (for statistics)
- m_Mle - Variable in class weka.clusterers.XMeans
-
Distortion.
- m_Model - Variable in class weka.classifiers.functions.LibLINEAR
-
LibLINEAR Model
- m_Model - Variable in class weka.classifiers.functions.LibSVM
-
LibSVM Model
- m_Model - Variable in class weka.classifiers.misc.SerializedClassifier
-
the serialized classifier model used for making predictions
- m_Model - Variable in class weka.clusterers.XMeans
-
model information, should increase readability.
- m_Model - Variable in class weka.gui.AttributeListPanel
-
The table model containing attribute names
- m_Model - Variable in class weka.gui.AttributeSelectionPanel
-
The table model containing attribute names and selection status
- m_Model - Variable in class weka.gui.ResultHistoryPanel
-
The list model
- m_Model - Variable in class weka.gui.sql.InfoPanel
-
the model for the list
- m_ModelFile - Variable in class weka.classifiers.misc.SerializedClassifier
-
the file where the serialized model is stored
- m_ModelFilter - Variable in class weka.gui.explorer.ClassifierPanel
-
Filter to ensure only model files are selected
- m_ModelFilter - Variable in class weka.gui.explorer.ClustererPanel
-
Filter to ensure only model files are selected
- m_modelHasChanged - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
-
- m_modelHasChangedLL - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
-
- m_modelName - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
-
- m_models - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
-
- m_Models - Variable in class weka.classifiers.mi.MIBoost
-
the models for the iterations
- m_modelSelection - Variable in class weka.classifiers.trees.ft.FTtree
-
ModelSelection object (for splitting)
- m_modelSelection - Variable in class weka.classifiers.trees.lmt.LMTNode
-
ModelSelection object (for splitting)
- m_modelType - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
-
- m_modelType - Variable in class weka.classifiers.trees.FT
-
Model Type, value: 0 is FT, 1 is FTLeaves, 2 is FTInner
- m_modePanel - Variable in class weka.gui.experiment.SimpleSetupPanel
-
The panel which switched between simple and advanced setup modes
- m_ModifyHeader - Variable in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
Modify header for nominal attributes?
- m_ModifyHeader - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
-
Modify header for nominal attributes?
- m_Monitor - Variable in class weka.gui.MemoryUsagePanel
-
the thread for monitoring the memory usage.
- m_Monitoring - Variable in class weka.gui.MemoryUsagePanel.MemoryMonitor
-
whether the thread is still running.
- m_MultiInstance - Variable in class weka.core.TestInstances
-
whether to generate Multi-Instance data or not
- m_MultinomialWord - Variable in class weka.classifiers.bayes.DMNBtext
-
- m_mustContainOR - Variable in class weka.associations.FPGrowth
-
Use OR rather than AND when considering must contain lists
- m_MWeight - Variable in class weka.classifiers.bayes.AODEsr
-
m value for m-estimation
- m_N - Variable in class weka.core.tokenizers.NGramTokenizer
-
the current length of the N-grams
- m_Name - Variable in class weka.core.PropertyPath.PathElement
-
the property
- m_Name - Variable in class weka.filters.unsupervised.attribute.Add
-
The name for the new attribute.
- m_Name - Variable in class weka.filters.unsupervised.attribute.AddID
-
the name of the attribute
- m_name - Variable in class weka.gui.treevisualizer.NamedColor
-
The name of the color
- m_NameCounter - Variable in class weka.gui.sql.ResultPanel
-
the counter for the tab names
- m_NB - Variable in class weka.classifiers.rules.DTNB
-
The naive Bayes half of the hybrid
- m_nCacheHits - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
-
number of kernel cache hits, used for printing statistics only
- m_nCount - Variable in class weka.classifiers.bayes.net.ADNode
-
count
- m_NCV - Variable in class weka.classifiers.lazy.LBR
-
for printing in n-fold cross validation
- m_ndtree - Variable in class weka.classifiers.meta.nestedDichotomies.ND
-
The tree of classes
- m_NearestNeighbors - Variable in class weka.filters.supervised.instance.SMOTE
-
the number of neighbors to use.
- m_negTrainInstances - Variable in class weka.classifiers.trees.ADTree
-
The training instances with negative class - referencing the training dataset
- m_NeighborListDebug - Variable in class weka.classifiers.mi.CitationKNN
-
- m_Neighbour - Variable in class weka.classifiers.mi.MINND
-
The number of nearest neighbour for prediction
- m_nEvals - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
-
number of kernel evaluations, used for printing statistics only
- m_nEvidence - Variable in class weka.classifiers.bayes.net.EditableBayesNet
-
evidence values, used for evidence propagation *
- m_NewBatch - Variable in class weka.filters.Filter
-
Record whether the filter is at the start of a batch
- m_NewBut - Variable in class weka.gui.experiment.SetupPanel
-
Click to create a new experiment with default settings
- m_NewBut - Variable in class weka.gui.experiment.SimpleSetupPanel
-
Click to create a new experiment with default settings
- m_NewOrderCols - Variable in class weka.filters.unsupervised.attribute.Reorder
-
Stores which columns to reorder
- m_newValidationFs - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
-
- m_Next - Variable in class weka.core.neighboursearch.NearestNeighbourSearch.NeighborNode
-
A link to the next neighbor instance.
- m_Next - Variable in class weka.core.Queue.QueueNode
-
The next node in the queue
- m_nextClassShouldBeZero - Variable in class weka.datagenerators.classifiers.classification.Agrawal
-
used for balancing the class
- m_nInstances - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
-
number of instances in data set
- m_NMax - Variable in class weka.core.tokenizers.NGramTokenizer
-
the maximum number of N
- m_nMaxNrOfParents - Variable in class weka.classifiers.bayes.net.search.SearchAlgorithm
-
Holds upper bound on number of parents
- m_nMCV - Variable in class weka.classifiers.bayes.net.VaryNode
-
most common value
- m_NMin - Variable in class weka.core.tokenizers.NGramTokenizer
-
the minimum number of N
- m_nNodes - Variable in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
-
nodes of the Bayes net in this junction node
- m_NNSearch - Variable in class weka.classifiers.lazy.IBk
-
for nearest-neighbor search.
- m_NNSearch - Variable in class weka.classifiers.lazy.LWL
-
The nearest neighbour search algorithm to use.
- m_noClass - Variable in class weka.estimators.Estimator
-
set if class is not important
- m_NodeColor - Variable in class weka.gui.treevisualizer.TreeVisualizer
-
the node color.
- m_nodeHeight - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
-
The nodeWidth and nodeHeight
- m_NodeNumber - Variable in class weka.core.neighboursearch.balltrees.BallNode
-
The node number/id.
- m_NodeNumber - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode
-
node number (only for debug).
- m_NodeRanges - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode
-
lowest and highest value and width (= high - low) for each
dimension.
- m_nodes - Variable in class weka.gui.graphvisualizer.BIFParser
-
These holds the nodes and edges of the graph
- m_nodes - Variable in class weka.gui.graphvisualizer.DotParser
-
These holds the nodes and edges of the graph
- m_nodes - Variable in class weka.gui.graphvisualizer.GraphVisualizer
-
Vector containing nodes
- m_nodes - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
-
FastVector containing nodes and edges
- m_nodesExpanded - Variable in class weka.classifiers.trees.ADTree
-
Statistics - the number of prediction nodes investigated during search
- m_nodesExpanded - Variable in class weka.classifiers.trees.LADTree
-
- m_NodesRectBounds - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode
-
The lo and high bounds of the hyper rectangle described by the
node.
- m_nodeWidth - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
-
The nodeWidth and nodeHeight
- m_NoisePercent - Variable in class weka.datagenerators.classifiers.classification.LED24
-
the noise rate
- m_NoiseRandom - Variable in class weka.datagenerators.classifiers.regression.MexicanHat
-
the random number generator for the noise
- m_NoiseRate - Variable in class weka.datagenerators.classifiers.regression.MexicanHat
-
the rate of the gaussian noise
- m_NoiseRate - Variable in class weka.datagenerators.clusterers.SubspaceCluster
-
noise rate in percent (option P, between 0 and 30)
- m_NoiseVariance - Variable in class weka.datagenerators.classifiers.regression.MexicanHat
-
the variance of the gaussian noise
- m_nominalAttIndices - Variable in class weka.classifiers.trees.ADTree
-
An array containing the inidices to the nominal attributes in the data
- m_NominalAttributes - Variable in class weka.core.converters.CSVLoader
-
The range of attributes to force to type nominal.
- m_nominalCols - Variable in class weka.datagenerators.ClusterGenerator
-
Stores which columns are nominal (default numeric)
- m_NominalIndexes - Variable in class weka.experiment.InstancesResultListener
-
For lookup of indices given a string value for each nominal attribute
- m_NominalMapping - Variable in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
If m_ModifyHeader, stores a mapping from old to new indexes
- m_NominalMapping - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
-
If m_ModifyHeader, stores a mapping from old to new indexes
- m_NominalStrings - Variable in class weka.experiment.InstancesResultListener
-
Contains strings seen so far for each nominal attribute
- m_NominalToBinary - Variable in class weka.classifiers.functions.GaussianProcesses
-
The filter used to make attributes numeric.
- m_NominalToBinary - Variable in class weka.classifiers.functions.LibLINEAR
-
The filter used to make attributes numeric.
- m_NominalToBinary - Variable in class weka.classifiers.functions.SimpleLogistic
-
Filter for converting nominal attributes to binary ones
- m_NominalToBinary - Variable in class weka.classifiers.functions.SMO
-
The filter used to make attributes numeric.
- m_NominalToBinary - Variable in class weka.classifiers.functions.SMOreg
-
The filter used to make attributes numeric.
- m_nominalToBinary - Variable in class weka.classifiers.functions.SPegasos
-
Convert nominal attributes to numerically coded binary ones
- m_NominalToBinary - Variable in class weka.classifiers.mi.MISMO
-
The filter used to make attributes numeric.
- m_nominalToBinary - Variable in class weka.classifiers.trees.ft.FTtree
-
Filter to convert nominal attributes to binary
- m_nominalToBinary - Variable in class weka.classifiers.trees.FT
-
Filter to replace nominal attributes
- m_nominalToBinary - Variable in class weka.classifiers.trees.lmt.LMTNode
-
Filter to convert nominal attributes to binary
- m_nominalToBinary - Variable in class weka.classifiers.trees.LMT
-
Filter to replace nominal attributes
- m_NominalToBinary - Variable in class weka.filters.unsupervised.attribute.KernelFilter
-
The filter used to make attributes numeric.
- m_NominalToBinaryFilter - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Filter for turning nominal values into numeric ones.
- m_NonSigWins - Variable in class weka.experiment.ResultMatrix
-
the non-significant wins
- m_NoPriors - Variable in class weka.classifiers.Evaluation
-
enables/disables the use of priors, e.g., if no training set is
present in case of de-serialized schemes
- m_NoPruning - Variable in class weka.classifiers.trees.REPTree
-
Don't prune
- m_NoReplacement - Variable in class weka.filters.supervised.instance.Resample
-
Whether to perform sampling with replacement or without.
- m_NoReplacement - Variable in class weka.filters.unsupervised.instance.Resample
-
Whether to perform sampling with replacement or without
- m_Norm - Variable in class weka.filters.unsupervised.instance.Normalize
-
The norm that each instance must have at the end
- M_NORMAL - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
-
- m_normal - Static variable in class weka.clusterers.Cobweb
-
Normal constant.
- m_normalizationMethod - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
-
The normalization method
- m_normalizationMethod - Variable in class weka.classifiers.pmml.consumer.Regression
-
The normalization to use
- m_Normalize - Variable in class weka.classifiers.functions.LibLINEAR
-
normalize input data
- m_Normalize - Variable in class weka.classifiers.functions.LibSVM
-
normalize input data
- m_normalize - Variable in class weka.classifiers.functions.SPegasos
-
Normalize the training data
- m_Normalize - Variable in class weka.classifiers.meta.RotationForest
-
Filter that normalized the attributes
- m_NormalizeDimWidths - Variable in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
-
Should we normalize the widths(ranges) of the dimensions (attributes)
before selecting the widest one.
- m_NormalizeNodeWidth - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
-
Stores whether if the width of a KDTree
node is normalized or not.
- m_NotCapabilities - Variable in class weka.core.FindWithCapabilities
-
the capabilities to look for to "not have".
- m_Notes - Variable in class weka.experiment.Experiment
-
User notes about the experiment
- m_NotesButton - Variable in class weka.gui.experiment.SetupPanel
-
A button for bringing up the notes
- m_NotesButton - Variable in class weka.gui.experiment.SimpleSetupPanel
-
A button for bringing up the notes
- m_NotesFrame - Variable in class weka.gui.experiment.SetupPanel
-
Frame for the notes
- m_NotesFrame - Variable in class weka.gui.experiment.SimpleSetupPanel
-
Frame for the notes
- m_NotesText - Variable in class weka.gui.experiment.SetupPanel
-
Area for user notes Default of 10 rows
- m_NotesText - Variable in class weka.gui.experiment.SimpleSetupPanel
-
Area for user notes Default of 10 rows
- m_nPositionX - Variable in class weka.classifiers.bayes.net.BIFReader
-
- m_nPositionX - Variable in class weka.classifiers.bayes.net.EditableBayesNet
-
location of nodes, used for graph drawing *
- m_nPositionY - Variable in class weka.classifiers.bayes.net.BIFReader
-
- m_nPositionY - Variable in class weka.classifiers.bayes.net.EditableBayesNet
-
- m_nSeed - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
-
seed for initializing random number generator
- m_nStartNode - Variable in class weka.classifiers.bayes.net.ADNode
-
first node in VaryNode array
- m_nSymbols - Variable in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
-
Holds number of symbols in distribution
- m_ntob - Variable in class weka.filters.unsupervised.attribute.RandomProjection
-
The NominalToBinary filter applied to the data before this filter
- m_nu - Variable in class weka.classifiers.functions.LibSVM
-
for NU_SVC, ONE_CLASS, and NU_SVR
- m_numAttribs - Variable in class weka.attributeSelection.BestFirst
-
number of attributes in the data
- m_numAttribs - Variable in class weka.attributeSelection.GreedyStepwise
-
number of attributes in the data
- m_numAttribs - Variable in class weka.attributeSelection.LinearForwardSelection
-
number of attributes in the data
- m_numAttribs - Variable in class weka.attributeSelection.SubsetSizeForwardSelection
-
number of attributes in the data
- m_NumAttribs - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Number of attributes.
- m_numAttributes - Variable in class weka.classifiers.bayes.NaiveBayesMultinomial
-
number of unique words
- m_NumAttributes - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
The number of attributes
- m_NumAttributes - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
The number of attributes
- m_NumAttributes - Variable in class weka.classifiers.lazy.KStar
-
The number of attributes
- m_NumAttributes - Variable in class weka.classifiers.meta.CVParameterSelection
-
The number of attributes in the data
- m_numAttributes - Variable in class weka.classifiers.rules.DecisionTable
-
The number of attributes in the dataset
- m_NumAttributes - Variable in class weka.core.SparseInstance
-
The maximum number of values that can be stored.
- m_NumAttributes - Variable in class weka.datagenerators.classifiers.classification.RandomRBF
-
Number of attribute the dataset should have
- m_NumAttributes - Variable in class weka.datagenerators.classifiers.classification.RDG1
-
Number of attribute the dataset should have
- m_NumAttributes - Variable in class weka.datagenerators.ClusterGenerator
-
Number of attribute the dataset should have
- m_NumAttributes - Variable in class weka.filters.unsupervised.attribute.RandomSubset
-
The number of attributes to randomly choose (>= 1 absolute number of
attributes, < 1 percentage).
- m_NumAttributesLab - Variable in class weka.gui.InstancesSummaryPanel
-
Displays the number of attributes
- m_numAttributesSelected - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
-
The number of attributes selected by the attribute selection phase
- m_NumAttributesUsed - Variable in class weka.classifiers.lazy.IBk
-
The number of attributes the contribute to a prediction.
- m_numAtts - Variable in class weka.classifiers.lazy.LBR
-
number of attributes for the dataset
- m_NumAttsSet - Variable in class weka.classifiers.lazy.LBR.Indexes
-
the number of attributes "in use" or set to a the original value (true or false)
- m_NumBags - Variable in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
-
the total number of bags
- m_Number - Variable in class weka.classifiers.lazy.LBR
-
the number of instance to be processed
- m_numberAdditionalMeasures - Variable in class weka.experiment.ClassifierSplitEvaluator
-
The number of additional measures that need to be filled in
after taking into account column constraints imposed by the final
destination for results
- m_numberAdditionalMeasures - Variable in class weka.experiment.DensityBasedClustererSplitEvaluator
-
The number of additional measures that need to be filled in
after taking into account column constraints imposed by the final
destination for results
- m_numberMerges - Variable in class weka.clusterers.Cobweb
-
the number of merges that happened
- m_numberOfClusters - Variable in class weka.clusterers.CLOPE
-
Number of clusters
- m_numberOfClusters - Variable in class weka.clusterers.Cobweb
-
Number of clusters (nodes in the tree).
- m_numberOfClustersDetermined - Variable in class weka.clusterers.CLOPE
-
whether the number of clusters was already determined
- m_numberOfClustersDetermined - Variable in class weka.clusterers.Cobweb
-
whether the number of clusters was already determined
- m_NumberOfGroups - Variable in class weka.classifiers.meta.RotationForest
-
Whether minGroup and maxGroup refer to the number of groups or their
size
- m_numberOfInputs - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
-
The number of inputs to the network
- m_NumberOfInstances - Variable in class weka.classifiers.lazy.LBR
-
the Number of Instances to be used in building a classifiers
- m_numberOfInstances - Variable in class weka.clusterers.CLOPE
-
Number of instances
- m_numberOfLayers - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
-
Number of hidden layers in the network
- m_NumberOfRepetitionsTField - Variable in class weka.gui.experiment.SimpleSetupPanel
-
Input field for number of repetitions
- m_numberOfTransactions - Variable in class weka.associations.FPGrowth.FrequentItemSets
-
The total number of transactions in the data
- m_numberSplits - Variable in class weka.clusterers.Cobweb
-
the number of splits that happened
- m_NumBins - Variable in class weka.classifiers.meta.RegressionByDiscretization
-
The number of discretization intervals.
- m_NumBins - Variable in class weka.filters.unsupervised.attribute.Discretize
-
The number of bins to divide the attribute into
- m_numBoostingIterations - Variable in class weka.classifiers.functions.SimpleLogistic
-
If non-negative, use this as fixed number of LogitBoost iterations
- m_numBoostingIterations - Variable in class weka.classifiers.trees.FT
-
if non-zero, use fixed number of iterations for LogitBoost
- m_numBoostingIterations - Variable in class weka.classifiers.trees.LMT
-
if non-zero, use fixed number of iterations for LogitBoost
- m_NumCacheHits - Variable in class weka.classifiers.functions.supportVector.KernelEvaluation
-
the number of cache hits
- m_NumCentroids - Variable in class weka.datagenerators.classifiers.classification.RandomRBF
-
the number of centroids to use for generation
- m_NumCiters - Variable in class weka.classifiers.mi.CitationKNN
-
Number of citers
- m_NumClasses - Variable in class weka.classifiers.bayes.BayesNet
-
The number of classes
- m_NumClasses - Variable in class weka.classifiers.bayes.NaiveBayes
-
The number of classes (or 1 for numeric class)
- m_numClasses - Variable in class weka.classifiers.bayes.NaiveBayesMultinomial
-
number of class values
- m_NumClasses - Variable in class weka.classifiers.Evaluation
-
The number of classes.
- m_NumClasses - Variable in class weka.classifiers.functions.Logistic
-
The number of the class labels
- m_NumClasses - Variable in class weka.classifiers.lazy.IBk
-
The number of class values (or 1 if predicting numeric).
- m_NumClasses - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
The number of class values
- m_NumClasses - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
The number of class values
- m_NumClasses - Variable in class weka.classifiers.lazy.KStar
-
The number of class values
- m_numClasses - Variable in class weka.classifiers.lazy.LBR
-
number of classes for dataset
- m_NumClasses - Variable in class weka.classifiers.meta.AdaBoostM1
-
The number of classes
- m_numClasses - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
-
The number of class vals in the training data (1 if class is numeric)
- m_NumClasses - Variable in class weka.classifiers.meta.LogitBoost
-
The number of classes
- m_NumClasses - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
The number of classes
- m_NumClasses - Variable in class weka.classifiers.mi.CitationKNN
-
The number of the class labels
- m_NumClasses - Variable in class weka.classifiers.mi.MDD
-
The number of the class labels
- m_NumClasses - Variable in class weka.classifiers.mi.MIBoost
-
The number of the class labels
- m_NumClasses - Variable in class weka.classifiers.mi.MIDD
-
The number of the class labels
- m_NumClasses - Variable in class weka.classifiers.mi.MIEMDD
-
The number of the class labels
- m_NumClasses - Variable in class weka.classifiers.mi.MILR
-
The number of the class labels
- m_NumClasses - Variable in class weka.classifiers.mi.MINND
-
The number of class labels in the data
- m_NumClasses - Variable in class weka.classifiers.mi.MIWrapper
-
The number of the class labels
- m_NumClasses - Variable in class weka.classifiers.misc.VFI
-
The number of classes
- m_numClasses - Variable in class weka.classifiers.trees.lmt.LogisticBase
-
The number of different classes
- m_numClasses - Variable in class weka.classifiers.trees.lmt.ResidualSplit
-
Number of classed
- m_NumClasses - Variable in class weka.core.TestInstances
-
the number of classes (in case of NOMINAL class)
- m_NumClasses - Variable in class weka.datagenerators.classifiers.classification.RandomRBF
-
Number of Classes the dataset should have
- m_NumClasses - Variable in class weka.datagenerators.classifiers.classification.RDG1
-
Number of Classes the dataset should have
- m_numClusterAttributes - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
number of attributes the cluster is defined for
- m_NumClusters - Variable in class weka.clusterers.FarthestFirst
-
number of clusters to generate
- m_NumClusters - Variable in class weka.clusterers.XMeans
-
The actual number of clusters.
- m_NumClusters - Variable in class weka.datagenerators.clusterers.BIRCHCluster
-
Number of Clusters the dataset should have
- m_NumComponents - Variable in class weka.filters.supervised.attribute.PLSFilter
-
the maximum number of components to generate
- m_NumDate - Variable in class weka.core.CheckScheme
-
the number of date attributes
- m_NumDate - Variable in class weka.core.TestInstances
-
the number of date attributes
- m_numericAttIndices - Variable in class weka.classifiers.trees.ADTree
-
An array containing the inidices to the numeric attributes in the data
- m_numericAttIndices - Variable in class weka.classifiers.trees.LADTree
-
- m_NumericClassData - Variable in class weka.classifiers.meta.LogitBoost
-
Dummy dataset with a numeric class
- m_NumericClassData - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Dummy dataset with a numeric class
- m_numericClassifyThreshold - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
-
The threshold for deciding when a numeric value is correctly classified
- m_NumericColumns - Variable in class weka.gui.sql.ResultSetHelper
-
whether a column is numeric.
- m_numericData - Variable in class weka.classifiers.trees.lmt.LogisticBase
-
Numeric version of the training data.
- m_numericDataHeader - Variable in class weka.classifiers.trees.lmt.LogisticBase
-
Header-only version of the numeric version of the training data
- m_NumEvals - Variable in class weka.classifiers.functions.supportVector.KernelEvaluation
-
the number of performed evaluations
- m_NumExamples - Variable in class weka.datagenerators.ClassificationGenerator
-
Number of instances
- m_NumExamples - Variable in class weka.datagenerators.RegressionGenerator
-
Number of instances
- m_NumExamplesAct - Variable in class weka.datagenerators.DataGenerator
-
Number of instances that should be produced into the dataset
this number is by default m_NumExamples,
but can be reset by the generator
- m_numFeatures - Variable in class weka.classifiers.trees.RandomForest
-
Number of features to consider in random feature selection.
- m_NumFiles - Variable in class weka.core.Debug.Log
-
the number of files for rotating the logs
- m_numFolds - Variable in class weka.attributeSelection.SubsetSizeForwardSelection
-
Number of cross validation folds for subset size determination (default =
5).
- m_NumFolds - Variable in class weka.classifiers.Evaluation
-
The number of folds for a cross-validation.
- m_numFolds - Variable in class weka.classifiers.functions.SMO
-
The number of folds for the internal cross-validation
- m_NumFolds - Variable in class weka.classifiers.meta.CVParameterSelection
-
The number of folds used in cross-validation
- m_NumFolds - Variable in class weka.classifiers.meta.Dagging
-
the number of folds to use to split the training data
- m_NumFolds - Variable in class weka.classifiers.meta.LogitBoost
-
The number of folds for the internal cross-validation.
- m_NumFolds - Variable in class weka.classifiers.meta.Stacking
-
Set the number of folds for the cross-validation
- m_numFolds - Variable in class weka.classifiers.mi.MISMO
-
The number of folds for the internal cross-validation
- m_NumFolds - Variable in class weka.classifiers.trees.RandomTree
-
Determines how much data is used for backfitting
- m_NumFolds - Variable in class weka.classifiers.trees.REPTree
-
Number of folds for reduced error pruning.
- m_NumFolds - Variable in class weka.experiment.CrossValidationResultProducer
-
The number of folds in the cross-validation
- m_numFolds - Variable in class weka.gui.experiment.SimpleSetupPanel
-
The number of folds for a cross-validation experiment
- m_numFoldsBoosting - Static variable in class weka.classifiers.trees.lmt.LogisticBase
-
Number of folds for cross-validating number of LogitBoost iterations
- m_numFoldsPruning - Variable in class weka.classifiers.trees.BFTree
-
Number of folds for the pruning.
- m_numFoldsPruning - Static variable in class weka.classifiers.trees.lmt.LMTNode
-
Number of folds for CART pruning
- m_numFoldsPruning - Variable in class weka.classifiers.trees.SimpleCart
-
Number of folds for minimal cost-complexity pruning.
- m_NumGenerated - Variable in class weka.classifiers.meta.LogitBoost
-
The number of successfully generated base classifiers.
- m_numHigherRegressions - Variable in class weka.classifiers.trees.ft.FTtree
-
Number of simple regression functions fit by LogitBoost at higher levels in the tree
- m_numHigherRegressions - Variable in class weka.classifiers.trees.lmt.LMTNode
-
Number of simple regression functions fit by LogitBoost at higher levels in the tree
- m_numIncorrectModel - Variable in class weka.classifiers.trees.lmt.LMTNode
-
Weighted number of training examples currently misclassified by the logistic model at the node
- m_numIncorrectModel - Variable in class weka.classifiers.trees.SimpleCart
-
Number of training examples misclassified by the model (subtree rooted).
- m_numIncorrectTree - Variable in class weka.classifiers.trees.lmt.LMTNode
-
Weighted number of training examples currently misclassified by the subtree rooted at the node
- m_numIncorrectTree - Variable in class weka.classifiers.trees.SimpleCart
-
Number of training examples misclassified by the model (subtree not rooted).
- m_numInputs - Variable in class weka.classifiers.functions.neural.NeuralConnection
-
The number of inputs.
- m_NumInstances - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
The number of instances in the dataset
- m_NumInstances - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
The number of instances in the dataset
- m_NumInstances - Variable in class weka.classifiers.lazy.KStar
-
The number of instances in the dataset
- m_numInstances - Variable in class weka.classifiers.trees.ft.FTtree
-
Number of instances at the node
- m_numInstances - Variable in class weka.classifiers.trees.lmt.LMTNode
-
Number of instances at the node
- m_numInstances - Variable in class weka.classifiers.trees.lmt.ResidualSplit
-
Number of instances in the set
- m_numInstances - Variable in class weka.classifiers.trees.m5.RuleNode
-
the number of instances reaching this node
- m_NumInstances - Variable in class weka.core.CheckScheme
-
The number of instances in the datasets
- m_NumInstances - Variable in class weka.core.neighboursearch.balltrees.BallNode
-
The number of instances/points in the node.
- m_NumInstances - Variable in class weka.core.TestInstances
-
the number of instances
- m_numInstances - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
number of instances for this cluster
- m_NumInstances - Variable in class weka.estimators.CheckEstimator
-
The number of instances in the datasets
- m_NumInstances - Variable in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
-
the total number of the propositional instance in the dataset
- m_NumInstances - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Number of instances.
- m_numInstancesConsumed - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
The number of instances consumed
- m_NumInstancesLab - Variable in class weka.gui.InstancesSummaryPanel
-
Displays the number of instances
- m_NumInstancesRelational - Variable in class weka.core.CheckScheme
-
the number of instances in relational attributes (applies also for bags
in multi-instance)
- m_NumInstancesRelational - Variable in class weka.core.TestInstances
-
the number of instances in relational attributes (applies also for bags
in multi-instance)
- m_numInsts - Variable in class weka.classifiers.functions.supportVector.CachedKernel
-
The number of instance in the dataset
- m_numInsts - Variable in class weka.classifiers.lazy.LBR
-
number of instances in dataset
- m_NumInstsSet - Variable in class weka.classifiers.lazy.LBR.Indexes
-
the number of instances "in use" or set to a the original value (true or false)
- m_numIntervals - Static variable in class weka.associations.PredictiveApriori
-
The number of intervals used for the prior estimation.
- m_numIntervals - Variable in class weka.associations.PriorEstimation
-
The number of intervals.
- m_numIrrelevantAttributes - Variable in class weka.datagenerators.classifiers.classification.LED24
-
used for generating the output, i.e., the additional noise attributes
- m_NumIterations - Variable in class weka.classifiers.bayes.DMNBtext
-
The number of iterations.
- m_numIterations - Variable in class weka.classifiers.functions.Winnow
-
The number of iterations
- m_NumIterations - Variable in class weka.classifiers.IteratedSingleClassifierEnhancer
-
The number of iterations.
- m_NumIterations - Variable in class weka.classifiers.meta.MetaCost
-
The number of iterations.
- m_NumIterationsPerformed - Variable in class weka.classifiers.meta.AdaBoostM1
-
The number of successfully generated base classifiers.
- m_NumIterationsPerformed - Variable in class weka.classifiers.meta.AdditiveRegression
-
The number of successfully generated base classifiers.
- m_NumLeaves - Variable in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
The number of leaf nodes in the built tree.
- m_NumLeaves - Variable in class weka.core.neighboursearch.CoverTree
-
Number of nodes in the tree.
- m_NumLeaves - Variable in class weka.core.neighboursearch.KDTree
-
Tree stats.
- m_NumNodes - Variable in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
The number of internal and leaf nodes in the
built tree.
- m_NumNodes - Variable in class weka.core.neighboursearch.CoverTree
-
Number of nodes in the tree.
- m_NumNodes - Variable in class weka.core.neighboursearch.KDTree
-
Tree stats.
- m_NumNominal - Variable in class weka.core.CheckScheme
-
the number of nominal attributes
- m_NumNominal - Variable in class weka.core.TestInstances
-
the number of nominal attributes
- m_NumNominalValues - Variable in class weka.core.TestInstances
-
the number of values for nominal attributes
- m_NumNumeric - Variable in class weka.core.CheckScheme
-
the number of numeric attributes
- m_NumNumeric - Variable in class weka.core.TestInstances
-
the number of numeric attributes
- m_numOfClasses - Variable in class weka.classifiers.trees.LADTree
-
- m_numOfCleansingIterations - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
-
The maximum number of cleansing iterations to perform (<1 = until fully cleansed)
- m_numOfCrossValidationFolds - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
-
The number of cross validation folds to perform (<2 = no cross validation)
- m_numOfSamplesPerGenerator - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
- m_numOfSamplesPerRegion - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
- m_numOutputs - Variable in class weka.classifiers.functions.neural.NeuralConnection
-
The number of outputs.
- m_numParameters - Variable in class weka.classifiers.trees.lmt.LogisticBase
-
Effective number of parameters used for AIC / BIC automatic stopping
- m_numParameters - Variable in class weka.classifiers.trees.m5.RuleNode
-
the number of paramters in the chosen model for this node---either
the subtree model or the linear model.
- m_NumPredictors - Variable in class weka.classifiers.functions.Logistic
-
The number of attributes in the model
- m_NumQueries - Variable in class weka.core.neighboursearch.PerformanceStats
-
The total number of queries looked at.
- m_numRandRules - Static variable in class weka.associations.PredictiveApriori
-
The number of rules created for the prior estimation.
- m_numRandRules - Variable in class weka.associations.PriorEstimation
-
The number of rnadom rules.
- m_NumReferences - Variable in class weka.classifiers.mi.CitationKNN
-
Number of references
- m_numRegressions - Variable in class weka.classifiers.trees.lmt.LogisticBase
-
The number of LogitBoost iterations performed.
- m_NumRelational - Variable in class weka.core.CheckScheme
-
the number of relational attributes
- m_NumRelational - Variable in class weka.core.TestInstances
-
the number of relational attributes
- m_NumRelationalDate - Variable in class weka.core.TestInstances
-
the number of date attributes in a relational attribute
- m_NumRelationalNominal - Variable in class weka.core.TestInstances
-
the number of nominal attributes in a relational attribute
- m_NumRelationalNominalValues - Variable in class weka.core.TestInstances
-
the number of values for nominal attributes in relational attributes
- m_NumRelationalNumeric - Variable in class weka.core.TestInstances
-
the number of numeric attributes in a relational attribute
- m_NumRelationalString - Variable in class weka.core.TestInstances
-
the number of string attributes in a relational attribute
- m_numRepetitions - Variable in class weka.gui.experiment.SimpleSetupPanel
-
The number of times to repeat the sub-experiment
- m_numRules - Variable in class weka.associations.Apriori
-
The maximum number of rules that are output.
- m_numRules - Variable in class weka.associations.PredictiveApriori
-
The maximum number of rules that are output.
- m_numRulesToFind - Variable in class weka.associations.FPGrowth
-
The number of rules to find
- m_NumRuns - Variable in class weka.classifiers.meta.LogitBoost
-
The number of runs for the internal cross-validation.
- m_NumSeqAttsSet - Variable in class weka.classifiers.lazy.LBR.Indexes
-
the number of sequential attributes "in use" or set to a the original value (true or false)
- m_NumSeqInstsSet - Variable in class weka.classifiers.lazy.LBR.Indexes
-
the number of sequential instances "in use" or set to a the original value (true or false)
- m_NumSplits - Variable in class weka.clusterers.XMeans
-
Number of splits prepared.
- m_NumSplitsDone - Variable in class weka.clusterers.XMeans
-
Number of splits accepted (including cutoff factor decisions).
- m_NumSplitsStillDone - Variable in class weka.clusterers.XMeans
-
Number of splits accepted just because of cutoff factor.
- m_NumString - Variable in class weka.core.CheckScheme
-
the number of string attributes
- m_NumString - Variable in class weka.core.TestInstances
-
the number of string attributes
- m_NumSubCmtys - Variable in class weka.classifiers.meta.MultiBoostAB
-
The number of sub-committees to use
- m_numSubsets - Variable in class weka.classifiers.trees.j48.ClassifierSplitModel
-
Number of created subsets.
- m_numToSelect - Variable in class weka.attributeSelection.GreedyStepwise
-
The number of attributes to select.
- m_NumTrain - Variable in class weka.classifiers.functions.GaussianProcesses
-
The number of training instances
- m_NumTrainClassVals - Variable in class weka.classifiers.Evaluation
-
Number of non-missing class training instances seen
- m_NumTrainInstances - Variable in class weka.filters.unsupervised.attribute.KernelFilter
-
The number of instances in the training data.
- m_numTrees - Variable in class weka.classifiers.trees.RandomForest
-
Number of trees in forest.
- m_numUsedAttributes - Variable in class weka.attributeSelection.LinearForwardSelection
-
number of top-ranked attributes that are taken into account for the
search
- m_numUsedAttributes - Variable in class weka.attributeSelection.SubsetSizeForwardSelection
-
number of top-ranked attributes that are taken into account for the
search
- m_numValues - Variable in class weka.datagenerators.clusterers.SubspaceCluster
-
if nominal, store number of values
- m_NumValues - Variable in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
the number of values to retain.
- m_NumXValFolds - Variable in class weka.classifiers.meta.MultiScheme
-
Number of folds to use for cross validation (0 means use training
error for selection)
- m_NumXValFolds - Variable in class weka.classifiers.meta.ThresholdSelector
-
The number of folds used in cross-validation
- m_Object - Variable in class weka.core.CheckGOE
-
the object to test
- m_Object - Variable in class weka.core.PropertyPath.PropertyContainer
-
the associated object
- m_Object - Variable in class weka.gui.GenericObjectEditor
-
The object being configured.
- m_ObjectNames - Variable in class weka.gui.GenericObjectEditor
-
The model containing the list of names to select from.
- m_ObjectPropertyPanel - Variable in class weka.gui.GenericObjectEditor
-
The property panel created for the objects.
- m_objectstream - Variable in class weka.core.converters.SerializedInstancesSaver
-
the output stream.
- m_Objs - Variable in class weka.gui.ResultHistoryPanel
-
A hashtable mapping names to arbitrary objects
- m_Offset - Variable in class weka.classifiers.meta.LogitBoost
-
The value by which the actual target value for the
true class is offset.
- m_offsetValue - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
-
- m_offsetVariable - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
-
- m_okBut - Variable in class weka.gui.GenericObjectEditor.GOEPanel
-
ok button.
- m_OkButton - Variable in class weka.gui.experiment.OutputFormatDialog
-
Click to activate the current set parameters.
- m_OkButton - Variable in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
-
the OK button.
- m_OkButton - Variable in class weka.gui.ViewerDialog
-
Click to activate the current set parameters
- m_oldText - Variable in class weka.gui.beans.Classifier
-
Holds original icon label text
- m_oldWidth - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
-
Used to determine if the positions need to be recalculated.
- m_omega - Variable in class weka.classifiers.functions.supportVector.Puk
-
Omega for the Puk kernel.
- m_OnDemandDirectory - Variable in class weka.attributeSelection.CostSensitiveASEvaluation
-
The directory used when loading cost files on demand, null indicates
current directory
- m_OnDemandDirectory - Variable in class weka.classifiers.meta.CostSensitiveClassifier
-
The directory used when loading cost files on demand, null indicates
current directory
- m_OnDemandDirectory - Variable in class weka.classifiers.meta.MetaCost
-
The directory used when loading cost files on demand, null indicates
current directory
- m_OnDemandDirectory - Variable in class weka.experiment.CostSensitiveClassifierSplitEvaluator
-
The directory used when loading cost files on demand, null indicates
current directory
- m_onlyClass - Variable in class weka.associations.Apriori
-
Only the class attribute of all Instances.
- m_onlyNumeric - Variable in class weka.classifiers.functions.SMOreg
-
Only numeric attributes in the dataset? If so, less need to filter
- m_OpenBut - Variable in class weka.gui.experiment.SetupPanel
-
Click to load an experiment
- m_OpenBut - Variable in class weka.gui.experiment.SimpleSetupPanel
-
Click to load an experiment
- m_OpenBut - Variable in class weka.gui.GenericObjectEditor.GOEPanel
-
Open object from disk.
- m_openBut - Variable in class weka.gui.visualize.VisualizePanel
-
Button for the user to open the visualized set of instances
- m_OpenDBBut - Variable in class weka.gui.explorer.PreprocessPanel
-
Click to load base instances from a Database
- m_OpenFileBut - Variable in class weka.gui.explorer.PreprocessPanel
-
Click to load base instances from a file
- m_OpenFileBut - Variable in class weka.gui.SetInstancesPanel
-
Click to open instances from a file
- m_OpenURLBut - Variable in class weka.gui.explorer.PreprocessPanel
-
Click to load base instances from a URL
- m_OpenURLBut - Variable in class weka.gui.SetInstancesPanel
-
Click to open instances from a URL
- m_operator - Variable in class weka.core.pmml.BuiltInArithmetic
-
The operator for this function
- m_optimizer - Variable in class weka.classifiers.functions.SMOreg
-
contains the algorithm used for learning
- m_OptionBlacklist - Static variable in class weka.datagenerators.DataGenerator
-
a black list for options not to be listed (for derived generators)
in the makeOptionString method
- m_OptionHandler - Variable in class weka.core.CheckOptionHandler
-
the optionhandler to test
- m_Options - Variable in class weka.classifiers.functions.supportVector.KernelEvaluation
-
user-supplied options
- m_opType - Variable in class weka.core.pmml.Expression
-
The optype of this Expression
- m_optype - Variable in class weka.core.pmml.FieldMetaInfo
-
The optype for the target
- m_optypeOverride - Variable in class weka.core.pmml.MiningFieldMetaInfo
-
optype overrides (override data dictionary type - NOT SUPPORTED AT PRESENT)
- m_OrderAlgorithmsFirstRBut - Variable in class weka.gui.experiment.SimpleSetupPanel
-
Radio button for choosing algorithms first in order of execution
- m_OrderDatasetsFirstRBut - Variable in class weka.gui.experiment.SimpleSetupPanel
-
Radio button for choosing datasets first in order of execution
- m_OriginalDataSet - Variable in class weka.associations.GeneralizedSequentialPatterns
-
original sequential data set to be used for sequential patterns extraction
- m_OriginalHeader - Variable in class weka.classifiers.meta.ClassificationViaClustering
-
the original training data header
- m_originalInstances - Static variable in class weka.datagenerators.classifiers.classification.LED24
-
the 7-bit LEDs
- m_originalPlot - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
-
The master plot
- m_originalPopSize - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_osi - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
used for offscreen drawing
- m_otherBayesNet - Variable in class weka.classifiers.bayes.BayesNet
-
Bayes network to compare the structure with.
- m_Out - Variable in class weka.experiment.CSVResultListener
-
The destination for results (typically connected to the output file)
- m_OutlierAttributePosition - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
-
the position of the outlier attribute
- m_OutlierFactor - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
-
the factor for detecting outliers
- m_outlierTreatmentMethod - Variable in class weka.core.pmml.MiningFieldMetaInfo
-
outlier treatmemnt method
- m_outlierTreatmentMethod - Variable in class weka.core.pmml.NormContinuous
-
Outlier treatment method (default = asIs)
- m_OutOfBagError - Variable in class weka.classifiers.meta.Bagging
-
The out of bag error that has been calculated
- m_Output - Variable in class weka.datagenerators.DataGenerator
-
PrintWriter for outputting the generated data
- m_Output - Variable in class weka.gui.explorer.DataGeneratorPanel
-
the generated output (as text)
- m_Output - Variable in class weka.gui.LogWindow
-
the output
- m_OutputAdditionalAttributesLab - Variable in class weka.gui.explorer.ClassifierPanel
-
Label for the text field with additional attributes in the output
- m_OutputAdditionalAttributesRange - Variable in class weka.gui.explorer.ClassifierPanel
-
the range of attributes to output
- m_OutputAdditionalAttributesText - Variable in class weka.gui.explorer.ClassifierPanel
-
Lists indices for additional attributes to output
- m_OutputArea - Variable in class weka.gui.SimpleCLIPanel
-
The output area canvas added to the frame.
- m_OutputCenterFile - Variable in class weka.clusterers.XMeans
-
file name of the output file for the cluster centers.
- m_OutputClassification - Variable in class weka.filters.supervised.attribute.AddClassification
-
whether to output the classification.
- m_OutputConfusionBut - Variable in class weka.gui.explorer.ClassifierPanel
-
Check to output a confusion matrix
- m_outputDef - Variable in class weka.core.pmml.BuiltInString
-
The output structure produced by this function
- m_outputDef - Variable in class weka.core.pmml.Discretize
-
The output structure of this discretization
- m_OutputDistribution - Variable in class weka.filters.supervised.attribute.AddClassification
-
whether to output the class distribution.
- m_OutputEntropyBut - Variable in class weka.gui.explorer.ClassifierPanel
-
Check to output entropy statistics
- m_OutputErrorFlag - Variable in class weka.filters.supervised.attribute.AddClassification
-
whether to output the error flag.
- m_OutputFile - Variable in class weka.experiment.CrossValidationResultProducer
-
The destination output file/directory for raw output
- m_OutputFile - Variable in class weka.experiment.CSVResultListener
-
The destination output file, null sends to System.out
- m_OutputFile - Variable in class weka.experiment.RandomSplitResultProducer
-
The destination output file/directory for raw output
- m_OutputFilename - Variable in class weka.core.converters.TextDirectoryLoader
-
whether to include the filename as an extra attribute
- m_OutputFileName - Variable in class weka.experiment.CSVResultListener
-
The name of the output file.
- m_OutputFilename - Variable in class weka.gui.GenericPropertiesCreator
-
the output props file for the GenericObjectEditor
- m_OutputFormat - Variable in class weka.core.Debug.Clock
-
the format of the output
- m_OutputFormatButton - Variable in class weka.gui.experiment.ResultsPanel
-
lets the user choose the format for the output.
- m_OutputFormatClasses - Static variable in class weka.gui.experiment.OutputFormatDialog
-
the different classes for outputting the comparison tables.
- m_OutputFormatComboBox - Variable in class weka.gui.experiment.OutputFormatDialog
-
lets the user choose the format for the output.
- m_OutputFormatDefined - Variable in class weka.filters.unsupervised.attribute.RandomProjection
-
Keeps track of output format if it is defined or not
- m_OutputFormatNames - Static variable in class weka.gui.experiment.OutputFormatDialog
-
the different names of matrices for outputting the comparison tables.
- m_outputItemSets - Variable in class weka.associations.Apriori
-
Output itemsets found?
- m_outputList - Variable in class weka.classifiers.functions.neural.NeuralConnection
-
The list of outputs from this unit.
- m_OutputModelBut - Variable in class weka.gui.explorer.ClassifierPanel
-
Check to output the model built from the training data
- m_OutputNumAtts - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
The number of attributes in the pc transformed data.
- m_outputNums - Variable in class weka.classifiers.functions.neural.NeuralConnection
-
The numbering for the connections at the other end of the out lines.
- m_OutputOffsetMultiplier - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
-
whether to add another attribute called "Offset", that lists the
'multiplier' by which the outlier/extreme value is away from the median,
i.e., value = median + 'multiplier' * IQR
automatically enables m_DetectionPerAttribute!
- m_OutputPerClassBut - Variable in class weka.gui.explorer.ClassifierPanel
-
Check to output true/false positives, precision/recall for each class
- m_OutputPredictionsTextBut - Variable in class weka.gui.explorer.ClassifierPanel
-
Check to output text predictions
- m_OutputProperties - Variable in class weka.gui.GenericPropertiesCreator
-
the output properties file with the filled in classes
- m_outputQueues - Variable in class weka.gui.beans.Classifier
-
Stores completed models and associated data sets.
- m_OutputRelAtts - Variable in class weka.filters.Filter
-
Indices of relational attributes in the output format
- m_outputs - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
-
The outputs of the network
- m_outputs - Variable in class weka.gui.beans.MetaBean
-
- m_OutputSourceCode - Variable in class weka.gui.explorer.ClassifierPanel
-
Whether to output the source code (only for classifiers importing
Sourcable)
- m_OutputStringAtts - Variable in class weka.filters.Filter
-
Indices of string attributes in the output format
- m_outputTypes - Variable in class weka.core.Debug.DBO
-
range of outputtyp
- m_OutRedirector - Variable in class weka.gui.SimpleCLIPanel
-
The thread that sends output from m_POO to the output box.
- m_OutText - Variable in class weka.gui.experiment.ResultsPanel
-
Displays the output of tests.
- m_OutText - Variable in class weka.gui.explorer.AssociationsPanel
-
The output area for associations
- m_OutText - Variable in class weka.gui.explorer.AttributeSelectionPanel
-
The output area for attribute selection results
- m_OutText - Variable in class weka.gui.explorer.ClassifierPanel
-
The output area for classification results
- m_OutText - Variable in class weka.gui.explorer.ClustererPanel
-
The output area for classification results
- m_OverwriteWarning - Variable in class weka.gui.ConverterFileChooser
-
whether to popup a dialog in case the file already exists (only save
dialog)
- m_Owner - Variable in class weka.core.Capabilities
-
the object that owns this capabilities instance
- m_Owner - Variable in class weka.core.logging.OutputLogger.OutputPrintStream
-
the owning logger.
- m_P - Variable in class weka.classifiers.BVDecomposeSegCVSub
-
Proportion of instances common between any two training sets.
- m_Packages - Variable in class weka.core.FindWithCapabilities
-
the packages to search in.
- m_Packages - Static variable in class weka.gui.SimpleCLIPanel.CommandlineCompletion
-
all the available packages.
- m_Padding - Variable in class weka.filters.unsupervised.attribute.Wavelet
-
the type of padding
- m_PanelApplications - Variable in class weka.gui.GUIChooser
-
the panel for the application buttons
- m_PanelButtons - Variable in class weka.gui.sql.SqlViewerDialog
-
the panel for the buttons
- m_panelHeight - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
- m_Panels - Variable in class weka.gui.explorer.Explorer
-
Contains all the additional panels apart from the pre-processing panel
- m_panelWidth - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
- m_Par - Variable in class weka.classifiers.functions.Logistic
-
The coefficients (optimized parameters) of the model
- m_Par - Variable in class weka.classifiers.mi.MDD
-
- m_Par - Variable in class weka.classifiers.mi.MIDD
-
- m_Par - Variable in class weka.classifiers.mi.MIEMDD
-
- m_Par - Variable in class weka.classifiers.mi.MILR
-
- m_parameterDefs - Variable in class weka.core.pmml.Function
-
The structure of the parameters to this function
- m_parameterList - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
-
- m_parameters - Variable in class weka.core.pmml.DefineFunction
-
The list of parameters expected by this function.
- m_paramMatrix - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
-
- m_parent - Variable in class weka.associations.FPGrowth.FPTreeNode
-
link to the parent node
- m_parent - Variable in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
-
The parent
- m_Parent - Variable in class weka.datagenerators.ClusterDefinition
-
the parent of the cluster
- m_Parent - Variable in class weka.gui.GUIChooser.ChildFrameSDI
-
the parent frame.
- m_Parent - Variable in class weka.gui.LogWindow.LogWindowPrintStream
-
the parent
- m_Parent - Variable in class weka.gui.Main.ChildFrameMDI
-
the parent frame.
- m_Parent - Variable in class weka.gui.Main.ChildFrameSDI
-
the parent frame.
- m_Parent - Variable in class weka.gui.sql.ConnectionPanel
-
the parent frame.
- m_Parent - Variable in class weka.gui.sql.InfoPanel
-
the parent of this panel
- m_Parent - Variable in class weka.gui.sql.QueryPanel
-
the parent of this panel.
- m_Parent - Variable in class weka.gui.sql.ResultPanel
-
the parent of this panel
- m_Parent - Variable in class weka.gui.sql.SqlViewer
-
the parent of this panel.
- m_Parent - Variable in class weka.gui.sql.SqlViewerDialog
-
the parent frame
- m_parentFrame - Variable in class weka.gui.beans.AssociatorCustomizer
-
- m_ParentFrame - Variable in class weka.gui.SetInstancesPanel
-
the parent frame.
- m_ParentSets - Variable in class weka.classifiers.bayes.BayesNet
-
The parent sets.
- m_Password - Variable in class weka.core.converters.DatabaseLoader
-
the database password to use
- m_password - Variable in class weka.experiment.DatabaseUtils
-
Database Password.
- m_Password - Variable in class weka.gui.sql.ConnectionPanel
-
the password to use for connecting to the DB.
- m_Password - Variable in class weka.gui.sql.event.ResultChangedEvent
-
the password that was used to connect to the DB
- m_Password - Variable in class weka.gui.sql.ResultSetTable
-
the password that was used to connect to the DB
- m_Password - Variable in class weka.gui.sql.SqlViewer
-
the password that was used to connect to the DB.
- m_Password - Variable in class weka.gui.sql.SqlViewerDialog
-
the password that was used to connect to the DB
- m_PasswordLab - Variable in class weka.gui.DatabaseConnectionDialog
-
- m_PasswordText - Variable in class weka.gui.DatabaseConnectionDialog
-
- m_Pattern - Variable in class weka.gui.AttributeSelectionPanel
-
Press to enter a perl regular expression for selection
- m_PatternBut - Variable in class weka.gui.ListSelectorDialog
-
Click to enter a regex pattern for selection
- m_PatternRegEx - Variable in class weka.gui.AttributeSelectionPanel
-
The current regular expression.
- m_PatternRegEx - Variable in class weka.gui.ListSelectorDialog
-
The current regular expression.
- m_PD - Variable in class weka.gui.experiment.AlgorithmListPanel
-
The currently displayed property dialog, if any
- m_pendingKnowledgeFlowLoad - Variable in class weka.gui.GUIChooser
-
Pending file to load on startup of the KnowledgeFlow
- m_percent - Variable in class weka.filters.unsupervised.attribute.RandomProjection
-
Stores the dimensionality the data should be reduced to as percentage of the original dimension
- m_Percentage - Variable in class weka.filters.supervised.instance.SMOTE
-
the percentage of SMOTE instances to create.
- m_percentage - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell
-
- m_Percentages - Variable in class weka.gui.MemoryUsagePanel
-
the threshold percentages to change color.
- m_PercentBut - Variable in class weka.gui.explorer.ClassifierPanel
-
Click to set test mode to generate a % split
- m_PercentBut - Variable in class weka.gui.explorer.ClustererPanel
-
Click to set test mode to generate a % split
- m_PercentLab - Variable in class weka.gui.explorer.ClassifierPanel
-
Label by where the % split is entered
- m_PercentLab - Variable in class weka.gui.explorer.ClustererPanel
-
Label by where the % split is entered
- m_PercentText - Variable in class weka.gui.explorer.ClassifierPanel
-
The field where the % split is entered
- m_PercentText - Variable in class weka.gui.explorer.ClustererPanel
-
The field where the % split is entered
- m_percOfTarget - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_percOfTargetLab - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_percPop - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_percPopLab - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_performancePanel - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
Displays the performance graphs(s)
- m_Performances - Variable in class weka.classifiers.meta.GridSearch.PerformanceTable
-
the performances
- m_PerformBut - Variable in class weka.gui.experiment.ResultsPanel
-
Click to start the test.
- m_PerformPrediction - Variable in class weka.filters.supervised.attribute.PLSFilter
-
whether to include the prediction, i.e., modifying the class attribute
- m_performRanking - Variable in class weka.attributeSelection.LinearForwardSelection
-
perform initial ranking to select top-ranked attributes
- m_performRanking - Variable in class weka.attributeSelection.SubsetSizeForwardSelection
-
perform initial ranking to select top-ranked attributes
- m_PerturbationFraction - Variable in class weka.datagenerators.classifiers.classification.Agrawal
-
the perturabation fraction
- m_Pivot - Variable in class weka.core.neighboursearch.balltrees.BallNode
-
The pivot/centre of the ball.
- m_pixHeight - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
- m_pixWidth - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
- m_plot - Variable in class weka.gui.visualize.VisualizePanel
-
The panel that displays the plot
- m_plot2D - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
-
The actual generic plotting panel
- m_plotAreaHeight - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
- m_plotAreaWidth - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
- m_plotCompanion - Variable in class weka.gui.visualize.Plot2D
-
An optional "compainion" of the panel.
- m_plotInstances - Variable in class weka.gui.visualize.AttributePanel
-
The instances to be plotted
- m_plotInstances - Variable in class weka.gui.visualize.Plot2D
-
The instances to be plotted
- m_plotInstances - Variable in class weka.gui.visualize.PlotData2D
-
The instances
- m_plotInstances - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
-
The instances from the master plot
- m_plotName - Variable in class weka.gui.visualize.PlotData2D
-
The name of this plot
- m_plotName - Variable in class weka.gui.visualize.VisualizePanel
-
The name of the plot (not currently displayed, but can be used
in the containing Frame or Panel)
- m_plotNameHTML - Variable in class weka.gui.visualize.PlotData2D
-
The name of this plot (possibly in html) suitable for using in a
tool tip text.
- m_plotResize - Variable in class weka.gui.visualize.Plot2D
-
if the user resizes the window, or the attributes selected for
the attributes change, then the lookup table for points needs
to be recalculated
- m_Plots - Variable in class weka.gui.GUIChooser
-
keeps track of the opened plots
- m_plots - Variable in class weka.gui.visualize.LegendPanel
-
the list of plot elements
- m_plots - Variable in class weka.gui.visualize.Plot2D
-
The plots to display
- m_plotSize - Variable in class weka.gui.visualize.MatrixPanel
-
The slider to adjust the size of the cells in the matrix
- m_plotSurround - Variable in class weka.gui.visualize.VisualizePanel
-
Panel that surrounds the plot panel with a titled border
- m_plotTrainingData - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
plot the training data
- m_plotTrainingData - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
- m_PLS1_b_hat - Variable in class weka.filters.supervised.attribute.PLSFilter
-
the b-hat vector for PLS1
- m_PLS1_P - Variable in class weka.filters.supervised.attribute.PLSFilter
-
the P matrix for PLS1
- m_PLS1_RegVector - Variable in class weka.filters.supervised.attribute.PLSFilter
-
the regression vector "r-hat" for PLS1
- m_PLS1_W - Variable in class weka.filters.supervised.attribute.PLSFilter
-
the W matrix for PLS1
- m_PMMLModelFilter - Variable in class weka.gui.explorer.ClassifierPanel
-
- m_pmmlVersion - Variable in class weka.classifiers.pmml.consumer.PMMLClassifier
-
PMML version
- m_POE - Variable in class weka.gui.SimpleCLIPanel
-
The new output stream for System.err.
- m_PointCount - Variable in class weka.core.neighboursearch.PerformanceStats
-
The number of data points looked at
for the current/last query.
- m_pointDrawn - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
-
A temporary array used to strike any instances that would be
drawn redundantly.
- m_pointLookup - Variable in class weka.gui.visualize.Plot2D
-
lookup table for plotted points
- m_pointLookup - Variable in class weka.gui.visualize.PlotData2D
-
Panel coordinate cache for data points
- m_pointSize - Variable in class weka.gui.visualize.MatrixPanel
-
The slider to adjust the size of the datapoints
- m_POO - Variable in class weka.gui.SimpleCLIPanel
-
The new output stream for System.out.
- m_Popup - Variable in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
-
the popup to display again.
- m_popupFrame - Variable in class weka.gui.beans.CostBenefitAnalysis
-
- m_popupFrame - Variable in class weka.gui.beans.DataVisualizer
-
- m_popupFrame - Variable in class weka.gui.beans.ModelPerformanceChart
-
- m_positiveIndex - Variable in class weka.associations.FPGrowth
-
The index (1 based) of binary attributes to treat as the positive value
- m_PostProcessor - Variable in class weka.core.CheckScheme
-
for post-processing the data even further
- m_PostProcessor - Variable in class weka.estimators.CheckEstimator
-
for post-processing the data even further
- m_posTrainInstances - Variable in class weka.classifiers.trees.ADTree
-
The training instances with positive class - referencing the training dataset
- m_powersOflambda - Variable in class weka.classifiers.functions.supportVector.StringKernel
-
the precalculated powers of lambda
- m_ppMatrix - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
-
- m_Precision - Variable in class weka.classifiers.meta.LogitBoost
-
The threshold on the improvement of the likelihood
- m_Precision - Variable in class weka.core.xml.XMLInstances
-
the precision for numbers
- m_preferredColourDimension - Variable in class weka.gui.visualize.VisualizePanel
-
- m_PreferredExtension - Variable in class weka.gui.beans.KnowledgeFlowApp
-
the preferred file extension
- m_preferredXDimension - Variable in class weka.gui.visualize.VisualizePanel
-
These hold the names of preferred columns to visualize on---if the
user has defined them in the Visualize.props file
- m_preferredYDimension - Variable in class weka.gui.visualize.VisualizePanel
-
- m_Prefixes - Variable in class weka.core.Tee
-
whether to add a prefix or not.
- m_premise - Variable in class weka.associations.FPGrowth.AssociationRule
-
The premise of the rule
- m_premise - Variable in class weka.associations.RuleItem
-
The premise of a rule.
- m_premiseCount - Variable in class weka.associations.PredictiveApriori
-
The minimum support.
- m_premiseSupport - Variable in class weka.associations.FPGrowth.AssociationRule
-
The support for the premise
- m_PreparedStatement - Variable in class weka.experiment.DatabaseUtils
-
The prepared statement used for database queries.
- m_Preprocessing - Variable in class weka.filters.supervised.attribute.PLSFilter
-
the type of preprocessing
- m_PreprocessPanel - Variable in class weka.gui.explorer.Explorer
-
The panel for preprocessing instances
- m_Present - Static variable in class weka.classifiers.functions.LibLINEAR
-
whether the liblinear classes are in the Classpath
- m_Present - Static variable in class weka.classifiers.functions.LibSVM
-
whether the libsvm classes are in the Classpath
- m_Present - Static variable in class weka.core.Jython
-
whether the Jython classes are in the Classpath
- m_Present - Static variable in class weka.core.stemmers.SnowballStemmer
-
whether the snowball stemmers are in the Classpath.
- m_Present - Static variable in class weka.core.xml.KOML
-
indicates whether
KOML
(Koala Object Markup Language) is present
- m_Present - Static variable in class weka.core.xml.XStream
-
indicates whether
XStream
is present
- m_PreserveOrderBut - Variable in class weka.gui.explorer.ClassifierPanel
-
Whether randomization is turned off to preserve order
- m_previousShapeIndex - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
The index of the previous plotted point that was highlighted
- m_PrintColNames - Variable in class weka.experiment.ResultMatrix
-
whether the names or numbers are output as column declarations
- m_Printer - Variable in class weka.gui.beans.StripChart
-
the class responsible for printing
- m_Printer - Variable in class weka.gui.ResultHistoryPanel
-
for printing the output to files
- m_Printer - Variable in class weka.gui.visualize.PrintablePanel
-
the class responsible for printing
- m_PrintRowNames - Variable in class weka.experiment.ResultMatrix
-
whether the names or numbers are output as row declarations
- m_printstream - Variable in class weka.gui.visualize.PostscriptGraphics
-
The output file
- m_PriorErrorEstimator - Variable in class weka.classifiers.Evaluation
-
Numeric class error estimator for prior
- m_priorEstimator - Variable in class weka.associations.PredictiveApriori
-
The prior estimator.
- m_priors - Variable in class weka.associations.PredictiveApriori
-
The hashtable containing the prior probabilities.
- m_priors - Variable in class weka.associations.PriorEstimation
-
Hashtable containing the estimated prior probabilities.
- m_priors - Variable in class weka.associations.RuleGeneration
-
Hashtable conatining the estimated prior probabilities.
- m_Priors - Variable in class weka.classifiers.bayes.NaiveBayesSimple
-
The prior probabilities of the classes.
- m_Priors - Variable in class weka.classifiers.lazy.LBR
-
The prior probabilities of the classes.
- m_priors - Variable in class weka.filters.unsupervised.attribute.ClusterMembership
-
The prior probability for each class
- m_PriorUpdate - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
-
Prior class object interface
- m_probabilityCache - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
cache of probabilities for fast replotting
- m_ProbabilityEstimates - Variable in class weka.classifiers.functions.LibLINEAR
-
whether to generate probability estimates instead of +1/-1 in case of
classification problems
- m_ProbabilityEstimates - Variable in class weka.classifiers.functions.LibSVM
-
whether to generate probability estimates instead of +1/-1 in case of
classification problems
- m_probOfClass - Variable in class weka.classifiers.bayes.NaiveBayesMultinomial
-
the probability of a class (i.e.
- m_probOfWordGivenClass - Variable in class weka.classifiers.bayes.NaiveBayesMultinomial
-
probability that a word (w) exists in a class (H) (i.e.
- m_processed_InstanceID - Variable in class weka.clusterers.CLOPE
-
Counter for the processed instances
- m_progress - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
-
The progress bar to show the progress
of the layout process
- m_projectedCounts - Variable in class weka.associations.FPGrowth.FPTreeNode
-
counts associated with projected versions of this node
- m_ProjectionFilter - Variable in class weka.classifiers.meta.RotationForest
-
The type of projection filter
- m_ProjectionFilters - Variable in class weka.classifiers.meta.RotationForest
-
The projection filters
- m_Prolog - Variable in class weka.core.OptionHandlerJavadoc
-
whether to include the "Valid options..." prolog in the Javadoc
- m_Prolog - Variable in class weka.core.TechnicalInformationHandlerJavadoc
-
whether to include the "Valid options..." prolog in the Javadoc
- m_Prop - Variable in class weka.classifiers.trees.RandomTree
-
The proportions of training instances going down each branch.
- m_Prop - Variable in class weka.classifiers.trees.REPTree.Tree
-
The proportions of training instances going down each branch.
- m_Properties - Static variable in class weka.core.logging.Logger
-
the properties file.
- m_Properties - Variable in class weka.core.xml.XMLSerialization
-
for handling properties (ignored/allowed)
- m_property - Variable in class weka.core.pmml.FieldMetaInfo.Value
-
- m_PropertyArray - Variable in class weka.experiment.Experiment
-
The array of values to set the property to
- m_PropertyNumber - Variable in class weka.experiment.Experiment
-
The current custom property value index when the experiment is running
- m_PropertyPath - Variable in class weka.experiment.Experiment
-
The path to the iterator property
- m_Props - Variable in class weka.classifiers.trees.BFTree
-
Branch proportions.
- m_Props - Variable in class weka.classifiers.trees.SimpleCart
-
Proportion for each branch.
- m_Prune - Variable in class weka.classifiers.trees.SimpleCart
-
If use minimal cost-compexity pruning.
- m_PruningMethod - Variable in class weka.classifiers.functions.supportVector.StringKernel
-
the pruning method
- m_PruningStrategy - Variable in class weka.classifiers.trees.BFTree
-
the pruning strategy
- m_PruningType - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
The pruning type used
- m_PSFontReplacement - Static variable in class weka.gui.visualize.PostscriptGraphics
-
the font replacement
- m_psGraphicsState - Variable in class weka.gui.visualize.PostscriptGraphics
-
The current global PostScript graphics state for all cloned objects
- m_Quality - Variable in class weka.gui.visualize.JPEGWriter
-
the quality of the image.
- m_Query - Variable in class weka.gui.sql.event.QueryExecuteEvent
-
the query that was executed
- m_Query - Variable in class weka.gui.sql.event.ResultChangedEvent
-
the query that is associated with the active result table
- m_Query - Variable in class weka.gui.sql.ResultSetTable
-
the query the table model is based on
- m_Query - Variable in class weka.gui.sql.SqlViewer
-
the currently selected query.
- m_Query - Variable in class weka.gui.sql.SqlViewerDialog
-
the currently selected query
- m_QueryExecuteListeners - Variable in class weka.gui.sql.QueryPanel
-
the connection listeners.
- m_QueryPanel - Variable in class weka.gui.sql.ResultPanel
-
the panel where to output the queries
- m_QueryPanel - Variable in class weka.gui.sql.SqlViewer
-
the query panel.
- m_Radius - Variable in class weka.classifiers.mi.MIOptimalBall
-
radius of the optimal ball
- m_Radius - Variable in class weka.core.neighboursearch.balltrees.BallNode
-
The radius of this ball (hyper sphere).
- m_RAE - Variable in class weka.classifiers.meta.GridSearch.Performance
-
the Relative absolute error
- m_Rand - Variable in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
-
Random number generator for selecting
an abitrary (random) point.
- m_RandClassCols - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
Set of colomns: each colomn representing a randomised version
of the train dataset class colomn
- m_RandClassCols - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
Set of colomns: each colomn representing a randomised version of
the train dataset class colomn
- m_RandClassCols - Variable in class weka.classifiers.lazy.KStar
-
Table of random class value colomns
- m_randNum - Variable in class weka.associations.PriorEstimation
-
The random number generator.
- m_random - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
-
random number generator
- m_Random - Variable in class weka.classifiers.meta.Decorate
-
The random number generator.
- m_Random - Variable in class weka.classifiers.meta.MultiBoostAB
-
Random number generator
- m_Random - Variable in class weka.classifiers.meta.Vote
-
the random number generator used for breaking ties in majority voting
- m_random - Variable in class weka.classifiers.trees.ADTree
-
The random number generator - used for the random search heuristic
- m_Random - Variable in class weka.core.TestInstances
-
the random number generator
- m_Random - Variable in class weka.datagenerators.DataGenerator
-
random number generator
- m_random - Variable in class weka.filters.unsupervised.attribute.RandomProjection
-
The random number generator used for generating the random matrix
- m_Random - Variable in class weka.filters.unsupervised.instance.Randomize
-
The current random number generator
- m_random - Variable in class weka.filters.unsupervised.instance.ReservoirSample
-
The random number generator
- m_RandomInitialAnchor - Variable in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
True if the initial anchor is chosen randomly.
- m_RandomInstance - Variable in class weka.classifiers.meta.LogitBoost
-
The random number generator used
- m_RandomInstance - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
The random number generator used
- m_randomize - Variable in class weka.experiment.RandomSplitResultProducer
-
Whether dataset is to be randomized
- m_Randomize - Variable in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
-
whether to randomize the output data
- m_RandomLab - Variable in class weka.gui.explorer.ClassifierPanel
-
the label for the random seed textfield
- m_randomSeed - Variable in class weka.classifiers.functions.SMO
-
The random number seed
- m_randomSeed - Variable in class weka.classifiers.mi.MISMO
-
The random number seed
- m_randomSeed - Variable in class weka.classifiers.trees.ADTree
-
Option - the seed to use for a random search
- m_randomSeed - Variable in class weka.classifiers.trees.RandomForest
-
The random seed.
- m_randomSeed - Variable in class weka.classifiers.trees.RandomTree
-
The random seed to use.
- m_RandomSeed - Variable in class weka.filters.supervised.instance.Resample
-
The random number generator seed.
- m_RandomSeed - Variable in class weka.filters.supervised.instance.SMOTE
-
the random seed to use.
- m_RandomSeed - Variable in class weka.filters.unsupervised.instance.Resample
-
The random number generator seed
- m_RandomSeed - Variable in class weka.filters.unsupervised.instance.ReservoirSample
-
The random number generator seed
- m_RandomSeedText - Variable in class weka.gui.explorer.ClassifierPanel
-
User specified random seed for cross validation or % split
- m_randomV - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_RandSeed - Variable in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
-
Seed for random number generator.
- m_Range - Variable in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
-
The classes that are grouped together at the current node
- m_Range - Variable in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
-
The classes that are grouped together at the current node
- m_RangeMode - Variable in class weka.classifiers.meta.ThresholdSelector
-
The range correction mode
- m_Ranges - Variable in class weka.core.NormalizableDistance
-
The range of the attributes.
- m_Ranges - Variable in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
-
The attribute ranges.
- m_rankedAtts - Variable in class weka.attributeSelection.GreedyStepwise
-
a ranked list of attribute indexes
- m_rankedSoFar - Variable in class weka.attributeSelection.GreedyStepwise
-
- m_RankingDiff - Variable in class weka.experiment.ResultMatrix
-
the difference between wins and losses
- m_RankingLosses - Variable in class weka.experiment.ResultMatrix
-
the losses in ranking
- m_rankingRequested - Variable in class weka.attributeSelection.GreedyStepwise
-
true if the user has requested a ranked list of attributes
- m_RankingWins - Variable in class weka.experiment.ResultMatrix
-
the wins in ranking
- m_Rate - Variable in class weka.classifiers.mi.MINND
-
The learning rate in the gradient descent
- m_RawData - Variable in class weka.datagenerators.classifiers.regression.Expression
-
the input data structure for the filter
- m_Readable - Variable in class weka.core.Tag
-
The descriptive text
- m_readIncrementally - Variable in class weka.gui.SetInstancesPanel
-
- m_ReadMethods - Variable in class weka.core.xml.XMLSerializationMethodHandler
-
for storing read methods
- m_RecalcHashCode - Variable in class weka.core.Trie
-
whether the structure got modified and the hash code needs to be
re-calculated
- m_receivedStopNotification - Variable in class weka.gui.beans.TestSetMaker
-
- m_receivedStopNotification - Variable in class weka.gui.beans.TrainingSetMaker
-
- m_ReducedHeader - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
-
The header of the dimensionally reduced data
- m_ReducedHeaders - Variable in class weka.classifiers.meta.RotationForest
-
Headers of the reduced datasets
- m_References - Variable in class weka.classifiers.mi.CitationKNN
-
R nearest references
- m_ReferencesDebug - Variable in class weka.classifiers.mi.CitationKNN
-
- m_regressions - Variable in class weka.classifiers.trees.lmt.LogisticBase
-
Array holding the simple regression functions fit by LogitBoost
- m_regressionTables - Variable in class weka.classifiers.pmml.consumer.Regression
-
The regression tables for this regression
- m_regressionTree - Variable in class weka.classifiers.trees.m5.M5Base
-
Make a regression tree/rule instead of a model tree/rule
- m_Relation - Variable in class weka.core.TestInstances
-
the name of the relation
- m_RelationalClassFormat - Variable in class weka.core.TestInstances
-
the format of the multi-instance data of the class
- m_RelationalFormat - Variable in class weka.core.TestInstances
-
the format of the multi-instance data
- m_RelationName - Variable in class weka.core.Instances
-
The dataset's name.
- m_RelationName - Variable in class weka.datagenerators.DataGenerator
-
Relation name the dataset should have
- m_RelationNameLab - Variable in class weka.gui.InstancesSummaryPanel
-
Displays the name of the relation
- m_relativeCheck - Variable in class weka.gui.experiment.DatasetListPanel
-
Make file paths relative to the user (start) directory.
- m_RemainderErrors - Variable in class weka.classifiers.lazy.LBR
-
the number of instances to be classified incorrectly
besides the subset.
- m_remoteHosts - Variable in class weka.experiment.RemoteExperiment
-
Holds the names of machines with remoteEngine servers running
- m_remoteHosts - Variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
-
Holds the names of machines with remoteEngine servers running
- m_RemoveAll - Variable in class weka.gui.AttributeSelectionPanel
-
Press to deselect all attributes
- m_removeAttributes - Variable in class weka.filters.unsupervised.attribute.AddCluster
-
Filter for removing attributes
- m_removeAttributes - Variable in class weka.filters.unsupervised.attribute.ClusterMembership
-
Filter for removing attributes
- m_RemoveButton - Variable in class weka.gui.explorer.PreprocessPanel
-
Button for removing attributes
- m_removeClassColumn - Variable in class weka.experiment.DensityBasedClustererSplitEvaluator
-
Remove the class column (if set) from the data
- m_RemovedPercentage - Variable in class weka.classifiers.meta.RotationForest
-
The percentage of instances to be removed
- m_removeFilter - Variable in class weka.filters.unsupervised.attribute.RemoveUseless
-
The filter used to remove attributes
- m_RemoveFilterName - Variable in class weka.experiment.ResultMatrix
-
whether to remove the filter name from the dataaset name
- m_RemoveFilterName - Variable in class weka.gui.experiment.OutputFormatDialog
-
whether to remove the filter names from the names.
- m_RemoveFilterNameCheckBox - Variable in class weka.gui.experiment.OutputFormatDialog
-
the checkbox for the removing of filter classnames.
- m_removeMissingCols - Variable in class weka.associations.Apriori
-
Remove columns with all missing values
- m_RemoveOldClass - Variable in class weka.filters.supervised.attribute.AddClassification
-
whether to remove the old class attribute.
- m_removePointsButton - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
- m_RemoveUnused - Variable in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
-
Whether unused attributes are left out of the output.
- m_RemoveUseless - Variable in class weka.classifiers.meta.RotationForest
-
Filter that remove useless attributes
- m_Repainters - Variable in class weka.gui.visualize.LegendPanel
-
a list of components that need to be repainted when a colour is
changed
- m_replaceMissing - Variable in class weka.classifiers.functions.SPegasos
-
Replace missing values
- m_replaceMissing - Variable in class weka.classifiers.trees.FT
-
Filter to replace missing values
- m_replaceMissing - Variable in class weka.classifiers.trees.LMT
-
Filter to replace missing values
- m_ReplaceMissing - Variable in class weka.filters.supervised.attribute.PLSFilter
-
whether to replace missing values
- m_replaceMissing - Variable in class weka.filters.unsupervised.attribute.RandomProjection
-
The ReplaceMissingValues filter
- m_ReplaceMissingFilter - Variable in class weka.clusterers.FarthestFirst
-
replace missing values in training instances
- m_ReplaceMissingFilter - Variable in class weka.clusterers.XMeans
-
replace missing values in training instances.
- m_ReplaceMissingFilter - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Filters for replacing missing values.
- m_ReplaceMissingValues - Variable in class weka.classifiers.functions.LibLINEAR
-
The filter used to get rid of missing values.
- m_ReplaceMissingValues - Variable in class weka.classifiers.functions.LibSVM
-
The filter used to get rid of missing values.
