All Packages  Class Hierarchy

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Index of all Fields and Methods

A

absDev(int, Instances). Static method in class weka.classifiers.m5.M5Utils
Returns the absolute deviation value of the instances values of an attribute
accept(File). Method in class weka.gui.ExtensionFileFilter
Returns true if the supplied file should be accepted (i.e.
accept(File, String). Method in class weka.gui.ExtensionFileFilter
Returns true if the file in the given directory with the given name should be accepted.
acceptResult(ResultProducer, Object[], Object[]). Method in class weka.experiment.AveragingResultProducer
Accepts results from a ResultProducer.
acceptResult(ResultProducer, Object[], Object[]). Method in class weka.experiment.CSVResultListener
Just prints out each result as it is received.
acceptResult(ResultProducer, Object[], Object[]). Method in class weka.experiment.DatabaseResultListener
Submit the result to the appropriate table of the database
acceptResult(ResultProducer, Object[], Object[]). Method in class weka.experiment.DatabaseResultProducer
Accepts results from a ResultProducer.
acceptResult(ResultProducer, Object[], Object[]). Method in class weka.experiment.InstancesResultListener
Collects each instance and adjusts the header information.
acceptResult(ResultProducer, Object[], Object[]). Method in interface weka.experiment.ResultListener
Accepts results from a ResultProducer.
actionPerformed(ActionEvent). Method in class weka.gui.experiment.DatasetListPanel
Handle actions when buttons get pressed.
actionPerformed(ActionEvent). Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Handles the various button clicking type activities.
actionPerformed(ActionEvent). Method in class weka.gui.streams.InstanceLoader
actionPerformed(ActionEvent). Method in class weka.gui.experiment.RunPanel
Controls starting and stopping the experiment.
actionPerformed(ActionEvent). Method in class weka.gui.SimpleCLI
Only gets called when return is pressed in the input area, which starts the command running.
actualNumBags(). Method in class weka.classifiers.j48.Distribution
Returns number of non-empty bags of distribution.
actualNumClasses(). Method in class weka.classifiers.j48.Distribution
Returns number of classes actually occuring in distribution.
actualNumClasses(int). Method in class weka.classifiers.j48.Distribution
Returns number of classes actually occuring in given bag.
AdaBoostM1(). Constructor for class weka.classifiers.AdaBoostM1
add(Cobweb. CTree, Cobweb. CTree). Method in class weka.clusterers.Cobweb
Adds an example to the tree.
add(double). Method in class weka.experiment.Stats
Adds a value to the observed values
add(double, double). Method in class weka.experiment.PairedStats
Add an observed pair of values.
add(double, double). Method in class weka.experiment.Stats
Adds a value that has been seen n times to the observed values
add(Instance). Method in class weka.core.Instances
Adds one instance to the end of the set.
add(int, double[]). Method in class weka.classifiers.j48.Distribution
Adds counts to given bag.
add(int, Instance). Method in class weka.classifiers.j48.Distribution
Adds given instance to given bag.
addActionListener(ActionListener). Method in class weka.gui.explorer.VisualizePanel
Add a listener for this visualize panel
addCVParameter(String). Method in class weka.classifiers.CVParameterSelection
Adds a scheme parameter to the list of parameters to be set by cross-validation
addElement(Object). Method in class weka.core.FastVector
Adds an element to this vector.
addErrs(double, double, float). Static method in class weka.classifiers.j48.Stats
Computes estimated extra error for given total number of instances and errors.
AddFilter(). Constructor for class weka.filters.AddFilter
addInstanceListener(InstanceListener). Method in class weka.gui.streams.InstanceJoiner
addInstanceListener(InstanceListener). Method in class weka.gui.streams.InstanceLoader
addInstanceListener(InstanceListener). Method in interface weka.gui.streams.InstanceProducer
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.
addObject(String, Object). Method in class weka.gui.ResultHistoryPanel
Adds an object to the results list
addPropertyChangeListener(PropertyChangeListener). Method in class weka.gui.CostMatrixEditor
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener). Method in class weka.gui.GenericArrayEditor
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener). Method in class weka.gui.GenericObjectEditor
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener). Method in class weka.gui.explorer.PreprocessPanel
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener). Method in class weka.gui.PropertySheetPanel
Adds a PropertyChangeListener.
addPropertyChangeListener(PropertyChangeListener). Method in class weka.gui.SetInstancesPanel
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener). Method in class weka.gui.experiment.SetupPanel
Adds a PropertyChangeListener who will be notified of value changes.
addRange(int, Instances, int, int). Method in class weka.classifiers.j48.Distribution
Adds all instances in given range to given bag.
addResult(String, StringBuffer). Method in class weka.gui.ResultHistoryPanel
Adds a new result to the result list.
addStringValue(String). Method in class weka.core.Attribute
Adds a string value to the list of valid strings for attributes of type STRING and returns the index of the string.
addValue(double, double). Method in class weka.estimators.DiscreteEstimator
Add a new data value to the current estimator.
addValue(double, double). Method in interface weka.estimators.Estimator
Add a new data value to the current estimator.
addValue(double, double). Method in class weka.estimators.KernelEstimator
Add a new data value to the current estimator.
addValue(double, double). Method in class weka.estimators.MahalanobisEstimator
Add a new data value to the current estimator.
addValue(double, double). Method in class weka.estimators.NormalEstimator
Add a new data value to the current estimator.
addValue(double, double). Method in class weka.estimators.PoissonEstimator
Add a new data value to the current estimator.
addValue(double, double, double). Method in interface weka.estimators.ConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double). Method in class weka.estimators.DDConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double). Method in class weka.estimators.DKConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double). Method in class weka.estimators.DNConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double). Method in class weka.estimators.KDConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double). Method in class weka.estimators.KKConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double). Method in class weka.estimators.NDConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double). Method in class weka.estimators.NNConditionalEstimator
Add a new data value to the current estimator.
addWeights(Instance, double[]). Method in class weka.classifiers.j48.Distribution
Adds given instance to all bags weighting it according to given weights.
advanceCounters(). Method in class weka.experiment.Experiment
Increments iteration counters appropriately.
AllFilter(). Constructor for class weka.filters.AllFilter
applyCostMatrix(Instances, Random). Method in class weka.classifiers.CostMatrix
Changes the dataset to reflect a given set of costs.
APPROVE_OPTION. Static variable in class weka.gui.ListSelectorDialog
Signifies an OK property selection
APPROVE_OPTION. Static variable in class weka.gui.PropertySelectorDialog
Signifies an OK property selection
Apriori(). Constructor for class weka.associations.Apriori
Constructor that allows to sets default values for the minimum confidence and the maximum number of rules the minimum confidence.
arrayToString(Object[]). Static method in class weka.experiment.DatabaseUtils
Converts an array of objects to a string by inserting a space between each element.
ASEvaluation(). Constructor for class weka.attributeSelection.ASEvaluation
ASSearch(). Constructor for class weka.attributeSelection.ASSearch
AssociationsPanel(). Constructor for class weka.gui.explorer.AssociationsPanel
Creates the associator panel
Associator(). Constructor for class weka.associations.Associator
attIndex(). Method in class weka.classifiers.j48.BinC45Split
Returns index of attribute for which split was generated.
attIndex(). Method in class weka.classifiers.j48.C45Split
Returns index of attribute for which split was generated.
attribute(int). Method in class weka.core.Instance
Returns the attribute with the given index.
attribute(int). Method in class weka.core.Instances
Returns an attribute.
Attribute(String). Constructor for class weka.core.Attribute
Constructor for a numeric attribute.
attribute(String). Method in class weka.core.Instances
Returns an attribute given its name.
Attribute(String, FastVector). Constructor for class weka.core.Attribute
Constructor for nominal attributes and string attributes.
AttributeEvaluator(). Constructor for class weka.attributeSelection.AttributeEvaluator
AttributeFilter(). Constructor for class weka.filters.AttributeFilter
AttributeSelection(). Constructor for class weka.attributeSelection.AttributeSelection
constructor.
AttributeSelectionFilter(). Constructor for class weka.filters.AttributeSelectionFilter
Constructor
AttributeSelectionPanel(). Constructor for class weka.gui.AttributeSelectionPanel
Creates the attribute selection panel with no initial instances.
AttributeSelectionPanel(). Constructor for class weka.gui.explorer.AttributeSelectionPanel
Creates the classifier panel
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.
AttributeSummaryPanel(). Constructor for class weka.gui.AttributeSummaryPanel
Creates the instances panel with no initial instances.
attrSplit(int, Instances). Method in class weka.classifiers.m5.SplitInfo
Finds the best splitting point for an attribute in the instances
AveragingResultProducer(). Constructor for class weka.experiment.AveragingResultProducer

B

backQuoteChars(String). Static method in class weka.core.Utils
Converts carriage returns and new lines in a string into \r and \n.
Bagging(). Constructor for class weka.classifiers.Bagging
BATCH_FINISHED. Static variable in class weka.gui.streams.InstanceEvent
Specifies that the batch of instances is finished
batchFilterFile(Filter, String[]). Static method in class weka.filters.Filter
Method for testing filters ability to process multiple batches.
batchFinished(). Method in class weka.filters.AttributeSelectionFilter
Signify that this batch of input to the filter is finished.
batchFinished(). Method in class weka.filters.DiscretizeFilter
Signifies that this batch of input to the filter is finished.
batchFinished(). Method in class weka.filters.Filter
Signify that this batch of input to the filter is finished.
batchFinished(). Method in class weka.gui.streams.InstanceJoiner
Signify that this batch of input to the filter is finished.
batchFinished(). Method in class weka.gui.streams.InstanceSavePanel
batchFinished(). Method in class weka.gui.streams.InstanceTable
batchFinished(). Method in class weka.gui.streams.InstanceViewer
batchFinished(). Method in class weka.filters.NominalToBinaryFilter
Signify that this batch of input to the filter is finished.
batchFinished(). Method in class weka.filters.NormalizationFilter
Signify that this batch of input to the filter is finished.
batchFinished(). Method in class weka.filters.RandomizeFilter
Signify that this batch of input to the filter is finished.
batchFinished(). Method in class weka.filters.ReplaceMissingValuesFilter
Signify that this batch of input to the filter is finished.
batchFinished(). Method in class weka.filters.ResampleFilter
Signify that this batch of input to the filter is finished.
batchFinished(). Method in class weka.filters.SplitDatasetFilter
Signify that this batch of input to the filter is finished.
batchFinished(). Method in class weka.filters.TimeSeriesTranslateFilter
Signifies that this batch of input to the filter is finished.
BestFirst(). Constructor for class weka.attributeSelection.BestFirst
Constructor
bestHost(Cobweb. CTree, Cobweb. CTree, double, double). Method in class weka.clusterers.Cobweb
Finds the best place to add a new node during training.
bestHostCluster(Cobweb. CTree, Cobweb. CTree, double, double). Method in class weka.clusterers.Cobweb
Finds the cluster that an unseen instance belongs to.
BinC45ModelSelection(int, Instances). Constructor for class weka.classifiers.j48.BinC45ModelSelection
Initializes the split selection method with the given parameters.
BinC45Split(int, int, double). Constructor for class weka.classifiers.j48.BinC45Split
Initializes the split model.
binomialStandardError(double, int). Static method in class weka.core.Statistics
Computes standard error for observed values of a binomial random variable.
buildAssociations(Instances). Method in class weka.associations.Apriori
Method that generates all large itemsets with a minimum support, and from these all association rules with a minimum confidence.
buildAssociations(Instances). Method in class weka.associations.Associator
Generates an associator.
buildClassifier(Instances). Method in class weka.classifiers.AdaBoostM1
Boosting method.
buildClassifier(Instances). Method in class weka.classifiers.Bagging
Bagging method.
buildClassifier(Instances). Method in class weka.classifiers.j48.BinC45Split
Creates a C4.5-type split on the given data.
buildClassifier(Instances). Method in class weka.classifiers.j48.C45PruneableClassifierTree
Method for building a pruneable classifier tree.
buildClassifier(Instances). Method in class weka.classifiers.j48.C45Split
Creates a C4.5-type split on the given data.
buildClassifier(Instances). Method in class weka.classifiers.ClassificationViaRegression
Builds the classifiers.
buildClassifier(Instances). Method in class weka.classifiers.Classifier
Generates a classifier.
buildClassifier(Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
Builds the classifier split model for the given set of instances.
buildClassifier(Instances). Method in class weka.classifiers.j48.ClassifierTree
Method for building a classifier tree.
buildClassifier(Instances). Method in class weka.classifiers.CostSensitiveClassifier
Builds the model of the base learner.
buildClassifier(Instances). Method in class weka.classifiers.CVParameterSelection
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.DecisionStump
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.DecisionTable
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.FilteredClassifier
Build the classifier on the filtered data.
buildClassifier(Instances). Method in class weka.classifiers.HyperPipes
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.IB1
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.IBk
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.Id3
Builds Id3 decision tree classifier.
buildClassifier(Instances). Method in class weka.classifiers.j48.J48
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.KernelDensity
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.LinearRegression
Builds a regression model for the given data.
buildClassifier(Instances). Method in class weka.classifiers.Logistic
Builds the classifier
buildClassifier(Instances). Method in class weka.classifiers.LogitBoost
Boosting method.
buildClassifier(Instances). Method in class weka.classifiers.LWR
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.m5.M5Prime
Construct a model tree by training instances
buildClassifier(Instances). Method in class weka.classifiers.j48.MakeDecList
Builds dec list.
buildClassifier(Instances). Method in class weka.classifiers.MultiClassClassifier
Builds the classifiers.
buildClassifier(Instances). Method in class weka.classifiers.MultiScheme
Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
buildClassifier(Instances). Method in class weka.classifiers.NaiveBayes
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.NaiveBayesSimple
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.j48.NoSplit
Creates a "no-split"-split for a given set of instances.
buildClassifier(Instances). Method in class weka.classifiers.OneR
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.j48.PART
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.Prism
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.j48.PruneableClassifierTree
Method for building a pruneable classifier tree.
buildClassifier(Instances). Method in class weka.classifiers.RegressionByDiscretization
Generates the classifier.
buildClassifier(Instances). Method in class weka.classifiers.SMO
Method for building the classifier.
buildClassifier(Instances). Method in class weka.classifiers.Stacking
Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
buildClassifier(Instances). Method in class weka.classifiers.VotedPerceptron
Builds the ensemble of perceptrons.
buildClassifier(Instances). Method in class weka.classifiers.ZeroR
Generates the classifier.
buildClusterer(Instances). Method in class weka.clusterers.Clusterer
Generates a clusterer.
buildClusterer(Instances). Method in class weka.clusterers.Cobweb
Builds the clusterer.
buildClusterer(Instances). Method in class weka.clusterers.EM
Classifies a given instance.
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.ConsistencySubsetEval
Generates a attribute evaluator.
buildEvaluator(Instances). Method in class weka.attributeSelection.GainRatioAttributeEval
Initializes a gain ratio attribute evaluator.
buildEvaluator(Instances). Method in class weka.attributeSelection.InfoGainAttributeEval
Initializes an information gain attribute evaluator.
buildEvaluator(Instances). Method in class weka.attributeSelection.OneRAttributeEval
Initializes an information gain attribute evaluator.
buildEvaluator(Instances). Method in class weka.attributeSelection.ReliefFAttributeEval
Initializes a ReliefF attribute evaluator.
buildEvaluator(Instances). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Initializes a symmetrical uncertainty attribute evaluator.
buildEvaluator(Instances). Method in class weka.attributeSelection.WrapperSubsetEval
Generates a attribute evaluator.
buildRule(Instances). Method in class weka.classifiers.j48.C45PruneableDecList
Method for building a pruned partial tree.
buildRule(Instances, Instances). Method in class weka.classifiers.j48.PruneableDecList
Method for building a pruned partial tree.
buildTree(Instances, boolean). Method in class weka.classifiers.j48.ClassifierTree
Builds the tree structure.
buildTree(Instances, Instances, boolean). Method in class weka.classifiers.j48.ClassifierTree
Builds the tree structure with hold out set
BVDecompose(). Constructor for class weka.classifiers.BVDecompose

