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AbstractDataSource
instance.
AbstractTestSetProducer
instance.
AbstractTrainAndTestSetProducer
instance.
AbstractTrainingSetProducer
instance.
acceptGraph
method here.
Associator
instance.
AttributeSummarizer
instance.
BatchClassifierEvent
instance.
BatchClassifierEvent
instance.
BatchClustererEvent
instance.
BeanConnection
instance.
BeanInstance
instance.
BeanInstance
instance given the fully
qualified name of the bean
BitMatrix
with the indicated
number of rows and columns.
BoundaryPanel
instance.
BoundaryPanelDistributed
instance.
BoundaryVisualizer
instance.
ChartEvent
instance.
ChartEvent
instance.
CheckGOE -W classname -- test options
Valid options are:
CheckOptionHandler -W optionHandlerClassName -- test options
Valid options are: weka.classifiers.Sourcable
interface.weka.filters.Sourcable
interface.Task
Classifier
instance.
Clusterer
instance.
Coordinates
for the given instance.
DiscreteEstimator.
- CumulativeDiscreteDistribution(DiscreteDistribution) -
Constructor for class weka.classifiers.misc.monotone.CumulativeDiscreteDistribution
- Create a
CumulativeDiscreteDistribution
based on a
DiscreteDistribution.
- CumulativeDiscreteDistribution(double[]) -
Constructor for class weka.classifiers.misc.monotone.CumulativeDiscreteDistribution
- Create a
CumulativeDiscreteDistribution
based on an
array of doubles.
- cumulativeDistributionForInstance(Instance) -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Calculates the cumulative class probabilities for the given test
instance.
- CustomizerCloseRequester - Interface in weka.gui.beans
- Customizers who want to be able to close the customizer window
themselves can implement this window.
- customizerClosing() -
Method in class weka.gui.beans.ClassAssignerCustomizer
-
- customizerClosing() -
Method in class weka.gui.beans.ClassifierCustomizer
-
- customizerClosing() -
Method in class weka.gui.beans.ClassValuePickerCustomizer
-
- customizerClosing() -
Method in interface weka.gui.beans.CustomizerClosingListener
- Customizer classes that want to know when they are being
disposed of can implement this method.
- CustomizerClosingListener - Interface in weka.gui.beans
-
- CustomPanelSupplier - Interface in weka.gui
- An interface for objects that are capable of supplying their own
custom GUI components.
- cutOffFactorTipText() -
Method in class weka.clusterers.XMeans
- Returns the tip text for this property.
- cutoffTipText() -
Method in class weka.clusterers.Cobweb
- Returns the tip text for this property
- cutpointsToString(double[], boolean[]) -
Static method in class weka.estimators.EstimatorUtils
- Returns a string representing the cutpoints
- CV_BASED -
Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
-
- CVBasedHyperparameter() -
Method in class weka.classifiers.bayes.BayesianLogisticRegression
- Method computes the best hyperparameter value by doing cross
-validation on the training data and compute the likelihood.
- CVParameterSelection - Class in weka.classifiers.meta
- Class for performing parameter selection by cross-validation for any classifier.
For more information, see:
R. - CVParameterSelection() -
Constructor for class weka.classifiers.meta.CVParameterSelection
-
- CVParametersTipText() -
Method in class weka.classifiers.meta.CVParameterSelection
- Returns the tip text for this property
- CVResultsString() -
Method in class weka.attributeSelection.AttributeSelection
- returns a string summarizing the results of repeated attribute
selection runs on splits of a dataset.
- CVTypeTipText() -
Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
-
start
immediately with the
FORMAT_SECONDS output format and CPU time if available
start
immediately, using
CPU time if available
DiscreteDistribution
based on a
DiscreteEstimator.
- DiscreteDistribution(CumulativeDiscreteDistribution) -
Constructor for class weka.classifiers.misc.monotone.DiscreteDistribution
- Create a
DiscreteDistribution
based on a
CumulativeDiscreteDistribution.
- DiscreteDistribution(double[]) -
Constructor for class weka.classifiers.misc.monotone.DiscreteDistribution
- Create a
DiscreteDistribution
based on an
array of doubles.
- DiscreteEstimator - Class in weka.estimators
- Simple symbolic probability estimator based on symbol counts.
- DiscreteEstimator(int, boolean) -
Constructor for class weka.estimators.DiscreteEstimator
- Constructor
- DiscreteEstimator(int, double) -
Constructor for class weka.estimators.DiscreteEstimator
- Constructor
- DiscreteEstimatorBayes - Class in weka.classifiers.bayes.net.estimate
- Symbolic probability estimator based on symbol counts and a prior.
- DiscreteEstimatorBayes(int, double) -
Constructor for class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
- Constructor
- DiscreteEstimatorFullBayes - Class in weka.classifiers.bayes.net.estimate
- Symbolic probability estimator based on symbol counts and a prior.
- DiscreteEstimatorFullBayes(int, double, double, DiscreteEstimatorBayes, DiscreteEstimatorBayes, double) -
Constructor for class weka.classifiers.bayes.net.estimate.DiscreteEstimatorFullBayes
- Constructor
- DiscreteFunction - Class in weka.classifiers.functions.pace
- Class for handling discrete functions.
- DiscreteFunction() -
Constructor for class weka.classifiers.functions.pace.DiscreteFunction
- Constructs an empty discrete function
- DiscreteFunction(DoubleVector) -
Constructor for class weka.classifiers.functions.pace.DiscreteFunction
- Constructs a discrete function with the point values provides and the
function values are all 1/n.
- DiscreteFunction(DoubleVector, DoubleVector) -
Constructor for class weka.classifiers.functions.pace.DiscreteFunction
- Constructs a discrete function with both the point values and
function values provided.
- Discretize - Class in weka.core.pmml
- Class encapsulating a Discretize Expression.
- Discretize(Element, FieldMetaInfo.Optype, ArrayList<Attribute>) -
Constructor for class weka.core.pmml.Discretize
- Constructs a Discretize Expression
- Discretize - Class in weka.filters.supervised.attribute
- An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
- Discretize() -
Constructor for class weka.filters.supervised.attribute.Discretize
- Constructor - initialises the filter
- Discretize - Class in weka.filters.unsupervised.attribute
- An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
- Discretize() -
Constructor for class weka.filters.unsupervised.attribute.Discretize
- Constructor - initialises the filter
- Discretize(String) -
Constructor for class weka.filters.unsupervised.attribute.Discretize
- Another constructor, sets the attribute indices immediately
- discretizeBinTipText() -
Method in class weka.classifiers.mi.MIBoost
- Returns the tip text for this property
- displayModelInOldFormatTipText() -
Method in class weka.classifiers.bayes.NaiveBayes
- Returns the tip text for this property
- displayModelInOldFormatTipText() -
Method in class weka.clusterers.EM
- Returns the tip text for this property
- displayResultset(int) -
Method in class weka.experiment.PairedTTester
- Checks whether the resultset with the given index shall be displayed.
- displayResultset(int) -
Method in interface weka.experiment.Tester
- Checks whether the resultset with the given index shall be displayed.
- displayRulesTipText() -
Method in class weka.classifiers.rules.DecisionTable
- Returns the tip text for this property
- displayStdDevsTipText() -
Method in class weka.clusterers.SimpleKMeans
- Returns the tip text for this property
- dispose() -
Method in class weka.gui.GUIChooser.ChildFrameSDI
- de-registers the child frame with the parent first.
- dispose() -
Method in class weka.gui.Main.ChildFrameMDI
- de-registers the child frame with the parent first.
- dispose() -
Method in class weka.gui.Main.ChildFrameSDI
- de-registers the child frame with the parent first.
- dispose() -
Method in class weka.gui.visualize.PostscriptGraphics
- Not implemented
- disposeSplash() -
Static method in class weka.gui.SplashWindow
- Closes the splash window.
- distance(Instance, Instance) -
Method in class weka.classifiers.mi.CitationKNN
- distance between two instances
- distance(DataObject) -
Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
- Calculates the distance between dataObject and this.dataObject
- distance(DataObject) -
Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
- Calculates the euclidian-distance between dataObject and this.dataObject
- distance(DataObject) -
Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
- Calculates the manhattan-distance between dataObject and this.dataObject
- distance(Instance, Instance, double, PerformanceStats) -
Method in class weka.core.AbstractStringDistanceFunction
- Calculates the distance between two instances.
- distance(Instance, Instance) -
Method in interface weka.core.DistanceFunction
- Calculates the distance between two instances.
- distance(Instance, Instance, PerformanceStats) -
Method in interface weka.core.DistanceFunction
- Calculates the distance between two instances.
- distance(Instance, Instance, double) -
Method in interface weka.core.DistanceFunction
- Calculates the distance between two instances.
- distance(Instance, Instance, double, PerformanceStats) -
Method in interface weka.core.DistanceFunction
- Calculates the distance between two instances.
- distance(Instance, Instance) -
Method in class weka.core.EuclideanDistance
- Calculates the distance between two instances.
- distance(Instance, Instance, PerformanceStats) -
Method in class weka.core.EuclideanDistance
- Calculates the distance (or similarity) between two instances.
- distance(Instance, Instance) -
Method in class weka.core.NormalizableDistance
- Calculates the distance between two instances.
- distance(Instance, Instance, PerformanceStats) -
Method in class weka.core.NormalizableDistance
- Calculates the distance between two instances.
- distance(Instance, Instance, double) -
Method in class weka.core.NormalizableDistance
- Calculates the distance between two instances.
- distance(Instance, Instance, double, PerformanceStats) -
Method in class weka.core.NormalizableDistance
- Calculates the distance between two instances.
- distanceFTipText() -
Method in class weka.clusterers.XMeans
- Returns the tip text for this property.
- DistanceFunction - Interface in weka.core
- Interface for any class that can compute and return distances between two
instances.
- distanceFunctionTipText() -
Method in class weka.clusterers.SimpleKMeans
- Returns the tip text for this property.
- distanceFunctionTipText() -
Method in class weka.core.neighboursearch.NearestNeighbourSearch
- Returns the tip text for this property.
- distanceSet(Instance, Instance) -
Method in class weka.classifiers.mi.CitationKNN
- Calculates the distance between two instances
- distanceTypeTipText() -
Method in class weka.classifiers.misc.OLM
- Returns the tip text for this property.
- distanceWeightingTipText() -
Method in class weka.classifiers.lazy.IBk
- Returns the tip text for this property.
- distinctCount -
Variable in class weka.core.AttributeStats
- The number of distinct values
- distMultTipText() -
Method in class weka.datagenerators.clusterers.BIRCHCluster
- Returns the tip text for this property
- distributedExperimentSelected() -
Method in class weka.gui.experiment.DistributeExperimentPanel
- Returns true if the distribute experiment checkbox is selected
- DistributeExperimentPanel - Class in weka.gui.experiment
- This panel enables an experiment to be distributed to multiple hosts;
it also allows remote host names to be specified.
- DistributeExperimentPanel() -
Constructor for class weka.gui.experiment.DistributeExperimentPanel
- Constructor
- DistributeExperimentPanel(Experiment) -
Constructor for class weka.gui.experiment.DistributeExperimentPanel
- Creates the panel with the supplied initial experiment.
- distribution() -
Method in class weka.classifiers.evaluation.NominalPrediction
- Gets the predicted probabilities
- distribution() -
Method in class weka.classifiers.trees.j48.ClassifierSplitModel
- Returns the distribution of class values induced by the model.
- Distribution - Class in weka.classifiers.trees.j48
- Class for handling a distribution of class values.
- Distribution(int, int) -
Constructor for class weka.classifiers.trees.j48.Distribution
- Creates and initializes a new distribution.
- Distribution(double[][]) -
Constructor for class weka.classifiers.trees.j48.Distribution
- Creates and initializes a new distribution using the given
array.
- Distribution(Instances) -
Constructor for class weka.classifiers.trees.j48.Distribution
- Creates a distribution with only one bag according
to instances in source.
- Distribution(Instances, ClassifierSplitModel) -
Constructor for class weka.classifiers.trees.j48.Distribution
- Creates a distribution according to given instances and
split model.
- Distribution(Distribution) -
Constructor for class weka.classifiers.trees.j48.Distribution
- Creates distribution with only one bag by merging all
bags of given distribution.
- Distribution(Distribution, int) -
Constructor for class weka.classifiers.trees.j48.Distribution
- Creates distribution with two bags by merging all bags apart of
the indicated one.
- distributionForInstance(Instance) -
Method in class weka.classifiers.bayes.AODE
- Calculates the class membership probabilities for the given test
instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.bayes.AODEsr
- Calculates the class membership probabilities for the given test
instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.bayes.BayesNet
- Calculates the class membership probabilities for the given test
instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.bayes.DMNBtext
- Calculates the class membership probabilities for the given test
instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.bayes.DMNBtext.DNBBinary
- Calculates the class membership probabilities for the given test
instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.bayes.HNB
- Calculates the class membership probabilities for the given test instance
- distributionForInstance(Instance) -
Method in class weka.classifiers.bayes.NaiveBayes
- Calculates the class membership probabilities for the given test
instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.bayes.NaiveBayesMultinomial
- Calculates the class membership probabilities for the given test
instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable
- Calculates the class membership probabilities for the given test
instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.bayes.NaiveBayesSimple
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(BayesNet, Instance) -
Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
- Calculates the class membership probabilities for the given test
instance.
- distributionForInstance(BayesNet, Instance) -
Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
- Calculates the class membership probabilities for the given test
instance.
- distributionForInstance(BayesNet, Instance) -
Method in class weka.classifiers.bayes.net.estimate.SimpleEstimator
- Calculates the class membership probabilities for the given test
instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.bayes.WAODE
- Calculates the class membership probabilities for the given test instance
- distributionForInstance(Instance) -
Method in class weka.classifiers.Classifier
- Predicts the class memberships for a given instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.functions.LibLINEAR
- Computes the distribution for a given instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.functions.LibSVM
- Computes the distribution for a given instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.functions.Logistic
- Computes the distribution for a given instance
- distributionForInstance(Instance) -
Method in class weka.classifiers.functions.MultilayerPerceptron
- Call this function to predict the class of an instance once a
classification model has been built with the buildClassifier call.
- distributionForInstance(Instance) -
Method in class weka.classifiers.functions.RBFNetwork
- Computes the distribution for a given instance
- distributionForInstance(Instance) -
Method in class weka.classifiers.functions.SimpleLogistic
- Returns class probabilities for an instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.functions.SMO
- Estimates class probabilities for given instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.functions.VotedPerceptron
- Outputs the distribution for the given output.
- distributionForInstance(Instance) -
Method in class weka.classifiers.JythonClassifier
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.lazy.IBk
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.lazy.KStar
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.lazy.LBR
- Calculates the class membership probabilities
for the given test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.lazy.LWL
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.AdaBoostM1
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.AttributeSelectedClassifier
- Classifies a given instance after attribute selection
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.Bagging
- Calculates the class membership probabilities for the given test
instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.ClassificationViaRegression
- Returns the distribution for an instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.CostSensitiveClassifier
- Returns class probabilities.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.CVParameterSelection
- Predicts the class distribution for the given test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.Dagging
- Calculates the class membership probabilities for the given test
instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.Decorate
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.END
- Calculates the class membership probabilities for the given test
instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.EnsembleSelection
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.FilteredClassifier
- Classifies a given instance after filtering.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.Grading
- Returns class probabilities for a given instance using the stacked classifier.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.LogitBoost
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.MetaCost
- Classifies a given instance after filtering.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.MultiClassClassifier
- Returns the distribution for an instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.MultiScheme
- Returns class probabilities.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
- Predicts the class distribution for a given instance
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
- Predicts the class distribution for a given instance
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.nestedDichotomies.ND
- Predicts the class distribution for a given instance
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.OrdinalClassClassifier
- Returns the distribution for an instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
- Computes class distribution of an instance using the best committee.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.RandomCommittee
- Calculates the class membership probabilities for the given test
instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.RandomSubSpace
- Calculates the class membership probabilities for the given test
instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.RotationForest
- Calculates the class membership probabilities for the given test
instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.Stacking
- Returns class probabilities.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.StackingC
- Classifies a given instance using the stacked classifier.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.ThresholdSelector
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.meta.Vote
- Classifies a given instance using the selected combination rule.
- distributionForInstance(Instance) -
Method in class weka.classifiers.mi.CitationKNN
- Computes the distribution for a given exemplar
- distributionForInstance(Instance) -
Method in class weka.classifiers.mi.MDD
- Computes the distribution for a given exemplar
- distributionForInstance(Instance) -
Method in class weka.classifiers.mi.MIBoost
- Computes the distribution for a given exemplar
- distributionForInstance(Instance) -
Method in class weka.classifiers.mi.MIDD
- Computes the distribution for a given exemplar
- distributionForInstance(Instance) -
Method in class weka.classifiers.mi.MIEMDD
- Computes the distribution for a given exemplar
- distributionForInstance(Instance) -
Method in class weka.classifiers.mi.MILR
- Computes the distribution for a given exemplar
- distributionForInstance(Instance) -
Method in class weka.classifiers.mi.MIOptimalBall
- Computes the distribution for a given multiple instance
- distributionForInstance(Instance) -
Method in class weka.classifiers.mi.MISMO
- Estimates class probabilities for given instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.mi.MISVM
- Computes the distribution for a given exemplar
- distributionForInstance(Instance) -
Method in class weka.classifiers.mi.MIWrapper
- Computes the distribution for a given exemplar
- distributionForInstance(Instance) -
Method in class weka.classifiers.mi.SimpleMI
- Computes the distribution for a given exemplar
- distributionForInstance(Instance) -
Method in class weka.classifiers.mi.TLDSimple
- Computes the distribution for a given exemplar
- distributionForInstance(Instance) -
Method in class weka.classifiers.misc.HyperPipes
- Classifies the given test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Calculates the class probabilities for the given test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.misc.OSDL
- Use
classifyInstance
from OSDLCore
and
assign probability one to the chosen label.
- distributionForInstance(Instance) -
Method in class weka.classifiers.misc.SerializedClassifier
- Calculates the class membership probabilities for the given test
instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.misc.VFI
- Classifies the given test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.pmml.consumer.GeneralRegression
- Classifies the given test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.pmml.consumer.NeuralNetwork
- Classifies the given test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.pmml.consumer.Regression
- Classifies the given test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.rules.ConjunctiveRule
- Computes class distribution for the given instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.rules.DecisionTable
- Calculates the class membership probabilities for the given
test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.rules.DTNB
- Calculates the class membership probabilities for the given
test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.rules.JRip
- Classify the test instance with the rule learner and provide
the class distributions
- distributionForInstance(Instance) -
Method in class weka.classifiers.rules.part.ClassifierDecList
- Returns class probabilities for a weighted instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.rules.PART
- Returns class probabilities for an instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.rules.part.MakeDecList
- Returns the class distribution for an instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.rules.ZeroR
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.ADTree
- Returns the class probability distribution for an instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.BFTree
- Computes class probabilities for instance using the decision tree.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.DecisionStump
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.FT
- Returns class probabilities for an instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.ft.FTInnerNode
- Returns the class probabilities for an instance given by the Functional tree.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.ft.FTLeavesNode
- Returns the class probabilities for an instance given by the Functional Leaves tree.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.ft.FTNode
- Returns the class probabilities for an instance given by the Functional Tree.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.ft.FTtree
- Returns the class probabilities for an instance given by the Functional tree.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.Id3
- Computes class distribution for instance using decision tree.
- distributionForInstance(Instance, boolean) -
Method in class weka.classifiers.trees.j48.ClassifierTree
- Returns class probabilities for a weighted instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.J48
- Returns class probabilities for an instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.J48graft
- Returns class probabilities for an instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.LADTree
- Returns the class probability distribution for an instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.LMT
- Returns class probabilities for an instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.lmt.LMTNode
- Returns the class probabilities for an instance given by the logistic model tree.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.lmt.LogisticBase
- Returns class probabilities for an instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.NBTree
- Returns class probabilities for an instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.RandomForest
- Returns the class probability distribution for an instance.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.RandomTree
- Computes class distribution of an instance using the decision tree.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.REPTree
- Computes class distribution of an instance using the tree.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.SimpleCart
- Computes class probabilities for instance using the decision tree.
- distributionForInstance(Instance) -
Method in class weka.classifiers.trees.UserClassifier
- Call this function to get a double array filled with the probability
of how likely each class type is the class of the instance.
- distributionForInstance(Instance) -
Method in class weka.clusterers.AbstractClusterer
- Predicts the cluster memberships for a given instance.
- distributionForInstance(Instance) -
Method in class weka.clusterers.AbstractDensityBasedClusterer
- Returns the cluster probability distribution for an instance.
- distributionForInstance(Instance) -
Method in interface weka.clusterers.Clusterer
- Predicts the cluster memberships for a given instance.
- distributionForInstance(Instance) -
Method in class weka.clusterers.FilteredClusterer
- Classifies a given instance after filtering.
- distributionsByOriginalIndex(double[]) -
Method in class weka.filters.supervised.attribute.ClassOrder
- Convert the given class distribution back to the distributions
with the original internal class index
- distributionSpreadTipText() -
Method in class weka.filters.supervised.instance.SpreadSubsample
- Returns the tip text for this property
- distributionTipText() -
Method in class weka.filters.unsupervised.attribute.RandomProjection
- Returns the tip text for this property
- DistributionUtils - Class in weka.classifiers.misc.monotone
- Class with some simple methods acting on
CumulativeDiscreteDistribution.- DistributionUtils() -
Constructor for class weka.classifiers.misc.monotone.DistributionUtils
-
- divergence(BayesNet) -
Method in class weka.classifiers.bayes.net.BIFReader
- calculates the divergence between the probability distribution
represented by this network and that of another, that is,
\sum_{x\in X} P(x)log P(x)/Q(x)
where X is the set of values the nodes in the network can take,
P(x) the probability of this network for configuration x
Q(x) the probability of the other network for configuration x
- divide(Instances, boolean) -
Static method in class weka.associations.LabeledItemSet
- Splits the class attribute away.
- dividedBy(DoubleVector) -
Method in class weka.core.matrix.DoubleVector
- Divided by another DoubleVector element by element
- dividedByEquals(DoubleVector) -
Method in class weka.core.matrix.DoubleVector
- Divided by another DoubleVector element by element in place
- DIVISION -
Static variable in interface weka.core.mathematicalexpression.sym
-
- DIVISION -
Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
-
- DKConditionalEstimator - Class in weka.estimators
- Conditional probability estimator for a discrete domain conditional upon
a numeric domain.
- DKConditionalEstimator(int, double) -
Constructor for class weka.estimators.DKConditionalEstimator
- Constructor
- dl(int) -
Method in class weka.core.Debug.DBO
- Return true if the debug level is set
same method as outpuTypeSet but better name
- DMNBtext - Class in weka.classifiers.bayes
- Class for building and using a Discriminative Multinomial Naive Bayes classifier.
- DMNBtext() -
Constructor for class weka.classifiers.bayes.DMNBtext
-
- DMNBtext.DNBBinary - Class in weka.classifiers.bayes
-
- DMNBtext.DNBBinary() -
Constructor for class weka.classifiers.bayes.DMNBtext.DNBBinary
-
- DNConditionalEstimator - Class in weka.estimators
- Conditional probability estimator for a discrete domain conditional upon
a numeric domain.
- DNConditionalEstimator(int, double) -
Constructor for class weka.estimators.DNConditionalEstimator
- Constructor
- dnorm(double) -
Static method in class weka.core.matrix.Maths
- Returns the density of the standard normal.
- dnorm(double, double, double) -
Static method in class weka.core.matrix.Maths
- Returns the density value of a standard normal.
- dnorm(double, DoubleVector, double) -
Static method in class weka.core.matrix.Maths
- Returns the density values of a set of normal distributions with
different means.
- dnormLog(double) -
Static method in class weka.core.matrix.Maths
- Returns the log-density of the standard normal.
- dnormLog(double, double, double) -
Static method in class weka.core.matrix.Maths
- Returns the log-density value of a standard normal.
- dnormLog(double, DoubleVector, double) -
Static method in class weka.core.matrix.Maths
- Returns the log-density values of a set of normal distributions with
different means.
- do_action(int, lr_parser, Stack, int) -
Method in class weka.core.mathematicalexpression.Parser
- Invoke a user supplied parse action.
- do_action(int, lr_parser, Stack, int) -
Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
- Invoke a user supplied parse action.
- doCommandlineCompletion(KeyEvent) -
Method in class weka.gui.SimpleCLIPanel
- performs commandline completion on packages and classnames.
- DOCTYPE -
Static variable in class weka.core.xml.XMLInstances
- the DTD
- DOCTYPE -
Static variable in class weka.core.xml.XMLOptions
- the DTD for the XML file.
- DOCTYPE -
Static variable in class weka.core.xml.XMLSerialization
- the DOCTYPE for the serialization
- doGrafting(Instances) -
Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG
- Initializes variables for grafting.
- doHistory(KeyEvent) -
Method in class weka.gui.SimpleCLIPanel
- Changes the currently displayed command line when certain keys
are pressed.
- doMetaConnection(BeanInstance, BeanInstance, EventSetDescriptor, JComponent) -
Static method in class weka.gui.beans.BeanConnection
-
- done() -
Method in interface weka.classifiers.IterativeClassifier
- Signal end of iterating, useful for any house-keeping/cleanup
- done() -
Method in class weka.classifiers.pmml.consumer.PMMLClassifier
- Signal that a scoring run has been completed.
- done() -
Method in class weka.classifiers.trees.ADTree
- Frees memory that is no longer needed for a final model - will no longer be able
to increment the classifier after calling this.
- done() -
Method in class weka.classifiers.trees.LADTree
-
- doNotOperateOnPerClassBasisTipText() -
Method in class weka.filters.unsupervised.attribute.StringToWordVector
- Returns the tip text for this property.
- doNotReplaceMissingValuesTipText() -
Method in class weka.classifiers.functions.LibLINEAR
- Returns the tip text for this property
- doNotReplaceMissingValuesTipText() -
Method in class weka.classifiers.functions.LibSVM
- Returns the tip text for this property
- dontNormalizeTipText() -
Method in class weka.core.NormalizableDistance
- Returns the tip text for this property.
- dontReplaceMissingValuesTipText() -
Method in class weka.clusterers.SimpleKMeans
- Returns the tip text for this property
- doRun(int) -
Method in class weka.experiment.AveragingResultProducer
- Gets the results for a specified run number.
- doRun(int) -
Method in class weka.experiment.CrossValidationResultProducer
- Gets the results for a specified run number.
- doRun(int) -
Method in class weka.experiment.DatabaseResultProducer
- Gets the results for a specified run number.
- doRun(int) -
Method in class weka.experiment.LearningRateResultProducer
- Gets the results for a specified run number.
- doRun(int) -
Method in class weka.experiment.RandomSplitResultProducer
- Gets the results for a specified run number.
- doRun(int) -
Method in interface weka.experiment.ResultProducer
- Gets the results for a specified run number.
- doRunKeys(int) -
Method in class weka.experiment.AveragingResultProducer
- Gets the keys for a specified run number.
- doRunKeys(int) -
Method in class weka.experiment.CrossValidationResultProducer
- Gets the keys for a specified run number.
- doRunKeys(int) -
Method in class weka.experiment.DatabaseResultProducer
- Gets the keys for a specified run number.
- doRunKeys(int) -
Method in class weka.experiment.LearningRateResultProducer
- Gets the keys for a specified run number.
- doRunKeys(int) -
Method in class weka.experiment.RandomSplitResultProducer
- Gets the keys for a specified run number.
- doRunKeys(int) -
Method in interface weka.experiment.ResultProducer
- Gets the keys for a specified run number.
- doTests() -
Method in class weka.associations.CheckAssociator
- Begin the tests, reporting results to System.out
- doTests() -
Method in class weka.attributeSelection.CheckAttributeSelection
- Begin the tests, reporting results to System.out
- doTests() -
Method in class weka.classifiers.CheckClassifier
- Begin the tests, reporting results to System.out
- doTests() -
Method in class weka.classifiers.functions.supportVector.CheckKernel
- Begin the tests, reporting results to System.out
- doTests() -
Method in class weka.clusterers.CheckClusterer
- Begin the tests, reporting results to System.out
- doTests() -
Method in class weka.core.Check
- Begin the tests, reporting results to System.out
- doTests() -
Method in class weka.core.CheckGOE
- Runs some diagnostic tests on the object.
- doTests() -
Method in class weka.core.CheckOptionHandler
- Runs some diagnostic tests on an optionhandler object.
- doTests() -
Method in class weka.core.CheckScheme
- Begin the tests, reporting results to System.out
- doTests() -
Method in class weka.estimators.CheckEstimator
- Begin the tests, reporting results to System.out
- dotMultiply(AlgVector) -
Method in class weka.core.AlgVector
- Returns the inner (or dot) product of two vectors
- DotParser - Class in weka.gui.graphvisualizer
- This class parses input in DOT format, and
builds the datastructures that are passed to it.
- DotParser(Reader, FastVector, FastVector) -
Constructor for class weka.gui.graphvisualizer.DotParser
- Dot parser Constructor
- DOUBLE -
Static variable in class weka.experiment.DatabaseUtils
- Type mapping for DOUBLE used for reading experiment results.
- DOUBLE -
Static variable in interface weka.gui.graphvisualizer.GraphConstants
- Types of Edges
- doubleToString(double, int) -
Static method in class weka.core.Utils
- Rounds a double and converts it into String.
- doubleToString(double, int, int) -
Static method in class weka.core.Utils
- Rounds a double and converts it into a formatted decimal-justified String.
- DoubleVector - Class in weka.core.matrix
- A vector specialized on doubles.
- DoubleVector() -
Constructor for class weka.core.matrix.DoubleVector
- Constructs a null vector.
- DoubleVector(int) -
Constructor for class weka.core.matrix.DoubleVector
- Constructs an n-vector of zeros.
- DoubleVector(int, double) -
Constructor for class weka.core.matrix.DoubleVector
- Constructs a constant n-vector.
- DoubleVector(double[]) -
Constructor for class weka.core.matrix.DoubleVector
- Constructs a vector directly from a double array
- doubt(Instance, Instance) -
Static method in class weka.classifiers.misc.monotone.InstancesUtil
- Checks it two instances give rise to doubt.
- dp(String) -
Method in class weka.core.Debug.DBO
- prints out text if verbose is on.
- dp(int, String) -
Method in class weka.core.Debug.DBO
- prints out text but only if debug level is set.
- dpln(String) -
Method in class weka.core.Debug.DBO
- prints out text + endofline if verbose is on.
- dpln(int, String) -
Method in class weka.core.Debug.DBO
- prints out text + endofline but only if parameter debug type is set.
- draw(Shape) -
Method in class weka.gui.visualize.PostscriptGraphics
-
- draw3DRect(int, int, int, int, boolean) -
Method in class weka.gui.visualize.PostscriptGraphics
- Draw an outlined rectangle with 3D effect in current pen color.
- Drawable - Interface in weka.core
- Interface to something that can be drawn as a graph.
- drawArc(int, int, int, int, int, int) -
Method in class weka.gui.visualize.PostscriptGraphics
- Not implemented
- drawBytes(byte[], int, int, int, int) -
Method in class weka.gui.visualize.PostscriptGraphics
- simply calls drawString(String,int,int)
- drawChars(char[], int, int, int, int) -
Method in class weka.gui.visualize.PostscriptGraphics
- simply calls drawString(String,int,int)
- drawGlyphVector(GlyphVector, float, float) -
Method in class weka.gui.visualize.PostscriptGraphics
-
- drawHighlight(Graphics, int, int) -
Method in class weka.classifiers.functions.neural.NeuralConnection
- Call this function to draw the node highlighted.
- drawImage(Image, int, int, Color, ImageObserver) -
Method in class weka.gui.visualize.PostscriptGraphics
- calls drawImage(Image,int,int,int,int,Color,ImageObserver)
- drawImage(Image, int, int, ImageObserver) -
Method in class weka.gui.visualize.PostscriptGraphics
- calls drawImage(Image,int,int,Color,ImageObserver) with Color.WHITE as
background color
- drawImage(Image, int, int, int, int, Color, ImageObserver) -
Method in class weka.gui.visualize.PostscriptGraphics
- PS see http://astronomy.swin.edu.au/~pbourke/geomformats/postscript/
Java http://show.docjava.com:8086/book/cgij/doc/ip/graphics/SimpleImageFrame.java.html
- drawImage(Image, int, int, int, int, ImageObserver) -
Method in class weka.gui.visualize.PostscriptGraphics
- calls drawImage(Image,int,int,int,int,Color,ImageObserver) with the color
WHITE as background
- drawImage(Image, int, int, int, int, int, int, int, int, Color, ImageObserver) -
Method in class weka.gui.visualize.PostscriptGraphics
- Not implemented
- drawImage(Image, int, int, int, int, int, int, int, int, ImageObserver) -
Method in class weka.gui.visualize.PostscriptGraphics
- calls drawImage(Image,int,int,int,int,int,int,int,int,Color,ImageObserver)
with Color.WHITE as background color
- drawImage(BufferedImage, BufferedImageOp, int, int) -
Method in class weka.gui.visualize.PostscriptGraphics
-
- drawImage(Image, AffineTransform, ImageObserver) -
Method in class weka.gui.visualize.PostscriptGraphics
-
- drawInputLines(Graphics, int, int) -
Method in class weka.classifiers.functions.neural.NeuralConnection
- Call this function to draw the nodes input connections.
- drawLine(int, int, int, int) -
Method in class weka.gui.visualize.PostscriptGraphics
- Draw a line in current pen color.
- drawNode(Graphics, int, int) -
Method in class weka.classifiers.functions.neural.NeuralConnection
- Call this function to draw the node.
- drawOutputLines(Graphics, int, int) -
Method in class weka.classifiers.functions.neural.NeuralConnection
- Call this function to draw the nodes output connections.
- drawOval(int, int, int, int) -
Method in class weka.gui.visualize.PostscriptGraphics
- Draw an Oval outline in current pen color.
- drawPolygon(int[], int[], int) -
Method in class weka.gui.visualize.PostscriptGraphics
- Not implemented
- drawPolyline(int[], int[], int) -
Method in class weka.gui.visualize.PostscriptGraphics
- Not implemented
- drawRect(int, int, int, int) -
Method in class weka.gui.visualize.PostscriptGraphics
- Draw an outlined rectangle in current pen color.
- drawRenderableImage(RenderableImage, AffineTransform) -
Method in class weka.gui.visualize.PostscriptGraphics
-
- drawRenderedImage(RenderedImage, AffineTransform) -
Method in class weka.gui.visualize.PostscriptGraphics
-
- drawRoundRect(int, int, int, int, int, int) -
Method in class weka.gui.visualize.PostscriptGraphics
- Not implemented
- drawString(AttributedCharacterIterator, int, int) -
Method in class weka.gui.visualize.PostscriptGraphics
- Not implemented
- drawString(String, int, int) -
Method in class weka.gui.visualize.PostscriptGraphics
- Draw text in current pen color.
