All Packages  Class Hierarchy

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Index of all Fields and Methods

A

abortExperiment(). Method in class weka.experiment.RemoteExperiment
Set the abort flag
absDev(int, Instances). Static method in class weka.classifiers.m5.M5Utils
Returns the absolute deviation value of the instances values of an attribute
AbstractLoader(). Constructor for class weka.core.converters.AbstractLoader
AbstractTimeSeriesFilter(). Constructor for class weka.filters.AbstractTimeSeriesFilter
ACCEPT. Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
States that the user has accepted the tree.
accept(File). Method in class weka.gui.ExtensionFileFilter
Returns true if the supplied file should be accepted (i.e.
accept(File, String). Method in class weka.gui.ExtensionFileFilter
Returns true if the file in the given directory with the given name should be accepted.
acceptResult(ResultProducer, Object[], Object[]). Method in class weka.experiment.AveragingResultProducer
Accepts results from a ResultProducer.
acceptResult(ResultProducer, Object[], Object[]). Method in class weka.experiment.CSVResultListener
Just prints out each result as it is received.
acceptResult(ResultProducer, Object[], Object[]). Method in class weka.experiment.DatabaseResultListener
Submit the result to the appropriate table of the database
acceptResult(ResultProducer, Object[], Object[]). Method in class weka.experiment.DatabaseResultProducer
Accepts results from a ResultProducer.
acceptResult(ResultProducer, Object[], Object[]). Method in class weka.experiment.InstancesResultListener
Collects each instance and adjusts the header information.
acceptResult(ResultProducer, Object[], Object[]). Method in class weka.experiment.LearningRateResultProducer
Accepts results from a ResultProducer.
acceptResult(ResultProducer, Object[], Object[]). Method in interface weka.experiment.ResultListener
Accepts results from a ResultProducer.
actEntropy. Variable in class weka.classifiers.kstar.KStarWrapper
used/reused to hold the actual entropy
actionPerformed(ActionEvent). Method in class weka.gui.experiment.DatasetListPanel
Handle actions when buttons get pressed.
actionPerformed(ActionEvent). Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Handles the various button clicking type activities.
actionPerformed(ActionEvent). Method in class weka.gui.experiment.HostListPanel
Handle actions when text is entered into the host field or the delete button is pressed.
actionPerformed(ActionEvent). Method in class weka.gui.streams.InstanceLoader
actionPerformed(ActionEvent). Method in class weka.gui.experiment.RunPanel
Controls starting and stopping the experiment.
actionPerformed(ActionEvent). Method in class weka.gui.SimpleCLI
Only gets called when return is pressed in the input area, which starts the command running.
actionPerformed(ActionEvent). Method in class weka.gui.treevisualizer.TreeVisualizer
Performs the action associated with the ActionEvent.
actual(). Method in class weka.classifiers.evaluation.NominalPrediction
Gets the actual class value.
actual(). Method in class weka.classifiers.evaluation.NumericPrediction
Gets the actual class value.
actual(). Method in interface weka.classifiers.evaluation.Prediction
Gets the actual class value.
actualNumBags(). Method in class weka.classifiers.j48.Distribution
Returns number of non-empty bags of distribution.
actualNumClasses(). Method in class weka.classifiers.j48.Distribution
Returns number of classes actually occuring in distribution.
actualNumClasses(int). Method in class weka.classifiers.j48.Distribution
Returns number of classes actually occuring in given bag.
AdaBoostM1(). Constructor for class weka.classifiers.AdaBoostM1
add(Cobweb. CTree, Cobweb. CTree). Method in class weka.clusterers.Cobweb
Adds an example to the tree.
add(double). Method in class weka.experiment.Stats
Adds a value to the observed values
add(double, double). Method in class weka.experiment.PairedStats
Add an observed pair of values.
add(double, double). Method in class weka.experiment.Stats
Adds a value that has been seen n times to the observed values
add(Instance). Method in class weka.core.Instances
Adds one instance to the end of the set.
add(int, double[]). Method in class weka.classifiers.j48.Distribution
Adds counts to given bag.
add(int, Instance). Method in class weka.classifiers.j48.Distribution
Adds given instance to given bag.
add(Matrix). Method in class weka.core.Matrix
Returns the sum of this matrix with another.
ADD_CHILDREN. Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
addActionListener(ActionListener). Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Add a listener interested in kowing about editor status changes
addActionListener(ActionListener). Method in class weka.gui.visualize.VisualizePanel
Add a listener for this visualize panel
addAttributePanelListener(AttributePanelListener). Method in class weka.gui.visualize.AttributePanel
Add a listener to the list of things listening to this panel
addCheckBoxActionListener(ActionListener). Method in class weka.gui.experiment.DistributeExperimentPanel
Enable objects to listen for changes to the check box
addChild(Edge). Method in class weka.gui.treevisualizer.Node
Set the value of children.
addChild(Splitter, ADTree). Method in class weka.classifiers.adtree.PredictionNode
Adds a child to this node.
addCVParameter(String). Method in class weka.classifiers.CVParameterSelection
Adds a scheme parameter to the list of parameters to be set by cross-validation
addElement(int, int, double). Method in class weka.core.Matrix
Add a value to an element.
addElement(Object). Method in class weka.core.FastVector
Adds an element to this vector.
addErrs(double, double, float). Static method in class weka.classifiers.j48.Stats
Computes estimated extra error for given total number of instances and errors.
AddFilter(). Constructor for class weka.filters.AddFilter
addInstanceListener(InstanceListener). Method in class weka.gui.streams.InstanceJoiner
addInstanceListener(InstanceListener). Method in class weka.gui.streams.InstanceLoader
addInstanceListener(InstanceListener). Method in interface weka.gui.streams.InstanceProducer
addInstanceNumberAttribute(). Method in class weka.gui.visualize.PlotData2D
Adds an instance number attribute to the plottable instances,
addInstWithUnknown(Instances, int). Method in class weka.classifiers.j48.Distribution
Adds all instances with unknown values for given attribute, weighted according to frequency of instances in each bag.
AdditiveRegression(). Constructor for class weka.classifiers.AdditiveRegression
Default constructor specifying DecisionStump as the classifier
AdditiveRegression(Classifier). Constructor for class weka.classifiers.AdditiveRegression
Constructor which takes base classifier as argument.
addObject(String, Object). Method in class weka.gui.ResultHistoryPanel
Adds an object to the results list
addPlot(PlotData2D). Method in class weka.gui.visualize.Plot2D
Add a plot to the list of plots to display
addPlot(PlotData2D). Method in class weka.gui.visualize.VisualizePanel
Set a new plot to the visualize panel
addPrediction(NominalPrediction). Method in class weka.classifiers.evaluation.ConfusionMatrix
Includes a prediction in the confusion matrix.
addPredictions(FastVector). Method in class weka.classifiers.evaluation.ConfusionMatrix
Includes a whole bunch of predictions in the confusion matrix.
addPropertyChangeListener(PropertyChangeListener). Method in class weka.gui.CostMatrixEditor
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener). Method in class weka.gui.GenericArrayEditor
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener). Method in class weka.gui.GenericObjectEditor
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener). Method in class weka.gui.explorer.PreprocessPanel
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener). Method in class weka.gui.PropertySheetPanel
Adds a PropertyChangeListener.
addPropertyChangeListener(PropertyChangeListener). Method in class weka.gui.SetInstancesPanel
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener). Method in class weka.gui.experiment.SetupPanel
Adds a PropertyChangeListener who will be notified of value changes.
addRange(int, Instances, int, int). Method in class weka.classifiers.j48.Distribution
Adds all instances in given range to given bag.
addReference(Instance). Method in class weka.classifiers.adtree.ReferenceInstances
Adds one instance reference to the end of the set.
addRemoteExperimentListener(RemoteExperimentListener). Method in class weka.experiment.RemoteExperiment
Add an object to the list of those interested in recieving update information from the RemoteExperiment
addRemoteHost(String). Method in class weka.experiment.RemoteExperiment
Add a host name to the list of remote hosts
addRepaintNotify(Component). Method in class weka.gui.visualize.ClassPanel
Adds a component that will need to be repainted if the user changes the colour of a label.
addRepaintNotify(Component). Method in class weka.gui.visualize.LegendPanel
Adds a component that will need to be repainted if the user changes the colour of a label.
addResult(String, StringBuffer). Method in class weka.gui.ResultHistoryPanel
Adds a new result to the result list.
addStringValue(Attribute, int). Method in class weka.core.Attribute
Adds a string value to the list of valid strings for attributes of type STRING and returns the index of the string.
addStringValue(String). Method in class weka.core.Attribute
Adds a string value to the list of valid strings for attributes of type STRING and returns the index of the string.
addValue(double, double). Method in class weka.estimators.DiscreteEstimator
Add a new data value to the current estimator.
addValue(double, double). Method in interface weka.estimators.Estimator
Add a new data value to the current estimator.
addValue(double, double). Method in class weka.estimators.KernelEstimator
Add a new data value to the current estimator.
addValue(double, double). Method in class weka.estimators.MahalanobisEstimator
Add a new data value to the current estimator.
addValue(double, double). Method in class weka.estimators.NormalEstimator
Add a new data value to the current estimator.
addValue(double, double). Method in class weka.estimators.PoissonEstimator
Add a new data value to the current estimator.
addValue(double, double, double). Method in interface weka.estimators.ConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double). Method in class weka.estimators.DDConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double). Method in class weka.estimators.DKConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double). Method in class weka.estimators.DNConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double). Method in class weka.estimators.KDConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double). Method in class weka.estimators.KKConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double). Method in class weka.estimators.NDConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double). Method in class weka.estimators.NNConditionalEstimator
Add a new data value to the current estimator.
addWeights(Instance, double[]). Method in class weka.classifiers.j48.Distribution
Adds given instance to all bags weighting it according to given weights.
adjustCenter(double). Method in class weka.gui.treevisualizer.Node
Will increase or decrease the postion of center.
ADTree(). Constructor for class weka.classifiers.adtree.ADTree
advanceCounters(). Method in class weka.experiment.Experiment
Increments iteration counters appropriately.
advanceCounters(). Method in class weka.experiment.RemoteExperiment
overides the one in Experiment
AllFilter(). Constructor for class weka.filters.AllFilter
appendElements(FastVector). Method in class weka.core.FastVector
Appends all elements of the supplied vector to this vector.
applyCostMatrix(Instances, Random). Method in class weka.classifiers.CostMatrix
Changes the dataset to reflect a given set of costs.
APPROVE_OPTION. Static variable in class weka.gui.ListSelectorDialog
Signifies an OK property selection
APPROVE_OPTION. Static variable in class weka.gui.PropertySelectorDialog
Signifies an OK property selection
Apriori(). Constructor for class weka.associations.Apriori
Constructor that allows to sets default values for the minimum confidence and the maximum number of rules the minimum confidence.
ArffLoader(). Constructor for class weka.core.converters.ArffLoader
arrayToString(Object[]). Static method in class weka.experiment.DatabaseUtils
Converts an array of objects to a string by inserting a space between each element.
ASEvaluation(). Constructor for class weka.attributeSelection.ASEvaluation
ASSearch(). Constructor for class weka.attributeSelection.ASSearch
assignIDs(int). Method in class weka.classifiers.j48.ClassifierTree
Assigns a uniqe id to every node in the tree.
AssociationsPanel(). Constructor for class weka.gui.explorer.AssociationsPanel
Creates the associator panel
Associator(). Constructor for class weka.associations.Associator
attIndex(). Method in class weka.classifiers.j48.BinC45Split
Returns index of attribute for which split was generated.
attIndex(). Method in class weka.classifiers.j48.C45Split
Returns index of attribute for which split was generated.
attribute(int). Method in class weka.core.Instance
Returns the attribute with the given index.
attribute(int). Method in class weka.core.Instances
Returns an attribute.
Attribute(String). Constructor for class weka.core.Attribute
Constructor for a numeric attribute.
attribute(String). Method in class weka.core.Instances
Returns an attribute given its name.
Attribute(String, FastVector). Constructor for class weka.core.Attribute
Constructor for nominal attributes and string attributes.
AttributeEvaluator(). Constructor for class weka.attributeSelection.AttributeEvaluator
attributeEvaluatorTipText(). Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
attributeEvaluatorTipText(). Method in class weka.attributeSelection.RankSearch
Returns the tip text for this property
AttributeExpressionFilter(). Constructor for class weka.filters.AttributeExpressionFilter
AttributeFilter(). Constructor for class weka.filters.AttributeFilter
attributeIndexTipText(). Method in class weka.filters.AddFilter
Returns the tip text for this property
attributeIndexTipText(). Method in class weka.filters.MakeIndicatorFilter
attributeIndicesTipText(). Method in class weka.filters.AbstractTimeSeriesFilter
Returns the tip text for this property
attributeIndicesTipText(). Method in class weka.filters.AttributeFilter
Returns the tip text for this property
attributeIndicesTipText(). Method in class weka.filters.CopyAttributesFilter
Returns the tip text for this property
attributeIndicesTipText(). Method in class weka.filters.DiscretizeFilter
Returns the tip text for this property
attributeIndicesTipText(). Method in class weka.filters.FirstOrderFilter
Returns the tip text for this property
attributeIndicesTipText(). Method in class weka.filters.NumericTransformFilter
Returns the tip text for this property
attributeNameTipText(). Method in class weka.filters.AddFilter
Returns the tip text for this property
AttributePanel(). Constructor for class weka.gui.visualize.AttributePanel
This constructs an attributePanel.
AttributePanelEvent(boolean, boolean, int). Constructor for class weka.gui.visualize.AttributePanelEvent
Constructor
AttributeSelectedClassifier(). Constructor for class weka.classifiers.AttributeSelectedClassifier
AttributeSelection(). Constructor for class weka.attributeSelection.AttributeSelection
constructor.
attributeSelectionChange(AttributePanelEvent). Method in interface weka.gui.visualize.AttributePanelListener
Called when the user clicks on an attribute bar
AttributeSelectionFilter(). Constructor for class weka.filters.AttributeSelectionFilter
Constructor
AttributeSelectionPanel(). Constructor for class weka.gui.AttributeSelectionPanel
Creates the attribute selection panel with no initial instances.
AttributeSelectionPanel(). Constructor for class weka.gui.explorer.AttributeSelectionPanel
Creates the classifier panel
attributeSparse(int). Method in class weka.core.Instance
Returns the attribute with the given index.
attributeSparse(int). Method in class weka.core.SparseInstance
Returns the attribute associated with the internal index.
AttributeStats(). Constructor for class weka.core.AttributeStats
attributeStats(int). Method in class weka.core.Instances
Calculates summary statistics on the values that appear in this set of instances for a specified attribute.
attributeString(Instances). Method in class weka.classifiers.adtree.Splitter
Gets the string describing the attributes the split depends on.
attributeString(Instances). Method in class weka.classifiers.adtree.TwoWayNominalSplit
Gets the string describing the attributes the split depends on.
attributeString(Instances). Method in class weka.classifiers.adtree.TwoWayNumericSplit
Gets the string describing the attributes the split depends on.
AttributeSummaryPanel(). Constructor for class weka.gui.AttributeSummaryPanel
Creates the instances panel with no initial instances.
attributeToDoubleArray(int). Method in class weka.core.Instances
Gets the value of all instances in this dataset for a particular attribute.
AttributeTypeFilter(). Constructor for class weka.filters.AttributeTypeFilter
attrSplit(int, Instances). Method in class weka.classifiers.m5.SplitInfo
Finds the best splitting point for an attribute in the instances
autoBuildTipText(). Method in class weka.classifiers.neural.NeuralNetwork
AveragingResultProducer(). Constructor for class weka.experiment.AveragingResultProducer
avgCost(). Method in class weka.classifiers.Evaluation
Gets the average cost, that is, total cost of misclassifications (incorrect plus unclassified) over the total number of instances.
avgProb. Variable in class weka.classifiers.kstar.KStarWrapper
used/reused to hold the average transformation probability

B

B_ENTROPY. Static variable in interface weka.classifiers.kstar.KStarConstants
B_SPHERE. Static variable in interface weka.classifiers.kstar.KStarConstants
Blend setting modes
backQuoteChars(String). Static method in class weka.core.Utils
Converts carriage returns and new lines in a string into \r and \n.
Bagging(). Constructor for class weka.classifiers.Bagging
BATCH_FINISHED. Static variable in class weka.gui.streams.InstanceEvent
Specifies that the batch of instances is finished
batchFilterFile(Filter, String[]). Static method in class weka.filters.Filter
Method for testing filters ability to process multiple batches.
batchFinished(). Method in class weka.filters.AbstractTimeSeriesFilter
Signifies that this batch of input to the filter is finished.
batchFinished(). Method in class weka.filters.AttributeSelectionFilter
Signify that this batch of input to the filter is finished.
batchFinished(). Method in class weka.filters.DiscretizeFilter
Signifies that this batch of input to the filter is finished.
batchFinished(). Method in class weka.filters.EmptyAttributeFilter
Signify that this batch of input to the filter is finished.
batchFinished(). Method in class weka.filters.Filter
Signify that this batch of input to the filter is finished.
batchFinished(). Method in class weka.gui.streams.InstanceJoiner
Signify that this batch of input to the filter is finished.
batchFinished(). Method in class weka.gui.streams.InstanceSavePanel
batchFinished(). Method in class weka.gui.streams.InstanceTable
batchFinished(). Method in class weka.gui.streams.InstanceViewer
batchFinished(). Method in class weka.filters.NominalToBinaryFilter
Signify that this batch of input to the filter is finished.
batchFinished(). Method in class weka.filters.NormalizationFilter
Signify that this batch of input to the filter is finished.
batchFinished(). Method in class weka.filters.RandomizeFilter
Signify that this batch of input to the filter is finished.
batchFinished(). Method in class weka.filters.ReplaceMissingValuesFilter
Signify that this batch of input to the filter is finished.
batchFinished(). Method in class weka.filters.ResampleFilter
Signify that this batch of input to the filter is finished.
batchFinished(). Method in class weka.filters.SplitDatasetFilter
Signify that this batch of input to the filter is finished.
batchFinished(). Method in class weka.filters.SpreadSubsampleFilter
Signify that this batch of input to the filter is finished.
batchFinished(). Method in class weka.filters.StringToNominalFilter
Signifies that this batch of input to the filter is finished.
BestFirst(). Constructor for class weka.attributeSelection.BestFirst
Constructor
bestHost(Cobweb. CTree, Cobweb. CTree, double, double). Method in class weka.clusterers.Cobweb
Finds the best place to add a new node during training.
bestHostCluster(Cobweb. CTree, Cobweb. CTree, double, double). Method in class weka.clusterers.Cobweb
Finds the cluster that an unseen instance belongs to.
biasTipText(). Method in class weka.classifiers.VFI
Returns the tip text for this property
binarizeNumericAttributesTipText(). Method in class weka.attributeSelection.ChiSquaredAttributeEval
Returns the tip text for this property
binarizeNumericAttributesTipText(). Method in class weka.attributeSelection.InfoGainAttributeEval
Returns the tip text for this property
BinarySparseInstance(double, double[]). Constructor for class weka.core.BinarySparseInstance
Constructor that generates a sparse instance from the given parameters.
BinarySparseInstance(double, int[], int). Constructor for class weka.core.BinarySparseInstance
Constructor that inititalizes instance variable with given values.
BinarySparseInstance(Instance). Constructor for class weka.core.BinarySparseInstance
Constructor that generates a sparse instance from the given instance.
BinarySparseInstance(int). Constructor for class weka.core.BinarySparseInstance
Constructor of an instance that sets weight to one, all values to 1, and the reference to the dataset to null.
BinarySparseInstance(SparseInstance). Constructor for class weka.core.BinarySparseInstance
Constructor that copies the info from the given instance.
BinC45ModelSelection(int, Instances). Constructor for class weka.classifiers.j48.BinC45ModelSelection
Initializes the split selection method with the given parameters.
BinC45Split(int, int, double). Constructor for class weka.classifiers.j48.BinC45Split
Initializes the split model.
binomialStandardError(double, int). Static method in class weka.core.Statistics
Computes standard error for observed values of a binomial random variable.
binsTipText(). Method in class weka.filters.DiscretizeFilter
Returns the tip text for this property
blocker(boolean). Method in class weka.classifiers.neural.NeuralNetwork
A function used to stop the code that called buildclassifier from continuing on before the user has finished the decision tree.
boost(). Method in class weka.classifiers.adtree.ADTree
Performs a single boosting iteration, using two-class optimized method.
branchInstanceGoesDown(Instance). Method in class weka.classifiers.adtree.Splitter
Gets the index of the branch that an instance applies to.
branchInstanceGoesDown(Instance). Method in class weka.classifiers.adtree.TwoWayNominalSplit
Gets the index of the branch that an instance applies to.
branchInstanceGoesDown(Instance). Method in class weka.classifiers.adtree.TwoWayNumericSplit
Gets the index of the branch that an instance applies to.
buildAssociations(Instances). Method in class weka.associations.Apriori
Method that generates all large itemsets with a minimum support, and from these all association rules with a minimum confidence.
buildAssociations(Instances). Method in class weka.associations.Associator
Generates an associator.
buildClassifier(Instances). Method in class weka.classifiers.AdaBoostM1
Boosting method.
buildClassifier(Instances). Method in class weka.classifiers.AdditiveRegression
Build the classifier on the supplied data
buildClassifier(Instances). Method in class weka.classifiers.adtree.ADTree
Builds a classifier for a set of instances.
buildClassifier(Instances). Method in class weka.classifiers.AttributeSelectedClassifier
Build the classifier on the dimensionally reduced data.
buildClassifier(Instances). Method in class weka.classifiers.Bagging
Bagging method.
buildClassifier(Instances). Method in class weka.classifiers.j48.BinC45Split
Creates a C4.5-type split on the given data.
buildClassifier(Instances). Method in class weka.classifiers.j48.C45PruneableClassifierTree
Method for building a pruneable classifier tree.
buildClassifier(Instances). Method in class weka.classifiers.j48.C45Split
Creates a C4.5-type split on the given data.
buildClassifier(Instances). Method in class weka.classifiers.ClassificationViaRegression
Builds the classifiers.
buildClassifier(Instances). Method in class weka.classifiers.Classifier
Generates a classifier.
buildClassifier(Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
Builds the classifier split model for the given set of instances.
buildClassifier(Instances). Method in class weka.classifiers.j48.ClassifierTree
Method for building a classifier tree.
buildClassifier(Instances). Method in class weka.classifiers.CostSensitiveClassifier
Builds the model of the base learner.
buildClassifier(Instances). Method in class weka.classifiers.CVParameterSelection
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.DecisionStump
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.DecisionTable
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.DistributionMetaClassifier
Builds the classifier.
buildClassifier(Instances). Method in class weka.classifiers.FilteredClassifier
Build the classifier on the filtered data.
buildClassifier(Instances). Method in class weka.classifiers.HyperPipes
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.IB1
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.IBk
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.Id3
Builds Id3 decision tree classifier.
buildClassifier(Instances). Method in class weka.classifiers.j48.J48
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.KernelDensity
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.kstar.KStar
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.LinearRegression
Builds a regression model for the given data.
buildClassifier(Instances). Method in class weka.classifiers.Logistic
Builds the classifier
buildClassifier(Instances). Method in class weka.classifiers.LogitBoost
Boosting method.
buildClassifier(Instances). Method in class weka.classifiers.LWR
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.m5.M5Prime
Construct a model tree by training instances
buildClassifier(Instances). Method in class weka.classifiers.j48.MakeDecList
Builds dec list.
buildClassifier(Instances). Method in class weka.classifiers.MetaCost
Builds the model of the base learner.
buildClassifier(Instances). Method in class weka.classifiers.MultiClassClassifier
Builds the classifiers.
buildClassifier(Instances). Method in class weka.classifiers.MultiScheme
Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
buildClassifier(Instances). Method in class weka.classifiers.NaiveBayes
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.NaiveBayesSimple
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.neural.NeuralNetwork
Call this function to build and train a neural network for the training data provided.
buildClassifier(Instances). Method in class weka.classifiers.j48.NoSplit
Creates a "no-split"-split for a given set of instances.
buildClassifier(Instances). Method in class weka.classifiers.OneR
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.j48.PART
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.Prism
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.j48.PruneableClassifierTree
Method for building a pruneable classifier tree.
buildClassifier(Instances). Method in class weka.classifiers.RegressionByDiscretization
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.SMO
Method for building the classifier.
buildClassifier(Instances). Method in class weka.classifiers.Stacking
Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
buildClassifier(Instances). Method in class weka.classifiers.ThresholdSelector
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.UserClassifier
Call this function to build a decision tree for the training data provided.
buildClassifier(Instances). Method in class weka.classifiers.VFI
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.VotedPerceptron
Builds the ensemble of perceptrons.
buildClassifier(Instances). Method in class weka.classifiers.ZeroR
Generates the classifier.
buildClusterer(Instances). Method in class weka.clusterers.Clusterer
Generates a clusterer.
buildClusterer(Instances). Method in class weka.clusterers.Cobweb
Builds the clusterer.
buildClusterer(Instances). Method in class weka.clusterers.DistributionMetaClusterer
Builds the clusterer.
buildClusterer(Instances). Method in class weka.clusterers.EM
Generates a clusterer.
buildClusterer(Instances). Method in class weka.clusterers.SimpleKMeans
Generates a clusterer.
buildDecList(Instances, boolean). Method in class weka.classifiers.j48.ClassifierDecList
Builds the partial tree without hold out set.
buildDecList(Instances, Instances, boolean). Method in class weka.classifiers.j48.ClassifierDecList
Builds the partial tree with hold out set
buildEvaluator(Instances). Method in class weka.attributeSelection.ASEvaluation
Generates a attribute evaluator.
buildEvaluator(Instances). Method in class weka.attributeSelection.CfsSubsetEval
Generates a attribute evaluator.
buildEvaluator(Instances). Method in class weka.attributeSelection.ChiSquaredAttributeEval
Initializes a chi-squared attribute evaluator.
buildEvaluator(Instances). Method in class weka.attributeSelection.ClassifierSubsetEval
Generates a attribute evaluator.
buildEvaluator(Instances). Method in class weka.attributeSelection.ConsistencySubsetEval
Generates a attribute evaluator.
buildEvaluator(Instances). Method in class weka.attributeSelection.GainRatioAttributeEval
Initializes a gain ratio attribute evaluator.
buildEvaluator(Instances). Method in class weka.attributeSelection.InfoGainAttributeEval
Initializes an information gain attribute evaluator.
buildEvaluator(Instances). Method in class weka.attributeSelection.OneRAttributeEval
Initializes an information gain attribute evaluator.
buildEvaluator(Instances). Method in class weka.attributeSelection.PrincipalComponents
Initializes principal components and performs the analysis
buildEvaluator(Instances). Method in class weka.attributeSelection.ReliefFAttributeEval
Initializes a ReliefF attribute evaluator.
buildEvaluator(Instances). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Initializes a symmetrical uncertainty attribute evaluator.
buildEvaluator(Instances). Method in class weka.attributeSelection.WrapperSubsetEval
Generates a attribute evaluator.
buildRule(Instances). Method in class weka.classifiers.j48.C45PruneableDecList
Method for building a pruned partial tree.
buildRule(Instances, Instances). Method in class weka.classifiers.j48.PruneableDecList
Method for building a pruned partial tree.
buildTree(Instances, boolean). Method in class weka.classifiers.j48.ClassifierTree
Builds the tree structure.
buildTree(Instances, Instances, boolean). Method in class weka.classifiers.j48.ClassifierTree
Builds the tree structure with hold out set
BVDecompose(). Constructor for class weka.classifiers.BVDecompose

C

C45Loader(). Constructor for class weka.core.converters.C45Loader
C45ModelSelection(int, Instances). Constructor for class weka.classifiers.j48.C45ModelSelection
Initializes the split selection method with the given parameters.
C45PruneableClassifierTree(ModelSelection, boolean, float, boolean, boolean). Constructor for class weka.classifiers.j48.C45PruneableClassifierTree
Constructor for pruneable tree structure.
C45PruneableDecList(ModelSelection, double, int). Constructor for class weka.classifiers.j48.C45PruneableDecList
Constructor for pruneable tree structure.
C45Split(int, int, double). Constructor for class weka.classifiers.j48.C45Split
Initializes the split model.
cacheKeyNameTipText(). Method in class weka.experiment.DatabaseResultListener
Returns the tip text for this property
calculateDerived(). Method in class weka.experiment.PairedStats
Calculates the derived statistics (significance etc).
calculateDerived(). Method in class weka.experiment.Stats
Tells the object to calculate any statistics that don't have their values automatically updated during add.
calculateStatistics(Instance, int, int, int). Method in class weka.experiment.PairedTTester
Computes a paired t-test comparison for a specified dataset between two resultsets.
calculateStdDevsTipText(). Method in class weka.experiment.AveragingResultProducer
Returns the tip text for this property
CANCEL_OPTION. Static variable in class weka.gui.ListSelectorDialog
Signifies a cancelled property selection
CANCEL_OPTION. Static variable in class weka.gui.PropertySelectorDialog
Signifies a cancelled property selection
capacity(). Method in class weka.core.FastVector
Returns the capacity of the vector.
CfsSubsetEval(). Constructor for class weka.attributeSelection.CfsSubsetEval
Constructor
check(double). Method in class weka.classifiers.j48.Distribution
Checks if at least two bags contain a minimum number of instances.
CheckClassifier(). Constructor for class weka.classifiers.CheckClassifier
checkForRemainingOptions(String[]). Static method in class weka.core.Utils
Checks if the given array contains any non-empty options.
checkForStringAttributes(). Method in class weka.core.Instances
Checks for string attributes in the dataset
checkInstance(Instance). Method in class weka.core.Instances
Checks if the given instance is compatible with this dataset.
checkModel(). Method in class weka.classifiers.j48.ClassifierSplitModel
Checks if generated model is valid.
CheckOptionHandler(). Constructor for class weka.core.CheckOptionHandler
checkOptionHandler(OptionHandler, String[]). Static method in class weka.core.CheckOptionHandler
Runs some diagnostic tests on an optionhandler object.
checkStatus(Object). Method in interface weka.experiment.Compute
Check on the status of a Task
checkStatus(Object). Method in class weka.experiment.RemoteEngine
Returns status information on a particular task
children(). Method in class weka.classifiers.adtree.PredictionNode
Enumerates the children of this node.
chiSquared(double[][], boolean). Static method in class weka.core.ContingencyTables
Returns chi-squared probability for a given matrix.
ChiSquaredAttributeEval(). Constructor for class weka.attributeSelection.ChiSquaredAttributeEval
Constructor
chiSquaredProbability(double, int). Static method in class weka.core.Statistics
Returns chi-squared probability for given value and degrees of freedom.
chiVal(double[][], boolean). Static method in class weka.core.ContingencyTables
Computes chi-squared statistic for a contingency table.
chooseIndex(). Method in class weka.classifiers.j48.C45PruneableDecList
Method for choosing a subset to expand.
chooseIndex(). Method in class weka.classifiers.j48.PruneableDecList
Method for choosing a subset to expand.
chooseLastIndex(). Method in class weka.classifiers.j48.C45PruneableDecList
Choose last index (ie.
chooseLastIndex(). Method in class weka.classifiers.j48.PruneableDecList
Choose last index (ie.
classAttribute(). Method in class weka.core.Instance
Returns class attribute.
classAttribute(). Method in class weka.core.Instances
Returns the class attribute.
classFirst(boolean). Method in class weka.experiment.Experiment
Sets whether the first attribute is treated as the class for all datasets involved in the experiment.
ClassificationViaRegression(). Constructor for class weka.classifiers.ClassificationViaRegression
Classifier(). Constructor for class weka.classifiers.Classifier
ClassifierDecList(ModelSelection). Constructor for class weka.classifiers.j48.ClassifierDecList
Constructor - just calls constructor of class DecList.
ClassifierPanel(). Constructor for class weka.gui.explorer.ClassifierPanel
Creates the classifier panel
ClassifierSplitEvaluator(). Constructor for class weka.experiment.ClassifierSplitEvaluator
No args constructor.
ClassifierSplitModel(). Constructor for class weka.classifiers.j48.ClassifierSplitModel
ClassifierSubsetEval(). Constructor for class weka.attributeSelection.ClassifierSubsetEval
classifierTipText(). Method in class weka.classifiers.AdditiveRegression
Returns the tip text for this property
classifierTipText(). Method in class weka.classifiers.AttributeSelectedClassifier
Returns the tip text for this property
classifierTipText(). Method in class weka.experiment.ClassifierSplitEvaluator
Returns the tip text for this property
classifierTipText(). Method in class weka.attributeSelection.ClassifierSubsetEval
Returns the tip text for this property
classifierTipText(). Method in class weka.classifiers.CostSensitiveClassifier
classifierTipText(). Method in class weka.experiment.RegressionSplitEvaluator
Returns the tip text for this property
classifierTipText(). Method in class weka.attributeSelection.WrapperSubsetEval
Returns the tip text for this property
ClassifierTree(ModelSelection). Constructor for class weka.classifiers.j48.ClassifierTree
Constructor.
CLASSIFY_CHILD. Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
Asks for another learning scheme to classify this node.
classifyInstance(Instance). Method in class weka.classifiers.AdditiveRegression
Classify an instance.
classifyInstance(Instance). Method in class weka.classifiers.Classifier
Classifies a given instance.
classifyInstance(Instance). Method in class weka.classifiers.j48.ClassifierDecList
Classifies an instance.
classifyInstance(Instance). Method in class weka.classifiers.j48.ClassifierSplitModel
Classifies a given instance.
classifyInstance(Instance). Method in class weka.classifiers.j48.ClassifierTree
Classifies an instance.
classifyInstance(Instance). Method in class weka.classifiers.CostSensitiveClassifier
Classifies a given instance by choosing the class with the minimum expected misclassification cost.
classifyInstance(Instance). Method in class weka.classifiers.CVParameterSelection
Predicts the class value for the given test instance.
classifyInstance(Instance). Method in class weka.classifiers.DistributionClassifier
Classifies the given test instance.
classifyInstance(Instance). Method in class weka.classifiers.IB1
Classifies the given test instance.
classifyInstance(Instance). Method in class weka.classifiers.Id3
Classifies a given test instance using the decision tree.
classifyInstance(Instance). Method in class weka.classifiers.j48.J48
Classifies an instance.
classifyInstance(Instance). Method in class weka.classifiers.LinearRegression
Classifies the given instance using the linear regression function.
classifyInstance(Instance). Method in class weka.classifiers.LWR
Predicts the class value for the given test instance.
classifyInstance(Instance). Method in class weka.classifiers.m5.M5Prime
Classifies the given test instance.
classifyInstance(Instance). Method in class weka.classifiers.j48.MakeDecList
Classifies an instance.
classifyInstance(Instance). Method in class weka.classifiers.MetaCost
Classifies a given test instance.
classifyInstance(Instance). Method in class weka.classifiers.MultiScheme
Classifies a given instance using the selected classifier.
classifyInstance(Instance). Method in class weka.classifiers.OneR
Classifies a given instance.
classifyInstance(Instance). Method in class weka.classifiers.j48.PART
Classifies an instance.
classifyInstance(Instance). Method in class weka.classifiers.Prism
Classifies a given instance.
classifyInstance(Instance). Method in class weka.classifiers.RegressionByDiscretization
Returns a predicted class for the test instance.
classifyInstance(Instance). Method in class weka.classifiers.Stacking
Classifies a given instance using the stacked classifier.
classifyInstance(Instance). Method in class weka.classifiers.ZeroR
Classifies a given instance.
classIndex(). Method in class weka.core.Instance
Returns the class attribute's index.
classIndex(). Method in class weka.core.Instances
Returns the class attribute's index.
classIsMissing(). Method in class weka.core.Instance
Tests if an instance's class is missing.
className(int). Method in class weka.classifiers.evaluation.ConfusionMatrix
Gets the name of one of the classes.
ClassPanel(). Constructor for class weka.gui.visualize.ClassPanel
classProb(int, Instance, int). Method in class weka.classifiers.j48.BinC45Split
Gets class probability for instance.
classProb(int, Instance, int). Method in class weka.classifiers.j48.C45Split
Gets class probability for instance.
classProb(int, Instance, int). Method in class weka.classifiers.j48.ClassifierSplitModel
Gets class probability for instance.
classProbLaplace(int, Instance, int). Method in class weka.classifiers.j48.ClassifierSplitModel
Gets class probability for instance.
classValue(). Method in class weka.core.Instance
Returns an instance's class value in internal format.
cleanup(). Method in class weka.classifiers.j48.BinC45ModelSelection
Sets reference to training data to null.
cleanup(). Method in class weka.classifiers.j48.C45ModelSelection
Sets reference to training data to null.
cleanup(Instances). Method in class weka.classifiers.j48.ClassifierDecList
Cleanup in order to save memory.
cleanup(Instances). Method in class weka.classifiers.j48.ClassifierTree
Cleanup in order to save memory.
clear(). Method in class weka.classifiers.kstar.LightHashTable
Clears this hashtable so that it contains no keys.
clone(). Method in class weka.classifiers.adtree.ADTree
Creates a clone that is identical to the current tree, but is independent.
clone(). Method in class weka.classifiers.j48.ClassifierSplitModel
Allows to clone a model (shallow copy).
clone(). Method in class weka.classifiers.evaluation.ConfusionMatrix
Creates and returns a clone of this object.
clone(). Method in class weka.classifiers.j48.Distribution
Clones distribution (Deep copy of distribution).
clone(). Method in interface weka.classifiers.IterativeClassifier
Performs a deep copy of the classifier, and a reference copy of the training instances (or a deep copy if required).
clone(). Method in class weka.core.Matrix
Creates and returns a clone of this object.
clone(). Method in class weka.classifiers.adtree.PredictionNode
Clones this node.
clone(). Method in class weka.classifiers.adtree.Splitter
Clones this node.
clone(). Method in class weka.classifiers.adtree.TwoWayNominalSplit
Clones this node.
clone(). Method in class weka.classifiers.adtree.TwoWayNumericSplit
Clones this node.
Clusterer(). Constructor for class weka.clusterers.Clusterer
ClustererPanel(). Constructor for class weka.gui.explorer.ClustererPanel
Creates the clusterer panel
ClusterEvaluation(). Constructor for class weka.clusterers.ClusterEvaluation
Constructor.
clusterInstance(Instance). Method in class weka.clusterers.Clusterer
Classifies a given instance.
clusterInstance(Instance). Method in class weka.clusterers.Cobweb
Clusters an instance.
clusterInstance(Instance). Method in class weka.clusterers.DistributionClusterer
Assigns an instance to a Cluster.
clusterInstance(Instance). Method in class weka.clusterers.SimpleKMeans
Classifies a given instance.
clusterResultsToString(). Method in class weka.clusterers.ClusterEvaluation
return the results of clustering.
Cobweb(). Constructor for class weka.clusterers.Cobweb
cochransCriterion(double[][]). Static method in class weka.core.ContingencyTables
Tests if Cochran's criterion is fullfilled for the given contingency table.
codingCost(). Method in class weka.classifiers.j48.C45Split
Returns coding cost for split (used in rule learner).
codingCost(). Method in class weka.classifiers.j48.ClassifierSplitModel
Returns coding costs of model.
collapse(). Method in class weka.classifiers.j48.C45PruneableClassifierTree
Collapses a tree to a node if training error doesn't increase.
Colors(). Constructor for class weka.gui.treevisualizer.Colors
combine(Function, Function). Static method in class weka.classifiers.m5.Function
Constructs a new function of which the variable list is a combination of those of two functions
combine(int[], int[]). Static method in class weka.classifiers.m5.Ivector
Outputs a new integer vector which contains all the values in two integer vectors; assuming list1 and list2 are incrementally sorted and no identical integers within each integer vector
compactify(). Method in class weka.core.Instances
Compactifies the set of instances.
compareOptions(String[], String[]). Static method in class weka.core.CheckOptionHandler
Compares the two given sets of options.
comparisonString(int, Instances). Method in class weka.classifiers.adtree.Splitter
Gets the string describing the comparision the split depends on for a particular branch.
comparisonString(int, Instances). Method in class weka.classifiers.adtree.TwoWayNominalSplit
Gets the string describing the comparision the split depends on for a particular branch.
comparisonString(int, Instances). Method in class weka.classifiers.adtree.TwoWayNumericSplit
Gets the string describing the comparision the split depends on for a particular branch.
confidenceForRule(ItemSet, ItemSet). Static method in class weka.associations.ItemSet
Outputs the confidence for a rule.
confusionMatrix(). Method in class weka.classifiers.Evaluation
Returns a copy of the confusion matrix.
ConfusionMatrix(String[]). Constructor for class weka.classifiers.evaluation.ConfusionMatrix
Creates the confusion matrix with the given class names.
connect(NeuralConnection, NeuralConnection). Static method in class weka.classifiers.neural.NeuralConnection
Connects two units together.
CONNECTED. Static variable in class weka.classifiers.neural.NeuralConnection
This flag is set once the unit has a connection.
connectToDatabase(). Method in class weka.experiment.DatabaseUtils
Opens a connection to the database
ConsistencySubsetEval(). Constructor for class weka.attributeSelection.ConsistencySubsetEval
Constructor.
CONST_AUTOMATIC_SHAPE. Static variable in class weka.gui.visualize.Plot2D
containedBy(Instance). Method in class weka.associations.ItemSet
Checks if an instance contains an item set.
containsKey(double). Method in class weka.classifiers.kstar.KStarCache
Checks if the specified key maps with an entry in the cache table
containsKey(double). Method in class weka.classifiers.kstar.LightHashTable
Tests if the specified double is a key in this hashtable.
ContingencyTables(). Constructor for class weka.core.ContingencyTables
ConverterUtils(). Constructor for class weka.core.converters.ConverterUtils
convertInstance(Instance). Method in interface weka.attributeSelection.AttributeTransformer
Transforms an instance in the format of the original data to the transformed space
convertInstance(Instance). Method in class weka.attributeSelection.PrincipalComponents
Transform an instance in original (unormalized) format.
convertNewLines(String). Static method in class weka.core.Utils
Converts carriage returns and new lines in a string into \r and \n.
convertToAttribX(double). Method in class weka.gui.visualize.Plot2D
convert a Panel x coordinate to a raw x value.
convertToAttribY(double). Method in class weka.gui.visualize.Plot2D
convert a Panel y coordinate to a raw y value.
convertToPanelX(double). Method in class weka.gui.visualize.Plot2D
Convert an raw x value to Panel x coordinate.
convertToPanelY(double). Method in class weka.gui.visualize.Plot2D
Convert an raw y value to Panel y coordinate.
convictionForRule(ItemSet, ItemSet, int, int). Method in class weka.associations.ItemSet
Outputs the conviction for a rule.
copy(). Method in class weka.core.Attribute
Produces a shallow copy of this attribute.
copy(). Method in class weka.core.BinarySparseInstance
Produces a shallow copy of this instance.
copy(). Method in interface weka.core.Copyable
This method produces a shallow copy of an object.
copy(). Method in class weka.classifiers.m5.Errors
Makes a copy of the Errors object
copy(). Method in class weka.core.FastVector
Produces a shallow copy of this vector.
copy(). Method in class weka.classifiers.m5.Function
Makes a copy of a function
copy(). Method in class weka.core.Instance
Produces a shallow copy of this instance.
copy(). Method in class weka.core.SparseInstance
Produces a shallow copy of this instance.
copy(). Method in class weka.classifiers.m5.SplitInfo
Makes a copy of this SplitInfo object
copy(double[], int). Static method in class weka.classifiers.m5.Dvector
Returns a copy of the first n elements of a double vector
copy(int[], int). Static method in class weka.classifiers.m5.Ivector
Makes a copy of the first n elements in an integer vector
copy(Node). Method in class weka.classifiers.m5.Node
Makes a copy of the tree under this node
CopyAttributesFilter(). Constructor for class weka.filters.CopyAttributesFilter
copyElements(). Method in class weka.core.FastVector
Clones the vector and shallow copies all its elements.
correct(). Method in class weka.classifiers.evaluation.ConfusionMatrix
Gets the number of correct classifications (that is, for which a correct prediction was made).
correct(). Method in class weka.classifiers.Evaluation
Gets the number of instances correctly classified (that is, for which a correct prediction was made).
correlation. Variable in class weka.experiment.PairedStats
The correlation coefficient
correlation(double[], double[], int). Static method in class weka.classifiers.m5.M5Utils
Returns the correlation coefficient of two double vectors
correlation(double[], double[], int). Static method in class weka.core.Utils
Returns the correlation coefficient of two double vectors.
correlationCoefficient(). Method in class weka.classifiers.Evaluation
Returns the correlation coefficient if the class is numeric.
CostCurve(). Constructor for class weka.classifiers.evaluation.CostCurve
CostMatrix(CostMatrix). Constructor for class weka.classifiers.CostMatrix
Creates a cost matrix identical to an existing matrix.
CostMatrix(int). Constructor for class weka.classifiers.CostMatrix
Creates a default cost matrix for the given number of classes.
CostMatrix(Reader). Constructor for class weka.classifiers.CostMatrix
Creates a cost matrix from a cost file.
CostMatrixEditor(). Constructor for class weka.gui.CostMatrixEditor
costMatrixSourceTipText(). Method in class weka.classifiers.CostSensitiveClassifier
costMatrixTipText(). Method in class weka.classifiers.CostSensitiveClassifier
CostSensitiveClassifier(). Constructor for class weka.classifiers.CostSensitiveClassifier
CostSensitiveClassifierSplitEvaluator(). Constructor for class weka.experiment.CostSensitiveClassifierSplitEvaluator
count. Variable in class weka.experiment.PairedStats
The number of data points seen
count. Variable in class weka.experiment.Stats
The number of values seen
CramersV(double[][]). Static method in class weka.core.ContingencyTables
Computes Cramer's V for a contingency table.
create(Reader). Method in class weka.gui.treevisualizer.TreeBuild
This will build A node structure from the dotty format passed.
createExperimentIndex(). Method in class weka.experiment.DatabaseUtils
Attempts to create the experiment index table
createExperimentIndexEntry(ResultProducer). Method in class weka.experiment.DatabaseUtils
Attempts to insert a results entry for the table into the experiment index.
createResultsTable(ResultProducer, String). Method in class weka.experiment.DatabaseUtils
Creates a results table for the supplied result producer.
crossoverProbTipText(). Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property
CrossValidateAttributes(). Method in class weka.attributeSelection.AttributeSelection
Perform a cross validation for attribute selection.
crossValidateModel(Classifier, Instances, int). Method in class weka.classifiers.Evaluation
Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
crossValidateModel(String, Instances, int, String[]). Static method in class weka.clusterers.ClusterEvaluation
Performs a cross-validation for a distribution clusterer on a set of instances.
crossValidateModel(String, Instances, int, String[]). Method in class weka.classifiers.Evaluation
Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
CrossValidationResultProducer(). Constructor for class weka.experiment.CrossValidationResultProducer
CSVLoader(). Constructor for class weka.core.converters.CSVLoader
CSVResultListener(). Constructor for class weka.experiment.CSVResultListener
CVParameterSelection(). Constructor for class weka.classifiers.CVParameterSelection
CVResultsString(). Method in class weka.attributeSelection.AttributeSelection
returns a string summarizing the results of repeated attribute selection runs on splits of a dataset.