C

C45ModelSelection(int, Instances). Constructor for class weka.classifiers.j48.C45ModelSelection
Initializes the split selection method with the given parameters.
C45PruneableClassifierTree(ModelSelection, boolean, float, boolean). Constructor for class weka.classifiers.j48.C45PruneableClassifierTree
Constructor for pruneable tree structure.
C45PruneableDecList(ModelSelection, double, int). Constructor for class weka.classifiers.j48.C45PruneableDecList
Constructor for pruneable tree structure.
C45Split(int, int, double). Constructor for class weka.classifiers.j48.C45Split
Initializes the split model.
calculateDerived(). Method in class weka.experiment.PairedStats
Calculates the derived statistics (significance etc).
calculateDerived(). Method in class weka.experiment.Stats
Tells the object to calculate any statistics that don't have their values automatically updated during add.
calculateStatistics(int, int, int, int). Method in class weka.experiment.PairedTTester
Computes a paired t-test comparison for a specified dataset between two resultsets.
CANCEL_OPTION. Static variable in class weka.gui.ListSelectorDialog
Signifies a cancelled property selection
CANCEL_OPTION. Static variable in class weka.gui.PropertySelectorDialog
Signifies a cancelled property selection
capacity(). Method in class weka.core.FastVector
Returns the capacity of the vector.
CfsSubsetEval(). Constructor for class weka.attributeSelection.CfsSubsetEval
Constructor
check(double). Method in class weka.classifiers.j48.Distribution
Checks if at least two bags contain a minimum number of instances.
CheckClassifier(). Constructor for class weka.classifiers.CheckClassifier
checkForRemainingOptions(String[]). Static method in class weka.core.Utils
Checks if the given array contains any non-empty options.
checkForStringAttributes(). Method in class weka.core.Instances
Checks for string attributes in the dataset
checkInstance(Instance). Method in class weka.core.Instances
Checks if the given instance is compatible with this dataset.
checkModel(). Method in class weka.classifiers.j48.ClassifierSplitModel
Checks if generated model is valid.
CheckOptionHandler(). Constructor for class weka.core.CheckOptionHandler
checkOptionHandler(OptionHandler, String[]). Static method in class weka.core.CheckOptionHandler
Runs some diagnostic tests on an optionhandler object.
chiSquared(double[][], boolean). Static method in class weka.core.ContingencyTables
Returns chi-squared probability for a given matrix.
chiSquaredProbability(double, int). Static method in class weka.core.Statistics
Returns chi-squared probability for given value and degrees of freedom.
chiVal(double[][], boolean). Static method in class weka.core.ContingencyTables
Computes chi-squared statistic for a contingency table.
chooseIndex(). Method in class weka.classifiers.j48.C45PruneableDecList
Method for choosing a subset to expand.
chooseIndex(). Method in class weka.classifiers.j48.PruneableDecList
Method for choosing a subset to expand.
chooseLastIndex(). Method in class weka.classifiers.j48.C45PruneableDecList
Choose last index (ie.
chooseLastIndex(). Method in class weka.classifiers.j48.PruneableDecList
Choose last index (ie.
classAttribute(). Method in class weka.core.Instance
Returns class attribute.
classAttribute(). Method in class weka.core.Instances
Returns the class attribute.
ClassificationViaRegression(). Constructor for class weka.classifiers.ClassificationViaRegression
Classifier(). Constructor for class weka.classifiers.Classifier
ClassifierDecList(ModelSelection). Constructor for class weka.classifiers.j48.ClassifierDecList
Constructor - just calls constructor of class DecList.
ClassifierPanel(). Constructor for class weka.gui.explorer.ClassifierPanel
Creates the classifier panel
ClassifierSplitEvaluator(). Constructor for class weka.experiment.ClassifierSplitEvaluator
ClassifierSplitModel(). Constructor for class weka.classifiers.j48.ClassifierSplitModel
ClassifierTree(ModelSelection). Constructor for class weka.classifiers.j48.ClassifierTree
Constructor.
classifyInstance(Instance). Method in class weka.classifiers.Classifier
Classifies a given instance.
classifyInstance(Instance). Method in class weka.classifiers.j48.ClassifierDecList
Classifies an instance.
classifyInstance(Instance). Method in class weka.classifiers.j48.ClassifierSplitModel
Classifies a given instance.
classifyInstance(Instance). Method in class weka.classifiers.j48.ClassifierTree
Classifies an instance.
classifyInstance(Instance). Method in class weka.classifiers.CostSensitiveClassifier
Classifies a given instance by choosing the class with the minimum expected misclassification cost.
classifyInstance(Instance). Method in class weka.classifiers.CVParameterSelection
Predicts the class value for the given test instance.
classifyInstance(Instance). Method in class weka.classifiers.DistributionClassifier
Classifies the given test instance.
classifyInstance(Instance). Method in class weka.classifiers.IB1
Classifies the given test instance.
classifyInstance(Instance). Method in class weka.classifiers.Id3
Classifies a given test instance using the decision tree.
classifyInstance(Instance). Method in class weka.classifiers.j48.J48
Classifies an instance.
classifyInstance(Instance). Method in class weka.classifiers.LinearRegression
Classifies the given instance using the linear regression function.
classifyInstance(Instance). Method in class weka.classifiers.LWR
Predicts the class value for the given test instance.
classifyInstance(Instance). Method in class weka.classifiers.m5.M5Prime
Classifies the given test instance.
classifyInstance(Instance). Method in class weka.classifiers.j48.MakeDecList
Classifies an instance.
classifyInstance(Instance). Method in class weka.classifiers.MultiScheme
Classifies a given instance using the selected classifier.
classifyInstance(Instance). Method in class weka.classifiers.OneR
Classifies a given instance.
classifyInstance(Instance). Method in class weka.classifiers.j48.PART
Classifies an instance.
classifyInstance(Instance). Method in class weka.classifiers.Prism
Classifies a given instance.
classifyInstance(Instance). Method in class weka.classifiers.RegressionByDiscretization
Returns a predicted class for the test instance.
classifyInstance(Instance). Method in class weka.classifiers.Stacking
Classifies a given instance using the stacked classifier.
classifyInstance(Instance). Method in class weka.classifiers.ZeroR
Classifies a given instance.
classIndex(). Method in class weka.core.Instance
Returns the class attribute's index.
classIndex(). Method in class weka.core.Instances
Returns the class attribute's index.
classIsMissing(). Method in class weka.core.Instance
Tests if an instance's class is missing.
classProb(int, Instance). Method in class weka.classifiers.j48.BinC45Split
Gets class probability for instance.
classProb(int, Instance). Method in class weka.classifiers.j48.C45Split
Gets class probability for instance.
classProb(int, Instance). Method in class weka.classifiers.j48.ClassifierSplitModel
Gets class probability for instance.
classValue(). Method in class weka.core.Instance
Returns an instance's class value in internal format.
cleanup(). Method in class weka.classifiers.j48.BinC45ModelSelection
Sets reference to training data to null.
cleanup(). Method in class weka.classifiers.j48.C45ModelSelection
Sets reference to training data to null.
cleanup(Instances). Method in class weka.classifiers.j48.ClassifierDecList
Cleanup in order to save memory.
cleanup(Instances). Method in class weka.classifiers.j48.ClassifierTree
Cleanup in order to save memory.
clone(). Method in class weka.classifiers.j48.ClassifierSplitModel
Allows to clone a model (shallow copy).
clone(). Method in class weka.classifiers.j48.Distribution
Clones distribution (Deep copy of distribution).
Clusterer(). Constructor for class weka.clusterers.Clusterer
ClustererPanel(). Constructor for class weka.gui.explorer.ClustererPanel
Creates the clusterer panel
ClusterEvaluation(). Constructor for class weka.clusterers.ClusterEvaluation
Constructor.
clusterInstance(Instance). Method in class weka.clusterers.Clusterer
Classifies a given instance.
clusterInstance(Instance). Method in class weka.clusterers.Cobweb
Clusters an instance.
clusterInstance(Instance). Method in class weka.clusterers.DistributionClusterer
Assigns an instance to a Cluster.
clusterResultsToString(). Method in class weka.clusterers.ClusterEvaluation
return the results of clustering.
Cobweb(). Constructor for class weka.clusterers.Cobweb
cochransCriterion(double[][]). Static method in class weka.core.ContingencyTables
Tests if Cochran's criterion is fullfilled for the given contingency table.
codingCost(). Method in class weka.classifiers.j48.C45Split
Returns coding cost for split (used in rule learner).
codingCost(). Method in class weka.classifiers.j48.ClassifierSplitModel
Returns coding costs of model.
collapse(). Method in class weka.classifiers.j48.C45PruneableClassifierTree
Collapses a tree to a node if training error doesn't increase.
combine(Function, Function). Static method in class weka.classifiers.m5.Function
Constructs a new function of which the variable list is a combination of those of two functions
combine(int[], int[]). Static method in class weka.classifiers.m5.Ivector
Outputs a new integer vector which contains all the values in two integer vectors; assuming list1 and list2 are incrementally sorted and no identical integers within each integer vector
compactify(). Method in class weka.core.Instances
Compactifies the set of instances.
compareOptions(String[], String[]). Static method in class weka.core.CheckOptionHandler
Compares the two given sets of options.
confidenceForRule(ItemSet, ItemSet). Static method in class weka.associations.ItemSet
Outputs the confidence for a rule.
confusionMatrix(). Method in class weka.classifiers.Evaluation
Returns a copy of the confusion matrix.
connectToDatabase(). Method in class weka.experiment.DatabaseUtils
Opens a connection to the database
ConsistencySubsetEval(). Constructor for class weka.attributeSelection.ConsistencySubsetEval
Constructor.
containedBy(Instance). Method in class weka.associations.ItemSet
Checks if an instance contains an item set.
ContingencyTables(). Constructor for class weka.core.ContingencyTables
copy(). Method in class weka.core.Attribute
Produces a shallow copy of this attribute.
copy(). Method in interface weka.core.Copyable
This method produces a shallow copy of an object.
copy(). Method in class weka.classifiers.m5.Errors
Makes a copy of the Errors object
copy(). Method in class weka.core.FastVector
Produces a shallow copy of this vector.
copy(). Method in class weka.classifiers.m5.Function
Makes a copy of a function
copy(). Method in class weka.core.Instance
Produces a shallow copy of this instance.
copy(). Method in class weka.classifiers.m5.SplitInfo
Makes a copy of this SplitInfo object
copy(double[], int). Static method in class weka.classifiers.m5.Dvector
Returns a copy of the first n elements of a double vector
copy(int[], int). Static method in class weka.classifiers.m5.Ivector
Makes a copy of the first n elements in an integer vector
copy(Node). Method in class weka.classifiers.m5.Node
Makes a copy of the tree under this node
CopyAttributesFilter(). Constructor for class weka.filters.CopyAttributesFilter
copyElements(). Method in class weka.core.FastVector
Clones the vector and shallow copies all its elements.
correct(). Method in class weka.classifiers.Evaluation
Gets the number of instances correctly classified (that is, for which a correct prediction was made).
correlation. Variable in class weka.experiment.PairedStats
The correlation coefficient
correlation(double[], double[], int). Static method in class weka.classifiers.m5.M5Utils
Returns the correlation coefficient of two double vectors
correlation(double[], double[], int). Static method in class weka.core.Utils
Returns the correlation coefficient of two double vectors.
correlationCoefficient(). Method in class weka.classifiers.Evaluation
Returns the correlation coefficient if the class is numeric.
COST_EXTENSION. Static variable in class weka.gui.CostMatrixEditor
The filename extension for cost files
CostMatrix(CostMatrix). Constructor for class weka.classifiers.CostMatrix
CostMatrix(int). Constructor for class weka.classifiers.CostMatrix
Creates a default cost matrix for the given number of classes.
CostMatrix(Reader). Constructor for class weka.classifiers.CostMatrix
Creates a cost matrix from a cost file.
CostMatrixEditor(). Constructor for class weka.gui.CostMatrixEditor
CostSensitiveClassifier(). Constructor for class weka.classifiers.CostSensitiveClassifier
count. Variable in class weka.experiment.PairedStats
The number of data points seen
count. Variable in class weka.experiment.Stats
The number of values seen
CramersV(double[][]). Static method in class weka.core.ContingencyTables
Computes Cramer's V for a contingency table.
createExperimentIndex(). Method in class weka.experiment.DatabaseUtils
Attempts to create the experiment index table
createExperimentIndexEntry(ResultProducer). Method in class weka.experiment.DatabaseUtils
Attempts to insert a results entry for the table into the experiment index.
createResultsTable(ResultProducer, String). Method in class weka.experiment.DatabaseUtils
Creates a results table for the supplied result producer.
CrossValidateAttributes(). Method in class weka.attributeSelection.AttributeSelection
Perform a cross validation for attribute selection.
crossValidateModel(Classifier, Instances, int). Method in class weka.classifiers.Evaluation
Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
crossValidateModel(String, Instances, int, String[]). Static method in class weka.clusterers.ClusterEvaluation
Performs a cross-validation for a distribution clusterer on a set of instances.
crossValidateModel(String, Instances, int, String[]). Method in class weka.classifiers.Evaluation
Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
CrossValidationResultProducer(). Constructor for class weka.experiment.CrossValidationResultProducer
CSVResultListener(). Constructor for class weka.experiment.CSVResultListener
CVParameterSelection(). Constructor for class weka.classifiers.CVParameterSelection
CVResultsString(). Method in class weka.attributeSelection.AttributeSelection
returns a string summarizing the results of repeated attribute selection runs on splits of a dataset.

D

DatabaseResultListener(). Constructor for class weka.experiment.DatabaseResultListener
Sets up the database drivers
DatabaseResultProducer(). Constructor for class weka.experiment.DatabaseResultProducer
Creates the DatabaseResultProducer, letting the parent constructor do it's thing.
DatabaseUtils(). Constructor for class weka.experiment.DatabaseUtils
Sets up the database drivers
dataset(). Method in class weka.core.Instance
Returns the dataset this instance has access to.
DATASET_FIELD_NAME. Static variable in class weka.experiment.CrossValidationResultProducer
DATASET_FIELD_NAME. Static variable in class weka.experiment.RandomSplitResultProducer
DatasetListPanel(). Constructor for class weka.gui.experiment.DatasetListPanel
Create the dataset list panel initially disabled.
DatasetListPanel(Experiment). Constructor for class weka.gui.experiment.DatasetListPanel
Creates the dataset list panel with the given experiment.
DDConditionalEstimator(int, int, boolean). Constructor for class weka.estimators.DDConditionalEstimator
Constructor
DecisionStump(). Constructor for class weka.classifiers.DecisionStump
DecisionTable(). Constructor for class weka.classifiers.DecisionTable
Constructor for a DecisionTable
decompose(). Method in class weka.classifiers.BVDecompose
Carry out the bias-variance decomposition
del(int, Instance). Method in class weka.classifiers.j48.Distribution
Deletes given instance from given bag.
delete(int). Method in class weka.core.Instances
Removes an instance at the given position from the set.
deleteAttributeAt(int). Method in class weka.core.Instance
Deletes an attribute at the given position (0 to numAttributes() - 1).
deleteAttributeAt(int). Method in class weka.core.Instances
Deletes an attribute at the given position (0 to numAttributes() - 1).
deleteItemSets(FastVector, int). 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.
description(). Method in class weka.core.Option
Returns the option's description.
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
disconnectFromDatabase(). Method in class weka.experiment.DatabaseUtils
Closes the connection to the database.
DiscreteEstimator(int, boolean). Constructor for class weka.estimators.DiscreteEstimator
Constructor
DiscretizeFilter(). Constructor for class weka.filters.DiscretizeFilter
Constructor - initialises the filter
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(). Constructor for class weka.classifiers.DistributionClassifier
DistributionClusterer(). Constructor for class weka.clusterers.DistributionClusterer
distributionForInstance(Instance). Method in class weka.classifiers.AdaBoostM1
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.Bagging
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.ClassificationViaRegression
Returns the distribution for an instance.
distributionForInstance(Instance). Method in class weka.classifiers.j48.ClassifierDecList
Returns class probabilities for a weighted instance.
distributionForInstance(Instance). Method in class weka.classifiers.j48.ClassifierTree
Returns class probabilities for a weighted instance.
distributionForInstance(Instance). Method in class weka.classifiers.DecisionStump
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.DecisionTable
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.DistributionClassifier
Predicts the class memberships for a given instance.
distributionForInstance(Instance). Method in class weka.clusterers.DistributionClusterer
Predicts the cluster memberships for a given instance.
distributionForInstance(Instance). Method in class weka.clusterers.EM
Predicts the cluster memberships for a given instance.
distributionForInstance(Instance). Method in class weka.classifiers.FilteredClassifier
Classifies a given instance after filtering.
distributionForInstance(Instance). Method in class weka.classifiers.HyperPipes
Classifies the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.IBk
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.Id3
Computes class distribution for instance using decision tree.
distributionForInstance(Instance). Method in class weka.classifiers.j48.J48
Returns class probabilities for an instance.
distributionForInstance(Instance). Method in class weka.classifiers.KernelDensity
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.Logistic
Computes the distribution for a given instance
distributionForInstance(Instance). Method in class weka.classifiers.LogitBoost
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.j48.MakeDecList
Returns the class distribution for an instance.
distributionForInstance(Instance). Method in class weka.classifiers.MultiClassClassifier
Returns the distribution for an instance.
distributionForInstance(Instance). Method in class weka.classifiers.NaiveBayes
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.NaiveBayesSimple
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.j48.PART
Returns class probabilities for an instance.
distributionForInstance(Instance). Method in class weka.classifiers.SMO
Outputs the distribution for the given output.
distributionForInstance(Instance). Method in class weka.classifiers.VotedPerceptron
Outputs the distribution for the given output.
distributionForInstance(Instance). Method in class weka.classifiers.ZeroR
Calculates the class membership probabilities for the given test instance.
DKConditionalEstimator(int, double). Constructor for class weka.estimators.DKConditionalEstimator
Constructor
DNConditionalEstimator(int, double). Constructor for class weka.estimators.DNConditionalEstimator
Constructor
doHistory(KeyEvent). Method in class weka.gui.SimpleCLI
Changes the currently displayed command line when certain keys are pressed.
doRun(int). Method in class weka.experiment.AveragingResultProducer
Gets the results for a specified run number.
doRun(int). Method in class weka.experiment.CrossValidationResultProducer
Gets the results for a specified run number.
doRun(int). Method in class weka.experiment.DatabaseResultProducer
Gets the results for a specified run number.
doRun(int). Method in class weka.experiment.RandomSplitResultProducer
Gets the results for a specified run number.
doRun(int). Method in interface weka.experiment.ResultProducer
Gets the results for a specified run number.
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.
dumpDistribution(). Method in class weka.classifiers.j48.Distribution
Prints distribution.
dumpLabel(int, Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
Prints label for subset index of instances (eg class).
dumpModel(Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
Prints the split model.
Dvector(). Constructor for class weka.classifiers.m5.Dvector

E

editableProperties(). Method in class weka.gui.PropertySheetPanel
Gets the number of editable properties for the current target.
elementAt(int). Method in class weka.core.FastVector
Returns the element at the given position.
elements(). Method in class weka.core.FastVector
Returns an enumeration of this vector.
elements(int). Method in class weka.core.FastVector
Returns an enumeration of this vector, skipping the element with the given index.
EM(). Constructor for class weka.clusterers.EM
Constructor.
empty(). Method in class weka.core.Queue
Checks if queue is empty.
entropy(double[]). Static method in class weka.core.ContingencyTables
Computes the entropy of the given array.
EntropyBasedSplitCrit(). Constructor for class weka.classifiers.j48.EntropyBasedSplitCrit
entropyConditionedOnColumns(double[][]). Static method in class weka.core.ContingencyTables
Computes conditional entropy of the rows given the columns.
entropyConditionedOnRows(double[][]). Static method in class weka.core.ContingencyTables
Computes conditional entropy of the columns given the rows.
entropyConditionedOnRows(double[][], double[][], double). Static method in class weka.core.ContingencyTables
Computes conditional entropy of the columns given the rows of the test matrix with respect to the train matrix.
entropyOverColumns(double[][]). Static method in class weka.core.ContingencyTables
Computes the columns' entropy for the given contingency table.
entropyOverRows(double[][]). Static method in class weka.core.ContingencyTables
Computes the rows' entropy for the given contingency table.
EntropySplitCrit(). Constructor for class weka.classifiers.j48.EntropySplitCrit
enumerateAttributes(). Method in class weka.core.Instance
Returns an enumeration of all the attributes.
enumerateAttributes(). Method in class weka.core.Instances
Returns an enumeration of all the attributes.
enumerateInstances(). Method in class weka.core.Instances
Returns an enumeration of all instances in the dataset.
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.
eq(double, double). Static method in class weka.core.Utils
Tests if a is equal to b.
eqDouble(double, double). Static method in class weka.classifiers.m5.M5Utils
Tests if two double values are equal to each other
equalHeaders(Instance). Method in class weka.core.Instance
Tests if the headers of two instances are equivalent.
equalHeaders(Instances). Method in class weka.core.Instances
Checks if two headers are equivalent.
equals(Object). Method in class weka.core.Attribute
Tests if given attribute is equal to this attribute.
equals(Object). Method in class weka.classifiers.Evaluation
Tests whether the current evaluation object is equal to another evaluation object
equals(Object). Method in class weka.associations.ItemSet
Tests if two item sets are equal.
equals(Object). Method in class weka.core.SelectedTag
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).
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
evaluateAttribute(int). Method in class weka.attributeSelection.AttributeEvaluator
evaluates an individual attribute
evaluateAttribute(int). Method in class weka.attributeSelection.GainRatioAttributeEval
evaluates an individual attribute by measuring the gain ratio of the class given the attribute.
evaluateAttribute(int). Method in class weka.attributeSelection.InfoGainAttributeEval
evaluates an individual attribute by measuring the amount of information gained about the class given the attribute.
evaluateAttribute(int). Method in class weka.attributeSelection.OneRAttributeEval
evaluates an individual attribute by measuring the amount of information gained about the class given the attribute.
evaluateAttribute(int). Method in class weka.attributeSelection.ReliefFAttributeEval
Evaluates an individual attribute using ReliefF's instance based approach.
evaluateAttribute(int). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
evaluates an individual attribute by measuring the symmetrical uncertainty between it and the class.
evaluateClusterer(Clusterer, String[]). Static method in class weka.clusterers.ClusterEvaluation
Evaluates a clusterer with the options given in an array of strings.
evaluateClusterer(Instances). Method in class weka.clusterers.ClusterEvaluation
Evaluate the clusterer on a set of instances.
evaluateModel(Classifier, Instances). Method in class weka.classifiers.Evaluation
Evaluates the classifier on a given set of instances.
evaluateModel(Classifier, String[]). Static method in class weka.classifiers.Evaluation
Evaluates a classifier with the options given in an array of strings.
evaluateModel(String, String[]). Static method in class weka.classifiers.Evaluation
Evaluates a classifier with the options given in an array of strings.
evaluateModelOnce(Classifier, Instance). Method in class weka.classifiers.Evaluation
Evaluates the classifier on a single instance.
evaluateModelOnce(double, Instance). Method in class weka.classifiers.Evaluation
Evaluates the supplied prediction on a single instance.
evaluateSubset(BitSet). Method in class weka.attributeSelection.CfsSubsetEval
evaluates a subset of attributes
evaluateSubset(BitSet). Method in class weka.attributeSelection.ConsistencySubsetEval
Evaluates a subset of attributes
evaluateSubset(BitSet). Method in class weka.attributeSelection.SubsetEvaluator
evaluates a subset of attributes
evaluateSubset(BitSet). Method in class weka.attributeSelection.WrapperSubsetEval
Evaluates a subset of attributes
Evaluation(Instances). Constructor for class weka.classifiers.Evaluation
Initializes all the counters for the evaluation.
Evaluation(Instances, CostMatrix, Random). Constructor for class weka.classifiers.Evaluation
Initializes all the counters for the evaluation and also takes a cost matrix as parameter.
execute(String). Method in class weka.experiment.DatabaseUtils
Executes a SQL query.
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.
Experiment(). Constructor for class weka.experiment.Experiment
Experimenter(). Constructor for class weka.gui.experiment.Experimenter
Creates the experiment environment gui with no initial experiment
experimentIndexExists(). Method in class weka.experiment.DatabaseUtils
Returns true if the experiment index exists.
Explorer(). Constructor for class weka.gui.explorer.Explorer
Creates the experiment environment gui with no initial experiment
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
falsePositives(int). Method in class weka.classifiers.Evaluation
Calculate the false positive rate with respect to a particular class.
FastVector(). Constructor for class weka.core.FastVector
Constructs an empty vector with initial capacity zero.
FastVector(int). Constructor for class weka.core.FastVector
Constructs a vector with the given capacity.
FastVector(int, int, double). Constructor for class weka.core.FastVector
Constructs a vector with the given capacity, capacity increment and capacity mulitplier.
FCriticalValue(double, int, int). Static method in class weka.core.Statistics
Critical value for given probability of F-distribution.
FileEditor(). Constructor for class weka.gui.FileEditor
Filter(). Constructor for class weka.filters.Filter
FilteredClassifier(). Constructor for class weka.classifiers.FilteredClassifier
filterFile(Filter, String[]). Static method in class weka.filters.Filter
Method for testing filters.
firstElement(). Method in class weka.core.FastVector
Returns the first element of the vector.
firstInstance(). Method in class weka.core.Instances
Returns the first instance in the set.
FirstOrderFilter(). Constructor for class weka.filters.FirstOrderFilter
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.
FOLD_FIELD_NAME. Static variable in class weka.experiment.CrossValidationResultProducer
FORMAT_AVAILABLE. Static variable in class weka.gui.streams.InstanceEvent
Specifies that the instance format is available
formulaeToString(boolean). Method in class weka.classifiers.m5.Node
Converts all the linear models at the leaves under the node to a string
forName(Class, String, String[]). Static method in class weka.core.Utils
Creates a new instance of an object given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]). Static method in class weka.attributeSelection.ASEvaluation
Creates a new instance of an attribute/subset evaluator given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]). Static method in class weka.attributeSelection.ASSearch
Creates a new instance of a search class given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]). Static method in class weka.associations.Associator
Creates a new instance of a associator given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]). Static method in class weka.classifiers.Classifier
Creates a new instance of a classifier given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]). Static method in class weka.clusterers.Clusterer
Creates a new instance of a clusterer given it's class name and (optional) arguments to pass to it's setOptions method.
ForwardSelection(). Constructor for class weka.attributeSelection.ForwardSelection
FProbability(double, int, int). Static method in class weka.core.Statistics
Computes probability of F-ratio.
Function(). Constructor for class weka.classifiers.m5.Function
Constructs a function of constant value
function(). Method in class weka.classifiers.m5.Node
Finds the appropriate order of the unsmoothed linear model at this node
Function(Instances). Constructor for class weka.classifiers.m5.Function
Constucts a function with all attributes except the class in the inst
Function(int). Constructor for class weka.classifiers.m5.Function
Constructs a function with one attribute