- m_ReplaceMissingValues - Variable in class weka.classifiers.functions.SimpleLogistic
-
Filter for replacing missing values
- m_Repulsion - Variable in class weka.clusterers.CLOPE
-
Specifies the repulsion
- m_RepulsionDefault - Variable in class weka.clusterers.CLOPE
-
Specifies the repulsion default
- m_resampleBt - Variable in class weka.gui.visualize.MatrixPanel
-
The label for resample percentage
- m_resamplePercent - Variable in class weka.gui.visualize.MatrixPanel
-
The text area for percentage to resample data
- m_rescaleConstant - Variable in class weka.core.pmml.TargetMetaInfo
-
re-scaling of target value (if defined)
- m_rescaleFactor - Variable in class weka.core.pmml.TargetMetaInfo
-
- m_Result - Variable in class weka.associations.AssociatorEvaluation
-
the result string
- m_Result - Variable in class weka.classifiers.functions.supportVector.KernelEvaluation
-
the result string
- m_Result - Variable in class weka.core.mathematicalexpression.Parser
-
for storing the result of the expresion.
- m_result - Variable in class weka.experiment.ClassifierSplitEvaluator
-
Holds the statistics for the most recent application of the classifier
- m_result - Variable in class weka.experiment.DensityBasedClustererSplitEvaluator
-
Holds the statistics for the most recent application of the clusterer
- m_result - Variable in class weka.experiment.RegressionSplitEvaluator
-
Holds the statistics for the most recent application of the classifier
- m_Result - Variable in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
-
for storing the result of the expression.
- m_Result - Variable in class weka.gui.experiment.OutputFormatDialog
-
the result of the user's action, either OK or CANCEL.
- m_Result - Variable in class weka.gui.ListSelectorDialog
-
Whether the selection was made or cancelled
- m_Result - Variable in class weka.gui.PropertySelectorDialog
-
Whether the selection was made or cancelled
- m_Result - Variable in class weka.gui.ViewerDialog
-
the result of the user's action, either OK or CANCEL
- m_ResultKeyBut - Variable in class weka.gui.experiment.ResultsPanel
-
Click to edit the columns used to determine the scheme.
- m_ResultKeyLabel - Variable in class weka.gui.experiment.ResultsPanel
-
Displays the currently selected column names for the scheme & options.
- m_ResultKeyList - Variable in class weka.gui.experiment.ResultsPanel
-
Displays the list of selected columns determining the scheme.
- m_ResultKeyModel - Variable in class weka.gui.experiment.ResultsPanel
-
Stores the list of attributes for selecting the scheme columns.
- m_ResultListener - Variable in class weka.experiment.AveragingResultProducer
-
The ResultListener to send results to
- m_ResultListener - Variable in class weka.experiment.CrossValidationResultProducer
-
The ResultListener to send results to
- m_ResultListener - Variable in class weka.experiment.DatabaseResultProducer
-
The ResultListener to send results to
- m_ResultListener - Variable in class weka.experiment.Experiment
-
Where results will be sent
- m_ResultListener - Variable in class weka.experiment.LearningRateResultProducer
-
The ResultListener to send results to
- m_ResultListener - Variable in class weka.experiment.RandomSplitResultProducer
-
The ResultListener to send results to
- m_ResultMatrix - Variable in class weka.experiment.PairedTTester
-
the instance of the class to produce the output.
- m_ResultMatrix - Variable in class weka.gui.experiment.OutputFormatDialog
-
the output format specific matrix.
- m_ResultMatrix - Variable in class weka.gui.experiment.ResultsPanel
-
the initial result matrix.
- m_ResultPanel - Variable in class weka.gui.sql.SqlViewer
-
the result panel.
- m_ResultPath - Variable in class weka.gui.PropertySelectorDialog
-
Stores the path to the selected property
- m_ResultProducer - Variable in class weka.experiment.AveragingResultProducer
-
The ResultProducer used to generate results
- m_ResultProducer - Variable in class weka.experiment.DatabaseResultListener
-
The ResultProducer to listen to
- m_ResultProducer - Variable in class weka.experiment.DatabaseResultProducer
-
The ResultProducer used to generate results
- m_ResultProducer - Variable in class weka.experiment.Experiment
-
The result producer
- m_ResultProducer - Variable in class weka.experiment.LearningRateResultProducer
-
The ResultProducer used to generate results
- m_Results - Variable in class weka.experiment.AveragingResultProducer
-
Collects the results from a single run
- m_Results - Variable in class weka.gui.ResultHistoryPanel
-
A Hashtable mapping names to result buffers
- m_ResultsDestinationCBox - Variable in class weka.gui.experiment.SimpleSetupPanel
-
Combo box for choosing experiment destination type
- m_ResultsDestinationPathLabel - Variable in class weka.gui.experiment.SimpleSetupPanel
-
Label for destination field
- m_ResultsDestinationPathTField - Variable in class weka.gui.experiment.SimpleSetupPanel
-
Input field for result destination path
- m_ResultSet - Variable in class weka.gui.sql.event.QueryExecuteEvent
-
the produced ResultSet, if any
- m_ResultSet - Variable in class weka.gui.sql.ResultSetHelper
-
the resultset to work on.
- m_ResultsetKeyColumns - Variable in class weka.experiment.PairedTTester
-
An array containing the indexes of just the selected columns
- m_ResultsetKeyColumnsRange - Variable in class weka.experiment.PairedTTester
-
The range of columns that specify a unique result set
(eg: scheme plus configuration)
- m_Resultsets - Variable in class weka.experiment.PairedTTester
-
Stores a vector for each resultset holding all instances in each set
- m_ResultsetsValid - Variable in class weka.experiment.PairedTTester
-
Indicates whether the instances have been partitioned
- m_ResultsPanel - Variable in class weka.gui.experiment.Experimenter
-
The panel for analysing experimental results
- m_ResultsPanel - Variable in class weka.gui.experiment.RunPanel
-
A pointer to the results panel
- m_ResultsTableName - Variable in class weka.experiment.DatabaseResultListener
-
The name of the current results table
- m_retrieval - Variable in class weka.core.converters.AbstractLoader
-
The current retrieval mode
- m_retrieval - Variable in class weka.core.converters.AbstractSaver
-
The current retrieval mode
- m_returnValue - Variable in class weka.gui.DatabaseConnectionDialog
-
- m_ReturnValue - Variable in class weka.gui.sql.SqlViewerDialog
-
the return value
- m_Ridge - Variable in class weka.classifiers.functions.Logistic
-
The ridge parameter.
- m_ridge - Variable in class weka.classifiers.functions.RBFNetwork
-
The ridge parameter for the logistic regression.
- m_Ridge - Variable in class weka.classifiers.mi.MILR
-
The ridge parameter.
- m_right - Variable in class weka.classifiers.meta.nestedDichotomies.ND.NDTree
-
The right successor
- m_right - Variable in class weka.classifiers.trees.m5.RuleNode
-
right child node
- m_Right - Variable in class weka.core.neighboursearch.balltrees.BallNode
-
The right child of the node.
- m_Right - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode
-
right subtree; contains instances with larger than split value.
- m_rightMargin - Variable in class weka.core.pmml.FieldMetaInfo.Interval
-
The right boundary value
- m_RLEditor - Variable in class weka.gui.experiment.SetupPanel
-
The ResultListener editor
- m_RLEditorPanel - Variable in class weka.gui.experiment.SetupPanel
-
The panel to contain the ResultListener editor
- m_rmatrix - Variable in class weka.filters.unsupervised.attribute.RandomProjection
-
The random matrix
- m_RMSE - Variable in class weka.classifiers.meta.GridSearch.Performance
-
the Root mean squared error
- m_rndmSeed - Variable in class weka.filters.unsupervised.attribute.RandomProjection
-
Stores the random seed used to generate the random matrix
- m_ROCs - Variable in class weka.gui.GUIChooser
-
keeps track of the opened ROCs
- m_root - Variable in class weka.classifiers.bayes.net.MarginCalculator
-
- m_root - Variable in class weka.classifiers.trees.ADTree
-
The root of the tree
- m_root - Variable in class weka.classifiers.trees.LADTree
-
- m_Root - Variable in class weka.core.neighboursearch.BallTree
-
The root node of the BallTree.
- m_Root - Variable in class weka.core.neighboursearch.CoverTree
-
The root node.
- m_Root - Variable in class weka.core.neighboursearch.KDTree
-
The root node of the tree.
- m_Root - Variable in class weka.core.Trie
-
the root node
- m_Root - Variable in class weka.core.Trie.TrieIterator
-
the node to use as root
- m_Root - Variable in class weka.gui.PropertySelectorDialog
-
The root of the property tree
- m_RootNode - Variable in class weka.core.xml.XMLDocument
-
the root node as String.
- m_RootObject - Variable in class weka.gui.PropertySelectorDialog
-
The object at the root of the tree
- m_RowCount - Variable in class weka.gui.sql.ResultSetHelper
-
the number of rows.
- m_RowHidden - Variable in class weka.experiment.ResultMatrix
-
whether a row is hidden
- m_RowIndex - Variable in class weka.gui.arffviewer.ArffTable.RelationalCellEditor
-
the row index this editor is for
- m_RowNames - Variable in class weka.experiment.ResultMatrix
-
the row names
- m_RowNameWidth - Variable in class weka.experiment.ResultMatrix
-
the size of the names of the rows
- m_RowOrder - Variable in class weka.experiment.ResultMatrix
-
the ordering of the rows
- m_RP - Variable in class weka.experiment.CSVResultListener
-
The ResultProducer sending us results
- m_RPEditor - Variable in class weka.gui.experiment.SetupPanel
-
The ResultProducer editor
- m_RPEditorPanel - Variable in class weka.gui.experiment.SetupPanel
-
The panel to contain the ResultProducer editor
- m_RRSE - Variable in class weka.classifiers.meta.GridSearch.Performance
-
the Root relative squared error
- m_RSeed - Variable in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Seed form random number generator.
- m_rseed - Variable in class weka.gui.visualize.MatrixPanel
-
Random seed for random subsample
- m_rules - Variable in class weka.associations.FPGrowth
-
Holds the rules
- m_ruleSet - Variable in class weka.classifiers.trees.m5.M5Base
-
the rule set
- m_rulesMustContain - Variable in class weka.associations.FPGrowth
-
If set, then only output rules containing these itmes
- m_ruleSupCounter - Variable in class weka.associations.LabeledItemSet
-
The support of the rule.
- m_RunColumn - Variable in class weka.experiment.PairedTTester
-
The index of the column containing the run number
- m_RunColumnSet - Variable in class weka.experiment.PairedTTester
-
The option setting for the run number column (-1 means last)
- m_RunLower - Variable in class weka.experiment.Experiment
-
Lower run number
- m_Running - Variable in class weka.core.Debug.Clock
-
whether the time is still clocked
- m_runningCount - Variable in class weka.gui.beans.FlowRunner
-
- m_RunNumber - Variable in class weka.experiment.Experiment
-
The current run number when the experiment is running
- m_runNumber - Variable in class weka.gui.beans.BatchClassifierEvent
-
The run number that this classifier was generated for
- m_runNumber - Variable in class weka.gui.beans.TestSetEvent
-
What run number is this training set from.
- m_runNumber - Variable in class weka.gui.beans.TrainingSetEvent
-
What run number is this training set from.
- m_RunNumberPanel - Variable in class weka.gui.experiment.SetupPanel
-
The panel for configuring run numbers
- m_RunPanel - Variable in class weka.gui.experiment.Experimenter
-
The panel for running the experiment
- m_RunThread - Variable in class weka.gui.experiment.RunPanel
-
The thread running the experiment
- m_RunThread - Variable in class weka.gui.explorer.AssociationsPanel
-
A thread that associator runs in
- m_RunThread - Variable in class weka.gui.explorer.AttributeSelectionPanel
-
A thread that attribute selection runs in
- m_RunThread - Variable in class weka.gui.explorer.ClassifierPanel
-
A thread that classification runs in
- m_RunThread - Variable in class weka.gui.explorer.ClustererPanel
-
A thread that clustering runs in
- m_RunThread - Variable in class weka.gui.SimpleCLIPanel
-
The thread currently running a class main method.
- m_Runtime - Variable in class weka.core.Memory
-
the current runtime variable
- m_RunUpper - Variable in class weka.experiment.Experiment
-
Upper run number
- m_samplesBase - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
- m_SampleSize - Variable in class weka.classifiers.meta.GridSearch
-
the sample size to search the initial grid with
- m_SampleSize - Variable in class weka.filters.unsupervised.instance.ReservoirSample
-
The subsample size, number of instances%
- m_SampleSizePercent - Variable in class weka.filters.supervised.instance.Resample
-
The subsample size, percent of original set, default 100%.
- m_SampleSizePercent - Variable in class weka.filters.unsupervised.instance.Resample
-
The subsample size, percent of original set, default 100%
- m_SaveBut - Variable in class weka.gui.experiment.SetupPanel
-
Click to save an experiment
- m_SaveBut - Variable in class weka.gui.experiment.SimpleSetupPanel
-
Click to save an experiment
- m_SaveBut - Variable in class weka.gui.explorer.PreprocessPanel
-
Click to apply filters and save the results
- m_SaveBut - Variable in class weka.gui.GenericObjectEditor.GOEPanel
-
Save object to disk.
- m_saveBut - Variable in class weka.gui.visualize.VisualizePanel
-
Button for the user to save the visualized set of instances
- m_SaveDialogTitle - Variable in class weka.gui.visualize.PrintableComponent
-
the title of the save dialog.
- m_saveInstanceData - Variable in class weka.classifiers.trees.ADTree
-
Option - whether the tree should remember the instance data
- m_saveInstances - Variable in class weka.classifiers.trees.m5.M5Base
-
Save instances at each node in an M5 tree for visualization purposes.
- m_saveInstances - Variable in class weka.clusterers.Cobweb
-
Output instances in graph representation of Cobweb tree (Allows
instances at nodes in the tree to be visualized in the Explorer).
- m_saveMemory - Variable in class weka.classifiers.rules.DecisionTable
-
- m_SaveOptionsBut - Variable in class weka.gui.experiment.AlgorithmListPanel
-
Click to edit the save the options from selected algorithm
- m_SaveOut - Variable in class weka.gui.explorer.AssociationsPanel
-
The buffer saving object for saving output
- m_SaveOutBut - Variable in class weka.gui.experiment.ResultsPanel
-
Click to save test output to a file.
- m_Saver - Variable in class weka.core.converters.ConverterUtils.DataSink
-
the saver to use for storing the data.
- m_SaverFileFilters - Static variable in class weka.gui.ConverterFileChooser
-
the file filters for the savers
- m_Scale - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
The scale parameter
- m_Scale - Variable in class weka.filters.unsupervised.attribute.Normalize
-
The scaling factor of the output range.
- m_ScalingEnabled - Variable in class weka.gui.visualize.JComponentWriter
-
whether scaling is enabled
- m_ScrollBarIncrementComponents - Variable in class weka.gui.beans.KnowledgeFlowApp
-
the scrollbar increment of the components scrollpane
- m_ScrollBarIncrementLayout - Variable in class weka.gui.beans.KnowledgeFlowApp
-
the scrollbar increment of the layout scrollpane
- m_Search - Variable in class weka.attributeSelection.CheckAttributeSelection
-
The search method to be used
- m_Search - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
-
The search method to use
- m_search - Variable in class weka.classifiers.rules.DecisionTable
-
The search method to use
- m_search_bestInsertionNode - Variable in class weka.classifiers.trees.ADTree
-
The best node to insert under, as found so far by the latest search
- m_search_bestInsertionNode - Variable in class weka.classifiers.trees.LADTree
-
- m_search_bestPathInstances - Variable in class weka.classifiers.trees.LADTree
-
- m_search_bestPathNegInstances - Variable in class weka.classifiers.trees.ADTree
-
The negative instances that apply to the best path found so far
- m_search_bestPathPosInstances - Variable in class weka.classifiers.trees.ADTree
-
The positive instances that apply to the best path found so far
- m_search_bestSplitter - Variable in class weka.classifiers.trees.ADTree
-
The best splitter to insert, as found so far by the latest search
- m_search_bestSplitter - Variable in class weka.classifiers.trees.LADTree
-
- m_search_smallestLeastSquares - Variable in class weka.classifiers.trees.LADTree
-
- m_search_smallestZ - Variable in class weka.classifiers.trees.ADTree
-
The smallest Z value found so far by the latest search
- m_searchDirection - Variable in class weka.attributeSelection.BestFirst
-
0 == backward search, 1 == forward search, 2 == bidirectional
- m_searchPath - Variable in class weka.classifiers.trees.ADTree
-
Option - the search mode
- m_SecondSuccessor - Variable in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
-
The second successor
- m_SecondSuccessor - Variable in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
-
The second successor
- m_seed - Variable in class weka.attributeSelection.CostSensitiveASEvaluation
-
random number seed
- m_seed - Variable in class weka.attributeSelection.SubsetSizeForwardSelection
-
Seed for cross validation subset size determination.
- m_seed - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
-
seed for randomizing the instances before CV
- m_Seed - Variable in class weka.classifiers.BVDecompose
-
The random number seed
- m_Seed - Variable in class weka.classifiers.BVDecomposeSegCVSub
-
The random number seed
- m_Seed - Variable in class weka.classifiers.functions.Winnow
-
Random seed used for shuffling the dataset, -1 == disable
- m_Seed - Variable in class weka.classifiers.RandomizableClassifier
-
The random number seed.
- m_Seed - Variable in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
-
The random number seed.
- m_Seed - Variable in class weka.classifiers.RandomizableMultipleClassifiersCombiner
-
The random number seed.
- m_Seed - Variable in class weka.classifiers.RandomizableSingleClassifierEnhancer
-
The random number seed.
- m_Seed - Variable in class weka.classifiers.trees.REPTree
-
Seed for random data shuffling.
- m_Seed - Variable in class weka.clusterers.RandomizableClusterer
-
The random number seed.
- m_Seed - Variable in class weka.clusterers.RandomizableDensityBasedClusterer
-
The random number seed.
- m_Seed - Variable in class weka.clusterers.RandomizableSingleClustererEnhancer
-
The random number seed.
- m_Seed - Variable in class weka.core.TestInstances
-
the seed value
- m_Seed - Variable in class weka.datagenerators.DataGenerator
-
random number generator seed
- m_Seed - Variable in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
-
the seed for randomizing, default is 1
- m_Seed - Variable in class weka.filters.unsupervised.attribute.RandomSubset
-
The seed value.
- m_Seed - Variable in class weka.filters.unsupervised.instance.Randomize
-
The random number seed
- m_SeedDefault - Variable in class weka.clusterers.RandomizableClusterer
-
the default seed value
- m_SeedDefault - Variable in class weka.clusterers.RandomizableDensityBasedClusterer
-
the default seed value
- m_SeedDefault - Variable in class weka.clusterers.RandomizableSingleClustererEnhancer
-
the default seed value
- m_SeedLab - Variable in class weka.gui.explorer.AttributeSelectionPanel
-
Label by where cv random seed is entered
- m_SeedText - Variable in class weka.gui.explorer.AttributeSelectionPanel
-
The field where the seed value is entered
- m_selAttrib - Variable in class weka.gui.visualize.MatrixPanel
-
The button to display a window to select attributes
- m_SelectBut - Variable in class weka.gui.ListSelectorDialog
-
Click to choose the currently selected property
- m_SelectBut - Variable in class weka.gui.PropertySelectorDialog
-
Click to choose the currently selected property
- m_SelectCols - Variable in class weka.filters.unsupervised.attribute.MathExpression
-
Stores which columns to select as a funky range
- m_SelectCols - Variable in class weka.filters.unsupervised.attribute.Remove
-
Stores which columns to select as a funky range
- m_Selected - Variable in class weka.core.SelectedTag
-
The index of the selected tag
- m_SelectedAttributes - Variable in class weka.filters.unsupervised.attribute.Copy
-
Stores the indexes of the selected attributes in order, once the
dataset is seen
- m_SelectedAttributes - Variable in class weka.filters.unsupervised.attribute.Remove
-
Stores the indexes of the selected attributes in order, once the
dataset is seen
- m_SelectedAttributes - Variable in class weka.filters.unsupervised.attribute.Reorder
-
Stores the indexes of the selected attributes in order, once the
dataset is seen
- m_SelectedCols - Variable in class weka.filters.unsupervised.attribute.AbstractTimeSeries
-
Stores which columns to copy
- m_SelectedIndex - Variable in class weka.core.SingleIndex
-
The selected index
- m_SelectedRange - Variable in class weka.filters.unsupervised.attribute.RELAGGS
-
the range of attributes to process (only relational ones will be processed)
- m_SelectedRange - Variable in class weka.filters.unsupervised.attribute.StringToWordVector
-
Range of columns to convert to word vectors.
- m_selectionTime - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
-
The time taken to select attributes in milliseconds
- m_Self - Variable in class weka.gui.ConverterFileChooser
-
the file chooser itself
- m_Self - Variable in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
-
the dialog itself.
- m_Self - Variable in class weka.gui.GUIChooser
-
the GUIChooser itself
- m_Self - Variable in class weka.gui.Main
-
the frame itself.
- m_SequentialAttIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes
-
an array of attribute indexes that are set to either true or false
- m_SequentialInstIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes
-
the array of instance indexes that are set to a either true or false
- m_SerializedClassifierFile - Variable in class weka.filters.supervised.attribute.AddClassification
-
The file from which to load a serialized classifier.
- m_SerializedHeader - Variable in class weka.filters.supervised.attribute.AddClassification
-
the header of the file the serialized classifier was trained with.
- m_setAutoCommit - Variable in class weka.experiment.DatabaseUtils
-
setAutoCommit on the database?
- m_SetCostsBut - Variable in class weka.gui.explorer.ClassifierPanel
-
for the cost matrix
- m_SetCostsFrame - Variable in class weka.gui.explorer.ClassifierPanel
-
The frame used to show the cost matrix editing panel
- m_SetCurrentMethod - Variable in class weka.core.stemmers.SnowballStemmer
-
the setCurrent method.
- m_setNumber - Variable in class weka.gui.beans.BatchClassifierEvent
-
The set number for the test set
- m_setNumber - Variable in class weka.gui.beans.BatchClustererEvent
-
The set number for the test set
- m_setNumber - Variable in class weka.gui.beans.TestSetEvent
-
what number is this test set (ie fold 2 of 10 folds)
- m_setNumber - Variable in class weka.gui.beans.TrainingSetEvent
-
what number is this training set (ie fold 2 of 10 folds)
- m_sets - Variable in class weka.associations.FPGrowth.FrequentItemSets
-
The list of frequent item sets
- m_SetTestBut - Variable in class weka.gui.explorer.ClassifierPanel
-
The button used to open a separate test dataset
- m_SetTestBut - Variable in class weka.gui.explorer.ClustererPanel
-
The button used to open a separate test dataset
- m_SetTestFrame - Variable in class weka.gui.explorer.ClassifierPanel
-
The frame used to show the test set selection panel
- m_SetTestFrame - Variable in class weka.gui.explorer.ClustererPanel
-
The frame used to show the test set selection panel
- m_SetupPanel - Variable in class weka.gui.experiment.Experimenter
-
The panel for configuring the experiment
- m_sFileName - Variable in class weka.classifiers.bayes.net.GUI
-
String containing file name storing current network
- m_ShapeCombo - Variable in class weka.gui.visualize.VisualizePanel
-
Lets the user select the shape they want to create for instance
selection.
- m_shapeSize - Variable in class weka.gui.visualize.PlotData2D
-
Additional optional information to control the size of points.
- m_shapeSizes - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
The size of the points being plotted
- m_shapeType - Variable in class weka.gui.visualize.PlotData2D
-
Additional optional information to control the point shape for this
data.
- m_showAttBars - Variable in class weka.gui.visualize.VisualizePanel
-
Show the attribute bar panel
- m_ShowAverage - Variable in class weka.experiment.ResultMatrix
-
whether the average for each column should be printed
- m_ShowAverage - Variable in class weka.gui.experiment.OutputFormatDialog
-
whether to show the average too.
- m_ShowAverageCheckBox - Variable in class weka.gui.experiment.OutputFormatDialog
-
the checkbox for outputting the average.
- m_ShowBorder - Variable in class weka.gui.treevisualizer.TreeVisualizer
-
whether to show the border or not.
- m_showClassPanel - Variable in class weka.gui.visualize.VisualizePanel
-
Show the class panel
- m_ShowStdDev - Variable in class weka.experiment.ResultMatrix
-
whether std.
- m_ShowStdDevs - Variable in class weka.experiment.PairedTTester
-
Indicates whether standard deviations should be displayed
- m_ShowStdDevs - Variable in class weka.gui.experiment.ResultsPanel
-
Lets the user select whether standard deviations are to be output
or not.
- m_ShowToolTip - Static variable in class weka.gui.visualize.PrintableComponent
-
whether to display the tooltip or not.
- m_shrinkage - Variable in class weka.classifiers.meta.AdditiveRegression
-
Shrinkage (Learning rate).
- m_Shrinkage - Variable in class weka.classifiers.meta.LogitBoost
-
The value of the shrinkage parameter
- m_Shrinking - Variable in class weka.classifiers.functions.LibSVM
-
use the shrinking heuristics
- m_Sigma - Variable in class weka.classifiers.BVDecompose
-
The calculated sigma (squared)
- m_sigma - Variable in class weka.classifiers.functions.supportVector.Puk
-
Sigma for the Puk kernel.
- m_Significance - Variable in class weka.experiment.ResultMatrix
-
the significance
- m_significanceLevel - Variable in class weka.associations.Apriori
-
Significance level for optional significance test.
- m_SignificanceLevel - Variable in class weka.experiment.PairedTTester
-
The significance level for comparisons
- m_SignificanceWidth - Variable in class weka.experiment.ResultMatrix
-
the size of the significance columns
- m_SigTex - Variable in class weka.gui.experiment.ResultsPanel
-
Lets the user edit the test significance.
- m_Silent - Variable in class weka.core.Check
-
Silent mode, for no output at all to stdout
- m_Silent - Variable in class weka.core.Javadoc
-
whether to suppress error messages (no printout in the console)
- m_Silent - Variable in class weka.estimators.CheckEstimator
-
Silent mode, for no output at all to stdout
- m_SimpleBut - Variable in class weka.gui.GUIChooser
-
Click to open the simplecli
- m_SimpleCLI - Variable in class weka.gui.GUIChooser
-
The SimpleCLI
- m_simplePanel - Variable in class weka.gui.experiment.SetupModePanel
-
The simple setup panel
- m_SimpleSetupRBut - Variable in class weka.gui.experiment.SetupModePanel
-
The button for choosing simple setup mode
- m_SIMPLS_B - Variable in class weka.filters.supervised.attribute.PLSFilter
-
the B matrix for SIMPLS (used for prediction)
- m_SIMPLS_W - Variable in class weka.filters.supervised.attribute.PLSFilter
-
the W matrix for SIMPLS
- m_sIndex - Variable in class weka.gui.visualize.Plot2D
-
- m_sIndex - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
-
- m_SingleName - Variable in class weka.gui.ResultHistoryPanel
-
The named result being viewed in the single-click display
- m_SingleText - Variable in class weka.gui.ResultHistoryPanel
-
An optional component for single-click display
- m_Singleton - Static variable in class weka.core.logging.Logger
-
the singleton instance of the logger.
- m_Size - Variable in class weka.core.Debug.Log
-
the size of the file (in bytes)
- m_Size - Variable in class weka.core.Queue
-
Store the c m_Tail.m_Nexturrent number of elements in the queue
- m_SizePer - Variable in class weka.classifiers.trees.BFTree
-
The training data size (0-1).
- m_SizePer - Variable in class weka.classifiers.trees.SimpleCart
-
Training data size.
- m_SkipIdentical - Variable in class weka.core.neighboursearch.LinearNNSearch
-
Whether to skip instances from the neighbours that are identical to the query instance.
- m_SmallestProb - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
Smallest probability of test attribute transforming into
train attribute
- m_SmallestProb - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
Smallest probability of test attribute transforming into train
attribute
- m_sons - Variable in class weka.classifiers.rules.part.ClassifierDecList
-
References to sons.
- m_sons - Variable in class weka.classifiers.trees.ft.FTtree
-
Array of children of the node
- m_sons - Variable in class weka.classifiers.trees.j48.ClassifierTree
-
References to sons.
- m_sons - Variable in class weka.classifiers.trees.lmt.LMTNode
-
Array of children of the node
- m_Sort - Variable in class weka.filters.unsupervised.attribute.AddValues
-
Whether to sort the values.
- m_SortColumn - Variable in class weka.experiment.PairedTTester
-
The column to sort on (-1 means default sorting)
- m_SortCombo - Variable in class weka.gui.experiment.ResultsPanel
-
Lets the user select which column to use for sorting.
- m_SortedEigens - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Sorted eigenvalues.
- m_SortedIndices - Variable in class weka.classifiers.trees.BFTree
-
Sorted indices.
- m_SortedIndices - Variable in class weka.filters.unsupervised.attribute.AddValues
-
the array with the sorted label indices
- m_SortModel - Variable in class weka.gui.experiment.ResultsPanel
-
The model embedded in m_SortCombo.
- m_SortOrder - Variable in class weka.experiment.PairedTTester
-
The sorting of the datasets (according to the sort column)
- m_SourceCode - Variable in class weka.classifiers.CheckSource
-
the generated source code
- m_SourceCode - Variable in class weka.filters.CheckSource
-
the generated source code
- m_SourceCodeClass - Variable in class weka.gui.explorer.ClassifierPanel
-
The name of the generated class (only applicable to Sourcable schemes)
- m_sourceFile - Variable in class weka.core.converters.AbstractFileLoader
-
Holds the source of the data set.
- m_sourceFile - Variable in class weka.core.converters.TextDirectoryLoader
-
Holds the source of the data set.
- m_sourceReader - Variable in class weka.core.converters.ArffLoader
-
The reader for the source file.
- m_sourceReader - Variable in class weka.core.converters.CSVLoader
-
The reader for the data.
- m_sourceReader - Variable in class weka.core.converters.LibSVMLoader
-
The reader for the source file.
- m_sourceReader - Variable in class weka.core.converters.SVMLightLoader
-
The reader for the source file.
- m_sourceReader - Variable in class weka.core.converters.XRFFLoader
-
The reader for the source file.
- m_span - Variable in class weka.gui.visualize.AttributePanel
-
The container window for the attribute bars, and also where the
X,Y or B get printed.
- m_span - Variable in class weka.gui.visualize.LegendPanel
-
the panel that contains the legend entries
- m_SparseFilter - Variable in class weka.classifiers.mi.MISVM
-
The filter used to transform the sparse datasets to nonsparse
- m_sparseIndices - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
- m_sparseIndices - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
-
- m_sparseIndices - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
- m_sparseWeights - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
Variables to hold weight vector in sparse form.
- m_sparseWeights - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
-
Variables to hold weight vector in sparse form.
- m_sparseWeights - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
Variables to hold weight vector in sparse form.
- m_SpinnerMaxRows - Variable in class weka.gui.sql.QueryPanel
-
the spinner for the maximum number of rows.
- m_SpinnerMaxSize - Variable in class weka.gui.LogWindow
-
the spinner for the max number of chars
- m_SplitAttrib - Variable in class weka.core.neighboursearch.balltrees.BallNode
-
The attribute that splits this node (not
always used).
- m_splitByDataSet - Variable in class weka.experiment.RemoteExperiment
-
If true, then sub experiments are created on the basis of data sets
rather than run number.
- m_splitByDataSet - Variable in class weka.gui.experiment.DistributeExperimentPanel
-
Split experiment up by data set.
- m_splitByRun - Variable in class weka.gui.experiment.DistributeExperimentPanel
-
Split experiment up by run number.
- m_splitCrit - Static variable in class weka.classifiers.rules.part.ClassifierDecList
-
To compute the entropy.
- m_SplitDim - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode
-
attribute to split on.
- m_SplitEvaluator - Variable in class weka.experiment.CrossValidationResultProducer
-
The SplitEvaluator used to generate results
- m_SplitEvaluator - Variable in class weka.experiment.RandomSplitResultProducer
-
The SplitEvaluator used to generate results
- m_splitListener - Variable in class weka.gui.visualize.VisualizePanel
-
An optional listener that we will inform when the user creates a
split to seperate instances.
- m_splitOnResiduals - Variable in class weka.classifiers.trees.LMT
-
split on residuals?
- m_splitPoint - Variable in class weka.classifiers.trees.lmt.ResidualSplit
-
The split point (for numeric attributes)
- m_SplitPoint - Variable in class weka.classifiers.trees.RandomTree
-
The split point.
- m_SplitPoint - Variable in class weka.classifiers.trees.REPTree.Tree
-
The split point.
- m_SplitString - Variable in class weka.classifiers.trees.BFTree
-
Split subset (for nominal attributes).
- m_SplitString - Variable in class weka.classifiers.trees.SimpleCart
-
Split subset used to split data for nominal attributes.
- m_SplitString - Variable in class weka.core.tokenizers.NGramTokenizer
-
all the available grams
- m_Splitter - Variable in class weka.core.neighboursearch.balltrees.TopDownConstructor
-
The BallSplitter algorithm used by the TopDown BallTree constructor, if it
is selected.
- m_Splitter - Variable in class weka.core.neighboursearch.KDTree
-
The node splitter.
- m_SplitVal - Variable in class weka.core.neighboursearch.balltrees.BallNode
-
The value of m_SpiltAttrib that splits this
node (not always used).
- m_SplitValue - Variable in class weka.classifiers.trees.BFTree
-
Split point (for numeric attributes).
- m_SplitValue - Variable in class weka.classifiers.trees.SimpleCart
-
Split point for a numeric attribute.
- m_SplitValue - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode
-
value to split on.
- m_SQLQ - Variable in class weka.gui.explorer.PreprocessPanel
-
Stores the last sql query executed
- m_SqlViewerFrame - Variable in class weka.gui.GUIChooser
-
The frame containing the SqlViewer
- m_st - Variable in class weka.core.converters.CSVLoader
-
Tokenizer for the data.
- m_Stamp - Variable in class weka.core.Debug.Timestamp
-
the actual date
- m_standardizeFilter - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Filter for standardizing the data
- m_Start - Variable in class weka.core.Debug.Clock
-
the start time
- m_Start - Variable in class weka.core.neighboursearch.balltrees.BallNode
-
The start index of the portion of the master index array,
which stores the indices of the instances/points the node
contains.
- m_Start - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode
-
The start index of the portion of the master index array,
which stores the indices of the instances/points the node
contains.
- m_startBut - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
- m_StartBut - Variable in class weka.gui.experiment.RunPanel
-
Click to start running the experiment
- m_StartBut - Variable in class weka.gui.explorer.AssociationsPanel
-
Click to start running the associator
- m_StartBut - Variable in class weka.gui.explorer.AttributeSelectionPanel
-
Click to start running the attribute selector
- m_StartBut - Variable in class weka.gui.explorer.ClassifierPanel
-
Click to start running the classifier
- m_StartBut - Variable in class weka.gui.explorer.ClustererPanel
-
Click to start running the clusterer
- m_starting - Variable in class weka.attributeSelection.BestFirst
-
holds an array of starting attributes
- m_starting - Variable in class weka.attributeSelection.GreedyStepwise
-
holds an array of starting attributes
- m_starting - Variable in class weka.attributeSelection.LinearForwardSelection
-
holds an array of starting attributes
- m_startPoint - Variable in class weka.attributeSelection.RankSearch
-
start from this point in the ranking
- m_startRange - Variable in class weka.attributeSelection.BestFirst
-
holds the start set for the search as a Range
- m_startRange - Variable in class weka.attributeSelection.GreedyStepwise
-
holds the start set for the search as a Range
- m_startRange - Variable in class weka.attributeSelection.LinearForwardSelection
-
holds the start set for the search as a Range
- m_startSequentially - Variable in class weka.gui.beans.FlowRunner
-
run each Startable bean sequentially? (default in parallel)
- m_StartTag - Variable in class weka.core.Javadoc
-
the start tag
- m_StartupListeners - Static variable in class weka.gui.Main
-
list of things to be notified when the startup process of
the KnowledgeFlow is complete.
- m_staticPotentialSplitters2way - Variable in class weka.classifiers.trees.LADTree
-
- m_Stats - Variable in class weka.core.neighboursearch.NearestNeighbourSearch
-
Performance statistics.
- m_StatsTable - Variable in class weka.gui.AttributeSummaryPanel
-
Displays other stats in a table
- m_StatusBox - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel
-
Controls whether the custom iterator is used or not
- m_StatusLab - Variable in class weka.gui.LogPanel
-
Displays the current status
- m_statusMessage - Variable in class weka.experiment.RemoteExperimentEvent
-
A status type message
- m_StdDev - Variable in class weka.experiment.ResultMatrix
-
the standard deviation
- m_StdDevPrec - Variable in class weka.experiment.ResultMatrix
-
the standard std.
- m_StdDevPrec - Variable in class weka.gui.experiment.OutputFormatDialog
-
the number of digits after the period (= precision) for printing the std.
- m_StdDevPrecSpinner - Variable in class weka.gui.experiment.OutputFormatDialog
-
the spinner to choose the precision for the std.
- m_stddevValue - Variable in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
standarddev; only used if gaussian
- m_StdDevWidth - Variable in class weka.experiment.ResultMatrix
-
the size of the std dev columns
- m_StdErr - Variable in class weka.core.logging.OutputLogger
-
the Tee instance to redirect stderr.
- m_StdOut - Variable in class weka.core.logging.OutputLogger
-
the Tee instance to redirect stdout.
- m_Stemmer - Variable in class weka.core.stemmers.SnowballStemmer
-
the current stemmer.
- m_Stemmers - Static variable in class weka.core.stemmers.SnowballStemmer
-
contains the all the found stemmers (language names).
- m_StemMethod - Variable in class weka.core.stemmers.SnowballStemmer
-
the stem method.
- m_StepSize - Variable in class weka.experiment.LearningRateResultProducer
-
The number of instances to add at each step
- m_StepX - Variable in class weka.classifiers.meta.GridSearch.Grid
-
the step size for the X axis
- m_StepY - Variable in class weka.classifiers.meta.GridSearch.Grid
-
the step size for the Y axis
- m_Stop - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
The stop parameter
- m_Stop - Variable in class weka.core.Debug.Clock
-
the end time
- m_StopBut - Variable in class weka.gui.experiment.RunPanel
-
Click to signal the running experiment to halt
- m_StopBut - Variable in class weka.gui.explorer.AssociationsPanel
-
Click to stop a running associator
- m_StopBut - Variable in class weka.gui.explorer.AttributeSelectionPanel
-
Click to stop a running classifier
- m_StopBut - Variable in class weka.gui.explorer.ClassifierPanel
-
Click to stop a running classifier
- m_StopBut - Variable in class weka.gui.explorer.ClustererPanel
-
Click to stop a running clusterer
- m_stopped - Variable in class weka.gui.beans.Loader
-
Asked to stop?
- m_stopPlotting - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Stop the plotting thread
- m_stopReplotting - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Stop any replotting threads
- m_Stopwords - Static variable in class weka.core.Stopwords
-
The default stopwords object (stoplist based on Rainbow)
- m_storage - Variable in class weka.classifiers.functions.supportVector.CachedKernel
-
Kernel cache
- m_StorePredictionsBut - Variable in class weka.gui.explorer.ClassifierPanel
-
Check to save the predictions in the results list for visualizing later on
- m_StorePredictionsBut - Variable in class weka.gui.explorer.ClustererPanel
-
Check to save the predictions in the results list for visualizing
later on
- m_STPMX - Variable in class weka.core.Optimization
-
- m_Str - Variable in class weka.core.tokenizers.AlphabeticTokenizer
-
the characters of the string
- m_Stream - Variable in class weka.core.converters.ConverterUtils.DataSink
-
the stream to store the data in (always in ARFF format).
- m_Streamable - Variable in class weka.filters.MultiFilter
-
caches the streamable state
- m_StreamableChecked - Variable in class weka.filters.MultiFilter
-
whether we already checked the streamable state
- m_StreamErr - Variable in class weka.core.logging.OutputLogger
-
the stream object used for logging stderr.
- m_StreamOut - Variable in class weka.core.logging.OutputLogger
-
the stream object used for logging stdout.
- m_Streams - Variable in class weka.core.Tee
-
the different PrintStreams.
- m_String - Variable in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
-
- m_StringAttributes - Variable in class weka.core.converters.CSVLoader
-
The range of attributes to force to type string.
- m_stringType - Variable in class weka.experiment.DatabaseUtils
-
string type for the create table statement.
- m_structure - Variable in class weka.core.converters.AbstractFileLoader
-
Holds the determined structure (header) of the data set.
- m_structure - Variable in class weka.core.converters.DatabaseLoader
-
The header information that is retrieved in the beginning of incremental loading
- m_structure - Variable in class weka.core.converters.TextDirectoryLoader
-
Holds the determined structure (header) of the data set.
- m_Style - Variable in class weka.gui.LogWindow.LogWindowPrintStream
-
the style of the printstream
- m_subExpComplete - Variable in class weka.experiment.RemoteExperiment
-
The status of each of the sub-experiments
- m_subExperiments - Variable in class weka.experiment.RemoteExperiment
-
The sub experiments
- m_subFlow - Variable in class weka.gui.beans.MetaBean
-
- m_subFlowPreview - Variable in class weka.gui.beans.MetaBean
-
- m_subInstances - Variable in class weka.classifiers.lazy.LBR
-
index of instances and attributes for the given dataset
- m_submit - Variable in class weka.gui.visualize.VisualizePanel
-
Button for the user to enter the splits.
- m_subOldErrorFlags - Variable in class weka.classifiers.lazy.LBR
-
following is defined by wangzh,
the number of instances to be classified incorrectly
on the subset.
- m_subSample - Variable in class weka.filters.unsupervised.instance.ReservoirSample
-
Holds the sub-sample (reservoir)
- m_SubSpaceSize - Variable in class weka.classifiers.meta.RandomSubSpace
-
The size of each bag sample, as a percentage of the training size
- m_Success - Variable in class weka.core.CheckGOE
-
whether the tests were successful
- m_Success - Variable in class weka.core.CheckOptionHandler
-
whether the tests were successful
- m_Successors - Variable in class weka.classifiers.trees.BFTree
-
Successor nodes.
- m_Successors - Variable in class weka.classifiers.trees.RandomTree
-
The subtrees appended to this tree.
- m_Successors - Variable in class weka.classifiers.trees.REPTree.Tree
-
The subtrees of this tree.
- m_Successors - Variable in class weka.classifiers.trees.SimpleCart
-
Successor nodes.
- m_SuitableData - Variable in class weka.classifiers.meta.AdditiveRegression
-
whether we have suitable data or nor (if not, ZeroR model is used)
- m_sum - Variable in class weka.associations.PriorEstimation
-
Sums up the confidences of all rules with a certain length.
- m_SumAbsErr - Variable in class weka.classifiers.Evaluation
-
Sum of absolute errors.
- m_SumC - Variable in class weka.core.neighboursearch.PerformanceStats
-
The sum of coordinates/attributes looked at
for all the queries.
- m_SumClass - Variable in class weka.classifiers.Evaluation
-
Sum of class values.
- m_SumClassPredicted - Variable in class weka.classifiers.Evaluation
-
Sum of predicted * class values.
- m_SumErr - Variable in class weka.classifiers.Evaluation
-
Sum of errors.
- m_SumIntNodes - Variable in class weka.core.neighboursearch.TreePerformanceStats
-
The sum of internal nodes looked
at for all the queries.
- m_SumKBInfo - Variable in class weka.classifiers.Evaluation
-
Total Kononenko & Bratko Information
- m_SumLeaves - Variable in class weka.core.neighboursearch.TreePerformanceStats
-
The sum of leaf nodes looked
at for all the queries.
- m_Summary - Variable in class weka.gui.explorer.ClustererPanel
-
The instances summary panel displayed by m_SetTestFrame
- m_Summary - Variable in class weka.gui.SetInstancesPanel
-
The instance summary component
- m_SumOfCounts - Variable in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
-
Hold the sum of counts
- m_SumOfEigenValues - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
sum of the eigenvalues.
- m_sumOfWeights - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
Stores the weight of the training instances
- m_sumOfWeights - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
Stores the weight of the training instances
- m_SumP - Variable in class weka.core.neighboursearch.PerformanceStats
-
The sum of data points looked
at for all the queries.
- m_SumPredicted - Variable in class weka.classifiers.Evaluation
-
Sum of predicted values.
- m_SumPriorAbsErr - Variable in class weka.classifiers.Evaluation
-
Sum of absolute errors of the prior
- m_SumPriorEntropy - Variable in class weka.classifiers.Evaluation
-
Total entropy of prior predictions
- m_SumPriorSqrErr - Variable in class weka.classifiers.Evaluation
-
Sum of absolute errors of the prior
- m_SumSchemeEntropy - Variable in class weka.classifiers.Evaluation
-
Total entropy of scheme predictions
- m_SumSqC - Variable in class weka.core.neighboursearch.PerformanceStats
-
The squared sum of coordinates/attributes looked at
for all the queries.
- m_SumSqIntNodes - Variable in class weka.core.neighboursearch.TreePerformanceStats
-
The squared sum of internal nodes looked
at for all the queries.
- m_SumSqLeaves - Variable in class weka.core.neighboursearch.TreePerformanceStats
-
The squared sum of leaf nodes looked
at for all the queries.
- m_SumSqP - Variable in class weka.core.neighboursearch.PerformanceStats
-
The squared sum of data points looked
at for all the queries.
- m_SumSqrClass - Variable in class weka.classifiers.Evaluation
-
Sum of squared class values.
- m_SumSqrErr - Variable in class weka.classifiers.Evaluation
-
Sum of squared errors.
- m_SumSqrPredicted - Variable in class weka.classifiers.Evaluation
-
Sum of squared predicted values.
- m_Superclass - Variable in class weka.core.FindWithCapabilities
-
the superclass from the GenericPropertiesCreator to retrieve the packages from.
- m_support - Variable in class weka.associations.FPGrowth.FrequentBinaryItemSet
-
the support of this item set
- m_Support - Variable in class weka.gui.experiment.SetupPanel
-
Manages sending notifications to people when we change the experiment,
at this stage, only the resultlistener so the resultpanel can update.
- m_Support - Variable in class weka.gui.experiment.SimpleSetupPanel
-
Manages sending notifications to people when we change the experiment,
at this stage, only the resultlistener so the resultpanel can update.
- m_Support - Variable in class weka.gui.explorer.PreprocessPanel
-
Manages sending notifications to people when we change the set of
working instances.
- m_Support - Variable in class weka.gui.GenericObjectEditor
-
Handles property change notification.
- m_Support - Variable in class weka.gui.SetInstancesPanel
-
Manages sending notifications to people when we change the set of
working instances.
- m_SupportCount - Variable in class weka.associations.gsp.Sequence
-
the support count of the Sequence
- m_supportVectors - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
The set of support vectors
- m_supportVectors - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
-
set of support vectors, that is, vectors with alpha(*)!=0
- m_supportVectors - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
The set of support vectors {i: 0 < m_alpha[i]}
- m_SVM - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
-
parent SMOreg class
- m_SVM - Variable in class weka.classifiers.mi.MISVM
-
The SMO classifier used to compute SVM soluton w,b for the dataset
- m_SVMType - Variable in class weka.classifiers.functions.LibLINEAR
-
the SVM solver type
- m_SVMType - Variable in class weka.classifiers.functions.LibSVM
-
the SVM type
- m_Symbols - Variable in class weka.core.mathematicalexpression.Parser
-
variable - value relation.
- m_Symbols - Variable in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
-
variable - value relation.
- m_SymFactory - Variable in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
-
- m_SystemInfoFrame - Variable in class weka.gui.GUIChooser
-
The frame containing the system info
- m_t - Variable in class weka.classifiers.functions.GaussianProcesses
-
The vector of target values.
- m_t - Variable in class weka.classifiers.functions.SPegasos
-
Holds the current iteration number
- m_TabbedPane - Variable in class weka.gui.experiment.Experimenter
-
The tabbed pane that controls which sub-pane we are working with
- m_TabbedPane - Variable in class weka.gui.explorer.Explorer
-
The tabbed pane that controls which sub-pane we are working with
- m_TabbedPane - Variable in class weka.gui.sql.ResultPanel
-
the tabbed pane for the results
- m_Table - Variable in class weka.classifiers.meta.GridSearch.PerformanceTable
-
the table with the values
- m_Table - Variable in class weka.gui.AttributeListPanel
-
The table displaying attribute names
- m_Table - Variable in class weka.gui.AttributeSelectionPanel
-
The table displaying attribute names and selection status
- m_Tags - Variable in class weka.core.SelectedTag
-
The set of tags to choose from
- m_Tail - Variable in class weka.core.Queue
-
Store a reference to the tail of the queue
- m_target - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
-
class values/desired output vector
- m_targetMetaInfo - Variable in class weka.core.pmml.MiningSchema
-
target meta info (may be null if not defined)
- m_TaskMonitor - Variable in class weka.gui.LogPanel
-
The panel for monitoring the number of running tasks (if supplied)
- m_tCounts - Variable in class weka.classifiers.lazy.LBR
-
All the counts for nominal attributes.
- m_TeeErr - Static variable in class weka.gui.LogWindow
-
for redirecting stderr
- m_TeeOut - Static variable in class weka.gui.LogWindow
-
for redirecting stdout
- m_Template - Variable in class weka.experiment.ClassifierSplitEvaluator
-
The template classifier
- m_Template - Variable in class weka.experiment.RegressionSplitEvaluator
-
The template classifier
- m_tempUndoFiles - Variable in class weka.gui.explorer.PreprocessPanel
-
Keeps track of undo points
- m_tempUndoIndex - Variable in class weka.gui.explorer.PreprocessPanel
-
The next available slot for an undo point
- m_Test - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
The test instance
- m_Test - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
The test instance
- m_test - Variable in class weka.classifiers.rules.part.ClassifierDecList
-
The pruning instances.
- m_test - Variable in class weka.classifiers.trees.j48.ClassifierTree
-
The pruning instances.
- m_Test - Variable in class weka.core.Capabilities
-
whether to perform any tests at all
- m_TesterClasses - Variable in class weka.gui.experiment.ResultsPanel
-
Lists all the available classes implementing the Tester-Interface.
- m_TesterClassesLabel - Variable in class weka.gui.experiment.ResultsPanel
-
Displays the currently selected Tester-Class.
- m_TesterClassesModel - Static variable in class weka.gui.experiment.ResultsPanel
-
Contains all the available classes implementing the Tester-Interface
(the display names).
- m_Testers - Static variable in class weka.gui.experiment.ResultsPanel
-
Contains all the available classes implementing the Tester-Interface
(the actual Classes).
- m_TestEvaluator - Variable in class weka.attributeSelection.CheckAttributeSelection
-
whether to test the evaluator (default) or the search method
- m_TestInstances - Variable in class weka.gui.explorer.AssociationsPanel
-
The user-supplied test set (if any)
- m_TestInstances - Variable in class weka.gui.explorer.ClustererPanel
-
The user-supplied test set (if any)
- m_testListeners - Variable in class weka.gui.beans.AbstractTrainAndTestSetProducer
-
Objects listening for test set events
- m_TestLoader - Variable in class weka.gui.explorer.ClassifierPanel
-
The loader used to load the user-supplied test set (if any)
- m_testOrTrain - Variable in class weka.gui.beans.BatchClustererEvent
-
Indicates if m_testSet is a training or a test set.
- m_TestsButton - Variable in class weka.gui.experiment.ResultsPanel
-
Lets the user select which scheme to base comparisons against.
- m_testSet - Variable in class weka.gui.beans.BatchClassifierEvent
-
Instances that can be used for testing the classifier
- m_testSet - Variable in class weka.gui.beans.BatchClustererEvent
-
Training or Test Instances
- m_testSet - Variable in class weka.gui.beans.TestSetEvent
-
The test set instances
- m_testSetListeners - Variable in class weka.gui.beans.PredictionAppender
-
Objects listening for test set events
- m_TestsList - Variable in class weka.gui.experiment.ResultsPanel
-
Holds the list of schemes to base the test against.
- m_TestsModel - Variable in class weka.gui.experiment.ResultsPanel
-
The model embedded in m_TestsList.
- m_TestSplitBut - Variable in class weka.gui.explorer.ClassifierPanel
-
Click to set test mode to a user-specified test set
- m_TestSplitBut - Variable in class weka.gui.explorer.ClustererPanel
-
Click to set test mode to a user-specified test set
- m_testTrainRatio - Variable in class weka.experiment.PairedStatsCorrected
-
The ratio used to correct the significane test
- m_text - Variable in class weka.gui.beans.TextEvent
-
The text
- m_TextQuery - Variable in class weka.gui.sql.QueryPanel
-
the textarea for the query.
- m_textTitle - Variable in class weka.gui.beans.TextEvent
-
The title for the text.
- m_TextURL - Variable in class weka.gui.sql.ConnectionPanel
-
the textfield for the URL.
- m_theInstances - Variable in class weka.classifiers.rules.DecisionTable
-
Holds the original training instances
- m_ThreadID - Variable in class weka.core.Debug.Clock
-
the thread ID
- m_ThreadMonitor - Variable in class weka.core.Debug.Clock
-
the thread monitor, if the system can measure the CPU time
- m_threshold - Variable in class weka.attributeSelection.GreedyStepwise
-
A threshold by which to discard attributes---used by the
AttributeSelection module
- m_Threshold - Variable in class weka.classifiers.functions.Winnow
-
Prediction threshold, <0 == numAttributes
- m_threshold - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
-
Threshold activation
- m_threshold - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_thresholdLab - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_thresholdSlider - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
The slider for adjusting the threshold
- m_tickSize - Variable in class weka.gui.visualize.Plot2D
-
Tick size
- m_Timestamps - Variable in class weka.core.Tee
-
whether to add timestamps or not.
- m_tnPrevious - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_Tokenizer - Variable in class weka.core.converters.ArffLoader.ArffReader
-
the tokenizer for reading the stream
- m_Tokenizer - Variable in class weka.core.tokenizers.WordTokenizer
-
the actual tokenizer
- m_tol - Variable in class weka.classifiers.functions.SMO
-
Tolerance for accuracy of result.
- m_tol - Variable in class weka.classifiers.mi.MISMO
-
Tolerance for accuracy of result.
- m_TOLX - Variable in class weka.core.Optimization
-
- m_ToolTipUserAsked - Static variable in class weka.gui.visualize.PrintableComponent
-
whether the user was already asked about the tooltip behavior.
- m_topOfTree - Variable in class weka.classifiers.trees.m5.Rule
-
the top of the m5 tree for this rule
- m_toSelectModel - Variable in class weka.classifiers.rules.part.ClassifierDecList
-
The model selection method.
- m_toSelectModel - Variable in class weka.classifiers.trees.j48.ClassifierTree
-
The model selection method.
- m_Total - Variable in class weka.core.Memory
-
the total memory that is used
- m_TotalCost - Variable in class weka.classifiers.Evaluation
-
The total cost of predictions (includes instance weights)
- m_TotalCount - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
Number of trai instances with no missing attribute values
- m_totalEvals - Variable in class weka.attributeSelection.BestFirst
-
total number of subsets evaluated during a search
- m_totalEvals - Variable in class weka.attributeSelection.LinearForwardSelection
-
total number of subsets evaluated during a search
- m_totalEvals - Variable in class weka.attributeSelection.SubsetSizeForwardSelection
-
total number of subsets evaluated during a search
- m_totalInstanceWeight - Variable in class weka.classifiers.trees.ft.FTtree
-
Total number of training instances.
- m_totalInstanceWeight - Variable in class weka.classifiers.trees.lmt.LMTNode
-
Total number of training instances.
- m_totalPopField - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
Population text field
- m_totalPopPrevious - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_totalSupport - Variable in class weka.associations.FPGrowth.AssociationRule
-
The total support for the item set (premise + consequence)
- m_totalTime - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
-
The time taken to select attributes AND build the classifier
- m_totalTrainInstances - Variable in class weka.classifiers.trees.SimpleCart
-
Total number of instances used to build the classifier.
- m_totalTransactions - Variable in class weka.associations.FPGrowth.AssociationRule
-
The total number of transactions in the data
- m_totalTransactions - Variable in class weka.associations.ItemSet
-
The total number of transactions
- m_totalTransactions - Variable in class weka.associations.RuleGeneration
-
The total number of transactions
- m_TotalWeight - Variable in class weka.classifiers.trees.BFTree
-
Total weights.
- m_tpPrevious - Variable in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
- m_tPriors - Variable in class weka.classifiers.lazy.LBR
-
The prior probabilities of the classes.
- m_Train - Variable in class weka.classifiers.lazy.IBk
-
The training instances used for classification.
- m_Train - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
The train instance
- m_Train - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
The train instance
- m_Train - Variable in class weka.classifiers.lazy.KStar
-
The training instances used for classification.
- m_Train - Variable in class weka.classifiers.lazy.LWL
-
The training instances used for classification.
- m_train - Variable in class weka.classifiers.rules.part.ClassifierDecList
-
The training instances.
- m_train - Variable in class weka.classifiers.trees.j48.ClassifierTree
-
The training instances.
- m_train - Variable in class weka.classifiers.trees.lmt.LogisticBase
-
Training data
- m_train - Variable in class weka.classifiers.trees.SimpleCart
-
Training data.
- m_TrainBags - Variable in class weka.classifiers.mi.CitationKNN
-
Training bags
- m_TrainBut - Variable in class weka.gui.explorer.AttributeSelectionPanel
-
Click to set test mode to test on training data
- m_TrainBut - Variable in class weka.gui.explorer.ClassifierPanel
-
Click to set test mode to test on training data
- m_TrainBut - Variable in class weka.gui.explorer.ClustererPanel
-
Click to set test mode to test on training data
- m_TrainClassVals - Variable in class weka.classifiers.Evaluation
-
Array containing all numeric training class values seen
- m_TrainClassWeights - Variable in class weka.classifiers.Evaluation
-
Array containing all numeric training class weights
- m_TrainCopy - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Keep a copy for the class attribute (if set).
- m_TrainFoldSize - Variable in class weka.classifiers.meta.CVParameterSelection
-
The number of instances in a training fold
- m_trainingData - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
training data
- m_trainingListeners - Variable in class weka.gui.beans.AbstractTrainAndTestSetProducer
-
Objects listening for trainin set events
- m_trainingSet - Variable in class weka.gui.beans.TrainingSetEvent
-
The training instances
- m_trainingSetListeners - Variable in class weka.gui.beans.PredictionAppender
-
Objects listening for training set events
- m_trainInstances - Variable in class weka.classifiers.trees.ADTree
-
The instances used to train the tree
- m_trainInstances - Variable in class weka.classifiers.trees.LADTree
-
- m_TrainInstances - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
The data to transform analyse/transform.
- m_TrainIterations - Variable in class weka.classifiers.BVDecompose
-
The number of train iterations
- m_TrainPercent - Variable in class weka.experiment.RandomSplitResultProducer
-
The percentage of instances to use for training
- m_trainPercent - Variable in class weka.gui.experiment.SimpleSetupPanel
-
The training percentage for a train/test split experiment
- m_TrainPoolSize - Variable in class weka.classifiers.BVDecompose
-
The number of instances used in the training pool
- m_TrainSet - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
The training instances used for classification.
- m_TrainSet - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
The training instances used for classification.
- m_trainSet - Variable in class weka.gui.beans.BatchClassifierEvent
-
Instances that were used to train the classifier (may be null if not available)
- m_TrainSize - Variable in class weka.classifiers.BVDecomposeSegCVSub
-
The training set size
- m_trainTotalWeight - Variable in class weka.classifiers.trees.ADTree
-
The total weight of the instances - used to speed Z calculations
- m_transactionsMustContain - Variable in class weka.associations.FPGrowth
-
If set, limit the transactions (instances) input to the
algorithm to those that contain these items
- m_transformationDictionary - Variable in class weka.core.pmml.MiningSchema
-
The transformation dictionary (if defined)
- m_TransformedFormat - Variable in class weka.filters.unsupervised.attribute.PrincipalComponents
-
The header for the transformed data format.
- m_TransformMethod - Variable in class weka.classifiers.mi.SimpleMI
-
the method used in transformation
- m_Translation - Variable in class weka.filters.unsupervised.attribute.Normalize
-
The translation of the output range.
- m_Traversal - Variable in class weka.classifiers.meta.GridSearch
-
the traversal
- m_tree - Variable in class weka.classifiers.trees.FT
-
root of the logistic model tree
- m_tree - Variable in class weka.classifiers.trees.LMT
-
root of the logistic model tree
- m_Tree - Variable in class weka.classifiers.trees.REPTree
-
The Tree object
- m_Tree - Variable in class weka.gui.PropertySelectorDialog
-
The component displaying the property tree
- m_TreeConstructor - Variable in class weka.core.neighboursearch.BallTree
-
The constructor method to use to build the tree.
- m_treeNodeOfCurrentObject - Variable in class weka.gui.GenericObjectEditor
-
The tree node of the current object so we can re-select it for the user.
- m_TreeStats - Variable in class weka.core.neighboursearch.BallTree
-
Tree Stats variables.
- m_TreeStats - Variable in class weka.core.neighboursearch.CoverTree
-
Tree Stats variables.
- m_TreeStats - Variable in class weka.core.neighboursearch.KDTree
-
Tree Stats variables.
- m_TreeVisualizers - Variable in class weka.gui.GUIChooser
-
keeps track of the opened tree visualizer instancs
- m_trialsValue - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
-
- m_trialsVariable - Variable in class weka.classifiers.pmml.consumer.GeneralRegression
-
- m_Trie - Static variable in class weka.gui.SimpleCLIPanel.CommandlineCompletion
-
a trie for storing the packages.
- m_TTester - Variable in class weka.gui.experiment.ResultsPanel
-
The PairedTTester object.
- m_type - Variable in class weka.classifiers.functions.neural.NeuralConnection
-
The type of unit this is.
- m_Type - Variable in class weka.classifiers.meta.GridSearch.PerformanceTable
-
the type of performance the table was generated for
- m_Type - Variable in class weka.core.AttributeLocator
-
the type of the attribute
- m_Type - Variable in class weka.core.TechnicalInformation
-
the type of this technical information
- m_Type - Variable in class weka.gui.sql.event.ConnectionEvent
-
the type of event, CONNECT or DISCONNECT
- m_Unclassified - Variable in class weka.classifiers.Evaluation
-
The weight of all unclassified instances.
- m_UndoBut - Variable in class weka.gui.explorer.PreprocessPanel
-
Click to revert back to the last saved point
- m_UndoButton - Variable in class weka.gui.ViewerDialog
-
Click to undo the last action
- m_UniformPerformance - Variable in class weka.classifiers.meta.GridSearch
-
whether all performances in the grid are the same
- m_UniqueLab - Variable in class weka.gui.AttributeSummaryPanel
-
Displays the number of unique values
- m_unitError - Variable in class weka.classifiers.functions.neural.NeuralConnection
-
The error value for this unit, NaN if not calculated.
- m_unitValue - Variable in class weka.classifiers.functions.neural.NeuralConnection
-
The output value for this unit, NaN if not calculated.
- m_UpBut - Variable in class weka.gui.experiment.AlgorithmListPanel
-
Click to move the selected algorithm(s) one up
- m_UpBut - Variable in class weka.gui.experiment.DatasetListPanel
-
Click to move the selected dataset(s) one up.
- m_updateBt - Variable in class weka.gui.visualize.MatrixPanel
-
The button that updates the display to reflect the changes made by the user.
- m_Upper - Variable in class weka.core.SingleIndex
-
Store the maximum value permitted.
- m_upperBoundMinSupport - Variable in class weka.associations.Apriori
-
The upper bound on the support
- m_upperBoundMinSupport - Variable in class weka.associations.FPGrowth
-
The upper bound on the minimum support
- m_UpperExtremeValue - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
-
the upper extreme value threshold (= Q3 + EVF*IQR)
- m_UpperOutlier - Variable in class weka.filters.unsupervised.attribute.InterquartileRange
-
the upper outlier threshold (= Q3 + OF*IQR)
- m_UpperSize - Variable in class weka.experiment.LearningRateResultProducer
-
The maximum number of instances to use.
- m_UpperText - Variable in class weka.gui.experiment.RunNumberPanel
-
Configures the upper run number
- m_URL - Variable in class weka.core.converters.ArffLoader
-
the url
- m_URL - Variable in class weka.core.converters.ConverterUtils.DataSource
-
the URL to load.
- m_URL - Variable in class weka.core.converters.DatabaseLoader
-
the JDBC URL to use
- m_URL - Variable in class weka.core.converters.LibSVMLoader
-
the url.
- m_URL - Variable in class weka.core.converters.SVMLightLoader
-
the url.
- m_URL - Variable in class weka.core.converters.XRFFLoader
-
the url
- m_URL - Variable in class weka.gui.sql.ConnectionPanel
-
the URL to use.
- m_URL - Variable in class weka.gui.sql.event.ResultChangedEvent
-
the connect string with which the query was run
- m_URL - Variable in class weka.gui.sql.ResultSetTable
-
the connect string with which the query was run
- m_URL - Variable in class weka.gui.sql.SqlViewer
-
the connect string with which the query was run.
- m_URL - Variable in class weka.gui.sql.SqlViewerDialog
-
the connect string with which the query was run
- m_URLFileLoaders - Static variable in class weka.core.converters.ConverterUtils
-
all available URL loaders (extension <-> classname).
- m_useAIC - Variable in class weka.classifiers.trees.FT
-
If true, the AIC is used to choose the best LogitBoost iteration
- m_UseAllK - Variable in class weka.classifiers.lazy.LWL
-
True if m_kNN should be set to all instances.
- m_UseBetterEncoding - Variable in class weka.filters.supervised.attribute.Discretize
-
Use better encoding of split point for MDL.
- m_UseCpuTime - Variable in class weka.core.Debug.Clock
-
whether to use the CPU time (by default TRUE)
- m_useCrossValidation - Variable in class weka.classifiers.functions.SimpleLogistic
-
If true, cross-validate number of LogitBoost iterations
- m_useCrossValidation - Variable in class weka.classifiers.trees.lmt.LogisticBase
-
Use cross-validation to determine best number of LogitBoost iterations ?
- m_useCustomColour - Variable in class weka.gui.visualize.PlotData2D
-
Custom colour for this plot
- m_UseCustomDimensions - Variable in class weka.gui.visualize.JComponentWriter
-
whether to use custom dimensions
- m_UseDiscretization - Variable in class weka.classifiers.bayes.NaiveBayes
-
Whether to use discretization than normal distribution
for numeric attributes
- m_UseEqualFrequency - Variable in class weka.classifiers.meta.RegressionByDiscretization
-
Use equal-frequency binning
- m_UseEqualFrequency - Variable in class weka.filters.unsupervised.attribute.Discretize
-
Use equal-frequency binning if unsupervised discretization turned on
- m_UseErrorRate - Variable in class weka.classifiers.trees.BFTree
-
If use error rate in internal cross-validation to fix the number of expansions - default
(if not, root mean squared error is used).
- m_useGaussian - Variable in class weka.filters.unsupervised.attribute.RandomProjection
-
Is the random matrix will be computed using
Gaussian distribution or not
- m_UseGini - Variable in class weka.classifiers.trees.BFTree
-
If use Gini index as the splitting criterion - default (if not, information is used).
- m_UseGUI - Variable in class weka.core.Memory
-
whether a GUI is present
- m_useIBk - Variable in class weka.classifiers.rules.DecisionTable
-
Use the IBk classifier rather than majority class
- m_UseKDTree - Variable in class weka.clusterers.XMeans
-
whether to use the KDTree (the KDTree is only initialized to be
configurable from the GUI).
- m_UseKernelEstimator - Variable in class weka.classifiers.bayes.NaiveBayes
-
Whether to use kernel density estimator rather than normal distribution
for numeric attributes
- m_UseKononenko - Variable in class weka.filters.supervised.attribute.Discretize
-
Use Kononenko's MDL criterion instead of Fayyad et al.'s
- m_UseOneSE - Variable in class weka.classifiers.trees.BFTree
-
If use the 1SE rule to make the decision.
- m_UseOneSE - Variable in class weka.classifiers.trees.SimpleCart
-
If use the 1SE rule to make final decision tree.
- m_UsePropertyIterator - Variable in class weka.experiment.Experiment
-
True if the exp should also iterate over a property of the RP
- m_User - Variable in class weka.core.converters.DatabaseLoader
-
the database user to use
- m_User - Variable in class weka.gui.sql.ConnectionPanel
-
the user to use for connecting to the DB.
- m_User - Variable in class weka.gui.sql.event.ResultChangedEvent
-
the user that was used to connect to the DB
- m_User - Variable in class weka.gui.sql.ResultSetTable
-
the user that was used to connect to the DB
- m_User - Variable in class weka.gui.sql.SqlViewer
-
the user that was used to connect to the DB.
- m_User - Variable in class weka.gui.sql.SqlViewerDialog
-
the user that was used to connect to the DB
- m_UserComponentsInXML - Variable in class weka.gui.beans.KnowledgeFlowApp
-
whether to store the user components in XML or in binary format
- m_useRelativePath - Variable in class weka.core.converters.AbstractFileLoader
-
use relative file paths
- m_useRelativePath - Variable in class weka.core.converters.AbstractFileSaver
-
use relative file paths
- m_useReplaceMissing - Variable in class weka.filters.unsupervised.attribute.RandomProjection
-
Should the missing values be replaced using
unsupervised.ReplaceMissingValues filter
- m_UseResampling - Variable in class weka.classifiers.meta.AdaBoostM1
-
Use boosting with reweighting?
- m_UseResampling - Variable in class weka.classifiers.meta.LogitBoost
-
Use boosting with reweighting?
- m_UseResampling - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Whether to use resampling
- m_userHasBeenAskedAboutConversion - Variable in class weka.gui.experiment.SimpleSetupPanel
-
Whether or not the user has consented for the experiment to be simplified
- m_userName - Variable in class weka.experiment.DatabaseUtils
-
Database username.
- m_UserNameLab - Variable in class weka.gui.DatabaseConnectionDialog
-
- m_UserNameText - Variable in class weka.gui.DatabaseConnectionDialog
-
- m_UserOptions - Variable in class weka.core.CheckOptionHandler
-
the user-supplied options
- m_UseStars - Variable in class weka.core.Javadoc
-
whether to include the stars in the Javadoc
- m_useUnpruned - Variable in class weka.classifiers.trees.m5.M5Base
-
Do not prune tree/rules
- m_UseWordwrap - Variable in class weka.gui.LogWindow
-
whether the JTextPane has wordwrap or not
- m_Validated - Variable in class weka.core.NormalizableDistance
-
Whether all the necessary preparations have been done.
- m_Validating - Variable in class weka.core.xml.XMLDocument
-
whether to use a validating parser or not.
- m_validationChunkSize - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
The size of the validation set
- m_validationFs - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
-
- m_validationSet - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
The instances used for validation
- m_validationSetChanged - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Whether the validation set has recently been changed
- m_value - Variable in class weka.core.pmml.FieldMetaInfo.Value
-
The value
- m_Value - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
-
Stores which value of a numeric attribute is to be used for filtering.
- m_Value - Variable in class weka.gui.SortedTableModel.SortContainer
-
the value to sort.
- m_ValueBuffer - Variable in class weka.core.converters.ArffLoader.ArffReader
-
Buffer of values for sparse instance
- m_valueIndex - Variable in class weka.associations.FPGrowth.BinaryItem
-
The index of the value considered to be positive
- m_Values - Variable in class weka.classifiers.meta.GridSearch
-
the best values
- m_Values - Variable in class weka.classifiers.meta.GridSearch.Performance
-
the value pair the classifier was built with
- m_values - Variable in class weka.core.pmml.DerivedFieldMetaInfo
-
the list of values (if the field is ordinal) - may be of size zero if none are specified.
- m_values - Variable in class weka.core.pmml.TargetMetaInfo
-
for categorical values.
- m_Values - Variable in class weka.core.TechnicalInformation
-
stores all the values associated with the fields (FIELD - String)
- m_Values - Variable in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
contains the values to retain
- m_Values - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
-
Stores which values of nominal attribute are to be used for filtering.
- m_Variance - Variable in class weka.classifiers.BVDecompose
-
The calculated variance
- m_Variance - Variable in class weka.classifiers.mi.MINND
-
The variance for each attribute of each exemplar
- m_VaryNodes - Variable in class weka.classifiers.bayes.net.ADNode
-
list of VaryNode children
- m_verbose - Variable in class weka.associations.Apriori
-
Report progress iteratively
- m_verbose - Variable in class weka.attributeSelection.LinearForwardSelection
-
for debugging
- m_verbose - Variable in class weka.attributeSelection.SubsetSizeForwardSelection
-
for debugging
- m_Verbose - Variable in class weka.classifiers.meta.Dagging
-
whether to output some progress information during building
- m_verboseOn - Variable in class weka.core.Debug.DBO
-
enables/disables output of debug information
- m_Viewer - Variable in class weka.gui.sql.SqlViewerDialog
-
the SQL panel
- m_visual - Variable in class weka.gui.beans.AbstractDataSink
-
Default visual for data sources
- m_visual - Variable in class weka.gui.beans.AbstractDataSource
-
Default visual for data sources
- m_visual - Variable in class weka.gui.beans.AbstractEvaluator
-
Default visual for evaluators
- m_visual - Variable in class weka.gui.beans.AbstractTestSetProducer
-
- m_visual - Variable in class weka.gui.beans.AbstractTrainAndTestSetProducer
-
- m_visual - Variable in class weka.gui.beans.AbstractTrainingSetProducer
-
- m_visual - Variable in class weka.gui.beans.Associator
-
- m_visual - Variable in class weka.gui.beans.ClassAssigner
-
- m_visual - Variable in class weka.gui.beans.Classifier
-
- m_visual - Variable in class weka.gui.beans.ClassValuePicker
-
- m_visual - Variable in class weka.gui.beans.Clusterer
-
- m_visual - Variable in class weka.gui.beans.CostBenefitAnalysis
-
- m_visual - Variable in class weka.gui.beans.DataVisualizer
-
- m_visual - Variable in class weka.gui.beans.Filter
-
- m_visual - Variable in class weka.gui.beans.GraphViewer
-
- m_visual - Variable in class weka.gui.beans.InstanceStreamToBatchMaker
-
- m_visual - Variable in class weka.gui.beans.MetaBean
-
- m_visual - Variable in class weka.gui.beans.ModelPerformanceChart
-
- m_visual - Variable in class weka.gui.beans.PredictionAppender
-
- m_visual - Variable in class weka.gui.beans.SerializedModelSaver
-
Default visual for data sources
- m_visual - Variable in class weka.gui.beans.StripChart
-
- m_visual - Variable in class weka.gui.beans.TextViewer
-
- m_visualizeDataSet - Variable in class weka.gui.beans.DataVisualizer
-
- m_visualLabel - Variable in class weka.gui.beans.BeanVisual
-
- m_visualName - Variable in class weka.gui.beans.BeanVisual
-
Name for the bean
- m_Vote - Variable in class weka.classifiers.meta.Dagging
-
the classifier used for voting
- m_WBias - Variable in class weka.classifiers.BVDecomposeSegCVSub
-
The calculated Webb bias
- m_Weight - Variable in class weka.classifiers.functions.LibLINEAR
-
- m_Weight - Variable in class weka.classifiers.functions.LibSVM
-
for C_SVC
- m_Weight - Variable in class weka.core.Instance
-
The instance's weight.
- m_weightByConfidence - Variable in class weka.classifiers.misc.VFI
-
Exponentially bias more confident intervals
- m_WeightKernel - Variable in class weka.classifiers.lazy.LWL
-
The weighting kernel method currently selected.
- m_WeightLabel - Variable in class weka.classifiers.functions.LibLINEAR
-
- m_WeightLabel - Variable in class weka.classifiers.functions.LibSVM
-
for C_SVC
- m_WeightMethod - Variable in class weka.classifiers.mi.MIWrapper
-
the single-instance weight setting method
- m_WeightMethod - Variable in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
-
the propositional instance weight setting method
- m_weights - Variable in class weka.classifiers.functions.SMO.BinarySMO
-
Weight vector for linear machine.
- m_weights - Variable in class weka.classifiers.functions.SPegasos
-
Stores the weights (+ bias in the last element)
- m_weights - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
-
weights for linear kernel
- m_Weights - Variable in class weka.classifiers.mi.MINND
-
The weight of each exemplar
- m_weights - Variable in class weka.classifiers.mi.MISMO.BinaryMISMO
-
Weight vector for linear machine.
- m_Weights - Variable in class weka.classifiers.trees.BFTree
-
Sorted weights.
- m_weightsUpdated - Variable in class weka.classifiers.functions.neural.NeuralConnection
-
True if the weights have already been updated.
- m_WeightThreshold - Variable in class weka.classifiers.meta.AdaBoostM1
-
Weight Threshold.
- m_WeightThreshold - Variable in class weka.classifiers.meta.LogitBoost
-
Weight thresholding.
- m_weightTrimBeta - Variable in class weka.classifiers.functions.SimpleLogistic
-
Threshold for trimming weights.
- m_weightTrimBeta - Variable in class weka.classifiers.trees.FT
-
Threshold for trimming weights.
- m_weightTrimBeta - Variable in class weka.classifiers.trees.lmt.LogisticBase
-
Threshold for trimming weights.