D

DatabaseResultListener(). Constructor for class weka.experiment.DatabaseResultListener
Sets up the database drivers
DatabaseResultProducer(). Constructor for class weka.experiment.DatabaseResultProducer
Creates the DatabaseResultProducer, letting the parent constructor do it's thing.
databaseURLTipText(). Method in class weka.experiment.DatabaseUtils
Returns the tip text for this property
DatabaseUtils(). Constructor for class weka.experiment.DatabaseUtils
Sets up the database drivers
dataset(). Method in class weka.core.Instance
Returns the dataset this instance has access to.
DATASET_FIELD_NAME. Static variable in class weka.experiment.CrossValidationResultProducer
DATASET_FIELD_NAME. Static variable in class weka.experiment.RandomSplitResultProducer
DatasetListPanel(). Constructor for class weka.gui.experiment.DatasetListPanel
Create the dataset list panel initially disabled.
DatasetListPanel(Experiment). Constructor for class weka.gui.experiment.DatasetListPanel
Creates the dataset list panel with the given experiment.
DDConditionalEstimator(int, int, boolean). Constructor for class weka.estimators.DDConditionalEstimator
Constructor
debugTipText(). Method in class weka.classifiers.AdditiveRegression
Returns the tip text for this property
debugTipText(). Method in class weka.filters.AttributeExpressionFilter
Returns the tip text for this property
debugTipText(). Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
decayTipText(). Method in class weka.classifiers.neural.NeuralNetwork
DecisionStump(). Constructor for class weka.classifiers.DecisionStump
DecisionTable(). Constructor for class weka.classifiers.DecisionTable
Constructor for a DecisionTable
decompose(). Method in class weka.classifiers.BVDecompose
Carry out the bias-variance decomposition
DEFAULT_SHAPE_SIZE. Static variable in class weka.gui.visualize.Plot2D
del(int, Instance). Method in class weka.classifiers.j48.Distribution
Deletes given instance from given bag.
delete(). Method in class weka.core.Instances
Removes all instances from the set.
delete(int). Method in class weka.core.Instances
Removes an instance at the given position from the set.
deleteAttributeAt(int). Method in class weka.core.Instance
Deletes an attribute at the given position (0 to numAttributes() - 1).
deleteAttributeAt(int). Method in class weka.core.Instances
Deletes an attribute at the given position (0 to numAttributes() - 1).
deleteItemSets(FastVector, int, int). Static method in class weka.associations.ItemSet
Deletes all item sets that don't have minimum support.
deleteStringAttributes(). Method in class weka.core.Instances
Deletes all string attributes in the dataset.
deleteTrailingZerosAndDot(StringBuffer). Static method in class weka.classifiers.m5.M5Utils
Deletes the trailing zeros and decimal point in a stringBuffer
deleteWithMissing(Attribute). Method in class weka.core.Instances
Removes all instances with missing values for a particular attribute from the dataset.
deleteWithMissing(int). Method in class weka.core.Instances
Removes all instances with missing values for a particular attribute from the dataset.
deleteWithMissingClass(). Method in class weka.core.Instances
Removes all instances with a missing class value from the dataset.
delRange(int, Instances, int, int). Method in class weka.classifiers.j48.Distribution
Deletes all instances in given range from given bag.
deltaTipText(). Method in class weka.associations.Apriori
Returns the tip text for this property
densityForInstance(Instance). Method in class weka.clusterers.DistributionClusterer
Computes the density for a given instance.
densityForInstance(Instance). Method in class weka.clusterers.DistributionMetaClusterer
Returns the density for an instance.
densityForInstance(Instance). Method in class weka.clusterers.EM
Computes the density for a given instance.
description(). Method in class weka.core.Option
Returns the option's description.
designatedClassTipText(). Method in class weka.classifiers.ThresholdSelector
determineBounds(). Method in class weka.gui.visualize.Plot2D
Determine the min and max values for axis and colouring attributes
determineColumnConstraints(ResultProducer). Method in class weka.experiment.AveragingResultProducer
Determines if there are any constraints (imposed by the destination) on the result columns to be produced by resultProducers.
determineColumnConstraints(ResultProducer). Method in class weka.experiment.CSVResultListener
Determines if there are any constraints (imposed by the destination) on the result columns to be produced by resultProducers.
determineColumnConstraints(ResultProducer). Method in class weka.experiment.DatabaseResultListener
Determines if there are any constraints (imposed by the destination) on any additional measures produced by resultProducers.
determineColumnConstraints(ResultProducer). Method in class weka.experiment.LearningRateResultProducer
Determines if there are any constraints (imposed by the destination) on the result columns to be produced by resultProducers.
determineColumnConstraints(ResultProducer). Method in interface weka.experiment.ResultListener
Determines if there are any constraints (imposed by the destination) on additional result columns to be produced by resultProducers.
DIAMOND_SHAPE. Static variable in class weka.gui.visualize.Plot2D
differencesProbability. Variable in class weka.experiment.PairedStats
The probability of obtaining the observed differences
differencesSignificance. Variable in class weka.experiment.PairedStats
A significance indicator: 0 if the differences are not significant > 0 if x significantly greater than y < 0 if x significantly less than y
differencesStats. Variable in class weka.experiment.PairedStats
The stats associated with the paired differences
directionTipText(). Method in class weka.attributeSelection.BestFirst
Returns the tip text for this property
disconnect(NeuralConnection, NeuralConnection). Static method in class weka.classifiers.neural.NeuralConnection
Disconnects two units.
disconnectFromDatabase(). Method in class weka.experiment.DatabaseUtils
Closes the connection to the database.
DiscreteEstimator(int, boolean). Constructor for class weka.estimators.DiscreteEstimator
Constructor
DiscretizeFilter(). Constructor for class weka.filters.DiscretizeFilter
Constructor - initialises the filter
distinctCount. Variable in class weka.core.AttributeStats
The number of distinct values
distributedExperimentSelected(). Method in class weka.gui.experiment.DistributeExperimentPanel
Returns true if the distribute experiment checkbox is selected
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.j48.ClassifierSplitModel
Returns the distribution of class values induced by the model.
distribution(). Method in class weka.classifiers.evaluation.NominalPrediction
Gets the predicted probabilities
Distribution(Distribution). Constructor for class weka.classifiers.j48.Distribution
Creates distribution with only one bag by merging all bags of given distribution.
Distribution(Distribution, int). Constructor for class weka.classifiers.j48.Distribution
Creates distribution with two bags by merging all bags apart of the indicated one.
Distribution(double[][]). Constructor for class weka.classifiers.j48.Distribution
Creates and initializes a new distribution using the given array.
Distribution(Instances). Constructor for class weka.classifiers.j48.Distribution
Creates a distribution with only one bag according to instances in source.
Distribution(Instances, ClassifierSplitModel). Constructor for class weka.classifiers.j48.Distribution
Creates a distribution according to given instances and split model.
Distribution(int, int). Constructor for class weka.classifiers.j48.Distribution
Creates and initializes a new distribution.
DistributionClassifier(). Constructor for class weka.classifiers.DistributionClassifier
distributionClassifierTipText(). Method in class weka.classifiers.MultiClassClassifier
distributionClassifierTipText(). Method in class weka.classifiers.ThresholdSelector
DistributionClusterer(). Constructor for class weka.clusterers.DistributionClusterer
distributionForInstance(Instance). Method in class weka.classifiers.AdaBoostM1
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.adtree.ADTree
Returns the class probability distribution for an instance.
distributionForInstance(Instance). Method in class weka.classifiers.AttributeSelectedClassifier
Classifies a given instance after attribute selection
distributionForInstance(Instance). Method in class weka.classifiers.Bagging
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.ClassificationViaRegression
Returns the distribution for an instance.
distributionForInstance(Instance). Method in class weka.classifiers.j48.ClassifierDecList
Returns class probabilities for a weighted instance.
distributionForInstance(Instance). Method in class weka.classifiers.DecisionStump
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.DecisionTable
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.DistributionClassifier
Predicts the class memberships for a given instance.
distributionForInstance(Instance). Method in class weka.clusterers.DistributionClusterer
Predicts the cluster memberships for a given instance.
distributionForInstance(Instance). Method in class weka.classifiers.DistributionMetaClassifier
Returns the distribution for an instance.
distributionForInstance(Instance). Method in class weka.clusterers.DistributionMetaClusterer
Returns the distribution for an instance.
distributionForInstance(Instance). Method in class weka.clusterers.EM
Predicts the cluster memberships for a given instance.
distributionForInstance(Instance). Method in class weka.classifiers.FilteredClassifier
Classifies a given instance after filtering.
distributionForInstance(Instance). Method in class weka.classifiers.HyperPipes
Classifies the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.IBk
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.Id3
Computes class distribution for instance using decision tree.
distributionForInstance(Instance). Method in class weka.classifiers.j48.J48
Returns class probabilities for an instance.
distributionForInstance(Instance). Method in class weka.classifiers.KernelDensity
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.kstar.KStar
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.Logistic
Computes the distribution for a given instance
distributionForInstance(Instance). Method in class weka.classifiers.LogitBoost
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.j48.MakeDecList
Returns the class distribution for an instance.
distributionForInstance(Instance). Method in class weka.classifiers.MultiClassClassifier
Returns the distribution for an instance.
distributionForInstance(Instance). Method in class weka.classifiers.NaiveBayes
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.NaiveBayesSimple
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.neural.NeuralNetwork
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.j48.PART
Returns class probabilities for an instance.
distributionForInstance(Instance). Method in class weka.classifiers.SMO
Outputs the distribution for the given output.
distributionForInstance(Instance). Method in class weka.classifiers.ThresholdSelector
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.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.classifiers.VFI
Classifies the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.VotedPerceptron
Outputs the distribution for the given output.
distributionForInstance(Instance). Method in class weka.classifiers.ZeroR
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance, boolean). Method in class weka.classifiers.j48.ClassifierTree
Returns class probabilities for a weighted instance.
DistributionMetaClassifier(). Constructor for class weka.classifiers.DistributionMetaClassifier
Default constructor
DistributionMetaClassifier(Classifier). Constructor for class weka.classifiers.DistributionMetaClassifier
Creates a new DistributionMetaClassifier instance, specifying the Classifier to wrap around.
DistributionMetaClusterer(). Constructor for class weka.clusterers.DistributionMetaClusterer
DKConditionalEstimator(int, double). Constructor for class weka.estimators.DKConditionalEstimator
Constructor
DNConditionalEstimator(int, double). Constructor for class weka.estimators.DNConditionalEstimator
Constructor
doHistory(KeyEvent). Method in class weka.gui.SimpleCLI
Changes the currently displayed command line when certain keys are pressed.
done(). Method in class weka.classifiers.adtree.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 interface weka.classifiers.IterativeClassifier
Signal end of iterating, useful for any house-keeping/cleanup
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.classifiers.CheckClassifier
Begin the tests, reporting results to System.out
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.
doubleToStringF(double, int, int). Static method in class weka.classifiers.m5.M5Utils
Rounds a double and converts it into a formatted right-justified String.
doubleToStringG(double, int, int). Static method in class weka.classifiers.m5.M5Utils
Rounds a double and converts it into a formatted right-justified String.
drawHighlight(Graphics, int, int). Method in class weka.classifiers.neural.NeuralConnection
Call this function to draw the node highlighted.
drawInputLines(Graphics, int, int). Method in class weka.classifiers.neural.NeuralConnection
Call this function to draw the nodes input connections.
drawNode(Graphics, int, int). Method in class weka.classifiers.neural.NeuralConnection
Call this function to draw the node.
drawOutputLines(Graphics, int, int). Method in class weka.classifiers.neural.NeuralConnection
Call this function to draw the nodes output connections.
dumpDistribution(). Method in class weka.classifiers.j48.Distribution
Prints distribution.
dumpLabel(int, Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
Prints label for subset index of instances (eg class).
dumpModel(Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
Prints the split model.
Dvector(). Constructor for class weka.classifiers.m5.Dvector

E

Edge(String, String, String). Constructor for class weka.gui.treevisualizer.Edge
This constructs an Edge with the specified label and parent , child serial tags.
editableProperties(). Method in class weka.gui.PropertySheetPanel
Gets the number of editable properties for the current target.
elementAt(int). Method in class weka.core.FastVector
Returns the element at the given position.
elements(). Method in class weka.core.FastVector
Returns an enumeration of this vector.
elements(int). Method in class weka.core.FastVector
Returns an enumeration of this vector, skipping the element with the given index.
EM(). Constructor for class weka.clusterers.EM
Constructor.
empty(). Method in class weka.core.Queue
Checks if queue is empty.
EmptyAttributeFilter(). Constructor for class weka.filters.EmptyAttributeFilter
entropy(double[]). Static method in class weka.core.ContingencyTables
Computes the entropy of the given array.
EntropyBasedSplitCrit(). Constructor for class weka.classifiers.j48.EntropyBasedSplitCrit
entropyConditionedOnColumns(double[][]). Static method in class weka.core.ContingencyTables
Computes conditional entropy of the rows given the columns.
entropyConditionedOnRows(double[][]). Static method in class weka.core.ContingencyTables
Computes conditional entropy of the columns given the rows.
entropyConditionedOnRows(double[][], double[][], double). Static method in class weka.core.ContingencyTables
Computes conditional entropy of the columns given the rows of the test matrix with respect to the train matrix.
entropyOverColumns(double[][]). Static method in class weka.core.ContingencyTables
Computes the columns' entropy for the given contingency table.
entropyOverRows(double[][]). Static method in class weka.core.ContingencyTables
Computes the rows' entropy for the given contingency table.
EntropySplitCrit(). Constructor for class weka.classifiers.j48.EntropySplitCrit
enumerateAttributes(). Method in class weka.core.Instance
Returns an enumeration of all the attributes.
enumerateAttributes(). Method in class weka.core.Instances
Returns an enumeration of all the attributes.
enumerateInstances(). Method in class weka.core.Instances
Returns an enumeration of all instances in the dataset.
enumerateMeasures(). Method in interface weka.core.AdditionalMeasureProducer
Returns an enumeration of the measure names.
enumerateMeasures(). Method in class weka.classifiers.AdditiveRegression
Returns an enumeration of the additional measure names
enumerateMeasures(). Method in class weka.classifiers.adtree.ADTree
Returns an enumeration of the additional measure names.
enumerateMeasures(). Method in class weka.classifiers.AttributeSelectedClassifier
Returns an enumeration of the additional measure names
enumerateMeasures(). Method in class weka.experiment.AveragingResultProducer
Returns an enumeration of any additional measure names that might be in the result producer
enumerateMeasures(). Method in class weka.experiment.ClassifierSplitEvaluator
Returns an enumeration of any additional measure names that might be in the classifier
enumerateMeasures(). Method in class weka.experiment.CrossValidationResultProducer
Returns an enumeration of any additional measure names that might be in the SplitEvaluator
enumerateMeasures(). Method in class weka.experiment.DatabaseResultProducer
Returns an enumeration of any additional measure names that might be in the result producer
enumerateMeasures(). Method in class weka.classifiers.DecisionTable
Returns an enumeration of the additional measure names
enumerateMeasures(). Method in class weka.classifiers.j48.J48
Returns an enumeration of the additional measure names
enumerateMeasures(). Method in class weka.experiment.LearningRateResultProducer
Returns an enumeration of any additional measure names that might be in the result producer
enumerateMeasures(). Method in class weka.classifiers.m5.M5Prime
Returns an enumeration of the additional measure names
enumerateMeasures(). Method in class weka.classifiers.j48.PART
Returns an enumeration of the additional measure names
enumerateMeasures(). Method in class weka.experiment.RandomSplitResultProducer
Returns an enumeration of any additional measure names that might be in the SplitEvaluator
enumerateMeasures(). Method in class weka.experiment.RegressionSplitEvaluator
Returns an enumeration of any additional measure names that might be in the classifier
enumerateValues(). Method in class weka.core.Attribute
Returns an enumeration of all the attribute's values if the attribute is nominal or a string, null otherwise.
EPSILON. Static variable in interface weka.classifiers.kstar.KStarConstants
eq(double, double). Static method in class weka.core.Utils
Tests if a is equal to b.
eqDouble(double, double). Static method in class weka.classifiers.m5.M5Utils
Tests if two double values are equal to each other
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.
equals(Object). Method in class weka.core.Attribute
Tests if given attribute is equal to this attribute.
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.associations.ItemSet
Tests if two item sets are equal.
equals(Object). Method in class weka.core.SelectedTag
Returns true if this SelectedTag equals another object
equals(Object). Method in class weka.core.SerializedObject
Compares this object with another for equality.
equalTo(Splitter). Method in class weka.classifiers.adtree.Splitter
Tests whether two splitters are equivalent.
equalTo(Splitter). Method in class weka.classifiers.adtree.TwoWayNominalSplit
Tests whether two splitters are equivalent.
equalTo(Splitter). Method in class weka.classifiers.adtree.TwoWayNumericSplit
Tests whether two splitters are equivalent.
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_EXHAUSTIVE. Static variable in class weka.classifiers.MultiClassClassifier
ERROR_NONE. Static variable in class weka.classifiers.MultiClassClassifier
The error correction modes
ERROR_RANDOM. Static variable in class weka.classifiers.MultiClassClassifier
ERROR_SHAPE. Static variable in class weka.gui.visualize.Plot2D
errorCorrectionModeTipText(). Method in class weka.classifiers.MultiClassClassifier
errorMsg(String). Static method in class weka.classifiers.m5.M5Utils
Prints error message and exits
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).
errors(Instances). Method in class weka.classifiers.m5.Function
Evaluates a function
errors(Instances, boolean). Method in class weka.classifiers.m5.Node
Evaluates a tree
Errors(int, int). Constructor for class weka.classifiers.m5.Errors
Constructs an object which could contain the evaluation results of a model
errorValue(boolean). Method in class weka.classifiers.neural.NeuralConnection
Call this to get the error value of this unit.
errorValue(boolean). Method in class weka.classifiers.neural.NeuralNode
Call this to get the error value of this unit.
errorValue(NeuralNode). Method in class weka.classifiers.neural.LinearUnit
This function calculates what the error value should be.
errorValue(NeuralNode). Method in interface weka.classifiers.neural.NeuralMethod
This function calculates what the error value should be.
errorValue(NeuralNode). Method in class weka.classifiers.neural.SigmoidUnit
This function calculates what the error value should be.
EVAL_CROSS_VALIDATION. Static variable in class weka.classifiers.ThresholdSelector
EVAL_TRAINING_SET. Static variable in class weka.classifiers.ThresholdSelector
EVAL_TUNED_SPLIT. Static variable in class weka.classifiers.ThresholdSelector
evaluateAttribute(int). Method in class weka.attributeSelection.AttributeEvaluator
evaluates an individual attribute
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.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.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.SymmetricalUncertAttributeEval
evaluates an individual attribute by measuring the symmetrical uncertainty between it and the class.
evaluateClusterer(Clusterer, String[]). Static method in class weka.clusterers.ClusterEvaluation
Evaluates a clusterer with the options given in an array of strings.
evaluateClusterer(Instances). Method in class weka.clusterers.ClusterEvaluation
Evaluate the clusterer on a set of instances.
evaluateModel(Classifier, Instances). Method in class weka.classifiers.Evaluation
Evaluates the classifier on a given set of instances.
evaluateModel(Classifier, String[]). Static method in class weka.classifiers.Evaluation
Evaluates a classifier with the options given in an array of strings.
evaluateModel(String, String[]). Static method in class weka.classifiers.Evaluation
Evaluates a classifier with the options given in an array of strings.
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 prediction on a single instance.
evaluateModelOnce(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). Method in class weka.attributeSelection.ConsistencySubsetEval
Evaluates a subset of attributes
evaluateSubset(BitSet). Method in class weka.attributeSelection.SubsetEvaluator
evaluates a subset of attributes
evaluateSubset(BitSet). Method in class weka.attributeSelection.WrapperSubsetEval
Evaluates a subset of attributes
evaluateSubset(BitSet, Instance, boolean). Method in class weka.attributeSelection.ClassifierSubsetEval
Evaluates a subset of attributes with respect to a single instance.
evaluateSubset(BitSet, Instance, boolean). Method in class weka.attributeSelection.HoldOutSubsetEvaluator
Evaluates a subset of attributes with respect to a single instance.
evaluateSubset(BitSet, Instances). Method in class weka.attributeSelection.ClassifierSubsetEval
Evaluates a subset of attributes with respect to a set of instances.
evaluateSubset(BitSet, Instances). Method in class weka.attributeSelection.HoldOutSubsetEvaluator
Evaluates a subset of attributes with respect to a set of instances.
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.
evaluationModeTipText(). Method in class weka.classifiers.ThresholdSelector
EvaluationUtils(). Constructor for class weka.classifiers.evaluation.EvaluationUtils
evaluatorTipText(). Method in class weka.classifiers.AttributeSelectedClassifier
Returns the tip text for this property
execute(). Method in class weka.experiment.RemoteExperimentSubTask
Run the experiment
execute(). Method in interface weka.experiment.Task
Execute this task.
execute(String). Method in class weka.experiment.DatabaseUtils
Executes a SQL 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(). Constructor for class weka.attributeSelection.ExhaustiveSearch
Constructor
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)
expectedCosts(double[]). 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(). Constructor for class weka.experiment.Experiment
Experimenter(boolean). Constructor for class weka.gui.experiment.Experimenter
Creates the experiment environment gui with no initial experiment
experimentIndexExists(). Method in class weka.experiment.DatabaseUtils
Returns true if the experiment index exists.
Explorer(). Constructor for class weka.gui.explorer.Explorer
Creates the experiment environment gui with no initial experiment
expressionTipText(). Method in class weka.filters.AttributeExpressionFilter
Returns the tip text for this property
ExtensionFileFilter(String, String). Constructor for class weka.gui.ExtensionFileFilter
Creates the ExtensionFileFilter

F

factor(int, int, double). Method in class weka.classifiers.m5.Node
Calculates a multiplication factor used at this node
FAILED. Static variable in class weka.experiment.TaskStatusInfo
FALLOUT_NAME. Static variable in class weka.classifiers.evaluation.ThresholdCurve
FALSE_NEG_NAME. Static variable in class weka.classifiers.evaluation.ThresholdCurve
FALSE_POS_NAME. Static variable in class weka.classifiers.evaluation.ThresholdCurve
falseNegativeRate(int). Method in class weka.classifiers.Evaluation
Calculate the false negative rate with respect to a particular class.
falsePositiveRate(int). Method in class weka.classifiers.Evaluation
Calculate the false positive rate with respect to a particular class.
FastVector(). Constructor for class weka.core.FastVector
Constructs an empty vector with initial capacity zero.
FastVector(int). Constructor for class weka.core.FastVector
Constructs a vector with the given capacity.
FastVector(int, int, double). Constructor for class weka.core.FastVector
Constructs a vector with the given capacity, capacity increment and capacity mulitplier.
FCriticalValue(double, int, int). Static method in class weka.core.Statistics
Critical value for given probability of F-distribution.
FILE_EXTENSION. Static variable in class weka.classifiers.CostMatrix
The filename extension that should be used for cost files
FILE_EXTENSION. Static variable in class weka.experiment.Experiment
The filename extension that should be used for experiment files
FILE_EXTENSION. Static variable in class weka.core.Instances
The filename extension that should be used for arff files
FileEditor(). Constructor for class weka.gui.FileEditor
Filter(). Constructor for class weka.filters.Filter
FilteredClassifier(). Constructor for class weka.classifiers.FilteredClassifier
Default constructor specifying ZeroR as the classifier and AllFilter as the filter.
FilteredClassifier(Classifier, Filter). Constructor for class weka.classifiers.FilteredClassifier
Constructor that specifies the subclassifier and filter to use.
filterFile(Filter, String[]). Static method in class weka.filters.Filter
Method for testing filters.
findNumBinsTipText(). Method in class weka.filters.DiscretizeFilter
Returns the tip text for this property
FINISHED. Static variable in class weka.experiment.TaskStatusInfo
finished(). Method in class weka.experiment.OutputZipper
Closes the zip file.
firstElement(). Method in class weka.core.FastVector
Returns the first element of the vector.
firstInstance(). Method in class weka.core.Instances
Returns the first instance in the set.
FirstOrderFilter(). Constructor for class weka.filters.FirstOrderFilter
FLOOR. Static variable in interface weka.classifiers.kstar.KStarConstants
FLOOR1. Static variable in interface weka.classifiers.kstar.KStarConstants
floorDouble(double). Static method in class weka.classifiers.m5.M5Utils
Returns the largest (closest to positive infinity) long integer value that is not greater than the argument.
fMeasure(int). Method in class weka.classifiers.Evaluation
Calculate the F-Measure with respect to a particular class.
FMEASURE_NAME. Static variable in class weka.classifiers.evaluation.ThresholdCurve
FOLD_FIELD_NAME. Static variable in class weka.experiment.CrossValidationResultProducer
foldsTipText(). Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
foldsTipText(). Method in class weka.attributeSelection.WrapperSubsetEval
Returns the tip text for this property
FORMAT_AVAILABLE. Static variable in class weka.gui.streams.InstanceEvent
Specifies that the instance format is available
formulaeToString(boolean). Method in class weka.classifiers.m5.Node
Converts all the linear models at the leaves under the node to a string
forName(Class, String, String[]). Static method in class weka.core.Utils
Creates a new instance of an object given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]). Static method in class weka.attributeSelection.ASEvaluation
Creates a new instance of an attribute/subset evaluator given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]). Static method in class weka.attributeSelection.ASSearch
Creates a new instance of a search class given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]). Static method in class weka.associations.Associator
Creates a new instance of a associator given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]). Static method in class weka.classifiers.Classifier
Creates a new instance of a classifier given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]). Static method in class weka.clusterers.Clusterer
Creates a new instance of a clusterer given it's class name and (optional) arguments to pass to it's setOptions method.
ForwardSelection(). Constructor for class weka.attributeSelection.ForwardSelection
FP_RATE_NAME. Static variable in class weka.classifiers.evaluation.ThresholdCurve
FProbability(double, int, int). Static method in class weka.core.Statistics
Computes probability of F-ratio.
Function(). Constructor for class weka.classifiers.m5.Function
Constructs a function of constant value
function(). Method in class weka.classifiers.m5.Node
Finds the appropriate order of the unsmoothed linear model at this node
Function(Instances). Constructor for class weka.classifiers.m5.Function
Constucts a function with all attributes except the class in the inst
Function(int). Constructor for class weka.classifiers.m5.Function
Constructs a function with one attribute