G

gainRatio(). Method in class weka.classifiers.j48.BinC45Split
Returns (C4.5-type) gain ratio for the generated split.
gainRatio(). Method in class weka.classifiers.j48.C45Split
Returns (C4.5-type) gain ratio for the generated split.
gainRatio(double[][]). Static method in class weka.core.ContingencyTables
Computes gain ratio for contingency table (split on rows).
GainRatioAttributeEval(). Constructor for class weka.attributeSelection.GainRatioAttributeEval
Constructor
GainRatioSplitCrit(). Constructor for class weka.classifiers.j48.GainRatioSplitCrit
generateRules(double, FastVector, int). Method in class weka.associations.ItemSet
Generates all rules for an item set.
generateRulesBruteForce(double, FastVector, int, int, double). Method in class weka.associations.ItemSet
Generates all significant rules for an item set.
GeneratorPropertyIteratorPanel(). Constructor for class weka.gui.experiment.GeneratorPropertyIteratorPanel
Creates the property iterator panel initially disabled.
GeneratorPropertyIteratorPanel(Experiment). Constructor for class weka.gui.experiment.GeneratorPropertyIteratorPanel
Creates the property iterator panel and sets the experiment.
GenericArrayEditor(). Constructor for class weka.gui.GenericArrayEditor
Sets up the array editor.
GenericObjectEditor(). Constructor for class weka.gui.GenericObjectEditor
GeneticSearch(). Constructor for class weka.attributeSelection.GeneticSearch
Constructor.
getAcuity(). Method in class weka.clusterers.Cobweb
get the accuity value
getArffFile(). Method in class weka.gui.streams.InstanceLoader
getArffFile(). Method in class weka.gui.streams.InstanceSavePanel
getAsText(). Method in class weka.gui.CostMatrixEditor
Returns null as we don't support getting/setting values as text.
getAsText(). Method in class weka.gui.GenericArrayEditor
Returns null as we don't support getting/setting values as text.
getAsText(). Method in class weka.gui.GenericObjectEditor
Returns null as we don't support getting/setting values as text.
getAsText(). Method in class weka.gui.SelectedTagEditor
Gets the current value as text.
getAttributeIndex(). Method in class weka.filters.AddFilter
Get the index where the attribute will be inserted
getAttributeIndex(). Method in class weka.filters.InstanceFilter
Get the attribute to be used for selection (-1 for last)
getAttributeIndex(). Method in class weka.filters.MakeIndicatorFilter
Get the index of the attribute used.
getAttributeIndex(). Method in class weka.filters.MergeTwoValuesFilter
Get the index of the attribute used.
getAttributeIndex(). Method in class weka.filters.SwapAttributeValuesFilter
Get the index of the attribute used.
getAttributeIndices(). Method in class weka.filters.AttributeFilter
Get the current range selection.
getAttributeIndices(). Method in class weka.filters.CopyAttributesFilter
Get the current range selection
getAttributeIndices(). Method in class weka.filters.DiscretizeFilter
Gets the current range selection
getAttributeIndices(). Method in class weka.filters.FirstOrderFilter
Get the current range selection
getAttributeIndices(). Method in class weka.filters.NumericTransformFilter
Get the current range selection
getAttributeIndices(). Method in class weka.filters.TimeSeriesTranslateFilter
Get the current range selection
getAttributeMax(int). Method in class weka.classifiers.IBk
Get an attributes maximum observed value
getAttributeMin(int). Method in class weka.classifiers.IBk
Get an attributes minimum observed value
getAttributeName(). Method in class weka.filters.AddFilter
Get the name of the attribute to be created
getAttributeSelectionMethod(). Method in class weka.classifiers.LinearRegression
Gets the method used to select attributes for use in the linear regression.
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.
getBias(). Method in class weka.classifiers.BVDecompose
Get the calculated bias squared
getBiasToUniformClass(). Method in class weka.filters.ResampleFilter
Gets the bias towards a uniform class.
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.
getCalculateStdDevs(). Method in class weka.experiment.AveragingResultProducer
Get the value of CalculateStdDevs.
getCIndex(). Method in class weka.gui.explorer.VisualizePanel
Get the index of the attribute selected for coloring
getClassifier(). Method in class weka.classifiers.AdaBoostM1
Get the classifier used as the classifier
getClassifier(). Method in class weka.classifiers.Bagging
Get the classifier used as the classifier
getClassifier(). Method in class weka.classifiers.BVDecompose
Gets the name of the classifier being analysed
getClassifier(). Method in class weka.classifiers.CheckClassifier
Get the classifier used as the classifier
getClassifier(). Method in class weka.classifiers.ClassificationViaRegression
Get the base classifier (regression scheme) used as the classifier
getClassifier(). Method in class weka.experiment.ClassifierSplitEvaluator
Get the value of Classifier.
getClassifier(). Method in class weka.classifiers.CostSensitiveClassifier
Gets the distribution classifier used.
getClassifier(). Method in class weka.classifiers.CVParameterSelection
Get the classifier used as the classifier
getClassifier(). Method in class weka.classifiers.FilteredClassifier
Gets the classifier used.
getClassifier(). Method in class weka.classifiers.LogitBoost
Get the classifier used as the classifier
getClassifier(). Method in class weka.classifiers.MultiClassClassifier
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.experiment.RegressionSplitEvaluator
Get the value of Classifier.
getClassifier(). Method in class weka.attributeSelection.WrapperSubsetEval
Get the classifier used as the base learner.
getClassifier(int). Method in class weka.classifiers.MultiScheme
Gets a single classifier from the set of available classifiers.
getClassifiers(). Method in class weka.classifiers.MultiScheme
Gets the list of possible classifers to choose from.
getClassIndex(). Method in class weka.classifiers.BVDecompose
Get the index (starting from 1) of the attribute used as the class.
getClassName(). Method in class weka.filters.NumericTransformFilter
Get the class containing the transformation method.
getClearEachDataset(). Method in class weka.gui.streams.InstanceViewer
getClusterAssignments(). Method in class weka.clusterers.ClusterEvaluation
Return an array of cluster assignments corresponding to the most recent set of instances clustered.
getCompatibilityState(). Method in class weka.experiment.AveragingResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState(). Method in class weka.experiment.CrossValidationResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState(). Method in class weka.experiment.DatabaseResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState(). Method in class weka.experiment.RandomSplitResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState(). Method in interface weka.experiment.ResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getConfidenceFactor(). Method in class weka.classifiers.j48.J48
Get the value of CF.
getConfidenceFactor(). Method in class weka.classifiers.j48.PART
Get the value of CF.
getCostMatrix(). Method in class weka.classifiers.CostSensitiveClassifier
Gets the misclassification cost matrix.
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.
getCustomEditor(). Method in class weka.gui.CostMatrixEditor
Returns the array editing component.
getCustomEditor(). Method in class weka.gui.FileEditor
Gets the custom editor component.
getCustomEditor(). Method in class weka.gui.GenericArrayEditor
Returns the array editing component.
getCustomEditor(). Method in class weka.gui.GenericObjectEditor
Returns the array editing component.
getCutoff(). Method in class weka.clusterers.Cobweb
get the cutoff
getCutPoints(int). Method in class weka.filters.DiscretizeFilter
Gets the cut points for an attribute
getCVParameter(int). Method in class weka.classifiers.CVParameterSelection
Gets the scheme paramter with the given index.
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
getDatasetColumn(). Method in class weka.experiment.PairedTTester
Get the value of DatasetColumn.
getDatasets(). Method in class weka.experiment.Experiment
Gets the datasets in the experiment.
getDebug(). Method in class weka.classifiers.AdaBoostM1
Get whether debugging is turned on
getDebug(). Method in class weka.classifiers.BVDecompose
Gets whether debugging is turned on
getDebug(). Method in class weka.classifiers.CheckClassifier
Get whether debugging is turned on
getDebug(). Method in class weka.classifiers.CVParameterSelection
Gets whether debugging is turned on
getDebug(). Method in class weka.clusterers.EM
Get debug mode
getDebug(). Method in class weka.classifiers.IBk
Get the value of Debug.
getDebug(). Method in class weka.gui.streams.InstanceCounter
getDebug(). Method in class weka.gui.streams.InstanceJoiner
getDebug(). Method in class weka.gui.streams.InstanceLoader
getDebug(). Method in class weka.gui.streams.InstanceSavePanel
getDebug(). Method in class weka.gui.streams.InstanceTable
getDebug(). Method in class weka.gui.streams.InstanceViewer
getDebug(). Method in class weka.classifiers.LinearRegression
Controls whether debugging output will be printed
getDebug(). Method in class weka.classifiers.Logistic
Gets whether debugging output will be printed.
getDebug(). Method in class weka.classifiers.LogitBoost
Get whether debugging is turned on
getDebug(). Method in class weka.classifiers.LWR
SGts whether debugging output should be produced
getDebug(). Method in class weka.classifiers.MultiScheme
Get whether debugging is turned on
getDebug(). Method in class weka.classifiers.RegressionByDiscretization
Gets whether debugging output will be printed
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.
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.
getEditor(). Method in class weka.gui.PropertyDialog
Gets the current property editor.
getElement(int, int). Method in class weka.core.Matrix
Returns the value of a cell in the matrix.
getError(). Method in class weka.classifiers.BVDecompose
Get the calculated error rate
getEstimatedErrorsForLeaf(). Method in class weka.classifiers.j48.C45PruneableDecList
Computes estimated errors for leaf.
getEstimator(double). Method in interface weka.estimators.ConditionalEstimator
Get a probability estimator for a value
getEstimator(double). Method in class weka.estimators.DDConditionalEstimator
Get a probability estimator for a value
getEstimator(double). Method in class weka.estimators.DKConditionalEstimator
Get a probability estimator for a value
getEstimator(double). Method in class weka.estimators.DNConditionalEstimator
Get a probability estimator for a value
getEstimator(double). Method in class weka.estimators.KDConditionalEstimator
Get a probability estimator for a value
getEstimator(double). Method in class weka.estimators.KKConditionalEstimator
Get a probability estimator for a value
getEstimator(double). Method in class weka.estimators.NDConditionalEstimator
Get a probability estimator for a value
getEstimator(double). Method in class weka.estimators.NNConditionalEstimator
Get a probability estimator for a value
getEvaluator(). Method in class weka.filters.AttributeSelectionFilter
Get the name of the attribute/subset evaluator
getExpectedResultsPerAverage(). Method in class weka.experiment.AveragingResultProducer
Get the value of ExpectedResultsPerAverage.
getExperiment(). Method in class weka.gui.experiment.SetupPanel
Gets the currently configured experiment.
getExponent(). Method in class weka.classifiers.SMO
Get the value of exponent.
getExponent(). Method in class weka.classifiers.VotedPerceptron
Get the value of exponent.
getFillWithMissing(). Method in class weka.filters.TimeSeriesTranslateFilter
Gets whether missing values should be used rather than removing instances where the translated value is not known (due to border effects).
getFilter(). Method in class weka.classifiers.FilteredClassifier
Gets the filter used.
getFindNumBins(). Method in class weka.filters.DiscretizeFilter
Get the value of FindNumBins.
getFirstValueIndex(). Method in class weka.filters.MergeTwoValuesFilter
Get the index of the first value used.
getFirstValueIndex(). Method in class weka.filters.SwapAttributeValuesFilter
Get the index of the first value used.
getFlag(char, String[]). Static method in class weka.core.Utils
Checks if the given array contains the flag "-Char".
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
getGenerateRanking(). Method in class weka.attributeSelection.ForwardSelection
Gets whether ranking has been requested.
getGenerateRanking(). Method in interface weka.attributeSelection.RankedOutputSearch
Gets whether the user has opted to see a ranked list of attributes rather than the normal result of the search
getGenerateRanking(). Method in class weka.attributeSelection.Ranker
This is a dummy method.
getHashtable(FastVector, int). Static method in class weka.associations.ItemSet
Return a hashtable filled with the given item sets.
getID(). Method in class weka.gui.streams.InstanceEvent
Get the event type
getID(). Method in class weka.core.Tag
getInstanceRange(). Method in class weka.filters.TimeSeriesTranslateFilter
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.explorer.VisualizePanel
Get the instances being plotted
getInstances(String). Method in class weka.experiment.InstanceQuery
Makes a database query to convert a table into a set of instances
getInstancesIndices(). Method in class weka.filters.SplitDatasetFilter
Gets ranges of instances selected.
getInvert(). Method in class weka.core.Range
Gets whether the range sense is inverted, i.e.
getInvertSelection(). Method in class weka.filters.AttributeFilter
Get whether the supplied columns are to be removed or kept
getInvertSelection(). Method in class weka.filters.CopyAttributesFilter
Get whether the supplied columns are to be removed or kept
getInvertSelection(). Method in class weka.filters.DiscretizeFilter
Gets whether the supplied columns are to be removed or kept
getInvertSelection(). Method in class weka.filters.InstanceFilter
Get whether the supplied columns are to be removed or kept
getInvertSelection(). Method in class weka.filters.NumericTransformFilter
Get whether the supplied columns are to be transformed or not
getInvertSelection(). Method in class weka.filters.SplitDatasetFilter
Gets if selection is to be inverted.
getInvertSelection(). Method in class weka.filters.TimeSeriesTranslateFilter
Get whether the supplied columns are to be removed or kept
getJavaInitializationString(). Method in class weka.gui.CostMatrixEditor
Supposedly returns an initialization string to create a classifier identical to the current one, including it's state, but this doesn't appear possible given that the initialization string isn't supposed to contain multiple statements.
getJavaInitializationString(). Method in class weka.gui.FileEditor
Returns a representation of the current property value as java source.
getJavaInitializationString(). Method in class weka.gui.GenericArrayEditor
Supposedly returns an initialization string to create a classifier identical to the current one, including it's state, but this doesn't appear possible given that the initialization string isn't supposed to contain multiple statements.
getJavaInitializationString(). Method in class weka.gui.GenericObjectEditor
Supposedly returns an initialization string to create a Object identical to the current one, including it's state, but this doesn't appear possible given that the initialization string isn't supposed to contain multiple statements.
getJavaInitializationString(). Method in class weka.gui.SelectedTagEditor
Returns a description of the property value as java source.
getKey(). Method in class weka.experiment.ClassifierSplitEvaluator
Gets the key describing the current SplitEvaluator.
getKey(). Method in class weka.experiment.RegressionSplitEvaluator
Gets the key describing the current SplitEvaluator.
getKey(). Method in interface weka.experiment.SplitEvaluator
Gets the key describing the current SplitEvaluator.
getKeyFieldName(). Method in class weka.experiment.AveragingResultProducer
Get the value of KeyFieldName.
getKeyNames(). Method in class weka.experiment.AveragingResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames(). Method in class weka.experiment.ClassifierSplitEvaluator
Gets the names of each of the key columns produced for a single run.
getKeyNames(). Method in class weka.experiment.CrossValidationResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames(). Method in class weka.experiment.DatabaseResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames(). Method in class weka.experiment.RandomSplitResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames(). Method in class weka.experiment.RegressionSplitEvaluator
Gets the names of each of the key columns produced for a single run.
getKeyNames(). Method in interface weka.experiment.ResultProducer
Gets the names of each of the key columns produced for a single run.
getKeyNames(). Method in interface weka.experiment.SplitEvaluator
Gets the names of each of the key columns produced for a single run.
getKeyTypes(). Method in class weka.experiment.AveragingResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes(). Method in class weka.experiment.ClassifierSplitEvaluator
Gets the data types of each of the key columns produced for a single run.
getKeyTypes(). Method in class weka.experiment.CrossValidationResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes(). Method in class weka.experiment.DatabaseResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes(). Method in class weka.experiment.RandomSplitResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes(). Method in class weka.experiment.RegressionSplitEvaluator
Gets the data types of each of the key columns produced for a single run.
getKeyTypes(). Method in interface weka.experiment.ResultProducer
Gets the data types of each of the key columns produced for a single run.
getKeyTypes(). Method in interface weka.experiment.SplitEvaluator
Gets the data types of each of the key columns produced for a single run.
getKNN(). Method in class weka.classifiers.IBk
Gets the number of neighbours the learner will use.
getKNN(). Method in class weka.classifiers.LWR
Gets the number of neighbours used for kernel bandwidth setting.
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.
getMakeBinary(). Method in class weka.filters.DiscretizeFilter
Gets whether binary attributes should be made for discretized ones.
getMaxCost(int). Method in class weka.classifiers.CostMatrix
Gets the maximum misclassification cost possible for a given actual class value
getMaxGenerations(). Method in class weka.attributeSelection.GeneticSearch
get the number of generations
getMaxIterations(). Method in class weka.classifiers.AdaBoostM1
Get the maximum number of boost iterations
getMaxIterations(). Method in class weka.clusterers.EM
Get the maximum number of iterations
getMaxIterations(). Method in class weka.classifiers.LogitBoost
Get the maximum number of boost iterations
getMaxK(). Method in class weka.classifiers.VotedPerceptron
Get the value of maxK.
getMaxStale(). Method in class weka.classifiers.DecisionTable
Gets the number of non improving decision tables
getMeanSquared(). Method in class weka.classifiers.IBk
Gets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
getMetaClassifier(). Method in class weka.classifiers.Stacking
Gets the meta classifier.
getMethodName(). Method in class weka.filters.NumericTransformFilter
Get the transformation method.
getMinBucketSize(). Method in class weka.classifiers.OneR
Get the value of minBucketSize.
getMinConfidence(). Method in class weka.associations.Apriori
Get the value of minConfidence.
getMinimizeExpectedCost(). Method in class weka.classifiers.CostSensitiveClassifier
Gets the value of MinimizeExpectedCost.
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.
getMinSupport(). Method in class weka.associations.Apriori
Get the value of minSupport.
getMissingMerge(). Method in class weka.attributeSelection.GainRatioAttributeEval
get whether missing values are being distributed or not
getMissingMerge(). Method in class weka.attributeSelection.InfoGainAttributeEval
get whether missing values are being distributed or not
getMissingMerge(). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
get whether missing values are being distributed or not
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.
getMutationProb(). Method in class weka.attributeSelection.GeneticSearch
get the probability of mutation
getName(). Method in class weka.gui.explorer.VisualizePanel
Returns the name associated with this plot.
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
getNotes(). Method in class weka.experiment.Experiment
Get the user notes.
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.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.experiment.CrossValidationResultProducer
Get the value of NumFolds.
