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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 - class weka.core.converters.AbstractLoader.: Abstract class for Loaders that contains default implementation of the setSource methods: Any of these methods that are not overwritten will result in throwing IOException.
AbstractLoader() - Constructor for class weka.core.converters.AbstractLoader
AbstractTimeSeriesFilter - class weka.filters.AbstractTimeSeriesFilter.: An abstract instance filter that assumes instances form time-series data and performs some merging of attribute values in the current instance with attribute attribute values of some previous (or future) instance.
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.CSVResultListener: Just prints out each result as it is received.
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 class weka.experiment.AveragingResultProducer: Accepts results from a ResultProducer.
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 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.SimpleCLI: Only gets called when return is pressed in the input area, which starts the command running.
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.experiment.RunPanel: Controls starting and stopping the experiment.
actionPerformed(ActionEvent) - Method in class weka.gui.experiment.DatasetListPanel: Handle actions when buttons get pressed.
actionPerformed(ActionEvent) - Method in class weka.gui.streams.InstanceLoader
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 - class weka.classifiers.AdaBoostM1.: Class for boosting a classifier using Freund & Schapire's Adaboost M1 method.
AdaBoostM1() - Constructor for class weka.classifiers.AdaBoostM1
ADD_CHILDREN - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
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.Stats: Adds a value that has been seen n times to the observed values
add(double, double) - Method in class weka.experiment.PairedStats: Add an observed pair of 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.
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
addCancelListener(ActionListener) - Method in class weka.gui.GenericObjectEditor.GOEPanel: This is used to hook an action listener to the cancel button
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
addDistinct(double, int) - Method in class weka.core.AttributeStats: Updates the counters for one more observed distinct value.
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 - class weka.filters.AddFilter.: An instance filter that adds a new attribute to the dataset.
AddFilter() - Constructor for class weka.filters.AddFilter
addInstanceListener(InstanceListener) - Method in class weka.gui.streams.InstanceLoader
addInstanceListener(InstanceListener) - Method in class weka.gui.streams.InstanceJoiner
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.
AdditionalMeasureProducer - interface weka.core.AdditionalMeasureProducer.: Interface to something that can produce measures other than those calculated by evaluation modules.
AdditiveRegression - class weka.classifiers.AdditiveRegression.: Meta classifier that enhances the performance of a regression base classifier.
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.
addMissing(Instances, int, boolean, boolean) - Method in class weka.classifiers.CheckClassifier: Add missing values to a dataset.
addObject(String, Object) - Method in class weka.gui.ResultHistoryPanel: Adds an object to the results list
addOkListener(ActionListener) - Method in class weka.gui.GenericObjectEditor.GOEPanel: This is used to hook an action listener to the ok button
addPlot(PlotData2D) - Method in class weka.gui.visualize.VisualizePanel: Set a new plot to the visualize panel
addPlot(PlotData2D) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel: Adds a plot.
addPlot(PlotData2D) - Method in class weka.gui.visualize.Plot2D: Add a plot to the list of plots to display
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.PropertySheetPanel: Adds a PropertyChangeListener.
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.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.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.explorer.PreprocessPanel: 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.
addToList(BitSet, double) - Method in class weka.attributeSelection.BestFirst.LinkedList2: adds an element (Link) to the list.
addToList(BitSet, double) - Method in class weka.classifiers.DecisionTable.LinkedList: Aadds an element (Link) to the list.
addValue(double, double) - Method in class weka.estimators.NormalEstimator: 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.PoissonEstimator: 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.DiscreteEstimator: 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.KDConditionalEstimator: 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.KKConditionalEstimator: 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.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.
addValue(double, double, double) - Method in interface weka.estimators.ConditionalEstimator: 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 - class weka.classifiers.adtree.ADTree.: Class for generating an alternating decision tree.
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 - class weka.filters.AllFilter.: A simple instance filter that passes all instances directly through.
AllFilter() - Constructor for class weka.filters.AllFilter
allocateInputs() - Method in class weka.classifiers.neural.NeuralConnection: This will allocate more space for input connection information if the arrays for this have been filled up.
allocateInputs() - Method in class weka.classifiers.neural.NeuralNode: This will allocate more space for input connection information if the arrays for this have been filled up.
allocateOutputs() - Method in class weka.classifiers.neural.NeuralConnection: Allocates more space for output connection information if the arrays have been filled up.
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.PropertySelectorDialog: Signifies an OK property selection
APPROVE_OPTION - Static variable in class weka.gui.ListSelectorDialog: Signifies an OK property selection
Apriori - class weka.associations.Apriori.: Class implementing an Apriori-type algorithm.
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 - class weka.core.converters.ArffLoader.: Reads a source that is in arff text format.
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 - class weka.attributeSelection.ASEvaluation.: Abstract attribute selection evaluation class
ASEvaluation() - Constructor for class weka.attributeSelection.ASEvaluation
ASSearch - class weka.attributeSelection.ASSearch.: Abstract attribute selection search class.
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 - class weka.gui.explorer.AssociationsPanel.: This panel allows the user to select, configure, and run a scheme that learns associations.
AssociationsPanel() - Constructor for class weka.gui.explorer.AssociationsPanel: Creates the associator panel
Associator - class weka.associations.Associator.: Abstract scheme for learning associations.
Associator() - Constructor for class weka.associations.Associator
attIndex() - Method in class weka.classifiers.j48.C45Split: Returns index of attribute for which split was generated.
attIndex() - Method in class weka.classifiers.j48.BinC45Split: Returns index of attribute for which split was generated.
Attribute - class weka.core.Attribute.: Class for handling an attribute.
attribute(int) - Method in class weka.core.Instances: Returns an attribute.
attribute(int) - Method in class weka.core.Instance: Returns the attribute with the given index.
attribute(String) - Method in class weka.core.Instances: Returns an attribute given its name.
Attribute(String) - Constructor for class weka.core.Attribute: Constructor for a numeric attribute.
Attribute(String, FastVector) - Constructor for class weka.core.Attribute: Constructor for nominal attributes and string attributes.
AttributeEvaluator - class weka.attributeSelection.AttributeEvaluator.: Abstract attribute evaluator.
AttributeEvaluator() - Constructor for class weka.attributeSelection.AttributeEvaluator
attributeEvaluatorTipText() - Method in class weka.attributeSelection.RankSearch: Returns the tip text for this property
attributeEvaluatorTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
AttributeExpressionFilter - class weka.filters.AttributeExpressionFilter.: Applys a mathematical expression involving attributes and numeric constants to a dataset.
AttributeExpressionFilter() - Constructor for class weka.filters.AttributeExpressionFilter
AttributeFilter - class weka.filters.AttributeFilter.: An instance filter that deletes a range of attributes from the dataset.
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.FirstOrderFilter: 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.DiscretizeFilter: 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.AbstractTimeSeriesFilter: 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 - class weka.gui.visualize.AttributePanel.: This panel displays one dimensional views of the attributes in a dataset.
AttributePanel.AttributeSpacing - class weka.gui.visualize.AttributePanel.AttributeSpacing.: inner inner class used for plotting the points into a bar for a particular attribute.
AttributePanel.AttributeSpacing(AttributePanel, Attribute, int) - Constructor for class weka.gui.visualize.AttributePanel.AttributeSpacing: This constructs the bar with the specified attribute and sets its index to be used for selecting by the mouse.
AttributePanel() - Constructor for class weka.gui.visualize.AttributePanel: This constructs an attributePanel.
AttributePanelEvent - class weka.gui.visualize.AttributePanelEvent.: Class encapsulating a change in the AttributePanel's selected x and y attributes.
AttributePanelEvent(boolean, boolean, int) - Constructor for class weka.gui.visualize.AttributePanelEvent: Constructor
AttributePanelListener - interface weka.gui.visualize.AttributePanelListener.: Interface for classes that want to listen for Attribute selection changes in the attribute panel
AttributeSelectedClassifier - class weka.classifiers.AttributeSelectedClassifier.: Class for running an arbitrary classifier on data that has been reduced through attribute selection.
AttributeSelectedClassifier() - Constructor for class weka.classifiers.AttributeSelectedClassifier
AttributeSelection - class weka.attributeSelection.AttributeSelection.: Attribute selection class.
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 - class weka.filters.AttributeSelectionFilter.: Filter for doing attribute selection.
AttributeSelectionFilter() - Constructor for class weka.filters.AttributeSelectionFilter: Constructor
AttributeSelectionPanel - class weka.gui.AttributeSelectionPanel.: Creates a panel that displays the attributes contained in a set of instances, letting the user toggle whether each attribute is selected or not (eg: so that unselected attributes can be removed before classification).
AttributeSelectionPanel - class weka.gui.explorer.AttributeSelectionPanel.: This panel allows the user to select and configure an attribute evaluator and a search method, set the attribute of the current dataset to be used as the class, and perform attribute selection using one of two selection modes (select using all the training data or perform a n-fold cross validation---on each trial selecting features using n-1 folds of the data).
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 - class weka.core.AttributeStats.: A Utility class that contains summary information on an the values that appear in a dataset for a particular attribute.
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 - class weka.gui.AttributeSummaryPanel.: This panel displays summary statistics about an attribute: name, type number/% of missing/unique values, number of distinct values.
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.
AttributeTransformer - interface weka.attributeSelection.AttributeTransformer.: Abstract attribute transformer.
AttributeTypeFilter - class weka.filters.AttributeTypeFilter.: An instance filter that deletes all attributes of a specified type from the dataset.
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
AVAILABLE - Static variable in class weka.experiment.RemoteExperiment
availableHost(int) - Method in class weka.experiment.RemoteExperiment: Pushes a host back onto the queue of available hosts and attempts to launch a waiting experiment (if any).
AveragingResultProducer - class weka.experiment.AveragingResultProducer.: AveragingResultProducer takes the results from a ResultProducer and submits the average to the result listener.
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 - class weka.classifiers.Bagging.: Class for bagging a classifier.
Bagging() - Constructor for class weka.classifiers.Bagging
BATCH - Static variable in class weka.core.converters.AbstractLoader: For representing that instances have been retrieved in batch mode
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.Filter: 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.ReplaceMissingValuesFilter: Signify 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.SplitDatasetFilter: 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.AttributeSelectionFilter: 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.DiscretizeFilter: Signifies that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.AbstractTimeSeriesFilter: Signifies that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.NominalToBinaryFilter: 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.
batchFinished() - Method in class weka.filters.ResampleFilter: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.gui.streams.InstanceViewer
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.InstanceTable
batchFinished() - Method in class weka.gui.streams.InstanceSavePanel
BestFirst - class weka.attributeSelection.BestFirst.: Class for performing a best first search.
BestFirst.Link2 - class weka.attributeSelection.BestFirst.Link2.: Class for a node in a linked list.
BestFirst.Link2(BestFirst, BitSet, double) - Constructor for class weka.attributeSelection.BestFirst.Link2
BestFirst.LinkedList2 - class weka.attributeSelection.BestFirst.LinkedList2.: Class for handling a linked list.
BestFirst.LinkedList2(BestFirst, int) - Constructor for class weka.attributeSelection.BestFirst.LinkedList2
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 - class weka.core.BinarySparseInstance.: Class for storing a binary-data-only instance as a sparse vector.
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 - class weka.classifiers.j48.BinC45ModelSelection.: Class for selecting a C4.5-like binary (!) split for a given dataset.
BinC45ModelSelection(int, Instances) - Constructor for class weka.classifiers.j48.BinC45ModelSelection: Initializes the split selection method with the given parameters.
BinC45Split - class weka.classifiers.j48.BinC45Split.: Class implementing a binary C4.5-like split on an attribute.
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.
bufferInput(Instance) - Method in class weka.filters.Filter: Adds the supplied input instance to the inputformat dataset for later processing.
buildAssociations(Instances) - Method in class weka.associations.Associator: Generates an associator.
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.
buildClassifier(Instances) - Method in class weka.classifiers.Classifier: Generates a classifier.
buildClassifier(Instances) - Method in class weka.classifiers.DecisionTable: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.MetaCost: Builds the model of the base learner.
buildClassifier(Instances) - Method in class weka.classifiers.Prism: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.DecisionStump: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.AdditiveRegression: Build the classifier on the supplied data
buildClassifier(Instances) - Method in class weka.classifiers.VotedPerceptron: Builds the ensemble of perceptrons.
buildClassifier(Instances) - Method in class weka.classifiers.Bagging: Bagging method.
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.IBk: Generates 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.KernelDensity: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.IB1: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.ThresholdSelector: Generates the classifier.
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.RegressionByDiscretization: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.ZeroR: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.NaiveBayesSimple: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.CVParameterSelection: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.AdaBoostM1: Boosting method.
buildClassifier(Instances) - Method in class weka.classifiers.Id3: Builds Id3 decision tree classifier.
buildClassifier(Instances) - Method in class weka.classifiers.FilteredClassifier: Build the classifier on the filtered data.
buildClassifier(Instances) - Method in class weka.classifiers.LinearRegression: Builds a regression model for the given data.
buildClassifier(Instances) - Method in class weka.classifiers.OneR: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.CostSensitiveClassifier: Builds the model of the base learner.
buildClassifier(Instances) - Method in class weka.classifiers.SMO: Method for building the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.MultiClassClassifier: Builds the classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.LWR: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.DistributionMetaClassifier: Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.Logistic: Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.VFI: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.LogitBoost: Boosting method.
buildClassifier(Instances) - Method in class weka.classifiers.HyperPipes: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.AttributeSelectedClassifier: Build the classifier on the dimensionally reduced data.
buildClassifier(Instances) - Method in class weka.classifiers.ClassificationViaRegression: Builds the classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.NaiveBayes: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.adtree.ADTree: Builds a classifier for a 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.j48.PruneableClassifierTree: Method for building a pruneable classifier tree.
buildClassifier(Instances) - Method in class weka.classifiers.j48.J48: Generates the 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.NoSplit: Creates a "no-split"-split for a given set of instances.
buildClassifier(Instances) - Method in class weka.classifiers.j48.PART: Generates the classifier.
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.j48.BinC45Split: Creates a C4.5-type split on the given data.
buildClassifier(Instances) - Method in class weka.classifiers.j48.MakeDecList: Builds dec list.
buildClassifier(Instances) - Method in class weka.classifiers.j48.C45PruneableClassifierTree: Method for building a pruneable classifier tree.
buildClassifier(Instances) - Method in class weka.classifiers.kstar.KStar: 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.neural.NeuralNetwork: Call this function to build and train a neural network for the training data provided.
buildClassifierUsingResampling(Instances) - Method in class weka.classifiers.AdaBoostM1: Boosting method.
buildClassifierWithWeights(Instances) - Method in class weka.classifiers.AdaBoostM1: Boosting method.
buildClusterer(Instances) - Method in class weka.clusterers.Clusterer: Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.SimpleKMeans: Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.DistributionMetaClusterer: Builds the clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.Cobweb: Builds the clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.EM: 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.PrincipalComponents: Initializes principal components and performs the analysis
buildEvaluator(Instances) - Method in class weka.attributeSelection.WrapperSubsetEval: 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.ReliefFAttributeEval: Initializes a ReliefF attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.GainRatioAttributeEval: Initializes a gain ratio attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Initializes a symmetrical uncertainty attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.OneRAttributeEval: Initializes an information gain attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.ClassifierSubsetEval: Generates a attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.InfoGainAttributeEval: Initializes an information gain attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.ConsistencySubsetEval: 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 - class weka.classifiers.BVDecompose.: Class for performing a Bias-Variance decomposition on any classifier using the method specified in:
BVDecompose() - Constructor for class weka.classifiers.BVDecompose

C

C45Loader - class weka.core.converters.C45Loader.: Reads C4.5 input files.
C45Loader() - Constructor for class weka.core.converters.C45Loader
C45ModelSelection - class weka.classifiers.j48.C45ModelSelection.: Class for selecting a C4.5-type split for a given dataset.
C45ModelSelection(int, Instances) - Constructor for class weka.classifiers.j48.C45ModelSelection: Initializes the split selection method with the given parameters.
C45PruneableClassifierTree - class weka.classifiers.j48.C45PruneableClassifierTree.: Class for handling a tree structure that can be pruned using C4.5 procedures.
C45PruneableClassifierTree(ModelSelection, boolean, float, boolean, boolean) - Constructor for class weka.classifiers.j48.C45PruneableClassifierTree: Constructor for pruneable tree structure.
C45PruneableDecList - class weka.classifiers.j48.C45PruneableDecList.: Class for handling a partial tree structure pruned using C4.5's pruning heuristic.
C45PruneableDecList(ModelSelection, double, int) - Constructor for class weka.classifiers.j48.C45PruneableDecList: Constructor for pruneable tree structure.
C45Split - class weka.classifiers.j48.C45Split.: Class implementing a C4.5-type split on an attribute.
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
calculateCutPoints() - Method in class weka.filters.DiscretizeFilter: Generate the cutpoints for each attribute
calculateCutPointsByBinning(int) - Method in class weka.filters.DiscretizeFilter: Set cutpoints for a single attribute.
calculateCutPointsByMDL(int, Instances) - Method in class weka.filters.DiscretizeFilter: Set cutpoints for a single attribute using MDL.
calculateDerived() - Method in class weka.experiment.Stats: Tells the object to calculate any statistics that don't have their values automatically updated during add.
calculateDerived() - Method in class weka.experiment.PairedStats: Calculates the derived statistics (significance etc).
calculateLogLikelihood(double[][], double[], Matrix, double[]) - Method in class weka.classifiers.Logistic: Calculates the log likelihood of the current set of coefficients (stored in m_Par), given the data.
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.PropertySelectorDialog: Signifies a cancelled property selection
CANCEL_OPTION - Static variable in class weka.gui.ListSelectorDialog: Signifies a cancelled property selection
cancelShapes() - Method in class weka.gui.visualize.VisualizePanel.PlotPanel: Sets the list of shapes to empty and also cancels the current shape being drawn (if applicable).
canHandleMissing(boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.classifiers.CheckClassifier: Checks basic missing value handling of the scheme.
canHandleNClasses(boolean, boolean, int) - Method in class weka.classifiers.CheckClassifier: Checks whether nominal schemes can handle more than two classes.
canHandleZeroTraining(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier: Checks whether the scheme can handle zero training instances.
canPredict(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier: Checks basic prediction of the scheme, for simple non-troublesome datasets.
canTakeOptions() - Method in class weka.classifiers.CheckClassifier: Checks whether the scheme can take command line options.
capacity() - Method in class weka.core.FastVector: Returns the capacity of the vector.
CfsSubsetEval - class weka.attributeSelection.CfsSubsetEval.: CFS attribute subset evaluator.
CfsSubsetEval() - Constructor for class weka.attributeSelection.CfsSubsetEval: Constructor
changeInputNum(int, int) - Method in class weka.classifiers.neural.NeuralConnection: Changes the connection value information for one of the connections.
changeOutputNum(int, int) - Method in class weka.classifiers.neural.NeuralConnection: Changes the connection value information for one of the connections.
check(double) - Method in class weka.classifiers.j48.Distribution: Checks if at least two bags contain a minimum number of instances.
CheckClassifier - class weka.classifiers.CheckClassifier.: Class for examining the capabilities and finding problems with classifiers.
CheckClassifier() - Constructor for class weka.classifiers.CheckClassifier
checkForDuplicateKeys(Object[]) - Method in class weka.experiment.AveragingResultProducer: Checks whether any duplicate results (with respect to a key template) were received.
checkForMultipleDifferences() - Method in class weka.experiment.AveragingResultProducer: Checks that the keys for a run only differ in one key field.
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 - class weka.core.CheckOptionHandler.: Simple command line checking of classes that implement OptionHandler.
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 - class weka.attributeSelection.ChiSquaredAttributeEval.: Class for Evaluating attributes individually by measuring the chi-squared statistic with respect to the class.
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.ClassifierDecList: Dummy method.
chooseIndex() - Method in class weka.classifiers.j48.PruneableDecList: Method for choosing a subset to expand.
chooseIndex() - Method in class weka.classifiers.j48.C45PruneableDecList: Method for choosing a subset to expand.
chooseLastIndex() - Method in class weka.classifiers.j48.ClassifierDecList: Dummy method.
chooseLastIndex() - Method in class weka.classifiers.j48.PruneableDecList: Choose last index (ie.
chooseLastIndex() - Method in class weka.classifiers.j48.C45PruneableDecList: Choose last index (ie.
classAttribute() - Method in class weka.core.Instances: Returns the class attribute.
classAttribute() - Method in class weka.core.Instance: Returns 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 - class weka.classifiers.ClassificationViaRegression.: Class for doing classification using regression methods.
ClassificationViaRegression() - Constructor for class weka.classifiers.ClassificationViaRegression
Classifier - class weka.classifiers.Classifier.: Abstract classifier.
Classifier() - Constructor for class weka.classifiers.Classifier
ClassifierDecList - class weka.classifiers.j48.ClassifierDecList.: Class for handling a rule (partial tree) for a decision list.
ClassifierDecList(ModelSelection) - Constructor for class weka.classifiers.j48.ClassifierDecList: Constructor - just calls constructor of class DecList.
ClassifierPanel - class weka.gui.explorer.ClassifierPanel.: This panel allows the user to select and configure a classifier, set the attribute of the current dataset to be used as the class, and evaluate the classifier using a number of testing modes (test on the training data, train/test on a percentage split, n-fold cross-validation, test on a separate split).
ClassifierPanel() - Constructor for class weka.gui.explorer.ClassifierPanel: Creates the classifier panel
ClassifierSplitEvaluator - class weka.experiment.ClassifierSplitEvaluator.: A SplitEvaluator that produces results for a classification scheme on a nominal class attribute.
ClassifierSplitEvaluator() - Constructor for class weka.experiment.ClassifierSplitEvaluator: No args constructor.
ClassifierSplitModel - class weka.classifiers.j48.ClassifierSplitModel.: Abstract class for classification models that can be used recursively to split the data.
ClassifierSplitModel() - Constructor for class weka.classifiers.j48.ClassifierSplitModel
ClassifierSubsetEval - class weka.attributeSelection.ClassifierSubsetEval.: Classifier subset evaluator.
ClassifierSubsetEval() - Constructor for class weka.attributeSelection.ClassifierSubsetEval
classifierTipText() - Method in class weka.attributeSelection.WrapperSubsetEval: 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.AdditiveRegression: Returns the tip text for this property
classifierTipText() - Method in class weka.classifiers.CostSensitiveClassifier
classifierTipText() - Method in class weka.classifiers.AttributeSelectedClassifier: Returns the tip text for this property
classifierTipText() - Method in class weka.experiment.RegressionSplitEvaluator: Returns the tip text for this property
classifierTipText() - Method in class weka.experiment.ClassifierSplitEvaluator: Returns the tip text for this property
ClassifierTree - class weka.classifiers.j48.ClassifierTree.: Class for handling a tree structure used for classification.
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.Classifier: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.DistributionClassifier: Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.MetaCost: Classifies a given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.Prism: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.AdditiveRegression: Classify an instance.
classifyInstance(Instance) - Method in class weka.classifiers.Stacking: Classifies a given instance using the stacked classifier.
classifyInstance(Instance) - Method in class weka.classifiers.IB1: Classifies the 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.RegressionByDiscretization: Returns a predicted class for the test instance.
classifyInstance(Instance) - Method in class weka.classifiers.ZeroR: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.CVParameterSelection: Predicts the class value for 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.LinearRegression: Classifies the given instance using the linear regression function.
classifyInstance(Instance) - Method in class weka.classifiers.OneR: Classifies a given 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.LWR: Predicts the class value for the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.j48.ClassifierDecList: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.j48.ClassifierTree: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.j48.J48: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.j48.ClassifierSplitModel: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.j48.PART: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.j48.MakeDecList: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.m5.M5Prime: Classifies the given test instance.
classIndex() - Method in class weka.core.Instances: Returns the class attribute's index.
classIndex() - Method in class weka.core.Instance: 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 - class weka.gui.visualize.ClassPanel.: This panel displays coloured labels for nominal attributes and a spectrum for numeric attributes.
ClassPanel() - Constructor for class weka.gui.visualize.ClassPanel
classProb(int, Instance, int) - Method in class weka.classifiers.j48.ClassifierSplitModel: 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.BinC45Split: 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.C45ModelSelection: Sets reference to training data to null.
cleanup() - Method in class weka.classifiers.j48.BinC45ModelSelection: 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.KStarCache.CacheTable: Clears this hashtable so that it contains no keys.
clear() - Method in class weka.classifiers.kstar.LightHashTable: Clears this hashtable so that it contains no keys.
clear(int) - Method in class weka.attributeSelection.GeneticSearch.GABitSet: unset a bit in the chromosome
clone() - Method in class weka.attributeSelection.GeneticSearch.GABitSet: makes a copy of this GABitSet
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.classifiers.adtree.Splitter: Clones this node.
clone() - Method in class weka.classifiers.adtree.TwoWayNominalSplit: Clones this node.
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.adtree.TwoWayNumericSplit: Clones this node.
clone() - Method in class weka.classifiers.adtree.PredictionNode: Clones this node.
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 class weka.classifiers.j48.ClassifierSplitModel: Allows to clone a model (shallow copy).
clone() - Method in class weka.core.Matrix: Creates and returns a clone of this object.
Clusterer - class weka.clusterers.Clusterer.: Abstract clusterer.
Clusterer() - Constructor for class weka.clusterers.Clusterer
ClustererPanel - class weka.gui.explorer.ClustererPanel.: This panel allows the user to select and configure a clusterer, and evaluate the clusterer using a number of testing modes (test on the training data, train/test on a percentage split, test on a separate split).
ClustererPanel() - Constructor for class weka.gui.explorer.ClustererPanel: Creates the clusterer panel
ClusterEvaluation - class weka.clusterers.ClusterEvaluation.: Class for evaluating clustering models.
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.SimpleKMeans: Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.DistributionClusterer: Assigns an instance to a Cluster.
clusterInstance(Instance) - Method in class weka.clusterers.Cobweb: Clusters an instance.
clusterResultsToString() - Method in class weka.clusterers.ClusterEvaluation: return the results of clustering.
Cobweb - class weka.clusterers.Cobweb.
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.ClassifierSplitModel: Returns coding costs of model.
codingCost() - Method in class weka.classifiers.j48.C45Split: Returns coding cost for split (used in rule learner).
collapse() - Method in class weka.classifiers.j48.C45PruneableClassifierTree: Collapses a tree to a node if training error doesn't increase.
Colors - class weka.gui.treevisualizer.Colors.: This class maintains a list that contains all the colornames from the dotty standard and what color (in RGB) they represent
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.
compareDatasets(Instances, Instances) - Method in class weka.classifiers.CheckClassifier: Compare two datasets to see if they differ.
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.
Compute - interface weka.experiment.Compute.: Interface to something that can accept remote connections and execute a task.
ConditionalEstimator - interface weka.estimators.ConditionalEstimator.: Interface for conditional probability estimators.
CONFIDENCE - Static variable in class weka.associations.Apriori: Metric types.
confidenceForRule(ItemSet, ItemSet) - Static method in class weka.associations.ItemSet: Outputs the confidence for a rule.
ConfusionMatrix - class weka.classifiers.evaluation.ConfusionMatrix.: Cells of this matrix correspond to counts of the number (or weight) of predictions for each actual value / predicted value combination.
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.
connectInput(NeuralConnection, int) - Method in class weka.classifiers.neural.NeuralConnection: This will connect the specified unit to be an input to this unit.
connectInput(NeuralConnection, int) - Method in class weka.classifiers.neural.NeuralNode: This will connect the specified unit to be an input to this unit.
CONNECTION_FAILED - Static variable in class weka.experiment.RemoteExperiment
connectOutput(NeuralConnection, int) - Method in class weka.classifiers.neural.NeuralConnection: This will connect the specified unit to be an output to this unit.
connectToDatabase() - Method in class weka.experiment.DatabaseUtils: Opens a connection to the database
ConsistencySubsetEval - class weka.attributeSelection.ConsistencySubsetEval.: Consistency attribute subset evaluator.
ConsistencySubsetEval.hashKey - class weka.attributeSelection.ConsistencySubsetEval.hashKey.: Class providing keys to the hash table.
ConsistencySubsetEval.hashKey(ConsistencySubsetEval, double[]) - Constructor for class weka.attributeSelection.ConsistencySubsetEval.hashKey: Constructor for a hashKey
ConsistencySubsetEval.hashKey(ConsistencySubsetEval, Instance, int) - Constructor for class weka.attributeSelection.ConsistencySubsetEval.hashKey: Constructor for a hashKey
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.KStarCache.CacheTable: Tests if the specified double is a key in this hashtable.
containsKey(double) - Method in class weka.classifiers.kstar.LightHashTable: Tests if the specified double is a key in this hashtable.
contents() - Method in class weka.core.Queue.QueueNode: Returns the contents in the node.
contents(Object) - Method in class weka.core.Queue.QueueNode: Sets the contents of the node.
ContingencyTables - class weka.core.ContingencyTables.: Class implementing some statistical routines for contingency tables.
ContingencyTables() - Constructor for class weka.core.ContingencyTables
ConverterUtils - class weka.core.converters.ConverterUtils.: Utility routines for the converter package.
ConverterUtils() - Constructor for class weka.core.converters.ConverterUtils
convertInstance(Instance) - Method in class weka.attributeSelection.PrincipalComponents: Transform an instance in original (unormalized) format.
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.filters.AttributeSelectionFilter: Convert a single instance over.
convertInstance(Instance) - Method in class weka.filters.DiscretizeFilter: Convert a single instance over.
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.
convertToRelativePath(File) - Method in class weka.gui.experiment.DatasetListPanel: Converts a File's absolute path to a path relative to the user (ie start) directory
CONVICTION - Static variable in class weka.associations.Apriori
convictionForRule(ItemSet, ItemSet, int, int) - Method in class weka.associations.ItemSet: Outputs the conviction for a rule.
copy() - Method in class weka.classifiers.m5.SplitInfo: Makes a copy of this SplitInfo object
copy() - Method in class weka.classifiers.m5.Function: Makes a copy of a function
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.core.Instance: 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.core.SparseInstance: Produces a shallow copy of this instance.
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(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
Copyable - interface weka.core.Copyable.: Interface implemented by classes that can produce "shallow" copies of their objects.
CopyAttributesFilter - class weka.filters.CopyAttributesFilter.: An instance filter that copies a range of attributes in the dataset.
CopyAttributesFilter() - Constructor for class weka.filters.CopyAttributesFilter
copyElements() - Method in class weka.core.FastVector: Clones the vector and shallow copies all its elements.
copyObject(Object) - Method in class weka.gui.GenericObjectEditor.GOEPanel: Makes a copy of an object using serialization
copyStringValues(Instance, boolean, Instances, Instances) - Method in class weka.filters.Filter: Takes string values referenced by an Instance and copies them from a source dataset to a destination dataset.
copyStringValues(Instance, boolean, Instances, int[], Instances, int[]) - Method in class weka.filters.Filter: Takes string values referenced by an Instance and copies them from a source dataset to a destination dataset.
correct() - Method in class weka.classifiers.Evaluation: Gets the number of instances correctly classified (that is, for which a correct prediction was made).
correct() - Method in class weka.classifiers.evaluation.ConfusionMatrix: Gets the number of correct classifications (that is, for which a correct prediction was made).
correctBuildInitialisation(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier: Checks whether the scheme correctly initialises models when buildClassifier is called.
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 - class weka.classifiers.evaluation.CostCurve.: Generates points illustrating probablity cost tradeoffs that can be obtained by varying the threshold value between classes.
CostCurve() - Constructor for class weka.classifiers.evaluation.CostCurve
CostMatrix - class weka.classifiers.CostMatrix.: Class for a misclassification cost matrix.
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 - class weka.gui.CostMatrixEditor.: A PropertyEditor for CostMatrices.
CostMatrixEditor() - Constructor for class weka.gui.CostMatrixEditor
costMatrixSourceTipText() - Method in class weka.classifiers.CostSensitiveClassifier
costMatrixTipText() - Method in class weka.classifiers.CostSensitiveClassifier
CostSensitiveClassifier - class weka.classifiers.CostSensitiveClassifier.: This metaclassifier makes its base classifier cost-sensitive.
CostSensitiveClassifier() - Constructor for class weka.classifiers.CostSensitiveClassifier
CostSensitiveClassifierSplitEvaluator - class weka.experiment.CostSensitiveClassifierSplitEvaluator.: A SplitEvaluator that produces results for a classification scheme on a nominal class attribute, including weighted misclassification costs.
CostSensitiveClassifierSplitEvaluator() - Constructor for class weka.experiment.CostSensitiveClassifierSplitEvaluator
count - Variable in class weka.experiment.Stats: The number of values seen
count - Variable in class weka.experiment.PairedStats: The number of data points 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.
createFileChooser() - Method in class weka.gui.GenericObjectEditor.GOEPanel
createNodes(DefaultMutableTreeNode) - Method in class weka.gui.PropertySelectorDialog: Creates the property tree below the current node.
createOptions() - Method in class weka.classifiers.CVParameterSelection: Create the options array to pass to the classifier.
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[]) - 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.
CrossValidationResultProducer - class weka.experiment.CrossValidationResultProducer.: Generates for each run, carries out an n-fold cross-validation, using the set SplitEvaluator to generate some results.
CrossValidationResultProducer() - Constructor for class weka.experiment.CrossValidationResultProducer
CSVLoader - class weka.core.converters.CSVLoader.: Reads a text file that is comma or tab delimited..
CSVLoader() - Constructor for class weka.core.converters.CSVLoader
CSVResultListener - class weka.experiment.CSVResultListener.: CSVResultListener outputs the received results in csv format to a Writer
CSVResultListener() - Constructor for class weka.experiment.CSVResultListener
CVParameterSelection - class weka.classifiers.CVParameterSelection.: Class for performing parameter selection by cross-validation for any classifier.
CVParameterSelection.CVParameter - class weka.classifiers.CVParameterSelection.CVParameter.
CVParameterSelection.CVParameter(CVParameterSelection, String) - Constructor for class weka.classifiers.CVParameterSelection.CVParameter: Constructs a CVParameter.
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 - class weka.experiment.DatabaseResultListener.: DatabaseResultListener takes the results from a ResultProducer and submits them to a central database.
DatabaseResultListener() - Constructor for class weka.experiment.DatabaseResultListener: Sets up the database drivers
DatabaseResultProducer - class weka.experiment.DatabaseResultProducer.: DatabaseResultProducer examines a database and extracts out the results produced by the specified ResultProducer and submits them to the specified ResultListener.
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 - class weka.experiment.DatabaseUtils.: DatabaseUtils provides utility functions for accessing the experiment database.
DatabaseUtils() - Constructor for class weka.experiment.DatabaseUtils: Sets up the database drivers
DATASET_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer
DATASET_FIELD_NAME - Static variable in class weka.experiment.RandomSplitResultProducer
dataset() - Method in class weka.core.Instance: Returns the dataset this instance has access to.
datasetIntegrity(boolean, boolean, boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier: Checks whether the scheme alters the training dataset during training.
DatasetListPanel - class weka.gui.experiment.DatasetListPanel.: This panel controls setting a list of datasets for an experiment to iterate over.
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 - class weka.estimators.DDConditionalEstimator.: Conditional probability estimator for a discrete domain conditional upon a discrete domain.
DDConditionalEstimator(int, int, boolean) - Constructor for class weka.estimators.DDConditionalEstimator: Constructor
debugTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
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
decayTipText() - Method in class weka.classifiers.neural.NeuralNetwork
DecisionStump - class weka.classifiers.DecisionStump.: Class for building and using a decision stump.
DecisionStump() - Constructor for class weka.classifiers.DecisionStump
DecisionTable - class weka.classifiers.DecisionTable.: Class for building and using a simple decision table majority classifier.
DecisionTable.hashKey - class weka.classifiers.DecisionTable.hashKey.: Class providing keys to the hash table
DecisionTable.hashKey(DecisionTable, double[]) - Constructor for class weka.classifiers.DecisionTable.hashKey: Constructor for a hashKey
DecisionTable.hashKey(DecisionTable, Instance, int) - Constructor for class weka.classifiers.DecisionTable.hashKey: Constructor for a hashKey
DecisionTable.Link - class weka.classifiers.DecisionTable.Link.: Class for a node in a linked list.
DecisionTable.Link(DecisionTable, BitSet, double) - Constructor for class weka.classifiers.DecisionTable.Link: The constructor.
DecisionTable.LinkedList - class weka.classifiers.DecisionTable.LinkedList.: Class for handling a linked list.
DecisionTable.LinkedList(DecisionTable) - Constructor for class weka.classifiers.DecisionTable.LinkedList
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_NUM_PRECISION - Static variable in class weka.classifiers.NaiveBayes: The precision parameter used for numeric attributes
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.Instances: Deletes an attribute at the given position (0 to numAttributes() - 1).
deleteAttributeAt(int) - Method in class weka.core.Instance: 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.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.LearningRateResultProducer: 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.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.DatabaseResultListener: Determines if there are any constraints (imposed by the destination) on any additional measures 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.
determineTemplate(int) - Method in class weka.experiment.AveragingResultProducer: Simulates a run to collect the keys the sub-resultproducer could generate.
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.
disconnectInput(NeuralConnection, int) - Method in class weka.classifiers.neural.NeuralConnection: This will disconnect the input with the specific connection number From this node (only on this end however).
disconnectInput(NeuralConnection, int) - Method in class weka.classifiers.neural.NeuralNode: This will disconnect the input with the specific connection number From this node (only on this end however).
disconnectOutput(NeuralConnection, int) - Method in class weka.classifiers.neural.NeuralConnection: This will disconnect the output with the specific connection number From this node (only on this end however).
DiscreteEstimator - class weka.estimators.DiscreteEstimator.: Simple symbolic probability estimator based on symbol counts.
DiscreteEstimator(int, boolean) - Constructor for class weka.estimators.DiscreteEstimator: Constructor
DiscretizeFilter - class weka.filters.DiscretizeFilter.: An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
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 - class weka.gui.experiment.DistributeExperimentPanel.: This panel enables an experiment to be distributed to multiple hosts; it also allows remote host names to be specified.
DistributeExperimentPanel() - Constructor for class weka.gui.experiment.DistributeExperimentPanel: Constructor
DistributeExperimentPanel(Experiment) - Constructor for class weka.gui.experiment.DistributeExperimentPanel: Creates the panel with the supplied initial experiment.
Distribution - class weka.classifiers.j48.Distribution.: Class for handling a distribution of class values.
distribution() - Method in class weka.classifiers.evaluation.NominalPrediction: Gets the predicted probabilities
distribution() - Method in class weka.classifiers.j48.ClassifierSplitModel: Returns the distribution of class values induced by the model.
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 - class weka.classifiers.DistributionClassifier.: Abstract classification model that produces (for each test instance) an estimate of the membership in each class (ie.
distributionClassifier() - Method in class weka.classifiers.CheckClassifier: Checks whether the scheme is a distribution classifier.
DistributionClassifier() - Constructor for class weka.classifiers.DistributionClassifier
distributionClassifierTipText() - Method in class weka.classifiers.ThresholdSelector
distributionClassifierTipText() - Method in class weka.classifiers.MultiClassClassifier
DistributionClusterer - class weka.clusterers.DistributionClusterer.: Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie.
DistributionClusterer() - Constructor for class weka.clusterers.DistributionClusterer
distributionForInstance(Instance) - Method in class weka.classifiers.DistributionClassifier: Predicts the class memberships for a given 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.DecisionStump: Calculates the class membership probabilities for 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.Bagging: 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.IBk: Calculates the class membership probabilities for the given test 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.ThresholdSelector: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.ZeroR: 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.AdaBoostM1: 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.FilteredClassifier: Classifies a given instance after filtering.
distributionForInstance(Instance) - Method in class weka.classifiers.SMO: Outputs the distribution for the given output.
distributionForInstance(Instance) - Method in class weka.classifiers.MultiClassClassifier: Returns the distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.DistributionMetaClassifier: Returns the distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.Logistic: Computes the distribution for a given instance
distributionForInstance(Instance) - Method in class weka.classifiers.VFI: Classifies the given test 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.HyperPipes: Classifies the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.AttributeSelectedClassifier: Classifies a given instance after attribute selection
distributionForInstance(Instance) - Method in class weka.classifiers.ClassificationViaRegression: 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.adtree.ADTree: Returns the class probability 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.j48.J48: Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.j48.PART: Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.j48.MakeDecList: Returns the class distribution for an 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.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.clusterers.DistributionClusterer: Predicts the cluster memberships for a given 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, boolean) - Method in class weka.classifiers.j48.ClassifierTree: Returns class probabilities for a weighted instance.
DistributionMetaClassifier - class weka.classifiers.DistributionMetaClassifier.: Class that wraps up a Classifier and presents it as a DistributionClassifier for ease of programmatically handling Classifiers in general -- only the one predict method (distributionForInstance) need be worried about.
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 - class weka.clusterers.DistributionMetaClusterer.: Class that wraps up a Clusterer and presents it as a DistributionClusterer for ease of programmatically handling Clusterers in general -- only the one predict method (distributionForInstance) need be worried about.
DistributionMetaClusterer() - Constructor for class weka.clusterers.DistributionMetaClusterer
DKConditionalEstimator - class weka.estimators.DKConditionalEstimator.: Conditional probability estimator for a discrete domain conditional upon a numeric domain.
DKConditionalEstimator(int, double) - Constructor for class weka.estimators.DKConditionalEstimator: Constructor
DNConditionalEstimator - class weka.estimators.DNConditionalEstimator.: Conditional probability estimator for a discrete domain conditional upon a numeric domain.
DNConditionalEstimator(int, double) - Constructor for class weka.estimators.DNConditionalEstimator: Constructor
doAverageResult(Object[]) - Method in class weka.experiment.AveragingResultProducer: Asks the resultlistener whether an average result is required, and if so, calculates it.
doesntUseTestClassVal(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier: Checks whether the classifier erroneously uses the class value of test instances (if provided).
doHistory(KeyEvent) - Method in class weka.gui.SimpleCLI: Changes the currently displayed command line when certain keys are pressed.
done() - Method in interface weka.classifiers.IterativeClassifier: Signal end of iterating, useful for any house-keeping/cleanup
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.
doRun(int) - Method in class weka.experiment.CrossValidationResultProducer: 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 class weka.experiment.AveragingResultProducer: 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 interface weka.experiment.ResultProducer: Gets the results 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.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 class weka.experiment.AveragingResultProducer: 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 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.
Drawable - interface weka.core.Drawable.: Interface to something that can be drawn as a graph.
drawDataPoint(double, double, double, double, int, int, Graphics) - Static method in class weka.gui.visualize.Plot2D: Draws a data point at a given set of panel coordinates at a given size and connects a line to the previous point.
drawDataPoint(double, double, int, int, Graphics) - Static method in class weka.gui.visualize.Plot2D: Draws a data point at a given set of panel coordinates at a given size.
drawHighlight(Graphics, int, int) - Method in class weka.classifiers.neural.NeuralConnection: Call this function to draw the node highlighted.
drawHighlight(Graphics, int, int) - Method in class weka.classifiers.neural.NeuralNetwork.NeuralEnd: 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.
drawNode(Graphics, int, int) - Method in class weka.classifiers.neural.NeuralNetwork.NeuralEnd: This will draw the node id to the graphics context.
drawOutputLines(Graphics, int, int) - Method in class weka.classifiers.neural.NeuralConnection: Call this function to draw the nodes output connections.
DRIVERS - Static variable in class weka.experiment.DatabaseUtils: Holds the jdbc drivers to be used (only to stop them being gc'ed)
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 - class weka.classifiers.m5.Dvector.: Class for handling a double vector.
Dvector() - Constructor for class weka.classifiers.m5.Dvector