- drawString(AttributedCharacterIterator, float, float) -
Method in class weka.gui.visualize.PostscriptGraphics
-
- drawString(String, float, float) -
Method in class weka.gui.visualize.PostscriptGraphics
-
- DT_EUCLID -
Static variable in class weka.classifiers.misc.OLM
- Use the Euclidian distance whenever a nearest neighbour
rule is fired.
- DT_HAMMING -
Static variable in class weka.classifiers.misc.OLM
- Use the Hamming distance, this is the number of
positions in which the instances differ, whenever a
nearest neighbour rule is fired
- DT_NONE -
Static variable in class weka.classifiers.misc.OLM
- No nearest neighbour rule will be fired when
classifying an instance for which there is no smaller rule
in the rule base?
- DTD_ANY -
Static variable in class weka.core.xml.XMLDocument
- the ANY placeholder.
- DTD_AT_LEAST_ONE -
Static variable in class weka.core.xml.XMLDocument
- the at least one marker.
- DTD_ATTLIST -
Static variable in class weka.core.xml.XMLDocument
- the AttList definition.
- DTD_CDATA -
Static variable in class weka.core.xml.XMLDocument
- the CDATA placeholder.
- DTD_DOCTYPE -
Static variable in class weka.core.xml.XMLDocument
- the DocType definition.
- DTD_ELEMENT -
Static variable in class weka.core.xml.XMLDocument
- the Element definition.
- DTD_IMPLIED -
Static variable in class weka.core.xml.XMLDocument
- the #IMPLIED placeholder.
- DTD_OPTIONAL -
Static variable in class weka.core.xml.XMLDocument
- the optional marker.
- DTD_PCDATA -
Static variable in class weka.core.xml.XMLDocument
- the #PCDATA placeholder.
- DTD_REQUIRED -
Static variable in class weka.core.xml.XMLDocument
- the #REQUIRED placeholder.
- DTD_SEPARATOR -
Static variable in class weka.core.xml.XMLDocument
- the option separator.
- DTD_ZERO_OR_MORE -
Static variable in class weka.core.xml.XMLDocument
- the zero or more marker.
- DTNB - Class in weka.classifiers.rules
- Class for building and using a decision table/naive bayes hybrid classifier.
- DTNB() -
Constructor for class weka.classifiers.rules.DTNB
-
- dumpDistribution() -
Method in class weka.classifiers.trees.j48.Distribution
- Prints distribution.
- dumpLabel(int, Instances) -
Method in class weka.classifiers.trees.j48.ClassifierSplitModel
- Prints label for subset index of instances (eg class).
- dumpLabelG(int, Instances) -
Method in class weka.classifiers.trees.j48.GraftSplit
- Prints label for subset index of instances (eg class).
- dumpModel(Instances) -
Method in class weka.classifiers.trees.j48.ClassifierSplitModel
- Prints the split model.
enumerateRequests
method here.
enumerateRequests
method here.
enumerateRequests
method here.
Enumeration
interface. EnumerationIterator
on basis of on
Enumeration.
- Environment - Class in weka.core
- This class encapsulates a map of all environment and java system properties.
- Environment() -
Constructor for class weka.core.Environment
-
- EOF -
Static variable in interface weka.core.mathematicalexpression.sym
-
- EOF -
Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
-
- EOF_sym() -
Method in class weka.core.mathematicalexpression.Parser
EOF
Symbol index.
- EOF_sym() -
Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
EOF
Symbol index.
- EPSILON -
Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
-
- EPSILON -
Static variable in class weka.classifiers.misc.FLR
-
- epsilon -
Static variable in class weka.gui.ensembleLibraryEditor.tree.NumberNode
- This is the maximum floating point value that we care about when testing
for equality.
- epsilonParameterTipText() -
Method in class weka.attributeSelection.SVMAttributeEval
- Returns a tip text for this property suitable for display in the
GUI
- epsilonParameterTipText() -
Method in class weka.classifiers.functions.supportVector.RegOptimizer
- Returns the tip text for this property
- EpsilonRange_ListElement - Class in weka.clusterers.forOPTICSAndDBScan.Utils
-
EpsilonRange_ListElement.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Sep 7, 2004
Time: 2:12:34 PM
$ Revision 1.4 $
- EpsilonRange_ListElement(double, DataObject) -
Constructor for class weka.clusterers.forOPTICSAndDBScan.Utils.EpsilonRange_ListElement
- Constructs a new Element that is stored in the ArrayList which is
built in the k_nextNeighbourQuery-method from a specified database.
- epsilonRangeQuery(double, DataObject) -
Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database
- Performs an epsilon range query for this dataObject
- epsilonRangeQuery(double, DataObject) -
Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase
- Performs an epsilon range query for this dataObject
- epsilonTipText() -
Method in class weka.classifiers.functions.SMO
- Returns the tip text for this property
- epsilonTipText() -
Method in class weka.classifiers.functions.SMOreg
- Returns the tip text for this property
- epsilonTipText() -
Method in class weka.classifiers.functions.supportVector.RegSMO
- Returns the tip text for this property
- epsilonTipText() -
Method in class weka.classifiers.mi.MISMO
- Returns the tip text for this property
- epsilonTipText() -
Method in class weka.clusterers.DBScan
- Returns the tip text for this property
- epsilonTipText() -
Method in class weka.clusterers.OPTICS
- Returns the tip text for this property
- epsTipText() -
Method in class weka.classifiers.functions.LibLINEAR
- Returns the tip text for this property
- epsTipText() -
Method in class weka.classifiers.functions.LibSVM
- Returns the tip text for this property
- epsTipText() -
Method in class weka.classifiers.functions.SMOreg
- Returns the tip text for this property
- EQ -
Static variable in interface weka.core.mathematicalexpression.sym
-
- eq(double, double) -
Static method in class weka.core.Utils
- Tests if a is equal to b.
- EQ -
Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
-
- equalCondset(Object) -
Method in class weka.associations.LabeledItemSet
- Compares two item sets
- equalExemplars(Instance, Instance) -
Method in class weka.classifiers.mi.CitationKNN
- Wether the instances of two exemplars are or are not equal
- equalHeaders(Instance) -
Method in class weka.core.Instance
- Tests if the headers of two instances are equivalent.
- equalHeaders(Instances) -
Method in class weka.core.Instances
- Checks if two headers are equivalent.
- equalIgnoreClass(Instance, Instance) -
Static method in class weka.classifiers.misc.monotone.InstancesUtil
- Compares two instances, ignoring the class attribute (if any)
- equals(Object) -
Method in class weka.associations.AssociatorEvaluation
- Tests whether the current evaluation object is equal to another
evaluation object
- equals(Object) -
Method in class weka.associations.gsp.Element
- Checks if two Elements are equal.
- equals(Object) -
Method in class weka.associations.gsp.Sequence
- Checks if two Sequences are equal.
- equals(Object) -
Method in class weka.associations.ItemSet
- Tests if two item sets are equal.
- equals(Object) -
Method in class weka.associations.LabeledItemSet
- Tests if two item sets are equal.
- equals(Object) -
Method in class weka.associations.RuleItem
- returns whether two RuleItems are equal
- equals(Object) -
Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey
- Tests if two instances are equal
- equals(Object) -
Method in class weka.classifiers.Evaluation
- Tests whether the current evaluation object is equal to another
evaluation object
- equals(Object) -
Method in class weka.classifiers.functions.supportVector.KernelEvaluation
- Tests whether the current evaluation object is equal to another
evaluation object
- equals(Object) -
Method in class weka.classifiers.misc.monotone.Coordinates
- Indicates if the object
o
equals this.
- equals(Object) -
Method in class weka.classifiers.misc.monotone.CumulativeDiscreteDistribution
- Indicates if the object
o
equals this.
- equals(Object) -
Method in class weka.classifiers.rules.DecisionTableHashKey
- Tests if two instances are equal
- equals(Object) -
Method in class weka.clusterers.ClusterEvaluation
- Tests whether the current evaluation object is equal to another
evaluation object
- equals(DataObject) -
Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
- Compares two DataObjects in respect to their attribute-values
- equals(DataObject) -
Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
- Compares two DataObjects in respect to their attribute-values
- equals(DataObject) -
Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
- Compares two DataObjects in respect to their attribute-values
- equals(Object) -
Method in class weka.core.Attribute
- Tests if given attribute is equal to this attribute.
- equals(Object) -
Method in class weka.core.AttributeLocator
- Indicates whether some other object is "equal to" this one.
- equals(Object) -
Method in class weka.core.ClassDiscovery.StringCompare
- Indicates whether some other object is "equal to" this Comparator.
- equals(Object) -
Method in class weka.core.SelectedTag
- Returns true if this SelectedTag equals another object
- equals(Object) -
Method in class weka.core.SerializedObject
-
- equals(Object) -
Method in class weka.core.Trie
- Compares the specified object with this collection for equality.
- equals(Object) -
Method in class weka.core.Trie.TrieNode
- Indicates whether some other object is "equal to" this one.
- equals(Object) -
Method in class weka.core.Version
- whether the given version string is equal to this version
- equals(Object) -
Method in class weka.estimators.Estimator
- Tests whether the current estimation object is equal to another
estimation object
- equals(Number, Number) -
Method in class weka.gui.ensembleLibraryEditor.tree.NumberNode
- tests for equality among two objects that are instances of one of the
child classes of java.lang.Number
- equals(Object) -
Method in class weka.gui.graphvisualizer.GraphEdge
-
- equals(Object) -
Method in class weka.gui.graphvisualizer.GraphNode
- Returns true if passed in argument is an instance
of GraphNode and is equal to this node.
- equalTo(Splitter) -
Method in class weka.classifiers.trees.adtree.Splitter
- Tests whether two splitters are equivalent.
- equalTo(Splitter) -
Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
- Tests whether two splitters are equivalent.
- equalTo(Splitter) -
Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
- Tests whether two splitters are equivalent.
- equalTo(Test) -
Method in class weka.datagenerators.Test
- Compares the test with the test that is given as parameter.
- equivalentTipText() -
Method in class weka.associations.Tertius
- Returns the tip text for this property.
- equivalentTo(Rule) -
Method in class weka.associations.tertius.Rule
- Test if this rule is equivalent to another rule.
- errms(StreamTokenizer, String) -
Static method in class weka.core.converters.ConverterUtils
- Throws error message with line number and last token read.
- error() -
Method in class weka.classifiers.evaluation.NumericPrediction
- Calculates the prediction error.
- error -
Static variable in interface weka.core.mathematicalexpression.sym
-
- error -
Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
-
- ERROR_SHAPE -
Static variable in class weka.gui.visualize.Plot2D
-
- error_sym() -
Method in class weka.core.mathematicalexpression.Parser
error
Symbol index.
- error_sym() -
Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser
error
Symbol index.
- ErrorBasedMeritEvaluator - Interface in weka.attributeSelection
- Interface for evaluators that calculate the "merit" of attributes/subsets
as the error of a learning scheme
- errorOnProbabilitiesTipText() -
Method in class weka.classifiers.functions.SimpleLogistic
- Returns the tip text for this property
- errorOnProbabilitiesTipText() -
Method in class weka.classifiers.trees.FT
- Returns the tip text for this property
- errorOnProbabilitiesTipText() -
Method in class weka.classifiers.trees.LMT
- Returns the tip text for this property
- errorRate() -
Method in class weka.classifiers.evaluation.ConfusionMatrix
- Returns the estimated error rate.
- errorRate() -
Method in class weka.classifiers.Evaluation
- Returns the estimated error rate or the root mean squared error
(if the class is numeric).
- errorValue(NeuralNode) -
Method in class weka.classifiers.functions.neural.LinearUnit
- This function calculates what the error value should be.
- errorValue(boolean) -
Method in class weka.classifiers.functions.neural.NeuralConnection
- Call this to get the error value of this unit.
- errorValue(NeuralNode) -
Method in interface weka.classifiers.functions.neural.NeuralMethod
- This function calculates what the error value should be.
- errorValue(boolean) -
Method in class weka.classifiers.functions.neural.NeuralNode
- Call this to get the error value of this unit.
- errorValue(NeuralNode) -
Method in class weka.classifiers.functions.neural.SigmoidUnit
- This function calculates what the error value should be.
- estimateCPTs() -
Method in class weka.classifiers.bayes.BayesNet
- estimateCPTs estimates the conditional probability tables for the Bayes
Net using the network structure.
- estimateCPTs(BayesNet) -
Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
- estimateCPTs estimates the conditional probability tables for the Bayes
Net using the network structure.
- estimateCPTs(BayesNet) -
Method in class weka.classifiers.bayes.net.estimate.BMAEstimator
- estimateCPTs estimates the conditional probability tables for the Bayes
Net using the network structure.
- estimateCPTs(BayesNet) -
Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
- estimateCPTs estimates the conditional probability tables for the Bayes
Net using the network structure.
- estimateCPTs(BayesNet) -
Method in class weka.classifiers.bayes.net.estimate.SimpleEstimator
- estimateCPTs estimates the conditional probability tables for the Bayes
Net using the network structure.
- estimatePrior() -
Method in class weka.associations.PriorEstimation
- Method to estimate the prior probabilities
- Estimator - Class in weka.estimators
- Abstract class for all estimators.
- Estimator() -
Constructor for class weka.estimators.Estimator
-
- estimatorTipText() -
Method in class weka.classifiers.bayes.BayesNet
- This will return a string describing the BayesNetEstimator.
- estimatorTipText() -
Method in class weka.classifiers.functions.PaceRegression
- Returns the tip text for this property
- EstimatorUtils - Class in weka.estimators
- Contains static utility functions for Estimators.
- EstimatorUtils() -
Constructor for class weka.estimators.EstimatorUtils
-
- ET_BOTH -
Static variable in class weka.classifiers.misc.OLM
- Combine both the minimal and maximal extension, and use the
midpoint of the resulting interval as prediction.
- ET_MAX -
Static variable in class weka.classifiers.misc.OLM
- Use only the maximal extension.
- ET_MIN -
Static variable in class weka.classifiers.misc.OLM
- Use only the minimal extension, as in the original algorithm
of Ben-David.
- EuclideanDistance - Class in weka.core
- Implementing Euclidean distance (or similarity) function.
One object defines not one distance but the data model in which the distances between objects of that data model can be computed.
Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low.
For more information, see:
Wikipedia. - EuclideanDistance() -
Constructor for class weka.core.EuclideanDistance
- Constructs an Euclidean Distance object, Instances must be still set.
- EuclideanDistance(Instances) -
Constructor for class weka.core.EuclideanDistance
- Constructs an Euclidean Distance object and automatically initializes the
ranges.
- EuclidianDataObject - Class in weka.clusterers.forOPTICSAndDBScan.DataObjects
-
EuclidianDataObject.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 19, 2004
Time: 5:50:22 PM
$ Revision 1.4 $
- EuclidianDataObject(Instance, String, Database) -
Constructor for class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
- Constructs a new DataObject.
- eval(int, int, Instance) -
Method in class weka.classifiers.functions.supportVector.CachedKernel
- Implements the abstract function of Kernel using the cache.
- eval(int, int, Instance) -
Method in class weka.classifiers.functions.supportVector.Kernel
- Computes the result of the kernel function for two instances.
- eval(int, int, Instance) -
Method in class weka.classifiers.functions.supportVector.NormalizedPolyKernel
- Computes the result of the kernel function for two instances.
- eval(int, int, Instance) -
Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
-
- eval(int, int, Instance) -
Method in class weka.classifiers.functions.supportVector.StringKernel
- Computes the result of the kernel function for two instances.
- EVAL_ACCURACY -
Static variable in class weka.classifiers.rules.DecisionTable
-
- EVAL_AUC -
Static variable in class weka.classifiers.rules.DecisionTable
-
- EVAL_CROSS_VALIDATION -
Static variable in class weka.classifiers.meta.ThresholdSelector
- n-fold cross-validation
- EVAL_DEFAULT -
Static variable in class weka.classifiers.rules.DecisionTable
- default is accuracy for discrete class and RMSE for numeric class
- EVAL_MAE -
Static variable in class weka.classifiers.rules.DecisionTable
-
- EVAL_RMSE -
Static variable in class weka.classifiers.rules.DecisionTable
-
- EVAL_TRAINING_SET -
Static variable in class weka.classifiers.meta.ThresholdSelector
- entire training set
- EVAL_TUNED_SPLIT -
Static variable in class weka.classifiers.meta.ThresholdSelector
- single tuned fold
- evalBoolean(String) -
Method in class weka.core.xml.XMLDocument
- Evaluates and returns the boolean result of the XPath expression.
- evalDouble(String) -
Method in class weka.core.xml.XMLDocument
- Evaluates and returns the double result of the XPath expression.
- evalString(String) -
Method in class weka.core.xml.XMLDocument
- Evaluates and returns the boolean result of the XPath expression.
- evaluate(String, String[]) -
Static method in class weka.associations.AssociatorEvaluation
- Evaluates an associator with the options given in an array of strings.
- evaluate(Associator, String[]) -
Static method in class weka.associations.AssociatorEvaluation
- Evaluates the associator with the given commandline options and returns
the evaluation string.
- evaluate(Associator, Instances) -
Method in class weka.associations.AssociatorEvaluation
- Evaluates the associator with the given commandline options and returns
the evaluation string.
- evaluate(Kernel, String[]) -
Static method in class weka.classifiers.functions.supportVector.KernelEvaluation
- Evaluates the Kernel with the given commandline options and returns
the evaluation string.
- evaluate(String, String[]) -
Static method in class weka.classifiers.functions.supportVector.KernelEvaluation
- Evaluates a kernel with the options given in an array of strings.
- evaluate(Kernel, Instances) -
Method in class weka.classifiers.functions.supportVector.KernelEvaluation
- Evaluates the Kernel with the given commandline options and returns
the evaluation string.
- evaluate(String, HashMap) -
Static method in class weka.core.MathematicalExpression
- Parses and evaluates the given expression.
- evaluateAttribute(int) -
Method in interface weka.attributeSelection.AttributeEvaluator
- evaluates an individual attribute
- evaluateAttribute(int) -
Method in class weka.attributeSelection.AttributeSetEvaluator
- evaluates an individual attribute
- evaluateAttribute(int[], int[]) -
Method in class weka.attributeSelection.AttributeSetEvaluator
- Evaluates a set of attributes
- evaluateAttribute(int) -
Method in class weka.attributeSelection.ChiSquaredAttributeEval
- evaluates an individual attribute by measuring its
chi-squared value.
- evaluateAttribute(int) -
Method in class weka.attributeSelection.CostSensitiveAttributeEval
- Evaluates an individual attribute.
- evaluateAttribute(int) -
Method in class weka.attributeSelection.FilteredAttributeEval
- Evaluates an individual attribute by delegating to the base
evaluator.
- evaluateAttribute(int) -
Method in class weka.attributeSelection.GainRatioAttributeEval
- evaluates an individual attribute by measuring the gain ratio
of the class given the attribute.
- evaluateAttribute(int) -
Method in class weka.attributeSelection.InfoGainAttributeEval
- evaluates an individual attribute by measuring the amount
of information gained about the class given the attribute.
- evaluateAttribute(int) -
Method in class weka.attributeSelection.LatentSemanticAnalysis
- Evaluates the merit of a transformed attribute.
- evaluateAttribute(int) -
Method in class weka.attributeSelection.OneRAttributeEval
- evaluates an individual attribute by measuring the amount
of information gained about the class given the attribute.
- evaluateAttribute(int) -
Method in class weka.attributeSelection.PrincipalComponents
- Evaluates the merit of a transformed attribute.
- evaluateAttribute(int) -
Method in class weka.attributeSelection.ReliefFAttributeEval
- Evaluates an individual attribute using ReliefF's instance based approach.
- evaluateAttribute(int) -
Method in class weka.attributeSelection.SVMAttributeEval
- Evaluates an attribute by returning the rank of the square of its coefficient in a
linear support vector machine.
- evaluateAttribute(int) -
Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
- evaluates an individual attribute by measuring the symmetrical
uncertainty between it and the class.
- evaluateAttribute(int) -
Method in class weka.attributeSelection.SymmetricalUncertAttributeSetEval
- evaluates an individual attribute by measuring the symmetrical
uncertainty between it and the class.
- evaluateAttribute(int[], int[]) -
Method in class weka.attributeSelection.SymmetricalUncertAttributeSetEval
- calculate symmetrical uncertainty between sets of attributes
- evaluateClusterer(Instances) -
Method in class weka.clusterers.ClusterEvaluation
- Evaluate the clusterer on a set of instances.
- evaluateClusterer(Instances, String) -
Method in class weka.clusterers.ClusterEvaluation
- Evaluate the clusterer on a set of instances.
- evaluateClusterer(Clusterer, String[]) -
Static method in class weka.clusterers.ClusterEvaluation
- Evaluates a clusterer with the options given in an array of
strings.
- evaluateExpression(Instance) -
Method in class weka.core.AttributeExpression
- Evaluate the expression using the supplied Instance.
- evaluateExpression(double[]) -
Method in class weka.core.AttributeExpression
- Evaluate the expression using the supplied array of attribute values.
- evaluateModel(String, String[]) -
Static method in class weka.classifiers.Evaluation
- Evaluates a classifier with the options given in an array of
strings.
- evaluateModel(Classifier, String[]) -
Static method in class weka.classifiers.Evaluation
- Evaluates a classifier with the options given in an array of
strings.
- evaluateModel(Classifier, Instances, Object...) -
Method in class weka.classifiers.Evaluation
- Evaluates the classifier on a given set of instances.
- evaluateModelOnce(Classifier, Instance) -
Method in class weka.classifiers.Evaluation
- Evaluates the classifier on a single instance.
- evaluateModelOnce(double[], Instance) -
Method in class weka.classifiers.Evaluation
- Evaluates the supplied distribution on a single instance.
- evaluateModelOnce(double, Instance) -
Method in class weka.classifiers.Evaluation
- Evaluates the supplied prediction on a single instance.
- evaluateModelOnceAndRecordPrediction(Classifier, Instance) -
Method in class weka.classifiers.Evaluation
- Evaluates the classifier on a single instance and records the
prediction (if the class is nominal).
- evaluateModelOnceAndRecordPrediction(double[], Instance) -
Method in class weka.classifiers.Evaluation
- Evaluates the supplied distribution on a single instance.
- evaluateSubset(BitSet) -
Method in class weka.attributeSelection.CfsSubsetEval
- evaluates a subset of attributes
- evaluateSubset(BitSet) -
Method in class weka.attributeSelection.ClassifierSubsetEval
- Evaluates a subset of attributes
- evaluateSubset(BitSet, Instances) -
Method in class weka.attributeSelection.ClassifierSubsetEval
- Evaluates a subset of attributes with respect to a set of instances.
- evaluateSubset(BitSet, Instance, boolean) -
Method in class weka.attributeSelection.ClassifierSubsetEval
- Evaluates a subset of attributes with respect to a single instance.
- evaluateSubset(BitSet) -
Method in class weka.attributeSelection.ConsistencySubsetEval
- Evaluates a subset of attributes
- evaluateSubset(BitSet) -
Method in class weka.attributeSelection.CostSensitiveSubsetEval
- Evaluates a subset of attributes.
- evaluateSubset(BitSet) -
Method in class weka.attributeSelection.FilteredSubsetEval
- evaluates a subset of attributes
- evaluateSubset(BitSet, Instances) -
Method in class weka.attributeSelection.HoldOutSubsetEvaluator
- Evaluates a subset of attributes with respect to a set of instances.
- evaluateSubset(BitSet, Instance, boolean) -
Method in class weka.attributeSelection.HoldOutSubsetEvaluator
- Evaluates a subset of attributes with respect to a single instance.
- evaluateSubset(BitSet) -
Method in interface weka.attributeSelection.SubsetEvaluator
- evaluates a subset of attributes
- evaluateSubset(BitSet) -
Method in class weka.attributeSelection.WrapperSubsetEval
- Evaluates a subset of attributes
- Evaluation - Class in weka.classifiers
- Class for evaluating machine learning models.
- Evaluation(Instances) -
Constructor for class weka.classifiers.Evaluation
- Initializes all the counters for the evaluation.
- Evaluation(Instances, CostMatrix) -
Constructor for class weka.classifiers.Evaluation
- Initializes all the counters for the evaluation and also takes a
cost matrix as parameter.
- EVALUATION_ACC -
Static variable in class weka.classifiers.meta.GridSearch
- evaluation via: Accuracy
- EVALUATION_CC -
Static variable in class weka.classifiers.meta.GridSearch
- evaluation via: Correlation coefficient
- EVALUATION_COMBINED -
Static variable in class weka.classifiers.meta.GridSearch
- evaluation via: Combined = (1-CC) + RRSE + RAE
- EVALUATION_KAPPA -
Static variable in class weka.classifiers.meta.GridSearch
- evaluation via: kappa statistic
- EVALUATION_MAE -
Static variable in class weka.classifiers.meta.GridSearch
- evaluation via: Mean absolute error
- EVALUATION_RAE -
Static variable in class weka.classifiers.meta.GridSearch
- evaluation via: Relative absolute error
- EVALUATION_RMSE -
Static variable in class weka.classifiers.meta.GridSearch
- evaluation via: Root mean squared error
- EVALUATION_RRSE -
Static variable in class weka.classifiers.meta.GridSearch
- evaluation via: Root relative squared error
- evaluationMeasureTipText() -
Method in class weka.classifiers.rules.DecisionTable
- Returns the tip text for this property
- evaluationModeTipText() -
Method in class weka.classifiers.meta.ThresholdSelector
-
- evaluationTipText() -
Method in class weka.classifiers.meta.GridSearch
- Returns the tip text for this property
- EvaluationUtils - Class in weka.classifiers.evaluation
- Contains utility functions for generating lists of predictions in
various manners.
- EvaluationUtils() -
Constructor for class weka.classifiers.evaluation.EvaluationUtils
-
- evaluatorTipText() -
Method in class weka.attributeSelection.CostSensitiveASEvaluation
- Returns the tip text for this property
- evaluatorTipText() -
Method in class weka.classifiers.meta.AttributeSelectedClassifier
- Returns the tip text for this property
- evaluatorTipText() -
Method in class weka.filters.supervised.attribute.AttributeSelection
- Returns the tip text for this property
- evalUsingTrainingDataTipText() -
Method in class weka.attributeSelection.OneRAttributeEval
- Returns a string for this option suitable for display in the gui
as a tip text
- EventConstraints - Interface in weka.gui.beans
- Interface for objects that want to be able to specify at any given
time whether their current configuration allows a particular event
to be generated.
- eventGeneratable(EventSetDescriptor) -
Method in class weka.gui.beans.Associator
- Returns true, if at the current time, the event described by the
supplied event descriptor could be generated.
- eventGeneratable(String) -
Method in class weka.gui.beans.Associator
- Returns true, if at the current time, the named event could
be generated.
- eventGeneratable(String) -
Method in class weka.gui.beans.ClassAssigner
- Returns true, if at the current time, the named event could
be generated.
- eventGeneratable(EventSetDescriptor) -
Method in class weka.gui.beans.Classifier
- Returns true, if at the current time, the event described by the
supplied event descriptor could be generated.
- eventGeneratable(String) -
Method in class weka.gui.beans.Classifier
- Returns true, if at the current time, the named event could
be generated.
- eventGeneratable(String) -
Method in class weka.gui.beans.ClassifierPerformanceEvaluator
- Returns true, if at the current time, the named event could
be generated.
- eventGeneratable(String) -
Method in class weka.gui.beans.ClassValuePicker
- Returns true, if at the current time, the named event could
be generated.
- eventGeneratable(EventSetDescriptor) -
Method in class weka.gui.beans.Clusterer
- Returns true, if at the current time, the event described by the
supplied event descriptor could be generated.
- eventGeneratable(String) -
Method in class weka.gui.beans.Clusterer
- Returns true, if at the current time, the named event could
be generated.
- eventGeneratable(String) -
Method in class weka.gui.beans.ClustererPerformanceEvaluator
- Returns true, if at the current time, the named event could
be generated.
- eventGeneratable(String) -
Method in class weka.gui.beans.CrossValidationFoldMaker
- Returns true, if at the current time, the named event could
be generated.
- eventGeneratable(String) -
Method in interface weka.gui.beans.EventConstraints
- Returns true if, at the current time, the named event could be
generated.
- eventGeneratable(String) -
Method in class weka.gui.beans.Filter
- Returns true, if at the current time, the named event could
be generated.
- eventGeneratable(String) -
Method in class weka.gui.beans.IncrementalClassifierEvaluator
- Returns true, if at the current time, the named event could
be generated.
- eventGeneratable(String) -
Method in class weka.gui.beans.InstanceStreamToBatchMaker
- Returns true if, at the current time, the named event could be
generated.
- eventGeneratable(String) -
Method in class weka.gui.beans.Loader
- Returns true if the named event can be generated at this time
- eventGeneratable(EventSetDescriptor) -
Method in class weka.gui.beans.MetaBean
- Returns true, if at the current time, the event described by the
supplied event descriptor could be generated.
- eventGeneratable(String) -
Method in class weka.gui.beans.MetaBean
- Returns true, if at the current time, the named event could
be generated.
- eventGeneratable(String) -
Method in class weka.gui.beans.PredictionAppender
- Returns true, if at the current time, the named event could
be generated.
- eventGeneratable(String) -
Method in class weka.gui.beans.TestSetMaker
- Returns true, if at the current time, the named event could
be generated.
- eventGeneratable(String) -
Method in class weka.gui.beans.TextViewer
- Returns true, if at the current time, the named event could
be generated.
- eventGeneratable(String) -
Method in class weka.gui.beans.TrainingSetMaker
- Returns true, if at the current time, the named event could
be generated.
- eventGeneratable(String) -
Method in class weka.gui.beans.TrainTestSplitMaker
- Returns true, if at the current time, the named event could
be generated.
- EXCLUDE_OPTIONS -
Variable in class weka.gui.ensembleLibraryEditor.DefaultModelsPanel
- options to exclude
- exclusiveTipText() -
Method in class weka.classifiers.rules.ConjunctiveRule
- Returns the tip text for this property
- execute() -
Method in class weka.classifiers.CheckSource
- performs the comparison test
- execute(String) -
Method in class weka.experiment.DatabaseUtils
- Executes a SQL query.
- execute() -
Method in class weka.experiment.RemoteExperimentSubTask
- Run the experiment
- execute() -
Method in interface weka.experiment.Task
- Execute this task.
- execute() -
Method in class weka.filters.CheckSource
- performs the comparison test
- execute() -
Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
- Perform the sub task
- execute() -
Method in class weka.gui.explorer.DataGeneratorPanel
- generates the instances, returns TRUE if successful
- execute() -
Method in class weka.gui.GenericPropertiesCreator
- generates the props-file for the GenericObjectEditor and stores it
- execute(boolean) -
Method in class weka.gui.GenericPropertiesCreator
- generates the props-file for the GenericObjectEditor and stores it only
if the the param
store
is TRUE.
- execute() -
Method in class weka.gui.sql.QueryPanel
- executes the current query.
- executeTask(Task) -
Method in interface weka.experiment.Compute
- Execute a task
- executeTask(Task) -
Method in class weka.experiment.RemoteEngine
- Takes a task object and queues it for execution
- ExhaustiveSearch - Class in weka.attributeSelection
- ExhaustiveSearch :
Performs an exhaustive search through the space of attribute subsets starting from the empty set of attrubutes. - ExhaustiveSearch() -
Constructor for class weka.attributeSelection.ExhaustiveSearch
- Constructor
- exists(TechnicalInformation.Field) -
Method in class weka.core.TechnicalInformation
- returns TRUE if the field is stored and has a value different from the
empty string.
- EXP -
Static variable in interface weka.core.mathematicalexpression.sym
-
- EXP -
Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
-
- EXP_INDEX_TABLE -
Static variable in class weka.experiment.DatabaseUtils
- The name of the table containing the index to experiments.
- EXP_RESULT_COL -
Static variable in class weka.experiment.DatabaseUtils
- The name of the column containing the results table name.
- EXP_RESULT_PREFIX -
Static variable in class weka.experiment.DatabaseUtils
- The prefix for result table names.
- EXP_SETUP_COL -
Static variable in class weka.experiment.DatabaseUtils
- The name of the column containing the experiment setup (parameters).
- EXP_TYPE_COL -
Static variable in class weka.experiment.DatabaseUtils
- The name of the column containing the experiment type (ResultProducer).
- expectation(double, int, double[], Hashtable) -
Static method in class weka.associations.RuleGeneration
- calculates the expected predctive accuracy of a rule
- expectedCosts(double[]) -
Method in class weka.classifiers.CostMatrix
- Calculates the expected misclassification cost for each possible class value,
given class probability estimates.
- expectedCosts(double[], Instance) -
Method in class weka.classifiers.CostMatrix
- Calculates the expected misclassification cost for each possible class value,
given class probability estimates.
- expectedResultsPerAverageTipText() -
Method in class weka.experiment.AveragingResultProducer
- Returns the tip text for this property
- Experiment - Class in weka.experiment
- Holds all the necessary configuration information for a standard
type experiment.
- Experiment() -
Constructor for class weka.experiment.Experiment
-
- Experimenter - Class in weka.gui.experiment
- The main class for the experiment environment.
- Experimenter(boolean) -
Constructor for class weka.gui.experiment.Experimenter
- Creates the experiment environment gui with no initial experiment
- ExperimenterDefaults - Class in weka.gui.experiment
- This class offers get methods for the default Experimenter settings in
the props file
weka/gui/experiment/Experimenter.props
. - ExperimenterDefaults() -
Constructor for class weka.gui.experiment.ExperimenterDefaults
-
- experimentIndexExists() -
Method in class weka.experiment.DatabaseUtils
- Returns true if the experiment index exists.
- EXPLICIT -
Static variable in class weka.associations.Tertius
- Way of handling missing values: min counterinstances
- Explorer - Class in weka.gui.explorer
- The main class for the Weka explorer.
- Explorer() -
Constructor for class weka.gui.explorer.Explorer
- Creates the experiment environment gui with no initial experiment
- Explorer.CapabilitiesFilterChangeEvent - Class in weka.gui.explorer
- This event can be fired in case the capabilities filter got changed
- Explorer.CapabilitiesFilterChangeEvent(Object, Capabilities) -
Constructor for class weka.gui.explorer.Explorer.CapabilitiesFilterChangeEvent
- Constructs a GOECapabilitiesFilterChangeEvent object.
- Explorer.CapabilitiesFilterChangeListener - Interface in weka.gui.explorer
- Interface for classes that listen for filter changes.