G

gainRatio(). Method in class weka.classifiers.j48.BinC45Split
Returns (C4.5-type) gain ratio for the generated split.
gainRatio(). Method in class weka.classifiers.j48.C45Split
Returns (C4.5-type) gain ratio for the generated split.
gainRatio(double[][]). Static method in class weka.core.ContingencyTables
Computes gain ratio for contingency table (split on rows).
GainRatioAttributeEval(). Constructor for class weka.attributeSelection.GainRatioAttributeEval
Constructor
GainRatioSplitCrit(). Constructor for class weka.classifiers.j48.GainRatioSplitCrit
generateRankingTipText(). Method in class weka.attributeSelection.ForwardSelection
Returns the tip text for this property
generateRankingTipText(). Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
generateRankingTipText(). Method in class weka.attributeSelection.Ranker
Returns the tip text for this property
generateRules(double, FastVector, int). Method in class weka.associations.ItemSet
Generates all rules for an item set.
generateRulesBruteForce(double, int, FastVector, int, int, double). Method in class weka.associations.ItemSet
Generates all significant rules for an item set.
GeneratorPropertyIteratorPanel(). Constructor for class weka.gui.experiment.GeneratorPropertyIteratorPanel
Creates the property iterator panel initially disabled.
GeneratorPropertyIteratorPanel(Experiment). Constructor for class weka.gui.experiment.GeneratorPropertyIteratorPanel
Creates the property iterator panel and sets the experiment.
GenericArrayEditor(). Constructor for class weka.gui.GenericArrayEditor
Sets up the array editor.
GenericObjectEditor(). Constructor for class weka.gui.GenericObjectEditor
GeneticSearch(). Constructor for class weka.attributeSelection.GeneticSearch
Constructor.
getAcuity(). Method in class weka.clusterers.Cobweb
get the accuity value
getAdjustWeights(). Method in class weka.filters.SpreadSubsampleFilter
Returns true if instance weights will be adjusted to maintain total weight per class.
getAdvanceDataSetFirst(). Method in class weka.experiment.Experiment
Get the value of m_DataSetFirstFirst.
getArffFile(). Method in class weka.gui.streams.InstanceLoader
getArffFile(). Method in class weka.gui.streams.InstanceSavePanel
getAsText(). Method in class weka.gui.CostMatrixEditor
Returns null as we don't support getting/setting values as text.
getAsText(). Method in class weka.gui.GenericArrayEditor
Returns null as we don't support getting/setting values as text.
getAsText(). Method in class weka.gui.GenericObjectEditor
Returns null as we don't support getting/setting values as text.
getAsText(). Method in class weka.gui.SelectedTagEditor
Gets the current value as text.
getAttribute1(). Method in class weka.gui.visualize.VisualizePanelEvent
getAttribute2(). Method in class weka.gui.visualize.VisualizePanelEvent
getAttributeEvaluator(). Method in class weka.attributeSelection.RaceSearch
Get the attribute evaluator used to generate the ranking.
getAttributeEvaluator(). Method in class weka.attributeSelection.RankSearch
Get the attribute evaluator used to generate the ranking.
getAttributeIndex(). Method in class weka.filters.AddFilter
Get the index where the attribute will be inserted
getAttributeIndex(). Method in class weka.filters.InstanceFilter
Get the attribute to be used for selection (-1 for last)
getAttributeIndex(). Method in class weka.filters.MakeIndicatorFilter
Get the index of the attribute used.
getAttributeIndex(). Method in class weka.filters.MergeTwoValuesFilter
Get the index of the attribute used.
getAttributeIndex(). Method in class weka.filters.StringToNominalFilter
Get the index of the attribute used.
getAttributeIndex(). Method in class weka.filters.SwapAttributeValuesFilter
Get the index of the attribute used.
getAttributeIndices(). Method in class weka.filters.AbstractTimeSeriesFilter
Get the current range selection
getAttributeIndices(). Method in class weka.filters.AttributeFilter
Get the current range selection.
getAttributeIndices(). Method in class weka.filters.CopyAttributesFilter
Get the current range selection
getAttributeIndices(). Method in class weka.filters.DiscretizeFilter
Gets the current range selection
getAttributeIndices(). Method in class weka.filters.FirstOrderFilter
Get the current range selection
getAttributeIndices(). Method in class weka.filters.NumericTransformFilter
Get the current range selection
getAttributeMax(int). Method in class weka.classifiers.IBk
Get an attributes maximum observed value
getAttributeMin(int). Method in class weka.classifiers.IBk
Get an attributes minimum observed value
getAttributeName(). Method in class weka.filters.AddFilter
Get the name of the attribute to be created
getAttributeSelectionMethod(). Method in class weka.classifiers.LinearRegression
Gets the method used to select attributes for use in the linear regression.
getAttributeType(). Method in class weka.filters.AttributeTypeFilter
Gets the type of attribute that will be deleted.
getAutoBuild(). Method in class weka.classifiers.neural.NeuralNetwork
getBagSizePercent(). Method in class weka.classifiers.Bagging
Gets the size of each bag, as a percentage of the training set size.
getBagSizePercent(). Method in class weka.classifiers.MetaCost
Gets the size of each bag, as a percentage of the training set size.
getBaseClassifier(int). Method in class weka.classifiers.Stacking
Gets the specific classifier from the set of base classifiers.
getBaseClassifiers(). Method in class weka.classifiers.Stacking
Gets the list of possible classifers to choose from.
getBaseExperiment(). Method in class weka.experiment.RemoteExperiment
Get the base experiment used by this remote experiment
getBias(). Method in class weka.classifiers.BVDecompose
Get the calculated bias squared
getBias(). Method in class weka.classifiers.VFI
Get the value of the bias parameter
getBiasToUniformClass(). Method in class weka.filters.ResampleFilter
Gets the bias towards a uniform class.
getBinarizeNumericAttributes(). Method in class weka.attributeSelection.ChiSquaredAttributeEval
get whether numeric attributes are just being binarized.
getBinarizeNumericAttributes(). Method in class weka.attributeSelection.InfoGainAttributeEval
get whether numeric attributes are just being binarized.
getBinaryAttributesNominal(). Method in class weka.filters.NominalToBinaryFilter
Gets if binary attributes are to be treated as nominal ones.
getBinarySplits(). Method in class weka.classifiers.j48.J48
Get the value of binarySplits.
getBinarySplits(). Method in class weka.classifiers.j48.PART
Get the value of binarySplits.
getBins(). Method in class weka.filters.DiscretizeFilter
Gets the number of bins numeric attributes will be divided into
getC(). Method in class weka.classifiers.SMO
Get the value of C.
getCacheKeyName(). Method in class weka.experiment.DatabaseResultListener
Get the value of CacheKeyName.
getCacheSize(). Method in class weka.classifiers.SMO
Get the size of the kernel cache
getCacheValues(double). Method in class weka.classifiers.kstar.KStarCache
Returns the values in the cache mapped by the specified key
getCalculatedNumToSelect(). Method in class weka.attributeSelection.ForwardSelection
Gets the calculated number of attributes to retain.
getCalculatedNumToSelect(). Method in class weka.attributeSelection.RaceSearch
Gets the calculated number of attributes to retain.
getCalculatedNumToSelect(). Method in interface weka.attributeSelection.RankedOutputSearch
Gets the calculated number of attributes to retain.
getCalculatedNumToSelect(). Method in class weka.attributeSelection.Ranker
Gets the calculated number to select.
getCalculateStdDevs(). Method in class weka.experiment.AveragingResultProducer
Get the value of CalculateStdDevs.
getCenter(). Method in class weka.gui.treevisualizer.Node
Get the value of center.
getChangeInWeights(). Method in class weka.classifiers.neural.NeuralNode
call this function to get the chnage in weights array.
getChild(int). Method in class weka.gui.treevisualizer.Node
Get the Edge for the child number 'i'.
getChildForBranch(int). Method in class weka.classifiers.adtree.Splitter
Gets the child for a branch of the split.
getChildForBranch(int). Method in class weka.classifiers.adtree.TwoWayNominalSplit
Gets the child for a branch of the split.
getChildForBranch(int). Method in class weka.classifiers.adtree.TwoWayNumericSplit
Gets the child for a branch of the split.
getChildren(). Method in class weka.classifiers.adtree.PredictionNode
Gets the children of this node.
getCindex(). Method in class weka.gui.visualize.PlotData2D
Get the currently set colouring index of the data
getCIndex(). Method in class weka.gui.visualize.VisualizePanel
Get the index of the attribute selected for coloring
getClassesToClusters(). Method in class weka.clusterers.ClusterEvaluation
Return the array (ordered by cluster number) of minimum error class to cluster mappings
getClassForIRStatistics(). Method in class weka.experiment.ClassifierSplitEvaluator
Get the value of ClassForIRStatistics.
getClassifier(). Method in class weka.classifiers.AdaBoostM1
Get the classifier used as the classifier
getClassifier(). Method in class weka.classifiers.AdditiveRegression
Gets the classifier used.
getClassifier(). Method in class weka.classifiers.AttributeSelectedClassifier
Gets the classifier used.
getClassifier(). Method in class weka.classifiers.Bagging
Get the classifier used as the classifier
getClassifier(). Method in class weka.classifiers.BVDecompose
Gets the name of the classifier being analysed
getClassifier(). Method in class weka.classifiers.CheckClassifier
Get the classifier used as the classifier
getClassifier(). Method in class weka.classifiers.ClassificationViaRegression
Get the base classifier (regression scheme) used as the classifier
getClassifier(). Method in class weka.experiment.ClassifierSplitEvaluator
Get the value of Classifier.
getClassifier(). Method in class weka.attributeSelection.ClassifierSubsetEval
Get the classifier used as the base learner.
getClassifier(). Method in class weka.classifiers.CostSensitiveClassifier
Gets the classifier used.
getClassifier(). Method in class weka.classifiers.CVParameterSelection
Get the classifier used as the classifier
getClassifier(). Method in class weka.classifiers.DistributionMetaClassifier
Get the classifier used as the classifier
getClassifier(). Method in class weka.classifiers.FilteredClassifier
Gets the classifier used.
getClassifier(). Method in class weka.classifiers.LogitBoost
Get the classifier used as the classifier
getClassifier(). Method in class weka.classifiers.MetaCost
Gets the distribution classifier used.
getClassifier(). Method in class weka.classifiers.RegressionByDiscretization
Get the classifier used as the classifier
getClassifier(). Method in class weka.experiment.RegressionSplitEvaluator
Get the value of Classifier.
getClassifier(). Method in class weka.attributeSelection.WrapperSubsetEval
Get the classifier used as the base learner.
getClassifier(int). Method in class weka.classifiers.MultiScheme
Gets a single classifier from the set of available classifiers.
getClassifiers(). Method in class weka.classifiers.MultiScheme
Gets the list of possible classifers to choose from.
getClassIndex(). Method in class weka.classifiers.BVDecompose
Get the index (starting from 1) of the attribute used as the class.
getClassName(). Method in class weka.filters.NumericTransformFilter
Get the class containing the transformation method.
getClearEachDataset(). Method in class weka.gui.streams.InstanceViewer
getClusterAssignments(). Method in class weka.clusterers.ClusterEvaluation
Return an array of cluster assignments corresponding to the most recent set of instances clustered.
getClusterer(). Method in class weka.clusterers.DistributionMetaClusterer
Get the clusterer used as the clusterer
getColor(). Method in class weka.gui.treevisualizer.Node
Get the value of color.
getCommand(). Method in class weka.gui.treevisualizer.TreeDisplayEvent
getCompatibilityState(). Method in class weka.experiment.AveragingResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState(). Method in class weka.experiment.CrossValidationResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState(). Method in class weka.experiment.DatabaseResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState(). Method in class weka.experiment.LearningRateResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState(). Method in class weka.experiment.RandomSplitResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState(). Method in interface weka.experiment.ResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getConfidenceFactor(). Method in class weka.classifiers.j48.J48
Get the value of CF.
getConfidenceFactor(). Method in class weka.classifiers.j48.PART
Get the value of CF.
getConfusionMatrix(). Method in class weka.classifiers.evaluation.TwoClassStats
Generates a ConfusionMatrix representing the current two-class statistics, using class names "negative" and "positive".
getCostMatrix(). Method in class weka.classifiers.CostSensitiveClassifier
Gets the misclassification cost matrix.
getCostMatrix(). Method in class weka.classifiers.MetaCost
Gets the misclassification cost matrix.
getCostMatrixSource(). Method in class weka.classifiers.CostSensitiveClassifier
Gets the source location method of the cost matrix.
getCostMatrixSource(). Method in class weka.classifiers.MetaCost
Gets the source location method of the cost matrix.
getCount(Node, int). Static method in class weka.gui.treevisualizer.Node
Recursively finds the number of visible nodes there are (this may accidentally count some of the invis nodes).
getCrossoverProb(). Method in class weka.attributeSelection.GeneticSearch
get the probability of crossover
getCrossVal(). Method in class weka.classifiers.DecisionTable
Gets the number of folds for cross validation
getCrossValidate(). Method in class weka.classifiers.IBk
Gets whether hold-one-out cross-validation will be used to select the best k value
getCurrentDatasetNumber(). Method in class weka.experiment.Experiment
When an experiment is running, this returns the current dataset number.
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). 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.CostCurve
Calculates the performance stats for the desired 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
Returns the array editing component.
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.
getCutoff(). Method in class weka.clusterers.Cobweb
get the cutoff
getCutPoints(int). Method in class weka.filters.DiscretizeFilter
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.CVParameterSelection
Gets the scheme paramter with the given index.
getCVPredictions(DistributionClassifier, 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.
getDatabaseURL(). Method in class weka.experiment.DatabaseUtils
Get the value of DatabaseURL.
getDataFileName(). Method in class weka.classifiers.BVDecompose
Get the name of the data file used for the decomposition
getDataSet(). Method in class weka.core.converters.AbstractLoader
Must be overridden by subclasses.
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.CSVLoader
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.
getDatasetKeyColumns(). Method in class weka.experiment.PairedTTester
Get the value of DatasetKeyColumns.
getDatasets(). Method in class weka.experiment.Experiment
Gets the datasets in the experiment.
getDebug(). Method in class weka.classifiers.AdaBoostM1
Get whether debugging is turned on
getDebug(). Method in class weka.classifiers.AdditiveRegression
Gets whether debugging has been turned on
getDebug(). Method in class weka.filters.AttributeExpressionFilter
Gets whether debug is set
getDebug(). Method in class weka.classifiers.BVDecompose
Gets whether debugging is turned on
getDebug(). Method in class weka.classifiers.CheckClassifier
Get whether debugging is turned on
getDebug(). Method in class weka.classifiers.CVParameterSelection
Gets whether debugging is turned on
getDebug(). Method in class weka.clusterers.EM
Get debug mode
getDebug(). Method in class weka.classifiers.IBk
Get the value of Debug.
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
getDebug(). Method in class weka.classifiers.LinearRegression
Controls whether debugging output will be printed
getDebug(). Method in class weka.classifiers.Logistic
Gets whether debugging output will be printed.
getDebug(). Method in class weka.classifiers.LogitBoost
Get whether debugging is turned on
getDebug(). Method in class weka.classifiers.LWR
SGts whether debugging output should be produced
getDebug(). Method in class weka.classifiers.MultiScheme
Get whether debugging is turned on
getDebug(). Method in class weka.attributeSelection.RaceSearch
Get whether output is to be verbose
getDebug(). Method in class weka.classifiers.RegressionByDiscretization
Gets whether debugging output will be printed
getDecay(). Method in class weka.classifiers.neural.NeuralNetwork
getDelta(). Method in class weka.associations.Apriori
Get the value of delta.
getDescription(). Method in class weka.gui.ExtensionFileFilter
Gets the description of accepted files.
getDesignatedClass(). Method in class weka.classifiers.ThresholdSelector
Gets the method to determine which class value to optimize.
getDirection(). Method in class weka.attributeSelection.BestFirst
Get the search direction
getDisplayRules(). Method in class weka.classifiers.DecisionTable
Gets whether rules are being printed
getDistanceWeighting(). Method in class weka.classifiers.IBk
Gets the distance weighting method used.
getDistributionClassifier(). Method in class weka.classifiers.MultiClassClassifier
Get the classifier used as the classifier
getDistributionClassifier(). Method in class weka.classifiers.ThresholdSelector
Get the DistributionClassifier used as the classifier.
getDistributionSpread(). Method in class weka.filters.SpreadSubsampleFilter
Gets the value for the distribution spread
getDontStratifyData(). Method in class weka.filters.SplitDatasetFilter
Gets whether stratification is not performed.
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.
getElement(int, int). Method in class weka.core.Matrix
Returns the value of a cell in the matrix.
getEntropicAutoBlend(). Method in class weka.classifiers.kstar.KStar
Get whether entropic blending being used
getEntry(double). Method in class weka.classifiers.kstar.LightHashTable
Returns the table entry to which the specified key is mapped in this hashtable.
getEpsilon(). Method in class weka.classifiers.SMO
Get the value of epsilon.
getError(). Method in class weka.classifiers.BVDecompose
Get the calculated error rate
getErrorCorrectionMode(). Method in class weka.classifiers.MultiClassClassifier
Gets the error correction mode used.
getEstimatedErrorsForLeaf(). Method in class weka.classifiers.j48.C45PruneableDecList
Computes estimated errors for leaf.
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
getEvaluationMode(). Method in class weka.classifiers.ThresholdSelector
Gets the evaluation mode used.
getEvaluator(). Method in class weka.classifiers.AttributeSelectedClassifier
Gets the attribute evaluator used
getEvaluator(). Method in class weka.filters.AttributeSelectionFilter
Get the name of the attribute/subset evaluator
getExecutionStatus(). Method in class weka.experiment.TaskStatusInfo
Get the execution status of this Task.
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.SetupPanel
Gets the currently configured experiment.
getExponent(). Method in class weka.classifiers.SMO
Get the value of exponent.
getExponent(). Method in class weka.classifiers.VotedPerceptron
Get the value of exponent.
getExpression(). Method in class weka.filters.AttributeExpressionFilter
Get the expression
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.
getFillWithMissing(). Method in class weka.filters.AbstractTimeSeriesFilter
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.classifiers.FilteredClassifier
Gets the filter used.
getFindNumBins(). Method in class weka.filters.DiscretizeFilter
Get the value of FindNumBins.
getFirstToken(StreamTokenizer). Static method in class weka.core.converters.ConverterUtils
Gets token, skipping empty lines.
getFirstValueIndex(). Method in class weka.filters.MergeTwoValuesFilter
Get the index of the first value used.
getFirstValueIndex(). Method in class weka.filters.SwapAttributeValuesFilter
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".
getFMeasure(). Method in class weka.classifiers.evaluation.TwoClassStats
Calculate the F-Measure.
getFold(). Method in class weka.filters.SplitDatasetFilter
Gets the fold which is selected.
getFolds(). Method in class weka.attributeSelection.WrapperSubsetEval
Get the number of folds used for accuracy estimation
getFoldsType(). Method in class weka.attributeSelection.RaceSearch
Get the xfold type
getGCount(Node, int). Static method in class weka.gui.treevisualizer.Node
Recursively finds the number of visible groups of siblings there are.
getGenerateRanking(). Method in class weka.attributeSelection.ForwardSelection
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.
getGlobalBlend(). Method in class weka.classifiers.kstar.KStar
Get the value of the global blend parameter
getGUI(). Method in class weka.classifiers.neural.NeuralNetwork
getHashtable(FastVector, int). Static method in class weka.associations.ItemSet
Return a hashtable filled with the given item sets.
getHeight(Node, int). Static method in class weka.gui.treevisualizer.Node
Recursively finds the number of visible levels there are.
getHiddenLayers(). Method in class weka.classifiers.neural.NeuralNetwork
getHoldOutFile(). Method in class weka.attributeSelection.ClassifierSubsetEval
Gets the file that holds hold out/test instances.
getID(). Method in class weka.gui.streams.InstanceEvent
Get the event type
getId(). Method in class weka.classifiers.neural.NeuralConnection
getID(). Method in class weka.core.Tag
Gets the numeric ID of the Tag.
getID(). Method in class weka.gui.treevisualizer.TreeDisplayEvent
getInputNums(). Method in class weka.classifiers.neural.NeuralConnection
Use this to get easy access to the input numbers.
getInputs(). Method in class weka.classifiers.neural.NeuralConnection
Use this to get easy access to the inputs.
getInstanceRange(). Method in class weka.filters.AbstractTimeSeriesFilter
Gets the number of instances forward to translate values between.
getInstances(). Method in class weka.gui.treevisualizer.Node
This will return the Instances object related to this node.
getInstances(). Method in class weka.experiment.PairedTTester
Get the value 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.visualize.VisualizePanel
Get the master plot's instances
getInstances1(). Method in class weka.gui.visualize.VisualizePanelEvent
getInstances2(). Method in class weka.gui.visualize.VisualizePanelEvent
getInstancesIndices(). Method in class weka.filters.SplitDatasetFilter
Gets ranges of instances selected.
getInvert(). Method in class weka.core.Range
Gets whether the range sense is inverted, i.e.
getInvertSelection(). Method in class weka.filters.AbstractTimeSeriesFilter
Get whether the supplied columns are to be removed or kept
getInvertSelection(). Method in class weka.filters.AttributeFilter
Get whether the supplied columns are to be removed or kept
getInvertSelection(). Method in class weka.filters.CopyAttributesFilter
Get whether the supplied columns are to be removed or kept
getInvertSelection(). Method in class weka.filters.DiscretizeFilter
Gets whether the supplied columns are to be removed or kept
getInvertSelection(). Method in class weka.filters.InstanceFilter
Get whether the supplied columns are to be removed or kept
getInvertSelection(). Method in class weka.filters.NumericTransformFilter
Get whether the supplied columns are to be transformed or not
getInvertSelection(). Method in class weka.filters.SplitDatasetFilter
Gets if selection is to be inverted.
getJavaInitializationString(). Method in class weka.gui.CostMatrixEditor
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.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.
getKey(). Method in class weka.experiment.ClassifierSplitEvaluator
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.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.
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.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.IBk
Gets the number of neighbours the learner will use.
getKNN(). Method in class weka.classifiers.LWR
Gets the number of neighbours used for kernel bandwidth setting.
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.
getLearningRate(). Method in class weka.classifiers.neural.NeuralNetwork
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.
getList(). Method in class weka.gui.ResultHistoryPanel
Gets the JList used by the results list
getLocallyPredictive(). Method in class weka.attributeSelection.CfsSubsetEval
Return true if including locally predictive attributes
getLower(). Method in class weka.gui.experiment.RunNumberPanel
Gets the current lower run number.
getLowerBoundMinSupport(). Method in class weka.associations.Apriori
Get the value of lowerBoundMinSupport.
getLowerOrderTerms(). Method in class weka.classifiers.SMO
Check whether lower-order terms are being used.
getLowerSize(). Method in class weka.experiment.LearningRateResultProducer
Get the value of LowerSize.
getMakeBinary(). Method in class weka.filters.DiscretizeFilter
Gets whether binary attributes should be made for discretized ones.
getMasterPlot(). Method in class weka.gui.visualize.Plot2D
Get the master plot
getMatchMissingValues(). Method in class weka.filters.InstanceFilter
Gets whether missing values are counted as a match.
getMaxC(). Method in class weka.gui.visualize.Plot2D
Return the current max value of the colouring attribute
getMaxCost(int). Method in class weka.classifiers.CostMatrix
Gets the maximum misclassification cost possible for a given actual class value
getMaxCount(). Method in class weka.filters.SpreadSubsampleFilter
Gets the value for the max count
getMaxGenerations(). Method in class weka.attributeSelection.GeneticSearch
get the number of generations
getMaxIterations(). Method in class weka.classifiers.AdaBoostM1
Get the maximum number of boost iterations
getMaxIterations(). Method in class weka.clusterers.EM
Get the maximum number of iterations
getMaxIterations(). Method in class weka.classifiers.LogitBoost
Get the maximum number of boost iterations
getMaxK(). Method in class weka.classifiers.VotedPerceptron
Get the value of maxK.
getMaxModels(). Method in class weka.classifiers.AdditiveRegression
Get the max number of models to generate
getMaxStale(). Method in class weka.classifiers.DecisionTable
Gets the number of non improving decision tables
getMaxX(). Method in class weka.gui.visualize.Plot2D
Return the current max value of the attribute plotted on the x axis
getMaxY(). Method in class weka.gui.visualize.Plot2D
Return the current max value of the attribute plotted on the y axis
getMeanSquared(). Method in class weka.classifiers.IBk
Gets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
getMeasure(String). Method in interface weka.core.AdditionalMeasureProducer
Returns the value of the named measure
getMeasure(String). Method in class weka.classifiers.AdditiveRegression
Returns the value of the named measure
getMeasure(String). Method in class weka.classifiers.adtree.ADTree
Returns the value of the named measure.
getMeasure(String). Method in class weka.classifiers.AttributeSelectedClassifier
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.classifiers.DecisionTable
Returns the value of the named measure
getMeasure(String). Method in class weka.classifiers.j48.J48
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.classifiers.m5.M5Prime
Returns the value of the named measure
getMeasure(String). Method in class weka.classifiers.j48.PART
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
getMetaClassifier(). Method in class weka.classifiers.Stacking
Gets the meta classifier.
getMethod(). Method in class weka.classifiers.neural.NeuralNode
getMethodName(). Method in class weka.filters.NumericTransformFilter
Get the transformation method.
getMetricType(). Method in class weka.associations.Apriori
Get the metric type
getMinBucketSize(). Method in class weka.classifiers.OneR
Get the value of minBucketSize.
getMinC(). Method in class weka.gui.visualize.Plot2D
Return the current min value of the colouring attribute
getMinimizeExpectedCost(). Method in class weka.classifiers.CostSensitiveClassifier
Gets the value of MinimizeExpectedCost.
getMinMetric(). Method in class weka.associations.Apriori
Get the value of minConfidence.
getMinNumObj(). Method in class weka.classifiers.j48.J48
Get the value of minNumObj.
getMinNumObj(). Method in class weka.classifiers.j48.PART
Get the value of minNumObj.
getMinStdDev(). Method in class weka.clusterers.EM
Get the minimum allowable standard deviation.
getMinX(). Method in class weka.gui.visualize.Plot2D
Return the current min value of the attribute plotted on the x axis
getMinY(). Method in class weka.gui.visualize.Plot2D
Return the current min value of the attribute plotted on the y axis
getMissingMerge(). Method in class weka.attributeSelection.ChiSquaredAttributeEval
get whether missing values are being distributed or not
getMissingMerge(). Method in class weka.attributeSelection.GainRatioAttributeEval
get whether missing values are being distributed or not
getMissingMerge(). Method in class weka.attributeSelection.InfoGainAttributeEval
get whether missing values are being distributed or not
getMissingMerge(). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
get whether missing values are being distributed or not
getMissingMode(). Method in class weka.classifiers.kstar.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
getModelType(). Method in class weka.classifiers.m5.M5Prime
Get the value of Model.
getModifyHeader(). Method in class weka.filters.InstanceFilter
Gets whether the header will be modified when selecting on nominal attributes.
getMomentum(). Method in class weka.classifiers.neural.NeuralNetwork
getMutationProb(). Method in class weka.attributeSelection.GeneticSearch
get the probability of mutation
getName(). Method in class weka.filters.AttributeExpressionFilter
Returns the name of the new attribute
getName(). Method in class weka.gui.visualize.VisualizePanel
Returns the name associated with this plot.
getNameAtIndex(int). Method in class weka.gui.ResultHistoryPanel
Gets the name of theitem in the list at the specified index
getNamedBuffer(String). Method in class weka.gui.ResultHistoryPanel
Gets the named buffer
getNamedObject(String). Method in class weka.gui.ResultHistoryPanel
Get the named object from the list
getNextInstance(). Method in class weka.core.converters.AbstractLoader
Must be overridden by subclasses.
getNextInstance(). 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(). 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(). Method in class weka.core.converters.CSVLoader
CSVLoader is unable to process a data set incrementally.
getNextInstance(). 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(). 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.
getNominalIndices(). Method in class weka.filters.InstanceFilter
Get the set of nominal value indices that will be used for selection
getNominalLabels(). Method in class weka.filters.AddFilter
Get the list of labels for nominal attribute creation
getNominalToBinaryFilter(). Method in class weka.classifiers.neural.NeuralNetwork
getNoNormalization(). Method in class weka.classifiers.IBk
Gets whether normalization is turned off.
getNormalize(). Method in class weka.attributeSelection.PrincipalComponents
Gets whether or not input data is to be normalized
getNormalizeAttributes(). Method in class weka.classifiers.neural.NeuralNetwork
getNormalizeData(). Method in class weka.classifiers.SMO
Check whether data is to be normalized.
getNormalizeNumericClass(). Method in class weka.classifiers.neural.NeuralNetwork
getNotes(). Method in class weka.experiment.Experiment
Get the user notes.
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.
getNumBins(). Method in class weka.classifiers.RegressionByDiscretization
Gets the number of bins the class attribute will be discretized into.
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.SimpleKMeans
gets the number of clusters to generate
getNumDatasets(). Method in class weka.experiment.PairedTTester
Gets the number of datasets in the resultsets
getNumeric(). Method in class weka.filters.MakeIndicatorFilter
Check if new attribute is to be numeric.
getNumFolds(). Method in class weka.experiment.CrossValidationResultProducer
Get the value of NumFolds.
getNumFolds(). Method in class weka.classifiers.CVParameterSelection
Get the number of folds used for cross-validation.
getNumFolds(). Method in class weka.classifiers.j48.J48
Get the value of numFolds.
getNumFolds(). Method in class weka.classifiers.MultiScheme
Gets the number of folds for cross-validation.
getNumFolds(). Method in class weka.classifiers.j48.PART
Get the value of numFolds.
getNumFolds(). Method in class weka.filters.SplitDatasetFilter
Gets the number of folds in which dataset is to be split into.
getNumFolds(). Method in class weka.classifiers.Stacking
Gets the number of folds for the cross-validation.
getNumInputs(). Method in class weka.classifiers.neural.NeuralConnection
getNumIterations(). Method in class weka.classifiers.Bagging
Gets the number of bagging iterations
getNumIterations(). Method in class weka.classifiers.MetaCost
Gets the number of bagging iterations
getNumIterations(). Method in class weka.classifiers.VotedPerceptron
Get the value of NumIterations.
getNumNeighbours(). Method in class weka.attributeSelection.ReliefFAttributeEval
Get the number of nearest neighbours
getNumOfBoostingIterations(). Method in class weka.classifiers.adtree.ADTree
Gets the number of boosting iterations.
getNumOfBranches(). Method in class weka.classifiers.adtree.Splitter
Gets the number of branches of the split.
getNumOfBranches(). Method in class weka.classifiers.adtree.TwoWayNominalSplit
Gets the number of branches of the split.
getNumOfBranches(). Method in class weka.classifiers.adtree.TwoWayNumericSplit
Gets the number of branches of the split.
getNumOutputs(). Method in class weka.classifiers.neural.NeuralConnection
getNumResultsets(). Method in class weka.experiment.PairedTTester
Gets the number of resultsets in the data.
getNumRules(). Method in class weka.associations.Apriori
Get the value of numRules.
getNumSymbols(). Method in class weka.estimators.DiscreteEstimator
Gets the number of symbols this estimator operates with
getNumToSelect(). Method in class weka.attributeSelection.ForwardSelection
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.IBk
Get the number of training instances the classifier is currently using
getNumXValFolds(). Method in class weka.classifiers.ThresholdSelector
Get the number of folds used for cross-validation.
getObject(). Method in class weka.core.SerializedObject
Gets the object stored in this SerializedObject.
getOnDemandDirectory(). Method in class weka.classifiers.CostSensitiveClassifier
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.
getOnDemandDirectory(). Method in class weka.classifiers.MetaCost
Returns the directory that will be searched for cost files when loading on demand.
getOptimizeBins(). Method in class weka.classifiers.RegressionByDiscretization
Gets whether the discretizer optimizes the number of bins
getOption(char, String[]). Static method in class weka.core.Utils
Gets an option indicated by a flag "-Char" from the given array of strings.
getOptions(). Method in class weka.filters.AbstractTimeSeriesFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.classifiers.AdaBoostM1
Gets the current settings of the Classifier.
getOptions(). Method in class weka.filters.AddFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.classifiers.AdditiveRegression
Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.adtree.ADTree
Gets the current settings of ADTree.
getOptions(). Method in class weka.associations.Apriori
Gets the current settings of the Apriori object.
getOptions(). Method in class weka.filters.AttributeExpressionFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.filters.AttributeFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.classifiers.AttributeSelectedClassifier
Gets the current settings of the Classifier.
getOptions(). Method in class weka.filters.AttributeSelectionFilter
Gets the current settings for the attribute selection (search, evaluator) etc.
getOptions(). Method in class weka.filters.AttributeTypeFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.experiment.AveragingResultProducer
Gets the current settings of the result producer.
getOptions(). Method in class weka.classifiers.Bagging
Gets the current settings of the Classifier.
getOptions(). Method in class weka.attributeSelection.BestFirst
Gets the current settings of BestFirst.
getOptions(). Method in class weka.classifiers.BVDecompose
Gets the current settings of the CheckClassifier.
getOptions(). Method in class weka.attributeSelection.CfsSubsetEval
Gets the current settings of CfsSubsetEval
getOptions(). Method in class weka.classifiers.CheckClassifier
Gets the current settings of the CheckClassifier.
getOptions(). Method in class weka.attributeSelection.ChiSquaredAttributeEval
Gets the current settings of WrapperSubsetEval.
getOptions(). Method in class weka.classifiers.ClassificationViaRegression
Gets the current settings of the Classifier.
getOptions(). Method in class weka.experiment.ClassifierSplitEvaluator
Gets the current settings of the Classifier.
getOptions(). Method in class weka.attributeSelection.ClassifierSubsetEval
Gets the current settings of ClassifierSubsetEval
getOptions(). Method in class weka.clusterers.Cobweb
Gets the current settings of Cobweb.
getOptions(). Method in class weka.filters.CopyAttributesFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.classifiers.CostSensitiveClassifier
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.classifiers.CVParameterSelection
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.classifiers.DecisionTable
Gets the current settings of the classifier.
getOptions(). Method in class weka.filters.DiscretizeFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.classifiers.DistributionMetaClassifier
Gets the current settings of the Classifier.
getOptions(). Method in class weka.clusterers.DistributionMetaClusterer
Gets the current settings of the Clusterer.
getOptions(). Method in class weka.clusterers.EM
Gets the current settings of EM.
getOptions(). Method in class weka.attributeSelection.ExhaustiveSearch
Gets the current settings of RandomSearch.
getOptions(). Method in class weka.experiment.Experiment
Gets the current settings of the experiment iterator.
getOptions(). Method in class weka.classifiers.FilteredClassifier
Gets the current settings of the Classifier.
getOptions(). Method in class weka.filters.FirstOrderFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.attributeSelection.ForwardSelection
Gets the current settings of ReliefFAttributeEval.
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.classifiers.IBk
Gets the current settings of IBk.
getOptions(). Method in class weka.attributeSelection.InfoGainAttributeEval
Gets the current settings of WrapperSubsetEval.
getOptions(). Method in class weka.filters.InstanceFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.experiment.InstanceQuery
Gets the current settings of InstanceQuery
getOptions(). Method in class weka.classifiers.j48.J48
Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.kstar.KStar
Gets the current settings of K*.
getOptions(). Method in class weka.experiment.LearningRateResultProducer
Gets the current settings of the result producer.
getOptions(). Method in class weka.classifiers.LinearRegression
Gets the current settings of the classifier.
getOptions(). Method in class weka.classifiers.Logistic
Gets the current settings of the classifier.
getOptions(). Method in class weka.classifiers.LogitBoost
Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.LWR
Gets the current settings of the classifier.
getOptions(). Method in class weka.classifiers.m5.M5Prime
Gets the current settings of the Classifier.
getOptions(). Method in class weka.filters.MakeIndicatorFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.filters.MergeTwoValuesFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.classifiers.MetaCost
Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.MultiClassClassifier
Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.MultiScheme
Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.NaiveBayes
Gets the current settings of the classifier.
getOptions(). Method in class weka.classifiers.neural.NeuralNetwork
Gets the current settings of NeuralNet.
getOptions(). Method in class weka.filters.NominalToBinaryFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.filters.NumericTransformFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.classifiers.OneR
Gets the current settings of the OneR classifier.
getOptions(). Method in interface weka.core.OptionHandler
Gets the current option settings for the OptionHandler.
getOptions(). Method in class weka.experiment.PairedTTester
Gets current settings of the PairedTTester.
getOptions(). Method in class weka.classifiers.j48.PART
Gets the current settings of the Classifier.
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.filters.RandomizeFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.attributeSelection.RandomSearch
Gets the current settings of RandomSearch.
getOptions(). Method in class weka.experiment.RandomSplitResultProducer
Gets the current settings of the result producer.
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.classifiers.RegressionByDiscretization
Gets the current settings of the Classifier.
getOptions(). Method in class weka.experiment.RegressionSplitEvaluator
Gets the current settings of the Classifier.
getOptions(). Method in class weka.attributeSelection.ReliefFAttributeEval
Gets the current settings of ReliefFAttributeEval.
getOptions(). Method in class weka.filters.ResampleFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.clusterers.SimpleKMeans
Gets the current settings of SimpleKMeans
getOptions(). Method in class weka.classifiers.SMO
Gets the current settings of the classifier.
getOptions(). Method in class weka.filters.SplitDatasetFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.filters.SpreadSubsampleFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.classifiers.Stacking
Gets the current settings of the Classifier.
getOptions(). Method in class weka.filters.StringToNominalFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.filters.SwapAttributeValuesFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Gets the current settings of WrapperSubsetEval.
getOptions(). Method in class weka.classifiers.ThresholdSelector
Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.VFI
Gets the current settings of VFI
getOptions(). Method in class weka.classifiers.VotedPerceptron
Gets the current settings of the classifier.
getOptions(). Method in class weka.attributeSelection.WrapperSubsetEval
Gets the current settings of WrapperSubsetEval.
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.
getOutputFormat(). Method in class weka.filters.Filter
Gets the format of the output instances.
getOutputNums(). Method in class weka.classifiers.neural.NeuralConnection
Use this to get easy access to the output numbers.
getOutputs(). Method in class weka.classifiers.neural.NeuralConnection
Use this to get easy access to the outputs.
getParent(int). Method in class weka.gui.treevisualizer.Node
Get the parent edge.
getPath(). Method in class weka.gui.PropertySelectorDialog
Gets the path of property nodes to the selected property.
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
getPlots(). Method in class weka.gui.visualize.Plot2D
Return the list of plots
getPopulationSize(). Method in class weka.attributeSelection.GeneticSearch
get the size of the population
getPrecision(). Method in class weka.classifiers.evaluation.TwoClassStats
Calculate the precision.
getPrediction(DistributionClassifier, Instance). Method in class weka.classifiers.evaluation.EvaluationUtils
Generate a single prediction for a test instance given the pre-trained classifier.
getProbability(double). Method in class weka.estimators.DiscreteEstimator
Get a probability estimate for a value
getProbability(double). Method in interface weka.estimators.Estimator
Get a probability estimate for a value.
getProbability(double). Method in class weka.estimators.KernelEstimator
Get a probability estimate for a value.
getProbability(double). Method in class weka.estimators.MahalanobisEstimator
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
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, 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, double). Method in class weka.estimators.KDConditionalEstimator
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, 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
getProduceLatex(). Method in class weka.experiment.PairedTTester
Get whether latex is output
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.
getPropertyPath(). Method in class weka.experiment.Experiment
Gets the path of properties taken to get to the custom property to iterate over.
getPruningFactor(). Method in class weka.classifiers.m5.M5Prime
Get the value of PruningFactor.
getQuery(). Method in class weka.experiment.InstanceQuery
Get the query to execute against the database
getRaceType(). Method in class weka.attributeSelection.RaceSearch
Get the race type
getRandomizeData(). Method in class weka.experiment.RandomSplitResultProducer
Get if dataset is to be randomized
getRandomSeed(). Method in class weka.classifiers.adtree.ADTree
Gets random seed for a random walk.
getRandomSeed(). Method in class weka.classifiers.neural.NeuralNetwork
getRandomSeed(). Method in class weka.filters.RandomizeFilter
Get the random number generator seed value.
getRandomSeed(). Method in class weka.filters.ResampleFilter
Gets the random number seed.
getRandomSeed(). Method in class weka.filters.SpreadSubsampleFilter
Gets the random number seed.
getRandomWidthFactor(). Method in class weka.classifiers.MultiClassClassifier
Gets the multiplier when generating random codes.
getRangeCorrection(). Method in class weka.classifiers.ThresholdSelector
Gets the confidence range correction mode used.
getRanges(). Method in class weka.core.Range
Gets the string representing the selected range of values
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.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.
getReadable(). Method in class weka.core.Tag
Gets the string description of the Tag.
getRecall(). Method in class weka.classifiers.evaluation.TwoClassStats
Calculate the recall.
getReducedErrorPruning(). Method in class weka.classifiers.j48.J48
Get the value of reducedErrorPruning.
getReducedErrorPruning(). Method in class weka.classifiers.j48.PART
Get the value of reducedErrorPruning.
getRefer(). Method in class weka.gui.treevisualizer.Node
Get the value of refer.
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
getReportFrequency(). Method in class weka.attributeSelection.GeneticSearch
get how often repports are generated
getRescaleKernel(). Method in class weka.classifiers.SMO
Check whether kernel is being rescaled.
getReset(). Method in class weka.classifiers.neural.NeuralNetwork
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.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.
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.
getResultListener(). Method in class weka.experiment.Experiment
Gets the result listener where results will be sent.
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.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.
getResultSet(). Method in class weka.experiment.DatabaseUtils
Gets the results generated by a previous query.
getResultsetKeyColumns(). Method in class weka.experiment.PairedTTester
Get the value of ResultsetKeyColumns.
getResultsetName(int). Method in class weka.experiment.PairedTTester
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.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.
getROCArea(Instances). Static method in class weka.classifiers.evaluation.ThresholdCurve
Calculates the area under the ROC curve.
getRoot(). Method in class weka.gui.treevisualizer.Node
Get the value of root.
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.
getRunColumn(). Method in class weka.experiment.PairedTTester
Get the value of RunColumn.
getRunLower(). Method in class weka.experiment.Experiment
Get the lower run number for the experiment.
getRunUpper(). Method in class weka.experiment.Experiment
Get the upper run number for the experiment.
getSampleSize(). Method in class weka.attributeSelection.ReliefFAttributeEval
Get the number of instances used for estimating attributes
getSampleSizePercent(). Method in class weka.filters.ResampleFilter
Gets the subsample size as a percentage of the original set.
getSaveInstanceData(). Method in class weka.classifiers.adtree.ADTree
Gets whether the tree is to save instance data.
getSaveInstanceData(). Method in class weka.classifiers.j48.J48
Check whether instance data is to be saved.
getSearch(). Method in class weka.classifiers.AttributeSelectedClassifier
Gets the search method used
getSearch(). Method in class weka.filters.AttributeSelectionFilter
Get the name of the search method
getSearchPath(). Method in class weka.classifiers.adtree.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
getSearchTermination(). Method in class weka.attributeSelection.BestFirst
Get the termination criterion (number of non-improving nodes).
getSecondValueIndex(). Method in class weka.filters.MergeTwoValuesFilter
Get the index of the second value used.
getSecondValueIndex(). Method in class weka.filters.SwapAttributeValuesFilter
Get the index of the second value used.
getSeed(). Method in class weka.classifiers.AdaBoostM1
Get seed for resampling.
getSeed(). Method in class weka.classifiers.Bagging
Gets the seed for the random number generations
getSeed(). Method in class weka.classifiers.BVDecompose
Gets the random number seed
getSeed(). Method in class weka.classifiers.CostSensitiveClassifier
Get seed for resampling.
getSeed(). Method in class weka.classifiers.CVParameterSelection
Gets the random number seed.
getSeed(). Method in class weka.clusterers.EM
Get 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.attributeSelection.GeneticSearch
get the value of the random number generator's seed
getSeed(). Method in class weka.classifiers.LogitBoost
Get seed for resampling.
getSeed(). Method in class weka.classifiers.MetaCost
Get seed for resampling.
getSeed(). Method in class weka.classifiers.MultiScheme
Gets the random number seed.
getSeed(). Method in class weka.attributeSelection.ReliefFAttributeEval
Get the seed used for randomly sampling instances.
getSeed(). Method in class weka.clusterers.SimpleKMeans
Get the random number seed
getSeed(). Method in class weka.filters.SplitDatasetFilter
Gets the random number seed used for shuffling the dataset.
getSeed(). Method in class weka.classifiers.Stacking
Gets the random number seed.
getSeed(). Method in class weka.classifiers.ThresholdSelector
Gets the random number seed.
getSeed(). Method in class weka.classifiers.VotedPerceptron
Get the value of Seed.
getSeed(). Method in class weka.attributeSelection.WrapperSubsetEval
Get the random number seed used for cross validation
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.
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.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.
getShape(). Method in class weka.gui.treevisualizer.Node
Get the value of shape.
getShowStdDevs(). Method in class weka.experiment.PairedTTester
Returns true if standard deviations have been requested.
getShrinkage(). Method in class weka.classifiers.AdditiveRegression
Get the shrinkage rate.
getSigma(). Method in class weka.classifiers.BVDecompose
Get the calculated sigma squared
getSigma(). Method in class weka.attributeSelection.ReliefFAttributeEval
Get the value of sigma.
getSignificanceLevel(). Method in class weka.associations.Apriori
Get the value of significanceLevel.
getSignificanceLevel(). Method in class weka.experiment.PairedTTester
Get the value of SignificanceLevel.
getSignificanceLevel(). Method in class weka.attributeSelection.RaceSearch
Get the significance level
getSIndex(). Method in class weka.gui.visualize.VisualizePanel
Get the index of the shape selected for creating splits.
getSource(). Method in class weka.gui.treevisualizer.Edge
Get the value of source.
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..
getSplitEvaluator(). Method in class weka.experiment.CrossValidationResultProducer
Get the SplitEvaluator.
getSplitEvaluator(). Method in class weka.experiment.RandomSplitResultProducer
Get the SplitEvaluator.
getSplitPoint(). Method in class weka.filters.InstanceFilter
Get the split point used for numeric selection
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.ExhaustiveSearch
Returns a list of attributes (and or attribute ranges) as a String
getStartSet(). Method in class weka.attributeSelection.ForwardSelection
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.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
getStatusMessage(). Method in class weka.experiment.TaskStatusInfo
Get the status message.
getStepSize(). Method in class weka.experiment.LearningRateResultProducer
Get the value of StepSize.
getStructure(). Method in class weka.core.converters.AbstractLoader
Must be overridden by subclasses.
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.CSVLoader
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.
getSubtreeRaising(). Method in class weka.classifiers.j48.J48
Get the value of subtreeRaising.
getSummary(). Method in class weka.gui.SetInstancesPanel
Gets the instances summary panel associated with this panel
getTags(). Method in class weka.gui.CostMatrixEditor
Returns null as we don't support getting values as tags.
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.core.SelectedTag
Gets the set of all valid Tags.
getTags(). Method in class weka.gui.SelectedTagEditor
Gets the list of tags that can be selected from.
getTarget(). Method in class weka.gui.treevisualizer.Edge
Get the value of target.
getTaskResult(). Method in class weka.experiment.TaskStatusInfo
Get the returnable result of this task.
getTestPredictions(DistributionClassifier, 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.
getThreshold(). Method in class weka.attributeSelection.ForwardSelection
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
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.
getToken(StreamTokenizer). Static method in class weka.core.converters.ConverterUtils
Gets token.
getToleranceParameter(). Method in class weka.classifiers.SMO
Get the value of tolerance parameter.
getTop(). Method in class weka.gui.treevisualizer.Node
Get the value of top.
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.
getTrainingTime(). Method in class weka.classifiers.neural.NeuralNetwork
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.
getTrainPoolSize(). Method in class weka.classifiers.BVDecompose
Get the number of instances in the training pool.
getTrainTestPredictions(DistributionClassifier, 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.
getTransformBackToOriginal(). Method in class weka.attributeSelection.PrincipalComponents
Gets whether the data is to be transformed back to the original space.
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.
getTwoClassStats(int). Method in class weka.classifiers.evaluation.ConfusionMatrix
Gets the performance with respect to one of the classes as a TwoClassStats object.
getType(). Method in class weka.classifiers.neural.NeuralConnection
getUnpruned(). Method in class weka.classifiers.j48.J48
Get the value of unpruned.
getUpper(). Method in class weka.gui.experiment.RunNumberPanel
Gets the current upper run number.
getUpperBoundMinSupport(). Method in class weka.associations.Apriori
Get the value of upperBoundMinSupport.
getUpperSize(). Method in class weka.experiment.LearningRateResultProducer
Get the value of UpperSize.
getUseBetterEncoding(). Method in class weka.filters.DiscretizeFilter
Gets whether better encoding is to be used for MDL.
getUseIBk(). Method in class weka.classifiers.DecisionTable
Gets whether IBk is being used instead of the majority class
getUseKernelEstimator(). Method in class weka.classifiers.NaiveBayes
Gets if kernel estimator is being used.
getUseKononenko(). Method in class weka.filters.DiscretizeFilter
Gets whether Kononenko's MDL criterion is to be used.
getUseLaplace(). Method in class weka.classifiers.j48.J48
Get the value of useLaplace.
getUseMDL(). Method in class weka.filters.DiscretizeFilter
Gets whether MDL will be used as the discretisation method.
getUsePropertyIterator(). Method in class weka.experiment.Experiment
Gets whether the custom property iterator should be used.
getUseResampling(). Method in class weka.classifiers.AdaBoostM1
Get whether resampling is turned on
getUseResampling(). Method in class weka.classifiers.LogitBoost
Get whether resampling is turned on
getUseTraining(). Method in class weka.attributeSelection.ClassifierSubsetEval
Get if training data is to be used instead of hold out/test data
getUseUnsmoothed(). Method in class weka.classifiers.m5.M5Prime
Get the value of UseUnsmoothed.
getValidationSetSize(). Method in class weka.classifiers.neural.NeuralNetwork
getValidationThreshold(). Method in class weka.classifiers.neural.NeuralNetwork
getValue(). Method in class weka.gui.CostMatrixEditor
Gets the current object array.
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.classifiers.adtree.PredictionNode
Gets the prediction value of the node.
getValueIndices(). Method in class weka.filters.MakeIndicatorFilter
Get the indices of the indicator values.
getValueRange(). Method in class weka.filters.MakeIndicatorFilter
Get the range containing the indicator values.
getValues(). Method in class weka.gui.visualize.VisualizePanelEvent
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
getVerbose(). Method in class weka.attributeSelection.ExhaustiveSearch
get whether or not output is verbose
getVerbose(). Method in class weka.attributeSelection.RandomSearch
get whether or not output is verbose
getVerbosity(). Method in class weka.classifiers.m5.M5Prime
Get the value of Verbosity.
getVisible(). Method in class weka.gui.treevisualizer.Node
Get the value of visible.
getWeightByConfidence(). Method in class weka.classifiers.VFI
Get whether feature intervals are being weighted by confidence
getWeightByDistance(). Method in class weka.attributeSelection.ReliefFAttributeEval
Get whether nearest neighbours are being weighted by distance
getWeightingKernel(). Method in class weka.classifiers.LWR
Gets the kernel weighting method to use.
getWeights(). Method in class weka.classifiers.neural.NeuralNode
call this function to get the weights array.
getWeightThreshold(). Method in class weka.classifiers.AdaBoostM1
Get the degree of weight thresholding
getWeightThreshold(). Method in class weka.classifiers.LogitBoost
Get the degree of weight thresholding
getWindowSize(). Method in class weka.classifiers.IBk
Gets the maximum number of instances allowed in the training pool.
getWorkingInstances(). Method in class weka.gui.explorer.PreprocessPanel
Gets the working set of instances.
getX(). Method in class weka.classifiers.neural.NeuralConnection
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
getY(). Method in class weka.classifiers.neural.NeuralConnection
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
globalInfo(). Method in class weka.filters.AddFilter
Returns a string describing this filter
globalInfo(). Method in class weka.classifiers.AdditiveRegression
Returns a string describing this attribute evaluator
globalInfo(). Method in class weka.classifiers.adtree.ADTree
globalInfo(). Method in class weka.filters.AllFilter
Returns a string describing this filter
globalInfo(). Method in class weka.associations.Apriori
Returns a string describing this associator
globalInfo(). Method in class weka.filters.AttributeExpressionFilter
Returns a string describing this filter
globalInfo(). Method in class weka.filters.AttributeFilter
Returns a string describing this filter
globalInfo(). Method in class weka.classifiers.AttributeSelectedClassifier
Returns a string describing this search method
globalInfo(). Method in class weka.experiment.AveragingResultProducer
Returns a string describing this result producer
globalInfo(). Method in class weka.attributeSelection.BestFirst
Returns a string describing this search method
globalInfo(). Method in class weka.core.converters.C45Loader
Returns a string describing this attribute evaluator
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.experiment.ClassifierSplitEvaluator
Returns a string describing this split 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.filters.CopyAttributesFilter
Returns a string describing this filter
globalInfo(). Method in class weka.classifiers.CostSensitiveClassifier
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.core.converters.CSVLoader
Returns a string describing this attribute evaluator
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.filters.DiscretizeFilter
Returns a string describing this filter
globalInfo(). Method in class weka.clusterers.EM
Returns a string describing this clusterer
globalInfo(). Method in class weka.attributeSelection.ExhaustiveSearch
Returns a string describing this search method
globalInfo(). Method in class weka.attributeSelection.ForwardSelection
Returns a string describing this search method
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.InfoGainAttributeEval
Returns a string describing this attribute 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.filters.MakeIndicatorFilter
globalInfo(). Method in class weka.classifiers.MultiClassClassifier
globalInfo(). Method in class weka.classifiers.neural.NeuralNetwork
This will return a string describing the classifier.
globalInfo(). Method in class weka.filters.NonSparseToSparseFilter
Returns a string describing this filter
globalInfo(). Method in class weka.filters.ObfuscateFilter
Returns a string describing this filter
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.experiment.RandomSplitResultProducer
Returns a string describing this result producer
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.experiment.RegressionSplitEvaluator
Returns a string describing this split evaluator
globalInfo(). Method in class weka.attributeSelection.ReliefFAttributeEval
Returns a string describing this attribute evaluator
globalInfo(). Method in class weka.clusterers.SimpleKMeans
Returns a string describing this clusterer
globalInfo(). Method in class weka.filters.SparseToNonSparseFilter
Returns a string describing this filter
globalInfo(). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Returns a string describing this attribute evaluator
globalInfo(). Method in class weka.classifiers.ThresholdSelector
globalInfo(). Method in class weka.classifiers.UserClassifier
This will return a string describing the classifier.
globalInfo(). Method in class weka.classifiers.VFI
Returns a string describing this search method
globalInfo(). Method in class weka.attributeSelection.WrapperSubsetEval
Returns a string describing this attribute evaluator
gr(double, double). Static method in class weka.core.Utils
Tests if a is smaller than b.
graph(). Method in class weka.classifiers.adtree.ADTree
Returns graph describing the tree.
graph(). Method in class weka.classifiers.j48.ClassifierTree
Returns graph describing the tree.
graph(). Method in class weka.classifiers.CostSensitiveClassifier
Returns graph describing the classifier (if possible).
graph(). Method in interface weka.core.Drawable
Returns a string that describes a graph representing the object.
graph(). Method in class weka.classifiers.j48.J48
Returns graph describing the tree.
graph(). Method in class weka.classifiers.UserClassifier
grOrEq(double, double). Static method in class weka.core.Utils
Tests if a is greater or equal to b.
GUIChooser(). Constructor for class weka.gui.GUIChooser
Creates the experiment environment gui with no initial experiment
GUITipText(). Method in class weka.classifiers.neural.NeuralNetwork