E

Edge - class weka.gui.treevisualizer.Edge.: This class is used in conjunction with the Node class to form a tree structure.
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 - class weka.clusterers.EM.: Simple EM (estimation maximisation) class.
EM() - Constructor for class weka.clusterers.EM: Constructor.
empty() - Method in class weka.core.Queue: Checks if queue is empty.
EmptyAttributeFilter - class weka.filters.EmptyAttributeFilter.: Removes all attributes that do not contain more than one distinct value.
EmptyAttributeFilter() - Constructor for class weka.filters.EmptyAttributeFilter
entropy(double[]) - Static method in class weka.core.ContingencyTables: Computes the entropy of the given array.
EntropyBasedSplitCrit - class weka.classifiers.j48.EntropyBasedSplitCrit.: "Abstract" class for computing splitting criteria based on the entropy of a class distribution.
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 - class weka.classifiers.j48.EntropySplitCrit.: Class for computing the entropy for a given distribution.
EntropySplitCrit() - Constructor for class weka.classifiers.j48.EntropySplitCrit
enumerateAttributes() - Method in class weka.core.Instances: Returns an enumeration of all the attributes.
enumerateAttributes() - Method in class weka.core.Instance: 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 class weka.classifiers.DecisionTable: Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.AdditiveRegression: 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.classifiers.adtree.ADTree: 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.classifiers.j48.PART: Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.m5.M5Prime: Returns an enumeration of the additional measure names
enumerateMeasures() - Method in interface weka.core.AdditionalMeasureProducer: Returns an enumeration of the measure names.
enumerateMeasures() - Method in class weka.experiment.RegressionSplitEvaluator: 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.LearningRateResultProducer: Returns an enumeration of any additional measure names that might be in the result producer
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.AveragingResultProducer: Returns an enumeration of any additional measure names that might be in the result producer
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.experiment.ClassifierSplitEvaluator: 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.associations.ItemSet: Tests if two item sets are equal.
equals(Object) - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey: Tests if two instances are equal
equals(Object) - Method in class weka.classifiers.DecisionTable.hashKey: Tests if two instances are equal
equals(Object) - Method in class weka.classifiers.Evaluation: Tests whether the current evaluation object is equal to another evaluation object
equals(Object) - Method in class weka.core.SelectedTag: Returns true if this SelectedTag equals another object
equals(Object) - Method in class weka.core.Attribute: Tests if given attribute is equal to this attribute.
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_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
error() - Method in class weka.classifiers.evaluation.NumericPrediction: Calculates the prediction error.
ErrorBasedMeritEvaluator - interface weka.attributeSelection.ErrorBasedMeritEvaluator.: Interface for evaluators that calculate the "merit" of attributes/subsets as the error of a learning scheme
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: Returns the estimated error rate or the root mean squared error (if the class is numeric).
errorRate() - Method in class weka.classifiers.evaluation.ConfusionMatrix: Returns the estimated error rate.
Errors - class weka.classifiers.m5.Errors.: Class for containing the evaluation results of a model
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.NeuralNetwork.NeuralEnd: Call this to get the error value of this unit, which in this case is the difference between the predicted class, and the actual class.
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.SigmoidUnit: This function calculates what the error value should be.
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.
Estimator - interface weka.estimators.Estimator.: Interface for probability estimators.
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.PrincipalComponents: Evaluates the merit of a transformed 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.ReliefFAttributeEval: Evaluates an individual attribute using ReliefF's instance based approach.
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.SymmetricalUncertAttributeEval: evaluates an individual attribute by measuring the symmetrical uncertainty between it and the class.
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.InfoGainAttributeEval: evaluates an individual attribute by measuring the amount of information gained about the class given the attribute.
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 distribution on a single instance.
evaluateModelOnce(double, Instance) - Method in class weka.classifiers.Evaluation: Evaluates the supplied prediction on a single instance.
evaluateProbability(double[]) - Method in class weka.classifiers.Logistic: Evaluate the probability for this point using the current coefficients
evaluateSubset(BitSet) - Method in class weka.attributeSelection.SubsetEvaluator: evaluates a subset of attributes
evaluateSubset(BitSet) - Method in class weka.attributeSelection.CfsSubsetEval: evaluates a subset of attributes
evaluateSubset(BitSet) - Method in class weka.attributeSelection.WrapperSubsetEval: 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, Instance, boolean) - Method in class weka.attributeSelection.HoldOutSubsetEvaluator: Evaluates a subset of attributes with respect to a single instance.
evaluateSubset(BitSet, Instance, boolean) - Method in class weka.attributeSelection.ClassifierSubsetEval: Evaluates a subset of attributes with respect to a single instance.
evaluateSubset(BitSet, Instances) - Method in class weka.attributeSelection.HoldOutSubsetEvaluator: Evaluates a subset of attributes with respect to a set of instances.
evaluateSubset(BitSet, Instances) - Method in class weka.attributeSelection.ClassifierSubsetEval: Evaluates a subset of attributes with respect to a set of instances.
Evaluation - class weka.classifiers.Evaluation.: Class for evaluating machine learning models.
Evaluation(Instances) - Constructor for class weka.classifiers.Evaluation: Initializes all the counters for the evaluation.
Evaluation(Instances, CostMatrix) - Constructor for class weka.classifiers.Evaluation: Initializes all the counters for the evaluation and also takes a cost matrix as parameter.
evaluationModeTipText() - Method in class weka.classifiers.ThresholdSelector
EvaluationUtils - class weka.classifiers.evaluation.EvaluationUtils.: Contains utility functions for generating lists of predictions in various manners.
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 - class weka.attributeSelection.ExhaustiveSearch.: Class for performing an exhaustive search.
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 - class weka.experiment.Experiment.: Holds all the necessary configuration information for a standard type experiment.
Experiment() - Constructor for class weka.experiment.Experiment
Experimenter - class weka.gui.experiment.Experimenter.: The main class for the experiment environment.
Experimenter(boolean) - Constructor for class weka.gui.experiment.Experimenter: Creates the experiment environment gui with no initial experiment
experimentIndexExists() - Method in class weka.experiment.DatabaseUtils: Returns true if the experiment index exists.
Explorer - class weka.gui.explorer.Explorer.: The main class for the Weka explorer.
Explorer() - Constructor for class weka.gui.explorer.Explorer: Creates the experiment environment gui with no initial experiment
expressionTipText() - Method in class weka.filters.AttributeExpressionFilter: Returns the tip text for this property
ExtensionFileFilter - class weka.gui.ExtensionFileFilter.: Provides a file filter for FileChoosers that accepts or rejects files based on their extension.
ExtensionFileFilter(String, String) - Constructor for class weka.gui.ExtensionFileFilter: Creates the ExtensionFileFilter

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 - class weka.core.FastVector.: Implements a fast vector class without synchronized methods.
FastVector.FastVectorEnumeration - class weka.core.FastVector.FastVectorEnumeration.: Class for enumerating the vector's elements.
FastVector.FastVectorEnumeration(FastVector, FastVector) - Constructor for class weka.core.FastVector.FastVectorEnumeration: Constructs an enumeration.
FastVector.FastVectorEnumeration(FastVector, FastVector, int) - Constructor for class weka.core.FastVector.FastVectorEnumeration: Constructs an enumeration with a special element.
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.core.Instances: The filename extension that should be used for arff files
FILE_EXTENSION - Static variable in class weka.experiment.Experiment: The filename extension that should be used for experiment files
FileEditor - class weka.gui.FileEditor.: A PropertyEditor for File objects that lets the user select a file.
FileEditor() - Constructor for class weka.gui.FileEditor
Filter - class weka.filters.Filter.: An abstract class for instance filters: objects that take instances as input, carry out some transformation on the instance and then output the instance.
Filter() - Constructor for class weka.filters.Filter
FilteredClassifier - class weka.classifiers.FilteredClassifier.: Class for running an arbitrary classifier on data that has been passed through an arbitrary 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.
filterInstances(Instances) - Method in class weka.gui.explorer.PreprocessPanel: Passes the supplied instances through all the filters that have been configured for use.
filterUserTestInstances() - Method in class weka.gui.explorer.ClassifierPanel: Attempts to filter the user specified test set through the most currently used set of filters (if any) from the pre-process panel.
filterUserTestInstances() - Method in class weka.gui.explorer.ClustererPanel: Attempts to filter the user specified test set through the most currently used set of filters (if any) from the pre-process panel.
findKeyIndex() - Method in class weka.experiment.AveragingResultProducer: Scans through the key field names of the result producer to find the index of the key field to average over.
findNumBins(int) - Method in class weka.filters.DiscretizeFilter: Optimizes the number of bins using leave-one-out cross-validation.
findNumBinsTipText() - Method in class weka.filters.DiscretizeFilter: Returns the tip text for this property
findParamsByCrossValidation(int, Instances) - Method in class weka.classifiers.CVParameterSelection: Finds the best parameter combination.
findThreshold(FastVector) - Method in class weka.classifiers.ThresholdSelector: Finds the best threshold, this implementation searches for the highest FMeasure.
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 - class weka.filters.FirstOrderFilter.: This instance filter takes a range of N numeric attributes and replaces them with N-1 numeric attributes, the values of which are the difference between consecutive attribute values from the original instance.
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.
flushInput() - Method in class weka.filters.Filter: This will remove all buffered instances from the inputformat dataset.
FMEASURE_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
fMeasure(int) - Method in class weka.classifiers.Evaluation: Calculate the F-Measure with respect to a particular class.
FOLD_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer
foldsTipText() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns the tip text for this property
foldsTipText() - Method in class weka.attributeSelection.RaceSearch: 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.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.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.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 - class weka.attributeSelection.ForwardSelection.: Class for performing a forward selection hill climbing search.
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 - class weka.classifiers.m5.Function.: Class for handling a linear function.
function() - Method in class weka.classifiers.m5.Node: Finds the appropriate order of the unsmoothed linear model at this node
Function() - Constructor for class weka.classifiers.m5.Function: Constructs a function of constant value
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.C45Split: Returns (C4.5-type) gain ratio for the generated split.
gainRatio() - Method in class weka.classifiers.j48.BinC45Split: Returns (C4.5-type) gain ratio for the generated split.
gainRatio(double[][]) - Static method in class weka.core.ContingencyTables: Computes gain ratio for contingency table (split on rows).
GainRatioAttributeEval - class weka.attributeSelection.GainRatioAttributeEval.: Class for Evaluating attributes individually by measuring gain ratio with respect to the class.
GainRatioAttributeEval() - Constructor for class weka.attributeSelection.GainRatioAttributeEval: Constructor
GainRatioSplitCrit - class weka.classifiers.j48.GainRatioSplitCrit.: Class for computing the gain ratio for a given distribution.
GainRatioSplitCrit() - Constructor for class weka.classifiers.j48.GainRatioSplitCrit
GAUSS - Static variable in class weka.classifiers.LWR
generateRankingTipText() - Method in class weka.attributeSelection.Ranker: 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.ForwardSelection: 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 - class weka.gui.experiment.GeneratorPropertyIteratorPanel.: This panel controls setting a list of values for an arbitrary resultgenerator property for an experiment to iterate over.
GeneratorPropertyIteratorPanel() - Constructor for class weka.gui.experiment.GeneratorPropertyIteratorPanel: Creates the property iterator panel initially disabled.
GeneratorPropertyIteratorPanel(Experiment) - Constructor for class weka.gui.experiment.GeneratorPropertyIteratorPanel: Creates the property iterator panel and sets the experiment.
GenericArrayEditor - class weka.gui.GenericArrayEditor.: A PropertyEditor for arrays of objects that themselves have property editors.
GenericArrayEditor() - Constructor for class weka.gui.GenericArrayEditor: Sets up the array editor.
GenericObjectEditor - class weka.gui.GenericObjectEditor.: A PropertyEditor for objects that themselves have been defined as editable in the GenericObjectEditor configuration file, which lists possible values that can be selected from, and themselves configured.
GenericObjectEditor.GOEPanel - class weka.gui.GenericObjectEditor.GOEPanel.: Handles the GUI side of editing values.
GenericObjectEditor.GOEPanel(GenericObjectEditor) - Constructor for class weka.gui.GenericObjectEditor.GOEPanel: Creates the GUI editor component
GenericObjectEditor() - Constructor for class weka.gui.GenericObjectEditor
GeneticSearch - class weka.attributeSelection.GeneticSearch.: Class for performing a genetic based search.
GeneticSearch.GABitSet - class weka.attributeSelection.GeneticSearch.GABitSet.
GeneticSearch.GABitSet(GeneticSearch) - Constructor for class weka.attributeSelection.GeneticSearch.GABitSet: Constructor
GeneticSearch() - Constructor for class weka.attributeSelection.GeneticSearch: Constructor.
get(int) - Method in class weka.attributeSelection.GeneticSearch.GABitSet: get the value of a bit in the chromosome
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.SelectedTagEditor: Gets the current value as text.
getAsText() - Method in class weka.gui.GenericObjectEditor: Returns null as we don't support getting/setting values as text.
getAttribute1() - Method in class weka.gui.visualize.VisualizePanelEvent
getAttribute2() - Method in class weka.gui.visualize.VisualizePanelEvent
getAttributeEvaluator() - Method in class weka.attributeSelection.RankSearch: Get the attribute evaluator used to generate the ranking.
getAttributeEvaluator() - Method in class weka.attributeSelection.RaceSearch: Get the attribute evaluator used to generate the ranking.
getAttributeIndex() - Method in class weka.filters.InstanceFilter: Get the attribute to be used for selection (-1 for last)
getAttributeIndex() - Method in class weka.filters.MergeTwoValuesFilter: Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.SwapAttributeValuesFilter: 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.AddFilter: Get the index where the attribute will be inserted
getAttributeIndex() - Method in class weka.filters.MakeIndicatorFilter: Get the index of the attribute used.
getAttributeIndices() - Method in class weka.filters.FirstOrderFilter: Get the current range selection
getAttributeIndices() - Method in class weka.filters.AttributeFilter: Get the current range selection.
getAttributeIndices() - Method in class weka.filters.DiscretizeFilter: Gets the current range selection
getAttributeIndices() - Method in class weka.filters.CopyAttributesFilter: Get the current range selection
getAttributeIndices() - Method in class weka.filters.AbstractTimeSeriesFilter: 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
getAttributeMax(int) - Method in class weka.classifiers.LWR: Gets an attributes maximum observed value
getAttributeMin(int) - Method in class weka.classifiers.IBk: Get an attributes minimum observed value
getAttributeMin(int) - Method in class weka.classifiers.LWR: Gets 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.MetaCost: Gets the size of each bag, as a percentage of the training set size.
getBagSizePercent() - Method in class weka.classifiers.Bagging: 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.
getBaseClassifierSpec(int) - Method in class weka.classifiers.Stacking: Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
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 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.
getCalculatedNumToSelect() - Method in class weka.attributeSelection.RaceSearch: Gets the calculated number of attributes to retain.
getCalculatedNumToSelect() - Method in class weka.attributeSelection.ForwardSelection: Gets the calculated number of attributes to retain.
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.
getChromosome() - Method in class weka.attributeSelection.GeneticSearch.GABitSet: get the chromosome
getCindex() - Method in class weka.gui.visualize.PlotData2D: Get the currently set colouring index of the data
getCIndex() - Method in class weka.gui.visualize.VisualizePanel: Get the index of the attribute selected for coloring
getClassesFromProperties() - Method in class weka.gui.GenericObjectEditor: Called when the class of object being edited changes.
getClassesToClusters() - Method in class weka.clusterers.ClusterEvaluation: Return the array (ordered by cluster number) of minimum error class to cluster mappings
getClassForIRStatistics() - Method in class weka.experiment.ClassifierSplitEvaluator: Get the value of ClassForIRStatistics.
getClassifier() - Method in class weka.attributeSelection.WrapperSubsetEval: Get the classifier used as the base learner.
getClassifier() - Method in class weka.attributeSelection.ClassifierSubsetEval: Get the classifier used as the base learner.
getClassifier() - Method in class weka.classifiers.MetaCost: Gets the distribution classifier used.
getClassifier() - Method in class weka.classifiers.AdditiveRegression: Gets the classifier used.
getClassifier() - Method in class weka.classifiers.Bagging: Get the classifier used as the classifier
getClassifier() - Method in class weka.classifiers.RegressionByDiscretization: Get the classifier used as the classifier
getClassifier() - Method in class weka.classifiers.CVParameterSelection: 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.AdaBoostM1: Get the classifier used as the classifier
getClassifier() - Method in class weka.classifiers.FilteredClassifier: Gets the classifier used.
getClassifier() - Method in class weka.classifiers.CostSensitiveClassifier: Gets the classifier used.
getClassifier() - Method in class weka.classifiers.DistributionMetaClassifier: Get the classifier used as the classifier
getClassifier() - Method in class weka.classifiers.CheckClassifier: Get the classifier used as the classifier
getClassifier() - Method in class weka.classifiers.LogitBoost: Get the classifier used as the classifier
getClassifier() - Method in class weka.classifiers.AttributeSelectedClassifier: Gets the classifier used.
getClassifier() - Method in class weka.classifiers.ClassificationViaRegression: Get the base classifier (regression scheme) used as the classifier
getClassifier() - Method in class weka.experiment.RegressionSplitEvaluator: Get the value of Classifier.
getClassifier() - Method in class weka.experiment.ClassifierSplitEvaluator: Get the value of Classifier.
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.
getClassifierSpec() - Method in class weka.classifiers.MetaCost: Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getClassifierSpec() - Method in class weka.classifiers.AdditiveRegression: Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getClassifierSpec() - Method in class weka.classifiers.FilteredClassifier: Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getClassifierSpec() - Method in class weka.classifiers.CostSensitiveClassifier: Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getClassifierSpec() - Method in class weka.classifiers.AttributeSelectedClassifier: Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getClassifierSpec(Classifier) - Method in class weka.classifiers.Stacking: Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getClassifierSpec(int) - Method in class weka.classifiers.MultiScheme: Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
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.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.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 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.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 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.MetaCost: Gets the misclassification cost matrix.
getCostMatrix() - Method in class weka.classifiers.CostSensitiveClassifier: Gets the misclassification cost matrix.
getCostMatrixSource() - Method in class weka.classifiers.MetaCost: Gets the source location method of the cost matrix.
getCostMatrixSource() - Method in class weka.classifiers.CostSensitiveClassifier: 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.ThresholdCurve: 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, 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.GenericArrayEditor: Returns the array editing component.
getCustomEditor() - Method in class weka.gui.FileEditor: Gets the custom editor 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.CSVLoader: Return the full data set.
getDataSet() - Method in class weka.core.converters.SerializedInstancesLoader: 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.C45Loader: Return the full data set.
getDataSet() - Method in class weka.core.converters.ArffLoader: 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.attributeSelection.RaceSearch: Get whether output is to be verbose
getDebug() - Method in class weka.classifiers.AdditiveRegression: Gets whether debugging has been turned on
getDebug() - Method in class weka.classifiers.IBk: Get the value of Debug.
getDebug() - Method in class weka.classifiers.MultiScheme: Get whether debugging is turned on
getDebug() - Method in class weka.classifiers.RegressionByDiscretization: Gets whether debugging output will be printed
getDebug() - Method in class weka.classifiers.CVParameterSelection: Gets whether debugging is turned on
getDebug() - Method in class weka.classifiers.BVDecompose: Gets whether debugging is turned on
getDebug() - Method in class weka.classifiers.AdaBoostM1: Get whether debugging is turned on
getDebug() - Method in class weka.classifiers.LinearRegression: Controls whether debugging output will be printed
getDebug() - Method in class weka.classifiers.LWR: SGts whether debugging output should be produced
getDebug() - Method in class weka.classifiers.Logistic: Gets whether debugging output will be printed.
getDebug() - Method in class weka.classifiers.CheckClassifier: Get whether debugging is turned on
getDebug() - Method in class weka.classifiers.LogitBoost: Get whether debugging is turned on
getDebug() - Method in class weka.clusterers.EM: Get debug mode
getDebug() - Method in class weka.filters.AttributeExpressionFilter: Gets whether debug is set
getDebug() - Method in class weka.gui.streams.InstanceCounter
getDebug() - Method in class weka.gui.streams.InstanceLoader
getDebug() - Method in class weka.gui.streams.InstanceViewer
getDebug() - Method in class weka.gui.streams.InstanceJoiner
getDebug() - Method in class weka.gui.streams.InstanceTable
getDebug() - Method in class weka.gui.streams.InstanceSavePanel
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.ThresholdSelector: Get the DistributionClassifier used as the classifier.
getDistributionClassifier() - Method in class weka.classifiers.MultiClassClassifier: Get the classifier 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.KStarCache.CacheTable: Returns the table entry to which the specified key is mapped in this hashtable.
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 class weka.estimators.DDConditionalEstimator: 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.DKConditionalEstimator: 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.DNConditionalEstimator: 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
getEstimator(double) - Method in interface weka.estimators.ConditionalEstimator: 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
getEvaluatorSpec() - Method in class weka.classifiers.AttributeSelectedClassifier: Gets the evaluator specification string, which contains the class name of the attribute evaluator and any options to it
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.VotedPerceptron: Get the value of exponent.
getExponent() - Method in class weka.classifiers.SMO: 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.
getFilesRecursively(File, Vector) - Method in class weka.gui.experiment.DatasetListPanel: Gets all the files in the given directory that match the currently selected extension.
getFillWithMissing() - Method in class weka.filters.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.
getFilters() - Method in class weka.gui.explorer.PreprocessPanel: Gets an array of all the filters that have been configured for use.
getFilterSpec() - Method in class weka.classifiers.FilteredClassifier: Gets the filter specification string, which contains the class name of the filter and any options to the filter
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.
getFitness() - Method in class weka.attributeSelection.GeneticSearch.GABitSet: gets the scaled fitness
getFlag(char, String[]) - Static method in class weka.core.Utils: Checks if the given array contains the flag "-Char".
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 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.
getGenerateRanking() - Method in class weka.attributeSelection.RaceSearch: Gets whether ranking has been requested.
getGenerateRanking() - Method in class weka.attributeSelection.ForwardSelection: Gets whether ranking has been requested.
getGlobalBlend() - Method in class weka.classifiers.kstar.KStar: Get the value of the global blend parameter
getGroup() - Method in class weka.attributeSelection.BestFirst.Link2: Get a group
getGroup() - Method in class weka.classifiers.DecisionTable.Link: Gets the group.
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.classifiers.neural.NeuralConnection
getID() - Method in class weka.core.Tag: Gets the numeric ID of the Tag.
getID() - Method in class weka.gui.streams.InstanceEvent: Get the event type
getID() - Method in class weka.gui.treevisualizer.TreeDisplayEvent
getInputFormat() - Method in class weka.filters.Filter: Gets the currently set inputformat instances.
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.
getInputStringIndex() - Method in class weka.filters.Filter: Returns an array containing the indices of all string attributes in the input format.
getInstance(StreamTokenizer, boolean) - Method in class weka.core.Instances: Reads a single instance using the tokenizer and appends it to the dataset.
getInstanceFull(StreamTokenizer, boolean) - Method in class weka.core.Instances: Reads a single instance using the tokenizer and appends it to the dataset.
getInstanceRange() - Method in class weka.filters.AbstractTimeSeriesFilter: Gets the number of instances forward to translate values between.
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.treevisualizer.Node: This will return the Instances object related to this node.
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.
getInstanceSparse(StreamTokenizer, boolean) - Method in class weka.core.Instances: Reads a single instance using the tokenizer and appends it to the dataset.
getInvert() - Method in class weka.core.Range: Gets whether the range sense is inverted, i.e.
getInvertSelection() - Method in class weka.filters.InstanceFilter: Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.SplitDatasetFilter: Gets if selection is to be inverted.
getInvertSelection() - Method in class weka.filters.AttributeFilter: 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.CopyAttributesFilter: Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.AbstractTimeSeriesFilter: 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
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.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.SelectedTagEditor: Returns a description of the property value as java source.
getJavaInitializationString() - Method in class weka.gui.FileEditor: Returns a representation of the current property value as java source.
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.
getKey() - Method in class weka.experiment.RegressionSplitEvaluator: Gets the key describing the current SplitEvaluator.
getKey() - Method in class weka.experiment.ClassifierSplitEvaluator: 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.RegressionSplitEvaluator: 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.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.AveragingResultProducer: 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.ClassifierSplitEvaluator: 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.
getKeyNames() - Method in interface weka.experiment.ResultProducer: Gets the names of each of the key 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 class weka.experiment.CrossValidationResultProducer: 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.AveragingResultProducer: 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.ClassifierSplitEvaluator: 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.
getKeyTypes() - Method in interface weka.experiment.ResultProducer: 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.Node: Get the value of label.
getLabel() - Method in class weka.gui.treevisualizer.Edge: Get the value of label.
getLearningRate() - Method in class weka.classifiers.neural.NeuralNetwork
getLine(int) - Method in class weka.gui.treevisualizer.Node: Returns the text String for the specfied line.
getLine(int) - Method in class weka.gui.treevisualizer.Edge: Returns line number n
getLink() - Method in class weka.classifiers.neural.NeuralNetwork.NeuralEnd
getLinkAt(int) - Method in class weka.attributeSelection.BestFirst.LinkedList2: returns the element (Link) at a specific index from the list.
getLinkAt(int) - Method in class weka.classifiers.DecisionTable.LinkedList: Returns the element (Link) at a specific index from the list.
getList() - Method in class weka.gui.ResultHistoryPanel: Gets the JList used by the results list
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.classifiers.LogitBoost: Get the maximum number of boost iterations
getMaxIterations() - Method in class weka.clusterers.EM: Get the maximum number of 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 class weka.classifiers.DecisionTable: 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.AttributeSelectedClassifier: 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.j48.J48: 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.classifiers.m5.M5Prime: Returns the value of the named measure
getMeasure(String) - Method in interface weka.core.AdditionalMeasureProducer: Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.RegressionSplitEvaluator: 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.LearningRateResultProducer: Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.RandomSplitResultProducer: Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.AveragingResultProducer: Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.DatabaseResultProducer: Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.ClassifierSplitEvaluator: Returns the value of the named measure
getMerit() - Method in class weka.classifiers.DecisionTable.Link: Gets the merit.
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.SymmetricalUncertAttributeEval: 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
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
getMostRecentFilters() - Method in class weka.gui.explorer.PreprocessPanel: gets a copy of the most recently applied filters.
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
getNewDecList(Instances, boolean) - Method in class weka.classifiers.j48.ClassifierDecList: Returns a newly created tree.
getNewDecList(Instances, boolean) - Method in class weka.classifiers.j48.C45PruneableDecList: Returns a newly created tree.
getNewDecList(Instances, Instances, boolean) - Method in class weka.classifiers.j48.ClassifierDecList: Returns a newly created tree.
getNewDecList(Instances, Instances, boolean) - Method in class weka.classifiers.j48.PruneableDecList: Returns a newly created tree.
getNewTree(Instances) - Method in class weka.classifiers.j48.ClassifierTree: Returns a newly created tree.
getNewTree(Instances) - Method in class weka.classifiers.j48.C45PruneableClassifierTree: Returns a newly created tree.
getNewTree(Instances, Instances) - Method in class weka.classifiers.j48.ClassifierTree: Returns a newly created tree.
getNewTree(Instances, Instances) - Method in class weka.classifiers.j48.PruneableClassifierTree: Returns a newly created tree.
getNextInstance() - Method in class weka.core.converters.AbstractLoader: Must be overridden by subclasses.
getNextInstance() - Method in class weka.core.converters.CSVLoader: CSVLoader is unable to process a data set incrementally.
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.
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.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.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.
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.SimpleKMeans: gets the number of clusters to generate
getNumClusters() - Method in class weka.clusterers.ClusterEvaluation: Return the number of clusters found for the most recent call to evaluateClusterer
getNumClusters() - Method in class weka.clusterers.EM: Get the number of clusters
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.classifiers.Stacking: Gets the number of folds for the cross-validation.
getNumFolds() - Method in class weka.classifiers.MultiScheme: Gets the number of folds for cross-validation.
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.j48.PART: Get the value of numFolds.
getNumFolds() - Method in class weka.experiment.CrossValidationResultProducer: 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.
getNumInputs() - Method in class weka.classifiers.neural.NeuralConnection
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.
getNumIterations() - Method in class weka.classifiers.Bagging: Gets the number of bagging iterations
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 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.
getNumToSelect() - Method in class weka.attributeSelection.RaceSearch: Gets the number of attributes to be retained.
getNumToSelect() - Method in class weka.attributeSelection.ForwardSelection: 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.
getObjective() - Method in class weka.attributeSelection.GeneticSearch.GABitSet: gets the objective merit
getOnDemandDirectory() - Method in class weka.classifiers.MetaCost: Returns the directory that will be searched for cost files when loading on demand.
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.
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.associations.Apriori: Gets the current settings of the Apriori object.
getOptions() - Method in class weka.attributeSelection.CfsSubsetEval: Gets the current settings of CfsSubsetEval
getOptions() - Method in class weka.attributeSelection.PrincipalComponents: Gets the current settings of PrincipalComponents
getOptions() - Method in class weka.attributeSelection.RankSearch: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.WrapperSubsetEval: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.BestFirst: Gets the current settings of BestFirst.
getOptions() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.ReliefFAttributeEval: Gets the current settings of ReliefFAttributeEval.
getOptions() - Method in class weka.attributeSelection.GainRatioAttributeEval: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.Ranker: Gets the current settings of ReliefFAttributeEval.
getOptions() - Method in class weka.attributeSelection.ExhaustiveSearch: Gets the current settings of RandomSearch.
getOptions() - Method in class weka.attributeSelection.ClassifierSubsetEval: Gets the current settings of ClassifierSubsetEval
getOptions() - Method in class weka.attributeSelection.RandomSearch: Gets the current settings of RandomSearch.
getOptions() - Method in class weka.attributeSelection.InfoGainAttributeEval: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.RaceSearch: Gets the current settings of BestFirst.
getOptions() - Method in class weka.attributeSelection.ForwardSelection: Gets the current settings of ReliefFAttributeEval.
getOptions() - Method in class weka.attributeSelection.GeneticSearch: Gets the current settings of ReliefFAttributeEval.
getOptions() - Method in class weka.classifiers.DecisionTable: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.MetaCost: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.AdditiveRegression: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.VotedPerceptron: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.Bagging: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.IBk: Gets the current settings of IBk.
getOptions() - Method in class weka.classifiers.Stacking: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.ThresholdSelector: 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.RegressionByDiscretization: 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.classifiers.BVDecompose: Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.classifiers.AdaBoostM1: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.FilteredClassifier: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.LinearRegression: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.OneR: Gets the current settings of the OneR classifier.
getOptions() - Method in class weka.classifiers.CostSensitiveClassifier: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.SMO: 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.LWR: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.DistributionMetaClassifier: 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.VFI: Gets the current settings of VFI
getOptions() - Method in class weka.classifiers.CheckClassifier: Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.classifiers.LogitBoost: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.AttributeSelectedClassifier: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.ClassificationViaRegression: 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.adtree.ADTree: Gets the current settings of ADTree.
getOptions() - Method in class weka.classifiers.j48.J48: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.j48.PART: 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.classifiers.m5.M5Prime: 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.clusterers.SimpleKMeans: Gets the current settings of SimpleKMeans
getOptions() - Method in class weka.clusterers.DistributionMetaClusterer: Gets the current settings of the Clusterer.
getOptions() - Method in class weka.clusterers.Cobweb: Gets the current settings of Cobweb.
getOptions() - Method in class weka.clusterers.EM: Gets the current settings of EM.
getOptions() - Method in interface weka.core.OptionHandler: Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.experiment.RegressionSplitEvaluator: 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.PairedTTester: Gets current settings of the PairedTTester.
getOptions() - Method in class weka.experiment.CSVResultListener: Gets the current settings of the Classifier.
getOptions() - Method in class weka.experiment.LearningRateResultProducer: Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.Experiment: Gets the current settings of the experiment iterator.
getOptions() - Method in class weka.experiment.InstanceQuery: Gets the current settings of InstanceQuery
getOptions() - Method in class weka.experiment.RandomSplitResultProducer: Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.AveragingResultProducer: Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.DatabaseResultProducer: Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.ClassifierSplitEvaluator: Gets the current settings of the Classifier.
getOptions() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Gets the current settings of the Classifier.
getOptions() - Method in class weka.filters.AttributeExpressionFilter: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.FirstOrderFilter: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.InstanceFilter: 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.filters.SplitDatasetFilter: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.RandomizeFilter: Gets the current settings of the filter.
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.filters.SwapAttributeValuesFilter: 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.filters.SpreadSubsampleFilter: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.DiscretizeFilter: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.CopyAttributesFilter: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.AbstractTimeSeriesFilter: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.NominalToBinaryFilter: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.StringToNominalFilter: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.AddFilter: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.ResampleFilter: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.MakeIndicatorFilter: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.NumericTransformFilter: Gets the current settings of the filter.
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.
getOutputStringIndex() - Method in class weka.filters.Filter: Returns an array containing the indices of all string attributes in the output format.
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.
getPredictions(Instances, int, int) - Method in class weka.classifiers.ThresholdSelector: Collects the classifier predictions using the specified evaluation method.
getProbability(double) - Method in class weka.estimators.NormalEstimator: 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.PoissonEstimator: 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.DiscreteEstimator: Get a probability estimate for a 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.KDConditionalEstimator: 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.KKConditionalEstimator: 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.NDConditionalEstimator: Get a probability estimate for a value
getProbability(double, double) - Method in class weka.estimators.NNConditionalEstimator: Get a probability estimate for a value
getProbability(double, double) - Method in interface weka.estimators.ConditionalEstimator: Get a probability for a value conditional on another 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
getRandom(int) - Method in class weka.classifiers.adtree.ADTree: Gets the next random value.
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.SpreadSubsampleFilter: Gets the random number seed.
getRandomSeed() - Method in class weka.filters.ResampleFilter: 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.RegressionSplitEvaluator: Gets the raw output from the classifier
getRawResultOutput() - Method in class weka.experiment.ClassifierSplitEvaluator: 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.RegressionSplitEvaluator: Gets the results for the supplied train and test datasets.
getResult(Instances, Instances) - Method in class weka.experiment.ClassifierSplitEvaluator: Gets the results for the supplied train and test datasets.
getResult(Instances, Instances) - Method in interface weka.experiment.SplitEvaluator: Gets the results for the supplied train and test datasets.
getResult(Instances, Instances) - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: 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.RegressionSplitEvaluator: 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.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.AveragingResultProducer: 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.ClassifierSplitEvaluator: 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.
getResultNames() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: 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.
getResultProducer() - Method in class weka.experiment.LearningRateResultProducer: 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.AveragingResultProducer: Get the ResultProducer.
getResultProducer() - Method in class weka.experiment.DatabaseResultProducer: 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.RegressionSplitEvaluator: 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.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.AveragingResultProducer: 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.ClassifierSplitEvaluator: 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.
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 interface weka.experiment.ResultProducer: Gets the data types of each of the result columns produced for a single run.
getRetrieval() - Method in class weka.core.converters.AbstractLoader
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
getSearchSpec() - Method in class weka.classifiers.AttributeSelectedClassifier: Gets the search specification string, which contains the class name of the search method and any options to it
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.attributeSelection.WrapperSubsetEval: Get the random number seed used for cross validation
getSeed() - Method in class weka.attributeSelection.ReliefFAttributeEval: Get the seed used for randomly sampling instances.
getSeed() - Method in class weka.attributeSelection.GeneticSearch: get the value of the random number generator's seed
getSeed() - Method in class weka.classifiers.MetaCost: Get seed for resampling.
getSeed() - Method in class weka.classifiers.VotedPerceptron: Get the value of Seed.
getSeed() - Method in class weka.classifiers.Bagging: Gets the seed for the random number generations
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.MultiScheme: Gets the random number seed.
getSeed() - Method in class weka.classifiers.CVParameterSelection: Gets the random number seed.
getSeed() - Method in class weka.classifiers.BVDecompose: Gets the random number seed
getSeed() - Method in class weka.classifiers.AdaBoostM1: Get seed for resampling.
getSeed() - Method in class weka.classifiers.CostSensitiveClassifier: Get seed for resampling.
getSeed() - Method in class weka.classifiers.LogitBoost: Get seed for resampling.
getSeed() - Method in class weka.classifiers.evaluation.EvaluationUtils: Gets the seed for randomization during cross-validation
getSeed() - Method in class weka.clusterers.SimpleKMeans: Get the random number seed
getSeed() - Method in class weka.clusterers.EM: Get the random number seed
getSeed() - Method in class weka.filters.SplitDatasetFilter: Gets the random number seed used for shuffling the dataset.
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.
getShapes() - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
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.attributeSelection.ReliefFAttributeEval: Get the value of sigma.
getSigma() - Method in class weka.classifiers.BVDecompose: Get the calculated sigma squared
getSignificanceLevel() - Method in class weka.associations.Apriori: Get the value of significanceLevel.
getSignificanceLevel() - Method in class weka.attributeSelection.RaceSearch: Get the significance level
getSignificanceLevel() - Method in class weka.experiment.PairedTTester: Get the value of SignificanceLevel.
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 interface weka.attributeSelection.StartSetHandler: Returns a list of attributes (and or attribute ranges) as a String
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.Ranker: 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.RandomSearch: 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
getStatusMessage() - Method in class weka.experiment.TaskStatusInfo: Get the status message.
getStepSize() - Method in class weka.experiment.LearningRateResultProducer: Get the value of StepSize.
getStringIndices(Instances) - Method in class weka.filters.Filter: Gets an array containing the indices of all string attributes.
getStructure() - Method in class weka.core.converters.AbstractLoader: Must be overridden by subclasses.
getStructure() - Method in class weka.core.converters.CSVLoader: Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.SerializedInstancesLoader: 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.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.ArffLoader: 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.core.SelectedTag: Gets the set of all valid Tags.
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.SelectedTagEditor: Gets the list of tags that can be selected from.
getTags() - Method in class weka.gui.GenericObjectEditor: Returns null as we don't support getting values as tags.
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 interface weka.attributeSelection.RankedOutputSearch: Gets the threshold by which attributes can be discarded.
getThreshold() - Method in class weka.attributeSelection.WrapperSubsetEval: Get the value of the threshold
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.RaceSearch: Get the threshold
getThreshold() - Method in class weka.attributeSelection.ForwardSelection: Returns the threshold so that the AttributeSelection module can discard attributes from the ranking.
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.
getTimestamp() - Static method in class weka.gui.LogPanel: Gets a string containing current date and time.
getTimestamp() - Static method in class weka.gui.SysErrLog: Gets a string containing current date and time.
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.classifiers.adtree.PredictionNode: Gets the prediction value of the node.
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.
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.associations.Apriori: Returns a string describing this associator
globalInfo() - Method in class weka.attributeSelection.CfsSubsetEval: 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.RankSearch: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.BestFirst: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.ReliefFAttributeEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.GainRatioAttributeEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.Ranker: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.OneRAttributeEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.ExhaustiveSearch: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.ClassifierSubsetEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.RandomSearch: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.InfoGainAttributeEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.RaceSearch: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.ConsistencySubsetEval: 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.GeneticSearch: Returns a string describing this search method
globalInfo() - Method in class weka.classifiers.AdditiveRegression: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.classifiers.UserClassifier: This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.ThresholdSelector
globalInfo() - Method in class weka.classifiers.CostSensitiveClassifier
globalInfo() - Method in class weka.classifiers.MultiClassClassifier
globalInfo() - Method in class weka.classifiers.VFI: Returns a string describing this search method
globalInfo() - Method in class weka.classifiers.AttributeSelectedClassifier: Returns a string describing this search method
globalInfo() - Method in class weka.classifiers.adtree.ADTree
globalInfo() - Method in class weka.classifiers.neural.NeuralNetwork: This will return a string describing the classifier.
globalInfo() - Method in class weka.clusterers.SimpleKMeans: Returns a string describing this clusterer
globalInfo() - Method in class weka.clusterers.EM: Returns a string describing this clusterer
globalInfo() - Method in class weka.core.converters.CSVLoader: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.core.converters.C45Loader: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.experiment.RegressionSplitEvaluator: Returns a string describing this split evaluator
globalInfo() - Method in class weka.experiment.CrossValidationResultProducer: Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.CSVResultListener: Returns a string describing this result listener
globalInfo() - Method in class weka.experiment.InstancesResultListener: Returns a string describing this result listener
globalInfo() - Method in class weka.experiment.LearningRateResultProducer: Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.RandomSplitResultProducer: Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.AveragingResultProducer: Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.DatabaseResultListener: Returns a string describing this result listener
globalInfo() - Method in class weka.experiment.DatabaseResultProducer: Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.ClassifierSplitEvaluator: Returns a string describing this split evaluator
globalInfo() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Returns a string describing this split evaluator
globalInfo() - Method in class weka.filters.SparseToNonSparseFilter: Returns a string describing this filter
globalInfo() - Method in class weka.filters.AllFilter: Returns a string describing this filter
globalInfo() - Method in class weka.filters.ObfuscateFilter: Returns a string describing this filter
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.filters.DiscretizeFilter: Returns a string describing this filter
globalInfo() - Method in class weka.filters.CopyAttributesFilter: Returns a string describing this filter
globalInfo() - Method in class weka.filters.AddFilter: Returns a string describing this filter
globalInfo() - Method in class weka.filters.MakeIndicatorFilter
globalInfo() - Method in class weka.filters.NonSparseToSparseFilter: Returns a string describing this filter
gr(double, double) - Static method in class weka.core.Utils: Tests if a is smaller than b.
graph() - Method in class weka.classifiers.UserClassifier
graph() - Method in class weka.classifiers.CostSensitiveClassifier: Returns graph describing the classifier (if possible).
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.j48.J48: Returns graph describing the tree.
graph() - Method in interface weka.core.Drawable: Returns a string that describes a graph representing the object.
graphTraverse(PredictionNode, StringBuffer, int, int, Instances) - Method in class weka.classifiers.adtree.ADTree: Traverses the tree, graphing each node.
grOrEq(double, double) - Static method in class weka.core.Utils: Tests if a is greater or equal to b.
GUIChooser - class weka.gui.GUIChooser.: The main class for the Weka GUIChooser.
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
hash - Variable in class weka.classifiers.kstar.KStarCache.TableEntry: attribute value hash code
hashCode() - Method in class weka.associations.ItemSet: Produces a hash code for a item set.
hashCode() - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey: Calculates a hash code
hashCode() - Method in class weka.classifiers.DecisionTable.hashKey: Calculates a hash code
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
hasMoreElements() - Method in class weka.core.FastVector.FastVectorEnumeration: Tests if there are any more elements to enumerate.
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
historyInput(Instance) - Method in class weka.filters.AbstractTimeSeriesFilter: Adds an instance to the history buffer.
HLINE - Static variable in class weka.gui.visualize.VisualizePanelEvent
holdOutFileTipText() - Method in class weka.attributeSelection.ClassifierSubsetEval: Returns the tip text for this property
HoldOutSubsetEvaluator - class weka.attributeSelection.HoldOutSubsetEvaluator.: Abstract attribute subset evaluator capable of evaluating subsets with respect to a data set that is distinct from that used to initialize/ train the subset evaluator.
HoldOutSubsetEvaluator() - Constructor for class weka.attributeSelection.HoldOutSubsetEvaluator
HostListPanel - class weka.gui.experiment.HostListPanel.: This panel controls setting a list of hosts for a RemoteExperiment to use.
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 - class weka.classifiers.HyperPipes.: Class implementing a HyperPipe classifier.
HyperPipes() - Constructor for class weka.classifiers.HyperPipes