getNumFolds(). Method in class weka.classifiers.CVParameterSelection
Get the number of folds used for cross-validation.
getNumFolds(). Method in class weka.classifiers.j48.J48
Get the value of numFolds.
getNumFolds(). Method in class weka.classifiers.MultiScheme
Gets the number of folds for cross-validation.
getNumFolds(). Method in class weka.classifiers.j48.PART
Get the value of numFolds.
getNumFolds(). Method in class weka.filters.SplitDatasetFilter
Gets the number of folds in which dataset is to be split into.
getNumFolds(). Method in class weka.classifiers.Stacking
Gets the number of folds for the cross-validation.
getNumIterations(). Method in class weka.classifiers.Bagging
Gets the number of bagging iterations
getNumIterations(). Method in class weka.classifiers.VotedPerceptron
Get the value of NumIterations.
getNumNeighbours(). Method in class weka.attributeSelection.ReliefFAttributeEval
Get the number of nearest neighbours
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
getNumTraining(). Method in class weka.classifiers.IBk
Get the number of training instances the classifier is currently using
getOptimizeBins(). Method in class weka.classifiers.RegressionByDiscretization
Gets whether the discretizer optimizes the number of bins
getOptimzeBinning(). Method in class weka.filters.DiscretizeFilter
Get if binning is to be optimized.
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.classifiers.AdaBoostM1
Gets the current settings of the Classifier.
getOptions(). Method in class weka.filters.AddFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.associations.Apriori
Gets the current settings of the Apriori object.
getOptions(). Method in class weka.filters.AttributeFilter
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.experiment.AveragingResultProducer
Gets the current settings of the result producer.
getOptions(). Method in class weka.classifiers.Bagging
Gets the current settings of the Classifier.
getOptions(). Method in class weka.attributeSelection.BestFirst
Gets the current settings of BestFirst.
getOptions(). Method in class weka.classifiers.BVDecompose
Gets the current settings of the CheckClassifier.
getOptions(). Method in class weka.attributeSelection.CfsSubsetEval
Gets the current settings of CfsSubsetEval
getOptions(). Method in class weka.classifiers.CheckClassifier
Gets the current settings of the CheckClassifier.
getOptions(). Method in class weka.classifiers.ClassificationViaRegression
Gets the current settings of the Classifier.
getOptions(). Method in class weka.experiment.ClassifierSplitEvaluator
Gets the current settings of the Classifier.
getOptions(). Method in class weka.clusterers.Cobweb
Gets the current settings of Cobweb.
getOptions(). Method in class weka.filters.CopyAttributesFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.classifiers.CostSensitiveClassifier
Gets the current settings of the Classifier.
getOptions(). Method in class weka.experiment.CrossValidationResultProducer
Gets the current settings of the result producer.
getOptions(). Method in class weka.experiment.CSVResultListener
Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.CVParameterSelection
Gets the current settings of the Classifier.
getOptions(). Method in class weka.experiment.DatabaseResultProducer
Gets the current settings of the result producer.
getOptions(). Method in class weka.classifiers.DecisionTable
Gets the current settings of the classifier.
getOptions(). Method in class weka.filters.DiscretizeFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.clusterers.EM
Gets the current settings of EM.
getOptions(). Method in class weka.attributeSelection.ExhaustiveSearch
Gets the current settings of RandomSearch.
getOptions(). Method in class weka.experiment.Experiment
Gets the current settings of the experiment iterator.
getOptions(). Method in class weka.classifiers.FilteredClassifier
Gets the current settings of the Classifier.
getOptions(). Method in class weka.filters.FirstOrderFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.attributeSelection.ForwardSelection
Gets the current settings of ReliefFAttributeEval.
getOptions(). Method in class weka.attributeSelection.GainRatioAttributeEval
Gets the current settings of WrapperSubsetEval.
getOptions(). Method in class weka.attributeSelection.GeneticSearch
Gets the current settings of ReliefFAttributeEval.
getOptions(). Method in class weka.classifiers.IBk
Gets the current settings of IBk.
getOptions(). Method in class weka.attributeSelection.InfoGainAttributeEval
Gets the current settings of WrapperSubsetEval.
getOptions(). Method in class weka.filters.InstanceFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.classifiers.j48.J48
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.Logistic
Gets the current settings of the classifier.
getOptions(). Method in class weka.classifiers.LogitBoost
Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.LWR
Gets the current settings of the classifier.
getOptions(). Method in class weka.classifiers.m5.M5Prime
Gets the current settings of the Classifier.
getOptions(). Method in class weka.filters.MakeIndicatorFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.filters.MergeTwoValuesFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.classifiers.MultiClassClassifier
Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.MultiScheme
Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.NaiveBayes
Gets the current settings of the classifier.
getOptions(). Method in class weka.filters.NominalToBinaryFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.filters.NumericTransformFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.classifiers.OneR
Gets the current settings of the OneR classifier.
getOptions(). Method in interface weka.core.OptionHandler
Gets the current option settings for the OptionHandler.
getOptions(). Method in class weka.experiment.PairedTTester
Gets current settings of the PairedTTester.
getOptions(). Method in class weka.classifiers.j48.PART
Gets the current settings of the Classifier.
getOptions(). Method in class weka.filters.RandomizeFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.attributeSelection.RandomSearch
Gets the current settings of RandomSearch.
getOptions(). Method in class weka.experiment.RandomSplitResultProducer
Gets the current settings of the result producer.
getOptions(). Method in class weka.attributeSelection.Ranker
Gets the current settings of ReliefFAttributeEval.
getOptions(). Method in class weka.classifiers.RegressionByDiscretization
Gets the current settings of the Classifier.
getOptions(). Method in class weka.experiment.RegressionSplitEvaluator
Gets the current settings of the Classifier.
getOptions(). Method in class weka.attributeSelection.ReliefFAttributeEval
Gets the current settings of ReliefFAttributeEval.
getOptions(). Method in class weka.filters.ResampleFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.classifiers.SMO
Gets the current settings of the classifier.
getOptions(). Method in class weka.filters.SplitDatasetFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.classifiers.Stacking
Gets the current settings of the Classifier.
getOptions(). Method in class weka.filters.SwapAttributeValuesFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Gets the current settings of WrapperSubsetEval.
getOptions(). Method in class weka.filters.TimeSeriesTranslateFilter
Gets the current settings of the filter.
getOptions(). Method in class weka.classifiers.VotedPerceptron
Gets the current settings of the classifier.
getOptions(). Method in class weka.attributeSelection.WrapperSubsetEval
Gets the current settings of WrapperSubsetEval.
getOutputFile(). Method in class weka.experiment.CSVResultListener
Get the value of OutputFile.
getPath(). Method in class weka.gui.PropertySelectorDialog
Gets the path of property nodes to the selected property.
getPopulationSize(). Method in class weka.attributeSelection.GeneticSearch
get the size of the population
getProbability(double). Method in class weka.estimators.DiscreteEstimator
Get a probability estimate for a value
getProbability(double). Method in interface weka.estimators.Estimator
Get a probability estimate for a value.
getProbability(double). Method in class weka.estimators.KernelEstimator
Get a probability estimate for a value.
getProbability(double). Method in class weka.estimators.MahalanobisEstimator
Get a probability estimate for a value
getProbability(double). Method in class weka.estimators.NormalEstimator
Get a probability estimate for a value
getProbability(double). Method in class weka.estimators.PoissonEstimator
Get a probability estimate for a value
getProbability(double, double). Method in interface weka.estimators.ConditionalEstimator
Get a probability for a value conditional on another value
getProbability(double, double). Method in class weka.estimators.DDConditionalEstimator
Get a probability estimate for a value
getProbability(double, double). Method in class weka.estimators.DKConditionalEstimator
Get a probability estimate for a value
getProbability(double, double). Method in class weka.estimators.DNConditionalEstimator
Get a probability estimate for a value
getProbability(double, double). Method in class weka.estimators.KDConditionalEstimator
Get a probability estimate for a value
getProbability(double, double). Method in class weka.estimators.KKConditionalEstimator
Get a probability estimate for a value
getProbability(double, double). Method in class weka.estimators.NDConditionalEstimator
Get a probability estimate for a value
getProbability(double, double). Method in class weka.estimators.NNConditionalEstimator
Get a probability estimate for a value
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.
getRandomSeed(). Method in class weka.filters.RandomizeFilter
Get the random number generator seed value.
getRandomSeed(). Method in class weka.filters.ResampleFilter
Gets the random number seed.
getRanges(). Method in class weka.core.Range
Gets the string representing the selected range of values
getReadable(). Method in class weka.core.Tag
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.
getReportFrequency(). Method in class weka.attributeSelection.GeneticSearch
get how often repports are generated
getResult(Instances, Instances). Method in class weka.experiment.ClassifierSplitEvaluator
Gets the results for the supplied train and test datasets.
getResult(Instances, Instances). Method in class weka.experiment.RegressionSplitEvaluator
Gets the results for the supplied train and test datasets.
getResult(Instances, Instances). Method in interface weka.experiment.SplitEvaluator
Gets the results for the supplied train and test datasets.
getResultFromTable(String, ResultProducer, Object[]). Method in class weka.experiment.DatabaseUtils
Executes a database query to extract a result for the supplied key from the database.
getResultListener(). Method in class weka.experiment.Experiment
Gets the result listener where results will be sent.
getResultNames(). Method in class weka.experiment.AveragingResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames(). Method in class weka.experiment.ClassifierSplitEvaluator
Gets the names of each of the result columns produced for a single run.
getResultNames(). Method in class weka.experiment.CrossValidationResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames(). Method in class weka.experiment.DatabaseResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames(). Method in class weka.experiment.RandomSplitResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames(). Method in class weka.experiment.RegressionSplitEvaluator
Gets the names of each of the result columns produced for a single run.
getResultNames(). Method in interface weka.experiment.ResultProducer
Gets the names of each of the result columns produced for a single run.
getResultNames(). Method in interface weka.experiment.SplitEvaluator
Gets the names of each of the result columns produced for a single run.
getResultProducer(). Method in class weka.experiment.AveragingResultProducer
Get the ResultProducer.
getResultProducer(). Method in class weka.experiment.DatabaseResultProducer
Get the ResultProducer.
getResultProducer(). Method in class weka.experiment.Experiment
Get the result producer used for the current experiment.
getResultSet(). Method in class weka.experiment.DatabaseUtils
Gets the results generated by a previous query.
getResultsetKeyColumns(). Method in class weka.experiment.PairedTTester
Get the value of ResultsetKeyColumns.
getResultsetName(int). Method in class weka.experiment.PairedTTester
Gets a string descriptive of the specified resultset.
getResultsTableName(ResultProducer). Method in class weka.experiment.DatabaseUtils
Gets the name of the experiment table that stores results from a particular ResultProducer.
getResultTypes(). Method in class weka.experiment.AveragingResultProducer
Gets the data types of each of the columns produced for a single run.
getResultTypes(). Method in class weka.experiment.ClassifierSplitEvaluator
Gets the data types of each of the result columns produced for a single run.
getResultTypes(). Method in class weka.experiment.CrossValidationResultProducer
Gets the data types of each of the columns produced for a single run.
getResultTypes(). Method in class weka.experiment.DatabaseResultProducer
Gets the data types of each of the columns produced for a single run.
getResultTypes(). Method in class weka.experiment.RandomSplitResultProducer
Gets the data types of each of the columns produced for a single run.
getResultTypes(). Method in class weka.experiment.RegressionSplitEvaluator
Gets the data types of each of the result columns produced for a single run.
getResultTypes(). Method in interface weka.experiment.ResultProducer
Gets the data types of each of the result columns produced for a single run.
getResultTypes(). Method in interface weka.experiment.SplitEvaluator
Gets the data types of each of the result columns produced for a single run.
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.
getSearch(). Method in class weka.filters.AttributeSelectionFilter
Get the name of the search method
getSearchPercent(). Method in class weka.attributeSelection.RandomSearch
get the percentage of the search space to consider
getSearchTermination(). Method in class weka.attributeSelection.BestFirst
Get the termination criterion (number of non-improving nodes).
getSecondValueIndex(). Method in class weka.filters.MergeTwoValuesFilter
Get the index of the second value used.
getSecondValueIndex(). Method in class weka.filters.SwapAttributeValuesFilter
Get the index of the second value used.
getSeed(). Method in class weka.classifiers.AdaBoostM1
Get seed for resampling.
getSeed(). Method in class weka.classifiers.Bagging
Gets the seed for the random number generations
getSeed(). Method in class weka.classifiers.BVDecompose
Gets the random number seed
getSeed(). Method in class weka.classifiers.CostSensitiveClassifier
Get seed for resampling.
getSeed(). Method in class weka.classifiers.CVParameterSelection
Gets the random number seed.
getSeed(). Method in class weka.clusterers.EM
Get the random number seed
getSeed(). Method in class weka.attributeSelection.GeneticSearch
get the value of the random number generator's seed
getSeed(). Method in class weka.classifiers.LogitBoost
Get seed for resampling.
getSeed(). Method in class weka.classifiers.MultiScheme
Gets the random number seed.
getSeed(). Method in class weka.attributeSelection.ReliefFAttributeEval
Get the seed used for randomly sampling instances.
getSeed(). Method in class weka.classifiers.SMO
Get the value of seed.
getSeed(). Method in class weka.filters.SplitDatasetFilter
Gets the random number seed used for shuffling the dataset.
getSeed(). Method in class weka.classifiers.Stacking
Gets the random number seed.
getSeed(). Method in class weka.classifiers.VotedPerceptron
Get the value of Seed.
getSeed(). Method in class weka.attributeSelection.WrapperSubsetEval
Get the random number seed used for cross validation
getSelectedAttributes(). Method in class weka.gui.AttributeSelectionPanel
Gets an array containing the indices of all selected attributes.
getSelectedBuffer(). Method in class weka.gui.ResultHistoryPanel
Gets the buffer associated with the currently selected item in the list.
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
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.
getSigma(). Method in class weka.classifiers.BVDecompose
Get the calculated sigma squared
getSigma(). Method in class weka.attributeSelection.ReliefFAttributeEval
Get the value of sigma.
getSignificanceLevel(). Method in class weka.associations.Apriori
Get the value of significanceLevel.
getSignificanceLevel(). Method in class weka.experiment.PairedTTester
Get the value of SignificanceLevel.
getSplitEvaluator(). Method in class weka.experiment.CrossValidationResultProducer
Get the SplitEvaluator.
getSplitEvaluator(). Method in class weka.experiment.RandomSplitResultProducer
Get the SplitEvaluator.
getSplitPoint(). Method in class weka.filters.InstanceFilter
Get the split point used for numeric selection
getStartSet(). Method in class weka.attributeSelection.BestFirst
Returns a list of attributes (and or attribute ranges) as a String
getStartSet(). Method in class weka.attributeSelection.ExhaustiveSearch
Returns a list of attributes (and or attribute ranges) as a String
getStartSet(). Method in class weka.attributeSelection.ForwardSelection
Returns a list of attributes (and or attribute ranges) as a String
getStartSet(). Method in class weka.attributeSelection.GeneticSearch
Returns a list of attributes (and or attribute ranges) as a String
getStartSet(). Method in class weka.attributeSelection.RandomSearch
Returns a list of attributes (and or attribute ranges) as a String
getStartSet(). Method in class weka.attributeSelection.Ranker
Returns a list of attributes (and or attribute ranges) as a String
getStartSet(). Method in interface weka.attributeSelection.StartSetHandler
Returns a list of attributes (and or attribute ranges) as a String
getSubtreeRaising(). Method in class weka.classifiers.j48.J48
Get the value of subtreeRaising.
getSummary(). Method in class weka.gui.SetInstancesPanel
Gets the instances summary panel associated with this panel
getTags(). Method in class weka.gui.CostMatrixEditor
Returns null as we don't support getting values as tags.
getTags(). Method in class weka.gui.GenericArrayEditor
Returns null as we don't support getting values as tags.
getTags(). Method in class weka.gui.GenericObjectEditor
Returns null as we don't support getting values as tags.
getTags(). Method in class weka.core.SelectedTag
getTags(). Method in class weka.gui.SelectedTagEditor
Gets the list of tags that can be selected from.
getThreshold(). Method in class weka.attributeSelection.ForwardSelection
Returns the threshold so that the AttributeSelection module can discard attributes from the ranking.
getThreshold(). Method in interface weka.attributeSelection.RankedOutputSearch
Gets the threshold by which attributes can be discarded.
getThreshold(). Method in class weka.attributeSelection.Ranker
Returns the threshold so that the AttributeSelection module can discard attributes from the ranking.
getThreshold(). Method in class weka.attributeSelection.WrapperSubsetEval
Get the value of the threshold
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.
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.
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.
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.
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
getUseUnsmoothed(). Method in class weka.classifiers.m5.M5Prime
Get the value of UseUnsmoothed.
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.
getValueIndex(). Method in class weka.filters.MakeIndicatorFilter
Get the index of the first value used.
getVariance(). Method in class weka.classifiers.BVDecompose
Get the calculated variance
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.
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.
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.
getXIndex(). Method in class weka.gui.explorer.VisualizePanel
Get the index of the attribute on the x axis
getYIndex(). Method in class weka.gui.explorer.VisualizePanel
Get the index of the attribute on the y axis
gr(double, double). Static method in class weka.core.Utils
Tests if a is smaller than b.
graph(). Method in class weka.classifiers.j48.ClassifierTree
Returns graph describing the tree.
graph(). Method in interface weka.core.Drawable
Returns a string that describes a graph representing the object.
graph(). Method in class weka.classifiers.j48.J48
Returns graph describing the tree.
grOrEq(double, double). Static method in class weka.core.Utils
Tests if a is greater or equal to b.
GUIChooser(). Constructor for class weka.gui.GUIChooser
Creates the experiment environment gui with no initial experiment