H

hasEnumAttr(Instances). Static method in class weka.classifiers.m5.M5Utils
Tests if enumerated attribute(s) exists in the instances
hashCode(). Method in class weka.associations.ItemSet
Produces a hash code for a item set.
hashCode(). Method in class weka.core.SerializedObject
Returns a hashcode for this object.
hasMissing(Instances). Static method in class weka.classifiers.m5.M5Utils
Tests if missing value(s) exists in the instances
hasMoreIterations(). Method in class weka.experiment.Experiment
Returns true if there are more iterations to carry out in the experiment.
header(int). Method in class weka.experiment.PairedTTester
Creates a "header" string describing the current resultsets.
headToString(). Static method in class weka.classifiers.m5.M5Utils
Prints the head lines of the output
hiddenLayersTipText(). Method in class weka.classifiers.neural.NeuralNetwork
HLINE. Static variable in class weka.gui.visualize.VisualizePanelEvent
holdOutFileTipText(). Method in class weka.attributeSelection.ClassifierSubsetEval
Returns the tip text for this property
HoldOutSubsetEvaluator(). Constructor for class weka.attributeSelection.HoldOutSubsetEvaluator
HostListPanel(). Constructor for class weka.gui.experiment.HostListPanel
Create the host list panel initially disabled.
HostListPanel(RemoteExperiment). Constructor for class weka.gui.experiment.HostListPanel
Creates the host list panel with the given experiment.
HyperPipes(). Constructor for class weka.classifiers.HyperPipes

I

IB1(). Constructor for class weka.classifiers.IB1
IBk(). Constructor for class weka.classifiers.IBk
IB1 classifer.
IBk(int). Constructor for class weka.classifiers.IBk
IBk classifier.
Id3(). Constructor for class weka.classifiers.Id3
Impurity(int, int, Instances, int). Constructor for class weka.classifiers.m5.Impurity
Constructs an Impurity object containing the impurity values of partitioning the instances using an attribute
incorrect(). Method in class weka.classifiers.evaluation.ConfusionMatrix
Gets the number of incorrect classifications (that is, for which an incorrect prediction was made).
incorrect(). Method in class weka.classifiers.Evaluation
Gets the number of instances incorrectly classified (that is, for which an incorrect prediction was made).
incremental(double, int). Method in class weka.classifiers.m5.Impurity
Incrementally computes the impurirty values
incremental(Measures). Method in class weka.classifiers.m5.Measures
Adds up performance measures for cross-validation
index(). Method in class weka.core.Attribute
Returns the index of this attribute.
index(int). Method in class weka.core.Instance
Returns the index of the attribute stored at the given position.
index(int). Method in class weka.core.SparseInstance
Returns the index of the attribute stored at the given position.
indexOf(Object). Method in class weka.core.FastVector
Searches for the first occurence of the given argument, testing for equality using the equals method.
indexOfValue(String). Method in class weka.core.Attribute
Returns the index of a given attribute value.
indicesToRangeList(int[]). Static method in class weka.core.Range
Creates a string representation of the indices in the supplied array.
info(int[]). Static method in class weka.core.Utils
Computes entropy for an array of integers.
infoGain(). Method in class weka.classifiers.j48.BinC45Split
Returns (C4.5-type) information gain for the generated split.
infoGain(). Method in class weka.classifiers.j48.C45Split
Returns (C4.5-type) information gain for the generated split.
InfoGainAttributeEval(). Constructor for class weka.attributeSelection.InfoGainAttributeEval
Constructor
InfoGainSplitCrit(). Constructor for class weka.classifiers.j48.InfoGainSplitCrit
initClassifier(Instances). Method in class weka.classifiers.adtree.ADTree
Sets up the tree ready to be trained, using two-class optimized method.
initClassifier(Instances). Method in interface weka.classifiers.IterativeClassifier
Inits an iterative classifier.
INITIAL_STEP. Static variable in interface weka.classifiers.kstar.KStarConstants
initialize(). Method in class weka.classifiers.CostMatrix
Sets the costs to default values (i.e.
initialize(). Method in class weka.classifiers.j48.Distribution
Sets all counts to zero.
initialize(). Method in class weka.experiment.Experiment
Prepares an experiment for running, initializing current iterator settings.
initialize(). Method in class weka.experiment.RemoteExperiment
Prepares a remote experiment for running, creates sub experiments
initialize(Instances). Method in class weka.classifiers.m5.Options
Initializes for constucting model trees
initialize(int, int, int). Method in class weka.classifiers.m5.SplitInfo
Resets the object of split information
INPUT. Static variable in class weka.classifiers.neural.NeuralConnection
This unit is an input unit.
input(Instance). Method in class weka.filters.AbstractTimeSeriesFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.AddFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.AllFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.AttributeExpressionFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.AttributeFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.AttributeSelectionFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.AttributeTypeFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.CopyAttributesFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.DiscretizeFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.EmptyAttributeFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.Filter
Input an instance for filtering.
input(Instance). Method in class weka.filters.FirstOrderFilter
Input an instance for filtering.
input(Instance). Method in class weka.gui.streams.InstanceCounter
input(Instance). Method in class weka.filters.InstanceFilter
Input an instance for filtering.
input(Instance). Method in class weka.gui.streams.InstanceJoiner
input(Instance). Method in class weka.gui.streams.InstanceSavePanel
input(Instance). Method in class weka.gui.streams.InstanceTable
input(Instance). Method in class weka.gui.streams.InstanceViewer
input(Instance). Method in class weka.filters.MakeIndicatorFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.MergeTwoValuesFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.NominalToBinaryFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.NonSparseToSparseFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.NormalizationFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.NullFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.NumericToBinaryFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.NumericTransformFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.ObfuscateFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.ReplaceMissingValuesFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.ResampleFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.SparseToNonSparseFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.SpreadSubsampleFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.StringToNominalFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.SwapAttributeValuesFilter
Input an instance for filtering.
inputFormat(Instances). Method in class weka.filters.Filter
inputFormat(Instances). Method in class weka.gui.streams.InstanceCounter
inputFormat(Instances). Method in class weka.gui.streams.InstanceJoiner
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.gui.streams.InstanceSavePanel
inputFormat(Instances). Method in class weka.gui.streams.InstanceTable
inputFormat(Instances). Method in class weka.gui.streams.InstanceViewer
insert(double, double, double). Method in class weka.classifiers.kstar.LightHashTable
Inserts a new entry in the hashtable using the specified key.
insertAttributeAt(Attribute, int). Method in class weka.core.Instances
Inserts an attribute at the given position (0 to numAttributes()) and sets all values to be missing.
insertAttributeAt(int). Method in class weka.core.Instance
Inserts an attribute at the given position (0 to numAttributes()).
insertElementAt(Object, int). Method in class weka.core.FastVector
Inserts an element at the given position.
insignificant(double, Instances). Method in class weka.classifiers.m5.Function
Detects the most insignificant variable in the funcion
Instance(double, double[]). Constructor for class weka.core.Instance
Constructor that inititalizes instance variable with given values.
Instance(Instance). Constructor for class weka.core.Instance
Constructor that copies the attribute values and the weight from the given instance.
Instance(int). Constructor for class weka.core.Instance
Constructor of an instance that sets weight to one, all values to be missing, and the reference to the dataset to null.
instance(int). Method in class weka.core.Instances
Returns the instance at the given position.
INSTANCE_AVAILABLE. Static variable in class weka.gui.streams.InstanceEvent
Specifies that an instance is available
InstanceCounter(). Constructor for class weka.gui.streams.InstanceCounter
InstanceEvent(Object, int). Constructor for class weka.gui.streams.InstanceEvent
Constructs an InstanceEvent with the specified source object and event type
InstanceFilter(). Constructor for class weka.filters.InstanceFilter
Default constructor
InstanceJoiner(). Constructor for class weka.gui.streams.InstanceJoiner
Setup the initial states of the member variables
InstanceLoader(). Constructor for class weka.gui.streams.InstanceLoader
instanceProduced(InstanceEvent). Method in class weka.gui.streams.InstanceCounter
instanceProduced(InstanceEvent). Method in class weka.gui.streams.InstanceJoiner
instanceProduced(InstanceEvent). Method in interface weka.gui.streams.InstanceListener
instanceProduced(InstanceEvent). Method in class weka.gui.streams.InstanceSavePanel
instanceProduced(InstanceEvent). Method in class weka.gui.streams.InstanceTable
instanceProduced(InstanceEvent). Method in class weka.gui.streams.InstanceViewer
InstanceQuery(). Constructor for class weka.experiment.InstanceQuery
Sets up the database drivers
Instances(Instances). Constructor for class weka.core.Instances
Constructor copying all instances and references to the header information from the given set of instances.
Instances(Instances, int). Constructor for class weka.core.Instances
Constructor creating an empty set of instances.
Instances(Instances, int, int). Constructor for class weka.core.Instances
Creates a new set of instances by copying a subset of another set.
Instances(Reader). Constructor for class weka.core.Instances
Reads an ARFF file from a reader, and assigns a weight of one to each instance.
Instances(Reader, int). Constructor for class weka.core.Instances
Reads the header of an ARFF file from a reader and reserves space for the given number of instances.
Instances(String, FastVector, int). Constructor for class weka.core.Instances
Creates an empty set of instances.
InstanceSavePanel(). Constructor for class weka.gui.streams.InstanceSavePanel
instancesDownBranch(int, Instances). Method in class weka.classifiers.adtree.Splitter
Gets the subset of instances that apply to a particluar branch of the split.
instancesDownBranch(int, Instances). Method in class weka.classifiers.adtree.TwoWayNominalSplit
Gets the subset of instances that apply to a particluar branch of the split.
instancesDownBranch(int, Instances). Method in class weka.classifiers.adtree.TwoWayNumericSplit
Gets the subset of instances that apply to a particluar branch of the split.
InstancesResultListener(). Constructor for class weka.experiment.InstancesResultListener
InstancesSummaryPanel(). Constructor for class weka.gui.InstancesSummaryPanel
Creates the instances panel with no initial instances.
InstanceTable(). Constructor for class weka.gui.streams.InstanceTable
InstanceViewer(). Constructor for class weka.gui.streams.InstanceViewer
intCount. Variable in class weka.core.AttributeStats
The number of int-like values
invertSelectionTipText(). Method in class weka.filters.AttributeFilter
Returns the tip text for this property
invertSelectionTipText(). Method in class weka.filters.CopyAttributesFilter
Returns the tip text for this property
invertSelectionTipText(). Method in class weka.filters.DiscretizeFilter
Returns the tip text for this property
isConnected(). Method in class weka.experiment.DatabaseUtils
Returns true if a database connection is active.
isEmpty(). Method in class weka.classifiers.kstar.LightHashTable
Tests if this hashtable maps no keys to values.
isInRange(int). Method in class weka.core.Range
Gets whether the supplied cardinal number is included in the current range.
isMissing(Attribute). Method in class weka.core.Instance
Tests if a specific value is "missing".
isMissing(int). 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".
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".
isNominal(). Method in class weka.core.Attribute
Test if the attribute is nominal.
isNominal(). Method in class weka.filters.InstanceFilter
Returns true if selection attribute is nominal.
isNumeric(). Method in class weka.core.Attribute
Tests if the attribute is numeric.
isNumeric(). Method in class weka.filters.InstanceFilter
Returns true if selection attribute is numeric.
isOutputFormatDefined(). Method in class weka.filters.Filter
Returns whether the output format is ready to be collected
isPaintable(). Method in class weka.gui.CostMatrixEditor
Returns true to indicate that we can paint a representation of the string array
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.
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.
isString(). Method in class weka.core.Attribute
Tests if the attribute is a string.
ItemSet(int). Constructor for class weka.associations.ItemSet
Constructor
itemStateChanged(ItemEvent). Method in class weka.gui.treevisualizer.TreeVisualizer
Performs the action associated with the ItemEvent.
Ivector(). Constructor for class weka.classifiers.m5.Ivector

J

J48(). Constructor for class weka.classifiers.j48.J48
joinOptions(String[]). Static method in class weka.core.Utils
Joins all the options in an option array into a single string, as might be used on the command line.

K

kappa(). Method in class weka.classifiers.Evaluation
Returns value of kappa statistic if class is nominal.
KBInformation(). Method in class weka.classifiers.Evaluation
Return the total Kononenko & Bratko Information score in bits
KBMeanInformation(). Method in class weka.classifiers.Evaluation
Return the Kononenko & Bratko Information score in bits per instance.
KBRelativeInformation(). Method in class weka.classifiers.Evaluation
Return the Kononenko & Bratko Relative Information score
KDConditionalEstimator(int, double). Constructor for class weka.estimators.KDConditionalEstimator
Constructor
KernelDensity(). Constructor for class weka.classifiers.KernelDensity
KernelEstimator(double). Constructor for class weka.estimators.KernelEstimator
Constructor that takes a precision argument.
keyFieldNameTipText(). Method in class weka.experiment.AveragingResultProducer
Returns the tip text for this property
KKConditionalEstimator(double). Constructor for class weka.estimators.KKConditionalEstimator
Constructor
KStar(). Constructor for class weka.classifiers.kstar.KStar
KStarCache(). Constructor for class weka.classifiers.kstar.KStarCache
KStarNominalAttribute(Instance, Instance, int, Instances, int[][], KStarCache). Constructor for class weka.classifiers.kstar.KStarNominalAttribute
Constructor
KStarNumericAttribute(Instance, Instance, int, Instances, int[][], KStarCache). Constructor for class weka.classifiers.kstar.KStarNumericAttribute
Constructor
KStarWrapper(). Constructor for class weka.classifiers.kstar.KStarWrapper

L

laplaceProb(int). Method in class weka.classifiers.j48.Distribution
Returns relative frequency of class over all bags with Laplace correction.
laplaceProb(int, int). Method in class weka.classifiers.j48.Distribution
Returns relative frequency of class for given bag.
lastElement(). Method in class weka.core.FastVector
Returns the last element of the vector.
lastInstance(). Method in class weka.core.Instances
Returns the last instance in the set.
launchNext(int, int). Method in class weka.experiment.RemoteExperiment
Launch a sub experiment on a remote host
leafNode(). Method in class weka.classifiers.m5.Node
Sets the node to a leaf
leafNum(Instance). Method in class weka.classifiers.m5.Node
Detects which leaf a instance falls into
LearningRateResultProducer(). Constructor for class weka.experiment.LearningRateResultProducer
learningRateTipText(). Method in class weka.classifiers.neural.NeuralNetwork
leftSide(Instances). Method in class weka.classifiers.j48.BinC45Split
Prints left side of condition..
leftSide(Instances). Method in class weka.classifiers.j48.C45Split
Prints left side of condition..
leftSide(Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
Prints left side of condition satisfied by instances.
leftSide(Instances). Method in class weka.classifiers.j48.NoSplit
Does nothing because no condition has to be satisfied.
legend(). Method in class weka.classifiers.adtree.ADTree
Returns the legend of the tree, describing how results are to be interpreted.
LegendPanel(). Constructor for class weka.gui.visualize.LegendPanel
Constructor
leverageForRule(ItemSet, ItemSet, int, int). Method in class weka.associations.ItemSet
Outputs the leverage for a rule.
liftForRule(ItemSet, ItemSet, int). Method in class weka.associations.ItemSet
Outputs the lift for a rule.
LightHashTable(). Constructor for class weka.classifiers.kstar.LightHashTable
Constructs a new hashtable with a default capacity and load factor.
LINE. Static variable in class weka.gui.visualize.VisualizePanelEvent
LinearRegression(). Constructor for class weka.classifiers.LinearRegression
LinearUnit(). Constructor for class weka.classifiers.neural.LinearUnit
listOptions(). Method in class weka.filters.AbstractTimeSeriesFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.AdaBoostM1
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.AddFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.AdditiveRegression
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.adtree.ADTree
Returns an enumeration describing the available options.
listOptions(). Method in class weka.associations.Apriori
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.AttributeExpressionFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.AttributeFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.AttributeSelectedClassifier
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.AttributeSelectionFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.AttributeTypeFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.experiment.AveragingResultProducer
Returns an enumeration describing the available options.
listOptions(). Method in class weka.classifiers.Bagging
Returns an enumeration describing the available options
listOptions(). Method in class weka.attributeSelection.BestFirst
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.BVDecompose
Returns an enumeration describing the available options
listOptions(). Method in class weka.attributeSelection.CfsSubsetEval
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.CheckClassifier
Returns an enumeration describing the available options
listOptions(). Method in class weka.attributeSelection.ChiSquaredAttributeEval
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.ClassificationViaRegression
Returns an enumeration describing the available options
listOptions(). Method in class weka.experiment.ClassifierSplitEvaluator
Returns an enumeration describing the available options.
listOptions(). Method in class weka.attributeSelection.ClassifierSubsetEval
Returns an enumeration describing the available options

-B
Class name of the classifier to use for accuracy estimation.

listOptions(). Method in class weka.clusterers.Cobweb
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.CopyAttributesFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.CostSensitiveClassifier
Returns an enumeration describing the available options
listOptions(). Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Returns an enumeration describing the available options.
listOptions(). Method in class weka.experiment.CrossValidationResultProducer
Returns an enumeration describing the available options.
listOptions(). Method in class weka.experiment.CSVResultListener
Returns an enumeration describing the available options.
listOptions(). Method in class weka.classifiers.CVParameterSelection
Returns an enumeration describing the available options
listOptions(). Method in class weka.experiment.DatabaseResultProducer
Returns an enumeration describing the available options.
listOptions(). Method in class weka.classifiers.DecisionTable
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.DiscretizeFilter
Gets an enumeration describing the available options
listOptions(). Method in class weka.classifiers.DistributionMetaClassifier
Returns an enumeration describing the available options
listOptions(). Method in class weka.clusterers.DistributionMetaClusterer
Returns an enumeration describing the available options
listOptions(). Method in class weka.clusterers.EM
Returns an enumeration describing the available options.
listOptions(). Method in class weka.attributeSelection.ExhaustiveSearch
Returns an enumeration describing the available options
listOptions(). Method in class weka.experiment.Experiment
Returns an enumeration describing the available options.
listOptions(). Method in class weka.classifiers.FilteredClassifier
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.FirstOrderFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.attributeSelection.ForwardSelection
Returns an enumeration describing the available options
listOptions(). Method in class weka.attributeSelection.GainRatioAttributeEval
Returns an enumeration describing the available options
listOptions(). Method in class weka.attributeSelection.GeneticSearch
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.IBk
Returns an enumeration describing the available options
listOptions(). Method in class weka.attributeSelection.InfoGainAttributeEval
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.InstanceFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.experiment.InstanceQuery
Returns an enumeration describing the available options

listOptions(). Method in class weka.classifiers.j48.J48
Returns an enumeration describing the available options Valid options are:

-U
Use unpruned tree.

-C confidence
Set confidence threshold for pruning.

listOptions(). Method in class weka.classifiers.kstar.KStar
Returns an enumeration describing the available options
listOptions(). Method in class weka.experiment.LearningRateResultProducer
Returns an enumeration describing the available options.
listOptions(). Method in class weka.classifiers.LinearRegression
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.Logistic
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.LogitBoost
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.LWR
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.m5.M5Prime
Returns an enumeration describing the available options.
listOptions(). Method in class weka.filters.MakeIndicatorFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.MergeTwoValuesFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.MetaCost
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.MultiClassClassifier
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.MultiScheme
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.NaiveBayes
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.neural.NeuralNetwork
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.NominalToBinaryFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.NumericTransformFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.OneR
Returns an enumeration describing the available options.
listOptions(). Method in interface weka.core.OptionHandler
Returns an enumeration of all the available options.
listOptions(). Method in class weka.experiment.PairedTTester
Lists options understood by this object.
listOptions(). Method in class weka.classifiers.j48.PART
Returns an enumeration describing the available options Valid options are:

-C confidence
Set confidence threshold for pruning.

listOptions(). Method in class weka.attributeSelection.PrincipalComponents
Returns an enumeration describing the available options

-N Don't normalize the input data.

listOptions(). Method in class weka.attributeSelection.RaceSearch
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.RandomizeFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.attributeSelection.RandomSearch
Returns an enumeration describing the available options
listOptions(). Method in class weka.experiment.RandomSplitResultProducer
Returns an enumeration describing the available options.
listOptions(). Method in class weka.attributeSelection.Ranker
Returns an enumeration describing the available options
listOptions(). Method in class weka.attributeSelection.RankSearch
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.RegressionByDiscretization
Returns an enumeration describing the available options
listOptions(). Method in class weka.experiment.RegressionSplitEvaluator
Returns an enumeration describing the available options.
listOptions(). Method in class weka.attributeSelection.ReliefFAttributeEval
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.ResampleFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.clusterers.SimpleKMeans
Returns an enumeration describing the available options.
listOptions(). Method in class weka.classifiers.SMO
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.SplitDatasetFilter
Gets an enumeration describing the available options.
listOptions(). Method in class weka.filters.SpreadSubsampleFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.Stacking
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.StringToNominalFilter
Returns an enumeration describing the available options.
listOptions(). Method in class weka.filters.SwapAttributeValuesFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.ThresholdSelector
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.VFI
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.VotedPerceptron
Returns an enumeration describing the available options
listOptions(). Method in class weka.attributeSelection.WrapperSubsetEval
Returns an enumeration describing the available options
ListSelectorDialog(Frame, JList). Constructor for class weka.gui.ListSelectorDialog
Create the list selection dialog.
lnFactorial(double). Static method in class weka.core.SpecialFunctions
Returns natural logarithm of factorial using gamma function.
lnGamma(double). Static method in class weka.core.SpecialFunctions
Returns natural logarithm of gamma function.
lnsrch(int, double[], double, double[], double[], double[], double, double[][], double[]). Method in class weka.classifiers.Logistic
Finds a new point x in the direction p from a point xold at which the value of the function has decreased sufficiently.
locallyPredictiveTipText(). Method in class weka.attributeSelection.CfsSubsetEval
Returns the tip text for this property
locateIndex(int). Method in class weka.core.SparseInstance
Locates the greatest index that is not greater than the given index.
LOG2. Static variable in interface weka.classifiers.kstar.KStarConstants
log2. Static variable in class weka.core.Utils
The natural logarithm of 2.
log2(double). Static method in class weka.core.Utils
Returns the logarithm of a for base 2.
log2Binomial(double, double). Static method in class weka.core.SpecialFunctions
Returns base 2 logarithm of binomial coefficient using gamma function.
log2Multinomial(double, double[]). Static method in class weka.core.SpecialFunctions
Returns base 2 logarithm of multinomial using gamma function.
log2MultipleHypergeometric(double[][]). Static method in class weka.core.ContingencyTables
Returns negative base 2 logarithm of multiple hypergeometric probability for a contingency table.
Logistic(). Constructor for class weka.classifiers.Logistic
LogitBoost(). Constructor for class weka.classifiers.LogitBoost
logMessage(String). Method in interface weka.gui.Logger
Sends the supplied message to the log area.
logMessage(String). Method in class weka.gui.LogPanel
Sends the supplied message to the log area.
logMessage(String). Method in class weka.gui.SysErrLog
Sends the supplied message to the log area.
LogPanel(). Constructor for class weka.gui.LogPanel
Creates the log panel
LogPanel(WekaTaskMonitor). Constructor for class weka.gui.LogPanel
Creates the log panel
lowerBoundMinSupportTipText(). Method in class weka.associations.Apriori
Returns the tip text for this property
lowerSizeTipText(). Method in class weka.experiment.LearningRateResultProducer
Returns the tip text for this property
lubksb(int, int[], double[]). Method in class weka.classifiers.m5.Matrix
LU backward substitution
lubksb(int[], double[]). Method in class weka.core.Matrix
Performs LU backward substitution.
ludcmp(). Method in class weka.core.Matrix
Performs LU decomposition.
ludcmp(int, int[]). Method in class weka.classifiers.m5.Matrix
LU decomposition
LWR(). Constructor for class weka.classifiers.LWR

M

M5Prime(). Constructor for class weka.classifiers.m5.M5Prime
M5Utils(). Constructor for class weka.classifiers.m5.M5Utils
M_AVERAGE. Static variable in interface weka.classifiers.kstar.KStarConstants
m_col. Variable in class weka.gui.treevisualizer.NamedColor
The actual color object
m_cols. Variable in class weka.gui.treevisualizer.Colors
The array with all the colors input
m_customColour. Variable in class weka.gui.visualize.PlotData2D
M_DELETE. Static variable in interface weka.classifiers.kstar.KStarConstants
Missing value handling mode
m_displayAllPoints. Variable in class weka.gui.visualize.PlotData2D
Display all points (ie.
m_experimentFinished. Variable in class weka.experiment.RemoteExperimentEvent
True if a remote experiment has finished
m_indexVal. Variable in class weka.gui.visualize.AttributePanelEvent
The index for the new attribute
m_logMessage. Variable in class weka.experiment.RemoteExperimentEvent
A log type message
M_MAXDIFF. Static variable in interface weka.classifiers.kstar.KStarConstants
m_messageString. Variable in class weka.experiment.RemoteExperimentEvent
The message
m_name. Variable in class weka.gui.treevisualizer.NamedColor
The name of the color
M_NORMAL. Static variable in interface weka.classifiers.kstar.KStarConstants
m_statusMessage. Variable in class weka.experiment.RemoteExperimentEvent
A status type message
m_useCustomColour. Variable in class weka.gui.visualize.PlotData2D
Custom colour for this plot
m_xChange. Variable in class weka.gui.visualize.AttributePanelEvent
True if the x selection changed
m_yChange. Variable in class weka.gui.visualize.AttributePanelEvent
True if the y selection changed
MahalanobisEstimator(Matrix, double, double). Constructor for class weka.estimators.MahalanobisEstimator
Constructor
main(String[]). Static method in class weka.classifiers.AdaBoostM1
Main method for testing this class.
main(String[]). Static method in class weka.filters.AddFilter
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.AdditiveRegression
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.adtree.ADTree
Main method for testing this class.
main(String[]). Static method in class weka.filters.AllFilter
Main method for testing this class.
main(String[]). Static method in class weka.associations.Apriori
Main method for testing this class.
main(String[]). Static method in class weka.core.converters.ArffLoader
Main method.
main(String[]). Static method in class weka.gui.explorer.AssociationsPanel
Tests out the Associator panel from the command line.
main(String[]). Static method in class weka.core.Attribute
Simple main method for testing this class.
main(String[]). Static method in class weka.filters.AttributeExpressionFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.AttributeFilter
Main method for testing this class.
main(String[]). Static method in class weka.gui.visualize.AttributePanel
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.AttributeSelectedClassifier
Main method for testing this class.
main(String[]). Static method in class weka.attributeSelection.AttributeSelection
Main method for testing this class.
main(String[]). Static method in class weka.filters.AttributeSelectionFilter
Main method for testing this class.
main(String[]). Static method in class weka.gui.AttributeSelectionPanel
Tests the attribute selection panel from the command line.
main(String[]). Static method in class weka.gui.explorer.AttributeSelectionPanel
Tests out the attribute selection panel from the command line.
main(String[]). Static method in class weka.gui.AttributeSummaryPanel
Tests out the attribute summary panel from the command line.
main(String[]). Static method in class weka.filters.AttributeTypeFilter
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.Bagging
Main method for testing this class.
main(String[]). Static method in class weka.core.BinarySparseInstance
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.BVDecompose
Test method for this class
main(String[]). Static method in class weka.core.converters.C45Loader
Main method for testing this class.
main(String[]). Static method in class weka.attributeSelection.CfsSubsetEval
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.CheckClassifier
Test method for this class
main(String[]). Static method in class weka.core.CheckOptionHandler
Main method for using the CheckOptionHandler.