- m_weightTrimBeta - Variable in class weka.classifiers.trees.LMT
-
Threshold for trimming weights.
- m_Width - Variable in class weka.classifiers.meta.GridSearch.Grid
-
the number of points on the X axis
- m_width - Variable in class weka.classifiers.pmml.consumer.NeuralNetwork
-
Width for radial basis
- m_WindowCount - Static variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
the number of visualizer windows we have open.
- m_WindowSize - Variable in class weka.classifiers.lazy.IBk
-
The maximum number of training instances allowed.
- m_Wins - Variable in class weka.experiment.ResultMatrix
-
the significant wins
- m_WithClass - Variable in class weka.classifiers.Evaluation
-
The weight of all instances that had a class assigned to them.
- m_Words - Variable in class weka.core.CheckScheme
-
for generating String attributes/classes
- m_Words - Variable in class weka.core.Stopwords
-
The hash set containing the list of stopwords
- m_Words - Variable in class weka.core.TestInstances
-
for generating String attributes/classes
- m_WordSeparators - Variable in class weka.core.CheckScheme
-
for generating String attributes/classes
- m_WordSeparators - Variable in class weka.core.TestInstances
-
for generating String attributes/classes
- m_wordsPerClass - Variable in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable
-
the word count per class
- m_WriteMethods - Variable in class weka.core.xml.XMLSerializationMethodHandler
-
for storing write methods
- m_WVariance - Variable in class weka.classifiers.BVDecomposeSegCVSub
-
The calculated Webb variance
- m_x - Variable in class weka.classifiers.functions.neural.NeuralConnection
-
The x coord of this unit purely for displaying purposes.
- m_x0 - Variable in class weka.classifiers.functions.SMOreg
-
- m_x1 - Variable in class weka.classifiers.functions.SMOreg
-
coefficients used by normalization filter for doing its linear transformation
so that result = svmoutput * m_x1 + m_x0
- m_X_Base - Variable in class weka.classifiers.meta.GridSearch
-
the base for
- m_X_Expression - Variable in class weka.classifiers.meta.GridSearch
-
The expression for the X property.
- m_X_Max - Variable in class weka.classifiers.meta.GridSearch
-
the maximum of X
- m_X_Min - Variable in class weka.classifiers.meta.GridSearch
-
the minimum of X
- m_X_Property - Variable in class weka.classifiers.meta.GridSearch
-
the X option to work on (without leading dash, preceding 'classifier.'
means to set the option for the classifier 'filter.' for the filter)
- m_X_Step - Variable in class weka.classifiers.meta.GridSearch
-
the step size of
- m_xAttBox - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
- m_xAttribute - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
- m_XaxisEnd - Variable in class weka.gui.visualize.Plot2D
-
- m_XaxisStart - Variable in class weka.gui.visualize.Plot2D
-
the offsets of the axes once label metrics are calculated
- m_xChange - Variable in class weka.gui.visualize.AttributePanelEvent
-
True if the x selection changed
- m_XCombo - Variable in class weka.gui.visualize.VisualizePanel
-
Lets the user select the attribute for the x axis
- m_xIndex - Variable in class weka.gui.visualize.AttributePanel
-
- m_xIndex - Variable in class weka.gui.visualize.Plot2D
-
Indexes of the attributes to go on the x and y axis and the attribute
to use for colouring and the current shape for drawing
- m_xIndex - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
-
Indexes of the attributes to go on the x and y axis and the attribute
to use for colouring and the current shape for drawing
- m_XMLDocument - Variable in class weka.core.xml.XMLOptions
-
the XML document.
- m_XMLFilter - Variable in class weka.gui.beans.KnowledgeFlowApp
-
A filter to ensure only KnowledgeFlow layout files in XML format get
shown in the chooser
- m_XMLFilter - Variable in class weka.gui.experiment.AlgorithmListPanel
-
A filter to ensure only experiment (in XML format) files get shown in the chooser
- m_XMLFilter - Variable in class weka.gui.experiment.SetupPanel
-
A filter to ensure only experiment (in XML format) files get shown in the chooser
- m_XMLFilter - Variable in class weka.gui.experiment.SimpleSetupPanel
-
A filter to ensure only experiment (in XML format) files get shown in the chooser
- m_XMLInstances - Variable in class weka.core.converters.XRFFLoader
-
the loaded XML document
- m_XMLInstances - Variable in class weka.core.converters.XRFFSaver
-
the generated XML document
- m_XPath - Variable in class weka.core.xml.XMLDocument
-
for XPath queries.
- m_xScale - Variable in class weka.gui.visualize.JComponentWriter
-
the x scale factor
- m_xScale - Variable in class weka.gui.visualize.PrintableComponent
-
the x scale factor.
- m_XStreamFilter - Variable in class weka.gui.beans.Classifier
-
- m_XStreamFilter - Variable in class weka.gui.beans.KnowledgeFlowApp
-
A filter to ensure only KnowledgeFlow files in XStream format
get shown in the chooser
- m_y - Variable in class weka.classifiers.functions.neural.NeuralConnection
-
The y coord of this unit purely for displaying purposes.
- m_Y_Base - Variable in class weka.classifiers.meta.GridSearch
-
the base for Y
- m_Y_Expression - Variable in class weka.classifiers.meta.GridSearch
-
The expression for the Y property.
- m_Y_Max - Variable in class weka.classifiers.meta.GridSearch
-
the maximum of Y
- m_Y_Min - Variable in class weka.classifiers.meta.GridSearch
-
the minimum of Y
- m_Y_Property - Variable in class weka.classifiers.meta.GridSearch
-
the Y option to work on (without leading dash, preceding 'classifier.'
means to set the option for the classifier 'filter.' for the filter)
- m_Y_Step - Variable in class weka.classifiers.meta.GridSearch
-
the step size of Y
- m_yAttBox - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
- m_yAttribute - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
-
- m_YaxisEnd - Variable in class weka.gui.visualize.Plot2D
-
- m_YaxisStart - Variable in class weka.gui.visualize.Plot2D
-
- m_yChange - Variable in class weka.gui.visualize.AttributePanelEvent
-
True if the y selection changed
- m_YCombo - Variable in class weka.gui.visualize.VisualizePanel
-
Lets the user select the attribute for the y axis
- m_yIndex - Variable in class weka.gui.visualize.AttributePanel
-
- m_yIndex - Variable in class weka.gui.visualize.Plot2D
-
- m_yIndex - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
-
- m_yScale - Variable in class weka.gui.visualize.JComponentWriter
-
the y scale factor
- m_yScale - Variable in class weka.gui.visualize.PrintableComponent
-
the y scale factor.
- m_Zero - Static variable in class weka.core.Optimization
-
Compute machine precision
- m_ZeroR - Variable in class weka.classifiers.lazy.LWL
-
a ZeroR model in case no model can be built from the data.
- m_ZeroR - Variable in class weka.classifiers.meta.AdaBoostM1
-
a ZeroR model in case no model can be built from the data
- m_zeroR - Variable in class weka.classifiers.meta.AdditiveRegression
-
The model for the mean
- m_ZeroR - Variable in class weka.classifiers.meta.ClassificationViaClustering
-
the default model
- m_ZeroR - Variable in class weka.classifiers.meta.LogitBoost
-
a ZeroR model in case no model can be built from the data
- m_zeroR - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
The default scheme used when committees aren't ready
- m_ZeroR - Variable in class weka.classifiers.meta.RandomSubSpace
-
a ZeroR model in case no model can be built from the data
- m_ZeroR - Variable in class weka.classifiers.misc.HyperPipes
-
a ZeroR model in case no model can be built from the data
- m_ZeroR - Variable in class weka.classifiers.trees.RandomTree
-
a ZeroR model in case no model can be built from the data
- m_zeroR - Variable in class weka.classifiers.trees.REPTree
-
ZeroR model that is used if no attributes are present.
- m_ZipDest - Variable in class weka.experiment.CrossValidationResultProducer
-
The output zipper to use for saving raw splitEvaluator output
- m_ZipDest - Variable in class weka.experiment.RandomSplitResultProducer
-
The output zipper to use for saving raw splitEvaluator output
- m_ZoomBoxColor - Variable in class weka.gui.treevisualizer.TreeVisualizer
-
the color of the zoombox.
- m_ZoomBoxXORColor - Variable in class weka.gui.treevisualizer.TreeVisualizer
-
the XOR color of the zoombox.
- MACHEP - Static variable in class weka.core.Statistics
-
Some constants
- MahalanobisEstimator - Class in weka.estimators
-
Simple probability estimator that places a single normal distribution
over the observed values.
- MahalanobisEstimator(Matrix, double, double) - Constructor for class weka.estimators.MahalanobisEstimator
-
Constructor
- main(String[]) - Static method in class weka.associations.Apriori
-
Main method.
- main(String[]) - Static method in class weka.associations.AssociatorEvaluation
-
A test method for this class.
- main(String[]) - Static method in class weka.associations.CheckAssociator
-
Test method for this class
- main(String[]) - Static method in class weka.associations.FilteredAssociator
-
Main method for running this class.
- main(String[]) - Static method in class weka.associations.FPGrowth
-
Main method.
- main(String[]) - Static method in class weka.associations.GeneralizedSequentialPatterns
-
Main method.
- main(String[]) - Static method in class weka.associations.PredictiveApriori
-
Main method.
- main(String[]) - Static method in class weka.associations.Tertius
-
Main method.
- main(String[]) - Static method in class weka.attributeSelection.AttributeSelection
-
Main method for testing this class.
- main(String[]) - Static method in class weka.attributeSelection.CfsSubsetEval
-
Main method for testing this class.
- main(String[]) - Static method in class weka.attributeSelection.CheckAttributeSelection
-
Test method for this class
- main(String[]) - Static method in class weka.attributeSelection.ChiSquaredAttributeEval
-
Main method.
- main(String[]) - Static method in class weka.attributeSelection.ClassifierSubsetEval
-
Main method for testing this class.
- main(String[]) - Static method in class weka.attributeSelection.ConsistencySubsetEval
-
Main method for testing this class.
- main(String[]) - Static method in class weka.attributeSelection.CostSensitiveAttributeEval
-
Main method for testing this class.
- main(String[]) - Static method in class weka.attributeSelection.CostSensitiveSubsetEval
-
Main method for testing this class.
- main(String[]) - Static method in class weka.attributeSelection.FilteredAttributeEval
-
Main method for testing this class.
- main(String[]) - Static method in class weka.attributeSelection.FilteredSubsetEval
-
Main method for testing this class.
- main(String[]) - Static method in class weka.attributeSelection.GainRatioAttributeEval
-
Main method.
- main(String[]) - Static method in class weka.attributeSelection.InfoGainAttributeEval
-
Main method for testing this class.
- main(String[]) - Static method in class weka.attributeSelection.LatentSemanticAnalysis
-
Main method for testing this class
- main(String[]) - Static method in class weka.attributeSelection.OneRAttributeEval
-
Main method for testing this class.
- main(String[]) - Static method in class weka.attributeSelection.PrincipalComponents
-
Main method for testing this class
- main(String[]) - Static method in class weka.attributeSelection.ReliefFAttributeEval
-
Main method for testing this class.
- main(String[]) - Static method in class weka.attributeSelection.SVMAttributeEval
-
Main method for testing this class.
- main(String[]) - Static method in class weka.attributeSelection.SymmetricalUncertAttributeEval
-
Main method for testing this class.
- main(String[]) - Static method in class weka.attributeSelection.WrapperSubsetEval
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.bayes.AODE
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.bayes.AODEsr
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.bayes.BayesNet
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.bayes.ComplementNaiveBayes
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.bayes.DMNBtext
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.bayes.HNB
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayes
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayesMultinomial
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayesSimple
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayesUpdateable
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.bayes.net.ADNode
-
for testing only
- main(String[]) - Static method in class weka.classifiers.bayes.net.BayesNetGenerator
-
Main method
- main(String[]) - Static method in class weka.classifiers.bayes.net.BIFReader
-
Loads the file specified as first parameter and prints it to stdout.
- main(String[]) - Static method in class weka.classifiers.bayes.net.EditableBayesNet
-
- main(String[]) - Static method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorFullBayes
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.bayes.net.GUI
-
Main method.
- main(String[]) - Static method in class weka.classifiers.bayes.net.MarginCalculator
-
- main(String[]) - Static method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
-
for testing the class
- main(String[]) - Static method in class weka.classifiers.bayes.WAODE
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.BVDecompose
-
Test method for this class
- main(String[]) - Static method in class weka.classifiers.BVDecomposeSegCVSub
-
Test method for this class
- main(String[]) - Static method in class weka.classifiers.CheckClassifier
-
Test method for this class
- main(String[]) - Static method in class weka.classifiers.CheckSource
-
Executes the tests, use "-h" to list the commandline options.
- main(String[]) - Static method in class weka.classifiers.evaluation.CostCurve
-
Tests the CostCurve generation from the command line.
- main(String[]) - Static method in class weka.classifiers.Evaluation
-
A test method for this class.
- main(String[]) - Static method in class weka.classifiers.evaluation.MarginCurve
-
Tests the MarginCurve generation from the command line.
- main(String[]) - Static method in class weka.classifiers.evaluation.ThresholdCurve
-
Tests the ThresholdCurve generation from the command line.
- main(String[]) - Static method in class weka.classifiers.functions.GaussianProcesses
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.functions.IsotonicRegression
-
Main method for testing this class
- main(String[]) - Static method in class weka.classifiers.functions.LeastMedSq
-
generate a Linear regression predictor for testing
- main(String[]) - Static method in class weka.classifiers.functions.LibLINEAR
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.functions.LibSVM
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.functions.LinearRegression
-
Generates a linear regression function predictor.
- main(String[]) - Static method in class weka.classifiers.functions.Logistic
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.functions.MultilayerPerceptron
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.functions.pace.ChisqMixture
-
Method to test this class
- main(String[]) - Static method in class weka.classifiers.functions.pace.DiscreteFunction
-
- main(String[]) - Static method in class weka.classifiers.functions.pace.NormalMixture
-
Method to test this class
- main(String[]) - Static method in class weka.classifiers.functions.pace.PaceMatrix
-
for testing only
- main(String[]) - Static method in class weka.classifiers.functions.PaceRegression
-
Generates a linear regression function predictor.
- main(String[]) - Static method in class weka.classifiers.functions.PLSClassifier
-
Main method for running this classifier from commandline.
- main(String[]) - Static method in class weka.classifiers.functions.RBFNetwork
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.functions.SimpleLinearRegression
-
Main method for testing this class
- main(String[]) - Static method in class weka.classifiers.functions.SimpleLogistic
-
Main method for testing this class
- main(String[]) - Static method in class weka.classifiers.functions.SMO
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.functions.SMOreg
-
Main method for running this classifier.
- main(String[]) - Static method in class weka.classifiers.functions.SPegasos
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.functions.supportVector.CheckKernel
-
Test method for this class
- main(String[]) - Static method in class weka.classifiers.functions.supportVector.KernelEvaluation
-
A test method for this class.
- main(String[]) - Static method in class weka.classifiers.functions.VotedPerceptron
-
Main method.
- main(String[]) - Static method in class weka.classifiers.functions.Winnow
-
Main method.
- main(String[]) - Static method in class weka.classifiers.lazy.IB1
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.lazy.IBk
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.lazy.KStar
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.lazy.LBR
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.lazy.LWL
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.AdaBoostM1
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.AdditiveRegression
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.AttributeSelectedClassifier
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.Bagging
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.ClassificationViaClustering
-
Runs the classifier with the given options
- main(String[]) - Static method in class weka.classifiers.meta.ClassificationViaRegression
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.CostSensitiveClassifier
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.CVParameterSelection
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.Dagging
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.Decorate
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.END
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.FilteredClassifier
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.Grading
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.GridSearch
-
Main method for running this classifier from commandline.
- main(String[]) - Static method in class weka.classifiers.meta.LogitBoost
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.MetaCost
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.MultiBoostAB
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.MultiClassClassifier
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.MultiScheme
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.nestedDichotomies.ND
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.OrdinalClassClassifier
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Main method for this class.
- main(String[]) - Static method in class weka.classifiers.meta.RandomCommittee
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.RandomSubSpace
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.RegressionByDiscretization
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.RotationForest
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.Stacking
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.StackingC
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.ThresholdSelector
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.meta.Vote
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.CitationKNN
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.MDD
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.MIBoost
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.MIDD
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.MIEMDD
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.MILR
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.MINND
-
Main method for testing.
- main(String[]) - Static method in class weka.classifiers.mi.MIOptimalBall
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.MISMO
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.MISVM
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.MIWrapper
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.SimpleMI
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.misc.HyperPipes
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.misc.SerializedClassifier
-
Runs the classifier with the given options
- main(String[]) - Static method in class weka.classifiers.misc.VFI
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.rules.ConjunctiveRule
-
Main method.
- main(String[]) - Static method in class weka.classifiers.rules.DecisionTable
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.rules.DTNB
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.rules.JRip
-
Main method.
- main(String[]) - Static method in class weka.classifiers.rules.M5Rules
-
Main method by which this class can be tested
- main(String[]) - Static method in class weka.classifiers.rules.NNge
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.rules.OneR
-
Main method for testing this class
- main(String[]) - Static method in class weka.classifiers.rules.PART
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.rules.Prism
-
Main method for testing this class
- main(String[]) - Static method in class weka.classifiers.rules.Ridor
-
Main method.
- main(String[]) - Static method in class weka.classifiers.rules.ZeroR
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.trees.ADTree
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.trees.BFTree
-
Main method.
- main(String[]) - Static method in class weka.classifiers.trees.DecisionStump
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.trees.FT
-
Main method for testing this class
- main(String[]) - Static method in class weka.classifiers.trees.Id3
-
Main method.
- main(String[]) - Static method in class weka.classifiers.trees.J48
-
Main method for testing this class
- main(String[]) - Static method in class weka.classifiers.trees.J48graft
-
Main method for testing this class
- main(String[]) - Static method in class weka.classifiers.trees.LADTree
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.trees.LMT
-
Main method for testing this class
- main(String[]) - Static method in class weka.classifiers.trees.M5P
-
Main method by which this class can be tested
- main(String[]) - Static method in class weka.classifiers.trees.NBTree
-
Main method for testing this class
- main(String[]) - Static method in class weka.classifiers.trees.RandomForest
-
Main method for this class.
- main(String[]) - Static method in class weka.classifiers.trees.RandomTree
-
Main method for this class.
- main(String[]) - Static method in class weka.classifiers.trees.REPTree
-
Main method for this class.
- main(String[]) - Static method in class weka.classifiers.trees.SimpleCart
-
Main method.
- main(String[]) - Static method in class weka.classifiers.trees.UserClassifier
-
Main method for testing this class.
- main(String[]) - Static method in class weka.clusterers.CheckClusterer
-
Test method for this class
- main(String[]) - Static method in class weka.clusterers.CLOPE
-
Main method for testing this class.
- main(String[]) - Static method in class weka.clusterers.ClusterEvaluation
-
Main method for testing this class.
- main(String[]) - Static method in class weka.clusterers.Cobweb
-
Main method.
- main(String[]) - Static method in class weka.clusterers.DBScan
-
Main Method for testing DBScan
- main(String[]) - Static method in class weka.clusterers.EM
-
Main method for testing this class.
- main(String[]) - Static method in class weka.clusterers.FarthestFirst
-
Main method for testing this class.
- main(String[]) - Static method in class weka.clusterers.FilteredClusterer
-
Main method for testing this class.
- main(String[]) - Static method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.OPTICS_Visualizer
-
Displays the GUI.
- main(String[]) - Static method in class weka.clusterers.HierarchicalClusterer
-
- main(String[]) - Static method in class weka.clusterers.MakeDensityBasedClusterer
-
Main method for testing this class.
- main(String[]) - Static method in class weka.clusterers.OPTICS
-
Main Method for testing OPTICS
- main(String[]) - Static method in class weka.clusterers.sIB
-
- main(String[]) - Static method in class weka.clusterers.SimpleKMeans
-
Main method for testing this class.
- main(String[]) - Static method in class weka.clusterers.XMeans
-
Main method for testing this class.
- main(String[]) - Static method in class weka.core.AlgVector
-
Main method for testing this class, can take an ARFF file as first argument.
- main(String[]) - Static method in class weka.core.AllJavadoc
-
Parses the given commandline parameters and generates the Javadoc.
- main(String[]) - Static method in class weka.core.Attribute
-
Simple main method for testing this class.
- main(String[]) - Static method in class weka.core.BinarySparseInstance
-
Main method for testing this class.
- main(String[]) - Static method in class weka.core.Capabilities
-
loads the given dataset and prints the Capabilities necessary to
process it.
- main(String[]) - Static method in class weka.core.CheckGOE
-
Main method for using the CheckGOE.
- main(String[]) - Static method in class weka.core.CheckOptionHandler
-
Main method for using the CheckOptionHandler.
- main(String[]) - Static method in class weka.core.ClassDiscovery
-
Possible calls:
weka.core.ClassDiscovery <packages>
Prints all the packages in the current classpath
weka.core.ClassDiscovery <classname> <packagename(s)>
Prints the classes it found.
- main(String[]) - Static method in class weka.core.ContingencyTables
-
Main method for testing this class.
- main(String[]) - Static method in class weka.core.converters.ArffLoader
-
Main method.
- main(String[]) - Static method in class weka.core.converters.ArffSaver
-
Main method.
- main(String[]) - Static method in class weka.core.converters.C45Loader
-
Main method for testing this class.
- main(String[]) - Static method in class weka.core.converters.C45Saver
-
Main method.
- main(String[]) - Static method in class weka.core.converters.ConverterUtils.DataSink
-
for testing only - takes a data file as input and a data file for the
output.
- main(String[]) - Static method in class weka.core.converters.ConverterUtils.DataSource
-
for testing only - takes a data file as input.
- main(String[]) - Static method in class weka.core.converters.CSVLoader
-
Main method.
- main(String[]) - Static method in class weka.core.converters.CSVSaver
-
Main method.
- main(String[]) - Static method in class weka.core.converters.DatabaseLoader
-
Main method.
- main(String[]) - Static method in class weka.core.converters.DatabaseSaver
-
Main method.
- main(String[]) - Static method in class weka.core.converters.LibSVMLoader
-
Main method.
- main(String[]) - Static method in class weka.core.converters.LibSVMSaver
-
Main method.
- main(String[]) - Static method in class weka.core.converters.SerializedInstancesLoader
-
Main method.
- main(String[]) - Static method in class weka.core.converters.SerializedInstancesSaver
-
Main method.
- main(String[]) - Static method in class weka.core.converters.SVMLightLoader
-
Main method.
- main(String[]) - Static method in class weka.core.converters.SVMLightSaver
-
Main method.
- main(String[]) - Static method in class weka.core.converters.TextDirectoryLoader
-
Main method.
- main(String[]) - Static method in class weka.core.converters.XRFFLoader
-
Main method.
- main(String[]) - Static method in class weka.core.converters.XRFFSaver
-
Main method.
- main(String[]) - Static method in class weka.core.Copyright
-
Only for testing
- main(String[]) - Static method in class weka.core.Environment
-
Main method for testing this class.
- main(String[]) - Static method in class weka.core.FindWithCapabilities
-
Executes the location of classes with parameters from the commandline.
- main(String[]) - Static method in class weka.core.GlobalInfoJavadoc
-
Parses the given commandline parameters and generates the Javadoc.
- main(String[]) - Static method in class weka.core.Instance
-
Main method for testing this class.
- main(String[]) - Static method in class weka.core.InstanceComparator
-
for testing only.
- main(String[]) - Static method in class weka.core.Instances
-
Main method for this class.
- main(String[]) - Static method in class weka.core.Jython
-
If no arguments are given, it just prints the presence of the Jython
classes, otherwise it expects a Jython filename to execute.
- main(String[]) - Static method in class weka.core.ListOptions
-
runs the javadoc producer with the given commandline options
- main(String[]) - Static method in class weka.core.mathematicalexpression.Parser
-
Runs the parser from commandline.
- main(String[]) - Static method in class weka.core.matrix.DoubleVector
-
- main(String[]) - Static method in class weka.core.matrix.IntVector
-
Tests the IntVector class
- main(String[]) - Static method in class weka.core.Matrix
-
Deprecated.
Main method for testing this class.
- main(String[]) - Static method in class weka.core.matrix.Matrix
-
Main method for testing this class.
- main(String[]) - Static method in class weka.core.Memory
-
prints only some statistics
- main(String[]) - Static method in class weka.core.neighboursearch.CoverTree
-
Method for testing the class from command line.
- main(String[]) - Static method in class weka.core.OptionHandlerJavadoc
-
Parses the given commandline parameters and generates the Javadoc.
- main(String[]) - Static method in class weka.core.pmml.Constant
-
- main(String[]) - Static method in class weka.core.pmml.PMMLFactory
-
- main(String[]) - Static method in class weka.core.PropertyPath
-
for testing only
- main(String[]) - Static method in class weka.core.Queue
-
Main method for testing this class.
- main(String[]) - Static method in class weka.core.RandomVariates
-
Main method for testing this class.
- main(String[]) - Static method in class weka.core.Range
-
Main method for testing this class.
- main(String[]) - Static method in class weka.core.RevisionUtils
-
For testing only.
- main(String[]) - Static method in class weka.core.SerializationHelper
-
Outputs information about a class on the commandline, takes class
name as arguments.
- main(String[]) - Static method in class weka.core.SingleIndex
-
Main method for testing this class.
- main(String[]) - Static method in class weka.core.SparseInstance
-
Main method for testing this class.
- main(String[]) - Static method in class weka.core.SpecialFunctions
-
Main method for testing this class.
- main(String[]) - Static method in class weka.core.Statistics
-
Main method for testing this class.
- main(String[]) - Static method in class weka.core.stemmers.IteratedLovinsStemmer
-
Runs the stemmer with the given options
- main(String[]) - Static method in class weka.core.stemmers.LovinsStemmer
-
Runs the stemmer with the given options
- main(String[]) - Static method in class weka.core.stemmers.NullStemmer
-
Runs the stemmer with the given options
- main(String[]) - Static method in class weka.core.stemmers.SnowballStemmer
-
Runs the stemmer with the given options.
- main(String[]) - Static method in class weka.core.Stopwords
-
Accepts the following parameter:
-i file
loads the stopwords from the given file
-o file
saves the stopwords to the given file
-p
outputs the current stopwords on stdout
Any additional parameters are interpreted as words to test as stopwords.
- main(String[]) - Static method in class weka.core.SystemInfo
-
for printing the system info to stdout.
- main(String[]) - Static method in class weka.core.TechnicalInformation
-
Prints some examples of technical informations if there are no
commandline options given.
- main(String[]) - Static method in class weka.core.TechnicalInformationHandlerJavadoc
-
Parses the given commandline parameters and generates the Javadoc.
- main(String[]) - Static method in class weka.core.TestInstances
-
for running the class from commandline, prints the generated data
to stdout
- main(String[]) - Static method in class weka.core.tokenizers.AlphabeticTokenizer
-
Runs the tokenizer with the given options and strings to tokenize.
- main(String[]) - Static method in class weka.core.tokenizers.NGramTokenizer
-
Runs the tokenizer with the given options and strings to tokenize.
- main(String[]) - Static method in class weka.core.tokenizers.WordTokenizer
-
Runs the tokenizer with the given options and strings to tokenize.
- main(String[]) - Static method in class weka.core.Trie
-
Only for testing (prints the built Trie).
- main(String[]) - Static method in class weka.core.Utils
-
Main method for testing this class.
- main(String[]) - Static method in class weka.core.Version
-
only for testing
- main(String[]) - Static method in class weka.core.xml.SerialUIDChanger
-
exchanges an old UID for a new one.
- main(String[]) - Static method in class weka.core.xml.XMLDocument
-
for testing only.
- main(String[]) - Static method in class weka.core.xml.XMLInstances
-
takes an XML document as first argument and then outputs the Instances
statistics
- main(String[]) - Static method in class weka.core.xml.XMLOptions
-
for testing only.
- main(String[]) - Static method in class weka.core.xml.XMLSerialization
-
for testing only.
- main(String[]) - Static method in class weka.datagenerators.classifiers.classification.Agrawal
-
Main method for executing this class.
- main(String[]) - Static method in class weka.datagenerators.classifiers.classification.BayesNet
-
Main method for executing this class.
- main(String[]) - Static method in class weka.datagenerators.classifiers.classification.LED24
-
Main method for executing this class.
- main(String[]) - Static method in class weka.datagenerators.classifiers.classification.RandomRBF
-
Main method for executing this class.
- main(String[]) - Static method in class weka.datagenerators.classifiers.classification.RDG1
-
Main method for testing this class.
- main(String[]) - Static method in class weka.datagenerators.classifiers.regression.Expression
-
Main method for testing this class.
- main(String[]) - Static method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Main method for testing this class.
- main(String[]) - Static method in class weka.datagenerators.clusterers.BIRCHCluster
-
Main method for testing this class.
- main(String[]) - Static method in class weka.datagenerators.clusterers.SubspaceCluster
-
Main method for testing this class.
- main(String[]) - Static method in class weka.estimators.CheckEstimator
-
Test method for this class
- main(String[]) - Static method in class weka.estimators.DDConditionalEstimator
-
Main method for testing this class.
- main(String[]) - Static method in class weka.estimators.DiscreteEstimator
-
Main method for testing this class.
- main(String[]) - Static method in class weka.estimators.DKConditionalEstimator
-
Main method for testing this class.
- main(String[]) - Static method in class weka.estimators.DNConditionalEstimator
-
Main method for testing this class.
- main(String[]) - Static method in class weka.estimators.KDConditionalEstimator
-
Main method for testing this class.
- main(String[]) - Static method in class weka.estimators.KernelEstimator
-
Main method for testing this class.
- main(String[]) - Static method in class weka.estimators.KKConditionalEstimator
-
Main method for testing this class.
- main(String[]) - Static method in class weka.estimators.MahalanobisEstimator
-
Main method for testing this class.
- main(String[]) - Static method in class weka.estimators.NDConditionalEstimator
-
Main method for testing this class.
- main(String[]) - Static method in class weka.estimators.NNConditionalEstimator
-
Main method for testing this class.
- main(String[]) - Static method in class weka.estimators.NormalEstimator
-
Main method for testing this class.
- main(String[]) - Static method in class weka.estimators.PoissonEstimator
-
Main method for testing this class.
- main(String[]) - Static method in class weka.experiment.CrossValidationResultProducer
-
Quick test of timestamp
- main(String[]) - Static method in class weka.experiment.Experiment
-
Configures/Runs the Experiment from the command line.
- main(String[]) - Static method in class weka.experiment.InstanceQuery
-
Test the class from the command line.
- main(String[]) - Static method in class weka.experiment.OutputZipper
-
Main method for testing this class
- main(String[]) - Static method in class weka.experiment.PairedCorrectedTTester
-
Test the class from the command line.
- main(String[]) - Static method in class weka.experiment.PairedStats
-
Tests the paired stats object from the command line.
- main(String[]) - Static method in class weka.experiment.PairedTTester
-
Test the class from the command line.
- main(String[]) - Static method in class weka.experiment.RemoteEngine
-
Main method.
- main(String[]) - Static method in class weka.experiment.RemoteExperiment
-
Configures/Runs the Experiment from the command line.
- main(String[]) - Static method in class weka.experiment.ResultMatrixCSV
-
for testing only
- main(String[]) - Static method in class weka.experiment.ResultMatrixGnuPlot
-
for testing only
- main(String[]) - Static method in class weka.experiment.ResultMatrixHTML
-
for testing only
- main(String[]) - Static method in class weka.experiment.ResultMatrixLatex
-
for testing only
- main(String[]) - Static method in class weka.experiment.ResultMatrixPlainText
-
for testing only
- main(String[]) - Static method in class weka.experiment.ResultMatrixSignificance
-
for testing only
- main(String[]) - Static method in class weka.experiment.Stats
-
Tests the paired stats object from the command line.
- main(String[]) - Static method in class weka.experiment.xml.XMLExperiment
-
for testing only.
- main(String[]) - Static method in class weka.filters.AllFilter
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.CheckSource
-
Executes the tests, use "-h" to list the commandline options.
- main(String[]) - Static method in class weka.filters.Filter
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.MultiFilter
-
Main method for executing this class.
- main(String[]) - Static method in class weka.filters.supervised.attribute.AddClassification
-
runs the filter with the given arguments.
- main(String[]) - Static method in class weka.filters.supervised.attribute.AttributeSelection
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.supervised.attribute.ClassOrder
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.supervised.attribute.Discretize
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.supervised.attribute.NominalToBinary
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.supervised.attribute.PLSFilter
-
runs the filter with the given arguments.
- main(String[]) - Static method in class weka.filters.supervised.instance.Resample
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.supervised.instance.SMOTE
-
Main method for running this filter.
- main(String[]) - Static method in class weka.filters.supervised.instance.SpreadSubsample
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.supervised.instance.StratifiedRemoveFolds
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.Add
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddCluster
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddExpression
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddID
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddNoise
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddValues
-
Main method for testing and running this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.Center
-
Main method for running this filter.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.ChangeDateFormat
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.ClassAssigner
-
Main method for executing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.ClusterMembership
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.Copy
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.Discretize
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.FirstOrder
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.InterquartileRange
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.KernelFilter
-
runs the filter with the given arguments
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.MakeIndicator
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.MathExpression
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.MergeTwoValues
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
-
Main method for running this filter.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.NominalToBinary
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.NominalToString
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.Normalize
-
Main method for running this filter.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Runs the filter from commandline, use "-h" to see all options.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.NumericToBinary
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.NumericToNominal
-
Runs the filter with the given parameters.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.NumericTransform
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.Obfuscate
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
-
Main method for executing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.PKIDiscretize
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Main method for running this filter.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
-
Main method for running this filter.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.RandomProjection
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.RandomSubset
-
Runs the filter with the given parameters.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.RELAGGS
-
runs the filter with the given arguments
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.Remove
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.RemoveType
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.RemoveUseless
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.Reorder
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.ReplaceMissingValues
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.Standardize
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.StringToNominal
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.SwapValues
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.TimeSeriesDelta
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.attribute.Wavelet
-
runs the filter with the given arguments
- main(String[]) - Static method in class weka.filters.unsupervised.instance.NonSparseToSparse
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.instance.Normalize
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.instance.Randomize
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveFolds
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.instance.RemovePercentage
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveRange
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.instance.Resample
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.instance.ReservoirSample
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.instance.SparseToNonSparse
-
Main method for testing this class.
- main(String[]) - Static method in class weka.filters.unsupervised.instance.SubsetByExpression
-
Main method for running this filter.
- main(String[]) - Static method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
-
Runs the parser from commandline.
- main(String[]) - Static method in class weka.gui.arffviewer.ArffViewer
-
shows the frame and it tries to load all the arff files that were
provided as arguments.
- main(String[]) - Static method in class weka.gui.AttributeListPanel
-
Tests the attribute list panel from the command line.
- main(String[]) - Static method in class weka.gui.AttributeSelectionPanel
-
Tests the attribute selection panel from the command line.
- main(String[]) - Static method in class weka.gui.AttributeSummaryPanel
-
Tests out the attribute summary panel from the command line.
- main(String[]) - Static method in class weka.gui.AttributeVisualizationPanel
-
Main method to test this class from command line
- main(String[]) - Static method in class weka.gui.beans.AttributeSummarizer
-
- main(String[]) - Static method in class weka.gui.beans.CostBenefitAnalysis
-
- main(String[]) - Static method in class weka.gui.beans.DataVisualizer
-
- main(String[]) - Static method in class weka.gui.beans.FlowRunner
-
Main method for testing this class.
- main(String[]) - Static method in class weka.gui.beans.KnowledgeFlow
-
Shows the splash screen, launches the application and then disposes
the splash screen.
- main(String[]) - Static method in class weka.gui.beans.KnowledgeFlowApp
-
Main method.
- main(String[]) - Static method in class weka.gui.beans.Loader
-
- main(String[]) - Static method in class weka.gui.beans.LogPanel
-
Main method to test this class.
- main(String[]) - Static method in class weka.gui.beans.ModelPerformanceChart
-
- main(String[]) - Static method in class weka.gui.beans.Saver
-
The main method for testing
- main(String[]) - Static method in class weka.gui.beans.ScatterPlotMatrix
-
- main(String[]) - Static method in class weka.gui.beans.StripChart
-
Tests out the StripChart from the command line
- main(String[]) - Static method in class weka.gui.beans.TextViewer
-
- main(String[]) - Static method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Main method for testing this class
- main(String[]) - Static method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
-
Main method for testing this class
- main(String[]) - Static method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
Main method for testing this class
- main(String[]) - Static method in class weka.gui.ConverterFileChooser
-
For testing the file chooser
- main(String[]) - Static method in class weka.gui.DatabaseConnectionDialog
-
for testing only
- main(String[]) - Static method in class weka.gui.experiment.AlgorithmListPanel
-
Tests out the algorithm list panel from the command line.
- main(String[]) - Static method in class weka.gui.experiment.DatasetListPanel
-
Tests out the dataset list panel from the command line.
- main(String[]) - Static method in class weka.gui.experiment.DistributeExperimentPanel
-
Tests out the panel from the command line.
- main(String[]) - Static method in class weka.gui.experiment.Experimenter
-
Tests out the experiment environment.
- main(String[]) - Static method in class weka.gui.experiment.ExperimenterDefaults
-
only for testing - prints the content of the props file
- main(String[]) - Static method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
-
Tests out the panel from the command line.
- main(String[]) - Static method in class weka.gui.experiment.HostListPanel
-
Tests out the host list panel from the command line.
- main(String[]) - Static method in class weka.gui.experiment.OutputFormatDialog
-
for testing only.
- main(String[]) - Static method in class weka.gui.experiment.ResultsPanel
-
Tests out the results panel from the command line.
- main(String[]) - Static method in class weka.gui.experiment.RunNumberPanel
-
Tests out the panel from the command line.
- main(String[]) - Static method in class weka.gui.experiment.RunPanel
-
Tests out the run panel from the command line.
- main(String[]) - Static method in class weka.gui.experiment.SetupPanel
-
Tests out the experiment setup from the command line.
- main(String[]) - Static method in class weka.gui.explorer.AssociationsPanel
-
Tests out the Associator panel from the command line.
- main(String[]) - Static method in class weka.gui.explorer.AttributeSelectionPanel
-
Tests out the attribute selection panel from the command line.
- main(String[]) - Static method in class weka.gui.explorer.ClassifierPanel
-
Tests out the classifier panel from the command line.
- main(String[]) - Static method in class weka.gui.explorer.ClustererPanel
-
Tests out the clusterer panel from the command line.
- main(String[]) - Static method in class weka.gui.explorer.Explorer
-
Tests out the explorer environment.
- main(String[]) - Static method in class weka.gui.explorer.ExplorerDefaults
-
only for testing - prints the content of the props file.
- main(String[]) - Static method in class weka.gui.explorer.PreprocessPanel
-
Tests out the instance-preprocessing panel from the command line.
- main(String[]) - Static method in class weka.gui.explorer.VisualizePanel
-
Tests out the visualize panel from the command line.
- main(String[]) - Static method in class weka.gui.GenericArrayEditor
-
Tests out the array editor from the command line.
- main(String[]) - Static method in class weka.gui.GenericObjectEditor
-
Tests out the Object editor from the command line.
- main(String[]) - Static method in class weka.gui.GenericPropertiesCreator
-
for generating props file:
no parameter:
see default constructor
1 parameter (i.e., filename):
see default constructor + setOutputFilename(String)
2 parameters (i.e, filenames):
see constructor with String argument + setOutputFilename(String)
- main(String[]) - Static method in class weka.gui.graphvisualizer.GraphVisualizer
-
Main method to load a text file with the
description of a graph from the command
line
- main(String[]) - Static method in class weka.gui.GUIChooser
-
Tests out the GUIChooser environment.
- main(String[]) - Static method in class weka.gui.HierarchyPropertyParser
-
Tests out the parser.
- main(String[]) - Static method in class weka.gui.hierarchyvisualizer.HierarchyVisualizer
-
Main method for testing this class.
- main(String[]) - Static method in class weka.gui.InstancesSummaryPanel
-
Tests out the instance summary panel from the command line.
- main(String[]) - Static method in class weka.gui.ListSelectorDialog
-
Tests out the list selector from the command line.
- main(String[]) - Static method in class weka.gui.LogPanel
-
Tests out the log panel from the command line.
- main(String[]) - Static method in class weka.gui.LogWindow
-
for testing only
- main(String[]) - Static method in class weka.gui.LookAndFeel
-
prints all the available LnFs to stdout
- Main - Class in weka.gui
-
Menu-based GUI for Weka, replacement for the GUIChooser.
- Main() - Constructor for class weka.gui.Main
-
default constructor.
- main(String[]) - Static method in class weka.gui.Main
-
starts the application.
- main(String[]) - Static method in class weka.gui.PropertySelectorDialog
-
Tests out the property selector from the command line.
- main(String[]) - Static method in class weka.gui.ResultHistoryPanel
-
Tests out the result history from the command line.
- main(String[]) - Static method in class weka.gui.SaveBuffer
-
Main method for testing this class
- main(String[]) - Static method in class weka.gui.SelectedTagEditor
-
Tests out the selectedtag editor from the command line.
- main(String[]) - Static method in class weka.gui.SimpleCLI
-
Method to start up the simple cli
- main(String[]) - Static method in class weka.gui.SimpleCLIPanel
-
Main method for testing this class.
- main(String[]) - Static method in class weka.gui.sql.SqlViewer
-
starts the SQL-Viewer interface.
- main(String[]) - Static method in class weka.gui.sql.SqlViewerDialog
-
for testing only
- main(String[]) - Static method in class weka.gui.treevisualizer.TreeVisualizer
-
Main method for testing this class.
- main(String[]) - Static method in class weka.gui.visualize.AttributePanel
-
Main method for testing this class.
- main(String[]) - Static method in class weka.gui.visualize.BMPWriter
-
for testing only
- main(String[]) - Static method in class weka.gui.visualize.ClassPanel
-
Main method for testing this class.
- main(String[]) - Static method in class weka.gui.visualize.JPEGWriter
-
for testing only.
- main(String[]) - Static method in class weka.gui.visualize.LegendPanel
-
Main method for testing this class
- main(String[]) - Static method in class weka.gui.visualize.MatrixPanel
-
Main method for testing this class
- main(String[]) - Static method in class weka.gui.visualize.Plot2D
-
Main method for testing this class
- main(String[]) - Static method in class weka.gui.visualize.PNGWriter
-
for testing only
- main(String[]) - Static method in class weka.gui.visualize.PostscriptWriter
-
for testing only
- main(String[]) - Static method in class weka.gui.visualize.ThresholdVisualizePanel
-
Starts the ThresholdVisualizationPanel with parameters from the command line.
- main(String[]) - Static method in class weka.gui.visualize.VisualizePanel
-
Main method for testing this class
- main(String[]) - Static method in class weka.gui.WekaTaskMonitor
-
Main method for testing this class
- Main.BackgroundDesktopPane - Class in weka.gui
-
DesktopPane with background image.
- Main.ChildFrameMDI - Class in weka.gui
-
Specialized JInternalFrame class.
- Main.ChildFrameSDI - Class in weka.gui
-
Specialized JFrame class.
- MainMenuExtension - Interface in weka.gui
-
Classes implementing this interface will be displayed in the "Extensions"
menu in the main GUI of Weka.
- MAJOR - Static variable in class weka.core.Version
-
the major version
- MAJORITY_VOTING_RULE - Static variable in class weka.classifiers.meta.Vote
-
combination rule: Majority Voting (only nominal classes)
- majorityClassTipText() - Method in class weka.classifiers.rules.Ridor
-
Returns the tip text for this property
- makeADTree(int, FastVector, Instances) - Static method in class weka.classifiers.bayes.net.ADNode
-
create sub tree
- makeADTree(Instances) - Static method in class weka.classifiers.bayes.net.ADNode
-
create AD tree from set of instances
- makeBallTree(BottomUpConstructor.TempNode, int, int, int[], int, double) - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor
-
Makes ball tree nodes of temp nodes that were used
in the merging process.
- makeBallTreeNodes(MiddleOutConstructor.TempNode, int, int, int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Makes BallTreeNodes out of TempNodes.
- makeBinaryTipText() - Method in class weka.filters.supervised.attribute.Discretize
-
Returns the tip text for this property
- makeBinaryTipText() - Method in class weka.filters.unsupervised.attribute.Discretize
-
Returns the tip text for this property
- makeCentersRandomly(Random, Instances, int) - Method in class weka.clusterers.XMeans
-
Generates new centers randomly.
- makeCopies(Associator, int) - Static method in class weka.associations.AbstractAssociator
-
Creates copies of the current associator.
- makeCopies(ASEvaluation, int) - Static method in class weka.attributeSelection.ASEvaluation
-
Creates copies of the current evaluator.
- makeCopies(ASSearch, int) - Static method in class weka.attributeSelection.ASSearch
-
Creates copies of the current search scheme.
- makeCopies(Object, int) - Method in class weka.attributeSelection.CheckAttributeSelection
-
returns deep copies of the given object
- makeCopies(Classifier, int) - Static method in class weka.classifiers.Classifier
-
Creates a given number of deep copies of the given classifier using serialization.
- makeCopies(Kernel, int) - Static method in class weka.classifiers.functions.supportVector.Kernel
-
Creates a given number of deep copies of the given kernel using
serialization.
- makeCopies(Clusterer, int) - Static method in class weka.clusterers.AbstractClusterer
-
Creates copies of the current clusterer.
- makeCopies(DensityBasedClusterer, int) - Static method in class weka.clusterers.AbstractDensityBasedClusterer
-
Creates copies of the current clusterer.
- makeCopies(Estimator, int) - Static method in class weka.estimators.Estimator
-
Creates a given number of deep copies of the given estimator using serialization.
- makeCopies(Filter, int) - Static method in class weka.filters.Filter
-
Creates a given number of deep copies of the given filter using
serialization.
- makeCopy(Associator) - Static method in class weka.associations.AbstractAssociator
-
Creates a deep copy of the given associator using serialization.
- makeCopy(Classifier) - Static method in class weka.classifiers.Classifier
-
Creates a deep copy of the given classifier using serialization.
- makeCopy(Kernel) - Static method in class weka.classifiers.functions.supportVector.Kernel
-
Creates a deep copy of the given kernel using serialization.
- makeCopy(Clusterer) - Static method in class weka.clusterers.AbstractClusterer
-
Creates a deep copy of the given clusterer using serialization.
- makeCopy(Estimator) - Static method in class weka.estimators.Estimator
-
Creates a deep copy of the given estimator using serialization.
- makeCopy(Filter) - Static method in class weka.filters.Filter
-
Creates a deep copy of the given filter using serialization.
- makeCopy(Object) - Static method in class weka.gui.GenericArrayEditor
-
Makes a copy of an object using serialization.
- makeCopy(Object) - Static method in class weka.gui.GenericObjectEditor
-
Makes a copy of an object using serialization.
- makeData(DataGenerator, String[]) - Static method in class weka.datagenerators.DataGenerator
-
Calls the data generator.
- MakeDecList - Class in weka.classifiers.rules.part
-
Class for handling a decision list.
- MakeDecList(ModelSelection, int) - Constructor for class weka.classifiers.rules.part.MakeDecList
-
Constructor for unpruned dec list.
- MakeDecList(ModelSelection, double, int) - Constructor for class weka.classifiers.rules.part.MakeDecList
-
Constructor for dec list pruned using C4.5 pruning.
- MakeDecList(ModelSelection, int, int, int) - Constructor for class weka.classifiers.rules.part.MakeDecList
-
Constructor for dec list pruned using hold-out pruning.
- MakeDensityBasedClusterer - Class in weka.clusterers
-
Class for wrapping a Clusterer to make it return a distribution and density.
- MakeDensityBasedClusterer() - Constructor for class weka.clusterers.MakeDensityBasedClusterer
-
Default constructor.
- MakeDensityBasedClusterer(Clusterer) - Constructor for class weka.clusterers.MakeDensityBasedClusterer
-
Contructs a MakeDensityBasedClusterer wrapping a given Clusterer.
- makeDistribution(double) - Method in class weka.classifiers.Evaluation
-
Convert a single prediction into a probability distribution
with all zero probabilities except the predicted value which
has probability 1.0;
- makeDistribution(double, int) - Static method in class weka.classifiers.evaluation.NominalPrediction
-
Convert a single prediction into a probability distribution
with all zero probabilities except the predicted value which
has probability 1.0.
- makeDistribution(Instances, double[]) - Method in class weka.classifiers.lazy.IBk
-
Turn the list of nearest neighbors into a probability distribution.
- makeDistribution() - Method in class weka.classifiers.mi.CitationKNN
-
Turn the references and citers list into a probability distribution
- makeGUIPanel(boolean) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
-
This methods makes the gui extra controls panel "m_controlsPanel"
- MakeIndicator - Class in weka.filters.unsupervised.attribute
-
A filter that creates a new dataset with a boolean attribute replacing a nominal attribute.
- MakeIndicator() - Constructor for class weka.filters.unsupervised.attribute.MakeIndicator
-
Constructor
- makeLeaf(Instances) - Method in class weka.classifiers.trees.BFTree
-
Make the node leaf node.
- makeLeaf(Instances) - Method in class weka.classifiers.trees.SimpleCart
-
Make the node leaf node.
- makeOptionsString(Stemmer) - Static method in class weka.core.stemmers.Stemming
-
lists all the options on the command line
- makeOptionStr(AbstractFileLoader) - Static method in class weka.core.converters.AbstractFileLoader
-
generates a string suitable for output on the command line displaying
all available options (currently only a simple usage).
- makeOptionStr(AbstractFileSaver) - Static method in class weka.core.converters.AbstractFileSaver
-
generates a string suitable for output on the command line displaying
all available options.
- makeOptionString(Associator) - Static method in class weka.associations.AssociatorEvaluation
-
Generates an option string to output on the commandline.
- makeOptionString(Classifier, boolean) - Static method in class weka.classifiers.Evaluation
-
Make up the help string giving all the command line options
- makeOptionString(Kernel) - Static method in class weka.classifiers.functions.supportVector.KernelEvaluation
-
Generates an option string to output on the commandline.
- makeOptionString(DataGenerator) - Static method in class weka.datagenerators.DataGenerator
-
returns all the options in a string
- makeProperHierarchy() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
-
- makeSuccessors(FastVector, Instances, int[][][], double[][][], double[][][], Attribute, boolean, boolean) - Method in class weka.classifiers.trees.BFTree
-
Generate successor nodes for a node and put them into BestFirstElements
according to gini gain or information gain in a descending order.
- makeTestDataset(int, int, int, int, int, int, int, int, int, boolean) - Method in class weka.associations.CheckAssociator
-
Make a simple set of instances, which can later be modified
for use in specific tests.
- makeTestDataset(int, int, int, int, int, int, int, int, int, int, boolean) - Method in class weka.associations.CheckAssociator
-
Make a simple set of instances with variable position of the class
attribute, which can later be modified for use in specific tests.
- makeTestDataset(int, int, int, int, int, int, int, int, int, boolean) - Method in class weka.attributeSelection.CheckAttributeSelection
-
Make a simple set of instances, which can later be modified
for use in specific tests.
- makeTestDataset(int, int, int, int, int, int, int, int, int, int, boolean) - Method in class weka.attributeSelection.CheckAttributeSelection
-
Make a simple set of instances with variable position of the class
attribute, which can later be modified for use in specific tests.
- makeTestDataset(int, int, int, int, int, int, int, int, int, boolean) - Method in class weka.classifiers.CheckClassifier
-
Make a simple set of instances, which can later be modified
for use in specific tests.
- makeTestDataset(int, int, int, int, int, int, int, int, int, int, boolean) - Method in class weka.classifiers.CheckClassifier
-
Make a simple set of instances with variable position of the class
attribute, which can later be modified for use in specific tests.
- makeTestDataset(int, int, int, int, int, int, int, int, int, boolean) - Method in class weka.classifiers.functions.supportVector.CheckKernel
-
Make a simple set of instances, which can later be modified
for use in specific tests.
- makeTestDataset(int, int, int, int, int, int, int, int, int, int, boolean) - Method in class weka.classifiers.functions.supportVector.CheckKernel
-
Make a simple set of instances with variable position of the class
attribute, which can later be modified for use in specific tests.
- makeTestDataset(int, int, int, int, int, int, int, boolean) - Method in class weka.clusterers.CheckClusterer
-
Make a simple set of instances with variable position of the class
attribute, which can later be modified for use in specific tests.
- makeTestDataset(int, int, int, CheckEstimator.AttrTypes, int, int) - Method in class weka.estimators.CheckEstimator
-
Make a simple set of instances, which can later be modified
for use in specific tests.
- makeTestDataset(int, int, int, CheckEstimator.AttrTypes, int, int, int) - Method in class weka.estimators.CheckEstimator
-
Make a simple set of instances with variable position of the class
attribute, which can later be modified for use in specific tests.
- makeTestValueList(int, int, Instances, int, int) - Method in class weka.estimators.CheckEstimator
-
Make a simple set of values.
- makeTestValueList(int, int, double, double, int) - Method in class weka.estimators.CheckEstimator
-
Make a simple set of values.
- makeTree(FastVector, Instances, int[][], double[][], double[][][], double[], double, double[], int, boolean, boolean, int) - Method in class weka.classifiers.trees.BFTree
-
Recursively build a best-first decision tree.
- makeTree(FastVector, BFTree, Instances, int[][], double[][], double[][][], double[], double, double[], int, boolean, boolean) - Method in class weka.classifiers.trees.BFTree
-
This method is to find the number of expansions based on internal
cross-validation for just pre-pruning.
- makeTree(FastVector, BFTree, Instances, Instances, FastVector, int[][], double[][], double[][][], double[], double, double[], int, boolean, boolean, boolean) - Method in class weka.classifiers.trees.BFTree
-
This method is to find the number of expansions based on internal
cross-validation for just post-pruning.
- makeTree(Instances, int, int[][], double[][], double[], double, double, boolean) - Method in class weka.classifiers.trees.SimpleCart
-
Make binary decision tree recursively.
- makeUniformDistribution(int) - Static method in class weka.classifiers.evaluation.NominalPrediction
-
Creates a uniform probability distribution -- where each of the
possible classes is assigned equal probability.
- makeVaryNode(int, FastVector, Instances) - Static method in class weka.classifiers.bayes.net.ADNode
-
create sub tree
- makeWeighted(CostMatrix) - Method in class weka.classifiers.evaluation.ConfusionMatrix
-
Makes a copy of this ConfusionMatrix after applying the
supplied CostMatrix to the cells.
- ManhattanDataObject - Class in weka.clusterers.forOPTICSAndDBScan.DataObjects
-
ManhattanDataObject.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 19, 2004
Time: 5:50:22 PM
$ Revision 1.4 $
- ManhattanDataObject(Instance, String, Database) - Constructor for class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
-
Constructs a new DataObject.
- ManhattanDistance - Class in weka.core
-
Implements the Manhattan distance (or Taxicab geometry).
- ManhattanDistance() - Constructor for class weka.core.ManhattanDistance
-
Constructs an Manhattan Distance object, Instances must be still set.
- ManhattanDistance(Instances) - Constructor for class weka.core.ManhattanDistance
-
Constructs an Manhattan Distance object and automatically initializes the
ranges.
- manualThresholdValueTipText() - Method in class weka.classifiers.meta.ThresholdSelector
-
- map(String, String) - Method in class weka.core.matrix.DoubleVector
-
Applies a method to the vector
- mapClasses(int, int, int[][], int[], double[], double[], int) - Static method in class weka.clusterers.ClusterEvaluation
-
Finds the minimum error mapping of classes to clusters.
- MappingInfo - Class in weka.core.pmml
-
Class that maintains the mapping between incoming data set structure
and that of the mining schema.
- MappingInfo(Instances, MiningSchema, Logger) - Constructor for class weka.core.pmml.MappingInfo
-
- mapToMiningSchema(Instances) - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
-
Map mining schema to incoming instances.
- margin() - Method in class weka.classifiers.evaluation.NominalPrediction
-
Calculates the prediction margin.
- MarginCalculator - Class in weka.classifiers.bayes.net
-
- MarginCalculator() - Constructor for class weka.classifiers.bayes.net.MarginCalculator
-
- MarginCalculator.JunctionTreeNode - Class in weka.classifiers.bayes.net
-
- MarginCalculator.JunctionTreeSeparator - Class in weka.classifiers.bayes.net
-
- MarginCurve - Class in weka.classifiers.evaluation
-
Generates points illustrating the prediction margin.
- MarginCurve() - Constructor for class weka.classifiers.evaluation.MarginCurve
-
- markovBlanketClassifierTipText() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
-
- markovBlanketClassifierTipText() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
-
- markovBlanketClassifierTipText() - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
-
- mAscending - Variable in class weka.gui.SortedTableModel
-
whether sorting is ascending or descending
- maskKeyword(String) - Method in class weka.experiment.DatabaseUtils
-
If the given string is a keyword, then the mask character will be
appended and returned.
- Matchable - Interface in weka.core
-
Interface to something that can be matched with tree matching
algorithms.
- matchesTemplate(Object[], Object[]) - Method in class weka.experiment.AveragingResultProducer
-
Compares a key to a template to see whether they match.
- matchesTemplate(Instance) - Method in class weka.experiment.PairedTTester.Dataset
-
Returns true if the two instances match on those attributes that have
been designated key columns (eg: scheme name and scheme options)
- matchesTemplate(Instance) - Method in class weka.experiment.PairedTTester.Resultset
-
Returns true if the two instances match on those attributes that have
been designated key columns (eg: scheme name and scheme options)
- matchMissingValuesTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Returns the tip text for this property
- MathematicalExpression - Class in weka.core
-
Class for evaluating a string adhering the following grammar:
- MathematicalExpression() - Constructor for class weka.core.MathematicalExpression
-
- MathExpression - Class in weka.filters.unsupervised.attribute
-
Modify numeric attributes according to a given expression
Valid options are:
- MathExpression() - Constructor for class weka.filters.unsupervised.attribute.MathExpression
-
Constructor
- Maths - Class in weka.core.matrix
-
Utility class.
- Maths() - Constructor for class weka.core.matrix.Maths
-
- matrix() - Method in class weka.classifiers.trees.j48.Distribution
-
Returns matrix with distribution of class values.
- Matrix - Class in weka.core
-
- Matrix(int, int) - Constructor for class weka.core.Matrix
-
Deprecated.
Constructs a matrix and initializes it with default values.
- Matrix(double[][]) - Constructor for class weka.core.Matrix
-
Deprecated.
Constructs a matrix using a given array.
- Matrix(Reader) - Constructor for class weka.core.Matrix
-
Deprecated.
Reads a matrix from a reader.
- Matrix - Class in weka.core.matrix
-
Jama = Java Matrix class.
- Matrix(int, int) - Constructor for class weka.core.matrix.Matrix
-
Construct an m-by-n matrix of zeros.
- Matrix(int, int, double) - Constructor for class weka.core.matrix.Matrix
-
Construct an m-by-n constant matrix.
- Matrix(double[][]) - Constructor for class weka.core.matrix.Matrix
-
Construct a matrix from a 2-D array.
- Matrix(double[][], int, int) - Constructor for class weka.core.matrix.Matrix
-
Construct a matrix quickly without checking arguments.
- Matrix(double[], int) - Constructor for class weka.core.matrix.Matrix
-
Construct a matrix from a one-dimensional packed array
- Matrix(Reader) - Constructor for class weka.core.matrix.Matrix
-
Reads a matrix from a reader.
- MATRIX_ON_DEMAND - Static variable in class weka.attributeSelection.CostSensitiveASEvaluation
-
load cost matrix on demand
- MATRIX_ON_DEMAND - Static variable in class weka.classifiers.meta.CostSensitiveClassifier
-
load cost matrix on demand
- MATRIX_ON_DEMAND - Static variable in class weka.classifiers.meta.MetaCost
-
load cost matrix on demand
- MATRIX_SUPPLIED - Static variable in class weka.attributeSelection.CostSensitiveASEvaluation
-
use explicit cost matrix
- MATRIX_SUPPLIED - Static variable in class weka.classifiers.meta.CostSensitiveClassifier
-
use explicit cost matrix
- MATRIX_SUPPLIED - Static variable in class weka.classifiers.meta.MetaCost
-
use explicit matrix
- MatrixPanel - Class in weka.gui.visualize
-
This panel displays a plot matrix of the user selected attributes
of a given data set.
- MatrixPanel() - Constructor for class weka.gui.visualize.MatrixPanel
-
Constructor
- max() - Method in class weka.core.matrix.DoubleVector
-
Returns the maximum value of all elements
- MAX - Static variable in class weka.core.neighboursearch.KDTree
-
The index of MAX value in attributes' range array.
- MAX - Static variable in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
-
Index of max value in an array of attributes' range.
- max - Variable in class weka.experiment.Stats
-
The maximum value seen, or Double.NaN if no values seen
- MAX_DECIMALS - Static variable in class weka.filters.unsupervised.attribute.NumericToNominal
-
the maximum number of decimals to use
- MAX_DIGITS - Static variable in class weka.core.converters.SVMLightSaver
-
the number of digits after the decimal point.
- MAX_FAILURES - Static variable in class weka.experiment.RemoteExperiment
-
allow at most 3 failures on a host before it is removed from the list
of usable hosts
- MAX_FAILURES - Static variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
-
- MAX_N - Static variable in class weka.associations.PriorEstimation
-
The maximum number of attributes for which a prior can be estimated.
- MAX_N - Static variable in class weka.associations.RuleGeneration
-
Threshold.
- MAX_POWER_OF_LAMBDA - Static variable in class weka.classifiers.functions.supportVector.StringKernel
-
powers of lambda are prepared prior to kernel evaluations.
- MAX_PRECISION - Static variable in class weka.gui.visualize.VisualizeUtils
-
Default maximum precision for the display of numeric values
- MAX_ROWS - Static variable in class weka.gui.sql.QueryPanel
-
the name for the max rows in the history.
- MAX_RULE - Static variable in class weka.classifiers.meta.Vote
-
combination rule: Maximum Probability
- max_set(Stack<CoverTree.DistanceNode>) - Method in class weka.core.neighboursearch.CoverTree
-
Returns the max distance of the reference point p in current node to
it's children nodes.
- MAX_SHAPES - Static variable in class weka.gui.visualize.Plot2D
-
- maxAbs() - Method in class weka.classifiers.functions.pace.PaceMatrix
-
Returns the maximum absolute value of all elements
- maxAbs(int, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
-
Returns the maximum absolute value of some elements of a column,
that is, the elements of A[i0:i1][j].
- maxBag() - Method in class weka.classifiers.trees.j48.Distribution
-
Returns index of bag containing maximum number of instances.
- maxBoostingIterationsTipText() - Method in class weka.classifiers.functions.SimpleLogistic
-
Returns the tip text for this property
- maxCardinalityTipText() - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
-
- maxCardinalityTipText() - Method in class weka.filters.unsupervised.attribute.RELAGGS
-
Returns the tip text for this property
- maxChunkSizeTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
- maxClass() - Method in class weka.classifiers.trees.j48.Distribution
-
Returns class with highest frequency over all bags.
- maxClass(int) - Method in class weka.classifiers.trees.j48.Distribution
-
Returns class with highest frequency for given bag.
- maxClassForSubsetOfInterest() - Method in class weka.classifiers.trees.j48.GraftSplit
-
- maxCountTipText() - Method in class weka.filters.supervised.instance.SpreadSubsample
-
Returns the tip text for this property
- maxDefaultTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Returns the tip text for this property
- maxDepthTipText() - Method in class weka.classifiers.trees.RandomForest
-
Returns the tip text for this property
- maxDepthTipText() - Method in class weka.classifiers.trees.RandomTree
-
Returns the tip text for this property
- maxDepthTipText() - Method in class weka.classifiers.trees.REPTree
-
Returns the tip text for this property
- MAXGAM - Static variable in class weka.core.Statistics
-
- maxGenerationsTipText() - Method in class weka.attributeSelection.GeneticSearch
-
Returns the tip text for this property
- maxGridExtensionsTipText() - Method in class weka.classifiers.meta.GridSearch
-
Returns the tip text for this property
- maxGroupTipText() - Method in class weka.classifiers.meta.RotationForest
-
Returns the tip text for this property
- maxImpurity() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo
-
Returns the impurity of this split
- maxImpurity() - Method in interface weka.classifiers.trees.m5.SplitEvaluate
-
Returns the impurity of this split
- maxImpurity() - Method in class weka.classifiers.trees.m5.YongSplitInfo
-
Returns the impurity of this split
- maximumAttributeNamesTipText() - Method in class weka.attributeSelection.LatentSemanticAnalysis
-
Returns the tip text for this property
- maximumAttributeNamesTipText() - Method in class weka.attributeSelection.PrincipalComponents
-
Returns the tip text for this property
- maximumAttributeNamesTipText() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Returns the tip text for this property.
- maximumAttributesTipText() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Returns the tip text for this property.
- maximumVariancePercentageAllowedTipText() - Method in class weka.filters.unsupervised.attribute.RemoveUseless
-
Returns the tip text for this property
- maxIndex(double[]) - Static method in class weka.core.Utils
-
Returns index of maximum element in a given
array of doubles.
- maxIndex(int[]) - Static method in class weka.core.Utils
-
Returns index of maximum element in a given
array of integers.
- maxInfoGain - Variable in class weka.classifiers.rules.JRip.Antd
-
The maximum infoGain achieved by this antecedent test
in the growing data
- maxInstancesInLeafTipText() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
Returns the tip text for this property.
- maxInstInLeafTipText() - Method in class weka.core.neighboursearch.KDTree
-
Tip text for this property.
- maxInstNumTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Returns the tip text for this property
- maxInstNumTipText() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Returns the tip text for this property
- maxIterations - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
-
Maximum number of iterations
- maxIterationsTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Returns the tip text for this property
- maxIterationsTipText() - Method in class weka.classifiers.mi.MIBoost
-
Returns the tip text for this property
- maxIterationsTipText() - Method in class weka.classifiers.mi.MISVM
-
Returns the tip text for this property
- maxIterationsTipText() - Method in class weka.clusterers.EM
-
Returns the tip text for this property
- maxIterationsTipText() - Method in class weka.clusterers.sIB
-
Returns the tip text for this property.
- maxIterationsTipText() - Method in class weka.clusterers.SimpleKMeans
-
Returns the tip text for this property
- maxIterationsTipText() - Method in class weka.clusterers.XMeans
-
Returns the tip text for this property.
- maxIterationsTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Returns the tip text for this property
- maxItsTipText() - Method in class weka.classifiers.functions.Logistic
-
Returns the tip text for this property
- maxItsTipText() - Method in class weka.classifiers.functions.RBFNetwork
-
Returns the tip text for this property
- maxKMeansForChildrenTipText() - Method in class weka.clusterers.XMeans
-
Returns the tip text for this property.
- maxKMeansTipText() - Method in class weka.clusterers.XMeans
-
Returns the tip text for this property.
- maxKTipText() - Method in class weka.classifiers.functions.VotedPerceptron
-
Returns the tip text for this property
- MAXLOG - Static variable in class weka.core.Statistics
-
- maxNrOfParentsTipText() - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
-
- maxNumberOfItemsTipText() - Method in class weka.associations.FPGrowth
-
Tip text for this property suitable for displaying
in the GUI.
- maxNumClustersTipText() - Method in class weka.clusterers.XMeans
-
Returns the tip text for this property.
- maxNumSupportPoints - Variable in class weka.classifiers.functions.pace.ChisqMixture
-
- maxParentSetSize(int) - Method in class weka.classifiers.bayes.net.ParentSet
-
reserve memory for parent set
- maxRadiusTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Returns the tip text for this property
- maxRangeTipText() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Returns the tip text for this property
- maxRelativeLeafRadiusTipText() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
Returns the tip text for this property.
- maxRuleSizeTipText() - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Returns the tip text for this property
- maxSubsequenceLengthTipText() - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Returns the tip text for this property
- maxThresholdTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Returns the tip text for this property
- MAYBE_SUPPORT - Static variable in class weka.gui.GenericObjectEditor.GOETreeNode
-
color for "maybe support".
- MDD - Class in weka.classifiers.mi
-
Modified Diverse Density algorithm, with collective assumption.
More information about DD:
Oded Maron (1998).
- MDD() - Constructor for class weka.classifiers.mi.MDD
-
- MDL - Static variable in interface weka.classifiers.bayes.net.search.local.Scoreable
-
- mean(double[]) - Static method in class weka.core.Utils
-
Computes the mean for an array of doubles.
- mean - Variable in class weka.experiment.Stats
-
The mean of values at the last calculateDerived() call
- meanAbsoluteError() - Method in class weka.classifiers.Evaluation
-
Returns the mean absolute error.
- meanOrMode(Instances, int[], int) - Method in class weka.clusterers.XMeans
-
Computes Mean Or Mode of one attribute on a subset of m_Instances.
- meanOrMode(int) - Method in class weka.core.Instances
-
Returns the mean (mode) for a numeric (nominal) attribute as
a floating-point value.
- meanOrMode(Attribute) - Method in class weka.core.Instances
-
Returns the mean (mode) for a numeric (nominal) attribute as a
floating-point value.
- meanPriorAbsoluteError() - Method in class weka.classifiers.Evaluation
-
Returns the mean absolute error of the prior.
- meanSquaredTipText() - Method in class weka.classifiers.lazy.IBk
-
Returns the tip text for this property.
- meanStddevTipText() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Returns the tip text for this property
- measureAICScore() - Method in class weka.classifiers.bayes.BayesNet
-
- measureAttributesUsed() - Method in class weka.classifiers.functions.SimpleLogistic
-
Returns the fraction of all attributes in the data that are used in the
logistic model (in percent).
- measureBayesScore() - Method in class weka.classifiers.bayes.BayesNet
-
- measureBDeuScore() - Method in class weka.classifiers.bayes.BayesNet
-
- measureCacheHits() - Method in class weka.classifiers.functions.SMOreg
-
number of kernel cache hits used during learing
- measureDivergence() - Method in class weka.classifiers.bayes.BayesNet
-
- measureEntropyScore() - Method in class weka.classifiers.bayes.BayesNet
-
- measureExamplesCounted() - Method in class weka.classifiers.trees.LADTree
-
Returns the number of examples "counted".
- measureExamplesProcessed() - Method in class weka.classifiers.trees.ADTree
-
Returns the number of examples "counted".
- measureExtraArcs() - Method in class weka.classifiers.bayes.BayesNet
-
- measureKernelEvaluations() - Method in class weka.classifiers.functions.SMOreg
-
number of kernel evaluations used in learing
- measureMaxDepth() - Method in class weka.core.neighboursearch.BallTree
-
Returns the depth of the tree.
- measureMaxDepth() - Method in class weka.core.neighboursearch.CoverTree
-
Returns the depth of the tree.
- measureMaxDepth() - Method in class weka.core.neighboursearch.KDTree
-
Returns the depth of the tree.
- measureMDLScore() - Method in class weka.classifiers.bayes.BayesNet
-
- measureMissingArcs() - Method in class weka.classifiers.bayes.BayesNet
-
- measureNodesExpanded() - Method in class weka.classifiers.trees.ADTree
-
Returns the number of nodes expanded.
- measureNodesExpanded() - Method in class weka.classifiers.trees.LADTree
-
Returns the number of nodes expanded.
- measureNumAttributesSelected() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
-
Additional measure --- number of attributes selected
- measureNumIterations() - Method in class weka.classifiers.meta.AdditiveRegression
-
return the number of iterations (base classifiers) completed
- measureNumLeaves() - Method in class weka.classifiers.trees.ADTree
-
Calls measure function for leaf size - the number of prediction nodes.
- measureNumLeaves() - Method in class weka.classifiers.trees.FT
-
Returns the number of leaves in the tree
- measureNumLeaves() - Method in class weka.classifiers.trees.J48
-
Returns the number of leaves
- measureNumLeaves() - Method in class weka.classifiers.trees.J48graft
-
Returns the number of leaves
- measureNumLeaves() - Method in class weka.classifiers.trees.LADTree
-
Calls measure function for leaf size.
- measureNumLeaves() - Method in class weka.classifiers.trees.LMT
-
Returns the number of leaves in the tree
- measureNumLeaves() - Method in class weka.classifiers.trees.NBTree
-
Returns the number of leaves
- measureNumLeaves() - Method in class weka.core.neighboursearch.BallTree
-
Returns the number of leaves.
- measureNumLeaves() - Method in class weka.core.neighboursearch.CoverTree
-
Returns the number of leaves.
- measureNumLeaves() - Method in class weka.core.neighboursearch.KDTree
-
Returns the number of leaves.
- measureNumPredictionLeaves() - Method in class weka.classifiers.trees.ADTree
-
Calls measure function for prediction leaf size - the number of
prediction nodes without children.
- measureNumPredictionLeaves() - Method in class weka.classifiers.trees.LADTree
-
Calls measure function for leaf size.
- measureNumRules() - Method in class weka.classifiers.rules.DecisionTable
-
Returns the number of rules
- measureNumRules() - Method in class weka.classifiers.rules.PART
-
Return the number of rules.
- measureNumRules() - Method in class weka.classifiers.trees.J48
-
Returns the number of rules (same as number of leaves)
- measureNumRules() - Method in class weka.classifiers.trees.J48graft
-
Returns the number of rules (same as number of leaves)
- measureNumRules() - Method in class weka.classifiers.trees.m5.M5Base
-
return the number of rules
- measureNumRules() - Method in class weka.classifiers.trees.NBTree
-
Returns the number of rules (same as number of leaves)
- measureOutOfBagError() - Method in class weka.classifiers.meta.Bagging
-
Gets the out of bag error that was calculated as the classifier
was built.
- measureOutOfBagError() - Method in class weka.classifiers.trees.RandomForest
-
Gets the out of bag error that was calculated as the classifier was built.
- measurePercentAttsUsedByDT() - Method in class weka.classifiers.rules.DTNB
-
Returns the number of rules
- measurePerformanceTipText() - Method in class weka.core.neighboursearch.NearestNeighbourSearch
-
Returns the tip text for this property.
- measureReversedArcs() - Method in class weka.classifiers.bayes.BayesNet
-
- measureSelectionTime() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
-
Additional measure --- time taken (milliseconds) to select the attributes
- measureTime() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
-
Additional measure --- time taken (milliseconds) to select attributes
and build the classifier
- measureTipText() - Method in class weka.classifiers.meta.ThresholdSelector
-
Tooltip for this property.
- measureTreeSize() - Method in class weka.classifiers.trees.ADTree
-
Calls measure function for tree size - the total number of nodes.
- measureTreeSize() - Method in class weka.classifiers.trees.BFTree
-
Return number of tree size.
- measureTreeSize() - Method in class weka.classifiers.trees.FT
-
Returns the size of the tree
- measureTreeSize() - Method in class weka.classifiers.trees.J48
-
Returns the size of the tree
- measureTreeSize() - Method in class weka.classifiers.trees.J48graft
-
Returns the size of the tree
- measureTreeSize() - Method in class weka.classifiers.trees.LADTree
-
Calls measure function for tree size.
- measureTreeSize() - Method in class weka.classifiers.trees.LMT
-
Returns the size of the tree
- measureTreeSize() - Method in class weka.classifiers.trees.NBTree
-
Returns the size of the tree
- measureTreeSize() - Method in class weka.classifiers.trees.SimpleCart
-
Return number of tree size.
- measureTreeSize() - Method in class weka.core.neighboursearch.BallTree
-
Returns the size of the tree.
- measureTreeSize() - Method in class weka.core.neighboursearch.CoverTree
-
Returns the size of the tree.
- measureTreeSize() - Method in class weka.core.neighboursearch.KDTree
-
Returns the size of the tree.
- MEDIAN_RULE - Static variable in class weka.classifiers.meta.Vote
-
combination rule: Median Probability (only numeric class)
- MedianDistanceFromArbitraryPoint - Class in weka.core.neighboursearch.balltrees
-
Class that splits a BallNode of a ball tree using Uhlmann's described method.
For information see:
Jeffrey K.
- MedianDistanceFromArbitraryPoint() - Constructor for class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
-
Constructor.
- MedianDistanceFromArbitraryPoint(int[], Instances, EuclideanDistance) - Constructor for class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
-
Constructor.
- MedianOfWidestDimension - Class in weka.core.neighboursearch.balltrees
-
Class that splits a BallNode of a ball tree based on the median value of the widest dimension of the points in the ball.
- MedianOfWidestDimension() - Constructor for class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
-
Constructor.
- MedianOfWidestDimension(int[], Instances, EuclideanDistance) - Constructor for class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
-
Constructor.
- MedianOfWidestDimension - Class in weka.core.neighboursearch.kdtrees
-
The class that splits a KDTree node based on the median value of a dimension in which the node's points have the widest spread.
For more information see also:
Jerome H.
- MedianOfWidestDimension() - Constructor for class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension
-
- Memory - Class in weka.core
-
A little helper class for Memory management.