- Explorer.ExplorerPanel - Interface in weka.gui.explorer
- A common interface for panels to be displayed in the Explorer
- Explorer.LogHandler - Interface in weka.gui.explorer
- A common interface for panels in the explorer that can handle logs
- ExplorerDefaults - Class in weka.gui.explorer
- This class offers get methods for the default Explorer settings in
the props file
weka/gui/explorer/Explorer.props
. - ExplorerDefaults() -
Constructor for class weka.gui.explorer.ExplorerDefaults
-
- ExponentialFormat - Class in weka.core.matrix
-
- ExponentialFormat() -
Constructor for class weka.core.matrix.ExponentialFormat
-
- ExponentialFormat(int) -
Constructor for class weka.core.matrix.ExponentialFormat
-
- ExponentialFormat(int, boolean) -
Constructor for class weka.core.matrix.ExponentialFormat
-
- ExponentialFormat(int, int, boolean, boolean) -
Constructor for class weka.core.matrix.ExponentialFormat
-
- exponentTipText() -
Method in class weka.classifiers.functions.supportVector.PolyKernel
- Returns the tip text for this property
- exponentTipText() -
Method in class weka.classifiers.functions.VotedPerceptron
- Returns the tip text for this property
- Expression - Class in weka.core.pmml
-
- Expression(FieldMetaInfo.Optype, ArrayList<Attribute>) -
Constructor for class weka.core.pmml.Expression
-
- Expression - Class in weka.datagenerators.classifiers.regression
- A data generator for generating y according to a given expression out of randomly generated x.
E.g., the mexican hat can be generated like this:
sin(abs(a1)) / abs(a1)
In addition to this function, the amplitude can be changed and gaussian noise can be added. - Expression() -
Constructor for class weka.datagenerators.classifiers.regression.Expression
- initializes the generator
- expressionTipText() -
Method in class weka.datagenerators.classifiers.regression.Expression
- Returns the tip text for this property
- expressionTipText() -
Method in class weka.filters.unsupervised.attribute.AddExpression
- Returns the tip text for this property
- expressionTipText() -
Method in class weka.filters.unsupervised.attribute.MathExpression
- Returns the tip text for this property
- expressionTipText() -
Method in class weka.filters.unsupervised.instance.SubsetByExpression
- Returns the tip text for this property.
- ExtensionFileFilter - Class in weka.gui
- Provides a file filter for FileChoosers that accepts or rejects files
based on their extension.
- ExtensionFileFilter(String, String) -
Constructor for class weka.gui.ExtensionFileFilter
- Creates the ExtensionFileFilter
- ExtensionFileFilter(String[], String) -
Constructor for class weka.gui.ExtensionFileFilter
- Creates an ExtensionFileFilter that accepts files that have any of
the extensions contained in the supplied array.
- extensionTypeTipText() -
Method in class weka.classifiers.misc.OLM
- Returns the tip text for this property.
- extraArcs(BayesNet) -
Method in class weka.classifiers.bayes.net.BIFReader
- Count nr of exta arcs from other network compared to current network
Note that an arc is not 'extra' if it is reversed.
- extract(RevisionHandler) -
Static method in class weka.core.RevisionUtils
- Extracts the revision string returned by the RevisionHandler.
- extract(String) -
Static method in class weka.core.RevisionUtils
- Extracts the revision string.
- extractFilterAttributes(String) -
Method in class weka.associations.GeneralizedSequentialPatterns
- Parses a given String containing attribute numbers which are used for
result filtering.
- extremeValuesAsOutliersTipText() -
Method in class weka.filters.unsupervised.attribute.InterquartileRange
- Returns the tip text for this property
- extremeValuesFactorTipText() -
Method in class weka.filters.unsupervised.attribute.InterquartileRange
- Returns the tip text for this property
readFromXML()
of the XMLSerialiation
class.
writeToXML()
of the XMLSerialiation
class.
GenericObjectEditor.props
file (= PROPERTY_FILE).ConfusionMatrix
representing the current
two-class statistics, using class names "negative" and "positive".
trim()
on the result).
index.
- 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.
- 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.EnsembleLibraryEditor
- Gets a GUI component with which the user can edit the cost matrix.
- getCustomEditor() -
Method in class weka.gui.EnsembleSelectionLibraryEditor
- 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.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.
- getDataDirectoryName(Instances) -
Static method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibrary
- Returns the unique name for the set of instances supplied.
- 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
- 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.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.associations.HotSpot
- Get whether debugging info is output
- 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.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
- getDefaultRenderer(PropertyEditor) -
Static method in class weka.gui.EnsembleLibraryEditor
- This is a helper function that creates a renderer for Default Objects.
- 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.
- getDefaultWorkingDirectory() -
Static method in class weka.classifiers.meta.EnsembleSelection
- This method tries to find a reasonable path name for the ensemble working
directory where models and files will be stored.
- 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.
- 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.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.
- getDescriptionText() -
Method in class weka.classifiers.EnsembleLibraryModel
- getter for the string representation
- 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.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.
- 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.
- 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.
- getDistanceType() -
Method in class weka.classifiers.misc.OLM
- Gets the distance type used by a nearest neighbour rule (if any).
- 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
- getDistributionArray(DiscreteEstimator) -
Static method in class weka.classifiers.misc.monotone.DistributionUtils
- Converts a
DiscreteEstimator
to an array of
doubles.
- 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.
- 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.
- getDontNormalize() -
Method in class weka.core.NormalizableDistance
- Gets whether if the attribute values are to be normazlied in distance
calculation.
- 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.ensembleLibraryEditor.tree.DefaultNode
- this returns the property editor that was provided for this object.
- getEditor() -
Method in class weka.gui.ensembleLibraryEditor.tree.GenericObjectNode
- A getter for the GenericObjectEditor for this node
- 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.
- getEditorValue(Object) -
Static method in class weka.gui.EnsembleLibraryEditor
- This method handles the different object editor types in weka to obtain
their current values.
- 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.
- getElementAt(int) -
Method in class weka.gui.ensembleLibraryEditor.ModelList.SortedListModel
-
- 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
- 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
- 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
- getEps() -
Method in class weka.classifiers.functions.SMOreg
- Get the value of eps.
- getEpsilon() -
Method in class weka.classifiers.functions.SMO
- Get the value of epsilon.
- getEpsilon() -
Method in class weka.classifiers.functions.SMOreg
- 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.
- 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.
- getErrorText() -
Method in class weka.classifiers.EnsembleLibraryModel
- getter for the error text
- 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
- getEvaluation() -
Method in class weka.classifiers.meta.GridSearch
- Gets the criterion used for evaluating the classifier performance.
- 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
- 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.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.
- getExtensionType() -
Method in class weka.classifiers.misc.OLM
- Gets the extension type.
- 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.
- 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
- 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.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(String) -
Static method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibraryModel
- The purpose of this method is to get an appropriate file name for a model
based on its string representation of a model.
- 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.
- 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
- 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.
- 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.functions.SVMreg
- 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.
- 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
-
- 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.
- 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.
- getFoldPrediction(Instance, int) -
Method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibraryModel
- Returns prediction of the classifier for the specified fold.
- 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.meta.ensembleSelection.EnsembleSelectionLibraryModel
- get the number of folds
- 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.
- 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
- 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
- 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.
- getGenerateDataOutput() -
Method in class weka.attributeSelection.FCBFSearch
- Returns the flag, by which the AttributeSelection module decide
whether create a new dataset according to the selected features.
- getGenerateRanking() -
Method in class weka.attributeSelection.FCBFSearch
- This is a dummy method.
- 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.
- 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(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
- 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
- getGreedySortInitialization() -
Method in class weka.classifiers.meta.EnsembleSelection
- Get the value of greedySortInitialization.
- 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
- 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.
- getHelpText() -
Method in class weka.gui.ensembleLibraryEditor.tree.GenericObjectNode
- getter for the tooltip text
- 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
-
- getHillclimbIterations() -
Method in class weka.classifiers.meta.EnsembleSelection
- Gets the number of hillclimbIterations.
- getHillclimbMetric() -
Method in class weka.classifiers.meta.EnsembleSelection
- Gets the hill climbing metric.
- getHillclimbPredictions() -
Method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibrary
- This method will get the predictions for all the models in the
ensemble library.
- 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.
- 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() -
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.
- 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.
- getIndex() -
Method in class weka.associations.tertius.Predicate
-
- 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
- getIndexofBiggest(List<Integer>) -
Method in class weka.attributeSelection.ScatterSearchV1
- get the index in a List where this have the biggest number
- getIndividualPerformance(Instances, int) -
Method in class weka.classifiers.meta.ensembleSelection.ModelBag
- Gets the individual performances of all the models in the bag.
- 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)
- 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
- 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() -
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() -
Method in class weka.gui.beans.InstanceEvent
- Get the instance
- getInstanceIndex(int) -
Method in class weka.classifiers.lazy.LBR.Indexes
- Returns the boolean value at the specified index in the Instance Index array
- getInstanceRange() -
Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
- Gets the number of instances forward to translate values between.
- getInstances() -
Method in 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
-
- getInstancesChecksum(Instances) -
Static method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibrary
- This method takes an Instances object and returns a checksum of its
toString method - that is the checksum of the .arff file that would
be created if the Instances object were transformed into an arff file
in the file system.
- 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
- 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
- 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.
- getInterpolationParameter() -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Returns the current value of the interpolation parameter.
- getInterpolationParameterLowerBound() -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Returns the lower bound for the interpolation parameter tuning
(0 <= x < 1).
- getInterpolationParameterUpperBound() -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Returns the upper bound for the interpolation parameter tuning
(0 < x <= 1).
- getInterpreter() -
Method in class weka.core.Jython
- returns the currently used Python Interpreter
- 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).
- getIteratorType() -
Method in class weka.gui.ensembleLibraryEditor.tree.NumberNode
- getter for this node's iteratorType which will be one of the three
enumerated values
- 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.EnsembleLibraryEditor
- 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
- getJythonModule() -
Method in class weka.classifiers.JythonClassifier
- Gets the Jython module.
- getJythonOptions() -
Method in class weka.classifiers.JythonClassifier
- Gets the Jython module options.
- getJythonPaths() -
Method in class weka.classifiers.JythonClassifier
- Gets the additional Jython paths.
- getKDTree() -
Method in class weka.clusterers.XMeans
- Gets the KDTree class.
- 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
- Gets the kernel to use.
- getKernel() -
Method in class weka.classifiers.functions.supportVector.CheckKernel
- Get the kernel being tested
- getKernel() -
Method in class weka.classifiers.functions.SVMreg
- 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.
- getKNN() -
Method in class weka.classifiers.lazy.IBk
- Gets the number of neighbours the learner will use.
- getKNN() -
Method in class weka.classifiers.lazy.LWL
- Gets the number of neighbours used for kernel bandwidth setting.
- getKValue() -
Method in class weka.classifiers.trees.RandomTree
- Get the value of K.
- 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.
- 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
-
- getLastLiteral() -
Method in class weka.associations.tertius.LiteralSet
- Give the last literal added to this set.
- 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.
- getLibrary() -
Method in class weka.classifiers.meta.EnsembleSelection
- Gets the ensemble library.
- getLibrary() -
Method in class weka.gui.ensembleLibraryEditor.ListModelsPanel
- returns the current library
- 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
- 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.
- 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.ensembleLibraryEditor.ModelList.ModelListRenderer
- This is the only method necessary to overload.
- 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
- 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.
- 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
- getLower() -
Method in class weka.gui.experiment.RunNumberPanel
- Gets the current lower run number.
- getLowerBound() -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Returns the current value of the lower bound for the interpolation
parameter.
- getLowerBoundMinSupport() -
Method in class weka.associations.Apriori
- 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
-
- 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(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.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.
- getMaxBranchingFactor() -
Method in class weka.associations.HotSpot
- Get the maximum branching factor
- 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.
- getMaximalCumulativeDiscreteDistribution(int) -
Static method in class weka.classifiers.misc.monotone.DistributionUtils
- Get the maximal
CumulativeDiscreteDistribution
over numClasses
elements.
- 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.
- 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.
- 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.
- 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.VisualizePlugin
- Get the maximum version of Weka, exclusive, the class
is designed to work with.
- 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.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
- 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.SVMreg
- 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.misc.FLR
- Returns the value of the named measure
- getMeasure(String) -
Method in class weka.classifiers.rules.DecisionTable
- Returns the value of the named measure
- getMeasure(String) -
Method in class weka.classifiers.rules.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.
- getMetric(Evaluation, int) -
Static method in class weka.classifiers.meta.ensembleSelection.EnsembleMetricHelper
- Given an Evaluation object and metric, call the appropriate function to get
the value for that metric and return it.
- getMetricType() -
Method in class weka.associations.Apriori
- Get the metric type
- 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.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.
- getMinimalCumulativeDiscreteDistribution(int) -
Static method in class weka.classifiers.misc.monotone.DistributionUtils
- Get the minimal
CumulativeDiscreteDistribution
over numClasses
elements.
- 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.
- getMinimizeTarget() -
Method in class weka.associations.HotSpot
- Get whether to minimize the target rather than maximize
- getMinImprovement() -
Method in class weka.associations.HotSpot
- Get the minimum improvement in the target necessary to add a test
- getMinimumBucketSize() -
Method in class weka.attributeSelection.OneRAttributeEval
- Get the minimum bucket size used by oneR
- 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.
- 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..
- getMinMaxExtension() -
Method in class weka.classifiers.misc.MinMaxExtension
- Return if the minimal extension is in effect.
- getMinMetric() -
Method in class weka.associations.Apriori
- 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.VisualizePlugin
- Get the minimum version of Weka, inclusive, the class
is designed to work with.
- 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.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
- getMissingMerge() -
Method in class weka.attributeSelection.SymmetricalUncertAttributeSetEval
- 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.
- getMissingSeperate() -
Method in class weka.attributeSelection.CfsSubsetEval
- Return true is missing is treated as a seperate value
- 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
- getModelClass() -
Method in class weka.classifiers.EnsembleLibraryModel
- getter for the modelClass
- getModelFile() -
Method in class weka.classifiers.misc.SerializedClassifier
- Gets the file containing the serialized model.
- getModelListFile() -
Method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibrary
- Gets the model list file that holds the list of models
in the ensemble library.
- getModelNames() -
Method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibrary
- This method will return a Set object containing all the
String representations of the models.
- 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.
- getModelRatio() -
Method in class weka.classifiers.meta.EnsembleSelection
- Get the value of modelRatio.
- getModels() -
Method in class weka.classifiers.EnsembleLibrary
- getter for the set of models in this library
- getModels() -
Method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibraryModel
- Returs the array of classifiers
- getModelType() -
Method in class weka.classifiers.trees.FT
- Get the type of functional tree model being used.
- getModelValueAt(int, int) -
Method in class weka.gui.arffviewer.ArffSortedTableModel
- returns the value at the given position
- getModelWeights() -
Method in class weka.classifiers.meta.ensembleSelection.ModelBag
- returns the model weights
- 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 class weka.classifiers.mi.TLD
- Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() -
Method in class weka.classifiers.mi.TLDSimple
- Returns the capabilities of this multi-instance classifier 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).
- 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.ensembleLibraryEditor.tree.CheckBoxNode
- gets the name of the parameter value represented by this node
which is stored as the node's user object
- getName() -
Method in class weka.gui.ensembleLibraryEditor.tree.DefaultNode
- gets the name of the parameter value represented by this node
- getName() -
Method in class weka.gui.ensembleLibraryEditor.tree.PropertyNode
- getter for the name to be displayed for this node
- 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
-
- 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.
- 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
- 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.attributeSelection.PrincipalComponents
- 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
- getNormalize() -
Method in class weka.filters.unsupervised.attribute.PrincipalComponents
- Gets whether or not input data is to be normalized.
- 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"
- getNumberFormat() -
Method in class weka.gui.ensembleLibraryEditor.tree.NumberNode
- A helper method to figure out what number format should be used to
display the numbers value in a formatted text box.
- 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
- getNumberOfPartsForInterpolationParameter() -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Gets the granularity for tuning the interpolation parameter.
- 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.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.
- 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.EnsembleSelection
- Gets the number of folds for the cross-validation.
- 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.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.misc.monotone.OSDLCore
- Returns the number of instances in the training set.
- 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.
- getNumModelBags() -
Method in class weka.classifiers.meta.EnsembleSelection
- Gets numModelBags.
- 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.
- 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.
- getNumRuns() -
Method in class weka.classifiers.meta.LogitBoost
- Get the value of NumRuns.
- getNumRuns() -
Method in class weka.classifiers.mi.TLD
- Returns the number of runs to perform.
- getNumRuns() -
Method in class weka.classifiers.mi.TLDSimple
- Returns the number of runs to perform.
- 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.classifiers.misc.monotone.CumulativeDiscreteDistribution
- Get the number of elements over which the cumulative
probability distribution is defined.
- getNumSymbols() -
Method in class weka.classifiers.misc.monotone.DiscreteDistribution
- Get the number of elements over which the
DiscreteDistribution
is defined.
- 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.FCBFSearch
- Gets the number of attributes to be retained.
- 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.SerializedObject
- Returns a serialized object.
- getObject() -
Method in class weka.gui.ensembleLibraryEditor.tree.GenericObjectNode
- getter for this node's object
- 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.
- getOneValue() -
Method in class weka.gui.ensembleLibraryEditor.tree.NumberNode
- figures out the class of this node's object and returns a new instance of
it initialized with the value of "1".
- 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.GeneralizedSequentialPatterns
- Returns an Array containing the current options settings.
- getOptions() -
Method in class weka.associations.HotSpot
- Gets the current settings of HotSpot.
- 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.FCBFSearch
- Gets the current settings of ReliefFAttributeEval.
- 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 ReliefFAttributeEval.
- 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.SymmetricalUncertAttributeSetEval
- 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.EnsembleLibraryModel
- getter for the classifier options
- 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.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.SVMreg
- Gets the current settings of the classifier.
- 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.JythonClassifier
- 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.EnsembleSelection
- 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.mi.TLD
- Gets the current settings of the Classifier.
- getOptions() -
Method in class weka.classifiers.mi.TLDSimple
- Gets the current settings of the Classifier.
- getOptions() -
Method in class weka.classifiers.misc.FLR
- Gets the current settings of the Classifier.
- getOptions() -
Method in class weka.classifiers.misc.MinMaxExtension
- Gets the current settings of this classifier.
- getOptions() -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Gets the current settings of the OSDLCore classifier.
- getOptions() -
Method in class weka.classifiers.misc.OLM
- Gets an array of string with the current options 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.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.C45Saver
- Gets the current settings of the C45Saver object.
- 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.
- getOptionsWereValid() -
Method in class weka.classifiers.EnsembleLibraryModel
- getter for the optionsWereValid member variable
- 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.DefineFunction
- Get the structure of the result produced by this function.
- 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.
- 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.
- getPackageName() -
Static method in class weka.gui.ensembleLibraryEditor.DefaultModelsPanel
- this bit of code grabs all of the .model.xml files located in
the ensemble selection package directory.
- 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.
- 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
- getParentPanel() -
Method in class weka.gui.ensembleLibraryEditor.tree.GenericObjectNode
- getter for the parent panel
- 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() -
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
- 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
- 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
- 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.
- getPredTargetColumn() -
Method in class weka.experiment.ClassifierSplitEvaluator
-
- getPrefix() -
Method in class weka.gui.beans.SerializedModelSaver
- Get the prefix to prepend to the model file names.
- 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
- getPrintColNames() -
Method in class weka.experiment.ResultMatrix
- returns whether column names or numbers instead are printed
- 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(int) -
Method in class weka.classifiers.misc.monotone.DiscreteDistribution
- Get the probability of finding the element at
a specified index.
- 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.
- 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
- getProjectionFilter() -
Method in class weka.classifiers.meta.RotationForest
- Gets the filter used to project the data.
- 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
- getPropertyEditor() -
Method in class weka.gui.ensembleLibraryEditor.tree.PropertyNode
- this returns the property editor that was provided for this object.
- 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.
- getRandom() -
Method in class weka.datagenerators.DataGenerator
- Gets the random generator.
- 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.MultilayerPerceptron
-
- 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(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.SVMreg
- 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.
- 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
- getReplacement() -
Method in class weka.classifiers.meta.EnsembleSelection
- Get the value of replacement.
- 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
- 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.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.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.HotSpot
- 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.FCBFSearch
- 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
- 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.SymmetricalUncertAttributeSetEval
- 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.CostMatrix
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.EnsembleLibrary
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.EnsembleLibraryModel
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.EnsembleLibraryModelComparator
- 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.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.supportVector.CheckKernel
- 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.SVMreg
- 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.JythonClassifier
- 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
- 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.ensembleSelection.EnsembleMetricHelper
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibrary
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibraryModel
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.meta.EnsembleSelection
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.meta.ensembleSelection.ModelBag
- 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.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.OrdinalClassClassifier
- 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
- 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.mi.TLD
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.mi.TLDSimple
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.misc.FLR
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.misc.HyperPipes
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.misc.MinMaxExtension
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.misc.monotone.AbsoluteLossFunction
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.misc.monotone.BooleanBitMatrix
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.misc.monotone.Coordinates
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.misc.monotone.CumulativeDiscreteDistribution
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.misc.monotone.DiscreteDistribution
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.misc.monotone.DistributionUtils
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.misc.monotone.EnumerationIterator
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.misc.monotone.InstancesComparator
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.misc.monotone.InstancesUtil
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.misc.monotone.MultiDimensionalSort
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.misc.monotone.ZeroOneLossFunction
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.misc.OLM
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.misc.OSDL
- 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
- Returns the revision string.
- getRevision() -
Method in class weka.classifiers.rules.JRip
- 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.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.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.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.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.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.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.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.ProtectedProperties
- Returns the revision string.
- getRevision() -
Method in class weka.core.Queue
- 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
- 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.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.sql.DbUtils
- Returns the revision string.
- getRhoa() -
Method in class weka.classifiers.misc.FLR
- Get rhoa
- 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.
- 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
- 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.beans.Saver
- Get the saver
- 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.
- 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
- 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
- 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.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.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.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.ensembleSelection.EnsembleSelectionLibraryModel
- Get the 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
- getSelected() -
Method in class weka.gui.ensembleLibraryEditor.tree.CheckBoxNode
- getter for the node state
- getSelected() -
Method in class weka.gui.ensembleLibraryEditor.tree.NumberNode
- getter for the node selected state
- 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.
- 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.
- getShowGUI() -
Method in class weka.clusterers.OPTICS
- Returns the flag for showing the OPTICS visualizer GUI.
- getShowRules() -
Method in class weka.classifiers.misc.FLR
- Get ShowRules parameter
- 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.Main
- Return the singleton instance of the Main GUI.
- 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
- getSize() -
Method in class weka.gui.ensembleLibraryEditor.ModelList.SortedListModel
-
- getSizePer() -
Method in class weka.classifiers.trees.BFTree
- Get training set size.
- getSizePer() -
Method in class weka.classifiers.trees.SimpleCart
- Get training set size.
- getSizeRuleBaseMax() -
Method in class weka.classifiers.misc.OLM
- Return the number of examples in the maximal rule base.
- getSizeRuleBaseMin() -
Method in class weka.classifiers.misc.OLM
- Return the number of examples in the minimal rule base.
- 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.classifiers.misc.OLM
- Returns if the instances are sorted prior to building the rule bases.
- 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)
- getSortInitializationRatio() -
Method in class weka.classifiers.meta.EnsembleSelection
- Get the value of sortInitializationRatio.
- 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.
- 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.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
- getStartPoint() -
Method in class weka.attributeSelection.RankSearch
- Get the point at which to start evaluating the ranking
- 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.FCBFSearch
- 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.
- 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(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.core.Trie.TrieNode
- returns the full string up to the root
- getStringChecksum(String) -
Static method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibraryModel
- Gets a checksum for the string defining this classifier.
- getStringRepresentation() -
Method in class weka.classifiers.EnsembleLibraryModel
- getter for the string representation
- 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() -
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)
- 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
- 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.HotSpot
- Get the minimum support
- 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
- 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.gui.arffviewer.ArffPanel
- returns the table component
- 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.
- 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.EnsembleLibraryEditor
- 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.associations.HotSpot
- Get the target index as a string
- 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
- getTargetIndex() -
Method in class weka.associations.HotSpot
- For a nominal target, get the index of the value of interest (1-based)
- 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.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.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.FCBFSearch
- 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.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.SymmetricalUncertAttributeSetEval
- 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.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.SVMreg
- 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.EnsembleSelection
- Return the technical information.
- 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.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.mi.TLD
- 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.TLDSimple
- 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.FLR
- 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.MinMaxExtension
- 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.monotone.OSDLCore
- 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.OLM
- 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.rules.Ridor
- 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.
- 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.
- 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.
- getText() -
Method in class weka.gui.ensembleLibraryEditor.tree.NumberNode
- returns the text to be displayed for this node
- 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.
- getThreshold() -
Method in class weka.attributeSelection.FCBFSearch
- Returns the threshold so that the AttributeSelection module can
discard attributes from the ranking.
- 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.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
- 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.
- 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.functions.SMOreg
- 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() -
Method in class weka.gui.ensembleLibraryEditor.tree.CheckBoxNode
- getter for the tooltip text
- getToolTipText() -
Method in class weka.gui.ensembleLibraryEditor.tree.DefaultNode
- getter for the tooltip text
- getToolTipText() -
Method in class weka.gui.ensembleLibraryEditor.tree.GenericObjectNode
- getter for the tooltip text
- getToolTipText() -
Method in class weka.gui.ensembleLibraryEditor.tree.NumberNode
- getter for the tooltip text
- getToolTipText() -
Method in class weka.gui.ensembleLibraryEditor.tree.PropertyNode
- getter for the tooltip text
- 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.
- 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.
- 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 .
- 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.
- getTree() -
Method in class weka.gui.ensembleLibraryEditor.tree.GenericObjectNode
- returns the current tree
- getTreeCellEditorComponent(JTree, Object, boolean, boolean, boolean, int) -
Method in class weka.gui.ensembleLibraryEditor.tree.ModelTreeNodeEditor
- This method uses the ModelTreeNodeRenderer class to get the individual
editors and then registers this classes editing event listeners with
them
- getTreeCellRendererComponent(JTree, Object, boolean, boolean, boolean, int, boolean) -
Method in class weka.gui.ensembleLibraryEditor.tree.ModelTreeNodeRenderer
- This is the method of this class that is responsible for figuring out how
to display each of the tree nodes.
- getTreshold() -
Method in class weka.attributeSelection.ScatterSearchV1
- Get the treshold
- 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
-
- getTuneInterpolationParameter() -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Returns whether the interpolation parameter is to be tuned based on the
bounds.
- getTwoClassStats(int) -
Method in class weka.classifiers.evaluation.ConfusionMatrix
- Gets the performance with respect to one of the classes
as a TwoClassStats object.
- getTwoValue() -
Method in class weka.gui.ensembleLibraryEditor.tree.NumberNode
- figures out the class of this node's object and returns a new instance of
it initialized with the value of "2".
- 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.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.
- getUpperBound() -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Returns the current value of the upper bound for the interpolation
parameter.
- getUpperBoundMinSupport() -
Method in class weka.associations.Apriori
- 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() -
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.
- 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
- getUsingCutOff() -
Method in class weka.classifiers.mi.TLD
- Returns whether an empirical cutoff is used
- getUsingCutOff() -
Method in class weka.classifiers.mi.TLDSimple
- Returns whether an empirical cutoff is 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
- getValidationPredictions() -
Method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibraryModel
- getter for validation predictions
- getValidationRatio() -
Method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibraryModel
- get validationRatio
- getValidationRatio() -
Method in class weka.classifiers.meta.EnsembleSelection
- Get the value of validationRatio.
- getValidationSetSize() -
Method in class weka.classifiers.functions.MultilayerPerceptron
-
- getValidationThreshold() -
Method in class weka.classifiers.functions.MultilayerPerceptron
-
- getValue(int) -
Method in class weka.classifiers.misc.monotone.Coordinates
- Get the value of the attribute with index
index,
ignoring the class attribute.
- 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.EnsembleLibraryEditor
- Gets the cost matrix that is being edited.
- getValue() -
Method in class weka.gui.ensembleLibraryEditor.tree.NumberNode
- getter for this node's object
- 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.
- 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.
- 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(double[]) -
Method in class weka.classifiers.misc.monotone.Coordinates
- Get the values of the coordinates.
- getValues() -
Method in class weka.core.pmml.TargetMetaInfo
- Get the values (discrete case only) for this Target.
- getValues() -
Method in class weka.gui.ensembleLibraryEditor.tree.GenericObjectNode
- This method iterates over all of the child nodes of this
GenericObjectNode and requests the verious sets of values that the
user has presumably specified.
- 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() -
Static method in class weka.core.Environment
- Get the names of the variables (keys) stored in the
internal map.
- getVariableValue(String) -
Static 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.
- 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
- getVerboseOutput() -
Method in class weka.classifiers.meta.EnsembleSelection
- Get the value of verboseOutput.
- 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.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(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
- getWeighted() -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Returns if the weighted version is in effect.
- 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.
- getWorkingDirectory() -
Method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibrary
- Gets the working Directory of the ensemble library.
- getWorkingDirectory() -
Method in class weka.classifiers.meta.EnsembleSelection
- Get the value of working directory.
- 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(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.
- 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() -
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() -
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).
- 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.
- getZeroValue() -
Method in class weka.gui.ensembleLibraryEditor.tree.NumberNode
- figures out the class of this node's object and returns a new instance of
it initialized with the value of "0".
- 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.GeneralizedSequentialPatterns
- Returns global information about the algorithm.
- globalInfo() -
Method in class weka.associations.HotSpot
- Returns a string describing this classifier
- 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.FCBFSearch
- 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.SymmetricalUncertAttributeSetEval
- 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.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.SVMreg
- Returns a string describing classifier
- 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.JythonClassifier
- 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.EnsembleSelection
- 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.mi.TLD
- Returns a string describing this filter
- globalInfo() -
Method in class weka.classifiers.mi.TLDSimple
- Returns a string describing this filter
- globalInfo() -
Method in class weka.classifiers.misc.FLR
- Returns a description of the classifier suitable for
displaying in the explorer/experimenter gui
- globalInfo() -
Method in class weka.classifiers.misc.HyperPipes
- Returns a string describing classifier
- globalInfo() -
Method in class weka.classifiers.misc.MinMaxExtension
- Returns a string describing the classifier.
- globalInfo() -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Returns a string describing the classifier.
- globalInfo() -
Method in class weka.classifiers.misc.OLM
- Returns a string describing the 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
- 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.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.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_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.
- 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() -
Method in class weka.associations.HotSpot
-
- 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.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 interface weka.core.Drawable
- Returns a string that describes a graph representing
the object.
- 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.
- GraphListener - Interface in weka.gui.beans
- Describe interface
TextListener
here. - 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
-
- graphType() -
Method in class weka.associations.HotSpot
- Returns the type of graph this scheme
represents.
- 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.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 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
-
- 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.
- greedySortInitializationTipText() -
Method in class weka.classifiers.meta.EnsembleSelection
- Returns the tip text for this property
- 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 -
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
- 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.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.
- GUIChooser.ChildFrameSDI(GUIChooser, String) -
Constructor for class weka.gui.GUIChooser.ChildFrameSDI
- constructs a new internal frame that knows about its parent.
- 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
-
true
if there are more elements in the iteration.
IncrementalClassifierEvent
instance.
InstanceEvent
instance that encapsulates
a single instance only.
InstanceEvent
instance which encapsulates
header information only.
readInstance(Reader)
method, one should use the
ArffLoader
or DataSource
class instead.
InstancesComparator
that compares
the attributes with the given index.
InstancesComparator
that compares
the attributes with the given index, with the possibility of
reversing the order.
Instance
and Instances,
not
provided by there respective classes.Instance
to an array of values that matches the
format of the mining schema.
CumulativeDiscreteDistribution.
- interpolate(CumulativeDiscreteDistribution, CumulativeDiscreteDistribution, double[]) -
Static method in class weka.classifiers.misc.monotone.DistributionUtils
- Compute a linear interpolation between the two given
CumulativeDiscreteDistribution.
- interpolate(DiscreteDistribution, DiscreteDistribution, double) -
Static method in class weka.classifiers.misc.monotone.DistributionUtils
- Compute a linear interpolation between the two given
DiscreteDistribution.
- interpolationParameterLowerBoundTipText() -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Returns the tip text for this property.
- interpolationParameterTipText() -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Returns the tip text for this property.
- interpolationParameterUpperBoundTipText() -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Returns the tip text for this property.
- InterquartileRange - Class in weka.filters.unsupervised.attribute
- A filter for detecting outliers and extreme values based on interquartile ranges.
- InterquartileRange() -
Constructor for class weka.filters.unsupervised.attribute.InterquartileRange
-
- intersectSubsets(ScatterSearchV1.Subset, ScatterSearchV1.Subset) -
Method in class weka.attributeSelection.ScatterSearchV1
- Intersects two subsets
- IntervalEstimator - Interface in weka.classifiers
- Interface for classifiers that can output confidence intervals
- IntVector - Class in weka.core.matrix
- A vector specialized on integers.
- IntVector() -
Constructor for class weka.core.matrix.IntVector
- Constructs a null vector.
- IntVector(int) -
Constructor for class weka.core.matrix.IntVector
- Constructs an n-vector of zeros.
- IntVector(int, int) -
Constructor for class weka.core.matrix.IntVector
- Constructs an n-vector of a constant
- IntVector(int[]) -
Constructor for class weka.core.matrix.IntVector
- Constructs a vector given an int array
- InvalidInputException - Exception in weka.gui.ensembleLibraryEditor.tree
- A custom Exception that is thrown when a user specifies an invalid
set of parameters to the LibraryEditor Tree GUI.
- InvalidInputException(String) -
Constructor for exception weka.gui.ensembleLibraryEditor.tree.InvalidInputException
- initializes the exception
- inverse() -
Method in class weka.core.matrix.Matrix
- Matrix inverse or pseudoinverse
- inverseIterator() -
Method in class weka.associations.tertius.SimpleLinkedList
-
- invertSelectionTipText() -
Method in class weka.core.NormalizableDistance
- Returns the tip text for this property.
- invertSelectionTipText() -
Method in class weka.filters.supervised.attribute.Discretize
- Returns the tip text for this property
- invertSelectionTipText() -
Method in class weka.filters.supervised.instance.Resample
- Returns the tip text for this property.
- invertSelectionTipText() -
Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
- Returns the tip text for this property
- invertSelectionTipText() -
Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
- Returns the tip text for this property
- invertSelectionTipText() -
Method in class weka.filters.unsupervised.attribute.Copy
- Returns the tip text for this property
- invertSelectionTipText() -
Method in class weka.filters.unsupervised.attribute.Discretize
- Returns the tip text for this property
- invertSelectionTipText() -
Method in class weka.filters.unsupervised.attribute.MathExpression
- Returns the tip text for this property
- invertSelectionTipText() -
Method in class weka.filters.unsupervised.attribute.NominalToBinary
- Returns the tip text for this property
- invertSelectionTipText() -
Method in class weka.filters.unsupervised.attribute.NumericCleaner
- Returns the tip text for this property
- invertSelectionTipText() -
Method in class weka.filters.unsupervised.attribute.NumericToNominal
- Returns the tip text for this property
- invertSelectionTipText() -
Method in class weka.filters.unsupervised.attribute.NumericTransform
- Returns the tip text for this property
- invertSelectionTipText() -
Method in class weka.filters.unsupervised.attribute.RELAGGS
- Returns the tip text for this property
- invertSelectionTipText() -
Method in class weka.filters.unsupervised.attribute.Remove
- Returns the tip text for this property
- invertSelectionTipText() -
Method in class weka.filters.unsupervised.attribute.RemoveType
- Returns the tip text for this property
- invertSelectionTipText() -
Method in class weka.filters.unsupervised.attribute.StringToWordVector
- Returns the tip text for this property.