Valid options are:

-W classname
The name of the class implementing an OptionHandler.

main(String[]). Static method in class weka.attributeSelection.ChiSquaredAttributeEval
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.ClassificationViaRegression
Main method for testing this class.
main(String[]). Static method in class weka.gui.explorer.ClassifierPanel
Tests out the classifier panel from the command line.
main(String[]). Static method in class weka.attributeSelection.ClassifierSubsetEval
Main method for testing this class.
main(String[]). Static method in class weka.gui.visualize.ClassPanel
Main method for testing this class.
main(String[]). Static method in class weka.gui.explorer.ClustererPanel
Tests out the clusterer panel from the command line.
main(String[]). Static method in class weka.clusterers.ClusterEvaluation
Main method for testing this class.
main(String[]). Static method in class weka.clusterers.Cobweb
main(String[]). Static method in class weka.attributeSelection.ConsistencySubsetEval
Main method for testing this class.
main(String[]). Static method in class weka.core.ContingencyTables
Main method for testing this class.
main(String[]). Static method in class weka.filters.CopyAttributesFilter
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.evaluation.CostCurve
Tests the CostCurve generation from the command line.
main(String[]). Static method in class weka.classifiers.CostMatrix
Tests out creation of a frequency dependent cost matrix from the command line.
main(String[]). Static method in class weka.gui.CostMatrixEditor
Tests out the array editor from the command line.
main(String[]). Static method in class weka.classifiers.CostSensitiveClassifier
Main method for testing this class.
main(String[]). Static method in class weka.experiment.CrossValidationResultProducer
main(String[]). Static method in class weka.core.converters.CSVLoader
Main method.
main(String[]). Static method in class weka.classifiers.CVParameterSelection
Main method for testing this class.
main(String[]). Static method in class weka.gui.experiment.DatasetListPanel
Tests out the dataset list panel from the command line.
main(String[]). Static method in class weka.estimators.DDConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.DecisionStump
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.DecisionTable
Main method for testing this class.
main(String[]). Static method in class weka.estimators.DiscreteEstimator
Main method for testing this class.
main(String[]). Static method in class weka.filters.DiscretizeFilter
Main method for testing this class.
main(String[]). Static method in class weka.gui.experiment.DistributeExperimentPanel
Tests out the panel from the command line.
main(String[]). Static method in class weka.classifiers.DistributionMetaClassifier
Main method for testing this class.
main(String[]). Static method in class weka.clusterers.DistributionMetaClusterer
Main method for testing this class.
main(String[]). Static method in class weka.estimators.DKConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.estimators.DNConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.clusterers.EM
Main method for testing this class.
main(String[]). Static method in class weka.filters.EmptyAttributeFilter
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.Evaluation
A test method for this class.
main(String[]). Static method in class weka.experiment.Experiment
Configures/Runs the Experiment from the command line.
main(String[]). Static method in class weka.gui.experiment.Experimenter
Tests out the experiment environment.
main(String[]). Static method in class weka.gui.explorer.Explorer
Tests out the explorer environment.
main(String[]). Static method in class weka.filters.Filter
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.FilteredClassifier
Main method for testing this class.
main(String[]). Static method in class weka.filters.FirstOrderFilter
Main method for testing this class.
main(String[]). Static method in class weka.attributeSelection.GainRatioAttributeEval
Main method for testing this class.
main(String[]). Static method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Tests out the panel from the command line.
main(String[]). Static method in class weka.gui.GenericArrayEditor
Tests out the array editor from the command line.
main(String[]). Static method in class weka.gui.GenericObjectEditor
Tests out the Object editor from the command line.
main(String[]). Static method in class weka.gui.GUIChooser
Tests out the GUIChooser environment.
main(String[]). Static method in class weka.gui.experiment.HostListPanel
Tests out the host list panel from the command line.
main(String[]). Static method in class weka.classifiers.HyperPipes
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.IB1
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.IBk
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.Id3
Main method.
main(String[]). Static method in class weka.attributeSelection.InfoGainAttributeEval
Main method for testing this class.
main(String[]). Static method in class weka.core.Instance
Main method for testing this class.
main(String[]). Static method in class weka.filters.InstanceFilter
Main method for testing this class.
main(String[]). Static method in class weka.experiment.InstanceQuery
Test the class from the command line.
main(String[]). Static method in class weka.core.Instances
Main method for this class -- just prints a summary of a set of instances.
main(String[]). Static method in class weka.gui.InstancesSummaryPanel
Tests out the instance summary panel from the command line.
main(String[]). Static method in class weka.classifiers.j48.J48
Main method for testing this class
main(String[]). Static method in class weka.estimators.KDConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.KernelDensity
Main method for testing this class.
main(String[]). Static method in class weka.estimators.KernelEstimator
Main method for testing this class.
main(String[]). Static method in class weka.estimators.KKConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.kstar.KStar
Main method for testing this class.
main(String[]). Static method in class weka.gui.visualize.LegendPanel
Main method for testing this class
main(String[]). Static method in class weka.classifiers.LinearRegression
Generates a linear regression function predictor.
main(String[]). Static method in class weka.gui.ListSelectorDialog
Tests out the list selector from the command line.
main(String[]). Static method in class weka.classifiers.Logistic
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.LogitBoost
Main method for testing this class.
main(String[]). Static method in class weka.gui.LogPanel
Tests out the log panel from the command line.
main(String[]). Static method in class weka.classifiers.LWR
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.m5.M5Prime
Main method for M5' algorithm
main(String[]). Static method in class weka.estimators.MahalanobisEstimator
Main method for testing this class.
main(String[]). Static method in class weka.filters.MakeIndicatorFilter
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.evaluation.MarginCurve
Tests the MarginCurve generation from the command line.
main(String[]). Static method in class weka.core.Matrix
Main method for testing this class.
main(String[]). Static method in class weka.filters.MergeTwoValuesFilter
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.MetaCost
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.MultiClassClassifier
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.MultiScheme
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.NaiveBayes
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.NaiveBayesSimple
Main method for testing this class.
main(String[]). Static method in class weka.estimators.NDConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.neural.NeuralNetwork
Main method for testing this class.
main(String[]). Static method in class weka.estimators.NNConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.filters.NominalToBinaryFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.NonSparseToSparseFilter
Main method for testing this class.
main(String[]). Static method in class weka.estimators.NormalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.filters.NormalizationFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.NullFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.NumericToBinaryFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.NumericTransformFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.ObfuscateFilter
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.OneR
Main method for testing this class
main(String[]). Static method in class weka.attributeSelection.OneRAttributeEval
Main method for testing this class.
main(String[]). Static method in class weka.experiment.OutputZipper
Main method for testing this class
main(String[]). Static method in class weka.experiment.PairedStats
Tests the paired stats object from the command line.
main(String[]). Static method in class weka.experiment.PairedTTester
Test the class from the command line.
main(String[]). Static method in class weka.classifiers.j48.PART
Main method for testing this class.
main(String[]). Static method in class weka.gui.visualize.Plot2D
Main method for testing this class
main(String[]). Static method in class weka.estimators.PoissonEstimator
Main method for testing this class.
main(String[]). Static method in class weka.gui.explorer.PreprocessPanel
Tests out the instance-preprocessing panel from the command line.
main(String[]). Static method in class weka.attributeSelection.PrincipalComponents
Main method for testing this class
main(String[]). Static method in class weka.classifiers.Prism
Main method for testing this class
main(String[]). Static method in class weka.gui.PropertySelectorDialog
Tests out the property selector from the command line.
main(String[]). Static method in class weka.core.Queue
Main method for testing this class.
main(String[]). Static method in class weka.filters.RandomizeFilter
Main method for testing this class.
main(String[]). Static method in class weka.core.Range
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.RegressionByDiscretization
Main method for testing this class.
main(String[]). Static method in class weka.attributeSelection.ReliefFAttributeEval
Main method for testing this class.
main(String[]). Static method in class weka.experiment.RemoteEngine
Main method.
main(String[]). Static method in class weka.experiment.RemoteExperiment
Configures/Runs the Experiment from the command line.
main(String[]). Static method in class weka.filters.ReplaceMissingValuesFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.ResampleFilter
Main method for testing this class.
main(String[]). Static method in class weka.gui.ResultHistoryPanel
Tests out the result history from the command line.
main(String[]). Static method in class weka.gui.experiment.ResultsPanel
Tests out the results panel from the command line.
main(String[]). Static method in class weka.gui.experiment.RunNumberPanel
Tests out the panel from the command line.
main(String[]). Static method in class weka.gui.experiment.RunPanel
Tests out the run panel from the command line.
main(String[]). Static method in class weka.gui.SaveBuffer
Main method for testing this class
main(String[]). Static method in class weka.gui.SelectedTagEditor
Tests out the selectedtag editor from the command line.
main(String[]). Static method in class weka.core.converters.SerializedInstancesLoader
Main method.
main(String[]). Static method in class weka.core.SerializedObject
Test routine, reads an arff file from stdin and measures memory usage (the arff file should have long string attribute values)
main(String[]). Static method in class weka.gui.experiment.SetupPanel
Tests out the experiment setup from the command line.
main(String[]). Static method in class weka.gui.SimpleCLI
Method to start up the simple cli
main(String[]). Static method in class weka.clusterers.SimpleKMeans
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.SMO
Main method for testing this class.
main(String[]). Static method in class weka.core.SparseInstance
Main method for testing this class.
main(String[]). Static method in class weka.filters.SparseToNonSparseFilter
Main method for testing this class.
main(String[]). Static method in class weka.core.SpecialFunctions
Main method for testing this class.
main(String[]). Static method in class weka.filters.SplitDatasetFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.SpreadSubsampleFilter
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.Stacking
Main method for testing this class.
main(String[]). Static method in class weka.core.Statistics
Main method for testing this class.
main(String[]). Static method in class weka.experiment.Stats
Tests the paired stats object from the command line.
main(String[]). Static method in class weka.filters.StringToNominalFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.SwapAttributeValuesFilter
Main method for testing this class.
main(String[]). Static method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.evaluation.ThresholdCurve
Tests the ThresholdCurve generation from the command line.
main(String[]). Static method in class weka.classifiers.ThresholdSelector
Main method for testing this class.
main(String[]). Static method in class weka.filters.TimeSeriesDeltaFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.TimeSeriesTranslateFilter
Main method for testing this class.
main(String[]). Static method in class weka.gui.treevisualizer.TreeVisualizer
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.UserClassifier
Main method for testing this class.
main(String[]). Static method in class weka.core.Utils
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.VFI
Main method for testing this class.
main(String[]). Static method in class weka.gui.visualize.VisualizePanel
Main method for testing this class
main(String[]). Static method in class weka.classifiers.VotedPerceptron
Main method.
main(String[]). Static method in class weka.gui.WekaTaskMonitor
Main method for testing this class
main(String[]). Static method in class weka.attributeSelection.WrapperSubsetEval
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.ZeroR
Main method for testing this class.
main2(String[]). Static method in class weka.core.SerializedObject
Test routine, reads text from stdin and measures memory usage
makeBinaryTipText(). Method in class weka.filters.DiscretizeFilter
Returns the tip text for this property
makeCopies(ASEvaluation, int). Static method in class weka.attributeSelection.ASEvaluation
Creates copies of the current evaluator.
makeCopies(Associator, int). Static method in class weka.associations.Associator
Creates copies of the current associator.
makeCopies(Classifier, int). Static method in class weka.classifiers.Classifier
Creates copies of the current classifier, which can then be used for boosting etc.
makeCopies(Clusterer, int). Static method in class weka.clusterers.Clusterer
Creates copies of the current clusterer.
MakeDecList(ModelSelection, double, int). Constructor for class weka.classifiers.j48.MakeDecList
Constructor for dec list pruned using C4.5 pruning.
MakeDecList(ModelSelection, int, int). Constructor for class weka.classifiers.j48.MakeDecList
Constructor for dec list pruned using hold-out pruning.
makeDistribution(double, int). Static method in class weka.classifiers.evaluation.NominalPrediction
Convert a single prediction into a probability distribution with all zero probabilities except the predicted value which has probability 1.0.
makeFrequencyDependentMatrix(Instances, double). Static method in class weka.classifiers.CostMatrix
Creates a cost matrix for the class attribute of the supplied instances, where the misclassification costs are higher for misclassifying a rare class as a frequent one.
MakeIndicatorFilter(). Constructor for class weka.filters.MakeIndicatorFilter
makeUniformDistribution(int). Static method in class weka.classifiers.evaluation.NominalPrediction
Creates a uniform probability distribution -- where each of the possible classes is assigned equal probability.
makeWeighted(CostMatrix). Method in class weka.classifiers.evaluation.ConfusionMatrix
Makes a copy of this ConfusionMatrix after applying the supplied CostMatrix to the cells.
margin(). Method in class weka.classifiers.evaluation.NominalPrediction
Calculates the prediction margin.
MarginCurve(). Constructor for class weka.classifiers.evaluation.MarginCurve
matrix(). Method in class weka.classifiers.j48.Distribution
Returns matrix with distribution of class values.
Matrix(int, int). Constructor for class weka.classifiers.m5.Matrix
Constructs a matrix
Matrix(int, int). Constructor for class weka.core.Matrix
Constructs a matrix.
Matrix(Reader). Constructor for class weka.core.Matrix
Reads a matrix from a reader.
MATRIX_ON_DEMAND. Static variable in class weka.classifiers.CostSensitiveClassifier
MATRIX_ON_DEMAND. Static variable in class weka.classifiers.MetaCost
MATRIX_SUPPLIED. Static variable in class weka.classifiers.CostSensitiveClassifier
MATRIX_SUPPLIED. Static variable in class weka.classifiers.MetaCost
max. Variable in class weka.experiment.Stats
The maximum value seen, or Double.NaN if no values seen
MAX_SHAPES. Static variable in class weka.gui.visualize.Plot2D
maxBag(). Method in class weka.classifiers.j48.Distribution
Returns index of bag containing maximum number of instances.
maxClass(). Method in class weka.classifiers.j48.Distribution
Returns class with highest frequency over all bags.
maxClass(int). Method in class weka.classifiers.j48.Distribution
Returns class with highest frequency for given bag.
maxGenerationsTipText(). Method in class weka.attributeSelection.GeneticSearch
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.
maxIterationsTipText(). Method in class weka.clusterers.EM
Returns the tip text for this property
maxModelsTipText(). Method in class weka.classifiers.AdditiveRegression
Returns the tip text for this property
mean. Variable in class weka.experiment.Stats
The mean of values at the last calculateDerived() call
mean(double[]). Static method in class weka.core.Utils
Computes the mean for an array of doubles.
meanAbsoluteError(). Method in class weka.classifiers.Evaluation
Returns the mean absolute error.
meanOrMode(Attribute). Method in class weka.core.Instances
Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanOrMode(int). 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.
measureExamplesProcessed(). Method in class weka.classifiers.adtree.ADTree
Returns the number of examples "counted".
measureNodesExpanded(). Method in class weka.classifiers.adtree.ADTree
Returns the number of nodes expanded.
measureNumAttributesSelected(). Method in class weka.classifiers.AttributeSelectedClassifier
Additional measure --- number of attributes selected
measureNumIterations(). Method in class weka.classifiers.AdditiveRegression
return the number of iterations (base classifiers) completed
measureNumLeaves(). Method in class weka.classifiers.adtree.ADTree
Calls measure function for leaf size - the number of prediction nodes.
measureNumLeaves(). Method in class weka.classifiers.j48.J48
Returns the number of leaves
measureNumLeaves(). Method in class weka.classifiers.m5.M5Prime
return the number of leaves in the tree
measureNumLinearModels(). Method in class weka.classifiers.m5.M5Prime
return the number of linear models
measureNumPredictionLeaves(). Method in class weka.classifiers.adtree.ADTree
Calls measure function for prediction leaf size - the number of prediction nodes without children.
measureNumRules(). Method in class weka.classifiers.DecisionTable
Returns the number of rules
measureNumRules(). Method in class weka.classifiers.j48.J48
Returns the number of rules (same as number of leaves)
measureNumRules(). Method in class weka.classifiers.m5.M5Prime
return the number of rules
measureNumRules(). Method in class weka.classifiers.j48.PART
Return the number of rules.
Measures(). Constructor for class weka.classifiers.m5.Measures
Constructs a Measures object which could containing the performance measures
measures(Instances, boolean). Method in class weka.classifiers.m5.Node
Computes performance measures of a tree
measureSelectionTime(). Method in class weka.classifiers.AttributeSelectedClassifier
Additional measure --- time taken (milliseconds) to select the attributes
measuresToString(Measures[], Instances, int, int, String). Method in class weka.classifiers.m5.Node
Converts the performance measures into a string
measureTime(). Method in class weka.classifiers.AttributeSelectedClassifier
Additional measure --- time taken (milliseconds) to select attributes and build the classifier
measureTreeSize(). Method in class weka.classifiers.adtree.ADTree
Calls measure function for tree size - the total number of nodes.
measureTreeSize(). Method in class weka.classifiers.j48.J48
Returns the size of the tree
merge(ADTree). Method in class weka.classifiers.adtree.ADTree
Merges two trees together.
merge(PredictionNode, ADTree). Method in class weka.classifiers.adtree.PredictionNode
Merges this node with another.
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.
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.
MergeTwoValuesFilter(). Constructor for class weka.filters.MergeTwoValuesFilter
MetaCost(). Constructor for class weka.classifiers.MetaCost
metricTypeTipText(). Method in class weka.associations.Apriori
Returns the tip text for this property
min. Variable in class weka.experiment.Stats
The minimum value seen, or Double.NaN if no values seen
minimizeExpectedCostTipText(). Method in class weka.classifiers.CostSensitiveClassifier
minIndex(double[]). Static method in class weka.core.Utils
Returns index of minimum element in a given array of doubles.
minIndex(int[]). Static method in class weka.core.Utils
Returns index of minimum element in a given array of integers.
minMetricTipText(). Method in class weka.associations.Apriori
Returns the tip text for this property
minProb. Variable in class weka.classifiers.kstar.KStarWrapper
used/reused to hold the smallest transformation probability
minsAndMaxs(Instances, double[][], int). Method in class weka.classifiers.j48.C45Split
Returns the minsAndMaxs of the index.th subset.
minStdDevTipText(). Method in class weka.clusterers.EM
Returns the tip text for this property
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.
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
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".
MODEL_LINEAR_REGRESSION. Static variable in class weka.classifiers.m5.M5Prime
MODEL_MODEL_TREE. Static variable in class weka.classifiers.m5.M5Prime
MODEL_REGRESSION_TREE. Static variable in class weka.classifiers.m5.M5Prime
ModelSelection(). Constructor for class weka.classifiers.j48.ModelSelection
momentumTipText(). Method in class weka.classifiers.neural.NeuralNetwork
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.treevisualizer.TreeVisualizer
Does nothing.
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.treevisualizer.TreeVisualizer
Determines what action the user wants to perform.
mouseReleased(MouseEvent). Method in class weka.gui.treevisualizer.TreeVisualizer
Performs the final stages of what the user wants to perform.
MultiClassClassifier(). Constructor for class weka.classifiers.MultiClassClassifier
multiply(Matrix). Method in class weka.core.Matrix
Reurns the multiplication of two matrices
multiply(Matrix, int, int, int). Method in class weka.classifiers.m5.Matrix
Reurns the multiplication of two matrices
multiResultsetFull(int, int). Method in class weka.experiment.PairedTTester
Creates a comparison table where a base resultset is compared to the other resultsets.
multiResultsetRanking(int). Method in class weka.experiment.PairedTTester
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.
multiResultsetWins(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.
MultiScheme(). Constructor for class weka.classifiers.MultiScheme
mutationProbTipText(). Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property

N

NaiveBayes(). Constructor for class weka.classifiers.NaiveBayes
NaiveBayesSimple(). Constructor for class weka.classifiers.NaiveBayesSimple
name(). Method in class weka.core.Attribute
Returns the attribute's name.
name(). Method in class weka.core.Option
Returns the option's name.
NamedColor(String, int, int, int). Constructor for class weka.gui.treevisualizer.NamedColor
nameTipText(). Method in class weka.filters.AttributeExpressionFilter
Returns the tip text for this property
NDConditionalEstimator(int, double). Constructor for class weka.estimators.NDConditionalEstimator
Constructor
NeuralConnection(String). Constructor for class weka.classifiers.neural.NeuralConnection
Constructs The unit with the basic connection information prepared for use.
NeuralNetwork(). Constructor for class weka.classifiers.neural.NeuralNetwork
The constructor.
NeuralNode(String, Random, NeuralMethod). Constructor for class weka.classifiers.neural.NeuralNode
newEnt(Distribution). Method in class weka.classifiers.j48.EntropyBasedSplitCrit
Computes entropy of distribution after splitting.
newNominalRule(Attribute, Instances, int[]). Method in class weka.classifiers.OneR
Create a rule branching on this nominal attribute.
newNumericRule(Attribute, Instances, int[]). Method in class weka.classifiers.OneR
Create a rule branching on this numeric attribute
newRule(Attribute, Instances). Method in class weka.classifiers.OneR
Create a rule branching on this attribute.
next(int). Method in class weka.classifiers.adtree.ADTree
Performs one iteration.
next(int). Method in interface weka.classifiers.IterativeClassifier
Performs one iteration.
nextIteration(). Method in class weka.experiment.Experiment
Carries out the next iteration of the experiment.
nextIteration(). Method in class weka.experiment.RemoteExperiment
Overides the one in Experiment
nextSplitAddedOrder(). Method in class weka.classifiers.adtree.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.
NNConditionalEstimator(). Constructor for class weka.estimators.NNConditionalEstimator
NO_COMMAND. Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
Node(Instances, Node). Constructor for class weka.classifiers.m5.Node
Constructs a new node
Node(Instances, Node, Options). Constructor for class weka.classifiers.m5.Node
Constructs the root of a tree
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.
NOMINAL. Static variable in class weka.core.Attribute
Constant set for nominal attributes.
nominalCounts. Variable in class weka.core.AttributeStats
Counts of each nominal value
nominalLabelsTipText(). Method in class weka.filters.AddFilter
Returns the tip text for this property
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.
NominalToBinaryFilter(). Constructor for class weka.filters.NominalToBinaryFilter
nominalToBinaryFilterTipText(). Method in class weka.classifiers.neural.NeuralNetwork
NONE. Static variable in class weka.gui.visualize.VisualizePanelEvent
No longer used
NonSparseToSparseFilter(). Constructor for class weka.filters.NonSparseToSparseFilter
NORM_EXPECTED_COST_NAME. Static variable in class weka.classifiers.evaluation.CostCurve
NormalEstimator(double). Constructor for class weka.estimators.NormalEstimator
Constructor that takes a precision argument.
NormalizationFilter(). Constructor for class weka.filters.NormalizationFilter
normalize(). Method in class weka.classifiers.CostMatrix
Normalizes the cost matrix so that diagonal elements are zero.
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.
normalizeAttributesTipText(). Method in class weka.classifiers.neural.NeuralNetwork
normalizeNumericClassTipText(). Method in class weka.classifiers.neural.NeuralNetwork
normalizeTipText(). Method in class weka.attributeSelection.PrincipalComponents
Returns the tip text for this property
normalProbability(double). Static method in class weka.core.Statistics
Returns probability that the standardized normal variate Z (mean = 0, standard deviation = 1) is less than z.
NoSplit(Distribution). Constructor for class weka.classifiers.j48.NoSplit
Creates "no-split"-split for given distribution.
NullFilter(). Constructor for class weka.filters.NullFilter
NUM_RAND_COLS. Static variable in interface weka.classifiers.kstar.KStarConstants
numArguments(). Method in class weka.core.Option
Returns the option's number of arguments.
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.
numBags(). Method in class weka.classifiers.j48.Distribution
Returns number of bags.
numberAttributesSelected(). Method in class weka.attributeSelection.AttributeSelection
Return the number of attributes selected from the most recent run of attribute selection
numberOfClusters(). Method in class 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.DistributionMetaClusterer
Returns the number of clusters.
numberOfClusters(). Method in class weka.clusterers.EM
Returns the number of clusters.
numberOfClusters(). Method in class weka.clusterers.SimpleKMeans
Returns the number of clusters.
numberOfLinearModels(). Method in class weka.classifiers.m5.Node
Counts the number of linear models in the tree.
numClasses(). Method in class weka.classifiers.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.
numClustersTipText(). Method in class weka.clusterers.EM
Returns the tip text for this property
numClustersTipText(). Method in class weka.clusterers.SimpleKMeans
Returns the tip text for this property
numColumns(). Method in class weka.core.Matrix
Returns the number of columns in the matrix.
numCorrect(). Method in class weka.classifiers.j48.Distribution
Returns perClass(maxClass()).
numCorrect(int). Method in class weka.classifiers.j48.Distribution
Returns perClassPerBag(index,maxClass(index)).
numDistinctValues(Attribute). Method in class weka.core.Instances
Returns the number of distinct values of a given attribute.
numDistinctValues(int). Method in class weka.core.Instances
Returns the number of distinct values of a given attribute.
NUMERIC. Static variable in class weka.core.Attribute
Constant set for numeric attributes.
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.MakeIndicatorFilter
NumericToBinaryFilter(). Constructor for class weka.filters.NumericToBinaryFilter
NumericTransformFilter(). Constructor for class weka.filters.NumericTransformFilter
Default constructor -- sets the default transform method to java.lang.Math.abs().
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.
numFoldsTipText(). Method in class weka.experiment.CrossValidationResultProducer
Returns the tip text for this property
numIncorrect(). Method in class weka.classifiers.j48.Distribution
Returns total-numCorrect().
numIncorrect(int). Method in class weka.classifiers.j48.Distribution
Returns perBag(index)-numCorrect(index).
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.
numLeaves(). Method in class weka.classifiers.j48.ClassifierTree
Returns number of leaves in tree structure.
numLeaves(int). Method in class weka.classifiers.m5.Node
Sets the leaves' numbers
numNeighboursTipText(). Method in class weka.attributeSelection.ReliefFAttributeEval
Returns the tip text for this property
numNodes(). Method in class weka.classifiers.j48.ClassifierTree
Returns number of nodes in tree structure.
numOfBoostingIterationsTipText(). Method in class weka.classifiers.adtree.ADTree
numParameters(). Method in class weka.classifiers.LinearRegression
Get the number of coefficients used in the model
numPendingOutput(). Method in class weka.filters.Filter
Returns the number of instances pending output
numRows(). Method in class weka.core.Matrix
Returns the number of rows in the matrix.
numRules(). Method in class weka.classifiers.j48.MakeDecList
Outputs the number of rules in the classifier.
numRulesTipText(). Method in class weka.associations.Apriori
Returns the tip text for this property
numSubsets(). Method in class weka.classifiers.j48.ClassifierSplitModel
Returns the number of created subsets for the split.
numToSelectTipText(). Method in class weka.attributeSelection.ForwardSelection
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
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.
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.
numXValFoldsTipText(). Method in class weka.classifiers.ThresholdSelector

O

ObfuscateFilter(). Constructor for class weka.filters.ObfuscateFilter
OFF. Static variable in interface weka.classifiers.kstar.KStarConstants
oldEnt(Distribution). Method in class weka.classifiers.j48.EntropyBasedSplitCrit
Computes entropy of distribution before splitting.
ON. Static variable in interface weka.classifiers.kstar.KStarConstants
Some usefull constants
onDemandDirectoryTipText(). Method in class weka.classifiers.CostSensitiveClassifier
onDemandDirectoryTipText(). Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Returns the tip text for this property
OneR(). Constructor for class weka.classifiers.OneR
OneRAttributeEval(). Constructor for class weka.attributeSelection.OneRAttributeEval
Constructor
onUnit(Graphics, int, int, int, int). Method in class weka.classifiers.neural.NeuralConnection
Call this function to determine if the point at x,y is on the unit.
openFrame(String). Method in class weka.gui.ResultHistoryPanel
Opens the named result in a separate frame.
OPTIMIZE_0. Static variable in class weka.classifiers.ThresholdSelector
OPTIMIZE_1. Static variable in class weka.classifiers.ThresholdSelector
OPTIMIZE_LFREQ. Static variable in class weka.classifiers.ThresholdSelector
OPTIMIZE_MFREQ. Static variable in class weka.classifiers.ThresholdSelector
OPTIMIZE_POS_NAME. Static variable in class weka.classifiers.ThresholdSelector
Option(String, String, int, String). Constructor for class weka.core.Option
Creates new option with the given parameters.
Options(Instances). Constructor for class weka.classifiers.m5.Options
Options(String[]). Constructor for class weka.classifiers.m5.Options
Constructs an object to store command line options and other necessary information
orderAdded. Variable in class weka.classifiers.adtree.Splitter
The number this node was in the order of nodes added to the tree
OUTPUT. Static variable in class weka.classifiers.neural.NeuralConnection
This unit is an output unit.
output(). Method in class weka.filters.Filter
Output an instance after filtering and remove from the output queue.
outputFileTipText(). Method in class weka.experiment.CrossValidationResultProducer
Returns the tip text for this property
outputFileTipText(). Method in class weka.experiment.CSVResultListener
Returns the tip text for this property
outputFileTipText(). Method in class weka.experiment.RandomSplitResultProducer
Returns the tip text for this property
outputFormat(). Method in class weka.filters.Filter
outputFormat(). Method in class weka.gui.streams.InstanceJoiner
Gets the format of the output instances.
outputFormat(). Method in class weka.gui.streams.InstanceLoader
outputFormat(). Method in interface weka.gui.streams.InstanceProducer
outputPeek(). Method in class weka.filters.Filter
Output an instance after filtering but do not remove from the output queue.
outputPeek(). Method in class weka.gui.streams.InstanceJoiner
Output an instance after filtering but do not remove from the output queue.
outputPeek(). Method in class weka.gui.streams.InstanceLoader
outputPeek(). Method in interface weka.gui.streams.InstanceProducer
outputValue(boolean). Method in class weka.classifiers.neural.NeuralConnection
Call this to get the output value of this unit.
outputValue(boolean). Method in class weka.classifiers.neural.NeuralNode
Call this to get the output value of this unit.
outputValue(NeuralNode). Method in class weka.classifiers.neural.LinearUnit
This function calculates what the output value should be.
outputValue(NeuralNode). Method in interface weka.classifiers.neural.NeuralMethod
This function calculates what the output value should be.
outputValue(NeuralNode). Method in class weka.classifiers.neural.SigmoidUnit
This function calculates what the output value should be.
OutputZipper(File). Constructor for class weka.experiment.OutputZipper
Constructor.
OVAL. Static variable in class weka.gui.visualize.VisualizePanelEvent

P

padLeft(String, int). Static method in class weka.core.Utils
Pads a string to a specified length, inserting spaces on the left as required.
padRight(String, int). Static method in class weka.core.Utils
Pads a string to a specified length, inserting spaces on the right as required.
paintComponent(Graphics). Method in class weka.gui.visualize.ClassPanel
Renders this component
paintComponent(Graphics). Method in class weka.gui.visualize.Plot2D
Renders this component
paintComponent(Graphics). Method in class weka.gui.PropertyPanel
Paints the component, using the property editor's paint method.
paintComponent(Graphics). Method in class weka.gui.treevisualizer.TreeVisualizer
Updates the screen contents.
paintValue(Graphics, Rectangle). Method in class weka.gui.CostMatrixEditor
Paints a representation of the current classifier.
paintValue(Graphics, Rectangle). Method in class weka.gui.FileEditor
Paints a representation of the current Object.
paintValue(Graphics, Rectangle). Method in class weka.gui.GenericArrayEditor
Paints a representation of the current classifier.
paintValue(Graphics, Rectangle). Method in class weka.gui.GenericObjectEditor
Paints a representation of the current Object.
PairedStats(double). Constructor for class weka.experiment.PairedStats
Creates a new PairedStats object with the supplied significance level.
PairedTTester(). Constructor for class weka.experiment.PairedTTester
parentClass. Variable in class weka.experiment.PropertyNode
The class of the object with this property
PART(). Constructor for class weka.classifiers.j48.PART
partitionOptions(String[]). Static method in class weka.core.Utils
Returns the secondary set of options (if any) contained in the supplied options array.
pctCorrect(). Method in class weka.classifiers.Evaluation
Gets the percentage of instances correctly classified (that is, for which a correct prediction was made).
pctIncorrect(). Method in class weka.classifiers.Evaluation
Gets the percentage of instances incorrectly classified (that is, for which an incorrect prediction was made).
pctUnclassified(). Method in class weka.classifiers.Evaluation
Gets the percentage of instances not classified (that is, for which no prediction was made by the classifier).
peek(). Method in class weka.core.Queue
Gets object from the front of the queue.
perBag(int). Method in class weka.classifiers.j48.Distribution
Returns number of (possibly fractional) instances in given bag.
perClass(int). Method in class weka.classifiers.j48.Distribution
Returns number of (possibly fractional) instances of given class.
perClassPerBag(int, int). Method in class weka.classifiers.j48.Distribution
Returns number of (possibly fractional) instances of given class in given bag.
place(Node). Method in interface weka.gui.treevisualizer.NodePlace
The function to call to postion the tree that starts at Node r
place(Node). Method in class weka.gui.treevisualizer.PlaceNode1
Call this function to have each node in the tree starting at 'r' placed in a visual (not logical, they already are) tree position.
place(Node). Method in class weka.gui.treevisualizer.PlaceNode2
The Funtion to call to have the nodes arranged.
PlaceNode1(). Constructor for class weka.gui.treevisualizer.PlaceNode1
PlaceNode2(). Constructor for class weka.gui.treevisualizer.PlaceNode2
Plot2D(). Constructor for class weka.gui.visualize.Plot2D
Constructor
PlotData2D(Instances). Constructor for class weka.gui.visualize.PlotData2D
Construct a new PlotData2D using the supplied instances
PLUS_SHAPE. Static variable in class weka.gui.visualize.Plot2D
PoissonEstimator(). Constructor for class weka.estimators.PoissonEstimator
POLYGON. Static variable in class weka.gui.visualize.VisualizePanelEvent
pop(). Method in class weka.core.Queue
Pops an object from the front of the queue.
populationSizeTipText(). Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property
postProcess(). Method in class weka.experiment.AveragingResultProducer
When this method is called, it indicates that no more requests to generate results for the current experiment will be sent.
postProcess(). Method in class weka.experiment.CrossValidationResultProducer
Perform any postprocessing.
postProcess(). Method in class weka.experiment.DatabaseResultProducer
When this method is called, it indicates that no more requests to generate results for the current experiment will be sent.
postProcess(). Method in class weka.experiment.Experiment
Signals that the experiment is finished running, so that cleanup can be done.
postProcess(). Method in class weka.experiment.LearningRateResultProducer
When this method is called, it indicates that no more requests to generate results for the current experiment will be sent.
postProcess(). Method in class weka.experiment.RandomSplitResultProducer
Perform any postprocessing.
postProcess(). Method in class weka.experiment.RemoteExperiment
overides the one in Experiment
postProcess(). Method in interface weka.experiment.ResultProducer
Perform any postprocessing.
postProcess(int[]). Method in class weka.attributeSelection.ASEvaluation
Provides a chance for a attribute evaluator to do any special post processing of the selected attribute set.
postProcess(int[]). Method in class weka.attributeSelection.CfsSubsetEval
Calls locallyPredictive in order to include locally predictive attributes (if requested).
postProcess(ResultProducer). Method in class weka.experiment.AveragingResultProducer
When this method is called, it indicates that no more results will be sent that need to be grouped together in any way.
postProcess(ResultProducer). Method in class weka.experiment.CSVResultListener
Perform any postprocessing.
postProcess(ResultProducer). Method in class weka.experiment.DatabaseResultListener
Perform any postprocessing.
postProcess(ResultProducer). Method in class weka.experiment.DatabaseResultProducer
When this method is called, it indicates that no more results will be sent that need to be grouped together in any way.
postProcess(ResultProducer). Method in class weka.experiment.InstancesResultListener
Perform any postprocessing.
postProcess(ResultProducer). Method in class weka.experiment.LearningRateResultProducer
When this method is called, it indicates that no more results will be sent that need to be grouped together in any way.
postProcess(ResultProducer). Method in interface weka.experiment.ResultListener
Perform any postprocessing.
precision(int). Method in class weka.classifiers.Evaluation
Calculate the precision with respect to a particular class.
PRECISION_NAME. Static variable in class weka.classifiers.evaluation.ThresholdCurve
predict(Instance). Method in class weka.classifiers.m5.Function
Returns the predicted value of instance i by a function
predict(Instance, boolean). Method in class weka.classifiers.m5.Node
Predicts the class value of an instance by the tree
predicted(). Method in class weka.classifiers.evaluation.NominalPrediction
Gets the predicted class value.
predicted(). Method in class weka.classifiers.evaluation.NumericPrediction
Gets the predicted class value.
predicted(). Method in interface weka.classifiers.evaluation.Prediction
Gets the predicted class value.
PredictionNode(double). Constructor for class weka.classifiers.adtree.PredictionNode
Creates a new prediction node.
predictionsToString(Instances, int, boolean). Method in class weka.classifiers.m5.Node
Converts the predictions by the tree under this node to a string
prefix(). Method in class weka.classifiers.j48.ClassifierTree
Returns tree in prefix order.
prefix(). Method in class weka.classifiers.j48.J48
Returns tree in prefix order.
prefix(). Method in interface weka.core.Matchable
Returns a string that describes a tree representing the object in prefix order.
prePlot(Graphics). Method in interface weka.gui.visualize.Plot2DCompanion
Something to be drawn before the plot itself
preProcess(). Method in class weka.experiment.AveragingResultProducer
Prepare to generate results.
preProcess(). Method in class weka.experiment.CrossValidationResultProducer
Prepare to generate results.
preProcess(). Method in class weka.experiment.DatabaseResultProducer
Prepare to generate results.
preProcess(). Method in class weka.experiment.LearningRateResultProducer
Prepare to generate results.
preProcess(). Method in class weka.experiment.RandomSplitResultProducer
Prepare to generate results.
preProcess(). Method in interface weka.experiment.ResultProducer
Prepare to generate results.
preProcess(ResultProducer). Method in class weka.experiment.AveragingResultProducer
Prepare for the results to be received.
preProcess(ResultProducer). Method in class weka.experiment.CSVResultListener
Prepare for the results to be received.
preProcess(ResultProducer). Method in class weka.experiment.DatabaseResultListener
Prepare for the results to be received.
preProcess(ResultProducer). Method in class weka.experiment.DatabaseResultProducer
Prepare for the results to be received.
preProcess(ResultProducer). Method in class weka.experiment.InstancesResultListener
Prepare for the results to be received.
preProcess(ResultProducer). Method in class weka.experiment.LearningRateResultProducer
Prepare for the results to be received.
preProcess(ResultProducer). Method in interface weka.experiment.ResultListener
Prepare for the results to be received.
PreprocessPanel(). Constructor for class weka.gui.explorer.PreprocessPanel
Creates the instances panel with no initial instances.
PrincipalComponents(). Constructor for class weka.attributeSelection.PrincipalComponents
print(double[], int, int). Static method in class weka.classifiers.m5.Dvector
Prints the indexed elements in a double vector
printFeatures(). Method in class weka.classifiers.DecisionTable
Returns a string description of the features selected
printOptions(String[]). Static method in class weka.core.CheckOptionHandler
Prints the given options to a string.
printValidOptions(). Method in class weka.classifiers.m5.Options
Prints valid command line options and simply explains the output
priorEntropy(). Method in class weka.classifiers.Evaluation
Calculate the entropy of the prior distribution
Prism(). Constructor for class weka.classifiers.Prism
prob(int). Method in class weka.classifiers.j48.Distribution
Returns relative frequency of class over all bags.
prob(int, int). Method in class weka.classifiers.j48.Distribution
Returns relative frequency of class for given bag.
PROB_COST_FUNC_NAME. Static variable in class weka.classifiers.evaluation.CostCurve
PROCESSING. Static variable in class weka.experiment.TaskStatusInfo
property. Variable in class weka.experiment.PropertyNode
Other info about the property
propertyChange(PropertyChangeEvent). Method in class weka.gui.PropertySheetPanel
Updates the property sheet panel with a changed property and also passed the event along.
PropertyDialog(PropertyEditor, int, int). Constructor for class weka.gui.PropertyDialog
Creates the editor frame.
PropertyNode(Object). Constructor for class weka.experiment.PropertyNode
Creates a mostly empty property.
PropertyNode(Object, PropertyDescriptor, Class). Constructor for class weka.experiment.PropertyNode
Creates a fully specified property node.
PropertyPanel(PropertyEditor). Constructor for class weka.gui.PropertyPanel
Create the panel with the supplied property editor.
PropertySelectorDialog(Frame, Object). Constructor for class weka.gui.PropertySelectorDialog
Create the property selection dialog.
PropertySheetPanel(). Constructor for class weka.gui.PropertySheetPanel
Creates the property sheet panel.
prune(). Method in class weka.classifiers.j48.C45PruneableClassifierTree
Prunes a tree using C4.5's pruning procedure.
prune(). Method in class weka.classifiers.m5.Node
Prunes the model tree
prune(). Method in class weka.classifiers.j48.PruneableClassifierTree
Prunes a tree.
PruneableClassifierTree(ModelSelection, boolean, int, boolean). Constructor for class weka.classifiers.j48.PruneableClassifierTree
Constructor for pruneable tree structure.
PruneableDecList(ModelSelection, int). Constructor for class weka.classifiers.j48.PruneableDecList
Constructor for pruneable partial tree structure.
pruneItemSets(FastVector, Hashtable). Static method in class weka.associations.ItemSet
Prunes a set of (k)-item sets using the given (k-1)-item sets.
pruneRules(FastVector[], double). Static method in class weka.associations.ItemSet
Prunes a set of rules.
PURE_INPUT. Static variable in class weka.classifiers.neural.NeuralConnection
This unit is a pure input unit.
PURE_OUTPUT. Static variable in class weka.classifiers.neural.NeuralConnection
This unit is a pure output unit.
push(Object). Method in class weka.core.Queue
Appends an object to the back of the queue.
putResultInTable(String, ResultProducer, Object[], Object[]). Method in class weka.experiment.DatabaseUtils
Executes a database query to insert a result for the supplied key into the database.