I

IB1 - class weka.classifiers.IB1.: IB1-type classifier.
IB1() - Constructor for class weka.classifiers.IB1
IBk - class weka.classifiers.IBk.: K-nearest neighbour classifier.
IBk() - Constructor for class weka.classifiers.IBk: IB1 classifer.
IBk(int) - Constructor for class weka.classifiers.IBk: IBk classifier.
Id3 - class weka.classifiers.Id3.: Class implementing an Id3 decision tree classifier.
Id3() - Constructor for class weka.classifiers.Id3
Impurity - class weka.classifiers.m5.Impurity.: Class for handling the impurity values when spliting the instances
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
IN_USE - Static variable in class weka.experiment.RemoteExperiment
incorrect() - Method in class weka.classifiers.Evaluation: Gets the number of instances incorrectly classified (that is, for which an incorrect prediction was made).
incorrect() - Method in class weka.classifiers.evaluation.ConfusionMatrix: Gets the number of incorrect classifications (that is, for which an incorrect prediction was made).
INCREMENTAL - Static variable in class weka.core.converters.AbstractLoader: For representing that instances have been retrieved incrementally
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
incrementFailed(int) - Method in class weka.experiment.RemoteExperiment: Increment the overall number of failures and the number of failures for a particular host
incrementFinished() - Method in class weka.experiment.RemoteExperiment: Increment the number of successfully completed sub experiments
indeX - Variable in class weka.classifiers.j48.ClassifierDecList: Which son to expand?
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.C45Split: Returns (C4.5-type) information gain for the generated split.
infoGain() - Method in class weka.classifiers.j48.BinC45Split: Returns (C4.5-type) information gain for the generated split.
InfoGainAttributeEval - class weka.attributeSelection.InfoGainAttributeEval.: Class for Evaluating attributes individually by measuring information gain with respect to the class.
InfoGainAttributeEval() - Constructor for class weka.attributeSelection.InfoGainAttributeEval: Constructor
InfoGainSplitCrit - class weka.classifiers.j48.InfoGainSplitCrit.: Class for computing the information gain for a given distribution.
InfoGainSplitCrit() - Constructor for class weka.classifiers.j48.InfoGainSplitCrit
initClassifier(Instances) - Method in interface weka.classifiers.IterativeClassifier: Inits an iterative classifier.
initClassifier(Instances) - Method in class weka.classifiers.adtree.ADTree: Sets up the tree ready to be trained, using two-class optimized method.
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.core.Matrix: Resets the elements to default values (i.e.
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.Filter: 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.AllFilter: 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.ObfuscateFilter: 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.ReplaceMissingValuesFilter: 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.FirstOrderFilter: Input an instance for filtering.
input(Instance) - Method in class weka.filters.InstanceFilter: 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.MergeTwoValuesFilter: 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.SwapAttributeValuesFilter: 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.SpreadSubsampleFilter: 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.CopyAttributesFilter: Input an instance for filtering.
input(Instance) - Method in class weka.filters.AbstractTimeSeriesFilter: 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.StringToNominalFilter: 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.ResampleFilter: Input an instance for filtering.
input(Instance) - Method in class weka.filters.MakeIndicatorFilter: 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.NumericTransformFilter: Input an instance for filtering.
input(Instance) - Method in class weka.filters.NullFilter: Input an instance for filtering.
input(Instance) - Method in class weka.gui.streams.InstanceCounter
input(Instance) - Method in class weka.gui.streams.InstanceViewer
input(Instance) - Method in class weka.gui.streams.InstanceJoiner
input(Instance) - Method in class weka.gui.streams.InstanceTable
input(Instance) - Method in class weka.gui.streams.InstanceSavePanel
inputFormat(Instances) - Method in class weka.filters.Filter: Deprecated. use setInputFormat(Instances) instead.
inputFormat(Instances) - Method in class weka.gui.streams.InstanceCounter
inputFormat(Instances) - Method in class weka.gui.streams.InstanceViewer
inputFormat(Instances) - Method in class weka.gui.streams.InstanceJoiner: Sets the format of the input instances.
inputFormat(Instances) - Method in class weka.gui.streams.InstanceTable
inputFormat(Instances) - Method in class weka.gui.streams.InstanceSavePanel
insert(double, double, double) - Method in class weka.classifiers.kstar.KStarCache.CacheTable: Inserts a new entry in the hashtable using the specified key.
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
inSplit(Instance) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel: This will check if an instance is inside or outside of the current shapes.
Instance - class weka.core.Instance.: Class for handling an instance.
INSTANCE_AVAILABLE - Static variable in class weka.gui.streams.InstanceEvent: Specifies that an instance is available
Instance() - Constructor for class weka.core.Instance: Private constructor for subclasses.
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) - Method in class weka.core.Instances: Returns the instance at the given position.
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.
InstanceCounter - class weka.gui.streams.InstanceCounter.: A bean that counts instances streamed to it.
InstanceCounter() - Constructor for class weka.gui.streams.InstanceCounter
InstanceEvent - class weka.gui.streams.InstanceEvent.: An event encapsulating an instance stream event.
InstanceEvent(Object, int) - Constructor for class weka.gui.streams.InstanceEvent: Constructs an InstanceEvent with the specified source object and event type
InstanceFilter - class weka.filters.InstanceFilter.: Filters instances according to the value of an attribute.
InstanceFilter() - Constructor for class weka.filters.InstanceFilter: Default constructor
InstanceJoiner - class weka.gui.streams.InstanceJoiner.: A bean that joins two streams of instances into one.
InstanceJoiner() - Constructor for class weka.gui.streams.InstanceJoiner: Setup the initial states of the member variables
InstanceListener - interface weka.gui.streams.InstanceListener.: An interface for objects interested in listening to streams of instances.
InstanceLoader - class weka.gui.streams.InstanceLoader.: A bean that produces a stream of instances from a file.
InstanceLoader() - Constructor for class weka.gui.streams.InstanceLoader
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceCounter
instanceProduced(InstanceEvent) - Method in interface weka.gui.streams.InstanceListener
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceViewer
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceJoiner
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceTable
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceSavePanel
InstanceProducer - interface weka.gui.streams.InstanceProducer.: An interface for objects capable of producing streams of instances.
InstanceQuery - class weka.experiment.InstanceQuery.: Convert the results of a database query into instances.
InstanceQuery() - Constructor for class weka.experiment.InstanceQuery: Sets up the database drivers
Instances - class weka.core.Instances.: Class for handling an ordered set of weighted instances.
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 - class weka.gui.streams.InstanceSavePanel.: A bean that saves a stream of instances to a file.
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 - class weka.experiment.InstancesResultListener.: InstancesResultListener outputs the received results in arff format to a Writer.
InstancesResultListener() - Constructor for class weka.experiment.InstancesResultListener
InstancesSummaryPanel - class weka.gui.InstancesSummaryPanel.: This panel just displays relation name, number of instances, and number of attributes.
InstancesSummaryPanel() - Constructor for class weka.gui.InstancesSummaryPanel: Creates the instances panel with no initial instances.
InstanceTable - class weka.gui.streams.InstanceTable.: A bean that takes a stream of instances and displays in a table.
InstanceTable() - Constructor for class weka.gui.streams.InstanceTable
InstanceViewer - class weka.gui.streams.InstanceViewer.: This is a very simple instance viewer - just displays the dataset as text output as it would be written to a file.
InstanceViewer() - Constructor for class weka.gui.streams.InstanceViewer
instanceWeights(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier: Checks whether the classifier can handle instance weights.
intCount - Variable in class weka.core.AttributeStats: The number of int-like values
INVERSE - Static variable in class weka.classifiers.LWR
invertSelectionTipText() - Method in class weka.filters.AttributeFilter: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.DiscretizeFilter: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.CopyAttributesFilter: Returns the tip text for this property
isCacheValid(Object[]) - Method in class weka.experiment.DatabaseResultListener: Checks whether the current cache contents are valid for the supplied key.
isConnected() - Method in class weka.experiment.DatabaseUtils: Returns true if a database connection is active.
isEmpty() - Method in class weka.classifiers.kstar.KStarCache.CacheTable: Tests if this hashtable maps no keys to values.
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.
isKeyInCache(ResultProducer, Object[]) - Method in class weka.experiment.DatabaseResultListener: Returns true if the supplied key is in the key cache (and thus we do not need to execute a database query).
isKeyInTable(String, ResultProducer, Object[]) - Method in class weka.experiment.DatabaseUtils: Executes a database query to see whether a result for the supplied key is already in the database.
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.GenericArrayEditor: 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.GenericObjectEditor: Returns true to indicate that we can paint a representation of the Object.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.CSVResultListener: Always says a result is required.
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 class weka.experiment.AveragingResultProducer: Determines whether the results for a specified key must be generated.
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 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.
isValidRange(String) - Method in class weka.core.Range: Determines if a string represents a valid index or simple range.
ItemSet - class weka.associations.ItemSet.: Class for storing a set of items.
ItemSet(int) - Constructor for class weka.associations.ItemSet: Constructor
itemStateChanged(ItemEvent) - Method in class weka.gui.GenericObjectEditor.GOEPanel: When the chooser selection is changed, ensures that the Object is changed appropriately.
itemStateChanged(ItemEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer: Performs the action associated with the ItemEvent.
IterativeClassifier - interface weka.classifiers.IterativeClassifier.: Interface for classifiers that can induce models of growing complexity one step at a time.
Ivector - class weka.classifiers.m5.Ivector.: Class for handling integer vector
Ivector() - Constructor for class weka.classifiers.m5.Ivector

J

J48 - class weka.classifiers.j48.J48.: Class for generating an unpruned or a pruned C4.5 decision tree.
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 - class weka.estimators.KDConditionalEstimator.: Conditional probability estimator for a numeric domain conditional upon a discrete domain (utilises separate kernel estimators for each discrete conditioning value).
KDConditionalEstimator(int, double) - Constructor for class weka.estimators.KDConditionalEstimator: Constructor
KernelDensity - class weka.classifiers.KernelDensity.: Class for building and using a very simple kernel density classifier.
KernelDensity() - Constructor for class weka.classifiers.KernelDensity
KernelEstimator - class weka.estimators.KernelEstimator.: Simple kernel density estimator.
KernelEstimator(double) - Constructor for class weka.estimators.KernelEstimator: Constructor that takes a precision argument.
key - Variable in class weka.classifiers.kstar.KStarCache.TableEntry: attribute value
keyFieldNameTipText() - Method in class weka.experiment.AveragingResultProducer: Returns the tip text for this property
KKConditionalEstimator - class weka.estimators.KKConditionalEstimator.: Conditional probability estimator for a numeric domain conditional upon a numeric domain.
KKConditionalEstimator(double) - Constructor for class weka.estimators.KKConditionalEstimator: Constructor
KStar - class weka.classifiers.kstar.KStar.: K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function.
KStar() - Constructor for class weka.classifiers.kstar.KStar
KStarCache - class weka.classifiers.kstar.KStarCache.: A class representing the caching system used to keep track of each attribute value and its corresponding scale factor or stop parameter.
KStarCache.CacheTable - class weka.classifiers.kstar.KStarCache.CacheTable.: A custom hashtable class to support the caching system.
KStarCache.CacheTable(KStarCache) - Constructor for class weka.classifiers.kstar.KStarCache.CacheTable: Constructs a new hashtable with a default capacity and load factor.
KStarCache.CacheTable(KStarCache, int, float) - Constructor for class weka.classifiers.kstar.KStarCache.CacheTable: Constructs a new hashtable with a default capacity and load factor.
KStarCache.TableEntry - class weka.classifiers.kstar.KStarCache.TableEntry.: Hashtable collision list.
KStarCache.TableEntry(KStarCache, int, double, double, double, KStarCache.TableEntry) - Constructor for class weka.classifiers.kstar.KStarCache.TableEntry: Constructor
KStarCache() - Constructor for class weka.classifiers.kstar.KStarCache
KStarConstants - interface weka.classifiers.kstar.KStarConstants.
KStarNominalAttribute - class weka.classifiers.kstar.KStarNominalAttribute.: A custom class which provides the environment for computing the transformation probability of a specified test instance nominal attribute to a specified train instance nominal attribute.
KStarNominalAttribute(Instance, Instance, int, Instances, int[][], KStarCache) - Constructor for class weka.classifiers.kstar.KStarNominalAttribute: Constructor
KStarNumericAttribute - class weka.classifiers.kstar.KStarNumericAttribute.: A custom class which provides the environment for computing the transformation probability of a specified test instance numeric attribute to a specified train instance numeric attribute.
KStarNumericAttribute(Instance, Instance, int, Instances, int[][], KStarCache) - Constructor for class weka.classifiers.kstar.KStarNumericAttribute: Constructor
KStarWrapper - class weka.classifiers.kstar.KStarWrapper.
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 - class weka.experiment.LearningRateResultProducer.: LearningRateResultProducer takes the results from a ResultProducer and submits the average to the result listener.
LearningRateResultProducer() - Constructor for class weka.experiment.LearningRateResultProducer
learningRateTipText() - Method in class weka.classifiers.neural.NeuralNetwork
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.
leftSide(Instances) - Method in class weka.classifiers.j48.C45Split: Prints left side of condition..
leftSide(Instances) - Method in class weka.classifiers.j48.BinC45Split: Prints left side of condition..
legend() - Method in class weka.classifiers.adtree.ADTree: Returns the legend of the tree, describing how results are to be interpreted.
LegendPanel - class weka.gui.visualize.LegendPanel.: This panel displays legends for a list of plots.
LegendPanel.LegendEntry - class weka.gui.visualize.LegendPanel.LegendEntry.: Inner class for handling legend entries
LegendPanel.LegendEntry(LegendPanel, PlotData2D, int) - Constructor for class weka.gui.visualize.LegendPanel.LegendEntry
LegendPanel() - Constructor for class weka.gui.visualize.LegendPanel: Constructor
LEVERAGE - Static variable in class weka.associations.Apriori
leverageForRule(ItemSet, ItemSet, int, int) - Method in class weka.associations.ItemSet: Outputs the leverage for a rule.
LIFT - Static variable in class weka.associations.Apriori
liftForRule(ItemSet, ItemSet, int) - Method in class weka.associations.ItemSet: Outputs the lift for a rule.
LightHashTable - class weka.classifiers.kstar.LightHashTable.
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
LINEAR - Static variable in class weka.classifiers.LWR
LinearRegression - class weka.classifiers.LinearRegression.: Class for using linear regression for prediction.
LinearRegression() - Constructor for class weka.classifiers.LinearRegression
LinearUnit - class weka.classifiers.neural.LinearUnit.: This can be used by the neuralnode to perform all it's computations (as a Linear unit).
LinearUnit() - Constructor for class weka.classifiers.neural.LinearUnit
listener - Variable in class weka.gui.visualize.VisualizePanel: An optional listener that we will inform when ComboBox selections change
listOptions() - Method in class weka.associations.Apriori: 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.attributeSelection.PrincipalComponents: 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.attributeSelection.WrapperSubsetEval: 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.attributeSelection.ChiSquaredAttributeEval: 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.attributeSelection.GainRatioAttributeEval: 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.attributeSelection.Ranker: 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.attributeSelection.ClassifierSubsetEval: 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.attributeSelection.InfoGainAttributeEval: Returns an enumeration describing the available options
listOptions() - Method in class weka.attributeSelection.RaceSearch: 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.GeneticSearch: 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.classifiers.MetaCost: 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.VotedPerceptron: 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.classifiers.IBk: 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.classifiers.ThresholdSelector: 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.RegressionByDiscretization: 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.classifiers.BVDecompose: 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.classifiers.FilteredClassifier: 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.OneR: 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.classifiers.SMO: 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.LWR: Returns 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.classifiers.Logistic: 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.CheckClassifier: 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.AttributeSelectedClassifier: 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.classifiers.NaiveBayes: 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.classifiers.j48.J48: Returns an enumeration describing the available options Valid options are:
listOptions() - Method in class weka.classifiers.j48.PART: Returns an enumeration describing the available options Valid options are:
listOptions() - Method in class weka.classifiers.kstar.KStar: 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.classifiers.neural.NeuralNetwork: 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.clusterers.DistributionMetaClusterer: Returns an enumeration describing the available options
listOptions() - Method in class weka.clusterers.Cobweb: Returns an enumeration describing the available options
listOptions() - Method in class weka.clusterers.EM: 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.RegressionSplitEvaluator: 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.PairedTTester: Lists options understood by this object.
listOptions() - Method in class weka.experiment.CSVResultListener: 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.experiment.Experiment: 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.experiment.RandomSplitResultProducer: 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.experiment.DatabaseResultProducer: 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.experiment.CostSensitiveClassifierSplitEvaluator: 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.FirstOrderFilter: 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.filters.MergeTwoValuesFilter: 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.RandomizeFilter: 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.filters.SwapAttributeValuesFilter: 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.filters.SpreadSubsampleFilter: 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.filters.CopyAttributesFilter: Returns an enumeration describing the available options
listOptions() - Method in class weka.filters.AbstractTimeSeriesFilter: 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.StringToNominalFilter: 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.filters.ResampleFilter: 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.NumericTransformFilter: Returns an enumeration describing the available options
ListSelectorDialog - class weka.gui.ListSelectorDialog.: A dialog to present the user with a list of items, that the user can make a selection from, or cancel the selection.
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.
loadCache(ResultProducer, Object[]) - Method in class weka.experiment.DatabaseResultListener: Executes a database query to fill the key cache
Loader - interface weka.core.converters.Loader.: Interface to something that can load Instances from an input source in some format.
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 class weka.classifiers.j48.EntropyBasedSplitCrit: The log of 2.
log2 - Static variable in class weka.core.Utils: The natural logarithm of 2.
LOG2 - Static variable in interface weka.classifiers.kstar.KStarConstants
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.
logFunc(double) - Method in class weka.classifiers.j48.EntropyBasedSplitCrit: Help method for computing entropy.
Logger - interface weka.gui.Logger.: Interface for objects that display log (permanent historical) and status (transient) messages.
Logistic - class weka.classifiers.Logistic.: Class for building and using a two-class logistic regression model with a ridge estimator.
Logistic() - Constructor for class weka.classifiers.Logistic
LogitBoost - class weka.classifiers.LogitBoost.: Class for boosting any classifier that can handle weighted instances.
LogitBoost() - Constructor for class weka.classifiers.LogitBoost
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.
logMessage(String) - Method in interface weka.gui.Logger: Sends the supplied message to the log area.
logMessage(String) - Method in class weka.gui.experiment.RunPanel: Sends the supplied message to the log panel log area.
LogPanel - class weka.gui.LogPanel.: This panel allows log and status messages to be posted.
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[], double[]) - Method in class weka.core.Matrix: Performs LU backward substitution.
lubksb(int, int[], double[]) - Method in class weka.classifiers.m5.Matrix: 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 - class weka.classifiers.LWR.: Locally-weighted regression.
LWR() - Constructor for class weka.classifiers.LWR