- invertSelectionTipText() -
Method in class weka.filters.unsupervised.instance.RemoveFolds
- Returns the tip text for this property
- invertSelectionTipText() -
Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
- Returns the tip text for this property
- invertSelectionTipText() -
Method in class weka.filters.unsupervised.instance.RemovePercentage
- Returns the tip text for this property
- invertSelectionTipText() -
Method in class weka.filters.unsupervised.instance.RemoveRange
- Returns the tip text for this property
- invertSelectionTipText() -
Method in class weka.filters.unsupervised.instance.RemoveWithValues
- Returns the tip text for this property
- invertSelectionTipText() -
Method in class weka.filters.unsupervised.instance.Resample
- Returns the tip text for this property
- invertTipText() -
Method in class weka.filters.unsupervised.instance.RemoveMisclassified
- Returns the tip text for this property
- invoke(String, Class[], Object[]) -
Method in class weka.core.Jython
- executes the specified method on the current interpreter and returns the
result, if any
- invoke(Object, String, Class[], Object[]) -
Static method in class weka.core.Jython
- executes the specified method and returns the result, if any
- invokeMain(String, String[]) -
Static method in class weka.gui.SplashWindow
- Invokes the main method of the provided class name.
- invokeMethod(String, String, String[]) -
Static method in class weka.gui.SplashWindow
- Invokes the named method of the provided class name.
- is(String) -
Method in class weka.core.Stopwords
- Returns true if the given string is a stop word.
- IS -
Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
-
- isALeaf() -
Method in class weka.core.neighboursearch.balltrees.BallNode
- Returns true if the node is a leaf node (if
both its left and right child are null).
- isALeaf() -
Method in class weka.core.neighboursearch.CoverTree.CoverTreeNode
- Returns whether if the node is a leaf or not.
- isALeaf() -
Method in class weka.core.neighboursearch.kdtrees.KDTreeNode
- Checks if node is a leaf.
- isAllowed(Class, String) -
Method in class weka.core.xml.PropertyHandler
- returns whether the given property (display name) is allowed for the
given class.
- isAllowed(Object, String) -
Method in class weka.core.xml.PropertyHandler
- returns whether the given property (display name) is allowed for the given
object .
- isArff(String) -
Static method in class weka.core.converters.ConverterUtils.DataSource
- returns whether the extension of the location is likely to be of ARFF
format, i.e., ending in ".arff" or ".arff.gz" (case-insensitive).
- isAttribute() -
Method in enum weka.core.Capabilities.Capability
- returns true if the capability is an attribute
- isAttributeCapability() -
Method in enum weka.core.Capabilities.Capability
- returns true if the capability is an attribute capability
- isAveragable() -
Method in class weka.core.Attribute
- Returns whether the attribute can be averaged meaningfully.
- isBoolean(int) -
Method in class weka.datagenerators.clusterers.SubspaceCluster
- Returns true if attribute is boolean
- isBusy() -
Method in class weka.gui.beans.Associator
- Returns true if.
- isBusy() -
Method in interface weka.gui.beans.BeanCommon
- Returns true if.
- isBusy() -
Method in class weka.gui.beans.ClassAssigner
- Returns true if.
- isBusy() -
Method in class weka.gui.beans.Classifier
- Returns true if.
- isBusy() -
Method in class weka.gui.beans.ClassifierPerformanceEvaluator
- Returns true if.
- isBusy() -
Method in class weka.gui.beans.ClassValuePicker
- Returns true if.
- isBusy() -
Method in class weka.gui.beans.Clusterer
- Returns true if.
- isBusy() -
Method in class weka.gui.beans.ClustererPerformanceEvaluator
- Returns true if.
- isBusy() -
Method in class weka.gui.beans.CrossValidationFoldMaker
- Returns true if.
- isBusy() -
Method in class weka.gui.beans.Filter
- Returns true if.
- isBusy() -
Method in class weka.gui.beans.IncrementalClassifierEvaluator
- Returns true if.
- isBusy() -
Method in class weka.gui.beans.InstanceStreamToBatchMaker
- Returns true if.
- isBusy() -
Method in class weka.gui.beans.Loader
- Returns true if.
- isBusy() -
Method in class weka.gui.beans.MetaBean
- Returns true if.
- isBusy() -
Method in class weka.gui.beans.PredictionAppender
- Returns true if.
- isBusy() -
Method in class weka.gui.beans.Saver
- Returns true if.
- isBusy() -
Method in class weka.gui.beans.SerializedModelSaver
- Returns true if.
- isBusy() -
Method in class weka.gui.beans.StripChart
- Returns true if.
- isBusy() -
Method in class weka.gui.beans.TestSetMaker
- Returns true if.
- isBusy() -
Method in class weka.gui.beans.TextViewer
- Returns true if.
- isBusy() -
Method in class weka.gui.beans.TrainingSetMaker
- Returns true if.
- isBusy() -
Method in class weka.gui.beans.TrainTestSplitMaker
- Returns true if.
- isCellEditable(int, int) -
Method in class weka.gui.arffviewer.ArffTableModel
- returns true if the cell at rowindex and columnindexis editable
- isCellEditable(EventObject) -
Method in class weka.gui.ensembleLibraryEditor.tree.ModelTreeNodeEditor
- This tells the JTree whether or not to let nodes in the tree be
edited.
- isCellEditable(int, int) -
Method in class weka.gui.SortedTableModel
- Returns true if the cell at rowIndex and columnIndex is editable.
- isCellEditable(int, int) -
Method in class weka.gui.sql.ResultSetTableModel
- returns true if the cell at rowindex and columnindexis editable.
- isChanged() -
Method in class weka.classifiers.bayes.net.EditableBayesNet
- return true when current state differs from the state the network was last saved
- isChanged() -
Method in class weka.gui.arffviewer.ArffPanel
- returns whether the content of the panel was changed
- isChanged() -
Method in class weka.gui.ViewerDialog
- returns whether the data has been changed
- isClass() -
Method in class weka.associations.tertius.Predicate
-
- isClass() -
Method in enum weka.core.Capabilities.Capability
- returns true if the capability is a class
- isClassCapability() -
Method in enum weka.core.Capabilities.Capability
- returns true if the capability is a other capability
- isClassname(String) -
Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion
- tests whether the given partial string is the name of a class with
classpath - it basically tests, whether the string consists only
of alphanumeric literals, underscores and dots.
- isConnected() -
Method in class weka.experiment.DatabaseUtils
- Returns true if a database connection is active.
- isConnected() -
Method in class weka.gui.sql.event.ConnectionEvent
- returns whether the connection is still open.
- isContainedBy(Instance) -
Method in class weka.associations.gsp.Element
- Checks if an Element is contained by a given Instance.
- isContinuous() -
Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
- checks, whether cluster sub type is continuous
- isCoreFileLoader(String) -
Static method in class weka.core.converters.ConverterUtils
- checks whether the given class is one of the hardcoded core file loaders.
- isCoreFileSaver(String) -
Static method in class weka.core.converters.ConverterUtils
- checks whether the given class is one of the hardcoded core file savers.
- isCover(Instance) -
Method in class weka.classifiers.rules.ConjunctiveRule
- Whether the instance covered by this rule
- isCpuTime() -
Method in class weka.core.Debug.Clock
- whether the measurement is based on the msecs returned from the System
class or on the more accurate CPU time.
- isCursorScrollable() -
Method in class weka.experiment.DatabaseUtils
- Checks whether cursors are scrollable in general, false otherwise
(also if not connected).
- isCursorScrollSensitive() -
Method in class weka.experiment.DatabaseUtils
- Returns whether the cursors only support forward movement or are
scroll sensitive (with ResultSet.CONCUR_READ_ONLY concurrency).
- isDate() -
Method in class weka.core.Attribute
- Tests if the attribute is a date type.
- isDebug() -
Method in class weka.classifiers.bayes.BayesianLogisticRegression
- Returns true if debug is turned on.
- isEmpty() -
Method in class weka.associations.gsp.Element
- Checks if the Element contains any events.
- isEmpty() -
Method in class weka.associations.tertius.LiteralSet
- Test if this set is empty.
- isEmpty() -
Method in class weka.associations.tertius.Rule
- Test if this rule is empty.
- isEmpty() -
Method in class weka.associations.tertius.SimpleLinkedList
-
- isEmpty() -
Method in class weka.classifiers.functions.pace.DiscreteFunction
- Returns true if it is empty.
- isEmpty() -
Method in class weka.classifiers.functions.pace.PaceMatrix
- Check if the matrix is empty
- isEmpty() -
Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable
- Tests if this hashtable maps no keys to values.
- isEmpty() -
Method in class weka.core.matrix.DoubleVector
- Checks if it is an empty vector
- isEmpty() -
Method in class weka.core.matrix.IntVector
- Returns true if the vector is empty
- isEmpty() -
Method in class weka.core.Trie
- Returns true if this collection contains no elements.
- isEnabled(Capabilities.Capability) -
Method in class weka.core.FindWithCapabilities
- whether the given capability is enabled.
- isEnabled() -
Method in class weka.core.Memory
- returns whether the memory management is enabled
- isEnabledNot(Capabilities.Capability) -
Method in class weka.core.FindWithCapabilities
- whether the given "not to have" capability is enabled.
- isEqual(ScatterSearchV1.Subset) -
Method in class weka.attributeSelection.ScatterSearchV1.Subset
-
- isFirstBatchDone() -
Method in class weka.filters.Filter
- Returns true if the first batch of instances got processed.
- isFullRank() -
Method in class weka.core.matrix.QRDecomposition
- Is the matrix full rank?
- isGaussian() -
Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
- checks, whether cluster type is gaussian
- isHidden() -
Method in class weka.gui.beans.BeanConnection
- Returns true if this connection is invisible
- isHierachic(String) -
Method in class weka.gui.HierarchyPropertyParser
- Whether the given string has a hierachy structure with
the seperators
- isHomogeneous(Instances) -
Static method in class weka.classifiers.misc.monotone.InstancesUtil
- Check if all instances have the same class value.
- isIgnored(String) -
Method in class weka.core.xml.PropertyHandler
- checks whether the given display name is an ignored property
- isIgnored(Class, String) -
Method in class weka.core.xml.PropertyHandler
- checks whether the given display name of a certain class is an ignored
property.
- isIgnored(Object, String) -
Method in class weka.core.xml.PropertyHandler
- checks whether the given display name of a given object is an ignored
property.
- isIncludedIn(Rule) -
Method in class weka.associations.tertius.Body
- Test if this Body is included in a rule.
- isIncludedIn(Rule) -
Method in class weka.associations.tertius.Head
- Test if this Head is included in a rule.
- isIncludedIn(Rule) -
Method in class weka.associations.tertius.LiteralSet
- Test if this LiteralSet is included in a rule.
- isIncremental() -
Method in class weka.core.converters.ConverterUtils.DataSource
- returns whether the loader is an incremental one.
- isInitialAnchorRandom() -
Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor
- Gets whether if the initial anchor is chosen randomly.
- isInRange(double) -
Method in class weka.core.Attribute
- Determines whether a value lies within the bounds of the attribute.
- isInRange(int) -
Method in class weka.core.Range
- Gets whether the supplied cardinal number is included in the current
range.
- isInteger() -
Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
- checks, whether cluster sub type is integer
- isKOML(String) -
Static method in class weka.core.xml.SerialUIDChanger
- checks whether the given filename ends with ".koml"
- isLeaf() -
Method in class weka.classifiers.trees.m5.RuleNode
- Return true if this node is a leaf
- isLeafReached() -
Method in class weka.gui.HierarchyPropertyParser
- Whether the current position is a leaf
- isLoadModelsPanelSelected() -
Method in class weka.gui.EnsembleSelectionLibraryEditor
- returns whether or not the LoadModelsPanel is currently selected
- isMissing(int) -
Method in class weka.core.Instance
- Tests if a specific value is "missing".
- isMissing(Attribute) -
Method in class weka.core.Instance
- Tests if a specific value is "missing".
- isMissing(int) -
Method in class weka.core.SparseInstance
- Tests if a specific value is "missing".
- ISMISSING -
Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
-
- isMissingAt(int, int) -
Method in class weka.gui.arffviewer.ArffSortedTableModel
- checks whether the value at the given position is missing
- isMissingAt(int, int) -
Method in class weka.gui.arffviewer.ArffTableModel
- checks whether the value at the given position is missing
- isMissingSparse(int) -
Method in class weka.core.Instance
- Tests if a specific value is "missing".
- isMissingValue(double) -
Static method in class weka.core.Instance
- Tests if the given value codes "missing".
- isMonitoring() -
Method in class weka.gui.MemoryUsagePanel
- Returns whether the thread is still running.
- isMonotone(Instances) -
Static method in class weka.classifiers.misc.monotone.InstancesUtil
- Checks if the given data set is monotone.
- isNewBatch() -
Method in class weka.filters.Filter
- Returns true if the a new batch was started, either a new instance of the
filter was created or the batchFinished() method got called.
- isNewer(Object) -
Method in class weka.core.Version
- checks whether this version is newer than the one from the given
version string
- isNominal() -
Method in class weka.core.Attribute
- Test if the attribute is nominal.
- isNominal(int) -
Method in class weka.datagenerators.clusterers.SubspaceCluster
- Returns true if attribute is nominal
- isNominal() -
Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
- Returns true if selection attribute is nominal.
- isNominal() -
Method in class weka.filters.unsupervised.instance.RemoveWithValues
- Returns true if selection attribute is nominal.
- isNonsingular() -
Method in class weka.core.matrix.LUDecomposition
- Is the matrix nonsingular?
- isNormalizeData() -
Method in class weka.classifiers.bayes.BayesianLogisticRegression
- Returns true if the data is to be normalized first
- isNotificationEnabled() -
Method in class weka.gui.arffviewer.ArffSortedTableModel
- returns whether the notification of changes is enabled
- isNotificationEnabled() -
Method in class weka.gui.arffviewer.ArffTableModel
- returns whether the notification of changes is enabled
- isNullAt(int, int) -
Method in class weka.gui.sql.ResultSetTableModel
- checks whether the value of the cell is NULL.
- isNumeric() -
Method in class weka.core.Attribute
- Tests if the attribute is numeric.
- isNumeric() -
Method in class weka.filters.unsupervised.instance.RemoveWithValues
- Returns true if selection attribute is numeric.
- isNumericAt(int) -
Method in class weka.gui.sql.ResultSetTableModel
- returns whether the column at the given index is numeric.
- isOlder(Object) -
Method in class weka.core.Version
- checks whether this version is older than the one from the given
version string
- isOpticsOutputs() -
Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
- Returns the flag for writing actions
- isOtherCapability() -
Method in enum weka.core.Capabilities.Capability
- returns true if the capability is a class capability
- IsotonicRegression - Class in weka.classifiers.functions
- Learns an isotonic regression model.
- IsotonicRegression() -
Constructor for class weka.classifiers.functions.IsotonicRegression
-
- isOutOfMemory() -
Method in class weka.core.Memory
- checks if there's still enough memory left.
- isOutputFormatDefined() -
Method in class weka.filters.Filter
- Returns whether the output format is ready to be collected
- isOutputFormatDefined() -
Method in class weka.filters.unsupervised.attribute.RemoveType
- Returns whether the output format is ready to be collected
- isPaintable() -
Method in class weka.gui.CostMatrixEditor
- Indicates whether the object can be represented graphically.
- isPaintable() -
Method in class weka.gui.EnsembleLibraryEditor
- Indicates whether the object can be represented graphically.
- isPaintable() -
Method in class weka.gui.FileEditor
- Returns true since this editor is paintable.
- isPaintable() -
Method in class weka.gui.GenericArrayEditor
- Returns true to indicate that we can paint a representation of the
string array.
- isPaintable() -
Method in class weka.gui.GenericObjectEditor
- Returns true to indicate that we can paint a representation of the
Object.
- isPaintable() -
Method in class weka.gui.SimpleDateFormatEditor
- Indicates whether the object can be represented graphically.
- isPanelSelected() -
Method in class weka.gui.arffviewer.ArffViewerMainPanel
- checks whether a panel is currently selected
- isPresent() -
Static method in class weka.classifiers.functions.LibLINEAR
- returns whether the liblinear classes are present or not, i.e.
- isPresent() -
Static method in class weka.classifiers.functions.LibSVM
- returns whether the libsvm classes are present or not, i.e.
- isPresent() -
Static method in class weka.core.Jython
- returns whether the Jython classes are present or not, i.e.
- isPresent() -
Static method in class weka.core.stemmers.SnowballStemmer
- returns whether Snowball is present or not, i.e.
- isPresent() -
Static method in class weka.core.xml.KOML
- returns whether KOML is present or not, i.e.
- isPresent() -
Static method in class weka.core.xml.XStream
- returns whether XStream is present or not, i.e.
- isProcessed() -
Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
- Gives information about the status of a dataObject
- isProcessed() -
Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
- Gives information about the status of a dataObject
- isProcessed() -
Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
- Gives information about the status of a dataObject
- isQuasiMonotone(Instances, Instances) -
Static method in class weka.classifiers.misc.monotone.InstancesUtil
- Test if a set of instances is quasi monotone.
- isRandom() -
Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
- checks, whether cluster type is random
- isReadOnly() -
Method in class weka.gui.arffviewer.ArffPanel
- returns whether the model is read-only
- isReadOnly() -
Method in class weka.gui.arffviewer.ArffSortedTableModel
- returns whether the model is read-only
- isReadOnly() -
Method in class weka.gui.arffviewer.ArffTable
- returns whether the model is read-only
- isReadOnly() -
Method in class weka.gui.arffviewer.ArffTableModel
- returns whether the model is read-only
- isRegular() -
Method in class weka.core.Attribute
- Returns whether the attribute values are equally spaced.
- isRelationValued() -
Method in class weka.core.Attribute
- Tests if the attribute is relation valued.
- isResultRequired(ResultProducer, Object[]) -
Method in class weka.experiment.AveragingResultProducer
- Determines whether the results for a specified key must be
generated.
- isResultRequired(ResultProducer, Object[]) -
Method in class weka.experiment.CSVResultListener
- Always says a result is required.
- isResultRequired(ResultProducer, Object[]) -
Method in class weka.experiment.DatabaseResultListener
- Always says a result is required.
- isResultRequired(ResultProducer, Object[]) -
Method in class weka.experiment.DatabaseResultProducer
- Determines whether the results for a specified key must be
generated.
- isResultRequired(ResultProducer, Object[]) -
Method in class weka.experiment.LearningRateResultProducer
- Determines whether the results for a specified key must be
generated.
- isResultRequired(ResultProducer, Object[]) -
Method in interface weka.experiment.ResultListener
- Determines whether the results for a specified key must be
generated.
- isRootReached() -
Method in class weka.gui.HierarchyPropertyParser
- Whether the current position is the root
- isRunning() -
Method in class weka.core.Debug.Clock
- whether the time is still being clocked
- isSaved() -
Method in class weka.classifiers.bayes.net.EditableBayesNet
- indicate the network state was saved
- isSequentialAttIndexValid() -
Method in class weka.classifiers.lazy.LBR.Indexes
- Returns whether or not the Sequential Attribute Index requires rebuilding due to a change
- isSequentialInstanceIndexValid() -
Method in class weka.classifiers.lazy.LBR.Indexes
- Returns whether or not the Sequential Instance Index requires rebuilding due to a change
- isSerializable(String) -
Static method in class weka.core.SerializationHelper
- checks whether a class is serializable
- isSerializable(Class) -
Static method in class weka.core.SerializationHelper
- checks whether a class is serializable
- isShowCoreDistances() -
Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
- Returns the flag for showCoreDistances
- isShowReachabilityDistances() -
Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
- Returns the flag for showReachabilityDistances
- isSorted() -
Method in class weka.gui.SortedTableModel
- returns whether the table was sorted
- isSPD() -
Method in class weka.core.matrix.CholeskyDecomposition
- Is the matrix symmetric and positive definite?
- isSquare() -
Method in class weka.core.matrix.Matrix
- returns whether the matrix is a square matrix or not.
- isStopword(String) -
Static method in class weka.core.Stopwords
- Returns true if the given string is a stop word.
- isStreamableFilter() -
Method in class weka.filters.MultiFilter
- tests whether all the enclosed filters are streamable
- isString() -
Method in class weka.core.Attribute
- Tests if the attribute is a string.
- isStructureOnly() -
Method in class weka.gui.beans.DataSetEvent
- Returns true if the encapsulated instances
contain just header information
- isStructureOnly() -
Method in class weka.gui.beans.TestSetEvent
- Returns true if the encapsulated instances
contain just header information
- isStructureOnly() -
Method in class weka.gui.beans.TrainingSetEvent
- Returns true if the encapsulated instances
contain just header information
- isSubclass(String, String) -
Static method in class weka.core.ClassDiscovery
- Checks whether the "otherclass" is a subclass of the given "superclass".
- isSubclass(Class, Class) -
Static method in class weka.core.ClassDiscovery
- Checks whether the "otherclass" is a subclass of the given "superclass".
- isSymmetric() -
Method in class weka.core.Matrix
- Deprecated. Returns true if the matrix is symmetric.
- isSymmetric() -
Method in class weka.core.matrix.Matrix
- Returns true if the matrix is symmetric.
- isUndoEnabled() -
Method in interface weka.core.Undoable
- returns whether undo support is enabled
- isUndoEnabled() -
Method in class weka.gui.arffviewer.ArffPanel
- returns whether undo support is enabled
- isUndoEnabled() -
Method in class weka.gui.arffviewer.ArffSortedTableModel
- returns whether undo support is enabled
- isUndoEnabled() -
Method in class weka.gui.arffviewer.ArffTableModel
- returns whether undo support is enabled
- isUniform() -
Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
- checks, whether cluster type is uniform
- isUseK2Prior() -
Method in class weka.classifiers.bayes.net.estimate.BMAEstimator
- Returns whether K2 prior is used
- isUseK2Prior() -
Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
-
- isUseVariant1() -
Method in class weka.classifiers.functions.supportVector.RegSMOImproved
- Whether variant 1 is used
- itemAt(int) -
Method in class weka.associations.ItemSet
- Gest the index of the value of the specified attribute
- items() -
Method in class weka.associations.ItemSet
- Gest the item set as an int array
- ItemSet - Class in weka.associations
- Class for storing a set of items.
- ItemSet(int) -
Constructor for class weka.associations.ItemSet
- Constructor
- ItemSet(int, int[]) -
Constructor for class weka.associations.ItemSet
- Constructor
- ItemSet(int[]) -
Constructor for class weka.associations.ItemSet
- Contsructor
- itemStateChanged(ItemEvent) -
Method in class weka.gui.ensembleLibraryEditor.tree.CheckBoxNode
- This is the listener that fires when the check box is actually toggled.
- itemStateChanged(ItemEvent) -
Method in class weka.gui.ensembleLibraryEditor.tree.CheckBoxNodeEditor
- This is the implementation of the itemListener for the CheckBoxNode.
- itemStateChanged(ItemEvent) -
Method in class weka.gui.ensembleLibraryEditor.tree.ModelTreeNodeEditor
- The item Listener that gets registered with all node editors that
have a widget that had itemStateChangeg events.
- itemStateChanged(ItemEvent) -
Method in class weka.gui.ensembleLibraryEditor.tree.NumberNodeEditor
- This is the Listener that while handle events from the checkBox ,if this
node has one.
- itemStateChanged(ItemEvent) -
Method in class weka.gui.treevisualizer.TreeVisualizer
- Performs the action associated with the ItemEvent.
- IteratedLovinsStemmer - Class in weka.core.stemmers
- An iterated version of the Lovins stemmer.
- IteratedLovinsStemmer() -
Constructor for class weka.core.stemmers.IteratedLovinsStemmer
-
- IteratedSingleClassifierEnhancer - Class in weka.classifiers
- Abstract utility class for handling settings common to
meta classifiers that build an ensemble from a single base learner.
- IteratedSingleClassifierEnhancer() -
Constructor for class weka.classifiers.IteratedSingleClassifierEnhancer
-
- iterationCounter -
Variable in class weka.classifiers.bayes.BayesianLogisticRegression
- Iteration counter
- IterativeClassifier - Interface in weka.classifiers
- Interface for classifiers that can induce models of growing
complexity one step at a time.
- iterator() -
Method in class weka.associations.tertius.SimpleLinkedList
-
- iterator() -
Method in class weka.core.Trie
- Returns an iterator over the elements in this collection.
- iterator() -
Method in class weka.gui.ensembleLibraryEditor.ModelList.SortedListModel
-
KnowledgeFlowApp
instance.
M5P
instance.
weka.core.matrix.Matrix
instead - only for
backwards compatibility. maxValue.
- 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.
- 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
- maxNrOfParentsTipText() -
Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
-
- maxNumClustersTipText() -
Method in class weka.clusterers.XMeans
- Returns the tip text for this property.
- 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() -
Method in class weka.classifiers.misc.monotone.DiscreteDistribution
- Calculate the mean of the distribution.
- 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(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
-
- 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
-
- 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.misc.FLR
- Additional measure Number of Rules
- 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() -
Method in class weka.classifiers.misc.monotone.DiscreteDistribution
- Calculate the median of the distribution.
- 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
- MemoryUsagePanel - Class in weka.gui
- A panel for displaying the memory usage.
- MemoryUsagePanel() -
Constructor for class weka.gui.MemoryUsagePanel
- default constructor.
- merge(Element, Element) -
Static method in class weka.associations.gsp.Element
- Merges two Elements into one.
- 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.
- 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.
- 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.
- 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
-
- 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
-
- 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
- METRIC_ACCURACY -
Static variable in class weka.classifiers.meta.ensembleSelection.EnsembleMetricHelper
- metric: Accuracy
- METRIC_ALL -
Static variable in class weka.classifiers.meta.ensembleSelection.EnsembleMetricHelper
- metric: All
- METRIC_FSCORE -
Static variable in class weka.classifiers.meta.ensembleSelection.EnsembleMetricHelper
- metric: FScore
- METRIC_PRECISION -
Static variable in class weka.classifiers.meta.ensembleSelection.EnsembleMetricHelper
- metric: Precision
- METRIC_RECALL -
Static variable in class weka.classifiers.meta.ensembleSelection.EnsembleMetricHelper
- metric: Recall
- METRIC_RMSE -
Static variable in class weka.classifiers.meta.ensembleSelection.EnsembleMetricHelper
- metric: RMSE
- METRIC_ROC -
Static variable in class weka.classifiers.meta.ensembleSelection.EnsembleMetricHelper
- metric: ROC
- 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
- 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.
- 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
- 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
- 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.
- minGroupTipText() -
Method in class weka.classifiers.meta.RotationForest
- Returns the tip text for this property
- minimalExtension(Instances, Instance) -
Static method in class weka.classifiers.misc.monotone.InstancesUtil
- Computes the minimal extension for a given instance.
- minimalExtension(Instances, Instance, double) -
Static method in class weka.classifiers.misc.monotone.InstancesUtil
- Computes the minimal extension of a given instance, but the
minimal value returned is
minValue.
- 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
-
- minimizeTargetTipText() -
Method in class weka.associations.HotSpot
- Returns the tip text for this property
- minimizeWindows() -
Method in class weka.gui.Main
- minimizes all windows.
- minImprovementTipText() -
Method in class weka.associations.HotSpot
- Returns the tip text for this property
- 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
- MinMaxExtension - Class in weka.classifiers.misc
- This class is an implementation of the minimal and maximal extension.
All attributes and the class are assumed to be ordinal. - MinMaxExtension() -
Constructor for class weka.classifiers.misc.MinMaxExtension
-
- minMaxExtensionTipText() -
Method in class weka.classifiers.misc.MinMaxExtension
- Returns the tip text for this property.
- minMetricTipText() -
Method in class weka.associations.Apriori
- 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
-
- 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.
- 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
- missingMergeTipText() -
Method in class weka.attributeSelection.SymmetricalUncertAttributeSetEval
- Returns the tip text for this property
- missingModeTipText() -
Method in class weka.classifiers.lazy.KStar
- Returns the tip text for this property
- missingSeperateTipText() -
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.
- 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
-
- MixtureDistribution - Class in weka.classifiers.functions.pace
- Abtract class for manipulating mixture distributions.
- MixtureDistribution() -
Constructor for class weka.classifiers.functions.pace.MixtureDistribution
-
- 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
-
- ModelBag - Class in weka.classifiers.meta.ensembleSelection
- This class is responsible for the duties of a bag of models.
- ModelBag(double[][][], double, boolean) -
Constructor for class weka.classifiers.meta.ensembleSelection.ModelBag
- Constructor for ModelBag.
- 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
- ModelList - Class in weka.gui.ensembleLibraryEditor
- This class is basically a customization of the JList class to allow it
to display LibraryModel objects.
- ModelList() -
Constructor for class weka.gui.ensembleLibraryEditor.ModelList
- The constructor simply initializes the model and the renderer.
- ModelList.ModelListRenderer - Class in weka.gui.ensembleLibraryEditor
- This nested helper class is responsible for rendering each Library
Model object.
- ModelList.ModelListRenderer() -
Constructor for class weka.gui.ensembleLibraryEditor.ModelList.ModelListRenderer
-
- ModelList.SortedListModel - Class in weka.gui.ensembleLibraryEditor
- This is a helper class that creates a custom list model for the ModelList class.
- ModelList.SortedListModel() -
Constructor for class weka.gui.ensembleLibraryEditor.ModelList.SortedListModel
- default constructor
- 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
-
- modelRatioTipText() -
Method in class weka.classifiers.meta.EnsembleSelection
- Returns the tip text for this property
- 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.
- ModelTreeNodeEditor - Class in weka.gui.ensembleLibraryEditor.tree
- This class is in charge of dynamically creating editor GUI objects on
demand for the main JTree class that will display our Classifier tree
model of parameters.
- ModelTreeNodeEditor(JTree) -
Constructor for class weka.gui.ensembleLibraryEditor.tree.ModelTreeNodeEditor
- default Constructor
- ModelTreeNodeRenderer - Class in weka.gui.ensembleLibraryEditor.tree
- This class renders a tree nodes.
- ModelTreeNodeRenderer() -
Constructor for class weka.gui.ensembleLibraryEditor.tree.ModelTreeNodeRenderer
- empty Constructor
- modelTypeTipText() -
Method in class weka.classifiers.trees.FT
- Returns the tip text for this property
- modes() -
Method in class weka.classifiers.misc.monotone.DiscreteDistribution
- Get a sorted array containing the indices of the elements with
maximal probability.
- 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
- modifyValue(int, double) -
Method in class weka.core.Instance
- Modifies the instances value for an attribute (floating point
representation).
- modifyValue(int, double) -
Method in class weka.core.SparseInstance
- Modifies the instances value for an attribute (floating point
representation).
- 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
- 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
- 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
- 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.
- MultiDimensionalSort - Class in weka.classifiers.misc.monotone
- Class for doing multidimensional sorting, using an array of
Comparator.- MultiDimensionalSort() -
Constructor for class weka.classifiers.misc.monotone.MultiDimensionalSort
-
- multiDimensionalSort(Object[], Comparator[]) -
Static method in class weka.classifiers.misc.monotone.MultiDimensionalSort
- Sort an array using different comparators.
- multiDimensionalSort(Object[], int, int, Comparator[]) -
Static method in class weka.classifiers.misc.monotone.MultiDimensionalSort
- Sort part of an array using different comparators.
- 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".
- 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.
- MultiNomialBMAEstimator - Class in weka.classifiers.bayes.net.estimate
- Multinomial BMA Estimator.
- MultiNomialBMAEstimator() -
Constructor for class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
-
- 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
- multiplyNumbers(Number, Number) -
Method in class weka.gui.ensembleLibraryEditor.tree.NumberNode
- multiplies two objects that are instances of one of the child classes of
java.lang.Number
- 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
index.
- nextSplitAddedOrder() -
Method in class weka.classifiers.trees.ADTree
- Returns the next number in the order that splitter nodes have been added to
the tree, and records that a new splitter has been added.
- NGramMaxSizeTipText() -
Method in class weka.core.tokenizers.NGramTokenizer
- Returns the tip text for this property.
- NGramMinSizeTipText() -
Method in class weka.core.tokenizers.NGramTokenizer
- Returns the tip text for this property.
- NGramTokenizer - Class in weka.core.tokenizers
- Splits a string into an n-gram with min and max grams.
- NGramTokenizer() -
Constructor for class weka.core.tokenizers.NGramTokenizer
-
- NNConditionalEstimator - Class in weka.estimators
- Conditional probability estimator for a numeric domain conditional upon
a numeric domain (using Mahalanobis distance).
- NNConditionalEstimator() -
Constructor for class weka.estimators.NNConditionalEstimator
-
- NNge - Class in weka.classifiers.rules
- Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules).
- NNge() -
Constructor for class weka.classifiers.rules.NNge
-
- nnls(PaceMatrix, IntVector) -
Method in class weka.classifiers.functions.pace.PaceMatrix
- Solves the nonnegative linear squares problem.
- nnlse(PaceMatrix, PaceMatrix, PaceMatrix, IntVector) -
Method in class weka.classifiers.functions.pace.PaceMatrix
- Solves the nonnegative least squares problem with equality
constraint.
- nnlse1(PaceMatrix, IntVector) -
Method in class weka.classifiers.functions.pace.PaceMatrix
- Solves the nonnegative least squares problem with equality
constraint.
- NNMMethod -
Static variable in class weka.classifiers.functions.pace.MixtureDistribution
- The nonnegative-measure-based method
- NO_CLASS -
Static variable in class weka.associations.CheckAssociator
- a "dummy" class type
- NO_CLASS -
Static variable in class weka.core.TestInstances
- can be used to avoid generating a class attribute
- NO_COMMAND -
Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
-
- NO_SUPPORT -
Static variable in class weka.gui.GenericObjectEditor.GOETreeNode
- color for "no support".
- Node - Class in weka.gui.treevisualizer
- This class records all the data about a particular node for displaying.
- Node(String, String, int, int, Color, String) -
Constructor for class weka.gui.treevisualizer.Node
- This will setup all the values of the node except for its top and center.
- NodePlace - Interface in weka.gui.treevisualizer
- This is an interface for classes that wish to take a node structure and
arrange them
- nodeSplitterTipText() -
Method in class weka.core.neighboursearch.KDTree
- Returns the tip text for this property.
- nodeToString() -
Method in class weka.classifiers.trees.m5.RuleNode
- Returns a description of this node (debugging purposes)
- nodeType -
Variable in class weka.gui.graphvisualizer.GraphNode
- Type of node.