- Memory() - Constructor for class weka.core.Memory
-
initializes the memory management without GUI support
- Memory(boolean) - Constructor for class weka.core.Memory
-
initializes the memory management
- MemoryMonitor() - Constructor for class weka.gui.MemoryUsagePanel.MemoryMonitor
-
default constructor.
- MemoryUsagePanel - Class in weka.gui
-
A panel for displaying the memory usage.
- MemoryUsagePanel() - Constructor for class weka.gui.MemoryUsagePanel
-
default constructor.
- MemoryUsagePanel.MemoryMonitor - Class in weka.gui
-
Specialized thread for monitoring the memory usage.
- menuBar - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuEdit - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuEditAttributeAsClass - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuEditClearSearch - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuEditCopy - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuEditDeleteAttribute - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuEditDeleteAttributes - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuEditDeleteInstance - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuEditDeleteInstances - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuEditRenameAttribute - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuEditSearch - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuEditSortInstances - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuEditUndo - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuFile - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuFileClose - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuFileCloseAll - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuFileExit - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuFileOpen - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuFileProperties - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuFileSave - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuFileSaveAs - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuView - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuViewAttributes - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuViewOptimalColWidths - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- menuViewValues - Variable in class weka.gui.arffviewer.ArffViewerMainPanel
-
- merge(Element, Element) - Static method in class weka.associations.gsp.Element
-
Merges two Elements into one.
- merge(Sequence, Sequence, boolean, boolean) - Static method in class weka.associations.gsp.Sequence
-
Merges two Sequences in the course of candidate generation.
- merge(SimpleLinkedList, Comparator) - Method in class weka.associations.tertius.SimpleLinkedList
-
- merge(ADTree) - Method in class weka.classifiers.trees.ADTree
-
Merges two trees together.
- merge(PredictionNode, ADTree) - Method in class weka.classifiers.trees.adtree.PredictionNode
-
Merges this node with another.
- merge(LADTree) - Method in class weka.classifiers.trees.LADTree
-
Merges two trees together.
- merge(LADTree.PredictionNode) - Method in class weka.classifiers.trees.LADTree.PredictionNode
-
- mergeAllItemSets(FastVector, int, int) - Static method in class weka.associations.AprioriItemSet
-
Merges all item sets in the set of (k-1)-item sets
to create the (k)-item sets and updates the counters.
- mergeAllItemSets(FastVector, int, int) - Static method in class weka.associations.ItemSet
-
Merges all item sets in the set of (k-1)-item sets
to create the (k)-item sets and updates the counters.
- mergeAllItemSets(FastVector, int, int) - Static method in class weka.associations.LabeledItemSet
-
Merges all item sets in the set of (k-1)-item sets
to create the (k)-item sets and updates the counters.
- mergeArrays(SimpleLinearRegression[][], SimpleLinearRegression[][]) - Method in class weka.classifiers.trees.ft.FTtree
-
Merges two arrays of regression functions into one
- mergeArrays(SimpleLinearRegression[][], SimpleLinearRegression[][]) - Method in class weka.classifiers.trees.lmt.LMTNode
-
Merges two arrays of regression functions into one
- mergeInstance(Instance) - Method in class weka.core.BinarySparseInstance
-
Merges this instance with the given instance and returns
the result.
- mergeInstance(Instance) - Method in class weka.core.Instance
-
Merges this instance with the given instance and returns
the result.
- mergeInstance(Instance) - Method in class weka.core.SparseInstance
-
Merges this instance with the given instance and returns
the result.
- mergeInstances(Instances, Instances) - Static method in class weka.core.Instances
-
Merges two sets of Instances together.
- mergeInstances(Instance, Instance) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
-
Creates a new instance the same as one instance (the "destination")
but with some attribute values copied from another instance
(the "source")
- mergeInstances(Instance, Instance) - Method in class weka.filters.unsupervised.attribute.TimeSeriesDelta
-
Creates a new instance the same as one instance (the "destination")
but with some attribute values copied from another instance
(the "source")
- mergeInstances(Instance, Instance) - Method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate
-
Creates a new instance the same as one instance (the "destination")
but with some attribute values copied from another instance
(the "source")
- mergeNodes(FastVector, int, int, int[]) - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor
-
Merges nodes into one top node.
- mergeNodes(Vector, int, int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Merges nodes created by createAnchorsHierarchy()
into one top node.
- MergeTwoValues - Class in weka.filters.unsupervised.attribute
-
Merges two values of a nominal attribute into one value.
- MergeTwoValues() - Constructor for class weka.filters.unsupervised.attribute.MergeTwoValues
-
- Messages - Class in weka.associations.gsp
-
Messages.
- Messages() - Constructor for class weka.associations.gsp.Messages
-
- Messages - Class in weka.associations
-
Messages.
- Messages() - Constructor for class weka.associations.Messages
-
- Messages - Class in weka.gui.arffviewer
-
Messages.
- Messages() - Constructor for class weka.gui.arffviewer.Messages
-
- Messages - Class in weka.gui.beans
-
Messages.
- Messages() - Constructor for class weka.gui.beans.Messages
-
- Messages - Class in weka.gui.beans.xml
-
Messages.
- Messages() - Constructor for class weka.gui.beans.xml.Messages
-
- Messages - Class in weka.gui.boundaryvisualizer
-
Messages.
- Messages() - Constructor for class weka.gui.boundaryvisualizer.Messages
-
- Messages - Class in weka.gui.experiment
-
Messages.
- Messages() - Constructor for class weka.gui.experiment.Messages
-
- Messages - Class in weka.gui.explorer
-
Messages.
- Messages() - Constructor for class weka.gui.explorer.Messages
-
- Messages - Class in weka.gui.graphvisualizer
-
Messages.
- Messages() - Constructor for class weka.gui.graphvisualizer.Messages
-
- Messages - Class in weka.gui.hierarchyvisualizer
-
Messages.
- Messages() - Constructor for class weka.gui.hierarchyvisualizer.Messages
-
- Messages - Class in weka.gui
-
Messages.
- Messages() - Constructor for class weka.gui.Messages
-
- Messages - Class in weka.gui.sql.event
-
Messages.
- Messages() - Constructor for class weka.gui.sql.event.Messages
-
- Messages - Class in weka.gui.sql
-
Messages.
- Messages() - Constructor for class weka.gui.sql.Messages
-
- Messages - Class in weka.gui.streams
-
Messages.
- Messages() - Constructor for class weka.gui.streams.Messages
-
- Messages - Class in weka.gui.treevisualizer
-
Messages.
- Messages() - Constructor for class weka.gui.treevisualizer.Messages
-
- Messages - Class in weka.gui.visualize
-
Messages.
- Messages() - Constructor for class weka.gui.visualize.Messages
-
- MEstimate(double, double, double) - Method in class weka.classifiers.bayes.AODEsr
-
Returns the probability estimate, using m-estimate
- mestWeightTipText() - Method in class weka.classifiers.bayes.AODEsr
-
Returns the tip text for this property
- MetaBean - Class in weka.gui.beans
-
A meta bean that encapsulates several other regular beans, useful for
grouping large KnowledgeFlows.
- MetaBean() - Constructor for class weka.gui.beans.MetaBean
-
- metaClassifierTipText() - Method in class weka.classifiers.meta.Stacking
-
Returns the tip text for this property
- MetaCost - Class in weka.classifiers.meta
-
This metaclassifier makes its base classifier cost-sensitive using the method specified in
Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive.
- MetaCost() - Constructor for class weka.classifiers.meta.MetaCost
-
- metaFormat(Instances) - Method in class weka.classifiers.meta.Grading
-
Makes the format for the level-1 data.
- metaFormat(Instances) - Method in class weka.classifiers.meta.Stacking
-
Makes the format for the level-1 data.
- metaInstance(Instance, int) - Method in class weka.classifiers.meta.Grading
-
Makes a level-1 instance from the given instance.
- metaInstance(Instance) - Method in class weka.classifiers.meta.Stacking
-
Makes a level-1 instance from the given instance.
- metaOption() - Method in class weka.classifiers.meta.Stacking
-
String describing option for setting meta classifier
- metaOption() - Method in class weka.classifiers.meta.StackingC
-
String describing option for setting meta classifier
- METHOD_1_AGAINST_1 - Static variable in class weka.classifiers.meta.MultiClassClassifier
-
1-against-1
- METHOD_1_AGAINST_ALL - Static variable in class weka.classifiers.meta.MultiClassClassifier
-
1-against-all
- METHOD_ERROR_EXHAUSTIVE - Static variable in class weka.classifiers.meta.MultiClassClassifier
-
exhaustive correction code
- METHOD_ERROR_RANDOM - Static variable in class weka.classifiers.meta.MultiClassClassifier
-
random correction code
- MethodHandler - Class in weka.core.xml
-
This class handles relationships between display names of properties
(or classes) and Methods that are associated with them.
- MethodHandler() - Constructor for class weka.core.xml.MethodHandler
-
initializes the handler
- methodNameTipText() - Method in class weka.filters.unsupervised.attribute.NumericTransform
-
Returns the tip text for this property
- methodTipText() - Method in class weka.classifiers.meta.MultiClassClassifier
-
- methodTipText() - Method in class weka.classifiers.mi.MIWrapper
-
Returns the tip text for this property
- metricString() - Method in class weka.associations.Apriori
-
Returns the metric string for the chosen metric type
- metricString() - Method in interface weka.associations.CARuleMiner
-
Gets name of the scoring metric used for car mining
- metricString() - Method in class weka.associations.PredictiveApriori
-
Returns the metric string for the chosen metric type.
- metricTypeTipText() - Method in class weka.associations.Apriori
-
Returns the tip text for this property
- metricTypeTipText() - Method in class weka.associations.FPGrowth
-
Tip text for this property suitable for displaying
in the GUI.
- MexicanHat - Class in weka.datagenerators.classifiers.regression
-
A data generator for the simple 'Mexian Hat' function:
y = sin|x| / |x|
In addition to this simple function, the amplitude can be changed and gaussian noise can be added.
- MexicanHat() - Constructor for class weka.datagenerators.classifiers.regression.MexicanHat
-
initializes the generator
- MIBoost - Class in weka.classifiers.mi
-
MI AdaBoost method, considers the geometric mean of posterior of instances inside a bag (arithmatic mean of log-posterior) and the expectation for a bag is taken inside the loss function.
For more information about Adaboost, see:
Yoav Freund, Robert E.
- MIBoost() - Constructor for class weka.classifiers.mi.MIBoost
-
- MIDD - Class in weka.classifiers.mi
-
Re-implement the Diverse Density algorithm, changes the testing procedure.
Oded Maron (1998).
- MIDD() - Constructor for class weka.classifiers.mi.MIDD
-
- MiddleOutConstructor - Class in weka.core.neighboursearch.balltrees
-
The class that builds a BallTree middle out.
For more information see also:
Andrew W.
- MiddleOutConstructor() - Constructor for class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Creates a new instance of MiddleOutConstructor.
- MiddleOutConstructor.ListNode - Class in weka.core.neighboursearch.balltrees
-
An element of MyIdxList.
- MiddleOutConstructor.MyIdxList - Class in weka.core.neighboursearch.balltrees
-
Class implementing a list.
- MiddleOutConstructor.TempNode - Class in weka.core.neighboursearch.balltrees
-
Temp class to represent either a leaf node or an internal node.
- midPoint(double, int) - Method in class weka.associations.PriorEstimation
-
calculates the mid point of an interval
- MidPointOfWidestDimension - Class in weka.core.neighboursearch.kdtrees
-
The class that splits a KDTree node based on the midpoint value of a dimension in which the node's points have the widest spread.
For more information see also:
Andrew Moore (1991).
- MidPointOfWidestDimension() - Constructor for class weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension
-
- midPoints() - Method in class weka.associations.PriorEstimation
-
split the interval [0,1] into a predefined number of intervals and calculates their mid points
- MIEMDD - Class in weka.classifiers.mi
-
EMDD model builds heavily upon Dietterich's Diverse Density (DD) algorithm.
It is a general framework for MI learning of converting the MI problem to a single-instance setting using EM.
- MIEMDD() - Constructor for class weka.classifiers.mi.MIEMDD
-
- MILR - Class in weka.classifiers.mi
-
Uses either standard or collective multi-instance assumption, but within linear regression.
- MILR() - Constructor for class weka.classifiers.mi.MILR
-
- MIN - Static variable in class weka.core.neighboursearch.KDTree
-
The index of MIN value in attributes' range array.
- MIN - Static variable in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
-
Index of min value in an array of attributes' range.
- min - Variable in class weka.experiment.Stats
-
The minimum value seen, or Double.NaN if no values seen
- MIN_RULE - Static variable in class weka.classifiers.meta.Vote
-
combination rule: Minimum Probability
- MIN_SF_PROB - Static variable in class weka.classifiers.Evaluation
-
The minimum probablility accepted from an estimator to avoid
taking log(0) in Sf calculations.
- MIN_VALUE - Static variable in class weka.classifiers.meta.ThresholdSelector
-
The minimum value for the criterion.
- minAbs(int, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
-
Returns the minimum absolute value of some elements of a column,
that is, the elements of A[i0:i1][j].
- minBagDistance(Instance, Instance) - Method in class weka.classifiers.mi.MIOptimalBall
-
Calculate the distance from one data point to a bag
- minBoxRelWidthTipText() - Method in class weka.core.neighboursearch.KDTree
-
Tip text for this property.
- minBucketSizeTipText() - Method in class weka.classifiers.rules.OneR
-
Returns the tip text for this property
- minChangeTipText() - Method in class weka.clusterers.sIB
-
Returns the tip text for this property.
- minChunkSizeTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
- minDataDLIfDeleted(int, double, boolean) - Method in class weka.classifiers.rules.RuleStats
-
Compute the minimal data description length of the ruleset
if the rule in the given position is deleted.
The min_data_DL_if_deleted = data_DL_if_deleted - potential
- minDataDLIfExists(int, double, boolean) - Method in class weka.classifiers.rules.RuleStats
-
Compute the minimal data description length of the ruleset
if the rule in the given position is NOT deleted.
The min_data_DL_if_n_deleted = data_DL_if_n_deleted - potential
- minDefaultTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Returns the tip text for this property
- mIndices - Variable in class weka.gui.SortedTableModel
-
the mapping between displayed and actual index
- mineCARs(Instances) - Method in class weka.associations.Apriori
-
Method that mines all class association rules with minimum support and
with a minimum confidence.
- mineCARs(Instances) - Method in interface weka.associations.CARuleMiner
-
Method for mining class association rules.
- mineCARs(Instances) - Method in class weka.associations.PredictiveApriori
-
Method that mines the n best class association rules.
- mineTree(FPGrowth.FPTreeRoot, FPGrowth.FrequentItemSets, int, FPGrowth.FrequentBinaryItemSet, int) - Method in class weka.associations.FPGrowth
-
Find large item sets in the FP-tree.
- minGroupTipText() - Method in class weka.classifiers.meta.RotationForest
-
Returns the tip text for this property
- minimax(Instances, int) - Static method in class weka.classifiers.mi.SimpleMI
-
Get the minimal and maximal value of a certain attribute in a certain data
- minimaxTipText() - Method in class weka.classifiers.mi.MISMO
-
Returns the tip text for this property
- minimizeExpectedCostTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
-
- minimizeWindows() - Method in class weka.gui.Main
-
minimizes all windows.
- minimumBucketSizeTipText() - Method in class weka.attributeSelection.OneRAttributeEval
-
Returns a string for this option suitable for display in the gui
as a tip text
- minIndex(int[]) - Static method in class weka.core.Utils
-
Returns index of minimum element in a given
array of integers.
- minIndex(double[]) - Static method in class weka.core.Utils
-
Returns index of minimum element in a given
array of doubles.
- MiningFieldMetaInfo - Class in weka.core.pmml
-
Class encapsulating information about a MiningField.
- MiningFieldMetaInfo(Element) - Constructor for class weka.core.pmml.MiningFieldMetaInfo
-
Constructs a new MiningFieldMetaInfo object.
- MiningSchema - Class in weka.core.pmml
-
This class encapsulates the mining schema from
a PMML xml file.
- MiningSchema(Element, Instances, TransformationDictionary) - Constructor for class weka.core.pmml.MiningSchema
-
Constructor for MiningSchema.
- minInstNumTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Returns the tip text for this property
- minInstNumTipText() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Returns the tip text for this property
- MINLOG - Static variable in class weka.core.Statistics
-
- minMetricTipText() - Method in class weka.associations.Apriori
-
Returns the tip text for this property
- minMetricTipText() - Method in class weka.associations.FPGrowth
-
Returns the tip text for this property
- MINND - Class in weka.classifiers.mi
-
Multiple-Instance Nearest Neighbour with Distribution learner.
It uses gradient descent to find the weight for each dimension of each exeamplar from the starting point of 1.0.
- MINND() - Constructor for class weka.classifiers.mi.MINND
-
- minNoTipText() - Method in class weka.classifiers.rules.ConjunctiveRule
-
Returns the tip text for this property
- minNoTipText() - Method in class weka.classifiers.rules.JRip
-
Returns the tip text for this property
- minNoTipText() - Method in class weka.classifiers.rules.Ridor
-
Returns the tip text for this property
- minNumClustersTipText() - Method in class weka.clusterers.XMeans
-
Returns the tip text for this property.
- minNumInstancesTipText() - Method in class weka.classifiers.trees.FT
-
Returns the tip text for this property
- minNumInstancesTipText() - Method in class weka.classifiers.trees.LMT
-
Returns the tip text for this property
- minNumInstancesTipText() - Method in class weka.classifiers.trees.m5.M5Base
-
Returns the tip text for this property
- minNumObjTipText() - Method in class weka.classifiers.rules.PART
-
Returns the tip text for this property
- minNumObjTipText() - Method in class weka.classifiers.trees.BFTree
-
Returns the tip text for this property
- minNumObjTipText() - Method in class weka.classifiers.trees.J48
-
Returns the tip text for this property
- minNumObjTipText() - Method in class weka.classifiers.trees.J48graft
-
Returns the tip text for this property
- minNumObjTipText() - Method in class weka.classifiers.trees.SimpleCart
-
Returns the tip text for this property
- minNumTipText() - Method in class weka.classifiers.trees.RandomTree
-
Returns the tip text for this property
- minNumTipText() - Method in class weka.classifiers.trees.REPTree
-
Returns the tip text for this property
- MINOR - Static variable in class weka.core.Version
-
the minor version
- minPointsTipText() - Method in class weka.clusterers.DBScan
-
Returns the tip text for this property
- minPointsTipText() - Method in class weka.clusterers.OPTICS
-
Returns the tip text for this property
- minProb - Variable in class weka.classifiers.lazy.kstar.KStarWrapper
-
used/reused to hold the smallest transformation probability
- minRadiusTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Returns the tip text for this property
- minRangeTipText() - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Returns the tip text for this property
- minRuleSizeTipText() - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Returns the tip text for this property
- minsAndMaxs(Instances, double[][], int) - Method in class weka.classifiers.trees.j48.C45Split
-
Returns the minsAndMaxs of the index.th subset.
- minStdDevTipText() - Method in class weka.classifiers.functions.RBFNetwork
-
Returns the tip text for this property
- minStdDevTipText() - Method in class weka.clusterers.EM
-
Returns the tip text for this property
- minStdDevTipText() - Method in class weka.clusterers.MakeDensityBasedClusterer
-
Returns the tip text for this property
- minSupportTipText() - Method in class weka.associations.GeneralizedSequentialPatterns
-
Returns the minimum support option tip text for the Weka GUI.
- minTermFreqTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Returns the tip text for this property.
- minThresholdTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Returns the tip text for this property
- MINUS - Static variable in interface weka.core.mathematicalexpression.sym
-
- minus(double) - Method in class weka.core.matrix.DoubleVector
-
Subtracts a value
- minus(DoubleVector) - Method in class weka.core.matrix.DoubleVector
-
Subtracts another DoubleVector element by element
- minus(Matrix) - Method in class weka.core.matrix.Matrix
-
C = A - B
- MINUS - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
-
- minusEquals(double) - Method in class weka.core.matrix.DoubleVector
-
Subtracts a value in place
- minusEquals(DoubleVector) - Method in class weka.core.matrix.DoubleVector
-
Subtracts another DoubleVector element by element in place
- minusEquals(Matrix) - Method in class weka.core.matrix.Matrix
-
A = A - B
- minVariancePropTipText() - Method in class weka.classifiers.trees.REPTree
-
Returns the tip text for this property
- MIOptimalBall - Class in weka.classifiers.mi
-
This classifier tries to find a suitable ball in the multiple-instance space, with a certain data point in the instance space as a ball center.
- MIOptimalBall() - Constructor for class weka.classifiers.mi.MIOptimalBall
-
- MIPolyKernel - Class in weka.classifiers.mi.supportVector
-
The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p
Valid options are:
- MIPolyKernel() - Constructor for class weka.classifiers.mi.supportVector.MIPolyKernel
-
default constructor - does nothing.
- MIPolyKernel(Instances, int, double, boolean) - Constructor for class weka.classifiers.mi.supportVector.MIPolyKernel
-
Creates a new MIPolyKernel
instance.
- MIRBFKernel - Class in weka.classifiers.mi.supportVector
-
The RBF kernel.
- MIRBFKernel() - Constructor for class weka.classifiers.mi.supportVector.MIRBFKernel
-
default constructor - does nothing.
- MIRBFKernel(Instances, int, double) - Constructor for class weka.classifiers.mi.supportVector.MIRBFKernel
-
Constructor.
- MISMO - Class in weka.classifiers.mi
-
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.
This implementation globally replaces all missing values and transforms nominal attributes into binary ones.
- MISMO() - Constructor for class weka.classifiers.mi.MISMO
-
- MISMO.BinaryMISMO - Class in weka.classifiers.mi
-
Class for building a binary support vector machine.
- MISSING_ID - Static variable in class weka.core.TechnicalInformation
-
will be returned if no ID can be generated
- MISSING_SHAPE - Static variable in class weka.gui.visualize.Plot2D
-
- MISSING_VALUE - Static variable in interface weka.classifiers.evaluation.Prediction
-
Constant representing a missing value.
- MISSING_VALUE - Static variable in class weka.core.Instance
-
Constant representing a missing value.
- missingArcs(BayesNet) - Method in class weka.classifiers.bayes.net.BIFReader
-
Count nr of arcs missing from other network compared to current network
Note that an arc is not 'missing' if it is reversed.
- missingCount - Variable in class weka.core.AttributeStats
-
The number of missing values
- missingMergeTipText() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
-
Returns the tip text for this property
- missingMergeTipText() - Method in class weka.attributeSelection.GainRatioAttributeEval
-
Returns the tip text for this property
- missingMergeTipText() - Method in class weka.attributeSelection.InfoGainAttributeEval
-
Returns the tip text for this property
- missingMergeTipText() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
-
Returns the tip text for this property
- missingModeTipText() - Method in class weka.classifiers.lazy.KStar
-
Returns the tip text for this property
- missingSeparateTipText() - Method in class weka.attributeSelection.CfsSubsetEval
-
Returns the tip text for this property
- missingValue() - Static method in class weka.core.Instance
-
Returns the double that codes "missing".
- missingValuesTipText() - Method in class weka.associations.Tertius
-
Returns the tip text for this property.
- missingValueTipText() - Method in class weka.core.converters.CSVLoader
-
Returns the tip text for this property.
- MISVM - Class in weka.classifiers.mi
-
Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL).
- MISVM() - Constructor for class weka.classifiers.mi.MISVM
-
- MIWrapper - Class in weka.classifiers.mi
-
A simple Wrapper method for applying standard propositional learners to multi-instance data.
For more information see:
E.
- MIWrapper() - Constructor for class weka.classifiers.mi.MIWrapper
-
- mixingDistribution - Variable in class weka.classifiers.functions.pace.MixtureDistribution
-
- MixtureDistribution - Class in weka.classifiers.functions.pace
-
Abtract class for manipulating mixture distributions.
- MixtureDistribution() - Constructor for class weka.classifiers.functions.pace.MixtureDistribution
-
- mModel - Variable in class weka.gui.SortedTableModel
-
the actual table model
- MODEL_FILE_EXTENSION - Static variable in class weka.gui.explorer.ClassifierPanel
-
The filename extension that should be used for model files
- MODEL_FILE_EXTENSION - Static variable in class weka.gui.explorer.ClustererPanel
-
The filename extension that should be used for model files
- MODEL_FT - Static variable in class weka.classifiers.trees.FT
-
model types
- MODEL_FTInner - Static variable in class weka.classifiers.trees.FT
-
- MODEL_FTLeaves - Static variable in class weka.classifiers.trees.FT
-
- modelBuilt() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
-
flag to indicate whether the model was built yet
- modelDistributionForInstance(Instance) - Method in class weka.classifiers.trees.ft.FTtree
-
Returns the class probabilities for an instance according to the logistic model at the node.
- modelDistributionForInstance(Instance) - Method in class weka.classifiers.trees.lmt.LMTNode
-
Returns the class probabilities for an instance according to the logistic model at the node.
- modelErrors() - Method in class weka.classifiers.trees.lmt.LMTNode
-
Updates the numIncorrectModel field for all nodes.
- modelErrors() - Method in class weka.classifiers.trees.SimpleCart
-
Updates the numIncorrectModel field for all nodes when subtree (to be
pruned) is rooted.
- modelFileTipText() - Method in class weka.classifiers.misc.SerializedClassifier
-
Returns the tip text for this property
- ModelPerformanceChart - Class in weka.gui.beans
-
Bean that can be used for displaying threshold curves (e.g.
- ModelPerformanceChart() - Constructor for class weka.gui.beans.ModelPerformanceChart
-
- ModelPerformanceChartBeanInfo - Class in weka.gui.beans
-
Bean info class for the model performance chart
- ModelPerformanceChartBeanInfo() - Constructor for class weka.gui.beans.ModelPerformanceChartBeanInfo
-
- ModelSelection - Class in weka.classifiers.trees.j48
-
Abstract class for model selection criteria.
- ModelSelection() - Constructor for class weka.classifiers.trees.j48.ModelSelection
-
- modelsToString() - Method in class weka.classifiers.trees.ft.FTtree
-
Returns a string describing the logistic regression function at the node.
- modelsToString() - Method in class weka.classifiers.trees.lmt.LMTNode
-
Returns a string describing the logistic regression function at the node.
- modelToString(DefaultListModel) - Method in class weka.gui.sql.SqlViewer
-
converts the given model into a comma-separated string.
- modelTypeTipText() - Method in class weka.classifiers.trees.FT
-
Returns the tip text for this property
- modifyHeader(Instances) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
modifies the header of the Instances and returns the format w/o
any instances
- modifyHeaderTipText() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
Returns the tip text for this property
- modifyHeaderTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Returns the tip text for this property
- momentumTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- moralize(BayesNet) - Method in class weka.classifiers.bayes.net.MarginCalculator
-
moralize DAG and calculate
adjacency matrix representation for a Bayes Network, effecively
converting the directed acyclic graph to an undirected graph.
- mostExplainingColumn(PaceMatrix, IntVector, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
-
Returns the index of the column that has the largest (squared)
response, when each of columns pvt[ks:] is moved to become the
ks-th column.
- mouseClicked(MouseEvent) - Method in class weka.gui.arffviewer.ArffPanel
-
Invoked when a mouse button has been pressed and released on a component
- mouseClicked(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
-
Does nothing.
- mouseDragged(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
-
Performs intermediate updates to what the user wishes to do.
- mouseEntered(MouseEvent) - Method in class weka.gui.arffviewer.ArffPanel
-
Invoked when the mouse enters a component.
- mouseEntered(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
-
Does nothing.
- mouseExited(MouseEvent) - Method in class weka.gui.arffviewer.ArffPanel
-
Invoked when the mouse exits a component
- mouseExited(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
-
Does nothing.
- mouseMoved(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
-
Does nothing.
- mousePressed(MouseEvent) - Method in class weka.gui.arffviewer.ArffPanel
-
Invoked when a mouse button has been pressed on a component
- mousePressed(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
-
Determines what action the user wants to perform.
- mouseReleased(MouseEvent) - Method in class weka.gui.arffviewer.ArffPanel
-
Invoked when a mouse button has been released on a component.
- mouseReleased(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
-
Performs the final stages of what the user wants to perform.
- MOVE_DOWN - Static variable in class weka.gui.JListHelper
-
moves items down
- MOVE_UP - Static variable in class weka.gui.JListHelper
-
moves items up
- moveBottom(JList) - Static method in class weka.gui.JListHelper
-
moves the selected items to the end
- moveCentroid(int, Instances, boolean) - Method in class weka.clusterers.SimpleKMeans
-
Move the centroid to it's new coordinates.
- moveDown(JList) - Static method in class weka.gui.JListHelper
-
moves the selected item down by 1
- MoveInstanceToBestCluster(Instance) - Method in class weka.clusterers.CLOPE
-
Move instance to best cluster
- moveItems(JList, int, int) - Static method in class weka.gui.JListHelper
-
moves the selected items by a certain amount of items in a given direction
- moveTop(JList) - Static method in class weka.gui.JListHelper
-
moves the selected items to the top
- moveUp(JList) - Static method in class weka.gui.JListHelper
-
moves the selected items up by 1
- MOVING - Static variable in class weka.gui.beans.KnowledgeFlowApp
-
- mSortColumn - Variable in class weka.gui.SortedTableModel
-
the sort column
- MultiBoostAB - Class in weka.classifiers.meta
-
Class for boosting a classifier using the MultiBoosting method.
MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees.
- MultiBoostAB() - Constructor for class weka.classifiers.meta.MultiBoostAB
-
- MultiClassClassifier - Class in weka.classifiers.meta
-
A metaclassifier for handling multi-class datasets with 2-class classifiers.
- MultiClassClassifier() - Constructor for class weka.classifiers.meta.MultiClassClassifier
-
Constructor.
- MultiFilter - Class in weka.filters
-
Applies several filters successively.
- MultiFilter() - Constructor for class weka.filters.MultiFilter
-
- MultiInstanceCapabilitiesHandler - Interface in weka.core
-
Multi-Instance classifiers can specify an additional Capabilities object
for the data in the relational attribute, since the format of multi-instance
data is fixed to "bag/NOMINAL,data/RELATIONAL,class".
- multiInstanceHandler() - Method in class weka.associations.CheckAssociator
-
Checks whether the scheme handles multi-instance data.
- multiInstanceHandler() - Method in class weka.attributeSelection.CheckAttributeSelection
-
Checks whether the scheme handles multi-instance data.
- multiInstanceHandler() - Method in class weka.classifiers.CheckClassifier
-
Checks whether the scheme handles multi-instance data.
- multiInstanceHandler() - Method in class weka.classifiers.functions.supportVector.CheckKernel
-
Checks whether the scheme handles multi-instance data.
- multiInstanceHandler() - Method in class weka.clusterers.CheckClusterer
-
Checks whether the scheme handles multi-instance data.
- MultiInstanceToPropositional - Class in weka.filters.unsupervised.attribute
-
Converts the multi-instance dataset into single instance dataset so that the Nominalize, Standardize and other type of filters or transformation can be applied to these data for the further preprocessing.
Note: the first attribute of the converted dataset is a nominal attribute and refers to the bagId.
- MultiInstanceToPropositional() - Constructor for class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
-
- MultilayerPerceptron - Class in weka.classifiers.functions
-
A Classifier that uses backpropagation to classify instances.
This network can be built by hand, created by an algorithm or both.
- MultilayerPerceptron() - Constructor for class weka.classifiers.functions.MultilayerPerceptron
-
The constructor.
- MultilayerPerceptron.NeuralEnd - Class in weka.classifiers.functions
-
This inner class is used to connect the nodes in the network up to
the data that they are classifying, Note that objects of this class are
only suitable to go on the attribute side or class side of the network
and not both.
- MultiNomialBMAEstimator - Class in weka.classifiers.bayes.net.estimate
-
Multinomial BMA Estimator.
- MultiNomialBMAEstimator() - Constructor for class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
-
- multinomialWordTipText() - Method in class weka.classifiers.bayes.DMNBtext
-
Returns the tip text for this property
- MultipleClassifiersCombiner - Class in weka.classifiers
-
Abstract utility class for handling settings common to
meta classifiers that build an ensemble from multiple classifiers.
- MultipleClassifiersCombiner() - Constructor for class weka.classifiers.MultipleClassifiersCombiner
-
- multiply(Matrix) - Method in class weka.core.Matrix
-
Deprecated.
Returns the multiplication of two matrices
- multiResultsetFull(int, int) - Method in class weka.experiment.PairedTTester
-
Creates a comparison table where a base resultset is compared to the
other resultsets.
- multiResultsetFull(int, int) - Method in interface weka.experiment.Tester
-
Creates a comparison table where a base resultset is compared to the
other resultsets.
- multiResultsetRanking(int) - Method in class weka.experiment.PairedTTester
-
returns a ranking of the resultsets
- multiResultsetRanking(int) - Method in interface weka.experiment.Tester
-
- multiResultsetSummary(int) - Method in class weka.experiment.PairedTTester
-
Carries out a comparison between all resultsets, counting the number
of datsets where one resultset outperforms the other.
- multiResultsetSummary(int) - Method in interface weka.experiment.Tester
-
Carries out a comparison between all resultsets, counting the number
of datsets where one resultset outperforms the other.
- multiResultsetWins(int, int[][]) - Method in class weka.experiment.PairedTTester
-
Carries out a comparison between all resultsets, counting the number
of datsets where one resultset outperforms the other.
- multiResultsetWins(int, int[][]) - Method in interface weka.experiment.Tester
-
Carries out a comparison between all resultsets, counting the number
of datsets where one resultset outperforms the other.
- MultiScheme - Class in weka.classifiers.meta
-
Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
- MultiScheme() - Constructor for class weka.classifiers.meta.MultiScheme
-
- mutationProbTipText() - Method in class weka.attributeSelection.GeneticSearch
-
Returns the tip text for this property
- MyHeap(int) - Constructor for class weka.core.neighboursearch.CoverTree.MyHeap
-
constructor.
- MyHeap(int) - Constructor for class weka.core.neighboursearch.NearestNeighbourSearch.MyHeap
-
constructor.
- MyHeapElement(double) - Constructor for class weka.core.neighboursearch.CoverTree.MyHeapElement
-
constructor.
- MyHeapElement(int, double) - Constructor for class weka.core.neighboursearch.NearestNeighbourSearch.MyHeapElement
-
constructor.
- MyIdxList() - Constructor for class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList
-
Constructor.
- MyIdxList(int) - Constructor for class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList
-
Constructor.
- s_fileFormatsAvailable - Static variable in class weka.gui.beans.SerializedModelSaver
-
Available file formats.
- s_startupListeners - Static variable in class weka.gui.beans.KnowledgeFlowApp
-
- sameClauseAs(Rule) - Method in class weka.associations.tertius.Rule
-
Test if this rule and another rule correspond to the same clause.
- sameClauseTipText() - Method in class weka.associations.Tertius
-
Returns the tip text for this property.
- SAMPLE_SIZE_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
-
attribute name: Sample Size
- sampleSizePercentTipText() - Method in class weka.classifiers.meta.GridSearch
-
Returns the tip text for this property
- sampleSizePercentTipText() - Method in class weka.filters.supervised.instance.Resample
-
Returns the tip text for this property.
- sampleSizePercentTipText() - Method in class weka.filters.unsupervised.instance.Resample
-
Returns the tip text for this property
- sampleSizeTipText() - Method in class weka.attributeSelection.ReliefFAttributeEval
-
Returns the tip text for this property
- sampleSizeTipText() - Method in class weka.classifiers.functions.LeastMedSq
-
Returns the tip text for this property
- sampleSizeTipText() - Method in class weka.filters.unsupervised.instance.ReservoirSample
-
Returns the tip text for this property
- sanitizeFilename(String) - Method in class weka.gui.beans.Saver
-
makes sure that the filename is valid, i.e., replaces slashes,
backslashes and colons with underscores ("_").
- sanitizeFilename(String) - Method in class weka.gui.beans.SerializedModelSaver
-
makes sure that the filename is valid, i.e., replaces slashes,
backslashes and colons with underscores ("_").
- satisfies(Instance) - Method in class weka.associations.tertius.AttributeValueLiteral
-
- satisfies(Instance) - Method in class weka.associations.tertius.Literal
-
- save(StringBuffer) - Method in class weka.gui.SaveBuffer
-
Save a buffer
- saveBatch() - Method in class weka.gui.beans.Saver
-
Saves instances in batch mode
- saveBinary(File, Object, Instances) - Static method in class weka.gui.beans.SerializedModelSaver
-
Save a model in binary form.
- saveBuffer() - Method in class weka.gui.experiment.ResultsPanel
-
Save the currently selected result buffer to a file.
- saveBuffer(String) - Method in class weka.gui.explorer.AssociationsPanel
-
Save the currently selected associator output to a file.
- saveBuffer(String) - Method in class weka.gui.explorer.AttributeSelectionPanel
-
Save the named buffer to a file.
- saveBuffer(String) - Method in class weka.gui.explorer.ClassifierPanel
-
Save the currently selected classifier output to a file.
- saveBuffer(String) - Method in class weka.gui.explorer.ClustererPanel
-
Save the currently selected clusterer output to a file.
- SaveBuffer - Class in weka.gui
-
This class handles the saving of StringBuffers to files.
- SaveBuffer(Logger, Component) - Constructor for class weka.gui.SaveBuffer
-
Constructor
- saveChanges() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
if the file is changed it pops up a dialog whether to change the
settings.
- saveChanges(boolean) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
if the file is changed it pops up a dialog whether to change the
settings.
- saveClassifier(String, Classifier, Instances) - Method in class weka.gui.explorer.ClassifierPanel
-
Saves the currently selected classifier
- saveClusterer(String, Clusterer, Instances, int[]) - Method in class weka.gui.explorer.ClustererPanel
-
Saves the currently selected clusterer
- saveComponent() - Method in class weka.gui.visualize.PrintableComponent
-
displays a save dialog for saving the panel to a file.
- saveComponent() - Method in interface weka.gui.visualize.PrintableHandler
-
displays a save dialog for saving the component to a file.
- saveComponent() - Method in class weka.gui.visualize.PrintablePanel
-
displays a save dialog for saving the panel to a file.
- saveFile() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
saves the current data into a file
- saveFileAs() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
saves the current data into a new file
- saveHistory() - Method in class weka.gui.SimpleCLIPanel
-
saves the current command history in the user's home directory.
- saveHistory() - Method in class weka.gui.sql.SqlViewer
-
saves the history properties of the SqlViewer in the user's home directory.
- saveImage(String) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
- saveInstanceDataTipText() - Method in class weka.classifiers.trees.ADTree
-
- saveInstanceDataTipText() - Method in class weka.classifiers.trees.J48
-
Returns the tip text for this property
- saveInstanceDataTipText() - Method in class weka.classifiers.trees.J48graft
-
Returns the tip text for this property
- saveInstanceDataTipText() - Method in class weka.clusterers.Cobweb
-
Returns the tip text for this property
- saveInstancesTipText() - Method in class weka.classifiers.trees.M5P
-
Returns the tip text for this property
- saveInstancesToFile(AbstractFileSaver, Instances) - Method in class weka.gui.explorer.PreprocessPanel
-
saves the data with the specified saver
- saveKOML(File, Object, Instances) - Static method in class weka.gui.beans.SerializedModelSaver
-
Save a model in KOML deep object serialized XML form.
- saveLayout(OutputStream) - Method in class weka.gui.beans.KnowledgeFlowApp
-
Save the knowledge flow into the OutputStream passed at input.
- saveModel() - Method in class weka.gui.beans.Classifier
-
- saveModel() - Method in class weka.gui.beans.Clusterer
-
- saveObject(Object) - Method in class weka.gui.GenericObjectEditor.GOEPanel
-
Saves an object to a file selected by the user.
- Saver - Interface in weka.core.converters
-
Interface to something that can save Instances to an output destination in some
format.
- Saver - Class in weka.gui.beans
-
Saves data sets using weka.core.converter classes
- Saver() - Constructor for class weka.gui.beans.Saver
-
Contsructor
- SAVER_DIALOG - Static variable in class weka.gui.ConverterFileChooser
-
the saver dialog
- SaverBeanInfo - Class in weka.gui.beans
-
Bean info class for the saver bean
- SaverBeanInfo() - Constructor for class weka.gui.beans.SaverBeanInfo
-
- SaverCustomizer - Class in weka.gui.beans
-
GUI Customizer for the saver bean
- SaverCustomizer() - Constructor for class weka.gui.beans.SaverCustomizer
-
Constructor
- saveSize() - Method in class weka.gui.sql.SqlViewer
-
obtains the size of the panel and saves it in the history.
- saveToFile(String, Object) - Static method in class weka.core.Debug
-
writes the serialized object to the speicified file
- saveTransformedData(Instances) - Method in class weka.gui.explorer.AttributeSelectionPanel
-
Popup a SaveDialog for saving the transformed data
- saveWeights() - Method in class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd
-
Call this to have the connection save the current
weights.
- saveWeights() - Method in class weka.classifiers.functions.neural.NeuralConnection
-
Call this to have the connection save the current
weights.
- saveWeights() - Method in class weka.classifiers.functions.neural.NeuralNode
-
Call this to have the connection save the current
weights.
- 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.
- saveXStream(File, Object, Instances) - Static method in class weka.gui.beans.SerializedModelSaver
-
Save a model in XStream deep object serialized XML form.
- scalarMultiply(double) - Method in class weka.core.AlgVector
-
Computes the scalar product of this vector with a scalar
- scale(int) - Method in class weka.gui.beans.BeanVisual
-
Reduce this BeanVisual's icon size by the given factor
- scale(double, double) - Method in class weka.gui.visualize.PostscriptGraphics
-
- scaleTipText() - Method in class weka.filters.unsupervised.attribute.Normalize
-
Returns the tip text for this property.
- Scanner - Class in weka.core.mathematicalexpression
-
A scanner for mathematical expressions.
- Scanner(InputStream, SymbolFactory) - Constructor for class weka.core.mathematicalexpression.Scanner
-
- Scanner(Reader) - Constructor for class weka.core.mathematicalexpression.Scanner
-
Creates a new scanner
There is also a java.io.InputStream version of this constructor.
- Scanner(InputStream) - Constructor for class weka.core.mathematicalexpression.Scanner
-
Creates a new scanner.
- Scanner - Class in weka.filters.unsupervised.instance.subsetbyexpression
-
A scanner for evaluating whether an Instance is to be included in a subset
or not.
- Scanner(InputStream, SymbolFactory) - Constructor for class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
-
- Scanner(Reader) - Constructor for class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
-
Creates a new scanner
There is also a java.io.InputStream version of this constructor.
- Scanner(InputStream) - Constructor for class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
-
Creates a new scanner.
- ScatterPlotMatrix - Class in weka.gui.beans
-
Bean that encapsulates weka.gui.visualize.MatrixPanel for displaying a
scatter plot matrix.
- ScatterPlotMatrix() - Constructor for class weka.gui.beans.ScatterPlotMatrix
-
- ScatterPlotMatrixBeanInfo - Class in weka.gui.beans
-
Bean info class for the scatter plot matrix bean
- ScatterPlotMatrixBeanInfo() - Constructor for class weka.gui.beans.ScatterPlotMatrixBeanInfo
-
- ScatterSearchV1 - Class in weka.attributeSelection
-
Class for performing the Sequential Scatter Search.
- ScatterSearchV1() - Constructor for class weka.attributeSelection.ScatterSearchV1
-
- ScatterSearchV1.Subset - Class in weka.attributeSelection
-
- Scoreable - Interface in weka.classifiers.bayes.net.search.local
-
Interface for allowing to score a classifier
- scoreTypeTipText() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
-
- scrollToVisible(int, int) - Method in class weka.gui.JTableHelper
-
Assumes table is contained in a JScrollPane.
- scrollToVisible(JTable, int, int) - Static method in class weka.gui.JTableHelper
-
Assumes table is contained in a JScrollPane.
- search() - Method in class weka.associations.Tertius
-
Search in the space of hypotheses the rules that have the highest
confirmation.
- search(ASEvaluation, Instances) - Method in class weka.attributeSelection.ASSearch
-
Searches the attribute subset/ranking space.
- search(ASEvaluation, Instances) - Method in class weka.attributeSelection.BestFirst
-
Searches the attribute subset space by best first search
- search(ASSearch, ASEvaluation, Instances) - Method in class weka.attributeSelection.CheckAttributeSelection
-
Performs a attribute selection with the given search and evaluation scheme
on the provided data.
- 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.GeneticSearch
-
Searches the attribute subset space using a genetic algorithm.
- search(ASEvaluation, Instances) - Method in class weka.attributeSelection.GreedyStepwise
-
Searches the attribute subset space by forward selection.
- search(ASEvaluation, Instances) - Method in class weka.attributeSelection.LinearForwardSelection
-
Searches the attribute subset space by linear forward selection
- 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 randomly.
- search(ASEvaluation, Instances) - Method in class weka.attributeSelection.Ranker
-
Kind of a dummy search algorithm.
- search(ASEvaluation, Instances) - Method in class weka.attributeSelection.RankSearch
-
Ranks attributes using the specified attribute evaluator and then
searches the ranking using the supplied subset evaluator.
- search(ASEvaluation, Instances) - Method in class weka.attributeSelection.ScatterSearchV1
-
Searches the attribute subset space using Scatter Search.
- search(ASEvaluation, Instances) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Searches the attribute subset space by subset size forward selection
- search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
-
Search for Bayes network structure using ICS algorithm
- search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
-
search determines the network structure/graph of the network
with a genetic search algorithm.
- search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.global.HillClimber
-
search determines the network structure/graph of the network
with the Taby algorithm.
- search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.global.K2
-
search determines the network structure/graph of the network
with the K2 algorithm, restricted by its initial structure (which can
be an empty graph, or a Naive Bayes graph.
- search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
-
search determines the network structure/graph of the network
with the repeated hill climbing.
- search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
-
- search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
-
search determines the network structure/graph of the network
with the Tabu search algorithm.
- search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
-
search determines the network structure/graph of the network
with a genetic search algorithm.
- search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.local.HillClimber
-
search determines the network structure/graph of the network
with the Taby algorithm.
- search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.local.K2
-
search determines the network structure/graph of the network
with the K2 algorithm, restricted by its initial structure (which can
be an empty graph, or a Naive Bayes graph.
- search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
-
search determines the network structure/graph of the network
- search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
-
search determines the network structure/graph of the network
with the repeated hill climbing.
- search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
-
- search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
-
search determines the network structure/graph of the network
with the Tabu search algorithm.
- search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
-
- search(ASEvaluation, Instances) - Method in class weka.classifiers.rules.DTNB.BackwardsWithDelete
-
- search() - Method in class weka.gui.arffviewer.ArffPanel
-
searches for a string in the cells
- search() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
searches for a string in the cells
- search(Vector, String) - Method in class weka.gui.HierarchyPropertyParser
-
Helper function to search for the given target string in a
given vector in which the elements' value may hopefully is equal
to the target.
- SEARCH_METHOD_FLOATING - Static variable in class weka.attributeSelection.LinearForwardSelection
-
- SEARCH_METHOD_FORWARD - Static variable in class weka.attributeSelection.LinearForwardSelection
-
search directions
- SearchAlgorithm - Class in weka.classifiers.bayes.net.search
-
This is the base class for all search algorithms for learning Bayes networks.
- SearchAlgorithm() - Constructor for class weka.classifiers.bayes.net.search.SearchAlgorithm
-
c'tor
- searchAlgorithmTipText() - Method in class weka.classifiers.bayes.BayesNet
-
- searchBackwardsTipText() - Method in class weka.attributeSelection.GreedyStepwise
-
Returns the tip text for this property
- searchFinish() - Method in class weka.core.neighboursearch.PerformanceStats
-
Signals end of the nearest neighbour search.
- searchFinish() - Method in class weka.core.neighboursearch.TreePerformanceStats
-
Signals end of the nearest neighbour search.
- SEARCHPATH_ALL - Static variable in class weka.classifiers.trees.ADTree
-
search mode: Expand all paths
- SEARCHPATH_HEAVIEST - Static variable in class weka.classifiers.trees.ADTree
-
search mode: Expand the heaviest path
- SEARCHPATH_RANDOM - Static variable in class weka.classifiers.trees.ADTree
-
search mode: Expand a random path
- SEARCHPATH_ZPURE - Static variable in class weka.classifiers.trees.ADTree
-
search mode: Expand the best z-pure path
- searchPathTipText() - Method in class weka.classifiers.trees.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)
- searchStart() - Method in class weka.core.neighboursearch.PerformanceStats
-
Signals start of the nearest neighbour search.
- searchStart() - Method in class weka.core.neighboursearch.TreePerformanceStats
-
Signals start of the nearest neighbour search.
- searchTerminationTipText() - Method in class weka.attributeSelection.BestFirst
-
Returns the tip text for this property
- searchTerminationTipText() - Method in class weka.attributeSelection.LinearForwardSelection
-
Returns the tip text for this property
- searchTipText() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
-
Returns the tip text for this property
- searchTipText() - Method in class weka.classifiers.rules.DecisionTable
-
Returns the tip text for this property
- searchTipText() - Method in class weka.filters.supervised.attribute.AttributeSelection
-
Returns the tip text for this property
- secondChoiceHeuristic(int) - Method in class weka.classifiers.functions.supportVector.RegSMO
-
applies heuristic for finding candidate that is expected to lead to
good gain when applying takeStep together with second candidate.
- secondInstanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceJoiner
-
- secondInstanceProduced(InstanceEvent) - Method in interface weka.gui.streams.SerialInstanceListener
-
- secondValueIndexTipText() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
-
- secondValueIndexTipText() - Method in class weka.filters.unsupervised.attribute.SwapValues
-
- SEED - Static variable in class weka.associations.PriorEstimation
-
The random seed used for the random rule generation step.
- seedTipText() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
-
Returns the tip text for this property
- seedTipText() - Method in class weka.attributeSelection.GeneticSearch
-
Returns the tip text for this property
- seedTipText() - Method in class weka.attributeSelection.OneRAttributeEval
-
Returns a string for this option suitable for display in the gui
as a tip text
- seedTipText() - Method in class weka.attributeSelection.RandomSearch
-
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.ScatterSearchV1
-
Returns the tip text for this property
- seedTipText() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Returns the tip text for this property
- seedTipText() - Method in class weka.attributeSelection.WrapperSubsetEval
-
Returns the tip text for this property
- seedTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Returns the tip text for this property
- seedTipText() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
-
- seedTipText() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
-
- seedTipText() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
-
- seedTipText() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
-
- seedTipText() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
-
- seedTipText() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
-
- seedTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- seedTipText() - Method in class weka.classifiers.functions.supportVector.RegOptimizer
-
Returns the tip text for this property
- seedTipText() - Method in class weka.classifiers.functions.VotedPerceptron
-
Returns the tip text for this property
- seedTipText() - Method in class weka.classifiers.functions.Winnow
-
Returns the tip text for this property
- seedTipText() - Method in class weka.classifiers.meta.MultiScheme
-
Returns the tip text for this property
- seedTipText() - Method in class weka.classifiers.RandomizableClassifier
-
Returns the tip text for this property
- seedTipText() - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
-
Returns the tip text for this property
- seedTipText() - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner
-
Returns the tip text for this property
- seedTipText() - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer
-
Returns the tip text for this property
- seedTipText() - Method in class weka.classifiers.rules.ConjunctiveRule
-
Returns the tip text for this property
- seedTipText() - Method in class weka.classifiers.rules.JRip
-
Returns the tip text for this property
- seedTipText() - Method in class weka.classifiers.rules.PART
-
Returns the tip text for this property
- seedTipText() - Method in class weka.classifiers.rules.Ridor
-
Returns the tip text for this property
- seedTipText() - Method in class weka.classifiers.trees.J48
-
Returns the tip text for this property
- seedTipText() - Method in class weka.classifiers.trees.RandomForest
-
Returns the tip text for this property
- seedTipText() - Method in class weka.classifiers.trees.RandomTree
-
Returns the tip text for this property
- seedTipText() - Method in class weka.classifiers.trees.REPTree
-
Returns the tip text for this property
- seedTipText() - Method in class weka.clusterers.Cobweb
-
Returns the tip text for this property
- seedTipText() - Method in class weka.clusterers.RandomizableClusterer
-
Returns the tip text for this property
- seedTipText() - Method in class weka.clusterers.RandomizableDensityBasedClusterer
-
Returns the tip text for this property
- seedTipText() - Method in class weka.clusterers.RandomizableSingleClustererEnhancer
-
Returns the tip text for this property
- seedTipText() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Returns the tip text for this property.
- seedTipText() - Method in class weka.datagenerators.DataGenerator
-
Returns the tip text for this property
- seedTipText() - Method in class weka.filters.supervised.attribute.ClassOrder
-
Returns the tip text for this property
- seedTipText() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
-
Returns the tip text for this property
- seedTipText() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
-
Returns the tip text for this property
- seedTipText() - Method in class weka.filters.unsupervised.attribute.RandomSubset
-
Returns the tip text for this property.
- seedTipText() - Method in class weka.filters.unsupervised.instance.RemoveFolds
-
Returns the tip text for this property
- seedTipText() - Method in class weka.gui.beans.CrossValidationFoldMaker
-
Tip text for this property
- seedTipText() - Method in class weka.gui.beans.TrainTestSplitMaker
-
Tip text for this property
- select(int, int, int, int) - Method in class weka.core.Instances
-
Implements computation of the kth-smallest element according
to Manber's "Introduction to Algorithms".
- select(double[], int[], int, int, int, int) - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
-
Implements computation of the kth-smallest element according
to Manber's "Introduction to Algorithms".
- select(int, int[], int, int, int) - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
-
Implements computation of the kth-smallest element according
to Manber's "Introduction to Algorithms".
- select(int, int[], int, int, int) - Method in class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension
-
Implements computation of the kth-smallest element according
to Manber's "Introduction to Algorithms".
- select(String) - Method in class weka.experiment.DatabaseUtils
-
Executes a SQL SELECT query that returns a ResultSet.
- SelectAttributes(ASEvaluation, String[]) - Static method in class weka.attributeSelection.AttributeSelection
-
Perform attribute selection with a particular evaluator and
a set of options specifying search method and input file etc.
- SelectAttributes(Instances) - Method in class weka.attributeSelection.AttributeSelection
-
Perform attribute selection on the supplied training instances.
- SelectAttributes(ASEvaluation, String[], Instances) - Static method in class weka.attributeSelection.AttributeSelection
-
Perform attribute selection with a particular evaluator and
a set of options specifying search method and options for the
search method and evaluator.
- selectAttributesCVSplit(Instances) - Method in class weka.attributeSelection.AttributeSelection
-
Select attributes for a split of the data.
- selectClasses(int, Random) - Method in class weka.classifiers.meta.RotationForest
-
Selects a non-empty subset of the classes
- selectedAttributes() - Method in class weka.attributeSelection.AttributeSelection
-
get the final selected set of attributes.
- SelectedTag - Class in weka.core
-
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.
- SelectedTag(String, Tag[]) - Constructor for class weka.core.SelectedTag
-
Creates a new SelectedTag
instance.
- SelectedTagEditor - Class in weka.gui
-
A PropertyEditor that uses tags, where the tags are obtained from a
weka.core.SelectedTag object.
- SelectedTagEditor() - Constructor for class weka.gui.SelectedTagEditor
-
- selectIndexProbabilistically(double[]) - Method in class weka.classifiers.meta.Decorate
-
Given cumulative probabilities select a nominal attribute value index
- SELECTING - Static variable in class weka.gui.beans.KnowledgeFlowApp
-
- SELECTION_BACKWARD - Static variable in class weka.attributeSelection.BestFirst
-
search direction: backward
- SELECTION_BIDIRECTIONAL - Static variable in class weka.attributeSelection.BestFirst
-
search direction: bidirectional
- SELECTION_FORWARD - Static variable in class weka.attributeSelection.BestFirst
-
search direction: forward
- SELECTION_GREEDY - Static variable in class weka.classifiers.functions.LinearRegression
-
Attribute selection method: Greedy method
- SELECTION_M5 - Static variable in class weka.classifiers.functions.LinearRegression
-
Attribute selection method: M5 method
- SELECTION_NONE - Static variable in class weka.classifiers.functions.LinearRegression
-
Attribute selection method: No attribute selection
- selectionThresholdTipText() - Method in class weka.attributeSelection.RaceSearch
-
Returns the tip text for this property
- selectModel(Instances) - Method in class weka.classifiers.trees.j48.BinC45ModelSelection
-
Selects C4.5-type split for the given dataset.
- selectModel(Instances, Instances) - Method in class weka.classifiers.trees.j48.BinC45ModelSelection
-
Selects C4.5-type split for the given dataset.
- selectModel(Instances) - Method in class weka.classifiers.trees.j48.C45ModelSelection
-
Selects C4.5-type split for the given dataset.
- selectModel(Instances, Instances) - Method in class weka.classifiers.trees.j48.C45ModelSelection
-
Selects C4.5-type split for the given dataset.
- selectModel(Instances) - Method in class weka.classifiers.trees.j48.ModelSelection
-
Selects a model for the given dataset.
- selectModel(Instances, Instances) - Method in class weka.classifiers.trees.j48.ModelSelection
-
Selects a model for the given train data using the given test data
- selectModel(Instances) - Method in class weka.classifiers.trees.j48.NBTreeModelSelection
-
Selects NBTree-type split for the given dataset.
- selectModel(Instances, Instances) - Method in class weka.classifiers.trees.j48.NBTreeModelSelection
-
Selects NBTree-type split for the given dataset.
- selectModel(Instances, double[][], double[][]) - Method in class weka.classifiers.trees.lmt.ResidualModelSelection
-
Selects split based on residuals for the given dataset.
- selectModel(Instances) - Method in class weka.classifiers.trees.lmt.ResidualModelSelection
-
Method not in use
- selectModel(Instances, Instances) - Method in class weka.classifiers.trees.lmt.ResidualModelSelection
-
Method not in use
- selectPattern() - Method in class weka.gui.ListSelectorDialog
-
opens a separate dialog for entering a regex pattern for selecting
elements from the provided list
- selectProperty() - Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
-
Gets the user to select a property of the current resultproducer.
- selectRegressions(SimpleLinearRegression[][]) - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Helper function for cutting back m_regressions to the set of classifiers
(corresponsing to the number of LogitBoost iterations) that gave the
smallest error.
- selectWeightQuantile(Instances, double) - Method in class weka.classifiers.meta.AdaBoostM1
-
Select only instances with weights that contribute to
the specified quantile of the weight distribution
- selectWeightQuantile(Instances, double) - Method in class weka.classifiers.meta.LogitBoost
-
Select only instances with weights that contribute to
the specified quantile of the weight distribution
- SEND_INSTANCES - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
-
Command to remove instances from this node and send them to the
VisualizePanel.
- separable(DoubleVector, int, int, double) - Method in class weka.classifiers.functions.pace.ChisqMixture
-
Return true if a value can be considered for mixture estimation
separately from the data indexed between i0 and i1
- separable(DoubleVector, int, int, double) - Method in class weka.classifiers.functions.pace.MixtureDistribution
-
Return true if a value can be considered for mixture estimatino
separately from the data indexed between i0 and i1
- separable(DoubleVector, int, int, double) - Method in class weka.classifiers.functions.pace.NormalMixture
-
Return true if a value can be considered for mixture estimatino
separately from the data indexed between i0 and i1
- separatingThreshold - Variable in class weka.classifiers.functions.pace.ChisqMixture
-
the separating threshold value
- separatingThreshold - Variable in class weka.classifiers.functions.pace.NormalMixture
-
the separating threshold
- seq(int, int) - Static method in class weka.core.matrix.IntVector
-
Generates an IntVector that stores all integers inclusively between
two integers.
- Sequence - Class in weka.associations.gsp
-
Class representing a sequence of elements/itemsets.
- Sequence() - Constructor for class weka.associations.gsp.Sequence
-
Constructor.
- Sequence(FastVector) - Constructor for class weka.associations.gsp.Sequence
-
Constructor accepting a set of elements as parameter.
- Sequence(int) - Constructor for class weka.associations.gsp.Sequence
-
Constructor accepting an int value as parameter to set the support count.
- SequentialDatabase - Class in weka.clusterers.forOPTICSAndDBScan.Databases
-
SequentialDatabase.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 20, 2004
Time: 1:23:38 PM
$ Revision 1.4 $
- SequentialDatabase(Instances) - Constructor for class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
-
Constructs a new sequential database and holds the original instances
- SERFileFilter - Class in weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI
-
SERFileFilter.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht
Date: Sep 15, 2004
Time: 6:54:56 PM
$ Revision 1.4 $
- SERFileFilter(String, String) - Constructor for class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERFileFilter
-
- SERIAL_VERSION_UID - Static variable in class weka.core.SerializationHelper
-
the field name of serialVersionUID.
- SerialInstanceListener - Interface in weka.gui.streams
-
Defines an interface for objects able to produce two output streams of
instances.
- SerializationHelper - Class in weka.core
-
A helper class for determining serialVersionUIDs and checking whether
classes contain one and/or need one.
- SerializationHelper() - Constructor for class weka.core.SerializationHelper
-
- serialize(Object) - Static method in class weka.core.xml.XStream
-
Serializes the supplied object xml
- SERIALIZED_OBJ_FILE_EXTENSION - Static variable in class weka.core.Instances
-
The filename extension that should be used for bin.
- SerializedClassifier - Class in weka.classifiers.misc
-
A wrapper around a serialized classifier model.
- SerializedClassifier() - Constructor for class weka.classifiers.misc.SerializedClassifier
-
- serializedClassifierFileTipText() - Method in class weka.filters.supervised.attribute.AddClassification
-
Returns the tip text for this property.
- SerializedInstancesLoader - Class in weka.core.converters
-
Reads a source that contains serialized Instances.
- SerializedInstancesLoader() - Constructor for class weka.core.converters.SerializedInstancesLoader
-
- SerializedInstancesSaver - Class in weka.core.converters
-
Serializes the instances to a file with extension bsi.
- SerializedInstancesSaver() - Constructor for class weka.core.converters.SerializedInstancesSaver
-
Constructor.
- SerializedModelSaver - Class in weka.gui.beans
-
A bean that saves serialized models
- SerializedModelSaver() - Constructor for class weka.gui.beans.SerializedModelSaver
-
Constructor.
- SerializedModelSaverBeanInfo - Class in weka.gui.beans
-
Bean info class for the serialized model saver bean
- SerializedModelSaverBeanInfo() - Constructor for class weka.gui.beans.SerializedModelSaverBeanInfo
-
- SerializedModelSaverCustomizer - Class in weka.gui.beans
-
GUI Customizer for the SerializedModelSaver bean
- SerializedModelSaverCustomizer() - Constructor for class weka.gui.beans.SerializedModelSaverCustomizer
-
Constructor
- SerializedObject - Class in weka.core
-
Class for storing an object in serialized form in memory.
- SerializedObject(Object) - Constructor for class weka.core.SerializedObject
-
Creates a new serialized object (without compression).
- SerializedObject(Object, boolean) - Constructor for class weka.core.SerializedObject
-
Creates a new serialized object.
- serializePMMLModel(PMMLModel, String) - Static method in class weka.core.pmml.PMMLFactory
-
Serialize a PMMLModel
object that encapsulates a PMML model
- serializePMMLModel(PMMLModel, File) - Static method in class weka.core.pmml.PMMLFactory
-
Serialize a PMMLModel
object that encapsulates a PMML model
- serializePMMLModel(PMMLModel, OutputStream) - Static method in class weka.core.pmml.PMMLFactory
-
Serialize a PMMLModel
object that encapsulates a PMML model
- SerialUIDChanger - Class in weka.core.xml
-
This class enables one to change the UID of a serialized object and therefore
not losing the data stored in the binary format.
- SerialUIDChanger() - Constructor for class weka.core.xml.SerialUIDChanger
-
- serialVersionUID - Static variable in class weka.classifiers.functions.LibLINEAR
-
serial UID
- serialVersionUID - Static variable in class weka.classifiers.functions.LibSVM
-
serial UID
- SERObject - Class in weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI
-
SERObject.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht
Date: Sep 15, 2004
Time: 9:43:00 PM
$ Revision 1.4 $
- SERObject(FastVector, int, int, double, int, boolean, String, String, int, String) - Constructor for class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
-
- set(int) - Method in class weka.attributeSelection.GeneticSearch.GABitSet
-
set a bit in the chromosome
- set(int, double) - Method in class weka.core.matrix.DoubleVector
-
Set a single element.
- set(double) - Method in class weka.core.matrix.DoubleVector
-
Set all elements to a value
- set(int, int, double) - Method in class weka.core.matrix.DoubleVector
-
Set some elements to a value
- set(int, int, double[], int) - Method in class weka.core.matrix.DoubleVector
-
Set some elements using a 2-D array
- set(DoubleVector) - Method in class weka.core.matrix.DoubleVector
-
Set the elements using a DoubleVector
- set(int, int, DoubleVector, int) - Method in class weka.core.matrix.DoubleVector
-
Set some elements using a DoubleVector.
- set(int) - Method in class weka.core.matrix.IntVector
-
Sets the value of an element.
- set(int, int, int[], int) - Method in class weka.core.matrix.IntVector
-
Sets the values of elements from an int array.
- set(int, int, IntVector, int) - Method in class weka.core.matrix.IntVector
-
Sets the values of elements from another IntVector.
- set(IntVector) - Method in class weka.core.matrix.IntVector
-
Sets the values of elements from another IntVector.
- set(int, int) - Method in class weka.core.matrix.IntVector
-
Sets a single element.
- set(int, int, double) - Method in class weka.core.matrix.Matrix
-
Set a single element.
- set(int, T) - Method in class weka.core.neighboursearch.covertrees.Stack
-
Sets the ith element in the stack.
- set(int, Object) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
-
Replaces the element at the specified position in this list with the
specified element.
- setAcuity(double) - Method in class weka.clusterers.Cobweb
-
set the acuity.
- setAdditionalMeasures(String[]) - Method in class weka.experiment.AveragingResultProducer
-
Set a list of method names for additional measures to look for
in SplitEvaluators.
- setAdditionalMeasures(String[]) - Method in class weka.experiment.ClassifierSplitEvaluator
-
Set a list of method names for additional measures to look for
in Classifiers.
- setAdditionalMeasures(String[]) - Method in class weka.experiment.CrossValidationResultProducer
-
Set a list of method names for additional measures to look for
in SplitEvaluators.
- setAdditionalMeasures(String[]) - Method in class weka.experiment.DatabaseResultProducer
-
Set a list of method names for additional measures to look for
in SplitEvaluators.
- setAdditionalMeasures(String[]) - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
-
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.RandomSplitResultProducer
-
Set a list of method names for additional measures to look for
in SplitEvaluators.
- setAdditionalMeasures(String[]) - Method in class weka.experiment.RegressionSplitEvaluator
-
Set a list of method names for additional measures to look for
in Classifiers.
- setAdditionalMeasures(String[]) - Method in interface weka.experiment.ResultProducer
-
Sets a list of method names for additional measures to look for
in SplitEvaluators.
- setAdditionalMeasures(String[]) - Method in interface weka.experiment.SplitEvaluator
-
Sets a list of method names for additional measures to look for
in SplitEvaluators.
- setAdjustWeights(boolean) - Method in class weka.filters.supervised.instance.SpreadSubsample
-
Sets whether the instance weights will be adjusted to maintain
total weight per class.
- setAdvanceDataSetFirst(boolean) - Method in class weka.experiment.Experiment
-
Set the value of m_AdvanceDataSetFirst.
- setAlgorithm(SelectedTag) - Method in class weka.filters.supervised.attribute.PLSFilter
-
Sets the type of algorithm to use
- setAlgorithm(SelectedTag) - Method in class weka.filters.unsupervised.attribute.Wavelet
-
Sets the type of algorithm to use
- setAlgorithmType(SelectedTag) - Method in class weka.classifiers.mi.MILR
-
Sets the algorithm type.
- setAllowUnclassifiedInstances(boolean) - Method in class weka.classifiers.trees.RandomTree
-
Set the value of AllowUnclassifiedInstances.
- setAlpha(double) - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
-
Set prior used in probability table estimation
- setAlpha(double) - Method in class weka.classifiers.functions.Winnow
-
Set the value of Alpha.
- setAmplitude(double) - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Sets the amplitude multiplier.
- setAnimated() - Method in class weka.gui.beans.BeanVisual
-
Set the animated version of the icon
- setAppendPredictedProbabilities(boolean) - Method in class weka.gui.beans.PredictionAppender
-
Set whether to append predicted probabilities rather than
class value (for discrete class data sets)
- setAppropriateNodeSize() - Method in class weka.classifiers.bayes.net.GUI
-
This method sets the node size that is appropriate considering the
maximum label size that is present.
- setAppropriateNodeSize() - Method in class weka.gui.graphvisualizer.GraphVisualizer
-
This method sets the node size that is appropriate
considering the maximum label size that is present.
- setAppropriateSize() - Method in class weka.classifiers.bayes.net.GUI
-
Sets the preferred size for m_GraphPanel GraphPanel to the minimum size that is
neccessary to display the graph.
- setAppropriateSize() - Method in class weka.gui.graphvisualizer.GraphVisualizer
-
Sets the preferred size for m_gp GraphPanel to the
minimum size that is neccessary to display the graph.
- setArffFile(String) - Method in class weka.gui.streams.InstanceLoader
-
- setArffFile(String) - Method in class weka.gui.streams.InstanceSavePanel
-
- setArray(int[]) - Method in class weka.core.matrix.IntVector
-
Sets the internal one-dimensional array.
- setArtificialSize(double) - Method in class weka.classifiers.meta.Decorate
-
Sets factor that determines number of artificial examples to generate.
- setAssociatedConnections(Vector) - Method in class weka.gui.beans.MetaBean
-
- setAssociator(Associator) - Method in class weka.associations.CheckAssociator
-
Set the associator to test.
- setAssociator(Associator) - Method in class weka.associations.SingleAssociatorEnhancer
-
Set the base associator.
- setAssociator(Associator) - Method in class weka.gui.beans.Associator
-
Set the associator for this wrapper
- setAsText(String) - Method in class weka.gui.CostMatrixEditor
-
Some objects can be represented as text, but a cost matrix cannot.
- setAsText(String) - Method in class weka.gui.GenericArrayEditor
-
Returns null as we don't support getting/setting values as text.
- setAsText(String) - Method in class weka.gui.GenericObjectEditor
-
Returns null as we don't support getting/setting values as text.
- setAsText(String) - Method in class weka.gui.SelectedTagEditor
-
Sets the current property value as text.
- setAsText(String) - Method in class weka.gui.SimpleDateFormatEditor
-
Sets the date format string.
- setAttIndex(int, boolean) - Method in class weka.classifiers.lazy.LBR.Indexes
-
Changes the boolean value at the specified index in the AttIndexes array
- setAttList_Irr(boolean[]) - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Sets the array that defines which of the attributes
are seen to be irrelevant.
- setAttribute(int) - Method in class weka.gui.AttributeSummaryPanel
-
Sets the attribute that statistics will be displayed for.
- setAttribute(int) - Method in class weka.gui.AttributeVisualizationPanel
-
Tells the panel which attribute to visualize.
- setAttributeEvaluator(ASEvaluation) - Method in class weka.attributeSelection.FilteredAttributeEval
-
Set the attribute evaluator to use
- setAttributeEvaluator(ASEvaluation) - Method in class weka.attributeSelection.RaceSearch
-
Set the attribute evaluator to use for generating the ranking.
- setAttributeEvaluator(ASEvaluation) - Method in class weka.attributeSelection.RankSearch
-
Set the attribute evaluator to use for generating the ranking.
- setAttributeID(int) - Method in class weka.experiment.ClassifierSplitEvaluator
-
Set the index of Attibute Identifying the instances
- setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.Add
-
Sets index of the attribute used.
- setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.AddNoise
-
Sets index of the attribute used.
- setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.AddValues
-
Sets index of the attribute used.
- setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
-
Sets the index of the attribute used.
- setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.MakeIndicator
-
Sets index of the attribute used.
- setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
-
Sets index of the attribute used.
- setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.SwapValues
-
Sets index of the attribute used.
- setAttributeIndex(String) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
Sets index of the attribute used.
- setAttributeIndex(String) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Sets index of the attribute used.
- setAttributeIndexes(String) - Method in class weka.filters.unsupervised.attribute.NominalToString
-
Sets index of the attribute used.
- setAttributeIndices(String) - Method in interface weka.core.DistanceFunction
-
Sets the range of attributes to use in the calculation of the distance.
- setAttributeIndices(String) - Method in class weka.core.NormalizableDistance
-
Sets the range of attributes to use in the calculation of the distance.
- setAttributeIndices(String) - Method in class weka.filters.supervised.attribute.Discretize
-
Sets which attributes are to be Discretized (only numeric
attributes among the selection will be Discretized).
- setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
-
Set which attributes are to be copied (or kept if invert is true)
- setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.Copy
-
Set which attributes are to be copied (or kept if invert is true)
- setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.Discretize
-
Sets which attributes are to be Discretized (only numeric
attributes among the selection will be Discretized).
- setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.FirstOrder
-
Set which attributes are to be deleted (or kept if invert is true)
- setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
-
Sets which attributes are to be used for interquartile calculations and
outlier/extreme value detection (only numeric attributes among the
selection will be used).
- setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.NominalToBinary
-
Sets which attributes are to be acted on.
- setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Sets the columns to use, e.g., first-last or first-3,5-last
- setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.NumericToNominal
-
Sets which attributes are to be "nominalized" (only numeric
attributes among the selection will be transformed).
- setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.NumericTransform
-
Set which attributes are to be transformed (or kept if invert is true).
- setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.Remove
-
Set which attributes are to be deleted (or kept if invert is true)
- setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.Reorder
-
Set which attributes are to be copied (or kept if invert is true)
- setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Sets which attributes are to be worked on.
- setAttributeIndicesArray(int[]) - Method in class weka.filters.supervised.attribute.Discretize
-
Sets which attributes are to be Discretized (only numeric
attributes among the selection will be Discretized).
- setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
-
Set which attributes are to be copied (or kept if invert is true)
- setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.Copy
-
Set which attributes are to be copied (or kept if invert is true)
- setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.Discretize
-
Sets which attributes are to be Discretized (only numeric
attributes among the selection will be Discretized).
- setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.FirstOrder
-
Set which attributes are to be deleted (or kept if invert is true)
- setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
-
Sets which attributes are to be used for interquartile calculations and
outlier/extreme value detection (only numeric attributes among the
selection will be used).
- setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.NumericToNominal
-
Sets which attributes are to be transoformed to nominal.
- setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.NumericTransform
-
Set which attributes are to be transformed (or kept if invert is true)
- setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.Remove
-
Set which attributes are to be deleted (or kept if invert is true)
- setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.Reorder
-
Set which attributes are to be copied (or kept if invert is true)
- setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Sets which attributes are to be processed.
- setAttributeName(String) - Method in class weka.filters.unsupervised.attribute.Add
-
Set the new attribute's name.
- setAttributeName(String) - Method in class weka.filters.unsupervised.attribute.AddID
-
Set the new attribute's name
- setAttributeNamePrefix(String) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Set the attribute name prefix.
- setAttributeRange(String) - Method in class weka.filters.unsupervised.attribute.StringToNominal
-
Sets range of indices of the attributes used.
- setAttributeSelectionMethod(SelectedTag) - Method in class weka.classifiers.functions.LinearRegression
-
Sets the method used to select attributes for use in the
linear regression.
- setAttributeType(SelectedTag) - Method in class weka.filters.unsupervised.attribute.Add
-
Sets the type of attribute to generate.
- setAttributeType(SelectedTag) - Method in class weka.filters.unsupervised.attribute.RemoveType
-
Sets the attribute type to be deleted by the filter.
- setAttributeTypes(Hashtable) - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
-
Sets the attribute - attribute-type relation to use.
- setAttributeTypeString(String) - Method in class weka.filters.unsupervised.attribute.RemoveType
-
Sets the attribute type to be deleted by the filter.
- setAttrIndexRange(String) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Sets which attributes are used in the cluster
attributes among the selection will be discretized.
- setAtts(int[], boolean) - Method in class weka.classifiers.lazy.LBR.Indexes
-
Changes the boolean value at the specified index in the InstIndexes array
- setAttsToEliminatePerIteration(int) - Method in class weka.attributeSelection.SVMAttributeEval
-
Set the constant rate of attribute elimination per iteration
- setAutoBuild(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
-
This will set whether the network is automatically built
or if it is left up to the user.
- setAutoKeyGeneration(boolean) - Method in class weka.core.converters.DatabaseSaver
-
En/Dis-ables the automatic generation of a primary key.
- setBackground(Color) - Method in class weka.gui.visualize.BMPWriter
-
sets the background color to use in creating the JPEG
- setBackground(Color) - Method in class weka.gui.visualize.JPEGWriter
-
sets the background color to use in creating the JPEG.
- setBackground(Color) - Method in class weka.gui.visualize.PNGWriter
-
sets the background color to use in creating the JPEG
- setBackground(Color) - Method in class weka.gui.visualize.PostscriptGraphics
-
- setBagSizePercent(int) - Method in class weka.classifiers.meta.Bagging
-
Sets the size of each bag, as a percentage of the training set size.