M

m_ActualCount - Variable in class weka.classifiers.kstar.KStarNumericAttribute: The number of train instances with no missing attribute values
m_AddBut - Variable in class weka.gui.experiment.DatasetListPanel: Click to add a dataset
m_AdditionalMeasures - Variable in class weka.experiment.RegressionSplitEvaluator: The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.CrossValidationResultProducer: The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.LearningRateResultProducer: The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.Experiment: Method names of additional measures of objects contained in the custom property iterator.
m_AdditionalMeasures - Variable in class weka.experiment.RandomSplitResultProducer: The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.AveragingResultProducer: The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.DatabaseResultProducer: The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.ClassifierSplitEvaluator: The names of any additional measures to look for in SplitEvaluators
m_advanceDataSetFirst - Variable in class weka.gui.experiment.SetupPanel: Click to advacne data set before custom generator
m_AdvanceDataSetFirst - Variable in class weka.experiment.Experiment: If true an experiment will advance the current data set befor any custom itererator
m_advanceIteratorFirst - Variable in class weka.gui.experiment.SetupPanel: Click to advance custom generator before data set
m_AEEPanel - Variable in class weka.gui.explorer.AttributeSelectionPanel: The panel showing the current attribute evaluation method
m_allTheRules - Variable in class weka.associations.Apriori: The list of all generated rules.
m_AnalysisResults - Variable in class weka.classifiers.CheckClassifier: The results of the analysis as a string
m_ApplyBut - Variable in class weka.gui.explorer.PreprocessPanel: Click to apply filters and replace the working dataset
m_ArffFilter - Variable in class weka.gui.SetInstancesPanel: Filter to ensure only arff files are selected
m_ArffFilter - Variable in class weka.gui.experiment.DatasetListPanel: A filter to ensure only arff files get selected
m_ArffFilter - Variable in class weka.gui.experiment.ResultsPanel: Filter to ensure only arff files are selected for result files
m_ArffFilter - Variable in class weka.gui.explorer.PreprocessPanel: Filter to ensure only arff files are selected
m_ArffFilter - Variable in class weka.gui.visualize.VisualizePanel: Filter to ensure only arff files are selected
m_ArrayEditor - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel: Allows editing of the custom property values
m_ASEPanel - Variable in class weka.gui.explorer.AttributeSelectionPanel: The panel showing the current search method
m_AssociationPanel - Variable in class weka.gui.explorer.Explorer: Label for a panel that still need to be implemented
m_AssociatorEditor - Variable in class weka.gui.explorer.AssociationsPanel: Lets the user configure the associator
m_AttPanel - Variable in class weka.gui.explorer.PreprocessPanel: Panel to let the user toggle attributes
m_attrib - Variable in class weka.gui.visualize.VisualizePanel: The panel that displays the attributes , using color to represent another attribute.
m_attrib - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing: The attribute itself.
m_attribIndex - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing: The index for this attribute.
m_Attribute - Variable in class weka.filters.InstanceFilter: Stores which attribute to be used for filtering
m_AttributeEvaluatorEditor - Variable in class weka.gui.explorer.AttributeSelectionPanel: Lets the user configure the attribute evaluator
m_AttributeNameLab - Variable in class weka.gui.AttributeSummaryPanel: Displays the name of the relation
m_Attributes - Variable in class weka.core.Instances: The attribute information.
m_AttributeSearchEditor - Variable in class weka.gui.explorer.AttributeSelectionPanel: Lets the user configure the search method
m_AttributeSelection - Variable in class weka.classifiers.AttributeSelectedClassifier: The attribute selection object
m_AttributeSelectionPanel - Variable in class weka.gui.explorer.Explorer: Label for a panel that still need to be implemented
m_AttributeSet - Variable in class weka.filters.InstanceFilter: Stores the attribute setting
m_AttributeStats - Variable in class weka.gui.AttributeSummaryPanel: Cached stats on the attributes we've summarized so far
m_AttributeType - Variable in class weka.filters.AddFilter: Record the type of attribute to insert
m_AttributeTypeLab - Variable in class weka.gui.AttributeSummaryPanel: Displays the type of attribute
m_AttributeTypes - Variable in class weka.experiment.InstancesResultListener: Stores the attribute types for each column
m_AttrIndex - Variable in class weka.classifiers.kstar.KStarNumericAttribute: The index of the attribute in the test and train instances
m_AttrIndex - Variable in class weka.classifiers.kstar.KStarNominalAttribute: The index of the nominal attribute in the test and train instances
m_AttSummaryPanel - Variable in class weka.gui.explorer.PreprocessPanel: Displays summary stats on the selected attribute
m_AttValues - Variable in class weka.core.Instance: The instance's attribute values.
M_AVERAGE - Static variable in interface weka.classifiers.kstar.KStarConstants
m_AverageProb - Variable in class weka.classifiers.kstar.KStarNumericAttribute: Average probability of test attribute transforming into train attribute
m_AverageProb - Variable in class weka.classifiers.kstar.KStarNominalAttribute: Average probability of test attribute transforming into train attribute
m_BagSizePercent - Variable in class weka.classifiers.MetaCost: The size of each bag sample, as a percentage of the training size
m_BagSizePercent - Variable in class weka.classifiers.Bagging: The size of each bag sample, as a percentage of the training size
m_barColour - Variable in class weka.gui.visualize.AttributePanel: The default colour to use for the background of the bars if a colour is not defined in Visualize.props
m_BaseClassifiers - Variable in class weka.classifiers.Stacking: The base classifiers.
m_BaseFormat - Variable in class weka.classifiers.Stacking: Format for base data
m_BaseInstances - Variable in class weka.gui.explorer.PreprocessPanel: The unadulterated instances
m_BaseInstPanel - Variable in class weka.gui.explorer.PreprocessPanel: Displays simple stats on the base instances
m_BestClassifierOptions - Variable in class weka.classifiers.CVParameterSelection: The set of all classifier options as determined by cross-validation
m_BestPerformance - Variable in class weka.classifiers.CVParameterSelection: The cross-validated performance of the best options
m_BestThreshold - Variable in class weka.classifiers.ThresholdSelector: The threshold that lead to the best performance
m_BestValue - Variable in class weka.classifiers.ThresholdSelector: The best value that has been observed
m_Betas - Variable in class weka.classifiers.AdaBoostM1: Array for storing the weights for the votes.
m_bias - Variable in class weka.classifiers.VFI: Bias towards more confident intervals
m_Bias - Variable in class weka.classifiers.BVDecompose: The calculated bias (squared)
m_BlendFactor - Variable in class weka.classifiers.kstar.KStarNumericAttribute: default sphere of influence blend setting
m_BlendFactor - Variable in class weka.classifiers.kstar.KStarNominalAttribute: default sphere of influence blend setting
m_BlendMethod - Variable in class weka.classifiers.kstar.KStarNumericAttribute: 0 = use specified blend, 1 = entropic blend setting
m_BlendMethod - Variable in class weka.classifiers.kstar.KStar: 0 = use specified blend, 1 = entropic blend setting
m_BlendMethod - Variable in class weka.classifiers.kstar.KStarNominalAttribute: B_SPHERE = use specified blend, B_ENTROPY = entropic blend setting
m_boostingIterations - Variable in class weka.classifiers.adtree.ADTree: Option - the number of boosting iterations o perform
m_Cache - Variable in class weka.classifiers.kstar.KStarNumericAttribute: A cache for storing attribute values and their corresponding scale parameters
m_Cache - Variable in class weka.classifiers.kstar.KStar: A custom data structure for caching distinct attribute values and their scale factor or stop parameter.
m_Cache - Variable in class weka.classifiers.kstar.KStarNominalAttribute: A cache for storing attribute values and their corresponding stop parameters
m_Cache - Variable in class weka.experiment.DatabaseResultListener: Stores the cached values
m_cached - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing: The x position of each point.
m_CacheKey - Variable in class weka.experiment.DatabaseResultListener: Stores the key for which the cache is valid
m_CacheKeyIndex - Variable in class weka.experiment.DatabaseResultListener: Stores the index of the key column holding the cache key data
m_CacheKeyName - Variable in class weka.experiment.DatabaseResultListener: Holds the name of the key field to cache upon, or null if no caching
m_CalculateStdDevs - Variable in class weka.experiment.AveragingResultProducer: True if standard deviation fields should be produced
m_cancel - Variable in class weka.gui.visualize.VisualizePanel: Button for the user to remove all splits.
m_CancelBut - Variable in class weka.gui.PropertySelectorDialog: Click to cancel the property selection
m_CancelBut - Variable in class weka.gui.ListSelectorDialog: Click to cancel the property selection
m_CEPanel - Variable in class weka.gui.explorer.ClassifierPanel: The panel showing the current classifier selection
m_CEPanel - Variable in class weka.gui.explorer.AssociationsPanel: The panel showing the current associator selection
m_cIndex - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
m_cIndex - Variable in class weka.gui.visualize.Plot2D
m_cIndex - Variable in class weka.gui.visualize.AttributePanel
m_ClassAttribute - Variable in class weka.classifiers.LogitBoost: The actual class attribute (for getting class names)
m_ClassCombo - Variable in class weka.gui.explorer.ClassifierPanel: Lets the user select the class column
m_ClassCombo - Variable in class weka.gui.explorer.AttributeSelectionPanel: Lets the user select the class column
m_ClassCombo - Variable in class weka.gui.explorer.ClustererPanel: Lets the user select the class column for classes to clusters based evaluation
m_ClassDistribution - Variable in class weka.classifiers.NaiveBayes: The class estimator.
m_ClassesToClustersBut - Variable in class weka.gui.explorer.ClustererPanel: Click to set test mode to classes to clusters based evaluation
m_ClassFirst - Variable in class weka.experiment.Experiment: True if the class attribute is the first attribute for all datasets involved in this experiment.
m_ClassFirst - Variable in class weka.gui.experiment.Experimenter: True if the class attribute is the first attribute for all datasets involved in this experiment.
m_Classifier - Variable in class weka.classifiers.MetaCost: The classifier
m_Classifier - Variable in class weka.classifiers.AdditiveRegression: Base classifier.
m_Classifier - Variable in class weka.classifiers.Bagging: The model base classifier to use
m_Classifier - Variable in class weka.classifiers.ThresholdSelector: The generated base classifier
m_Classifier - Variable in class weka.classifiers.MultiScheme: The classifier that had the best performance on training data.
m_Classifier - Variable in class weka.classifiers.RegressionByDiscretization: The subclassifier.
m_Classifier - Variable in class weka.classifiers.CVParameterSelection: The generated base classifier
m_Classifier - Variable in class weka.classifiers.BVDecompose: An instantiated base classifier used for getting and testing options.
m_Classifier - Variable in class weka.classifiers.AdaBoostM1: The model base classifier to use
m_Classifier - Variable in class weka.classifiers.FilteredClassifier: The classifier
m_Classifier - Variable in class weka.classifiers.CostSensitiveClassifier: The classifier
m_Classifier - Variable in class weka.classifiers.CheckClassifier: The classifier to be examined
m_Classifier - Variable in class weka.classifiers.LogitBoost: An instantiated base classifier used for getting and testing options
m_Classifier - Variable in class weka.classifiers.AttributeSelectedClassifier: The classifier
m_Classifier - Variable in class weka.experiment.RegressionSplitEvaluator: The classifier used for evaluation
m_Classifier - Variable in class weka.experiment.ClassifierSplitEvaluator: The classifier used for evaluation
m_ClassifierEditor - Variable in class weka.gui.explorer.ClassifierPanel: Lets the user configure the classifier
m_ClassifierIndex - Variable in class weka.classifiers.MultiScheme: The index into the vector for the selected scheme
m_ClassifierOptions - Variable in class weka.classifiers.CVParameterSelection: The base classifier options (not including those being set by cross-validation)
m_ClassifierOptions - Variable in class weka.classifiers.BVDecompose: The options to be passed to the base classifier.
m_ClassifierOptions - Variable in class weka.classifiers.CheckClassifier: The options to be passed to the base classifier.
m_ClassifierOptions - Variable in class weka.experiment.RegressionSplitEvaluator: The classifier options (if any)
m_ClassifierOptions - Variable in class weka.experiment.ClassifierSplitEvaluator: The classifier options (if any)
m_ClassifierPanel - Variable in class weka.gui.explorer.Explorer: The panel for running classifiers
m_Classifiers - Variable in class weka.classifiers.Bagging: Array for storing the generated base classifiers.
m_Classifiers - Variable in class weka.classifiers.MultiScheme: The list of classifiers
m_Classifiers - Variable in class weka.classifiers.AdaBoostM1: Array for storing the generated base classifiers.
m_Classifiers - Variable in class weka.classifiers.LogitBoost: Array for storing the generated base classifiers.
m_ClassifierVersion - Variable in class weka.experiment.RegressionSplitEvaluator: The classifier version
m_ClassifierVersion - Variable in class weka.experiment.ClassifierSplitEvaluator: The classifier version
m_ClassIndex - Variable in class weka.classifiers.BVDecompose: The index of the class attribute
m_ClassIndex - Variable in class weka.classifiers.Logistic: The index of the class attribute
m_ClassIndex - Variable in class weka.classifiers.VFI: The index of the class attribute
m_ClassIndex - Variable in class weka.classifiers.HyperPipes: The index of the class attribute
m_ClassIndex - Variable in class weka.core.Instances: The class attribute's index
m_ClassMeans - Variable in class weka.classifiers.RegressionByDiscretization: The mean values for each Discretized class interval.
m_ClassMode - Variable in class weka.classifiers.ThresholdSelector: Method to determine which class to optimize for
m_ClassNames - Variable in class weka.classifiers.evaluation.ConfusionMatrix: Stores the names of the classes
m_classPanel - Variable in class weka.gui.visualize.VisualizePanel: The panel that displays the legend for the colouring attribute
m_classSurround - Variable in class weka.gui.visualize.VisualizePanel: Panel that surrounds the class panel with a titled border
m_ClassType - Variable in class weka.classifiers.IBk: The class attribute type
m_ClassType - Variable in class weka.classifiers.kstar.KStarNumericAttribute: The class attribute type
m_ClassType - Variable in class weka.classifiers.kstar.KStar: The class attribute type
m_ClassType - Variable in class weka.classifiers.kstar.KStarNominalAttribute: The class attribute type
m_CLPanel - Variable in class weka.gui.explorer.ClustererPanel: The panel showing the current clusterer selection
m_ClustererEditor - Variable in class weka.gui.explorer.ClustererPanel: Lets the user configure the clusterer
m_ClustererPanel - Variable in class weka.gui.explorer.Explorer: Label for a panel that still need to be implemented
m_col - Variable in class weka.gui.treevisualizer.NamedColor: The actual color object
m_colorList - Variable in class weka.gui.visualize.VisualizePanel: The list of the colors used
m_colorList - Variable in class weka.gui.visualize.Plot2D: The list of the colors used
m_colorList - Variable in class weka.gui.visualize.AttributePanel: The colour map to use for colouring points
m_ColourCombo - Variable in class weka.gui.visualize.VisualizePanel: Lets the user select the attribute to use for colouring
m_cols - Variable in class weka.gui.treevisualizer.Colors: The array with all the colors input
m_CommandHistory - Variable in class weka.gui.SimpleCLI: The history of commands entered interactively
m_CompareCombo - Variable in class weka.gui.experiment.ResultsPanel: Lets the user select which performance measure to analyze
m_CompareModel - Variable in class weka.gui.experiment.ResultsPanel: The model embedded in m_CompareCombo
m_ComputeRandomCols - Variable in class weka.classifiers.kstar.KStar: Flag turning on and off the computation of random class colomns
m_ConfigureBut - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel: Click to select the property to iterate over
m_configureHostNames - Variable in class weka.gui.experiment.DistributeExperimentPanel: Popup the HostListPanel
m_ConfigureListener - Variable in class weka.gui.experiment.ResultsPanel: An actionlisteners that updates ttest settings
m_Connection - Variable in class weka.experiment.DatabaseUtils: The database connection
m_connectPoints - Variable in class weka.gui.visualize.PlotData2D: Additional optional information to control the drawing of lines between consecutive points.
m_Contents - Variable in class weka.core.Queue.QueueNode: The nodes contents
m_CopyCols - Variable in class weka.filters.CopyAttributesFilter: Stores which columns to copy
m_CostFile - Variable in class weka.classifiers.MetaCost: The name of the cost file, for command line options
m_CostFile - Variable in class weka.classifiers.CostSensitiveClassifier: The name of the cost file, for command line options
m_CostMatrix - Variable in class weka.classifiers.MetaCost: The cost matrix
m_CostMatrix - Variable in class weka.classifiers.CostSensitiveClassifier: The cost matrix
m_CostMatrixEditor - Variable in class weka.gui.explorer.ClassifierPanel: The cost matrix editor for evaluation costs
m_counter - Variable in class weka.associations.ItemSet: Counter for how many transactions contain this item set.
m_CountFieldName - Variable in class weka.experiment.AveragingResultProducer: The name of the field that will contain the number of results averaged over.
m_counts - Variable in class weka.classifiers.VFI: The class counts for each interval of each attribute
m_CrossValidate - Variable in class weka.classifiers.IBk: Whether to select k by cross validation
m_CurrentInstances - Variable in class weka.experiment.Experiment: The dataset currently being used
m_CurrentProperty - Variable in class weka.experiment.Experiment: The custom property value that has actually been set
m_CurrentSize - Variable in class weka.experiment.LearningRateResultProducer: The current dataset size during stepping
m_CurrentVis - Variable in class weka.gui.explorer.ClassifierPanel: The current visualization object
m_CurrentVis - Variable in class weka.gui.explorer.AttributeSelectionPanel: The current visualization object
m_CurrentVis - Variable in class weka.gui.explorer.ClustererPanel: The current visualization object
m_customColour - Variable in class weka.gui.visualize.PlotData2D
m_CutPoints - Variable in class weka.filters.DiscretizeFilter: Store the current cutpoints
m_CVBut - Variable in class weka.gui.explorer.ClassifierPanel: Click to set test mode to cross-validation
m_CVBut - Variable in class weka.gui.explorer.AttributeSelectionPanel: Click to set evaluation mode to cross-validation
m_CVLab - Variable in class weka.gui.explorer.ClassifierPanel: Label by where the cv folds are entered
m_CVLab - Variable in class weka.gui.explorer.AttributeSelectionPanel: Label by where the cv folds are entered
m_CVParams - Variable in class weka.classifiers.CVParameterSelection: The set of parameters to cross-validate over
m_CVText - Variable in class weka.gui.explorer.ClassifierPanel: The field where the cv folds are entered
m_CVText - Variable in class weka.gui.explorer.AttributeSelectionPanel: The field where the cv folds are entered
m_cycles - Variable in class weka.associations.Apriori: Number of cycles used before required number of rules was one.
m_DatabaseQueryEditor - Variable in class weka.gui.explorer.PreprocessPanel
m_DatabaseURL - Variable in class weka.experiment.DatabaseUtils: Database URL
m_DataFileName - Variable in class weka.classifiers.BVDecompose: The name of the data file used for the decomposition
m_Dataset - Variable in class weka.core.Instance: The dataset the instance has access to.
m_Dataset - Variable in class weka.core.converters.SerializedInstancesLoader: Holds the structure (header) of the data set.
m_DatasetKeyBut - Variable in class weka.gui.experiment.ResultsPanel: Click to edit the columns used to determine the scheme
m_DatasetKeyColumns - Variable in class weka.experiment.PairedTTester: An array containing the indexes of just the selected columns
m_DatasetKeyColumnsRange - Variable in class weka.experiment.PairedTTester: The range of columns that specify a unique "dataset" (eg: scheme plus configuration)
m_DatasetKeyLabel - Variable in class weka.gui.experiment.ResultsPanel: Displays the currently selected column names for the scheme & options
m_DatasetKeyList - Variable in class weka.gui.experiment.ResultsPanel: Displays the list of selected columns determining the scheme
m_DatasetKeyModel - Variable in class weka.gui.experiment.ResultsPanel: Stores the list of attributes for selecting the scheme columns
m_DatasetListPanel - Variable in class weka.gui.experiment.SetupPanel: The panel for configuring selected datasets
m_DatasetModel - Variable in class weka.gui.experiment.ResultsPanel: The model embedded in m_DatasetCombo
m_DatasetNumber - Variable in class weka.experiment.Experiment: The current dataset number when the experiment is running
m_Datasets - Variable in class weka.experiment.Experiment: An array of dataset files
m_DatasetSpecifiers - Variable in class weka.experiment.PairedTTester: The list of dataset specifiers
m_Debug - Variable in class weka.classifiers.MultiScheme: Debugging mode, gives extra output if true
m_Debug - Variable in class weka.classifiers.RegressionByDiscretization: Whether debugging output will be printed
m_Debug - Variable in class weka.classifiers.CVParameterSelection: Debugging mode, gives extra output if true
m_Debug - Variable in class weka.classifiers.BVDecompose: Debugging mode, gives extra output if true
m_Debug - Variable in class weka.classifiers.AdaBoostM1: Debugging mode, gives extra output if true
m_Debug - Variable in class weka.classifiers.LWR: True if debugging output should be printed
m_Debug - Variable in class weka.classifiers.Logistic: Debugging output
m_Debug - Variable in class weka.classifiers.CheckClassifier: Debugging mode, gives extra output if true
m_Debug - Variable in class weka.classifiers.LogitBoost: Debugging mode, gives extra output if true
m_Debug - Variable in class weka.experiment.DatabaseUtils: True if debugging output should be printed
m_Debug - Variable in class weka.experiment.DatabaseResultListener: True if debugging output should be printed
m_debugOutput - Variable in class weka.experiment.CrossValidationResultProducer: Save raw output of split evaluators --- for debugging purposes
m_debugOutput - Variable in class weka.experiment.RandomSplitResultProducer: Save raw output of split evaluators --- for debugging purposes
m_DefaultColors - Variable in class weka.gui.visualize.VisualizePanel: default colours for colouring discrete class
m_DefaultColors - Variable in class weka.gui.visualize.ClassPanel: default colours for colouring discrete class
m_DefaultColors - Variable in class weka.gui.visualize.Plot2D: default colours for colouring discrete class
m_DefaultColors - Variable in class weka.gui.visualize.AttributePanel: default colours for colouring discrete class
M_DELETE - Static variable in interface weka.classifiers.kstar.KStarConstants: Missing value handling mode
m_DeleteBut - Variable in class weka.gui.experiment.HostListPanel: Click to remove the selected host from the list
m_DeleteBut - Variable in class weka.gui.experiment.DatasetListPanel: Click to remove the selected dataset from the list
m_DeleteType - Variable in class weka.filters.AttributeTypeFilter: Stores which type of attribute to delete
m_delta - Variable in class weka.associations.Apriori: Delta by which m_minSupport is decreased in each iteration.
m_DeltaCols - Variable in class weka.filters.FirstOrderFilter: Stores which columns to take differences between
m_Description - Variable in class weka.gui.ExtensionFileFilter: The text description of the types of files accepted
m_DesignatedClass - Variable in class weka.classifiers.ThresholdSelector: Designated class value, determined during building
m_DiscretizeCols - Variable in class weka.filters.DiscretizeFilter: Stores which columns to Discretize
m_Discretizer - Variable in class weka.classifiers.RegressionByDiscretization: The discretization filter.
m_displayAllPoints - Variable in class weka.gui.visualize.PlotData2D: Display all points (ie.
m_Distances - Variable in class weka.classifiers.kstar.KStarNumericAttribute: The set of disctances from the test attribute to the set of train attributes
m_DistanceWeighting - Variable in class weka.classifiers.IBk: Whether the neighbours should be distance-weighted
m_DistinctLab - Variable in class weka.gui.AttributeSummaryPanel: Displays the number of distinct values
m_DistributeExperimentPanel - Variable in class weka.gui.experiment.SetupPanel: The panel for enabling a distributed experiment
m_distribution - Variable in class weka.classifiers.j48.ClassifierSplitModel: Distribution of class values.
m_Distribution - Variable in class weka.classifiers.kstar.KStarNominalAttribute: Distribution of the attribute value in the train dataset
m_Distributions - Variable in class weka.classifiers.NaiveBayes: The attribute estimators.
m_doesProduce - Variable in class weka.experiment.RegressionSplitEvaluator: Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current classifier can produce
m_doesProduce - Variable in class weka.experiment.ClassifierSplitEvaluator: Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current classifier can produce
m_DontNormalize - Variable in class weka.classifiers.IBk: True if normalization is turned off
m_Elements - Variable in class weka.core.Matrix: The data in the matrix.
m_enableDistributedExperiment - Variable in class weka.gui.experiment.DistributeExperimentPanel: Distribute the current experiment to remote hosts
m_Error - Variable in class weka.classifiers.BVDecompose: The error rate
m_ErrRedirector - Variable in class weka.gui.SimpleCLI: The thread that sends output from m_POE to the output box
m_EvalMode - Variable in class weka.classifiers.ThresholdSelector: The evaluation mode
m_Evaluator - Variable in class weka.classifiers.AttributeSelectedClassifier: The attribute evaluator to use
m_EvalWRTCostsBut - Variable in class weka.gui.explorer.ClassifierPanel: Check to evaluate w.r.t a cost matrix
m_examplesCounted - Variable in class weka.classifiers.adtree.ADTree: Statistics - the number of instances processed during search
m_Exp - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel: The experiment this all applies to
m_Exp - Variable in class weka.gui.experiment.SetupPanel: The experiment being configured
m_Exp - Variable in class weka.gui.experiment.HostListPanel: The remote experiment to set the host list of
m_Exp - Variable in class weka.gui.experiment.RunPanel: The experiment to run
m_Exp - Variable in class weka.gui.experiment.DatasetListPanel: The experiment to set the dataset list of
m_Exp - Variable in class weka.gui.experiment.ResultsPanel: An experiment (used for identifying a result source) -- optional
m_Exp - Variable in class weka.gui.experiment.RunNumberPanel: The experiment being configured
m_ExpectedResultsPerAverage - Variable in class weka.experiment.AveragingResultProducer: The number of results expected to average over for each run
m_ExperimenterBut - Variable in class weka.gui.GUIChooser: Click to open the Explorer
m_ExperimenterFrame - Variable in class weka.gui.GUIChooser: The frame containing the experiment interface
m_experimentFinished - Variable in class weka.experiment.RemoteExperimentEvent: True if a remote experiment has finished
m_ExpFilter - Variable in class weka.gui.experiment.SetupPanel: A filter to ensure only experiment files get shown in the chooser
m_ExplorerBut - Variable in class weka.gui.GUIChooser: Click to open the Explorer
m_ExplorerFrame - Variable in class weka.gui.GUIChooser: The frame containing the explorer interface
m_Extension - Variable in class weka.gui.ExtensionFileFilter: The filename extension of accepted files
m_FileChooser - Variable in class weka.gui.FileEditor: The file chooser used for selecting files
m_FileChooser - Variable in class weka.gui.SetInstancesPanel: The file chooser for selecting arff files
m_FileChooser - Variable in class weka.gui.experiment.SetupPanel: The file chooser for selecting experiments
m_FileChooser - Variable in class weka.gui.experiment.DatasetListPanel: The file chooser component
m_FileChooser - Variable in class weka.gui.experiment.ResultsPanel: The file chooser for selecting result files
m_FileChooser - Variable in class weka.gui.explorer.PreprocessPanel: The file chooser for selecting arff files
m_FileChooser - Variable in class weka.gui.visualize.VisualizePanel: file chooser for saving instances
m_FillWithMissing - Variable in class weka.filters.AbstractTimeSeriesFilter: True if missing values should be used rather than removing instances where the translated value is not known (due to border effects).
m_Filter - Variable in class weka.classifiers.FilteredClassifier: The filter
m_FilteredInstances - Variable in class weka.classifiers.FilteredClassifier: The instance structure of the filtered instances
m_Filters - Variable in class weka.gui.explorer.PreprocessPanel: Lets the user add a series of filters
m_FiltersCopy - Variable in class weka.gui.explorer.PreprocessPanel: A copy of the most recently applied filters
m_FindNumBins - Variable in class weka.filters.DiscretizeFilter: Find the number of bins using cross-validated entropy.
m_Finished - Variable in class weka.experiment.Experiment: True if the experiment has finished running
m_First - Variable in class weka.gui.LogPanel: An indicator for whether text has been output yet
m_FramedOutput - Variable in class weka.gui.ResultHistoryPanel: A Hashtable mapping names to output text components
m_FromDBaseBut - Variable in class weka.gui.experiment.ResultsPanel: Click to load results from a database
m_FromExpBut - Variable in class weka.gui.experiment.ResultsPanel: Click to get results from the destination given in the experiment
m_FromFileBut - Variable in class weka.gui.experiment.ResultsPanel: Click to load results from a file
m_FromLab - Variable in class weka.gui.experiment.ResultsPanel: Displays a message about the current result set
m_GeneratorPropertyPanel - Variable in class weka.gui.experiment.SetupPanel: The panel that configures iteration on custom resultproducer property
m_GlobalBlend - Variable in class weka.classifiers.kstar.KStar: default sphere of influence blend setting
m_globalCounts - Variable in class weka.classifiers.VFI: The global class counts
m_HandleRightClicks - Variable in class weka.gui.ResultHistoryPanel: Let the result history list handle right clicks in the default manner---ie, pop up a window displaying the buffer
m_hashtables - Variable in class weka.associations.Apriori: The same information stored in hash tables.
m_Head - Variable in class weka.core.Queue: Store a reference to the head of the queue
m_heights - Variable in class weka.gui.visualize.AttributePanel: Holds the random height for each instance.
m_HighThreshold - Variable in class weka.classifiers.ThresholdSelector: The upper threshold used as the basis of correction
m_History - Variable in class weka.filters.AbstractTimeSeriesFilter: Stores the historical instances to copy values between
m_History - Variable in class weka.gui.experiment.ResultsPanel: A panel controlling results viewing
m_History - Variable in class weka.gui.explorer.ClassifierPanel: A panel controlling results viewing
m_History - Variable in class weka.gui.explorer.AssociationsPanel: A panel controlling results viewing
m_History - Variable in class weka.gui.explorer.AttributeSelectionPanel: A panel controlling results viewing
m_History - Variable in class weka.gui.explorer.ClustererPanel: A panel controlling results viewing
m_HistoryPos - Variable in class weka.gui.SimpleCLI: The current position in the command history
m_HostField - Variable in class weka.gui.experiment.HostListPanel: The field with which to enter host names
m_hostList - Variable in class weka.gui.experiment.DistributeExperimentPanel: The host list panel
m_HyperPipes - Variable in class weka.classifiers.HyperPipes: Stores the HyperPipe for each class
m_id - Variable in class weka.classifiers.j48.ClassifierTree: The id for the node.
m_id - Variable in class weka.classifiers.neural.NeuralConnection: The string that uniquely (provided naming is done properly) identifies this unit.
m_ID - Variable in class weka.core.Tag: The ID
m_ignoreBut - Variable in class weka.gui.explorer.ClustererPanel: The button used to popup a list for choosing attributes to ignore while clustering
m_ignoreKeyList - Variable in class weka.gui.explorer.ClustererPanel
m_ignoreKeyModel - Variable in class weka.gui.explorer.ClustererPanel
m_IncludeAll - Variable in class weka.gui.AttributeSelectionPanel: Press to select all attributes
m_IncrementalIndex - Variable in class weka.core.converters.SerializedInstancesLoader: The current index position for incremental reading
m_indexVal - Variable in class weka.gui.visualize.AttributePanelEvent: The index for the new attribute
m_Indices - Variable in class weka.core.SparseInstance: The index of the attribute associated with each stored value.
m_IndicesBuffer - Variable in class weka.core.Instances: Buffer of indices for sparse instance
m_InitFlag - Variable in class weka.classifiers.kstar.KStar: Flag turning on and off the initialisation of config variables
m_Input - Variable in class weka.gui.SimpleCLI: The command input area
m_InputFormat - Variable in class weka.gui.streams.InstanceJoiner: The input format for instances
m_inputList - Variable in class weka.classifiers.neural.NeuralConnection: The list of inputs to this unit.
m_inputNums - Variable in class weka.classifiers.neural.NeuralConnection: The numbering for the connections at the other end of the input lines.
m_InputStringIndex - Variable in class weka.filters.AttributeFilter: Contains an index of string attributes in the input format that will survive the filtering process
m_InputStringIndex - Variable in class weka.filters.CopyAttributesFilter: Contains an index of string attributes in the input format that survive the filtering process -- some entries may be duplicated
m_Insert - Variable in class weka.filters.AddFilter: The location to insert the new attribute
m_InstanceQuery - Variable in class weka.gui.experiment.ResultsPanel: Does any database querying for us
m_InstanceRange - Variable in class weka.filters.AbstractTimeSeriesFilter: The number of instances forward to translate values between.
m_instances - Variable in class weka.associations.Apriori: The instances (transactions) to be used for generating the association rules.
m_Instances - Variable in class weka.classifiers.VFI: The training data
m_Instances - Variable in class weka.classifiers.HyperPipes: The structure of the training data
m_Instances - Variable in class weka.classifiers.NaiveBayes: The dataset header for the purposes of printing out a semi-intelligible model
m_Instances - Variable in class weka.core.Instances: The instances.
m_Instances - Variable in class weka.experiment.CrossValidationResultProducer: The dataset of interest
m_Instances - Variable in class weka.experiment.PairedTTester: The set of instances we will analyse
m_Instances - Variable in class weka.experiment.InstancesResultListener: Stores the instances created so far, before assigning to a header
m_Instances - Variable in class weka.experiment.LearningRateResultProducer: The dataset of interest
m_Instances - Variable in class weka.experiment.RandomSplitResultProducer: The dataset of interest
m_Instances - Variable in class weka.experiment.AveragingResultProducer: The dataset of interest
m_Instances - Variable in class weka.experiment.DatabaseResultProducer: The dataset of interest
m_Instances - Variable in class weka.gui.AttributeSummaryPanel: The instances we're playing with
m_Instances - Variable in class weka.gui.InstancesSummaryPanel: The instances we're playing with
m_Instances - Variable in class weka.gui.SetInstancesPanel: The current set of instances loaded
m_Instances - Variable in class weka.gui.experiment.ResultsPanel: The instances we're extracting results from
m_Instances - Variable in class weka.gui.explorer.ClassifierPanel: The main set of instances we're playing with
m_Instances - Variable in class weka.gui.explorer.AssociationsPanel: The main set of instances we're playing with
m_Instances - Variable in class weka.gui.explorer.AttributeSelectionPanel: The main set of instances we're playing with
m_Instances - Variable in class weka.gui.explorer.ClustererPanel: The main set of instances we're playing with
m_intervalBounds - Variable in class weka.classifiers.VFI: The lower bounds for each attribute
m_Inverse - Variable in class weka.filters.InstanceFilter: Inverse of test to be used?
m_Invert - Variable in class weka.gui.AttributeSelectionPanel: Press to invert the current selection
m_IOThread - Variable in class weka.gui.SetInstancesPanel: The thread we do loading in
m_IOThread - Variable in class weka.gui.explorer.PreprocessPanel: A thread to loading/saving instances from a file or URL
m_isEmpty - Variable in class weka.classifiers.j48.ClassifierDecList: True if node is empty.
m_isEmpty - Variable in class weka.classifiers.j48.ClassifierTree: True if node is empty.
m_isLeaf - Variable in class weka.classifiers.j48.ClassifierDecList: True if node is leaf.
m_isLeaf - Variable in class weka.classifiers.j48.ClassifierTree: True if node is leaf.
m_items - Variable in class weka.associations.ItemSet: The items stored as an array of of ints.
m_Jitter - Variable in class weka.gui.visualize.VisualizePanel: The jitter slider
m_JitterLab - Variable in class weka.gui.visualize.VisualizePanel: Label for the jitter slider
m_KeyFieldName - Variable in class weka.experiment.AveragingResultProducer: The name of the key field to average over
m_KeyIndex - Variable in class weka.experiment.AveragingResultProducer: The index of the field to average over in the resultproducers key
m_Keys - Variable in class weka.experiment.AveragingResultProducer: Collects the keys from a single run
m_kNN - Variable in class weka.classifiers.IBk: The number of neighbours to use for classification (currently)
m_kNN - Variable in class weka.classifiers.LWR: The number of neighbours used to select the kernel bandwidth
m_kNNUpper - Variable in class weka.classifiers.IBk: The value of kNN provided by the user.
m_kNNValid - Variable in class weka.classifiers.IBk: Whether the value of k selected by cross validation has been invalidated by a change in the training instances
m_Labels - Variable in class weka.filters.AddFilter: The list of labels for nominal attribute
m_lastAddedSplitNum - Variable in class weka.classifiers.adtree.ADTree: The number of the last splitter added to the tree
m_LastURL - Variable in class weka.gui.SetInstancesPanel: Stores the last URL that instances were loaded from
m_LastURL - Variable in class weka.gui.explorer.PreprocessPanel: Stores the last URL that instances were loaded from
m_latexOutput - Variable in class weka.experiment.PairedTTester: Produce tables in latex format
m_legendPanel - Variable in class weka.gui.visualize.VisualizePanel: The panel that displays legend info if there is more than one plot
m_List - Variable in class weka.gui.ResultHistoryPanel: The list component
m_List - Variable in class weka.gui.ListSelectorDialog: The list component
m_List - Variable in class weka.gui.experiment.HostListPanel: The component displaying the host list
m_List - Variable in class weka.gui.experiment.DatasetListPanel: The component displaying the dataset list
m_Listeners - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel: Listeners who want to be notified about editing status of this panel
m_Listeners - Variable in class weka.gui.visualize.AttributePanel: The list of things listening to this panel
m_LL - Variable in class weka.classifiers.Logistic: The log-likelihood of the built model
m_LLn - Variable in class weka.classifiers.Logistic: The log-likelihood of the null model
m_LoadThread - Variable in class weka.gui.experiment.ResultsPanel: A thread to load results instances from a file or database
m_localModel - Variable in class weka.classifiers.j48.ClassifierDecList: Local model at node.
m_localModel - Variable in class weka.classifiers.j48.ClassifierTree: Local model at node.
m_Log - Variable in class weka.gui.experiment.RunPanel
m_Log - Variable in class weka.gui.explorer.ClassifierPanel: The destination for log/status messages
m_Log - Variable in class weka.gui.explorer.PreprocessPanel
m_Log - Variable in class weka.gui.explorer.AssociationsPanel: The destination for log/status messages
m_Log - Variable in class weka.gui.explorer.AttributeSelectionPanel: The destination for log/status messages
m_Log - Variable in class weka.gui.explorer.ClustererPanel: The destination for log/status messages
m_Log - Variable in class weka.gui.visualize.VisualizePanel: the logger
m_logMessage - Variable in class weka.experiment.RemoteExperimentEvent: A log type message
m_LogPanel - Variable in class weka.gui.explorer.Explorer: The panel for log and status messages
m_LogText - Variable in class weka.gui.LogPanel: Displays the log messages
m_lowerBoundMinSupport - Variable in class weka.associations.Apriori: The lower bound for the minimum support.
m_LowerSize - Variable in class weka.experiment.LearningRateResultProducer: The minimum number of instances to use.
m_LowerText - Variable in class weka.gui.experiment.RunNumberPanel: Configures the lower run number
m_LowThreshold - Variable in class weka.classifiers.ThresholdSelector: The lower threshold used as the basis of correction
m_Ls - Variable in class weka.associations.Apriori: The set of all sets of itemsets L.
m_MakeBinary - Variable in class weka.filters.DiscretizeFilter: Output binary attributes for discretized attributes.
m_masterName - Variable in class weka.gui.visualize.Plot2D: The name of the master plot
m_masterPlot - Variable in class weka.gui.visualize.Plot2D: The master plot
m_MatchMissingValues - Variable in class weka.filters.InstanceFilter: True if missing values should count as a match
m_MatrixSource - Variable in class weka.classifiers.MetaCost: Indicates the current cost matrix source
m_MatrixSource - Variable in class weka.classifiers.CostSensitiveClassifier: Indicates the current cost matrix source
m_Max - Variable in class weka.classifiers.IBk: The maximum values for numeric attributes.
m_Max - Variable in class weka.classifiers.LWR: The maximum values for numeric attributes.
m_maxC - Variable in class weka.gui.visualize.Plot2D
m_maxC - Variable in class weka.gui.visualize.AttributePanel: Holds the min and max values of the colouring attributes
m_maxC - Variable in class weka.gui.visualize.PlotData2D
M_MAXDIFF - Static variable in interface weka.classifiers.kstar.KStarConstants
m_maxEntrop - Variable in class weka.classifiers.VFI: The maximum entropy for the class
m_MaxIterations - Variable in class weka.classifiers.AdaBoostM1: The maximum number of boost iterations
m_MaxIterations - Variable in class weka.classifiers.LogitBoost: The maximum number of boost iterations
m_maxModels - Variable in class weka.classifiers.AdditiveRegression: Maximum number of models to produce.
m_maxVal - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing: The min and max values for this attribute.
m_maxX - Variable in class weka.gui.visualize.Plot2D: Holds the min and max values of the x, y and colouring attributes over all plots
m_maxX - Variable in class weka.gui.visualize.PlotData2D: Holds the min and max values of the x, y and colouring attributes for this plot
m_maxY - Variable in class weka.gui.visualize.Plot2D
m_maxY - Variable in class weka.gui.visualize.PlotData2D
m_MeanSquared - Variable in class weka.classifiers.IBk: Whether to minimise mean squared error rather than mean absolute error when cross-validating on numeric prediction tasks
m_messageString - Variable in class weka.experiment.RemoteExperimentEvent: The message
m_MetaClassifier - Variable in class weka.classifiers.Stacking: The meta classifier.
m_MetaFormat - Variable in class weka.classifiers.Stacking: Format for meta data
m_metricType - Variable in class weka.associations.Apriori: The selected metric type.
m_Min - Variable in class weka.classifiers.IBk: The minimum values for numeric attributes.
m_Min - Variable in class weka.classifiers.LWR: The minimum values for numeric attributes.
m_minC - Variable in class weka.gui.visualize.Plot2D
m_minC - Variable in class weka.gui.visualize.AttributePanel
m_minC - Variable in class weka.gui.visualize.PlotData2D
m_MinimizeExpectedCost - Variable in class weka.classifiers.CostSensitiveClassifier: True if the costs should be used by selecting the minimum expected cost (false means weight training data by the costs)
m_minMetric - Variable in class weka.associations.Apriori: The minimum metric score.
m_minSupport - Variable in class weka.associations.Apriori: The minimum support.
m_minVal - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
m_minX - Variable in class weka.gui.visualize.Plot2D
m_minX - Variable in class weka.gui.visualize.PlotData2D
m_minY - Variable in class weka.gui.visualize.Plot2D
m_minY - Variable in class weka.gui.visualize.PlotData2D
m_MissingLab - Variable in class weka.gui.AttributeSummaryPanel: Displays the number of missing values
m_MissingMode - Variable in class weka.classifiers.kstar.KStarNumericAttribute: missing value treatment
m_MissingMode - Variable in class weka.classifiers.kstar.KStar: missing value treatment
m_MissingMode - Variable in class weka.classifiers.kstar.KStarNominalAttribute: missing value treatment
m_MissingProb - Variable in class weka.classifiers.kstar.KStarNumericAttribute: Probability of test attribute transforming into train attribute with missing value
m_MissingProb - Variable in class weka.classifiers.kstar.KStarNominalAttribute: Probability of test attribute transforming into train attribute with missing value
m_Model - Variable in class weka.gui.AttributeSelectionPanel: The table model containingn attribute names and selection status
m_Model - Variable in class weka.gui.ResultHistoryPanel: The list model
m_ModifyHeader - Variable in class weka.filters.InstanceFilter: Modify header for nominal attributes?
m_name - Variable in class weka.gui.treevisualizer.NamedColor: The name of the color
m_Name - Variable in class weka.filters.AddFilter: The name for the new attribute
m_negTrainInstances - Variable in class weka.classifiers.adtree.ADTree: The training instances with negative class - referencing the training dataset
m_NewBatch - Variable in class weka.filters.Filter: Record whether the filter is at the start of a batch
m_NewBut - Variable in class weka.gui.experiment.SetupPanel: Click to create a new experiment with default settings
m_Next - Variable in class weka.core.Queue.QueueNode: The next node in the queue
m_nodesExpanded - Variable in class weka.classifiers.adtree.ADTree: Statistics - the number of prediction nodes investigated during search
m_nominalAttIndices - Variable in class weka.classifiers.adtree.ADTree: An array containing the inidices to the nominal attributes in the data
m_NominalIndexes - Variable in class weka.experiment.InstancesResultListener: For lookup of indices given a string value for each nominal attribute
m_NominalMapping - Variable in class weka.filters.InstanceFilter: If m_ModifyHeader, stores a mapping from old to new indexes
m_NominalStrings - Variable in class weka.experiment.InstancesResultListener: Contains strings seen so far for each nominal attribute
M_NORMAL - Static variable in interface weka.classifiers.kstar.KStarConstants
m_Notes - Variable in class weka.experiment.Experiment: User notes about the experiment
m_NotesText - Variable in class weka.gui.experiment.SetupPanel: Area for user notes Default of 5 rows
m_NumAttributes - Variable in class weka.classifiers.CVParameterSelection: The number of attributes in the data
m_NumAttributes - Variable in class weka.classifiers.kstar.KStarNumericAttribute: The number of attributes
m_NumAttributes - Variable in class weka.classifiers.kstar.KStar: The number of attributes
m_NumAttributes - Variable in class weka.classifiers.kstar.KStarNominalAttribute: The number of attributes
m_NumAttributes - Variable in class weka.core.SparseInstance: The maximum number of values that can be stored.
m_NumAttributesLab - Variable in class weka.gui.InstancesSummaryPanel: Displays the number of attributes
m_numAttributesSelected - Variable in class weka.classifiers.AttributeSelectedClassifier: The number of attributes selected by the attribute selection phase
m_NumAttributesUsed - Variable in class weka.classifiers.IBk: The number of attributes the contribute to a prediction
m_numberAdditionalMeasures - Variable in class weka.experiment.ClassifierSplitEvaluator: The number of additional measures that need to be filled in after taking into account column constraints imposed by the final destination for results
m_NumBins - Variable in class weka.classifiers.RegressionByDiscretization: The number of classes in the Discretized training data.
m_NumBins - Variable in class weka.filters.DiscretizeFilter: The number of bins to divide the attribute into
m_numClasses - Variable in class weka.classifiers.AttributeSelectedClassifier: The number of class vals in the training data (1 if class is numeric)
m_NumClasses - Variable in class weka.classifiers.IBk: The number of class values (or 1 if predicting numeric)
m_NumClasses - Variable in class weka.classifiers.AdaBoostM1: The number of classes
m_NumClasses - Variable in class weka.classifiers.VFI: The number of classes
m_NumClasses - Variable in class weka.classifiers.LogitBoost: The number of classes
m_NumClasses - Variable in class weka.classifiers.NaiveBayes: The number of classes (or 1 for numeric class)
m_NumClasses - Variable in class weka.classifiers.kstar.KStarNumericAttribute: The number of class values
m_NumClasses - Variable in class weka.classifiers.kstar.KStar: The number of class values
m_NumClasses - Variable in class weka.classifiers.kstar.KStarNominalAttribute: The number of class values
m_numericAttIndices - Variable in class weka.classifiers.adtree.ADTree: An array containing the inidices to the numeric attributes in the data
m_NumericClassData - Variable in class weka.classifiers.LogitBoost: Dummy dataset with a numeric class
m_NumFolds - Variable in class weka.classifiers.Stacking: Set the number of folds for the cross-validation
m_NumFolds - Variable in class weka.classifiers.CVParameterSelection: The number of folds used in cross-validation
m_NumFolds - Variable in class weka.experiment.CrossValidationResultProducer: The number of folds in the cross-validation
m_numInputs - Variable in class weka.classifiers.neural.NeuralConnection: The number of inputs.
m_NumInstances - Variable in class weka.classifiers.kstar.KStarNumericAttribute: The number of instances in the dataset
m_NumInstances - Variable in class weka.classifiers.kstar.KStar: The number of instances in the dataset
m_NumInstances - Variable in class weka.classifiers.kstar.KStarNominalAttribute: The number of instances in the dataset
m_NumInstancesLab - Variable in class weka.gui.InstancesSummaryPanel: Displays the number of instances
m_NumIterations - Variable in class weka.classifiers.MetaCost: The number of iterations.
m_NumIterations - Variable in class weka.classifiers.Bagging: The number of iterations.
m_NumIterations - Variable in class weka.classifiers.AdaBoostM1: The number of successfully generated base classifiers.
m_NumIterations - Variable in class weka.classifiers.LogitBoost: The number of successfully generated base classifiers.
m_numOutputs - Variable in class weka.classifiers.neural.NeuralConnection: The number of outputs.
m_NumPredictors - Variable in class weka.classifiers.Logistic: The number of attributes in the model
m_numRules - Variable in class weka.associations.Apriori: The maximum number of rules that are output.
m_numSubsets - Variable in class weka.classifiers.j48.ClassifierSplitModel: Number of created subsets.
m_NumXValFolds - Variable in class weka.classifiers.ThresholdSelector: The number of folds used in cross-validation
m_NumXValFolds - Variable in class weka.classifiers.MultiScheme: Number of folds to use for cross validation (0 means use training error for selection)
m_Objs - Variable in class weka.gui.ResultHistoryPanel: A hashtable mapping names to arbitrary objects
m_oldWidth - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing: Used to determine if the positions need to be recalculated.
m_OnDemandDirectory - Variable in class weka.classifiers.MetaCost: The directory used when loading cost files on demand, null indicates current directory
m_OnDemandDirectory - Variable in class weka.classifiers.CostSensitiveClassifier: The directory used when loading cost files on demand, null indicates current directory