- NOISE -
Static variable in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
-
- noisePercentTipText() -
Method in class weka.datagenerators.classifiers.classification.LED24
- Returns the tip text for this property
- noiseRateTipText() -
Method in class weka.datagenerators.classifiers.regression.MexicanHat
- Returns the tip text for this property
- noiseRateTipText() -
Method in class weka.datagenerators.clusterers.BIRCHCluster
- Returns the tip text for this property
- noiseRateTipText() -
Method in class weka.datagenerators.clusterers.SubspaceCluster
- Returns the tip text for this property
- noiseThresholdTipText() -
Method in class weka.associations.Tertius
- Returns the tip text for this property.
- noiseTipText() -
Method in class weka.classifiers.functions.GaussianProcesses
- Returns the tip text for this property
- noiseVarianceTipText() -
Method in class weka.datagenerators.classifiers.regression.MexicanHat
- Returns the tip text for this property
- NOMINAL -
Static variable in class weka.core.Attribute
- Constant set for nominal attributes.
- nominalColsTipText() -
Method in class weka.datagenerators.ClusterGenerator
- Returns the tip text for this property
- nominalCounts -
Variable in class weka.core.AttributeStats
- Counts of each nominal value
- nominalIndicesTipText() -
Method in class weka.filters.unsupervised.instance.RemoveWithValues
- Returns the tip text for this property
- nominalLabelsTipText() -
Method in class weka.filters.unsupervised.attribute.Add
- Returns the tip text for this property.
- NominalLossFunction - Interface in weka.classifiers.misc.monotone
- Interface for incorporating different loss functions.
- NominalPrediction - Class in weka.classifiers.evaluation
- Encapsulates an evaluatable nominal prediction: the predicted probability
distribution plus the actual class value.
- NominalPrediction(double, double[]) -
Constructor for class weka.classifiers.evaluation.NominalPrediction
- Creates the NominalPrediction object with a default weight of 1.0.
- NominalPrediction(double, double[], double) -
Constructor for class weka.classifiers.evaluation.NominalPrediction
- Creates the NominalPrediction object.
- NominalToBinary - Class in weka.filters.supervised.attribute
- Converts all nominal attributes into binary numeric attributes.
- NominalToBinary() -
Constructor for class weka.filters.supervised.attribute.NominalToBinary
-
- NominalToBinary - Class in weka.filters.unsupervised.attribute
- Converts all nominal attributes into binary numeric attributes.
- NominalToBinary() -
Constructor for class weka.filters.unsupervised.attribute.NominalToBinary
- Constructor - initialises the filter
- nominalToBinaryFilterTipText() -
Method in class weka.classifiers.functions.MultilayerPerceptron
-
- NominalToString - Class in weka.filters.unsupervised.attribute
- Converts a nominal attribute (i.e.
- NominalToString() -
Constructor for class weka.filters.unsupervised.attribute.NominalToString
-
- NON_NUMERIC -
Static variable in class weka.filters.unsupervised.attribute.InterquartileRange
- indicator for non-numeric attributes
- NONE -
Static variable in interface weka.core.converters.Saver
- The retrieval modes
- NONE -
Static variable in class weka.gui.visualize.VisualizePanelEvent
- No longer used
- NonSparseToSparse - Class in weka.filters.unsupervised.instance
- An instance filter that converts all incoming instances into sparse format.
- NonSparseToSparse() -
Constructor for class weka.filters.unsupervised.instance.NonSparseToSparse
-
- noPruningTipText() -
Method in class weka.classifiers.trees.REPTree
- Returns the tip text for this property
- noReplacementTipText() -
Method in class weka.filters.supervised.instance.Resample
- Returns the tip text for this property.
- noReplacementTipText() -
Method in class weka.filters.unsupervised.instance.Resample
- Returns the tip text for this property
- norm() -
Method in class weka.core.AlgVector
- Returns the norm of the vector
- norm1() -
Method in class weka.core.matrix.DoubleVector
- Returns the L1-norm of the vector
- norm1() -
Method in class weka.core.matrix.Matrix
- One norm
- norm2() -
Method in class weka.core.matrix.DoubleVector
- Returns the L2-norm of the vector
- norm2() -
Method in class weka.core.matrix.Matrix
- Two norm
- norm2() -
Method in class weka.core.matrix.SingularValueDecomposition
- Two norm
- NORM_BASED -
Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
- Methods for selecting the hyperparameter value
- NORM_EXPECTED_COST_NAME -
Static variable in class weka.classifiers.evaluation.CostCurve
- attribute name: Normalized Expected Cost
- NORMAL -
Static variable in interface weka.gui.graphvisualizer.GraphConstants
- NORMAL node - node actually contained in graphs description
- normalDistribution -
Static variable in class weka.core.matrix.Maths
- Distribution type: noraml
- NormalEstimator - Class in weka.estimators
- Simple probability estimator that places a single normal distribution
over the observed values.
- NormalEstimator(double) -
Constructor for class weka.estimators.NormalEstimator
- Constructor that takes a precision argument.
- normalInverse(double) -
Static method in class weka.core.Statistics
- Returns the value, x, for which the area under the
Normal (Gaussian) probability density function (integrated from
minus infinity to x) is equal to the argument y
(assumes mean is zero, variance is one).
- NormalizableDistance - Class in weka.core
- Represents the abstract ancestor for normalizable distance functions, like
Euclidean or Manhattan distance.
- NormalizableDistance() -
Constructor for class weka.core.NormalizableDistance
- Invalidates the distance function, Instances must be still set.
- NormalizableDistance(Instances) -
Constructor for class weka.core.NormalizableDistance
- Initializes the distance function and automatically initializes the
ranges.
- normalize() -
Method in class weka.classifiers.CostMatrix
- Normalizes the matrix so that the diagonal contains zeros.
- normalize() -
Method in class weka.classifiers.functions.pace.DiscreteFunction
- Normalizes the function values with L1-norm.
- normalize(double[]) -
Static method in class weka.core.Utils
- Normalizes the doubles in the array by their sum.
- normalize(double[], double) -
Static method in class weka.core.Utils
- Normalizes the doubles in the array using the given value.
- Normalize - Class in weka.filters.unsupervised.attribute
- Normalizes all numeric values in the given dataset (apart from the class attribute, if set).
- Normalize() -
Constructor for class weka.filters.unsupervised.attribute.Normalize
-
- Normalize - Class in weka.filters.unsupervised.instance
- An instance filter that normalize instances considering only numeric attributes and ignoring class index.
- Normalize() -
Constructor for class weka.filters.unsupervised.instance.Normalize
-
- normalizeAttributesTipText() -
Method in class weka.classifiers.functions.MultilayerPerceptron
-
- NormalizeData -
Variable in class weka.classifiers.bayes.BayesianLogisticRegression
- Choose whether to normalize data or not
- normalizeDataTipText() -
Method in class weka.classifiers.bayes.BayesianLogisticRegression
- Returns the tip text for this property
- normalizeDimWidthsTipText() -
Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension
- Returns the tip text for this property.
- normalizedKernel(char[], char[]) -
Method in class weka.classifiers.functions.supportVector.StringKernel
- evaluates the normalized kernel between s and t.
- normalizeDocLengthTipText() -
Method in class weka.filters.unsupervised.attribute.StringToWordVector
- Returns the tip text for this property.
- NormalizedPolyKernel - Class in weka.classifiers.functions.supportVector
- The normalized polynomial kernel.
K(x,y) = <x,y>/sqrt(<x,x><y,y>) where <x,y> = PolyKernel(x,y)
Valid options are: - NormalizedPolyKernel() -
Constructor for class weka.classifiers.functions.supportVector.NormalizedPolyKernel
- default constructor - does nothing
- NormalizedPolyKernel(Instances, int, double, boolean) -
Constructor for class weka.classifiers.functions.supportVector.NormalizedPolyKernel
- Creates a new
NormalizedPolyKernel
instance.
- normalizeNodeWidthTipText() -
Method in class weka.core.neighboursearch.KDTree
- Tip text for this property.
- normalizeNumericClassTipText() -
Method in class weka.classifiers.functions.MultilayerPerceptron
-
- normalizeTipText() -
Method in class weka.attributeSelection.LatentSemanticAnalysis
- Returns the tip text for this property
- normalizeTipText() -
Method in class weka.attributeSelection.PrincipalComponents
- Returns the tip text for this property
- normalizeTipText() -
Method in class weka.classifiers.functions.LibLINEAR
- Returns the tip text for this property
- normalizeTipText() -
Method in class weka.classifiers.functions.LibSVM
- Returns the tip text for this property
- normalizeTipText() -
Method in class weka.filters.unsupervised.attribute.PrincipalComponents
- Returns the tip text for this property.
- normalizeWordWeightsTipText() -
Method in class weka.classifiers.bayes.ComplementNaiveBayes
- Returns the tip text for this property
- NormalMixture - Class in weka.classifiers.functions.pace
- Class for manipulating normal mixture distributions.
- NormalMixture() -
Constructor for class weka.classifiers.functions.pace.NormalMixture
- Contructs an empty NormalMixture
- normalProbability(double) -
Static method in class weka.core.Statistics
- Returns the area under the Normal (Gaussian) probability density
function, integrated from minus infinity to x
(assumes mean is zero, variance is one).
- normBasedHyperParameter() -
Method in class weka.classifiers.bayes.BayesianLogisticRegression
- This function computes the norm-based hyperparameters
and stores them in the m_Hyperparameters.
- NormContinuous - Class in weka.core.pmml
- Class encapsulating a NormContinuous Expression.
- NormContinuous(Element, FieldMetaInfo.Optype, ArrayList<Attribute>) -
Constructor for class weka.core.pmml.NormContinuous
-
- NormDiscrete - Class in weka.core.pmml
- Class encapsulating a NormDiscrete Expression.
- NormDiscrete(Element, FieldMetaInfo.Optype, ArrayList<Attribute>) -
Constructor for class weka.core.pmml.NormDiscrete
- Constructor.
- normF() -
Method in class weka.core.matrix.Matrix
- Frobenius norm
- normInf() -
Method in class weka.core.matrix.Matrix
- Infinity norm
- normTipText() -
Method in class weka.filters.unsupervised.instance.Normalize
- Returns the tip text for this property
- normVector() -
Method in class weka.core.AlgVector
- Norms this vector to length 1.0
- NORTH_CONNECTOR -
Static variable in class weka.gui.beans.BeanVisual
-
- NoSplit - Class in weka.classifiers.trees.j48
- Class implementing a "no-split"-split.
- NoSplit(Distribution) -
Constructor for class weka.classifiers.trees.j48.NoSplit
- Creates "no-split"-split for given distribution.
- NoSupportForMissingValuesException - Exception in weka.core
- Exception that is raised by an object that is unable to process
data with missing values.
- NoSupportForMissingValuesException() -
Constructor for exception weka.core.NoSupportForMissingValuesException
- Creates a new NoSupportForMissingValuesException with no message.
- NoSupportForMissingValuesException(String) -
Constructor for exception weka.core.NoSupportForMissingValuesException
- Creates a new NoSupportForMissingValuesException.
- NOT -
Static variable in interface weka.core.mathematicalexpression.sym
-
- NOT -
Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
-
- NOT_DRAWABLE -
Static variable in interface weka.core.Drawable
-
- NOT_ITERATOR -
Static variable in class weka.gui.ensembleLibraryEditor.tree.NumberNode
- the enumerated value indicating a node is not an iterator
- notCoveredInstances() -
Method in class weka.classifiers.trees.m5.Rule
- Get the instances not covered by this rule
- notifyCapabilitiesFilterListener(Capabilities) -
Method in class weka.gui.explorer.Explorer
- notifies all the listeners of a change
- notifyListener() -
Method in class weka.gui.arffviewer.ArffPanel
- notfies all listener of the change
- notifyListener(TableModelEvent) -
Method in class weka.gui.arffviewer.ArffSortedTableModel
- notfies all listener of the change of the model
- notifyListener(TableModelEvent) -
Method in class weka.gui.arffviewer.ArffTableModel
- notfies all listener of the change of the model
- notUnifyNormTipText() -
Method in class weka.clusterers.sIB
- Returns the tip text for this property.
- nrOfGoodOperationsTipText() -
Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
-
- nrOfLookAheadStepsTipText() -
Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
-
- nrOfRedundant(Instances) -
Static method in class weka.classifiers.misc.monotone.InstancesUtil
- Counts the number of redundant pairs in the sense of OLM.
- nrOfReversedPreferences(Instances) -
Static method in class weka.classifiers.misc.monotone.InstancesUtil
- Gather some statistics regarding reversed preferences.
- nrStochasticReversedPreference(Instances) -
Static method in class weka.classifiers.misc.monotone.InstancesUtil
- Find the number of stochastic reversed preferences in the dataset.
- NullStemmer - Class in weka.core.stemmers
- A dummy stemmer that performs no stemming at all.
- NullStemmer() -
Constructor for class weka.core.stemmers.NullStemmer
-
- NUM_RAND_COLS -
Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
-
- numAllConditions(Instances) -
Static method in class weka.classifiers.rules.RuleStats
- Compute the number of all possible conditions that could
appear in a rule of a given data.
- numAntdsTipText() -
Method in class weka.classifiers.rules.ConjunctiveRule
- Returns the tip text for this property
- numArcsTipText() -
Method in class weka.datagenerators.classifiers.classification.BayesNet
- Returns the tip text for this property
- numArguments() -
Method in class weka.core.Option
- Returns the option's number of arguments.
- numAttemptsOfGeneOptionTipText() -
Method in class weka.classifiers.rules.NNge
- Returns the tip text for this property
- numAttributes() -
Method in class weka.core.Instance
- Returns the number of attributes.
- numAttributes() -
Method in class weka.core.Instances
- Returns the number of attributes.
- numAttributes() -
Method in class weka.core.SparseInstance
- Returns the number of attributes.
- numAttributesTipText() -
Method in class weka.datagenerators.classifiers.classification.BayesNet
- Returns the tip text for this property
- numAttributesTipText() -
Method in class weka.datagenerators.classifiers.classification.RandomRBF
- Returns the tip text for this property
- numAttributesTipText() -
Method in class weka.datagenerators.classifiers.classification.RDG1
- Returns the tip text for this property
- numAttributesTipText() -
Method in class weka.datagenerators.clusterers.SubspaceCluster
- Returns the tip text for this property
- numAttributesTipText() -
Method in class weka.datagenerators.ClusterGenerator
- Returns the tip text for this property
- numAttributesTipText() -
Method in class weka.filters.unsupervised.attribute.RandomSubset
- Returns the tip text for this property.
- numBags() -
Method in class weka.classifiers.trees.j48.Distribution
- Returns number of bags.
- NUMBER -
Static variable in interface weka.core.mathematicalexpression.sym
-
- NUMBER -
Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
-
- numberAttributesSelected() -
Method in class weka.attributeSelection.AttributeSelection
- Return the number of attributes selected from the most recent
run of attribute selection
- NumberClassNotFoundException - Exception in weka.gui.ensembleLibraryEditor.tree
- This is a custom exception that gets thrown when the NumberNode class
or its editor cannot determine the correct child class of
java.lang.Number for a numeric parameter.
- NumberClassNotFoundException(String) -
Constructor for exception weka.gui.ensembleLibraryEditor.tree.NumberClassNotFoundException
- initializes the exception
- numberInInterval(Instance, Instance) -
Static method in class weka.classifiers.misc.monotone.InstancesUtil
- Calculatus the number of elements in the closed interval
[low,up].
- numberLiteralsTipText() -
Method in class weka.associations.Tertius
- Returns the tip text for this property.
- NumberNode - Class in weka.gui.ensembleLibraryEditor.tree
- This subclass is responsible for allowing users to specify either a minimum,
maximum, or iterator value for Integer attributes.
- NumberNode(String, Number, int, boolean, String) -
Constructor for class weka.gui.ensembleLibraryEditor.tree.NumberNode
- The constructor simply initializes all of the member variables
- NumberNodeEditor - Class in weka.gui.ensembleLibraryEditor.tree
- This class is responsible for creating the number editor GUI to allow users
to specify ranges of numerical values.
- NumberNodeEditor(NumberNode) -
Constructor for class weka.gui.ensembleLibraryEditor.tree.NumberNodeEditor
- The constructor builds a user interface based on the information queried
from the node passed in.
- numberOfAttributesTipText() -
Method in class weka.filters.unsupervised.attribute.RandomProjection
- Returns the tip text for this property
- numberOfClusters() -
Method in class weka.clusterers.AbstractClusterer
- Returns the number of clusters.
- numberOfClusters() -
Method in class weka.clusterers.CLOPE
-
- numberOfClusters() -
Method in interface weka.clusterers.Clusterer
- Returns the number of clusters.
- numberOfClusters() -
Method in class weka.clusterers.Cobweb
- Returns the number of clusters.
- numberOfClusters() -
Method in class weka.clusterers.DBScan
- Returns the number of clusters.
- numberOfClusters() -
Method in class weka.clusterers.EM
- Returns the number of clusters.
- numberOfClusters() -
Method in class weka.clusterers.FarthestFirst
- Returns the number of clusters.
- numberOfClusters() -
Method in class weka.clusterers.MakeDensityBasedClusterer
- Returns the number of clusters.
- numberOfClusters() -
Method in class weka.clusterers.OPTICS
- Returns the number of clusters.
- numberOfClusters() -
Method in class weka.clusterers.sIB
- Get the number of clusters
- numberOfClusters() -
Method in class weka.clusterers.SimpleKMeans
- Returns the number of clusters.
- numberOfClusters() -
Method in class weka.clusterers.SingleClustererEnhancer
- Returns the number of clusters.
- numberOfClusters() -
Method in class weka.clusterers.XMeans
- Returns the number of clusters.
- NumberOfClustersRequestable - Interface in weka.clusterers
- Interface to a clusterer that can generate a requested number of
clusters
- numberOfGreaterVectors(Instance) -
Static method in class weka.classifiers.misc.monotone.InstancesUtil
- Calculatutes the number of vectors in the data space that are
greater or equal than the given instance.
- numberOfGroupsTipText() -
Method in class weka.classifiers.meta.RotationForest
- Returns the tip text for this property
- numberOfLinearModels() -
Method in class weka.classifiers.trees.m5.RuleNode
- Get the number of linear models in the tree
- numberOfPartsForInterpolationParameterTipText() -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Returns the tip text for this property.
- numberOfSmallerVectors(Instance) -
Static method in class weka.classifiers.misc.monotone.InstancesUtil
- Calculatutes the number of vectors in the data space that are smaller
or equal than the given instance.
- numBinsTipText() -
Method in class weka.classifiers.meta.RegressionByDiscretization
- Returns the tip text for this property
- numBoostingIterationsTipText() -
Method in class weka.classifiers.functions.SimpleLogistic
- Returns the tip text for this property
- numBoostingIterationsTipText() -
Method in class weka.classifiers.trees.FT
- Returns the tip text for this property
- numBoostingIterationsTipText() -
Method in class weka.classifiers.trees.LMT
- Returns the tip text for this property
- numCacheHits() -
Method in class weka.classifiers.functions.supportVector.CachedKernel
- Returns the number of cache hits on dot products.
- numCacheHits() -
Method in class weka.classifiers.functions.supportVector.Kernel
- Returns the number of dot product cache hits.
- numCacheHits() -
Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
- Returns the number of dot product cache hits.
- numCacheHits() -
Method in class weka.classifiers.functions.supportVector.StringKernel
- Returns the number of dot product cache hits.
- numCentroidsTipText() -
Method in class weka.datagenerators.classifiers.classification.RandomRBF
- Returns the tip text for this property
- numChildren() -
Method in class weka.gui.HierarchyPropertyParser
- The number of the children nodes.
- numCitersTipText() -
Method in class weka.classifiers.mi.CitationKNN
- Returns the tip text for this property
- numClassAttributeValues() -
Method in class weka.classifiers.functions.SMO
-
- numClassAttributeValues() -
Method in class weka.classifiers.mi.MISMO
- Returns the number of values of the class attribute.
- numClasses() -
Method in class weka.classifiers.trees.j48.Distribution
- Returns number of classes.
- numClasses() -
Method in class weka.core.Instance
- Returns the number of class labels.
- numClasses() -
Method in class weka.core.Instances
- Returns the number of class labels.
- numClassesTipText() -
Method in class weka.datagenerators.classifiers.classification.RandomRBF
- Returns the tip text for this property
- numClassesTipText() -
Method in class weka.datagenerators.classifiers.classification.RDG1
- Returns the tip text for this property
- numClustersTipText() -
Method in class weka.classifiers.functions.RBFNetwork
- Returns the tip text for this property
- numClustersTipText() -
Method in class weka.clusterers.EM
- Returns the tip text for this property
- numClustersTipText() -
Method in class weka.clusterers.FarthestFirst
- Returns the tip text for this property
- numClustersTipText() -
Method in class weka.clusterers.sIB
- Returns the tip text for this property.
- numClustersTipText() -
Method in class weka.clusterers.SimpleKMeans
- Returns the tip text for this property
- numClustersTipText() -
Method in class weka.datagenerators.clusterers.BIRCHCluster
- Returns the tip text for this property
- numColumns() -
Method in class weka.classifiers.CostMatrix
- Same as size
- numColumns() -
Method in class weka.core.Matrix
- Deprecated. Returns the number of columns in the matrix.
- numComponentsTipText() -
Method in class weka.filters.supervised.attribute.PLSFilter
- Returns the tip text for this property
- numCorrect() -
Method in class weka.classifiers.trees.j48.Distribution
- Returns perClass(maxClass()).
- numCorrect(int) -
Method in class weka.classifiers.trees.j48.Distribution
- Returns perClassPerBag(index,maxClass(index)).
- numCyclesTipText() -
Method in class weka.datagenerators.clusterers.BIRCHCluster
- Returns the tip text for this property
- numDistinctValues(int) -
Method in class weka.core.Instances
- Returns the number of distinct values of a given attribute.
- numDistinctValues(Attribute) -
Method in class weka.core.Instances
- Returns the number of distinct values of a given attribute.
- numElements() -
Method in class weka.classifiers.functions.supportVector.SMOset
- Returns the number of elements in the set.
- numElements() -
Method in class weka.core.AlgVector
- Returns the number of elements in the vector.
- NUMERIC -
Static variable in class weka.core.Attribute
- Constant set for numeric attributes.
- NumericCleaner - Class in weka.filters.unsupervised.attribute
- A filter that 'cleanses' the numeric data from values that are too small, too big or very close to a certain value (e.g., 0) and sets these values to a pre-defined default.
- NumericCleaner() -
Constructor for class weka.filters.unsupervised.attribute.NumericCleaner
-
- NumericPrediction - Class in weka.classifiers.evaluation
- Encapsulates an evaluatable numeric prediction: the predicted class value
plus the actual class value.
- NumericPrediction(double, double) -
Constructor for class weka.classifiers.evaluation.NumericPrediction
- Creates the NumericPrediction object with a default weight of 1.0.
- NumericPrediction(double, double, double) -
Constructor for class weka.classifiers.evaluation.NumericPrediction
- Creates the NumericPrediction object.
- numericStats -
Variable in class weka.core.AttributeStats
- Stats on numeric value distributions
- numericTipText() -
Method in class weka.filters.unsupervised.attribute.MakeIndicator
-
- NumericToBinary - Class in weka.filters.unsupervised.attribute
- Converts all numeric attributes into binary attributes (apart from the class attribute, if set): if the value of the numeric attribute is exactly zero, the value of the new attribute will be zero.
- NumericToBinary() -
Constructor for class weka.filters.unsupervised.attribute.NumericToBinary
-
- NumericToNominal - Class in weka.filters.unsupervised.attribute
- A filter for turning numeric attributes into nominal ones.
- NumericToNominal() -
Constructor for class weka.filters.unsupervised.attribute.NumericToNominal
-
- NumericTransform - Class in weka.filters.unsupervised.attribute
- Transforms numeric attributes using a given transformation method.
- NumericTransform() -
Constructor for class weka.filters.unsupervised.attribute.NumericTransform
- Default constructor -- sets the default transform method
to java.lang.Math.abs().
- numEvals() -
Method in class weka.classifiers.functions.supportVector.CachedKernel
- Returns the number of time Eval has been called.
- numEvals() -
Method in class weka.classifiers.functions.supportVector.Kernel
- Returns the number of kernel evaluation performed.
- numEvals() -
Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
- Returns the number of kernel evaluation performed.
- numEvals() -
Method in class weka.classifiers.functions.supportVector.StringKernel
- Returns the number of kernel evaluation performed.
- numExamplesTipText() -
Method in class weka.datagenerators.ClassificationGenerator
- Returns the tip text for this property
- numExamplesTipText() -
Method in class weka.datagenerators.RegressionGenerator
- Returns the tip text for this property
- numFalseNegatives(int) -
Method in class weka.classifiers.Evaluation
- Calculate number of false negatives with respect to a particular class.
- numFalsePositives(int) -
Method in class weka.classifiers.Evaluation
- Calculate number of false positives with respect to a particular class.
- numFeaturesTipText() -
Method in class weka.classifiers.trees.RandomForest
- Returns the tip text for this property
- numFoldersMIOptionTipText() -
Method in class weka.classifiers.rules.NNge
- Returns the tip text for this property
- NumFolds -
Variable in class weka.classifiers.bayes.BayesianLogisticRegression
- NumFolds for CV based Hyperparameters selection
- numFoldsPruningTipText() -
Method in class weka.classifiers.trees.BFTree
- Returns the tip text for this property
- numFoldsPruningTipText() -
Method in class weka.classifiers.trees.SimpleCart
- Returns the tip text for this property
- numFoldsTipText() -
Method in class weka.classifiers.bayes.BayesianLogisticRegression
- Returns the tip text for this property
- numFoldsTipText() -
Method in class weka.classifiers.functions.SMO
- Returns the tip text for this property
- numFoldsTipText() -
Method in class weka.classifiers.meta.CVParameterSelection
- Returns the tip text for this property
- numFoldsTipText() -
Method in class weka.classifiers.meta.Dagging
- Returns the tip text for this property
- numFoldsTipText() -
Method in class weka.classifiers.meta.EnsembleSelection
- Returns the tip text for this property
- numFoldsTipText() -
Method in class weka.classifiers.meta.LogitBoost
- Returns the tip text for this property
- numFoldsTipText() -
Method in class weka.classifiers.meta.MultiScheme
- Returns the tip text for this property
- numFoldsTipText() -
Method in class weka.classifiers.meta.Stacking
- Returns the tip text for this property
- numFoldsTipText() -
Method in class weka.classifiers.mi.MISMO
- Returns the tip text for this property
- numFoldsTipText() -
Method in class weka.classifiers.rules.PART
- Returns the tip text for this property
- numFoldsTipText() -
Method in class weka.classifiers.trees.J48
- Returns the tip text for this property
- numFoldsTipText() -
Method in class weka.classifiers.trees.REPTree
- Returns the tip text for this property
- numFoldsTipText() -
Method in class weka.experiment.CrossValidationResultProducer
- Returns the tip text for this property
- numFoldsTipText() -
Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
- Returns the tip text for this property
- numFoldsTipText() -
Method in class weka.filters.unsupervised.instance.RemoveFolds
- Returns the tip text for this property
- numFoldsTipText() -
Method in class weka.filters.unsupervised.instance.RemoveMisclassified
- Returns the tip text for this property
- numIncorrect() -
Method in class weka.classifiers.trees.j48.Distribution
- Returns total-numCorrect().
- numIncorrect(int) -
Method in class weka.classifiers.trees.j48.Distribution
- Returns perBag(index)-numCorrect(index).
- numInnerNodes() -
Method in class weka.classifiers.trees.SimpleCart
- Method to count the number of inner nodes in the tree.
- numInstances() -
Method in class weka.classifiers.Evaluation
- Gets the number of test instances that had a known class value
(actually the sum of the weights of test instances with known
class value).
- numInstances() -
Method in class weka.core.Instances
- Returns the number of instances in the dataset.
- numInstances() -
Method in class weka.core.neighboursearch.balltrees.BallNode
- Returns the number of instances in the
hyper-spherical region of this node.
- numInstances() -
Method in class weka.core.neighboursearch.kdtrees.KDTreeNode
- Returns the number of Instances
in the rectangular region defined
by this node.
- numIrrelevantTipText() -
Method in class weka.datagenerators.classifiers.classification.RDG1
- Returns the tip text for this property
- numIterationsTipText() -
Method in class weka.classifiers.bayes.DMNBtext
- Returns the tip text for this property
- numIterationsTipText() -
Method in class weka.classifiers.functions.VotedPerceptron
- Returns the tip text for this property
- numIterationsTipText() -
Method in class weka.classifiers.functions.Winnow
- Returns the tip text for this property
- numIterationsTipText() -
Method in class weka.classifiers.IteratedSingleClassifierEnhancer
- Returns the tip text for this property
- numIterationsTipText() -
Method in class weka.classifiers.meta.Decorate
- Returns the tip text for this property
- numIterationsTipText() -
Method in class weka.classifiers.meta.MetaCost
- Returns the tip text for this property
- numLeaves() -
Method in class weka.classifiers.trees.BFTree
- Compute number of leaf nodes.
- numLeaves() -
Method in class weka.classifiers.trees.ft.FTtree
- Returns the number of leaves (normal count).
- numLeaves() -
Method in class weka.classifiers.trees.j48.ClassifierTree
- Returns number of leaves in tree structure.
- numLeaves() -
Method in class weka.classifiers.trees.lmt.LMTNode
- Returns the number of leaves (normal count).
- numLeaves(int) -
Method in class weka.classifiers.trees.m5.RuleNode
- Sets the leaves' numbers
- numLeaves() -
Method in class weka.classifiers.trees.SimpleCart
- Compute number of leaf nodes.
- numLiterals() -
Method in class weka.associations.tertius.LiteralSet
- Give the number of literals in this set.
- numLiterals() -
Method in class weka.associations.tertius.Predicate
-
- numLiterals() -
Method in class weka.associations.tertius.Rule
- Give the number of literals in this rule.
- numModelBagsTipText() -
Method in class weka.classifiers.meta.EnsembleSelection
- Returns the tip text for this property
- numNeighboursTipText() -
Method in class weka.attributeSelection.ReliefFAttributeEval
- Returns the tip text for this property
- numNeighboursTipText() -
Method in class weka.classifiers.mi.MINND
- Returns the tip text for this property
- numNodes() -
Method in class weka.classifiers.trees.BFTree
- Compute size of the tree.
- numNodes() -
Method in class weka.classifiers.trees.ft.FTtree
- Returns the number of nodes.
- numNodes() -
Method in class weka.classifiers.trees.j48.ClassifierTree
- Returns number of nodes in tree structure.
- numNodes() -
Method in class weka.classifiers.trees.lmt.LMTNode
- Returns the number of nodes.
- numNodes() -
Method in class weka.classifiers.trees.RandomTree
- Computes size of the tree.
- numNodes() -
Method in class weka.classifiers.trees.REPTree
- Computes size of the tree.
- numNodes() -
Method in class weka.classifiers.trees.SimpleCart
- Compute size of the tree.
- numNumericTipText() -
Method in class weka.datagenerators.classifiers.classification.RDG1
- Returns the tip text for this property
- numOfBoostingIterationsTipText() -
Method in class weka.classifiers.trees.ADTree
-
- numOfBoostingIterationsTipText() -
Method in class weka.classifiers.trees.LADTree
-
- numParameters() -
Method in class weka.classifiers.functions.LinearRegression
- Get the number of coefficients used in the model
- numParameters() -
Method in class weka.classifiers.functions.PaceRegression
- Get the number of coefficients used in the model
- numParameters() -
Method in class weka.classifiers.trees.m5.PreConstructedLinearModel
- Return the number of parameters (coefficients) in the linear model
- numPendingOutput() -
Method in class weka.filters.Filter
- Returns the number of instances pending output
- numPendingOutput() -
Method in class weka.filters.unsupervised.attribute.RemoveType
- Returns the number of instances pending output
- numReferencesTipText() -
Method in class weka.classifiers.mi.CitationKNN
- Returns the tip text for this property
- numRestartsTipText() -
Method in class weka.clusterers.sIB
- Returns the tip text for this property.
- numRows() -
Method in class weka.classifiers.CostMatrix
- Same as size
- numRows() -
Method in class weka.core.Matrix
- Deprecated. Returns the number of rows in the matrix.
- numRules() -
Method in class weka.classifiers.rules.part.MakeDecList
- Outputs the number of rules in the classifier.
- numRulesTipText() -
Method in class weka.associations.Apriori
- Returns the tip text for this property
- numRulesTipText() -
Method in class weka.associations.PredictiveApriori
- Returns the tip text for this property
- numRunsTipText() -
Method in class weka.classifiers.meta.LogitBoost
- Returns the tip text for this property
- numRunsTipText() -
Method in class weka.classifiers.mi.TLD
- Returns the tip text for this property
- numRunsTipText() -
Method in class weka.classifiers.mi.TLDSimple
- Returns the tip text for this property
- numSubCmtysTipText() -
Method in class weka.classifiers.meta.MultiBoostAB
- Returns the tip text for this property
- numSubsets() -
Method in class weka.classifiers.trees.j48.ClassifierSplitModel
- Returns the number of created subsets for the split.
- numSubsetSizeCVFoldsTipText() -
Method in class weka.attributeSelection.SubsetSizeForwardSelection
- Returns the tip text for this property
- numTestingNoisesTipText() -
Method in class weka.classifiers.mi.MINND
- Returns the tip text for this property
- numToSelectTipText() -
Method in class weka.attributeSelection.FCBFSearch
- Returns the tip text for this property
- numToSelectTipText() -
Method in class weka.attributeSelection.GreedyStepwise
- Returns the tip text for this property
- numToSelectTipText() -
Method in class weka.attributeSelection.RaceSearch
- Returns the tip text for this property
- numToSelectTipText() -
Method in class weka.attributeSelection.Ranker
- Returns the tip text for this property
- numTrainingNoisesTipText() -
Method in class weka.classifiers.mi.MINND
- Returns the tip text for this property
- numTreesTipText() -
Method in class weka.classifiers.trees.RandomForest
- Returns the tip text for this property
- numTrueNegatives(int) -
Method in class weka.classifiers.Evaluation
- Calculate the number of true negatives with respect to a particular class.
- numTruePositives(int) -
Method in class weka.classifiers.Evaluation
- Calculate the number of true positives with respect to a particular class.
- numUsedAttributesTipText() -
Method in class weka.attributeSelection.LinearForwardSelection
- Returns the tip text for this property
- numUsedAttributesTipText() -
Method in class weka.attributeSelection.SubsetSizeForwardSelection
- Returns the tip text for this property
- numValues() -
Method in class weka.core.Attribute
- Returns the number of attribute values.
- numValues() -
Method in class weka.core.Instance
- Returns the number of values present.
- numValues() -
Method in class weka.core.SparseInstance
- Returns the number of values in the sparse vector.