- setBagSizePercent(int) - Method in class weka.classifiers.meta.MetaCost
-
Sets the size of each bag, as a percentage of the training set size.
- setBalanceClass(boolean) - Method in class weka.datagenerators.classifiers.classification.Agrawal
-
Sets whether the class is balanced.
- setBalanced(boolean) - Method in class weka.classifiers.functions.Winnow
-
Set the value of Balanced.
- setBallSplitter(BallSplitter) - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
-
Sets the ball splitting algorithm to be used by the
TopDown constructor.
- setBallTreeConstructor(BallTreeConstructor) - Method in class weka.core.neighboursearch.BallTree
-
Sets the BallTreeConstructor for building the BallTree
(default TopDownConstructor).
- setBase(double) - Method in class weka.core.neighboursearch.CoverTree
-
Sets the base to use for expansion constant.
- setBaseExperiment(Experiment) - Method in class weka.experiment.RemoteExperiment
-
Set the base experiment.
- setBeanConnection(BeanConnection, Vector) - Method in class weka.gui.beans.xml.XMLBeans
-
puts the given BeanConnection onto the next null in the given Vector,
or at the end of the list, if no null is found.
- setBeanContext(BeanContext) - Method in class weka.gui.beans.AbstractDataSource
-
Set a bean context for this bean
- setBeanContext(BeanContext) - Method in class weka.gui.beans.CostBenefitAnalysis
-
- setBeanContext(BeanContext) - Method in class weka.gui.beans.DataVisualizer
-
Set a bean context for this bean
- setBeanContext(BeanContext) - Method in class weka.gui.beans.GraphViewer
-
Set a bean context for this bean
- setBeanContext(BeanContext) - Method in class weka.gui.beans.Loader
-
Set a bean context for this bean
- setBeanContext(BeanContext) - Method in class weka.gui.beans.ModelPerformanceChart
-
Set a bean context for this bean
- setBeanContext(BeanContext) - Method in class weka.gui.beans.TextViewer
-
Set a bean context for this bean
- setBeanInstances(Vector, JComponent) - Static method in class weka.gui.beans.BeanInstance
-
Describe setBeanInstances
method here.
- setBeta(double) - Method in class weka.classifiers.functions.Winnow
-
Set the value of Beta.
- setBias(double) - Method in class weka.classifiers.functions.LibLINEAR
-
Sets bias term value (default 1)
No bias term is added if value < 0
- setBias(double) - Method in class weka.classifiers.misc.VFI
-
Set the value of the exponential bias towards more confident intervals
- setBiasToUniformClass(double) - Method in class weka.filters.supervised.instance.Resample
-
Sets the bias towards a uniform class.
- setBIFFile(String) - Method in class weka.classifiers.bayes.BayesNet
-
Set name of network in BIF file to compare with
- setBIFFile(String) - Method in class weka.classifiers.bayes.net.search.fixed.FromFile
-
Set name of network in BIF file to read structure from
- setBinarizeNumericAttributes(boolean) - Method in class weka.attributeSelection.ChiSquaredAttributeEval
-
Binarize numeric attributes.
- setBinarizeNumericAttributes(boolean) - Method in class weka.attributeSelection.InfoGainAttributeEval
-
Binarize numeric attributes.
- setBinaryAttributesNominal(boolean) - Method in class weka.filters.supervised.attribute.NominalToBinary
-
Sets if binary attributes are to be treates as nominal ones.
- setBinaryAttributesNominal(boolean) - Method in class weka.filters.unsupervised.attribute.NominalToBinary
-
Sets if binary attributes are to be treates as nominal ones.
- setBinarySplits(boolean) - Method in class weka.classifiers.rules.PART
-
Set the value of binarySplits.
- setBinarySplits(boolean) - Method in class weka.classifiers.trees.J48
-
Set the value of binarySplits.
- setBinarySplits(boolean) - Method in class weka.classifiers.trees.J48graft
-
Set the value of binarySplits.
- setBins(int) - Method in class weka.filters.unsupervised.attribute.Discretize
-
Sets the number of bins to divide each selected numeric attribute into
- setBins(int) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
-
Ignored
- setBinSplit(boolean) - Method in class weka.classifiers.trees.FT
-
Set the value of binarySplits.
- setBinValue(double) - Method in class weka.clusterers.XMeans
-
Sets the distance value between true and false of binary attributes.
- setBlendFactor(int) - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
Set the blending factor
- setBlendMethod(int) - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
Set the blending method
- setBooleanCols(Range) - Method in class weka.datagenerators.ClusterGenerator
-
Sets which attributes are boolean.
- setBooleanIndices(String) - Method in class weka.datagenerators.ClusterGenerator
-
Sets which attributes are boolean
- setBuildLogisticModels(boolean) - Method in class weka.classifiers.functions.SMO
-
Set the value of buildLogisticModels.
- setBuildLogisticModels(boolean) - Method in class weka.classifiers.mi.MISMO
-
Set the value of buildLogisticModels.
- setBuildRegressionTree(boolean) - Method in class weka.classifiers.trees.m5.M5Base
-
Set the value of regressionTree.
- setButtons() - Method in class weka.gui.sql.ConnectionPanel
-
sets the buttons according to the connected-state.
- setButtons(ListSelectionEvent) - Method in class weka.gui.sql.InfoPanel
-
sets the state of the buttons according to the selection state of the
JList
- setButtons() - Method in class weka.gui.sql.QueryPanel
-
sets the buttons according to the connected-state.
- setButtons() - Method in class weka.gui.sql.ResultPanel
-
sets the state of the buttons
- setButtons() - Method in class weka.gui.ViewerDialog
-
sets the state of the buttons
- setC(double) - Method in class weka.classifiers.functions.SMO
-
Set the value of C.
- setC(double) - Method in class weka.classifiers.functions.SMOreg
-
Set the value of C.
- setC(double) - Method in class weka.classifiers.mi.MISMO
-
Set the value of C.
- setC(double) - Method in class weka.classifiers.mi.MISVM
-
Set the value of C.
- setCacheKeyName(String) - Method in class weka.experiment.DatabaseResultListener
-
Set the value of CacheKeyName.
- setCacheSize(double) - Method in class weka.classifiers.functions.LibSVM
-
Sets cache memory size in MB (default 40)
- setCacheSize(int) - Method in class weka.classifiers.functions.supportVector.CachedKernel
-
Sets the size of the cache to use (a prime number)
- setCacheSize(int) - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Sets the size of the cache to use (a prime number)
- setCalcOutOfBag(boolean) - Method in class weka.classifiers.meta.Bagging
-
Set whether the out of bag error is calculated.
- setCalculateStdDevs(boolean) - Method in class weka.experiment.AveragingResultProducer
-
Set the value of CalculateStdDevs.
- setCancelButton(boolean) - Method in class weka.gui.GenericObjectEditor.GOEPanel
-
Enables/disables the cancel button.
- setCanChangeClassInDialog(boolean) - Method in class weka.gui.GenericObjectEditor
-
Sets whether the user can change the class in the dialog.
- setCapabilities(Capabilities) - Method in class weka.core.FindWithCapabilities
-
Uses the given Capabilities for the search.
- setCapabilities(Capabilities) - Method in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
-
sets the initial capabilities.
- setCapabilitiesFilter(Capabilities) - Method in class weka.gui.ConverterFileChooser
-
sets the capabilities that the savers must have.
- setCapabilitiesFilter(Capabilities) - Method in class weka.gui.GenericObjectEditor
-
Sets the capabilities to use for filtering.
- setCapacity(int) - Method in class weka.core.FastVector
-
Sets the vector's capacity to the given value.
- setCapacity(int) - Method in class weka.core.matrix.DoubleVector
-
Sets the capacity of the vector
- setCapacity(int) - Method in class weka.core.matrix.IntVector
-
Sets the capacity of the vector
- setCar(boolean) - Method in class weka.associations.Apriori
-
Sets class association rule mining
- setCar(boolean) - Method in class weka.associations.PredictiveApriori
-
Sets class association rule mining
- setCardinality(int) - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
Sets the cardinality of the attributes (incl class attribute)
- setCell(int, int, Object) - Method in class weka.classifiers.CostMatrix
-
Set the value of a particular cell in the matrix
- setCellValue(double, double, double, int) - Method in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell
-
Set the value of a cell.
- setCenter(double) - Method in class weka.gui.treevisualizer.Node
-
Set the value of center.
- setCenterData(boolean) - Method in class weka.attributeSelection.PrincipalComponents
-
Set whether to center (rather than standardize)
the data.
- setCenterData(boolean) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Set whether to center (rather than standardize)
the data.
- setCenteredLocation() - Method in class weka.gui.arffviewer.ArffViewer
-
positions the window at the center of the screen
- setChanged(boolean) - Method in class weka.gui.arffviewer.ArffPanel
-
can only reset the changed state to FALSE
- setChar(Character) - Method in class weka.core.Trie.TrieNode
-
sets the character this node represents
- setCharSet(String) - Method in class weka.core.converters.TextDirectoryLoader
-
Set the character set to use when reading text files (an empty string
indicates that the default character set will be used).
- setChecked(boolean) - Method in class weka.gui.CheckBoxList.CheckBoxListItem
-
sets the checked state of the item
- setChecked(int, boolean) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
-
sets the checked state of the element at the given index
- setChecked(int, boolean) - Method in class weka.gui.CheckBoxList
-
sets the checked state of the element at the given index
- setCheckErrorRate(boolean) - Method in class weka.classifiers.rules.JRip
-
Sets whether to check for error rate is in stopping criterion
- setChecksTurnedOff(boolean) - Method in class weka.classifiers.functions.SMO
-
Disables or enables the checks (which could be time-consuming).
- setChecksTurnedOff(boolean) - Method in class weka.classifiers.functions.supportVector.Kernel
-
Disables or enables the checks (which could be time-consuming).
- setChecksTurnedOff(boolean) - Method in class weka.classifiers.mi.MISMO
-
Disables or enables the checks (which could be time-consuming).
- setChecksTurnedOff(boolean) - Method in class weka.filters.unsupervised.attribute.KernelFilter
-
Disables or enables the checks (which could be time-consuming).
- setChildForBranch(int, PredictionNode) - Method in class weka.classifiers.trees.adtree.Splitter
-
Sets the child for a branch of the split.
- setChildForBranch(int, PredictionNode) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
-
Sets the child for a branch of the split.
- setChildForBranch(int, PredictionNode) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
-
Sets the child for a branch of the split.
- setChildForBranch(int, LADTree.PredictionNode) - Method in class weka.classifiers.trees.LADTree.Splitter
-
- setChildForBranch(int, LADTree.PredictionNode) - Method in class weka.classifiers.trees.LADTree.TwoWayNominalSplit
-
- setChildForBranch(int, LADTree.PredictionNode) - Method in class weka.classifiers.trees.LADTree.TwoWayNumericSplit
-
- setChromosome(BitSet) - Method in class weka.attributeSelection.GeneticSearch.GABitSet
-
set the chromosome
- setCindex(int, double, double) - Method in class weka.gui.visualize.AttributePanel
-
Set the index of the attribute by which to colour the data.
- setCindex(int) - Method in class weka.gui.visualize.AttributePanel
-
Set the index of the attribute by which to colour the data.
- setCindex(int) - Method in class weka.gui.visualize.ClassPanel
-
Set the index of the attribute to display coloured labels for
- setCindex(int) - Method in class weka.gui.visualize.Plot2D
-
Set the index of the attribute to use for colouring
- setCindex(int) - Method in class weka.gui.visualize.PlotData2D
-
Set the colouring index of the data
- setCindex(int) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
-
Set the index of the attribute to use for colouring
- setClass(Attribute) - Method in class weka.core.Instances
-
Sets the class attribute.
- setClassColumn(String) - Method in class weka.gui.beans.ClassAssigner
-
- setClassFlag(boolean) - Method in class weka.datagenerators.ClusterGenerator
-
Sets the class flag, if class flag is set,
the cluster is listed as class atrribute in an extra attribute.
- setClassForIRStatistics(int) - Method in class weka.experiment.ClassifierSplitEvaluator
-
Set the value of ClassForIRStatistics.
- setClassification(boolean) - Method in class weka.associations.Tertius
-
Set the value of classification.
- setClassifier(Classifier) - Method in class weka.attributeSelection.ClassifierSubsetEval
-
Set the classifier to use for accuracy estimation
- setClassifier(Classifier) - Method in class weka.attributeSelection.WrapperSubsetEval
-
Set the classifier to use for accuracy estimation
- setClassifier(Classifier) - Method in class weka.classifiers.BVDecompose
-
Set the classifiers being analysed
- setClassifier(Classifier) - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Set the classifiers being analysed
- setClassifier(Classifier) - Method in class weka.classifiers.CheckClassifier
-
Set the classifier for boosting.
- setClassifier(Classifier) - Method in class weka.classifiers.CheckSource
-
Sets the classifier to use for the comparison.
- setClassifier(Classifier) - Method in class weka.classifiers.meta.GridSearch
-
Set the base learner.
- setClassifier(Classifier) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Set the base learner.
- setClassifier(Classifier) - Method in class weka.classifiers.SingleClassifierEnhancer
-
Set the base learner.
- setClassifier(Classifier) - Method in class weka.experiment.ClassifierSplitEvaluator
-
Sets the classifier.
- setClassifier(Classifier) - Method in class weka.experiment.RegressionSplitEvaluator
-
Sets the classifier.
- setClassifier(Classifier) - Method in class weka.filters.supervised.attribute.AddClassification
-
Sets the classifier to classify instances with.
- setClassifier(Classifier) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Sets the classifier to classify instances with.
- setClassifier(Classifier) - Method in class weka.gui.beans.BatchClassifierEvent
-
Set the classifier
- setClassifier(Classifier) - Method in class weka.gui.beans.IncrementalClassifierEvent
-
- setClassifier(Classifier) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Set the classifier to use.
- setClassifier(Classifier) - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
Set a classifier to use
- setClassifier(Classifier) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
-
Set the classifier to use
- setClassifierName(String) - Method in class weka.experiment.ClassifierSplitEvaluator
-
Set the Classifier to use, given it's class name.
- setClassifierName(String) - Method in class weka.experiment.RegressionSplitEvaluator
-
Set the Classifier to use, given it's class name.
- setClassifiers(Classifier[]) - Method in class weka.classifiers.meta.MultiScheme
-
Sets the list of possible classifers to choose from.
- setClassifiers(Classifier[]) - Method in class weka.classifiers.MultipleClassifiersCombiner
-
Sets the list of possible classifers to choose from.
- setClassifierTemplate(Classifier) - Method in class weka.gui.beans.Classifier
-
Set the classifier for this wrapper
- setClassifyIterations(int) - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Sets the number of times an instance is classified
- setClassIndex(int) - Method in class weka.associations.Apriori
-
Sets the class index
- setClassIndex(int) - Method in interface weka.associations.CARuleMiner
-
Sets the class index for the class association rule miner
- setClassIndex(int) - Method in class weka.associations.FilteredAssociator
-
Sets the class index
- setClassIndex(int) - Method in class weka.associations.PredictiveApriori
-
Sets the class index
- setClassIndex(int) - Method in class weka.associations.Tertius
-
Set the value of classIndex.
- setClassIndex(int) - Method in class weka.classifiers.BVDecompose
-
Sets index of attribute to discretize on
- setClassIndex(int) - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Sets index of attribute to discretize on
- setClassIndex(int) - Method in class weka.classifiers.CheckSource
-
Sets the class index of the dataset.
- setClassIndex(String) - Method in class weka.core.converters.LibSVMSaver
-
Sets index of the class attribute.
- setClassIndex(String) - Method in class weka.core.converters.SVMLightSaver
-
Sets index of the class attribute.
- setClassIndex(String) - Method in class weka.core.converters.XRFFSaver
-
Sets index of the class attribute.
- setClassIndex(String) - Method in class weka.core.FindWithCapabilities
-
sets the class index, -1 for none, first and last are also valid.
- setClassIndex(int) - Method in class weka.core.Instances
-
Sets the class index of the set.
- setClassIndex(int) - Method in class weka.core.TestInstances
-
sets the class index (0-based)
- setClassIndex(int) - Method in class weka.filters.CheckSource
-
Sets the class index of the dataset.
- setClassIndex(String) - Method in class weka.filters.unsupervised.attribute.ClassAssigner
-
sets the class index.
- setClassIndex(int) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Sets the attribute on which misclassifications are based.
- setClassMissing() - Method in class weka.core.Instance
-
Sets the class value of an instance to be "missing".
- setClassname(String) - Method in class weka.core.AllJavadoc
-
sets the classname of the class to generate the Javadoc for
- setClassname(String) - Method in class weka.core.Javadoc
-
sets the classname of the class to generate the Javadoc for
- setClassname(String) - Method in class weka.core.ListOptions
-
sets the classname of the class to generate the Javadoc for
- setClassName(String) - Method in class weka.filters.unsupervised.attribute.NumericTransform
-
Sets the class containing the transformation method.
- setClassOrder(int) - Method in class weka.filters.supervised.attribute.ClassOrder
-
Set the wanted class order
- setClassType(int) - Method in class weka.core.TestInstances
-
sets the class attribute type
- 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.
- setClassValue(String) - Method in class weka.filters.supervised.instance.SMOTE
-
Sets the index of the class value to which SMOTE should be applied.
- setClassValue(String) - Method in class weka.gui.beans.ClassValuePicker
-
Set the class value index considered to be the "positive"
class value.
- setClearEachDataset(boolean) - Method in class weka.gui.streams.InstanceViewer
-
- setClip(int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics
-
Not implemented
- setClip(Shape) - Method in class weka.gui.visualize.PostscriptGraphics
-
Not implemented
- setCloseTo(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Set the "close to" number.
- setCloseToDefault(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Set the "close to" default.
- setCloseToTolerance(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Set the "close to" Tolerance.
- setClusterDefinitions(ClusterDefinition[]) - Method in class weka.datagenerators.clusterers.SubspaceCluster
-
sets the clusters to use
- setClusterer(Clusterer) - Method in class weka.attributeSelection.UnsupervisedSubsetEvaluator
-
Set the clusterer to use
- setClusterer(Clusterer) - Method in class weka.classifiers.meta.ClassificationViaClustering
-
Set the base clusterer.
- setClusterer(Clusterer) - Method in class weka.clusterers.CheckClusterer
-
Set the clusterer for testing.
- setClusterer(Clusterer) - Method in class weka.clusterers.ClusterEvaluation
-
set the clusterer
- setClusterer(Clusterer) - Method in class weka.clusterers.MakeDensityBasedClusterer
-
Sets the clusterer to wrap.
- setClusterer(Clusterer) - Method in class weka.clusterers.SingleClustererEnhancer
-
Set the base clusterer.
- setClusterer(DensityBasedClusterer) - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
-
Sets the clusterer.
- setClusterer(Clusterer) - Method in class weka.filters.unsupervised.attribute.AddCluster
-
Sets the clusterer to assign clusters with.
- setClusterer(Clusterer) - Method in class weka.gui.beans.Clusterer
-
Set the clusterer for this wrapper
- setClustererName(String) - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
-
Set the Clusterer to use, given it's class name.
- setClusteringSeed(int) - Method in class weka.classifiers.functions.RBFNetwork
-
Set the random seed to be passed on to K-means.
- setClusterLabel(int) - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
-
Sets the clusterID (cluster), to which this DataObject belongs to
- setClusterLabel(int) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
-
Sets the clusterID (cluster), to which this DataObject belongs to
- setClusterLabel(int) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
-
Sets the clusterID (cluster), to which this DataObject belongs to
- setClusterSubType(SelectedTag) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Sets the cluster sub type.
- setClusterType(SelectedTag) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Sets the cluster type.
- setCoef0(double) - Method in class weka.classifiers.functions.LibSVM
-
Sets coef (default 0)
- setColHidden(int, boolean) - Method in class weka.experiment.ResultMatrix
-
sets the hidden status of the column (if the index is valid)
- setColName(int, String) - Method in class weka.experiment.ResultMatrix
-
sets the name of the column (if the index is valid)
- setColNameWidth(int) - Method in class weka.experiment.ResultMatrix
-
sets the width for the column names (0 = optimal)
- setColor(Color) - Method in class weka.gui.treevisualizer.Node
-
Set the value of color.
- setColor(Color) - Method in class weka.gui.visualize.PostscriptGraphics
-
Set current pen color.
- setColOrder(int[]) - Method in class weka.experiment.ResultMatrix
-
sets the ordering of the columns, null means default
- setColoringIndex(int) - Method in class weka.gui.AttributeVisualizationPanel
-
Set the coloring (class) index for the plot
- setColoringIndex(int) - Method in class weka.gui.beans.AttributeSummarizer
-
Set the coloring index for the attribute summary plots
- setColors(FastVector) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Set a vector of Color objects for the classes
- setColourIndex(int) - Method in class weka.gui.visualize.VisualizePanel
-
Sets the index used for colouring.
- setColours(FastVector) - Method in class weka.gui.visualize.AttributePanel
-
Sets a list of colours to use for colouring data points
- setColours(FastVector) - Method in class weka.gui.visualize.ClassPanel
-
Set a list of colours to use for colouring labels
- setColours(FastVector) - Method in class weka.gui.visualize.Plot2D
-
Set a list of colours to use when colouring points according
to class values or cluster numbers
- setColours(FastVector) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
-
Set a list of colours to use for plotting points
- setColumn(int, double[]) - Method in class weka.core.Matrix
-
Deprecated.
Sets a column of the matrix to the given column.
- setColumnDimension(int) - Method in class weka.classifiers.functions.pace.PaceMatrix
-
Set the column dimenion of the matrix
- setCombination(SelectedTag) - Method in class weka.attributeSelection.ScatterSearchV1
-
Set the kind of combination
- setCombinationRule(SelectedTag) - Method in class weka.classifiers.meta.Vote
-
Sets the combination rule to use.
- setComboSizes() - Method in class weka.gui.experiment.ResultsPanel
-
Sets the combo-boxes to a fixed size so they don't take up too much room
that would be better devoted to the test output box.
- setComplexityParameter(double) - Method in class weka.attributeSelection.SVMAttributeEval
-
Set the value of C for SMO
- setComponent(JComponent) - Method in class weka.gui.visualize.JComponentWriter
-
sets the component to print to an output format
- setComposite(Composite) - Method in class weka.gui.visualize.PostscriptGraphics
-
- setCompressOutput(boolean) - Method in class weka.core.converters.ArffSaver
-
Sets whether to compress the output.
- setCompressOutput(boolean) - Method in class weka.core.converters.XRFFSaver
-
Sets whether to compress the output.
- setConfidenceFactor(float) - Method in class weka.classifiers.rules.PART
-
Set the value of CF.
- setConfidenceFactor(float) - Method in class weka.classifiers.trees.J48
-
Set the value of CF.
- setConfidenceFactor(float) - Method in class weka.classifiers.trees.J48graft
-
Set the value of CF.
- setConfirmationThreshold(double) - Method in class weka.associations.Tertius
-
Set the value of confirmationThreshold.
- setConfirmationValues(int) - Method in class weka.associations.Tertius
-
Set the value of confirmationValues.
- setConfirmExit(boolean) - Method in class weka.gui.arffviewer.ArffViewer
-
whether to present a MessageBox on Exit or not
- setConfirmExit(boolean) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
whether to present a MessageBox on Exit or not
- setConnections(Vector) - Static method in class weka.gui.beans.BeanConnection
-
Describe setConnections
method here.
- setConnectPoints(boolean[]) - Method in class weka.gui.visualize.PlotData2D
-
Set whether consecutive points should be connected by lines
- setConnectPoints(FastVector) - Method in class weka.gui.visualize.PlotData2D
-
Set whether consecutive points should be connected by lines
- setConsequent(double) - Method in class weka.classifiers.rules.JRip.RipperRule
-
Sets the internal representation of the class label to be predicted
- setConservativeForwardSelection(boolean) - Method in class weka.attributeSelection.GreedyStepwise
-
Set whether attributes should continue to be added during
a forward search as long as merit does not decrease
- setContainChildBalls(boolean) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
Sets whether if a parent ball should completely enclose
its two child balls.
- setConvertNominal(boolean) - Method in class weka.classifiers.trees.LMT
-
Set the value of convertNominal.
- setConvertNominalToBinary(boolean) - Method in class weka.classifiers.functions.LibLINEAR
-
Whether to turn on conversion of nominal attributes
to binary.
- setCoreConvertersOnly(boolean) - Method in class weka.gui.ConverterFileChooser
-
Whether to display only the hardocded core converters.
- setCoreDistance(double) - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
-
Sets a new coreDistance for this dataObject
- setCoreDistance(double) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
-
Sets a new coreDistance for this dataObject
- setCoreDistance(double) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
-
Sets a new coreDistance for this dataObject
- setCoreDistanceColor(Color) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
-
Sets a new color for the coreDistance
- setCost(double) - Method in class weka.classifiers.functions.LibLINEAR
-
Sets the cost parameter C (default 1)
- setCost(double) - Method in class weka.classifiers.functions.LibSVM
-
Sets the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
- setCostMatrix(CostMatrix) - Method in class weka.attributeSelection.CostSensitiveASEvaluation
-
Sets the misclassification cost matrix.
- setCostMatrix(CostMatrix) - Method in class weka.classifiers.meta.CostSensitiveClassifier
-
Sets the misclassification cost matrix.
- setCostMatrix(CostMatrix) - Method in class weka.classifiers.meta.MetaCost
-
Sets the misclassification cost matrix.
- setCostMatrixSource(SelectedTag) - Method in class weka.attributeSelection.CostSensitiveASEvaluation
-
Sets the source location of the cost matrix.
- setCostMatrixSource(SelectedTag) - Method in class weka.classifiers.meta.CostSensitiveClassifier
-
Sets the source location of the cost matrix.
- setCostMatrixSource(SelectedTag) - Method in class weka.classifiers.meta.MetaCost
-
Sets the source location of the cost matrix.
- setCount(int, double) - Method in class weka.experiment.ResultMatrix
-
sets the count for the row (if the index is valid)
- setCounter(int) - Method in class weka.associations.ItemSet
-
Sets the counter
- setCountWidth(int) - Method in class weka.experiment.ResultMatrix
-
sets the width for the counts (0 = optimal)
- setCreatorApplication(Document) - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
-
Set the name of the application (if specified) that created this
model
- setCreatorApplication(Document) - Method in interface weka.core.pmml.PMMLModel
-
Set the name of the application (if specified) that created this.
- setCriticalValue(int) - Method in class weka.classifiers.bayes.AODEsr
-
Sets the critical value
- setCrossoverProb(double) - Method in class weka.attributeSelection.GeneticSearch
-
set the probability of crossover
- setCrossVal(int) - Method in class weka.classifiers.rules.DecisionTable
-
Sets the number of folds for cross validation (1 = leave one out)
- setCrossValidate(boolean) - Method in class weka.classifiers.lazy.IBk
-
Sets whether hold-one-out cross-validation will be used
to select the best k value.
- setCurrentFilename(String) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
sets the filename of the current tab
- setCurrentInstance(Instance) - Method in class weka.gui.beans.IncrementalClassifierEvent
-
Set the current instance for this event
- setCurveData(PlotData2D, Attribute) - Method in class weka.gui.beans.CostBenefitAnalysis
-
Set the threshold curve data to use.
- setCustomColour(Color) - Method in class weka.gui.visualize.PlotData2D
-
Set a custom colour to use for this plot.
- setCustomHeight(int) - Method in class weka.gui.visualize.JComponentWriter
-
sets the custom height to use
- setCustomName(String) - Method in class weka.gui.beans.Associator
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in interface weka.gui.beans.BeanCommon
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.ClassAssigner
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.Classifier
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.ClassValuePicker
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.Clusterer
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.ClustererPerformanceEvaluator
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.CostBenefitAnalysis
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.CrossValidationFoldMaker
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.Filter
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.IncrementalClassifierEvaluator
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.Loader
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.MetaBean
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.PredictionAppender
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.Saver
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.SerializedModelSaver
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.StripChart
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.TestSetMaker
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.TextViewer
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.TrainingSetMaker
-
Set a custom (descriptive) name for this bean
- setCustomName(String) - Method in class weka.gui.beans.TrainTestSplitMaker
-
Set a custom (descriptive) name for this bean
- setCustomWidth(int) - Method in class weka.gui.visualize.JComponentWriter
-
sets the custom width to use
- setCutoff(double) - Method in class weka.clusterers.Cobweb
-
set the cutoff
- setCutOffFactor(double) - Method in class weka.clusterers.XMeans
-
Sets a new cutoff factor.
- setCVisible(boolean) - Method in class weka.gui.treevisualizer.Node
-
Sets all the children of this node either to visible or invisible
- setCVParameters(Object[]) - Method in class weka.classifiers.meta.CVParameterSelection
-
Set method for CVParameters.
- setCVType(SelectedTag) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
-
set cross validation strategy to be used in searching for networks.
- setData(Instances) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
Assuming a network structure is defined and we want to learn from data,
the data set must be put if correct order first and possibly discretized/missing
values filled in before proceeding to CPT learning.
- setData(Instances) - Method in class weka.classifiers.rules.RuleStats
-
Set the data of the stats, overwriting the old one if any
- setDatabase_distanceType(String) - Method in class weka.clusterers.DBScan
-
Sets a new distance-type
- setDatabase_distanceType(String) - Method in class weka.clusterers.OPTICS
-
Sets a new distance-type
- setDatabase_Type(String) - Method in class weka.clusterers.DBScan
-
Sets a new database-type
- setDatabase_Type(String) - Method in class weka.clusterers.OPTICS
-
Sets a new database-type
- setDatabaseOutput(File) - Method in class weka.clusterers.OPTICS
-
Sets the the file to save the generated database to.
- setDatabaseURL(String) - Method in class weka.experiment.DatabaseUtils
-
Set the value of DatabaseURL.
- setDataFileName(String) - Method in class weka.classifiers.BVDecompose
-
Sets the name of the data file used for the decomposition
- setDataFileName(String) - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Sets the name of the dataset file.
- setDataGenerator(DataGenerator) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Set the data generator to use for generating new instances
- setDataGenerator(DataGenerator) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
-
Set the density estimator to use
- setDataPoint(double[]) - Method in class weka.gui.beans.ChartEvent
-
Set the data point
- setDataSeqID(int) - Method in class weka.associations.GeneralizedSequentialPatterns
-
Sets the attribute representing the data sequence ID.
- setDataset(File) - Method in class weka.classifiers.CheckSource
-
Sets the dataset to use for testing.
- setDataset(Instances) - Method in class weka.core.Instance
-
Sets the reference to the dataset.
- setDataset(File) - Method in class weka.filters.CheckSource
-
Sets the dataset to use for testing.
- setDataSet(PlotData2D, Attribute) - Method in class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel
-
Set the threshold data for the panel to use.
- setDatasetFormat(Instances) - Method in class weka.datagenerators.DataGenerator
-
Sets the format of the dataset that is to be generated.
- setDatasetKeyColumns(Range) - Method in class weka.experiment.PairedTTester
-
Set the value of DatasetKeyColumns.
- setDatasetKeyColumns(Range) - Method in interface weka.experiment.Tester
-
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
- setDataType(int) - Method in class weka.gui.beans.xml.XMLBeans
-
sets what kind of data is to be read/written
- setDateAttributes(String) - Method in class weka.core.converters.CSVLoader
-
Set the attribute range to be forced to type date.
- setDateFormat(String) - Method in class weka.core.converters.CSVLoader
-
Set the format to use for parsing date values.
- setDateFormat(String) - Method in class weka.filters.unsupervised.attribute.Add
-
Set the date format, complying to ISO-8601.
- setDateFormat(String) - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
-
Sets the output date format.
- setDateFormat(SimpleDateFormat) - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
-
Sets the output date format.
- setDB(boolean) - Method in class weka.gui.beans.Loader
-
- setDebug(boolean) - Method in class weka.associations.GeneralizedSequentialPatterns
-
Set debugging mode.
- setDebug(boolean) - Method in class weka.attributeSelection.RaceSearch
-
Set whether verbose output should be generated.
- setDebug(boolean) - Method in class weka.attributeSelection.ScatterSearchV1
-
Set whether verbose output should be generated.
- setDebug(boolean) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
- setDebug(boolean) - Method in class weka.classifiers.BVDecompose
-
Sets debugging mode
- setDebug(boolean) - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Sets debugging mode
- setDebug(boolean) - Method in class weka.classifiers.Classifier
-
Set debugging mode.
- setDebug(boolean) - Method in class weka.classifiers.functions.LeastMedSq
-
sets whether or not debugging output shouild be printed
- setDebug(boolean) - Method in class weka.classifiers.functions.LinearRegression
-
Controls whether debugging output will be printed
- setDebug(boolean) - Method in class weka.classifiers.functions.Logistic
-
Sets whether debugging output will be printed.
- setDebug(boolean) - Method in class weka.classifiers.functions.PaceRegression
-
Controls whether debugging output will be printed
- setDebug(boolean) - Method in class weka.classifiers.functions.supportVector.Kernel
-
Enables or disables the output of debug information (if the derived
kernel supports that)
- setDebug(boolean) - Method in class weka.classifiers.meta.MultiScheme
-
Set debugging mode
- setDebug(boolean) - Method in class weka.classifiers.rules.JRip
-
Sets whether debug information is output to the console
- setDebug(boolean) - Method in class weka.clusterers.EM
-
Set debug mode - verbose output
- setDebug(boolean) - Method in class weka.clusterers.HierarchicalClusterer
-
Set debugging mode.
- setDebug(boolean) - Method in class weka.clusterers.sIB
-
Set debug mode - verbose output
- setDebug(boolean) - Method in class weka.core.Check
-
Set debugging mode
- setDebug(boolean) - Method in class weka.core.converters.TextDirectoryLoader
-
Sets whether to print some debug information.
- setDebug(boolean) - Method in class weka.core.Debug.Random
-
sets whether to print the generated random values or not
- setDebug(boolean) - Method in class weka.core.Optimization
-
Set whether in debug mode
- setDebug(boolean) - Method in class weka.datagenerators.DataGenerator
-
Sets the debug flag.
- setDebug(boolean) - Method in class weka.estimators.CheckEstimator
-
Set debugging mode
- setDebug(boolean) - Method in class weka.estimators.Estimator
-
Set debugging mode.
- setDebug(boolean) - Method in class weka.experiment.DatabaseUtils
-
Sets whether there should be printed some debugging output to stderr or not.
- setDebug(boolean) - Method in class weka.filters.SimpleFilter
-
Sets the debugging mode
- setDebug(boolean) - Method in class weka.filters.unsupervised.attribute.AddExpression
-
Set debug mode.
- setDebug(boolean) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion
-
sets debug mode on/off.
- setDebug(boolean) - Method in class weka.gui.streams.InstanceCounter
-
- setDebug(boolean) - Method in class weka.gui.streams.InstanceJoiner
-
- setDebug(boolean) - Method in class weka.gui.streams.InstanceLoader
-
- setDebug(boolean) - Method in class weka.gui.streams.InstanceSavePanel
-
- setDebug(boolean) - Method in class weka.gui.streams.InstanceTable
-
- setDebug(boolean) - Method in class weka.gui.streams.InstanceViewer
-
- setDebugLevel(int) - Method in class weka.clusterers.XMeans
-
Sets the debug level.
- setDebugVectorsFile(File) - Method in class weka.clusterers.XMeans
-
Sets the file that has the random vectors stored.
- setDecay(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- setDecimals(int) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Set the number of decimals to round to.
- setDefaultColourList(Color[]) - Method in class weka.gui.visualize.AttributePanel
-
- setDefaultColourList(Color[]) - Method in class weka.gui.visualize.ClassPanel
-
- setDefaults() - Method in class weka.datagenerators.ClusterDefinition
-
sets the default values
- setDefaults() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
sets the default values
- setDefaultValue() - Method in class weka.gui.GenericObjectEditor
-
Sets the current object to be the default, taken as the first item in
the chooser.
- setDefaultWeight(double) - Method in class weka.classifiers.functions.Winnow
-
Set the value of defaultWeight.
- setDegree(int) - Method in class weka.classifiers.functions.LibSVM
-
Sets the degree of the kernel
- setDegreesOfFreedom(int) - Method in class weka.experiment.PairedStats
-
Sets the degrees of freedom (if calibration is required).
- setDeleteEmptyBins(boolean) - Method in class weka.classifiers.meta.RegressionByDiscretization
-
Sets the number of bins to divide each selected numeric attribute into
- setDelimiters(String) - Method in class weka.core.tokenizers.CharacterDelimitedTokenizer
-
Set the value of delimiters.
- setDelta(double) - Method in class weka.associations.Apriori
-
Set the value of delta.
- setDelta(double) - Method in class weka.associations.FPGrowth
-
Set the value of delta.
- setDelta(double) - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
-
Sets the m_fDelta.
- setDelta(double) - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
-
Sets the m_fDelta.
- setDensityBasedClusterer(DensityBasedClusterer) - Method in class weka.filters.unsupervised.attribute.ClusterMembership
-
Set the clusterer for use in filtering
- setDerived(int) - Method in class weka.gui.AttributeSummaryPanel
-
Sets the gui elements for fields that are stored in the AttributeStats
structure.
- setDescendantPopulationSize(int) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
-
- setDescendantPopulationSize(int) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
-
- setDescendents(ArrayList, C45PruneableClassifierTreeG) - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG
-
add the grafted nodes at originalLeaf's position in tree.
- setDesign(boolean) - Method in class weka.gui.beans.AttributeSummarizer
-
Set whether the appearance of this bean should be design or
application
- setDesignatedClass(SelectedTag) - Method in class weka.classifiers.meta.ThresholdSelector
-
Sets the method to determine which class value to optimize.
- setDesiredSize(int) - Method in class weka.classifiers.meta.Decorate
-
Sets the desired size of the committee.
- setDesiredWeightOfInstancesPerInterval(double) - Method in class weka.filters.unsupervised.attribute.Discretize
-
Set the DesiredWeightOfInstancesPerInterval value.
- setDestination(File) - Method in class weka.core.converters.AbstractFileSaver
-
Sets the destination file (and directories if necessary).
- setDestination(OutputStream) - Method in class weka.core.converters.AbstractFileSaver
-
Sets the destination output stream.
- setDestination(File) - Method in class weka.core.converters.AbstractSaver
-
Default implementation throws an IOException.
- setDestination(OutputStream) - Method in class weka.core.converters.AbstractSaver
-
Default implementation throws an IOException.
- setDestination(OutputStream) - Method in class weka.core.converters.ArffSaver
-
Sets the destination output stream.
- setDestination(String, String, String) - Method in class weka.core.converters.DatabaseSaver
-
Sets the database url.
- setDestination(String) - Method in class weka.core.converters.DatabaseSaver
-
Sets the database url.
- setDestination() - Method in class weka.core.converters.DatabaseSaver
-
Sets the database url using the DatabaseUtils file.
- setDestination(File) - Method in interface weka.core.converters.Saver
-
Resets the Saver object and sets the destination to be
the supplied File object.
- setDestination(OutputStream) - Method in interface weka.core.converters.Saver
-
Resets the Saver object and sets the destination to be
the supplied InputStream.
- setDestination(OutputStream) - Method in class weka.core.converters.SerializedInstancesSaver
-
Sets the destination output stream.
- setDestination(OutputStream) - Method in class weka.core.converters.XRFFSaver
-
Sets the destination output stream.
- setDetectionPerAttribute(boolean) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
-
Set whether an Outlier/ExtremeValue attribute pair is generated for
each numeric attribute ("true") or just one pair for all numeric
attributes together ("false").
- setDir(String) - Method in class weka.core.converters.AbstractFileSaver
-
Sets the directory where the instances should be stored
- setDir(String) - Method in class weka.core.converters.AbstractSaver
-
Default implementation throws an IOException.
- setDir(String) - Method in interface weka.core.converters.Saver
-
Sets the directory of the output file.
- setDir(String) - Method in class weka.core.Javadoc
-
sets the dir containing the file that is to be updated.
- setDirAndPrefix(String, String) - Method in class weka.core.converters.AbstractFileSaver
-
Sets the directory and the file prefix.
- setDirAndPrefix(String, String) - Method in class weka.core.converters.AbstractSaver
-
Default implementation throws an IOException.
- setDirAndPrefix(String, String) - Method in interface weka.core.converters.Saver
-
Sets the file prefix and the directory.
- setDirection(SelectedTag) - Method in class weka.attributeSelection.BestFirst
-
Set the search direction
- setDirectory(File) - Method in class weka.core.converters.TextDirectoryLoader
-
sets the source directory
- setDirectory(File) - Method in class weka.gui.beans.SerializedModelSaver
-
Set the directory that the model(s) will be saved into.
- setDiscretizeBin(int) - Method in class weka.classifiers.mi.MIBoost
-
Set the number of bins in discretization
- setDisplayConnectors(boolean) - Method in class weka.gui.beans.BeanVisual
-
Turn on/off the connector points
- setDisplayConnectors(boolean, Color) - Method in class weka.gui.beans.BeanVisual
-
Turn on/off the connector points
- setDisplayedFromDialog() - Method in class weka.gui.experiment.ResultsPanel
-
- setDisplayedResultsets(int[]) - Method in class weka.experiment.PairedTTester
-
Sets the indicies of the datasets to display (null
means all).
- setDisplayedResultsets(int[]) - Method in interface weka.experiment.Tester
-
Sets the indicies of the datasets to display (null
means all).
- setDisplayModelInOldFormat(boolean) - Method in class weka.classifiers.bayes.NaiveBayes
-
Set whether to display model output in the old, original
format.
- setDisplayModelInOldFormat(boolean) - Method in class weka.clusterers.EM
-
Set whether to display model output in the old, original
format.
- setDisplayRules(boolean) - Method in class weka.classifiers.rules.DecisionTable
-
Sets whether rules are to be printed
- setDisplayStdDevs(boolean) - Method in class weka.clusterers.SimpleKMeans
-
Sets whether standard deviations and nominal count
Should be displayed in the clustering output
- setDistanceF(DistanceFunction) - Method in class weka.clusterers.XMeans
-
gets the "binary" distance value.
- setDistanceFunction(DistanceFunction) - Method in class weka.clusterers.HierarchicalClusterer
-
- setDistanceFunction(DistanceFunction) - Method in class weka.clusterers.SimpleKMeans
-
sets the distance function to use for instance comparison.
- setDistanceFunction(DistanceFunction) - Method in class weka.core.neighboursearch.CoverTree
-
Sets the distance function to use for nearest neighbour search.
- setDistanceFunction(DistanceFunction) - Method in class weka.core.neighboursearch.KDTree
-
sets the distance function to use for nearest neighbour search.
- setDistanceFunction(DistanceFunction) - Method in class weka.core.neighboursearch.NearestNeighbourSearch
-
sets the distance function to use for nearest neighbour search.
- setDistanceIsBranchLength(boolean) - Method in class weka.clusterers.HierarchicalClusterer
-
- setDistanceWeighting(SelectedTag) - Method in class weka.classifiers.lazy.IBk
-
Sets the distance weighting method used.
- setDistMult(double) - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Sets the distance multiplier.
- setDistribution(String, double[][]) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
specify distribution of a node
- setDistribution(int, double[][]) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
specify distribution of a node
- setDistribution(SelectedTag) - Method in class weka.filters.unsupervised.attribute.RandomProjection
-
Sets the distribution to use for calculating the random matrix
- setDistributionSpread(double) - Method in class weka.filters.supervised.instance.SpreadSubsample
-
Sets the value for the distribution spread
- setDocType(String) - Method in class weka.core.xml.XMLDocument
-
sets the DOCTYPE-String to use in the XML output.
- setDocument(Document) - Method in class weka.core.xml.XMLDocument
-
sets the DOM document to use.
- setDoNotOperateOnPerClassBasis(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Set the DoNotOperateOnPerClassBasis value.
- setDoNotReplaceMissingValues(boolean) - Method in class weka.classifiers.functions.LibLINEAR
-
Whether to turn off automatic replacement of missing values.
- setDoNotReplaceMissingValues(boolean) - Method in class weka.classifiers.functions.LibSVM
-
Whether to turn off automatic replacement of missing values.
- setDontFilterAfterFirstBatch(boolean) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Set whether to apply the filter to instances that arrive once
the first (training) batch has been seen.
- setDontNormalize(boolean) - Method in class weka.classifiers.functions.SPegasos
-
Turn normalization off/on.
- setDontNormalize(boolean) - Method in class weka.core.NormalizableDistance
-
Sets whether if the attribute values are to be normalized in distance
calculation.
- setDontReplaceMissing(boolean) - Method in class weka.classifiers.functions.SPegasos
-
Turn global replacement of missing values off/on.
- setDontReplaceMissingValues(boolean) - Method in class weka.clusterers.SimpleKMeans
-
Sets whether missing values are to be replaced
- setElement(int, int, double) - Method in class weka.classifiers.CostMatrix
-
Set the value of a cell as a double
- setElement(int, double) - Method in class weka.core.AlgVector
-
Sets an element of the matrix to the given value.
- setElement(int, int, double) - Method in class weka.core.Matrix
-
Deprecated.
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.
- setElementAt(Object, int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel
-
Sets the component at the specified index of this list to be the
specified object.
- setElements(FastVector) - Method in class weka.associations.gsp.Sequence
-
Sets the Elements of the Sequence.
- setElements(double[]) - Method in class weka.core.AlgVector
-
Sets the elements of the vector to values of the given array.
- setEliminateColinearAttributes(boolean) - Method in class weka.classifiers.functions.LinearRegression
-
Set the value of EliminateColinearAttributes.
- setEnabled(boolean) - Method in class weka.core.Debug
-
sets whether the logging is enabled or not
- setEnabled(boolean) - Method in class weka.core.Memory
-
sets whether the memory management is enabled
- setEnabled(boolean) - Method in class weka.gui.GenericObjectEditor
-
Sets whether the editor is "enabled", meaning that the current
values will be painted.
- setEnabled(boolean) - Method in class weka.gui.PropertyPanel
-
Passes on enabled/disabled status to the custom
panel (if one is set).
- setEnclosureCharacters(String) - Method in class weka.core.converters.CSVLoader
-
Set the character(s) to use/recognize as string enclosures
- setEntropicAutoBlend(boolean) - Method in class weka.classifiers.lazy.KStar
-
Set whether entropic blending is to be used.
- setEnumerateColNames(boolean) - Method in class weka.experiment.ResultMatrix
-
sets whether the column names are prefixed with "(x)" where "x" is
the index
- setEnumerateRowNames(boolean) - Method in class weka.experiment.ResultMatrix
-
sets whether to the row names or numbers instead are enumerateed
- setEnvironment(Environment) - Method in class weka.core.converters.AbstractFileLoader
-
Set the environment variables to use.
- setEnvironment(Environment) - Method in class weka.core.converters.AbstractFileSaver
-
Set the environment variables to use.
- setEnvironment(Environment) - Method in interface weka.core.EnvironmentHandler
-
Set environment variables to use.
- setEnvironment(Environment) - Method in class weka.gui.beans.FlowRunner
-
Set the environment variables to use.
- setEnvironment(Environment) - Method in class weka.gui.beans.KnowledgeFlowApp
-
Set the environment variables to use.
- setEnvironment(Environment) - Method in class weka.gui.beans.Loader
-
Set environment variables to use.
- setEnvironment(Environment) - Method in class weka.gui.beans.Saver
-
Set environment variables to use.
- setEnvironment(Environment) - Method in class weka.gui.beans.SerializedModelSaver
-
Set environment variables to use.
- setEpochs(int) - Method in class weka.classifiers.functions.SPegasos
-
Set the number of epochs to use
- setEps(double) - Method in class weka.classifiers.functions.LibLINEAR
-
Sets tolerance of termination criterion (default 0.001)
- setEps(double) - Method in class weka.classifiers.functions.LibSVM
-
Sets tolerance of termination criterion (default 0.001)
- setEpsilon(double) - Method in class weka.classifiers.functions.SMO
-
Set the value of epsilon.
- setEpsilon(double) - Method in class weka.classifiers.functions.supportVector.RegSMO
-
Set the value of epsilon.
- setEpsilon(double) - Method in class weka.classifiers.mi.MISMO
-
Set the value of epsilon.
- setEpsilon(double) - Method in class weka.clusterers.DBScan
-
Sets a new value for epsilon
- setEpsilon(double) - Method in class weka.clusterers.OPTICS
-
Sets a new value for epsilon
- setEpsilonParameter(double) - Method in class weka.attributeSelection.SVMAttributeEval
-
Set the value of P for SMO
- setEpsilonParameter(double) - Method in class weka.classifiers.functions.supportVector.RegOptimizer
-
Set the value of epsilon parameter of the epsilon insensitive loss function.
- setErrorOnProbabilities(boolean) - Method in class weka.classifiers.functions.SimpleLogistic
-
Set the value of errorOnProbabilities.
- setErrorOnProbabilities(boolean) - Method in class weka.classifiers.trees.FT
-
Set the value of errorOnProbabilities.
- setErrorOnProbabilities(boolean) - Method in class weka.classifiers.trees.LMT
-
Set the value of errorOnProbabilities.
- setEstimator(BayesNetEstimator) - Method in class weka.classifiers.bayes.BayesNet
-
Set the Estimator Algorithm used in calculating the CPTs
- setEstimator(SelectedTag) - Method in class weka.classifiers.functions.PaceRegression
-
Sets the estimator.
- setEstimator(Estimator) - Method in class weka.estimators.CheckEstimator
-
Set the estimator for boosting.
- setEuclideanDistanceFunction(EuclideanDistance) - Method in class weka.core.neighboursearch.balltrees.BallSplitter
-
Sets the distance function used to (or to be used
to) build the tree.
- setEuclideanDistanceFunction(EuclideanDistance) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
Sets the distance function to use to build the
tree.
- setEuclideanDistanceFunction(EuclideanDistance) - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
-
Sets the EuclideanDistance object to use for
splitting nodes.
- setEvaluation(SelectedTag) - Method in class weka.classifiers.meta.GridSearch
-
Sets the criterion to use for evaluating the classifier performance.
- setEvaluationMeasure(SelectedTag) - Method in class weka.classifiers.rules.DecisionTable
-
Sets the performance evaluation measure to use for selecting attributes
for the decision table
- setEvaluationMode(SelectedTag) - Method in class weka.classifiers.meta.ThresholdSelector
-
Sets the evaluation mode used.
- setEvaluator(ASEvaluation) - Method in class weka.attributeSelection.AttributeSelection
-
set the attribute/subset evaluator
- setEvaluator(ASEvaluation) - Method in class weka.attributeSelection.CheckAttributeSelection
-
Set the evaluator to test.
- setEvaluator(ASEvaluation) - Method in class weka.attributeSelection.CostSensitiveASEvaluation
-
Set the base evaluator.
- setEvaluator(ASEvaluation) - Method in class weka.attributeSelection.CostSensitiveAttributeEval
-
Set the base evaluator.
- setEvaluator(ASEvaluation) - Method in class weka.attributeSelection.CostSensitiveSubsetEval
-
Set the base evaluator.
- setEvaluator(ASEvaluation) - Method in class weka.classifiers.meta.AttributeSelectedClassifier
-
Sets the attribute evaluator
- setEvaluator(ASEvaluation) - Method in class weka.filters.supervised.attribute.AttributeSelection
-
set attribute/subset evaluator
- setEvalUsingTrainingData(boolean) - Method in class weka.attributeSelection.OneRAttributeEval
-
Use the training data to evaluate attributes rather than cross validation
- setEvents(int[]) - Method in class weka.associations.gsp.Element
-
Sets the events Array of an Element.
- setEvidence(int, int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
set evidence state of a node.
- setEvidence(int, int) - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
-
- setEvidence(int, int) - Method in class weka.classifiers.bayes.net.MarginCalculator
-
- setExclusive(boolean) - Method in class weka.classifiers.rules.ConjunctiveRule
-
Sets whether exclusive expressions for nominal attributes splits are
considered
- setExecutionSlots(int) - Method in class weka.gui.beans.Classifier
-
Set the number of execution slots (threads) to use to
train models with.
- setExecutionStatus(int) - Method in class weka.experiment.TaskStatusInfo
-
Set the execution status of this Task.
- setExitIfNoWindowsOpen(boolean) - Static method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
Sets whether System.exit gets called when no more windows are open.
- setExitOnClose(boolean) - Method in class weka.gui.arffviewer.ArffViewer
-
whether to do a System.exit(0) on close
- setExitOnClose(boolean) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
whether to do a System.exit(0) on close
- setExpectedResultsPerAverage(int) - Method in class weka.experiment.AveragingResultProducer
-
Set the value of ExpectedResultsPerAverage.
- setExperiment(Experiment) - Method in class weka.experiment.RemoteExperimentSubTask
-
Set the experiment for this sub task
- setExperiment(Experiment) - Method in class weka.gui.experiment.AlgorithmListPanel
-
Tells the panel to act on a new experiment.
- setExperiment(Experiment) - Method in class weka.gui.experiment.DatasetListPanel
-
Tells the panel to act on a new experiment.
- setExperiment(Experiment) - Method in class weka.gui.experiment.DistributeExperimentPanel
-
Sets the experiment to be configured.
- setExperiment(Experiment) - Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
-
Sets the experiment which will have the custom properties edited.
- setExperiment(RemoteExperiment) - Method in class weka.gui.experiment.HostListPanel
-
Tells the panel to act on a new experiment.
- setExperiment(Experiment) - Method in class weka.gui.experiment.ResultsPanel
-
Tells the panel to use a new experiment.
- setExperiment(Experiment) - Method in class weka.gui.experiment.RunNumberPanel
-
Sets the experiment to be configured.
- setExperiment(Experiment) - Method in class weka.gui.experiment.RunPanel
-
Sets the experiment the panel operates on.
- setExperiment(Experiment) - Method in class weka.gui.experiment.SetupPanel
-
Sets the experiment to configure.
- setExperiment(Experiment) - Method in class weka.gui.experiment.SimpleSetupPanel
-
Sets the experiment to configure.
- setExplicitPropsFile(boolean) - Method in class weka.gui.GenericPropertiesCreator
-
if FALSE, the locating of a props-file of the Utils-class is used,
otherwise it's tried to load the specified file
- setExplorer(Explorer) - Method in class weka.gui.explorer.AssociationsPanel
-
Sets the Explorer to use as parent frame (used for sending notifications
about changes in the data)
- setExplorer(Explorer) - Method in class weka.gui.explorer.AttributeSelectionPanel
-
Sets the Explorer to use as parent frame (used for sending notifications
about changes in the data)
- setExplorer(Explorer) - Method in class weka.gui.explorer.ClassifierPanel
-
Sets the Explorer to use as parent frame (used for sending notifications
about changes in the data)
- setExplorer(Explorer) - Method in class weka.gui.explorer.ClustererPanel
-
Sets the Explorer to use as parent frame (used for sending notifications
about changes in the data)
- setExplorer(Explorer) - Method in interface weka.gui.explorer.Explorer.ExplorerPanel
-
Sets the Explorer to use as parent frame (used for sending notifications
about changes in the data)
- setExplorer(Explorer) - Method in class weka.gui.explorer.PreprocessPanel
-
Sets the Explorer to use as parent frame (used for sending notifications
about changes in the data)
- setExplorer(Explorer) - Method in class weka.gui.explorer.VisualizePanel
-
Sets the Explorer to use as parent frame (used for sending notifications
about changes in the data)
- setExponent(double) - Method in class weka.classifiers.functions.supportVector.NormalizedPolyKernel
-
Sets the exponent value (must be different from 1.0).
- setExponent(double) - Method in class weka.classifiers.functions.supportVector.PolyKernel
-
Sets the exponent value.
- setExponent(double) - Method in class weka.classifiers.functions.VotedPerceptron
-
Set the value of exponent.
- setExpression(String) - Method in class weka.datagenerators.classifiers.regression.Expression
-
Sets the mathematical expression to generate y out of x.
- setExpression(String) - Method in class weka.filters.unsupervised.attribute.AddExpression
-
Set the expression to apply
- setExpression(String) - Method in class weka.filters.unsupervised.attribute.MathExpression
-
Set the expression to apply
- setExpression(String) - Method in class weka.filters.unsupervised.instance.SubsetByExpression
-
Sets the expression used for filtering.
- setExtremeValuesAsOutliers(boolean) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
-
Set whether extreme values are also tagged as outliers.
- setExtremeValuesFactor(double) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
-
Sets the factor for determining the thresholds for extreme values.
- 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
- setFastRegression(boolean) - Method in class weka.classifiers.trees.LMT
-
Set the value of fastRegression.
- setField(Object, String, Object) - Method in class weka.classifiers.functions.LibLINEAR
-
sets the specified field
- setField(Object, String, int, Object) - Method in class weka.classifiers.functions.LibLINEAR
-
sets the specified field in an array
- setField(Object, String, Object) - Method in class weka.classifiers.functions.LibSVM
-
sets the specified field
- setField(Object, String, int, Object) - Method in class weka.classifiers.functions.LibSVM
-
sets the specified field in an array
- setFieldDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.DerivedFieldMetaInfo
-
Upadate the field definitions for this derived field
- setFieldDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.Discretize
-
Set the field definitions for this Expression to use
- setFieldDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.Expression
-
Set the field definitions for this Expression to use
- setFieldDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.FieldRef
-
- setFieldDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.NormContinuous
-
Set the field definitions for this Expression to use
- setFieldDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.NormDiscrete
-
Set the field definitions for this Expression to use
- setFile(File) - Method in class weka.core.converters.AbstractFileLoader
-
sets the source File
- setFile(File) - Method in class weka.core.converters.AbstractFileSaver
-
Sets the destination file.
- setFile(File) - Method in class weka.core.converters.AbstractSaver
-
Default implementation throws an IOException.
- setFile(File) - Method in class weka.core.converters.ArffLoader
-
sets the source File
- setFile(File) - Method in class weka.core.converters.ArffSaver
-
Sets the destination file.
- setFile(File) - Method in interface weka.core.converters.FileSourcedConverter
-
Set the file to load from/ to save in
- setFile(File) - Method in interface weka.core.converters.Saver
-
Sets the output file
- setFile(File) - Method in class weka.core.converters.XRFFSaver
-
Sets the destination file.
- setFile(File) - Method in class weka.gui.visualize.JComponentWriter
-
sets the file to store the output in
- setFileExtension(String) - Method in class weka.core.converters.AbstractFileSaver
-
Sets ihe file extension.
- setFileFormat(Tag) - Method in class weka.gui.beans.SerializedModelSaver
-
Set the file format to use for saving.
- setFileMustExist(boolean) - Method in class weka.gui.ConverterFileChooser
-
Whether the selected file must exst (only open dialog).
- setFilename(String) - Method in class weka.core.FindWithCapabilities
-
Sets the dataset filename to base the capabilities on.
- setFilename(String) - Method in class weka.gui.arffviewer.ArffPanel
-
sets the filename
- setFilename(int, String) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
sets the filename of the specified panel
- setFilePrefix(String) - Method in class weka.core.converters.AbstractFileSaver
-
Sets the file name prefix
- setFilePrefix(String) - Method in class weka.core.converters.AbstractSaver
-
Default implementation throws an IOException.
- setFilePrefix(String) - Method in interface weka.core.converters.Saver
-
Sets the file prefix.
- setFillWithMissing(boolean) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
-
Sets whether missing values should be used rather than removing instances
where the translated value is not known (due to border effects).
- setFilter(Filter) - Method in class weka.associations.FilteredAssociator
-
Sets the filter
- setFilter(Filter) - Method in class weka.attributeSelection.FilteredAttributeEval
-
Set the filter to use
- setFilter(Filter) - Method in class weka.attributeSelection.FilteredSubsetEval
-
Set the filter to use
- setFilter(Filter) - Method in class weka.classifiers.functions.PLSClassifier
-
Set the PLS filter (only used for setup).
- setFilter(Filter) - Method in class weka.classifiers.meta.FilteredClassifier
-
Sets the filter
- setFilter(Filter) - Method in class weka.classifiers.meta.GridSearch
-
Set the kernel filter (only used for setup).
- setFilter(Filter) - Method in class weka.clusterers.FilteredClusterer
-
Sets the filter.
- setFilter(Filter) - Method in class weka.filters.CheckSource
-
Sets the filter to use for the comparison.
- setFilter(Filter) - Method in class weka.filters.unsupervised.attribute.Wavelet
-
Set the preprocessing filter (only used for setup).
- setFilter(Filter) - Method in class weka.gui.beans.Filter
-
Set the filter to be wrapped by this bean
- setFilterAfterFirstBatch(boolean) - Method in class weka.filters.unsupervised.instance.SubsetByExpression
-
Set whether to apply the filter to instances that arrive once
the first (training) batch has been seen.
- setFilterAttributes(String) - Method in class weka.associations.GeneralizedSequentialPatterns
-
Sets the String containing the attributes which are used for output
filtering.
- setFilters(Filter[]) - Method in class weka.filters.MultiFilter
-
Sets the list of possible filters to choose from.
- setFilters(Filter[]) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
-
Sets the list of possible filters to choose from.
- setFilterType(SelectedTag) - Method in class weka.attributeSelection.SVMAttributeEval
-
The filtering mode to pass to SMO
- setFilterType(SelectedTag) - Method in class weka.classifiers.functions.GaussianProcesses
-
Sets how the training data will be transformed.
- setFilterType(SelectedTag) - Method in class weka.classifiers.functions.SMO
-
Sets how the training data will be transformed.
- setFilterType(SelectedTag) - Method in class weka.classifiers.functions.SMOreg
-
Sets how the training data will be transformed.
- setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MDD
-
Sets how the training data will be transformed.
- setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MIDD
-
Sets how the training data will be transformed.
- setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MIEMDD
-
Sets how the training data will be transformed.
- setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MIOptimalBall
-
Sets how the training data will be transformed.
- setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MISMO
-
Sets how the training data will be transformed.
- setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MISVM
-
Sets how the training data will be transformed.
- setFindAllRulesForSupportLevel(boolean) - Method in class weka.associations.FPGrowth
-
If true then turn off the iterative support reduction method
of finding x rules that meet the minimum support and metric
thresholds and just return all the rules that meet the
lower bound on minimum support and the minimum metric.
- setFindNumBins(boolean) - Method in class weka.filters.unsupervised.attribute.Discretize
-
Set the value of FindNumBins.
- setFindNumBins(boolean) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
-
Set the value of FindNumBins.
- setFirstValueIndex(String) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
-
Sets index of the first value used.
- setFirstValueIndex(String) - Method in class weka.filters.unsupervised.attribute.SwapValues
-
Sets index of the first value used.
- setFitness(double) - Method in class weka.attributeSelection.GeneticSearch.GABitSet
-
sets the scaled fitness
- setFlags() - Method in class weka.core.Range
-
Sets the flags array.
- setFlow(Vector) - Method in class weka.gui.beans.KnowledgeFlowApp
-
Set the flow for the KnowledgeFlow to edit.
- setFlows(Vector) - Method in class weka.gui.beans.FlowRunner
-
Set the vector holding the flows(s) to run
- setFocus() - Method in class weka.gui.sql.ConnectionPanel
-
sets the focus in a designated control.
- setFocus() - Method in class weka.gui.sql.InfoPanel
-
sets the focus in a designated control
- setFocus() - Method in class weka.gui.sql.QueryPanel
-
sets the focus in a designated control.
- setFocus() - Method in class weka.gui.sql.ResultPanel
-
sets the focus in a designated control
- setFold(int) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
-
Selects a fold.
- setFold(int) - Method in class weka.filters.unsupervised.instance.RemoveFolds
-
Selects a fold.
- setFoldColumn(int) - Method in class weka.experiment.PairedTTester
-
Set the value of FoldColumn.
- setFoldColumn(int) - Method in interface weka.experiment.Tester
-
Set the value of FoldColumn.
- setFolds(int) - Method in class weka.attributeSelection.AttributeSelection
-
set the number of folds for cross validation
- setFolds(int) - Method in class weka.attributeSelection.OneRAttributeEval
-
Set the number of folds to use for cross validation
- setFolds(int) - Method in class weka.attributeSelection.WrapperSubsetEval
-
Set the number of folds to use for accuracy estimation
- setFolds(int) - Method in class weka.classifiers.rules.ConjunctiveRule
-
the number of folds to use
- setFolds(int) - Method in class weka.classifiers.rules.JRip
-
Sets the number of folds to use
- setFolds(int) - Method in class weka.classifiers.rules.Ridor
-
- setFolds(int) - Method in class weka.gui.beans.CrossValidationFoldMaker
-
Set the number of folds for the cross validation
- setFoldsType(SelectedTag) - Method in class weka.attributeSelection.RaceSearch
-
Set the xfold type
- setFont(Font) - Method in class weka.gui.visualize.PostscriptGraphics
-
Set current font.
- setFormat(String) - Method in class weka.core.Debug.Timestamp
-
sets the format for the timestamp
- setFormat() - Method in class weka.gui.experiment.OutputFormatDialog
-
sets the class of the chosen result matrix.
- setForwardSelectionMethod(SelectedTag) - Method in class weka.attributeSelection.LinearForwardSelection
-
Set the search direction
- setFrequencyLimit(int) - Method in class weka.classifiers.bayes.AODE
-
Sets the frequency limit
- setFrequencyLimit(int) - Method in class weka.classifiers.bayes.AODEsr
-
Sets the frequency limit
- setFrequencyThreshold(double) - Method in class weka.associations.Tertius
-
Set the value of frequencyThreshold.
- setFunction(SelectedTag) - Method in class weka.datagenerators.classifiers.classification.Agrawal
-
Sets the function for generating the data.
- setFunctionValue(int, double) - Method in class weka.classifiers.functions.pace.DiscreteFunction
-
Sets a particular function value
- setGamma(double) - Method in class weka.classifiers.functions.LibSVM
-
Sets gamma (default = 1/no of attributes)
- setGamma(double) - Method in class weka.classifiers.functions.supportVector.RBFKernel
-
Sets the gamma value.
- setGenerateRanking(boolean) - Method in class weka.attributeSelection.GreedyStepwise
-
Records whether the user has requested a ranked list of attributes.
- setGenerateRanking(boolean) - Method in class weka.attributeSelection.RaceSearch
-
Records whether the user has requested a ranked list of attributes.
- setGenerateRanking(boolean) - Method in interface weka.attributeSelection.RankedOutputSearch
-
Sets whether or not ranking is to be performed.
- setGenerateRanking(boolean) - Method in class weka.attributeSelection.Ranker
-
This is a dummy set method---Ranker is ONLY capable of producing
a ranked list of attributes for attribute evaluators.
- setGenerateRules(boolean) - Method in class weka.classifiers.trees.m5.M5Base
-
Generate rules (decision list) rather than a tree
- setGenerator(DataGenerator) - Method in class weka.gui.explorer.DataGeneratorPanel
-
sets the generator to use initially
- setGeneratorOption(BayesNetGenerator, String, String) - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
sets a specific option/value of the generator (option must be w/o
then '-')
- setGeneratorOption(String, String) - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
sets a specific option/value of the generator (option must be w/o
then '-')
- setGeneratorOptions(BayesNetGenerator, Vector) - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
sets the given options of the BayesNetGenerator
- setGeneratorOptions(Vector) - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
sets the given options of the BayesNetGenerator
- setGeneratorSamplesBase(double) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Set the base for computing the number of samples to obtain from each
generator.
- setGeneratorSamplesBase(double) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
-
Set the base for computing the number of samples to obtain from each
generator.
- setGlobalBlend(int) - Method in class weka.classifiers.lazy.KStar
-
Set the global blend parameter
- setGlobalModel(NBTreeNoSplit) - Method in class weka.classifiers.trees.j48.NBTreeSplit
-
Set the global naive bayes model for this node
- setGridIsExtendable(boolean) - Method in class weka.classifiers.meta.GridSearch
-
Set whether the grid can be extended dynamically.
- setGridWidth(int) - Method in class weka.gui.beans.AttributeSummarizer
-
Set the width of the grid of plots
- setGroupIdentifier(long) - Method in class weka.gui.beans.BatchClassifierEvent
-
- setGUI(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
-
This will set whether A GUI is brought up to allow interaction by the user
with the neural network during training.
- setGUIType(SelectedTag) - Method in class weka.gui.Main
-
Sets the type of GUI to use.
- setHandler(CapabilitiesHandler) - Method in class weka.core.FindWithCapabilities
-
sets the Capabilities handler to generate the data for.
- setHandler(CapabilitiesHandler) - Method in class weka.core.TestInstances
-
sets the Capabilities handler to generate the data for
- 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.
- setHashtable(Hashtable) - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
-
Set hashtable from END.
- setHashtable(Hashtable) - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
-
Set hashtable from END.
- setHashtable(Hashtable) - Method in class weka.classifiers.meta.nestedDichotomies.ND
-
Set hashtable from END.
- setHDRank(int) - Method in class weka.classifiers.mi.CitationKNN
-
Sets the rank associated to the Hausdorff distance
- setHeader(int) - Method in class weka.gui.AttributeSummaryPanel
-
Sets the labels for fields we can determine just from the instance
header.
- setHeuristic(boolean) - Method in class weka.classifiers.trees.BFTree
-
Set if use heuristic search for nominal attributes in multi-class problems.
- setHeuristic(boolean) - Method in class weka.classifiers.trees.SimpleCart
-
Set if use heuristic search for nominal attributes in multi-class problems.
- setHeuristicStop(int) - Method in class weka.classifiers.functions.SimpleLogistic
-
Set the value of heuristicStop.
- setHeuristicStop(int) - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Sets the option "heuristicStop".
- setHidden(boolean) - Method in class weka.gui.beans.BeanConnection
-
Make this connection invisible on the display
- setHiddenLayers(String) - Method in class weka.classifiers.functions.MultilayerPerceptron
-
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
- setHistory(DefaultListModel) - Method in class weka.gui.sql.ConnectionPanel
-
sets the local history to the given one.
- setHistory(DefaultListModel) - Method in class weka.gui.sql.QueryPanel
-
sets the local history to the given one.
- setHoldOutFile(File) - Method in class weka.attributeSelection.ClassifierSubsetEval
-
Set the file that contains hold out/test instances
- setHornClauses(boolean) - Method in class weka.associations.Tertius
-
Set the value of hornClauses.
- setHyperparameterRange(String) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Set the range of hyperparameter values to consider
during CV-based selection
- setHyperparameterSelection(SelectedTag) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Set the method used to select the hyperparameter
- setHyperparameterValue(double) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Set the hyperparameter value.
- setID(int) - Method in class weka.core.Tag
-
Sets the numeric ID of the Tag.
- setIDFTransform(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Sets whether if the word frequencies in a document should be transformed
into:
fij*log(num of Docs/num of Docs with word i)
where fij is the frequency of word i in document(instance) j.
- setIDIndex(String) - Method in class weka.filters.unsupervised.attribute.AddID
-
Sets index of the attribute used.
- setIDStr(String) - Method in class weka.core.Tag
-
Sets the string ID of the Tag.
- setIgnoreClass(boolean) - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer
-
Set the IgnoreClass value.
- setIgnoredAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.AddCluster
-
Sets the ranges of attributes to be ignored.
- setIgnoredAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.ClusterMembership
-
Sets the ranges of attributes to be ignored.
- setIgnoredProperties(String) - Method in class weka.core.CheckGOE
-
Sets the properties to ignore in checkToolTips().
- setIgnoreRange(String) - Method in class weka.filters.unsupervised.attribute.MathExpression
-
Set which attributes are to be ignored
- setIncludeClass(boolean) - Method in class weka.core.InstanceComparator
-
sets whether the class should be included (= TRUE) in the comparison
- setIncludeClass(boolean) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Sets whether the class can be cleaned, too.
- setIndex(int) - Method in class weka.core.pmml.MiningFieldMetaInfo
-
Set the index of this field in the mining schema Instances
- setInitAsNaiveBayes(boolean) - Method in class weka.classifiers.bayes.net.search.global.HillClimber
-
Sets whether to init as naive bayes
- setInitAsNaiveBayes(boolean) - Method in class weka.classifiers.bayes.net.search.global.K2
-
Sets whether to init as naive bayes
- setInitAsNaiveBayes(boolean) - Method in class weka.classifiers.bayes.net.search.local.HillClimber
-
Sets whether to init as naive bayes
- setInitAsNaiveBayes(boolean) - Method in class weka.classifiers.bayes.net.search.local.K2
-
Sets whether to init as naive bayes
- setInitFile(File) - Method in class weka.filters.unsupervised.attribute.KernelFilter
-
Sets the file to initialize the filter with, can be null.
- setInitFileClassIndex(String) - Method in class weka.filters.unsupervised.attribute.KernelFilter
-
Sets class index of the file to initialize the filter with.
- setInitialAnchorRandom(boolean) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Sets whether if the initial anchor is chosen randomly.
- setInputCenterFile(File) - Method in class weka.clusterers.XMeans
-
Sets the file to read the list of centers from.
- setInputFilename(String) - Method in class weka.gui.GenericPropertiesCreator
-
sets the file to get the information about the packages from.
- setInputFormat(Instances) - Method in class weka.filters.AllFilter
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.Filter
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.SimpleFilter
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.supervised.attribute.ClassOrder
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.supervised.attribute.Discretize
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.supervised.attribute.NominalToBinary
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.supervised.instance.Resample
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.supervised.instance.SMOTE
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.supervised.instance.SpreadSubsample
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Add
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddCluster
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddExpression
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddID
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddNoise
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddValues
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Center
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.ClusterMembership
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Copy
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Discretize
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.FirstOrder
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.MakeIndicator
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.MathExpression
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.NominalToBinary
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.NominalToString
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Normalize
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.NumericToBinary
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.NumericTransform
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Obfuscate
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.RandomProjection
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Remove
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.RemoveType
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.RemoveUseless
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Reorder
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Standardize
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.StringToNominal
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.SwapValues
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.TimeSeriesDelta
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.NonSparseToSparse
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.Normalize
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.Randomize
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveFolds
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemovePercentage
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveRange
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.Resample
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.ReservoirSample
-
Sets the format of the input instances.
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.SparseToNonSparse
-
Sets the format of the input instances.
- setInputOrder(SelectedTag) - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Sets the input order.
- setInputs(Vector) - Method in class weka.gui.beans.MetaBean
-
- setInstance(Instance) - Method in class weka.gui.beans.InstanceEvent
-
Set the instance
- setInstanceIndex(int, boolean) - Method in class weka.classifiers.lazy.LBR.Indexes
-
Changes the boolean value at the specified index in the InstIndexes array
- setInstanceList(int[]) - Method in class weka.core.neighboursearch.balltrees.BallSplitter
-
Sets the master index array containing indices of the
training instances.
- setInstanceList(int[]) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
Sets the master index array that points to
instances in m_Instances, so that only this array
is manipulated, and m_Instances is left
untouched.
- setInstanceList(int[]) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Sets the master index array that points to
instances in m_Instances, so that only this array
is manipulated, and m_Instances is left
untouched.
- setInstanceList(int[]) - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
-
Sets the master index array containing indices of the
training instances.
- setInstanceRange(int) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
-
Sets the number of instances forward to translate values between.
- setInstances(Instances) - Method in class weka.core.converters.AbstractSaver
-
Sets instances that should be stored.
- setInstances(Instances) - Method in class weka.core.converters.LibSVMSaver
-
Sets instances that should be stored.
- setInstances(Instances) - Method in interface weka.core.converters.Saver
-
Sets the instances to be saved
- setInstances(Instances) - Method in class weka.core.converters.SVMLightSaver
-
Sets instances that should be stored.
- setInstances(Instances) - Method in class weka.core.converters.XRFFSaver
-
Sets instances that should be stored.
- setInstances(Instances) - Method in interface weka.core.DistanceFunction
-
Sets the instances.
- setInstances(Instances) - Method in class weka.core.neighboursearch.BallTree
-
Builds the BallTree based on the given set of instances.
- setInstances(Instances) - Method in class weka.core.neighboursearch.balltrees.BallSplitter
-
Sets the training instances on which the tree is
(or is to be) built.
- setInstances(Instances) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
Sets the instances on which the tree is to be built.
- setInstances(Instances) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Sets the instances on which the tree is to be built.
- setInstances(Instances) - Method in class weka.core.neighboursearch.CoverTree
-
Builds the Cover Tree on the given set of instances.
- setInstances(Instances) - Method in class weka.core.neighboursearch.KDTree
-
Builds the KDTree on the given set of instances.
- setInstances(Instances) - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
-
Sets the training instances on which the tree is (or is
to be) built.
- setInstances(Instances) - Method in class weka.core.neighboursearch.LinearNNSearch
-
Sets the instances comprising the current neighbourhood.
- setInstances(Instances) - Method in class weka.core.neighboursearch.NearestNeighbourSearch
-
Sets the instances.
- setInstances(Instances) - Method in class weka.core.NormalizableDistance
-
Sets the instances.
- setInstances(Instances) - Method in class weka.core.xml.XMLInstances
-
builds up the XML structure based on the given data
- setInstances(Instances) - Method in class weka.experiment.AveragingResultProducer
-
Sets the dataset that results will be obtained for.