Q

queryTipText(). Method in class weka.experiment.InstanceQuery
Returns the tip text for this property
Queue(). Constructor for class weka.core.Queue
quote(String). Static method in class weka.core.Utils
Quotes a string if it contains special characters.

R

RaceSearch(). Constructor for class weka.attributeSelection.RaceSearch
raceTypeTipText(). Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
randEntropy. Variable in class weka.classifiers.kstar.KStarWrapper
used/reused to hold the random entropy
randomize(Random). Method in class weka.core.Instances
Shuffles the instances in the set so that they are ordered randomly.
randomizeDataTipText(). Method in class weka.experiment.RandomSplitResultProducer
Returns the tip text for this property
RandomizeFilter(). Constructor for class weka.filters.RandomizeFilter
RandomSearch(). Constructor for class weka.attributeSelection.RandomSearch
Constructor
randomSeedTipText(). Method in class weka.classifiers.adtree.ADTree
randomSeedTipText(). Method in class weka.classifiers.neural.NeuralNetwork
RandomSplitResultProducer(). Constructor for class weka.experiment.RandomSplitResultProducer
randomWidthFactorTipText(). Method in class weka.classifiers.MultiClassClassifier
Range(). Constructor for class weka.core.Range
Default constructor.
Range(String). Constructor for class weka.core.Range
Constructor to set initial range.
RANGE_BOUNDS. Static variable in class weka.classifiers.ThresholdSelector
RANGE_NONE. Static variable in class weka.classifiers.ThresholdSelector
rangeCorrectionTipText(). Method in class weka.classifiers.ThresholdSelector
rankedAttributes(). Method in class weka.attributeSelection.AttributeSelection
get the final ranking of the attributes.
rankedAttributes(). Method in class weka.attributeSelection.ForwardSelection
Produces a ranked list of attributes.
rankedAttributes(). Method in class weka.attributeSelection.RaceSearch
rankedAttributes(). Method in interface weka.attributeSelection.RankedOutputSearch
Returns a X by 2 list of attribute indexes and corresponding evaluations from best (highest) to worst.
rankedAttributes(). Method in class weka.attributeSelection.Ranker
Sorts the evaluated attribute list
Ranker(). Constructor for class weka.attributeSelection.Ranker
Constructor
RankSearch(). Constructor for class weka.attributeSelection.RankSearch
rawOutputTipText(). Method in class weka.experiment.CrossValidationResultProducer
Returns the tip text for this property
rawOutputTipText(). Method in class weka.experiment.RandomSplitResultProducer
Returns the tip text for this property
readInstance(Reader). Method in class weka.core.Instances
Reads a single instance from the reader and appends it to the dataset.
readOldFormat(Reader). Method in class weka.classifiers.CostMatrix
Reads misclassification cost matrix from given reader.
readProperties(String). Static method in class weka.core.Utils
Reads properties that inherit from three locations.
realCount. Variable in class weka.core.AttributeStats
The number of real-like values (i.e.
recall(int). Method in class weka.classifiers.Evaluation
Calculate the recall with respect to a particular class.
RECALL_NAME. Static variable in class weka.classifiers.evaluation.ThresholdCurve
RECTANGLE. Static variable in class weka.gui.visualize.VisualizePanelEvent
reduceDimensionality(Instance). Method in class weka.attributeSelection.AttributeSelection
reduce the dimensionality of a single instance to include only those attributes chosen by the last run of attribute selection.
reduceDimensionality(Instances). Method in class weka.attributeSelection.AttributeSelection
reduce the dimensionality of a set of instances to include only those attributes chosen by the last run of attribute selection.
reduceMatrix(double[][]). Static method in class weka.core.ContingencyTables
Reduces a matrix by deleting all zero rows and columns.
ReferenceInstances(Instances, int). Constructor for class weka.classifiers.adtree.ReferenceInstances
Creates an empty set of instances.
regression(Function). Method in class weka.classifiers.m5.Node
Computes the coefficients of a linear model using the instances at this node
regression(Matrix). Method in class weka.core.Matrix
Performs a (ridged) linear regression.
regression(Matrix, double[]). Method in class weka.core.Matrix
Performs a weighted (ridged) linear regression.
regression(Matrix, int, int). Method in class weka.classifiers.m5.Matrix
Linear regression
RegressionByDiscretization(). Constructor for class weka.classifiers.RegressionByDiscretization
RegressionSplitEvaluator(). Constructor for class weka.experiment.RegressionSplitEvaluator
No args constructor.
RELATION_NAME. Static variable in class weka.classifiers.evaluation.CostCurve
The name of the relation used in cost curve datasets
RELATION_NAME. Static variable in class weka.classifiers.evaluation.ThresholdCurve
The name of the relation used in threshold curve datasets
relationName(). Method in class weka.core.Instances
Returns the relation's name.
relativeAbsoluteError(). Method in class weka.classifiers.Evaluation
Returns the relative absolute error.
ReliefFAttributeEval(). Constructor for class weka.attributeSelection.ReliefFAttributeEval
Constructor
RemoteEngine(String). Constructor for class weka.experiment.RemoteEngine
Constructor
RemoteExperiment(Experiment). Constructor for class weka.experiment.RemoteExperiment
Construct a new RemoteExperiment using a base Experiment
RemoteExperimentEvent(boolean, boolean, boolean, String). Constructor for class weka.experiment.RemoteExperimentEvent
Constructor
remoteExperimentStatus(RemoteExperimentEvent). Method in interface weka.experiment.RemoteExperimentListener
Called when progress has been made in a remote experiment
RemoteExperimentSubTask(). Constructor for class weka.experiment.RemoteExperimentSubTask
remove(int). Method in class weka.classifiers.m5.Function
Removes a term from the function
REMOVE_CHILDREN. Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
removeAllElements(). Method in class weka.core.FastVector
Removes all components from this vector and sets its size to zero.
removeAllElements(). Method in class weka.core.Queue
Removes all objects from the queue.
removeAllInputs(). Method in class weka.classifiers.neural.NeuralConnection
This function will remove all the inputs to this unit.
removeAllInputs(). Method in class weka.classifiers.neural.NeuralNode
This function will remove all the inputs to this unit.
removeAllMissingColsTipText(). Method in class weka.associations.Apriori
Returns the tip text for this property
removeAllOutputs(). Method in class weka.classifiers.neural.NeuralConnection
This function will remove all outputs to this unit.
removeAllPlots(). Method in class weka.gui.visualize.Plot2D
Clears all plots
removeElementAt(int). Method in class weka.core.FastVector
Deletes an element from this vector.
removeInstanceListener(InstanceListener). Method in class weka.gui.streams.InstanceJoiner
removeInstanceListener(InstanceListener). Method in class weka.gui.streams.InstanceLoader
removeInstanceListener(InstanceListener). Method in interface weka.gui.streams.InstanceProducer
removeNotify(). Method in class weka.gui.PropertyPanel
removePropertyChangeListener(PropertyChangeListener). Method in class weka.gui.CostMatrixEditor
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener). Method in class weka.gui.GenericArrayEditor
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener). Method in class weka.gui.GenericObjectEditor
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener). Method in class weka.gui.explorer.PreprocessPanel
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener). Method in class weka.gui.PropertySheetPanel
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener). Method in class weka.gui.SetInstancesPanel
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener). Method in class weka.gui.experiment.SetupPanel
Removes a PropertyChangeListener.
removeResult(String). Method in class weka.gui.ResultHistoryPanel
Removes one of the result buffers from the history.
removeSubstring(String, String). Static method in class weka.core.Utils
Removes all occurrences of a string from another string.
renameAttribute(Attribute, String). Method in class weka.core.Instances
Renames an attribute.
renameAttribute(int, String). Method in class weka.core.Instances
Renames an attribute.
renameAttributeValue(Attribute, String, String). Method in class weka.core.Instances
Renames the value of a nominal (or string) attribute value.
renameAttributeValue(int, int, String). Method in class weka.core.Instances
Renames the value of a nominal (or string) attribute value.
replaceMissingValues(double[]). Method in class weka.core.BinarySparseInstance
Does nothing, since we don't support missing values.
replaceMissingValues(double[]). Method in class weka.core.Instance
Replaces all missing values in the instance with the values contained in the given array.
replaceMissingValues(double[]). Method in class weka.core.SparseInstance
Replaces all missing values in the instance with the values contained in the given array.
ReplaceMissingValuesFilter(). Constructor for class weka.filters.ReplaceMissingValuesFilter
replaceSubstring(String, String, String). Static method in class weka.core.Utils
Replaces with a new string, all occurrences of a string from another string.
reportFrequencyTipText(). Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property
resample(Random). Method in class weka.core.Instances
Creates a new dataset of the same size using random sampling with replacement.
ResampleFilter(). Constructor for class weka.filters.ResampleFilter
resampleWithWeights(Random). Method in class weka.core.Instances
Creates a new dataset of the same size using random sampling with replacement according to the current instance weights.
resampleWithWeights(Random, double[]). Method in class weka.core.Instances
Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
reset(). Method in class weka.core.converters.ArffLoader
Resets the Loader ready to read a new data set
reset(). Method in class weka.core.converters.C45Loader
Resets the Loader ready to read a new data set
reset(). Method in class weka.core.converters.CSVLoader
Resets the loader ready to read a new data set
reset(). Method in class weka.classifiers.neural.NeuralConnection
Call this to reset the unit for another run.
reset(). Method in class weka.classifiers.neural.NeuralNode
Call this to reset the value and error for this unit, ready for the next run.
reset(). Method in class weka.core.converters.SerializedInstancesLoader
Resets the Loader ready to read a new data set
resetDistribution(Instances). Method in class weka.classifiers.j48.BinC45Split
Sets distribution associated with model.
resetDistribution(Instances). Method in class weka.classifiers.j48.C45Split
Sets distribution associated with model.
resetDistribution(Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
Sets distribution associated with model.
resetOptions(). Method in class weka.associations.Apriori
Resets the options to the default values.
resetTipText(). Method in class weka.classifiers.neural.NeuralNetwork
ResultHistoryPanel(JTextComponent). Constructor for class weka.gui.ResultHistoryPanel
Create the result history object
resultProducerTipText(). Method in class weka.experiment.AveragingResultProducer
Returns the tip text for this property
resultProducerTipText(). Method in class weka.experiment.DatabaseResultProducer
Returns the tip text for this property
resultProducerTipText(). Method in class weka.experiment.LearningRateResultProducer
Returns the tip text for this property
resultsetKey(). Method in class weka.experiment.PairedTTester
Creates a key that maps resultset numbers to their descriptions.
ResultsPanel(). Constructor for class weka.gui.experiment.ResultsPanel
Creates the results panel with no initial experiment.
retrieveInstances(). Method in class weka.experiment.InstanceQuery
Makes a database query using the query set through the -Q option to convert a table into a set of instances
retrieveInstances(String). Method in class weka.experiment.InstanceQuery
Makes a database query to convert a table into a set of instances
rightSide(int, Instances). Method in class weka.classifiers.j48.BinC45Split
Prints the condition satisfied by instances in a subset.
rightSide(int, Instances). Method in class weka.classifiers.j48.C45Split
Prints the condition satisfied by instances in a subset.
rightSide(int, Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
Prints left side of condition satisfied by instances in subset index.
rightSide(int, Instances). Method in class weka.classifiers.j48.NoSplit
Does nothing because no condition has to be satisfied.
ROOT_FINDER_ACCURACY. Static variable in interface weka.classifiers.kstar.KStarConstants
ROOT_FINDER_MAX_ITER. Static variable in interface weka.classifiers.kstar.KStarConstants
How close the root finder for numeric and nominal have to get
rootMeanPriorSquaredError(). Method in class weka.classifiers.Evaluation
Returns the root mean prior squared error.
rootMeanSquaredError(). Method in class weka.classifiers.Evaluation
Returns the root mean squared error.
rootRelativeSquaredError(). Method in class weka.classifiers.Evaluation
Returns the root relative squared error if the class is numeric.
round(double). Static method in class weka.core.Utils
Rounds a double to the next nearest integer value.
roundDouble(double). Static method in class weka.classifiers.m5.M5Utils
Rounds a double
roundDouble(double, int). Static method in class weka.core.Utils
Rounds a double to the given number of decimal places.
RUN_FIELD_NAME. Static variable in class weka.experiment.CrossValidationResultProducer
RUN_FIELD_NAME. Static variable in class weka.experiment.RandomSplitResultProducer
runCommand(String). Method in class weka.gui.SimpleCLI
Executes a simple cli command.
runExperiment(). Method in class weka.experiment.Experiment
Runs all iterations of the experiment, continuing past errors.
runExperiment(). Method in class weka.experiment.RemoteExperiment
Overides runExperiment in Experiment
RunNumberPanel(). Constructor for class weka.gui.experiment.RunNumberPanel
Creates the panel with no initial experiment.
RunNumberPanel(Experiment). Constructor for class weka.gui.experiment.RunNumberPanel
Creates the panel with the supplied initial experiment.
RunPanel(). Constructor for class weka.gui.experiment.RunPanel
Creates the run panel with no initial experiment.
RunPanel(Experiment). Constructor for class weka.gui.experiment.RunPanel
Creates the panel with the supplied initial experiment.