- numValuesTipText() -
Method in class weka.filters.unsupervised.instance.RemoveFrequentValues
- Returns the tip text for this property
- numXValFoldsTipText() -
Method in class weka.classifiers.meta.ThresholdSelector
-
- nuTipText() -
Method in class weka.classifiers.functions.LibSVM
- Returns the tip text for this property
java weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.OPTICS_Visualizer [file.ser]
performRequest
method here.
performRequest
method here.
performRequest
method here.
PolyKernel
instance.
PredictionAppender
instance.
java.awt.Dialog
or from
java.awt.Frame
) or, if none available,
using (Frame) null
as owner.
readInstance(Reader)
method, one should use the
ArffLoader
or DataSource
class instead.
reduce_goto
table.
reduce_goto
table.
RemoteResult
instance.
DefaultTableModel
which is a table of zero columns and zero rows.
RuleNode
instance.
SelectedTag
instance.
SelectedTag
instance.
PMMLModel
object that encapsulates a PMML model
PMMLModel
object that encapsulates a PMML model
PMMLModel
object that encapsulates a PMML model
true.
- set(int, int, boolean) -
Method in class weka.classifiers.misc.monotone.BooleanBitMatrix
- Sets the bit at the specified position to the specified
value.
- set(int, int) -
Method in class weka.classifiers.misc.monotone.BooleanBitMatrix
- Sets the bit at the specified position to
true.
- 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.
- setActionListener(ActionListener) -
Method in class weka.gui.ensembleLibraryEditor.tree.NumberNodeEditor
- This method provides a way for the ModelTreeNodeEditor to register an
actionListener with this editor.
- 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.classifiers.meta.EnsembleSelection
- Sets the Algorithm to use
- 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.
- 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)
- 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.
- setArffFile(String) -
Method in class weka.gui.streams.InstanceLoader
-
- setArffFile(String) -
Method in class weka.gui.streams.InstanceSavePanel
-
- 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.EnsembleLibraryEditor
- Some objects can be represented as text, but a library 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.
- 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
- setAveragingType(SelectedTag) -
Method in class weka.classifiers.misc.OLM
- Sets the averaging type to use in phase 1 of the algorithm.
- 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.
- setBalanced(boolean) -
Method in class weka.classifiers.misc.monotone.OSDLCore
- If
balanced
is true
then the balanced
version of OSDL will be used, otherwise the ordinary version of
OSDL will be in effect.
- 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.
- setBeanContext(BeanContext) -
Method in class weka.gui.beans.AbstractDataSource
- Set a bean context for this bean
- 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.
- setBinaryWord(boolean) -
Method in class weka.classifiers.bayes.DMNBtext
- Sets whether use binary text representation
- 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
- setBounds(Instances) -
Method in class weka.classifiers.misc.FLR
- Sets the metric space from the training set using the min-max stats, in case -B option is not used.
- setBoundsFile(String) -
Method in class weka.classifiers.misc.FLR
- Set Boundaries File
- setBoxSelected(boolean) -
Method in class weka.gui.ensembleLibraryEditor.tree.CheckBoxNode
- sets whether the box is selected
- 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.
- 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.functions.SVMreg
- 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.
- 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
- setCenter(double) -
Method in class weka.gui.treevisualizer.Node
- Set the value of center.
- 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
- 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.SMOreg
- 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).
- setChecksum(String) -
Method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibraryModel
- set the checksum
- setChildForBranch(int, PredictionNode) -
Method in class weka.classifiers.trees.adtree.Splitter
- Sets the child for a branch of the split.
- setChildForBranch(int, PredictionNode) -
Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
- Sets the child for a branch of the split.
- setChildForBranch(int, PredictionNode) -
Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
- Sets the child for a branch of the split.
- setCindex(int, 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
- 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.
- setClassificationType(SelectedTag) -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Sets the classification type.
- setClassificationType(SelectedTag) -
Method in class weka.classifiers.misc.OLM
- Sets the classification type.
- 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.Classifier
- Set the classifier for this wrapper
- 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.
- 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.
- setClassValueIndex(int) -
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
- 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.
- 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.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
- 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
- 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.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.
- 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
- 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.associations.HotSpot
- Set whether debugging info is output
- 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.ensembleSelection.EnsembleSelectionLibrary
- Set debug flag for the library and all its models.
- setDebug(boolean) -
Method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibraryModel
- This is used to propagate the m_Debug flag of the EnsembleSelection
classifier to this class.
- 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.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.
- 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.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
- 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.
- setDescriptionText(String) -
Method in class weka.classifiers.EnsembleLibraryModel
- setter for the description text
- 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(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.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.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.
- setDistanceType(SelectedTag) -
Method in class weka.classifiers.misc.OLM
- Sets the distance type to be used by a nearest neighbour rule (if any).
- 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.
- setDontNormalize(boolean) -
Method in class weka.core.NormalizableDistance
- Sets whether if the attribute values are to be normalized in distance
calculation.
- 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(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.
- 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
- 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)
- setEps(double) -
Method in class weka.classifiers.functions.SMOreg
- Set the value of eps.
- setEpsilon(double) -
Method in class weka.classifiers.functions.SMO
- Set the value of epsilon.
- setEpsilon(double) -
Method in class weka.classifiers.functions.SMOreg
- 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.
- setErrorText(String) -
Method in class weka.classifiers.EnsembleLibraryModel
- setter for the error text
- 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
- 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.
- setExtensionType(SelectedTag) -
Method in class weka.classifiers.misc.OLM
- Sets the extension type to use.
- 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.
- 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 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
- 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.classifiers.meta.ensembleSelection.EnsembleSelectionLibraryModel
- Sets the .elm file name for this library model
- 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
- 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.functions.SVMreg
- 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.
- 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.
- 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.meta.ensembleSelection.EnsembleSelectionLibraryModel
- Set the number of folds for cross validation.
- 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
- 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.
- setGenerateDataOutput(boolean) -
Method in class weka.attributeSelection.FCBFSearch
- Sets the flag, by which the AttributeSelection module decide
whether create a new dataset according to the selected features.
- setGenerateRanking(boolean) -
Method in class weka.attributeSelection.FCBFSearch
- This is a dummy set method---Ranker is ONLY capable of producing
a ranked list of attributes for attribute evaluators.
- 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.
- setGenerator(DataGenerator) -
Method in class weka.gui.explorer.DataGeneratorPanel
- sets the generator to use initially
- 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
- setGreedySortInitialization(boolean) -
Method in class weka.classifiers.meta.EnsembleSelection
- Set the value of greedySortInitialization.
- 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
- 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
- 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
- setHillclimbIterations(int) -
Method in class weka.classifiers.meta.EnsembleSelection
- Sets the number of hillclimbIterations.
- setHillclimbMetric(SelectedTag) -
Method in class weka.classifiers.meta.EnsembleSelection
- Sets the hill climbing metric.
- 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.
- setInstancesFromDB(InstanceQuery) -
Method in class weka.gui.explorer.PreprocessPanel
- Loads instances from a database
- 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.
- setInstancesFromFile(AbstractFileLoader) -
Method in class weka.gui.explorer.PreprocessPanel
- Loads results from a set of instances retrieved with the supplied loader.
- 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.
- 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.
- 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().
- setInterpolationParameter(double) -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Sets the interpolation parameter.
- setInterpolationParameterBounds(double, double) -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Sets the interpolation bounds for the interpolation parameter.
- setInterpolationParameterLowerBound(double) -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Sets the lower bound for the interpolation parameter tuning
(0 <= x < 1).
- setInterpolationParameterUpperBound(double) -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Sets the upper bound for the interpolation parameter tuning
(0 < x <= 1).
- 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
- setItemListener(ItemListener) -
Method in class weka.gui.ensembleLibraryEditor.tree.CheckBoxNodeEditor
- This method provides a way for the ModelTreeNodeEditor
to register an itemListener with this editor.
- setItemListener(ItemListener) -
Method in class weka.gui.ensembleLibraryEditor.tree.NumberNodeEditor
- This method provides a way for the ModelTreeNodeEditor to register an
itemListener with this editor.
- setIteratorType(int) -
Method in class weka.gui.ensembleLibraryEditor.tree.NumberNode
- setter for this nodes iteratorType which should be one of the three
enumerated values
- setJitter(int) -
Method in class weka.gui.visualize.Plot2D
- Set level of jitter and repaint the plot using the new jitter value
- setJythonModule(File) -
Method in class weka.classifiers.JythonClassifier
- Sets the Jython module.
- setJythonOptions(String) -
Method in class weka.classifiers.JythonClassifier
- Sets the Jython module options.
- setJythonPaths(String) -
Method in class weka.classifiers.JythonClassifier
- Sets the additional Jython paths.
- 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.functions.SVMreg
- 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.
- 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
- 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.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
- setLibrary(EnsembleSelectionLibrary) -
Method in class weka.classifiers.meta.EnsembleSelection
- Sets the ensemble library.
- setLibrary(EnsembleLibrary) -
Method in class weka.gui.ensembleLibraryEditor.ListModelsPanel
- this is necessay to set the Library object after initiialization
- setLibrary(EnsembleSelectionLibrary) -
Method in class weka.gui.ensembleLibraryEditor.LoadModelsPanel
- Sets the library to use
- setLikelihoodThreshold(double) -
Method in class weka.classifiers.meta.LogitBoost
- Set the value of Precision.
- 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.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)
- setLowerBoundMinSupport(double) -
Method in class weka.associations.Apriori
- 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
-
- setMasterPlot(PlotData2D) -
Method in class weka.gui.visualize.Plot2D
- Set the master plot.
- setMasterPlot(PlotData2D) -
Method in class weka.gui.visualize.VisualizePanel
- Set the master plot for the visualize panel
- setMatchMissingValues(boolean) -
Method in class weka.filters.unsupervised.instance.RemoveWithValues
- Sets whether missing values are counted as a match.
- setMatrix(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.
- setMaxBranchingFactor(int) -
Method in class weka.associations.HotSpot
- Set the maximum branching factor
- 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.
- setMaxExtension() -
Method in class weka.classifiers.misc.MinMaxExtension
- After calling this method, the next classification will use the maximal
extension.
- 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
- 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
- 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.
- setMinExtension() -
Method in class weka.classifiers.misc.MinMaxExtension
- After calling this method, the next classification will use the minimal
extension.
- 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.
- setMinimizeTarget(boolean) -
Method in class weka.associations.HotSpot
- Set whether to minimize the target rather than maximize
- setMinImprovement(double) -
Method in class weka.associations.HotSpot
- Set the minimum improvement in the target necessary to add a test
- 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
- 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.
- setMinMaxExtension(boolean) -
Method in class weka.classifiers.misc.MinMaxExtension
- Chooses between the minimal and maximal extension of the algorithm.
- 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.
- 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
- setMissingMerge(boolean) -
Method in class weka.attributeSelection.SymmetricalUncertAttributeSetEval
- 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.
- setMissingSeperate(boolean) -
Method in class weka.attributeSelection.CfsSubsetEval
- Treat missing as a seperate value
- 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.
- setModelListFile(String) -
Method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibrary
- Sets the model list file that holds the list of models
in the ensemble library.
- setModelRatio(double) -
Method in class weka.classifiers.meta.EnsembleSelection
- Set the value of modelRatio.
- setModels(TreeSet) -
Method in class weka.classifiers.EnsembleLibrary
- setter for the set of models in this library
- 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)
- 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.ensembleLibraryEditor.tree.CheckBoxNode
- sets the name of the parameter value represented by this node
and stores it as the node's user object
- 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
- 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.attributeSelection.PrincipalComponents
- 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
- setNormalize(boolean) -
Method in class weka.filters.unsupervised.attribute.PrincipalComponents
- Set whether input data will be normalized.
- 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
- setNumberOfPartsForInterpolationParameter(int) -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Sets the granularity for tuning the interpolation parameter.
- 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.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.
- 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.
- 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.ensembleSelection.EnsembleSelectionLibrary
- Set the number of folds for cross validation.
- setNumFolds(int) -
Method in class weka.classifiers.meta.EnsembleSelection
- Sets the number of folds for the cross-validation.
- 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.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
- setNumModelBags(int) -
Method in class weka.classifiers.meta.EnsembleSelection
- Sets numModelBags.
- 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.
- setNumRuns(int) -
Method in class weka.classifiers.meta.LogitBoost
- Set the value of NumRuns.
- setNumRuns(int) -
Method in class weka.classifiers.mi.TLD
- Sets the number of runs to perform.
- setNumRuns(int) -
Method in class weka.classifiers.mi.TLDSimple
- Sets the number of runs to perform.
- 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.FCBFSearch
- Specify the number of attributes to select from the ranked list.
- 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.ensembleLibraryEditor.tree.GenericObjectNode
- setter for this nodes object
- 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.GeneralizedSequentialPatterns
- Parses a given list of options.
- setOptions(String[]) -
Method in class weka.associations.HotSpot
- 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.FCBFSearch
- 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.SymmetricalUncertAttributeSetEval
- 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.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.SVMreg
- 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.JythonClassifier
- 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.EnsembleSelection
- Valid options are:
- 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.mi.TLD
- Parses a given list of options.
- setOptions(String[]) -
Method in class weka.classifiers.mi.TLDSimple
- Parses a given list of options.
- setOptions(String[]) -
Method in class weka.classifiers.misc.FLR
- Parses a given list of options.
- setOptions(String[]) -
Method in class weka.classifiers.misc.MinMaxExtension
- Parses the options for this object.
- setOptions(String[]) -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Parses the options for this object.
- setOptions(String[]) -
Method in class weka.classifiers.misc.OLM
- Parses the options for this object.
- 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.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.C45Saver
- 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.
- setOptionsWereValid(boolean) -
Method in class weka.classifiers.EnsembleLibraryModel
- setter for the optionsWereValid member variable
- 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
- 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 interface weka.gui.beans.CustomizerCloseRequester
- A reference to the parent is passed in
- 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.
- 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.
- 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.
- 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.
- 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.
- setPropertyChangeListener(PropertyChangeListener) -
Method in class weka.gui.ensembleLibraryEditor.tree.GenericObjectNodeEditor
- Sets the prop change listener
- setPropertyChangeListener(PropertyChangeListener) -
Method in class weka.gui.ensembleLibraryEditor.tree.NumberNodeEditor
- This method provides a way for the ModelTreeNodeEditor to register a
PropertyListener with this editor.
- 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.
- 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(long) -
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.
- 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.
- 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.SVMreg
- 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.
- setReplacement(boolean) -
Method in class weka.classifiers.meta.EnsembleSelection
- Set the value of replacement.
- 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.AbstractSaver
- Sets the retrieval mode.
- setRetrieval(int) -
Method in interface weka.core.converters.Saver
- Sets the retrieval mode
- setRhoa(double) -
Method in class weka.classifiers.misc.FLR
- Set rhoa
- 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
- 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.M5P
- Set whether to save instance data at each node in the
tree for visualization purposes
- setSaver(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.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.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.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.ensembleSelection.EnsembleSelectionLibraryModel
- Set the 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.
- setSelected(boolean) -
Method in class weka.gui.ensembleLibraryEditor.tree.CheckBoxNode
- setter for the node state
- setSelected(boolean) -
Method in class weka.gui.ensembleLibraryEditor.tree.NumberNode
- setter for the node selected state
- setSelectedColumn(int) -
Method in class weka.gui.arffviewer.ArffTable
- sets the selected column
- 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
- 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
- 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.
- 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
- setShowRules(boolean) -
Method in class weka.classifiers.misc.FLR
- Set ShowRules flag
- 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
- 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
- setSort(boolean) -
Method in class weka.classifiers.misc.OLM
- Sets if the instances are to be sorted prior to building the rule bases.
- 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.
- setSortInitializationRatio(double) -
Method in class weka.classifiers.meta.EnsembleSelection
- Set the value of sortInitializationRatio.
- 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
- setStartSet(String) -
Method in class weka.attributeSelection.BestFirst
- Sets a starting set of attributes for the search.
- setStartSet(String) -
Method in class weka.attributeSelection.FCBFSearch
- 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.
- 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(double) -
Method in class weka.associations.HotSpot
- Set the minimum support
- setSuppressErrorMessage(boolean) -
Method in class weka.classifiers.functions.SimpleLinearRegression
- Turn off the error message that is reported when no useful attribute is found.
- setSVMReg(SVMreg) -
Method in class weka.classifiers.functions.supportVector.RegOptimizer
- sets the parent SVM
- 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.
- setTableName(String) -
Method in class weka.core.converters.DatabaseSaver
- Sets the table's name
- 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(String) -
Method in class weka.associations.HotSpot
- Set the target index
- 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
- setTargetIndex(String) -
Method in class weka.associations.HotSpot
- For a nominal target, set the index of the value of interest (1-based)
- setTaskResult(Object) -
Method in class weka.experiment.TaskStatusInfo
- Set the returnable result for this task..
- setTestBaseFromDialog() -
Method in class weka.gui.experiment.ResultsPanel
-
- 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
- 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.FCBFSearch
- Set the threshold by which the AttributeSelection module can discard
attributes.
- 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.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.functions.SMOreg
- 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.
- 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.
- setTree(JTree) -
Method in class weka.gui.ensembleLibraryEditor.tree.GenericObjectNode
- It seems kind of dumb that the reference to the tree model is passed in
seperately - but know that this is actually necessary.
- setTreshold(double) -
Method in class weka.attributeSelection.ScatterSearchV1
- Set the treshold
- 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.
- setTuneInterpolationParameter(boolean) -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Sets whether the interpolation parameter is to be tuned based on the
bounds.
- 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
- 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.
- 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
- 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.
- 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.
- 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.
- setUserObject(Object) -
Method in class weka.gui.ensembleLibraryEditor.tree.CheckBoxNode
- this is a simple filter for the setUserObject method.
- setUserObject(Object) -
Method in class weka.gui.ensembleLibraryEditor.tree.DefaultNode
- this is a simple filter for the setUserObject method.
- setUserObject(Object) -
Method in class weka.gui.ensembleLibraryEditor.tree.GenericObjectNode
- this is a simple filter for the setUserObject method.
- setUserObject(Object) -
Method in class weka.gui.ensembleLibraryEditor.tree.NumberNode
- this is a simple filter for the setUserObject method.
- 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
- setUsingCutOff(boolean) -
Method in class weka.classifiers.mi.TLD
- Sets whether to use an empirical cutoff.
- setUsingCutOff(boolean) -
Method in class weka.classifiers.mi.TLDSimple
- Sets whether to use an empirical cutoff.
- 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
- setValidationPredictions(double[][]) -
Method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibraryModel
- setter for validation predictions
- setValidationRatio(double) -
Method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibrary
- Sets the validation-set ratio.
- setValidationRatio(double) -
Method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibraryModel
- Sets the validation set ratio (only meaningful if folds == 1)
- setValidationRatio(double) -
Method in class weka.classifiers.meta.EnsembleSelection
- Set the value of validationRatio.
- 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(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(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.EnsembleLibraryEditor
- Sets the value of the Library to be edited.
- setValue(Number) -
Method in class weka.gui.ensembleLibraryEditor.tree.NumberNode
- setter for this nodes object
- setValue(Object) -
Method in class weka.gui.EnsembleSelectionLibraryEditor
- Sets the value of the Library 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)
- 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.
- 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
- setVerboseOutput(boolean) -
Method in class weka.classifiers.meta.EnsembleSelection
- Set the value of verboseOutput.
- 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.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
- setWeighted(boolean) -
Method in class weka.classifiers.misc.monotone.OSDLCore
- If
weighted
is true
then the
weighted version of the OSDL is used.
- 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).
- 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
- setWorkingDirectory(File) -
Method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibrary
- Sets the working Directory of the ensemble library.
- setWorkingDirectory(File) -
Method in class weka.classifiers.meta.EnsembleSelection
- Set the value of working directory.
- 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.
- 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
- 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
- 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
- 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.
- shear(double, double) -
Method in class weka.gui.visualize.PostscriptGraphics
-
- 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.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.
- 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
- 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
- showRules() -
Method in class weka.classifiers.misc.FLR
- Returns the induced set of Fuzzy Lattice Rules
- showRulesTipText() -
Method in class weka.classifiers.misc.FLR
- Returns the tip text for this property
- 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
- shuffle(Random) -
Method in class weka.classifiers.meta.ensembleSelection.ModelBag
- Shuffle the models.
- 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.
- 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
- 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.
- SimpleCLIPanel.CommandlineCompletion() -
Constructor for class weka.gui.SimpleCLIPanel.CommandlineCompletion
- default constructor.
- 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.LinkedListInverseIterator() -
Constructor for class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator
-
- SimpleLinkedList.LinkedListIterator - Class in weka.associations.tertius
-
- SimpleLinkedList.LinkedListIterator() -
Constructor for class weka.associations.tertius.SimpleLinkedList.LinkedListIterator
-
- 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).
- 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.tertius.SimpleLinkedList
-
- size() -
Method in class weka.classifiers.CostMatrix
- The number of rows (and columns)
- size() -
Method in class weka.classifiers.EnsembleLibrary
- Returns the number of models in the ensemble library
- 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.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.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.
- smallerOrEqual(Coordinates) -
Method in class weka.classifiers.misc.monotone.Coordinates
- Checks if
this
is smaller or equal than cc.
- smallerOrEqual(Instance, Instance) -
Static method in class weka.classifiers.misc.monotone.InstancesUtil
- Compares two instances in the data space, this is, ignoring the class
attribute.
- 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.
- SMO.BinarySMO() -
Constructor for class weka.classifiers.functions.SMO.BinarySMO
-
- smoothingParameterTipText() -
Method in class weka.classifiers.bayes.ComplementNaiveBayes
- Returns the tip text for this property
- SMOreg - Class in weka.classifiers.functions
- Implements Alex Smola and Bernhard Scholkopf's sequential minimal optimization algorithm for training a support vector regression model.
- 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
-
- 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
- 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.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).
- 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
- sortInitializationRatioTipText() -
Method in class weka.classifiers.meta.EnsembleSelection
- Returns the tip text for this property
- sortInitialize(int, boolean, Instances, int) -
Method in class weka.classifiers.meta.ensembleSelection.ModelBag
- Sort initialize the bag.
- 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.classifiers.misc.OLM
- Returns the tip text for this property.
- 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
- 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(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
-
- 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
- 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
- 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.
- 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.
- 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
-
- 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
-
- 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
- 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
- startClock() -
Method in class weka.core.Debug
- starts the clock
- 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.FCBFSearch
- 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
- 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.ensembleLibraryEditor.DefaultModelsPanel
- this listener event fires when the use switches back to this panel
it checks to se if the working directory has changed since they were
here last.
- stateChanged(ChangeEvent) -
Method in class weka.gui.ensembleLibraryEditor.LoadModelsPanel
- this listener event fires when the use switches back to this panel it
checks to se if the working directory has changed since they were here
last.
- 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.LogPanel
- Sends the supplied message to the status area.
- statusMessage(String) -
Method in interface weka.gui.Logger
- Sends the supplied message to the status line.
- statusMessage(String) -
Method in class weka.gui.LogPanel
- Sends the supplied message to the status line.
- statusMessage(String) -
Method in class weka.gui.SysErrLog
- Sends the supplied message to the status line.
- stdDev -
Variable in class weka.experiment.Stats
- The std deviation of values at the last calculateDerived() call
- 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
- stochasticDominatedBy(CumulativeDiscreteDistribution) -
Method in class weka.classifiers.misc.monotone.CumulativeDiscreteDistribution
- Returns if
this
is dominated by cdf.
- stochasticDominatedBy(DiscreteDistribution) -
Method in class weka.classifiers.misc.monotone.DiscreteDistribution
- Checks if
this
is dominated by dd.
- 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.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
-
- 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.
- 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.
- 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).
- StreamableFilter - Interface in weka.filters
- Interface for filters can work with a stream of instances.
- strictlySmaller(Coordinates) -
Method in class weka.classifiers.misc.monotone.Coordinates
- Checks if
this
is strictly smaller than cc.
- strictlySmaller(Instance, Instance) -
Static method in class weka.classifiers.misc.monotone.InstancesUtil
- Compares two instances in the data space, this is ignoring the class
attribute.
- 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
-
- 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.
- 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).
- 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.
- 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
-
- 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.
- 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
- 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) -
Static 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
- 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.EnsembleLibraryEditor
- Indicates whether the library 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!)
- supportTipText() -
Method in class weka.associations.HotSpot
- Returns the tip text for this property
- 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.
- SVMOutput(int, Instance) -
Method in class weka.classifiers.functions.SMO.BinarySMO
- Computes SVM output for given instance.
- SVMOutput(Instance) -
Method in class weka.classifiers.functions.supportVector.RegOptimizer
-
- SVMreg - Class in weka.classifiers.functions
- SVMreg implements the support vector machine for regression.
- SVMreg() -
Constructor for class weka.classifiers.functions.SVMreg
-
- 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
- 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.
- 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
- SymmetricalUncertAttributeSetEval - Class in weka.attributeSelection
- SymmetricalUncertAttributeSetEval :
Evaluates the worth of a set attributes by measuring the symmetrical uncertainty with respect to another set of attributes. - SymmetricalUncertAttributeSetEval() -
Constructor for class weka.attributeSelection.SymmetricalUncertAttributeSetEval
- 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
Tag
simply associates a numeric ID with a String description.Tag
instance.
Tag
instance.
CumulativeDiscreteDistribution
that is the maximum of the two given
CumulativeDiscreteDistribution.
- takeMin(CumulativeDiscreteDistribution, CumulativeDiscreteDistribution) -
Static method in class weka.classifiers.misc.monotone.DistributionUtils
- Create a new
CumulativeDiscreteDistribution
that is the minimum of the two given
CumulativeDiscreteDistribution.
- TAN - Class in weka.classifiers.bayes.net.search.global
- This Bayes Network learning algorithm determines the maximum weight spanning tree and returns a Naive Bayes network augmented with a tree.
For more information see:
N. - TAN() -
Constructor for class weka.classifiers.bayes.net.search.global.TAN
-
- TAN - Class in weka.classifiers.bayes.net.search.local
- This Bayes Network learning algorithm determines the maximum weight spanning tree and returns a Naive Bayes network augmented with a tree.
For more information see:
N. - TAN() -
Constructor for class weka.classifiers.bayes.net.search.local.TAN
-
- TAN -
Static variable in interface weka.core.mathematicalexpression.sym
-
- TAN -
Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
-
- target(double[], double[][], int, double[]) -
Method in class weka.classifiers.mi.MINND
- Compute the target function to minimize in gradient descent
The formula is:
1/2*sum[i=1..p](f(X, Xi)-var(Y, Yi))^2
where p is the number of exemplars and Y is the class label.
- targetIndexTipText() -
Method in class weka.associations.HotSpot
- Returns the tip text for this property
- TargetMetaInfo - Class in weka.core.pmml
- Class to encapsulate information about a Target.
- targetTipText() -
Method in class weka.associations.HotSpot
- Returns the tip text for this property
- Task - Interface in weka.experiment
- Interface to something that can be remotely executed as a task.
- taskFinished() -
Method in class weka.gui.LogPanel
- Record a task ending
- taskFinished() -
Method in interface weka.gui.TaskLogger
- Tells the task logger that a task has completed
- taskFinished() -
Method in class weka.gui.WekaTaskMonitor
- Tells the panel that a task has completed
- TaskLogger - Interface in weka.gui
- Interface for objects that display log and display information on
running tasks.
- taskStarted() -
Method in class weka.gui.LogPanel
- Record the starting of a new task
- taskStarted() -
Method in interface weka.gui.TaskLogger
- Tells the task logger that a new task has been started
- taskStarted() -
Method in class weka.gui.WekaTaskMonitor
- Tells the panel that a new task has been started
- TaskStatusInfo - Class in weka.experiment
- A class holding information for tasks being executed
on RemoteEngines.
- TaskStatusInfo() -
Constructor for class weka.experiment.TaskStatusInfo
-
- tauVal(double[][]) -
Static method in class weka.core.ContingencyTables
- Computes Goodman and Kruskal's tau-value for a contingency table.
- TechnicalInformation - Class in weka.core
- Used for paper references in the Javadoc and for BibTex generation.
- TechnicalInformation(TechnicalInformation.Type) -
Constructor for class weka.core.TechnicalInformation
- Initializes the information with the given type
- TechnicalInformation(TechnicalInformation.Type, String) -
Constructor for class weka.core.TechnicalInformation
- Initializes the information with the given type
- TechnicalInformation.Field - Enum in weka.core
- the possible fields
- TechnicalInformation.Type - Enum in weka.core
- the different types of information
- TechnicalInformationHandler - Interface in weka.core
- For classes that are based on some kind of publications.
- TechnicalInformationHandlerJavadoc - Class in weka.core
- Generates Javadoc comments from the TechnicalInformationHandler's data.
- TechnicalInformationHandlerJavadoc() -
Constructor for class weka.core.TechnicalInformationHandlerJavadoc
- default constructor
- Tee - Class in weka.core
- This class pipelines print/println's to several PrintStreams.
- Tee() -
Constructor for class weka.core.Tee
- initializes the object, with a default printstream
- Tee(PrintStream) -
Constructor for class weka.core.Tee
- initializes the object with the given default printstream, e.g.,
System.out.
- Tertius - Class in weka.associations
- Finds rules according to confirmation measure (Tertius-type algorithm).
For more information see:
P. - Tertius() -
Constructor for class weka.associations.Tertius
- Constructor that sets the options to the default values.
- test(Attribute) -
Method in class weka.core.Capabilities
- Test the given attribute, whether it can be processed by the handler,
given its capabilities.
- test(Attribute, boolean) -
Method in class weka.core.Capabilities
- Test the given attribute, whether it can be processed by the handler,
given its capabilities.
- test(Instances) -
Method in class weka.core.Capabilities
- Tests the given data, whether it can be processed by the handler,
given its capabilities.
- test(Instances, int, int) -
Method in class weka.core.Capabilities
- Tests a certain range of attributes of the given data, whether it can be
processed by the handler, given its capabilities.
- test(String[]) -
Static method in class weka.core.Instances
- Method for testing this class.
- Test - Class in weka.datagenerators
- Class to represent a test.
- Test(int, double, Instances) -
Constructor for class weka.datagenerators.Test
- Constructor
- Test(int, double, Instances, boolean) -
Constructor for class weka.datagenerators.Test
- Constructor
- TEST -
Static variable in class weka.gui.beans.BatchClustererEvent
-
- testCapabilities(Instances, int) -
Method in class weka.estimators.Estimator
- Test if the estimator can handle the data.
- testCV(int, int) -
Method in class weka.core.Instances
- Creates the test set for one fold of a cross-validation on
the dataset.
- Tester - Interface in weka.experiment
- Interface for different kinds of Testers in the Experimenter.
- TestInstances - Class in weka.core
- Generates artificial datasets for testing.
- TestInstances() -
Constructor for class weka.core.TestInstances
- the default constructor
- TESTMETHOD_ARITHMETIC -
Static variable in class weka.classifiers.mi.MIWrapper
- arithmetic average
- TESTMETHOD_GEOMETRIC -
Static variable in class weka.classifiers.mi.MIWrapper
- geometric average
- TESTMETHOD_MAXPROB -
Static variable in class weka.classifiers.mi.MIWrapper
- max probability of positive bag
- testOptions() -
Method in class weka.classifiers.EnsembleLibraryModel
- This method will attempt to instantiate this classifier with the given
options.
- TestSetEvent - Class in weka.gui.beans
- Event encapsulating a test set
- TestSetEvent(Object, Instances) -
Constructor for class weka.gui.beans.TestSetEvent
- Creates a new
TestSetEvent
- TestSetEvent(Object, Instances, int, int) -
Constructor for class weka.gui.beans.TestSetEvent
- Creates a new
TestSetEvent
- TestSetEvent(Object, Instances, int, int, int, int) -
Constructor for class weka.gui.beans.TestSetEvent
- Creates a new
TestSetEvent
- TestSetListener - Interface in weka.gui.beans
- Interface to something that can accpet test set events
- TestSetMaker - Class in weka.gui.beans
- Bean that accepts data sets and produces test sets
- TestSetMaker() -
Constructor for class weka.gui.beans.TestSetMaker
-
- TestSetMakerBeanInfo - Class in weka.gui.beans
- Bean info class for the test set maker bean.
- TestSetMakerBeanInfo() -
Constructor for class weka.gui.beans.TestSetMakerBeanInfo
-
- TestSetProducer - Interface in weka.gui.beans
- Interface to something that can produce test sets
- testType() -
Method in class weka.classifiers.trees.j48.GraftSplit
- returns the test type
- testWithFail(Attribute) -
Method in class weka.core.Capabilities
- tests the given attribute by calling the test(Attribute,boolean) method
and throws an exception if the test fails.
- testWithFail(Attribute, boolean) -
Method in class weka.core.Capabilities
- tests the given attribute by calling the test(Attribute,boolean) method
and throws an exception if the test fails.
- testWithFail(Instances, int, int) -
Method in class weka.core.Capabilities
- tests the given data by calling the test(Instances,int,int) method and
throws an exception if the test fails.
- testWithFail(Instances) -
Method in class weka.core.Capabilities
- tests the given data by calling the test(Instances) method and throws
an exception if the test fails.
- TEXT -
Static variable in class weka.experiment.DatabaseUtils
- Type mapping for TEXT used for reading, e.g., text blobs.
- TextDirectoryLoader - Class in weka.core.converters
- Loads all text files in a directory and uses the subdirectory names as class labels.
- TextDirectoryLoader() -
Constructor for class weka.core.converters.TextDirectoryLoader
- default constructor
- TextEvent - Class in weka.gui.beans
- Event that encapsulates some textual information
- TextEvent(Object, String, String) -
Constructor for class weka.gui.beans.TextEvent
- Creates a new
TextEvent
instance.
- TextListener - Interface in weka.gui.beans
- Interface to something that can process a TextEvent
- TextViewer - Class in weka.gui.beans
- Bean that collects and displays pieces of text
- TextViewer() -
Constructor for class weka.gui.beans.TextViewer
-
- TextViewerBeanInfo - Class in weka.gui.beans
- Bean info class for the text viewer
- TextViewerBeanInfo() -
Constructor for class weka.gui.beans.TextViewerBeanInfo
-
- TFTransformTipText() -
Method in class weka.filters.unsupervised.attribute.StringToWordVector
- Returns the tip text for this property.
- theoryDL(int) -
Method in class weka.classifiers.rules.RuleStats
- The description length of the theory for a given rule.
- Threshold -
Variable in class weka.classifiers.bayes.BayesianLogisticRegression
- Threshold for binary classification of probabilisitic estimate
- THRESHOLD_NAME -
Static variable in class weka.classifiers.evaluation.CostCurve
- attribute name: Threshold
- THRESHOLD_NAME -
Static variable in class weka.classifiers.evaluation.ThresholdCurve
- attribute name: Threshold
- ThresholdCurve - Class in weka.classifiers.evaluation
- Generates points illustrating prediction tradeoffs that can be obtained
by varying the threshold value between classes.