H

hasEnumAttr(Instances). Static method in class weka.classifiers.m5.M5Utils
Tests if enumerated attribute(s) exists in the instances
hashCode(). Method in class weka.associations.ItemSet
Produces a hash code for a item set.
hasMissing(Instances). Static method in class weka.classifiers.m5.M5Utils
Tests if missing value(s) exists in the instances
hasMoreIterations(). Method in class weka.experiment.Experiment
Returns true if there are more iterations to carry out in the experiment.
header(int). Method in class weka.experiment.PairedTTester
Creates a "header" string describing the current resultsets.
headToString(). Static method in class weka.classifiers.m5.M5Utils
Prints the head lines of the output
HyperPipes(). Constructor for class weka.classifiers.HyperPipes

I

IB1(). Constructor for class weka.classifiers.IB1
IBk(). Constructor for class weka.classifiers.IBk
IB1 classifer.
IBk(int). Constructor for class weka.classifiers.IBk
IBk classifier.
Id3(). Constructor for class weka.classifiers.Id3
Impurity(int, int, Instances, int). Constructor for class weka.classifiers.m5.Impurity
Constructs an Impurity object containing the impurity values of partitioning the instances using an attribute
incorrect(). Method in class weka.classifiers.Evaluation
Gets the number of instances incorrectly classified (that is, for which an incorrect prediction was made).
incremental(double, int). Method in class weka.classifiers.m5.Impurity
Incrementally computes the impurirty values
incremental(Measures). Method in class weka.classifiers.m5.Measures
Adds up performance measures for cross-validation
index(). Method in class weka.core.Attribute
Returns the index of this attribute.
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.
info(int[]). Static method in class weka.core.Utils
Computes entropy for an array of integers.
infoGain(). Method in class weka.classifiers.j48.BinC45Split
Returns (C4.5-type) information gain for the generated split.
infoGain(). Method in class weka.classifiers.j48.C45Split
Returns (C4.5-type) information gain for the generated split.
InfoGainAttributeEval(). Constructor for class weka.attributeSelection.InfoGainAttributeEval
Constructor
InfoGainSplitCrit(). Constructor for class weka.classifiers.j48.InfoGainSplitCrit
initialize(). Method in class weka.classifiers.CostMatrix
Sets the costs to default values (i.e.
initialize(). Method in class weka.classifiers.j48.Distribution
Sets all counts to zero.
initialize(). Method in class weka.experiment.Experiment
Prepares an experiment for running, initializing current iterator settings.
initialize(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(Instance). Method in class weka.filters.AddFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.AllFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.AttributeFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.AttributeSelectionFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.CopyAttributesFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.DiscretizeFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.Filter
Input an instance for filtering.
input(Instance). Method in class weka.filters.FirstOrderFilter
Input an instance for filtering.
input(Instance). Method in class weka.gui.streams.InstanceCounter
input(Instance). Method in class weka.filters.InstanceFilter
Input an instance for filtering.
input(Instance). Method in class weka.gui.streams.InstanceJoiner
input(Instance). Method in class weka.gui.streams.InstanceSavePanel
input(Instance). Method in class weka.gui.streams.InstanceTable
input(Instance). Method in class weka.gui.streams.InstanceViewer
input(Instance). Method in class weka.filters.MakeIndicatorFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.MergeTwoValuesFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.NominalToBinaryFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.NormalizationFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.NullFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.NumericTransformFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.ReplaceMissingValuesFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.ResampleFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.SwapAttributeValuesFilter
Input an instance for filtering.
input(Instance). Method in class weka.filters.TimeSeriesTranslateFilter
Input an instance for filtering.
inputFormat(Instances). Method in class weka.filters.AddFilter
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.filters.AllFilter
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.filters.AttributeFilter
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.filters.CopyAttributesFilter
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.filters.DiscretizeFilter
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.filters.Filter
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.filters.FirstOrderFilter
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.gui.streams.InstanceCounter
inputFormat(Instances). Method in class weka.filters.InstanceFilter
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.gui.streams.InstanceJoiner
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.gui.streams.InstanceSavePanel
inputFormat(Instances). Method in class weka.gui.streams.InstanceTable
inputFormat(Instances). Method in class weka.gui.streams.InstanceViewer
inputFormat(Instances). Method in class weka.filters.MakeIndicatorFilter
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.filters.MergeTwoValuesFilter
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.filters.NominalToBinaryFilter
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.filters.NormalizationFilter
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.filters.NullFilter
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.filters.NumericTransformFilter
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.filters.RandomizeFilter
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.filters.ReplaceMissingValuesFilter
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.filters.ResampleFilter
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.filters.SplitDatasetFilter
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.filters.SwapAttributeValuesFilter
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.filters.TimeSeriesDeltaFilter
Sets the format of the input instances.
inputFormat(Instances). Method in class weka.filters.TimeSeriesTranslateFilter
Sets the format of the input instances.
insertAttributeAt(Attribute, int). Method in class weka.core.Instances
Inserts an attribute at the given position (0 to numAttributes()) and sets all values to be missing.
insertAttributeAt(int). Method in class weka.core.Instance
Inserts an attribute at the given position (0 to numAttributes()).
insertElementAt(Object, int). Method in class weka.core.FastVector
Inserts an element at the given position.
insignificant(double, Instances). Method in class weka.classifiers.m5.Function
Detects the most insignificant variable in the funcion
Instance(double, double[]). Constructor for class weka.core.Instance
Constructor that inititalizes instance variable with given values.
Instance(Instance). Constructor for class weka.core.Instance
Constructor that copies the attribute values and the weight from the given instance.
Instance(int). Constructor for class weka.core.Instance
Constructor of an instance that sets weight to one, all values to be missing, and the reference to the dataset to null.
instance(int). Method in class weka.core.Instances
Returns the instance at the given position.
INSTANCE_AVAILABLE. Static variable in class weka.gui.streams.InstanceEvent
Specifies that an instance is available
InstanceCounter(). Constructor for class weka.gui.streams.InstanceCounter
InstanceEvent(Object, int). Constructor for class weka.gui.streams.InstanceEvent
Constructs an InstanceEvent with the specified source object and event type
InstanceFilter(). Constructor for class weka.filters.InstanceFilter
InstanceJoiner(). Constructor for class weka.gui.streams.InstanceJoiner
Setup the initial states of the member variables
InstanceLoader(). Constructor for class weka.gui.streams.InstanceLoader
instanceProduced(InstanceEvent). Method in class weka.gui.streams.InstanceCounter
instanceProduced(InstanceEvent). Method in class weka.gui.streams.InstanceJoiner
instanceProduced(InstanceEvent). Method in interface weka.gui.streams.InstanceListener
instanceProduced(InstanceEvent). Method in class weka.gui.streams.InstanceSavePanel
instanceProduced(InstanceEvent). Method in class weka.gui.streams.InstanceTable
instanceProduced(InstanceEvent). Method in class weka.gui.streams.InstanceViewer
InstanceQuery(). Constructor for class weka.experiment.InstanceQuery
Sets up the database drivers
Instances(Instances). Constructor for class weka.core.Instances
Constructor copying all instances and references to the header information from the given set of instances.
Instances(Instances, int). Constructor for class weka.core.Instances
Constructor creating an empty set of instances.
Instances(Instances, int, int). Constructor for class weka.core.Instances
Creates a new set of instances by copying a subset of another set.
Instances(Reader). Constructor for class weka.core.Instances
Reads an ARFF file from a reader, and assigns a weight of one to each instance.
Instances(Reader, int). Constructor for class weka.core.Instances
Reads the header of an ARFF file from a reader and reserves space for the given number of instances.
Instances(String, FastVector, int). Constructor for class weka.core.Instances
Creates an empty set of instances.
InstanceSavePanel(). Constructor for class weka.gui.streams.InstanceSavePanel
InstancesResultListener(). Constructor for class weka.experiment.InstancesResultListener
InstancesSummaryPanel(). Constructor for class weka.gui.InstancesSummaryPanel
Creates the instances panel with no initial instances.
InstanceTable(). Constructor for class weka.gui.streams.InstanceTable
InstanceViewer(). Constructor for class weka.gui.streams.InstanceViewer
isConnected(). Method in class weka.experiment.DatabaseUtils
Returns true if a database connection is active.
isInRange(int). Method in class weka.core.Range
Gets whether the supplied cardinal number is included in the current range.
isMissing(Attribute). Method in class weka.core.Instance
Tests if a specific value is "missing".
isMissing(int). Method in class weka.core.Instance
Tests if a specific value is "missing".
isMissingValue(double). Static method in class weka.core.Instance
Tests if the given value codes "missing".
isNominal(). Method in class weka.core.Attribute
Test if the attribute is nominal.
isNominal(). Method in class weka.filters.InstanceFilter
Returns true if selection attribute is nominal.
isNumeric(). Method in class weka.core.Attribute
Tests if the attribute is numeric.
isNumeric(). Method in class weka.filters.InstanceFilter
Returns true if selection attribute is numeric.
isOutputFormatDefined(). Method in class weka.filters.Filter
Returns whether the output format is ready to be collected
isPaintable(). Method in class weka.gui.CostMatrixEditor
Returns true to indicate that we can paint a representation of the string array
isPaintable(). Method in class weka.gui.FileEditor
Returns true since this editor is paintable.
isPaintable(). Method in class weka.gui.GenericArrayEditor
Returns true to indicate that we can paint a representation of the string array
isPaintable(). Method in class weka.gui.GenericObjectEditor
Returns true to indicate that we can paint a representation of the Object.
isResultRequired(ResultProducer, Object[]). Method in class weka.experiment.AveragingResultProducer
Determines whether the results for a specified key must be generated.
isResultRequired(ResultProducer, Object[]). Method in class weka.experiment.CSVResultListener
Always says a result is required.
isResultRequired(ResultProducer, Object[]). Method in class weka.experiment.DatabaseResultListener
Always says a result is required.
isResultRequired(ResultProducer, Object[]). Method in class weka.experiment.DatabaseResultProducer
Determines whether the results for a specified key must be generated.
isResultRequired(ResultProducer, Object[]). Method in interface weka.experiment.ResultListener
Determines whether the results for a specified key must be generated.
isString(). Method in class weka.core.Attribute
Tests if the attribute is a string.
ItemSet(). Constructor for class weka.associations.ItemSet
Ivector(). Constructor for class weka.classifiers.m5.Ivector

J

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

K

KBInformation(). Method in class weka.classifiers.Evaluation
Return the total Kononenko & Bratko Information score in bits
KBMeanInformation(). Method in class weka.classifiers.Evaluation
Return the Kononenko & Bratko Information score in bits per instance.
KBRelativeInformation(). Method in class weka.classifiers.Evaluation
Return the Kononenko & Bratko Relative Information score
KDConditionalEstimator(int, double). Constructor for class weka.estimators.KDConditionalEstimator
Constructor
KernelDensity(). Constructor for class weka.classifiers.KernelDensity
KernelEstimator(double). Constructor for class weka.estimators.KernelEstimator
Constructor that takes a precision argument.
KKConditionalEstimator(double). Constructor for class weka.estimators.KKConditionalEstimator
Constructor

L

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.
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
leftSide(Instances). Method in class weka.classifiers.j48.BinC45Split
Prints left side of condition..
leftSide(Instances). Method in class weka.classifiers.j48.C45Split
Prints left side of condition..
leftSide(Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
Prints left side of condition satisfied by instances.
leftSide(Instances). Method in class weka.classifiers.j48.NoSplit
Does nothing because no condition has to be satisfied.
LinearRegression(). Constructor for class weka.classifiers.LinearRegression
listOptions(). Method in class weka.classifiers.AdaBoostM1
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.AddFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.associations.Apriori
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.AttributeSelectionFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.experiment.AveragingResultProducer
Returns an enumeration describing the available options.
listOptions(). Method in class weka.classifiers.Bagging
Returns an enumeration describing the available options
listOptions(). Method in class weka.attributeSelection.BestFirst
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.BVDecompose
Returns an enumeration describing the available options
listOptions(). Method in class weka.attributeSelection.CfsSubsetEval
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.CheckClassifier
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.ClassificationViaRegression
Returns an enumeration describing the available options
listOptions(). Method in class weka.experiment.ClassifierSplitEvaluator
Returns an enumeration describing the available options.
listOptions(). Method in class weka.clusterers.Cobweb
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.CopyAttributesFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.CostSensitiveClassifier
Returns an enumeration describing the available options
listOptions(). Method in class weka.experiment.CrossValidationResultProducer
Returns an enumeration describing the available options.
listOptions(). Method in class weka.experiment.CSVResultListener
Returns an enumeration describing the available options.
listOptions(). Method in class weka.classifiers.CVParameterSelection
Returns an enumeration describing the available options
listOptions(). Method in class weka.experiment.DatabaseResultProducer
Returns an enumeration describing the available options.
listOptions(). Method in class weka.classifiers.DecisionTable
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.DiscretizeFilter
Gets an enumeration describing the available options
listOptions(). Method in class weka.clusterers.EM
Returns an enumeration describing the available options.
listOptions(). Method in class weka.attributeSelection.ExhaustiveSearch
Returns an enumeration describing the available options
listOptions(). Method in class weka.experiment.Experiment
Returns an enumeration describing the available options.
listOptions(). Method in class weka.classifiers.FilteredClassifier
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.FirstOrderFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.attributeSelection.ForwardSelection
Returns an enumeration describing the available options
listOptions(). Method in class weka.attributeSelection.GainRatioAttributeEval
Returns an enumeration describing the available options
listOptions(). Method in class weka.attributeSelection.GeneticSearch
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.IBk
Returns an enumeration describing the available options
listOptions(). Method in class weka.attributeSelection.InfoGainAttributeEval
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.InstanceFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.j48.J48
Returns an enumeration describing the available options Valid options are:

-U
Use unpruned tree.

-C confidence
Set confidence threshold for pruning.

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

-C confidence
Set confidence threshold for pruning.

listOptions(). Method in class weka.filters.RandomizeFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.attributeSelection.RandomSearch
Returns an enumeration describing the available options
listOptions(). Method in class weka.experiment.RandomSplitResultProducer
Returns an enumeration describing the available options.
listOptions(). Method in class weka.attributeSelection.Ranker
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.RegressionByDiscretization
Returns an enumeration describing the available options
listOptions(). Method in class weka.experiment.RegressionSplitEvaluator
Returns an enumeration describing the available options.
listOptions(). Method in class weka.attributeSelection.ReliefFAttributeEval
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.ResampleFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.SMO
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.SplitDatasetFilter
Gets an enumeration describing the available options.
listOptions(). Method in class weka.classifiers.Stacking
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.SwapAttributeValuesFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Returns an enumeration describing the available options
listOptions(). Method in class weka.filters.TimeSeriesTranslateFilter
Returns an enumeration describing the available options
listOptions(). Method in class weka.classifiers.VotedPerceptron
Returns an enumeration describing the available options
listOptions(). Method in class weka.attributeSelection.WrapperSubsetEval
Returns an enumeration describing the available options
ListSelectorDialog(Frame, JList). Constructor for class weka.gui.ListSelectorDialog
Create the list selection dialog.
lnFactorial(double). Static method in class weka.core.SpecialFunctions
Returns natural logarithm of factorial using gamma function.
lnGamma(double). Static method in class weka.core.SpecialFunctions
Returns natural logarithm of gamma function.
log2. Static variable in class weka.core.Utils
The natural logarithm of 2.
log2(double). Static method in class weka.core.Utils
Returns the logarithm of a for base 2.
log2Binomial(double, double). Static method in class weka.core.SpecialFunctions
Returns base 2 logarithm of binomial coefficient using gamma function.
log2Multinomial(double, double[]). Static method in class weka.core.SpecialFunctions
Returns base 2 logarithm of multinomial using gamma function.
log2MultipleHypergeometric(double[][]). Static method in class weka.core.ContingencyTables
Returns negative base 2 logarithm of multiple hypergeometric probability for a contingency table.
Logistic(). Constructor for class weka.classifiers.Logistic
LogitBoost(). Constructor for class weka.classifiers.LogitBoost
logMessage(String). Method in interface weka.gui.Logger
Sends the supplied message to the log area.
logMessage(String). Method in class weka.gui.LogPanel
Sends the supplied message to the log area.
logMessage(String). Method in class weka.gui.SysErrLog
Sends the supplied message to the log area.
LogPanel(). Constructor for class weka.gui.LogPanel
Creates the log panel
lubksb(int, int[], double[]). Method in class weka.classifiers.m5.Matrix
LU backward substitution
lubksb(int[], double[]). Method in class weka.core.Matrix
Performs LU backward substitution.
ludcmp(). Method in class weka.core.Matrix
Performs LU decomposition.
ludcmp(int, int[]). Method in class weka.classifiers.m5.Matrix
LU decomposition
LWR(). Constructor for class weka.classifiers.LWR

M

M5Prime(). Constructor for class weka.classifiers.m5.M5Prime
M5Utils(). Constructor for class weka.classifiers.m5.M5Utils
MahalanobisEstimator(Matrix, double, double). Constructor for class weka.estimators.MahalanobisEstimator
Constructor
main(String[]). Static method in class weka.classifiers.AdaBoostM1
Main method for testing this class.
main(String[]). Static method in class weka.filters.AddFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.AllFilter
Main method for testing this class.
main(String[]). Static method in class weka.associations.Apriori
Main method for testing this class.
main(String[]). Static method in class weka.gui.explorer.AssociationsPanel
Tests out the Associator panel from the command line.
main(String[]). Static method in class weka.core.Attribute
Simple main method for testing this class.
main(String[]). Static method in class weka.filters.AttributeFilter
Main method for testing this class.
main(String[]). Static method in class weka.attributeSelection.AttributeSelection
Main method for testing this class.
main(String[]). Static method in class weka.filters.AttributeSelectionFilter
Main method for testing this class.
main(String[]). Static method in class weka.gui.AttributeSelectionPanel
Tests the attribute selection panel from the command line.
main(String[]). Static method in class weka.gui.explorer.AttributeSelectionPanel
Tests out the attribute selection panel from the command line.
main(String[]). Static method in class weka.gui.AttributeSummaryPanel
Tests out the attribute summary panel from the command line.
main(String[]). Static method in class weka.classifiers.Bagging
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.attributeSelection.CfsSubsetEval
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.CheckClassifier
Test method for this class
main(String[]). Static method in class weka.core.CheckOptionHandler
Main method for using the CheckOptionHandler.