S

sampleSizeTipText(). Method in class weka.attributeSelection.ReliefFAttributeEval
Returns the tip text for this property
save(StringBuffer). Method in class weka.gui.SaveBuffer
Save a buffer
SaveBuffer(Logger, Component). Constructor for class weka.gui.SaveBuffer
Constructor
saveInstanceDataTipText(). Method in class weka.classifiers.adtree.ADTree
saveWorkingInstancesToFileQ(). Method in class weka.gui.explorer.PreprocessPanel
Queries the user for a file to save instances as, then saves the instances in a background process.
search(ASEvaluation, Instances). Method in class weka.attributeSelection.ASSearch
Searches the attribute subset/ranking space.
search(ASEvaluation, Instances). Method in class weka.attributeSelection.BestFirst
Searches the attribute subset space by best first search
search(ASEvaluation, Instances). Method in class weka.attributeSelection.ExhaustiveSearch
Searches the attribute subset space using an exhaustive search.
search(ASEvaluation, Instances). Method in class weka.attributeSelection.ForwardSelection
Searches the attribute subset space by forward selection.
search(ASEvaluation, Instances). Method in class weka.attributeSelection.GeneticSearch
Searches the attribute subset space using a genetic algorithm.
search(ASEvaluation, Instances). Method in class weka.attributeSelection.RaceSearch
Searches the attribute subset space by racing cross validation errors of competing subsets
search(ASEvaluation, Instances). Method in class weka.attributeSelection.RandomSearch
Searches the attribute subset space using a genetic algorithm.
search(ASEvaluation, Instances). Method in class weka.attributeSelection.Ranker
Kind of a dummy search algorithm.
search(ASEvaluation, Instances). Method in class weka.attributeSelection.RankSearch
Ranks attributes using the specified attribute evaluator and then searches the ranking using the supplied subset evaluator.
SEARCHPATH_ALL. Static variable in class weka.classifiers.adtree.ADTree
The search modes
SEARCHPATH_HEAVIEST. Static variable in class weka.classifiers.adtree.ADTree
SEARCHPATH_RANDOM. Static variable in class weka.classifiers.adtree.ADTree
SEARCHPATH_ZPURE. Static variable in class weka.classifiers.adtree.ADTree
searchPathTipText(). Method in class weka.classifiers.adtree.ADTree
searchPercentTipText(). Method in class weka.attributeSelection.RandomSearch
Returns the tip text for this property
searchPoints(int, int, boolean). Method in class weka.gui.visualize.Plot2D
Pops up a window displaying attribute information on any instances at a point+-plotting_point_size (in panel coordinates)
searchTerminationTipText(). Method in class weka.attributeSelection.BestFirst
Returns the tip text for this property
searchTipText(). Method in class weka.classifiers.AttributeSelectedClassifier
Returns the tip text for this property
secondInstanceProduced(InstanceEvent). Method in class weka.gui.streams.InstanceJoiner
secondInstanceProduced(InstanceEvent). Method in interface weka.gui.streams.SerialInstanceListener
seedTipText(). Method in class weka.classifiers.CostSensitiveClassifier
seedTipText(). Method in class weka.clusterers.EM
Returns the tip text for this property
seedTipText(). Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property
seedTipText(). Method in class weka.attributeSelection.ReliefFAttributeEval
Returns the tip text for this property
seedTipText(). Method in class weka.clusterers.SimpleKMeans
Returns the tip text for this property
seedTipText(). Method in class weka.classifiers.ThresholdSelector
seedTipText(). Method in class weka.attributeSelection.WrapperSubsetEval
Returns the tip text for this property
SelectAttributes(ASEvaluation, String[]). Static method in class weka.attributeSelection.AttributeSelection
Perform attribute selection with a particular evaluator and a set of options specifying search method and input file etc.
SelectAttributes(ASEvaluation, String[], Instances). Static method in class weka.attributeSelection.AttributeSelection
Perform attribute selection with a particular evaluator and a set of options specifying search method and options for the search method and evaluator.
SelectAttributes(Instances). Method in class weka.attributeSelection.AttributeSelection
Perform attribute selection on the supplied training instances.
selectAttributesCVSplit(Instances). Method in class weka.attributeSelection.AttributeSelection
Select attributes for a split of the data.
selectedAttributes(). Method in class weka.attributeSelection.AttributeSelection
get the final selected set of attributes.
SelectedTag(int, Tag[]). Constructor for class weka.core.SelectedTag
Creates a new SelectedTag instance.
SelectedTagEditor(). Constructor for class weka.gui.SelectedTagEditor
selectionThresholdTipText(). Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
selectModel(Instances). Method in class weka.classifiers.j48.BinC45ModelSelection
Selects C4.5-type split for the given dataset.
selectModel(Instances). Method in class weka.classifiers.j48.C45ModelSelection
Selects C4.5-type split for the given dataset.
selectModel(Instances). Method in class weka.classifiers.j48.ModelSelection
Selects a model for the given dataset.
selectModel(Instances, Instances). Method in class weka.classifiers.j48.BinC45ModelSelection
Selects C4.5-type split for the given dataset.
selectModel(Instances, Instances). Method in class weka.classifiers.j48.C45ModelSelection
Selects C4.5-type split for the given dataset.
selectModel(Instances, Instances). Method in class weka.classifiers.j48.ModelSelection
Selects a model for the given train data using the given test data
SEND_INSTANCES. Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
Command to remove instances from this node and send them to the VisualizePanel.
separatorToString(). Static method in class weka.classifiers.m5.M5Utils
Prints sepearating line
SerializedInstancesLoader(). Constructor for class weka.core.converters.SerializedInstancesLoader
SerializedObject(Object). Constructor for class weka.core.SerializedObject
Serializes the supplied object into a byte array without compression.
SerializedObject(Object, boolean). Constructor for class weka.core.SerializedObject
Serializes the supplied object into a byte array.
setAcuity(int). Method in class weka.clusterers.Cobweb
set the accuity.
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.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.SpreadSubsampleFilter
Sets whether the instance weights will be adjusted to maintain total weight per class.
setAdvanceDataSetFirst(boolean). Method in class weka.experiment.Experiment
Set the value of m_AdvanceDataSetFirst.
setArffFile(String). Method in class weka.gui.streams.InstanceLoader
setArffFile(String). Method in class weka.gui.streams.InstanceSavePanel
setAsText(String). Method in class weka.gui.CostMatrixEditor
Returns null as we don't support getting/setting values as text.
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.
setAttribute(int). Method in class weka.gui.AttributeSummaryPanel
Sets the attribute that statistics will be displayed for.
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.
setAttributeIndex(int). Method in class weka.filters.AddFilter
Set the index where the attribute will be inserted
setAttributeIndex(int). Method in class weka.filters.InstanceFilter
Sets attribute to be used for selection
setAttributeIndex(int). Method in class weka.filters.MakeIndicatorFilter
Sets index of of the attribute used.
setAttributeIndex(int). Method in class weka.filters.MergeTwoValuesFilter
Sets index of the attribute used.
setAttributeIndex(int). Method in class weka.filters.StringToNominalFilter
Sets index of the attribute used.
setAttributeIndex(int). Method in class weka.filters.SwapAttributeValuesFilter
Sets index of the attribute used.
setAttributeIndices(String). Method in class weka.filters.AbstractTimeSeriesFilter
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndices(String). Method in class weka.filters.AttributeFilter
Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndices(String). Method in class weka.filters.CopyAttributesFilter
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndices(String). Method in class weka.filters.DiscretizeFilter
Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeIndices(String). Method in class weka.filters.FirstOrderFilter
Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndices(String). Method in class weka.filters.NumericTransformFilter
Set which attributes are to be transformed (or kept if invert is true).
setAttributeIndicesArray(int[]). Method in class weka.filters.AbstractTimeSeriesFilter
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndicesArray(int[]). Method in class weka.filters.AttributeFilter
Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndicesArray(int[]). Method in class weka.filters.CopyAttributesFilter
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndicesArray(int[]). Method in class weka.filters.DiscretizeFilter
Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeIndicesArray(int[]). Method in class weka.filters.FirstOrderFilter
Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndicesArray(int[]). Method in class weka.filters.NumericTransformFilter
Set which attributes are to be transformed (or kept if invert is true)
setAttributeName(String). Method in class weka.filters.AddFilter
Set the new attribute's name
setAttributeSelectionMethod(SelectedTag). Method in class weka.classifiers.LinearRegression
Sets the method used to select attributes for use in the linear regression.
setAttributeType(SelectedTag). Method in class weka.filters.AttributeTypeFilter
Sets the type of attribute to delete.
setAutoBuild(boolean). Method in class weka.classifiers.neural.NeuralNetwork
This will set whether the network is automatically built or if it is left up to the user.
setBagSizePercent(int). Method in class weka.classifiers.Bagging
Sets the size of each bag, as a percentage of the training set size.
setBagSizePercent(int). Method in class weka.classifiers.MetaCost
Sets the size of each bag, as a percentage of the training set size.
setBaseClassifiers(Classifier[]). Method in class weka.classifiers.Stacking
Sets the list of possible classifers to choose from.
setBaseExperiment(Experiment). Method in class weka.experiment.RemoteExperiment
Set the base experiment.
setBaseInstances(Instances). Method in class weka.gui.explorer.PreprocessPanel
Tells the panel to use a new base set of instances.
setBaseInstancesFromDB(InstanceQuery). Method in class weka.gui.explorer.PreprocessPanel
Loads instances from a database
setBaseInstancesFromDBQ(). Method in class weka.gui.explorer.PreprocessPanel
Queries the user for a URL to a database to load instances from, then loads the instances in a background process.
setBaseInstancesFromFile(File). Method in class weka.gui.explorer.PreprocessPanel
Loads results from a set of instances contained in the supplied file.
setBaseInstancesFromFileQ(). Method in class weka.gui.explorer.PreprocessPanel
Queries the user for a file to load instances from, then loads the instances in a background process.
setBaseInstancesFromURL(URL). Method in class weka.gui.explorer.PreprocessPanel
Loads instances from a URL.
setBaseInstancesFromURLQ(). Method in class weka.gui.explorer.PreprocessPanel
Queries the user for a URL to load instances from, then loads the instances in a background process.
setBias(double). Method in class weka.classifiers.VFI
Set the value of the exponential bias towards more confident intervals
setBiasToUniformClass(double). Method in class weka.filters.ResampleFilter
Sets the bias towards a uniform class.
setBinarizeNumericAttributes(boolean). Method in class weka.attributeSelection.ChiSquaredAttributeEval
Binarize numeric attributes.
setBinarizeNumericAttributes(boolean). Method in class weka.attributeSelection.InfoGainAttributeEval
Binarize numeric attributes.
setBinaryAttributesNominal(boolean). Method in class weka.filters.NominalToBinaryFilter
Sets if binary attributes are to be treates as nominal ones.
setBinarySplits(boolean). Method in class weka.classifiers.j48.J48
Set the value of binarySplits.
setBinarySplits(boolean). Method in class weka.classifiers.j48.PART
Set the value of binarySplits.
setBins(int). Method in class weka.filters.DiscretizeFilter
Sets the number of bins to divide each selected numeric attribute into
setBlendFactor(int). Method in class weka.classifiers.kstar.KStarNumericAttribute
Set the blending factor
setBlendMethod(int). Method in class weka.classifiers.kstar.KStarNumericAttribute
Set the blending method
setC(double). Method in class weka.classifiers.SMO
Set the value of C.
setCacheKeyName(String). Method in class weka.experiment.DatabaseResultListener
Set the value of CacheKeyName.
setCacheSize(int). Method in class weka.classifiers.SMO
Set the value of the kernel cache.
setCalculateStdDevs(boolean). Method in class weka.experiment.AveragingResultProducer
Set the value of CalculateStdDevs.
setCapacity(int). Method in class weka.core.FastVector
Sets the vector's capacity to the given value.
setCenter(double). Method in class weka.gui.treevisualizer.Node
Set the value of center.
setChildForBranch(int, PredictionNode). Method in class weka.classifiers.adtree.Splitter
Sets the child for a branch of the split.
setChildForBranch(int, PredictionNode). Method in class weka.classifiers.adtree.TwoWayNominalSplit
Sets the child for a branch of the split.
setChildForBranch(int, PredictionNode). Method in class weka.classifiers.adtree.TwoWayNumericSplit
Sets the child for a branch of the split.
setCindex(int). Method in class weka.gui.visualize.AttributePanel
Set the index of the attribute by which to colour the data.
setCindex(int). Method in class weka.gui.visualize.Plot2D
Set the index of the attribute to use for colouring
setCindex(int). Method in class weka.gui.visualize.PlotData2D
Set the colouring index of the data
setCindex(int, double, double). Method in class weka.gui.visualize.AttributePanel
Set the index of the attribute by which to colour the data.
setClass(Attribute). Method in class weka.core.Instances
Sets the class attribute.
setClassForIRStatistics(int). Method in class weka.experiment.ClassifierSplitEvaluator
Set the value of ClassForIRStatistics.
setClassifier(Classifier). Method in class weka.classifiers.AdaBoostM1
Set the classifier for boosting.
setClassifier(Classifier). Method in class weka.classifiers.AdditiveRegression
Sets the classifier
setClassifier(Classifier). Method in class weka.classifiers.AttributeSelectedClassifier
Sets the classifier
setClassifier(Classifier). Method in class weka.classifiers.Bagging
Set the classifier for bagging.
setClassifier(Classifier). Method in class weka.classifiers.BVDecompose
Set the classifiers being analysed
setClassifier(Classifier). Method in class weka.classifiers.CheckClassifier
Set the classifier for boosting.
setClassifier(Classifier). Method in class weka.classifiers.ClassificationViaRegression
Set the base classifier.
setClassifier(Classifier). Method in class weka.experiment.ClassifierSplitEvaluator
Sets the classifier.
setClassifier(Classifier). Method in class weka.attributeSelection.ClassifierSubsetEval
Set the classifier to use for accuracy estimation
setClassifier(Classifier). Method in class weka.classifiers.CostSensitiveClassifier
Sets the distribution classifier
setClassifier(Classifier). Method in class weka.classifiers.CVParameterSelection
Set the classifier for boosting.
setClassifier(Classifier). Method in class weka.classifiers.DistributionMetaClassifier
Set the base classifier.
setClassifier(Classifier). Method in class weka.classifiers.FilteredClassifier
Sets the classifier
setClassifier(Classifier). Method in class weka.classifiers.LogitBoost
Set the classifier for boosting.
setClassifier(Classifier). Method in class weka.classifiers.MetaCost
Sets the distribution classifier
setClassifier(Classifier). Method in class weka.classifiers.RegressionByDiscretization
Set the classifier for boosting.
setClassifier(Classifier). Method in class weka.experiment.RegressionSplitEvaluator
Sets the classifier.
setClassifier(Classifier). Method in class weka.attributeSelection.WrapperSubsetEval
Set the classifier to use for accuracy estimation
setClassifierName(String). Method in class weka.experiment.ClassifierSplitEvaluator
Set the Classifier to use, given it's class name.
setClassifierName(String). Method in class weka.experiment.RegressionSplitEvaluator
Set the Classifier to use, given it's class name.
setClassifiers(Classifier[]). Method in class weka.classifiers.MultiScheme
Sets the list of possible classifers to choose from.
setClassIndex(int). Method in class weka.classifiers.BVDecompose
Sets index of attribute to discretize on
setClassIndex(int). Method in class weka.core.Instances
Sets the class index of the set.
setClassMissing(). Method in class weka.core.Instance
Sets the class value of an instance to be "missing".
setClassName(String). Method in class weka.filters.NumericTransformFilter
Sets the class containing the transformation method.
setClassType(Class). Method in class weka.gui.GenericObjectEditor
Sets the class of values that can be edited.
setClassValue(double). Method in class weka.core.Instance
Sets the class value of an instance to the given value (internal floating-point format).
setClassValue(String). Method in class weka.core.Instance
Sets the class value of an instance to the given value.
setClearEachDataset(boolean). Method in class weka.gui.streams.InstanceViewer
setClusterer(Clusterer). Method in class weka.clusterers.ClusterEvaluation
set the clusterer
setClusterer(Clusterer). Method in class weka.clusterers.DistributionMetaClusterer
Set the base clusterer.
setColor(Color). Method in class weka.gui.treevisualizer.Node
Set the value of color.
setColourIndex(int). Method in class weka.gui.visualize.VisualizePanel
Sets the index used for colouring.
setColours(FastVector). Method in class weka.gui.visualize.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
Sets a column of the matrix to the given column.
setConfidenceFactor(float). Method in class weka.classifiers.j48.J48
Set the value of CF.
setConfidenceFactor(float). Method in class weka.classifiers.j48.PART
Set the value of CF.
setConnectPoints(boolean[]). Method in class weka.gui.visualize.PlotData2D
Set whether consecutive points should be connected by lines
setConnectPoints(FastVector). Method in class weka.gui.visualize.PlotData2D
Set whether consecutive points should be connected by lines
setCostMatrix(CostMatrix). Method in class weka.classifiers.CostSensitiveClassifier
Sets the misclassification cost matrix.
setCostMatrix(CostMatrix). Method in class weka.classifiers.MetaCost
Sets the misclassification cost matrix.
setCostMatrixSource(SelectedTag). Method in class weka.classifiers.CostSensitiveClassifier
Sets the source location of the cost matrix.
setCostMatrixSource(SelectedTag). Method in class weka.classifiers.MetaCost
Sets the source location of the cost matrix.
setCrossoverProb(double). Method in class weka.attributeSelection.GeneticSearch
set the probability of crossover
setCrossVal(int). Method in class weka.classifiers.DecisionTable
Sets the number of folds for cross validation (1 = leave one out)
setCrossValidate(boolean). Method in class weka.classifiers.IBk
Sets whether hold-one-out cross-validation will be used to select the best k value
setCustomColour(Color). Method in class weka.gui.visualize.PlotData2D
Set a custom colour to use for this plot.
setCutoff(int). Method in class weka.clusterers.Cobweb
set the cutoff
setCVisible(boolean). Method in class weka.gui.treevisualizer.Node
Sets all the children of this node either to visible or invisible
setDatabaseURL(String). Method in class weka.experiment.DatabaseUtils
Set the value of DatabaseURL.
setDataFileName(String). Method in class weka.classifiers.BVDecompose
Sets the maximum number of boost iterations
setDataset(Instances). Method in class weka.core.Instance
Sets the reference to the dataset.
setDatasetKeyColumns(Range). Method in class weka.experiment.PairedTTester
Set the value of DatasetKeyColumns.
setDatasetKeyFromDialog(). Method in class weka.gui.experiment.ResultsPanel
setDatasets(DefaultListModel). Method in class weka.experiment.Experiment
Set the datasets to use in the experiment
setDatasets(DefaultListModel). Method in class weka.experiment.RemoteExperiment
Set the datasets to use in the experiment
setDebug(boolean). Method in class weka.classifiers.AdaBoostM1
Set debugging mode
setDebug(boolean). Method in class weka.classifiers.AdditiveRegression
Set whether debugging output is produced.
setDebug(boolean). Method in class weka.filters.AttributeExpressionFilter
Set debug mode.
setDebug(boolean). Method in class weka.classifiers.BVDecompose
Sets debugging mode
setDebug(boolean). Method in class weka.classifiers.CheckClassifier
Set debugging mode
setDebug(boolean). Method in class weka.classifiers.CVParameterSelection
Sets debugging mode
setDebug(boolean). Method in class weka.clusterers.EM
Set debug mode - verbose output
setDebug(boolean). Method in class weka.classifiers.IBk
Set the value of Debug.
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
setDebug(boolean). Method in class weka.classifiers.LinearRegression
Controls whether debugging output will be printed
setDebug(boolean). Method in class weka.classifiers.Logistic
Sets whether debugging output will be printed.
setDebug(boolean). Method in class weka.classifiers.LogitBoost
Set debugging mode
setDebug(boolean). Method in class weka.classifiers.LWR
Sets whether debugging output should be produced
setDebug(boolean). Method in class weka.classifiers.MultiScheme
Set debugging mode
setDebug(boolean). Method in class weka.attributeSelection.RaceSearch
Set whether verbose output should be generated.
setDebug(boolean). Method in class weka.classifiers.RegressionByDiscretization
Sets whether debugging output will be printed
setDecay(boolean). Method in class weka.classifiers.neural.NeuralNetwork
setDefaultValue(). Method in class weka.gui.GenericObjectEditor
Sets the current object to be the default, taken as the first item in the chooser
setDelta(double). Method in class weka.associations.Apriori
Set the value of delta.
setDesignatedClass(SelectedTag). Method in class weka.classifiers.ThresholdSelector
Sets the method to determine which class value to optimize.
setDirection(SelectedTag). Method in class weka.attributeSelection.BestFirst
Set the search direction
setDisplayRules(boolean). Method in class weka.classifiers.DecisionTable
Sets whether rules are to be printed
setDistanceWeighting(SelectedTag). Method in class weka.classifiers.IBk
Sets the distance weighting method used.
setDistributionClassifier(DistributionClassifier). Method in class weka.classifiers.MultiClassClassifier
Set the base classifier.
setDistributionClassifier(DistributionClassifier). Method in class weka.classifiers.ThresholdSelector
Set the DistributionClassifier for which threshold is set.
setDistributionSpread(double). Method in class weka.filters.SpreadSubsampleFilter
Sets the value for the distribution spread
setDontStratifyData(boolean). Method in class weka.filters.SplitDatasetFilter
Sets whether stratification is not performed.
setDoXval(boolean). Method in class weka.clusterers.ClusterEvaluation
set whether or not to do cross validation
setElement(int, int, double). Method in class weka.core.Matrix
Sets an element of the matrix to the given value.
setElementAt(Object, int). Method in class weka.core.FastVector
Sets the element at the given index.
setEnabled(boolean). Method in class weka.gui.GenericObjectEditor
Sets whether the editor is "enabled", meaning that the current values will be painted.
setEntropicAutoBlend(boolean). Method in class weka.classifiers.kstar.KStar
Set whether entropic blending is to be used.
setEpsilon(double). Method in class weka.classifiers.SMO
Set the value of epsilon.
setErrorCorrectionMode(SelectedTag). Method in class weka.classifiers.MultiClassClassifier
Sets the error correction mode used.
setEvaluationMode(SelectedTag). Method in class weka.classifiers.ThresholdSelector
Sets the evaluation mode used.
setEvaluator(ASEvaluation). Method in class weka.classifiers.AttributeSelectedClassifier
Sets the attribute evaluator
setEvaluator(ASEvaluation). Method in class weka.attributeSelection.AttributeSelection
set the attribute/subset evaluator
setEvaluator(ASEvaluation). Method in class weka.filters.AttributeSelectionFilter
set a string holding the name of a attribute/subset evaluator
setExecutionStatus(int). Method in class weka.experiment.TaskStatusInfo
Set the execution status of this Task.
setExpectedResultsPerAverage(int). Method in class weka.experiment.AveragingResultProducer
Set the value of ExpectedResultsPerAverage.
setExperiment(Experiment). Method in class weka.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(Experiment). Method in class weka.experiment.RemoteExperimentSubTask
Set the experiment for this sub task
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(RemoteExperiment). Method in class weka.gui.experiment.HostListPanel
Tells the panel to act on a new experiment.
setExponent(double). Method in class weka.classifiers.SMO
Set the value of exponent.
setExponent(double). Method in class weka.classifiers.VotedPerceptron
Set the value of exponent.
setExpression(String). Method in class weka.filters.AttributeExpressionFilter
Set the expression to apply
setFalseNegative(double). Method in class weka.classifiers.evaluation.TwoClassStats
Sets the number of positive instances predicted as negative
setFalsePositive(double). Method in class weka.classifiers.evaluation.TwoClassStats
Sets the number of negative instances predicted as positive
setFillWithMissing(boolean). Method in class weka.filters.AbstractTimeSeriesFilter
Sets whether missing values should be used rather than removing instances where the translated value is not known (due to border effects).
setFilter(Filter). Method in class weka.classifiers.FilteredClassifier
Sets the filter
setFindNumBins(boolean). Method in class weka.filters.DiscretizeFilter
Set the value of FindNumBins.
setFirstValueIndex(int). Method in class weka.filters.MergeTwoValuesFilter
Sets index of the first value used.
setFirstValueIndex(int). Method in class weka.filters.SwapAttributeValuesFilter
Sets index of the first value used.
setFold(int). Method in class weka.filters.SplitDatasetFilter
Selects a fold.
setFolds(int). Method in class weka.attributeSelection.AttributeSelection
set the number of folds for cross validation
setFolds(int). Method in class weka.clusterers.ClusterEvaluation
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
setFoldsType(SelectedTag). Method in class weka.attributeSelection.RaceSearch
Set the xfold type
setGenerateRanking(boolean). Method in class weka.attributeSelection.ForwardSelection
Records whether the user has requested a ranked list of attributes.
setGenerateRanking(boolean). Method in class weka.attributeSelection.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.
setGlobalBlend(int). Method in class weka.classifiers.kstar.KStar
Set the global blend parameter
setGUI(boolean). Method in class weka.classifiers.neural.NeuralNetwork
This will set whether A GUI is brought up to allow interaction by the user with the neural network during training.
setHandleRightClicks(boolean). Method in class weka.gui.ResultHistoryPanel
Set whether the result history list should handle right clicks or whether the parent object will handle them.
setHiddenLayers(String). Method in class weka.classifiers.neural.NeuralNetwork
This will set what the hidden layers are made up of when auto build is enabled.
setHighlight(String). Method in class weka.gui.treevisualizer.TreeVisualizer
Set the highlight for the node with the given id
setHoldOutFile(File). Method in class weka.attributeSelection.ClassifierSubsetEval
Set the file that contains hold out/test instances
setInputFormat(Instances). Method in class weka.filters.AbstractTimeSeriesFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.AddFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.AllFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.AttributeExpressionFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.AttributeFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.AttributeTypeFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.CopyAttributesFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.DiscretizeFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.EmptyAttributeFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.Filter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.FirstOrderFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.InstanceFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.MakeIndicatorFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.MergeTwoValuesFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.NominalToBinaryFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.NonSparseToSparseFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.NormalizationFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.NullFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.NumericToBinaryFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.NumericTransformFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.ObfuscateFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.RandomizeFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.ReplaceMissingValuesFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.ResampleFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.SparseToNonSparseFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.SplitDatasetFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.SpreadSubsampleFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.StringToNominalFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.SwapAttributeValuesFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.TimeSeriesDeltaFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.TimeSeriesTranslateFilter
Sets the format of the input instances.
setInstanceRange(int). Method in class weka.filters.AbstractTimeSeriesFilter
Sets the number of instances forward to translate values between.
setInstances(Instances). Method in class weka.gui.explorer.AssociationsPanel
Tells the panel to use a new set of instances.
setInstances(Instances). Method in class weka.gui.visualize.AttributePanel
This sets the instances to be drawn into the attribute panel
setInstances(Instances). Method in class weka.gui.AttributeSelectionPanel
Sets the instances who's attribute names will be displayed.
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.AttributeSummaryPanel
Tells the panel to use a new set of instances.
setInstances(Instances). Method in class weka.experiment.AveragingResultProducer
Sets the dataset that results will be obtained for.
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 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.gui.InstancesSummaryPanel
Tells the panel to use a new set of instances.
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.gui.visualize.Plot2D
Sets the master plot from a set 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 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.SetInstancesPanel
Updates the set of instances that is currently held by the panel
setInstances(Instances). Method in class weka.gui.visualize.VisualizePanel
Tells the panel to use a new set of instances.
setInstancesFromFileQ(). Method in class weka.gui.SetInstancesPanel
Queries the user for a file to load instances from, then loads the instances in a background process.
setInstancesFromURLQ(). Method in class weka.gui.SetInstancesPanel
Queries the user for a URL to load instances from, then loads the instances in a background process.
setInstancesIndices(String). Method in class weka.filters.SplitDatasetFilter
Sets the ranges of instances to be selected.
SetInstancesPanel(). Constructor for class weka.gui.SetInstancesPanel
Create the panel.
setInvert(boolean). Method in class weka.core.Range
Sets whether the range sense is inverted, i.e.
setInvertSelection(boolean). Method in class weka.filters.AbstractTimeSeriesFilter
Set whether selected columns should be removed or kept.
setInvertSelection(boolean). Method in class weka.filters.AttributeFilter
Set whether selected columns should be removed or kept.
setInvertSelection(boolean). Method in class weka.filters.CopyAttributesFilter
Set whether selected columns should be removed or kept.
setInvertSelection(boolean). Method in class weka.filters.DiscretizeFilter
Sets whether selected columns should be removed or kept.
setInvertSelection(boolean). Method in class weka.filters.InstanceFilter
Set whether selected values should be removed or kept.
setInvertSelection(boolean). Method in class weka.filters.NumericTransformFilter
Set whether selected columns should be transformed or not.
setInvertSelection(boolean). Method in class weka.filters.SplitDatasetFilter
Sets if selection is to be inverted.
setJitter(int). Method in class weka.gui.visualize.Plot2D
Set level of jitter and repaint the plot using the new jitter value
setKeyFieldName(String). Method in class weka.experiment.AveragingResultProducer
Set the value of KeyFieldName.
setKNN(int). Method in class weka.classifiers.IBk
Set the number of neighbours the learner is to use.
setKNN(int). Method in class weka.classifiers.LWR
Sets the number of neighbours used for kernel bandwidth setting.
setLearningRate(double). Method in class weka.classifiers.neural.NeuralNetwork
The learning rate can be set using this command.
setLocallyPredictive(boolean). Method in class weka.attributeSelection.CfsSubsetEval
Include locally predictive attributes
setLog(Logger). Method in class weka.gui.explorer.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.PreprocessPanel
Sets the Logger to receive informational messages
setLog(Logger). Method in class weka.gui.visualize.VisualizePanel
Sets the Logger to receive informational messages
setLowerBoundMinSupport(double). Method in class weka.associations.Apriori
Set the value of lowerBoundMinSupport.
setLowerOrderTerms(boolean). Method in class weka.classifiers.SMO
Set whether lower-order terms are to be used.
setLowerSize(int). Method in class weka.experiment.LearningRateResultProducer
Set the value of LowerSize.
setMakeBinary(boolean). Method in class weka.filters.DiscretizeFilter
Sets whether binary attributes should be made for discretized ones.
setMasterPlot(PlotData2D). Method in class weka.gui.visualize.Plot2D
Set the master plot.
setMasterPlot(PlotData2D). Method in class weka.gui.visualize.VisualizePanel
Set the master plot for the visualize panel
setMatchMissingValues(boolean). Method in class weka.filters.InstanceFilter
Sets whether missing values are counted as a match.
setMaxCount(double). Method in class weka.filters.SpreadSubsampleFilter
Sets the value for the max count
setMaxGenerations(int). Method in class weka.attributeSelection.GeneticSearch
set the number of generations to evaluate
setMaxIterations(int). Method in class weka.classifiers.AdaBoostM1
Set the maximum number of boost iterations
setMaxIterations(int). Method in class weka.clusterers.EM
Set the maximum number of iterations to perform
setMaxIterations(int). Method in class weka.classifiers.LogitBoost
Set the maximum number of boost iterations
setMaxK(int). Method in class weka.classifiers.VotedPerceptron
Set the value of maxK.
setMaxModels(int). Method in class weka.classifiers.AdditiveRegression
Set the maximum number of models to generate
setMaxStale(int). Method in class weka.classifiers.DecisionTable
Sets the number of non improving decision tables to consider before abandoning the search.
setMeanSquared(boolean). Method in class weka.classifiers.IBk
Sets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
setMetaClassifier(Classifier). Method in class weka.classifiers.Stacking
Adds meta classifier
setMethod(NeuralMethod). Method in class weka.classifiers.neural.NeuralNode
Set how this node should operate (note that the neural method has no internal state, so the same object can be used by any number of nodes.
setMethodName(String). Method in class weka.filters.NumericTransformFilter
Set the transformation method.
setMetricType(SelectedTag). Method in class weka.associations.Apriori
Set the metric type for ranking rules
setMinBucketSize(int). Method in class weka.classifiers.OneR
Set the value of minBucketSize.
setMinimizeExpectedCost(boolean). Method in class weka.classifiers.CostSensitiveClassifier
Set the value of MinimizeExpectedCost.
setMinMetric(double). Method in class weka.associations.Apriori
Set the value of minConfidence.
setMinNumObj(int). Method in class weka.classifiers.j48.J48
Set the value of minNumObj.
setMinNumObj(int). Method in class weka.classifiers.j48.PART
Set the value of minNumObj.
setMinStdDev(double). Method in class weka.clusterers.EM
Set the minimum value for standard deviation when calculating normal density.
setMissing(Attribute). Method in class weka.core.Instance
Sets a specific value to be "missing".
setMissing(int). Method in class weka.core.Instance
Sets a specific value to be "missing".
setMissingMerge(boolean). Method in class weka.attributeSelection.ChiSquaredAttributeEval
distribute the counts for missing values across observed values
setMissingMerge(boolean). Method in class weka.attributeSelection.GainRatioAttributeEval
distribute the counts for missing values across observed values
setMissingMerge(boolean). Method in class weka.attributeSelection.InfoGainAttributeEval
distribute the counts for missing values across observed values
setMissingMerge(boolean). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
distribute the counts for missing values across observed values
setMissingMode(int). Method in class weka.classifiers.kstar.KStarNumericAttribute
Set the missing value mode.
setMissingMode(SelectedTag). Method in class weka.classifiers.kstar.KStar
Sets the method to use for handling missing values.
setMissingSeperate(boolean). Method in class weka.attributeSelection.CfsSubsetEval
Treat missing as a seperate value
setModelType(SelectedTag). Method in class weka.classifiers.m5.M5Prime
Set the value of Model.
setModifyHeader(boolean). Method in class weka.filters.InstanceFilter
Sets whether the header will be modified when selecting on nominal attributes.
setMomentum(double). Method in class weka.classifiers.neural.NeuralNetwork
The momentum can be set using this command.
setMutationProb(double). Method in class weka.attributeSelection.GeneticSearch
set the probability of mutation
setName(String). Method in class weka.filters.AttributeExpressionFilter
Set the name for the new attribute.
setName(String). Method in class weka.gui.visualize.VisualizePanel
Set a name for this plot
setNominalIndices(String). Method in class weka.filters.InstanceFilter
Set which nominal labels are to be included in the selection.
setNominalIndicesArr(int[]). Method in class weka.filters.InstanceFilter
Set which values of a nominal attribute are to be used for selection.
setNominalLabels(String). Method in class weka.filters.AddFilter
Set the labels for nominal attribute creation.
setNominalToBinaryFilter(boolean). Method in class weka.classifiers.neural.NeuralNetwork
setNoNormalization(boolean). Method in class weka.classifiers.IBk
Set whether normalization is turned off.
setNormalize(boolean). Method in class weka.attributeSelection.PrincipalComponents
Set whether input data will be normalized.
setNormalizeAttributes(boolean). Method in class weka.classifiers.neural.NeuralNetwork
setNormalizeData(boolean). Method in class weka.classifiers.SMO
Set whether data is to be normalized.
setNormalizeNumericClass(boolean). Method in class weka.classifiers.neural.NeuralNetwork
setNotes(String). Method in class weka.experiment.Experiment
Set the user notes.
setNotes(String). Method in class weka.experiment.RemoteExperiment
Set the user notes.
setNumBins(int). Method in class weka.classifiers.RegressionByDiscretization
Sets the number of bins the class attribute will be discretized into.
setNumClusters(int). Method in class weka.clusterers.EM
Set the number of clusters (-1 to select by CV).
setNumClusters(int). Method in class weka.clusterers.SimpleKMeans
set the number of clusters to generate
setNumeric(boolean). Method in class weka.filters.MakeIndicatorFilter
Sets if the new Attribute is to be numeric.
setNumFolds(int). Method in class weka.experiment.CrossValidationResultProducer
Set the value of NumFolds.
setNumFolds(int). Method in class weka.classifiers.CVParameterSelection
Set the number of folds used for cross-validation.
setNumFolds(int). Method in class weka.classifiers.j48.J48
Set the value of numFolds.
setNumFolds(int). Method in class weka.classifiers.MultiScheme
Sets the number of folds for cross-validation.
setNumFolds(int). Method in class weka.classifiers.j48.PART
Set the value of numFolds.
setNumFolds(int). Method in class weka.filters.SplitDatasetFilter
Sets the number of folds the dataset is split into.
setNumFolds(int). Method in class weka.classifiers.Stacking
Sets the number of folds for the cross-validation.
setNumIterations(int). Method in class weka.classifiers.Bagging
Sets the number of bagging iterations
setNumIterations(int). Method in class weka.classifiers.MetaCost
Sets the number of bagging iterations
setNumIterations(int). Method in class weka.classifiers.VotedPerceptron
Set the value of NumIterations.
setNumNeighbours(int). Method in class weka.attributeSelection.ReliefFAttributeEval
Set the number of nearest neighbours
setNumOfBoostingIterations(int). Method in class weka.classifiers.adtree.ADTree
Sets the number of boosting iterations.
setNumRules(int). Method in class weka.associations.Apriori
Set the value of numRules.
setNumToSelect(int). Method in class weka.attributeSelection.ForwardSelection
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.
setNumXValFolds(int). Method in class weka.classifiers.ThresholdSelector
Set the number of folds used for cross-validation.
setOnDemandDirectory(File). Method in class weka.classifiers.CostSensitiveClassifier
Sets the directory that will be searched for cost files when loading on demand.
setOnDemandDirectory(File). Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Sets the directory that will be searched for cost files when loading on demand.
setOnDemandDirectory(File). Method in class weka.classifiers.MetaCost
Sets the directory that will be searched for cost files when loading on demand.
setOptimizeBins(boolean). Method in class weka.classifiers.RegressionByDiscretization
Sets whether the discretizer optimizes the number of bins
setOptions(int, int, int). Method in class weka.classifiers.kstar.KStarNominalAttribute
Sets the options.
setOptions(int, int, int). Method in class weka.classifiers.kstar.KStarNumericAttribute
Set options.
setOptions(String[]). Method in class weka.filters.AbstractTimeSeriesFilter
Parses a given list of options controlling the behaviour of this object.
setOptions(String[]). Method in class weka.classifiers.AdaBoostM1
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.AddFilter
Parses a list of options for this object.
setOptions(String[]). Method in class weka.classifiers.AdditiveRegression
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.adtree.ADTree
Parses a given list of options.
setOptions(String[]). Method in class weka.associations.Apriori
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.AttributeExpressionFilter
Parses a list of options for this object.
setOptions(String[]). Method in class weka.filters.AttributeFilter
Parses a given list of options controlling the behaviour of this object.
setOptions(String[]). Method in class weka.classifiers.AttributeSelectedClassifier
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.AttributeSelectionFilter
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.AttributeTypeFilter
Parses a given list of options controlling the behaviour of this object.
setOptions(String[]). Method in class weka.experiment.AveragingResultProducer
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.Bagging
Parses a given list of options.
setOptions(String[]). Method in class weka.attributeSelection.BestFirst
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.attributeSelection.CfsSubsetEval
Parses and sets a given list of options.
setOptions(String[]). Method in class weka.classifiers.CheckClassifier
Parses a given list of options.
setOptions(String[]). Method in class weka.attributeSelection.ChiSquaredAttributeEval
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.ClassificationViaRegression
Sets a given list of options.
setOptions(String[]). Method in class weka.experiment.ClassifierSplitEvaluator
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.clusterers.Cobweb
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.CopyAttributesFilter
Parses a given list of options controlling the behaviour of this object.
setOptions(String[]). Method in class weka.classifiers.CostSensitiveClassifier
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.classifiers.CVParameterSelection
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.classifiers.DecisionTable
Parses the options for this object.
setOptions(String[]). Method in class weka.filters.DiscretizeFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.classifiers.DistributionMetaClassifier
Parses a given list of options.
setOptions(String[]). Method in class weka.clusterers.DistributionMetaClusterer
Parses a given list of options.
setOptions(String[]). Method in class weka.clusterers.EM
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.experiment.Experiment
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.FilteredClassifier
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.FirstOrderFilter
Parses a given list of options controlling the behaviour of this object.
setOptions(String[]). Method in class weka.attributeSelection.ForwardSelection
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.classifiers.IBk
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.filters.InstanceFilter
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.classifiers.j48.J48
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.kstar.KStar
Parses a given list of options.
setOptions(String[]). Method in class weka.experiment.LearningRateResultProducer
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.LinearRegression
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.Logistic
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.LogitBoost
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.LWR
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.m5.M5Prime
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.MakeIndicatorFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.filters.MergeTwoValuesFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.classifiers.MetaCost
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.MultiClassClassifier
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.MultiScheme
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.NaiveBayes
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.neural.NeuralNetwork
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.NominalToBinaryFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.filters.NumericTransformFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.classifiers.OneR
Parses a given list of options.
setOptions(String[]). Method in interface weka.core.OptionHandler
Sets the OptionHandler's options using the given list.
setOptions(String[]). Method in class weka.experiment.PairedTTester
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.j48.PART
Parses a given list of options.
setOptions(String[]). Method in class weka.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.filters.RandomizeFilter
Parses a list of options for this object.
setOptions(String[]). Method in class weka.attributeSelection.RandomSearch
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.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.classifiers.RegressionByDiscretization
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.attributeSelection.ReliefFAttributeEval
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.ResampleFilter
Parses a list of options for this object.
setOptions(String[]). Method in class weka.clusterers.SimpleKMeans
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.SMO
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.SplitDatasetFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.filters.SpreadSubsampleFilter
Parses a list of options for this object.
setOptions(String[]). Method in class weka.classifiers.Stacking
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.StringToNominalFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.filters.SwapAttributeValuesFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.ThresholdSelector
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.VFI
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.VotedPerceptron
Parses a given list of options.
setOptions(String[]). Method in class weka.attributeSelection.WrapperSubsetEval
Parses a given list of options.
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.
setParent(Edge). Method in class weka.gui.treevisualizer.Node
Set the value of parent.
setPlotCompanion(Plot2DCompanion). Method in class weka.gui.visualize.Plot2D
Set a companion class.
setPlotList(FastVector). Method in class weka.gui.visualize.LegendPanel
Set the list of plots to generate legend entries for
setPlotName(String). Method in class weka.gui.visualize.PlotData2D
Set the name of this plot
setPopulationSize(int). Method in class weka.attributeSelection.GeneticSearch
set the population size
setPreprocess(PreprocessPanel). Method in class weka.gui.explorer.ClassifierPanel
Sets the preprocess panel through which user selected filters can be applied to any supplied test data
setPreprocess(PreprocessPanel). Method in class weka.gui.explorer.ClustererPanel
Sets the preprocess panel through which user selected filters can be applied to any supplied test data
setPriors(Instances). Method in class weka.classifiers.Evaluation
Sets the class prior probabilities
setProduceLatex(boolean). Method in class weka.experiment.PairedTTester
Set whether latex is output
setPropertyArray(Object). Method in class weka.experiment.Experiment
Sets the array of values to set the custom property to.
setPropertyArray(Object). Method in class weka.experiment.RemoteExperiment
Sets the array of values to set the custom property to.
setPropertyPath(PropertyNode[]). Method in class weka.experiment.Experiment
Sets the path of properties taken to get to the custom property to iterate over.
setPropertyPath(PropertyNode[]). Method in class weka.experiment.RemoteExperiment
Sets the path of properties taken to get to the custom property to iterate over.
setPruningFactor(double). Method in class weka.classifiers.m5.M5Prime
Set the value of PruningFactor.
setQuery(String). Method in class weka.experiment.InstanceQuery
Set the query to execute against the database
setRaceType(SelectedTag). Method in class weka.attributeSelection.RaceSearch
Set the race type
setRandomizeData(boolean). Method in class weka.experiment.RandomSplitResultProducer
Set to true if dataset is to be randomized
setRandomSeed(int). Method in class weka.classifiers.adtree.ADTree
Sets random seed for a random walk.
setRandomSeed(int). Method in class weka.filters.RandomizeFilter
Set the random number generator seed value.
setRandomSeed(int). Method in class weka.filters.ResampleFilter
Sets the random number seed.
setRandomSeed(int). Method in class weka.filters.SpreadSubsampleFilter
Sets the random number seed.
setRandomSeed(long). Method in class weka.classifiers.neural.NeuralNetwork
This seeds the random number generator, that is used when a random number is needed for the network.
setRandomWidthFactor(double). Method in class weka.classifiers.MultiClassClassifier
Sets the multiplier when generating random codes.
setRangeCorrection(SelectedTag). Method in class weka.classifiers.ThresholdSelector
Sets the confidence range correction mode used.
setRanges(String). Method in class weka.core.Range
Sets the ranges from a string representation.
setRanking(boolean). Method in class weka.attributeSelection.AttributeSelection
produce a ranking (if possible with the set search and evaluator)
setRawOutput(boolean). Method in class weka.experiment.CrossValidationResultProducer
Set to true if raw split evaluator output is to be saved
setRawOutput(boolean). Method in class weka.experiment.RandomSplitResultProducer
Set to true if raw split evaluator output is to be saved
setReducedErrorPruning(boolean). Method in class weka.classifiers.j48.J48
Set the value of reducedErrorPruning.
setReducedErrorPruning(boolean). Method in class weka.classifiers.j48.PART
Set the value of reducedErrorPruning.
setRefer(String). Method in class weka.gui.treevisualizer.Node
Set the value of refer.
setRelationName(String). Method in class weka.core.Instances
Sets the relation's name.
setRemoveAllMissingCols(boolean). Method in class weka.associations.Apriori
Remove columns containing all missing values.
setReportFrequency(int). Method in class weka.attributeSelection.GeneticSearch
set how often reports are generated
setRescaleKernel(boolean). Method in class weka.classifiers.SMO
Set whether kernel is to be rescaled.
setReset(boolean). Method in class weka.classifiers.neural.NeuralNetwork
This sets the network up to be able to reset itself with the current settings and the learning rate at half of what it is currently.
setResultKeyFromDialog(). Method in class weka.gui.experiment.ResultsPanel
setResultListener(ResultListener). Method in 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.
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.
setRoot(boolean). Method in class weka.gui.treevisualizer.Node
Set the value of root.
setRow(int, double[]). Method in class weka.core.Matrix
Sets a row of the matrix to the given row.
setRsource(String). Method in class weka.gui.treevisualizer.Edge
Set the value of rsource.
setRtarget(String). Method in class weka.gui.treevisualizer.Edge
Set the value of rtarget.
setRunColumn(int). Method in class weka.experiment.PairedTTester
Set the value of RunColumn.
setRunLower(int). Method in class weka.experiment.Experiment
Set the lower run number for the experiment.
setRunLower(int). Method in class weka.experiment.RemoteExperiment
Set the lower run number for the experiment.
setRunUpper(int). Method in class weka.experiment.Experiment
Set the upper run number for the experiment.
setRunUpper(int). Method in class weka.experiment.RemoteExperiment
Set the upper run number for the experiment.
setSampleSize(int). Method in class weka.attributeSelection.ReliefFAttributeEval
Set the number of instances to sample for attribute estimation
setSampleSizePercent(double). Method in class weka.filters.ResampleFilter
Sets the size of the subsample, as a percentage of the original set.
setSaveInstanceData(boolean). Method in class weka.classifiers.adtree.ADTree
Sets whether the tree is to save instance data.
setSaveInstanceData(boolean). Method in class weka.classifiers.j48.J48
Set whether instance data is to be saved.
setSearch(ASSearch). Method in class weka.classifiers.AttributeSelectedClassifier
Sets the search method
setSearch(ASSearch). Method in class weka.attributeSelection.AttributeSelection
set the search method
setSearch(ASSearch). Method in class weka.filters.AttributeSelectionFilter
Set as string holding the name of a search class
setSearchPath(SelectedTag). Method in class weka.classifiers.adtree.ADTree
Sets the method of searching the tree for a new insertion.
setSearchPercent(double). Method in class weka.attributeSelection.RandomSearch
set the percentage of the search space to consider
setSearchTermination(int). Method in class weka.attributeSelection.BestFirst
Set the numnber of non-improving nodes to consider before terminating search.
setSecondValueIndex(int). Method in class weka.filters.MergeTwoValuesFilter
Sets index of the second value used.
setSecondValueIndex(int). Method in class weka.filters.SwapAttributeValuesFilter
Sets index of the second value used.
setSeed(int). Method in class weka.classifiers.AdaBoostM1
Set seed for resampling.
setSeed(int). Method in class weka.attributeSelection.AttributeSelection
set the seed for use in cross validation
setSeed(int). Method in class weka.classifiers.Bagging
Set the seed for random number generation.
setSeed(int). Method in class weka.classifiers.BVDecompose
Sets the random number seed
setSeed(int). Method in class weka.clusterers.ClusterEvaluation
set the seed to use for cross validation
setSeed(int). Method in class weka.classifiers.CostSensitiveClassifier
Set seed for resampling.
setSeed(int). Method in class weka.classifiers.CVParameterSelection
Sets the seed for random number generation.
setSeed(int). Method in class weka.clusterers.EM
Set the random number seed
setSeed(int). Method in class weka.classifiers.evaluation.EvaluationUtils
Sets the seed for randomization during cross-validation
setSeed(int). Method in class weka.attributeSelection.GeneticSearch
set the seed for random number generation
setSeed(int). Method in class weka.classifiers.LogitBoost
Set seed for resampling.
setSeed(int). Method in class weka.classifiers.MetaCost
Set seed for resampling.
setSeed(int). Method in class weka.classifiers.MultiScheme
Sets the seed for random number generation.
setSeed(int). Method in class weka.attributeSelection.ReliefFAttributeEval
Set the random number seed for randomly sampling instances.
setSeed(int). Method in class weka.clusterers.SimpleKMeans
Set the random number seed
setSeed(int). Method in class weka.classifiers.Stacking
Sets the seed for random number generation.
setSeed(int). Method in class weka.classifiers.ThresholdSelector
Sets the seed for random number generation.
setSeed(int). Method in class weka.classifiers.VotedPerceptron
Set the value of Seed.
setSeed(int). Method in class weka.attributeSelection.WrapperSubsetEval
Set the seed to use for cross validation
setSeed(long). Method in class weka.filters.SplitDatasetFilter
Sets the random number seed for shuffling the dataset.
setSelectionThreshold(double). Method in class weka.attributeSelection.RaceSearch
Set the threshold by which the AttributeSelection module can discard attributes.
setShape(int). Method in class weka.gui.treevisualizer.Node
Set the value of shape.
setShapes(FastVector). Method in class weka.gui.visualize.VisualizePanel
This will set the shapes for the instances.
setShapeSize(FastVector). Method in class weka.gui.visualize.PlotData2D
Set the shape sizes for the plot data
setShapeSize(int[]). Method in class weka.gui.visualize.PlotData2D
Set the shape sizes for the plot data
setShapeType(FastVector). Method in class weka.gui.visualize.PlotData2D
Set the shape type for the plot data
setShapeType(int[]). Method in class weka.gui.visualize.PlotData2D
Set the shape type for the plot data
setShowStdDevs(boolean). Method in class weka.experiment.PairedTTester
Set whether standard deviations are displayed or not.
setShrinkage(double). Method in class weka.classifiers.AdditiveRegression
Set the shrinkage parameter
setSigma(int). Method in class weka.attributeSelection.ReliefFAttributeEval
Sets the sigma value.
setSignificanceLevel(double). Method in class weka.associations.Apriori
Set the value of significanceLevel.
setSignificanceLevel(double). Method in class weka.experiment.PairedTTester
Set the value of SignificanceLevel.
setSignificanceLevel(double). Method in class weka.attributeSelection.RaceSearch
Sets the significance level to use
setSIndex(int). Method in class weka.gui.visualize.VisualizePanel
Set the shape for creating splits.
setSingle(String). Method in class weka.gui.ResultHistoryPanel
Sets the single-click display to view the named result.
setSource(File). Method in class weka.core.converters.AbstractLoader
Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File). Method in class weka.core.converters.ArffLoader
Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File). Method in class weka.core.converters.C45Loader
Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File). Method in class weka.core.converters.CSVLoader
Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File). Method in interface weka.core.converters.Loader
Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File). Method in class weka.core.converters.SerializedInstancesLoader
Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(InputStream). Method in class weka.core.converters.AbstractLoader
Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(InputStream). Method in class weka.core.converters.ArffLoader
Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(InputStream). Method in interface weka.core.converters.Loader
Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(InputStream). Method in class weka.core.converters.SerializedInstancesLoader
Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(Node). Method in class weka.gui.treevisualizer.Edge
Set the value of source.
setSparseData(boolean). Method in class weka.experiment.InstanceQuery
Sets whether data should be encoded as sparse instances
setSplitByDataSet(boolean). Method in class weka.experiment.RemoteExperiment
Set whether sub experiments are to be created on the basis of data set.
setSplitEvaluator(SplitEvaluator). Method in class weka.experiment.CrossValidationResultProducer
Set the SplitEvaluator.
setSplitEvaluator(SplitEvaluator). Method in class weka.experiment.RandomSplitResultProducer
Set the SplitEvaluator.
setSplitPoint(double). Method in class weka.filters.InstanceFilter
Split point to be used for selection on numeric attribute.
setSplitPoint(Instances). Method in class weka.classifiers.j48.BinC45Split
Sets split point to greatest value in given data smaller or equal to old split point.
setSplitPoint(Instances). Method in class weka.classifiers.j48.C45Split
Sets split point to greatest value in given data smaller or equal to old split point.
setStartSet(String). Method in class weka.attributeSelection.BestFirst
Sets a starting set of attributes for the search.
setStartSet(String). Method in class weka.attributeSelection.ExhaustiveSearch
Sets a starting set of attributes for the search.
setStartSet(String). Method in class weka.attributeSelection.ForwardSelection
Sets a starting set of attributes for the search.
setStartSet(String). Method in class weka.attributeSelection.GeneticSearch
Sets a starting set of attributes for the search.
setStartSet(String). Method in class weka.attributeSelection.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.
setStatusMessage(String). Method in class weka.experiment.TaskStatusInfo
Set the status message.
setStepSize(int). Method in class weka.experiment.LearningRateResultProducer
Set the value of StepSize.
setSubtreeRaising(boolean). Method in class weka.classifiers.j48.J48
Set the value of subtreeRaising.
setTarget(Node). Method in class weka.gui.treevisualizer.Edge
Set the value of target.
setTarget(Object). Method in class weka.gui.PropertySheetPanel
Sets a new target object for customisation.
setTaskResult(Object). Method in class weka.experiment.TaskStatusInfo
Set the returnable result for this task..
setTestBaseFromDialog(). Method in class weka.gui.experiment.ResultsPanel
setThreshold(double). Method in class weka.attributeSelection.AttributeSelection
set the threshold by which to select features from a ranked list
setThreshold(double). Method in class weka.attributeSelection.ForwardSelection
Set the threshold by which the AttributeSelection module can discard attributes.
setThreshold(double). Method in class weka.attributeSelection.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
setToleranceParameter(double). Method in class weka.classifiers.SMO
Set the value of tolerance parameter.
setTop(double). Method in class weka.gui.treevisualizer.Node
Set the value of top.
setTrainingTime(int). Method in class weka.classifiers.neural.NeuralNetwork
Set the number of training epochs to perform.
setTrainIterations(int). Method in class weka.classifiers.BVDecompose
Sets the maximum number of boost iterations
setTrainPercent(double). Method in class weka.experiment.RandomSplitResultProducer
Set the value of TrainPercent.
setTrainPoolSize(int). Method in class weka.classifiers.BVDecompose
Set the number of instances in the training pool.
setTransformBackToOriginal(boolean). Method in class weka.attributeSelection.PrincipalComponents
Sets whether the data should be transformed back to the original space
setTrueNegative(double). Method in class weka.classifiers.evaluation.TwoClassStats
Sets the number of negative instances predicted as negative
setTruePositive(double). Method in class weka.classifiers.evaluation.TwoClassStats
Sets the number of positive instances predicted as positive
setType(int). Method in class weka.classifiers.neural.NeuralConnection
setUnpruned(boolean). Method in class weka.classifiers.j48.J48
Set the value of unpruned.
setUpComboBoxes(Instances). Method in class weka.gui.visualize.VisualizePanel
SetupPanel(). Constructor for class weka.gui.experiment.SetupPanel
Creates the setup panel with no initial experiment.
SetupPanel(Experiment). Constructor for class weka.gui.experiment.SetupPanel
Creates the setup panel with the supplied initial experiment.
setUpper(int). Method in class weka.core.Range
Sets the value of "last".
setUpperBoundMinSupport(double). Method in class weka.associations.Apriori
Set the value of upperBoundMinSupport.
setUpperSize(int). Method in class weka.experiment.LearningRateResultProducer
Set the value of UpperSize.
setUseBetterEncoding(boolean). Method in class weka.filters.DiscretizeFilter
Sets whether better encoding is to be used for MDL.
setUseIBk(boolean). Method in class weka.classifiers.DecisionTable
Sets whether IBk should be used instead of the majority class
setUseKernelEstimator(boolean). Method in class weka.classifiers.NaiveBayes
Sets if kernel estimator is to be used.
setUseKononenko(boolean). Method in class weka.filters.DiscretizeFilter
Sets whether Kononenko's MDL criterion is to be used.
setUseLaplace(boolean). Method in class weka.classifiers.j48.J48
Set the value of useLaplace.
setUseMDL(boolean). Method in class weka.filters.DiscretizeFilter
Sets whether MDL will be used as the discretisation method.
setUsePropertyIterator(boolean). Method in class weka.experiment.Experiment
Sets whether the custom property iterator should be used.
setUsePropertyIterator(boolean). Method in class weka.experiment.RemoteExperiment
Sets whether the custom property iterator should be used.
setUseResampling(boolean). Method in class weka.classifiers.AdaBoostM1
Set resampling mode
setUseResampling(boolean). Method in class weka.classifiers.LogitBoost
Set resampling mode
setUseTraining(boolean). Method in class weka.attributeSelection.ClassifierSubsetEval
Set if training data is to be used instead of hold out/test data
setUseUnsmoothed(boolean). Method in class weka.classifiers.m5.M5Prime
Set the value of UseUnsmoothed.
setValidationSetSize(int). Method in class weka.classifiers.neural.NeuralNetwork
This will set the size of the validation set.
setValidationThreshold(int). Method in class weka.classifiers.neural.NeuralNetwork
This sets the threshold to use for when validation testing is being done.
setValue(Attribute, double). Method in class weka.core.Instance
Sets a specific value in the instance to the given value (internal floating-point format).
setValue(Attribute, String). Method in class weka.core.Instance
Sets a value of an nominal or string attribute to the given value.
setValue(double). Method in class weka.classifiers.adtree.PredictionNode
Sets the prediction value of the node.
setValue(int, double). Method in class weka.core.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, double). Method in class weka.core.SparseInstance
Sets a specific value in the instance to the given value (internal floating-point format).
setValue(int, String). Method in class weka.core.Instance
Sets a value of a nominal or string attribute to the given value.
setValue(Object). Method in class weka.gui.CostMatrixEditor
Sets the current object array.
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.
setValueIndex(int). Method in class weka.filters.MakeIndicatorFilter
Sets index of the indicator value.
setValueIndices(String). Method in class weka.filters.MakeIndicatorFilter
Sets indices of the indicator values.
setValueIndicesArray(int[]). Method in class weka.filters.MakeIndicatorFilter
Set which attributes are to be deleted (or kept if invert is true)
setValueSparse(int, double). Method in class weka.core.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
setVerbose(boolean). Method in class weka.attributeSelection.ExhaustiveSearch
set whether or not to output new best subsets as the search proceeds
setVerbose(boolean). Method in class weka.attributeSelection.RandomSearch
set whether or not to output new best subsets as the search proceeds
setVerbosity(int). Method in class weka.classifiers.m5.M5Prime
Set the value of Verbosity.
setWeight(double). Method in class weka.core.Instance
Sets the weight of an instance.
setWeightByConfidence(boolean). Method in class weka.classifiers.VFI
Set weighting by confidence
setWeightByDistance(boolean). Method in class weka.attributeSelection.ReliefFAttributeEval
Set the nearest neighbour weighting method
setWeightingKernel(int). Method in class weka.classifiers.LWR
Sets the kernel weighting method to use.
setWeightThreshold(int). Method in class weka.classifiers.AdaBoostM1
Set weight threshold
setWeightThreshold(int). Method in class weka.classifiers.LogitBoost
Set weight thresholding
setWindowSize(int). Method in class weka.classifiers.IBk
Sets the maximum number of instances allowed in the training pool.
setWorkingInstances(Instances). Method in class weka.gui.explorer.PreprocessPanel
Tells the panel to use a new working set of instances.
setWorkingInstancesFromFilters(). Method in class weka.gui.explorer.PreprocessPanel
Applies the current filters and attribute selection settings and sets the result as the working dataset.
setX(double). Method in class weka.classifiers.neural.NeuralConnection
setX(int). Method in class weka.gui.visualize.AttributePanel
shows which bar is the current x attribute.
setXindex(int). Method in class weka.gui.visualize.Plot2D
Set the index of the attribute to go on the x axis
setXindex(int). Method in class weka.gui.visualize.PlotData2D
Set the x index of the data.
setXIndex(int). Method in class weka.gui.visualize.VisualizePanel
Set the index of the attribute for the x axis
setXval(boolean). Method in class weka.attributeSelection.AttributeSelection
do a cross validation
setXY_VisualizeIndexes(int, int). Method in class weka.gui.explorer.ClassifierPanel
Set the default attributes to use on the x and y axis of a new visualization object.
setXY_VisualizeIndexes(int, int). Method in class weka.gui.explorer.ClustererPanel
Set the default attributes to use on the x and y axis of a new visualization object.
setY(double). Method in class weka.classifiers.neural.NeuralConnection
setY(int). Method in class weka.gui.visualize.AttributePanel
shows which bar is the current y attribute.
setYindex(int). Method in class weka.gui.visualize.Plot2D
Set the index of the attribute to go on the y axis
setYindex(int). Method in class weka.gui.visualize.PlotData2D
Set the y index of the data
setYIndex(int). Method in class weka.gui.visualize.VisualizePanel
Set the index of the attribute for the y axis
SFEntropyGain(). Method in class weka.classifiers.Evaluation
Returns the total SF, which is the null model entropy minus the scheme entropy.
SFMeanEntropyGain(). Method in class weka.classifiers.Evaluation
Returns the SF per instance, which is the null model entropy minus the scheme entropy, per instance.
SFMeanPriorEntropy(). Method in class weka.classifiers.Evaluation
Returns the entropy per instance for the null model
SFMeanSchemeEntropy(). Method in class weka.classifiers.Evaluation
Returns the entropy per instance for the scheme
SFPriorEntropy(). Method in class weka.classifiers.Evaluation
Returns the total entropy for the null model
SFSchemeEntropy(). Method in class weka.classifiers.Evaluation
Returns the total entropy for the scheme
shift(int, int, Instance). Method in class weka.classifiers.j48.Distribution
Shifts given instance from one bag to another one.
shiftRange(int, int, Instances, int, int). Method in class weka.classifiers.j48.Distribution
Shifts all instances in given range from one bag to another one.
showDialog(). Method in class weka.gui.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.
shrinkageTipText(). Method in class weka.classifiers.AdditiveRegression
Returns the tip text for this property
sigLevel. Variable in class weka.experiment.PairedStats
The significance level for comparisons
sigmaTipText(). Method in class weka.attributeSelection.ReliefFAttributeEval
Returns the tip text for this property
SigmoidUnit(). Constructor for class weka.classifiers.neural.SigmoidUnit
significanceLevelTipText(). Method in class weka.associations.Apriori
Returns the tip text for this property
significanceLevelTipText(). Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
SimpleCLI(). Constructor for class weka.gui.SimpleCLI
Constructor
SimpleKMeans(). Constructor for class weka.clusterers.SimpleKMeans
singleNodeToString(). Method in class weka.classifiers.m5.Node
Converts the information stored at this node to a string
singletons(Instances). Static method in class weka.associations.ItemSet
Converts the header info of the given set of instances into a set of item sets (singletons).
size(). Method in class weka.classifiers.evaluation.ConfusionMatrix
Gets the number of classes.
size(). Method in class weka.classifiers.CostMatrix
Gets the number of classes.
size(). Method in class weka.core.FastVector
Returns the vector's current size.
size(). Method in class weka.classifiers.kstar.LightHashTable
Returns the number of keys in this hashtable.
size(). Method in class weka.core.Queue
Gets queue's size.
sm(double, double). Static method in class weka.core.Utils
Tests if a is smaller than b.
SMALL. Static variable in class weka.core.Utils
The small deviation allowed in double comparisons
SMO(). Constructor for class weka.classifiers.SMO
smoothen(). Method in class weka.classifiers.m5.Node
Smoothens all unsmoothed formulae at the tree leaves under this node.
smoothenFormula(Node). Method in class weka.classifiers.m5.Node
Recursively smoothens the unsmoothed linear model at this node with the unsmoothed linear models at the nodes above this
smoothenValue(double, double, int, int). Static method in class weka.classifiers.m5.M5Utils
Returns the smoothed values according to the smoothing formula (np+kq)/(n+k)
smOrEq(double, double). Static method in class weka.core.Utils
Tests if a is smaller or equal to b.
sort(Attribute). Method in class weka.core.Instances
Sorts the instances based on an attribute.
sort(double[]). Static method in class weka.core.Utils
Sorts a given array of doubles in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
sort(int). Method in class weka.core.Instances
Sorts the instances based on an attribute.
sort(int[]). Static method in class weka.core.Utils
Sorts a given array of integers in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
sourceClass(int, Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
sourceExpression(int, Instances). Method in class weka.classifiers.j48.BinC45Split
Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances). Method in class weka.classifiers.j48.C45Split
Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
sourceExpression(int, Instances). Method in class weka.classifiers.j48.NoSplit
Returns a string containing java source code equivalent to the test made at this node.
sparseDataTipText(). Method in class weka.experiment.InstanceQuery
Returns the tip text for this property
SparseInstance(double, double[]). Constructor for class weka.core.SparseInstance
Constructor that generates a sparse instance from the given parameters.
SparseInstance(double, double[], int[], int). Constructor for class weka.core.SparseInstance
Constructor that inititalizes instance variable with given values.
SparseInstance(Instance). Constructor for class weka.core.SparseInstance
Constructor that generates a sparse instance from the given instance.
SparseInstance(int). Constructor for class weka.core.SparseInstance
Constructor of an instance that sets weight to one, all values to be missing, and the reference to the dataset to null.
SparseInstance(SparseInstance). Constructor for class weka.core.SparseInstance
Constructor that copies the info from the given instance.
SparseToNonSparseFilter(). Constructor for class weka.filters.SparseToNonSparseFilter
SpecialFunctions(). Constructor for class weka.core.SpecialFunctions
sphere. Variable in class weka.classifiers.kstar.KStarWrapper
used/reused to hold the sphere size
split(Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
Splits the given set of instances into subsets.
split(Instances). Method in class weka.classifiers.m5.Node
Splits the node recursively, unless there are few instances or instances have similar values of the class attribute
SplitCriterion(). Constructor for class weka.classifiers.j48.SplitCriterion
splitCritValue(Distribution). Method in class weka.classifiers.j48.EntropySplitCrit
Computes entropy for given distribution.
splitCritValue(Distribution). Method in class weka.classifiers.j48.GainRatioSplitCrit
This method is a straightforward implementation of the gain ratio criterion for the given distribution.
splitCritValue(Distribution). Method in class weka.classifiers.j48.InfoGainSplitCrit
This method is a straightforward implementation of the information gain criterion for the given distribution.
splitCritValue(Distribution). Method in class weka.classifiers.j48.SplitCriterion
Computes result of splitting criterion for given distribution.
splitCritValue(Distribution, Distribution). Method in class weka.classifiers.j48.EntropySplitCrit
Computes entropy of test distribution with respect to training distribution.
splitCritValue(Distribution, Distribution). Method in class weka.classifiers.j48.SplitCriterion
Computes result of splitting criterion for given training and test distributions.
splitCritValue(Distribution, Distribution, Distribution). Method in class weka.classifiers.j48.SplitCriterion
Computes result of splitting criterion for given training and test distributions and given default distribution.
splitCritValue(Distribution, Distribution, int). Method in class weka.classifiers.j48.SplitCriterion
Computes result of splitting criterion for given training and test distributions and given number of classes.
splitCritValue(Distribution, double). Method in class weka.classifiers.j48.InfoGainSplitCrit
This method computes the information gain in the same way C4.5 does.
splitCritValue(Distribution, double, double). Method in class weka.classifiers.j48.GainRatioSplitCrit
This method computes the gain ratio in the same way C4.5 does.
splitCritValue(Distribution, double, double). Method in class weka.classifiers.j48.InfoGainSplitCrit
This method computes the information gain in the same way C4.5 does.
SplitDatasetFilter(). Constructor for class weka.filters.SplitDatasetFilter
splitEnt(Distribution). Method in class weka.classifiers.j48.EntropyBasedSplitCrit
Computes entropy after splitting without considering the class values.
splitEvaluatorTipText(). Method in class weka.experiment.CrossValidationResultProducer
Returns the tip text for this property
splitEvaluatorTipText(). Method in class weka.experiment.RandomSplitResultProducer
Returns the tip text for this property
SplitInfo(int, int, int). Constructor for class weka.classifiers.m5.SplitInfo
Constructs an object which contains the split information
splitOptions(String). Static method in class weka.core.Utils
Split up a string containing options into an array of strings, one for each option.
Splitter(). Constructor for class weka.classifiers.adtree.Splitter
SpreadSubsampleFilter(). Constructor for class weka.filters.SpreadSubsampleFilter
sqrSum(int, Instances). Static method in class weka.classifiers.m5.M5Utils
Returns the squared sum of the instances values of an attribute
stableSort(double[]). Static method in class weka.core.Utils
Sorts a given array of doubles in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
Stacking(). Constructor for class weka.classifiers.Stacking
startSetTipText(). Method in class weka.attributeSelection.BestFirst
Returns the tip text for this property
startSetTipText(). Method in class weka.attributeSelection.ExhaustiveSearch
Returns the tip text for this property
startSetTipText(). Method in class weka.attributeSelection.ForwardSelection
Returns the tip text for this property
startSetTipText(). Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property
startSetTipText(). Method in class weka.attributeSelection.RandomSearch
Returns the tip text for this property
startSetTipText(). Method in class weka.attributeSelection.Ranker
Returns the tip text for this property
Statistics(). Constructor for class weka.core.Statistics
Stats(). Constructor for class weka.classifiers.j48.Stats
Stats(). Constructor for class weka.experiment.Stats
statusMessage(String). Method in interface weka.gui.Logger
Sends the supplied message to the status line.
statusMessage(String). Method in class weka.gui.LogPanel
Sends the supplied message to the status line.
statusMessage(String). Method in class weka.gui.SysErrLog
Sends the supplied message to the status line.
stdDev. Variable in class weka.experiment.Stats
The std deviation of values at the last calculateDerived() call
stdDev(int, Instances). Static method in class weka.classifiers.m5.M5Utils
Returns the standard deviation value of the instances values of an attribute
STEP_FIELD_NAME. Static variable in class weka.experiment.LearningRateResultProducer
stepSizeTipText(). Method in class weka.experiment.LearningRateResultProducer
Returns the tip text for this property
store(double, double, double). Method in class weka.classifiers.kstar.KStarCache
Stores the specified values in the cahce table for easy retrieval.
stratify(int). Method in class weka.core.Instances
Stratifies a set of instances according to its class values if the class attribute is nominal (so that afterwards a stratified cross-validation can be performed).
STRING. Static variable in class weka.core.Attribute
Constant set for attributes with string values.
stringFreeStructure(). Method in class weka.core.Instances
Create a copy of the structure, but "cleanse" string types (i.e.
stringSize(FontMetrics). Method in class weka.gui.treevisualizer.Edge
This will calculate how large a rectangle using the FontMetrics passed that the lines of the label will take up
stringSize(FontMetrics). Method in class weka.gui.treevisualizer.Node
This will return the width and height of the rectangle that the text will fit into.
StringToNominalFilter(). Constructor for class weka.filters.StringToNominalFilter
stringValue(Attribute). Method in class weka.core.Instance
Returns the value of a nominal (or string) attribute for the instance.
stringValue(int). Method in class weka.core.Instance
Returns the value of a nominal (or string) attribute for the instance.
studentTConfidenceInterval(int, double, double). Static method in class weka.core.Statistics
Computes absolute size of half of a student-t confidence interval for given degrees of freedom, probability, and observed value.
sub(int, Instance). Method in class weka.classifiers.j48.Distribution
Subtracts given instance from given bag.
SubsetEvaluator(). Constructor for class weka.attributeSelection.SubsetEvaluator
subtract(Distribution). Method in class weka.classifiers.j48.Distribution
Subtracts the given distribution from this one.
subtract(double). Method in class weka.experiment.Stats
Removes a value to the observed values (no checking is done that the value being removed was actually added).
subtract(double, double). Method in class weka.experiment.PairedStats
Removes an observed pair of values.
subtract(ItemSet). Method in class weka.associations.ItemSet
Subtracts an item set from another one.
sum. Variable in class weka.experiment.Stats
The sum of values seen
sum(double[]). Static method in class weka.core.Utils
Computes the sum of the elements of an array of doubles.
sum(int, Instances). Static method in class weka.classifiers.m5.M5Utils
Returns the sum of the instances values of an attribute
sum(int[]). Static method in class weka.core.Utils
Computes the sum of the elements of an array of integers.
sumOfWeights(). Method in class weka.core.Instances
Computes the sum of all the instances' weights.
sumSq. Variable in class weka.experiment.Stats
The sum of values squared seen
support(). Method in class weka.associations.ItemSet
Outputs the support for an item set.
supportsCustomEditor(). Method in class weka.gui.CostMatrixEditor
Returns true because we do support a custom editor.
supportsCustomEditor(). Method in class weka.gui.FileEditor
Returns true because we do support a custom editor.
supportsCustomEditor(). Method in class weka.gui.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.
swap(int, int). Method in class weka.core.FastVector
Swaps two elements in the vector.
SwapAttributeValuesFilter(). Constructor for class weka.filters.SwapAttributeValuesFilter
symmetricalUncertainty(double[][]). Static method in class weka.core.ContingencyTables
Calculates the symmetrical uncertainty for base 2.
SymmetricalUncertAttributeEval(). Constructor for class weka.attributeSelection.SymmetricalUncertAttributeEval
Constructor
synopsis(). Method in class weka.core.Option
Returns the option's synopsis.
SysErrLog(). Constructor for class weka.gui.SysErrLog