m_OnDemandDirectory - Variable in class weka.experiment.CostSensitiveClassifierSplitEvaluator: The directory used when loading cost files on demand, null indicates current directory
m_OpenBut - Variable in class weka.gui.experiment.SetupPanel: Click to load an experiment
m_OpenDBBut - Variable in class weka.gui.explorer.PreprocessPanel: Click to load base instances from a Database
m_OpenFileBut - Variable in class weka.gui.SetInstancesPanel: Click to open instances from a file
m_OpenFileBut - Variable in class weka.gui.explorer.PreprocessPanel: Click to load base instances from a file
m_OpenURLBut - Variable in class weka.gui.SetInstancesPanel: Click to open instances from a URL
m_OpenURLBut - Variable in class weka.gui.explorer.PreprocessPanel: Click to load base instances from a URL
m_OptimizeBins - Variable in class weka.classifiers.RegressionByDiscretization: Whether the Discretizer will optimise the number of bins
m_originalPlot - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel: The master plot
m_Out - Variable in class weka.experiment.CSVResultListener: The destination for results (typically connected to the output file)
m_OutputArea - Variable in class weka.gui.SimpleCLI: The output area canvas added to the frame
m_OutputConfusionBut - Variable in class weka.gui.explorer.ClassifierPanel: Check to output a confusion matrix
m_OutputEntropyBut - Variable in class weka.gui.explorer.ClassifierPanel: Check to output entropy statistics
m_OutputFile - Variable in class weka.experiment.CrossValidationResultProducer: The destination output file/directory for raw output
m_OutputFile - Variable in class weka.experiment.CSVResultListener: The destination output file, null sends to System.out
m_OutputFile - Variable in class weka.experiment.RandomSplitResultProducer: The destination output file/directory for raw output
m_outputItemSets - Variable in class weka.associations.Apriori: Output itemsets found?
m_outputList - Variable in class weka.classifiers.neural.NeuralConnection: The list of outputs from this unit.
m_OutputModelBut - Variable in class weka.gui.explorer.ClassifierPanel: Check to output the model built from the training data
m_outputNums - Variable in class weka.classifiers.neural.NeuralConnection: The numbering for the connections at the other end of the out lines.
m_OutputPerClassBut - Variable in class weka.gui.explorer.ClassifierPanel: Check to output true/false positives, precision/recall for each class
m_OutRedirector - Variable in class weka.gui.SimpleCLI: The thread that sends output from m_POO to the output box
m_OutText - Variable in class weka.gui.experiment.ResultsPanel: Displays the output of tests
m_OutText - Variable in class weka.gui.explorer.ClassifierPanel: The output area for classification results
m_OutText - Variable in class weka.gui.explorer.AssociationsPanel: The output area for associations
m_OutText - Variable in class weka.gui.explorer.AttributeSelectionPanel: The output area for attribute selection results
m_OutText - Variable in class weka.gui.explorer.ClustererPanel: The output area for classification results
m_Par - Variable in class weka.classifiers.Logistic: The coefficients of the model
m_PercentBut - Variable in class weka.gui.explorer.ClassifierPanel: Click to set test mode to generate a % split
m_PercentBut - Variable in class weka.gui.explorer.ClustererPanel: Click to set test mode to generate a % split
m_PercentLab - Variable in class weka.gui.explorer.ClassifierPanel: Label by where the % split is entered
m_PercentLab - Variable in class weka.gui.explorer.ClustererPanel: Label by where the % split is entered
m_PercentText - Variable in class weka.gui.explorer.ClassifierPanel: The field where the % split is entered
m_PercentText - Variable in class weka.gui.explorer.ClustererPanel: The field where the % split is entered
m_PerformBut - Variable in class weka.gui.experiment.ResultsPanel: Click to start the test
m_plot - Variable in class weka.gui.visualize.VisualizePanel: The panel that displays the plot
m_plot2D - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel: The actual generic plotting panel
m_plotCompanion - Variable in class weka.gui.visualize.Plot2D: An optional "compainion" of the panel.
m_plotInstances - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel: The instances from the master plot
m_plotInstances - Variable in class weka.gui.visualize.Plot2D: The instances to be plotted
m_plotInstances - Variable in class weka.gui.visualize.AttributePanel: The instances to be plotted
m_plotInstances - Variable in class weka.gui.visualize.PlotData2D: The instances
m_plotName - Variable in class weka.gui.visualize.VisualizePanel: The name of the plot (not currently displayed, but can be used in the containing Frame or Panel)
m_plotName - Variable in class weka.gui.visualize.PlotData2D: The name of this plot
m_plots - Variable in class weka.gui.visualize.Plot2D: The plots to display
m_plots - Variable in class weka.gui.visualize.LegendPanel: the list of plot elements
m_plotSurround - Variable in class weka.gui.visualize.VisualizePanel: Panel that surrounds the plot panel with a titled border
m_POE - Variable in class weka.gui.SimpleCLI: The new output stream for System.err
m_pointDrawn - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing: A temporary array used to strike any instances that would be drawn redundantly.
m_pointLookup - Variable in class weka.gui.visualize.PlotData2D: Panel coordinate cache for data points
m_POO - Variable in class weka.gui.SimpleCLI: The new output stream for System.out
m_posTrainInstances - Variable in class weka.classifiers.adtree.ADTree: The training instances with positive class - referencing the training dataset
m_preferredColourDimension - Variable in class weka.gui.visualize.VisualizePanel
m_preferredXDimension - Variable in class weka.gui.visualize.VisualizePanel: These hold the names of preferred columns to visualize on---if the user has defined them in the Visualize.props file
m_preferredYDimension - Variable in class weka.gui.visualize.VisualizePanel
m_Preprocess - Variable in class weka.gui.explorer.ClassifierPanel: The preprocess panel through which filters can be applied to user supplied test data sets
m_Preprocess - Variable in class weka.gui.explorer.ClustererPanel: The pre-process object from which to fetch filters for applying to a user specified test set
m_PreprocessPanel - Variable in class weka.gui.explorer.Explorer: The panel for preprocessing instances
m_PropertyArray - Variable in class weka.experiment.Experiment: The array of values to set the property to
m_PropertyNumber - Variable in class weka.experiment.Experiment: The current custom property value index when the experiment is running
m_PropertyPath - Variable in class weka.experiment.Experiment: The path to the iterator property
m_RandClassCols - Variable in class weka.classifiers.kstar.KStarNumericAttribute: Set of colomns: each colomn representing a randomised version of the train dataset class colomn
m_RandClassCols - Variable in class weka.classifiers.kstar.KStar: Table of random class value colomns
m_RandClassCols - Variable in class weka.classifiers.kstar.KStarNominalAttribute: Set of colomns: each colomn representing a randomised version of the train dataset class colomn
m_random - Variable in class weka.classifiers.adtree.ADTree: The random number generator - used for the random search heuristic
m_Random - Variable in class weka.filters.RandomizeFilter: The current random number generator
m_randomize - Variable in class weka.experiment.RandomSplitResultProducer: Whether dataset is to be randomized
m_randomSeed - Variable in class weka.classifiers.adtree.ADTree: Option - the seed to use for a random search
m_RangeMode - Variable in class weka.classifiers.ThresholdSelector: The range correction mode
m_Readable - Variable in class weka.core.Tag: The descriptive text
m_ReducedHeader - Variable in class weka.classifiers.AttributeSelectedClassifier: The header of the dimensionally reduced data
m_RelationName - Variable in class weka.core.Instances: The dataset's name.
m_RelationNameLab - Variable in class weka.gui.InstancesSummaryPanel: Displays the name of the relation
m_relativeCheck - Variable in class weka.gui.experiment.DatasetListPanel: Make file paths relative to the user (start) directory
m_remoteHosts - Variable in class weka.experiment.RemoteExperiment: Holds the names of machines with remoteEngine servers running
m_RemoveAll - Variable in class weka.gui.AttributeSelectionPanel: Press to deselect all attributes
m_removeMissingCols - Variable in class weka.associations.Apriori
m_Repainters - Variable in class weka.gui.visualize.LegendPanel: a list of components that need to be repainted when a colour is changed
m_ReplaceBut - Variable in class weka.gui.explorer.PreprocessPanel: Click to replace the base dataset with the working dataset
m_result - Variable in class weka.experiment.RegressionSplitEvaluator: Holds the statistics for the most recent application of the classifier
m_result - Variable in class weka.experiment.ClassifierSplitEvaluator: Holds the statistics for the most recent application of the classifier
m_Result - Variable in class weka.gui.PropertySelectorDialog: Whether the selection was made or cancelled
m_Result - Variable in class weka.gui.ListSelectorDialog: Whether the selection was made or cancelled
m_ResultKeyBut - Variable in class weka.gui.experiment.ResultsPanel: Click to edit the columns used to determine the scheme
m_ResultKeyLabel - Variable in class weka.gui.experiment.ResultsPanel: Displays the currently selected column names for the scheme & options
m_ResultKeyList - Variable in class weka.gui.experiment.ResultsPanel: Displays the list of selected columns determining the scheme
m_ResultKeyModel - Variable in class weka.gui.experiment.ResultsPanel: Stores the list of attributes for selecting the scheme columns
m_ResultListener - Variable in class weka.experiment.CrossValidationResultProducer: The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.LearningRateResultProducer: The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.Experiment: Where results will be sent
m_ResultListener - Variable in class weka.experiment.RandomSplitResultProducer: The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.AveragingResultProducer: The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.DatabaseResultProducer: The ResultListener to send results to
m_ResultPath - Variable in class weka.gui.PropertySelectorDialog: Stores the path to the selected property
m_ResultProducer - Variable in class weka.experiment.LearningRateResultProducer: The ResultProducer used to generate results
m_ResultProducer - Variable in class weka.experiment.Experiment: The result producer
m_ResultProducer - Variable in class weka.experiment.AveragingResultProducer: The ResultProducer used to generate results
m_ResultProducer - Variable in class weka.experiment.DatabaseResultListener: The ResultProducer to listen to
m_ResultProducer - Variable in class weka.experiment.DatabaseResultProducer: The ResultProducer used to generate results
m_Results - Variable in class weka.experiment.AveragingResultProducer: Collects the results from a single run
m_Results - Variable in class weka.gui.ResultHistoryPanel: A Hashtable mapping names to result buffers
m_ResultsetKeyColumns - Variable in class weka.experiment.PairedTTester: An array containing the indexes of just the selected columns
m_ResultsetKeyColumnsRange - Variable in class weka.experiment.PairedTTester: The range of columns that specify a unique result set (eg: scheme plus configuration)
m_Resultsets - Variable in class weka.experiment.PairedTTester: Stores a vector for each resultset holding all instances in each set
m_ResultsetsValid - Variable in class weka.experiment.PairedTTester: Indicates whether the instances have been partitioned
m_ResultsPanel - Variable in class weka.gui.experiment.Experimenter: The panel for analysing experimental results
m_ResultsTableName - Variable in class weka.experiment.DatabaseResultListener: The name of the current results table
m_Retrieval - Variable in class weka.core.converters.AbstractLoader
m_Ridge - Variable in class weka.classifiers.Logistic: The ridge parameter.
m_RLEditor - Variable in class weka.gui.experiment.SetupPanel: The ResultListener editor
m_RLEditorPanel - Variable in class weka.gui.experiment.SetupPanel: The panel to contain the ResultListener editor
m_root - Variable in class weka.classifiers.adtree.ADTree: The root of the tree
m_Root - Variable in class weka.gui.PropertySelectorDialog: The root of the property tree
m_RootObject - Variable in class weka.gui.PropertySelectorDialog: The object at the root of the tree
m_RP - Variable in class weka.experiment.CSVResultListener: The ResultProducer sending us results
m_RPEditor - Variable in class weka.gui.experiment.SetupPanel: The ResultProducer editor
m_RPEditorPanel - Variable in class weka.gui.experiment.SetupPanel: The panel to contain the ResultProducer editor
m_RunColumn - Variable in class weka.experiment.PairedTTester: The index of the column containing the run number
m_RunColumnSet - Variable in class weka.experiment.PairedTTester: The option setting for the run number column (-1 means last)
m_RunCombo - Variable in class weka.gui.experiment.ResultsPanel: Lets the user select which column contains the run number
m_RunLower - Variable in class weka.experiment.Experiment: Lower run number
m_RunModel - Variable in class weka.gui.experiment.ResultsPanel: The model embedded in m_RunCombo
m_RunNumber - Variable in class weka.experiment.Experiment: The current run number when the experiment is running
m_RunNumberPanel - Variable in class weka.gui.experiment.SetupPanel: The panel for configuring run numbers
m_RunPanel - Variable in class weka.gui.experiment.Experimenter: The panel for running the experiment
m_RunThread - Variable in class weka.gui.SimpleCLI: The thread currently running a class main method
m_RunThread - Variable in class weka.gui.experiment.RunPanel: The thread running the experiment
m_RunThread - Variable in class weka.gui.explorer.ClassifierPanel: A thread that classification runs in
m_RunThread - Variable in class weka.gui.explorer.AssociationsPanel: A thread that associator runs in
m_RunThread - Variable in class weka.gui.explorer.AttributeSelectionPanel: A thread that attribute selection runs in
m_RunThread - Variable in class weka.gui.explorer.ClustererPanel: A thread that clustering runs in
m_RunUpper - Variable in class weka.experiment.Experiment: Upper run number
m_saveBut - Variable in class weka.gui.visualize.VisualizePanel: Button for the user to save the visualized set of instances
m_SaveBut - Variable in class weka.gui.experiment.SetupPanel: Click to save an experiment
m_SaveBut - Variable in class weka.gui.explorer.PreprocessPanel: Click to apply filters and save the results
m_saveInstanceData - Variable in class weka.classifiers.adtree.ADTree: Option - whether the tree should remember the instance data
m_SaveOut - Variable in class weka.gui.explorer.AssociationsPanel: The buffer saving object for saving output
m_SaveOutBut - Variable in class weka.gui.experiment.ResultsPanel: Click to save test output to a file
m_SaveOutBut - Variable in class weka.gui.explorer.AssociationsPanel: Click to save the output associated with the currently selected result
m_Scale - Variable in class weka.classifiers.kstar.KStarNumericAttribute: The scale parameter
m_Search - Variable in class weka.classifiers.AttributeSelectedClassifier: The search method to use
m_search_bestInsertionNode - Variable in class weka.classifiers.adtree.ADTree: The best node to insert under, as found so far by the latest search
m_search_bestPathNegInstances - Variable in class weka.classifiers.adtree.ADTree: The negative instances that apply to the best path found so far
m_search_bestPathPosInstances - Variable in class weka.classifiers.adtree.ADTree: The positive instances that apply to the best path found so far
m_search_bestSplitter - Variable in class weka.classifiers.adtree.ADTree: The best splitter to insert, as found so far by the latest search
m_search_smallestZ - Variable in class weka.classifiers.adtree.ADTree: The smallest Z value found so far by the latest search
m_searchPath - Variable in class weka.classifiers.adtree.ADTree: Option - the search mode
m_Seed - Variable in class weka.classifiers.MetaCost: Seed for reweighting using resampling.
m_Seed - Variable in class weka.classifiers.Bagging: The seed for random number generation.
m_Seed - Variable in class weka.classifiers.Stacking: Random number seed
m_Seed - Variable in class weka.classifiers.ThresholdSelector: Random number seed
m_Seed - Variable in class weka.classifiers.MultiScheme: Random number seed
m_Seed - Variable in class weka.classifiers.CVParameterSelection: Random number seed
m_Seed - Variable in class weka.classifiers.BVDecompose: The random number seed
m_Seed - Variable in class weka.classifiers.AdaBoostM1: Seed for boosting with resampling.
m_Seed - Variable in class weka.classifiers.CostSensitiveClassifier: Seed for reweighting using resampling.
m_Seed - Variable in class weka.classifiers.LogitBoost: Seed for boosting with resampling.
m_Seed - Variable in class weka.filters.RandomizeFilter: The random number seed
m_SeedLab - Variable in class weka.gui.explorer.AttributeSelectionPanel: Label by where cv random seed is entered
m_SeedText - Variable in class weka.gui.explorer.AttributeSelectionPanel: The field where the seed value is entered
m_SelectBut - Variable in class weka.gui.PropertySelectorDialog: Click to choose the currently selected property
m_SelectBut - Variable in class weka.gui.ListSelectorDialog: Click to choose the currently selected property
m_SelectCols - Variable in class weka.filters.AttributeFilter: Stores which columns to select as a funky range
m_Selected - Variable in class weka.core.SelectedTag: The index of the selected tag
m_SelectedAttributes - Variable in class weka.filters.AttributeFilter: Stores the indexes of the selected attributes in order, once the dataset is seen
m_SelectedAttributes - Variable in class weka.filters.CopyAttributesFilter: Stores the indexes of the selected attributes in order, once the dataset is seen
m_SelectedCols - Variable in class weka.filters.AbstractTimeSeriesFilter: Stores which columns to copy
m_selectionTime - Variable in class weka.classifiers.AttributeSelectedClassifier: The time taken to select attributes in milliseconds
m_SetCostsBut - Variable in class weka.gui.explorer.ClassifierPanel
m_SetCostsFrame - Variable in class weka.gui.explorer.ClassifierPanel: The frame used to show the cost matrix editing panel
m_SetTestBut - Variable in class weka.gui.explorer.ClassifierPanel: The button used to open a separate test dataset
m_SetTestBut - Variable in class weka.gui.explorer.ClustererPanel: The button used to open a separate test dataset
m_SetTestFrame - Variable in class weka.gui.explorer.ClassifierPanel: The frame used to show the test set selection panel
m_SetTestFrame - Variable in class weka.gui.explorer.ClustererPanel: The frame used to show the test set selection panel
m_SetupPanel - Variable in class weka.gui.experiment.Experimenter: The panel for configuring the experiment
m_ShapeCombo - Variable in class weka.gui.visualize.VisualizePanel: Lets the user select the shape they want to create for instance selection.
m_shapeSize - Variable in class weka.gui.visualize.PlotData2D: Additional optional information to control the size of points.
m_shapeType - Variable in class weka.gui.visualize.PlotData2D: Additional optional information to control the point shape for this data.
m_showAttBars - Variable in class weka.gui.visualize.VisualizePanel: Show the attribute bar panel
m_ShowStdDevs - Variable in class weka.experiment.PairedTTester: Indicates whether standard deviations should be displayed
m_ShowStdDevs - Variable in class weka.gui.experiment.ResultsPanel: Lets the user select whether standard deviations are to be output or not
m_shrinkage - Variable in class weka.classifiers.AdditiveRegression: Shrinkage (Learning rate).
m_Sigma - Variable in class weka.classifiers.BVDecompose: The calculated sigma (squared)
m_significanceLevel - Variable in class weka.associations.Apriori: Significance level for optional significance test.
m_SignificanceLevel - Variable in class weka.experiment.PairedTTester: The significance level for comparisons
m_SigTex - Variable in class weka.gui.experiment.ResultsPanel: Lets the user edit the test significance
m_SimpleBut - Variable in class weka.gui.GUIChooser: Click to open the simplecli
m_SimpleCLI - Variable in class weka.gui.GUIChooser: The SimpleCLI
m_sIndex - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
m_sIndex - Variable in class weka.gui.visualize.Plot2D
m_SingleName - Variable in class weka.gui.ResultHistoryPanel: The named result being viewed in the single-click display
m_SingleText - Variable in class weka.gui.ResultHistoryPanel: An optional component for single-click display
m_Size - Variable in class weka.core.Queue: Store the current number of elements in the queue
m_SmallestProb - Variable in class weka.classifiers.kstar.KStarNumericAttribute: Smallest probability of test attribute transforming into train attribute
m_SmallestProb - Variable in class weka.classifiers.kstar.KStarNominalAttribute: Smallest probability of test attribute transforming into train attribute
m_sons - Variable in class weka.classifiers.j48.ClassifierDecList: References to sons.
m_sons - Variable in class weka.classifiers.j48.ClassifierTree: References to sons.
m_sourceFile - Variable in class weka.core.converters.CSVLoader: Holds the source of the data set.
m_sourceFile - Variable in class weka.core.converters.C45Loader: Holds the source of the data set.
m_span - Variable in class weka.gui.visualize.LegendPanel: the panel that contains the legend entries
m_span - Variable in class weka.gui.visualize.AttributePanel: The container window for the attribute bars, and also where the X,Y or B get printed.
m_splitByDataSet - Variable in class weka.experiment.RemoteExperiment: If true, then sub experiments are created on the basis of data sets rather than run number.
m_splitByDataSet - Variable in class weka.gui.experiment.DistributeExperimentPanel: Split experiment up by data set.
m_splitByRun - Variable in class weka.gui.experiment.DistributeExperimentPanel: Split experiment up by run number.
m_SplitEvaluator - Variable in class weka.experiment.CrossValidationResultProducer: The SplitEvaluator used to generate results
m_SplitEvaluator - Variable in class weka.experiment.RandomSplitResultProducer: The SplitEvaluator used to generate results
m_splitListener - Variable in class weka.gui.visualize.VisualizePanel: An optional listener that we will inform when the user creates a split to seperate instances.
m_SQLQ - Variable in class weka.gui.explorer.PreprocessPanel: Stores the last sql query executed
m_StartBut - Variable in class weka.gui.experiment.RunPanel: Click to start running the experiment
m_StartBut - Variable in class weka.gui.explorer.ClassifierPanel: Click to start running the classifier
m_StartBut - Variable in class weka.gui.explorer.AssociationsPanel: Click to start running the associator
m_StartBut - Variable in class weka.gui.explorer.AttributeSelectionPanel: Click to start running the attribute selector
m_StartBut - Variable in class weka.gui.explorer.ClustererPanel: Click to start running the clusterer
m_Statement - Variable in class weka.experiment.DatabaseUtils: The statement used for database queries
m_StatsTable - Variable in class weka.gui.AttributeSummaryPanel: Displays other stats in a table
m_StatusBox - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel: Controls whether the custom iterator is used or not
m_StatusLab - Variable in class weka.gui.LogPanel: Displays the current status
m_statusMessage - Variable in class weka.experiment.RemoteExperimentEvent: A status type message
m_StepSize - Variable in class weka.experiment.LearningRateResultProducer: The number of instances to add at each step
m_Stop - Variable in class weka.classifiers.kstar.KStarNominalAttribute: The stop parameter
m_StopBut - Variable in class weka.gui.experiment.RunPanel: Click to signal the running experiment to halt
m_StopBut - Variable in class weka.gui.explorer.ClassifierPanel: Click to stop a running classifier
m_StopBut - Variable in class weka.gui.explorer.AssociationsPanel: Click to stop a running associator
m_StopBut - Variable in class weka.gui.explorer.AttributeSelectionPanel: Click to stop a running classifier
m_StopBut - Variable in class weka.gui.explorer.ClustererPanel: Click to stop a running clusterer
m_StorePredictionsBut - Variable in class weka.gui.explorer.ClassifierPanel: Check to save the predictions in the results list for visualizing later on
m_StorePredictionsBut - Variable in class weka.gui.explorer.ClustererPanel: Check to save the predictions in the results list for visualizing later on
m_structure - Variable in class weka.core.converters.CSVLoader: Holds the determined structure (header) of the data set.
m_structure - Variable in class weka.core.converters.C45Loader: Holds the determined structure (header) of the data set.
m_structure - Variable in class weka.core.converters.ArffLoader: Holds the determined structure (header) of the data set.
m_subExpComplete - Variable in class weka.experiment.RemoteExperiment: The status of each of the sub-experiments
m_subExperiments - Variable in class weka.experiment.RemoteExperiment: The sub experiments
m_submit - Variable in class weka.gui.visualize.VisualizePanel: Button for the user to enter the splits.
m_Summary - Variable in class weka.gui.SetInstancesPanel: The instance summary component
m_Summary - Variable in class weka.gui.explorer.ClassifierPanel: The instances summary panel displayed by m_SetTestFrame
m_Summary - Variable in class weka.gui.explorer.ClustererPanel: The instances summary panel displayed by m_SetTestFrame
m_Support - Variable in class weka.gui.SetInstancesPanel: Manages sending notifications to people when we change the set of working instances.
m_Support - Variable in class weka.gui.experiment.SetupPanel: Manages sending notifications to people when we change the experiment, at this stage, only the resultlistener so the resultpanel can update.
m_Support - Variable in class weka.gui.explorer.PreprocessPanel: Manages sending notifications to people when we change the set of working instances.
m_TabbedPane - Variable in class weka.gui.experiment.Experimenter: The tabbed pane that controls which sub-pane we are working with
m_TabbedPane - Variable in class weka.gui.explorer.Explorer: The tabbed pane that controls which sub-pane we are working with
m_Table - Variable in class weka.gui.AttributeSelectionPanel: The table displaying attribute names and selection status
m_Tags - Variable in class weka.core.SelectedTag: The set of tags to choose from
m_Tail - Variable in class weka.core.Queue: Store a reference to the tail of the queue
m_TaskMonitor - Variable in class weka.gui.LogPanel: The panel for monitoring the number of running tasks (if supplied)
m_test - Variable in class weka.classifiers.j48.ClassifierDecList: The pruning instances.
m_test - Variable in class weka.classifiers.j48.ClassifierTree: The pruning instances.
m_Test - Variable in class weka.classifiers.kstar.KStarNumericAttribute: The test instance
m_Test - Variable in class weka.classifiers.kstar.KStarNominalAttribute: The test instance
m_TestInstances - Variable in class weka.gui.explorer.ClassifierPanel: The user-supplied test set (if any)
m_TestInstances - Variable in class weka.gui.explorer.AssociationsPanel: The user-supplied test set (if any)
m_TestInstances - Variable in class weka.gui.explorer.ClustererPanel: The user-supplied test set (if any)
m_TestInstancesCopy - Variable in class weka.gui.explorer.ClassifierPanel: The user supplied test set after preprocess filters have been applied
m_TestInstancesCopy - Variable in class weka.gui.explorer.ClustererPanel: The user supplied test set after preprocess filters have been applied
m_TestsButton - Variable in class weka.gui.experiment.ResultsPanel: Lets the user select which scheme to base comparisons against
m_TestsList - Variable in class weka.gui.experiment.ResultsPanel: Holds the list of schemes to base the test against
m_TestsModel - Variable in class weka.gui.experiment.ResultsPanel: The model embedded in m_TestsList
m_TestSplitBut - Variable in class weka.gui.explorer.ClassifierPanel: Click to set test mode to a user-specified test set
m_TestSplitBut - Variable in class weka.gui.explorer.ClustererPanel: Click to set test mode to a user-specified test set
m_toSelectModel - Variable in class weka.classifiers.j48.ClassifierDecList: The model selection method.
m_toSelectModel - Variable in class weka.classifiers.j48.ClassifierTree: The model selection method.
m_TotalCount - Variable in class weka.classifiers.kstar.KStarNominalAttribute: Number of trai instances with no missing attribute values
m_totalTime - Variable in class weka.classifiers.AttributeSelectedClassifier: The time taken to select attributes AND build the classifier
m_totalTransactions - Variable in class weka.associations.ItemSet: The total number of transactions
m_train - Variable in class weka.classifiers.j48.ClassifierDecList: The training instances.
m_train - Variable in class weka.classifiers.j48.ClassifierTree: The training instances.
m_Train - Variable in class weka.classifiers.IBk: The training instances used for classification.
m_Train - Variable in class weka.classifiers.LWR: The training instances used for classification.
m_Train - Variable in class weka.classifiers.kstar.KStarNumericAttribute: The train instance
m_Train - Variable in class weka.classifiers.kstar.KStar: The training instances used for classification.
m_Train - Variable in class weka.classifiers.kstar.KStarNominalAttribute: The train instance
m_TrainBut - Variable in class weka.gui.explorer.ClassifierPanel: Click to set test mode to test on training data
m_TrainBut - Variable in class weka.gui.explorer.AttributeSelectionPanel: Click to set test mode to test on training data
m_TrainBut - Variable in class weka.gui.explorer.ClustererPanel: Click to set test mode to test on training data
m_TrainFoldSize - Variable in class weka.classifiers.CVParameterSelection: The number of instances in a training fold
m_trainInstances - Variable in class weka.classifiers.adtree.ADTree: The instances used to train the tree
m_TrainIterations - Variable in class weka.classifiers.BVDecompose: The number of train iterations
m_TrainPercent - Variable in class weka.experiment.RandomSplitResultProducer: The percentage of instances to use for training
m_TrainPoolSize - Variable in class weka.classifiers.BVDecompose: The number of instances used in the training pool
m_TrainSet - Variable in class weka.classifiers.kstar.KStarNumericAttribute: The training instances used for classification.
m_TrainSet - Variable in class weka.classifiers.kstar.KStarNominalAttribute: The training instances used for classification.
m_trainTotalWeight - Variable in class weka.classifiers.adtree.ADTree: The total weight of the instances - used to speed Z calculations
m_Tree - Variable in class weka.gui.PropertySelectorDialog: The component displaying the property tree
m_TTester - Variable in class weka.gui.experiment.ResultsPanel: The PairedTTester object
m_type - Variable in class weka.classifiers.neural.NeuralConnection: The type of unit this is.
m_UniqueLab - Variable in class weka.gui.AttributeSummaryPanel: Displays the number of unique values
m_unitError - Variable in class weka.classifiers.neural.NeuralConnection: The error value for this unit, NaN if not calculated.
m_unitValue - Variable in class weka.classifiers.neural.NeuralConnection: The output value for this unit, NaN if not calculated.
m_upperBoundMinSupport - Variable in class weka.associations.Apriori: The upper bound on the support
m_UpperSize - Variable in class weka.experiment.LearningRateResultProducer: The maximum number of instances to use.
m_UpperText - Variable in class weka.gui.experiment.RunNumberPanel: Configures the upper run number
m_UseAllK - Variable in class weka.classifiers.LWR: True if m_kNN should be set to all instances
m_UseBetterEncoding - Variable in class weka.filters.DiscretizeFilter: Use better encoding of split point for MDL.
m_useCustomColour - Variable in class weka.gui.visualize.PlotData2D: Custom colour for this plot
m_UseKernelEstimator - Variable in class weka.classifiers.NaiveBayes: Whether to use kernel density estimator rather than normal distribution for numeric attributes
m_UseKononenko - Variable in class weka.filters.DiscretizeFilter: Use Kononenko's MDL criterion instead of Fayyad et al.'s
m_UseMDL - Variable in class weka.filters.DiscretizeFilter: True if discretisation will be done by MDL rather than binning
m_UsePropertyIterator - Variable in class weka.experiment.Experiment: True if the exp should also iterate over a property of the RP
m_UserDir - Variable in class weka.gui.experiment.DatasetListPanel: The user (start) directory
m_UseResampling - Variable in class weka.classifiers.AdaBoostM1: Use boosting with reweighting?
m_UseResampling - Variable in class weka.classifiers.LogitBoost: Use boosting with reweighting?
m_Value - Variable in class weka.filters.InstanceFilter: Stores which value of a numeric attribute is to be used for filtering.
m_ValueBuffer - Variable in class weka.core.Instances: Buffer of values for sparse instance
m_Values - Variable in class weka.filters.InstanceFilter: Stores which values of nominal attribute are to be used for filtering.
m_Variance - Variable in class weka.classifiers.BVDecompose: The calculated variance
m_verbose - Variable in class weka.associations.Apriori: Report progress iteratively
m_VisualizePanel - Variable in class weka.gui.explorer.Explorer: Label for a panel that still need to be implemented
m_visXIndex - Variable in class weka.gui.explorer.ClassifierPanel: default x index for visualizing
m_visXIndex - Variable in class weka.gui.explorer.ClustererPanel: default x index for visualizing
m_visYIndex - Variable in class weka.gui.explorer.ClassifierPanel: default y index for visualizing
m_visYIndex - Variable in class weka.gui.explorer.ClustererPanel: default y index for visualizing
m_Weight - Variable in class weka.core.Instance: The instance's weight.
m_weightByConfidence - Variable in class weka.classifiers.VFI: Exponentially bias more confident intervals
m_WeightKernel - Variable in class weka.classifiers.LWR: The weighting kernel method currently selected
m_weightsUpdated - Variable in class weka.classifiers.neural.NeuralConnection: True if the weights have already been updated.
m_WeightThreshold - Variable in class weka.classifiers.AdaBoostM1: Weight Threshold.
m_WeightThreshold - Variable in class weka.classifiers.LogitBoost: Weight thresholding.
m_WindowSize - Variable in class weka.classifiers.IBk: The maximum number of training instances allowed.
m_WorkingInstances - Variable in class weka.gui.explorer.PreprocessPanel: The working (filtered) copy
m_WorkingInstPanel - Variable in class weka.gui.explorer.PreprocessPanel: Displays simple stats on the working instances
m_x - Variable in class weka.classifiers.neural.NeuralConnection: The x coord of this unit purely for displaying purposes.
m_xChange - Variable in class weka.gui.visualize.AttributePanelEvent: True if the x selection changed
m_XCombo - Variable in class weka.gui.visualize.VisualizePanel: Lets the user select the attribute for the x axis
m_xIndex - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel: Indexes of the attributes to go on the x and y axis and the attribute to use for colouring and the current shape for drawing
m_xIndex - Variable in class weka.gui.visualize.Plot2D: Indexes of the attributes to go on the x and y axis and the attribute to use for colouring and the current shape for drawing
m_xIndex - Variable in class weka.gui.visualize.AttributePanel
m_y - Variable in class weka.classifiers.neural.NeuralConnection: The y coord of this unit purely for displaying purposes.
m_yChange - Variable in class weka.gui.visualize.AttributePanelEvent: True if the y selection changed
m_YCombo - Variable in class weka.gui.visualize.VisualizePanel: Lets the user select the attribute for the y axis
m_yIndex - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
m_yIndex - Variable in class weka.gui.visualize.Plot2D
m_yIndex - Variable in class weka.gui.visualize.AttributePanel
m_ZipDest - Variable in class weka.experiment.CrossValidationResultProducer: The output zipper to use for saving raw splitEvaluator output
m_ZipDest - Variable in class weka.experiment.RandomSplitResultProducer: The output zipper to use for saving raw splitEvaluator output
M5Prime - class weka.classifiers.m5.M5Prime.: Class for contructing and evaluating model trees; M5' algorithm.
M5Prime() - Constructor for class weka.classifiers.m5.M5Prime
M5Utils - class weka.classifiers.m5.M5Utils.: Class for some small methods used in M5Java
M5Utils() - Constructor for class weka.classifiers.m5.M5Utils
MahalanobisEstimator - class weka.estimators.MahalanobisEstimator.: Simple probability estimator that places a single normal distribution over the observed values.
MahalanobisEstimator(Matrix, double, double) - Constructor for class weka.estimators.MahalanobisEstimator: Constructor
main(String[]) - Static method in class weka.associations.Apriori: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.CfsSubsetEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.PrincipalComponents: 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.attributeSelection.ChiSquaredAttributeEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.ReliefFAttributeEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.AttributeSelection: 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.attributeSelection.SymmetricalUncertAttributeEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.OneRAttributeEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.ClassifierSubsetEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.InfoGainAttributeEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.ConsistencySubsetEval: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.DecisionTable: 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.Prism: 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.AdditiveRegression: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.VotedPerceptron: Main method.
main(String[]) - Static method in class weka.classifiers.Bagging: 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.classifiers.IBk: 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.classifiers.KernelDensity: 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.ThresholdSelector: 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.RegressionByDiscretization: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.ZeroR: 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.classifiers.CVParameterSelection: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.BVDecompose: Test method for this class
main(String[]) - Static method in class weka.classifiers.AdaBoostM1: Main method for testing this class.
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.classifiers.Id3: Main method.
main(String[]) - Static method in class weka.classifiers.Evaluation: A test method for this class.
main(String[]) - Static method in class weka.classifiers.FilteredClassifier: 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.classifiers.OneR: Main method for testing this class
main(String[]) - Static method in class weka.classifiers.CostSensitiveClassifier: 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.classifiers.MultiClassClassifier: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.LWR: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.DistributionMetaClassifier: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.Logistic: 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.classifiers.CheckClassifier: Test method for this class
main(String[]) - Static method in class weka.classifiers.LogitBoost: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.HyperPipes: 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.classifiers.ClassificationViaRegression: 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.adtree.ADTree: 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.evaluation.ThresholdCurve: Tests the ThresholdCurve generation from the command line.
main(String[]) - Static method in class weka.classifiers.evaluation.MarginCurve: Tests the MarginCurve generation from the command line.
main(String[]) - Static method in class weka.classifiers.j48.J48: Main method for testing this class
main(String[]) - Static method in class weka.classifiers.j48.PART: 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.classifiers.m5.M5Prime: Main method for M5' algorithm
main(String[]) - Static method in class weka.classifiers.neural.NeuralNetwork: Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.SimpleKMeans: 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.clusterers.Cobweb
main(String[]) - Static method in class weka.clusterers.ClusterEvaluation: 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.core.Matrix: Main method for testing this class.
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.core.CheckOptionHandler: Main method for using the CheckOptionHandler.
main(String[]) - Static method in class weka.core.Statistics: Main method for testing this class.
main(String[]) - Static method in class weka.core.SpecialFunctions: Main method for testing this class.
main(String[]) - Static method in class weka.core.Instance: Main method for testing this class.
main(String[]) - Static method in class weka.core.Queue: Main method for testing this class.
main(String[]) - Static method in class weka.core.SparseInstance: Main method for testing this class.
main(String[]) - Static method in class weka.core.Range: Main method for testing this class.
main(String[]) - Static method in class weka.core.Attribute: Simple main method for testing this class.
main(String[]) - Static method in class weka.core.BinarySparseInstance: Main method for testing this class.
main(String[]) - Static method in class weka.core.Utils: 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.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.core.converters.CSVLoader: Main method.
main(String[]) - Static method in class weka.core.converters.SerializedInstancesLoader: Main method.
main(String[]) - Static method in class weka.core.converters.C45Loader: Main method for testing this class.
main(String[]) - Static method in class weka.core.converters.ArffLoader: Main method.
main(String[]) - Static method in class weka.estimators.NormalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.DDConditionalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.KDConditionalEstimator: 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.KKConditionalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.DNConditionalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.PoissonEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.NDConditionalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.KernelEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.MahalanobisEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.DiscreteEstimator: 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.experiment.CrossValidationResultProducer
main(String[]) - Static method in class weka.experiment.PairedTTester: Test the class from the command line.
main(String[]) - Static method in class weka.experiment.Stats: Tests the paired stats object from the command line.
main(String[]) - Static method in class weka.experiment.Experiment: Configures/Runs the Experiment from the command line.
main(String[]) - Static method in class weka.experiment.InstanceQuery: Test the class from the command line.
main(String[]) - Static method in class weka.experiment.RemoteEngine: Main method.
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.RemoteExperiment: Configures/Runs the Experiment from the command line.
main(String[]) - Static method in class weka.filters.Filter: 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.filters.AllFilter: 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.ObfuscateFilter: 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.ReplaceMissingValuesFilter: 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.FirstOrderFilter: 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.filters.EmptyAttributeFilter: 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.filters.SplitDatasetFilter: 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.filters.AttributeSelectionFilter: Main method for testing this class.
main(String[]) - Static method in class weka.filters.AttributeTypeFilter: 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.filters.AttributeFilter: 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.filters.DiscretizeFilter: 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.filters.NominalToBinaryFilter: 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.filters.TimeSeriesDeltaFilter: Main method for testing this class.
main(String[]) - Static method in class weka.filters.StringToNominalFilter: 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.filters.ResampleFilter: 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.filters.NonSparseToSparseFilter: 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.NullFilter: Main method for testing this class.
main(String[]) - Static method in class weka.gui.GUIChooser: Tests out the GUIChooser environment.
main(String[]) - Static method in class weka.gui.AttributeSummaryPanel: Tests out the attribute summary panel from the command line.
main(String[]) - Static method in class weka.gui.SaveBuffer: Main method for testing this class
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.gui.GenericArrayEditor: Tests out the array editor from the command line.
main(String[]) - Static method in class weka.gui.AttributeSelectionPanel: Tests the attribute selection panel from the command line.
main(String[]) - Static method in class weka.gui.SelectedTagEditor: Tests out the selectedtag editor from the command line.
main(String[]) - Static method in class weka.gui.WekaTaskMonitor: 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.gui.GenericObjectEditor: Tests out the Object editor from the command line.
main(String[]) - Static method in class weka.gui.InstancesSummaryPanel: Tests out the instance summary panel from the command line.
main(String[]) - Static method in class weka.gui.SimpleCLI: Method to start up the simple cli
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.PropertySelectorDialog: Tests out the property selector from the command line.
main(String[]) - Static method in class weka.gui.ListSelectorDialog: Tests out the list selector from the command line.
main(String[]) - Static method in class weka.gui.experiment.Experimenter: Tests out the experiment environment.
main(String[]) - Static method in class weka.gui.experiment.GeneratorPropertyIteratorPanel: Tests out the panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.SetupPanel: Tests out the experiment setup from the command line.
main(String[]) - Static method in class weka.gui.experiment.HostListPanel: Tests out the host list panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.RunPanel: Tests out the run panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.DatasetListPanel: Tests out the dataset list panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.ResultsPanel: Tests out the results panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.DistributeExperimentPanel: Tests out the panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.RunNumberPanel: Tests out the panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.ClassifierPanel: Tests out the classifier panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.PreprocessPanel: Tests out the instance-preprocessing panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.Explorer: Tests out the explorer environment.
main(String[]) - Static method in class weka.gui.explorer.AssociationsPanel: Tests out the Associator panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.AttributeSelectionPanel: Tests out the attribute selection panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.ClustererPanel: Tests out the clusterer panel from the command line.
main(String[]) - Static method in class weka.gui.treevisualizer.TreeVisualizer: Main method for testing this class.
main(String[]) - Static method in class weka.gui.visualize.VisualizePanel: 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.visualize.Plot2D: 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.gui.visualize.AttributePanel: 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 - class weka.classifiers.j48.MakeDecList.: Class for handling a decision list.
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 - class weka.filters.MakeIndicatorFilter.: Creates a new dataset with a boolean attribute replacing a nominal attribute.
MakeIndicatorFilter() - Constructor for class weka.filters.MakeIndicatorFilter
makeTestDataset(int, int, int, int, int, boolean) - Method in class weka.classifiers.CheckClassifier: Make a simple set of instances, which can later be modified for use in specific tests.
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 - class weka.classifiers.evaluation.MarginCurve.: Generates points illustrating the prediction margin.
MarginCurve() - Constructor for class weka.classifiers.evaluation.MarginCurve
Matchable - interface weka.core.Matchable.: Interface to something that can be matched with tree matching algorithms.
matchesTemplate(Object[], Object[]) - Method in class weka.experiment.AveragingResultProducer: Compares a key to a template to see whether they match.
Matrix - class weka.classifiers.m5.Matrix.: Class for handling a matrix
Matrix - class weka.core.Matrix.: Class for performing operations on a matrix of floating-point values.
MATRIX_ON_DEMAND - Static variable in class weka.classifiers.MetaCost
MATRIX_ON_DEMAND - Static variable in class weka.classifiers.CostSensitiveClassifier
MATRIX_SUPPLIED - Static variable in class weka.classifiers.MetaCost
MATRIX_SUPPLIED - Static variable in class weka.classifiers.CostSensitiveClassifier
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.
max - Variable in class weka.experiment.Stats: The maximum value seen, or Double.NaN if no values seen
MAX_FAILURES - Static variable in class weka.experiment.RemoteExperiment: allow at most 3 failures on a host before it is removed from the list of usable hosts
MAX_PRECISION - Static variable in class weka.gui.visualize.VisualizeUtils: Default maximum precision for the display of numeric values
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.j48.PART: Return the number of rules.
measureNumRules() - Method in class weka.classifiers.m5.M5Prime: return the number of rules
Measures - class weka.classifiers.m5.Measures.: Class for performance measures
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.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.
mergeInstance(Instance) - Method in class weka.core.BinarySparseInstance: Merges this instance with the given instance and returns the result.
mergeInstances(Instance, Instance) - Method in class weka.filters.AbstractTimeSeriesFilter: Creates a new instance the same as one instance (the "destination") but with some attribute values copied from another instance (the "source")
mergeInstances(Instance, Instance) - Method in class weka.filters.TimeSeriesTranslateFilter: Creates a new instance the same as one instance (the "destination") but with some attribute values copied from another instance (the "source")
mergeInstances(Instance, Instance) - Method in class weka.filters.TimeSeriesDeltaFilter: Creates a new instance the same as one instance (the "destination") but with some attribute values copied from another instance (the "source")
mergeInstances(Instances, Instances) - Static method in class weka.core.Instances: Merges two sets of Instances together.
MergeTwoValuesFilter - class weka.filters.MergeTwoValuesFilter.: Merges two values of a nominal attribute.
MergeTwoValuesFilter() - Constructor for class weka.filters.MergeTwoValuesFilter
MetaCost - class weka.classifiers.MetaCost.: This metaclassifier makes its base classifier cost-sensitive using the method specified in
MetaCost() - Constructor for class weka.classifiers.MetaCost
metaFormat(Instances) - Method in class weka.classifiers.Stacking: Makes the format for the level-1 data.
metaInstance(Instance) - Method in class weka.classifiers.Stacking: Makes a level-1 instance from the given instance.
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
MIN_VALUE - Static variable in class weka.classifiers.ThresholdSelector: The minimum value for the criterion.
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.
MISSING_VALUE - Static variable in class weka.core.Instance: 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.SymmetricalUncertAttributeEval: Returns the tip text for this property
missingMergeTipText() - Method in class weka.attributeSelection.InfoGainAttributeEval: 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 - class weka.classifiers.j48.ModelSelection.: Abstract class for model selection criteria.
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 - class weka.classifiers.MultiClassClassifier.: Class for handling multi-class datasets with 2-class distribution classifiers.
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 - class weka.classifiers.MultiScheme.: Class for selecting a classifier from among several using cross validation on the training data.
MultiScheme() - Constructor for class weka.classifiers.MultiScheme
mutationProbTipText() - Method in class weka.attributeSelection.GeneticSearch: Returns the tip text for this property