Valid options are:

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

main(String[]). Static method in class weka.classifiers.ClassificationViaRegression
Main method for testing this class.
main(String[]). Static method in class weka.gui.explorer.ClassifierPanel
Tests out the classifier panel from the command line.
main(String[]). Static method in class weka.gui.explorer.ClustererPanel
Tests out the clusterer panel from the command line.
main(String[]). Static method in class weka.clusterers.ClusterEvaluation
Main method for testing this class.
main(String[]). Static method in class weka.clusterers.Cobweb
main(String[]). Static method in class weka.attributeSelection.ConsistencySubsetEval
Main method for testing this class.
main(String[]). Static method in class weka.core.ContingencyTables
Main method for testing this class.
main(String[]). Static method in class weka.filters.CopyAttributesFilter
Main method for testing this class.
main(String[]). Static method in class weka.gui.CostMatrixEditor
Tests out the array editor from the command line.
main(String[]). Static method in class weka.classifiers.CostSensitiveClassifier
Main method for testing this class.
main(String[]). Static method in class weka.experiment.CrossValidationResultProducer
main(String[]). Static method in class weka.classifiers.CVParameterSelection
Main method for testing this class.
main(String[]). Static method in class weka.gui.experiment.DatasetListPanel
Tests out the dataset list panel from the command line.
main(String[]). Static method in class weka.estimators.DDConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.DecisionStump
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.DecisionTable
Main method for testing this class.
main(String[]). Static method in class weka.estimators.DiscreteEstimator
Main method for testing this class.
main(String[]). Static method in class weka.filters.DiscretizeFilter
Main method for testing this class.
main(String[]). Static method in class weka.estimators.DKConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.estimators.DNConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.clusterers.EM
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.Evaluation
A test method for this class.
main(String[]). Static method in class weka.experiment.Experiment
Configures/Runs the Experiment from the command line.
main(String[]). Static method in class weka.gui.experiment.Experimenter
Tests out the experiment environment.
main(String[]). Static method in class weka.gui.explorer.Explorer
Tests out the explorer environment.
main(String[]). Static method in class weka.classifiers.FilteredClassifier
Main method for testing this class.
main(String[]). Static method in class weka.filters.FirstOrderFilter
Main method for testing this class.
main(String[]). Static method in class weka.attributeSelection.GainRatioAttributeEval
Main method for testing this class.
main(String[]). Static method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Tests out the panel from the command line.
main(String[]). Static method in class weka.gui.GenericArrayEditor
Tests out the array editor from the command line.
main(String[]). Static method in class weka.gui.GenericObjectEditor
Tests out the Object editor from the command line.
main(String[]). Static method in class weka.gui.GUIChooser
Tests out the GUIChooser environment.
main(String[]). Static method in class weka.classifiers.HyperPipes
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.IB1
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.IBk
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.Id3
Main method.
main(String[]). Static method in class weka.attributeSelection.InfoGainAttributeEval
Main method for testing this class.
main(String[]). Static method in class weka.core.Instance
Main method for testing this class.
main(String[]). Static method in class weka.filters.InstanceFilter
Main method for testing this class.
main(String[]). Static method in class weka.experiment.InstanceQuery
Test the class from the command line.
main(String[]). Static method in class weka.core.Instances
Main method for this class -- just prints a summary of a set of instances.
main(String[]). Static method in class weka.gui.InstancesSummaryPanel
Tests out the instance summary panel from the command line.
main(String[]). Static method in class weka.classifiers.j48.J48
Main method for testing this class
main(String[]). Static method in class weka.estimators.KDConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.KernelDensity
Main method for testing this class.
main(String[]). Static method in class weka.estimators.KernelEstimator
Main method for testing this class.
main(String[]). Static method in class weka.estimators.KKConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.LinearRegression
Generates a linear regression function predictor.
main(String[]). Static method in class weka.gui.ListSelectorDialog
Tests out the list selector from the command line.
main(String[]). Static method in class weka.classifiers.Logistic
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.LogitBoost
Main method for testing this class.
main(String[]). Static method in class weka.gui.LogPanel
Tests out the log panel from the command line.
main(String[]). Static method in class weka.classifiers.LWR
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.m5.M5Prime
Main method for M5' algorithm
main(String[]). Static method in class weka.estimators.MahalanobisEstimator
Main method for testing this class.
main(String[]). Static method in class weka.filters.MakeIndicatorFilter
Main method for testing this class.
main(String[]). Static method in class weka.core.Matrix
Main method for testing this class.
main(String[]). Static method in class weka.filters.MergeTwoValuesFilter
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.MultiClassClassifier
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.MultiScheme
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.NaiveBayes
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.NaiveBayesSimple
Main method for testing this class.
main(String[]). Static method in class weka.estimators.NDConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.estimators.NNConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.filters.NominalToBinaryFilter
Main method for testing this class.
main(String[]). Static method in class weka.estimators.NormalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.filters.NormalizationFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.NullFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.NumericTransformFilter
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.OneR
Main method for testing this class
main(String[]). Static method in class weka.attributeSelection.OneRAttributeEval
Main method for testing this class.
main(String[]). Static method in class weka.experiment.PairedStats
Tests the paired stats object from the command line.
main(String[]). Static method in class weka.experiment.PairedTTester
Test the class from the command line.
main(String[]). Static method in class weka.classifiers.j48.PART
Main method for testing this class.
main(String[]). Static method in class weka.estimators.PoissonEstimator
Main method for testing this class.
main(String[]). Static method in class weka.gui.explorer.PreprocessPanel
Tests out the instance-preprocessing panel from the command line.
main(String[]). Static method in class weka.classifiers.Prism
Main method for testing this class
main(String[]). Static method in class weka.gui.PropertySelectorDialog
Tests out the property selector from the command line.
main(String[]). Static method in class weka.core.Queue
Main method for testing this class.
main(String[]). Static method in class weka.filters.RandomizeFilter
Main method for testing this class.
main(String[]). Static method in class weka.core.Range
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.RegressionByDiscretization
Main method for testing this class.
main(String[]). Static method in class weka.attributeSelection.ReliefFAttributeEval
Main method for testing this class.
main(String[]). Static method in class weka.filters.ReplaceMissingValuesFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.ResampleFilter
Main method for testing this class.
main(String[]). Static method in class weka.gui.ResultHistoryPanel
Tests out the result history from the command line.
main(String[]). Static method in class weka.gui.experiment.ResultsPanel
Tests out the results panel from the command line.
main(String[]). Static method in class weka.gui.experiment.RunNumberPanel
Tests out the panel from the command line.
main(String[]). Static method in class weka.gui.experiment.RunPanel
Tests out the run panel from the command line.
main(String[]). Static method in class weka.gui.SelectedTagEditor
Tests out the selectedtag editor 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.SimpleCLI
Method to start up the simple cli
main(String[]). Static method in class weka.classifiers.SMO
Main method for testing this class.
main(String[]). Static method in class weka.core.SpecialFunctions
Main method for testing this class.
main(String[]). Static method in class weka.filters.SplitDatasetFilter
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.Stacking
Main method for testing this class.
main(String[]). Static method in class weka.core.Statistics
Main method for testing this class.
main(String[]). Static method in class weka.filters.SwapAttributeValuesFilter
Main method for testing this class.
main(String[]). Static method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Main method for testing this class.
main(String[]). Static method in class weka.filters.TimeSeriesDeltaFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.TimeSeriesTranslateFilter
Main method for testing this class.
main(String[]). Static method in class weka.core.Utils
Main method for testing this class.
main(String[]). Static method in class weka.gui.explorer.VisualizePanel
Main method for testing this class
main(String[]). Static method in class weka.classifiers.VotedPerceptron
Main method.
main(String[]). Static method in class weka.attributeSelection.WrapperSubsetEval
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.ZeroR
Main method for testing this class.
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.
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.
MakeIndicatorFilter(). Constructor for class weka.filters.MakeIndicatorFilter
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
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.
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.
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.
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
measuresToString(Measures[], Instances, int, int, String). Method in class weka.classifiers.m5.Node
Converts the performance measures into a string
mergeAllItemSets(FastVector, 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.
mergeInstances(Instances, Instances). Static method in class weka.core.Instances
Merges two sets of Instances together.
MergeTwoValuesFilter(). Constructor for class weka.filters.MergeTwoValuesFilter
min. Variable in class weka.experiment.Stats
The minimum value seen
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.
minsAndMaxs(Instances, double[][], int). Method in class weka.classifiers.j48.C45Split
Returns the minsAndMaxs of the index.th subset.
missingValue(). Static method in class weka.core.Instance
Returns the double that codes "missing".
MODEL_LINEAR_REGRESSION. Static variable in class weka.classifiers.m5.M5Prime
MODEL_MODEL_TREE. Static variable in class weka.classifiers.m5.M5Prime
MODEL_REGRESSION_TREE. Static variable in class weka.classifiers.m5.M5Prime
ModelSelection(). Constructor for class weka.classifiers.j48.ModelSelection
MultiClassClassifier(). Constructor for class weka.classifiers.MultiClassClassifier
multiply(Matrix). Method in class weka.core.Matrix
Reurns the multiplication of two matrices
multiply(Matrix, int, int, int). Method in class weka.classifiers.m5.Matrix
Reurns the multiplication of two matrices
multiResultsetFull(int, int). Method in class weka.experiment.PairedTTester
Creates a comparison table where a base resultset is compared to the other resultsets.
multiResultsetRanking(int). Method in class weka.experiment.PairedTTester
multiResultsetSummary(int). Method in class weka.experiment.PairedTTester
Carries out a comparison between all resultsets, counting the number of datsets where one resultset outperforms the other.
multiResultsetWins(int). Method in class weka.experiment.PairedTTester
Carries out a comparison between all resultsets, counting the number of datsets where one resultset outperforms the other.
MultiScheme(). Constructor for class weka.classifiers.MultiScheme

N

NaiveBayes(). Constructor for class weka.classifiers.NaiveBayes
NaiveBayesSimple(). Constructor for class weka.classifiers.NaiveBayesSimple
name(). Method in class weka.core.Attribute
Returns the attribute's name.
name(). Method in class weka.core.Option
Returns the option's name.
NDConditionalEstimator(int, double). Constructor for class weka.estimators.NDConditionalEstimator
Constructor
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.
nextIteration(). Method in class weka.experiment.Experiment
Carries out the next iteration of the experiment.
NNConditionalEstimator(). Constructor for class weka.estimators.NNConditionalEstimator
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
NOMINAL. Static variable in class weka.core.Attribute
Constant set for nominal attributes.
NominalToBinaryFilter(). Constructor for class weka.filters.NominalToBinaryFilter
NormalEstimator(double). Constructor for class weka.estimators.NormalEstimator
Constructor that takes a precision argument.
NormalizationFilter(). Constructor for class weka.filters.NormalizationFilter
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.
normalProbability(double). Static method in class weka.core.Statistics
Returns probability that the standardized normal variate Z (mean = 0, standard deviation = 1) is less than z.
NoSplit(Distribution). Constructor for class weka.classifiers.j48.NoSplit
Creates "no-split"-split for given distribution.
NullFilter(). Constructor for class weka.filters.NullFilter
numArguments(). Method in class weka.core.Option
Returns the option's number of arguments.
numAttributes(). Method in class weka.core.Instance
Returns the number of attributes.
numAttributes(). Method in class weka.core.Instances
Returns the number of attributes.
numBags(). Method in class weka.classifiers.j48.Distribution
Returns number of bags.
numberOfClusters(). Method in class weka.clusterers.Clusterer
Returns the number of clusters.
numberOfClusters(). Method in class weka.clusterers.Cobweb
Returns the number of clusters.
numberOfClusters(). Method in class weka.clusterers.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.Instance
Returns the number of class labels.
numClasses(). Method in class weka.core.Instances
Returns the number of class labels.
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.
NumericTransformFilter(). Constructor for class weka.filters.NumericTransformFilter
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
numNodes(). Method in class weka.classifiers.j48.ClassifierTree
Returns number of nodes in tree structure.
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.
numSubsets(). Method in class weka.classifiers.j48.ClassifierSplitModel
Returns the number of created subsets for the split.
numValues(). Method in class weka.core.Attribute
Returns the number of attribute values.

O

oldEnt(Distribution). Method in class weka.classifiers.j48.EntropyBasedSplitCrit
Computes entropy of distribution before splitting.
OneR(). Constructor for class weka.classifiers.OneR
OneRAttributeEval(). Constructor for class weka.attributeSelection.OneRAttributeEval
Constructor
openFrame(String). Method in class weka.gui.ResultHistoryPanel
Opens the named result in a separate frame.
Option(String, String, int, String). Constructor for class weka.core.Option
Creates new option with the given parameters.
Options(Instances). Constructor for class weka.classifiers.m5.Options
Options(String[]). Constructor for class weka.classifiers.m5.Options
Constructs an object to store command line options and other necessary information
output(). Method in class weka.filters.Filter
Output an instance after filtering and remove from the output queue.
outputFormat(). Method in class weka.filters.Filter
Gets the format of the output instances.
outputFormat(). Method in class weka.gui.streams.InstanceJoiner
Gets the format of the output instances.
outputFormat(). Method in class weka.gui.streams.InstanceLoader
outputFormat(). Method in interface weka.gui.streams.InstanceProducer
outputPeek(). Method in class weka.filters.Filter
Output an instance after filtering but do not remove from the output queue.
outputPeek(). Method in class weka.gui.streams.InstanceJoiner
Output an instance after filtering but do not remove from the output queue.
outputPeek(). Method in class weka.gui.streams.InstanceLoader
outputPeek(). Method in interface weka.gui.streams.InstanceProducer

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.
paintValue(Graphics, Rectangle). Method in class weka.gui.CostMatrixEditor
Paints a representation of the current classifier.
paintValue(Graphics, Rectangle). Method in class weka.gui.FileEditor
Paints a representation of the current Object.
paintValue(Graphics, Rectangle). Method in class weka.gui.GenericArrayEditor
Paints a representation of the current classifier.
paintValue(Graphics, Rectangle). Method in class weka.gui.GenericObjectEditor
Paints a representation of the current Object.
PairedStats(double). Constructor for class weka.experiment.PairedStats
Creates a new PairedStats object with the supplied significance level.
PairedTTester(). Constructor for class weka.experiment.PairedTTester
parentClass. Variable in class weka.experiment.PropertyNode
The class of the object with this property
PART(). Constructor for class weka.classifiers.j48.PART
partitionOptions(String[]). Static method in class weka.core.Utils
Returns the secondary set of options (if any) contained in the supplied options array.
pctCorrect(). Method in class weka.classifiers.Evaluation
Gets the percentage of instances correctly classified (that is, for which a correct prediction was made).
pctIncorrect(). Method in class weka.classifiers.Evaluation
Gets the percentage of instances incorrectly classified (that is, for which an incorrect prediction was made).
pctUnclassified(). Method in class weka.classifiers.Evaluation
Gets the percentage of instances not classified (that is, for which no prediction was made by the classifier).
peek(). Method in class weka.core.Queue
Gets object from the front of the queue.
perBag(int). Method in class weka.classifiers.j48.Distribution
Returns number of (possibly fractional) instances in given bag.
perClass(int). Method in class weka.classifiers.j48.Distribution
Returns number of (possibly fractional) instances of given class.
perClassPerBag(int, int). Method in class weka.classifiers.j48.Distribution
Returns number of (possibly fractional) instances of given class in given bag.
PoissonEstimator(). Constructor for class weka.estimators.PoissonEstimator
pop(). Method in class weka.core.Queue
Pops an object from the front of the queue.
postProcess(). Method in class weka.experiment.AveragingResultProducer
When this method is called, it indicates that no more requests to generate results for the current experiment will be sent.
postProcess(). Method in class weka.experiment.CrossValidationResultProducer
Perform any postprocessing.
postProcess(). Method in class weka.experiment.DatabaseResultProducer
When this method is called, it indicates that no more requests to generate results for the current experiment will be sent.
postProcess(). Method in class weka.experiment.Experiment
Signals that the experiment is finished running, so that cleanup can be done.
postProcess(). Method in class weka.experiment.RandomSplitResultProducer
Perform any postprocessing.
postProcess(). Method in interface weka.experiment.ResultProducer
Perform any postprocessing.
postProcess(int[]). Method in class weka.attributeSelection.ASEvaluation
Provides a chance for a attribute evaluator to do any special post processing of the selected attribute set.
postProcess(int[]). Method in class weka.attributeSelection.CfsSubsetEval
Calls locallyPredictive in order to include locally predictive attributes (if requested).
postProcess(ResultProducer). Method in class weka.experiment.AveragingResultProducer
When this method is called, it indicates that no more results will be sent that need to be grouped together in any way.
postProcess(ResultProducer). Method in class weka.experiment.CSVResultListener
Perform any postprocessing.
postProcess(ResultProducer). Method in class weka.experiment.DatabaseResultListener
Perform any postprocessing.
postProcess(ResultProducer). Method in class weka.experiment.DatabaseResultProducer
When this method is called, it indicates that no more results will be sent that need to be grouped together in any way.
postProcess(ResultProducer). Method in class weka.experiment.InstancesResultListener
Perform any postprocessing.
postProcess(ResultProducer). Method in interface weka.experiment.ResultListener
Perform any postprocessing.
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
predictionsToString(Instances, int, boolean). Method in class weka.classifiers.m5.Node
Converts the predictions by the tree under this node to a string
prefix(). Method in class weka.classifiers.j48.ClassifierTree
Returns tree in prefix order.
prefix(). Method in class weka.classifiers.j48.J48
Returns tree in prefix order.
prefix(). Method in interface weka.core.Matchable
Returns a string that describes a tree representing the object in prefix order.
preProcess(). Method in class weka.experiment.AveragingResultProducer
Prepare to generate results.
preProcess(). Method in class weka.experiment.CrossValidationResultProducer
Prepare to generate results.
preProcess(). Method in class weka.experiment.DatabaseResultProducer
Prepare to generate results.
preProcess(). Method in class weka.experiment.RandomSplitResultProducer
Prepare to generate results.
preProcess(). Method in interface weka.experiment.ResultProducer
Prepare to generate results.
preProcess(ResultProducer). Method in class weka.experiment.AveragingResultProducer
Prepare for the results to be received.
preProcess(ResultProducer). Method in class weka.experiment.CSVResultListener
Prepare for the results to be received.
preProcess(ResultProducer). Method in class weka.experiment.DatabaseResultListener
Prepare for the results to be received.
preProcess(ResultProducer). Method in class weka.experiment.DatabaseResultProducer
Prepare for the results to be received.
preProcess(ResultProducer). Method in class weka.experiment.InstancesResultListener
Prepare for the results to be received.
preProcess(ResultProducer). Method in interface weka.experiment.ResultListener
Prepare for the results to be received.
PreprocessPanel(). Constructor for class weka.gui.explorer.PreprocessPanel
Creates the instances panel with no initial instances.
print(double[], int, int). Static method in class weka.classifiers.m5.Dvector
Prints the indexed elements in a double vector
printFeatures(). Method in class weka.classifiers.DecisionTable
Returns a string description of the features selected
printOptions(String[]). Static method in class weka.core.CheckOptionHandler
Prints the given options to a string.
printValidOptions(). Method in class weka.classifiers.m5.Options
Prints valid command line options and simply explains the output
priorEntropy(). Method in class weka.classifiers.Evaluation
Calculate the entropy of the prior distribution
Prism(). Constructor for class weka.classifiers.Prism
prob(int). Method in class weka.classifiers.j48.Distribution
Returns relative frequency of class over all bags.
prob(int, int). Method in class weka.classifiers.j48.Distribution
Returns relative frequency of class for given bag.
property. Variable in class weka.experiment.PropertyNode
Other info about the property
propertyChange(PropertyChangeEvent). Method in class weka.gui.PropertySheetPanel
Updates the property sheet panel with a changed property and also passed the event along.
PropertyDialog(PropertyEditor, int, int). Constructor for class weka.gui.PropertyDialog
Creates the editor frame.
PropertyNode(Object). Constructor for class weka.experiment.PropertyNode
Creates a mostly empty property.
PropertyNode(Object, PropertyDescriptor, Class). Constructor for class weka.experiment.PropertyNode
Creates a fully specified property node.
PropertyPanel(PropertyEditor). Constructor for class weka.gui.PropertyPanel
Create the panel with the supplied property editor.
PropertySelectorDialog(Frame, Object). Constructor for class weka.gui.PropertySelectorDialog
Create the property selection dialog.
PropertySheetPanel(). Constructor for class weka.gui.PropertySheetPanel
Creates the property sheet panel.
prune(). Method in class weka.classifiers.j48.C45PruneableClassifierTree
Prunes a tree using C4.5's pruning procedure.
prune(). Method in class weka.classifiers.m5.Node
Prunes the model tree
prune(). Method in class weka.classifiers.j48.PruneableClassifierTree
Prunes a tree.
PruneableClassifierTree(ModelSelection, boolean, int). Constructor for class weka.classifiers.j48.PruneableClassifierTree
Constructor for pruneable tree structure.
PruneableDecList(ModelSelection, int). Constructor for class weka.classifiers.j48.PruneableDecList
Constructor for pruneable partial tree structure.
pruneItemSets(FastVector, Hashtable). Static method in class weka.associations.ItemSet
Prunes a set of (k)-item sets using the given (k-1)-item sets.
pruneRules(FastVector[], double). Static method in class weka.associations.ItemSet
Prunes a set of rules.
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