T

tableExists(String). Method in class weka.experiment.DatabaseUtils
Checks that a given table exists.
Tag(int, String). Constructor for class weka.core.Tag
Creates a new Tag instance.
TAGS_ATTRIBUTES. Static variable in class weka.filters.AttributeTypeFilter
TAGS_ERROR. Static variable in class weka.classifiers.MultiClassClassifier
TAGS_EVAL. Static variable in class weka.classifiers.ThresholdSelector
TAGS_MATRIX_SOURCE. Static variable in class weka.classifiers.CostSensitiveClassifier
TAGS_MATRIX_SOURCE. Static variable in class weka.classifiers.MetaCost
TAGS_MISSING. Static variable in class weka.classifiers.kstar.KStar
Define possible missing value handling methods
TAGS_MODEL_TYPES. Static variable in class weka.classifiers.m5.M5Prime
TAGS_OPTIMIZE. Static variable in class weka.classifiers.ThresholdSelector
TAGS_RANGE. Static variable in class weka.classifiers.ThresholdSelector
TAGS_SEARCHPATH. Static variable in class weka.classifiers.adtree.ADTree
TAGS_SELECTION. Static variable in class weka.associations.Apriori
TAGS_SELECTION. Static variable in class weka.attributeSelection.BestFirst
TAGS_SELECTION. Static variable in class weka.classifiers.LinearRegression
TAGS_SELECTION. Static variable in class weka.attributeSelection.RaceSearch
TAGS_WEIGHTING. Static variable in class weka.classifiers.IBk
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
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(). 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.
test(String[]). Static method in class weka.core.Instances
Method for testing this class.
testCV(int, int). Method in class weka.core.Instances
Creates the test set for one fold of a cross-validation on the dataset.
THRESHOLD_NAME. Static variable in class weka.classifiers.evaluation.CostCurve
THRESHOLD_NAME. Static variable in class weka.classifiers.evaluation.ThresholdCurve
ThresholdCurve(). Constructor for class weka.classifiers.evaluation.ThresholdCurve
ThresholdSelector(). Constructor for class weka.classifiers.ThresholdSelector
thresholdTipText(). Method in class weka.attributeSelection.ForwardSelection
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
TimeSeriesDeltaFilter(). Constructor for class weka.filters.TimeSeriesDeltaFilter
TimeSeriesTranslateFilter(). Constructor for class weka.filters.TimeSeriesTranslateFilter
TIMESTAMP_FIELD_NAME. Static variable in class weka.experiment.CrossValidationResultProducer
TIMESTAMP_FIELD_NAME. Static variable in class weka.experiment.RandomSplitResultProducer
TO_BE_RUN. Static variable in class weka.experiment.TaskStatusInfo
toArray(). Method in class weka.core.FastVector
Returns all the elements of this vector as an array
toClassDetailsString(). Method in class weka.classifiers.Evaluation
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.
toCumulativeMarginDistributionString(). Method in class weka.classifiers.Evaluation
Output the cumulative margin distribution as a string suitable for input for gnuplot or similar package.
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.
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.
toResultsString(). Method in class weka.attributeSelection.AttributeSelection
get a description of the attribute selection
toSource(String). Method in class weka.classifiers.AdaBoostM1
Returns the boosted model as Java source code.
toSource(String). Method in class weka.classifiers.j48.ClassifierTree
Returns source code for the tree as an if-then statement.
toSource(String). Method in class weka.classifiers.DecisionStump
Returns the decision tree as Java source code.
toSource(String). Method in class weka.classifiers.j48.J48
Returns tree as an if-then statement.
toSource(String). Method in class weka.classifiers.LogitBoost
Returns the boosted model as Java source code.
toSource(String). Method in interface weka.classifiers.Sourcable
Returns a string that describes the classifier as source.
toString(). Method in class weka.classifiers.AdaBoostM1
Returns description of the boosted classifier.
toString(). Method in class weka.classifiers.AdditiveRegression
Returns textual description of the classifier.
toString(). Method in class weka.classifiers.adtree.ADTree
Returns a description of the classifier.
toString(). Method in class weka.associations.Apriori
Outputs the size of all the generated sets of itemsets and the rules.
toString(). Method in class weka.core.Attribute
Returns a description of this attribute in ARFF format.
toString(). Method in class weka.classifiers.AttributeSelectedClassifier
Output a representation of this classifier
toString(). Method in class weka.core.AttributeStats
Returns a human readable representation of this AttributeStats instance.
toString(). Method in class weka.experiment.AveragingResultProducer
Gets a text descrption of the result producer.
toString(). Method in class weka.classifiers.Bagging
Returns description of the bagged classifier.
toString(). Method in class weka.attributeSelection.BestFirst
returns a description of the search as a String
toString(). Method in class weka.core.BinarySparseInstance
Returns the description of one instance in sparse format.
toString(). Method in class weka.classifiers.BVDecompose
Returns description of the bias-variance decomposition results.
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.classifiers.ClassificationViaRegression
Prints the classifiers.
toString(). Method in class weka.classifiers.j48.ClassifierDecList
Prints rules.
toString(). Method in class weka.experiment.ClassifierSplitEvaluator
Returns a text description of the split evaluator.
toString(). Method in class weka.attributeSelection.ClassifierSubsetEval
Returns a string describing classifierSubsetEval
toString(). Method in class weka.classifiers.j48.ClassifierTree
Prints tree structure.
toString(). Method in class weka.clusterers.Cobweb
Returns a description of the clusterer as a string.
toString(). Method in class weka.classifiers.evaluation.ConfusionMatrix
Calls toString() with a default title.
toString(). Method in class weka.attributeSelection.ConsistencySubsetEval
returns a description of the evaluator
toString(). Method in class weka.classifiers.CostSensitiveClassifier
Output a representation of this classifier
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.classifiers.CVParameterSelection
Returns description of the cross-validated classifier.
toString(). Method in class weka.experiment.DatabaseResultProducer
Gets a text descrption of the result producer.
toString(). Method in class weka.estimators.DDConditionalEstimator
Display a representation of this estimator
toString(). Method in class weka.classifiers.DecisionStump
Returns a description of the classifier.
toString(). Method in class weka.classifiers.DecisionTable
Returns a description of the classifier.
toString(). Method in class weka.estimators.DiscreteEstimator
Display a representation of this estimator
toString(). Method in class weka.classifiers.DistributionMetaClassifier
Prints the classifiers.
toString(). Method in class weka.clusterers.DistributionMetaClusterer
Prints the clusterers.
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.clusterers.EM
Outputs the generated clusters into a string.
toString(). Method in class weka.classifiers.m5.Errors
Converts the evaluation results of a model to a string
toString(). Method in class weka.attributeSelection.ExhaustiveSearch
prints a description of the search
toString(). Method in class weka.experiment.Experiment
Gets a string representation of the experiment configuration.
toString(). Method in class weka.classifiers.FilteredClassifier
Output a representation of this classifier
toString(). Method in class weka.attributeSelection.ForwardSelection
returns a description of the search.
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.classifiers.HyperPipes
Returns a description of this classifier.
toString(). Method in class weka.classifiers.IB1
Returns a description of this classifier.
toString(). Method in class weka.classifiers.IBk
Returns a description of this classifier.
toString(). Method in class weka.classifiers.Id3
Prints the decision tree using the private toString method from below.
toString(). Method in class weka.classifiers.m5.Impurity
Converts an Impurity object to a string
toString(). Method in class weka.attributeSelection.InfoGainAttributeEval
Describe the attribute evaluator
toString(). Method in class weka.core.Instance
Returns the description of one instance.
toString(). Method in class weka.core.Instances
Returns the dataset as a string in ARFF format.
toString(). Method in class weka.classifiers.j48.J48
Returns a description of the classifier.
toString(). Method in class weka.estimators.KDConditionalEstimator
Display a representation of this estimator
toString(). Method in class weka.classifiers.KernelDensity
Returns a description of the classifier.
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.classifiers.kstar.KStar
Returns a description of this classifier.
toString(). Method in class weka.experiment.LearningRateResultProducer
Gets a text descrption of the result producer.
toString(). Method in class weka.classifiers.LinearRegression
Outputs the linear regression model as a string.
toString(). Method in class weka.classifiers.Logistic
Gets a string describing the classifier.
toString(). Method in class weka.classifiers.LogitBoost
Returns description of the boosted classifier.
toString(). Method in class weka.classifiers.LWR
Returns a description of this classifier.
toString(). Method in class weka.classifiers.m5.M5Prime
Converts the output of the training process into a string
toString(). Method in class weka.estimators.MahalanobisEstimator
Display a representation of this estimator
toString(). Method in class weka.classifiers.j48.MakeDecList
Outputs the classifier into a string.
toString(). Method in class weka.core.Matrix
Converts a matrix to a string
toString(). Method in class weka.classifiers.MetaCost
Output a representation of this classifier
toString(). Method in class weka.classifiers.MultiClassClassifier
Prints the classifiers.
toString(). Method in class weka.classifiers.MultiScheme
Output a representation of this classifier
toString(). Method in class weka.classifiers.NaiveBayes
Returns a description of the classifier.
toString(). Method in class weka.classifiers.NaiveBayesSimple
Returns a description of the classifier.
toString(). Method in class weka.estimators.NDConditionalEstimator
Display a representation of this estimator
toString(). Method in class weka.classifiers.neural.NeuralNetwork
toString(). Method in class weka.estimators.NNConditionalEstimator
Display a representation of this estimator
toString(). Method in class weka.classifiers.evaluation.NominalPrediction
Gets a human readable representation of this prediction.
toString(). Method in class weka.estimators.NormalEstimator
Display a representation of this estimator
toString(). Method in class weka.classifiers.evaluation.NumericPrediction
Gets a human readable representation of this prediction.
toString(). Method in class weka.classifiers.OneR
Returns a description of the classifier
toString(). Method in class weka.attributeSelection.OneRAttributeEval
Return a description of the evaluator
toString(). Method in class weka.experiment.PairedStats
Returns statistics on the paired comparison.
toString(). Method in class weka.classifiers.j48.PART
Returns a description of the classifier
toString(). Method in class weka.estimators.PoissonEstimator
Display a representation of this estimator
toString(). Method in class weka.attributeSelection.PrincipalComponents
Returns a description of this attribute transformer
toString(). Method in class weka.classifiers.Prism
Prints a description of the classifier.
toString(). Method in class weka.experiment.PropertyNode
Returns a string description of this property.
toString(). Method in class weka.core.Queue
Produces textual description of queue.
toString(). Method in class weka.attributeSelection.RaceSearch
toString(). Method in class weka.attributeSelection.RandomSearch
prints a description of the search
toString(). Method in class weka.experiment.RandomSplitResultProducer
Gets a text descrption of the result producer.
toString(). Method in class weka.core.Range
Constructs a representation of the current range.
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.classifiers.RegressionByDiscretization
Returns a description of the classifier.
toString(). Method in class weka.experiment.RegressionSplitEvaluator
Returns a text description of the split evaluator.
toString(). Method in class weka.attributeSelection.ReliefFAttributeEval
Return a description of the ReliefF attribute evaluator.
toString(). Method in class weka.experiment.RemoteExperiment
Overides toString in Experiment
toString(). Method in class weka.core.SerializedObject
Returns a text representation of the state of this object.
toString(). Method in class weka.clusterers.SimpleKMeans
return a string describing this clusterer
toString(). Method in class weka.classifiers.SMO
Prints out the classifier.
toString(). Method in class weka.core.SparseInstance
Returns the description of one instance in sparse format.
toString(). Method in class weka.classifiers.Stacking
Output a representation of this classifier
toString(). Method in class weka.experiment.Stats
Returns a string summarising the stats so far.
toString(). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Return a description of the evaluator
toString(). Method in class weka.classifiers.ThresholdSelector
Returns description of the cross-validated classifier.
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.UserClassifier
toString(). Method in class weka.classifiers.m5.Values
Converts the stats to a string
toString(). Method in class weka.classifiers.VFI
Returns a description of this classifier.
toString(). Method in class weka.classifiers.VotedPerceptron
Returns textual description of classifier.
toString(). Method in class weka.attributeSelection.WrapperSubsetEval
Returns a string describing the wrapper
toString(). Method in class weka.classifiers.ZeroR
Returns a description of the classifier.
toString(Attribute). Method in class weka.core.Instance
Returns the description of one value of the instance as a string.
toString(double, double, String, String). Method in class weka.classifiers.m5.Measures
Converts the performance measures to a string
toString(Instances). Method in class weka.associations.ItemSet
Returns the contents of an item set as a string.
toString(Instances). Method in class weka.classifiers.m5.Options
Prints information stored in an 'Options' object, basically containing command line options
toString(Instances). Method in class weka.classifiers.m5.SplitInfo
Converts the spliting information to string
toString(Instances, int). Method in class weka.classifiers.m5.Function
Converts a function to a string
toString(int). Method in class weka.core.Instance
Returns the description of one value of the instance as a string.
toString(int, int, int, int). Method in class weka.classifiers.m5.Matrix
Converts a matrix to a string
toString(int[], int, int). Static method in class weka.classifiers.m5.Ivector
Converts a string
toString(String). Method in class weka.classifiers.evaluation.ConfusionMatrix
Outputs the performance statistics as a classification confusion matrix.
toSummaryString(). Method in class weka.classifiers.CVParameterSelection
toSummaryString(). Method in class weka.classifiers.Evaluation
Calls toSummaryString() with no title and no complexity stats
toSummaryString(). Method in class weka.core.Instances
Generates a string summarizing the set of instances.
toSummaryString(). Method in class weka.classifiers.j48.J48
Returns a superconcise version of the model
toSummaryString(). Method in class weka.classifiers.j48.PART
Returns a superconcise version of the model
toSummaryString(). Method in interface weka.core.Summarizable
Returns a string that summarizes the object.
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.
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.j48.Distribution
Returns total number of (possibly fractional) instances.
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.
TP_RATE_NAME. Static variable in class weka.classifiers.evaluation.ThresholdCurve
trainCV(int, int). Method in class weka.core.Instances
Creates the training set for one fold of a cross-validation on the dataset.
trainingTimeTipText(). Method in class weka.classifiers.neural.NeuralNetwork
trainPercentTipText(). Method in class weka.experiment.RandomSplitResultProducer
Returns the tip text for this property
transformBackToOriginalTipText(). Method in class weka.attributeSelection.PrincipalComponents
Returns the tip text for this property
transformedData(). Method in interface weka.attributeSelection.AttributeTransformer
Returns the transformed data
transformedData(). 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.PrincipalComponents
Returns just the header for the transformed data (ie.
transpose(). Method in class weka.core.Matrix
Returns the transpose of a matrix.
transpose(int, int). Method in class weka.classifiers.m5.Matrix
Returns the transpose of a matrix [0:n-1][0:m-1]
transProb(). Method in class weka.classifiers.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.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".
TreeBuild(). Constructor for class weka.gui.treevisualizer.TreeBuild
Upon construction this will only setup the color table for quick reference of a color.
TreeDisplayEvent(int, String). Constructor for class weka.gui.treevisualizer.TreeDisplayEvent
Constructs an event with the specified command and what the command is applied to.
treeToString(int, double). Method in class weka.classifiers.m5.Node
Converts the tree under this node to a string
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.
TreeVisualizer(TreeDisplayListener, String, NodePlace). Constructor for class weka.gui.treevisualizer.TreeVisualizer
Constructs Displayer to display a tree provided in a dot format.
TRIANGLEDOWN_SHAPE. Static variable in class weka.gui.visualize.Plot2D
TRIANGLEUP_SHAPE. Static variable in class weka.gui.visualize.Plot2D
trimToSize(). Method in class weka.core.FastVector
Sets the vector's capacity to its size.
TRUE_NEG_NAME. Static variable in class weka.classifiers.evaluation.ThresholdCurve
TRUE_POS_NAME. Static variable in class weka.classifiers.evaluation.ThresholdCurve
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.
TwoClassStats(double, double, double, double). Constructor for class weka.classifiers.evaluation.TwoClassStats
Creates the TwoClassStats with the given initial performance values.
TwoWayNominalSplit(int, int). Constructor for class weka.classifiers.adtree.TwoWayNominalSplit
Creates a new two-way nominal splitter.
TwoWayNumericSplit(int, double). Constructor for class weka.classifiers.adtree.TwoWayNumericSplit
Creates a new two-way numeric splitter.
type(). Method in class weka.core.Attribute
Returns the attribute's type as an integer.
typeName(int). Static method in class weka.experiment.DatabaseUtils
Returns the name associated with a SQL type.

U

UnassignedClassException(). Constructor for class weka.core.UnassignedClassException
Creates a new UnassignedClassException instance with no detail message.
UnassignedClassException(String). Constructor for class weka.core.UnassignedClassException
Creates a new UnassignedClassException instance with a specified message.
UnassignedDatasetException(). Constructor for class weka.core.UnassignedDatasetException
Creates a new UnassignedDatasetException instance with no detail message.
UnassignedDatasetException(String). Constructor for class weka.core.UnassignedDatasetException
Creates a new UnassignedDatasetException instance with a specified message.
unclassified(). Method in class weka.classifiers.Evaluation
Gets the number of instances not classified (that is, for which no prediction was made by the classifier).
UNCONNECTED. Static variable in class weka.classifiers.neural.NeuralConnection
This unit is not connected to any others.
uniqueCount. Variable in class weka.core.AttributeStats
The number of values that only appear once
UnsupervisedAttributeEvaluator(). Constructor for class weka.attributeSelection.UnsupervisedAttributeEvaluator
UnsupervisedSubsetEvaluator(). Constructor for class weka.attributeSelection.UnsupervisedSubsetEvaluator
UnsupportedAttributeTypeException(). Constructor for class weka.core.UnsupportedAttributeTypeException
Creates a new UnsupportedAttributeTypeException instance with no detail message.
UnsupportedAttributeTypeException(String). Constructor for class weka.core.UnsupportedAttributeTypeException
Creates a new UnsupportedAttributeTypeException instance with a specified message.
UnsupportedClassTypeException(). Constructor for class weka.core.UnsupportedClassTypeException
Creates a new UnsupportedClassTypeException instance with no detail message.
UnsupportedClassTypeException(String). Constructor for class weka.core.UnsupportedClassTypeException
Creates a new UnsupportedClassTypeException instance with a specified message.
updateClassifier(Instance). Method in class weka.classifiers.HyperPipes
Updates the classifier.
updateClassifier(Instance). Method in class weka.classifiers.IB1
Updates the classifier.
updateClassifier(Instance). Method in class weka.classifiers.IBk
Adds the supplied instance to the training set
updateClassifier(Instance). Method in class weka.classifiers.kstar.KStar
Adds the supplied instance to the training set
updateClassifier(Instance). Method in class weka.classifiers.LWR
Adds the supplied instance to the training set
updateClassifier(Instance). Method in class weka.classifiers.NaiveBayes
Updates the classifier with the given instance.
updateClassifier(Instance). Method in interface weka.classifiers.UpdateableClassifier
Updates a classifier using the given instance.
upDateCounter(Instance). Method in class weka.associations.ItemSet
Updates counter of item set with respect to given transaction.
upDateCounters(FastVector, Instances). Static method in class weka.associations.ItemSet
Updates counters for a set of item sets and a set of instances.
updatePriors(Instance). Method in class weka.classifiers.Evaluation
Updates the class prior probabilities (when incrementally training)
updateResult(String). Method in class weka.gui.ResultHistoryPanel
Tells any component currently displaying the named result that the contents of the result text in the StringBuffer have been updated.
updateWeights(double, double). Method in class weka.classifiers.neural.NeuralConnection
Call this function to update the weight values at this unit.
updateWeights(double, double). Method in class weka.classifiers.neural.NeuralNode
Call this function to update the weight values at this unit.
updateWeights(NeuralNode, double, double). Method in class weka.classifiers.neural.LinearUnit
This function will calculate what the change in weights should be and also update them.
updateWeights(NeuralNode, double, double). Method in interface weka.classifiers.neural.NeuralMethod
This function will calculate what the change in weights should be and also update them.
updateWeights(NeuralNode, double, double). Method in class weka.classifiers.neural.SigmoidUnit
This function will calculate what the change in weights should be and also update them.
upperBoundMinSupportTipText(). Method in class weka.associations.Apriori
Returns the tip text for this property
upperSizeTipText(). Method in class weka.experiment.LearningRateResultProducer
Returns the tip text for this property
useBetterEncodingTipText(). Method in class weka.filters.DiscretizeFilter
Returns the tip text for this property
useFilter(Instances, Filter). Static method in class weka.filters.Filter
Filters an entire set of instances through a filter and returns the new set.
useKononenkoTipText(). Method in class weka.filters.DiscretizeFilter
Returns the tip text for this property
useMDLTipText(). Method in class weka.filters.DiscretizeFilter
Returns the tip text for this property
UserClassifier(). Constructor for class weka.classifiers.UserClassifier
Constructor
userCommand(TreeDisplayEvent). Method in interface weka.gui.treevisualizer.TreeDisplayListener
Gets called when the user selects something, in the tree display.
userCommand(TreeDisplayEvent). Method in class weka.classifiers.UserClassifier
Receives user choices from the tree view, and then deals with these choices.
userDataEvent(VisualizePanelEvent). Method in class weka.classifiers.UserClassifier
This receives shapes from the data view.
userDataEvent(VisualizePanelEvent). Method in interface weka.gui.visualize.VisualizePanelListener
This method receives an object containing the shapes, instances inside and outside these shapes and the attributes these shapes were created in.
useTrainingTipText(). Method in class weka.attributeSelection.ClassifierSubsetEval
Returns the tip text for this property
Utils(). Constructor for class weka.core.Utils

V

validation(Instances). Method in class weka.classifiers.m5.Node
Computes performance measures for both unsmoothed and smoothed models
validationSetSizeTipText(). Method in class weka.classifiers.neural.NeuralNetwork
validationThresholdTipText(). Method in class weka.classifiers.neural.NeuralNetwork
value. Variable in class weka.experiment.PropertyNode
The current property value
value(Attribute). Method in class weka.core.Instance
Returns an instance's attribute value in internal format.
value(int). Method in class weka.core.Attribute
Returns a value of a nominal or string attribute.
value(int). Method in class weka.core.BinarySparseInstance
Returns an instance's attribute value in internal format.
value(int). Method in class weka.core.Instance
Returns an instance's attribute value in internal format.
value(int). Method in class weka.core.SparseInstance
Returns an instance's attribute value in internal format.
valueIndicesTipText(). Method in class weka.filters.MakeIndicatorFilter
valueNode(). Method in class weka.classifiers.m5.Node
Takes a constant value as the function at the node
Values(int, int, int, Instances). Constructor for class weka.classifiers.m5.Values
Constructs an object which stores some statistics of the instances such as sum, squared sum, variance, standard deviation
valueSparse(int). Method in class weka.core.BinarySparseInstance
Returns an instance's attribute value in internal format.
valueSparse(int). Method in class weka.core.Instance
Returns an instance's attribute value in internal format.
variance(Attribute). Method in class weka.core.Instances
Computes the variance for a numeric attribute.
variance(double[]). Static method in class weka.core.Utils
Computes the variance for an array of doubles.
variance(int). Method in class weka.core.Instances
Computes the variance for a numeric attribute.
variance(int, Instances). Static method in class weka.classifiers.m5.M5Utils
Returns the variance value of the instances values of an attribute
varianceCoveredTipText(). Method in class weka.attributeSelection.PrincipalComponents
Returns the tip text for this property
verboseTipText(). Method in class weka.attributeSelection.ExhaustiveSearch
Returns the tip text for this property
verboseTipText(). Method in class weka.attributeSelection.RandomSearch
Returns the tip text for this property
VFI(). Constructor for class weka.classifiers.VFI
VisualizePanel(). Constructor for class weka.gui.visualize.VisualizePanel
Constructor
VisualizePanel(VisualizePanelListener). Constructor for class weka.gui.visualize.VisualizePanel
This constructor allows a VisualizePanelListener to be set.
VisualizePanelEvent(FastVector, Instances, Instances, int, int). Constructor for class weka.gui.visualize.VisualizePanelEvent
This constructor creates the event with all the parameters set.
VisualizeUtils(). Constructor for class weka.gui.visualize.VisualizeUtils
VLINE. Static variable in class weka.gui.visualize.VisualizePanelEvent
VotedPerceptron(). Constructor for class weka.classifiers.VotedPerceptron

W

weight(). Method in class weka.core.Instance
Returns the instance's weight.
weight(). Method in class weka.classifiers.evaluation.NominalPrediction
Gets the weight assigned to this prediction.
weight(). Method in class weka.classifiers.evaluation.NumericPrediction
Gets the weight assigned to this prediction.
weight(). Method in interface weka.classifiers.evaluation.Prediction
Gets the weight assigned to this prediction.
weight(Instance). Method in class weka.classifiers.j48.ClassifierDecList
Returns the weight a rule assigns to an instance.
WEIGHT_INVERSE. Static variable in class weka.classifiers.IBk
WEIGHT_NONE. Static variable in class weka.classifiers.IBk
WEIGHT_SIMILARITY. Static variable in class weka.classifiers.IBk
weightByConfidenceTipText(). Method in class weka.classifiers.VFI
Returns the tip text for this property
weightByDistanceTipText(). Method in class weka.attributeSelection.ReliefFAttributeEval
Returns the tip text for this property
weights(Instance). Method in class weka.classifiers.j48.BinC45Split
Returns weights if instance is assigned to more than one subset.
weights(Instance). Method in class weka.classifiers.j48.C45Split
Returns weights if instance is assigned to more than one subset.
weights(Instance). Method in class weka.classifiers.j48.ClassifierSplitModel
Returns weights if instance is assigned to more than one subset.
weights(Instance). Method in class weka.classifiers.j48.NoSplit
Always returns null because there is only one subset.
weightValue(int). Method in class weka.classifiers.neural.NeuralConnection
Call this to get the weight value on a particular connection.
weightValue(int). Method in class weka.classifiers.neural.NeuralNode
Call this to get the weight value on a particular connection.
WekaException(). Constructor for class weka.core.WekaException
Creates a new WekaException instance with no detail message.
WekaException(String). Constructor for class weka.core.WekaException
Creates a new WekaException instance with a specified message.
WekaTaskMonitor(). Constructor for class weka.gui.WekaTaskMonitor
Constructor
whichSubset(Instance). Method in class weka.classifiers.j48.BinC45Split
Returns index of subset instance is assigned to.
whichSubset(Instance). Method in class weka.classifiers.j48.C45Split
Returns index of subset instance is assigned to.
whichSubset(Instance). Method in class weka.classifiers.j48.ClassifierSplitModel
Returns index of subset instance is assigned to.
whichSubset(Instance). Method in class weka.classifiers.j48.NoSplit
Always returns 0 because only there is only one subset.
WrapperSubsetEval(). Constructor for class weka.attributeSelection.WrapperSubsetEval
Constructor.
write(Writer). Method in class weka.core.Matrix
Writes out a matrix

X

X_SHAPE. Static variable in class weka.gui.visualize.Plot2D
xlogx(int). Static method in class weka.core.Utils
Returns c*log2(c) for a given integer value c.
xStats. Variable in class weka.experiment.PairedStats
The stats associated with the data in column 1
XVALTAGS_SELECTION. Static variable in class weka.attributeSelection.RaceSearch
xySum. Variable in class weka.experiment.PairedStats
The sum of the products

Y

yStats. Variable in class weka.experiment.PairedStats
The stats associated with the data in column 2

Z

ZeroR(). Constructor for class weka.classifiers.ZeroR
zipit(String, String). Method in class weka.experiment.OutputZipper
Saves a string to either an individual gzipped file or as an entry in a zip file.