- setInstances(Instances) - Method in class weka.experiment.CrossValidationResultProducer
-
Sets the dataset that results will be obtained for.
- setInstances(Instances) - Method in class weka.experiment.DatabaseResultProducer
-
Sets the dataset that results will be obtained for.
- setInstances(Instances) - Method in class weka.experiment.LearningRateResultProducer
-
Sets the dataset that results will be obtained for.
- setInstances(Instances) - Method in class weka.experiment.PairedTTester
-
Set the value of Instances.
- setInstances(Instances) - Method in class weka.experiment.RandomSplitResultProducer
-
Sets the dataset that results will be obtained for.
- setInstances(Instances) - Method in interface weka.experiment.ResultProducer
-
Sets the dataset that results will be obtained for.
- setInstances(Instances) - Method in interface weka.experiment.Tester
-
Set the value of Instances.
- setInstances(Instances) - Method in class weka.gui.arffviewer.ArffPanel
-
displays the given instances, i.e.
- setInstances(Instances) - Method in class weka.gui.arffviewer.ArffSortedTableModel
-
sets the data
- setInstances(Instances) - Method in class weka.gui.arffviewer.ArffTableModel
-
sets the data
- setInstances(Instances) - Method in class weka.gui.AttributeListPanel
-
Sets the instances who's attribute names will be displayed.
- setInstances(Instances) - Method in class weka.gui.AttributeSelectionPanel
-
Sets the instances who's attribute names will be displayed.
- setInstances(Instances) - Method in class weka.gui.AttributeSummaryPanel
-
Tells the panel to use a new set of instances.
- setInstances(Instances) - Method in class weka.gui.AttributeVisualizationPanel
-
Sets the instances for use
- setInstances(Instances) - Method in class weka.gui.beans.AttributeSummarizer
-
Set instances for this bean.
- setInstances(Instances) - Method in class weka.gui.beans.DataVisualizer
-
Set instances for this bean.
- setInstances(Instances) - Method in class weka.gui.beans.ScatterPlotMatrix
-
Set instances for this bean.
- setInstances(Instances) - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
Set the training instances
- setInstances(Instances) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
-
Set the training data
- setInstances(Instances) - Method in class weka.gui.experiment.ResultsPanel
-
Sets up the panel with a new set of instances, attempting
to guess the correct settings for various columns.
- setInstances(Instances) - Method in class weka.gui.explorer.AssociationsPanel
-
Tells the panel to use a new set of instances.
- setInstances(Instances) - Method in class weka.gui.explorer.AttributeSelectionPanel
-
Tells the panel to use a new set of instances.
- setInstances(Instances) - Method in class weka.gui.explorer.ClassifierPanel
-
Tells the panel to use a new set of instances.
- setInstances(Instances) - Method in class weka.gui.explorer.ClustererPanel
-
Tells the panel to use a new set of instances.
- setInstances(Instances) - Method in interface weka.gui.explorer.Explorer.ExplorerPanel
-
Tells the panel to use a new set of instances.
- setInstances(Instances) - Method in class weka.gui.explorer.PreprocessPanel
-
Tells the panel to use a new base set of instances.
- setInstances(Instances) - Method in class weka.gui.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.ViewerDialog
-
sets the instances to display
- setInstances(Instances) - Method in class weka.gui.visualize.AttributePanel
-
This sets the instances to be drawn into the attribute panel
- setInstances(Instances) - Method in class weka.gui.visualize.ClassPanel
-
Set the instances.
- setInstances(Instances) - Method in class weka.gui.visualize.MatrixPanel
-
This method changes the Instances object of this class to a new one.
- setInstances(Instances) - Method in class weka.gui.visualize.Plot2D
-
Sets the master plot from a set of instances
- setInstances(Instances) - Method in class weka.gui.visualize.VisualizePanel
-
Tells the panel to use a new set of instances.
- setInstancesFromDatabaseTable(String) - Method in class weka.gui.experiment.ResultsPanel
-
Queries a database to load results from the specified table name.
- setInstancesFromDB(InstanceQuery) - Method in class weka.gui.explorer.PreprocessPanel
-
Loads instances from a database
- setInstancesFromDBaseQuery() - Method in class weka.gui.experiment.ResultsPanel
-
Queries the user enough to make a database query to retrieve experiment
results.
- setInstancesFromDBQ(String, String, String, String) - Method in class weka.gui.explorer.PreprocessPanel
-
Loads instances from an SQL query the user provided with the
SqlViewerDialog, then loads the instances in a background process.
- setInstancesFromExp(Experiment) - Method in class weka.gui.experiment.ResultsPanel
-
Examines the supplied experiment to determine the results destination
and attempts to load the results.
- setInstancesFromFile(File) - Method in class weka.gui.experiment.ResultsPanel
-
Loads results from a set of instances contained in the supplied
file.
- setInstancesFromFile(AbstractFileLoader) - Method in class weka.gui.explorer.PreprocessPanel
-
Loads results from a set of instances retrieved with the supplied loader.
- setInstancesFromFile(File) - Method in class weka.gui.SetInstancesPanel
-
Loads results from a set of instances contained in the supplied
file.
- setInstancesFromFileQ() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
Queries the user for a file to load instances from, then loads the
instances in a background process.
- setInstancesFromFileQ() - Method in class weka.gui.explorer.PreprocessPanel
-
Queries the user for a file to load instances from, then loads the
instances in a background process.
- 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.
- setInstancesFromURL(URL) - Method in class weka.gui.explorer.PreprocessPanel
-
Loads instances from a URL.
- setInstancesFromURL(URL) - Method in class weka.gui.SetInstancesPanel
-
Loads instances from a URL.
- setInstancesFromURLQ() - Method in class weka.gui.explorer.PreprocessPanel
-
Queries the user for a URL to load instances from, then loads the
instances in a background process.
- 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.unsupervised.instance.RemoveRange
-
Sets the ranges of instances to be selected.
- SetInstancesPanel - Class in weka.gui
-
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.
- setInstNums(String) - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Sets the upper and lower boundary for instances per cluster.
- setInstNums(String) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Sets the upper and lower boundary for instances for this cluster.
- setInsts(int[], boolean) - Method in class weka.classifiers.lazy.LBR.Indexes
-
Changes the boolean value at the specified index in the InstIndexes array
- setInterAnchorDistances(Vector, MiddleOutConstructor.TempNode, Vector) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Sets the distances of a supplied new
anchor to all the rest of the
previous anchor points.
- setInternalCacheSize(int) - Method in class weka.classifiers.functions.supportVector.StringKernel
-
sets the size of the internal cache for intermediate results.
- setInternals(boolean) - Method in class weka.classifiers.bayes.WAODE
-
Sets whether internals about classifier are printed via toString().
- setInterval(int) - Method in class weka.gui.MemoryUsagePanel.MemoryMonitor
-
Sets the refresh interval in msecs.
- setInvert(boolean) - Method in class weka.core.Range
-
Sets whether the range sense is inverted, i.e.
- setInvert(boolean) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Set whether selection is inverted.
- setInvertSelection(boolean) - Method in interface weka.core.DistanceFunction
-
Sets whether the matching sense of attribute indices is inverted or not.
- setInvertSelection(boolean) - Method in class weka.core.NormalizableDistance
-
Sets whether the matching sense of attribute indices is inverted or not.
- setInvertSelection(boolean) - Method in class weka.filters.supervised.attribute.Discretize
-
Sets whether selected columns should be removed or kept.
- setInvertSelection(boolean) - Method in class weka.filters.supervised.instance.Resample
-
Sets whether the selection is inverted (only if instances are drawn WIHTOUT
replacement).
- setInvertSelection(boolean) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
-
Sets if selection is to be inverted.
- setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
-
Set whether selected columns should be removed or kept.
- setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.Copy
-
Set whether selected columns should be removed or kept.
- setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.Discretize
-
Sets whether selected columns should be removed or kept.
- setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.MathExpression
-
Set whether selected columns should be select or unselect.
- setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.NominalToBinary
-
Sets whether selected columns should be removed or kept.
- setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Sets whether the selection of the indices is inverted or not
- setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.NumericToNominal
-
Sets whether selected columns should be worked on or all the others apart
from these.
- setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.NumericTransform
-
Set whether selected columns should be transformed or not.
- setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.RELAGGS
-
Sets whether selected columns should be processed or skipped.
- setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.Remove
-
Set whether selected columns should be removed or kept.
- setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.RemoveType
-
Set whether selected columns should be removed or kept.
- setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Sets whether selected columns should be processed or skipped.
- setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.RemoveFolds
-
Sets if selection is to be inverted.
- setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
Set whether selected values should be removed or kept.
- setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.RemovePercentage
-
Sets if selection is to be inverted.
- setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.RemoveRange
-
Sets if selection is to be inverted.
- setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Set whether selected values should be removed or kept.
- setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.Resample
-
Sets whether the selection is inverted (only if instances are drawn WIHTOUT
replacement).
- setItem(int[]) - Method in class weka.associations.ItemSet
-
Sets an item sets
- setItemAt(int, int) - Method in class weka.associations.ItemSet
-
Sets the index of an attribute value
- setJitter(int) - Method in class weka.gui.visualize.Plot2D
-
Set level of jitter and repaint the plot using the new jitter value
- setJitter(int) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
-
Set level of jitter and repaint the plot using the new jitter value
- setKDTree(KDTree) - Method in class weka.clusterers.XMeans
-
Sets the KDTree class.
- setKernel(Kernel) - Method in class weka.classifiers.functions.GaussianProcesses
-
Sets the kernel to use.
- setKernel(Kernel) - Method in class weka.classifiers.functions.SMO.BinarySMO
-
sets the kernel to use
- setKernel(Kernel) - Method in class weka.classifiers.functions.SMO
-
sets the kernel to use
- setKernel(Kernel) - Method in class weka.classifiers.functions.SMOreg
-
sets the kernel to use
- setKernel(Kernel) - Method in class weka.classifiers.functions.supportVector.CheckKernel
-
Set the lernel to test.
- setKernel(Kernel) - Method in class weka.classifiers.mi.MISMO.BinaryMISMO
-
sets the kernel to use
- setKernel(Kernel) - Method in class weka.classifiers.mi.MISMO
-
Sets the kernel to use.
- setKernel(Kernel) - Method in class weka.classifiers.mi.MISVM
-
Sets the kernel to use.
- setKernel(Kernel) - Method in class weka.filters.unsupervised.attribute.KernelFilter
-
Sets the kernel to use.
- setKernelBandwidth(int) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
-
Set the kernel bandwidth (number of nearest neighbours to cover)
- setKernelFactorExpression(String) - Method in class weka.filters.unsupervised.attribute.KernelFilter
-
Sets the expression for the kernel.
- setKernelMatrix(Matrix) - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
-
Set the kernel matrix.
- setKernelMatrixFile(File) - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
-
Sets the file holding the kernel matrix
- setKernelType(SelectedTag) - Method in class weka.classifiers.functions.LibSVM
-
Sets type of kernel function (default KERNELTYPE_RBF)
- setKey(String) - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
-
Sets the key for this DataObject
- setKey(String) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
-
Sets the key for this DataObject
- setKey(String) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
-
Sets the key for this DataObject
- setKeyFieldName(String) - Method in class weka.experiment.AveragingResultProducer
-
Set the value of KeyFieldName.
- setKeys(String) - Method in class weka.core.converters.DatabaseLoader
-
Sets the key columns of a database table
- setKeywords(String) - Method in class weka.experiment.DatabaseUtils
-
Sets the keywords (comma-separated list) to use.
- setKeywordsMaskChar(String) - Method in class weka.experiment.DatabaseUtils
-
Sets the mask character to append to table or attribute names that
are a reserved keyword.
- setKNN(int) - Method in class weka.classifiers.lazy.IBk
-
Set the number of neighbours the learner is to use.
- setKNN(int) - Method in class weka.classifiers.lazy.LWL
-
Sets the number of neighbours used for kernel bandwidth setting.
- setKValue(int) - Method in class weka.classifiers.trees.RandomTree
-
Set the value of K.
- setLabels(String) - Method in class weka.filters.unsupervised.attribute.AddValues
-
Sets the comma-separated list of labels.
- setLambda(double) - Method in class weka.classifiers.functions.SPegasos
-
Set the value of lambda to use
- setLambda(double) - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Sets the lambda constant used in the string kernel
- setLearningRate(double) - Method in class weka.classifiers.functions.MultilayerPerceptron
-
The learning rate can be set using this command.
- setLegendText(Vector) - Method in class weka.gui.beans.ChartEvent
-
Set the legend text vector
- setLikelihoodThreshold(double) - Method in class weka.classifiers.meta.LogitBoost
-
Set the value of Precision.
- setLink(boolean, int) - Method in class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd
-
Call this function to set What this end unit represents.
- setLinkType(SelectedTag) - Method in class weka.clusterers.HierarchicalClusterer
-
- setListData(Object[]) - Method in class weka.gui.CheckBoxList
-
Constructs a CheckBoxListModel from an array of objects and then applies
setModel to it.
- setListData(Vector) - Method in class weka.gui.CheckBoxList
-
Constructs a CheckBoxListModel from a Vector and then applies setModel
to it.
- setLNorm(double) - Method in class weka.filters.unsupervised.instance.Normalize
-
Set the L-norm to used
- setLoader(Loader) - Method in class weka.gui.beans.Loader
-
Set the loader to use
- setLocallyPredictive(boolean) - Method in class weka.attributeSelection.CfsSubsetEval
-
Include locally predictive attributes
- setLocationProbs(int, double[]) - Method in class weka.gui.boundaryvisualizer.RemoteResult
-
Store the classifier's distribution for a particular pixel in the
visualization
- setLog(Logger) - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
-
Set a logger to use.
- setLog(Debug.Log) - Method in class weka.core.Debug.Random
-
the log to use, if it is null then stdout is used
- setLog(Logger) - Method in interface weka.core.pmml.PMMLModel
-
Set a logger to use.
- setLog(Logger) - Method in class weka.gui.beans.AbstractDataSink
-
Set a log for this bean
- setLog(Logger) - Method in class weka.gui.beans.AbstractEvaluator
-
Set a logger
- setLog(Logger) - Method in class weka.gui.beans.AbstractTestSetProducer
-
Set a logger
- setLog(Logger) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
-
Set a log for this bean
- setLog(Logger) - Method in class weka.gui.beans.AbstractTrainingSetProducer
-
Set a logger
- setLog(Logger) - Method in class weka.gui.beans.Associator
-
Set a logger
- setLog(Logger) - Method in interface weka.gui.beans.BeanCommon
-
Set a logger
- setLog(Logger) - Method in class weka.gui.beans.ClassAssigner
-
- setLog(Logger) - Method in class weka.gui.beans.Classifier
-
Set a logger
- setLog(Logger) - Method in class weka.gui.beans.ClassValuePicker
-
- setLog(Logger) - Method in class weka.gui.beans.Clusterer
-
Set a logger
- setLog(Logger) - Method in class weka.gui.beans.CostBenefitAnalysis
-
Set a logger
- setLog(Logger) - Method in class weka.gui.beans.Filter
-
Set a logger
- setLog(Logger) - Method in class weka.gui.beans.FlowRunner
-
- setLog(Logger) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
-
Set a logger
- setLog(Logger) - Method in class weka.gui.beans.Loader
-
Set a logger
- setLog(Logger) - Method in interface weka.gui.beans.LogWriter
-
Set a logger
- setLog(Logger) - Method in class weka.gui.beans.MetaBean
-
Set a logger
- setLog(Logger) - Method in class weka.gui.beans.PredictionAppender
-
Set a logger
- setLog(Logger) - Method in class weka.gui.beans.SerializedModelSaver
-
Set a log for this bean.
- setLog(Logger) - Method in class weka.gui.beans.StripChart
-
Set a logger
- setLog(Logger) - Method in class weka.gui.beans.TextViewer
-
Set a logger
- setLog(Logger) - Method in class weka.gui.explorer.AssociationsPanel
-
Sets the Logger to receive informational messages
- setLog(Logger) - Method in class weka.gui.explorer.AttributeSelectionPanel
-
Sets the Logger to receive informational messages
- setLog(Logger) - Method in class weka.gui.explorer.ClassifierPanel
-
Sets the Logger to receive informational messages
- setLog(Logger) - Method in class weka.gui.explorer.ClustererPanel
-
Sets the Logger to receive informational messages
- setLog(Logger) - Method in class weka.gui.explorer.DataGeneratorPanel
-
Sets the Logger to receive informational messages
- setLog(Logger) - Method in interface weka.gui.explorer.Explorer.LogHandler
-
Sets the Logger to receive informational messages
- setLog(Logger) - Method in class weka.gui.explorer.PreprocessPanel
-
Sets the Logger to receive informational messages
- setLog(Logger) - Method in class weka.gui.visualize.VisualizePanel
-
Sets the Logger to receive informational messages
- setLogFile(File) - Method in class weka.classifiers.meta.GridSearch
-
Sets the log file to use.
- setLookAndFeel(String) - Static method in class weka.gui.LookAndFeel
-
sets the look and feel to the specified class
- setLookAndFeel() - Static method in class weka.gui.LookAndFeel
-
sets the look and feel to the one in the props-file or if not set the
default one of the system
- setLookupCacheSize(int) - Method in class weka.attributeSelection.BestFirst
-
Set the maximum size of the evaluated subset cache (hashtable).
- setLookupCacheSize(int) - Method in class weka.attributeSelection.LinearForwardSelection
-
Set the maximum size of the evaluated subset cache (hashtable).
- setLookupCacheSize(int) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Set the maximum size of the evaluated subset cache (hashtable).
- setLoss(double) - Method in class weka.classifiers.functions.LibSVM
-
Sets the epsilon in loss function of epsilon-SVR (default 0.1)
- setLossFunction(SelectedTag) - Method in class weka.classifiers.functions.SPegasos
-
Set the loss function to use.
- setLowerBoundMinSupport(double) - Method in class weka.associations.Apriori
-
Set the value of lowerBoundMinSupport.
- setLowerBoundMinSupport(double) - Method in class weka.associations.FPGrowth
-
Set the value of lowerBoundMinSupport.
- setLowerCaseTokens(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Sets whether if the tokens are to be downcased or not.
- setLowerSize(int) - Method in class weka.experiment.LearningRateResultProducer
-
Set the value of LowerSize.
- setMajorityClass(boolean) - Method in class weka.classifiers.rules.Ridor
-
- setMakeBinary(boolean) - Method in class weka.filters.supervised.attribute.Discretize
-
Sets whether binary attributes should be made for discretized ones.
- setMakeBinary(boolean) - Method in class weka.filters.unsupervised.attribute.Discretize
-
Sets whether binary attributes should be made for discretized ones.
- setManualThresholdValue(double) - Method in class weka.classifiers.meta.ThresholdSelector
-
Sets the value for a manual threshold.
- setMargin(int, double[]) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
set marginal distibution for a node
- setMarkovBlanketClassifier(boolean) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
-
- setMarkovBlanketClassifier(boolean) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
-
- setMarkovBlanketClassifier(boolean) - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
-
- setMasterPlot(PlotData2D) - Method in class weka.gui.visualize.Plot2D
-
Set the master plot.
- setMasterPlot(PlotData2D) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
-
Clears all existing plots and sets a new master plot
- setMasterPlot(PlotData2D) - Method in class weka.gui.visualize.VisualizePanel
-
Set the master plot for the visualize panel
- setMatchMissingValues(boolean) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Sets whether missing values are counted as a match.
- setMatrix(int, int, int, int, double) - Method in class weka.classifiers.functions.pace.PaceMatrix
-
Set the submatrix A[i0:i1][j0:j1] with a same value
- setMatrix(int, int, int, DoubleVector) - Method in class weka.classifiers.functions.pace.PaceMatrix
-
Set the submatrix A[i0:i1][j] with the values stored in a
DoubleVector
- setMatrix(double[], boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix
-
Set the whole matrix from a 1-D array
- setMatrix(int, int, int, int, Matrix) - Method in class weka.core.matrix.Matrix
-
Set a submatrix.
- setMatrix(int[], int[], Matrix) - Method in class weka.core.matrix.Matrix
-
Set a submatrix.
- setMatrix(int[], int, int, Matrix) - Method in class weka.core.matrix.Matrix
-
Set a submatrix.
- setMatrix(int, int, int[], Matrix) - Method in class weka.core.matrix.Matrix
-
Set a submatrix.
- setMax(double) - Method in class weka.gui.beans.ChartEvent
-
Set the max y value
- setMaxBoostingIterations(int) - Method in class weka.classifiers.functions.SimpleLogistic
-
Set the value of maxBoostingIterations.
- setMaxCardinality(int) - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
-
sets the cardinality
- setMaxCardinality(int) - Method in class weka.filters.unsupervised.attribute.RELAGGS
-
Sets the maximum number of values allowed for nominal attributes, before
they're skipped.
- setMaxChunkSize(int) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Set the maximum chunk size
- setMaxCount(double) - Method in class weka.filters.supervised.instance.SpreadSubsample
-
Sets the value for the max count
- setMaxDefault(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Set the naximum default.
- setMaxDepth(int) - Method in class weka.classifiers.trees.RandomForest
-
Set the maximum depth of the tree, 0 for unlimited.
- setMaxDepth(int) - Method in class weka.classifiers.trees.RandomTree
-
Set the maximum depth of the tree, 0 for unlimited.
- setMaxDepth(int) - Method in class weka.classifiers.trees.REPTree
-
Set the value of MaxDepth.
- setMaxGenerations(int) - Method in class weka.attributeSelection.GeneticSearch
-
set the number of generations to evaluate
- setMaxGridExtensions(int) - Method in class weka.classifiers.meta.GridSearch
-
Sets the maximum number of grid extensions, -1 for unlimited.
- setMaxGroup(int) - Method in class weka.classifiers.meta.RotationForest
-
Sets the maximum size of a group.
- setMaximumAttributeNames(int) - Method in class weka.attributeSelection.LatentSemanticAnalysis
-
Sets maximum number of attributes to include in
transformed attribute names.
- setMaximumAttributeNames(int) - Method in class weka.attributeSelection.PrincipalComponents
-
Sets maximum number of attributes to include in
transformed attribute names.
- setMaximumAttributeNames(int) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Sets maximum number of attributes to include in
transformed attribute names.
- setMaximumAttributes(int) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Sets maximum number of PC attributes to retain.
- setMaximumVariancePercentageAllowed(double) - Method in class weka.filters.unsupervised.attribute.RemoveUseless
-
Sets the maximum variance attributes are allowed to have before they are
deleted by the filter.
- setMaxInstancesInLeaf(int) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
Sets the maximum number of instances allowed in a leaf.
- setMaxInstancesInLeaf(int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Sets the maximum number of instances allowed in a leaf.
- setMaxInstInLeaf(int) - Method in class weka.core.neighboursearch.KDTree
-
Sets the maximum number of instances in a leaf.
- setMaxInstNum(int) - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Sets the upper boundary for instances per cluster.
- setMaxInstNum(int) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Sets the upper boundary for instances per cluster.
- setMaxIteration(int) - Method in class weka.core.Optimization
-
Set the maximal number of iterations in searching (Default 200)
- setMaxIterations(int) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Set the maximum number of iterations to perform
- setMaxIterations(int) - Method in class weka.classifiers.mi.MIBoost
-
Set the maximum number of boost iterations
- setMaxIterations(int) - Method in class weka.classifiers.mi.MISVM
-
Sets the maximum number of iterations.
- setMaxIterations(int) - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Sets the parameter "maxIterations".
- setMaxIterations(int) - Method in class weka.clusterers.EM
-
Set the maximum number of iterations to perform
- setMaxIterations(int) - Method in class weka.clusterers.sIB
-
Set the max number of iterations
- setMaxIterations(int) - Method in class weka.clusterers.SimpleKMeans
-
set the maximum number of iterations to be executed
- setMaxIterations(int) - Method in class weka.clusterers.XMeans
-
Sets the maximum number of iterations to perform.
- setMaxIterations(int) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Sets the maximum number of cleansing iterations to perform
- < 1 means go until fully cleansed
- setMaxIts(int) - Method in class weka.classifiers.functions.Logistic
-
Set the value of MaxIts.
- setMaxIts(int) - Method in class weka.classifiers.functions.RBFNetwork
-
Set the value of MaxIts.
- setMaxK(int) - Method in class weka.classifiers.functions.VotedPerceptron
-
Set the value of maxK.
- setMaxKMeans(int) - Method in class weka.clusterers.XMeans
-
Set the maximum number of iterations to perform in KMeans.
- setMaxKMeansForChildren(int) - Method in class weka.clusterers.XMeans
-
Sets the maximum number of iterations KMeans that is performed
on the child centers.
- setMaxNrOfParents(int) - Method in class weka.classifiers.bayes.net.search.global.HillClimber
-
Sets the max number of parents
- setMaxNrOfParents(int) - Method in class weka.classifiers.bayes.net.search.global.K2
-
Sets the max number of parents
- setMaxNrOfParents(int) - Method in class weka.classifiers.bayes.net.search.local.HillClimber
-
Sets the max number of parents
- setMaxNrOfParents(int) - Method in class weka.classifiers.bayes.net.search.local.K2
-
Sets the max number of parents
- setMaxNrOfParents(int) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
-
Sets the max number of parents
- setMaxNumberOfItems(int) - Method in class weka.associations.FPGrowth
-
Set the maximum number of items to include in large items sets.
- setMaxNumClusters(int) - Method in class weka.clusterers.XMeans
-
Sets the maximum number of clusters to generate.
- setMaxPlots(int) - Method in class weka.gui.beans.AttributeSummarizer
-
Set the maximum number of plots to display
- setMaxRadius(double) - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Sets the upper boundary for the radiuses of the clusters.
- setMaxRange(double) - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Sets the upper boundary for the range of x
- setMaxRelativeLeafRadius(double) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
Sets the maximum relative radius, allowed for a leaf node.
- setMaxRows(int) - Method in class weka.gui.sql.QueryPanel
-
sets the maximum number of rows to display.
- setMaxRuleSize(int) - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Sets the maximum number of tests in rules.
- setMaxSubsequenceLength(int) - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Sets the maximum length of the subsequence.
- setMaxThreshold(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Set the maximum threshold.
- setMDLTheoryWeight(double) - Method in class weka.classifiers.rules.RuleStats
-
Set the weight of theory in MDL calcualtion
- setMean(int, int, double) - Method in class weka.experiment.ResultMatrix
-
sets the mean at the given position (if the position is valid)
- setMeanPrec(int) - Method in class weka.experiment.ResultMatrix
-
sets the precision for the means
- setMeanPrec(int) - Method in class weka.gui.experiment.OutputFormatDialog
-
Sets the precision of the mean output.
- setMeanSquared(boolean) - Method in class weka.classifiers.lazy.IBk
-
Sets whether the mean squared error is used rather than mean
absolute error when doing cross-validation.
- setMeanStddev(String) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Sets mean and standarddeviation.
- setMeanWidth(int) - Method in class weka.experiment.ResultMatrix
-
sets the width for the mean (0 = optimal)
- setMeasure(SelectedTag) - Method in class weka.classifiers.meta.ThresholdSelector
-
set measure used for determining threshold
- setMeasurePerformance(boolean) - Method in class weka.core.neighboursearch.BallTree
-
Sets whether to calculate the performance statistics or not.
- setMeasurePerformance(boolean) - Method in class weka.core.neighboursearch.KDTree
-
Sets whether to calculate the performance statistics or not.
- setMeasurePerformance(boolean) - Method in class weka.core.neighboursearch.NearestNeighbourSearch
-
Sets whether to calculate the performance statistics or not.
- setMestWeight(double) - Method in class weka.classifiers.bayes.AODEsr
-
Sets the weight for m-estimate
- setMetaClassifier(Classifier) - Method in class weka.classifiers.meta.Stacking
-
Adds meta classifier
- setMethod(NeuralMethod) - Method in class weka.classifiers.functions.neural.NeuralNode
-
Set how this node should operate (note that the neural method has no
internal state, so the same object can be used by any number of nodes.
- setMethod(SelectedTag) - Method in class weka.classifiers.meta.MultiClassClassifier
-
Sets the method used.
- setMethod(SelectedTag) - Method in class weka.classifiers.mi.MIWrapper
-
Set the method used in testing.
- setMethodName(String) - Method in class weka.filters.unsupervised.attribute.NumericTransform
-
Set the transformation method.
- setMetricType(SelectedTag) - Method in class weka.associations.Apriori
-
Set the metric type for ranking rules
- setMetricType(SelectedTag) - Method in class weka.associations.FPGrowth
-
Set the metric type to use.
- setMin(double) - Method in class weka.gui.beans.ChartEvent
-
Set the min y value
- setMinBoxRelWidth(double) - Method in class weka.core.neighboursearch.KDTree
-
Sets the minimum relative box width.
- setMinBucketSize(int) - Method in class weka.classifiers.rules.OneR
-
Set the value of minBucketSize.
- setMinChange(int) - Method in class weka.clusterers.sIB
-
set the minimum number of changes
- setMinChunkSize(int) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Set the minimum chunk size
- setMinDefault(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Set the minimum default.
- setMinGroup(int) - Method in class weka.classifiers.meta.RotationForest
-
Sets the minimum size of a group.
- setMinimax(boolean) - Method in class weka.classifiers.mi.MISMO
-
Set if the MIMinimax feature space is to be used.
- setMinimizeExpectedCost(boolean) - Method in class weka.classifiers.meta.CostSensitiveClassifier
-
Set the value of MinimizeExpectedCost.
- setMinimumBucketSize(int) - Method in class weka.attributeSelection.OneRAttributeEval
-
Set the minumum bucket size used by OneR
- setMinimumNumberInstances(int) - Method in class weka.core.Capabilities
-
sets the minimum number of instances that have to be in the dataset
- setMiningSchemaInstances(Instances) - Method in class weka.core.pmml.MiningFieldMetaInfo
-
Set the Instances that represent the mining schema.
- setMinInstNum(int) - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Sets the lower boundary for instances per cluster.
- setMinInstNum(int) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Sets the lower boundary for instances per cluster.
- setMinMaxValues() - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database
-
Sets the minimum and maximum values for each attribute in different arrays
by walking through every DataObject of the database
- setMinMaxValues() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
-
Sets the minimum and maximum values for each attribute in different arrays
by walking through every DataObject of the database
- setMinMaxX(double, double) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
-
Set the minimum and maximum values of the x axis fixed dimension
- setMinMaxY(double, double) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
-
Set the minimum and maximum values of the y axis fixed dimension
- setMinMetric(double) - Method in class weka.associations.Apriori
-
Set the value of minConfidence.
- setMinMetric(double) - Method in class weka.associations.FPGrowth
-
Set the value of minConfidence.
- setMinNo(double) - Method in class weka.classifiers.rules.ConjunctiveRule
-
Sets the minimum total weight of the instances in a rule
- setMinNo(double) - Method in class weka.classifiers.rules.JRip
-
Sets the minimum total weight of the instances in a rule
- setMinNo(double) - Method in class weka.classifiers.rules.Ridor
-
- setMinNum(double) - Method in class weka.classifiers.trees.RandomTree
-
Set the value of MinNum.
- setMinNum(double) - Method in class weka.classifiers.trees.REPTree
-
Set the value of MinNum.
- setMinNumClusters(int) - Method in class weka.clusterers.XMeans
-
Sets the minimum number of clusters to generate.
- setMinNumInstances(int) - Method in class weka.classifiers.trees.FT
-
Set the value of minNumInstances.
- setMinNumInstances(int) - Method in class weka.classifiers.trees.LMT
-
Set the value of minNumInstances.
- setMinNumInstances(double) - Method in class weka.classifiers.trees.m5.M5Base
-
Set the minimum number of instances to allow at a leaf node
- setMinNumInstances(double) - Method in class weka.classifiers.trees.m5.Rule
-
Set the minumum number of instances to allow at a leaf node
- setMinNumInstances(double) - Method in class weka.classifiers.trees.m5.RuleNode
-
Set the minumum number of instances to allow at a leaf node
- setMinNumObj(int) - Method in class weka.classifiers.rules.PART
-
Set the value of minNumObj.
- setMinNumObj(int) - Method in class weka.classifiers.trees.BFTree
-
Set minimal number of instances at the terminal nodes.
- setMinNumObj(int) - Method in class weka.classifiers.trees.J48
-
Set the value of minNumObj.
- setMinNumObj(int) - Method in class weka.classifiers.trees.J48graft
-
Set the value of minNumObj.
- setMinNumObj(double) - Method in class weka.classifiers.trees.SimpleCart
-
Set minimal number of instances at the terminal nodes.
- setMinPoints(int) - Method in class weka.clusterers.DBScan
-
Sets a new value for minPoints
- setMinPoints(int) - Method in class weka.clusterers.OPTICS
-
Sets a new value for minPoints
- setMinRadius(double) - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Sets the lower boundary for the radiuses of the clusters.
- setMinRange(double) - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Sets the lower boundary for the range of x
- setMinRuleSize(int) - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Sets the minimum number of tests in rules.
- setMinStdDev(double) - Method in class weka.classifiers.functions.RBFNetwork
-
Set the MinStdDev value.
- setMinStdDev(double) - Method in class weka.clusterers.EM
-
Set the minimum value for standard deviation when calculating
normal density.
- setMinStdDev(double) - Method in class weka.clusterers.MakeDensityBasedClusterer
-
Set the minimum value for standard deviation when calculating
normal density.
- setMinStdDevPerAtt(double[]) - Method in class weka.clusterers.EM
-
- setMinSupport(double) - Method in class weka.associations.GeneralizedSequentialPatterns
-
Sets the minimum support threshold.
- setMinTermFreq(int) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Set the MinTermFreq value.
- setMinThreshold(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Set the minimum threshold.
- setMinVarianceProp(double) - Method in class weka.classifiers.trees.REPTree
-
Set the value of MinVarianceProp.
- setMissing(int) - Method in class weka.core.Instance
-
Sets a specific value to be "missing".
- setMissing(Attribute) - Method in class weka.core.Instance
-
Sets a specific value to be "missing".
- setMissingMerge(boolean) - Method in class weka.attributeSelection.ChiSquaredAttributeEval
-
distribute the counts for missing values across observed values
- setMissingMerge(boolean) - Method in class weka.attributeSelection.GainRatioAttributeEval
-
distribute the counts for missing values across observed values
- setMissingMerge(boolean) - Method in class weka.attributeSelection.InfoGainAttributeEval
-
distribute the counts for missing values across observed values
- setMissingMerge(boolean) - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
-
distribute the counts for missing values across observed values
- setMissingMode(int) - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
Set the missing value mode.
- setMissingMode(SelectedTag) - Method in class weka.classifiers.lazy.KStar
-
Sets the method to use for handling missing values.
- setMissingSeparate(boolean) - Method in class weka.attributeSelection.CfsSubsetEval
-
Treat missing as a separate value
- setMissingValue(String) - Method in class weka.core.converters.CSVLoader
-
Sets the placeholder for missing values.
- setMissingValues(SelectedTag) - Method in class weka.associations.Tertius
-
Set the value of missingValues.
- setMixingDistribution(DiscreteFunction) - Method in class weka.classifiers.functions.pace.MixtureDistribution
-
Sets the mixing distribution
- setModel(Classifier) - Method in class weka.classifiers.misc.SerializedClassifier
-
Sets the fully built model to use, if one doesn't want to load a model
from a file or already deserialized a model from somewhere else.
- setModel(TableModel) - Method in class weka.gui.arffviewer.ArffTable
-
sets the new model
- setModel(ListModel) - Method in class weka.gui.CheckBoxList
-
sets the model - must be an instance of CheckBoxListModel
- setModel(TableModel) - Method in class weka.gui.SortedTableModel
-
sets the model to use
- setModelFile(File) - Method in class weka.classifiers.misc.SerializedClassifier
-
Sets the file containing the serialized model.
- setModelType(SelectedTag) - Method in class weka.classifiers.trees.FT
-
Set the Functional Tree type.
- setModePanel(SetupModePanel) - Method in class weka.gui.experiment.SimpleSetupPanel
-
Sets the panel used to switch between simple and advanced modes.
- setModifyHeader(boolean) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
Sets whether the header will be modified when selecting on nominal
attributes.
- setModifyHeader(boolean) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Sets whether the header will be modified when selecting on nominal
attributes.
- setMomentum(double) - Method in class weka.classifiers.functions.MultilayerPerceptron
-
The momentum can be set using this command.
- setMultiInstance(boolean) - Method in class weka.core.TestInstances
-
sets whether multi-instance data should be generated (with a fixed
data structure)
- setMultinomialWord(boolean) - Method in class weka.classifiers.bayes.DMNBtext
-
Sets whether use binary text representation
- setMutationProb(double) - Method in class weka.attributeSelection.GeneticSearch
-
set the probability of mutation
- setName(String) - Method in class weka.filters.unsupervised.attribute.AddExpression
-
Set the name for the new attribute.
- setName(String) - Method in class weka.gui.visualize.VisualizePanel
-
Set a name for this plot
- setNearestNeighbors(int) - Method in class weka.filters.supervised.instance.SMOTE
-
Sets the number of nearest neighbors to use.
- setNearestNeighbourSearchAlgorithm(NearestNeighbourSearch) - Method in class weka.classifiers.lazy.IBk
-
Sets the nearestNeighbourSearch algorithm to be used for finding nearest
neighbour(s).
- setNearestNeighbourSearchAlgorithm(NearestNeighbourSearch) - Method in class weka.classifiers.lazy.LWL
-
Sets the nearestNeighbourSearch algorithm to be used for finding nearest
neighbour(s).
- setNegation(Literal) - Method in class weka.associations.tertius.Literal
-
- setNegation(SelectedTag) - Method in class weka.associations.Tertius
-
Set the value of negation.
- setNewToolTip(String) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
-
Displays a toolTip for the selected DataObject
- setNGramMaxSize(int) - Method in class weka.core.tokenizers.NGramTokenizer
-
Sets the max size of the Ngram.
- setNGramMinSize(int) - Method in class weka.core.tokenizers.NGramTokenizer
-
Sets the min size of the Ngram.
- setNoClass(boolean) - Method in class weka.core.TestInstances
-
whether to have no class, e.g., for clusterers; otherwise the class
attribute index is set to last
- setNodeName(int, String) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
change the name of a node
- setNodesEdges(FastVector, FastVector) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
-
Sets the nodes and edges for this LayoutEngine.
- setNodesEdges(FastVector, FastVector) - Method in interface weka.gui.graphvisualizer.LayoutEngine
-
This method sets the nodes and edges vectors of the LayoutEngine
- setNodeSize(int, int) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
-
Sets the size of a node.
- setNodeSize(int, int) - Method in interface weka.gui.graphvisualizer.LayoutEngine
-
This method sets the allowed size of the node
- setNodeSplitter(KDTreeNodeSplitter) - Method in class weka.core.neighboursearch.KDTree
-
Sets the splitting method to use to split the nodes of the KDTree.
- setNodeWidthNormalization(boolean) - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
-
Sets whether if a nodes region is normalized
or not.
- setNoise(double) - Method in class weka.classifiers.functions.GaussianProcesses
-
Set the level of Gaussian Noise.
- setNoisePercent(double) - Method in class weka.datagenerators.classifiers.classification.LED24
-
Sets the noise percentage.
- setNoiseRate(double) - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Sets the gaussian noise rate.
- setNoiseRate(double) - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Sets the percentage of noise set.
- setNoiseRate(double) - Method in class weka.datagenerators.clusterers.SubspaceCluster
-
Sets the percentage of noise set.
- setNoiseThreshold(double) - Method in class weka.associations.Tertius
-
Set the value of noiseThreshold.
- setNoiseVariance(double) - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Sets the noise variance
- setNominal() - Method in class weka.gui.visualize.ClassPanel
-
Sets the legend to be for a nominal variable
- setNominalAttributes(String) - Method in class weka.core.converters.CSVLoader
-
Sets the attribute range to be forced to type nominal.
- setNominalCols(Range) - Method in class weka.datagenerators.ClusterGenerator
-
Sets which attributes are nominal.
- setNominalIndices(String) - Method in class weka.datagenerators.ClusterGenerator
-
Sets which attributes are nominal
- setNominalIndices(String) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Set which nominal labels are to be included in the selection.
- setNominalIndicesArr(int[]) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Set which values of a nominal attribute are to be used for
selection.
- setNominalLabels(String) - Method in class weka.filters.unsupervised.attribute.Add
-
Set the labels for nominal attribute creation.
- setNominalToBinaryFilter(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- setNoPruning(boolean) - Method in class weka.classifiers.trees.REPTree
-
Set the value of NoPruning.
- setNoReplacement(boolean) - Method in class weka.filters.supervised.instance.Resample
-
Sets whether instances are drawn with or with out replacement.
- setNoReplacement(boolean) - Method in class weka.filters.unsupervised.instance.Resample
-
Sets whether instances are drawn with or with out replacement.
- setNorm(double) - Method in class weka.filters.unsupervised.instance.Normalize
-
Set the norm of the instances
- setNormalize(boolean) - Method in class weka.attributeSelection.LatentSemanticAnalysis
-
Set whether input data will be normalized.
- setNormalize(boolean) - Method in class weka.classifiers.functions.LibLINEAR
-
whether to normalize input data
- setNormalize(boolean) - Method in class weka.classifiers.functions.LibSVM
-
whether to normalize input data
- setNormalizeAttributes(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- setNormalizeData(boolean) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Set whether to normalize the data or not
- setNormalizeDimWidths(boolean) - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
-
Should we normalize the widths(ranges) of the dimensions (attributes)
before selecting the widest one.
- setNormalizeDocLength(SelectedTag) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Sets whether if the word frequencies for a document (instance) should
be normalized or not.
- setNormalizeNodeWidth(boolean) - Method in class weka.core.neighboursearch.KDTree
-
Sets the flag for normalizing the widths of a KDTree Node by the width of
the dimension in the universe.
- setNormalizeNumericClass(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
-
- setNormalizeWordWeights(boolean) - Method in class weka.classifiers.bayes.ComplementNaiveBayes
-
Sets whether if the word weights for each class should be normalized
- setNotCapabilities(Capabilities) - Method in class weka.core.FindWithCapabilities
-
Uses the given "not to have" Capabilities for the search.
- setNotes(String) - Method in class weka.experiment.Experiment
-
Set the user notes.
- setNotes(String) - Method in class weka.experiment.RemoteExperiment
-
Set the user notes.
- setNotificationEnabled(boolean) - Method in class weka.gui.arffviewer.ArffSortedTableModel
-
sets whether the notification of changes is enabled
- setNotificationEnabled(boolean) - Method in class weka.gui.arffviewer.ArffTableModel
-
sets whether the notification of changes is enabled
- setNotUnifyNorm(boolean) - Method in class weka.clusterers.sIB
-
Set whether to normalize instances to unify prior probability
before building the clusterer
- setNrOfGoodOperations(int) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
-
Sets the number of "good operations"
- setNrOfLookAheadSteps(int) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
-
Sets the number of look-ahead steps
- setNu(double) - Method in class weka.classifiers.functions.LibSVM
-
Sets nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
- setNumAllConds(double) - Method in class weka.classifiers.rules.RuleStats
-
Set the number of all conditions that could appear
in a rule in this RuleStats object, if the number set
is smaller than 0 (typically -1), then it calcualtes
based on the data store
- setNumAntds(int) - Method in class weka.classifiers.rules.ConjunctiveRule
-
Sets the number of antecedants
- setNumArcs(int) - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
Sets the number of arcs for the bayesian net
- setNumAttemptsOfGeneOption(int) - Method in class weka.classifiers.rules.NNge
-
Sets the number of attempts for generalisation.
- setNumAttributes(int) - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
Sets the number of attributes the dataset should have.
- setNumAttributes(int) - Method in class weka.datagenerators.classifiers.classification.RandomRBF
-
Sets the number of attributes the dataset should have.
- setNumAttributes(int) - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Sets the number of attributes the dataset should have.
- setNumAttributes(int) - Method in class weka.datagenerators.clusterers.SubspaceCluster
-
Sets the number of attributes the dataset should have.
- setNumAttributes(int) - Method in class weka.datagenerators.ClusterGenerator
-
Sets the number of attributes the dataset should have.
- setNumAttributes(double) - Method in class weka.filters.unsupervised.attribute.RandomSubset
-
Set the number of attributes.
- setNumberLiterals(int) - Method in class weka.associations.Tertius
-
Set the value of numberLiterals.
- setNumberOfAttributes(int) - Method in class weka.filters.unsupervised.attribute.RandomProjection
-
Sets the number of attributes (dimensions) the data should be reduced to
- setNumberOfGroups(boolean) - Method in class weka.classifiers.meta.RotationForest
-
Set whether minGroup and maxGroup refer to the number of groups or their
size
- setNumBins(int) - Method in class weka.classifiers.meta.RegressionByDiscretization
-
Sets the number of bins to divide each selected numeric attribute into
- setNumBoostingIterations(int) - Method in class weka.classifiers.functions.SimpleLogistic
-
Set the value of numBoostingIterations.
- setNumBoostingIterations(int) - Method in class weka.classifiers.trees.FT
-
Set the value of numBoostingIterations.
- setNumBoostingIterations(int) - Method in class weka.classifiers.trees.LMT
-
Set the value of numBoostingIterations.
- setNumCentroids(int) - Method in class weka.datagenerators.classifiers.classification.RandomRBF
-
Sets the number of centroids to use.
- setNumCiters(int) - Method in class weka.classifiers.mi.CitationKNN
-
Sets the number of citers considered to estimate
the class prediction of tests bags
- setNumClasses(int) - Method in class weka.core.TestInstances
-
sets the number of classes
- setNumClasses(int) - Method in class weka.datagenerators.classifiers.classification.RandomRBF
-
Sets the number of classes the dataset should have.
- setNumClasses(int) - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Sets the number of classes the dataset should have.
- setNumClusters(int) - Method in class weka.classifiers.functions.RBFNetwork
-
Set the number of clusters for K-means to generate.
- setNumClusters(int) - Method in class weka.clusterers.EM
-
Set the number of clusters (-1 to select by CV).
- setNumClusters(int) - Method in class weka.clusterers.FarthestFirst
-
set the number of clusters to generate
- setNumClusters(int) - Method in class weka.clusterers.HierarchicalClusterer
-
- setNumClusters(int) - Method in class weka.clusterers.MakeDensityBasedClusterer
-
Set the number of clusters to generate.
- setNumClusters(int) - Method in interface weka.clusterers.NumberOfClustersRequestable
-
Set the number of clusters to generate
- setNumClusters(int) - Method in class weka.clusterers.sIB
-
Set the number of clusters
- setNumClusters(int) - Method in class weka.clusterers.SimpleKMeans
-
set the number of clusters to generate
- setNumClusters(int) - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Sets the number of clusters the dataset should have.
- setNumComponents(int) - Method in class weka.filters.supervised.attribute.PLSFilter
-
sets the maximum number of attributes to use.
- setNumCycles(int) - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Sets the the number of cycles.
- setNumDate(int) - Method in class weka.core.CheckScheme
-
sets the number of data attributes
- setNumDate(int) - Method in class weka.core.TestInstances
-
sets the number of date attributes
- setNumeric(boolean) - Method in class weka.filters.unsupervised.attribute.MakeIndicator
-
Sets if the new Attribute is to be numeric.
- setNumeric() - Method in class weka.gui.visualize.ClassPanel
-
Sets the legend to be for a numeric variable
- setNumericPriorsFromBuffer() - Method in class weka.classifiers.Evaluation
-
Sets up the priors for numeric class attributes from the
training class values that have been seen so far.
- setNumExamples(int) - Method in class weka.datagenerators.ClassificationGenerator
-
Sets the number of examples, given by option.
- setNumExamples(int) - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
Sets the number of examples, given by option.
- setNumExamples(int) - Method in class weka.datagenerators.RegressionGenerator
-
Sets the number of examples, given by option.
- setNumExamplesAct(int) - Method in class weka.datagenerators.DataGenerator
-
Sets the number of examples the dataset should have.
- setNumFeatures(int) - Method in class weka.classifiers.trees.RandomForest
-
Set the number of features to use in random selection.
- setNumFoldersMIOption(int) - Method in class weka.classifiers.rules.NNge
-
Sets the number of folder for mutual information.
- setNumFolds(int) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Set the number of folds to use for CV-based hyperparameter
selection
- setNumFolds(int) - Method in class weka.classifiers.functions.SMO
-
Set the value of numFolds.
- setNumFolds(int) - Method in class weka.classifiers.meta.CVParameterSelection
-
Sets the number of folds for the cross-validation.
- setNumFolds(int) - Method in class weka.classifiers.meta.Dagging
-
Sets the number of folds to use for splitting the training set.
- setNumFolds(int) - Method in class weka.classifiers.meta.LogitBoost
-
Set the value of NumFolds.
- setNumFolds(int) - Method in class weka.classifiers.meta.MultiScheme
-
Sets the number of folds for cross-validation.
- setNumFolds(int) - Method in class weka.classifiers.meta.Stacking
-
Sets the number of folds for the cross-validation.
- setNumFolds(int) - Method in class weka.classifiers.mi.MISMO
-
Set the value of numFolds.
- setNumFolds(int) - Method in class weka.classifiers.rules.PART
-
Set the value of numFolds.
- setNumFolds(int) - Method in class weka.classifiers.trees.J48
-
Set the value of numFolds.
- setNumFolds(int) - Method in class weka.classifiers.trees.RandomTree
-
Set the value of NumFolds.
- setNumFolds(int) - Method in class weka.classifiers.trees.REPTree
-
Set the value of NumFolds.
- setNumFolds(int) - Method in class weka.experiment.CrossValidationResultProducer
-
Set the value of NumFolds.
- setNumFolds(int) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
-
Sets the number of folds the dataset is split into.
- setNumFolds(int) - Method in class weka.filters.unsupervised.instance.RemoveFolds
-
Sets the number of folds the dataset is split into.
- setNumFolds(int) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Sets the number of cross-validation folds to use
- < 2 means no cross-validation.
- setNumFoldsPruning(int) - Method in class weka.classifiers.trees.BFTree
-
Set number of folds in internal cross-validation.
- setNumFoldsPruning(int) - Method in class weka.classifiers.trees.SimpleCart
-
Set number of folds in internal cross-validation.
- setNumInstances(int) - Method in class weka.core.CheckScheme
-
Sets the number of instances to use in the datasets (some classifiers
might require more instances).
- setNumInstances(int) - Method in class weka.core.TestInstances
-
sets the number of instances to produce
- setNumInstances(Random) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Sets the real number of instances for this cluster.
- setNumInstances(int) - Method in class weka.estimators.CheckEstimator
-
Sets the number of instances to use in the datasets (some estimators
might require more instances).
- setNumInstancesRelational(int) - Method in class weka.core.CheckScheme
-
sets the number of instances in relational/bag attributes to produce
- setNumInstancesRelational(int) - Method in class weka.core.TestInstances
-
sets the number of instances in relational/bag attributes to produce
- setNumIrrelevant(int) - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Sets the number of irrelevant attributes.
- setNumIterations(int) - Method in class weka.classifiers.bayes.DMNBtext
-
Sets the number of iterations to be performed
- setNumIterations(int) - Method in class weka.classifiers.functions.VotedPerceptron
-
Set the value of NumIterations.
- setNumIterations(int) - Method in class weka.classifiers.functions.Winnow
-
Set the value of numIterations.
- setNumIterations(int) - Method in class weka.classifiers.IteratedSingleClassifierEnhancer
-
Sets the number of bagging iterations
- setNumIterations(int) - Method in class weka.classifiers.meta.MetaCost
-
Sets the number of bagging iterations
- setNumNeighbours(int) - Method in class weka.attributeSelection.ReliefFAttributeEval
-
Set the number of nearest neighbours
- setNumNeighbours(int) - Method in class weka.classifiers.mi.MINND
-
Sets the number of nearest neighbours to estimate
the class prediction of tests bags
- setNumNominal(int) - Method in class weka.core.CheckScheme
-
sets the number of nominal attributes
- setNumNominal(int) - Method in class weka.core.TestInstances
-
sets the number of nominal attributes
- setNumNominalValues(int) - Method in class weka.core.TestInstances
-
sets the number of values for nominal attributes
- setNumNumeric(int) - Method in class weka.core.CheckScheme
-
sets the number of numeric attributes
- setNumNumeric(int) - Method in class weka.core.TestInstances
-
sets the number of numeric attributes
- setNumNumeric(int) - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Sets the number of numerical attributes.
- setNumOfBoostingIterations(int) - Method in class weka.classifiers.trees.ADTree
-
Sets the number of boosting iterations.
- setNumOfBoostingIterations(int) - Method in class weka.classifiers.trees.LADTree
-
Sets the number of boosting iterations.
- setNumReferences(int) - Method in class weka.classifiers.mi.CitationKNN
-
Sets the number of references considered to estimate
the class prediction of tests bags
- setNumRelational(int) - Method in class weka.core.CheckScheme
-
sets the number of relational attributes
- setNumRelational(int) - Method in class weka.core.TestInstances
-
sets the number of relational attributes
- setNumRelationalDate(int) - Method in class weka.core.TestInstances
-
sets the number of date attributes in a relational attribute
- setNumRelationalNominal(int) - Method in class weka.core.TestInstances
-
sets the number of nominal attributes in a relational attribute
- setNumRelationalNominalValues(int) - Method in class weka.core.TestInstances
-
sets the number of values for nominal attributes in a relational attribute
- setNumRelationalNumeric(int) - Method in class weka.core.TestInstances
-
sets the number of numeric attributes in a relational attribute
- setNumRelationalString(int) - Method in class weka.core.TestInstances
-
sets the number of string attributes in a relational attribute
- setNumRestarts(int) - Method in class weka.clusterers.sIB
-
Set the number of restarts
- setNumRules(int) - Method in class weka.associations.Apriori
-
Set the value of numRules.
- setNumRules(int) - Method in class weka.associations.PredictiveApriori
-
Set the value of required rules.
- setNumRulesToFind(int) - Method in class weka.associations.FPGrowth
-
Set the desired number of rules to find.
- setNumRuns(int) - Method in class weka.classifiers.meta.LogitBoost
-
Set the value of NumRuns.
- setNumSamplesPerRegion(int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Set the number of points to uniformly sample from a region (fixed
dimensions).
- setNumSamplesPerRegion(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
-
Set the number of points to uniformly sample from a region (fixed
dimensions).
- setNumString(int) - Method in class weka.core.CheckScheme
-
sets the number of string attributes
- setNumString(int) - Method in class weka.core.TestInstances
-
sets the number of string attributes
- setNumSubCmtys(int) - Method in class weka.classifiers.meta.MultiBoostAB
-
Set the number of sub committees to use
- setNumSubsetSizeCVFolds(int) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Set the number of cross validation folds for subset size determination
(default = 5).
- setNumTestingNoises(int) - Method in class weka.classifiers.mi.MINND
-
Sets The number of nearest neighbour exemplars in the
selection of noises in the test data
- setNumToSelect(int) - Method in class weka.attributeSelection.GreedyStepwise
-
Specify the number of attributes to select from the ranked list
(if generating a ranking).
- setNumToSelect(int) - Method in class weka.attributeSelection.RaceSearch
-
Specify the number of attributes to select from the ranked list
(if generating a ranking).
- setNumToSelect(int) - Method in interface weka.attributeSelection.RankedOutputSearch
-
Specify the number of attributes to select from the ranked list.
- setNumToSelect(int) - Method in class weka.attributeSelection.Ranker
-
Specify the number of attributes to select from the ranked list.
- setNumTrainingNoises(int) - Method in class weka.classifiers.mi.MINND
-
Sets the number of nearest neighbour instances in the
selection of noises in the training data
- setNumTrees(int) - Method in class weka.classifiers.trees.RandomForest
-
Set the value of numTrees.
- setNumUsedAttributes(int) - Method in class weka.attributeSelection.LinearForwardSelection
-
Set the number of top-ranked attributes that taken into account by the
search process.
- setNumUsedAttributes(int) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Set the number of top-ranked attributes that taken into account by the
search process.
- setNumValues(int) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
Sets how many values are retained
- setNumXValFolds(int) - Method in class weka.classifiers.meta.ThresholdSelector
-
Set the number of folds used for cross-validation.
- setObject(Object) - Method in class weka.core.CheckGOE
-
Set the object to work on..
- setObject(Object) - Method in class weka.gui.beans.AssociatorCustomizer
-
Set the classifier object to be edited
- setObject(Object) - Method in class weka.gui.beans.ClassAssignerCustomizer
-
Set the bean to be edited
- setObject(Object) - Method in class weka.gui.beans.ClassifierCustomizer
-
Set the classifier object to be edited
- setObject(Object) - Method in class weka.gui.beans.ClassValuePickerCustomizer
-
Set the bean to be edited
- setObject(Object) - Method in class weka.gui.beans.ClustererCustomizer
-
Set the Clusterer object to be edited
- setObject(Object) - Method in class weka.gui.beans.CrossValidationFoldMakerCustomizer
-
Set the object to be edited
- setObject(Object) - Method in class weka.gui.beans.FilterCustomizer
-
Set the filter bean to be edited
- setObject(Object) - Method in class weka.gui.beans.IncrementalClassifierEvaluatorCustomizer
-
Set the object to be edited
- setObject(Object) - Method in class weka.gui.beans.LoaderCustomizer
-
Set the loader to be customized
- setObject(Object) - Method in class weka.gui.beans.PredictionAppenderCustomizer
-
Set the object to be edited
- setObject(Object) - Method in class weka.gui.beans.SaverCustomizer
-
Set the saver to be customized
- setObject(Object) - Method in class weka.gui.beans.SerializedModelSaverCustomizer
-
Set the model saver to be customized
- setObject(Object) - Method in class weka.gui.beans.StripChartCustomizer
-
Set the StripChart object to be customized
- setObject(Object) - Method in class weka.gui.beans.TrainTestSplitMakerCustomizer
-
Set the TrainTestSplitMaker to be customized
- setObject(Object) - Method in class weka.gui.GenericObjectEditor
-
Sets the current Object.
- setObjective(double) - Method in class weka.attributeSelection.GeneticSearch.GABitSet
-
sets the objective merit value
- setOfSequencesToString(FastVector, Instances, FastVector) - Static method in class weka.associations.gsp.Sequence
-
Returns a String representation of a set of Sequences where the numeric
value of each event/item is represented by its respective nominal value.
- setOkButtonText(String) - Method in class weka.gui.GenericObjectEditor.GOEPanel
-
Allows customization of the action label on the dialog.
- setOmega(double) - Method in class weka.classifiers.functions.supportVector.Puk
-
Sets the omega value.
- setOn(boolean) - Method in class weka.gui.visualize.ClassPanel
-
Enables the panel
- setOnDemandDirectory(File) - Method in class weka.attributeSelection.CostSensitiveASEvaluation
-
Sets the directory that will be searched for cost files when
loading on demand.
- setOnDemandDirectory(File) - Method in class weka.classifiers.meta.CostSensitiveClassifier
-
Sets the directory that will be searched for cost files when
loading on demand.
- setOnDemandDirectory(File) - Method in class weka.classifiers.meta.MetaCost
-
Sets the directory that will be searched for cost files when
loading on demand.
- setOnDemandDirectory(File) - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
-
Sets the directory that will be searched for cost files when
loading on demand.
- setOptimalColumnWidth(int) - Method in class weka.gui.JTableHelper
-
sets the optimal column width for the given column
- setOptimalColumnWidth(JTable, int) - Static method in class weka.gui.JTableHelper
-
sets the optimal column width for the given column
- setOptimalColumnWidth() - Method in class weka.gui.JTableHelper
-
sets the optimal column width for all columns
- setOptimalColumnWidth(JTable) - Static method in class weka.gui.JTableHelper
-
sets the optimal column width for alls column if the given table
- setOptimalColWidth() - Method in class weka.gui.arffviewer.ArffPanel
-
calculates the optimal column width for the current column
- setOptimalColWidths() - Method in class weka.gui.arffviewer.ArffPanel
-
calculates the optimal column widths for all columns
- setOptimalColWidths() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
sets the optimal column width for all columns
- setOptimalHeaderWidth(int) - Method in class weka.gui.JTableHelper
-
sets the optimal header width for the given column
- setOptimalHeaderWidth(JTable, int) - Static method in class weka.gui.JTableHelper
-
sets the optimal header width for the given column
- setOptimalHeaderWidth() - Method in class weka.gui.JTableHelper
-
sets the optimal header width for all columns
- setOptimalHeaderWidth(JTable) - Static method in class weka.gui.JTableHelper
-
sets the optimal header width for alls column if the given table
- setOptimizations(int) - Method in class weka.classifiers.rules.JRip
-
Sets the number of optimization runs
- setOptionHandler(OptionHandler) - Method in class weka.core.CheckOptionHandler
-
Set the OptionHandler to work on..
- setOptions(String[]) - Method in class weka.associations.Apriori
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.associations.CheckAssociator
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.associations.FilteredAssociator
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.associations.FPGrowth
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.associations.GeneralizedSequentialPatterns
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.associations.PredictiveApriori
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.associations.SingleAssociatorEnhancer
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.associations.Tertius
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.attributeSelection.BestFirst
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.attributeSelection.CfsSubsetEval
-
Parses and sets a given list of options.
- setOptions(String[]) - Method in class weka.attributeSelection.CheckAttributeSelection
-
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.ClassifierSubsetEval
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.attributeSelection.CostSensitiveASEvaluation
-
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.FilteredAttributeEval
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.attributeSelection.FilteredSubsetEval
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.attributeSelection.GainRatioAttributeEval
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.attributeSelection.GeneticSearch
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.attributeSelection.GreedyStepwise
-
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.LatentSemanticAnalysis
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.attributeSelection.LinearForwardSelection
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.attributeSelection.OneRAttributeEval
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.attributeSelection.PrincipalComponents
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.attributeSelection.RaceSearch
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.attributeSelection.RandomSearch
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.attributeSelection.Ranker
-
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.ReliefFAttributeEval
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.attributeSelection.ScatterSearchV1
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.attributeSelection.SVMAttributeEval
-
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.WrapperSubsetEval
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.AODE
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.AODEsr
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.BayesNet
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.ComplementNaiveBayes
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.DMNBtext
-
- setOptions(String[]) - Method in class weka.classifiers.bayes.NaiveBayes
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.BayesNetGenerator
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.fixed.FromFile
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.HillClimber
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.K2
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.TAN
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.HillClimber
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.K2
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.TAN
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.bayes.WAODE
-
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.BVDecomposeSegCVSub
-
Sets the OptionHandler's options using the given list.
- setOptions(String[]) - Method in class weka.classifiers.CheckClassifier
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.CheckSource
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.Classifier
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.GaussianProcesses
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.LeastMedSq
-
Sets the OptionHandler's options using the given list.
- setOptions(String[]) - Method in class weka.classifiers.functions.LibLINEAR
-
Sets the classifier options
Valid options are:
- setOptions(String[]) - Method in class weka.classifiers.functions.LibSVM
-
Sets the classifier options
Valid options are:
- setOptions(String[]) - Method in class weka.classifiers.functions.LinearRegression
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.Logistic
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.MultilayerPerceptron
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.PaceRegression
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.PLSClassifier
-
Parses the options for this object.
- setOptions(String[]) - Method in class weka.classifiers.functions.RBFNetwork
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.SimpleLogistic
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.SMO
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.SMOreg
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.SPegasos
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.CachedKernel
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.CheckKernel
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.Kernel
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.PolyKernel
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.Puk
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.RBFKernel
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.RegOptimizer
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.RegSMO
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.VotedPerceptron
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.functions.Winnow
-
Parses a given list of options.
Valid options are:
- setOptions(String[]) - Method in class weka.classifiers.IteratedSingleClassifierEnhancer
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.lazy.IBk
-
Parses a given list of options.
- setOptions(int, int, int) - Method in class weka.classifiers.lazy.kstar.KStarNominalAttribute
-
Sets the options.
- setOptions(int, int, int) - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute
-
Set options.
- setOptions(String[]) - Method in class weka.classifiers.lazy.KStar
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.lazy.LWL
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.AdaBoostM1
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.AdditiveRegression
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.AttributeSelectedClassifier
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.Bagging
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.ClassificationViaClustering
-
Parses the options for this object.
- setOptions(String[]) - Method in class weka.classifiers.meta.CostSensitiveClassifier
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.CVParameterSelection
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.Dagging
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.Decorate
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.FilteredClassifier
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.GridSearch
-
Parses the options for this object.
- setOptions(String[]) - Method in class weka.classifiers.meta.LogitBoost
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.MetaCost
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.MultiBoostAB
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.MultiClassClassifier
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.MultiScheme
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.OrdinalClassClassifier
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.RandomSubSpace
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.RegressionByDiscretization
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.RotationForest
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.Stacking
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.ThresholdSelector
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.meta.Vote
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.CitationKNN
-
Sets the OptionHandler's options using the given list.
- setOptions(String[]) - Method in class weka.classifiers.mi.MDD
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.MIBoost
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.MIDD
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.MIEMDD
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.MILR
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.MINND
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.MIOptimalBall
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.MISMO
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.MISVM
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.MIWrapper
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.SimpleMI
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.misc.SerializedClassifier
-
Parses the options for this object.
- setOptions(String[]) - Method in class weka.classifiers.misc.VFI
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.MultipleClassifiersCombiner
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.RandomizableClassifier
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.rules.ConjunctiveRule
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.rules.DecisionTable
-
Parses the options for this object.
- setOptions(String[]) - Method in class weka.classifiers.rules.DTNB
-
Parses the options for this object.
- setOptions(String[]) - Method in class weka.classifiers.rules.JRip
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.rules.NNge
-
Sets the OptionHandler's options using the given list.
- setOptions(String[]) - Method in class weka.classifiers.rules.OneR
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.rules.PART
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.rules.Ridor
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.SingleClassifierEnhancer
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.trees.ADTree
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.trees.BFTree
-
Parses the options for this object.
- setOptions(String[]) - Method in class weka.classifiers.trees.FT
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.trees.J48
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.trees.J48graft
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.trees.LADTree
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.trees.LMT
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.trees.m5.M5Base
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.trees.M5P
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.trees.RandomForest
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.trees.RandomTree
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.trees.REPTree
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.trees.SimpleCart
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.clusterers.CheckClusterer
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.clusterers.CLOPE
-
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.DBScan
-
Sets the OptionHandler's options using the given list.
- setOptions(String[]) - Method in class weka.clusterers.EM
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.clusterers.FarthestFirst
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.clusterers.FilteredClusterer
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.clusterers.HierarchicalClusterer
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.clusterers.MakeDensityBasedClusterer
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.clusterers.OPTICS
-
Sets the OptionHandler's options using the given list.
- setOptions(String[]) - Method in class weka.clusterers.RandomizableClusterer
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.clusterers.RandomizableDensityBasedClusterer
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.clusterers.RandomizableSingleClustererEnhancer
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.clusterers.sIB
-
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.SingleClustererEnhancer
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.clusterers.XMeans
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.Check
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.CheckGOE
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.CheckOptionHandler
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.CheckScheme
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.converters.AbstractFileSaver
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.converters.ArffSaver
-
Parses the options for this object.
- setOptions(String[]) - Method in class weka.core.converters.C45Saver
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.converters.CSVLoader
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.converters.DatabaseLoader
-
Sets the options.
- setOptions(String[]) - Method in class weka.core.converters.DatabaseSaver
-
Sets the options.
- setOptions(String[]) - Method in class weka.core.converters.LibSVMSaver
-
Parses the options for this object.
- setOptions(String[]) - Method in class weka.core.converters.SVMLightSaver
-
Parses the options for this object.
- setOptions(String[]) - Method in class weka.core.converters.TextDirectoryLoader
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.converters.XRFFSaver
-
Parses the options for this object.
- setOptions(String[]) - Method in class weka.core.FindWithCapabilities
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.Javadoc
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.ListOptions
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.neighboursearch.BallTree
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.neighboursearch.balltrees.BallSplitter
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.neighboursearch.CoverTree
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.neighboursearch.KDTree
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.neighboursearch.LinearNNSearch
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.neighboursearch.NearestNeighbourSearch
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.NormalizableDistance
-
Parses a given list of options.
- setOptions(String[]) - Method in interface weka.core.OptionHandler
-
Sets the OptionHandler's options using the given list.
- setOptions(String[]) - Method in class weka.core.OptionHandlerJavadoc
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.stemmers.SnowballStemmer
-
Parses the options.
- setOptions(String[]) - Method in class weka.core.TechnicalInformationHandlerJavadoc
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.TestInstances
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.tokenizers.CharacterDelimitedTokenizer
-
Sets the OptionHandler's options using the given list.
- setOptions(String[]) - Method in class weka.core.tokenizers.NGramTokenizer
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.core.tokenizers.Tokenizer
-
Sets the OptionHandler's options using the given list.
- setOptions(String[]) - Method in class weka.datagenerators.ClassificationGenerator
-
Sets the options.
- setOptions(String[]) - Method in class weka.datagenerators.classifiers.classification.Agrawal
-
Parses a list of options for this object.
- setOptions(String[]) - Method in class weka.datagenerators.classifiers.classification.BayesNet
-
Parses a list of options for this object.
- setOptions(String[]) - Method in class weka.datagenerators.classifiers.classification.LED24
-
Parses a list of options for this object.
- setOptions(String[]) - Method in class weka.datagenerators.classifiers.classification.RandomRBF
-
Parses a list of options for this object.
- setOptions(String[]) - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Parses a list of options for this object.
- setOptions(String[]) - Method in class weka.datagenerators.classifiers.regression.Expression
-
Parses a list of options for this object.
- setOptions(String[]) - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Parses a list of options for this object.
- setOptions(String[]) - Method in class weka.datagenerators.ClusterDefinition
-
Parses a list of options for this object.
- setOptions(String[]) - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Parses a list of options for this object.
- setOptions(String[]) - Method in class weka.datagenerators.clusterers.SubspaceCluster
-
Parses a list of options for this object.
- setOptions(String[]) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Parses a list of options for this object.
- setOptions(String[]) - Method in class weka.datagenerators.ClusterGenerator
-
Sets the options.
- setOptions(String[]) - Method in class weka.datagenerators.DataGenerator
-
Parses a list of options for this object.
- setOptions(String[]) - Method in class weka.datagenerators.RegressionGenerator
-
Sets the options.
- setOptions(String[]) - Method in class weka.estimators.CheckEstimator
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.estimators.Estimator
-
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.ClassifierSplitEvaluator
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.experiment.CrossValidationResultProducer
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.experiment.CSVResultListener
-
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.DensityBasedClustererSplitEvaluator
-
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.InstanceQuery
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.experiment.LearningRateResultProducer
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.experiment.PairedTTester
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.experiment.RandomSplitResultProducer
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.experiment.RegressionSplitEvaluator
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.CheckSource
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.MultiFilter
-
Parses a list of options for this object.
- setOptions(String[]) - Method in class weka.filters.SimpleFilter
-
Parses a list of options for this object.
- setOptions(String[]) - Method in class weka.filters.supervised.attribute.AddClassification
-
Parses the options for this object.
- setOptions(String[]) - Method in class weka.filters.supervised.attribute.AttributeSelection
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.supervised.attribute.ClassOrder
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.supervised.attribute.Discretize
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.supervised.attribute.NominalToBinary
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.supervised.attribute.PLSFilter
-
Parses the options for this object.
- setOptions(String[]) - Method in class weka.filters.supervised.instance.Resample
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.supervised.instance.SMOTE
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.supervised.instance.SpreadSubsample
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
-
Parses a given list of options controlling the behaviour of this object.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Add
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AddCluster
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AddExpression
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AddID
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AddNoise
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AddValues
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.ClassAssigner
-
Parses a list of options for this object.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.ClusterMembership
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Copy
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Discretize
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.FirstOrder
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
-
Parses a list of options for this object.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.KernelFilter
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.MakeIndicator
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.MathExpression
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.NominalToBinary
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.NominalToString
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Normalize
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.NumericCleaner
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.NumericToNominal
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.NumericTransform
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
-
Parses a list of options for this object.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer
-
Parses a list of options for this object.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Parses a list of options for this object.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.RandomProjection
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.RandomSubset
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.RELAGGS
-
Parses the options for this object.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Remove
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.RemoveType
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.RemoveUseless
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Reorder
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.StringToNominal
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.SwapValues
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Wavelet
-
Parses the options for this object.
- setOptions(String[]) - Method in class weka.filters.unsupervised.instance.Normalize
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.instance.Randomize
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveFolds
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemovePercentage
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveRange
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.instance.Resample
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.instance.ReservoirSample
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.filters.unsupervised.instance.SubsetByExpression
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.gui.Main
-
Parses the options for this object.
- setOriginalCoords(Vector) - Method in class weka.gui.beans.MetaBean
-
sets the vector containing the original coordinates (instances of class
Point) for the inputs
- setOutlierFactor(double) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
-
Sets the factor for determining the thresholds for outliers.
- setOutput(PrintWriter) - Method in class weka.datagenerators.DataGenerator
-
Sets the print writer.
- setOutputCenterFile(File) - Method in class weka.clusterers.XMeans
-
Sets file to write the list of centers to.
- setOutputClassification(boolean) - Method in class weka.filters.supervised.attribute.AddClassification
-
Set whether the classification of the classifier is output.
- setOutputDistribution(boolean) - Method in class weka.filters.supervised.attribute.AddClassification
-
Set whether the Distribution of the classifier is output.
- setOutputErrorFlag(boolean) - Method in class weka.filters.supervised.attribute.AddClassification
-
Set whether the classification of the classifier is output.
- setOutputFile(File) - Method in class weka.experiment.CrossValidationResultProducer
-
Set the value of OutputFile.
- setOutputFile(File) - Method in class weka.experiment.CSVResultListener
-
Set the value of OutputFile.
- setOutputFile(File) - Method in class weka.experiment.RandomSplitResultProducer
-
Set the value of OutputFile.
- setOutputFilename(boolean) - Method in class weka.core.converters.TextDirectoryLoader
-
Sets whether the filename will be stored as an extra attribute.
- setOutputFileName(String) - Method in class weka.experiment.CSVResultListener
-
Set the value of OutputFileName.
- setOutputFilename(String) - Method in class weka.gui.GenericPropertiesCreator
-
sets the file to output the properties for the GEO to
- setOutputFormat(int) - Method in class weka.core.Debug.Clock
-
sets the format of the output
- setOutputFormat(Instances) - Method in class weka.filters.Filter
-
Sets the format of output instances.
- setOutputFormat() - Method in class weka.filters.supervised.attribute.AttributeSelection
-
Set the output format.
- setOutputFormat() - Method in class weka.filters.supervised.attribute.Discretize
-
Set the output format.
- setOutputFormat() - Method in class weka.filters.unsupervised.attribute.Discretize
-
Set the output format.
- setOutputFormat() - Method in class weka.filters.unsupervised.attribute.RandomProjection
-
Sets the output format
- setOutputFormat() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
Set the output format.
- setOutputFormatFromDialog() - Method in class weka.gui.experiment.ResultsPanel
-
displays the Dialog for the output format and sets the chosen settings,
if the user approves.
- setOutputItemSets(boolean) - Method in class weka.associations.Apriori
-
Sets whether itemsets are output as well
- setOutputOffsetMultiplier(boolean) - Method in class weka.filters.unsupervised.attribute.InterquartileRange
-
Set whether an additional attribute "Offset" is generated per
Outlier/ExtremeValue attribute pair that lists the multiplier the value
is off the median: value = median + 'multiplier' * IQR.
- setOutputPerClassInfoRetrievalStats(boolean) - Method in class weka.gui.beans.IncrementalClassifierEvaluator
-
Set whether to output per-class information retrieval
statistics (nominal class only).
- setOutputs(Vector) - Method in class weka.gui.beans.MetaBean
-
- setOutputTypes(String) - Method in class weka.core.Debug.DBO
-
Switches the outputs on that are requested from the option O
- setOutputWordCounts(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Sets whether output instances contain 0 or 1 indicating word
presence, or word counts.
- setOverwriteWarning(boolean) - Method in class weka.gui.ConverterFileChooser
-
Whether a warning is popped up if the file that is to be saved already
exists (only save dialog).
- setOwner(CapabilitiesHandler) - Method in class weka.core.Capabilities
-
sets the owner of this capabilities object
- setP(double) - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Set the proportion of instances that are common between two training sets
used to train a classifier.
- setPadding(SelectedTag) - Method in class weka.filters.unsupervised.attribute.Wavelet
-
Sets the type of Padding to use
- setPaint(Paint) - Method in class weka.gui.visualize.PostscriptGraphics
-
- setPaintMode() - Method in class weka.gui.visualize.PostscriptGraphics
-
Not implemented
- setPanelHeight(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
-
Set the height of the visualization
- setPanelWidth(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
-
Set the width of the visualization
- setParameterDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.BuiltInArithmetic
-
Set the structure of the parameters that are expected as input by
this function.
- setParameterDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.BuiltInMath
-
Set the structure of the parameters that are expected as input by
this function.
- setParameterDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.BuiltInString
-
Set the structure of the parameters that are expected as input by
this function.
- setParameterDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.DefineFunction
-
Set the structure of the parameters that are expected as input by
this function.
- setParameterDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.Function
-
Set the structure of the parameters that are expected as input by
this function.
- SetParent(int, int) - Method in class weka.classifiers.bayes.net.ParentSet
-
sets index parent of parent specified by index
- setParent(ClusterGenerator) - Method in class weka.datagenerators.ClusterDefinition
-
sets the parent datagenerator this cluster belongs to
- setParent(SubspaceCluster) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
sets the parent datagenerator this cluster belongs to
- setParent(Container) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
sets the new parent frame
- setParent(Edge) - Method in class weka.gui.treevisualizer.Node
-
Set the value of parent.
- setParentFrame(JFrame) - Method in class weka.gui.beans.AssociatorCustomizer
-
- setParentFrame(JFrame) - Method in class weka.gui.beans.ClassAssignerCustomizer
-
- setParentFrame(JFrame) - Method in class weka.gui.beans.ClassifierCustomizer
-
- setParentFrame(JFrame) - Method in class weka.gui.beans.ClassValuePickerCustomizer
-
- setParentFrame(JFrame) - Method in class weka.gui.beans.ClustererCustomizer
-
- setParentFrame(JFrame) - Method in interface weka.gui.beans.CustomizerCloseRequester
-
A reference to the parent is passed in
- setParentFrame(JFrame) - Method in class weka.gui.beans.FilterCustomizer
-
- setParentFrame(JFrame) - Method in class weka.gui.beans.LoaderCustomizer
-
- setParentFrame(JFrame) - Method in class weka.gui.beans.SaverCustomizer
-
- setParentFrame(JFrame) - Method in class weka.gui.beans.SerializedModelSaverCustomizer
-
- setParentFrame(JFrame) - Method in class weka.gui.SetInstancesPanel
-
Sets the frame, this panel resides in.
- setParentSeparator(MarginCalculator.JunctionTreeSeparator) - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
-
- setPassword(String) - Method in interface weka.core.converters.DatabaseConverter
-
- setPassword(String) - Method in class weka.core.converters.DatabaseLoader
-
Sets user password for the database
- setPassword(String) - Method in class weka.core.converters.DatabaseSaver
-
Sets the database password.
- setPassword(String) - Method in class weka.experiment.DatabaseUtils
-
Set the database password.
- setPassword(String) - Method in class weka.gui.sql.ConnectionPanel
-
sets the Password.
- setPattern(SelectedTag) - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Sets the pattern type.
- setPercent(int) - Method in class weka.filters.unsupervised.attribute.AddNoise
-
Sets the size of noise data, as a percentage of the original set.
- setPercent(double) - Method in class weka.filters.unsupervised.attribute.RandomProjection
-
Sets the percent the attributes (dimensions) of the data should be reduced to
- setPercent() - Method in class weka.gui.visualize.MatrixPanel
-
Calculates the percentage to resample
- setPercentage(double) - Method in class weka.filters.supervised.instance.SMOTE
-
Sets the percentage of SMOTE instances to create.
- setPercentage(double) - Method in class weka.filters.unsupervised.instance.RemovePercentage
-
Sets the percentage of intances to select.
- setPercentCompleted(int) - Method in class weka.gui.boundaryvisualizer.RemoteResult
-
Set the progress for this row so far
- setPercentThreshold(int) - Method in class weka.attributeSelection.SVMAttributeEval
-
Set the threshold below which percentage elimination reverts to
constant elimination.
- setPercentToEliminatePerIteration(int) - Method in class weka.attributeSelection.SVMAttributeEval
-
Set the percentage of attributes to eliminate per iteration
- setPerformPrediction(boolean) - Method in class weka.filters.supervised.attribute.PLSFilter
-
Sets whether to update the class attribute with the predicted value.
- setPerformRanking(boolean) - Method in class weka.attributeSelection.LinearForwardSelection
-
Perform initial ranking to select top-ranked attributes.
- setPerformRanking(boolean) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Perform initial ranking to select top-ranked attributes.
- setPeriodicPruning(double) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Sets the rate at which the dictionary is periodically pruned, as a
percentage of the dataset size.
- setPerturbationFraction(double) - Method in class weka.datagenerators.classifiers.classification.Agrawal
-
Sets the perturbation fraction.
- setPivot(Instance) - Method in class weka.core.neighboursearch.balltrees.BallNode
-
Sets the pivot/centre of this nodes
ball.
- setPixHeight(double) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
-
Set the height of a pixel
- setPixWidth(double) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
-
Set the width of a pixel
- setPlotCompanion(Plot2DCompanion) - Method in class weka.gui.visualize.Plot2D
-
Set a companion class.
- setPlotList(FastVector) - Method in class weka.gui.visualize.LegendPanel
-
Set the list of plots to generate legend entries for
- setPlotName(String) - Method in class weka.gui.visualize.PlotData2D
-
Set the name of this plot
- setPlotNameHTML(String) - Method in class weka.gui.visualize.PlotData2D
-
Set the plot name for use in a tool tip text.
- setPlotTrainingData(boolean) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Set whether to superimpose the training data
plot
- setPlus(int, int, double) - Method in class weka.classifiers.functions.pace.PaceMatrix
-
Add a value to an element and reset the element
- setPlus(int, double) - Method in class weka.core.matrix.DoubleVector
-
Adds a value to an element
- setPMMLVersion(Document) - Method in class weka.classifiers.pmml.consumer.PMMLClassifier
-
Set the version of PMML used for this model.
- setPMMLVersion(Document) - Method in interface weka.core.pmml.PMMLModel
-
Set the version of the PMML.
- setPoints(MiddleOutConstructor.TempNode, int, int, int[]) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Sets the points of an anchor node.
- setPointValue(int, double) - Method in class weka.classifiers.functions.pace.DiscreteFunction
-
Sets a particular point value
- setPopulationSize(int) - Method in class weka.attributeSelection.GeneticSearch
-
set the population size
- setPopulationSize(int) - Method in class weka.attributeSelection.ScatterSearchV1
-
Set the population size
- setPopulationSize(int) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
-
- setPopulationSize(int) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
-
- setPopup(JPopupMenu) - Method in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
-
sets the JPopupMenu to display again after closing the dialog.
- setPosition(int, int, int) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
set position of node
- setPosition(int, int, int, FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
Set position of node.
- setPositiveIndex(int) - Method in class weka.associations.FPGrowth
-
Set the index of the attribute value to consider as positive
for binary attributes in normal dense instances.
- setPostProcessor(CheckScheme.PostProcessor) - Method in class weka.core.CheckScheme
-
sets the PostProcessor to use
- setPostProcessor(CheckEstimator.PostProcessor) - Method in class weka.estimators.CheckEstimator
-
sets the PostProcessor to use
- setPredTargetColumn(boolean) - Method in class weka.experiment.ClassifierSplitEvaluator
-
Set the flag for prediction and target output.
- setPreferredScrollableViewportSize(Dimension) - Method in class weka.gui.AttributeSelectionPanel
-
- setPrefix(String) - Method in class weka.gui.beans.SerializedModelSaver
-
Set the prefix to prepend to the model file names.
- setPreprocessing(SelectedTag) - Method in class weka.filters.supervised.attribute.PLSFilter
-
Sets the type of preprocessing to use
- setPreprocessing(Filter) - Method in class weka.filters.unsupervised.attribute.KernelFilter
-
Sets the filter to use for preprocessing (use the AllFilter for no
preprocessing)
- setPreserveInstancesOrder(boolean) - Method in class weka.clusterers.SimpleKMeans
-
Sets whether order of instances must be preserved
- setPrintColNames(boolean) - Method in class weka.experiment.ResultMatrix
-
sets whether the column names or numbers instead are printed.
- setPrintNewick(boolean) - Method in class weka.clusterers.HierarchicalClusterer
-
- setPrintRowNames(boolean) - Method in class weka.experiment.ResultMatrix
-
sets whether the row names or numbers instead are printed
deactivating automatically sets m_EnumerateColNames to TRUE.
- setPriorClass(SelectedTag) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Set the type of prior to use.
- setPriors(Instances) - Method in class weka.classifiers.Evaluation
-
Sets the class prior probabilities
- setProbabilityEstimates(boolean) - Method in class weka.classifiers.functions.LibLINEAR
-
Returns whether probability estimates are generated instead of -1/+1 for
classification problems.
- setProbabilityEstimates(boolean) - Method in class weka.classifiers.functions.LibSVM
-
Returns whether probability estimates are generated instead of -1/+1 for
classification problems.
- setProcessed(boolean) - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
-
Marks this dataObject as processed
- setProcessed(boolean) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
-
Marks this dataObject as processed
- setProcessed(boolean) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
-
Marks this dataObject as processed
- setProjectionFilter(Filter) - Method in class weka.classifiers.meta.RotationForest
-
Sets the filter used to project the data.
- setProlog(boolean) - Method in class weka.core.OptionHandlerJavadoc
-
sets whether to add the "Valid options are..." prolog
- setProlog(boolean) - Method in class weka.core.TechnicalInformationHandlerJavadoc
-
sets whether to add the "Valid options are..." prolog
- setProperty(String, String) - Method in class weka.core.ProtectedProperties
-
Overrides a method to prevent the properties from being modified.
- setProperty(int, Object) - Method in class weka.experiment.Experiment
-
Recursively sets the custom property value, by setting all values
along the property path.
- setPropertyArray(Object) - Method in class weka.experiment.Experiment
-
Sets the array of values to set the custom property to.
- setPropertyArray(Object) - Method in class weka.experiment.RemoteExperiment
-
Sets the array of values to set the custom property to.
- setPropertyPath(PropertyNode[]) - Method in class weka.experiment.Experiment
-
Sets the path of properties taken to get to the custom property
to iterate over.
- setPropertyPath(PropertyNode[]) - Method in class weka.experiment.RemoteExperiment
-
Sets the path of properties taken to get to the custom property
to iterate over.
- setPruningMethod(SelectedTag) - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Sets the method used to for pruning.
- setPruningStrategy(SelectedTag) - Method in class weka.classifiers.trees.BFTree
-
Sets the pruning strategy.
- setPruningType(SelectedTag) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Set the pruning type
- setQuality(float) - Method in class weka.gui.visualize.JPEGWriter
-
sets the quality the JPEG is saved in.
- setQuery(String) - Method in class weka.core.converters.DatabaseLoader
-
Sets the query to execute against the database
- setQuery(String) - Method in class weka.experiment.InstanceQuery
-
Set the query to execute against the database
- setQuery(String) - Method in class weka.gui.sql.QueryPanel
-
sets the query in the textarea.
- setQueryPanel(QueryPanel) - Method in class weka.gui.sql.ResultPanel
-
sets the QueryPanel to use for displaying the query
- setRaceType(SelectedTag) - Method in class weka.attributeSelection.RaceSearch
-
Set the race type
- setRadius(double) - Method in class weka.core.neighboursearch.balltrees.BallNode
-
Sets the radius of the node's
ball.
- setRadiuses(String) - Method in class weka.datagenerators.clusterers.BIRCHCluster
-
Sets the upper and lower boundary for the radius of the clusters.
- setRandom(Random) - Method in class weka.datagenerators.DataGenerator
-
Sets the random generator.
- setRandomize(boolean) - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
-
Sets whether the order of the generated data is randomized
- setRandomizeData(boolean) - Method in class weka.experiment.RandomSplitResultProducer
-
Set to true if dataset is to be randomized
- setRandomOrder(boolean) - Method in class weka.classifiers.bayes.net.search.global.K2
-
Set random order flag
- setRandomOrder(boolean) - Method in class weka.classifiers.bayes.net.search.local.K2
-
Set random order flag
- setRandomSeed(long) - Method in class weka.classifiers.functions.LeastMedSq
-
Set the seed for the random number generator
- setRandomSeed(int) - Method in class weka.classifiers.functions.SMO
-
Set the value of randomSeed.
- setRandomSeed(int) - Method in class weka.classifiers.mi.MISMO
-
Set the value of randomSeed.
- setRandomSeed(int) - Method in class weka.classifiers.trees.ADTree
-
Sets random seed for a random walk.
- setRandomSeed(int) - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
-
Sets the seed for random number generator.
- setRandomSeed(int) - Method in class weka.filters.supervised.instance.Resample
-
Sets the random number seed.
- setRandomSeed(int) - Method in class weka.filters.supervised.instance.SMOTE
-
Sets the random number seed.
- setRandomSeed(int) - Method in class weka.filters.supervised.instance.SpreadSubsample
-
Sets the random number seed.
- setRandomSeed(int) - Method in class weka.filters.unsupervised.attribute.AddNoise
-
Sets the random number seed.
- setRandomSeed(long) - Method in class weka.filters.unsupervised.attribute.RandomProjection
-
Sets the random seed of the random number generator
- setRandomSeed(int) - Method in class weka.filters.unsupervised.instance.Randomize
-
Set the random number generator seed value.
- setRandomSeed(int) - Method in class weka.filters.unsupervised.instance.Resample
-
Sets the random number seed.
- setRandomSeed(int) - Method in class weka.filters.unsupervised.instance.ReservoirSample
-
Sets the random number seed.
- setRandomWidthFactor(double) - Method in class weka.classifiers.meta.MultiClassClassifier
-
Sets the multiplier when generating random codes.
- setRange(String) - Method in class weka.datagenerators.classifiers.regression.MexicanHat
-
Sets the upper and lower boundary for the range of x
- setRangeCorrection(SelectedTag) - Method in class weka.classifiers.meta.ThresholdSelector
-
Sets the confidence range correction mode used.
- setRanges(String) - Method in class weka.core.Range
-
Sets the ranges from a string representation.
- setRanges(Range[]) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
-
Sets the list of possible Ranges to choose from.
- setRank(double) - Method in class weka.attributeSelection.LatentSemanticAnalysis
-
Sets the desired matrix rank (or coverage proportion) for feature-space reduction
- setRanking(boolean) - Method in class weka.attributeSelection.AttributeSelection
-
produce a ranking (if possible with the set search and evaluator)
- setRanking(int[][]) - Method in class weka.experiment.ResultMatrix
-
sets the ranking data based on the wins
- 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
- setReachabilityDistance(double) - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
-
Sets a new reachability-distance for this dataObject
- setReachabilityDistance(double) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
-
Sets a new reachability-distance for this dataObject
- setReachabilityDistance(double) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
-
Sets a new reachability-distance for this dataObject
- setReachabilityDistanceColor(Color) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
-
Sets a new color for the reachabilityDistance
- setReadable(String) - Method in class weka.core.Tag
-
Sets the string description of the Tag.
- setReadIncrementally(boolean) - Method in class weka.gui.SetInstancesPanel
-
Sets whether or not instances should be read incrementally
by the Loader.
- setReadOnly(boolean) - Method in class weka.gui.arffviewer.ArffPanel
-
sets whether the model is read-only
- setReadOnly(boolean) - Method in class weka.gui.arffviewer.ArffSortedTableModel
-
sets whether the model is read-only
- setReadOnly(boolean) - Method in class weka.gui.arffviewer.ArffTable
-
sets whether the model is read-only
- setReadOnly(boolean) - Method in class weka.gui.arffviewer.ArffTableModel
-
sets whether the model is read-only
- setReducedErrorPruning(boolean) - Method in class weka.classifiers.rules.PART
-
Set the value of reducedErrorPruning.
- setReducedErrorPruning(boolean) - Method in class weka.classifiers.trees.J48
-
Set the value of reducedErrorPruning.
- setRefer(String) - Method in class weka.gui.treevisualizer.Node
-
Set the value of refer.
- setRefreshFreq(int) - Method in class weka.gui.beans.StripChart
-
Set how often (in x axis points) to refresh the display
- setRegOptimizer(RegOptimizer) - Method in class weka.classifiers.functions.SMOreg
-
sets the learning algorithm
- setRegressionTree(boolean) - Method in class weka.classifiers.trees.m5.Rule
-
Set the value of regressionTree.
- setRegressionTree(boolean) - Method in class weka.classifiers.trees.m5.RuleNode
-
Set the value of regressionTree.
- setRelabel(boolean) - Method in class weka.classifiers.trees.J48graft
-
Set the value of relabelling.
- setRelation(String) - Method in class weka.core.TestInstances
-
sets the name of the relation
- setRelationalClassFormat(Instances) - Method in class weka.core.TestInstances
-
sets the structure for the relational class attribute
- setRelationalFormat(int, Instances) - Method in class weka.core.TestInstances
-
sets the structure for the bags for the relational attribute
- setRelationForTableName(boolean) - Method in class weka.core.converters.DatabaseSaver
-
En/Dis-ables that the relation name is used for the name of the table (default enabled).
- setRelationName(String) - Method in class weka.core.Instances
-
Sets the relation's name.
- setRelationName(String) - Method in class weka.datagenerators.DataGenerator
-
Sets the relation name the dataset should have.
- setRelationNameForFilename(boolean) - Method in class weka.gui.beans.Saver
-
Set whether to use the relation name as the primary part
of the filename.
- setRemoteHosts(DefaultListModel) - Method in class weka.experiment.RemoteExperiment
-
Set the list of remote host names
- setRemoteHosts(Vector) - Method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
-
Set a list of host names of machines to distribute processing to
- setRemoveAllMissingCols(boolean) - Method in class weka.associations.Apriori
-
Remove columns containing all missing values.
- setRemoveClassColumn(boolean) - Method in class weka.experiment.DensityBasedClustererSplitEvaluator
-
Set whether the class column should be removed from the data.
- setRemovedPercentage(int) - Method in class weka.classifiers.meta.RotationForest
-
Sets the percentage of instance to be removed
- setRemoveFilterName(boolean) - Method in class weka.experiment.ResultMatrix
-
sets whether to remove the filter classname from the dataset name
- setRemoveFilterName(boolean) - Method in class weka.gui.experiment.OutputFormatDialog
-
sets whether to remove the filter classname from the dataset name.
- setRemoveOldClass(boolean) - Method in class weka.filters.supervised.attribute.AddClassification
-
Set whether the old class attribute is removed.
- setRemoveUnused(boolean) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter
-
Sets whether unused attributes (ones that are not covered by any of the
ranges) are removed from the output.
- setRenderingHint(RenderingHints.Key, Object) - Method in class weka.gui.visualize.PostscriptGraphics
-
- setRenderingHints(Map) - Method in class weka.gui.visualize.PostscriptGraphics
-
- setRepeatLiterals(boolean) - Method in class weka.associations.Tertius
-
Set the value of repeatLiterals.
- setReplaceMissing(boolean) - Method in class weka.filters.supervised.attribute.PLSFilter
-
Sets whether to replace missing values.
- setReplaceMissingValues(boolean) - Method in class weka.filters.unsupervised.attribute.RandomProjection
-
Sets either to use replace missing values filter or not
- setReportFrequency(int) - Method in class weka.attributeSelection.GeneticSearch
-
set how often reports are generated
- setRepulsion(double) - Method in class weka.clusterers.CLOPE
-
set the repulsion
- setReset(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
-
This sets the network up to be able to reset itself with the current
settings and the learning rate at half of what it is currently.
- setReset(boolean) - Method in class weka.gui.beans.ChartEvent
-
Set the reset flag
- setResult(Double) - Method in class weka.core.mathematicalexpression.Parser
-
Sets the result of the evaluation.
- setResult(Boolean) - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
-
Sets the result of the evaluation.
- setResultKeyFromDialog() - Method in class weka.gui.experiment.ResultsPanel
-
- setResultListener(ResultListener) - Method in class weka.experiment.AveragingResultProducer
-
Sets the object to send results of each run to.
- setResultListener(ResultListener) - Method in class weka.experiment.CrossValidationResultProducer
-
Sets the object to send results of each run to.
- setResultListener(ResultListener) - Method in class weka.experiment.DatabaseResultProducer
-
Sets the object to send results of each run to.
- setResultListener(ResultListener) - Method in class weka.experiment.Experiment
-
Sets the result listener where results will be sent.
- 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.RandomSplitResultProducer
-
Sets the object to send results of each run to.
- setResultListener(ResultListener) - Method in class weka.experiment.RemoteExperiment
-
Sets the result listener where results will be sent.
- setResultListener(ResultListener) - Method in interface weka.experiment.ResultProducer
-
Sets the object to send results of each run to.
- setResultMatrix(ResultMatrix) - Method in class weka.experiment.PairedTTester
-
Sets the matrix to use to produce the output.
- setResultMatrix(ResultMatrix) - Method in interface weka.experiment.Tester
-
Sets the matrix to use to produce the output.
- setResultMatrix(Class) - Method in class weka.gui.experiment.OutputFormatDialog
-
Sets the matrix to use as initial selected output format.
- setResultProducer(ResultProducer) - Method in class weka.experiment.AveragingResultProducer
-
Set the ResultProducer.
- setResultProducer(ResultProducer) - Method in class weka.experiment.DatabaseResultProducer
-
Set the ResultProducer.
- setResultProducer(ResultProducer) - Method in class weka.experiment.Experiment
-
Set the result producer used for the current experiment.
- setResultProducer(ResultProducer) - Method in class weka.experiment.LearningRateResultProducer
-
Set the ResultProducer.
- setResultProducer(ResultProducer) - Method in class weka.experiment.RemoteExperiment
-
Set the result producer used for the current experiment.
- setResultsetKeyColumns(Range) - Method in class weka.experiment.PairedTTester
-
Set the value of ResultsetKeyColumns.
- setResultsetKeyColumns(Range) - Method in interface weka.experiment.Tester
-
Set the value of ResultsetKeyColumns.
- setResultsPanel(ResultsPanel) - Method in class weka.gui.experiment.RunPanel
-
Sets the pointer to the results panel.
- setResultVector(FastVector) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
-
Sets a new resultVector
- setRetrieval(int) - Method in class weka.core.converters.AbstractLoader
-
Sets the retrieval mode.
- setRetrieval(int) - Method in class weka.core.converters.AbstractSaver
-
Sets the retrieval mode.
- setRetrieval(int) - Method in interface weka.core.converters.Loader
-
Sets the retrieval mode.
- setRetrieval(int) - Method in interface weka.core.converters.Saver
-
Sets the retrieval mode
- setRidge(double) - Method in class weka.classifiers.functions.LinearRegression
-
Set the value of Ridge.
- setRidge(double) - Method in class weka.classifiers.functions.Logistic
-
Sets the ridge in the log-likelihood.
- setRidge(double) - Method in class weka.classifiers.functions.RBFNetwork
-
Sets the ridge value for logistic or linear regression.
- setRidge(double) - Method in class weka.classifiers.mi.MILR
-
Sets the ridge in the log-likelihood.
- setRocAnalysis(boolean) - Method in class weka.associations.Tertius
-
Set the value of rocAnalysis.
- setROCString(String) - Method in class weka.gui.visualize.ThresholdVisualizePanel
-
Set the string with ROC area
- setRoot(boolean) - Method in class weka.gui.treevisualizer.Node
-
Set the value of root.
- setRootNode(String) - Method in class weka.core.xml.XMLDocument
-
sets the root node to use in the XML output.
- setRow(int, double[]) - Method in class weka.core.Matrix
-
Deprecated.
Sets a row of the matrix to the given row.
- setRowDimension(int) - Method in class weka.classifiers.functions.pace.PaceMatrix
-
Set the row dimenion of the matrix
- setRowHidden(int, boolean) - Method in class weka.experiment.ResultMatrix
-
sets the hidden status of the row (if the index is valid)
- setRowName(int, String) - Method in class weka.experiment.ResultMatrix
-
sets the name of the row (if the index is valid)
- setRowNameWidth(int) - Method in class weka.experiment.ResultMatrix
-
sets the width for the row names (0 = optimal)
- setRowNumber(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
-
Set the row number for this sub task
- setRowOrder(int[]) - Method in class weka.experiment.ResultMatrix
-
sets the ordering of the rows, null means default
- setRsource(String) - Method in class weka.gui.treevisualizer.Edge
-
Set the value of rsource.
- setRtarget(String) - Method in class weka.gui.treevisualizer.Edge
-
Set the value of rtarget.
- setRuleset(FastVector) - Method in class weka.classifiers.rules.RuleStats
-
Set the ruleset of the stats, overwriting the old one if any
- setRulesMustContain(String) - Method in class weka.associations.FPGrowth
-
Set the comma separated list of items that rules
must contain in order to be output.
- setRunColumn(int) - Method in class weka.experiment.PairedTTester
-
Set the value of RunColumn.
- setRunColumn(int) - Method in interface weka.experiment.Tester
-
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.
- setRuns(int) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
-
Sets the number of runs
- setRuns(int) - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
-
Sets the number of runs
- setRuns(int) - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
-
Sets the m_nRuns.
- setRuns(int) - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
-
Sets the number of runs
- setRuns(int) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
-
Sets the number of runs
- setRuns(int) - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
-
Sets the number of runs
- setRuns(int) - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
-
Sets the m_nRuns.
- setRuns(int) - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
-
Sets the number of runs
- setRunUpper(int) - Method in class weka.experiment.Experiment
-
Set the upper run number for the experiment.
- setRunUpper(int) - Method in class weka.experiment.RemoteExperiment
-
Set the upper run number for the experiment.
- setSampleSize(int) - Method in class weka.attributeSelection.ReliefFAttributeEval
-
Set the number of instances to sample for attribute estimation
- setSampleSize(int) - Method in class weka.classifiers.functions.LeastMedSq
-
sets number of samples
- setSampleSize(int) - Method in class weka.filters.unsupervised.instance.ReservoirSample
-
Sets the size of the subsample.
- setSampleSizePercent(double) - Method in class weka.classifiers.meta.GridSearch
-
Sets the sample size for the initial grid search.
- setSampleSizePercent(double) - Method in class weka.filters.supervised.instance.Resample
-
Sets the size of the subsample, as a percentage of the original set.
- setSampleSizePercent(double) - Method in class weka.filters.unsupervised.instance.Resample
-
Sets the size of the subsample, as a percentage of the original set.
- setSaveDialogTitle(String) - Method in class weka.gui.visualize.PrintableComponent
-
sets the title for the save dialog.
- setSaveDialogTitle(String) - Method in interface weka.gui.visualize.PrintableHandler
-
sets the title for the save dialog
- setSaveDialogTitle(String) - Method in class weka.gui.visualize.PrintablePanel
-
sets the title for the save dialog
- setSaveInstanceData(boolean) - Method in class weka.classifiers.trees.ADTree
-
Sets whether the tree is to save instance data.
- setSaveInstanceData(boolean) - Method in class weka.classifiers.trees.J48
-
Set whether instance data is to be saved.
- setSaveInstanceData(boolean) - Method in class weka.classifiers.trees.J48graft
-
Set whether instance data is to be saved.
- setSaveInstanceData(boolean) - Method in class weka.clusterers.Cobweb
-
Set the value of saveInstances.
- setSaveInstances(boolean) - Method in class weka.classifiers.trees.m5.Rule
-
Sets whether instances at each node in an M5 tree should be saved
for visualization purposes.
- setSaveInstances(boolean) - Method in class weka.classifiers.trees.m5.RuleNode
-
Set whether to save instances for visualization purposes.
- setSaveInstances(boolean) - Method in class weka.classifiers.trees.M5P
-
Set whether to save instance data at each node in the
tree for visualization purposes
- setSaverTemplate(Saver) - Method in class weka.gui.beans.Saver
-
Set the loader to use
- setScale(double) - Method in class weka.filters.unsupervised.attribute.Normalize
-
Sets the scaling factor.
- setScale(double, double) - Method in class weka.gui.visualize.JComponentWriter
-
sets the scale factor - is ignored since we always create a screenshot!
- setScale(double, double) - Method in class weka.gui.visualize.PrintableComponent
-
sets the scale factor.
- setScale(double, double) - Method in interface weka.gui.visualize.PrintableHandler
-
sets the scale factor
- setScale(double, double) - Method in class weka.gui.visualize.PrintablePanel
-
sets the scale factor
- setScalingEnabled(boolean) - Method in class weka.gui.visualize.JComponentWriter
-
sets whether to enable scaling
- setScoreType(SelectedTag) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
-
set quality measure to be used in searching for networks.
- setSearch(ASSearch) - Method in class weka.attributeSelection.AttributeSelection
-
set the search method
- setSearch(ASSearch) - Method in class weka.attributeSelection.CheckAttributeSelection
-
Set the search method to test.
- setSearch(ASSearch) - Method in class weka.classifiers.meta.AttributeSelectedClassifier
-
Sets the search method
- setSearch(ASSearch) - Method in class weka.classifiers.rules.DecisionTable
-
Sets the search method to use
- setSearch(ASSearch) - Method in class weka.classifiers.rules.DTNB
-
Sets the search method to use
- setSearch(ASSearch) - Method in class weka.filters.supervised.attribute.AttributeSelection
-
Set search class
- setSearchAlgorithm(SearchAlgorithm) - Method in class weka.classifiers.bayes.BayesNet
-
Set the SearchAlgorithm used in searching for network structures.
- setSearchBackwards(boolean) - Method in class weka.attributeSelection.GreedyStepwise
-
Set whether to search backwards instead of forwards
- setSearchPath(SelectedTag) - Method in class weka.classifiers.trees.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
- setSearchString(String) - Method in class weka.gui.arffviewer.ArffTable
-
sets the search string to look for in the table, NULL or "" disables
the search
- setSearchTermination(int) - Method in class weka.attributeSelection.BestFirst
-
Set the numnber of non-improving nodes to consider before terminating
search.
- setSearchTermination(int) - Method in class weka.attributeSelection.LinearForwardSelection
-
Set the numnber of non-improving nodes to consider before terminating
search.
- setSecondValueIndex(String) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
-
Sets index of the second value used.
- setSecondValueIndex(String) - Method in class weka.filters.unsupervised.attribute.SwapValues
-
Sets index of the second value used.
- setSeed(int) - Method in class weka.attributeSelection.AttributeSelection
-
set the seed for use in cross validation
- setSeed(int) - Method in class weka.attributeSelection.CostSensitiveASEvaluation
-
Set the seed for random number generation.
- setSeed(int) - Method in class weka.attributeSelection.GeneticSearch
-
set the seed for random number generation
- setSeed(int) - Method in class weka.attributeSelection.OneRAttributeEval
-
Set the random number seed for cross validation
- setSeed(int) - Method in class weka.attributeSelection.RandomSearch
-
Set the random seed to use
- setSeed(int) - Method in class weka.attributeSelection.ReliefFAttributeEval
-
Set the random number seed for randomly sampling instances.
- setSeed(int) - Method in class weka.attributeSelection.ScatterSearchV1
-
set the seed for random number generation
- setSeed(int) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Seed for cross validation subset size determination.
- setSeed(int) - Method in class weka.attributeSelection.WrapperSubsetEval
-
Set the seed to use for cross validation
- setSeed(int) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Set the seed for randomizing the instances for CV-based
hyperparameter selection
- setSeed(int) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
-
Sets the random number seed
- setSeed(int) - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
-
Sets the random number seed
- setSeed(int) - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
-
Sets the random number seed
- setSeed(int) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
-
Sets the random number seed
- setSeed(int) - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
-
Sets the random number seed
- setSeed(int) - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
-
Sets the random number seed
- setSeed(int) - Method in class weka.classifiers.BVDecompose
-
Sets the random number seed
- setSeed(int) - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Sets the random number seed
- setSeed(int) - Method in class weka.classifiers.evaluation.EvaluationUtils
-
Sets the seed for randomization during cross-validation
- setSeed(int) - Method in class weka.classifiers.functions.MultilayerPerceptron
-
This seeds the random number generator, that is used when a random
number is needed for the network.
- setSeed(int) - Method in class weka.classifiers.functions.supportVector.RegOptimizer
-
Sets the seed value for the random number generator
- setSeed(int) - Method in class weka.classifiers.functions.VotedPerceptron
-
Set the value of Seed.
- setSeed(int) - Method in class weka.classifiers.functions.Winnow
-
Set the value of Seed.
- setSeed(int) - Method in class weka.classifiers.meta.MultiScheme
-
Sets the seed for random number generation.
- setSeed(int) - Method in class weka.classifiers.RandomizableClassifier
-
Set the seed for random number generation.
- setSeed(int) - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
-
Set the seed for random number generation.
- setSeed(int) - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner
-
Set the seed for random number generation.
- setSeed(int) - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer
-
Set the seed for random number generation.
- setSeed(long) - Method in class weka.classifiers.rules.ConjunctiveRule
-
sets the seed for randomizing the data
- setSeed(long) - Method in class weka.classifiers.rules.JRip
-
Sets the seed value to use in randomizing the data
- setSeed(int) - Method in class weka.classifiers.rules.PART
-
Set the value of Seed.
- setSeed(int) - Method in class weka.classifiers.rules.Ridor
-
- setSeed(int) - Method in class weka.classifiers.trees.J48
-
Set the value of Seed.
- setSeed(int) - Method in class weka.classifiers.trees.RandomForest
-
Set the seed for random number generation.
- setSeed(int) - Method in class weka.classifiers.trees.RandomTree
-
Set the seed for random number generation.
- setSeed(int) - Method in class weka.classifiers.trees.REPTree
-
Set the value of Seed.
- setSeed(int) - Method in class weka.clusterers.RandomizableClusterer
-
Set the seed for random number generation.
- setSeed(int) - Method in class weka.clusterers.RandomizableDensityBasedClusterer
-
Set the seed for random number generation.
- setSeed(int) - Method in class weka.clusterers.RandomizableSingleClustererEnhancer
-
Set the seed for random number generation.
- setSeed(long) - Method in class weka.core.Debug.Random
-
Sets the seed of this random number generator using a single long seed.
- setSeed(int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Sets the seed for random number generator
(that is used for selecting the first anchor
point randomly).
- setSeed(int) - Method in interface weka.core.Randomizable
-
Set the seed for random number generation.
- setSeed(int) - Method in class weka.core.TestInstances
-
sets the seed value for the random number generator
- setSeed(int) - Method in class weka.datagenerators.DataGenerator
-
Sets the random number seed.
- setSeed(long) - Method in class weka.filters.supervised.attribute.ClassOrder
-
Set randomization seed
- setSeed(long) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
-
Sets the random number seed for shuffling the dataset.
- setSeed(int) - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
-
Sets the new seed for randomizing the order of the generated data
- setSeed(int) - Method in class weka.filters.unsupervised.attribute.RandomSubset
-
Set the seed value for the random number generator.
- setSeed(long) - Method in class weka.filters.unsupervised.instance.RemoveFolds
-
Sets the random number seed for shuffling the dataset.
- setSeed(int) - Method in class weka.gui.beans.CrossValidationFoldMaker
-
Set the seed
- setSeed(int) - Method in class weka.gui.beans.TrainTestSplitMaker
-
Set the random seed
- setSeed(int) - Method in interface weka.gui.boundaryvisualizer.DataGenerator
-
Set a seed for random number generation (if needed).
- setSeed(int) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
-
Initializes a new random number generator using the
supplied seed.
- setSelectedAttributes(boolean[]) - Method in class weka.gui.AttributeSelectionPanel
-
Set the selected attributes in the widget.
- setSelectedColumn(int) - Method in class weka.gui.arffviewer.ArffTable
-
sets the selected column
- setSelectedItem(JComboBox, String) - Method in class weka.gui.experiment.ResultsPanel
-
Sets the selected item of an combobox, since using setSelectedItem(...)
doesn't work, if one checks object references!
- setSelectedItem(JComboBox, String) - Method in class weka.gui.experiment.SimpleSetupPanel
-
Sets the selected item of an combobox, since using setSelectedItem(...)
doesn't work, if one checks object references!
- setSelectedRange(String) - Method in class weka.filters.unsupervised.attribute.RELAGGS
-
Set the range of attributes to process.
- setSelectedRange(String) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Set the value of m_SelectedRange.
- setSelectionThreshold(double) - Method in class weka.attributeSelection.RaceSearch
-
Set the threshold by which the AttributeSelection module can discard
attributes.
- setSeparatingThreshold(double) - Method in class weka.classifiers.functions.pace.ChisqMixture
-
Sets the separating threshold value
- setSeparatingThreshold(double) - Method in class weka.classifiers.functions.pace.NormalMixture
-
Sets the separating threshold value
- setSeperator(String) - Method in class weka.gui.HierarchyPropertyParser
-
Set the seperator between levels.
- setSequentialAttIndex(boolean) - Method in class weka.classifiers.lazy.LBR.Indexes
-
A Sequential Attribute index is all those Attributes that are set to the specified value placed in a sequential array.
- setSequentialDataset(boolean) - Method in class weka.classifiers.lazy.LBR.Indexes
-
Sets both the Instance and Attribute indexes to a specified value
- setSequentialInstanceIndex(boolean) - Method in class weka.classifiers.lazy.LBR.Indexes
-
A Sequential Instance index is all those Instances that are set to the specified value placed in a sequential array.
- setSerializedClassifierFile(File) - Method in class weka.filters.supervised.attribute.AddClassification
-
Sets the file pointing to a serialized, trained classifier.
- setShape(int) - Method in class weka.gui.treevisualizer.Node
-
Set the value of shape.
- setShapes(FastVector) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
-
This can be used to set the shapes that should appear.
- setShapes(FastVector) - Method in class weka.gui.visualize.VisualizePanel
-
This will set the shapes for the instances.
- setShapeSize(int[]) - Method in class weka.gui.visualize.PlotData2D
-
Set the shape sizes for the plot data
- setShapeSize(FastVector) - Method in class weka.gui.visualize.PlotData2D
-
Set the shape sizes for the plot data
- setShapeType(int[]) - Method in class weka.gui.visualize.PlotData2D
-
Set the shape type for the plot data
- setShapeType(FastVector) - Method in class weka.gui.visualize.PlotData2D
-
Set the shape type for the plot data
- setShowAttBars(boolean) - Method in class weka.gui.visualize.VisualizePanel
-
Set whether the attribute bars should be shown or not.
- setShowAverage(boolean) - Method in class weka.experiment.ResultMatrix
-
sets whether to display the average per column or not
- setShowAverage(boolean) - Method in class weka.gui.experiment.OutputFormatDialog
-
sets whether the average for each column is displayed.
- setShowClassPanel(boolean) - Method in class weka.gui.visualize.VisualizePanel
-
Set whether the class panel should be shown or not.
- setShowCoreDistances(boolean) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
-
Sets the flag for showCoreDistances
- setShowGUI(boolean) - Method in class weka.clusterers.OPTICS
-
Sets the flag for displaying the GUI.
- setShowReachabilityDistances(boolean) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
-
Sets the flag for showReachabilityDistances
- setShowStdDev(boolean) - Method in class weka.experiment.ResultMatrix
-
sets whether to display the std deviations or not
- setShowStdDev(boolean) - Method in class weka.experiment.ResultMatrixSignificance
-
sets whether to display the std deviations or not - always false!
- setShowStdDevs(boolean) - Method in class weka.experiment.PairedTTester
-
Set whether standard deviations are displayed or not.
- setShowStdDevs(boolean) - Method in interface weka.experiment.Tester
-
Set whether standard deviations are displayed or not.
- setShrinkage(double) - Method in class weka.classifiers.meta.AdditiveRegression
-
Set the shrinkage parameter
- setShrinkage(double) - Method in class weka.classifiers.meta.LogitBoost
-
Set the value of Shrinkage.
- setShrinking(boolean) - Method in class weka.classifiers.functions.LibSVM
-
whether to use the shrinking heuristics
- setShuffle(int) - Method in class weka.classifiers.rules.Ridor
-
- setSigma(int) - Method in class weka.attributeSelection.ReliefFAttributeEval
-
Sets the sigma value.
- setSigma(double) - Method in class weka.classifiers.functions.supportVector.Puk
-
Sets the sigma value.
- setSignificance(int, int, int) - Method in class weka.experiment.ResultMatrix
-
sets the significance at the given position (if the position is valid)
- 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.
- setSignificanceLevel(double) - Method in interface weka.experiment.Tester
-
Set the value of SignificanceLevel.
- setSignificanceWidth(int) - Method in class weka.experiment.ResultMatrix
-
sets the width for the significance (0 = optimal)
- setSilent(boolean) - Method in class weka.core.AllJavadoc
-
sets whether to suppress output in the console
- setSilent(boolean) - Method in class weka.core.Check
-
Set slient mode, i.e., no output at all to stdout
- setSilent(boolean) - Method in class weka.core.Javadoc
-
sets whether to suppress output in the console
- setSilent(boolean) - Method in class weka.estimators.CheckEstimator
-
Set slient mode, i.e., no output at all to stdout
- setSindex(int) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
-
Set the index of the attribute to use for the shape.
- 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.
- setSingleIndex(String) - Method in class weka.core.SingleIndex
-
Sets the index from a string representation.
- setSize(int) - Method in class weka.core.matrix.DoubleVector
-
Sets the size of the vector
- setSize(int) - Method in class weka.core.matrix.IntVector
-
Sets the size of the vector.
- setSize(int, int) - Method in class weka.experiment.ResultMatrix
-
clears the content of the matrix and sets the new size
- setSizePer(double) - Method in class weka.classifiers.trees.BFTree
-
Set training set size.
- setSizePer(double) - Method in class weka.classifiers.trees.SimpleCart
-
Set training set size.
- setSkipIdentical(boolean) - Method in class weka.core.neighboursearch.LinearNNSearch
-
Sets the property to skip identical instances (with distance zero from
the target) from the set of neighbours returned.
- setSmoothing(boolean) - Method in class weka.classifiers.trees.m5.Rule
-
Smooth predictions
- setSmoothingParameter(double) - Method in class weka.classifiers.bayes.ComplementNaiveBayes
-
Sets the smoothing value used to avoid zero WordGivenClass probabilities
- setSMOReg(SMOreg) - Method in class weka.classifiers.functions.supportVector.RegOptimizer
-
sets the parent SVM
- setSort(boolean) - Method in class weka.filters.unsupervised.attribute.AddValues
-
Sets whether the labels are sorted.
- setSortColumn(int) - Method in class weka.experiment.PairedTTester
-
Set the column to sort on, -1 means the default sorting.
- setSortColumn(int) - Method in interface weka.experiment.Tester
-
Set the column to sort on, -1 means the default sorting.
- setSource(File) - Method in class weka.core.converters.AbstractFileLoader
-
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.AbstractLoader
-
Default implementation throws an IOException.
- setSource(InputStream) - Method in class weka.core.converters.AbstractLoader
-
Default implementation throws an IOException.
- setSource(URL) - Method in class weka.core.converters.ArffLoader
-
Resets the Loader object and sets the source of the data set to be
the supplied url.
- setSource(InputStream) - Method in class weka.core.converters.ArffLoader
-
Resets the Loader object and sets the source of the data set to be
the supplied InputStream.
- setSource(File) - Method in class weka.core.converters.C45Loader
-
Resets the Loader object and sets the source of the data set to be
the supplied File object.
- setSource(InputStream) - Method in class weka.core.converters.CSVLoader
-
Resets the Loader object and sets the source of the data set to be the
supplied Stream 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(String, String, String) - Method in class weka.core.converters.DatabaseLoader
-
Sets the database url, user and pw
- setSource(String) - Method in class weka.core.converters.DatabaseLoader
-
Sets the database url
- setSource() - Method in class weka.core.converters.DatabaseLoader
-
Sets the database url using the DatabaseUtils file
- setSource(URL) - Method in class weka.core.converters.LibSVMLoader
-
Resets the Loader object and sets the source of the data set to be
the supplied url.
- setSource(InputStream) - Method in class weka.core.converters.LibSVMLoader
-
Resets the Loader object and sets the source of the data set to be
the supplied InputStream.
- setSource(File) - Method in interface weka.core.converters.Loader
-
Resets the Loader object and sets the source of the data set to be
the supplied File object.
- setSource(InputStream) - Method in interface weka.core.converters.Loader
-
Resets the Loader object and sets the source of the data set to be
the supplied InputStream.
- setSource(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(URL) - Method in class weka.core.converters.SVMLightLoader
-
Resets the Loader object and sets the source of the data set to be
the supplied url.
- setSource(InputStream) - Method in class weka.core.converters.SVMLightLoader
-
Resets the Loader object and sets the source of the data set to be
the supplied InputStream.
- setSource(File) - Method in class weka.core.converters.TextDirectoryLoader
-
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.XRFFLoader
-
Resets the Loader object and sets the source of the data set to be
the supplied File object.
- setSource(URL) - Method in class weka.core.converters.XRFFLoader
-
Resets the Loader object and sets the source of the data set to be
the supplied url.
- setSource(InputStream) - Method in class weka.core.converters.XRFFLoader
-
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.
- setSourceCode(Classifier) - Method in class weka.classifiers.CheckSource
-
Sets the class to test.
- setSourceCode(Filter) - Method in class weka.filters.CheckSource
-
Sets the class to test.
- 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.
- setSplitOnResiduals(boolean) - Method in class weka.classifiers.trees.LMT
-
Set the value of splitOnResiduals.
- setSplitPoint(Instances) - Method in class weka.classifiers.trees.j48.BinC45Split
-
Sets split point to greatest value in given data smaller or equal to
old split point.
- setSplitPoint(Instances) - Method in class weka.classifiers.trees.j48.C45Split
-
Sets split point to greatest value in given data smaller or equal to
old split point.
- setSplitPoint(double) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Split point to be used for selection on numeric attribute.
- setStartEndIndices(int, int) - Method in class weka.core.neighboursearch.balltrees.BallNode
-
Sets the the start and end index of the
portion of the master index array that is
assigned to this node.
- setStartPoint(int) - Method in class weka.attributeSelection.RankSearch
-
Set the point at which to start evaluating the ranking
- setStartSequentially(boolean) - Method in class weka.gui.beans.FlowRunner
-
Set whether to launch Startable beans one after the other
or all in parallel.
- setStartSet(String) - Method in class weka.attributeSelection.BestFirst
-
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.GreedyStepwise
-
Sets a starting set of attributes for the search.
- setStartSet(String) - Method in class weka.attributeSelection.LinearForwardSelection
-
Sets a starting set of attributes for the search.
- setStartSet(String) - Method in class weka.attributeSelection.RandomSearch
-
Sets a starting set of attributes for the search.
- setStartSet(String) - Method in class weka.attributeSelection.Ranker
-
Sets a starting set of attributes for the search.
- setStartSet(String) - Method in interface weka.attributeSelection.StartSetHandler
-
Sets a starting set of attributes for the search.
- setStatic() - Method in class weka.gui.beans.BeanVisual
-
Set the static version of the icon
- setStatus(int) - Method in class weka.gui.beans.IncrementalClassifierEvent
-
Set the status
- setStatus(int) - Method in class weka.gui.beans.InstanceEvent
-
Set the status
- setStatusFrequency(int) - Method in class weka.gui.beans.IncrementalClassifierEvaluator
-
Set how often progress is reported to the status bar.
- setStatusMessage(String) - Method in class weka.experiment.TaskStatusInfo
-
Set the status message.
- setStdDev(int, int, double) - Method in class weka.experiment.ResultMatrix
-
sets the std deviation at the given position (if the position is valid)
- setStdDevPrec(int) - Method in class weka.experiment.ResultMatrix
-
sets the precision for the standard deviation
- setStdDevPrec(int) - Method in class weka.gui.experiment.OutputFormatDialog
-
Sets the precision of the std.
- setStdDevWidth(int) - Method in class weka.experiment.ResultMatrix
-
sets the width for the std dev (0 = optimal)
- setStemmer(String) - Method in class weka.core.stemmers.SnowballStemmer
-
sets the stemmer with the given name, e.g., "porter".
- setStemmer(Stemmer) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
the stemming algorithm to use, null means no stemming at all (i.e., the
NullStemmer is used).
- setStepSize(int) - Method in class weka.attributeSelection.RankSearch
-
Set the number of attributes to add from the rankining
in each iteration
- setStepSize(int) - Method in class weka.experiment.LearningRateResultProducer
-
Set the value of StepSize.
- setStopwords(File) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
sets the file containing the stopwords, null or a directory unset the
stopwords.
- setStringAttributes(String) - Method in class weka.core.converters.CSVLoader
-
Sets the attribute range to be forced to type string.
- setStroke(Stroke) - Method in class weka.gui.visualize.PostscriptGraphics
-
- setStructure(Instances) - Method in class weka.core.converters.AbstractSaver
-
Sets the strcuture of the instances for the first step of incremental saving.
- setStructure(Instances) - Method in class weka.gui.beans.IncrementalClassifierEvent
-
Set the instances structure
- setStructure(Instances) - Method in class weka.gui.beans.InstanceEvent
-
Set the instances structure
- setSubFlow(Vector) - Method in class weka.gui.beans.MetaBean
-
- setSubFlowPreview(ImageIcon) - Method in class weka.gui.beans.MetaBean
-
- setSubsequenceLength(int) - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Sets the length of the subsequence.
- setSubsetEvaluator(ASEvaluation) - Method in class weka.attributeSelection.FilteredSubsetEval
-
Set the subset evaluator to use
- setSubsetSizeEvaluator(ASEvaluation) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Set the subset evaluator to use for subset size determination.
- setSubSpaceSize(double) - Method in class weka.classifiers.meta.RandomSubSpace
-
Sets the size of each subSpace, as a percentage of the training set size.
- setSubtreeRaising(boolean) - Method in class weka.classifiers.trees.J48
-
Set the value of subtreeRaising.
- setSubtreeRaising(boolean) - Method in class weka.classifiers.trees.J48graft
-
Set the value of subtreeRaising.
- setSummary(int[][], int[][]) - Method in class weka.experiment.ResultMatrix
-
sets the non-significant and significant wins of the resultsets
- setSupport(int) - Method in class weka.associations.FPGrowth.FrequentBinaryItemSet
-
Set the support for this item set.
- setSupportCount(int) - Method in class weka.associations.gsp.Sequence
-
Sets the support count of the Sequence.
- setSuppressErrorMessage(boolean) - Method in class weka.classifiers.functions.SimpleLinearRegression
-
Turn off the error message that is reported when no useful attribute is found.
- setSVMType(SelectedTag) - Method in class weka.classifiers.functions.LibLINEAR
-
Sets type of SVM (default SVMTYPE_L2)
- setSVMType(SelectedTag) - Method in class weka.classifiers.functions.LibSVM
-
Sets type of SVM (default SVMTYPE_C_SVC)
- setSymbols(HashMap) - Method in class weka.core.mathematicalexpression.Parser
-
Sets the variable - value relation to use.
- setSymbols(HashMap) - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
-
Sets the variable - value relation to use.
- setSyntax(StreamTokenizer) - Method in class weka.gui.graphvisualizer.DotParser
-
This method sets the syntax of the StreamTokenizer.
- setTable(AttributeStats, int) - Method in class weka.gui.AttributeSummaryPanel
-
Creates a tablemodel for the attribute being displayed
- setTableName(String) - Method in class weka.core.converters.DatabaseSaver
-
Sets the table's name.
- setTabTitle(JComponent) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
sets the title of the tab that contains the given component
- setTabuList(int) - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
-
Sets the Tabu List length.
- setTabuList(int) - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
-
Sets the Tabu List length.
- setTarget(Object) - Method in class weka.gui.PropertySheetPanel
-
Sets a new target object for customisation.
- setTarget(Node) - Method in class weka.gui.treevisualizer.Edge
-
Set the value of target.
- setTargetClass(int) - Method in class weka.classifiers.bayes.DMNBtext.DNBBinary
-
Sets the Target Class
- setTaskResult(Object) - Method in class weka.experiment.TaskStatusInfo
-
Set the returnable result for this task..
- setter(CoverTree.MyHeap, double, int) - Method in class weka.core.neighboursearch.CoverTree
-
Initializes a heap with k values of the the given upper_bound.
- setTestBaseFromDialog() - Method in class weka.gui.experiment.ResultsPanel
-
- setTester() - Method in class weka.gui.experiment.ResultsPanel
-
sets the currently selected Tester-Class.
- setTestEvaluator(boolean) - Method in class weka.attributeSelection.CheckAttributeSelection
-
Sets whether the evaluator or the search method is being tested.
- setTestSet(DataSetEvent) - Method in class weka.gui.beans.BatchClassifierEvent
-
Set the test set
- setTestSet() - Method in class weka.gui.explorer.ClassifierPanel
-
Sets the user test set.
- setTestSet() - Method in class weka.gui.explorer.ClustererPanel
-
Sets the user test set.
- setText(String) - Method in class weka.gui.beans.BeanVisual
-
Set the label for the visual.
- setTFTransform(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Sets whether if the word frequencies should be transformed into
log(1+fij) where fij is the frequency of word i in document(instance) j.
- setThreshold(double) - Method in class weka.attributeSelection.GreedyStepwise
-
Set the threshold by which the AttributeSelection module can discard
attributes.
- setThreshold(double) - Method in class weka.attributeSelection.RaceSearch
-
Sets the threshold for comparisons
- setThreshold(double) - Method in interface weka.attributeSelection.RankedOutputSearch
-
Sets a threshold by which attributes can be discarded from the
ranking.
- setThreshold(double) - Method in class weka.attributeSelection.Ranker
-
Set the threshold by which the AttributeSelection module can discard
attributes.
- setThreshold(double) - Method in class weka.attributeSelection.ScatterSearchV1
-
Set the treshold
- setThreshold(double) - Method in class weka.attributeSelection.WrapperSubsetEval
-
Set the value of the threshold for repeating cross validation
- setThreshold(double) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Set the threshold to use.
- setThreshold(double) - Method in class weka.classifiers.functions.PaceRegression
-
Set threshold for the olsc estimator
- setThreshold(double) - Method in class weka.classifiers.functions.Winnow
-
Set the value of Threshold.
- setThreshold(double) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
-
Sets the threshold for the max error when predicting a numeric class.
- setTimes(int, int, double) - Method in class weka.classifiers.functions.pace.PaceMatrix
-
Multiply a value with an element and reset the element
- setTimes(int, double) - Method in class weka.core.matrix.DoubleVector
-
Multiplies a value to an element
- setTokenizer(Tokenizer) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
the tokenizer algorithm to use.
- setTolerance(double) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Set the tolerance value
- setTolerance(double) - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
-
sets the tolerance
- setToleranceParameter(double) - Method in class weka.attributeSelection.SVMAttributeEval
-
Set the value of T for SMO
- setToleranceParameter(double) - Method in class weka.classifiers.functions.SMO
-
Set the value of tolerance parameter.
- setToleranceParameter(double) - Method in class weka.classifiers.mi.MISMO
-
Set the value of tolerance parameter.
- setTop(double) - Method in class weka.gui.treevisualizer.Node
-
Set the value of top.
- setTrainingData(Instances) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Set the training data to use
- setTrainingTime(int) - Method in class weka.classifiers.functions.MultilayerPerceptron
-
Set the number of training epochs to perform.
- setTrainIterations(int) - Method in class weka.classifiers.BVDecompose
-
Sets the maximum number of boost iterations
- setTrainPercent(double) - Method in class weka.experiment.RandomSplitResultProducer
-
Set the value of TrainPercent.
- setTrainPercent(double) - Method in class weka.gui.beans.TrainTestSplitMaker
-
Set the percentage of data to be in the training portion of the split
- setTrainPoolSize(int) - Method in class weka.classifiers.BVDecompose
-
Set the number of instances in the training pool.
- setTrainSet(DataSetEvent) - Method in class weka.gui.beans.BatchClassifierEvent
-
Set the training set
- setTrainSize(int) - Method in class weka.classifiers.BVDecomposeSegCVSub
-
Set the training size.
- setTransactionsMustContain(String) - Method in class weka.associations.FPGrowth
-
Set the comma separated list of items that transactions
must contain in order to be considered for large
item sets and rules.
- setTransform(AffineTransform) - Method in class weka.gui.visualize.PostscriptGraphics
-
- setTransformAllValues(boolean) - Method in class weka.filters.supervised.attribute.NominalToBinary
-
Sets whether all nominal values are transformed into new attributes, not
just if there are more than 2.
- setTransformAllValues(boolean) - Method in class weka.filters.unsupervised.attribute.NominalToBinary
-
Sets whether all nominal values are transformed into new attributes, not
just if there are more than 2.
- setTransformBackToOriginal(boolean) - Method in class weka.attributeSelection.PrincipalComponents
-
Sets whether the data should be transformed back to the original
space
- setTransformMethod(SelectedTag) - Method in class weka.classifiers.mi.SimpleMI
-
Set the method used in transformation.
- setTranslation(double) - Method in class weka.filters.unsupervised.attribute.Normalize
-
Sets the translation.
- setTraversal(SelectedTag) - Method in class weka.classifiers.meta.GridSearch
-
Sets the type of traversal for the grid.
- setTrimingThreshold(double) - Method in class weka.classifiers.functions.pace.ChisqMixture
-
Sets the triming thresholding value.
- setTrimingThreshold(double) - Method in class weka.classifiers.functions.pace.NormalMixture
-
Sets the triming thresholding value.
- setTrueNegative(double) - Method in class weka.classifiers.evaluation.TwoClassStats
-
Sets the number of negative instances predicted as negative
- setTruePositive(double) - Method in class weka.classifiers.evaluation.TwoClassStats
-
Sets the number of positive instances predicted as positive
- setTStart(double) - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
-
Sets the m_fTStart.
- setTStart(double) - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
-
Sets the m_fTStart.
- setTTester() - Method in class weka.gui.experiment.ResultsPanel
-
Updates the test chooser with possible tests.
- setType(SelectedTag) - Method in class weka.attributeSelection.LinearForwardSelection
-
Set the type
- setType(SelectedTag) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Set the type
- setType(int) - Method in class weka.classifiers.functions.neural.NeuralConnection
-
- setUndoEnabled(boolean) - Method in interface weka.core.Undoable
-
sets whether undo support is enabled
- setUndoEnabled(boolean) - Method in class weka.gui.arffviewer.ArffPanel
-
sets whether undo support is enabled
- setUndoEnabled(boolean) - Method in class weka.gui.arffviewer.ArffSortedTableModel
-
sets whether undo support is enabled
- setUndoEnabled(boolean) - Method in class weka.gui.arffviewer.ArffTableModel
-
sets whether undo support is enabled
- setUnpruned(boolean) - Method in class weka.classifiers.rules.PART
-
Set the value of unpruned.
- setUnpruned(boolean) - Method in class weka.classifiers.trees.J48
-
Set the value of unpruned.
- setUnpruned(boolean) - Method in class weka.classifiers.trees.J48graft
-
Set the value of unpruned.
- setUnpruned(boolean) - Method in class weka.classifiers.trees.m5.M5Base
-
Use unpruned tree/rules
- setUnpruned(boolean) - Method in class weka.classifiers.trees.m5.Rule
-
Use unpruned tree/rules
- setup(Object, double, double) - Method in class weka.classifiers.meta.GridSearch
-
returns a fully configures object (a copy of the provided one)
- setup(Instances) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Initializes the filter with the given input data.
- setupAttribLists() - Method in class weka.gui.visualize.MatrixPanel
-
Sets up the UI's attributes lists
- setUpBoundaryPanel() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
Sets up the BoundaryPanel object so that it is ready for plotting.
- setUpComboBoxes(Instances) - Method in class weka.gui.visualize.ThresholdVisualizePanel
-
This overloads VisualizePanel's setUpComboBoxes to add
ActionListeners to watch for when the X/Y Axis comboboxes
are changed.
- setUpComboBoxes(Instances) - Method in class weka.gui.visualize.VisualizePanel
-
initializes the comboboxes based on the data
- setUpdateIncrementalClassifier(boolean) - Method in class weka.gui.beans.Classifier
-
Set whether an incremental classifier will be updated on the
incoming instance stream.
- setUpEvaluator() - Method in class weka.classifiers.rules.DecisionTable
-
Sets up a dummy subset evaluator that basically just delegates
evaluation to the estimatePerformance method in DecisionTable
- setUpEvaluator() - Method in class weka.classifiers.rules.DTNB
-
Sets up a dummy subset evaluator that basically just delegates
evaluation to the estimatePerformance method in DecisionTable
- setUpFile() - Method in class weka.gui.beans.LoaderCustomizer
-
- setUpFile() - Method in class weka.gui.beans.SaverCustomizer
-
Sets up dialog for saving instances in a file
- setUpFile() - Method in class weka.gui.beans.SerializedModelSaverCustomizer
-
Sets up dialog for saving models to a file
- setupFileChooser() - Method in class weka.gui.beans.Classifier
-
- setUpFinal() - Method in class weka.gui.beans.AttributeSummarizer
-
- setUpFinal() - Method in class weka.gui.beans.CostBenefitAnalysis
-
- setUpFinal() - Method in class weka.gui.beans.DataVisualizer
-
- setUpFinal() - Method in class weka.gui.beans.GraphViewer
-
- setUpFinal() - Method in class weka.gui.beans.ModelPerformanceChart
-
- setUpFinal() - Method in class weka.gui.beans.ScatterPlotMatrix
-
- setUpFinal() - Method in class weka.gui.beans.TextViewer
-
- SetupModePanel - Class in weka.gui.experiment
-
This panel switches between simple and advanced experiment setup panels.
- SetupModePanel() - Constructor for class weka.gui.experiment.SetupModePanel
-
Creates the setup panel with no initial experiment.
- SetupPanel - Class in weka.gui.experiment
-
This panel controls the configuration of an experiment.
- SetupPanel(Experiment) - Constructor for class weka.gui.experiment.SetupPanel
-
Creates the setup panel with the supplied initial experiment.
- SetupPanel() - Constructor for class weka.gui.experiment.SetupPanel
-
Creates the setup panel with no initial experiment.
- setUpper(int) - Method in class weka.core.Range
-
Sets the value of "last".
- setUpper(int) - Method in class weka.core.SingleIndex
-
Sets the value of "last".
- setUpperBoundMinSupport(double) - Method in class weka.associations.Apriori
-
Set the value of upperBoundMinSupport.
- setUpperBoundMinSupport(double) - Method in class weka.associations.FPGrowth
-
Set the value of upperBoundMinSupport.
- setUpperSize(int) - Method in class weka.experiment.LearningRateResultProducer
-
Set the value of UpperSize.
- setUpVisualizableInstances(Instances) - Static method in class weka.gui.explorer.ClassifierPanel
-
Sets up the structure for the visualizable instances.
- setUpVisualizableInstances(Instances, ClusterEvaluation) - Static method in class weka.gui.explorer.ClustererPanel
-
Sets up the structure for the visualizable instances.
- setURL(String) - Method in class weka.core.converters.ArffLoader
-
Set the url to load from
- setUrl(String) - Method in interface weka.core.converters.DatabaseConverter
-
- setUrl(String) - Method in class weka.core.converters.DatabaseLoader
-
Sets the database URL
- setUrl(String) - Method in class weka.core.converters.DatabaseSaver
-
Sets the database URL.
- setURL(String) - Method in class weka.core.converters.LibSVMLoader
-
Set the url to load from.
- setURL(String) - Method in class weka.core.converters.SVMLightLoader
-
Set the url to load from.
- setURL(String) - Method in interface weka.core.converters.URLSourcedLoader
-
Set the url to load from
- setURL(String) - Method in class weka.core.converters.XRFFLoader
-
Set the url to load from
- setURL(String) - Method in class weka.gui.sql.ConnectionPanel
-
sets the URL.
- setUseADTree(boolean) - Method in class weka.classifiers.bayes.BayesNet
-
Set whether ADTree structure is used or not
- setUseAIC(boolean) - Method in class weka.classifiers.functions.SimpleLogistic
-
Set the value of useAIC.
- setUseAIC(boolean) - Method in class weka.classifiers.trees.FT
-
Set the value of useAIC.
- setUseAIC(boolean) - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Set the value of useAIC.
- setUseAIC(boolean) - Method in class weka.classifiers.trees.LMT
-
Set the value of useAIC.
- setUseArcReversal(boolean) - Method in class weka.classifiers.bayes.net.search.global.HillClimber
-
set use the arc reversal operation
- setUseArcReversal(boolean) - Method in class weka.classifiers.bayes.net.search.local.HillClimber
-
set use the arc reversal operation
- setUseBetterEncoding(boolean) - Method in class weka.filters.supervised.attribute.Discretize
-
Sets whether better encoding is to be used for MDL.
- setUseCpuTime(boolean) - Method in class weka.core.Debug.Clock
-
enables/disables the use of CPU time (if measurement of CPU time is
available).
- setUseCrossOver(boolean) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
-
- setUseCrossOver(boolean) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
-
- setUseCrossValidation(boolean) - Method in class weka.classifiers.functions.SimpleLogistic
-
Set the value of useCrossValidation.
- setUseCustomDimensions(boolean) - Method in class weka.gui.visualize.JComponentWriter
-
sets whether to use custom dimensions for the image
- setUseEqualFrequency(boolean) - Method in class weka.classifiers.meta.RegressionByDiscretization
-
Set the value of UseEqualFrequency.
- setUseEqualFrequency(boolean) - Method in class weka.filters.unsupervised.attribute.Discretize
-
Set the value of UseEqualFrequency.
- setUseEqualFrequency(boolean) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
-
Set the value of UseEqualFrequency.
- setUseErrorRate(boolean) - Method in class weka.classifiers.trees.BFTree
-
Set if use error rate in internal cross-validation.
- setUseGini(boolean) - Method in class weka.classifiers.trees.BFTree
-
Set if use Gini index as splitting criterion.
- setUseIBk(boolean) - Method in class weka.classifiers.rules.DecisionTable
-
Sets whether IBk should be used instead of the majority class
- setUseK2Prior(boolean) - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator
-
Sets the UseK2Prior.
- setUseK2Prior(boolean) - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
-
Sets the UseK2Prior.
- setUseKDTree(boolean) - Method in class weka.clusterers.XMeans
-
Sets whether to use the KDTree or not.
- setUseKernelEstimator(boolean) - Method in class weka.classifiers.bayes.NaiveBayes
-
Sets if kernel estimator is to be used.
- setUseKononenko(boolean) - Method in class weka.filters.supervised.attribute.Discretize
-
Sets whether Kononenko's MDL criterion is to be used.
- setUseLaplace(boolean) - Method in class weka.classifiers.bayes.AODEsr
-
Sets if laplace correction is to be used.
- setUseLaplace(boolean) - Method in class weka.classifiers.trees.J48
-
Set the value of useLaplace.
- setUseLaplace(boolean) - Method in class weka.classifiers.trees.J48graft
-
Set the value of useLaplace.
- setUseLeastValues(boolean) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
-
Sets whether to use values with least or most instances
- setUseLowerOrder(boolean) - Method in class weka.classifiers.functions.supportVector.PolyKernel
-
Sets whether to use lower-order terms.
- setUseMEstimates(boolean) - Method in class weka.classifiers.bayes.AODE
-
Sets if m-estimates is to be used.
- setUseMissing(boolean) - Method in class weka.filters.unsupervised.attribute.AddNoise
-
Sets the flag if missing values are treated as extra values.
- setUseMutation(boolean) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
-
- setUseMutation(boolean) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
-
- setUseNormalization(boolean) - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Sets whether to use normalization.
- setUseOneSE(boolean) - Method in class weka.classifiers.trees.BFTree
-
Set if use the 1SE rule to choose final model.
- setUseOneSE(boolean) - Method in class weka.classifiers.trees.SimpleCart
-
Set if use the 1SE rule to choose final model.
- setUseORForMustContainList(boolean) - Method in class weka.associations.FPGrowth
-
Set whether to use OR rather than AND when considering
must contain lists.
- setUsePairwiseCoupling(boolean) - Method in class weka.classifiers.meta.MultiClassClassifier
-
Set whether to use pairwise coupling with 1-vs-1
classification to improve probability estimates.
- setUseProb(boolean) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
-
- 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.
- setUsePrune(boolean) - Method in class weka.classifiers.trees.SimpleCart
-
Set if use minimal cost-complexity pruning.
- setUsePruning(boolean) - Method in class weka.classifiers.rules.JRip
-
Sets whether pruning is performed
- setUser(String) - Method in interface weka.core.converters.DatabaseConverter
-
- setUser(String) - Method in class weka.core.converters.DatabaseLoader
-
Sets the database user
- setUser(String) - Method in class weka.core.converters.DatabaseSaver
-
Sets the database user.
- setUser(String) - Method in class weka.gui.sql.ConnectionPanel
-
sets the User.
- setUseRelativePath(boolean) - Method in class weka.core.converters.AbstractFileLoader
-
Set whether to use relative rather than absolute paths
- setUseRelativePath(boolean) - Method in class weka.core.converters.AbstractFileSaver
-
Set whether to use relative rather than absolute paths
- setUseRelativePath(boolean) - Method in interface weka.core.converters.FileSourcedConverter
-
Set whether to use relative rather than absolute paths
- setUseRelativePath(boolean) - Method in class weka.gui.beans.SerializedModelSaver
-
Set whether to use relative paths for the directory.
- setUseResampling(boolean) - Method in class weka.classifiers.meta.AdaBoostM1
-
Set resampling mode
- setUseResampling(boolean) - Method in class weka.classifiers.meta.LogitBoost
-
Set resampling mode
- setUseResampling(boolean) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Set resampling mode
- setUsername(String) - Method in class weka.experiment.DatabaseUtils
-
Set the database username.
- setUserOptions(String[]) - Method in class weka.classifiers.functions.supportVector.KernelEvaluation
-
sets the option the user supplied for the kernel
- setUserOptions(String[]) - Method in class weka.core.CheckOptionHandler
-
Sets the user-supplied options (creates a copy)
- setUseStars(boolean) - Method in class weka.core.AllJavadoc
-
sets whether to prefix the Javadoc with "*"
- setUseStars(boolean) - Method in class weka.core.Javadoc
-
sets whether to prefix the Javadoc with "*"
- setUseStoplist(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Sets whether if the words that are on a stoplist are to be ignored (The
stop list is in weka.core.StopWords).
- setUseSupervisedDiscretization(boolean) - Method in class weka.classifiers.bayes.NaiveBayes
-
Set whether supervised discretization is to be used.
- setUseSupervisedDiscretization(boolean) - Method in class weka.classifiers.bayes.NaiveBayesUpdateable
-
Set whether supervised discretization is to be used.
- setUseTournamentSelection(boolean) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
-
- setUseTournamentSelection(boolean) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
-
- setUseTraining(boolean) - Method in class weka.attributeSelection.ClassifierSubsetEval
-
Set if training data is to be used instead of hold out/test data
- setUseTree(boolean) - Method in class weka.classifiers.trees.m5.Rule
-
Use an m5 tree rather than generate rules
- setUseUnsmoothed(boolean) - Method in class weka.classifiers.trees.m5.M5Base
-
Use unsmoothed predictions
- setUseVariant1(boolean) - Method in class weka.classifiers.functions.supportVector.RegSMOImproved
-
Sets whether to use variant 1
- setValidating(boolean) - Method in class weka.core.xml.XMLDocument
-
sets whether to use a validating parser or not.
Note: this does clear the current DOM document!
- setValidating(boolean) - Method in class weka.core.xml.XMLOptions
-
sets whether to use a validating parser or not.
- setValidationChunkSize(int) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
-
Set the validation chunk size
- setValidationSetSize(int) - Method in class weka.classifiers.functions.MultilayerPerceptron
-
This will set the size of the validation set.
- setValidationThreshold(int) - Method in class weka.classifiers.functions.MultilayerPerceptron
-
This sets the threshold to use for when validation testing is being done.
- setValue(Object, String, double) - Method in class weka.classifiers.meta.GridSearch
-
tries to set the value as double, integer (just casts it to int!) or
boolean (false if 0, otherwise true) in the object according to the
specified path.
- setValue(double) - Method in class weka.classifiers.trees.adtree.PredictionNode
-
Sets the prediction value of the node.
- setValue(int, double) - Method in class weka.core.BinarySparseInstance
-
Sets a specific value in the instance to the given value
(internal floating-point format).
- setValue(int, double) - Method in class weka.core.Instance
-
Sets a specific value in the instance to the given value
(internal floating-point format).
- setValue(int, String) - Method in class weka.core.Instance
-
Sets a value of a nominal or string attribute to the given
value.
- setValue(Attribute, double) - Method in class weka.core.Instance
-
Sets a specific value in the instance to the given value
(internal floating-point format).
- setValue(Attribute, String) - Method in class weka.core.Instance
-
Sets a value of an nominal or string attribute to the given
value.
- setValue(Object, PropertyPath.Path, Object) - Static method in class weka.core.PropertyPath
-
set the given value specified by the given path in the object
- setValue(Object, String, Object) - Static method in class weka.core.PropertyPath
-
set the given value specified by the given path in the object
- setValue() - Method in class weka.core.SingleIndex
-
Translates a single string selection into it's internal 0-based equivalent
- 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(TechnicalInformation.Field, String) - Method in class weka.core.TechnicalInformation
-
sets the value for the given field, overwrites any previously existing one.
- setValue(Object) - Method in class weka.gui.CostMatrixEditor
-
Sets the value of the CostMatrix to be edited.
- setValue(Object) - Method in class weka.gui.GenericArrayEditor
-
Sets the current object array.
- setValue(Object) - Method in class weka.gui.GenericObjectEditor
-
Sets the current Object.
- setValue(Object) - Method in class weka.gui.SimpleDateFormatEditor
-
Sets the value of the date format to be edited.
- setValueAt(Object, int, int) - Method in class weka.gui.arffviewer.ArffTableModel
-
sets the value in the cell at columnIndex and rowIndex to aValue.
- setValueAt(Object, int, int, boolean) - Method in class weka.gui.arffviewer.ArffTableModel
-
sets the value in the cell at columnIndex and rowIndex to aValue.
- setValueAt(Object, int, int) - Method in class weka.gui.SortedTableModel
-
Sets the value in the cell at columnIndex and rowIndex to aValue.
- setValueAt(Object, int, int) - Method in class weka.gui.sql.ResultSetTableModel
-
sets the value in the cell at columnIndex and rowIndex to aValue.
- setValueIndex(int) - Method in class weka.filters.unsupervised.attribute.MakeIndicator
-
Sets index of the indicator value.
- setValueIndices(String) - Method in class weka.filters.unsupervised.attribute.MakeIndicator
-
Sets indices of the indicator values.
- setValueIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.MakeIndicator
-
Set which attributes are to be deleted (or kept if invert is true)
- setValues(double[]) - Method in class weka.classifiers.trees.LADTree.PredictionNode
-
- setValuesList(String) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Sets the ranges for each attribute.
- setValuesList(String, double[], double[], String) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
Sets the ranges for each attribute.
- setValuesOutput(SelectedTag) - Method in class weka.associations.Tertius
-
Set the value of valuesOutput.
- setValueSparse(int, double) - Method in class weka.core.BinarySparseInstance
-
Sets a specific value in the instance to the given value
(internal floating-point format).
- setValueSparse(int, double) - Method in class weka.core.Instance
-
Sets a specific value in the instance to the given value
(internal floating-point format).
- setValueSparse(int, double) - Method in class weka.core.SparseInstance
-
Sets a specific value in the instance to the given value
(internal floating-point format).
- setVarianceCovered(double) - Method in class weka.attributeSelection.PrincipalComponents
-
Sets the amount of variance to account for when retaining
principal components
- setVarianceCovered(double) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents
-
Sets the amount of variance to account for when retaining
principal components.
- setVector(Matrix, Matrix, int) - Method in class weka.filters.supervised.attribute.PLSFilter
-
stores the data from the (column) vector in the matrix at the specified
index
- setVerbose(boolean) - Method in class weka.associations.Apriori
-
Sets verbose mode
- 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.LinearForwardSelection
-
Set whether verbose output should be generated.
- setVerbose(boolean) - Method in class weka.attributeSelection.RandomSearch
-
set whether or not to output new best subsets as the search proceeds
- setVerbose(boolean) - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Set whether verbose output should be generated.
- setVerbose(boolean) - Method in class weka.classifiers.meta.Dagging
-
Set the verbose state.
- setVerboseOn() - Method in class weka.core.Debug.DBO
-
Set the verbose on flag on
- setVerticalAdjustment(int) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
-
Sets a new value for the vertical verticalAdjustment
- setVisible(boolean) - Method in class weka.gui.Main
-
Shows or hides this component depending on the value of parameter b.
- setVisible(boolean) - Method in class weka.gui.sql.SqlViewerDialog
-
displays the dialog if TRUE
- setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractDataSink
-
Set the visual for this data source
- setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractDataSource
-
Set the visual for this data source
- setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractEvaluator
-
Set the visual
- setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractTestSetProducer
-
Set the visual for this bean
- setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
-
Set the visual for this bean
- setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractTrainingSetProducer
-
Set the visual for this bean
- setVisual(BeanVisual) - Method in class weka.gui.beans.Associator
-
Sets the visual appearance of this wrapper bean
- setVisual(BeanVisual) - Method in class weka.gui.beans.ClassAssigner
-
- setVisual(BeanVisual) - Method in class weka.gui.beans.Classifier
-
Sets the visual appearance of this wrapper bean
- setVisual(BeanVisual) - Method in class weka.gui.beans.ClassValuePicker
-
- setVisual(BeanVisual) - Method in class weka.gui.beans.Clusterer
-
Sets the visual appearance of this wrapper bean
- setVisual(BeanVisual) - Method in class weka.gui.beans.CostBenefitAnalysis
-
- setVisual(BeanVisual) - Method in class weka.gui.beans.DataVisualizer
-
Set the visual appearance of this bean
- setVisual(BeanVisual) - Method in class weka.gui.beans.Filter
-
Set the visual appearance of this bean
- setVisual(BeanVisual) - Method in class weka.gui.beans.GraphViewer
-
Set the visual appearance of this bean
- setVisual(BeanVisual) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
-
Sets the visual appearance of this wrapper bean
- setVisual(BeanVisual) - Method in class weka.gui.beans.MetaBean
-
Sets the visual appearance of this wrapper bean
- setVisual(BeanVisual) - Method in class weka.gui.beans.ModelPerformanceChart
-
Set the visual appearance of this bean
- setVisual(BeanVisual) - Method in class weka.gui.beans.PredictionAppender
-
Set the visual for this data source
- setVisual(BeanVisual) - Method in class weka.gui.beans.SerializedModelSaver
-
Set the visual for this data source.