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

randomize(Random). Method in class weka.core.Instances
Shuffles the instances in the set so that they are ordered randomly.
RandomizeFilter(). Constructor for class weka.filters.RandomizeFilter
RandomSearch(). Constructor for class weka.attributeSelection.RandomSearch
Constructor
RandomSplitResultProducer(). Constructor for class weka.experiment.RandomSplitResultProducer
Range(). Constructor for class weka.core.Range
rankedAttributes(). Method in class weka.attributeSelection.AttributeSelection
get the final ranking of the attributes.
rankedAttributes(). Method in class weka.attributeSelection.ForwardSelection
Produces a ranked list of attributes.
rankedAttributes(). Method in interface weka.attributeSelection.RankedOutputSearch
Returns a X by 2 list of attribute indexes and corresponding evaluations from best (highest) to worst.
rankedAttributes(). Method in class weka.attributeSelection.Ranker
Sorts the evaluated attribute list
Ranker(). Constructor for class weka.attributeSelection.Ranker
Constructor
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.
reduceMatrix(double[][]). Static method in class weka.core.ContingencyTables
Reduces a matrix by deleting all zero rows and columns.
regression(Function). Method in class weka.classifiers.m5.Node
Computes the coefficients of a linear model using the instances at this node
regression(Matrix). Method in class weka.core.Matrix
Performs a (ridged) linear regression.
regression(Matrix, double[]). Method in class weka.core.Matrix
Performs a weighted (ridged) linear regression.
regression(Matrix, int, int). Method in class weka.classifiers.m5.Matrix
Linear regression
RegressionByDiscretization(). Constructor for class weka.classifiers.RegressionByDiscretization
RegressionSplitEvaluator(). Constructor for class weka.experiment.RegressionSplitEvaluator
relationName(). Method in class weka.core.Instances
Returns the relation's name.
relativeAbsoluteError(). Method in class weka.classifiers.Evaluation
Returns the relative absolute error.
ReliefFAttributeEval(). Constructor for class weka.attributeSelection.ReliefFAttributeEval
Constructor
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.
removeElementAt(int). Method in class weka.core.FastVector
Deletes an element from this vector.
removeInstanceListener(InstanceListener). Method in class weka.gui.streams.InstanceJoiner
removeInstanceListener(InstanceListener). Method in class weka.gui.streams.InstanceLoader
removeInstanceListener(InstanceListener). Method in interface weka.gui.streams.InstanceProducer
removeNotify(). Method in class weka.gui.PropertyPanel
removePropertyChangeListener(PropertyChangeListener). Method in class weka.gui.CostMatrixEditor
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener). Method in class weka.gui.GenericArrayEditor
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener). Method in class weka.gui.GenericObjectEditor
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener). Method in class weka.gui.explorer.PreprocessPanel
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener). Method in class weka.gui.PropertySheetPanel
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener). Method in class weka.gui.SetInstancesPanel
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener). Method in class weka.gui.experiment.SetupPanel
Removes a PropertyChangeListener.
removeResult(String). Method in class weka.gui.ResultHistoryPanel
Removes one of the result buffers from the history.
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 modes and means contained in the given array.
ReplaceMissingValuesFilter(). Constructor for class weka.filters.ReplaceMissingValuesFilter
resample(Random). Method in class weka.core.Instances
Creates a new dataset of the same size using random sampling with replacement.
ResampleFilter(). Constructor for class weka.filters.ResampleFilter
resampleWithWeights(Random, 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.
resetOptions(). Method in class weka.associations.Apriori
Resets the options to the default values.
ResultHistoryPanel(JTextComponent). Constructor for class weka.gui.ResultHistoryPanel
Create the result history object
resultsetKey(). Method in class weka.experiment.PairedTTester
Creates a key that maps resultset numbers to their descriptions.
ResultsPanel(). Constructor for class weka.gui.experiment.ResultsPanel
Creates the results panel with no initial experiment.
rightSide(int, Instances). Method in class weka.classifiers.j48.BinC45Split
Prints the condition satisfied by instances in a subset.
rightSide(int, Instances). Method in class weka.classifiers.j48.C45Split
Prints the condition satisfied by instances in a subset.
rightSide(int, Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
Prints left side of condition satisfied by instances in subset index.
rightSide(int, Instances). Method in class weka.classifiers.j48.NoSplit
Does nothing because no condition has to be satisfied.
rootMeanPriorSquaredError(). Method in class weka.classifiers.Evaluation
Returns the root mean prior squared error.
rootMeanSquaredError(). Method in class weka.classifiers.Evaluation
Returns the root mean squared error.
rootRelativeSquaredError(). Method in class weka.classifiers.Evaluation
Returns the root relative squared error if the class is numeric.
round(double). Static method in class weka.core.Utils
Rounds a double to the next nearest integer value.
roundDouble(double). Static method in class weka.classifiers.m5.M5Utils
Rounds a double
roundDouble(double, int). Static method in class weka.core.Utils
Rounds a double to the given number of decimal places.
RUN_FIELD_NAME. Static variable in class weka.experiment.CrossValidationResultProducer
RUN_FIELD_NAME. Static variable in class weka.experiment.RandomSplitResultProducer
runCommand(String). Method in class weka.gui.SimpleCLI
Executes a simple cli command.
runExperiment(). Method in class weka.experiment.Experiment
Runs all iterations of the experiment, continuing past errors.
RunNumberPanel(). Constructor for class weka.gui.experiment.RunNumberPanel
Creates the panel with no initial experiment.
RunNumberPanel(Experiment). Constructor for class weka.gui.experiment.RunNumberPanel
Creates the panel with the supplied initial experiment.
RunPanel(). Constructor for class weka.gui.experiment.RunPanel
Creates the run panel with no initial experiment.
RunPanel(Experiment). Constructor for class weka.gui.experiment.RunPanel
Creates the panel with the supplied initial experiment.