N

NaiveBayes - class weka.classifiers.NaiveBayes.: Class for a Naive Bayes classifier using estimator classes.
NaiveBayes() - Constructor for class weka.classifiers.NaiveBayes
NaiveBayesSimple - class weka.classifiers.NaiveBayesSimple.: Class for building and using a simple Naive Bayes classifier.
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 - class weka.gui.treevisualizer.NamedColor.: This class contains a color name and the rgb values of that color
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 - class weka.estimators.NDConditionalEstimator.: Conditional probability estimator for a numeric domain conditional upon a discrete domain (utilises separate normal estimators for each discrete conditioning value).
NDConditionalEstimator(int, double) - Constructor for class weka.estimators.NDConditionalEstimator: Constructor
NeuralConnection - class weka.classifiers.neural.NeuralConnection.: Abstract unit in a NeuralNetwork.
NeuralConnection(String) - Constructor for class weka.classifiers.neural.NeuralConnection: Constructs The unit with the basic connection information prepared for use.
NeuralMethod - interface weka.classifiers.neural.NeuralMethod.: This is an interface used to create classes that can be used by the neuralnode to perform all it's computations.
NeuralNetwork - class weka.classifiers.neural.NeuralNetwork.: A Classifier that uses backpropagation to classify instances.
NeuralNetwork.NeuralEnd - class weka.classifiers.neural.NeuralNetwork.NeuralEnd.: This inner class is used to connect the nodes in the network up to the data that they are classifying, Note that objects of this class are only suitable to go on the attribute side or class side of the network and not both.
NeuralNetwork.NeuralEnd(NeuralNetwork, String) - Constructor for class weka.classifiers.neural.NeuralNetwork.NeuralEnd
NeuralNetwork() - Constructor for class weka.classifiers.neural.NeuralNetwork: The constructor.
NeuralNode - class weka.classifiers.neural.NeuralNode.: This class is used to represent a node in the neuralnet.
NeuralNode(String, Random, NeuralMethod) - Constructor for class weka.classifiers.neural.NeuralNode
newColorAttribute(int, Instances) - Method in class weka.gui.visualize.VisualizePanel: Sets the Colors in use for a different attrib if it is not a nominal attrib and or does not have more possible values then this will do nothing.
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 - Variable in class weka.classifiers.kstar.KStarCache.TableEntry: next table entry (separate chaining)
next() - Method in class weka.core.Queue.QueueNode: Gets the next node in the queue.
next(int) - Method in interface weka.classifiers.IterativeClassifier: Performs one iteration.
next(int) - Method in class weka.classifiers.adtree.ADTree: Performs one iteration.
next(Queue.QueueNode) - Method in class weka.core.Queue.QueueNode: Sets the next node in the queue, and returns it.
nextElement() - Method in class weka.core.FastVector.FastVectorEnumeration: Returns the next element.
nextID() - Static method in class weka.classifiers.j48.ClassifierTree: Gets the next unique node ID.
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 - class weka.estimators.NNConditionalEstimator.: Conditional probability estimator for a numeric domain conditional upon a numeric domain (using Mahalanobis distance).
NNConditionalEstimator() - Constructor for class weka.estimators.NNConditionalEstimator
NO_COMMAND - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
NO_SOURCE - Static variable in class weka.gui.AttributeSummaryPanel: Message shown when no instances have been loaded and no attribute set
NO_SOURCE - Static variable in class weka.gui.InstancesSummaryPanel: Message shown when no instances have been loaded
NO_SOURCE - Static variable in class weka.gui.experiment.ResultsPanel: Message shown when no experimental results have been loaded
Node - class weka.classifiers.m5.Node.: Class for handing a node in the tree or the subtree under this node
Node - class weka.gui.treevisualizer.Node.: This class records all the data about a particular node for displaying.
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.
NodePlace - interface weka.gui.treevisualizer.NodePlace.: This is an interface for classes that wish to take a node structure and arrange them
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 - class weka.classifiers.evaluation.NominalPrediction.: Encapsulates an evaluatable nominal prediction: the predicted probability distribution plus the actual class value.
NominalPrediction(double, double[]) - Constructor for class weka.classifiers.evaluation.NominalPrediction: Creates the NominalPrediction object with a default weight of 1.0.
NominalPrediction(double, double[], double) - Constructor for class weka.classifiers.evaluation.NominalPrediction: Creates the NominalPrediction object.
NominalToBinaryFilter - class weka.filters.NominalToBinaryFilter.: Converts all nominal attributes into binary numeric attributes.
NominalToBinaryFilter() - Constructor for class weka.filters.NominalToBinaryFilter
nominalToBinaryFilterTipText() - Method in class weka.classifiers.neural.NeuralNetwork
NONE - Static variable in class weka.core.converters.AbstractLoader: For state where no instances have been retrieved yet
NONE - Static variable in class weka.gui.visualize.VisualizePanelEvent: No longer used
NonSparseToSparseFilter - class weka.filters.NonSparseToSparseFilter.: A filter that converts all incoming instances into sparse format.
NonSparseToSparseFilter() - Constructor for class weka.filters.NonSparseToSparseFilter
NORM_EXPECTED_COST_NAME - Static variable in class weka.classifiers.evaluation.CostCurve
Norm(double) - Static method in class weka.classifiers.Logistic: Returns probability.
NormalEstimator - class weka.estimators.NormalEstimator.: Simple probability estimator that places a single normal distribution over the observed values.
NormalEstimator(double) - Constructor for class weka.estimators.NormalEstimator: Constructor that takes a precision argument.
NormalizationFilter - class weka.filters.NormalizationFilter.: Normalizes all numeric values in the given dataset.
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 - class weka.classifiers.j48.NoSplit.: Class implementing a "no-split"-split.
NoSplit(Distribution) - Constructor for class weka.classifiers.j48.NoSplit: Creates "no-split"-split for given distribution.
NOT_RUNNING - Static variable in class weka.gui.experiment.RunPanel: The message displayed when no experiment is running
notifyInstanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceLoader
notifyInstanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceJoiner
NullFilter - class weka.filters.NullFilter.: A simple instance filter that allows no instances to pass through.
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.Instances: Returns the number of attributes.
numAttributes() - Method in class weka.core.Instance: 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.SimpleKMeans: Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.DistributionMetaClusterer: Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.Cobweb: Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.EM: 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.Instances: Returns the number of class labels.
numClasses() - Method in class weka.core.Instance: Returns the number of class labels.
numClustersTipText() - Method in class weka.clusterers.SimpleKMeans: Returns the tip text for this property
numClustersTipText() - Method in class weka.clusterers.EM: 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 - class weka.classifiers.evaluation.NumericPrediction.: Encapsulates an evaluatable numeric prediction: the predicted class value plus the actual class value.
NumericPrediction(double, double) - Constructor for class weka.classifiers.evaluation.NumericPrediction: Creates the NumericPrediction object with a default weight of 1.0.
NumericPrediction(double, double, double) - Constructor for class weka.classifiers.evaluation.NumericPrediction: Creates the NumericPrediction object.
numericStats - Variable in class weka.core.AttributeStats: Stats on numeric value distributions
numericTipText() - Method in class weka.filters.MakeIndicatorFilter
NumericToBinaryFilter - class weka.filters.NumericToBinaryFilter.: Converts all numeric attributes into binary attributes (apart from the class attribute): if the value of the numeric attribute is exactly zero, the value of the new attribute will be zero.
NumericToBinaryFilter() - Constructor for class weka.filters.NumericToBinaryFilter
NumericTransformFilter - class weka.filters.NumericTransformFilter.: Transforms numeric attributes using a given transformation method.
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.
numOfAllNodes(PredictionNode) - Method in class weka.classifiers.adtree.ADTree: Returns the total number of nodes in a tree.
numOfBoostingIterationsTipText() - Method in class weka.classifiers.adtree.ADTree
numOfPredictionLeafNodes(PredictionNode) - Method in class weka.classifiers.adtree.ADTree: Returns the number of leaf nodes in a tree - prediction nodes without children.
numOfPredictionNodes(PredictionNode) - Method in class weka.classifiers.adtree.ADTree: Returns the number of prediction nodes in a tree.
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.Ranker: 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.ForwardSelection: 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.Instance: Returns the number of values present.
numValues() - Method in class weka.core.SparseInstance: Returns the number of values in the sparse vector.
numValues() - Method in class weka.core.Attribute: Returns the number of attribute values.
numXValFoldsTipText() - Method in class weka.classifiers.ThresholdSelector