- ThresholdCurve() -
Constructor for class weka.classifiers.evaluation.ThresholdCurve
-
- ThresholdDataEvent - Class in weka.gui.beans
- Event encapsulating classifier performance data based on
varying a threshold over the classifier's predicted probabilities
- ThresholdDataEvent(Object, PlotData2D) -
Constructor for class weka.gui.beans.ThresholdDataEvent
-
- ThresholdDataListener - Interface in weka.gui.beans
- Interface to something that can accept ThresholdDataEvents
- ThresholdSelector - Class in weka.classifiers.meta
- A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier.
- ThresholdSelector() -
Constructor for class weka.classifiers.meta.ThresholdSelector
- Constructor.
- thresholdTipText() -
Method in class weka.attributeSelection.FCBFSearch
- Returns the tip text for this property
- thresholdTipText() -
Method in class weka.attributeSelection.GreedyStepwise
- Returns the tip text for this property
- thresholdTipText() -
Method in class weka.attributeSelection.RaceSearch
- Returns the tip text for this property
- thresholdTipText() -
Method in class weka.attributeSelection.Ranker
- Returns the tip text for this property
- thresholdTipText() -
Method in class weka.attributeSelection.WrapperSubsetEval
- Returns the tip text for this property
- thresholdTipText() -
Method in class weka.classifiers.bayes.BayesianLogisticRegression
- Returns the tip text for this property
- thresholdTipText() -
Method in class weka.classifiers.functions.PaceRegression
- Returns the tip text for this property
- thresholdTipText() -
Method in class weka.classifiers.functions.Winnow
- Returns the tip text for this property
- thresholdTipText() -
Method in class weka.filters.unsupervised.instance.RemoveMisclassified
- Returns the tip text for this property
- ThresholdVisualizePanel - Class in weka.gui.visualize
- This panel is a VisualizePanel, with the added ablility to display the
area under the ROC curve if an ROC curve is chosen.
- ThresholdVisualizePanel() -
Constructor for class weka.gui.visualize.ThresholdVisualizePanel
- default constructor
- TIE_STRING -
Variable in class weka.experiment.ResultMatrix
- tie string
- TIME -
Static variable in class weka.experiment.DatabaseUtils
- Type mapping for TIME used for reading TIME columns.
- times(int, int, int, PaceMatrix, int) -
Method in class weka.classifiers.functions.pace.PaceMatrix
- Multiplication between a row (or part of a row) of the first matrix
and a column (or part or a column) of the second matrix.
- TIMES -
Static variable in interface weka.core.mathematicalexpression.sym
-
- times(double) -
Method in class weka.core.matrix.DoubleVector
- Multiplies a scalar
- times(DoubleVector) -
Method in class weka.core.matrix.DoubleVector
- Multiplies another DoubleVector element by element
- times(double) -
Method in class weka.core.matrix.Matrix
- Multiply a matrix by a scalar, C = s*A
- times(Matrix) -
Method in class weka.core.matrix.Matrix
- Linear algebraic matrix multiplication, A * B
- TIMES -
Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
-
- TIMES_EQUAL -
Static variable in class weka.gui.ensembleLibraryEditor.tree.NumberNode
- the enumerated value indicating a node is a *= iterator
- timesEquals(double) -
Method in class weka.classifiers.functions.pace.DiscreteFunction
- All function values are multiplied by a double
- timesEquals(double) -
Method in class weka.core.matrix.DoubleVector
- Multiply a vector by a scalar in place, u = s * u
- timesEquals(DoubleVector) -
Method in class weka.core.matrix.DoubleVector
- Multiplies another DoubleVector element by element in place
- timesEquals(double) -
Method in class weka.core.matrix.Matrix
- Multiply a matrix by a scalar in place, A = s*A
- TimeSeriesDelta - Class in weka.filters.unsupervised.attribute
- An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the difference between the current value and the equivalent attribute attribute value of some previous (or future) instance.
- TimeSeriesDelta() -
Constructor for class weka.filters.unsupervised.attribute.TimeSeriesDelta
-
- TimeSeriesTranslate - Class in weka.filters.unsupervised.attribute
- An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the equivalent attribute values of some previous (or future) instance.
- TimeSeriesTranslate() -
Constructor for class weka.filters.unsupervised.attribute.TimeSeriesTranslate
-
- TIMESTAMP_FIELD_NAME -
Static variable in class weka.experiment.CrossValidationResultProducer
- The name of the result field containing the timestamp
- TIMESTAMP_FIELD_NAME -
Static variable in class weka.experiment.RandomSplitResultProducer
- The name of the result field containing the timestamp
- TLD - Class in weka.classifiers.mi
- Two-Level Distribution approach, changes the starting value of the searching algorithm, supplement the cut-off modification and check missing values.
For more information see:
Xin Xu (2003). - TLD() -
Constructor for class weka.classifiers.mi.TLD
-
- TLDSimple - Class in weka.classifiers.mi
- A simpler version of TLD, mu random but sigma^2 fixed and estimated via data.
For more information see:
Xin Xu (2003). - TLDSimple() -
Constructor for class weka.classifiers.mi.TLDSimple
-
- TO_BE_RUN -
Static variable in class weka.experiment.TaskStatusInfo
-
- toArray() -
Method in class weka.classifiers.misc.monotone.CumulativeDiscreteDistribution
- Get an array representation of the cumulative probability
distribution.
- toArray() -
Method in class weka.classifiers.misc.monotone.DiscreteDistribution
- Convert the
DiscreteDistribution
to an
array of doubles.
- toArray() -
Method in class weka.core.FastVector
- Returns all the elements of this vector as an array
- toArray() -
Method in class weka.core.Trie
- Returns an array containing all of the elements in this collection.
- toArray(T[]) -
Method in class weka.core.Trie
- Returns an array containing all of the elements in this collection; the
runtime type of the returned array is that of the specified array.
- toArray() -
Method in class weka.core.xml.XMLOptions
- returns the current DOM document as string array.
- toArray() -
Method in class weka.gui.CheckBoxList.CheckBoxListModel
- Returns an array containing all of the elements in this list in the
correct order.
- toBibTex() -
Method in class weka.core.TechnicalInformation
- Returns a BibTex string representing this technical information.
- toClassDetailsString() -
Method in class weka.classifiers.Evaluation
- Generates a breakdown of the accuracy for each class (with default title),
incorporating various information-retrieval statistics, such as
true/false positive rate, precision/recall/F-Measure.
- toClassDetailsString(String) -
Method in class weka.classifiers.Evaluation
- Generates a breakdown of the accuracy for each class,
incorporating various information-retrieval statistics, such as
true/false positive rate, precision/recall/F-Measure.
- toCommandLine() -
Method in class weka.core.xml.XMLOptions
- returns the given DOM document as command line.
- toCumulativeMarginDistributionString() -
Method in class weka.classifiers.Evaluation
- Output the cumulative margin distribution as a string suitable
for input for gnuplot or similar package.
- toDataDouble(Instance) -
Static method in class weka.classifiers.misc.monotone.InstancesUtil
- Returns an array containing the attribute values (in internal floating
point format) of the given instance in data space, this is, the class
attribute (if any) is removed.
- toDoubleArray() -
Method in class weka.core.BinarySparseInstance
- Returns the values of each attribute as an array of doubles.
- toDoubleArray() -
Method in class weka.core.Instance
- Returns the values of each attribute as an array of doubles.
- toDoubleArray() -
Method in class weka.core.SparseInstance
- Returns the values of each attribute as an array of doubles.
- toGraph() -
Method in class weka.classifiers.trees.RandomTree
- Outputs the decision tree as a graph
- toGraph(StringBuffer, int) -
Method in class weka.classifiers.trees.RandomTree
- Outputs one node for graph.
- tokenize(String) -
Method in class weka.core.tokenizers.AlphabeticTokenizer
- Sets the string to tokenize.
- tokenize(String) -
Method in class weka.core.tokenizers.NGramTokenizer
- Sets the string to tokenize.
- tokenize(String) -
Method in class weka.core.tokenizers.Tokenizer
- Sets the string to tokenize.
- tokenize(Tokenizer, String[]) -
Static method in class weka.core.tokenizers.Tokenizer
- initializes the given tokenizer with the given options and runs the
tokenizer over all the remaining strings in the options array.
- tokenize(String) -
Method in class weka.core.tokenizers.WordTokenizer
- Sets the string to tokenize.
- tokenize(String) -
Method in class weka.gui.HierarchyPropertyParser
- Tokenize the given string based on the seperator and
put the tokens into an array of strings
- Tokenizer - Class in weka.core.tokenizers
- A superclass for all tokenizer algorithms.
- Tokenizer() -
Constructor for class weka.core.tokenizers.Tokenizer
-
- tokenizerTipText() -
Method in class weka.filters.unsupervised.attribute.StringToWordVector
- Returns the tip text for this property.
- Tolerance -
Variable in class weka.classifiers.bayes.BayesianLogisticRegression
- Tolerance criteria for the stopping criterion.
- toleranceParameterTipText() -
Method in class weka.attributeSelection.SVMAttributeEval
- Returns a tip text for this property suitable for display in the
GUI
- toleranceParameterTipText() -
Method in class weka.classifiers.functions.SMO
- Returns the tip text for this property
- toleranceParameterTipText() -
Method in class weka.classifiers.functions.SMOreg
- Returns the tip text for this property
- toleranceParameterTipText() -
Method in class weka.classifiers.mi.MISMO
- Returns the tip text for this property
- toleranceTipText() -
Method in class weka.classifiers.bayes.BayesianLogisticRegression
- Returns the tip text for this property
- toleranceTipText() -
Method in class weka.classifiers.functions.supportVector.RegSMOImproved
- Returns the tip text for this property
- toMatlab() -
Method in class weka.classifiers.CostMatrix
- converts the Matrix into a single line Matlab string: matrix is enclosed
by parentheses, rows are separated by semicolon and single cells by
blanks, e.g., [1 2; 3 4].
- toMatlab() -
Method in class weka.core.matrix.Matrix
- converts the Matrix into a single line Matlab string: matrix is enclosed
by parentheses, rows are separated by semicolon and single cells by
blanks, e.g., [1 2; 3 4].
- toMatlab() -
Method in class weka.core.Matrix
- Deprecated. converts the Matrix into a single line Matlab string: matrix is enclosed
by parentheses, rows are separated by semicolon and single cells by
blanks, e.g., [1 2; 3 4].
- toMatrixString() -
Method in class weka.classifiers.Evaluation
- Calls toMatrixString() with a default title.
- toMatrixString(String) -
Method in class weka.classifiers.Evaluation
- Outputs the performance statistics as a classification confusion
matrix.
- toMegaByte(long) -
Static method in class weka.core.Memory
- returns the amount of bytes as MB
- toNominalString(Instances) -
Method in class weka.associations.gsp.Element
- Returns a String representation of an Element where the numeric value
of each event/item is represented by its respective nominal value.
- toNominalString(Instances) -
Method in class weka.associations.gsp.Sequence
- Returns a String representation of a Sequences where the numeric value
of each event/item is represented by its respective nominal value.
- toOptionList(Tag[]) -
Static method in class weka.core.Tag
- returns a list that can be used in the listOption methods to list all
the available ID strings, e.g.: <0|1|2> or <what|ever>
- toOptionSynopsis(Tag[]) -
Static method in class weka.core.Tag
- returns a string that can be used in the listOption methods to list all
the available options, i.e., "\t\tID = Text\n" for each option
- toOutput() -
Method in class weka.gui.visualize.JComponentWriter
- saves the current component to the currently set file.
- toOutput(JComponentWriter, JComponent, File) -
Static method in class weka.gui.visualize.JComponentWriter
- outputs the given component with the given writer in the specified file
- toOutput(JComponentWriter, JComponent, File, int, int) -
Static method in class weka.gui.visualize.JComponentWriter
- outputs the given component with the given writer in the specified file.
- TopDownConstructor - Class in weka.core.neighboursearch.balltrees
- The class implementing the TopDown construction method of ball trees.
- TopDownConstructor() -
Constructor for class weka.core.neighboursearch.balltrees.TopDownConstructor
- Creates a new instance of TopDownConstructor.
- topOfTree() -
Method in class weka.classifiers.trees.m5.Rule
- Returns the top of the tree.
- toPrologString() -
Method in class weka.datagenerators.Test
- Returns the test represented by a string in Prolog notation.
- toResultsString() -
Method in class weka.attributeSelection.AttributeSelection
- get a description of the attribute selection
- toSource(String) -
Method in class weka.classifiers.meta.AdaBoostM1
- Returns the boosted model as Java source code.
- toSource(String) -
Method in class weka.classifiers.meta.LogitBoost
- Returns the boosted model as Java source code.
- toSource(String) -
Method in class weka.classifiers.rules.OneR
- Returns a string that describes the classifier as source.
- toSource(String) -
Method in class weka.classifiers.rules.ZeroR
- Returns a string that describes the classifier as source.
- toSource(String) -
Method in interface weka.classifiers.Sourcable
- Returns a string that describes the classifier as source.
- toSource(String) -
Method in class weka.classifiers.trees.DecisionStump
- Returns the decision tree as Java source code.
- toSource(String) -
Method in class weka.classifiers.trees.Id3
- Returns a string that describes the classifier as source.
- toSource(String) -
Method in class weka.classifiers.trees.j48.ClassifierTree
- Returns source code for the tree as an if-then statement.
- toSource(String) -
Method in class weka.classifiers.trees.J48
- Returns tree as an if-then statement.
- toSource(String) -
Method in class weka.classifiers.trees.J48graft
- Returns tree as an if-then statement.
- toSource(String) -
Method in class weka.classifiers.trees.REPTree
- Returns the tree as if-then statements.
- toSource(String) -
Method in class weka.core.Capabilities
- turns the capabilities object into source code.
- toSource(String, int) -
Method in class weka.core.Capabilities
- turns the capabilities object into source code.
- toSource(String, Instances) -
Method in class weka.filters.AllFilter
- Returns a string that describes the filter as source.
- toSource(String, Instances) -
Method in interface weka.filters.Sourcable
- Returns a string that describes the filter as source.
- toSource(String, Instances) -
Method in class weka.filters.unsupervised.attribute.Center
- Returns a string that describes the filter as source.
- toSource(String, Instances) -
Method in class weka.filters.unsupervised.attribute.Normalize
- Returns a string that describes the filter as source.
- toSource(String, Instances) -
Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues
- Returns a string that describes the filter as source.
- toSource(String, Instances) -
Method in class weka.filters.unsupervised.attribute.Standardize
- Returns a string that describes the filter as source.
- toString() -
Method in class weka.associations.Apriori
- Outputs the size of all the generated sets of itemsets and the rules.
- toString(Instances) -
Method in class weka.associations.AprioriItemSet
- Returns the contents of an item set as a string.
- toString() -
Method in class weka.associations.AssociatorEvaluation
- returns the current result
- toString() -
Method in class weka.associations.FilteredAssociator
- Output a representation of this associator
- toString() -
Method in class weka.associations.GeneralizedSequentialPatterns
- Returns a String containing the result information of the algorithm.
- toString() -
Method in class weka.associations.gsp.Element
- Returns a String representation of an Element.
- toString() -
Method in class weka.associations.gsp.Sequence
- Returns a String representation of a Sequence.
- toString() -
Method in class weka.associations.HotSpot
- Return the tree as a string
- toString(Instances) -
Method in class weka.associations.ItemSet
- Returns the contents of an item set as a string.
- toString() -
Method in class weka.associations.PredictiveApriori
- Outputs the association rules.
- toString() -
Method in class weka.associations.tertius.AttributeValueLiteral
-
- toString() -
Method in class weka.associations.tertius.Body
- Gives a String representation of this set of literals as a conjunction.
- toString() -
Method in class weka.associations.tertius.Head
- Gives a String representation of this set of literals as a disjunction.
- toString() -
Method in class weka.associations.tertius.Literal
-
- toString() -
Method in class weka.associations.tertius.LiteralSet
- Gives a String representation for this set of literals.
- toString() -
Method in class weka.associations.tertius.Predicate
-
- toString() -
Method in class weka.associations.tertius.Rule
- Retrun a String for this rule.
- toString() -
Method in class weka.associations.tertius.SimpleLinkedList
-
- toString() -
Method in class weka.associations.Tertius
- Outputs the best rules found with their confirmation value and number
of counter-instances.
- toString() -
Method in class weka.attributeSelection.BestFirst.Link2
-
- toString() -
Method in class weka.attributeSelection.BestFirst
- returns a description of the search as a String
- toString() -
Method in class weka.attributeSelection.CfsSubsetEval
- returns a string describing CFS
- toString() -
Method in class weka.attributeSelection.ChiSquaredAttributeEval
- Describe the attribute evaluator
- toString() -
Method in class weka.attributeSelection.ClassifierSubsetEval
- Returns a string describing classifierSubsetEval
- toString(Instances, int) -
Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey
- Convert a hash entry to a string
- toString() -
Method in class weka.attributeSelection.ConsistencySubsetEval
- returns a description of the evaluator
- toString() -
Method in class weka.attributeSelection.CostSensitiveASEvaluation
- Output a representation of this evaluator
- toString() -
Method in class weka.attributeSelection.ExhaustiveSearch
- prints a description of the search
- toString() -
Method in class weka.attributeSelection.FCBFSearch
- returns a description of the search as a String
- toString() -
Method in class weka.attributeSelection.FilteredAttributeEval
- Describe the attribute evaluator
- toString() -
Method in class weka.attributeSelection.FilteredSubsetEval
- Describe the attribute evaluator
- toString() -
Method in class weka.attributeSelection.GainRatioAttributeEval
- Return a description of the evaluator
- toString() -
Method in class weka.attributeSelection.GeneticSearch
- returns a description of the search
- toString() -
Method in class weka.attributeSelection.GreedyStepwise
- returns a description of the search.
- toString() -
Method in class weka.attributeSelection.InfoGainAttributeEval
- Describe the attribute evaluator
- toString() -
Method in class weka.attributeSelection.LatentSemanticAnalysis
- Returns a description of this attribute transformer
- toString() -
Method in class weka.attributeSelection.LFSMethods.Link2
-
- toString() -
Method in class weka.attributeSelection.LinearForwardSelection
- returns a description of the search as a String
- toString() -
Method in class weka.attributeSelection.OneRAttributeEval
- Return a description of the evaluator
- toString() -
Method in class weka.attributeSelection.PrincipalComponents
- Returns a description of this attribute transformer
- toString() -
Method in class weka.attributeSelection.RaceSearch
- Returns a string represenation
- toString() -
Method in class weka.attributeSelection.RandomSearch
- prints a description of the search
- toString() -
Method in class weka.attributeSelection.Ranker
- returns a description of the search as a String
- toString() -
Method in class weka.attributeSelection.RankSearch
- returns a description of the search as a String
- toString() -
Method in class weka.attributeSelection.ReliefFAttributeEval
- Return a description of the ReliefF attribute evaluator.
- toString() -
Method in class weka.attributeSelection.ScatterSearchV1
- returns a description of the search.
- toString() -
Method in class weka.attributeSelection.SubsetSizeForwardSelection
- returns a description of the search as a String
- toString() -
Method in class weka.attributeSelection.SVMAttributeEval
- Return a description of the evaluator
- toString() -
Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
- Return a description of the evaluator
- toString() -
Method in class weka.attributeSelection.SymmetricalUncertAttributeSetEval
- Return a description of the evaluator
- toString() -
Method in class weka.attributeSelection.WrapperSubsetEval
- Returns a string describing the wrapper
- toString() -
Method in class weka.classifiers.bayes.AODE
- Returns a description of the classifier.
- toString() -
Method in class weka.classifiers.bayes.AODEsr
- Returns a description of the classifier.
- toString() -
Method in class weka.classifiers.bayes.BayesianLogisticRegression
- Outputs the linear regression model as a string.
- toString() -
Method in class weka.classifiers.bayes.BayesNet
- Returns a description of the classifier.
- toString() -
Method in class weka.classifiers.bayes.ComplementNaiveBayes
- Prints out the internal model built by the classifier.
- toString() -
Method in class weka.classifiers.bayes.DMNBtext.DNBBinary
- Returns a string representation of the classifier.
- toString() -
Method in class weka.classifiers.bayes.DMNBtext
- Returns a string representation of the classifier.
- toString() -
Method in class weka.classifiers.bayes.HNB
- returns a string representation of the classifier
- toString() -
Method in class weka.classifiers.bayes.NaiveBayes
- Returns a description of the classifier.
- toString() -
Method in class weka.classifiers.bayes.NaiveBayesMultinomial
- Returns a string representation of the classifier.
- toString() -
Method in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable
- Returns a string representation of the classifier.
- toString() -
Method in class weka.classifiers.bayes.NaiveBayesSimple
- Returns a description of the classifier.
- toString() -
Method in class weka.classifiers.bayes.net.BayesNetGenerator
- Returns either the net (if BIF format) or the generated instances
- toString() -
Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes
- Display a representation of this estimator
- toString() -
Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
-
- toString() -
Method in class weka.classifiers.bayes.net.MarginCalculator
-
- toString() -
Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
- a string representation of the algorithm
- toString() -
Method in class weka.classifiers.bayes.WAODE
- returns a string representation of the classifier
- toString() -
Method in class weka.classifiers.BVDecompose
- Returns description of the bias-variance decomposition results.
- toString() -
Method in class weka.classifiers.BVDecomposeSegCVSub
- Returns description of the bias-variance decomposition results.
- toString() -
Method in class weka.classifiers.CostMatrix
- Converts a matrix to a string.
- toString() -
Method in class weka.classifiers.EnsembleLibraryModel
- This method converts the current set of arguments and the
class name to a string value representing the command line
invocation
- toString() -
Method in class weka.classifiers.evaluation.ConfusionMatrix
- Calls toString() with a default title.
- toString(String) -
Method in class weka.classifiers.evaluation.ConfusionMatrix
- Outputs the performance statistics as a classification confusion
matrix.
- toString() -
Method in class weka.classifiers.evaluation.NominalPrediction
- Gets a human readable representation of this prediction.
- toString() -
Method in class weka.classifiers.evaluation.NumericPrediction
- Gets a human readable representation of this prediction.
- toString() -
Method in class weka.classifiers.evaluation.TwoClassStats
- Returns a string containing the various performance measures
for the current object
- toString() -
Method in class weka.classifiers.functions.GaussianProcesses
- Prints out the classifier.
- toString() -
Method in class weka.classifiers.functions.IsotonicRegression
- Returns a description of this classifier as a string
- toString() -
Method in class weka.classifiers.functions.LeastMedSq
- Returns a string representing the best
LinearRegression classifier found.
- toString() -
Method in class weka.classifiers.functions.LibLINEAR
- returns a string representation
- toString() -
Method in class weka.classifiers.functions.LibSVM
- returns a string representation
- toString() -
Method in class weka.classifiers.functions.LinearRegression
- Outputs the linear regression model as a string.
- toString() -
Method in class weka.classifiers.functions.Logistic
- Gets a string describing the classifier.
- toString() -
Method in class weka.classifiers.functions.MultilayerPerceptron
-
- toString() -
Method in class weka.classifiers.functions.pace.ChisqMixture
- Converts to a string
- toString() -
Method in class weka.classifiers.functions.pace.DiscreteFunction
- Converts the discrete function to string.
- toString() -
Method in class weka.classifiers.functions.pace.MixtureDistribution
- Converts to a string
- toString() -
Method in class weka.classifiers.functions.pace.NormalMixture
- Converts to a string
- toString() -
Method in class weka.classifiers.functions.pace.PaceMatrix
- Converts matrix to string
- toString(int, boolean) -
Method in class weka.classifiers.functions.pace.PaceMatrix
- Converts matrix to string
- toString() -
Method in class weka.classifiers.functions.PaceRegression
- Outputs the linear regression model as a string.
- toString() -
Method in class weka.classifiers.functions.PLSClassifier
- returns a string representation of the classifier
- toString() -
Method in class weka.classifiers.functions.RBFNetwork
- Returns a description of this classifier as a String
- toString() -
Method in class weka.classifiers.functions.SimpleLinearRegression
- Returns a description of this classifier as a string
- toString() -
Method in class weka.classifiers.functions.SimpleLogistic
- Returns a description of the logistic model (attributes/coefficients).
- toString() -
Method in class weka.classifiers.functions.SMO.BinarySMO
- Prints out the classifier.
- toString() -
Method in class weka.classifiers.functions.SMO
- Prints out the classifier.
- toString() -
Method in class weka.classifiers.functions.SMOreg
- Prints out the classifier.
- toString() -
Method in class weka.classifiers.functions.supportVector.KernelEvaluation
- returns the current result
- toString() -
Method in class weka.classifiers.functions.supportVector.NormalizedPolyKernel
- returns a string representation for the Kernel
- toString() -
Method in class weka.classifiers.functions.supportVector.PolyKernel
- returns a string representation for the Kernel
- toString() -
Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
- returns a string representation for the Kernel
- toString() -
Method in class weka.classifiers.functions.supportVector.Puk
- returns a string representation for the Kernel
- toString() -
Method in class weka.classifiers.functions.supportVector.RBFKernel
- returns a string representation for the Kernel
- toString() -
Method in class weka.classifiers.functions.supportVector.RegOptimizer
- Prints out the classifier.
- toString() -
Method in class weka.classifiers.functions.SVMreg
- Prints out the classifier.
- toString() -
Method in class weka.classifiers.functions.VotedPerceptron
- Returns textual description of classifier.
- toString() -
Method in class weka.classifiers.functions.Winnow
- Returns textual description of the classifier.
- toString() -
Method in class weka.classifiers.JythonClassifier
- Returns a description of the classifier.
- toString() -
Method in class weka.classifiers.lazy.IB1
- Returns a description of this classifier.
- toString() -
Method in class weka.classifiers.lazy.IBk
- Returns a description of this classifier.
- toString() -
Method in class weka.classifiers.lazy.KStar
- Returns a description of this classifier.
- toString() -
Method in class weka.classifiers.lazy.LBR
- Returns a description of the classifier.
- toString() -
Method in class weka.classifiers.lazy.LWL
- Returns a description of this classifier.
- toString() -
Method in class weka.classifiers.meta.AdaBoostM1
- Returns description of the boosted classifier.
- toString() -
Method in class weka.classifiers.meta.AdditiveRegression
- Returns textual description of the classifier.
- toString() -
Method in class weka.classifiers.meta.AttributeSelectedClassifier
- Output a representation of this classifier
- toString() -
Method in class weka.classifiers.meta.Bagging
- Returns description of the bagged classifier.
- toString() -
Method in class weka.classifiers.meta.ClassificationViaClustering
- Returns a string representation of the classifier.
- toString() -
Method in class weka.classifiers.meta.ClassificationViaRegression
- Prints the classifiers.
- toString() -
Method in class weka.classifiers.meta.CostSensitiveClassifier
- Output a representation of this classifier
- toString() -
Method in class weka.classifiers.meta.CVParameterSelection
- Returns description of the cross-validated classifier.
- toString() -
Method in class weka.classifiers.meta.Dagging
- Returns description of the classifier.
- toString() -
Method in class weka.classifiers.meta.Decorate
- Returns description of the Decorate classifier.
- toString() -
Method in class weka.classifiers.meta.END
- Returns description of the committee.
- toString() -
Method in class weka.classifiers.meta.EnsembleSelection
- Output a representation of this classifier
- toString() -
Method in class weka.classifiers.meta.FilteredClassifier
- Output a representation of this classifier
- toString() -
Method in class weka.classifiers.meta.Grading
- Output a representation of this classifier
- toString() -
Method in class weka.classifiers.meta.GridSearch
- returns a string representation of the classifier
- toString() -
Method in class weka.classifiers.meta.LogitBoost
- Returns description of the boosted classifier.
- toString() -
Method in class weka.classifiers.meta.MetaCost
- Output a representation of this classifier
- toString() -
Method in class weka.classifiers.meta.MultiBoostAB
- Returns description of the boosted classifier.
- toString() -
Method in class weka.classifiers.meta.MultiClassClassifier
- Prints the classifiers.
- toString() -
Method in class weka.classifiers.meta.MultiScheme
- Output a representation of this classifier
- toString() -
Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
- Outputs the classifier as a string.
- toString() -
Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
- Outputs the classifier as a string.
- toString() -
Method in class weka.classifiers.meta.nestedDichotomies.ND
- Outputs the classifier as a string.
- toString() -
Method in class weka.classifiers.meta.OrdinalClassClassifier
- Prints the classifiers.
- toString() -
Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
- Returns description of the boosted classifier.
- toString() -
Method in class weka.classifiers.meta.RandomCommittee
- Returns description of the committee.
- toString() -
Method in class weka.classifiers.meta.RandomSubSpace
- Returns description of the bagged classifier.
- toString() -
Method in class weka.classifiers.meta.RegressionByDiscretization
- Returns a description of the classifier.
- toString() -
Method in class weka.classifiers.meta.RotationForest
- Returns description of the Rotation Forest classifier.
- toString() -
Method in class weka.classifiers.meta.Stacking
- Output a representation of this classifier
- toString() -
Method in class weka.classifiers.meta.StackingC
- Output a representation of this classifier
- toString() -
Method in class weka.classifiers.meta.ThresholdSelector
- Returns description of the cross-validated classifier.
- toString() -
Method in class weka.classifiers.meta.Vote
- Output a representation of this classifier
- toString() -
Method in class weka.classifiers.mi.CitationKNN
- returns a string representation of the classifier
- toString() -
Method in class weka.classifiers.mi.MDD
- Gets a string describing the classifier.
- toString() -
Method in class weka.classifiers.mi.MIBoost
- Gets a string describing the classifier.
- toString() -
Method in class weka.classifiers.mi.MIDD
- Gets a string describing the classifier.
- toString() -
Method in class weka.classifiers.mi.MIEMDD
- Gets a string describing the classifier.
- toString() -
Method in class weka.classifiers.mi.MILR
- Gets a string describing the classifier.
- toString() -
Method in class weka.classifiers.mi.MISMO
- Prints out the classifier.
- toString() -
Method in class weka.classifiers.mi.MIWrapper
- Gets a string describing the classifier.
- toString() -
Method in class weka.classifiers.mi.SimpleMI
- Gets a string describing the classifier.
- toString() -
Method in class weka.classifiers.mi.TLDSimple
- Gets a string describing the classifier.
- toString() -
Method in class weka.classifiers.misc.FLR
- Returns a description of the classifier.
- toString() -
Method in class weka.classifiers.misc.HyperPipes
- Returns a description of this classifier.
- toString() -
Method in class weka.classifiers.misc.MinMaxExtension
- returns a string representation of this classifier
- toString() -
Method in class weka.classifiers.misc.monotone.AbsoluteLossFunction
- Returns a string with the name of the loss function.
- toString() -
Method in class weka.classifiers.misc.monotone.BooleanBitMatrix
- Create a compact string representation of the matrix.
- toString() -
Method in class weka.classifiers.misc.monotone.Coordinates
- Get a string representation of this object.
- toString() -
Method in class weka.classifiers.misc.monotone.CumulativeDiscreteDistribution
- Get a string representation of the cumulative probability
distribution.
- toString() -
Method in class weka.classifiers.misc.monotone.DiscreteDistribution
- Get a string representation of the given
DiscreteDistribution.
- toString() -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Returns a description of the classifier.
- toString() -
Method in class weka.classifiers.misc.monotone.ZeroOneLossFunction
- Returns a string with the name of the loss function.
- toString() -
Method in class weka.classifiers.misc.OLM
- Returns a string description of the classifier.
- toString() -
Method in class weka.classifiers.misc.SerializedClassifier
- Returns a string representation of the classifier
- toString() -
Method in class weka.classifiers.misc.VFI
- Returns a description of this classifier.
- toString() -
Method in class weka.classifiers.pmml.consumer.GeneralRegression
- Return a textual description of this general regression.
- toString() -
Method in class weka.classifiers.pmml.consumer.NeuralNetwork
-
- toString() -
Method in class weka.classifiers.pmml.consumer.Regression
- Return a textual description of this Regression model.
- toString(String, String) -
Method in class weka.classifiers.rules.ConjunctiveRule
- Prints this rule with the specified class label
- toString() -
Method in class weka.classifiers.rules.ConjunctiveRule
- Prints this rule
- toString() -
Method in class weka.classifiers.rules.DecisionTable
- Returns a description of the classifier.
- toString(Instances, int) -
Method in class weka.classifiers.rules.DecisionTableHashKey
- Convert a hash entry to a string
- toString() -
Method in class weka.classifiers.rules.DTNB
-
- toString() -
Method in class weka.classifiers.rules.JRip
- Prints the all the rules of the rule learner.
- toString() -
Method in class weka.classifiers.rules.NNge
- Returns a description of this classifier.
- toString() -
Method in class weka.classifiers.rules.OneR
- Returns a description of the classifier
- toString() -
Method in class weka.classifiers.rules.part.ClassifierDecList
- Prints rules.
- toString() -
Method in class weka.classifiers.rules.part.MakeDecList
- Outputs the classifier into a string.
- toString() -
Method in class weka.classifiers.rules.PART
- Returns a description of the classifier
- toString() -
Method in class weka.classifiers.rules.Prism
- Prints a description of the classifier.
- toString() -
Method in class weka.classifiers.rules.Ridor
- Prints the all the rules of the rule learner.
- toString() -
Method in class weka.classifiers.rules.ZeroR
- Returns a description of the classifier.
- toString() -
Method in class weka.classifiers.trees.ADTree
- Returns a description of the classifier.
- toString() -
Method in class weka.classifiers.trees.BFTree
- Prints the decision tree using the protected toString method from below.
- toString() -
Method in class weka.classifiers.trees.DecisionStump
- Returns a description of the classifier.
- toString() -
Method in class weka.classifiers.trees.ft.FTtree
- Returns a description of the Functional tree (tree structure and logistic models)
- toString() -
Method in class weka.classifiers.trees.FT
- Returns a description of the classifier.
- toString() -
Method in class weka.classifiers.trees.Id3
- Prints the decision tree using the private toString method from below.
- toString() -
Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG
- Prints tree structure.
- toString() -
Method in class weka.classifiers.trees.j48.ClassifierTree
- Prints tree structure.
- toString(Instances) -
Method in class weka.classifiers.trees.j48.GraftSplit
- method for returning information about this GraftSplit
- toString() -
Method in class weka.classifiers.trees.j48.NBTreeClassifierTree
- Prints tree structure.
- toString() -
Method in class weka.classifiers.trees.j48.NBTreeNoSplit
- Return a textual description of the node
- toString() -
Method in class weka.classifiers.trees.J48
- Returns a description of the classifier.
- toString() -
Method in class weka.classifiers.trees.J48graft
- Returns a description of the classifier.
- toString() -
Method in class weka.classifiers.trees.LADTree
- Returns a description of the classifier.
- toString() -
Method in class weka.classifiers.trees.lmt.LMTNode
- Returns a description of the logistic model tree (tree structure and logistic models)
- toString() -
Method in class weka.classifiers.trees.lmt.LogisticBase
- Returns a description of the logistic model (i.e., attributes and
coefficients).
- toString() -
Method in class weka.classifiers.trees.LMT
- Returns a description of the classifier.