- setVisual(BeanVisual) - Method in class weka.gui.beans.StripChart
-
Set the visual appearance of this bean
- setVisual(BeanVisual) - Method in class weka.gui.beans.TextViewer
-
Describe setVisual
method here.
- setVisual(BeanVisual) - Method in interface weka.gui.beans.Visible
-
Set a new visual representation
- setVoteFlag(boolean) - Method in class weka.datagenerators.classifiers.classification.RDG1
-
Sets the vote flag.
- setWeight(int) - Method in class weka.classifiers.bayes.AODE
-
Sets the weight for m-estimate
- setWeight(double) - Method in class weka.core.Attribute
-
Sets the new attribute's weight
- setWeight(double) - Method in class weka.core.Instance
-
Sets the weight of an instance.
- setWeightByConfidence(boolean) - Method in class weka.classifiers.misc.VFI
-
Set weighting by confidence
- setWeightByDistance(boolean) - Method in class weka.attributeSelection.ReliefFAttributeEval
-
Set the nearest neighbour weighting method
- setWeightingDimensions(boolean[]) - Method in interface weka.gui.boundaryvisualizer.DataGenerator
-
Set the dimensions to be used in computing a weight for
each instance generated
- setWeightingDimensions(boolean[]) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
-
Set which dimensions to use when computing a weight for the next
instance to generate
- setWeightingKernel(int) - Method in class weka.classifiers.lazy.LWL
-
Sets the kernel weighting method to use.
- setWeightingValues(double[]) - Method in interface weka.gui.boundaryvisualizer.DataGenerator
-
Set the values of the dimensions (chosen via setWeightingDimensions)
to be used when computing instance weights
- setWeightingValues(double[]) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
-
Set the values for the weighting dimensions to be used when computing
the weight for the next instance to be generated
- setWeightMethod(SelectedTag) - Method in class weka.classifiers.mi.MIWrapper
-
The new method for weighting the instances.
- setWeightMethod(SelectedTag) - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
-
The new method for weighting the instances.
- setWeights(String) - Method in class weka.classifiers.functions.LibLINEAR
-
Sets the parameters C of class i to weight[i]*C (default 1).
- setWeights(String) - Method in class weka.classifiers.functions.LibSVM
-
Sets the parameters C of class i to weight[i]*C, for C-SVC (default 1).
- setWeights(Instances, double) - Method in class weka.classifiers.meta.AdaBoostM1
-
Sets the weights for the next iteration.
- setWeights(Instances, double) - Method in class weka.classifiers.meta.MultiBoostAB
-
Sets the weights for the next iteration.
- setWeightThreshold(int) - Method in class weka.classifiers.meta.AdaBoostM1
-
Set weight threshold
- setWeightThreshold(int) - Method in class weka.classifiers.meta.LogitBoost
-
Set weight thresholding
- setWeightTrimBeta(double) - Method in class weka.classifiers.functions.SimpleLogistic
-
Set the value of weightTrimBeta.
- setWeightTrimBeta(double) - Method in class weka.classifiers.trees.FT
-
Set the value of weightTrimBeta.
- setWeightTrimBeta(double) - Method in class weka.classifiers.trees.lmt.LogisticBase
-
Sets the option "weightTrimBeta".
- setWeightTrimBeta(double) - Method in class weka.classifiers.trees.LMT
-
Set the value of weightTrimBeta.
- setWholeDataErr(boolean) - Method in class weka.classifiers.rules.Ridor
-
- setWindowSize(int) - Method in class weka.classifiers.lazy.IBk
-
Sets the maximum number of instances allowed in the training
pool.
- setWords(String) - Method in class weka.core.CheckScheme
-
Sets the comma-separated list of words to use for generating strings.
- setWords(String) - Method in class weka.core.TestInstances
-
Sets the comma-separated list of words to use for generating strings.
- setWordSeparators(String) - Method in class weka.core.CheckScheme
-
sets the word separators (chars) to use for assembling strings.
- setWordSeparators(String) - Method in class weka.core.TestInstances
-
sets the word separators (chars) to use for assembling strings.
- setWordsToKeep(int) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Sets the number of words (per class if there is a class attribute
assigned) to attempt to keep.
- setWordwrap(boolean) - Method in class weka.gui.LogWindow
-
toggles the wordwrap
override wordwrap from:
http://forum.java.sun.com/thread.jspa?threadID=498535&messageID=2356174
- setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Associator
-
Sets the algorithm (associator) for this bean
- setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Classifier
-
Sets the algorithm (classifier) for this bean
- setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Clusterer
-
Sets the algorithm (clusterer) for this bean
- setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Filter
-
Set the filter to be wrapped by this bean
- setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Loader
-
Set the loader
- setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Saver
-
Set the saver
- setWrappedAlgorithm(Object) - Method in interface weka.gui.beans.WekaWrapper
-
Set the algorithm.
- setWriteMode(int) - Method in class weka.core.converters.AbstractSaver
-
Sets the write mode.
- setWriteOPTICSresults(boolean) - Method in class weka.clusterers.OPTICS
-
Sets the flag for writing actions
- setX(double) - Method in class weka.classifiers.functions.neural.NeuralConnection
-
- setX(int) - Method in class weka.gui.beans.BeanInstance
-
Sets the x coordinate of this bean
- setX(int) - Method in class weka.gui.visualize.AttributePanel
-
shows which bar is the current x attribute.
- setXAttribute(int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Set the x attribute index
- setXAttribute(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
-
Set the x axis fixed dimension
- setXBase(double) - Method in class weka.classifiers.meta.GridSearch
-
Set the value of the base for X.
- setXExpression(String) - Method in class weka.classifiers.meta.GridSearch
-
Set the expression for the X value.
- setXindex(int) - Method in class weka.gui.visualize.Plot2D
-
Set the index of the attribute to go on the x axis
- setXindex(int) - Method in class weka.gui.visualize.PlotData2D
-
Set the x index of the data.
- setXindex(int) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
-
Set the index of the attribute to go on the x axis
- setXIndex(int) - Method in class weka.gui.visualize.VisualizePanel
-
Set the index of the attribute for the x axis
- setXLabelFreq(int) - Method in class weka.gui.beans.StripChart
-
Set the frequency for printing x label values
- setXMax(double) - Method in class weka.classifiers.meta.GridSearch
-
Set the value of the Maximum of X.
- setXMin(double) - Method in class weka.classifiers.meta.GridSearch
-
Set the value of the minimum of X.
- setXML(Reader) - Method in class weka.core.xml.XMLInstances
-
reads the XML structure from the given reader
- setXORMode(Color) - Method in class weka.gui.visualize.PostscriptGraphics
-
Not implemented
- setXProperty(String) - Method in class weka.classifiers.meta.GridSearch
-
Set the X property.
- setXStep(double) - Method in class weka.classifiers.meta.GridSearch
-
Set the value of the step size for X.
- setXval(boolean) - Method in class weka.attributeSelection.AttributeSelection
-
do a cross validation
- setXY(int, int) - Method in class weka.gui.beans.BeanInstance
-
Set the x and y coordinates of this bean
- setY(double) - Method in class weka.classifiers.functions.neural.NeuralConnection
-
- setY(int) - Method in class weka.gui.beans.BeanInstance
-
Sets the y coordinate of this bean
- setY(int) - Method in class weka.gui.visualize.AttributePanel
-
shows which bar is the current y attribute.
- setYAttribute(int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Set the y attribute index
- setYAttribute(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
-
Set the y axis fixed dimension
- setYBase(double) - Method in class weka.classifiers.meta.GridSearch
-
Set the value of the base for Y.
- setYExpression(String) - Method in class weka.classifiers.meta.GridSearch
-
Set the expression for the Y value.
- setYindex(int) - Method in class weka.gui.visualize.Plot2D
-
Set the index of the attribute to go on the y axis
- setYindex(int) - Method in class weka.gui.visualize.PlotData2D
-
Set the y index of the data
- setYindex(int) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
-
Set the index of the attribute to go on the y axis
- setYIndex(int) - Method in class weka.gui.visualize.VisualizePanel
-
Set the index of the attribute for the y axis
- setYMax(double) - Method in class weka.classifiers.meta.GridSearch
-
Set the value of the Maximum of Y.
- setYMin(double) - Method in class weka.classifiers.meta.GridSearch
-
Set the value of the minimum of Y.
- setYProperty(String) - Method in class weka.classifiers.meta.GridSearch
-
Set the Y property (normally the classifier).
- setYStep(double) - Method in class weka.classifiers.meta.GridSearch
-
Set the value of the step size for Y.
- SEVERE - Static variable in class weka.core.Debug
-
the log level Severe
- sf - Variable in class weka.core.mathematicalexpression.Scanner
-
- 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
- sgn(double) - Static method in class weka.classifiers.bayes.BayesianLogisticRegression
-
Sign for a given value.
- ShadowCounts() - Constructor for class weka.associations.FPGrowth.ShadowCounts
-
- shear(double, double) - Method in class weka.gui.visualize.PostscriptGraphics
-
- shell(double, double, double) - Method in class weka.core.neighboursearch.CoverTree
-
Function to check if a child node can be inside a query ball,
without calculating the child node's distance to the query.
- shift(int, int, Instance) - Method in class weka.classifiers.trees.j48.Distribution
-
Shifts given instance from one bag to another one.
- shift(int, int) - Method in class weka.core.matrix.IntVector
-
Shifts an element to another position.
- shiftBeans(BeanInstance, boolean) - Method in class weka.gui.beans.MetaBean
-
Move coords of all inputs and outputs of this meta bean
to the coords of the supplied BeanInstance.
- shiftRange(int, int, Instances, int, int) - Method in class weka.classifiers.trees.j48.Distribution
-
Shifts all instances in given range from one bag to another one.
- shiftToEnd(int) - Method in class weka.core.matrix.IntVector
-
Shifts an element to the end of the vector.
- SHORT - Static variable in class weka.experiment.DatabaseUtils
-
Type mapping for SHORT used for reading experiment results.
- show(Component, int, int) - Method in class weka.gui.GenericObjectEditor.JTreePopupMenu
-
Displays the menu, making sure it will fit on the screen.
- showAttributes() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
displays all the attributes, returns the selected item or NULL if canceled
- showChart() - Method in class weka.gui.beans.StripChart
-
Popup the chart panel
- showDialog(Component, String) - Method in class weka.gui.ConverterFileChooser
-
Pops a custom file chooser dialog with a custom approve button.
- showDialog() - Method in class weka.gui.experiment.OutputFormatDialog
-
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.
- 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.sql.ConnectionPanel
-
displays the database dialog.
- showDialog() - Method in class weka.gui.ViewerDialog
-
Pops up the modal dialog and waits for Cancel or OK.
- showDialog(Instances) - Method in class weka.gui.ViewerDialog
-
Pops up the modal dialog and waits for Cancel or OK.
- showExplorer(String) - Method in class weka.gui.GUIChooser
-
- showGeneratedInstances(String) - Method in class weka.gui.explorer.PreprocessPanel
-
displays a dialog with the generated instances from the DataGenerator
- showGUITipText() - Method in class weka.clusterers.OPTICS
-
Returns the tip text for this property.
- showHistory() - Method in class weka.gui.sql.ConnectionPanel
-
displays the query history.
- showHistory() - Method in class weka.gui.sql.QueryPanel
-
displays the query history.
- showInputBox(Component, String, String, Object) - Static method in class weka.gui.ComponentHelper
-
pops up an input dialog
- showKnowledgeFlow(String) - Method in class weka.gui.GUIChooser
-
- showMessageBox(Component, String, String, int, int) - Static method in class weka.gui.ComponentHelper
-
displays a message box with the given title, message, buttons and icon
ant the dimension.
- showOpenDialog(Component) - Method in class weka.gui.ConverterFileChooser
-
Pops up an "Open File" file chooser dialog.
- showOutOfMemory() - Method in class weka.core.Memory
-
prints an error message if OutOfMemory (and if GUI is present a dialog),
otherwise nothing happens.
- showPopup() - Method in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog
-
if a JPopupMenu is set, it is displayed again.
- showProperties() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
displays some properties of the instances
- showPropertyDialog() - Method in class weka.gui.PropertyPanel
-
Displays the property edit dialog for the panel.
- showResults() - Method in class weka.gui.beans.GraphViewer
-
Popup a result list from which the user can select a graph to view
- showResults() - Method in class weka.gui.beans.TextViewer
-
Popup a component to display the selected text
- showSaveDialog(Component) - Method in class weka.gui.ConverterFileChooser
-
Pops up an "Save File" file chooser dialog.
- showTree() - Method in class weka.gui.HierarchyPropertyParser
-
Show the whole tree in text format
- showValues() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
displays all the distinct values for an attribute
- showWindow(Container) - Method in class weka.gui.Main
-
brings child frame to the top.
- showWindow(Class) - Method in class weka.gui.Main
-
brings the first frame to the top that is of the specified
window class.
- shrinkageTipText() - Method in class weka.classifiers.meta.AdditiveRegression
-
Returns the tip text for this property
- shrinkageTipText() - Method in class weka.classifiers.meta.LogitBoost
-
Returns the tip text for this property
- shrinkingTipText() - Method in class weka.classifiers.functions.LibSVM
-
Returns the tip text for this property
- shuffleTipText() - Method in class weka.classifiers.rules.Ridor
-
Returns the tip text for this property
- sIB - Class in weka.clusterers
-
Cluster data using the sequential information bottleneck algorithm.
Note: only hard clustering scheme is supported.
- sIB() - Constructor for class weka.clusterers.sIB
-
- 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
- sigmaTipText() - Method in class weka.classifiers.functions.supportVector.Puk
-
Returns the tip text for this property
- SigmoidUnit - Class in weka.classifiers.functions.neural
-
This can be used by the
neuralnode to perform all it's computations (as a sigmoid unit).
- SigmoidUnit() - Constructor for class weka.classifiers.functions.neural.SigmoidUnit
-
- sign() - Method in class weka.core.matrix.DoubleVector
-
Returns the signs of all elements in terms of -1, 0 and +1.
- sign - Variable in class weka.core.matrix.ExponentialFormat
-
- SIGNIFICANCE_LOSS - Static variable in class weka.experiment.ResultMatrix
-
loss
- SIGNIFICANCE_TIE - Static variable in class weka.experiment.ResultMatrix
-
tie
- SIGNIFICANCE_WIN - Static variable in class weka.experiment.ResultMatrix
-
win
- 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
- SIGNIFICANT - Static variable in class weka.associations.Tertius
-
Way of handling missing values: missing as a particular value
- SIGNLOWER - Static variable in class weka.classifiers.lazy.LBR
-
significantly lower
- simetricDif(ScatterSearchV1.Subset, ScatterSearchV1.Subset, int) - Method in class weka.attributeSelection.ScatterSearchV1
-
- SimetricDiference(ScatterSearchV1.Subset, BitSet) - Method in class weka.attributeSelection.ScatterSearchV1
-
Calculate the Simetric Diference of two subsets
- SimpleBatchFilter - Class in weka.filters
-
This filter is a superclass for simple batch filters.
- SimpleBatchFilter() - Constructor for class weka.filters.SimpleBatchFilter
-
- SimpleCart - Class in weka.classifiers.trees
-
Class implementing minimal cost-complexity pruning.
Note when dealing with missing values, use "fractional instances" method instead of surrogate split method.
For more information, see:
Leo Breiman, Jerome H.
- SimpleCart() - Constructor for class weka.classifiers.trees.SimpleCart
-
- SimpleCLI - Class in weka.gui
-
Creates a very simple command line for invoking the main method of
classes.
- SimpleCLI() - Constructor for class weka.gui.SimpleCLI
-
Constructor
- SimpleCLIPanel - Class in weka.gui
-
Creates a very simple command line for invoking the main method of
classes.
- SimpleCLIPanel() - Constructor for class weka.gui.SimpleCLIPanel
-
Constructor.
- SimpleCLIPanel.CommandlineCompletion - Class in weka.gui
-
A class for commandline completion of classnames.
- SimpleDateFormatEditor - Class in weka.gui
-
Class for editing SimpleDateFormat strings.
- SimpleDateFormatEditor() - Constructor for class weka.gui.SimpleDateFormatEditor
-
Constructs a new SimpleDateFormatEditor.
- SimpleEstimator - Class in weka.classifiers.bayes.net.estimate
-
SimpleEstimator is used for estimating the conditional probability tables of a Bayes network once the structure has been learned.
- SimpleEstimator() - Constructor for class weka.classifiers.bayes.net.estimate.SimpleEstimator
-
- SimpleFilter - Class in weka.filters
-
This filter contains common behavior of the SimpleBatchFilter and the
SimpleStreamFilter.
- SimpleFilter() - Constructor for class weka.filters.SimpleFilter
-
- SimpleKMeans - Class in weka.clusterers
-
Cluster data using the k means algorithm
Valid options are:
- SimpleKMeans() - Constructor for class weka.clusterers.SimpleKMeans
-
the default constructor
- SimpleLinearRegression - Class in weka.classifiers.functions
-
Learns a simple linear regression model.
- SimpleLinearRegression() - Constructor for class weka.classifiers.functions.SimpleLinearRegression
-
- SimpleLinkedList - Class in weka.associations.tertius
-
- SimpleLinkedList() - Constructor for class weka.associations.tertius.SimpleLinkedList
-
- SimpleLinkedList.LinkedListInverseIterator - Class in weka.associations.tertius
-
- SimpleLinkedList.LinkedListIterator - Class in weka.associations.tertius
-
- SimpleLog() - Constructor for class weka.core.Debug.SimpleLog
-
default constructor, uses only stdout
- SimpleLog(String) - Constructor for class weka.core.Debug.SimpleLog
-
Creates a logger that writes into the specified file.
- SimpleLog(String, boolean) - Constructor for class weka.core.Debug.SimpleLog
-
Creates a logger that writes into the specified file.
- SimpleLogger() - Constructor for class weka.gui.beans.FlowRunner.SimpleLogger
-
- SimpleLogistic - Class in weka.classifiers.functions
-
Classifier for building linear logistic regression models.
- SimpleLogistic() - Constructor for class weka.classifiers.functions.SimpleLogistic
-
Constructor for creating SimpleLogistic object with standard options.
- SimpleLogistic(int, boolean, boolean) - Constructor for class weka.classifiers.functions.SimpleLogistic
-
Constructor for creating SimpleLogistic object.
- SimpleMI - Class in weka.classifiers.mi
-
Reduces MI data into mono-instance data.
- SimpleMI() - Constructor for class weka.classifiers.mi.SimpleMI
-
- SimpleSetupPanel - Class in weka.gui.experiment
-
This panel controls the configuration of an experiment.
- SimpleSetupPanel(Experiment) - Constructor for class weka.gui.experiment.SimpleSetupPanel
-
Creates the setup panel with the supplied initial experiment.
- SimpleSetupPanel() - Constructor for class weka.gui.experiment.SimpleSetupPanel
-
Creates the setup panel with no initial experiment.
- SimpleStreamFilter - Class in weka.filters
-
This filter is a superclass for simple stream filters.
- SimpleStreamFilter() - Constructor for class weka.filters.SimpleStreamFilter
-
- SimulatedAnnealing - Class in weka.classifiers.bayes.net.search.global
-
This Bayes Network learning algorithm uses the general purpose search method of simulated annealing to find a well scoring network structure.
For more information see:
R.R.
- SimulatedAnnealing() - Constructor for class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
-
- SimulatedAnnealing - Class in weka.classifiers.bayes.net.search.local
-
This Bayes Network learning algorithm uses the general purpose search method of simulated annealing to find a well scoring network structure.
For more information see:
R.R.
- SimulatedAnnealing() - Constructor for class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
-
- SIN - Static variable in interface weka.core.mathematicalexpression.sym
-
- SIN - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
-
- SINE - Static variable in class weka.datagenerators.clusterers.BIRCHCluster
-
Constant set for choice of pattern.
- SingleAssociatorEnhancer - Class in weka.associations
-
Abstract utility class for handling settings common to meta
associators that use a single base associator.
- SingleAssociatorEnhancer() - Constructor for class weka.associations.SingleAssociatorEnhancer
-
- SingleClassifierEnhancer - Class in weka.classifiers
-
Abstract utility class for handling settings common to meta
classifiers that use a single base learner.
- SingleClassifierEnhancer() - Constructor for class weka.classifiers.SingleClassifierEnhancer
-
- SingleClustererEnhancer - Class in weka.clusterers
-
Meta-clusterer for enhancing a base clusterer.
- SingleClustererEnhancer() - Constructor for class weka.clusterers.SingleClustererEnhancer
-
- singleConsequence(Instances) - Static method in class weka.associations.CaRuleGeneration
-
generates a consequence of length 1 for a class association rule.
- singleConsequence(Instances, int, FastVector) - Static method in class weka.associations.RuleGeneration
-
generates a consequence of length 1 for an association rule.
- SingleIndex - Class in weka.core
-
Class representing a single cardinal number.
- SingleIndex() - Constructor for class weka.core.SingleIndex
-
Default constructor.
- SingleIndex(String) - Constructor for class weka.core.SingleIndex
-
Constructor to set initial index.
- singletons(Instances) - Static method in class weka.associations.AprioriItemSet
-
Converts the header info of the given set of instances into a set
of item sets (singletons).
- singletons(Instances) - Static method in class weka.associations.CaRuleGeneration
-
Converts the header info of the given set of instances into a set
of item sets (singletons).
- 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).
- singletons(Instances, Instances) - Static method in class weka.associations.LabeledItemSet
-
Converts the header info of the given set of instances into a set
of item sets (singletons).
- singleVariance(double, double, double) - Method in class weka.classifiers.trees.REPTree.Tree
-
Computes the variance for a single set
- SINGULAR_DUMMY - Static variable in interface weka.gui.graphvisualizer.GraphConstants
-
SINGULAR_DUMMY node - node with only one outgoing edge
i.e.
- SingularValueDecomposition - Class in weka.core.matrix
-
Singular Value Decomposition.
- SingularValueDecomposition(Matrix) - Constructor for class weka.core.matrix.SingularValueDecomposition
-
Construct the singular value decomposition
- size() - Method in class weka.associations.FPGrowth.FrequentItemSets
-
Get the number of item sets.
- size() - Method in class weka.associations.tertius.SimpleLinkedList
-
- size() - Method in class weka.classifiers.CostMatrix
-
The number of rows (and columns)
- size() - Method in class weka.classifiers.evaluation.ConfusionMatrix
-
Gets the number of classes.
- size() - Method in class weka.classifiers.functions.pace.DiscreteFunction
-
Returns the size of the point set.
- size() - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable
-
Returns the number of keys in this hashtable.
- size() - Method in class weka.classifiers.rules.JRip.RipperRule
-
the number of antecedents of the rule
- size() - Method in class weka.classifiers.rules.Rule
-
The size of the rule.
- size() - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database
-
Returns the size of the database (the number of dataObjects in the database)
- size() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
-
Returns the size of the database (the number of dataObjects in the database)
- size() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueue
-
Returns the queue's size
- size() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueue
-
Returns the queue's size
- size() - Method in class weka.core.FastVector
-
Returns the vector's current size.
- size() - Method in class weka.core.matrix.DoubleVector
-
Gets the size of the vector.
- size() - Method in class weka.core.matrix.IntVector
-
Gets the size of the vector.
- size() - Method in class weka.core.neighboursearch.CoverTree.MyHeap
-
returns the size of the heap.
- size() - Method in class weka.core.neighboursearch.NearestNeighbourSearch.MyHeap
-
returns the size of the heap.
- size() - Method in class weka.core.PropertyPath.Path
-
returns the number of path elements of this structure
- size() - Method in class weka.core.Queue
-
Gets queue's size.
- size() - Method in class weka.core.Tee
-
returns the number of streams currently in the list.
- size() - Method in class weka.core.Trie
-
Returns the number of elements in this collection.
- size() - Method in class weka.core.Trie.TrieNode
-
returns the number of stored strings, i.e., leaves
- size() - Method in class weka.core.xml.MethodHandler
-
returns the number of currently stored Methods
- sizePerTipText() - Method in class weka.classifiers.trees.BFTree
-
Returns the tip text for this property
- sizePerTipText() - Method in class weka.classifiers.trees.SimpleCart
-
Returns the tip text for this property
- skipIdenticalTipText() - Method in class weka.core.neighboursearch.LinearNNSearch
-
Returns the tip text for this property.
- SlidingMidPointOfWidestSide - Class in weka.core.neighboursearch.kdtrees
-
The class that splits a node into two based on the midpoint value of the dimension in which the node's rectangle is widest.
- SlidingMidPointOfWidestSide() - Constructor for class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide
-
- 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 in weka.classifiers.functions
-
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.
This implementation globally replaces all missing values and transforms nominal attributes into binary ones.
- SMO() - Constructor for class weka.classifiers.functions.SMO
-
- SMO.BinarySMO - Class in weka.classifiers.functions
-
Class for building a binary support vector machine.
- smoothingOriginal(double, double, double) - Static method in class weka.classifiers.trees.m5.RuleNode
-
Applies the m5 smoothing procedure to a prediction
- smoothingParameterTipText() - Method in class weka.classifiers.bayes.ComplementNaiveBayes
-
Returns the tip text for this property
- SMOreg - Class in weka.classifiers.functions
-
SMOreg implements the support vector machine for regression.
- SMOreg() - Constructor for class weka.classifiers.functions.SMOreg
-
- smOrEq(double, double) - Static method in class weka.core.Utils
-
Tests if a is smaller or equal to b.
- SMOset - Class in weka.classifiers.functions.supportVector
-
Stores a set of integer of a given size.
- SMOset(int) - Constructor for class weka.classifiers.functions.supportVector.SMOset
-
Creates a new set of the given size.
- SMOTE - Class in weka.filters.supervised.instance
-
Resamples a dataset by applying the Synthetic Minority Oversampling TEchnique (SMOTE).
- SMOTE() - Constructor for class weka.filters.supervised.instance.SMOTE
-
- SNOWBALL_PROGRAM - Static variable in class weka.core.stemmers.SnowballStemmer
-
the snowball program, all stemmers are derived from.
- SnowballStemmer - Class in weka.core.stemmers
-
A wrapper class for the Snowball stemmers.
- SnowballStemmer() - Constructor for class weka.core.stemmers.SnowballStemmer
-
initializes the stemmer ("porter").
- SnowballStemmer(String) - Constructor for class weka.core.stemmers.SnowballStemmer
-
initializes the stemmer with the given stemmer.
- solve(Matrix) - Method in class weka.core.matrix.CholeskyDecomposition
-
Solve A*X = B
- solve(Matrix) - Method in class weka.core.matrix.LUDecomposition
-
Solve A*X = B
- solve(Matrix) - Method in class weka.core.matrix.Matrix
-
Solve A*X = B
- solve(Matrix) - Method in class weka.core.matrix.QRDecomposition
-
Least squares solution of A*X = B
- solve(double[]) - Method in class weka.core.Matrix
-
Deprecated.
Solve A*X = B using backward substitution.
- solveTranspose(Matrix) - Method in class weka.core.matrix.Matrix
-
Solve X*A = B, which is also A'*X' = B'
- solveTriangle(Matrix, double[], boolean, boolean[]) - Static method in class weka.core.Optimization
-
Solve the linear equation of TX=B where T is a triangle matrix
It can be solved using back/forward substitution, with O(N^2)
complexity
- SOME_OTHER_FAILURE - Static variable in class weka.experiment.RemoteExperiment
-
status of the remote host: some other failure
- SOME_OTHER_FAILURE - Static variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
-
- son(int) - Method in class weka.classifiers.rules.part.ClassifierDecList
-
Method just exists to make program easier to read.
- sort(Comparator<FPGrowth.FrequentBinaryItemSet>) - Method in class weka.associations.FPGrowth.FrequentItemSets
-
Sort the item sets according to the supplied comparator.
- sort() - Method in class weka.associations.FPGrowth.FrequentItemSets
-
Sort the item sets.
- sort(Comparator) - Method in class weka.associations.tertius.SimpleLinkedList
-
- sort() - Method in class weka.classifiers.functions.pace.DiscreteFunction
-
Sorts the point values of the discrete function.
- sort(int) - Method in class weka.core.Instances
-
Sorts the instances based on an attribute.
- sort(Attribute) - Method in class weka.core.Instances
-
Sorts the instances based on an attribute.
- sort() - Method in class weka.core.matrix.DoubleVector
-
Sorts the array in place
- sort() - Method in class weka.core.matrix.IntVector
-
Sorts the elements in place
- sort(int[]) - Static method in class weka.core.Utils
-
Sorts a given array of integers in ascending order and returns an
array of integers with the positions of the elements of the original
array in the sorted array.
- sort(double[]) - Static method in class weka.core.Utils
-
Sorts a given array of doubles in ascending order and returns an
array of integers with the positions of the elements of the
original array in the sorted array.
- sort(int) - Method in class weka.experiment.PairedTTester.Dataset
-
Sorts the instances in the dataset by the run number.
- sort(int) - Method in class weka.experiment.PairedTTester.Resultset
-
Sorts the instances in each dataset by the run number.
- sort(int) - Method in class weka.gui.SortedTableModel
-
sorts the table over the given column (ascending)
- sort(int, boolean) - Method in class weka.gui.SortedTableModel
-
sorts the table over the given column, either ascending or descending
- sortArray(double[]) - Method in class weka.classifiers.mi.MIOptimalBall
-
Sort the array.
- sortClassesByRoot(String) - Static method in class weka.gui.GenericObjectEditor
-
parses the given string of classes separated by ", " and returns the
a hashtable with as many entries as there are different root elements in
the class names (the key is the root element).
- SortContainer(Comparable, int) - Constructor for class weka.gui.SortedTableModel.SortContainer
-
Initializes the container.
- SortedTableModel - Class in weka.gui
-
Represents a TableModel with sorting functionality.
- SortedTableModel() - Constructor for class weka.gui.SortedTableModel
-
initializes with no model
- SortedTableModel(TableModel) - Constructor for class weka.gui.SortedTableModel
-
initializes with the given model
- SortedTableModel.SortContainer - Class in weka.gui
-
Helper class for sorting the columns.
- sortInstances() - Method in class weka.gui.arffviewer.ArffPanel
-
sorts the instances via the currently selected column
- sortInstances(int) - Method in class weka.gui.arffviewer.ArffSortedTableModel
-
sorts the instances via the given attribute
- sortInstances(int) - Method in class weka.gui.arffviewer.ArffTableModel
-
sorts the instances via the given attribute
- sortInstances() - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
sorts the current selected attribute
- sortTipText() - Method in class weka.filters.unsupervised.attribute.AddValues
-
Returns the tip text for this property
- sortWithIndex() - Method in class weka.core.matrix.DoubleVector
-
Sorts the array in place with index returned
- sortWithIndex(int, int, IntVector) - Method in class weka.core.matrix.DoubleVector
-
Sorts the array in place with index changed
- Sourcable - Interface in weka.classifiers
-
Interface for classifiers that can be converted to Java source.
- Sourcable - Interface in weka.filters
-
Interface for filters that can be converted to Java source.
- sourceClass(int, Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
-
- sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.BinC45Split
-
Returns a string containing java source code equivalent to the test
made at this node.
- sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.C45Split
-
Returns a string containing java source code equivalent to the test
made at this node.
- sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
-
- sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.GraftSplit
-
Returns a string containing java source code equivalent to the test
made at this node.
- sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.NBTreeNoSplit
-
Returns a string containing java source code equivalent to the test
made at this node.
- sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.NBTreeSplit
-
Returns a string containing java source code equivalent to the test
made at this node.
- sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.NoSplit
-
Returns a string containing java source code equivalent to the test
made at this node.
- sourceExpression(int, Instances) - Method in class weka.classifiers.trees.lmt.ResidualSplit
-
Method not in use
- sourceExpression(int) - Method in class weka.classifiers.trees.REPTree.Tree
-
Returns a string containing java source code equivalent to the test
made at this node.
- SOUTH_CONNECTOR - Static variable in class weka.gui.beans.BeanVisual
-
- spaceHorizontal(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
space out set of nodes evenly between left and right most node in the list
- spaceVertical(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet
-
space out set of nodes evenly between top and bottom most node in the list
- SPARSE1 - Static variable in class weka.filters.unsupervised.attribute.RandomProjection
-
distribution type: sparse 1
- SPARSE2 - Static variable in class weka.filters.unsupervised.attribute.RandomProjection
-
distribution type: sparse 2
- sparseDataTipText() - Method in class weka.experiment.InstanceQuery
-
Returns the tip text for this property
- sparseIndices() - Method in class weka.classifiers.functions.SMO
-
Returns the indices in sparse format.
- sparseIndices() - Method in class weka.classifiers.mi.MISMO
-
Returns the indices in sparse format.
- SparseInstance - Class in weka.core
-
Class for storing an instance as a sparse vector.
- SparseInstance() - Constructor for class weka.core.SparseInstance
-
- SparseInstance(Instance) - Constructor for class weka.core.SparseInstance
-
Constructor that generates a sparse instance from the given
instance.
- SparseInstance(SparseInstance) - Constructor for class weka.core.SparseInstance
-
Constructor that copies the info from the given instance.
- SparseInstance(double, double[]) - Constructor for class weka.core.SparseInstance
-
Constructor that generates a sparse instance from the given
parameters.
- SparseInstance(double, double[], int[], int) - Constructor for class weka.core.SparseInstance
-
Constructor that inititalizes instance variable with given
values.
- SparseInstance(int) - Constructor for class weka.core.SparseInstance
-
Constructor of an instance that sets weight to one, all values to
be missing, and the reference to the dataset to null.
- SparseToNonSparse - Class in weka.filters.unsupervised.instance
-
An instance filter that converts all incoming sparse instances into non-sparse format.
- SparseToNonSparse() - Constructor for class weka.filters.unsupervised.instance.SparseToNonSparse
-
- sparseWeights() - Method in class weka.classifiers.functions.SMO
-
Returns the weights in sparse format.
- sparseWeights() - Method in class weka.classifiers.mi.MISMO
-
Returns the weights in sparse format.
- SpecialFunctions - Class in weka.core
-
Class implementing some mathematical functions.
- SpecialFunctions() - Constructor for class weka.core.SpecialFunctions
-
- SPECIFIC_VALUE - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
-
- specifier(int) - Method in class weka.experiment.PairedTTester.DatasetSpecifiers
-
Get the template at the given position.
- SPegasos - Class in weka.classifiers.functions
-
Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al.
- SPegasos() - Constructor for class weka.classifiers.functions.SPegasos
-
- sphere - Variable in class weka.classifiers.lazy.kstar.KStarWrapper
-
used/reused to hold the sphere size
- splash(Image) - Static method in class weka.gui.SplashWindow
-
Open's a splash window using the specified image.
- splash(URL) - Static method in class weka.gui.SplashWindow
-
Open's a splash window using the specified image.
- SplashWindow - Class in weka.gui
-
A Splash window.
- split(Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
-
Splits the given set of instances into subsets.
- split() - Method in class weka.classifiers.trees.m5.RuleNode
-
Finds an attribute and split point for this node
- split(Stack<CoverTree.DistanceNode>, Stack<CoverTree.DistanceNode>, int) - Method in class weka.core.neighboursearch.CoverTree
-
Splits a given point_set into near and far based on the given
scale/level.
- splitAtt() - Method in class weka.classifiers.trees.m5.RuleNode
-
Get the index of the splitting attribute for this node
- splitAttr() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo
-
Returns the attribute used in this split
- splitAttr() - Method in interface weka.classifiers.trees.m5.SplitEvaluate
-
Returns the attribute used in this split
- splitAttr() - Method in class weka.classifiers.trees.m5.YongSplitInfo
-
Returns the attribute used in this split
- splitCenter(Random, Instance, double, Instances) - Method in class weka.clusterers.XMeans
-
Split centers in their region.
- splitCenters(Random, Instances, Instances) - Method in class weka.clusterers.XMeans
-
Split centers in their region.
- SplitCriterion - Class in weka.classifiers.trees.j48
-
Abstract class for computing splitting criteria
with respect to distributions of class values.
- SplitCriterion() - Constructor for class weka.classifiers.trees.j48.SplitCriterion
-
- splitCritValue(Distribution) - Method in class weka.classifiers.trees.j48.EntropySplitCrit
-
Computes entropy for given distribution.
- splitCritValue(Distribution, Distribution) - Method in class weka.classifiers.trees.j48.EntropySplitCrit
-
Computes entropy of test distribution with respect to training distribution.
- splitCritValue(Distribution) - Method in class weka.classifiers.trees.j48.GainRatioSplitCrit
-
This method is a straightforward implementation of the gain
ratio criterion for the given distribution.
- splitCritValue(Distribution, double, double) - Method in class weka.classifiers.trees.j48.GainRatioSplitCrit
-
This method computes the gain ratio in the same way C4.5 does.
- splitCritValue(Distribution) - Method in class weka.classifiers.trees.j48.InfoGainSplitCrit
-
This method is a straightforward implementation of the information
gain criterion for the given distribution.
- splitCritValue(Distribution, double) - Method in class weka.classifiers.trees.j48.InfoGainSplitCrit
-
This method computes the information gain in the same way
C4.5 does.
- splitCritValue(Distribution, double, double) - Method in class weka.classifiers.trees.j48.InfoGainSplitCrit
-
This method computes the information gain in the same way
C4.5 does.
- splitCritValue(Distribution) - Method in class weka.classifiers.trees.j48.SplitCriterion
-
Computes result of splitting criterion for given distribution.
- splitCritValue(Distribution, Distribution) - Method in class weka.classifiers.trees.j48.SplitCriterion
-
Computes result of splitting criterion for given training and
test distributions.
- splitCritValue(Distribution, Distribution, int) - Method in class weka.classifiers.trees.j48.SplitCriterion
-
Computes result of splitting criterion for given training and
test distributions and given number of classes.
- splitCritValue(Distribution, Distribution, Distribution) - Method in class weka.classifiers.trees.j48.SplitCriterion
-
Computes result of splitting criterion for given training and
test distributions and given default distribution.
- splitData(Instances, double, double) - Method in class weka.classifiers.rules.JRip.Antd
-
- splitData(Instances, double, double) - Method in class weka.classifiers.rules.JRip.NominalAntd
-
Implements the splitData function.
- splitData(Instances, double, double) - Method in class weka.classifiers.rules.JRip.NumericAntd
-
Implements the splitData function.
- splitData(int[][][], double[][][], Attribute, double, String, int[][], double[][], Instances) - Method in class weka.classifiers.trees.BFTree
-
Split data into two subsets and store sorted indices and weights for two
successor nodes.
- splitData(Instances) - Method in class weka.classifiers.trees.RandomTree
-
Splits instances into subsets based on the given split.
- splitData(int[][][][], double[][][][], int, double, int[][], double[][], Instances) - Method in class weka.classifiers.trees.REPTree.Tree
-
Splits instances into subsets.
- splitData(int[][][], double[][][], Attribute, double, String, int[][], double[][], Instances) - Method in class weka.classifiers.trees.SimpleCart
-
Split data into two subsets and store sorted indices and weights for two
successor nodes.
- splitEnt(Distribution) - Method in class weka.classifiers.trees.j48.EntropyBasedSplitCrit
-
Computes entropy after splitting without considering the
class values.
- SplitEvaluate - Interface in weka.classifiers.trees.m5
-
Interface for objects that determine a split point on an attribute
- SplitEvaluator - Interface in weka.experiment
-
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
- splitItemSet(int, int[]) - Method in class weka.associations.PriorEstimation
-
splits an item set into premise and consequence and constructs therefore
an association rule.
- splitNode(BallNode, int) - Method in class weka.core.neighboursearch.balltrees.BallSplitter
-
Splits a node into two.
- splitNode(BallNode, int) - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint
-
Splits a ball into two.
- splitNode(BallNode, int) - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
-
Splits a ball into two.
- splitNode(BallNode, int) - Method in class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren
-
Splits a ball into two.
- splitNode(KDTreeNode, int, double[][], double[][]) - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
-
Splits a node into two.
- splitNode(KDTreeNode, int, double[][], double[][]) - Method in class weka.core.neighboursearch.kdtrees.KMeansInpiredMethod
-
Splits a node into two such that the overall sum of squared distances
of points to their centres on both sides of the (axis-parallel)
splitting plane is minimum.
- splitNode(KDTreeNode, int, double[][], double[][]) - Method in class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension
-
Splits a node into two based on the median value of the dimension
in which the points have the widest spread.
- splitNode(KDTreeNode, int, double[][], double[][]) - Method in class weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension
-
Splits a node into two based on the midpoint value of the dimension
in which the points have the widest spread.
- splitNode(KDTreeNode, int, double[][], double[][]) - Method in class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide
-
Splits a node into two based on the midpoint value of the dimension
in which the node's rectangle is widest.
- splitNodes(BallNode, int, double) - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor
-
Recursively splits nodes of a ball tree until
<=m_MaxInstancesInLeaf instances remain in a node.
- splitNodes(KDTreeNode, double[][], int) - Method in class weka.core.neighboursearch.KDTree
-
Recursively splits nodes of a tree starting from the supplied node.
- splitOnResidualsTipText() - Method in class weka.classifiers.trees.LMT
-
Returns the tip text for this property
- splitOptions(String) - Static method in class weka.core.Utils
-
Split up a string containing options into an array of strings,
one for each option.
- splitPoint() - Method in class weka.classifiers.trees.j48.GraftSplit
-
- splitPointTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
-
Returns the tip text for this property
- Splitter - Class in weka.classifiers.trees.adtree
-
Abstract class representing a splitter node in an alternating tree.
- Splitter() - Constructor for class weka.classifiers.trees.adtree.Splitter
-
- Splitter() - Constructor for class weka.classifiers.trees.LADTree.Splitter
-
- splitVal() - Method in class weka.classifiers.trees.m5.RuleNode
-
Get the split point for this node
- splitValue() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo
-
Returns the split value
- splitValue() - Method in interface weka.classifiers.trees.m5.SplitEvaluate
-
Returns the split value
- splitValue() - Method in class weka.classifiers.trees.m5.YongSplitInfo
-
Returns the split value
- SpreadSubsample - Class in weka.filters.supervised.instance
-
Produces a random subsample of a dataset.
- SpreadSubsample() - Constructor for class weka.filters.supervised.instance.SpreadSubsample
-
- sqDifference(int, double, double) - Method in class weka.core.EuclideanDistance
-
Returns the squared difference of two values of an attribute.
- SqlViewer - Class in weka.gui.sql
-
Represents a little tool for querying SQL databases.
- SqlViewer(JFrame) - Constructor for class weka.gui.sql.SqlViewer
-
initializes the SqlViewer.
- SqlViewerDialog - Class in weka.gui.sql
-
A little dialog containing the SqlViewer.
- SqlViewerDialog(JFrame) - Constructor for class weka.gui.sql.SqlViewerDialog
-
initializes the dialog
- SQRT - Static variable in interface weka.core.mathematicalexpression.sym
-
- sqrt() - Method in class weka.core.matrix.DoubleVector
-
Returns the square-root of all the elements in the vector
- sqrt() - Method in class weka.core.matrix.Matrix
-
returns the square root of the matrix, i.e., X from the equation
X*X = A.
Steps in the Calculation (see
sqrtm
in Matlab):
perform eigenvalue decomposition
[V,D]=eig(A)
take the square root of all elements in D (only the ones with
positive sign are considered for further computation)
S=sqrt(D)
calculate the root
X=V*S/V, which can be also written as X=(V'\(V*S)')'
Note: since this method uses other high-level methods, it generates
several instances of matrices.
- SQRT - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
-
- SQRTH - Static variable in class weka.core.Statistics
-
- SQTPI - Static variable in class weka.core.Statistics
-
- square() - Method in class weka.core.matrix.DoubleVector
-
Returns the squared vector
- square(double) - Static method in class weka.core.matrix.Maths
-
Returns the square of a value
- src - Variable in class weka.gui.graphvisualizer.GraphEdge
-
The index of source node in Nodes vector
- srcLbl - Variable in class weka.gui.graphvisualizer.GraphEdge
-
Label of source node
- 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.
- Stack<T> - Class in weka.core.neighboursearch.covertrees
-
Class implementing a stack.
- Stack() - Constructor for class weka.core.neighboursearch.covertrees.Stack
-
Constructor.
- Stack(int) - Constructor for class weka.core.neighboursearch.covertrees.Stack
-
Constructor.
- Stacking - Class in weka.classifiers.meta
-
Combines several classifiers using the stacking method.
- Stacking() - Constructor for class weka.classifiers.meta.Stacking
-
- StackingC - Class in weka.classifiers.meta
-
Implements StackingC (more efficient version of stacking).
For more information, see
A.K.
- StackingC() - Constructor for class weka.classifiers.meta.StackingC
-
The constructor.
- Standardize - Class in weka.filters.unsupervised.attribute
-
Standardizes all numeric attributes in the given dataset to have zero mean and unit variance (apart from the class attribute, if set).
- Standardize() - Constructor for class weka.filters.unsupervised.attribute.Standardize
-
- start() - Method in class weka.core.Debug.Clock
-
saves the current system time (or CPU time) in msec as start time
- start() - Method in class weka.gui.beans.Loader
-
Start loading
- start() - Method in interface weka.gui.beans.Startable
-
Start the flow running
- start() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Start the plotting thread
- start() - Method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
-
Start processing
- start_production() - Method in class weka.core.mathematicalexpression.Parser
-
Indicates start production.
- start_production() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
-
Indicates start production.
- start_state() - Method in class weka.core.mathematicalexpression.Parser
-
Indicates start state.
- start_state() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
-
Indicates start state.
- Startable - Interface in weka.gui.beans
-
Interface to something that is a start point for a flow and
can be launched programatically.
- startApp() - Static method in class weka.gui.beans.KnowledgeFlow
-
Static method that can be called from a running program
to launch the KnowledgeFlow
- startAssociator() - Method in class weka.gui.explorer.AssociationsPanel
-
Starts running the currently configured associator with the current
settings.
- startAttributeSelection() - Method in class weka.gui.explorer.AttributeSelectionPanel
-
Starts running the currently configured attribute evaluator and
search method.
- startClassifier() - Method in class weka.gui.explorer.ClassifierPanel
-
Starts running the currently configured classifier with the current
settings.
- startClock() - Method in class weka.core.Debug
-
starts the clock
- startClusterer() - Method in class weka.gui.explorer.ClustererPanel
-
Starts running the currently configured clusterer with the current
settings.
- startLoading() - Method in class weka.gui.beans.Loader
-
Start loading data
- startPlotThread() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Starts the plotting thread.
- startPointTipText() - Method in class weka.attributeSelection.RankSearch
-
Returns the tip text for this property
- StartSetHandler - Interface in weka.attributeSelection
-
Interface for search methods capable of doing something sensible
given a starting set of attributes.
- startSetTipText() - Method in class weka.attributeSelection.BestFirst
-
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.GreedyStepwise
-
Returns the tip text for this property
- startSetTipText() - Method in class weka.attributeSelection.LinearForwardSelection
-
Returns the tip text for this property
- startSetTipText() - Method in class weka.attributeSelection.RandomSearch
-
Returns the tip text for this property
- startSetTipText() - Method in class weka.attributeSelection.Ranker
-
Returns the tip text for this property
- startSetToString() - Method in class weka.attributeSelection.GreedyStepwise
-
converts the array of starting attributes to a string.
- startUpComplete() - Method in interface weka.gui.beans.StartUpListener
-
- StartUpListener - Interface in weka.gui.beans
-
Interface to something that can be notified of a successful startup
- stateChanged(ChangeEvent) - Method in class weka.gui.arffviewer.ArffPanel
-
Invoked when the target of the listener has changed its state.
- stateChanged(ChangeEvent) - Method in class weka.gui.arffviewer.ArffViewerMainPanel
-
Invoked when the target of the listener has changed its state.
- stateChanged(ChangeEvent) - Method in class weka.gui.LogWindow
-
Invoked when the target of the listener has changed its state.
- stateChanged(ChangeEvent) - Method in class weka.gui.sql.ResultPanel
-
Invoked when the target of the listener has changed its state.
- stateChanged(ChangeEvent) - Method in class weka.gui.ViewerDialog
-
Invoked when the target of the listener has changed its state.
- Statistics - Class in weka.core
-
Class implementing some distributions, tests, etc.
- Statistics() - Constructor for class weka.core.Statistics
-
- Stats - Class in weka.classifiers.trees.j48
-
Class implementing a statistical routine needed by J48 to
compute its error estimate.
- Stats() - Constructor for class weka.classifiers.trees.j48.Stats
-
- Stats - Class in weka.experiment
-
A class to store simple statistics
- Stats() - Constructor for class weka.experiment.Stats
-
- statusFrequencyTipText() - Method in class weka.gui.beans.IncrementalClassifierEvaluator
-
Return a tip text string for this property
- statusMessage(String) - Method in class weka.gui.beans.FlowRunner.SimpleLogger
-
- statusMessage(String) - Method in class weka.gui.beans.LogPanel
-
Sends the supplied message to the status area.
- statusMessage(String) - Method in class weka.gui.experiment.RunPanel
-
Sends the supplied message to the log panel status line.
- statusMessage(String) - Method in interface weka.gui.Logger
-
Sends the supplied message to the status line.
- statusMessage(String) - Method in class weka.gui.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(int, Instances) - Static method in class weka.classifiers.trees.m5.Rule
-
Returns the standard deviation value of the supplied attribute index.
- stdDev - Variable in class weka.experiment.Stats
-
The std deviation of values at the last calculateDerived() call
- stealPoints(MiddleOutConstructor.TempNode, Vector, Vector) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
-
Removes points from old anchors that
are nearer to the given new anchor and
adds them to the list of points of the
new anchor.
- stem(String) - Method in class weka.core.stemmers.IteratedLovinsStemmer
-
Iterated stemming of the given word.
- stem(String) - Method in class weka.core.stemmers.LovinsStemmer
-
Returns the stemmed version of the given word.
- stem(String) - Method in class weka.core.stemmers.NullStemmer
-
Returns the word as it is.
- stem(String) - Method in class weka.core.stemmers.SnowballStemmer
-
Returns the word in its stemmed form.
- stem(String) - Method in interface weka.core.stemmers.Stemmer
-
Stems the given word and returns the stemmed version
- Stemmer - Interface in weka.core.stemmers
-
Interface for all stemming algorithms.
- stemmerTipText() - Method in class weka.core.stemmers.SnowballStemmer
-
Returns the tip text for this property.
- stemmerTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Returns the tip text for this property.
- Stemming - Class in weka.core.stemmers
-
A helper class for using the stemmers.
- Stemming() - Constructor for class weka.core.stemmers.Stemming
-
- stemString(String) - Method in class weka.core.stemmers.LovinsStemmer
-
Stems everything in the given string.
- STEP_FIELD_NAME - Static variable in class weka.experiment.LearningRateResultProducer
-
The name of the key field containing the learning rate step number
- steplsqr(PaceMatrix, IntVector, int, int, boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix
-
Stepwise least squares QR-decomposition of the problem
A x = b
- stepSizeTipText() - Method in class weka.attributeSelection.RankSearch
-
Returns the tip text for this property
- stepSizeTipText() - Method in class weka.experiment.LearningRateResultProducer
-
Returns the tip text for this property
- stmt(StreamTokenizer) - Method in class weka.gui.graphvisualizer.DotParser
-
- stmtList(StreamTokenizer) - Method in class weka.gui.graphvisualizer.DotParser
-
- stop() - Method in class weka.core.Debug.Clock
-
saves the current system (or CPU time) in msec as stop time
- STOP - Static variable in class weka.core.Trie.TrieNode
-
the stop character
- stop() - Method in class weka.gui.beans.AbstractDataSink
-
Stop any processing that the bean might be doing.
- stop() - Method in class weka.gui.beans.AbstractEvaluator
-
Stop any processing that the bean might be doing.
- stop() - Method in class weka.gui.beans.AbstractTestSetProducer
-
Stop any processing that the bean might be doing.
- stop() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
-
Stop any processing that the bean might be doing.
- stop() - Method in class weka.gui.beans.AbstractTrainingSetProducer
-
Stop any processing that the bean might be doing.
- stop() - Method in class weka.gui.beans.Associator
-
Stop any associator action
- stop() - Method in interface weka.gui.beans.BeanCommon
-
Stop any processing that the bean might be doing.
- stop() - Method in class weka.gui.beans.ClassAssigner
-
- stop() - Method in class weka.gui.beans.Classifier
-
Stop any classifier action
- stop() - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
-
Try and stop any action
- stop() - Method in class weka.gui.beans.ClassValuePicker
-
- stop() - Method in class weka.gui.beans.Clusterer
-
Stop any clusterer action
- stop() - Method in class weka.gui.beans.ClustererPerformanceEvaluator
-
Try and stop any action
- stop() - Method in class weka.gui.beans.CostBenefitAnalysis
-
Stop any processing that the bean might be doing.
- stop() - Method in class weka.gui.beans.CrossValidationFoldMaker
-
Stop any action
- stop() - Method in class weka.gui.beans.Filter
-
Stop all action if possible
- stop() - Method in class weka.gui.beans.IncrementalClassifierEvaluator
-
Stop all action
- stop() - Method in class weka.gui.beans.InstanceStreamToBatchMaker
-
Stop any action (if possible).
- stop() - Method in class weka.gui.beans.Loader
-
Stop any loading action.
- stop() - Method in class weka.gui.beans.MetaBean
-
Stop all encapsulated beans
- stop() - Method in class weka.gui.beans.PredictionAppender
-
- stop() - Method in class weka.gui.beans.Saver
-
Stops the bean
- stop() - Method in class weka.gui.beans.SerializedModelSaver
-
Stop any processing that the bean might be doing.
- stop() - Method in class weka.gui.beans.StripChart
-
Stop any processing that the bean might be doing.
- stop() - Method in class weka.gui.beans.TestSetMaker
-
- stop() - Method in class weka.gui.beans.TextViewer
-
Stop any processing that the bean might be doing.
- stop() - Method in class weka.gui.beans.TrainingSetMaker
-
Stop any action
- stop() - Method in class weka.gui.beans.TrainTestSplitMaker
-
Stop processing
- stopAllFlows() - Method in class weka.gui.beans.FlowRunner
-
- stopAssociator() - Method in class weka.gui.explorer.AssociationsPanel
-
Stops the currently running Associator (if any).
- stopAttributeSelection() - Method in class weka.gui.explorer.AttributeSelectionPanel
-
Stops the currently running attribute selection (if any).
- stopClassifier() - Method in class weka.gui.explorer.ClassifierPanel
-
Stops the currently running classifier (if any).
- stopClock(String) - Method in class weka.core.Debug
-
stops the clock and prints the message associated with the time, but only
if the logging is enabled.
- stopClusterer() - Method in class weka.gui.explorer.ClustererPanel
-
Stops the currently running clusterer (if any).
- stopIteration(int, int) - Method in class weka.clusterers.XMeans
-
Checks if iterationCount has to be checked and if yes
(this means max is > 0) compares it with max.
- stopKMeansIteration(int, int) - Method in class weka.clusterers.XMeans
-
Controls that counter does not exceed max iteration value.
- stopMonitoring() - Method in class weka.gui.MemoryUsagePanel.MemoryMonitor
-
stops the monitoring thread.
- stopMonitoring() - Method in class weka.gui.MemoryUsagePanel
-
stops the monitoring thread.
- stoppingCriterion() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
-
This method implements the stopping criterion
function.
- stopPlotting() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
-
Stop the plotting thread
- stopPlotting() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
-
Stops the plotting thread.
- stopThreads() - Method in class weka.core.Memory
-
stops all the current threads, to make a restart possible
- Stopwords - Class in weka.core
-
Class that can test whether a given string is a stop word.
- Stopwords() - Constructor for class weka.core.Stopwords
-
initializes the stopwords (based on
Rainbow).
- stopwordsTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
-
Returns the tip text for this property.
- store(double, double, double) - Method in class weka.classifiers.lazy.kstar.KStarCache
-
Stores the specified values in the cahce table for easy retrieval.
- storeOutputProperties() - Method in class weka.gui.GenericPropertiesCreator
-
stores the generated output properties file
- StratifiedRemoveFolds - Class in weka.filters.supervised.instance
-
This filter takes a dataset and outputs a specified fold for cross validation.
- StratifiedRemoveFolds() - Constructor for class weka.filters.supervised.instance.StratifiedRemoveFolds
-
- stratify(Instances, int, Random) - Static method in class weka.classifiers.rules.RuleStats
-
Stratify the given data into the given number of bags based on the class
values.
- stratify(int) - Method in class weka.core.Instances
-
Stratifies a set of instances according to its class values
if the class attribute is nominal (so that afterwards a
stratified cross-validation can be performed).
- stratStep(int) - Method in class weka.core.Instances
-
Help function needed for stratification of set.
- StreamableFilter - Interface in weka.filters
-
Interface for filters can work with a stream of instances.
- STRING - Static variable in class weka.core.Attribute
-
Constant set for attributes with string values.
- STRING - Static variable in class weka.experiment.DatabaseUtils
-
Type mapping for STRING used for reading experiment results.
- STRING - Static variable in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
-
lexical states
- STRING - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
-
- stringAttributesTipText() - Method in class weka.core.converters.CSVLoader
-
Returns the tip text for this property.
- StringCompare() - Constructor for class weka.core.ClassDiscovery.StringCompare
-
- stringFreeStructure() - Method in class weka.core.Instances
-
Create a copy of the structure if the data has string or
relational attributes, "cleanses" string types (i.e.
- StringKernel - Class in weka.classifiers.functions.supportVector
-
Implementation of the subsequence kernel (SSK) as described in [1] and of the subsequence kernel with lambda pruning (SSK-LP) as described in [2].
For more information, see
Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J.
- StringKernel() - Constructor for class weka.classifiers.functions.supportVector.StringKernel
-
default constructor
- StringKernel(Instances, int, int, double, boolean) - Constructor for class weka.classifiers.functions.supportVector.StringKernel
-
creates a new StringKernel object.
- StringLocator - Class in weka.core
-
This class locates and records the indices of String attributes,
recursively in case of Relational attributes.
- StringLocator(Instances) - Constructor for class weka.core.StringLocator
-
initializes the StringLocator with the given data
- StringLocator(Instances, int, int) - Constructor for class weka.core.StringLocator
-
Initializes the StringLocator with the given data.
- StringLocator(Instances, int[]) - Constructor for class weka.core.StringLocator
-
Initializes the AttributeLocator with the given data.
- 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.
- stringToBoolean(String) - Method in class weka.core.xml.XMLSerialization
-
turns the given string into a boolean, if a positive number is given,
then zero is considered FALSE, every other number TRUE; the empty string
is also considered being FALSE
- stringToLevel(String) - Static method in class weka.core.Debug.Log
-
turns the string representing a level, e.g., "FINE" or "ALL" into
the corresponding level (case-insensitive).
- stringToLevel(String) - Static method in class weka.core.Debug
-
turns the string representing a level, e.g., "FINE" or "ALL" into
the corresponding level (case-insensitive).
- stringToModel(String) - Method in class weka.gui.sql.SqlViewer
-
transforms the given, comma-separated string into a DefaultListModel.
- StringToNominal - Class in weka.filters.unsupervised.attribute
-
Converts a string attribute (i.e.
- StringToNominal() - Constructor for class weka.filters.unsupervised.attribute.StringToNominal
-
- StringToWordVector - Class in weka.filters.unsupervised.attribute
-
Converts String attributes into a set of attributes representing word occurrence (depending on the tokenizer) information from the text contained in the strings.
- StringToWordVector() - Constructor for class weka.filters.unsupervised.attribute.StringToWordVector
-
Default constructor.
- StringToWordVector(int) - Constructor for class weka.filters.unsupervised.attribute.StringToWordVector
-
Constructor that allows specification of the target number of words
in the output.
- stringValue(int) - Method in class weka.core.Instance
-
Returns the value of a nominal, string, date, or relational attribute
for the instance as a string.
- stringValue(Attribute) - Method in class weka.core.Instance
-
Returns the value of a nominal, string, date, or relational attribute
for the instance as a string.
- stringWithoutHeader() - Method in class weka.core.Instances
-
Returns the instances in the dataset as a string in ARFF format.
- StripChart - Class in weka.gui.beans
-
Bean that can display a horizontally scrolling strip chart.
- StripChart() - Constructor for class weka.gui.beans.StripChart
-
- StripChartBeanInfo - Class in weka.gui.beans
-
Bean info class for the strip chart bean
- StripChartBeanInfo() - Constructor for class weka.gui.beans.StripChartBeanInfo
-
- StripChartCustomizer - Class in weka.gui.beans
-
GUI Customizer for the strip chart bean
- StripChartCustomizer() - Constructor for class weka.gui.beans.StripChartCustomizer
-
- STRUCTURE_READY - Static variable in class weka.core.converters.AbstractSaver
-
- StructureProducer - Interface in weka.gui.beans
-
Interface for something that can describe the structure of what
is encapsulated in a named event type as an empty set of
Instances (i.e.
- STYLE_STDERR - Static variable in class weka.gui.LogWindow
-
the name of the style for stderr
- STYLE_STDOUT - Static variable in class weka.gui.LogWindow
-
the name of the style for stdout
- sub(int, Instance) - Method in class weka.classifiers.trees.j48.Distribution
-
Subtracts given instance from given bag.
- subFlowContains(BeanInstance) - Method in class weka.gui.beans.MetaBean
-
- subgrid(int, int, int, int) - Method in class weka.classifiers.meta.GridSearch.Grid
-
returns a subgrid with the same step sizes, but different borders
- subList(int, int) - Method in class weka.core.neighboursearch.covertrees.Stack
-
Returns a sublist of the elements in the
stack.
- subpath(int) - Method in class weka.core.PropertyPath.Path
-
returns a subpath of the current structure, starting with the specified
element index up to the end
- subpath(int, int) - Method in class weka.core.PropertyPath.Path
-
returns a subpath of the current structure, starting with the specified
element index up.
- subsequenceLengthTipText() - Method in class weka.classifiers.functions.supportVector.StringKernel
-
Returns the tip text for this property
- Subset(BitSet, double) - Constructor for class weka.attributeSelection.ScatterSearchV1.Subset
-
- SubsetByExpression - Class in weka.filters.unsupervised.instance
-
Filters instances according to a user-specified expression.
Grammar:
boolexpr_list ::= boolexpr_list boolexpr_part | boolexpr_part;
boolexpr_part ::= boolexpr:e {: parser.setResult(e); :} ;
boolexpr ::= BOOLEAN
| true
| false
| expr < expr
| expr <= expr
| expr > expr
| expr >= expr
| expr = expr
| ( boolexpr )
| not boolexpr
| boolexpr and boolexpr
| boolexpr or boolexpr
| ATTRIBUTE is STRING
;
expr ::= NUMBER
| ATTRIBUTE
| ( expr )
| opexpr
| funcexpr
;
opexpr ::= expr + expr
| expr - expr
| expr * expr
| expr / expr
;
funcexpr ::= abs ( expr )
| sqrt ( expr )
| log ( expr )
| exp ( expr )
| sin ( expr )
| cos ( expr )
| tan ( expr )
| rint ( expr )
| floor ( expr )
| pow ( expr for base , expr for exponent )
| ceil ( expr )
;
Notes:
- NUMBER
any integer or floating point number
(but not in scientific notation!)
- STRING
any string surrounded by single quotes;
the string may not contain a single quote though.
- ATTRIBUTE
the following placeholders are recognized for
attribute values:
- CLASS for the class value in case a class attribute is set.
- ATTxyz with xyz a number from 1 to # of attributes in the
dataset, representing the value of indexed attribute.
Examples:
- extracting only mammals and birds from the 'zoo' UCI dataset:
(CLASS is 'mammal') or (CLASS is 'bird')
- extracting only animals with at least 2 legs from the 'zoo' UCI dataset:
(ATT14 >= 2)
- extracting only instances with non-missing 'wage-increase-second-year'
from the 'labor' UCI dataset:
not ismissing(ATT3)
Valid options are:
- SubsetByExpression() - Constructor for class weka.filters.unsupervised.instance.SubsetByExpression
-
- subsetDL(double, double, double) - Static method in class weka.classifiers.rules.RuleStats
-
Subset description length:
S(t,k,p) = -k*log2(p)-(n-k)log2(1-p)
Details see Quilan: "MDL and categorical theories (Continued)",ML95
- subsetEstimate(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture
-
Returns the estimate of optimal subset selection.
- SubsetEvaluator - Interface in weka.attributeSelection
-
Interface for attribute subset evaluators.
- subsetEvaluatorTipText() - Method in class weka.attributeSelection.FilteredSubsetEval
-
Returns the tip text for this property
- subsetOfInterest() - Method in class weka.classifiers.trees.j48.GraftSplit
-
- subsetSizeEvaluatorTipText() - Method in class weka.attributeSelection.SubsetSizeForwardSelection
-
Returns the tip text for this property
- SubsetSizeForwardSelection - Class in weka.attributeSelection
-
SubsetSizeForwardSelection:
Extension of LinearForwardSelection.
- SubsetSizeForwardSelection() - Constructor for class weka.attributeSelection.SubsetSizeForwardSelection
-
Constructor
- SubspaceCluster - Class in weka.datagenerators.clusterers
-
A data generator that produces data points in hyperrectangular subspace clusters.
- SubspaceCluster() - Constructor for class weka.datagenerators.clusterers.SubspaceCluster
-
initializes the generator, sets the number of clusters to 0, since user
has to specify them explicitly
- SubspaceClusterDefinition - Class in weka.datagenerators.clusterers
-
A single cluster for the SubspaceCluster datagenerator
Valid options are:
- SubspaceClusterDefinition() - Constructor for class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
initializes the cluster, without a parent cluster (necessary for GOE)
- SubspaceClusterDefinition(ClusterGenerator) - Constructor for class weka.datagenerators.clusterers.SubspaceClusterDefinition
-
initializes the cluster with default values
- subSpaceSizeTipText() - Method in class weka.classifiers.meta.RandomSubSpace
-
Returns the tip text for this property
- substitute(String) - Method in class weka.core.Environment
-
Substitute a variable names for their values in the given string.
- substract(AlgVector) - Method in class weka.core.AlgVector
-
Returns the difference of this vector minus another.
- subsumes(Rule) - Method in class weka.associations.tertius.Rule
-
Test if this rule subsumes another rule.
- subsumptionTipText() - Method in class weka.associations.Tertius
-
Returns the tip text for this property.
- subtract(AprioriItemSet) - Method in class weka.associations.AprioriItemSet
-
Subtracts an item set from another one.
- subtract(Distribution) - Method in class weka.classifiers.trees.j48.Distribution
-
Subtracts the given distribution from this one.
- subtract(double, double) - Method in class weka.experiment.PairedStats
-
Removes an observed pair of values.
- subtract(double[], double[]) - Method in class weka.experiment.PairedStats
-
Removes an array of observed pair of values.
- subtract(double) - Method in class weka.experiment.Stats
-
Removes a value to the observed values (no checking is done
that the value being removed was actually added).
- subtract(double, double) - Method in class weka.experiment.Stats
-
Subtracts a value that has been seen n times from the observed values
- subtreeRaisingTipText() - Method in class weka.classifiers.trees.J48
-
Returns the tip text for this property
- subtreeRaisingTipText() - Method in class weka.classifiers.trees.J48graft
-
Returns the tip text for this property
- subvector(int, int) - Method in class weka.core.matrix.DoubleVector
-
Returns a subvector.
- subvector(IntVector) - Method in class weka.core.matrix.DoubleVector
-
Returns a subvector.
- subvector(int, int) - Method in class weka.core.matrix.IntVector
-
Returns a subvector.
- subvector(IntVector) - Method in class weka.core.matrix.IntVector
-
Returns a subvector as indexed by an IntVector.
- sum() - Method in class weka.core.matrix.DoubleVector
-
Returns the sum of all elements in the vector.
- sum(double[]) - Static method in class weka.core.Utils
-
Computes the sum of the elements of an array of doubles.
- sum(int[]) - Static method in class weka.core.Utils
-
Computes the sum of the elements of an array of integers.
- sum - Variable in class weka.experiment.Stats
-
The sum of values seen
- sum2(int, int, int, boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix
-
Squared sum of a column or row in a matrix
- sum2(boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix
-
Squared sum of columns or rows of a matrix
- sum2() - Method in class weka.core.matrix.DoubleVector
-
Returns the squared sum of all elements in the vector.
- sum2(DoubleVector) - Method in class weka.core.matrix.DoubleVector
-
Returns ||u-v||^2
- Summarizable - Interface in weka.core
-
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
- supervisedEstimator() - Method in class weka.estimators.CheckEstimator
-
Checks whether the estimator is supervised.
- SupervisedFilter - Interface in weka.filters
-
Interface for filters that make use of a class attribute.
- support() - Method in class weka.associations.ItemSet
-
Outputs the support for an item set.
- support() - Method in class weka.associations.LabeledItemSet
-
Outputs the support for an item set.
- supportPoints(DoubleVector, int) - Method in class weka.classifiers.functions.pace.ChisqMixture
-
Contructs the set of support points for mixture estimation.
- supportPoints(DoubleVector, int) - Method in class weka.classifiers.functions.pace.MixtureDistribution
-
Contructs the set of support points for mixture estimation.
- supportPoints(DoubleVector, int) - Method in class weka.classifiers.functions.pace.NormalMixture
-
Contructs the set of support points for mixture estimation.
- supports(Capabilities) - Method in class weka.core.Capabilities
-
Returns true if the currently set capabilities support at least all of
the capabiliites of the given Capabilities object (checks only the enum!)
- supportsCustomEditor() - Method in class weka.gui.CostMatrixEditor
-
Indicates whether the cost matrix can be edited in a GUI, which it can.
- supportsCustomEditor() - Method in class weka.gui.FileEditor
-
Returns true because we do support a custom editor.
- supportsCustomEditor() - Method in class weka.gui.GenericArrayEditor
-
Returns true because we do support a custom editor.
- supportsCustomEditor() - Method in class weka.gui.GenericObjectEditor
-
Returns true because we do support a custom editor.
- supportsCustomEditor() - Method in class weka.gui.SimpleDateFormatEditor
-
Indicates whether the date format can be edited in a GUI, which it can.
- supportsMaybe(Capabilities) - Method in class weka.core.Capabilities
-
Returns true if the currently set capabilities support (or have a
dependency) at least all of the capabilities of the given Capabilities
object (checks only the enum!)
- supportThreshold - Variable in class weka.classifiers.functions.pace.ChisqMixture
-
- svd() - Method in class weka.core.matrix.Matrix
-
Singular Value Decomposition
- SVMAttributeEval - Class in weka.attributeSelection
-
SVMAttributeEval :
Evaluates the worth of an attribute by using an SVM classifier.
- SVMAttributeEval() - Constructor for class weka.attributeSelection.SVMAttributeEval
-
Constructor
- SVMLightLoader - Class in weka.core.converters
-
Reads a source that is in svm light format.
For more information about svm light see:
http://svmlight.joachims.org/
- SVMLightLoader() - Constructor for class weka.core.converters.SVMLightLoader
-
- SVMLightSaver - Class in weka.core.converters
-
Writes to a destination that is in svm light format.
For more information about svm light see:
http://svmlight.joachims.org/
Valid options are:
- SVMLightSaver() - Constructor for class weka.core.converters.SVMLightSaver
-
Constructor.
- svmlightToArray(String) - Method in class weka.core.converters.SVMLightLoader
-
turns a svm light row into a double array with the class as the last
entry.
- SVMOutput(int, Instance) - Method in class weka.classifiers.functions.SMO.BinarySMO
-
Computes SVM output for given instance.
- SVMOutput(int) - Method in class weka.classifiers.functions.supportVector.RegOptimizer
-
SVMOutput of an instance in the training set, m_data
This uses the cache, unlike SVMOutput(Instance)
- SVMOutput(Instance) - Method in class weka.classifiers.functions.supportVector.RegOptimizer
-
- SVMOutput(int, Instance) - Method in class weka.classifiers.mi.MISMO.BinaryMISMO
-
Computes SVM output for given instance.
- SVMTYPE_C_SVC - Static variable in class weka.classifiers.functions.LibSVM
-
SVM type C-SVC (classification)
- SVMTYPE_EPSILON_SVR - Static variable in class weka.classifiers.functions.LibSVM
-
SVM type epsilon-SVR (regression)
- SVMTYPE_L1LOSS_SVM_DUAL - Static variable in class weka.classifiers.functions.LibLINEAR
-
SVM solver type L1-loss support vector machines (dual)
- SVMTYPE_L2_LR - Static variable in class weka.classifiers.functions.LibLINEAR
-
SVM solver type L2-regularized logistic regression
- SVMTYPE_L2LOSS_SVM - Static variable in class weka.classifiers.functions.LibLINEAR
-
SVM solver type L2-loss support vector machines (primal)
- SVMTYPE_L2LOSS_SVM_DUAL - Static variable in class weka.classifiers.functions.LibLINEAR
-
SVM solver type L2-loss support vector machines (dual)
- SVMTYPE_MCSVM_CS - Static variable in class weka.classifiers.functions.LibLINEAR
-
SVM solver type multi-class support vector machines by Crammer and Singer
- SVMTYPE_NU_SVC - Static variable in class weka.classifiers.functions.LibSVM
-
SVM type nu-SVC (classification)
- SVMTYPE_NU_SVR - Static variable in class weka.classifiers.functions.LibSVM
-
SVM type nu-SVR (regression)
- SVMTYPE_ONE_CLASS_SVM - Static variable in class weka.classifiers.functions.LibSVM
-
SVM type one-class SVM (classification)
- SVMTypeTipText() - Method in class weka.classifiers.functions.LibLINEAR
-
Returns the tip text for this property
- SVMTypeTipText() - Method in class weka.classifiers.functions.LibSVM
-
Returns the tip text for this property
- swap(int, int) - Method in class weka.core.FastVector
-
Swaps two elements in the vector.
- swap(int, int) - Method in class weka.core.Instances
-
Swaps two instances in the set.
- swap(int, int) - Method in class weka.core.matrix.DoubleVector
-
Swaps the values stored at i and j
- swap(int, int) - Method in class weka.core.matrix.IntVector
-
Swaps the values stored at i and j
- SWAP(int, int, Stack<CoverTree.d_node>) - Method in class weka.core.neighboursearch.CoverTree
-
Swap two nodes in a cover set.
- SwapValues - Class in weka.filters.unsupervised.attribute
-
Swaps two values of a nominal attribute.
- SwapValues() - Constructor for class weka.filters.unsupervised.attribute.SwapValues
-
- switchToAdvanced(Experiment) - Method in class weka.gui.experiment.SetupModePanel
-
Switches to the advanced setup mode.
- switchToBars() - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
-
- switchToLegend() - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
-
Remove the attibute panel and replace it with the legend panel
- switchToSimple(Experiment) - Method in class weka.gui.experiment.SetupModePanel
-
Switches to the simple setup mode only if allowed to.
- sym - Interface in weka.core.mathematicalexpression
-
CUP generated interface containing symbol constants.
- sym - Interface in weka.filters.unsupervised.instance.subsetbyexpression
-
CUP generated interface containing symbol constants.
- symmetricalUncertainty(double[][]) - Static method in class weka.core.ContingencyTables
-
Calculates the symmetrical uncertainty for base 2.
- SymmetricalUncertAttributeEval - Class in weka.attributeSelection
-
SymmetricalUncertAttributeEval :
Evaluates the worth of an attribute by measuring the symmetrical uncertainty with respect to the class.
- SymmetricalUncertAttributeEval() - Constructor for class weka.attributeSelection.SymmetricalUncertAttributeEval
-
Constructor
- Sync(BayesNet) - Method in class weka.classifiers.bayes.net.BIFReader
-
synchronizes the node ordering of this Bayes network with
those in the other network (if possible).
- synopsis() - Method in class weka.core.Option
-
Returns the option's synopsis.
- SysErrLog - Class in weka.gui
-
This Logger just sends messages to System.err.
- SysErrLog() - Constructor for class weka.gui.SysErrLog
-
- SystemInfo - Class in weka.core
-
This class prints some information about the system setup, like Java
version, JVM settings etc.
- SystemInfo() - Constructor for class weka.core.SystemInfo
-
initializes the object and reads the system information