O

ObfuscateFilter - class weka.filters.ObfuscateFilter.: A simple instance filter that renames the relation, all attribute names and all nominal (and string) attribute values.
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 - class weka.classifiers.OneR.: Class for building and using a 1R classifier.
OneR() - Constructor for class weka.classifiers.OneR
OneRAttributeEval - class weka.attributeSelection.OneRAttributeEval.: Class for Evaluating attributes individually by using the OneR classifier.
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.
onUnit(Graphics, int, int, int, int) - Method in class weka.classifiers.neural.NeuralNetwork.NeuralEnd: 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.
openHelpFrame() - Method in class weka.gui.PropertySheetPanel
openObject() - Method in class weka.gui.GenericObjectEditor.GOEPanel: Opens an object from a file selected by the user.
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 - class weka.core.Option.: Class to store information about an option.
Option(String, String, int, String) - Constructor for class weka.core.Option: Creates new option with the given parameters.
OptionHandler - interface weka.core.OptionHandler.: Interface to something that understands options.
Options - class weka.classifiers.m5.Options.: Class for handing options
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: Deprecated. use getOutputFormat() instead.
outputFormat() - Method in class weka.gui.streams.InstanceLoader
outputFormat() - Method in class weka.gui.streams.InstanceJoiner: Gets the format of the output instances.
outputFormat() - Method in interface weka.gui.streams.InstanceProducer
outputFormatPeek() - Method in class weka.filters.Filter: Returns a reference to the current output format without copying it.
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.InstanceLoader
outputPeek() - Method in class weka.gui.streams.InstanceJoiner: Output an instance after filtering but do not remove from the output queue.
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.NeuralNetwork.NeuralEnd: 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.SigmoidUnit: This function calculates what the output value should be.
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.
OutputZipper - class weka.experiment.OutputZipper.: OutputZipper writes output to either gzipped files or to a multi entry zip file.
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.PropertyPanel: Paints the component, using the property editor's paint method.
paintComponent(Graphics) - Method in class weka.gui.treevisualizer.TreeVisualizer: Updates the screen contents.
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.visualize.AttributePanel.AttributeSpacing: paints all the visible instances to the panel , and recalculates their position if need be.
paintNominal(Graphics) - Method in class weka.gui.visualize.ClassPanel: Renders the legend for a nominal colouring attribute
paintNumeric(Graphics) - Method in class weka.gui.visualize.ClassPanel: Renders the legend for a numeric colouring attribute
paintValue(Graphics, Rectangle) - Method in class weka.gui.CostMatrixEditor: Paints a representation of the current classifier.
paintValue(Graphics, Rectangle) - Method in class weka.gui.GenericArrayEditor: 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.GenericObjectEditor: Paints a representation of the current Object.
PairedStats - class weka.experiment.PairedStats.: A class for storing stats on a paired comparison (t-test and correlation)
PairedStats(double) - Constructor for class weka.experiment.PairedStats: Creates a new PairedStats object with the supplied significance level.
PairedTTester - class weka.experiment.PairedTTester.: Calculates T-Test statistics on data stored in a set of instances.
PairedTTester() - Constructor for class weka.experiment.PairedTTester
parentClass - Variable in class weka.experiment.PropertyNode: The class of the object with this property
PART - class weka.classifiers.j48.PART.: Class for generating a PART decision list.
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.
performTest() - Method in class weka.gui.experiment.ResultsPanel: Carries out a t-test using the current configuration.
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 - class weka.gui.treevisualizer.PlaceNode1.: This class will place the Nodes of a tree.
PlaceNode1() - Constructor for class weka.gui.treevisualizer.PlaceNode1
PlaceNode2 - class weka.gui.treevisualizer.PlaceNode2.: This class will place the Nodes of a tree.
PlaceNode2() - Constructor for class weka.gui.treevisualizer.PlaceNode2
Plot2D - class weka.gui.visualize.Plot2D.: This class plots datasets in two dimensions.
Plot2D() - Constructor for class weka.gui.visualize.Plot2D: Constructor
Plot2DCompanion - interface weka.gui.visualize.Plot2DCompanion.: Interface for classes that need to draw to the Plot2D panel *before* Plot2D renders anything (eg.
PlotData2D - class weka.gui.visualize.PlotData2D.: This class is a container for plottable data.
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
pmiss - Variable in class weka.classifiers.kstar.KStarCache.TableEntry: transformation probability to missing value
PoissonEstimator - class weka.estimators.PoissonEstimator.: Simple probability estimator that places a single Poisson distribution over the observed values.
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.CrossValidationResultProducer: Perform any postprocessing.
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.Experiment: Signals that the experiment is finished running, so that cleanup can be done.
postProcess() - Method in class weka.experiment.RandomSplitResultProducer: Perform any postprocessing.
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.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.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.CSVResultListener: Perform any postprocessing.
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 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.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 interface weka.experiment.ResultListener: Perform any postprocessing.
PRECISION_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
precision(int) - Method in class weka.classifiers.Evaluation: Calculate the precision with respect to a particular class.
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.
Prediction - interface weka.classifiers.evaluation.Prediction.: Encapsulates a single evaluatable prediction: the predicted value plus the actual class value.
PredictionNode - class weka.classifiers.adtree.PredictionNode.: Class representing a prediction node in an alternating tree.
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
predictionValueForInstance(Instance, PredictionNode, double) - Method in class weka.classifiers.adtree.ADTree: Returns the class prediction value (vote) for an instance.
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.
prepareData() - Method in class weka.experiment.PairedTTester: Separates the instances into resultsets and by dataset/run.
prePlot(Graphics) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel: Renders the polygons if necessary
prePlot(Graphics) - Method in interface weka.gui.visualize.Plot2DCompanion: Something to be drawn before the plot itself
preProcess() - Method in class weka.experiment.CrossValidationResultProducer: 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 class weka.experiment.AveragingResultProducer: Prepare to generate results.
preProcess() - Method in class weka.experiment.DatabaseResultProducer: Prepare to generate results.
preProcess() - Method in interface weka.experiment.ResultProducer: Prepare to generate results.
preProcess(ResultProducer) - Method in class weka.experiment.CSVResultListener: 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 class weka.experiment.AveragingResultProducer: 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 interface weka.experiment.ResultListener: Prepare for the results to be received.
PreprocessPanel - class weka.gui.explorer.PreprocessPanel.: This panel controls simple preprocessing of instances.
PreprocessPanel() - Constructor for class weka.gui.explorer.PreprocessPanel: Creates the instances panel with no initial instances.
PrincipalComponents - class weka.attributeSelection.PrincipalComponents.: Class for performing principal components analysis/transformation.
PrincipalComponents() - Constructor for class weka.attributeSelection.PrincipalComponents
print_hash_code() - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey: Prints the hash code
print_hash_code() - Method in class weka.classifiers.DecisionTable.hashKey: Prints the hash code
print(double[], int, int) - Static method in class weka.classifiers.m5.Dvector: Prints the indexed elements in a double vector
printAttributeSummary(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier: Print out a short summary string for the dataset characteristics
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 - class weka.classifiers.Prism.: Class for building and using a PRISM classifier.
Prism() - Constructor for class weka.classifiers.Prism
PROB_COST_FUNC_NAME - Static variable in class weka.classifiers.evaluation.CostCurve
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.
processColour(String, Color) - Static method in class weka.gui.visualize.VisualizeUtils: Parses a string containing either a named colour or r,g,b values.
PROCESSING - Static variable in class weka.experiment.TaskStatusInfo
PROPERTIES - Static variable in class weka.experiment.DatabaseUtils: Properties associated with the database connection
property - Variable in class weka.experiment.PropertyNode: Other info about the property
PROPERTY_FILE - Static variable in class weka.experiment.DatabaseUtils: The name of the properties file
PROPERTY_FILE - Static variable in class weka.gui.GenericObjectEditor: The name of the properties file
PROPERTY_FILE - Static variable in class weka.gui.visualize.VisualizeUtils: The name of the properties file
propertyChange(PropertyChangeEvent) - Method in class weka.gui.PropertySheetPanel: Updates the property sheet panel with a changed property and also passed the event along.
PropertyDialog - class weka.gui.PropertyDialog.: Support for PropertyEditors with custom editors: puts the editor into a separate frame.
PropertyDialog(PropertyEditor, int, int) - Constructor for class weka.gui.PropertyDialog: Creates the editor frame.
PropertyNode - class weka.experiment.PropertyNode.: Stores information on a property of an object: the class of the object with the property; the property descriptor, and the current value.
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 - class weka.gui.PropertyPanel.: Support for drawing a property value in a component.
PropertyPanel(PropertyEditor) - Constructor for class weka.gui.PropertyPanel: Create the panel with the supplied property editor.
PropertySelectorDialog - class weka.gui.PropertySelectorDialog.: Allows the user to select any (supported) property of an object, including properties that any of it's property values may have.
PropertySelectorDialog(Frame, Object) - Constructor for class weka.gui.PropertySelectorDialog: Create the property selection dialog.
PropertySheetPanel - class weka.gui.PropertySheetPanel.: Displays a property sheet where (supported) properties of the target object may be edited.
PropertySheetPanel() - Constructor for class weka.gui.PropertySheetPanel: Creates the property sheet panel.
prune() - Method in class weka.classifiers.j48.PruneableClassifierTree: Prunes a tree.
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
PruneableClassifierTree - class weka.classifiers.j48.PruneableClassifierTree.: Class for handling a tree structure that can be pruned using a pruning set.
PruneableClassifierTree(ModelSelection, boolean, int, boolean) - Constructor for class weka.classifiers.j48.PruneableClassifierTree: Constructor for pruneable tree structure.
PruneableDecList - class weka.classifiers.j48.PruneableDecList.: Class for handling a partial tree structure that can be pruned using a pruning set.
PruneableDecList(ModelSelection, int) - Constructor for class weka.classifiers.j48.PruneableDecList: Constructor for pruneable partial tree structure.
pruneEnd() - Method in class weka.classifiers.j48.ClassifierDecList: Dummy method.
pruneEnd() - Method in class weka.classifiers.j48.PruneableDecList: Prunes the end of the rule.
pruneEnd() - Method in class weka.classifiers.j48.C45PruneableDecList: Prunes the end of the rule.
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(Instance) - Method in class weka.filters.Filter: Adds an output instance to the queue.
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 - class weka.core.Queue.: Class representing a FIFO queue.
Queue.QueueNode - class weka.core.Queue.QueueNode.: Represents one node in the queue.
Queue.QueueNode(Queue, Object) - Constructor for class weka.core.Queue.QueueNode: Creates a queue node with the given contents
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 - class weka.attributeSelection.RaceSearch.: Class for performing a racing search.
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 - class weka.filters.RandomizeFilter.: This filter randomly shuffles the order of instances passed through it.
RandomizeFilter() - Constructor for class weka.filters.RandomizeFilter
RandomSearch - class weka.attributeSelection.RandomSearch.: Class for performing a random search.
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 - class weka.experiment.RandomSplitResultProducer.: Generates a single train/test split and calls the appropriate SplitEvaluator to generate some results.
RandomSplitResultProducer() - Constructor for class weka.experiment.RandomSplitResultProducer
randomWidthFactorTipText() - Method in class weka.classifiers.MultiClassClassifier
Range - class weka.core.Range.: Class representing a range of cardinal numbers.
RANGE_BOUNDS - Static variable in class weka.classifiers.ThresholdSelector
RANGE_NONE - Static variable in class weka.classifiers.ThresholdSelector
Range() - Constructor for class weka.core.Range: Default constructor.
Range(String) - Constructor for class weka.core.Range: Constructor to set initial range.
rangeCorrectionTipText() - Method in class weka.classifiers.ThresholdSelector
rangeLower(String) - Method in class weka.core.Range: Translates a range into it's lower index.
rangeSingle(String) - Method in class weka.core.Range: Translates a single string selection into it's internal 0-based equivalent
rangeUpper(String) - Method in class weka.core.Range: Translates a range into it's upper index.
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.AttributeSelection: get the final ranking of the attributes.
rankedAttributes() - Method in class weka.attributeSelection.Ranker: Sorts the evaluated attribute list
rankedAttributes() - Method in class weka.attributeSelection.RaceSearch
rankedAttributes() - Method in class weka.attributeSelection.ForwardSelection: Produces a ranked list of attributes.
RankedOutputSearch - interface weka.attributeSelection.RankedOutputSearch.: Interface for search methods capable of producing a ranked list of attributes.
Ranker - class weka.attributeSelection.Ranker.: Class for ranking the attributes evaluated by a AttributeEvaluator Valid options are:
Ranker() - Constructor for class weka.attributeSelection.Ranker: Constructor
RankSearch - class weka.attributeSelection.RankSearch.: Class for evaluating a attribute ranking (given by a specified evaluator) using a specified subset evaluator.
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
readHeader(StreamTokenizer) - Method in class weka.core.Instances: Reads and stores header of an ARFF file.
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_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
recall(int) - Method in class weka.classifiers.Evaluation: Calculate the recall with respect to a particular class.
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 - class weka.classifiers.adtree.ReferenceInstances.: Simple class that extends the Instances class making it possible to create subsets of instances that reference their source set.
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 - class weka.classifiers.RegressionByDiscretization.: Class for a regression scheme that employs any distribution classifier on a copy of the data that has the class attribute discretized.
RegressionByDiscretization() - Constructor for class weka.classifiers.RegressionByDiscretization
RegressionSplitEvaluator - class weka.experiment.RegressionSplitEvaluator.: A SplitEvaluator that produces results for a classification scheme on a numeric class attribute.
RegressionSplitEvaluator() - Constructor for class weka.experiment.RegressionSplitEvaluator: No args constructor.
rehash() - Method in class weka.classifiers.kstar.LightHashTable: Rehashes the contents of the hashtable into a hashtable with a larger capacity.
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 - class weka.attributeSelection.ReliefFAttributeEval.: Class for Evaluating attributes individually using ReliefF.
ReliefFAttributeEval() - Constructor for class weka.attributeSelection.ReliefFAttributeEval: Constructor
RemoteEngine - class weka.experiment.RemoteEngine.: A general purpose server for executing Task objects sent via RMI.
RemoteEngine(String) - Constructor for class weka.experiment.RemoteEngine: Constructor
RemoteExperiment - class weka.experiment.RemoteExperiment.: Holds all the necessary configuration information for a distributed experiment.
RemoteExperiment(Experiment) - Constructor for class weka.experiment.RemoteExperiment: Construct a new RemoteExperiment using a base Experiment
RemoteExperimentEvent - class weka.experiment.RemoteExperimentEvent.: Class encapsulating information on progress of a remote experiment
RemoteExperimentEvent(boolean, boolean, boolean, String) - Constructor for class weka.experiment.RemoteExperimentEvent: Constructor
RemoteExperimentListener - interface weka.experiment.RemoteExperimentListener.: Interface for classes that want to listen for updates on RemoteExperiment progress
remoteExperimentStatus(RemoteExperimentEvent) - Method in interface weka.experiment.RemoteExperimentListener: Called when progress has been made in a remote experiment
RemoteExperimentSubTask - class weka.experiment.RemoteExperimentSubTask.: Class to encapsulate an experiment as a task that can be executed on a remote host.
RemoteExperimentSubTask() - Constructor for class weka.experiment.RemoteExperimentSubTask
REMOVE_CHILDREN - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
remove(int) - Method in class weka.classifiers.m5.Function: Removes a term from the function
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
removeCancelListener(ActionListener) - Method in class weka.gui.GenericObjectEditor.GOEPanel: This is used to remove an action listener from the cancel button
removeElementAt(int) - Method in class weka.core.FastVector: Deletes an element from this vector.
removeInstanceListener(InstanceListener) - Method in class weka.gui.streams.InstanceLoader
removeInstanceListener(InstanceListener) - Method in class weka.gui.streams.InstanceJoiner
removeInstanceListener(InstanceListener) - Method in interface weka.gui.streams.InstanceProducer
removeLinkAt(int) - Method in class weka.attributeSelection.BestFirst.LinkedList2: removes an element (Link) at a specific index from the list.
removeLinkAt(int) - Method in class weka.classifiers.DecisionTable.LinkedList: Removes an element (Link) at a specific index from the list.
removeNotify() - Method in class weka.gui.PropertyPanel
removeOkListener(ActionListener) - Method in class weka.gui.GenericObjectEditor.GOEPanel: This is used to remove an action listener from the ok button
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.PropertySheetPanel: Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.GenericObjectEditor: 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.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.explorer.PreprocessPanel: 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.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.
replaceMissingValues(double[]) - Method in class weka.core.BinarySparseInstance: Does nothing, since we don't support missing values.
ReplaceMissingValuesFilter - class weka.filters.ReplaceMissingValuesFilter.: Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
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 - class weka.filters.ResampleFilter.: Produces a random subsample of a dataset.
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.classifiers.neural.NeuralConnection: Call this to reset the unit for another run.
reset() - Method in class weka.classifiers.neural.NeuralNetwork.NeuralEnd: Call this to reset the value and error for this unit, ready for the next 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.CSVLoader: Resets the loader ready to read a new data set
reset() - Method in class weka.core.converters.SerializedInstancesLoader: 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.ArffLoader: Resets the Loader ready to read a new data set
resetDistribution(Instances) - Method in class weka.classifiers.j48.ClassifierSplitModel: 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.BinC45Split: Sets distribution associated with model.
resetHistory() - Method in class weka.filters.AbstractTimeSeriesFilter: Clears any instances from the history queue.
resetID() - Static method in class weka.classifiers.j48.ClassifierTree: Resets the unique node ID counter (e.g.
resetOptions() - Method in class weka.associations.Apriori: Resets the options to the default values.
resetOptions() - Method in class weka.attributeSelection.CfsSubsetEval
resetOptions() - Method in class weka.attributeSelection.RankSearch: Reset the search method.
resetOptions() - Method in class weka.attributeSelection.WrapperSubsetEval
resetOptions() - Method in class weka.attributeSelection.BestFirst: Reset options to default values
resetOptions() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Reset options to their default values
resetOptions() - Method in class weka.attributeSelection.ReliefFAttributeEval: Reset options to their default values
resetOptions() - Method in class weka.attributeSelection.GainRatioAttributeEval: reset options to default values
resetOptions() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: set options to default values
resetOptions() - Method in class weka.attributeSelection.Ranker: Resets stuff to default values
resetOptions() - Method in class weka.attributeSelection.OneRAttributeEval: rests to defaults.
resetOptions() - Method in class weka.attributeSelection.ClassifierSubsetEval: reset to defaults
resetOptions() - Method in class weka.attributeSelection.InfoGainAttributeEval: Reset options to their default values
resetOptions() - Method in class weka.attributeSelection.RaceSearch: Reset the search method.
resetOptions() - Method in class weka.classifiers.DecisionTable: Resets the options.
resetOptions() - Method in class weka.clusterers.EM: Reset to default options
resetOptions() - Method in class weka.filters.AttributeSelectionFilter: set options to their default values
resetQueue() - Method in class weka.filters.Filter: Clears the output queue.
resetTipText() - Method in class weka.classifiers.neural.NeuralNetwork
ResultHistoryPanel - class weka.gui.ResultHistoryPanel.: A component that accepts named stringbuffers and displays the name in a list box.
ResultHistoryPanel(JTextComponent) - Constructor for class weka.gui.ResultHistoryPanel: Create the result history object
ResultListener - interface weka.experiment.ResultListener.: Interface for objects able to listen for results obtained by a ResultProducer
ResultProducer - interface weka.experiment.ResultProducer.: This interface defines the methods required for an object that produces results for different randomizations of a dataset.
resultProducerTipText() - Method in class weka.experiment.LearningRateResultProducer: Returns the tip text for this property
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
resultsetKey() - Method in class weka.experiment.PairedTTester: Creates a key that maps resultset numbers to their descriptions.
ResultsPanel - class weka.gui.experiment.ResultsPanel.: This panel controls simple analysis of experimental results.
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.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.
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.BinC45Split: Prints the condition satisfied by instances in a subset.
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.
RtoP(double[], int) - Static method in class weka.classifiers.LogitBoost: Convert from function responses to probabilities
RUN_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer
RUN_FIELD_NAME - Static variable in class weka.experiment.RandomSplitResultProducer
runBasicTest(boolean, boolean, boolean, int, boolean, boolean, int, int, int, FastVector) - Method in class weka.classifiers.CheckClassifier: Runs a text on the datasets with the given characteristics.
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 - class weka.gui.experiment.RunNumberPanel.: This panel controls configuration of lower and upper run numbers in an 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 - class weka.gui.experiment.RunPanel.: This panel controls the running of an 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 - class weka.gui.SaveBuffer.: This class handles the saving of StringBuffers to files.
saveBuffer() - Method in class weka.gui.experiment.ResultsPanel: Save the currently selected result buffer to a file.
saveBuffer() - Method in class weka.gui.explorer.AssociationsPanel: Save the currently selected associator output to a file.
SaveBuffer(Logger, Component) - Constructor for class weka.gui.SaveBuffer: Constructor
saveBuffer(String) - Method in class weka.gui.explorer.ClassifierPanel: Save the currently selected classifier output to a file.
saveBuffer(String) - Method in class weka.gui.explorer.AttributeSelectionPanel: Save the named buffer to a file.
saveBuffer(String) - Method in class weka.gui.explorer.ClustererPanel: Save the currently selected clusterer output to a file.
saveInstanceDataTipText() - Method in class weka.classifiers.adtree.ADTree
saveInstancesToFile(File, Instances) - Method in class weka.gui.explorer.PreprocessPanel: Saves the filtered instances to the supplied file.
saveObject(Object) - Method in class weka.gui.GenericObjectEditor.GOEPanel: Opens an object from a file selected by the user.
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.RankSearch: Ranks attributes using the specified attribute evaluator and then searches the ranking using the supplied subset evaluator.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.BestFirst: Searches the attribute subset space by best first search
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.Ranker: Kind of a dummy search algorithm.
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.RandomSearch: 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.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.
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 interface weka.gui.streams.SerialInstanceListener
secondInstanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceJoiner
seedTipText() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns the tip text for this property
seedTipText() - Method in class weka.attributeSelection.ReliefFAttributeEval: Returns the tip text for this property
seedTipText() - Method in class weka.attributeSelection.GeneticSearch: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.ThresholdSelector
seedTipText() - Method in class weka.classifiers.CostSensitiveClassifier
seedTipText() - Method in class weka.clusterers.SimpleKMeans: Returns the tip text for this property
seedTipText() - Method in class weka.clusterers.EM: Returns the tip text for this property
SelectAttributes(ASEvaluation, String[]) - Static method in class weka.attributeSelection.AttributeSelection: Perform attribute selection with a particular evaluator and a set of options specifying search method and input file etc.
SelectAttributes(ASEvaluation, String[], Instances) - Static method in class weka.attributeSelection.AttributeSelection: Perform attribute selection with a particular evaluator and a set of options specifying search method and options for the search method and evaluator.
SelectAttributes(Instances) - Method in class weka.attributeSelection.AttributeSelection: Perform attribute selection on the supplied training instances.
selectAttributesCVSplit(Instances) - Method in class weka.attributeSelection.AttributeSelection: Select attributes for a split of the data.
selectedAttributes() - Method in class weka.attributeSelection.AttributeSelection: get the final selected set of attributes.
SelectedTag - class weka.core.SelectedTag.: Represents a selected value from a finite set of values, where each value is a Tag (i.e.
SelectedTag(int, Tag[]) - Constructor for class weka.core.SelectedTag: Creates a new SelectedTag instance.
SelectedTagEditor - class weka.gui.SelectedTagEditor.: A PropertyEditor that uses tags, where the tags are obtained from a weka.core.SelectedTag object.
SelectedTagEditor() - Constructor for class weka.gui.SelectedTagEditor
selectionThresholdTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
selectModel(Instances) - Method in class weka.classifiers.j48.ModelSelection: Selects a model for the given dataset.
selectModel(Instances) - Method in class weka.classifiers.j48.C45ModelSelection: Selects C4.5-type split for the given dataset.
selectModel(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.ModelSelection: Selects a model for the given train data using the given test data
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.BinC45ModelSelection: Selects C4.5-type split for the given dataset.
selectProperty() - Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel: Gets the user to select a property of the current resultproducer.
selectWeightQuantile(Instances, double) - Method in class weka.classifiers.AdaBoostM1: Select only instances with weights that contribute to the specified quantile of the weight distribution
selectWeightQuantile(Instances, double) - Method in class weka.classifiers.LogitBoost: Select only instances with weights that contribute to the specified quantile of the weight distribution
SEND_INSTANCES - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent: Command to remove instances from this node and send them to the VisualizePanel.
separatorToString() - Static method in class weka.classifiers.m5.M5Utils: Prints sepearating line
SerialInstanceListener - interface weka.gui.streams.SerialInstanceListener.: Defines an interface for objects able to produce two output streams of instances.
SerializedInstancesLoader - class weka.core.converters.SerializedInstancesLoader.: Reads a source that contains serialized Instances.
SerializedInstancesLoader() - Constructor for class weka.core.converters.SerializedInstancesLoader
SerializedObject - class weka.core.SerializedObject.: This class stores an object serialized in memory.
SerializedObject(Object) - Constructor for class weka.core.SerializedObject: Serializes the supplied object into a byte array without compression.
SerializedObject(Object, boolean) - Constructor for class weka.core.SerializedObject: Serializes the supplied object into a byte array.
set(int) - Method in class weka.attributeSelection.GeneticSearch.GABitSet: set a bit in the chromosome
setAcuity(int) - Method in class weka.clusterers.Cobweb: set the accuity.
setAdditionalMeasures(String[]) - Method in class weka.experiment.RegressionSplitEvaluator: Set a list of method names for additional measures to look for in Classifiers.
setAdditionalMeasures(String[]) - Method in class weka.experiment.CrossValidationResultProducer: 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.AveragingResultProducer: Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.DatabaseResultProducer: Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.ClassifierSplitEvaluator: Set a list of method names for additional measures to look for in Classifiers.
setAdditionalMeasures(String[]) - Method in interface weka.experiment.SplitEvaluator: Sets a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in interface weka.experiment.ResultProducer: 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.SelectedTagEditor: Sets the current property value as text.
setAsText(String) - Method in class weka.gui.GenericObjectEditor: Returns null as we don't support getting/setting values as text.
setAttribute(int) - Method in class weka.gui.AttributeSummaryPanel: Sets the attribute that statistics will be displayed for.
setAttributeEvaluator(ASEvaluation) - Method in class weka.attributeSelection.RankSearch: Set the attribute evaluator to use for generating the ranking.
setAttributeEvaluator(ASEvaluation) - Method in class weka.attributeSelection.RaceSearch: Set the attribute evaluator to use for generating the ranking.
setAttributeIndex(int) - Method in class weka.filters.InstanceFilter: Sets attribute to be used for selection
setAttributeIndex(int) - Method in class weka.filters.MergeTwoValuesFilter: Sets index of the attribute used.
setAttributeIndex(int) - Method in class weka.filters.SwapAttributeValuesFilter: Sets index of the attribute used.
setAttributeIndex(int) - Method in class weka.filters.StringToNominalFilter: Sets index of the attribute used.
setAttributeIndex(int) - Method in class weka.filters.AddFilter: Set the index where the attribute will be inserted
setAttributeIndex(int) - Method in class weka.filters.MakeIndicatorFilter: Sets index of of the attribute used.
setAttributeIndices(String) - Method in class weka.filters.FirstOrderFilter: Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndices(String) - Method in class weka.filters.AttributeFilter: Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndices(String) - Method in class weka.filters.DiscretizeFilter: Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
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.AbstractTimeSeriesFilter: Set which attributes are to be copied (or kept if invert is true)
setAttributeIndices(String) - Method in class weka.filters.NumericTransformFilter: Set which attributes are to be transformed (or kept if invert is true).
setAttributeIndicesArray(int[]) - Method in class weka.filters.FirstOrderFilter: Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.AttributeFilter: Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.DiscretizeFilter: Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
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.AbstractTimeSeriesFilter: Set which attributes are to be copied (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.NumericTransformFilter: Set which attributes are to be transformed (or kept if invert is true)
setAttributeName(String) - Method in class weka.filters.AddFilter: Set the new attribute's name
setAttributeSelectionMethod(SelectedTag) - Method in class weka.classifiers.LinearRegression: Sets the method used to select attributes for use in the linear regression.
setAttributeType(SelectedTag) - Method in class weka.filters.AttributeTypeFilter: Sets the type of attribute to delete.
setAutoBuild(boolean) - Method in class weka.classifiers.neural.NeuralNetwork: This will set whether the network is automatically built or if it is left up to the user.
setBagSizePercent(int) - Method in class weka.classifiers.MetaCost: Sets the size of each bag, as a percentage of the training set size.
setBagSizePercent(int) - Method in class weka.classifiers.Bagging: Sets the size of each bag, as a percentage of the training set size.
setBaseClassifiers(Classifier[]) - Method in class weka.classifiers.Stacking: Sets the list of possible classifers to choose from.
setBaseExperiment(Experiment) - Method in class weka.experiment.RemoteExperiment: Set the base experiment.
setBaseInstances(Instances) - Method in class weka.gui.explorer.PreprocessPanel: Tells the panel to use a new base set of instances.
setBaseInstancesFromDB(InstanceQuery) - Method in class weka.gui.explorer.PreprocessPanel: Loads instances from a database
setBaseInstancesFromDBQ() - Method in class weka.gui.explorer.PreprocessPanel: Queries the user for a URL to a database to load instances from, then loads the instances in a background process.
setBaseInstancesFromFile(File) - Method in class weka.gui.explorer.PreprocessPanel: Loads results from a set of instances contained in the supplied file.
setBaseInstancesFromFileQ() - Method in class weka.gui.explorer.PreprocessPanel: Queries the user for a file to load instances from, then loads the instances in a background process.
setBaseInstancesFromURL(URL) - Method in class weka.gui.explorer.PreprocessPanel: Loads instances from a URL.
setBaseInstancesFromURLQ() - Method in class weka.gui.explorer.PreprocessPanel: Queries the user for a URL to load instances from, then loads the instances in a background process.
setBias(double) - Method in class weka.classifiers.VFI: Set the value of the exponential bias towards more confident intervals
setBiasToUniformClass(double) - Method in class weka.filters.ResampleFilter: Sets the bias towards a uniform class.
setBinarizeNumericAttributes(boolean) - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Binarize numeric attributes.
setBinarizeNumericAttributes(boolean) - Method in class weka.attributeSelection.InfoGainAttributeEval: Binarize numeric attributes.
setBinaryAttributesNominal(boolean) - Method in class weka.filters.NominalToBinaryFilter: Sets if binary attributes are to be treates as nominal ones.
setBinarySplits(boolean) - Method in class weka.classifiers.j48.J48: Set the value of binarySplits.
setBinarySplits(boolean) - Method in class weka.classifiers.j48.PART: Set the value of binarySplits.
setBins(int) - Method in class weka.filters.DiscretizeFilter: Sets the number of bins to divide each selected numeric attribute into
setBlendFactor(int) - Method in class weka.classifiers.kstar.KStarNumericAttribute: Set the blending factor
setBlendMethod(int) - Method in class weka.classifiers.kstar.KStarNumericAttribute: Set the blending method
setC(double) - Method in class weka.classifiers.SMO: Set the value of C.
setCacheKeyName(String) - Method in class weka.experiment.DatabaseResultListener: Set the value of CacheKeyName.
setCacheSize(int) - Method in class weka.classifiers.SMO: Set the value of the kernel cache.
setCalculateStdDevs(boolean) - Method in class weka.experiment.AveragingResultProducer: Set the value of CalculateStdDevs.
setCapacity(int) - Method in class weka.core.FastVector: Sets the vector's capacity to the given value.
setCenter(double) - Method in class weka.gui.treevisualizer.Node: Set the value of center.
setChildForBranch(int, PredictionNode) - Method in class weka.classifiers.adtree.Splitter: Sets the child for a branch of the split.
setChildForBranch(int, PredictionNode) - Method in class weka.classifiers.adtree.TwoWayNominalSplit: Sets the child for a branch of the split.
setChildForBranch(int, PredictionNode) - Method in class weka.classifiers.adtree.TwoWayNumericSplit: Sets the child for a branch of the split.
setChromosome(BitSet) - Method in class weka.attributeSelection.GeneticSearch.GABitSet: set the chromosome
setCindex(int) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel: Set the index of the attribute to use for colouring
setCindex(int) - Method in class weka.gui.visualize.ClassPanel: Set the index of the attribute to display coloured labels for
setCindex(int) - Method in class weka.gui.visualize.Plot2D: Set the index of the attribute to use for colouring
setCindex(int) - Method in class weka.gui.visualize.AttributePanel: Set the index of the attribute by which to colour the data.
setCindex(int) - Method in class weka.gui.visualize.PlotData2D: Set the colouring index of the data
setCindex(int, double, double) - Method in class weka.gui.visualize.AttributePanel: Set the index of the attribute by which to colour the data.
setClass(Attribute) - Method in class weka.core.Instances: Sets the class attribute.
setClassForIRStatistics(int) - Method in class weka.experiment.ClassifierSplitEvaluator: Set the value of ClassForIRStatistics.
setClassifier(Classifier) - Method in class weka.attributeSelection.WrapperSubsetEval: Set the classifier to use for accuracy estimation
setClassifier(Classifier) - Method in class weka.attributeSelection.ClassifierSubsetEval: Set the classifier to use for accuracy estimation
setClassifier(Classifier) - Method in class weka.classifiers.MetaCost: Sets the distribution classifier
setClassifier(Classifier) - Method in class weka.classifiers.AdditiveRegression: Sets the classifier
setClassifier(Classifier) - Method in class weka.classifiers.Bagging: Set the classifier for bagging.
setClassifier(Classifier) - Method in class weka.classifiers.RegressionByDiscretization: Set the classifier for boosting.
setClassifier(Classifier) - Method in class weka.classifiers.CVParameterSelection: Set the classifier for boosting.
setClassifier(Classifier) - Method in class weka.classifiers.BVDecompose: Set the classifiers being analysed
setClassifier(Classifier) - Method in class weka.classifiers.AdaBoostM1: Set the classifier for boosting.
setClassifier(Classifier) - Method in class weka.classifiers.FilteredClassifier: Sets the classifier
setClassifier(Classifier) - Method in class weka.classifiers.CostSensitiveClassifier: Sets the distribution classifier
setClassifier(Classifier) - Method in class weka.classifiers.DistributionMetaClassifier: Set the base classifier.
setClassifier(Classifier) - Method in class weka.classifiers.CheckClassifier: Set the classifier for boosting.
setClassifier(Classifier) - Method in class weka.classifiers.LogitBoost: Set the classifier for boosting.
setClassifier(Classifier) - Method in class weka.classifiers.AttributeSelectedClassifier: Sets the classifier
setClassifier(Classifier) - Method in class weka.classifiers.ClassificationViaRegression: Set the base classifier.
setClassifier(Classifier) - Method in class weka.experiment.RegressionSplitEvaluator: Sets the classifier.
setClassifier(Classifier) - Method in class weka.experiment.ClassifierSplitEvaluator: Sets the classifier.
setClassifierName(String) - Method in class weka.experiment.RegressionSplitEvaluator: Set the Classifier to use, given it's class name.
setClassifierName(String) - Method in class weka.experiment.ClassifierSplitEvaluator: Set the Classifier to use, given it's class name.
setClassifiers(Classifier[]) - Method in class weka.classifiers.MultiScheme: Sets the list of possible classifers to choose from.
setClassIndex(int) - Method in class weka.classifiers.BVDecompose: Sets index of attribute to discretize on
setClassIndex(int) - Method in class weka.core.Instances: Sets the class index of the set.
setClassMissing() - Method in class weka.core.Instance: Sets the class value of an instance to be "missing".
setClassName(String) - Method in class weka.filters.NumericTransformFilter: Sets the class containing the transformation method.
setClassType(Class) - Method in class weka.gui.GenericObjectEditor: Sets the class of values that can be edited.
setClassValue(double) - Method in class weka.core.Instance: Sets the class value of an instance to the given value (internal floating-point format).
setClassValue(String) - Method in class weka.core.Instance: Sets the class value of an instance to the given value.
setClearEachDataset(boolean) - Method in class weka.gui.streams.InstanceViewer
setClusterer(Clusterer) - Method in class weka.clusterers.DistributionMetaClusterer: Set the base clusterer.
setClusterer(Clusterer) - Method in class weka.clusterers.ClusterEvaluation: set the clusterer
setColor(Color) - Method in class weka.gui.treevisualizer.Node: Set the value of color.
setColourIndex(int) - Method in class weka.gui.visualize.VisualizePanel: Sets the index used for colouring.
setColours(FastVector) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel: Set a list of colours to use for plotting points
setColours(FastVector) - Method in class weka.gui.visualize.ClassPanel: Set a list of colours to use for colouring labels
setColours(FastVector) - Method in class weka.gui.visualize.Plot2D: Set a list of colours to use when colouring points according to class values or cluster numbers
setColours(FastVector) - Method in class weka.gui.visualize.AttributePanel: Sets a list of colours to use for colouring data points
setColumn(int, double[]) - Method in class weka.core.Matrix: Sets a column of the matrix to the given column.
setComboSizes() - Method in class weka.gui.experiment.ResultsPanel: Sets the combo-boxes to a fixed size so they don't take up too much room that would be better devoted to the test output box
setConfidenceFactor(float) - Method in class weka.classifiers.j48.J48: Set the value of CF.
setConfidenceFactor(float) - Method in class weka.classifiers.j48.PART: Set the value of CF.
setConnectPoints(boolean[]) - Method in class weka.gui.visualize.PlotData2D: Set whether consecutive points should be connected by lines
setConnectPoints(FastVector) - Method in class weka.gui.visualize.PlotData2D: Set whether consecutive points should be connected by lines
setCostMatrix(CostMatrix) - Method in class weka.classifiers.MetaCost: Sets the misclassification cost matrix.
setCostMatrix(CostMatrix) - Method in class weka.classifiers.CostSensitiveClassifier: Sets the misclassification cost matrix.
setCostMatrixSource(SelectedTag) - Method in class weka.classifiers.MetaCost: Sets the source location of the cost matrix.
setCostMatrixSource(SelectedTag) - Method in class weka.classifiers.CostSensitiveClassifier: Sets the source location of the cost matrix.
setCrossoverProb(double) - Method in class weka.attributeSelection.GeneticSearch: set the probability of crossover
setCrossVal(int) - Method in class weka.classifiers.DecisionTable: Sets the number of folds for cross validation (1 = leave one out)
setCrossValidate(boolean) - Method in class weka.classifiers.IBk: Sets whether hold-one-out cross-validation will be used to select the best k value
setCustomColour(Color) - Method in class weka.gui.visualize.PlotData2D: Set a custom colour to use for this plot.
setCutoff(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.attributeSelection.RaceSearch: Set whether verbose output should be generated.
setDebug(boolean) - Method in class weka.classifiers.AdditiveRegression: Set whether debugging output is produced.
setDebug(boolean) - Method in class weka.classifiers.IBk: Set the value of Debug.
setDebug(boolean) - Method in class weka.classifiers.MultiScheme: Set debugging mode
setDebug(boolean) - Method in class weka.classifiers.RegressionByDiscretization: Sets whether debugging output will be printed
setDebug(boolean) - Method in class weka.classifiers.CVParameterSelection: Sets debugging mode
setDebug(boolean) - Method in class weka.classifiers.BVDecompose: Sets debugging mode
setDebug(boolean) - Method in class weka.classifiers.AdaBoostM1: Set debugging mode
setDebug(boolean) - Method in class weka.classifiers.LinearRegression: Controls whether debugging output will be printed
setDebug(boolean) - Method in class weka.classifiers.LWR: Sets whether debugging output should be produced
setDebug(boolean) - Method in class weka.classifiers.Logistic: Sets whether debugging output will be printed.
setDebug(boolean) - Method in class weka.classifiers.CheckClassifier: Set debugging mode
setDebug(boolean) - Method in class weka.classifiers.LogitBoost: Set debugging mode
setDebug(boolean) - Method in class weka.clusterers.EM: Set debug mode - verbose output
setDebug(boolean) - Method in class weka.filters.AttributeExpressionFilter: Set debug mode.
setDebug(boolean) - Method in class weka.gui.streams.InstanceCounter
setDebug(boolean) - Method in class weka.gui.streams.InstanceLoader
setDebug(boolean) - Method in class weka.gui.streams.InstanceViewer
setDebug(boolean) - Method in class weka.gui.streams.InstanceJoiner
setDebug(boolean) - Method in class weka.gui.streams.InstanceTable
setDebug(boolean) - Method in class weka.gui.streams.InstanceSavePanel
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.
setDerived(int) - Method in class weka.gui.AttributeSummaryPanel: Sets the gui elements for fields that are stored in the AttributeStats structure.
setDesignatedClass(SelectedTag) - Method in class weka.classifiers.ThresholdSelector: Sets the method to determine which class value to optimize.
setDirection(SelectedTag) - Method in class weka.attributeSelection.BestFirst: Set the search direction
setDisplayRules(boolean) - Method in class weka.classifiers.DecisionTable: Sets whether rules are to be printed
setDistanceWeighting(SelectedTag) - Method in class weka.classifiers.IBk: Sets the distance weighting method used.
setDistributionClassifier(DistributionClassifier) - Method in class weka.classifiers.ThresholdSelector: Set the DistributionClassifier for which threshold is set.
setDistributionClassifier(DistributionClassifier) - Method in class weka.classifiers.MultiClassClassifier: Set the base classifier.
setDistributionSpread(double) - Method in class weka.filters.SpreadSubsampleFilter: Sets the value for the distribution spread
setDontStratifyData(boolean) - Method in class weka.filters.SplitDatasetFilter: Sets whether stratification is not performed.
setDoXval(boolean) - Method in class weka.clusterers.ClusterEvaluation: set whether or not to do cross validation
setElement(int, int, double) - Method in class weka.core.Matrix: Sets an element of the matrix to the given value.
setElementAt(Object, int) - Method in class weka.core.FastVector: Sets the element at the given index.
setEnabled(boolean) - Method in class weka.gui.GenericObjectEditor: Sets whether the editor is "enabled", meaning that the current values will be painted.
setEntropicAutoBlend(boolean) - Method in class weka.classifiers.kstar.KStar: Set whether entropic blending is to be used.
setEpsilon(double) - Method in class weka.classifiers.SMO: Set the value of epsilon.
setErrorCorrectionMode(SelectedTag) - Method in class weka.classifiers.MultiClassClassifier: Sets the error correction mode used.
setEvaluationMode(SelectedTag) - Method in class weka.classifiers.ThresholdSelector: Sets the evaluation mode used.
setEvaluator(ASEvaluation) - Method in class weka.attributeSelection.AttributeSelection: set the attribute/subset evaluator
setEvaluator(ASEvaluation) - Method in class weka.classifiers.AttributeSelectedClassifier: Sets the attribute evaluator
setEvaluator(ASEvaluation) - Method in class weka.filters.AttributeSelectionFilter: set a string holding the name of a attribute/subset evaluator
setExecutionStatus(int) - Method in class weka.experiment.TaskStatusInfo: Set the execution status of this Task.
setExpectedResultsPerAverage(int) - Method in class weka.experiment.AveragingResultProducer: Set the value of ExpectedResultsPerAverage.
setExperiment(Experiment) - Method in class weka.experiment.RemoteExperimentSubTask: Set the experiment for this sub task
setExperiment(Experiment) - Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel: Sets the experiment which will have the custom properties edited.
setExperiment(Experiment) - Method in class weka.gui.experiment.SetupPanel: Sets the experiment to configure.
setExperiment(Experiment) - Method in class weka.gui.experiment.RunPanel: Sets the experiment the panel operates on.
setExperiment(Experiment) - Method in class weka.gui.experiment.DatasetListPanel: Tells the panel to act on a new experiment.
setExperiment(Experiment) - Method in class weka.gui.experiment.ResultsPanel: Tells the panel to use 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.RunNumberPanel: Sets the experiment to be configured.
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.VotedPerceptron: Set the value of exponent.
setExponent(double) - Method in class weka.classifiers.SMO: 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.
setFitness(double) - Method in class weka.attributeSelection.GeneticSearch.GABitSet: sets the scaled fitness
setFlags() - Method in class weka.core.Range: Sets the flags array.
setFold(int) - Method in class weka.filters.SplitDatasetFilter: Selects a fold.
setFolds(int) - Method in class weka.attributeSelection.WrapperSubsetEval: Set the number of folds to use for accuracy estimation
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
setFoldsType(SelectedTag) - Method in class weka.attributeSelection.RaceSearch: Set the xfold type
setFromExpEnabled() - Method in class weka.gui.experiment.ResultsPanel: Updates whether the current experiment is of a type that we can determine the results destination.
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.
setGenerateRanking(boolean) - Method in class weka.attributeSelection.RaceSearch: Records whether the user has requested a ranked list of attributes.
setGenerateRanking(boolean) - Method in class weka.attributeSelection.ForwardSelection: Records whether the user has requested a ranked list of attributes.
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.
setHeader(int) - Method in class weka.gui.AttributeSummaryPanel: Sets the labels for fields we can determine just from the instance header.
setHiddenLayers(String) - Method in class weka.classifiers.neural.NeuralNetwork: This will set what the hidden layers are made up of when auto build is enabled.
setHighlight(String) - Method in class weka.gui.treevisualizer.TreeVisualizer: Set the highlight for the node with the given id
setHoldOutFile(File) - Method in class weka.attributeSelection.ClassifierSubsetEval: Set the file that contains hold out/test instances
setInputFormat(Instances) - Method in class weka.filters.Filter: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.SparseToNonSparseFilter: 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.NumericToBinaryFilter: 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.NormalizationFilter: 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.AttributeExpressionFilter: 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.EmptyAttributeFilter: 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.SplitDatasetFilter: 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.AttributeTypeFilter: 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.AttributeFilter: 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.DiscretizeFilter: 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.AbstractTimeSeriesFilter: 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.TimeSeriesTranslateFilter: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.TimeSeriesDeltaFilter: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.StringToNominalFilter: 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.ResampleFilter: 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.NonSparseToSparseFilter: 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.NullFilter: 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.experiment.CrossValidationResultProducer: Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.PairedTTester: Set the value of Instances.
setInstances(Instances) - Method in class weka.experiment.LearningRateResultProducer: Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.RandomSplitResultProducer: Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.AveragingResultProducer: Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.DatabaseResultProducer: 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.AttributeSummaryPanel: Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.AttributeSelectionPanel: Sets the instances who's attribute names will be displayed.
setInstances(Instances) - Method in class weka.gui.InstancesSummaryPanel: Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.SetInstancesPanel: Updates the set of instances that is currently held by the panel
setInstances(Instances) - Method in class weka.gui.experiment.ResultsPanel: Sets up the panel with a new set of instances, attempting to guess the correct settings for various columns.
setInstances(Instances) - Method in class weka.gui.explorer.ClassifierPanel: Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.explorer.AssociationsPanel: Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.explorer.AttributeSelectionPanel: 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.gui.visualize.VisualizePanel: Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.visualize.ClassPanel: Set the instances.
setInstances(Instances) - Method in class weka.gui.visualize.Plot2D: Sets the master plot from a set of instances
setInstances(Instances) - Method in class weka.gui.visualize.AttributePanel: This sets the instances to be drawn into the attribute panel
setInstancesFromDatabaseTable(String) - Method in class weka.gui.experiment.ResultsPanel: Queries a database to load results from the specified table name.
setInstancesFromDBaseQuery() - Method in class weka.gui.experiment.ResultsPanel: Queries the user enough to make a database query to retrieve experiment results.
setInstancesFromExp(Experiment) - Method in class weka.gui.experiment.ResultsPanel: Examines the supplied experiment to determine the results destination and attempts to load the results.
setInstancesFromFile(File) - Method in class weka.gui.SetInstancesPanel: Loads results from a set of instances contained in the supplied file.
setInstancesFromFile(File) - Method in class weka.gui.experiment.ResultsPanel: Loads results from a set of instances contained in the supplied file.
setInstancesFromFileQ() - Method in class weka.gui.SetInstancesPanel: Queries the user for a file to load instances from, then loads the instances in a background process.
setInstancesFromURL(URL) - Method in class weka.gui.SetInstancesPanel: Loads instances from a URL.
setInstancesFromURLQ() - Method in class weka.gui.SetInstancesPanel: Queries the user for a URL to load instances from, then loads the instances in a background process.
setInstancesIndices(String) - Method in class weka.filters.SplitDatasetFilter: Sets the ranges of instances to be selected.
SetInstancesPanel - class weka.gui.SetInstancesPanel.: A panel that displays an instance summary for a set of instances and lets the user open a set of instances from either a file or URL.
SetInstancesPanel() - Constructor for class weka.gui.SetInstancesPanel: Create the panel.
setInvert(boolean) - Method in class weka.core.Range: Sets whether the range sense is inverted, i.e.
setInvertSelection(boolean) - Method in class weka.filters.InstanceFilter: Set whether selected values should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.SplitDatasetFilter: Sets if selection is to be inverted.
setInvertSelection(boolean) - Method in class weka.filters.AttributeFilter: Set whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.DiscretizeFilter: Sets whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.CopyAttributesFilter: Set whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.AbstractTimeSeriesFilter: Set whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.NumericTransformFilter: Set whether selected columns should be transformed or not.
setJitter(int) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel: Set level of jitter and repaint the plot using the new jitter value
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.
setLink(boolean, int) - Method in class weka.classifiers.neural.NeuralNetwork.NeuralEnd: Call this function to set What this end unit represents.
setLocallyPredictive(boolean) - Method in class weka.attributeSelection.CfsSubsetEval: Include locally predictive attributes
setLog(Logger) - Method in class weka.gui.explorer.ClassifierPanel: 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.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.ClustererPanel: 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.VisualizePanel: Set the master plot for the visualize panel
setMasterPlot(PlotData2D) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel: Clears all existing plots and sets a new master plot
setMasterPlot(PlotData2D) - Method in class weka.gui.visualize.Plot2D: Set the master plot.
setMatchMissingValues(boolean) - Method in class weka.filters.InstanceFilter: Sets whether missing values are counted as a match.
setMaxCount(double) - Method in class weka.filters.SpreadSubsampleFilter: Sets the value for the max count
setMaxGenerations(int) - Method in class weka.attributeSelection.GeneticSearch: set the number of generations to evaluate
setMaxIterations(int) - Method in class weka.classifiers.AdaBoostM1: Set the maximum number of boost iterations
setMaxIterations(int) - Method in class weka.classifiers.LogitBoost: Set the maximum number of boost iterations
setMaxIterations(int) - Method in class weka.clusterers.EM: Set the maximum number of iterations to perform
setMaxK(int) - Method in class weka.classifiers.VotedPerceptron: Set the value of maxK.
setMaxModels(int) - Method in class weka.classifiers.AdditiveRegression: Set the maximum number of models to generate
setMaxStale(int) - Method in class weka.classifiers.DecisionTable: Sets the number of non improving decision tables to consider before abandoning the search.
setMeanSquared(boolean) - Method in class weka.classifiers.IBk: Sets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
setMetaClassifier(Classifier) - Method in class weka.classifiers.Stacking: Adds meta classifier
setMethod(NeuralMethod) - Method in class weka.classifiers.neural.NeuralNode: Set how this node should operate (note that the neural method has no internal state, so the same object can be used by any number of nodes.
setMethodName(String) - Method in class weka.filters.NumericTransformFilter: Set the transformation method.
setMetricType(SelectedTag) - Method in class weka.associations.Apriori: Set the metric type for ranking rules
setMinBucketSize(int) - Method in class weka.classifiers.OneR: Set the value of minBucketSize.
setMinimizeExpectedCost(boolean) - Method in class weka.classifiers.CostSensitiveClassifier: Set the value of MinimizeExpectedCost.
setMinMetric(double) - Method in class weka.associations.Apriori: Set the value of minConfidence.
setMinNumObj(int) - Method in class weka.classifiers.j48.J48: Set the value of minNumObj.
setMinNumObj(int) - Method in class weka.classifiers.j48.PART: Set the value of minNumObj.
setMinStdDev(double) - Method in class weka.clusterers.EM: Set the minimum value for standard deviation when calculating normal density.
setMissing(Attribute) - Method in class weka.core.Instance: Sets a specific value to be "missing".
setMissing(int) - Method in class weka.core.Instance: Sets a specific value to be "missing".
setMissingMerge(boolean) - Method in class weka.attributeSelection.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.SymmetricalUncertAttributeEval: distribute the counts for missing values across observed values
setMissingMerge(boolean) - Method in class weka.attributeSelection.InfoGainAttributeEval: distribute the counts for missing values across observed values
setMissingMode(int) - Method in class weka.classifiers.kstar.KStarNumericAttribute: Set the missing value mode.
setMissingMode(SelectedTag) - Method in class weka.classifiers.kstar.KStar: Sets the method to use for handling missing values.
setMissingSeperate(boolean) - Method in class weka.attributeSelection.CfsSubsetEval: Treat missing as a seperate value
setModelType(SelectedTag) - Method in class weka.classifiers.m5.M5Prime: Set the value of Model.
setModifyHeader(boolean) - Method in class weka.filters.InstanceFilter: Sets whether the header will be modified when selecting on nominal attributes.
setMomentum(double) - Method in class weka.classifiers.neural.NeuralNetwork: The momentum can be set using this command.
setMutationProb(double) - Method in class weka.attributeSelection.GeneticSearch: set the probability of mutation
setName(String) - Method in class weka.filters.AttributeExpressionFilter: Set the name for the new attribute.
setName(String) - Method in class weka.gui.visualize.VisualizePanel: Set a name for this plot
setNominal() - Method in class weka.gui.visualize.ClassPanel: Sets the legend to be for a nominal variable
setNominalIndices(String) - Method in class weka.filters.InstanceFilter: Set which nominal labels are to be included in the selection.
setNominalIndicesArr(int[]) - Method in class weka.filters.InstanceFilter: Set which values of a nominal attribute are to be used for selection.
setNominalLabels(String) - Method in class weka.filters.AddFilter: Set the labels for nominal attribute creation.
setNominalToBinaryFilter(boolean) - Method in class weka.classifiers.neural.NeuralNetwork
setNoNormalization(boolean) - Method in class weka.classifiers.IBk: Set whether normalization is turned off.
setNormalize(boolean) - Method in class weka.attributeSelection.PrincipalComponents: Set whether input data will be normalized.
setNormalizeAttributes(boolean) - Method in class weka.classifiers.neural.NeuralNetwork
setNormalizeData(boolean) - Method in class weka.classifiers.SMO: Set whether data is to be normalized.
setNormalizeNumericClass(boolean) - Method in class weka.classifiers.neural.NeuralNetwork
setNotes(String) - Method in class weka.experiment.Experiment: Set the user notes.
setNotes(String) - Method in class weka.experiment.RemoteExperiment: Set the user notes.
setNumBins(int) - Method in class weka.classifiers.RegressionByDiscretization: Sets the number of bins the class attribute will be discretized into.
setNumClusters(int) - Method in class weka.clusterers.SimpleKMeans: set the number of clusters to generate
setNumClusters(int) - Method in class weka.clusterers.EM: Set the number of clusters (-1 to select by CV).
setNumeric() - Method in class weka.gui.visualize.ClassPanel: Sets the legend to be for a numeric variable
setNumeric(boolean) - Method in class weka.filters.MakeIndicatorFilter: Sets if the new Attribute is to be numeric.
setNumFolds(int) - Method in class weka.classifiers.Stacking: Sets the number of folds for the cross-validation.
setNumFolds(int) - Method in class weka.classifiers.MultiScheme: Sets the number of folds for cross-validation.
setNumFolds(int) - Method in class weka.classifiers.CVParameterSelection: Set the number of folds used for cross-validation.
setNumFolds(int) - Method in class weka.classifiers.j48.J48: Set the value of numFolds.
setNumFolds(int) - Method in class weka.classifiers.j48.PART: Set the value of numFolds.
setNumFolds(int) - Method in class weka.experiment.CrossValidationResultProducer: Set the value of NumFolds.
setNumFolds(int) - Method in class weka.filters.SplitDatasetFilter: Sets the number of folds the dataset is split into.
setNumIterations(int) - Method in class weka.classifiers.MetaCost: Sets the number of bagging iterations
setNumIterations(int) - Method in class weka.classifiers.VotedPerceptron: Set the value of NumIterations.
setNumIterations(int) - Method in class weka.classifiers.Bagging: Sets the number of bagging iterations
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 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.
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 class weka.attributeSelection.ForwardSelection: Specify the number of attributes to select from the ranked list (if generating a ranking).
setNumXValFolds(int) - Method in class weka.classifiers.ThresholdSelector: Set the number of folds used for cross-validation.
setObjective(double) - Method in class weka.attributeSelection.GeneticSearch.GABitSet: sets the objective merit value
setOn(boolean) - Method in class weka.gui.visualize.ClassPanel: Enables the panel
setOnDemandDirectory(File) - Method in class weka.classifiers.MetaCost: Sets the directory that will be searched for cost files when loading on demand.
setOnDemandDirectory(File) - Method in class weka.classifiers.CostSensitiveClassifier: Sets the directory that will be searched for cost files when loading on demand.
setOnDemandDirectory(File) - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Sets the directory that will be searched for cost files when loading on demand.
setOptimizeBins(boolean) - Method in class weka.classifiers.RegressionByDiscretization: Sets whether the discretizer optimizes the number of bins
setOptions(int, int, int) - Method in class weka.classifiers.kstar.KStarNumericAttribute: Set options.
setOptions(int, int, int) - Method in class weka.classifiers.kstar.KStarNominalAttribute: Sets the options.
setOptions(String[]) - Method in class weka.associations.Apriori: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.CfsSubsetEval: Parses and sets 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.RankSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.WrapperSubsetEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.BestFirst: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ReliefFAttributeEval: 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.SymmetricalUncertAttributeEval: 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.ExhaustiveSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ClassifierSubsetEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.RandomSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.InfoGainAttributeEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.RaceSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ForwardSelection: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.GeneticSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.DecisionTable: 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.AdditiveRegression: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.VotedPerceptron: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.Bagging: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.IBk: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.Stacking: 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.MultiScheme: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.RegressionByDiscretization: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.CVParameterSelection: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.BVDecompose: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.AdaBoostM1: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.FilteredClassifier: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.LinearRegression: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.OneR: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.CostSensitiveClassifier: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.SMO: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.MultiClassClassifier: 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.DistributionMetaClassifier: 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.VFI: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.CheckClassifier: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.LogitBoost: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.AttributeSelectedClassifier: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.ClassificationViaRegression: Sets 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.adtree.ADTree: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.j48.J48: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.j48.PART: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.kstar.KStar: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.m5.M5Prime: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.neural.NeuralNetwork: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.SimpleKMeans: 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.Cobweb: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.EM: 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.RegressionSplitEvaluator: 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.PairedTTester: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.CSVResultListener: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.LearningRateResultProducer: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.Experiment: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.InstanceQuery: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.RandomSplitResultProducer: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.AveragingResultProducer: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.DatabaseResultProducer: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.ClassifierSplitEvaluator: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.AttributeExpressionFilter: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.FirstOrderFilter: Parses a given list of options controlling the behaviour of this object.
setOptions(String[]) - Method in class weka.filters.InstanceFilter: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.MergeTwoValuesFilter: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.SplitDatasetFilter: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.RandomizeFilter: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.AttributeSelectionFilter: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.AttributeTypeFilter: Parses a given list of options controlling the behaviour of this object.
setOptions(String[]) - Method in class weka.filters.SwapAttributeValuesFilter: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.AttributeFilter: Parses a given list of options controlling the behaviour of this object.
setOptions(String[]) - Method in class weka.filters.SpreadSubsampleFilter: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.DiscretizeFilter: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.CopyAttributesFilter: Parses a given list of options controlling the behaviour of this object.
setOptions(String[]) - Method in class weka.filters.AbstractTimeSeriesFilter: Parses a given list of options controlling the behaviour of this object.
setOptions(String[]) - Method in class weka.filters.NominalToBinaryFilter: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.StringToNominalFilter: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.AddFilter: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.ResampleFilter: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.MakeIndicatorFilter: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.NumericTransformFilter: Parses the options for this object.
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.
setOutputFormat() - Method in class weka.filters.AttributeSelectionFilter: Set the output format.
setOutputFormat() - Method in class weka.filters.DiscretizeFilter: Set the output format.
setOutputFormat(Instances) - Method in class weka.filters.Filter: Sets the format of output instances.
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
setProperty(int, Object) - Method in class weka.experiment.Experiment: Recursively sets the custom property value, by setting all values along the property path.
setPropertyArray(Object) - Method in class weka.experiment.Experiment: Sets the array of values to set the custom property to.
setPropertyArray(Object) - Method in class weka.experiment.RemoteExperiment: Sets the array of values to set the custom property to.
setPropertyPath(PropertyNode[]) - Method in class weka.experiment.Experiment: Sets the path of properties taken to get to the custom property to iterate over.
setPropertyPath(PropertyNode[]) - Method in class weka.experiment.RemoteExperiment: Sets the path of properties taken to get to the custom property to iterate over.
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.SpreadSubsampleFilter: Sets the random number seed.
setRandomSeed(int) - Method in class weka.filters.ResampleFilter: 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.CrossValidationResultProducer: Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.LearningRateResultProducer: Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.Experiment: Sets the result listener where results will be sent.
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.AveragingResultProducer: 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.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.LearningRateResultProducer: Set the ResultProducer.
setResultProducer(ResultProducer) - Method in class weka.experiment.Experiment: Set the result producer used for the current experiment.
setResultProducer(ResultProducer) - Method in class weka.experiment.AveragingResultProducer: Set the ResultProducer.
setResultProducer(ResultProducer) - Method in class weka.experiment.DatabaseResultProducer: 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.
setRetrieval(int) - Method in class weka.core.converters.AbstractLoader
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.attributeSelection.AttributeSelection: set the search method
setSearch(ASSearch) - Method in class weka.classifiers.AttributeSelectedClassifier: Sets the search method
setSearch(ASSearch) - Method in class weka.filters.AttributeSelectionFilter: Set as string holding the name of a search class
setSearchPath(SelectedTag) - Method in class weka.classifiers.adtree.ADTree: Sets the method of searching the tree for a new insertion.
setSearchPercent(double) - Method in class weka.attributeSelection.RandomSearch: set the percentage of the search space to consider
setSearchTermination(int) - Method in class weka.attributeSelection.BestFirst: Set the numnber of non-improving nodes to consider before terminating search.
setSecondValueIndex(int) - Method in class weka.filters.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.attributeSelection.WrapperSubsetEval: Set the seed to use for cross validation
setSeed(int) - Method in class weka.attributeSelection.ReliefFAttributeEval: Set the random number seed for randomly sampling instances.
setSeed(int) - Method in class weka.attributeSelection.AttributeSelection: set the seed for use in cross validation
setSeed(int) - Method in class weka.attributeSelection.GeneticSearch: set the seed for random number generation
setSeed(int) - Method in class weka.classifiers.MetaCost: Set seed for resampling.
setSeed(int) - Method in class weka.classifiers.VotedPerceptron: Set the value of Seed.
setSeed(int) - Method in class weka.classifiers.Bagging: Set the seed for random number generation.
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.MultiScheme: Sets the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.CVParameterSelection: Sets the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.BVDecompose: Sets the random number seed
setSeed(int) - Method in class weka.classifiers.AdaBoostM1: Set seed for resampling.
setSeed(int) - Method in class weka.classifiers.CostSensitiveClassifier: Set seed for resampling.
setSeed(int) - Method in class weka.classifiers.LogitBoost: Set seed for resampling.
setSeed(int) - Method in class weka.classifiers.evaluation.EvaluationUtils: Sets the seed for randomization during cross-validation
setSeed(int) - Method in class weka.clusterers.SimpleKMeans: Set the random number seed
setSeed(int) - Method in class weka.clusterers.ClusterEvaluation: set the seed to use for cross validation
setSeed(int) - Method in class weka.clusterers.EM: Set the random number seed
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.
setShapes(FastVector) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel: This can be used to set the shapes that should appear.
setShapeSize(FastVector) - Method in class weka.gui.visualize.PlotData2D: Set the shape sizes for the plot data
setShapeSize(int[]) - Method in class weka.gui.visualize.PlotData2D: Set the shape sizes for the plot data
setShapeType(FastVector) - Method in class weka.gui.visualize.PlotData2D: Set the shape type for the plot data
setShapeType(int[]) - Method in class weka.gui.visualize.PlotData2D: Set the shape type for the plot data
setShowStdDevs(boolean) - Method in class weka.experiment.PairedTTester: Set whether standard deviations are displayed or not.
setShrinkage(double) - Method in class weka.classifiers.AdditiveRegression: Set the shrinkage parameter
setSigma(int) - Method in class weka.attributeSelection.ReliefFAttributeEval: Sets the sigma value.
setSignificanceLevel(double) - Method in class weka.associations.Apriori: Set the value of significanceLevel.
setSignificanceLevel(double) - Method in class weka.attributeSelection.RaceSearch: Sets the significance level to use
setSignificanceLevel(double) - Method in class weka.experiment.PairedTTester: Set the value of SignificanceLevel.
setSindex(int) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel: Set the index of the attribute to use for the shape.
setSIndex(int) - Method in class weka.gui.visualize.VisualizePanel: Set the shape for creating splits.
setSingle(String) - Method in class weka.gui.ResultHistoryPanel: Sets the single-click display to view the named result.
setSource(File) - Method in class weka.core.converters.AbstractLoader: Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File) - Method in class weka.core.converters.CSVLoader: Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File) - Method in class weka.core.converters.SerializedInstancesLoader: Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File) - Method in interface weka.core.converters.Loader: Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File) - Method in class weka.core.converters.C45Loader: Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File) - Method in class weka.core.converters.ArffLoader: 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.SerializedInstancesLoader: 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.ArffLoader: 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.C45Split: Sets split point to greatest value in given data smaller or equal to old split point.
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.
setStartSet(String) - Method in interface weka.attributeSelection.StartSetHandler: Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.BestFirst: Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.Ranker: Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.ExhaustiveSearch: Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.RandomSearch: Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.ForwardSelection: Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.GeneticSearch: Sets a starting set of attributes for the search.
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.
setTable(AttributeStats, int) - Method in class weka.gui.AttributeSummaryPanel: Creates a tablemodel for the attribute being displayed
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
setTestSet() - Method in class weka.gui.explorer.ClassifierPanel: Sets the user test set.
setTestSet() - Method in class weka.gui.explorer.ClustererPanel: Sets the user test set.
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.WrapperSubsetEval: Set the value of the threshold for repeating cross validation
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.Ranker: 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 class weka.attributeSelection.ForwardSelection: Set the threshold by which the AttributeSelection module can discard attributes.
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
setTTester() - Method in class weka.gui.experiment.ResultsPanel: Updates the test chooser with possible tests
setType(int) - Method in class weka.classifiers.neural.NeuralConnection
setUnpruned(boolean) - Method in class weka.classifiers.j48.J48: Set the value of unpruned.
setUpComboBoxes(Instances) - Method in class weka.gui.visualize.VisualizePanel
SetupPanel - class weka.gui.experiment.SetupPanel.: This panel controls the configuration of an experiment.
SetupPanel() - Constructor for class weka.gui.experiment.SetupPanel: Creates the setup panel with no initial experiment.
SetupPanel(Experiment) - Constructor for class weka.gui.experiment.SetupPanel: Creates the setup panel with the supplied initial experiment.
setUpper(int) - Method in class weka.core.Range: Sets the value of "last".
setUpperBoundMinSupport(double) - Method in class weka.associations.Apriori: Set the value of upperBoundMinSupport.
setUpperSize(int) - Method in class weka.experiment.LearningRateResultProducer: Set the value of UpperSize.
setUseBetterEncoding(boolean) - Method in class weka.filters.DiscretizeFilter: Sets whether better encoding is to be used for MDL.
setUseIBk(boolean) - Method in class weka.classifiers.DecisionTable: Sets whether IBk should be used instead of the majority class
setUseKernelEstimator(boolean) - Method in class weka.classifiers.NaiveBayes: Sets if kernel estimator is to be used.
setUseKononenko(boolean) - Method in class weka.filters.DiscretizeFilter: Sets whether Kononenko's MDL criterion is to be used.
setUseLaplace(boolean) - Method in class weka.classifiers.j48.J48: Set the value of useLaplace.
setUseMDL(boolean) - Method in class weka.filters.DiscretizeFilter: Sets whether MDL will be used as the discretisation method.
setUsePropertyIterator(boolean) - Method in class weka.experiment.Experiment: Sets whether the custom property iterator should be used.
setUsePropertyIterator(boolean) - Method in class weka.experiment.RemoteExperiment: Sets whether the custom property iterator should be used.
setUseResampling(boolean) - Method in class weka.classifiers.AdaBoostM1: Set resampling mode
setUseResampling(boolean) - Method in class weka.classifiers.LogitBoost: Set resampling mode
setUseTraining(boolean) - Method in class weka.attributeSelection.ClassifierSubsetEval: Set if training data is to be used instead of hold out/test data
setUseUnsmoothed(boolean) - Method in class weka.classifiers.m5.M5Prime: Set the value of UseUnsmoothed.
setValidationSetSize(int) - Method in class weka.classifiers.neural.NeuralNetwork: This will set the size of the validation set.
setValidationThreshold(int) - Method in class weka.classifiers.neural.NeuralNetwork: This sets the threshold to use for when validation testing is being done.
setValue(Attribute, double) - Method in class weka.core.Instance: Sets a specific value in the instance to the given value (internal floating-point format).
setValue(Attribute, String) - Method in class weka.core.Instance: Sets a value of an nominal or string attribute to the given value.
setValue(double) - Method in class weka.classifiers.adtree.PredictionNode: Sets the prediction value of the node.
setValue(int, double) - Method in class weka.core.Instance: Sets a specific value in the instance to the given value (internal floating-point format).
setValue(int, double) - Method in class weka.core.SparseInstance: Sets a specific value in the instance to the given value (internal floating-point format).
setValue(int, double) - Method in class weka.core.BinarySparseInstance: Sets a specific value in the instance to the given value (internal floating-point format).
setValue(int, String) - Method in class weka.core.Instance: Sets a value of a nominal or string attribute to the given value.
setValue(Object) - Method in class weka.gui.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.Instance: Sets a specific value in the instance to the given value (internal floating-point format).
setValueSparse(int, double) - Method in class weka.core.SparseInstance: Sets a specific value in the instance to the given value (internal floating-point format).
setValueSparse(int, double) - Method in class weka.core.BinarySparseInstance: Sets a specific value in the instance to the given value (internal floating-point format).
setVarianceCovered(double) - Method in class weka.attributeSelection.PrincipalComponents: Sets the amount of variance to account for when retaining principal components
setVerbose(boolean) - Method in class weka.attributeSelection.ExhaustiveSearch: set whether or not to output new best subsets as the search proceeds
setVerbose(boolean) - Method in class weka.attributeSelection.RandomSearch: set whether or not to output new best subsets as the search proceeds
setVerbosity(int) - Method in class weka.classifiers.m5.M5Prime: Set the value of Verbosity.
setWeight(double) - Method in class weka.core.Instance: Sets the weight of an instance.
setWeightByConfidence(boolean) - Method in class weka.classifiers.VFI: Set weighting by confidence
setWeightByDistance(boolean) - Method in class weka.attributeSelection.ReliefFAttributeEval: Set the nearest neighbour weighting method
setWeightingKernel(int) - Method in class weka.classifiers.LWR: Sets the kernel weighting method to use.
setWeightThreshold(int) - Method in class weka.classifiers.AdaBoostM1: Set weight threshold
setWeightThreshold(int) - Method in class weka.classifiers.LogitBoost: Set weight thresholding
setWindowSize(int) - Method in class weka.classifiers.IBk: Sets the maximum number of instances allowed in the training pool.
setWorkingInstances(Instances) - Method in class weka.gui.explorer.PreprocessPanel: Tells the panel to use a new working set of instances.
setWorkingInstancesFromFilters() - Method in class weka.gui.explorer.PreprocessPanel: Applies the current filters and attribute selection settings and sets the result as the working dataset.
setX(double) - Method in class weka.classifiers.neural.NeuralConnection
setX(int) - Method in class weka.gui.visualize.AttributePanel: shows which bar is the current x attribute.
setXindex(int) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel: Set the index of the attribute to go on the x axis
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.VisualizePanel.PlotPanel: Set the index of the attribute to go on the y axis
setYindex(int) - Method in class weka.gui.visualize.Plot2D: Set the index of the attribute to go on the y axis
setYindex(int) - Method in class weka.gui.visualize.PlotData2D: Set the y index of the data
setYIndex(int) - Method in class weka.gui.visualize.VisualizePanel: Set the index of the attribute for the y axis
SFEntropyGain() - Method in class weka.classifiers.Evaluation: Returns the total SF, which is the null model entropy minus the scheme entropy.
SFMeanEntropyGain() - Method in class weka.classifiers.Evaluation: Returns the SF per instance, which is the null model entropy minus the scheme entropy, per instance.
SFMeanPriorEntropy() - Method in class weka.classifiers.Evaluation: Returns the entropy per instance for the null model
SFMeanSchemeEntropy() - Method in class weka.classifiers.Evaluation: Returns the entropy per instance for the scheme
SFPriorEntropy() - Method in class weka.classifiers.Evaluation: Returns the total entropy for the null model
SFSchemeEntropy() - Method in class weka.classifiers.Evaluation: Returns the total entropy for the scheme
shift(int, int, Instance) - Method in class weka.classifiers.j48.Distribution: Shifts given instance from one bag to another one.
shiftRange(int, int, Instances, int, int) - Method in class weka.classifiers.j48.Distribution: Shifts all instances in given range from one bag to another one.
showDialog() - Method in class weka.gui.PropertySelectorDialog: Pops up the modal dialog and waits for cancel or a selection.
showDialog() - Method in class weka.gui.ListSelectorDialog: Pops up the modal dialog and waits for cancel or a selection.
shrinkageTipText() - Method in class weka.classifiers.AdditiveRegression: Returns the tip text for this property
sigLevel - Variable in class weka.experiment.PairedStats: The significance level for comparisons
sigmaTipText() - Method in class weka.attributeSelection.ReliefFAttributeEval: Returns the tip text for this property
SigmoidUnit - class weka.classifiers.neural.SigmoidUnit.: This can be used by the neuralnode to perform all it's computations (as a sigmoid unit).
SigmoidUnit() - Constructor for class weka.classifiers.neural.SigmoidUnit
significanceLevelTipText() - Method in class weka.associations.Apriori: Returns the tip text for this property
significanceLevelTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
SimpleCLI - class weka.gui.SimpleCLI.: Creates a very simple command line for invoking the main method of classes.
SimpleCLI() - Constructor for class weka.gui.SimpleCLI: Constructor
SimpleKMeans - class weka.clusterers.SimpleKMeans.: Simple k means clustering class.
SimpleKMeans() - Constructor for class weka.clusterers.SimpleKMeans
singleNodeToString() - Method in class weka.classifiers.m5.Node: Converts the information stored at this node to a string
singletons(Instances) - Static method in class weka.associations.ItemSet: Converts the header info of the given set of instances into a set of item sets (singletons).
size() - Method in class weka.classifiers.CostMatrix: Gets the number of classes.
size() - Method in class weka.classifiers.evaluation.ConfusionMatrix: Gets the number of classes.
size() - Method in class weka.classifiers.kstar.KStarCache.CacheTable: Returns the number of keys in this hashtable.
size() - Method in class weka.classifiers.kstar.LightHashTable: Returns the number of keys in this hashtable.
size() - Method in class weka.core.FastVector: Returns the vector's current size.
size() - Method in class weka.core.Queue: Gets queue's size.
sm(double, double) - Static method in class weka.core.Utils: Tests if a is smaller than b.
SMALL - Static variable in class weka.core.Utils: The small deviation allowed in double comparisons
SMO - class weka.classifiers.SMO.: Implements John C.
SMO() - Constructor for class weka.classifiers.SMO
smoothen() - Method in class weka.classifiers.m5.Node: Smoothens all unsmoothed formulae at the tree leaves under this node.
smoothenFormula(Node) - Method in class weka.classifiers.m5.Node: Recursively smoothens the unsmoothed linear model at this node with the unsmoothed linear models at the nodes above this
smoothenValue(double, double, int, int) - Static method in class weka.classifiers.m5.M5Utils: Returns the smoothed values according to the smoothing formula (np+kq)/(n+k)
smOrEq(double, double) - Static method in class weka.core.Utils: Tests if a is smaller or equal to b.
SOME_OTHER_FAILURE - Static variable in class weka.experiment.RemoteExperiment
sort(Attribute) - Method in class weka.core.Instances: Sorts the instances based on an attribute.
sort(double[]) - Static method in class weka.core.Utils: Sorts a given array of doubles in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
sort(int) - Method in class weka.core.Instances: Sorts the instances based on an attribute.
sort(int[]) - Static method in class weka.core.Utils: Sorts a given array of integers in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
Sourcable - interface weka.classifiers.Sourcable.: Interface for classifiers that can be converted to Java source.
sourceClass(int, Instances) - Method in class weka.classifiers.j48.ClassifierSplitModel
sourceExpression(int, Instances) - Method in class weka.classifiers.j48.ClassifierSplitModel
sourceExpression(int, Instances) - Method in class weka.classifiers.j48.NoSplit: Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances) - Method in class weka.classifiers.j48.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.BinC45Split: Returns a string containing java source code equivalent to the test made at this node.
sparseDataTipText() - Method in class weka.experiment.InstanceQuery: Returns the tip text for this property
SparseInstance - class weka.core.SparseInstance.: Class for storing an instance as a sparse vector.
SparseInstance() - Constructor for class weka.core.SparseInstance
SparseInstance(double, double[]) - Constructor for class weka.core.SparseInstance: Constructor that generates a sparse instance from the given parameters.
SparseInstance(double, double[], int[], int) - Constructor for class weka.core.SparseInstance: Constructor that inititalizes instance variable with given values.
SparseInstance(Instance) - Constructor for class weka.core.SparseInstance: Constructor that generates a sparse instance from the given instance.
SparseInstance(int) - Constructor for class weka.core.SparseInstance: Constructor of an instance that sets weight to one, all values to be missing, and the reference to the dataset to null.
SparseInstance(SparseInstance) - Constructor for class weka.core.SparseInstance: Constructor that copies the info from the given instance.
SparseToNonSparseFilter - class weka.filters.SparseToNonSparseFilter.: A filter that converts all incoming sparse instances into non-sparse format.
SparseToNonSparseFilter() - Constructor for class weka.filters.SparseToNonSparseFilter
SpecialFunctions - class weka.core.SpecialFunctions.: Class implementing some mathematical functions.
SpecialFunctions() - Constructor for class weka.core.SpecialFunctions
sphere - Variable in class weka.classifiers.kstar.KStarWrapper: used/reused to hold the sphere size
split(Instances) - Method in class weka.classifiers.j48.ClassifierSplitModel: Splits the given set of instances into subsets.
split(Instances) - Method in class weka.classifiers.m5.Node: Splits the node recursively, unless there are few instances or instances have similar values of the class attribute
SplitCriterion - class weka.classifiers.j48.SplitCriterion.: Abstract class for computing splitting criteria with respect to distributions of class values.
SplitCriterion() - Constructor for class weka.classifiers.j48.SplitCriterion
splitCritValue(Distribution) - Method in class weka.classifiers.j48.SplitCriterion: Computes result of splitting criterion for given distribution.
splitCritValue(Distribution) - Method in class weka.classifiers.j48.EntropySplitCrit: Computes entropy for given distribution.
splitCritValue(Distribution) - Method in class weka.classifiers.j48.InfoGainSplitCrit: This method is a straightforward implementation of the information gain criterion for the given distribution.
splitCritValue(Distribution) - Method in class weka.classifiers.j48.GainRatioSplitCrit: This method is a straightforward implementation of the gain ratio criterion for the given distribution.
splitCritValue(Distribution, Distribution) - Method in class weka.classifiers.j48.SplitCriterion: Computes result of splitting criterion for given training and test distributions.
splitCritValue(Distribution, Distribution) - Method in class weka.classifiers.j48.EntropySplitCrit: Computes entropy of test distribution with respect to training distribution.
splitCritValue(Distribution, Distribution, Distribution) - Method in class weka.classifiers.j48.SplitCriterion: Computes result of splitting criterion for given training and test distributions and given default distribution.
splitCritValue(Distribution, Distribution, int) - Method in class weka.classifiers.j48.SplitCriterion: Computes result of splitting criterion for given training and test distributions and given number of classes.
splitCritValue(Distribution, double) - Method in class weka.classifiers.j48.InfoGainSplitCrit: This method computes the information gain in the same way C4.5 does.
splitCritValue(Distribution, double, double) - Method in class weka.classifiers.j48.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.
SplitDatasetFilter - class weka.filters.SplitDatasetFilter.: This filter takes a dataset and outputs a subset of it.
SplitDatasetFilter() - Constructor for class weka.filters.SplitDatasetFilter
splitEnt(Distribution) - Method in class weka.classifiers.j48.EntropyBasedSplitCrit: Computes entropy after splitting without considering the class values.
SplitEvaluator - interface weka.experiment.SplitEvaluator.: Interface to objects able to generate a fixed set of results for a particular split of a dataset.
splitEvaluatorTipText() - Method in class weka.experiment.CrossValidationResultProducer: Returns the tip text for this property
splitEvaluatorTipText() - Method in class weka.experiment.RandomSplitResultProducer: Returns the tip text for this property
SplitInfo - class weka.classifiers.m5.SplitInfo.: Stores split information.
SplitInfo(int, int, int) - Constructor for class weka.classifiers.m5.SplitInfo: Constructs an object which contains the split information
splitOptions(String) - Static method in class weka.core.Utils: Split up a string containing options into an array of strings, one for each option.
splitOptions(String) - Static method in class weka.gui.SimpleCLI: Split up a string containing options into an array of strings, one for each option.
Splitter - class weka.classifiers.adtree.Splitter.: Abstract class representing a splitter node in an alternating tree.
Splitter() - Constructor for class weka.classifiers.adtree.Splitter
SpreadSubsampleFilter - class weka.filters.SpreadSubsampleFilter.: Produces a random subsample of a dataset.
SpreadSubsampleFilter() - Constructor for class weka.filters.SpreadSubsampleFilter
sqrSum(int, Instances) - Static method in class weka.classifiers.m5.M5Utils: Returns the squared sum of the instances values of an attribute
stableSort(double[]) - Static method in class weka.core.Utils: Sorts a given array of doubles in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
Stacking - class weka.classifiers.Stacking.: Implements stacking.
Stacking() - Constructor for class weka.classifiers.Stacking
startAssociator() - Method in class weka.gui.explorer.AssociationsPanel: Starts running the currently configured associator with the current settings.
startAttributeSelection() - Method in class weka.gui.explorer.AttributeSelectionPanel: Starts running the currently configured attribute evaluator and search method.
startClassifier() - Method in class weka.gui.explorer.ClassifierPanel: Starts running the currently configured classifier with the current settings.
startClusterer() - Method in class weka.gui.explorer.ClustererPanel: Starts running the currently configured clusterer with the current settings.
StartSetHandler - interface weka.attributeSelection.StartSetHandler.: Interface for search methods capable of doing something sensible given a starting set of attributes.
startSetTipText() - Method in class weka.attributeSelection.BestFirst: Returns the tip text for this property
startSetTipText() - Method in class weka.attributeSelection.Ranker: 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.RandomSearch: Returns the tip text for this property
startSetTipText() - Method in class weka.attributeSelection.ForwardSelection: Returns the tip text for this property
startSetTipText() - Method in class weka.attributeSelection.GeneticSearch: Returns the tip text for this property
Statistics - class weka.core.Statistics.: Class implementing some distributions, tests, etc.
Statistics() - Constructor for class weka.core.Statistics
Stats - class weka.classifiers.j48.Stats.: Class implementing a statistical routine needed by J48.
Stats - class weka.experiment.Stats.: A class to store simple statistics
Stats() - Constructor for class weka.classifiers.j48.Stats
Stats() - Constructor for class weka.experiment.Stats
statusMessage(String) - Method in 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.
statusMessage(String) - Method in interface weka.gui.Logger: Sends the supplied message to the status line.
statusMessage(String) - Method in class weka.gui.experiment.RunPanel: Sends the supplied message to the log panel 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
stopAssociator() - Method in class weka.gui.explorer.AssociationsPanel: Stops the currently running Associator (if any).
stopAttributeSelection() - Method in class weka.gui.explorer.AttributeSelectionPanel: Stops the currently running attribute selection (if any).
stopClassifier() - Method in class weka.gui.explorer.ClassifierPanel: Stops the currently running classifier (if any).
stopClusterer() - Method in class weka.gui.explorer.ClustererPanel: Stops the currently running clusterer (if any).
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.Node: This will return the width and height of the rectangle that the text will fit into.
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
StringToNominalFilter - class weka.filters.StringToNominalFilter.: Converts a string attribute (i.e.
StringToNominalFilter() - Constructor for class weka.filters.StringToNominalFilter
stringValue(Attribute) - Method in class weka.core.Instance: Returns the value of a nominal (or string) attribute for the instance.
stringValue(int) - Method in class weka.core.Instance: Returns the value of a nominal (or string) attribute for the instance.
studentTConfidenceInterval(int, double, double) - Static method in class weka.core.Statistics: Computes absolute size of half of a student-t confidence interval for given degrees of freedom, probability, and observed value.
sub(int, Instance) - Method in class weka.classifiers.j48.Distribution: Subtracts given instance from given bag.
SubsetEvaluator - class weka.attributeSelection.SubsetEvaluator.: Abstract attribute subset evaluator.
SubsetEvaluator() - Constructor for class weka.attributeSelection.SubsetEvaluator
subtract(Distribution) - Method in class weka.classifiers.j48.Distribution: Subtracts the given distribution from this one.
subtract(double) - Method in class weka.experiment.Stats: Removes a value to the observed values (no checking is done that the value being removed was actually added).
subtract(double, double) - Method in class weka.experiment.PairedStats: Removes an observed pair of values.
subtract(ItemSet) - Method in class weka.associations.ItemSet: Subtracts an item set from another one.
sum - Variable in class weka.experiment.Stats: The sum of values seen
sum(double[]) - Static method in class weka.core.Utils: Computes the sum of the elements of an array of doubles.
sum(int[]) - Static method in class weka.core.Utils: Computes the sum of the elements of an array of integers.
sum(int, Instances) - Static method in class weka.classifiers.m5.M5Utils: Returns the sum of the instances values of an attribute
Summarizable - interface weka.core.Summarizable.: Interface to something that provides a short textual summary (as opposed to toString() which is usually a fairly complete description) of itself.
sumOfWeights() - Method in class weka.core.Instances: Computes the sum of all the instances' weights.
sumSq - Variable in class weka.experiment.Stats: The sum of values squared seen
support() - Method in class weka.associations.ItemSet: Outputs the support for an item set.
supportsCustomEditor() - Method in class weka.gui.CostMatrixEditor: 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.FileEditor: 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 - class weka.filters.SwapAttributeValuesFilter.: Swaps two values of a nominal attribute.
SwapAttributeValuesFilter() - Constructor for class weka.filters.SwapAttributeValuesFilter
switchToLegend() - Method in class weka.gui.visualize.VisualizePanel.PlotPanel: Remove the attibute panel and replace it with the legend panel
symmetricalUncertainty(double[][]) - Static method in class weka.core.ContingencyTables: Calculates the symmetrical uncertainty for base 2.
SymmetricalUncertAttributeEval - class weka.attributeSelection.SymmetricalUncertAttributeEval.: Class for Evaluating attributes individually by measuring symmetrical uncertainty with respect to the class.
SymmetricalUncertAttributeEval() - Constructor for class weka.attributeSelection.SymmetricalUncertAttributeEval: Constructor
synopsis() - Method in class weka.core.Option: Returns the option's synopsis.
SysErrLog - class weka.gui.SysErrLog.: This Logger just sends messages to System.err.
SysErrLog() - Constructor for class weka.gui.SysErrLog