- toString() -
Method in class weka.classifiers.trees.m5.Impurity
- Converts an Impurity object to a string
- toString() -
Method in class weka.classifiers.trees.m5.M5Base
- Returns a description of the classifier
- toString() -
Method in class weka.classifiers.trees.m5.PreConstructedLinearModel
- Returns a textual description of this linear model
- toString() -
Method in class weka.classifiers.trees.m5.Rule
- Return a description of the m5 tree or rule
- toString() -
Method in class weka.classifiers.trees.m5.RuleNode
- print the linear model at this node
- toString() -
Method in class weka.classifiers.trees.m5.Values
- Converts the stats to a string
- toString(Instances) -
Method in class weka.classifiers.trees.m5.YongSplitInfo
- Converts the spliting information to string
- toString() -
Method in class weka.classifiers.trees.NBTree
- Returns a description of the classifier.
- toString() -
Method in class weka.classifiers.trees.RandomForest
- Outputs a description of this classifier.
- toString() -
Method in class weka.classifiers.trees.RandomTree
- Outputs the decision tree.
- toString() -
Method in class weka.classifiers.trees.REPTree
- Outputs the decision tree.
- toString() -
Method in class weka.classifiers.trees.SimpleCart
- Prints the decision tree using the protected toString method from below.
- toString() -
Method in class weka.classifiers.trees.UserClassifier
-
- toString() -
Method in class weka.clusterers.CLOPE
- return a string describing this clusterer
- toString() -
Method in class weka.clusterers.Cobweb
- Returns a description of the clusterer as a string.
- toString() -
Method in class weka.clusterers.DBScan
- Returns a description of the clusterer
- toString() -
Method in class weka.clusterers.EM
- Outputs the generated clusters into a string.
- toString() -
Method in class weka.clusterers.FarthestFirst
- return a string describing this clusterer
- toString() -
Method in class weka.clusterers.FilteredClusterer
- Output a representation of this clusterer.
- toString() -
Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject
-
- toString() -
Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
-
- toString() -
Method in class weka.clusterers.MakeDensityBasedClusterer
- Returns a description of the clusterer.
- toString() -
Method in class weka.clusterers.OPTICS
- Returns a description of the clusterer
- toString() -
Method in class weka.clusterers.sIB
-
- toString() -
Method in class weka.clusterers.SimpleKMeans
- return a string describing this clusterer
- toString() -
Method in class weka.clusterers.XMeans
- Return a string describing this clusterer.
- toString() -
Method in class weka.core.AlgVector
- Converts a vector to a string
- toString() -
Method in class weka.core.Attribute
- Returns a description of this attribute in ARFF format.
- toString() -
Method in class weka.core.AttributeExpression
-
- toString() -
Method in class weka.core.AttributeLocator
- returns a string representation of this object
- toString() -
Method in class weka.core.AttributeStats
- Returns a human readable representation of this AttributeStats instance.
- toString() -
Method in class weka.core.BinarySparseInstance
- Returns the description of one instance in sparse format.
- toString() -
Method in enum weka.core.Capabilities.Capability
- returns the display string of the capability
- toString() -
Method in class weka.core.Capabilities
- returns a string representation of the capabilities
- toString() -
Method in class weka.core.Debug.Clock
- returns the elapsed time, getStop() - getStart(), as string
- toString() -
Method in class weka.core.Debug.Log
- returns a string representation of the logger
- toString() -
Method in class weka.core.Debug.Random
- returns a string representation of this number generator
- toString() -
Method in class weka.core.Debug.SimpleLog
- returns a string representation of the logger
- toString() -
Method in class weka.core.Debug.Timestamp
- returns the timestamp as string in the specified format
- toString() -
Method in class weka.core.Instance
- Returns the description of one instance.
- toString(int) -
Method in class weka.core.Instance
- Returns the description of one value of the instance as a
string.
- toString(Attribute) -
Method in class weka.core.Instance
- Returns the description of one value of the instance as a
string.
- toString() -
Method in class weka.core.Instances
- Returns the dataset as a string in ARFF format.
- toString() -
Method in class weka.core.matrix.DoubleVector
- Convert the DoubleVecor to a string
- toString(int, boolean) -
Method in class weka.core.matrix.DoubleVector
- Convert the DoubleVecor to a string
- toString() -
Method in class weka.core.matrix.IntVector
- Converts the IntVecor to a string
- toString(int, boolean) -
Method in class weka.core.matrix.IntVector
- Convert the IntVecor to a string
- toString() -
Method in class weka.core.matrix.LinearRegression
- returns the coefficients in a string representation
- toString() -
Method in class weka.core.matrix.Matrix
- Converts a matrix to a string.
- toString() -
Method in class weka.core.Matrix
- Deprecated. Converts a matrix to a string
- toString() -
Method in class weka.core.NormalizableDistance
- Returns an empty string.
- toString() -
Method in class weka.core.pmml.BuiltInArithmetic
-
- toString(String) -
Method in class weka.core.pmml.BuiltInArithmetic
-
- toString() -
Method in class weka.core.pmml.BuiltInMath
-
- toString() -
Method in class weka.core.pmml.BuiltInString
-
- toString(String) -
Method in class weka.core.pmml.Constant
-
- toString() -
Method in class weka.core.pmml.DefineFunction
-
- toString(String) -
Method in class weka.core.pmml.DefineFunction
-
- toString() -
Method in class weka.core.pmml.DerivedFieldMetaInfo
-
- toString(String) -
Method in class weka.core.pmml.Discretize
-
- toString() -
Method in class weka.core.pmml.Expression
-
- toString(String) -
Method in class weka.core.pmml.Expression
-
- toString() -
Method in enum weka.core.pmml.FieldMetaInfo.Interval.Closure
-
- toString(double, double) -
Method in enum weka.core.pmml.FieldMetaInfo.Interval.Closure
-
- toString() -
Method in class weka.core.pmml.FieldMetaInfo.Interval
-
- toString() -
Method in enum weka.core.pmml.FieldMetaInfo.Optype
-
- toString() -
Method in enum weka.core.pmml.FieldMetaInfo.Value.Property
-
- toString() -
Method in class weka.core.pmml.FieldMetaInfo.Value
-
- toString(String) -
Method in class weka.core.pmml.FieldRef
-
- toString() -
Method in class weka.core.pmml.Function
-
- toString(String) -
Method in class weka.core.pmml.Function
-
- toString() -
Method in class weka.core.pmml.MiningFieldMetaInfo
- Return a textual representation of this MiningField.
- toString() -
Method in class weka.core.pmml.MiningSchema
- Get a textual description of the mining schema.
- toString(String) -
Method in class weka.core.pmml.NormContinuous
-
- toString(String) -
Method in class weka.core.pmml.NormDiscrete
-
- toString() -
Method in class weka.core.PropertyPath.Path
- returns the structure again as a dot-path
- toString() -
Method in class weka.core.PropertyPath.PathElement
- returns the element once again as string
- toString() -
Method in class weka.core.Queue
- Produces textual description of queue.
- toString() -
Method in class weka.core.Range
- Constructs a representation of the current range.
- toString() -
Method in class weka.core.SelectedTag
- returns the selected tag in string representation
- toString() -
Method in class weka.core.SingleIndex
- Constructs a representation of the current range.
- toString() -
Method in class weka.core.SparseInstance
- Returns the description of one instance in sparse format.
- toString() -
Method in class weka.core.stemmers.LovinsStemmer
- returns a string representation of the stemmer
- toString() -
Method in class weka.core.stemmers.NullStemmer
- returns a string representation of the stemmer
- toString() -
Method in class weka.core.stemmers.SnowballStemmer
- returns a string representation of the stemmer.
- toString() -
Method in class weka.core.Stopwords
- returns the current stopwords in a string
- toString() -
Method in class weka.core.SystemInfo
- returns a string representation of all the system properties
- toString() -
Method in class weka.core.Tag
- returns the IDStr
- toString() -
Method in enum weka.core.TechnicalInformation.Field
- returns the display string of the Type
- toString() -
Method in class weka.core.TechnicalInformation
- Returns a plain-text string representing this technical information.
- toString() -
Method in enum weka.core.TechnicalInformation.Type
- returns the display string of the Type
- toString() -
Method in class weka.core.Tee
- returns only the classname and the number of streams
- toString() -
Method in class weka.core.TestInstances
- returns a string representation of the object
- toString() -
Method in class weka.core.Trie
- returns the trie in string representation
- toString() -
Method in class weka.core.Trie.TrieNode
- returns the node in a string representation
- toString() -
Method in class weka.core.Version
- returns the current version as string
- toString() -
Method in class weka.core.xml.MethodHandler
- returns the internal Hashtable (propety/class - method relationship) in
a string representation
- toString() -
Method in class weka.core.xml.XMLDocument
- returns the current DOM document as XML-string.
- toString() -
Method in class weka.core.xml.XMLOptions
- returns the object in a string representation (as indented XML output).
- toString() -
Method in class weka.core.xml.XMLSerializationMethodHandler
- returns the read and write method handlers as string
- toString() -
Method in class weka.datagenerators.ClusterDefinition
- returns a string representation of the cluster
- toString() -
Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
- Make a string from the cluster features.
- toString() -
Method in class weka.datagenerators.Test
- Returns the test represented by a string.
- toString() -
Method in class weka.estimators.DDConditionalEstimator
- Display a representation of this estimator
- toString() -
Method in class weka.estimators.DiscreteEstimator
- Display a representation of this estimator
- toString() -
Method in class weka.estimators.DKConditionalEstimator
- Display a representation of this estimator
- toString() -
Method in class weka.estimators.DNConditionalEstimator
- Display a representation of this estimator
- toString() -
Method in class weka.estimators.KDConditionalEstimator
- Display a representation of this estimator
- toString() -
Method in class weka.estimators.KernelEstimator
- Display a representation of this estimator
- toString() -
Method in class weka.estimators.KKConditionalEstimator
- Display a representation of this estimator
- toString() -
Method in class weka.estimators.MahalanobisEstimator
- Display a representation of this estimator
- toString() -
Method in class weka.estimators.NDConditionalEstimator
- Display a representation of this estimator
- toString() -
Method in class weka.estimators.NNConditionalEstimator
- Display a representation of this estimator
- toString() -
Method in class weka.estimators.NormalEstimator
- Display a representation of this estimator
- toString() -
Method in class weka.estimators.PoissonEstimator
- Display a representation of this estimator
- toString() -
Method in class weka.experiment.AveragingResultProducer
- Gets a text descrption of the result producer.
- toString() -
Method in class weka.experiment.ClassifierSplitEvaluator
- Returns a text description of the split evaluator.
- toString() -
Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
- Returns a text description of the split evaluator.
- toString() -
Method in class weka.experiment.CrossValidationResultProducer
- Gets a text descrption of the result producer.
- toString() -
Method in class weka.experiment.DatabaseResultProducer
- Gets a text descrption of the result producer.
- toString() -
Method in class weka.experiment.DensityBasedClustererSplitEvaluator
- Returns a text description of the split evaluator.
- toString() -
Method in class weka.experiment.Experiment
- Gets a string representation of the experiment configuration.
- toString() -
Method in class weka.experiment.LearningRateResultProducer
- Gets a text descrption of the result producer.
- toString() -
Method in class weka.experiment.PairedStats
- Returns statistics on the paired comparison.
- toString() -
Method in class weka.experiment.PropertyNode
- Returns a string description of this property.
- toString() -
Method in class weka.experiment.RandomSplitResultProducer
- Gets a text descrption of the result producer.
- toString() -
Method in class weka.experiment.RegressionSplitEvaluator
- Returns a text description of the split evaluator.
- toString() -
Method in class weka.experiment.RemoteExperiment
- Overides toString in Experiment
- toString() -
Method in class weka.experiment.ResultMatrix
- returns the matrix as a string
- toString() -
Method in class weka.experiment.Stats
- Returns a string summarising the stats so far.
- toString() -
Method in class weka.filters.Filter
- Returns a description of the filter, by default only the classname.
- toString() -
Method in class weka.gui.arffviewer.ArffViewer
- returns only the classname
- toString() -
Method in class weka.gui.arffviewer.ArffViewerMainPanel
- returns only the classname
- toString() -
Method in class weka.gui.ensembleLibraryEditor.tree.CheckBoxNode
- ToString methods prints out the toString method of this nodes user
object
- toString() -
Method in class weka.gui.ensembleLibraryEditor.tree.DefaultNode
- ToString method simply prints out the user object toString for this node
- toString() -
Method in class weka.gui.ensembleLibraryEditor.tree.GenericObjectNode
- returns always null
- toString() -
Method in class weka.gui.ensembleLibraryEditor.tree.NumberNode
- returns a string representation
- toString() -
Method in class weka.gui.ensembleLibraryEditor.tree.PropertyNode
- returns a string representation
- toString() -
Method in class weka.gui.GenericObjectEditor.GOETreeNode
- returns a string representation of this treenode.
- toString() -
Method in class weka.gui.graphvisualizer.GraphEdge
-
- toString() -
Method in class weka.gui.sql.event.ConnectionEvent
- returns the event in a string representation
- toString() -
Method in class weka.gui.sql.event.HistoryChangedEvent
- returns the event in a string representation
- toString() -
Method in class weka.gui.sql.event.QueryExecuteEvent
- returns the event in a string representation
- toString() -
Method in class weka.gui.sql.event.ResultChangedEvent
- returns the event in a string representation
- toStringHeader() -
Method in class weka.experiment.ResultMatrix
- returns the header of the matrix as a string
- toStringHeader() -
Method in class weka.experiment.ResultMatrixCSV
- returns the header of the matrix as a string
- toStringHeader() -
Method in class weka.experiment.ResultMatrixGnuPlot
- returns the header of the matrix as a string
- toStringHeader() -
Method in class weka.experiment.ResultMatrixHTML
- returns the header of the matrix as a string
- toStringHeader() -
Method in class weka.experiment.ResultMatrixLatex
- returns the header of the matrix as a string
- toStringHeader() -
Method in class weka.experiment.ResultMatrixPlainText
- returns the header of the matrix as a string
- toStringHeader() -
Method in class weka.experiment.ResultMatrixSignificance
- returns the header of the matrix as a string
- toStringKey() -
Method in class weka.experiment.ResultMatrix
- returns returns a key for all the col names, for better readability if
the names got cut off
- toStringKey() -
Method in class weka.experiment.ResultMatrixCSV
- returns returns a key for all the col names, for better readability if
the names got cut off
- toStringKey() -
Method in class weka.experiment.ResultMatrixGnuPlot
- returns returns a key for all the col names, for better readability if
the names got cut off
- toStringKey() -
Method in class weka.experiment.ResultMatrixHTML
- returns returns a key for all the col names, for better readability if
the names got cut off
- toStringKey() -
Method in class weka.experiment.ResultMatrixLatex
- returns returns a key for all the col names, for better readability if
the names got cut off
- toStringKey() -
Method in class weka.experiment.ResultMatrixPlainText
- returns returns a key for all the col names, for better readability if
the names got cut off
- toStringKey() -
Method in class weka.experiment.ResultMatrixSignificance
- returns returns a key for all the col names, for better readability if
the names got cut off
- toStringMatrix() -
Method in class weka.experiment.ResultMatrix
- returns the matrix as a string
- toStringMatrix() -
Method in class weka.experiment.ResultMatrixCSV
- returns the matrix in CSV format
- toStringMatrix() -
Method in class weka.experiment.ResultMatrixGnuPlot
- returns the matrix in CSV format
- toStringMatrix() -
Method in class weka.experiment.ResultMatrixHTML
- returns the matrix in an HTML table
- toStringMatrix() -
Method in class weka.experiment.ResultMatrixLatex
- returns the matrix as latex table
- toStringMatrix() -
Method in class weka.experiment.ResultMatrixPlainText
- returns the matrix as plain text
- toStringMatrix() -
Method in class weka.experiment.ResultMatrixSignificance
- returns the matrix as plain text
- toStringRanking() -
Method in class weka.experiment.ResultMatrix
- returns the ranking in a string representation
- toStringRanking() -
Method in class weka.experiment.ResultMatrixCSV
- returns the ranking in a string representation
- toStringRanking() -
Method in class weka.experiment.ResultMatrixGnuPlot
- returns the ranking in a string representation
- toStringRanking() -
Method in class weka.experiment.ResultMatrixHTML
- returns the ranking in a string representation
- toStringRanking() -
Method in class weka.experiment.ResultMatrixLatex
- returns the ranking in a string representation
- toStringRanking() -
Method in class weka.experiment.ResultMatrixPlainText
- returns the ranking in a string representation
- toStringRanking() -
Method in class weka.experiment.ResultMatrixSignificance
- returns the ranking in a string representation
- toStringSummary() -
Method in class weka.experiment.ResultMatrix
- returns the summary as string
- toStringSummary() -
Method in class weka.experiment.ResultMatrixCSV
- returns the summary as string
- toStringSummary() -
Method in class weka.experiment.ResultMatrixGnuPlot
- returns the summary as string
- toStringSummary() -
Method in class weka.experiment.ResultMatrixHTML
- returns the summary as string
- toStringSummary() -
Method in class weka.experiment.ResultMatrixLatex
- returns the summary as string
- toStringSummary() -
Method in class weka.experiment.ResultMatrixPlainText
- returns the summary as string
- toStringSummary() -
Method in class weka.experiment.ResultMatrixSignificance
- returns the summary as string
- toSummaryString() -
Method in class weka.associations.AssociatorEvaluation
- returns a summary string of the evaluation with a no title
- toSummaryString(String) -
Method in class weka.associations.AssociatorEvaluation
- returns a summary string of the evaluation with a default title
- toSummaryString() -
Method in class weka.classifiers.Evaluation
- Calls toSummaryString() with no title and no complexity stats
- toSummaryString(boolean) -
Method in class weka.classifiers.Evaluation
- Calls toSummaryString() with a default title.
- toSummaryString(String, boolean) -
Method in class weka.classifiers.Evaluation
- Outputs the performance statistics in summary form.
- toSummaryString() -
Method in class weka.classifiers.functions.supportVector.KernelEvaluation
- returns a summary string of the evaluation with a no title
- toSummaryString(String) -
Method in class weka.classifiers.functions.supportVector.KernelEvaluation
- returns a summary string of the evaluation with a default title
- toSummaryString() -
Method in class weka.classifiers.meta.CVParameterSelection
- A concise description of the model.
- toSummaryString() -
Method in class weka.classifiers.meta.GridSearch
- Returns a string that summarizes the object.
- toSummaryString() -
Method in class weka.classifiers.misc.FLR
- Returns a superconcise version of the model
- toSummaryString() -
Method in class weka.classifiers.rules.PART
- Returns a superconcise version of the model
- toSummaryString() -
Method in class weka.classifiers.trees.J48
- Returns a superconcise version of the model
- toSummaryString() -
Method in class weka.classifiers.trees.J48graft
- Returns a superconcise version of the model
- toSummaryString() -
Method in class weka.classifiers.trees.NBTree
- Returns a superconcise version of the model
- toSummaryString() -
Method in class weka.core.Instances
- Generates a string summarizing the set of instances.
- toSummaryString() -
Method in interface weka.core.Summarizable
- Returns a string that summarizes the object.
- total() -
Method in class weka.classifiers.evaluation.ConfusionMatrix
- Gets the number of predictions that were made
(actually the sum of the weights of predictions where the
class value was known).
- total() -
Method in class weka.classifiers.trees.j48.Distribution
- Returns total number of (possibly fractional) instances.
- TOTAL_UNIFORM -
Static variable in class weka.datagenerators.clusterers.SubspaceCluster
- cluster type: total uniform
- totalCost() -
Method in class weka.classifiers.Evaluation
- Gets the total cost, that is, the cost of each prediction times the
weight of the instance, summed over all instances.
- totalCount -
Variable in class weka.core.AttributeStats
- The total number of values (i.e.
- totalForSubset(int) -
Method in class weka.classifiers.trees.j48.GraftSplit
-
- totalForSubsetOfInterest() -
Method in class weka.classifiers.trees.j48.GraftSplit
-
- totalLoss(Classifier, Instances, NominalLossFunction) -
Static method in class weka.classifiers.misc.monotone.InstancesUtil
- Calulates the total loss over the
instances
,
using the trained classifier
and the
specified lossFunction.
- toXML(Object) -
Method in class weka.core.xml.XMLSerialization
- extracts all accesible properties from the given object
- toXMLBIF03() -
Method in class weka.classifiers.bayes.BayesNet
- Returns a description of the classifier in XML BIF 0.3 format.
- toXMLBIF03() -
Method in class weka.classifiers.bayes.net.EditableBayesNet
- returns network in XMLBIF format
- toXMLBIF03(FastVector) -
Method in class weka.classifiers.bayes.net.EditableBayesNet
- return fragment of network in XMLBIF format
- toXMLBIF03() -
Method in class weka.classifiers.bayes.net.MarginCalculator
-
- TP_RATE -
Static variable in class weka.classifiers.meta.ThresholdSelector
- true-positive rate
- TP_RATE_NAME -
Static variable in class weka.classifiers.evaluation.ThresholdCurve
- attribute name: True Positive Rate
- trace() -
Method in class weka.core.matrix.Matrix
- Matrix trace.
- train(Instances, int) -
Method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibraryModel
- Train the classifier for the specified fold on the given data
- trainAll(Instances, String, int) -
Method in class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibrary
- This method will iterate through the TreeMap of models and
train all models that do not currently exist (are not
yet trained).
- trainCV(int, int) -
Method in class weka.core.Instances
- Creates the training set for one fold of a cross-validation
on the dataset.
- trainCV(int, int, Random) -
Method in class weka.core.Instances
- Creates the training set for one fold of a cross-validation
on the dataset.
- TRAINING -
Static variable in class weka.gui.beans.BatchClustererEvent
-
- TrainingSetEvent - Class in weka.gui.beans
- Event encapsulating a training set
- TrainingSetEvent(Object, Instances) -
Constructor for class weka.gui.beans.TrainingSetEvent
- Creates a new
TrainingSetEvent
- TrainingSetEvent(Object, Instances, int, int) -
Constructor for class weka.gui.beans.TrainingSetEvent
- Creates a new
TrainingSetEvent
- TrainingSetEvent(Object, Instances, int, int, int, int) -
Constructor for class weka.gui.beans.TrainingSetEvent
- Creates a new
TrainingSetEvent
- TrainingSetListener - Interface in weka.gui.beans
- Interface to something that can accept and process training set events
- TrainingSetMaker - Class in weka.gui.beans
- Bean that accepts a data sets and produces a training set
- TrainingSetMaker() -
Constructor for class weka.gui.beans.TrainingSetMaker
-
- TrainingSetMakerBeanInfo - Class in weka.gui.beans
- Bean info class for the training set maker bean
- TrainingSetMakerBeanInfo() -
Constructor for class weka.gui.beans.TrainingSetMakerBeanInfo
-
- TrainingSetProducer - Interface in weka.gui.beans
- Interface to something that can produce a training set
- trainingTimeTipText() -
Method in class weka.classifiers.functions.MultilayerPerceptron
-
- trainPercentTipText() -
Method in class weka.experiment.RandomSplitResultProducer
- Returns the tip text for this property
- trainPercentTipText() -
Method in class weka.gui.beans.TrainTestSplitMaker
- Tip text info for this property
- TrainTestSplitMaker - Class in weka.gui.beans
- Bean that accepts data sets, training sets, test sets and produces
both a training and test set by randomly spliting the data
- TrainTestSplitMaker() -
Constructor for class weka.gui.beans.TrainTestSplitMaker
-
- TrainTestSplitMakerBeanInfo - Class in weka.gui.beans
- Bean info class for the train test split maker bean
- TrainTestSplitMakerBeanInfo() -
Constructor for class weka.gui.beans.TrainTestSplitMakerBeanInfo
-
- TrainTestSplitMakerCustomizer - Class in weka.gui.beans
- GUI customizer for the train test split maker bean
- TrainTestSplitMakerCustomizer() -
Constructor for class weka.gui.beans.TrainTestSplitMakerCustomizer
-
- transform(Instances) -
Method in class weka.classifiers.mi.SimpleMI
- Implements MITransform (3 type of transformation) 1.arithmatic average;
2.geometric centor; 3.merge minima and maxima attribute value together
- transform(AffineTransform) -
Method in class weka.gui.visualize.PostscriptGraphics
-
- transformAllValuesTipText() -
Method in class weka.filters.supervised.attribute.NominalToBinary
- Returns the tip text for this property
- transformAllValuesTipText() -
Method in class weka.filters.unsupervised.attribute.NominalToBinary
- Returns the tip text for this property
- transformBackToOriginalTipText() -
Method in class weka.attributeSelection.PrincipalComponents
- Returns the tip text for this property
- transformedData(Instances) -
Method in interface weka.attributeSelection.AttributeTransformer
- Transform the supplied data set (assumed to be the same format
as the training data)
- transformedData(Instances) -
Method in class weka.attributeSelection.LatentSemanticAnalysis
- Transform the supplied data set (assumed to be the same format
as the training data)
- transformedData(Instances) -
Method in class weka.attributeSelection.PrincipalComponents
- Gets the transformed training data.
- transformedHeader() -
Method in interface weka.attributeSelection.AttributeTransformer
- Returns just the header for the transformed data (ie.
- transformedHeader() -
Method in class weka.attributeSelection.LatentSemanticAnalysis
- Returns just the header for the transformed data (ie.
- transformedHeader() -
Method in class weka.attributeSelection.PrincipalComponents
- Returns just the header for the transformed data (ie.
- TRANSFORMMETHOD_ARITHMETIC -
Static variable in class weka.classifiers.mi.SimpleMI
- arithmetic average
- TRANSFORMMETHOD_GEOMETRIC -
Static variable in class weka.classifiers.mi.SimpleMI
- geometric average
- TRANSFORMMETHOD_MINIMAX -
Static variable in class weka.classifiers.mi.SimpleMI
- using minimax combined features of a bag
- transformMethodTipText() -
Method in class weka.classifiers.mi.SimpleMI
- Returns the tip text for this property
- translate(int, int) -
Method in class weka.gui.visualize.PostscriptGraphics
- Translates the origin of the graphics context to the point (x, y) in the
current coordinate system.
- translate(double, double) -
Method in class weka.gui.visualize.PostscriptGraphics
-
- translateDBColumnType(String) -
Method in class weka.experiment.DatabaseUtils
- translates the column data type string to an integer value that indicates
which data type / get()-Method to use in order to retrieve values from the
database (see DatabaseUtils.Properties, InstanceQuery()).
- translationTipText() -
Method in class weka.filters.unsupervised.attribute.Normalize
- Returns the tip text for this property.
- transpose() -
Method in class weka.classifiers.misc.monotone.BooleanBitMatrix
- Swap the rows and the columns of the
BooleanBitMatrix.
- transpose() -
Method in class weka.core.matrix.Matrix
- Matrix transpose.
- transpose() -
Method in class weka.core.Matrix
- Deprecated. Returns the transpose of a matrix.
- transposeInPlace() -
Method in class weka.classifiers.misc.monotone.BooleanBitMatrix
- Swaps the rows and the columns of the
BooleanBitMatrix,
without creating a new object.
- transProb() -
Method in class weka.classifiers.lazy.kstar.KStarNominalAttribute
- Calculates the probability of the indexed nominal attribute of the test
instance transforming into the indexed nominal attribute of the training
instance.
- transProb() -
Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute
- Calculates the transformation probability of the attribute indexed
"m_AttrIndex" in test instance "m_Test" to the same attribute in
the train instance "m_Train".
- TRAVERSAL_BY_COLUMN -
Static variable in class weka.classifiers.meta.GridSearch
- column-wise grid traversal
- TRAVERSAL_BY_ROW -
Static variable in class weka.classifiers.meta.GridSearch
- row-wise grid traversal
- traversalTipText() -
Method in class weka.classifiers.meta.GridSearch
- Returns the tip text for this property
- TREE -
Static variable in interface weka.core.Drawable
-
- TreeBuild - Class in weka.gui.treevisualizer
- This class will parse a dotty file and construct a tree structure from it
with Edge's and Node's
- TreeBuild() -
Constructor for class weka.gui.treevisualizer.TreeBuild
- Upon construction this will only setup the color table for quick
reference of a color.
- TreeDisplayEvent - Class in weka.gui.treevisualizer
- An event containing the user selection from the tree display
- TreeDisplayEvent(int, String) -
Constructor for class weka.gui.treevisualizer.TreeDisplayEvent
- Constructs an event with the specified command
and what the command is applied to.
- TreeDisplayListener - Interface in weka.gui.treevisualizer
- Interface implemented by classes that wish to recieve user selection events
from a tree displayer.
- treeErrors() -
Method in class weka.classifiers.trees.lmt.LMTNode
- Updates the numIncorrectTree field for all nodes.
- treeErrors() -
Method in class weka.classifiers.trees.SimpleCart
- Updates the numIncorrectTree field for all nodes.
- TreePerformanceStats - Class in weka.core.neighboursearch
- The class that measures the performance of a tree based
nearest neighbour search algorithm.
- TreePerformanceStats() -
Constructor for class weka.core.neighboursearch.TreePerformanceStats
- Default constructor.
- treeToString(int) -
Method in class weka.classifiers.trees.m5.RuleNode
- Recursively builds a textual description of the tree
- TreeVisualizer - Class in weka.gui.treevisualizer
- Class for displaying a Node structure in Swing.
- TreeVisualizer(TreeDisplayListener, String, NodePlace) -
Constructor for class weka.gui.treevisualizer.TreeVisualizer
- Constructs Displayer to display a tree provided in a dot format.
- TreeVisualizer(TreeDisplayListener, Node, NodePlace) -
Constructor for class weka.gui.treevisualizer.TreeVisualizer
- Constructs Displayer with the specified Node as the top
of the tree, and uses the NodePlacer to place the Nodes.
- tresholdTipText() -
Method in class weka.attributeSelection.ScatterSearchV1
- Returns the tip text for this property
- TRIANGLEDOWN_SHAPE -
Static variable in class weka.gui.visualize.Plot2D
-
- TRIANGLEUP_SHAPE -
Static variable in class weka.gui.visualize.Plot2D
-
- Trie - Class in weka.core
- A class representing a Trie data structure for strings.
- Trie() -
Constructor for class weka.core.Trie
- initializes the data structure
- Trie.TrieIterator - Class in weka.core
- Represents an iterator over a trie
- Trie.TrieIterator(Trie.TrieNode) -
Constructor for class weka.core.Trie.TrieIterator
- initializes the iterator
- Trie.TrieNode - Class in weka.core
- Represents a node in the trie.
- Trie.TrieNode(char) -
Constructor for class weka.core.Trie.TrieNode
- initializes the node
- Trie.TrieNode(Character) -
Constructor for class weka.core.Trie.TrieNode
- initializes the node
- trim(DoubleVector) -
Method in class weka.classifiers.functions.pace.ChisqMixture
- Trims the small values of the estaimte
- trim(DoubleVector) -
Method in class weka.classifiers.functions.pace.NormalMixture
- Trims the small values of the estaimte
- trim() -
Method in class weka.gui.LogWindow
- trims the JTextPane, if too big
- trimToSize() -
Method in class weka.core.FastVector
- Sets the vector's capacity to its size.
- TRUE -
Static variable in interface weka.core.mathematicalexpression.sym
-
- TRUE -
Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
-
- TRUE_NEG -
Static variable in class weka.classifiers.meta.ThresholdSelector
- true-negative
- TRUE_NEG_NAME -
Static variable in class weka.classifiers.evaluation.ThresholdCurve
- attribute name: True Negatives
- TRUE_POS -
Static variable in class weka.classifiers.meta.ThresholdSelector
- true-positive
- TRUE_POS_NAME -
Static variable in class weka.classifiers.evaluation.ThresholdCurve
- attribute name: True Positives
- trueNegativeRate(int) -
Method in class weka.classifiers.Evaluation
- Calculate the true negative rate with respect to a particular class.
- truePositiveRate(int) -
Method in class weka.classifiers.Evaluation
- Calculate the true positive rate with respect to a particular class.
- TStartTipText() -
Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
-
- TStartTipText() -
Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
-
- tuneInterpolationParameter() -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Tune the interpolation parameter using the current
settings of the classifier.
- tuneInterpolationParameter(double, double, int, int) -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Tunes the interpolation parameter using the given settings.
- tuneInterpolationParameterTipText() -
Method in class weka.classifiers.misc.monotone.OSDLCore
- Returns the tip text for this property.
- turnChecksOff() -
Method in class weka.classifiers.functions.LinearRegression
- Turns off checks for missing values, etc.
- turnChecksOff() -
Method in class weka.classifiers.functions.SMO
- Turns off checks for missing values, etc.
- turnChecksOff() -
Method in class weka.classifiers.functions.SMOreg
- Turns off checks for missing values, etc.
- turnChecksOff() -
Method in class weka.classifiers.mi.MISMO
- Turns off checks for missing values, etc.
- turnChecksOn() -
Method in class weka.classifiers.functions.LinearRegression
- Turns on checks for missing values, etc.
- turnChecksOn() -
Method in class weka.classifiers.functions.SMO
- Turns on checks for missing values, etc.
- turnChecksOn() -
Method in class weka.classifiers.functions.SMOreg
- Turns on checks for missing values, etc.
- turnChecksOn() -
Method in class weka.classifiers.mi.MISMO
- Turns on checks for missing values, etc.
- TwoClassStats - Class in weka.classifiers.evaluation
- Encapsulates performance functions for two-class problems.
- TwoClassStats(double, double, double, double) -
Constructor for class weka.classifiers.evaluation.TwoClassStats
- Creates the TwoClassStats with the given initial performance values.
- TwoWayNominalSplit - Class in weka.classifiers.trees.adtree
- Class representing a two-way split on a nominal attribute, of the form:
either 'is some_value' or 'is not some_value'.
- TwoWayNominalSplit(int, int) -
Constructor for class weka.classifiers.trees.adtree.TwoWayNominalSplit
- Creates a new two-way nominal splitter.
- TwoWayNumericSplit - Class in weka.classifiers.trees.adtree
- Class representing a two-way split on a numeric attribute, of the form:
either 'is < some_value' or 'is >= some_value'.
- TwoWayNumericSplit(int, double) -
Constructor for class weka.classifiers.trees.adtree.TwoWayNumericSplit
- Creates a new two-way numeric splitter.
- type() -
Method in class weka.core.Attribute
- Returns the attribute's type as an integer.
- typeIsNumeric(int) -
Static method in class weka.gui.sql.ResultSetHelper
- returns whether the SQL type is numeric (and therefore the justification
should be right).
- typeName(int) -
Static method in class weka.experiment.DatabaseUtils
- Returns the name associated with a SQL type.
- typeTipText() -
Method in class weka.attributeSelection.LinearForwardSelection
- Returns the tip text for this property
- typeTipText() -
Method in class weka.attributeSelection.SubsetSizeForwardSelection
- Returns the tip text for this property
- typeToClass(int) -
Static method in class weka.gui.sql.ResultSetHelper
- Returns the class associated with a SQL type.
BufferedWriter
.
options
from the Experiment, since these are handled
by the get/set-methods.clear()
, which
adds initial methods automatically.
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