T

tableExists(String) - Method in class weka.experiment.DatabaseUtils: Checks that a given table exists.
Tag - class weka.core.Tag.: A Tag simply associates a numeric ID with a String description.
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.MetaCost
TAGS_MATRIX_SOURCE - Static variable in class weka.classifiers.CostSensitiveClassifier
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.attributeSelection.RaceSearch
TAGS_SELECTION - Static variable in class weka.classifiers.LinearRegression
TAGS_WEIGHTING - Static variable in class weka.classifiers.IBk
Task - interface weka.experiment.Task.: Interface to something that can be remotely executed as a task.
taskFinished() - Method in class weka.gui.WekaTaskMonitor: Tells the panel that a task has completed
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
TaskLogger - interface weka.gui.TaskLogger.: Interface for objects that display log and display information on running tasks.
taskStarted() - Method in class weka.gui.WekaTaskMonitor: Tells the panel that a new task has been started
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
TaskStatusInfo - class weka.experiment.TaskStatusInfo.: A class holding information for tasks being executed on RemoteEngines.
TaskStatusInfo() - Constructor for class weka.experiment.TaskStatusInfo
tauVal(double[][]) - Static method in class weka.core.ContingencyTables: Computes Goodman and Kruskal's tau-value for a contingency table.
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.
testsPerClassType(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier: Run a battery of tests for a given class attribute type
testWRTZeroR(Classifier, Evaluation, Instances, Instances) - Method in class weka.classifiers.CheckClassifier: Determine whether the scheme performs worse than ZeroR during testing
THRESHOLD_NAME - Static variable in class weka.classifiers.evaluation.CostCurve
THRESHOLD_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
ThresholdCurve - class weka.classifiers.evaluation.ThresholdCurve.: Generates points illustrating prediction tradeoffs that can be obtained by varying the threshold value between classes.
ThresholdCurve() - Constructor for class weka.classifiers.evaluation.ThresholdCurve
ThresholdSelector - class weka.classifiers.ThresholdSelector.: Class for selecting a threshold on a probability output by a distribution classifier.
ThresholdSelector() - Constructor for class weka.classifiers.ThresholdSelector
thresholdTipText() - Method in class weka.attributeSelection.WrapperSubsetEval: 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.RaceSearch: Returns the tip text for this property
thresholdTipText() - Method in class weka.attributeSelection.ForwardSelection: Returns the tip text for this property
TimeSeriesDeltaFilter - class weka.filters.TimeSeriesDeltaFilter.: An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the difference between the current value and the equivalent attribute attribute value of some previous (or future) instance.
TimeSeriesDeltaFilter() - Constructor for class weka.filters.TimeSeriesDeltaFilter
TimeSeriesTranslateFilter - class weka.filters.TimeSeriesTranslateFilter.: An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the equivalent attribute attribute values of some previous (or future) instance.
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
toByteArray(Object, boolean) - Static method in class weka.core.SerializedObject: Serializes the supplied object to a byte 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.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.
toDoubleArray() - Method in class weka.core.BinarySparseInstance: 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 interface weka.classifiers.Sourcable: Returns a string that describes the classifier as source.
toSource(String) - Method in class weka.classifiers.DecisionStump: Returns the decision tree as Java source code.
toSource(String) - Method in class weka.classifiers.AdaBoostM1: Returns the boosted model as Java source code.
toSource(String) - Method in class weka.classifiers.LogitBoost: 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.j48.J48: Returns tree as an if-then statement.
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.attributeSelection.CfsSubsetEval: returns a string describing CFS
toString() - Method in class weka.attributeSelection.PrincipalComponents: Returns a description of this attribute transformer
toString() - Method in class weka.attributeSelection.RankSearch: returns a description of the search as a String
toString() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns a string describing the wrapper
toString() - Method in class weka.attributeSelection.BestFirst: returns a description of the search as a String
toString() - Method in class weka.attributeSelection.BestFirst.Link2
toString() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Describe the attribute evaluator
toString() - Method in class weka.attributeSelection.ReliefFAttributeEval: Return a description of the ReliefF attribute evaluator.
toString() - Method in class weka.attributeSelection.GainRatioAttributeEval: Return a description of the evaluator
toString() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Return a description of the evaluator
toString() - Method in class weka.attributeSelection.Ranker: returns a description of the search as a String
toString() - Method in class weka.attributeSelection.OneRAttributeEval: Return a description of the evaluator
toString() - Method in class weka.attributeSelection.ExhaustiveSearch: prints a description of the search
toString() - Method in class weka.attributeSelection.ClassifierSubsetEval: Returns a string describing classifierSubsetEval
toString() - Method in class weka.attributeSelection.RandomSearch: prints a description of the search
toString() - Method in class weka.attributeSelection.InfoGainAttributeEval: Describe the attribute evaluator
toString() - Method in class weka.attributeSelection.RaceSearch
toString() - Method in class weka.attributeSelection.ConsistencySubsetEval: returns a description of the evaluator
toString() - Method in class weka.attributeSelection.ForwardSelection: returns a description of the search.
toString() - Method in class weka.attributeSelection.GeneticSearch: returns a description of the search
toString() - Method in class weka.classifiers.DecisionTable: Returns a description of the classifier.
toString() - Method in class weka.classifiers.DecisionTable.Link: Returns string representation.
toString() - Method in class weka.classifiers.MetaCost: Output a representation of this classifier
toString() - Method in class weka.classifiers.Prism: Prints a description of the classifier.
toString() - Method in class weka.classifiers.DecisionStump: Returns a description of the classifier.
toString() - Method in class weka.classifiers.AdditiveRegression: Returns textual description of the classifier.
toString() - Method in class weka.classifiers.VotedPerceptron: Returns textual description of classifier.
toString() - Method in class weka.classifiers.Bagging: Returns description of the bagged classifier.
toString() - Method in class weka.classifiers.UserClassifier
toString() - Method in class weka.classifiers.IBk: Returns a description of this classifier.
toString() - Method in class weka.classifiers.Stacking: Output a representation of this classifier
toString() - Method in class weka.classifiers.KernelDensity: Returns a description of the classifier.
toString() - Method in class weka.classifiers.IB1: Returns a description of this classifier.
toString() - Method in class weka.classifiers.ThresholdSelector: Returns description of the cross-validated classifier.
toString() - Method in class weka.classifiers.MultiScheme: Output a representation of this classifier
toString() - Method in class weka.classifiers.RegressionByDiscretization: Returns a description of the classifier.
toString() - Method in class weka.classifiers.ZeroR: Returns a description of the classifier.
toString() - Method in class weka.classifiers.NaiveBayesSimple: Returns a description of the classifier.
toString() - Method in class weka.classifiers.CVParameterSelection: Returns description of the cross-validated classifier.
toString() - Method in class weka.classifiers.CVParameterSelection.CVParameter: Returns a CVParameter as a string.
toString() - Method in class weka.classifiers.BVDecompose: Returns description of the bias-variance decomposition results.
toString() - Method in class weka.classifiers.AdaBoostM1: Returns description of the boosted 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.FilteredClassifier: Output a representation of this classifier
toString() - Method in class weka.classifiers.LinearRegression: Outputs the linear regression model as a string.
toString() - Method in class weka.classifiers.OneR: Returns a description of the classifier
toString() - Method in class weka.classifiers.CostSensitiveClassifier: Output a representation of this classifier
toString() - Method in class weka.classifiers.SMO: Prints out the classifier.
toString() - Method in class weka.classifiers.MultiClassClassifier: Prints the classifiers.
toString() - Method in class weka.classifiers.LWR: Returns a description of this classifier.
toString() - Method in class weka.classifiers.DistributionMetaClassifier: Prints the classifiers.
toString() - Method in class weka.classifiers.Logistic: Gets a string describing the classifier.
toString() - Method in class weka.classifiers.VFI: Returns a description of this classifier.
toString() - Method in class weka.classifiers.LogitBoost: Returns description of the boosted classifier.
toString() - Method in class weka.classifiers.HyperPipes: Returns a description of this classifier.
toString() - Method in class weka.classifiers.AttributeSelectedClassifier: Output a representation of this classifier
toString() - Method in class weka.classifiers.ClassificationViaRegression: Prints the classifiers.
toString() - Method in class weka.classifiers.NaiveBayes: Returns a description of the classifier.
toString() - Method in class weka.classifiers.adtree.ADTree: Returns a description of the classifier.
toString() - Method in class weka.classifiers.evaluation.NominalPrediction: Gets a human readable representation of this prediction.
toString() - Method in class weka.classifiers.evaluation.NumericPrediction: Gets a human readable representation of this prediction.
toString() - Method in class weka.classifiers.evaluation.ConfusionMatrix: Calls toString() with a default title.
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.j48.ClassifierDecList: Prints rules.
toString() - Method in class weka.classifiers.j48.ClassifierTree: Prints tree structure.
toString() - Method in class weka.classifiers.j48.J48: Returns a description of the classifier.
toString() - Method in class weka.classifiers.j48.PART: Returns a description of the classifier
toString() - Method in class weka.classifiers.j48.MakeDecList: Outputs the classifier into a string.
toString() - Method in class weka.classifiers.kstar.KStar: 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.classifiers.m5.Values: Converts the stats to a string
toString() - Method in class weka.classifiers.m5.Impurity: Converts an Impurity object to 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.classifiers.neural.NeuralNetwork
toString() - Method in class weka.clusterers.SimpleKMeans: return a string describing this clusterer
toString() - Method in class weka.clusterers.DistributionMetaClusterer: Prints the clusterers.
toString() - Method in class weka.clusterers.Cobweb: Returns a description of the clusterer as a string.
toString() - Method in class weka.clusterers.EM: Outputs the generated clusters into a string.
toString() - Method in class weka.core.Matrix: Converts a matrix to a string
toString() - Method in class weka.core.Instances: Returns the dataset as a string in ARFF format.
toString() - Method in class weka.core.Instance: Returns the description of one instance.
toString() - Method in class weka.core.Queue: Produces textual description of queue.
toString() - Method in class weka.core.SparseInstance: Returns the description of one instance in sparse format.
toString() - Method in class weka.core.AttributeStats: Returns a human readable representation of this AttributeStats instance.
toString() - Method in class weka.core.Range: Constructs a representation of the current range.
toString() - Method in class weka.core.Attribute: Returns a description of this attribute in ARFF format.
toString() - Method in class weka.core.BinarySparseInstance: Returns the description of one instance in sparse format.
toString() - Method in class weka.core.SerializedObject: Returns a text representation of the state of this object.
toString() - Method in class weka.estimators.NormalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.DDConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.KDConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.DKConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.KKConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.DNConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.PoissonEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.NDConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.KernelEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.MahalanobisEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.DiscreteEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.NNConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.experiment.RegressionSplitEvaluator: Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.CrossValidationResultProducer: Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.LearningRateResultProducer: Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.Stats: Returns a string summarising the stats so far.
toString() - Method in class weka.experiment.Experiment: Gets a string representation of the experiment configuration.
toString() - Method in class weka.experiment.RandomSplitResultProducer: Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.AveragingResultProducer: Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.DatabaseResultProducer: Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.PairedStats: Returns statistics on the paired comparison.
toString() - Method in class weka.experiment.PropertyNode: Returns a string description of this property.
toString() - Method in class weka.experiment.ClassifierSplitEvaluator: Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.RemoteExperiment: Overides toString in Experiment
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.SplitInfo: Converts the spliting information to string
toString(Instances) - Method in class weka.classifiers.m5.Options: Prints information stored in an 'Options' object, basically containing command line options
toString(Instances, int) - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey: Convert a hash entry to a string
toString(Instances, int) - Method in class weka.classifiers.DecisionTable.hashKey: Convert a hash entry to a 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) - Static method in class weka.classifiers.m5.Ivector: Converts a string
toString(int, int, int, int) - Method in class weka.classifiers.m5.Matrix: Converts a matrix to a string
toString(PredictionNode, int) - Method in class weka.classifiers.adtree.ADTree: Traverses the tree, forming a string that describes it.
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.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 class weka.core.Instances: Generates a string summarizing the set of instances.
toSummaryString() - Method in interface weka.core.Summarizable: Returns a string that summarizes the object.
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 class weka.attributeSelection.PrincipalComponents: Gets the transformed training data.
transformedData() - Method in interface weka.attributeSelection.AttributeTransformer: Returns the transformed data
transformedHeader() - Method in class weka.attributeSelection.PrincipalComponents: Returns just the header for the transformed data (ie.
transformedHeader() - Method in interface weka.attributeSelection.AttributeTransformer: 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.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".
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.
TreeBuild - class weka.gui.treevisualizer.TreeBuild.: This class will parse a dotty file and construct a tree structure from it with Edge's and Node's
TreeBuild() - Constructor for class weka.gui.treevisualizer.TreeBuild: Upon construction this will only setup the color table for quick reference of a color.
TreeDisplayEvent - class weka.gui.treevisualizer.TreeDisplayEvent.: An event containing the user selection from the tree display
TreeDisplayEvent(int, String) - Constructor for class weka.gui.treevisualizer.TreeDisplayEvent: Constructs an event with the specified command and what the command is applied to.
TreeDisplayListener - interface weka.gui.treevisualizer.TreeDisplayListener.: Interface implemented by classes that wish to recieve user selection events from a tree displayer.
treeToString(int, double) - Method in class weka.classifiers.m5.Node: Converts the tree under this node to a string
TreeVisualizer - class weka.gui.treevisualizer.TreeVisualizer.: Class for displaying a Node structure in Swing.
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 - class weka.classifiers.evaluation.TwoClassStats.: Encapsulates performance functions for two-class problems.
TwoClassStats(double, double, double, double) - Constructor for class weka.classifiers.evaluation.TwoClassStats: Creates the TwoClassStats with the given initial performance values.
TwoWayNominalSplit - class weka.classifiers.adtree.TwoWayNominalSplit.: Class representing a two-way split on a nominal attribute, of the form: either 'is some_value' or 'is not some_value'.
TwoWayNominalSplit(int, int) - Constructor for class weka.classifiers.adtree.TwoWayNominalSplit: Creates a new two-way nominal splitter.
TwoWayNumericSplit - class weka.classifiers.adtree.TwoWayNumericSplit.: Class representing a two-way split on a numeric attribute, of the form: either 'is < some_value' or 'is >= some_value'.
TwoWayNumericSplit(int, double) - Constructor for class weka.classifiers.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 - exception weka.core.UnassignedClassException.: UnassignedClassException is used when a method requires access to the Attribute designated as the class attribute in a set of Instances, but the Instances does not have any class attribute assigned (such as by setClassIndex()).
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 - exception weka.core.UnassignedDatasetException.: UnassignedDatasetException is used when a method of an Instance is called that requires access to the Instance structure, but that the Instance does not contain a reference to any Instances (as set by Instance.setDataset(), or when an Instance is added to a set of Instances)).
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 - class weka.attributeSelection.UnsupervisedAttributeEvaluator.: Abstract unsupervised attribute evaluator.
UnsupervisedAttributeEvaluator() - Constructor for class weka.attributeSelection.UnsupervisedAttributeEvaluator
UnsupervisedSubsetEvaluator - class weka.attributeSelection.UnsupervisedSubsetEvaluator.: Abstract unsupervised attribute subset evaluator.
UnsupervisedSubsetEvaluator() - Constructor for class weka.attributeSelection.UnsupervisedSubsetEvaluator
UnsupportedAttributeTypeException - exception weka.core.UnsupportedAttributeTypeException.: UnsupportedAttributeTypeException is used in situations where the throwing object is not able to accept Instances with the supplied structure, because one or more of the Attributes in the Instances are of the wrong type.
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 - exception weka.core.UnsupportedClassTypeException.: UnsupportedClassTypeException is used in situations where the throwing object is not able to accept Instances with the supplied structure, because the class Attribute is of the wrong type.
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.
UpdateableClassifier - interface weka.classifiers.UpdateableClassifier.: Interface to incremental classification models that can learn using one instance at a time.
updateableClassifier() - Method in class weka.classifiers.CheckClassifier: Checks whether the scheme can build models incrementally.
updateChildPropertySheet() - Method in class weka.gui.GenericObjectEditor.GOEPanel: Updates the child property sheet, and creates if needed
updateChooser() - Method in class weka.gui.GenericObjectEditor.GOEPanel: Called to update the list of values to be selected from
updateClassifier(Instance) - Method in class weka.classifiers.IBk: Adds the supplied instance to the training set
updateClassifier(Instance) - Method in interface weka.classifiers.UpdateableClassifier: Updates a classifier using the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.IB1: Updates the classifier.
updateClassifier(Instance) - Method in class weka.classifiers.LWR: Adds the supplied instance to the training set
updateClassifier(Instance) - Method in class weka.classifiers.HyperPipes: Updates the classifier.
updateClassifier(Instance) - Method in class weka.classifiers.NaiveBayes: Updates the classifier with the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.kstar.KStar: Adds the supplied instance to the training set
updateClassType() - Method in class weka.gui.GenericObjectEditor.GOEPanel: Called when the class of object being edited changes.
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.
updateOptions() - Method in class weka.experiment.RegressionSplitEvaluator: Updates the options that the current classifier is using.
updateOptions() - Method in class weka.experiment.ClassifierSplitEvaluator: Updates the options that the current classifier is using.
updatePriors(Instance) - Method in class weka.classifiers.Evaluation: Updates the class prior probabilities (when incrementally training)
updateRadioLinks() - Method in class weka.gui.explorer.ClassifierPanel: Updates the enabled status of the input fields and labels.
updateRadioLinks() - Method in class weka.gui.explorer.AttributeSelectionPanel: Updates the enabled status of the input fields and labels.
updateRadioLinks() - Method in class weka.gui.explorer.ClustererPanel: Updates the enabled status of the input fields and labels.
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.
updateResultsTableName(ResultProducer) - Method in class weka.experiment.DatabaseResultListener: Determines the table name that results will be inserted into.
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.SigmoidUnit: 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.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.
updatingEquality(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier: Checks whether an updateable scheme produces the same model when trained incrementally as when batch trained.
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 - class weka.classifiers.UserClassifier.: Class for generating an user defined decision tree.
UserClassifier() - Constructor for class weka.classifiers.UserClassifier: Constructor
userCommand(TreeDisplayEvent) - Method in class weka.classifiers.UserClassifier: Receives user choices from the tree view, and then deals with these choices.
userCommand(TreeDisplayEvent) - Method in interface weka.gui.treevisualizer.TreeDisplayListener: Gets called when the user selects something, in the tree display.
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 - class weka.core.Utils.: Class implementing some simple utility methods.
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.classifiers.kstar.KStarCache.TableEntry: scale factor or stop parameter
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.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.
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.
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 - class weka.classifiers.m5.Values.: Stores some statistics.
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.Instance: Returns an instance's attribute value in internal format.
valueSparse(int) - Method in class weka.core.BinarySparseInstance: 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
VERY_SMALL - Static variable in class weka.classifiers.LogitBoost: A very small number, below which weights cannot fall
VFI - class weka.classifiers.VFI.: Class implementing the voting feature interval classifier.
VFI() - Constructor for class weka.classifiers.VFI
VISUALIZE_PROPERTIES - Static variable in class weka.gui.visualize.VisualizeUtils: Contains the visualization properties
visualize(String, int, int) - Method in class weka.gui.explorer.ClassifierPanel: Handles constructing a popup menu with visualization options.
visualize(String, int, int) - Method in class weka.gui.explorer.AttributeSelectionPanel: Handles constructing a popup menu with visualization options
visualizeClassifierErrors(VisualizePanel) - Method in class weka.gui.explorer.ClassifierPanel: Pops up a VisualizePanel for visualizing the data and errors for the classifier from the currently selected item in the results list
visualizeClusterAssignments(VisualizePanel) - Method in class weka.gui.explorer.ClustererPanel: Pops up a visualize panel to display cluster assignments
visualizeClusterer(String, int, int) - Method in class weka.gui.explorer.ClustererPanel: Handles constructing a popup menu with visualization options
VisualizePanel - class weka.gui.visualize.VisualizePanel.: This panel allows the user to visualize a dataset (and if provided) a classifier's/clusterer's predictions in two dimensions.
VisualizePanel.PlotPanel - class weka.gui.visualize.VisualizePanel.PlotPanel.: Inner class to handle plotting
VisualizePanel.PlotPanel(VisualizePanel) - Constructor for class weka.gui.visualize.VisualizePanel.PlotPanel: Constructor
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 - class weka.gui.visualize.VisualizePanelEvent.: This event Is fired to a listeners 'userDataEvent' function when The user on the VisualizePanel clicks submit.
VisualizePanelEvent(FastVector, Instances, Instances, int, int) - Constructor for class weka.gui.visualize.VisualizePanelEvent: This constructor creates the event with all the parameters set.
VisualizePanelListener - interface weka.gui.visualize.VisualizePanelListener.: Interface implemented by a class that is interested in receiving submited shapes from a visualize panel.
visualizeTransformedData(VisualizePanel) - Method in class weka.gui.explorer.AttributeSelectionPanel: Popup a visualize panel for viewing transformed data
visualizeTree(String, String) - Method in class weka.gui.explorer.ClassifierPanel: Pops up a TreeVisualizer for the classifier from the currently selected item in the results list
visualizeTree(String, String) - Method in class weka.gui.explorer.ClustererPanel: Pops up a TreeVisualizer for the clusterer from the currently selected item in the results list
VisualizeUtils - class weka.gui.visualize.VisualizeUtils.: This class contains utility routines for visualization
VisualizeUtils() - Constructor for class weka.gui.visualize.VisualizeUtils
VLINE - Static variable in class weka.gui.visualize.VisualizePanelEvent
VotedPerceptron - class weka.classifiers.VotedPerceptron.: Implements the voted perceptron algorithm by Freund and Schapire.
VotedPerceptron() - Constructor for class weka.classifiers.VotedPerceptron

W

waitingExperiment(int) - Method in class weka.experiment.RemoteExperiment: Push an experiment back on the queue of waiting experiments
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
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() - Method in class weka.core.Instance: Returns the instance's weight.
weight(Instance) - Method in class weka.classifiers.j48.ClassifierDecList: Returns the weight a rule assigns to an instance.
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
WeightedInstancesHandler - interface weka.core.WeightedInstancesHandler.: Interface to something that makes use of the information provided by instance weights.
weightedInstancesHandler() - Method in class weka.classifiers.CheckClassifier: Checks whether the scheme says it can handle instance weights.
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.
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.BinC45Split: Returns weights if instance is assigned to more than one subset.
weightsForInstance(Instance) - Method in class weka.clusterers.EM: Returns the weights (indicating cluster membership) for a given instance
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.
weka.associations - package weka.associations
weka.attributeSelection - package weka.attributeSelection
weka.classifiers - package weka.classifiers
weka.classifiers.adtree - package weka.classifiers.adtree
weka.classifiers.evaluation - package weka.classifiers.evaluation
weka.classifiers.j48 - package weka.classifiers.j48
weka.classifiers.kstar - package weka.classifiers.kstar
weka.classifiers.m5 - package weka.classifiers.m5
weka.classifiers.neural - package weka.classifiers.neural
weka.clusterers - package weka.clusterers
weka.core - package weka.core
weka.core.converters - package weka.core.converters
weka.estimators - package weka.estimators
weka.experiment - package weka.experiment
weka.filters - package weka.filters
weka.gui - package weka.gui
weka.gui.experiment - package weka.gui.experiment
weka.gui.explorer - package weka.gui.explorer
weka.gui.streams - package weka.gui.streams
weka.gui.treevisualizer - package weka.gui.treevisualizer
weka.gui.visualize - package weka.gui.visualize
WekaException - exception weka.core.WekaException.: WekaException is used when some Weka-specific checked exception must be raised.
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.
wekaStaticWrapper(Sourcable, String) - Static method in class weka.classifiers.Evaluation: Wraps a static classifier in enough source to test using the weka class libraries.
WekaTaskMonitor - class weka.gui.WekaTaskMonitor.: This panel records the number of weka tasks running and displays a simple bird animation while their are active tasks
WekaTaskMonitor() - Constructor for class weka.gui.WekaTaskMonitor: Constructor
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.
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.BinC45Split: Returns index of subset instance is assigned to.
WrapperSubsetEval - class weka.attributeSelection.WrapperSubsetEval.: Wrapper attribute subset evaluator.
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

Z_MAX - Static variable in class weka.classifiers.LogitBoost: A threshold for responses (Friedman suggests between 2 and 4)
ZeroR - class weka.classifiers.ZeroR.: Class for building and using a 0-R classifier.
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.

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