S

saveWorkingInstancesToFileQ(). Method in class weka.gui.explorer.PreprocessPanel
Queries the user for a file to save instances as, then saves the instances in a background process.
search(ASEvaluation, Instances). Method in class weka.attributeSelection.ASSearch
Searches the attribute subset/ranking space.
search(ASEvaluation, Instances). Method in class weka.attributeSelection.BestFirst
Searches the attribute subset space by best first search
search(ASEvaluation, Instances). Method in class weka.attributeSelection.ExhaustiveSearch
Searches the attribute subset space using a genetic algorithm.
search(ASEvaluation, Instances). Method in class weka.attributeSelection.ForwardSelection
Searches the attribute subset space by forward selection.
search(ASEvaluation, Instances). Method in class weka.attributeSelection.GeneticSearch
Searches the attribute subset space using a genetic algorithm.
search(ASEvaluation, Instances). Method in class weka.attributeSelection.RandomSearch
Searches the attribute subset space using a genetic algorithm.
search(ASEvaluation, Instances). Method in class weka.attributeSelection.Ranker
Kind of a dummy search algorithm.
secondInstanceProduced(InstanceEvent). Method in class weka.gui.streams.InstanceJoiner
secondInstanceProduced(InstanceEvent). Method in interface weka.gui.streams.SerialInstanceListener
SelectAttributes(ASEvaluation, String[]). Static method in class weka.attributeSelection.AttributeSelection
Perform attribute selection with a particular evaluator and a set of options specifying search method and input file etc.
SelectAttributes(ASEvaluation, String[], Instances). Static method in class weka.attributeSelection.AttributeSelection
Perform attribute selection with a particular evaluator and a set of options specifying search method and options for the search method and evaluator.
SelectAttributes(Instances). Method in class weka.attributeSelection.AttributeSelection
Perform attribute selection on the supplied training instances.
selectAttributesCVSplit(Instances). Method in class weka.attributeSelection.AttributeSelection
Select attributes for a split of the data.
selectedAttributes(). Method in class weka.attributeSelection.AttributeSelection
get the final selected set of attributes.
SelectedTag(int, Tag[]). Constructor for class weka.core.SelectedTag
SelectedTagEditor(). Constructor for class weka.gui.SelectedTagEditor
selectModel(Instances). Method in class weka.classifiers.j48.BinC45ModelSelection
Selects C4.5-type split for the given dataset.
selectModel(Instances). Method in class weka.classifiers.j48.C45ModelSelection
Selects C4.5-type split for the given dataset.
selectModel(Instances). Method in class weka.classifiers.j48.ModelSelection
Selects a model for the given dataset.
selectModel(Instances, Instances). Method in class weka.classifiers.j48.BinC45ModelSelection
Selects C4.5-type split for the given dataset.
selectModel(Instances, Instances). Method in class weka.classifiers.j48.C45ModelSelection
Selects C4.5-type split for the given dataset.
selectModel(Instances, Instances). Method in class weka.classifiers.j48.ModelSelection
Selects a model for the given train data using the given test data
separatorToString(). Static method in class weka.classifiers.m5.M5Utils
Prints sepearating line
setAcuity(int). Method in class weka.clusterers.Cobweb
set the accuity.
setArffFile(String). Method in class weka.gui.streams.InstanceLoader
setArffFile(String). Method in class weka.gui.streams.InstanceSavePanel
setAsText(String). Method in class weka.gui.CostMatrixEditor
Returns null as we don't support getting/setting values as text.
setAsText(String). Method in class weka.gui.GenericArrayEditor
Returns null as we don't support getting/setting values as text.
setAsText(String). Method in class weka.gui.GenericObjectEditor
Returns null as we don't support getting/setting values as text.
setAsText(String). Method in class weka.gui.SelectedTagEditor
Sets the current property value as text.
setAttribute(int). Method in class weka.gui.AttributeSummaryPanel
Sets the attribute that statistics will be displayed for.
setAttributeIndex(int). Method in class weka.filters.AddFilter
Set the index where the attribute will be inserted
setAttributeIndex(int). Method in class weka.filters.InstanceFilter
Sets attribute to be used for selection
setAttributeIndex(int). Method in class weka.filters.MakeIndicatorFilter
Sets index of of the attribute used.
setAttributeIndex(int). Method in class weka.filters.MergeTwoValuesFilter
Sets index of the attribute used.
setAttributeIndex(int). Method in class weka.filters.SwapAttributeValuesFilter
Sets index of the attribute used.
setAttributeIndices(String). Method in class weka.filters.AttributeFilter
Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndices(String). Method in class weka.filters.CopyAttributesFilter
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndices(String). Method in class weka.filters.DiscretizeFilter
Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeIndices(String). Method in class weka.filters.FirstOrderFilter
Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndices(String). Method in class weka.filters.NumericTransformFilter
Set which attributes are to be transformed (or kept if invert is true).
setAttributeIndices(String). Method in class weka.filters.TimeSeriesTranslateFilter
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndicesArray(int[]). Method in class weka.filters.AttributeFilter
Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndicesArray(int[]). Method in class weka.filters.CopyAttributesFilter
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndicesArray(int[]). Method in class weka.filters.DiscretizeFilter
Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeIndicesArray(int[]). Method in class weka.filters.FirstOrderFilter
Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndicesArray(int[]). Method in class weka.filters.NumericTransformFilter
Set which attributes are to be transformed (or kept if invert is true)
setAttributeIndicesArray(int[]). Method in class weka.filters.TimeSeriesTranslateFilter
Set which attributes are to be copied (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.
setBaseClassifiers(Classifier[]). Method in class weka.classifiers.Stacking
Sets the list of possible classifers to choose from.
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.
setBiasToUniformClass(double). Method in class weka.filters.ResampleFilter
Sets the bias towards a uniform class.
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
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.
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.
setClass(Attribute). Method in class weka.core.Instances
Sets the class attribute.
setClassifier(Classifier). Method in class weka.classifiers.AdaBoostM1
Set the classifier for boosting.
setClassifier(Classifier). Method in class weka.classifiers.Bagging
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.CheckClassifier
Set the classifier for boosting.
setClassifier(Classifier). Method in class weka.classifiers.ClassificationViaRegression
Set the base classifier.
setClassifier(Classifier). Method in class weka.experiment.ClassifierSplitEvaluator
Sets the classifier.
setClassifier(Classifier). Method in class weka.classifiers.CostSensitiveClassifier
Sets the distribution classifier
setClassifier(Classifier). Method in class weka.classifiers.CVParameterSelection
Set the classifier for boosting.
setClassifier(Classifier). Method in class weka.classifiers.FilteredClassifier
Sets the classifier
setClassifier(Classifier). Method in class weka.classifiers.LogitBoost
Set the classifier for boosting.
setClassifier(Classifier). Method in class weka.classifiers.MultiClassClassifier
Set the base classifier.
setClassifier(Classifier). Method in class weka.classifiers.RegressionByDiscretization
Set the classifier for boosting.
setClassifier(Classifier). Method in class weka.experiment.RegressionSplitEvaluator
Sets the classifier.
setClassifier(Classifier). Method in class weka.attributeSelection.WrapperSubsetEval
Set the classifier to use for accuracy estimation
setClassifierName(String). Method in class weka.experiment.ClassifierSplitEvaluator
Set the Classifier to use, given it's class name.
setClassifierName(String). Method in class weka.experiment.RegressionSplitEvaluator
Set the Classifier to use, given it's class name.
setClassifiers(Classifier[]). Method in class weka.classifiers.MultiScheme
Sets the list of possible classifers to choose from.
setClassIndex(int). Method in class weka.classifiers.BVDecompose
Sets index of attribute to discretize on
setClassIndex(int). Method in class weka.core.Instances
Sets the class index of the set.
setClassMissing(). Method in class weka.core.Instance
Sets the class value of an instance to be "missing".
setClassName(String). Method in class weka.filters.NumericTransformFilter
Sets the class containing the transformation method.
setClassType(Class). Method in class weka.gui.GenericObjectEditor
Sets the class of values that can be edited.
setClassValue(double). Method in class weka.core.Instance
Sets the class value of an instance to the given value (internal floating-point format).
setClassValue(String). Method in class weka.core.Instance
Sets the class value of an instance to the given value.
setClearEachDataset(boolean). Method in class weka.gui.streams.InstanceViewer
setClusterer(Clusterer). Method in class weka.clusterers.ClusterEvaluation
set the clusterer
setColourIndex(int). Method in class weka.gui.explorer.VisualizePanel
Sets the index used for colouring.
setColumn(int, double[]). Method in class weka.core.Matrix
Sets a column of the matrix to the given column.
setConfidenceFactor(float). Method in class weka.classifiers.j48.J48
Set the value of CF.
setConfidenceFactor(float). Method in class weka.classifiers.j48.PART
Set the value of CF.
setCostMatrix(CostMatrix). Method in class weka.classifiers.CostSensitiveClassifier
Sets the misclassification 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
setCutoff(int). Method in class weka.clusterers.Cobweb
set the cutoff
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.
setDatasetColumn(int). Method in class weka.experiment.PairedTTester
Set the value of DatasetColumn.
setDebug(boolean). Method in class weka.classifiers.AdaBoostM1
Set debugging mode
setDebug(boolean). Method in class weka.classifiers.BVDecompose
Sets debugging mode
setDebug(boolean). Method in class weka.classifiers.CheckClassifier
Set debugging mode
setDebug(boolean). Method in class weka.classifiers.CVParameterSelection
Sets debugging mode
setDebug(boolean). Method in class weka.clusterers.EM
Set debug mode - verbose output
setDebug(boolean). Method in class weka.classifiers.IBk
Set the value of Debug.
setDebug(boolean). Method in class weka.gui.streams.InstanceCounter
setDebug(boolean). Method in class weka.gui.streams.InstanceJoiner
setDebug(boolean). Method in class weka.gui.streams.InstanceLoader
setDebug(boolean). Method in class weka.gui.streams.InstanceSavePanel
setDebug(boolean). Method in class weka.gui.streams.InstanceTable
setDebug(boolean). Method in class weka.gui.streams.InstanceViewer
setDebug(boolean). Method in class weka.classifiers.LinearRegression
Controls whether debugging output will be printed
setDebug(boolean). Method in class weka.classifiers.Logistic
Sets whether debugging output will be printed.
setDebug(boolean). Method in class weka.classifiers.LogitBoost
Set debugging mode
setDebug(boolean). Method in class weka.classifiers.LWR
Sets whether debugging output should be produced
setDebug(boolean). Method in class weka.classifiers.MultiScheme
Set debugging mode
setDebug(boolean). Method in class weka.classifiers.RegressionByDiscretization
Sets whether debugging output will be printed
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.
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.
setDistribution(Distribution). Method in class weka.classifiers.j48.ClassifierSplitModel
Sets distribution associated with model.
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.
setEvaluator(ASEvaluation). Method in class weka.attributeSelection.AttributeSelection
set the attribute/subset evaluator
setEvaluator(ASEvaluation). Method in class weka.filters.AttributeSelectionFilter
set a string holding the name of a attribute/subset evaluator
setExpectedResultsPerAverage(int). Method in class weka.experiment.AveragingResultProducer
Set the value of ExpectedResultsPerAverage.
setExperiment(Experiment). Method in class weka.gui.experiment.DatasetListPanel
Tells the panel to act on a new experiment.
setExperiment(Experiment). Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Sets the experiment which will have the custom properties edited.
setExperiment(Experiment). Method in class weka.gui.experiment.ResultsPanel
Tells the panel to use a new experiment.
setExperiment(Experiment). Method in class weka.gui.experiment.RunNumberPanel
Sets the experiment to be configured.
setExperiment(Experiment). Method in class weka.gui.experiment.RunPanel
Sets the experiment the panel operates on.
setExperiment(Experiment). Method in class weka.gui.experiment.SetupPanel
Sets the experiment to configure.
setExponent(double). Method in class weka.classifiers.SMO
Set the value of exponent.
setExponent(double). Method in class weka.classifiers.VotedPerceptron
Set the value of exponent.
setFillWithMissing(boolean). Method in class weka.filters.TimeSeriesTranslateFilter
Sets whether missing values should be used rather than removing instances where the translated value is not known (due to border effects).
setFilter(Filter). Method in class weka.classifiers.FilteredClassifier
Sets the filter
setFindNumBins(boolean). Method in class weka.filters.DiscretizeFilter
Set the value of FindNumBins.
setFirstValueIndex(int). Method in class weka.filters.MergeTwoValuesFilter
Sets index of the first value used.
setFirstValueIndex(int). Method in class weka.filters.SwapAttributeValuesFilter
Sets index of the first value used.
setFold(int). Method in class weka.filters.SplitDatasetFilter
Selects a fold.
setFolds(int). Method in class weka.attributeSelection.AttributeSelection
set the number of folds for cross validation
setFolds(int). Method in class weka.clusterers.ClusterEvaluation
set the number of folds to use for cross validation
setFolds(int). Method in class weka.attributeSelection.WrapperSubsetEval
Set the number of folds to use for accuracy estimation
setGenerateRanking(boolean). Method in class weka.attributeSelection.ForwardSelection
Records whether the user has requested a ranked list of attributes.
setGenerateRanking(boolean). Method in interface weka.attributeSelection.RankedOutputSearch
Sets whether or not ranking is to be performed.
setGenerateRanking(boolean). Method in class weka.attributeSelection.Ranker
This is a dummy set method---Ranker is ONLY capable of producing a ranked list of attributes for attribute evaluators.
setInstanceRange(int). Method in class weka.filters.TimeSeriesTranslateFilter
Sets the number of instances forward to translate values between.
setInstances(Instances). Method in class weka.gui.explorer.AssociationsPanel
Tells the panel to use a new set of instances.
setInstances(Instances). Method in class weka.gui.AttributeSelectionPanel
Sets the instances who's attribute names will be displayed.
setInstances(Instances). Method in class weka.gui.explorer.AttributeSelectionPanel
Tells the panel to use a new set of instances.
setInstances(Instances). Method in class weka.gui.AttributeSummaryPanel
Tells the panel to use a new set of instances.
setInstances(Instances). Method in class weka.experiment.AveragingResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances). Method in class weka.gui.explorer.ClassifierPanel
Tells the panel to use a new set of instances.
setInstances(Instances). Method in class weka.gui.explorer.ClustererPanel
Tells the panel to use a new set of instances.
setInstances(Instances). Method in class weka.experiment.CrossValidationResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances). Method in class weka.experiment.DatabaseResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances). Method in class weka.gui.InstancesSummaryPanel
Tells the panel to use a new set of instances.
setInstances(Instances). Method in class weka.experiment.PairedTTester
Set the value of Instances.
setInstances(Instances). Method in class weka.experiment.RandomSplitResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances). Method in interface weka.experiment.ResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances). Method in class weka.gui.experiment.ResultsPanel
Sets up the panel with a new set of instances, attempting to guess the correct settings for various columns.
setInstances(Instances). Method in class weka.gui.SetInstancesPanel
Updates the set of instances that is currently held by the panel
setInstances(Instances). Method in class weka.gui.explorer.VisualizePanel
Tells the panel to use a new set of instances.
setInstancesFromFileQ(). Method in class weka.gui.SetInstancesPanel
Queries the user for a file to load instances from, then loads the instances in a background process.
setInstancesFromURLQ(). Method in class weka.gui.SetInstancesPanel
Queries the user for a URL to load instances from, then loads the instances in a background process.
setInstancesIndices(String). Method in class weka.filters.SplitDatasetFilter
Sets the ranges of instances to be selected.
SetInstancesPanel(). Constructor for class weka.gui.SetInstancesPanel
Create the panel.
setInvert(boolean). Method in class weka.core.Range
Sets whether the range sense is inverted, i.e.
setInvertSelection(boolean). Method in class weka.filters.AttributeFilter
Set whether selected columns should be removed or kept.
setInvertSelection(boolean). Method in class weka.filters.CopyAttributesFilter
Set whether selected columns should be removed or kept.
setInvertSelection(boolean). Method in class weka.filters.DiscretizeFilter
Sets whether selected columns should be removed or kept.
setInvertSelection(boolean). Method in class weka.filters.InstanceFilter
Set whether selected values should be removed or kept.
setInvertSelection(boolean). Method in class weka.filters.NumericTransformFilter
Set whether selected columns should be transformed or not.
setInvertSelection(boolean). Method in class weka.filters.SplitDatasetFilter
Sets if selection is to be inverted.
setInvertSelection(boolean). Method in class weka.filters.TimeSeriesTranslateFilter
Set whether selected columns should be removed or kept.
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.
setLocallyPredictive(boolean). Method in class weka.attributeSelection.CfsSubsetEval
Include locally predictive attributes
setLog(Logger). Method in class weka.gui.explorer.AssociationsPanel
Sets the Logger to receive informational messages
setLog(Logger). Method in class weka.gui.explorer.AttributeSelectionPanel
Sets the Logger to receive informational messages
setLog(Logger). Method in class weka.gui.explorer.ClassifierPanel
Sets the Logger to receive informational messages
setLog(Logger). Method in class weka.gui.explorer.ClustererPanel
Sets the Logger to receive informational messages
setLog(Logger). Method in class weka.gui.explorer.PreprocessPanel
Sets the Logger to receive informational messages
setLowerBoundMinSupport(double). Method in class weka.associations.Apriori
Set the value of lowerBoundMinSupport.
setMakeBinary(boolean). Method in class weka.filters.DiscretizeFilter
Sets whether binary attributes should be made for discretized ones.
setMaxGenerations(int). Method in class weka.attributeSelection.GeneticSearch
set the number of generations to evaluate
setMaxIterations(int). Method in class weka.classifiers.AdaBoostM1
Set the maximum number of boost iterations
setMaxIterations(int). Method in class weka.clusterers.EM
Set the maximum number of iterations to perform
setMaxIterations(int). Method in class weka.classifiers.LogitBoost
Set the maximum number of boost iterations
setMaxK(int). Method in class weka.classifiers.VotedPerceptron
Set the value of maxK.
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
setMethodName(String). Method in class weka.filters.NumericTransformFilter
Set the transformation method.
setMinBucketSize(int). Method in class weka.classifiers.OneR
Set the value of minBucketSize.
setMinConfidence(double). Method in class weka.associations.Apriori
Set the value of minConfidence.
setMinimizeExpectedCost(boolean). Method in class weka.classifiers.CostSensitiveClassifier
Set the value of MinimizeExpectedCost.
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.
setMinSupport(double). Method in class weka.associations.Apriori
Set the value of minSupport.
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.GainRatioAttributeEval
distribute the counts for missing values across observed values
setMissingMerge(boolean). Method in class weka.attributeSelection.InfoGainAttributeEval
distribute the counts for missing values across observed values
setMissingMerge(boolean). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
distribute the counts for missing values across observed values
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.
setMutationProb(double). Method in class weka.attributeSelection.GeneticSearch
set the probability of mutation
setName(String). Method in class weka.gui.explorer.VisualizePanel
Set a name for this plot
setNominalIndices(String). Method in class weka.filters.InstanceFilter
Set which nominal labels are to be included in the selection.
setNominalIndicesArr(int[]). Method in class weka.filters.InstanceFilter
Set which values of a nominal attribute are to be used for selection.
setNominalLabels(String). Method in class weka.filters.AddFilter
Set the labels for nominal attribute creation.
setNotes(String). Method in class weka.experiment.Experiment
Set the user notes.
setNumBins(int). Method in class weka.classifiers.RegressionByDiscretization
Sets the number of bins the class attribute will be discretized into.
setNumClusters(int). Method in class weka.clusterers.EM
Set the number of clusters (-1 to select by CV).
setNumeric(boolean). Method in class weka.filters.MakeIndicatorFilter
Sets if the new Attribute is to be numeric.
setNumFolds(int). Method in class weka.experiment.CrossValidationResultProducer
Set the value of NumFolds.
setNumFolds(int). Method in class weka.classifiers.CVParameterSelection
Set the number of folds used for cross-validation.
setNumFolds(int). Method in class weka.classifiers.j48.J48
Set the value of numFolds.
setNumFolds(int). Method in class weka.classifiers.MultiScheme
Sets the number of folds for cross-validation.
setNumFolds(int). Method in class weka.classifiers.j48.PART
Set the value of numFolds.
setNumFolds(int). Method in class weka.filters.SplitDatasetFilter
Sets the number of folds the dataset is split into.
setNumFolds(int). Method in class weka.classifiers.Stacking
Sets the number of folds for the cross-validation.
setNumIterations(int). Method in class weka.classifiers.Bagging
Sets the number of bagging iterations
setNumIterations(int). Method in class weka.classifiers.VotedPerceptron
Set the value of NumIterations.
setNumNeighbours(int). Method in class weka.attributeSelection.ReliefFAttributeEval
Set the number of nearest neighbours
setNumRules(int). Method in class weka.associations.Apriori
Set the value of numRules.
setOptimizeBinning(boolean). Method in class weka.filters.DiscretizeFilter
Sets if binning is to be optimized.
setOptimizeBins(boolean). Method in class weka.classifiers.RegressionByDiscretization
Sets whether the discretizer optimizes the number of bins
setOptions(String[]). Method in class weka.classifiers.AdaBoostM1
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.AddFilter
Parses a list of options for this object.
setOptions(String[]). Method in class weka.associations.Apriori
Parses a given list of options.
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.AttributeSelectionFilter
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.classifiers.Bagging
Parses a given list of options.
setOptions(String[]). Method in class weka.attributeSelection.BestFirst
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.BVDecompose
Parses a given list of options.
setOptions(String[]). Method in class weka.attributeSelection.CfsSubsetEval
Parses and sets a given list of options.
setOptions(String[]). Method in class weka.classifiers.CheckClassifier
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.ClassificationViaRegression
Sets a given list of options.
setOptions(String[]). Method in class weka.experiment.ClassifierSplitEvaluator
Parses a given list of options.
setOptions(String[]). Method in class weka.clusterers.Cobweb
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.CopyAttributesFilter
Parses a given list of options controlling the behaviour of this object.
setOptions(String[]). Method in class weka.classifiers.CostSensitiveClassifier
Parses a given list of options.
setOptions(String[]). Method in class weka.experiment.CrossValidationResultProducer
Parses a given list of options.
setOptions(String[]). Method in class weka.experiment.CSVResultListener
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.CVParameterSelection
Parses a given list of options.
setOptions(String[]). Method in class weka.experiment.DatabaseResultProducer
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.DecisionTable
Parses the options for this object.
setOptions(String[]). Method in class weka.filters.DiscretizeFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.clusterers.EM
Parses a given list of options.
setOptions(String[]). Method in class weka.attributeSelection.ExhaustiveSearch
Parses a given list of options.
setOptions(String[]). Method in class weka.experiment.Experiment
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.FilteredClassifier
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.FirstOrderFilter
Parses a given list of options controlling the behaviour of this object.
setOptions(String[]). Method in class weka.attributeSelection.ForwardSelection
Parses a given list of options.
setOptions(String[]). Method in class weka.attributeSelection.GainRatioAttributeEval
Parses a given list of options.
setOptions(String[]). Method in class weka.attributeSelection.GeneticSearch
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.IBk
Parses a given list of options.
setOptions(String[]). Method in class weka.attributeSelection.InfoGainAttributeEval
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.InstanceFilter
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.j48.J48
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.LinearRegression
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.Logistic
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.LogitBoost
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.LWR
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.m5.M5Prime
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.MakeIndicatorFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.filters.MergeTwoValuesFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.classifiers.MultiClassClassifier
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.MultiScheme
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.NaiveBayes
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.NominalToBinaryFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.filters.NumericTransformFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.classifiers.OneR
Parses a given list of options.
setOptions(String[]). Method in interface weka.core.OptionHandler
Sets the OptionHandler's options using the given list.
setOptions(String[]). Method in class weka.experiment.PairedTTester
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.j48.PART
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.RandomizeFilter
Parses a list of options for this object.
setOptions(String[]). Method in class weka.attributeSelection.RandomSearch
Parses a given list of options.
setOptions(String[]). Method in class weka.experiment.RandomSplitResultProducer
Parses a given list of options.
setOptions(String[]). Method in class weka.attributeSelection.Ranker
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.RegressionByDiscretization
Parses a given list of options.
setOptions(String[]). Method in class weka.experiment.RegressionSplitEvaluator
Parses a given list of options.
setOptions(String[]). Method in class weka.attributeSelection.ReliefFAttributeEval
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.ResampleFilter
Parses a list of options for this object.
setOptions(String[]). Method in class weka.classifiers.SMO
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.SplitDatasetFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.classifiers.Stacking
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.SwapAttributeValuesFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.TimeSeriesTranslateFilter
Parses a given list of options controlling the behaviour of this object.
setOptions(String[]). Method in class weka.classifiers.VotedPerceptron
Parses a given list of options.
setOptions(String[]). Method in class weka.attributeSelection.WrapperSubsetEval
Parses a given list of options.
setOutputFile(File). Method in class weka.experiment.CSVResultListener
Set the value of OutputFile.
setPopulationSize(int). Method in class weka.attributeSelection.GeneticSearch
set the population size
setPredictions(double[]). Method in class weka.gui.explorer.VisualizePanel
Set the classifier's predictions.
setPredictionsNumeric(boolean). Method in class weka.gui.explorer.VisualizePanel
Specify whether the classifier's predictions are for a numeric class
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
setPropertyArray(Object). Method in class weka.experiment.Experiment
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.
setPruningFactor(double). Method in class weka.classifiers.m5.M5Prime
Set the value of PruningFactor.
setRandomSeed(int). Method in class weka.filters.RandomizeFilter
Set the random number generator seed value.
setRandomSeed(int). Method in class weka.filters.ResampleFilter
Sets the random number seed.
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 an evaluator
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.
setRelationName(String). Method in class weka.core.Instances
Sets the relation's name.
setReportFrequency(int). Method in class weka.attributeSelection.GeneticSearch
set how often reports are generated
setResultKeyFromDialog(). Method in class weka.gui.experiment.ResultsPanel
setResultListener(ResultListener). Method in class weka.experiment.AveragingResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener). Method in class weka.experiment.CrossValidationResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener). Method in class weka.experiment.DatabaseResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener). Method in class weka.experiment.Experiment
Sets the result listener where results will be sent.
setResultListener(ResultListener). Method in class weka.experiment.RandomSplitResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener). Method in interface weka.experiment.ResultProducer
Sets the object to send results of each run to.
setResultProducer(ResultProducer). Method in class weka.experiment.AveragingResultProducer
Set the ResultProducer.
setResultProducer(ResultProducer). Method in class weka.experiment.DatabaseResultProducer
Set the ResultProducer.
setResultProducer(ResultProducer). Method in class weka.experiment.Experiment
Set the result producer used for the current experiment.
setResultsetKeyColumns(Range). Method in class weka.experiment.PairedTTester
Set the value of ResultsetKeyColumns.
setRow(int, double[]). Method in class weka.core.Matrix
Sets a row of the matrix to the given row.
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.
setRunUpper(int). Method in class weka.experiment.Experiment
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.
setSearch(ASSearch). Method in class weka.attributeSelection.AttributeSelection
set the search method
setSearch(ASSearch). Method in class weka.filters.AttributeSelectionFilter
Set as string holding the name of a search class
setSearchPercent(double). Method in class weka.attributeSelection.RandomSearch
set the percentage of the search space to consider
setSearchTermination(int). Method in class weka.attributeSelection.BestFirst
Set the numnber of non-improving nodes to consider before terminating search.
setSecondValueIndex(int). Method in class weka.filters.MergeTwoValuesFilter
Sets index of the second value used.
setSecondValueIndex(int). Method in class weka.filters.SwapAttributeValuesFilter
Sets index of the second value used.
setSeed(int). Method in class weka.classifiers.AdaBoostM1
Set seed for resampling.
setSeed(int). Method in class weka.attributeSelection.AttributeSelection
set the seed for use in cross validation
setSeed(int). Method in class weka.classifiers.Bagging
Set the seed for random number generation.
setSeed(int). Method in class weka.classifiers.BVDecompose
Sets the random number seed
setSeed(int). Method in class weka.clusterers.ClusterEvaluation
set the seed to use for cross validation
setSeed(int). Method in class weka.classifiers.CostSensitiveClassifier
Set seed for resampling.
setSeed(int). Method in class weka.classifiers.CVParameterSelection
Sets the seed for random number generation.
setSeed(int). Method in class weka.clusterers.EM
Set the random number seed
setSeed(int). Method in class weka.attributeSelection.GeneticSearch
set the seed for random number generation
setSeed(int). Method in class weka.classifiers.LogitBoost
Set seed for resampling.
setSeed(int). Method in class weka.classifiers.MultiScheme
Sets the seed for random number generation.
setSeed(int). Method in class weka.attributeSelection.ReliefFAttributeEval
Set the random number seed for randomly sampling instances.
setSeed(int). Method in class weka.classifiers.SMO
Set the value of seed.
setSeed(int). Method in class weka.classifiers.Stacking
Sets the seed for random number generation.
setSeed(int). Method in class weka.classifiers.VotedPerceptron
Set the value of Seed.
setSeed(int). Method in class weka.attributeSelection.WrapperSubsetEval
Set the seed to use for cross validation
setSeed(long). Method in class weka.filters.SplitDatasetFilter
Sets the random number seed for shuffling the dataset.
setSigma(int). Method in class weka.attributeSelection.ReliefFAttributeEval
Sets the sigma value.
setSignificanceLevel(double). Method in class weka.associations.Apriori
Set the value of significanceLevel.
setSignificanceLevel(double). Method in class weka.experiment.PairedTTester
Set the value of SignificanceLevel.
setSingle(String). Method in class weka.gui.ResultHistoryPanel
Sets the single-click display to view the named result.
setSplitEvaluator(SplitEvaluator). Method in class weka.experiment.CrossValidationResultProducer
Set the SplitEvaluator.
setSplitEvaluator(SplitEvaluator). Method in class weka.experiment.RandomSplitResultProducer
Set the SplitEvaluator.
setSplitPoint(double). Method in class weka.filters.InstanceFilter
Split point to be used for selection on numeric attribute.
setSplitPoint(Instances). Method in class weka.classifiers.j48.BinC45Split
Sets split point to greatest value in given data smaller or equal to old split point.
setSplitPoint(Instances). Method in class weka.classifiers.j48.C45Split
Sets split point to greatest value in given data smaller or equal to old split point.
setStartSet(String). Method in class weka.attributeSelection.BestFirst
Sets a starting set of attributes for the search.
setStartSet(String). Method in class weka.attributeSelection.ExhaustiveSearch
Sets a starting set of attributes for the search.
setStartSet(String). Method in class weka.attributeSelection.ForwardSelection
Sets a starting set of attributes for the search.
setStartSet(String). Method in class weka.attributeSelection.GeneticSearch
Sets a starting set of attributes for the search.
setStartSet(String). Method in class weka.attributeSelection.RandomSearch
Sets a starting set of attributes for the search.
setStartSet(String). Method in class weka.attributeSelection.Ranker
Sets a starting set of attributes for the search.
setStartSet(String). Method in interface weka.attributeSelection.StartSetHandler
Sets a starting set of attributes for the search.
setSubtreeRaising(boolean). Method in class weka.classifiers.j48.J48
Set the value of subtreeRaising.
setTarget(Object). Method in class weka.gui.PropertySheetPanel
Sets a new target object for customisation.
setThreshold(double). Method in class weka.attributeSelection.AttributeSelection
set the threshold by which to select features from a ranked list
setThreshold(double). Method in class weka.attributeSelection.ForwardSelection
Set the threshold by which the AttributeSelection module can discard attributes.
setThreshold(double). Method in interface weka.attributeSelection.RankedOutputSearch
Sets a threshold by which attributes can be discarded from the ranking.
setThreshold(double). Method in class weka.attributeSelection.Ranker
Set the threshold by which the AttributeSelection module can discard attributes.
setThreshold(double). Method in class weka.attributeSelection.WrapperSubsetEval
Set the value of the threshold for repeating cross validation
setTrainIterations(int). Method in class weka.classifiers.BVDecompose
Sets the maximum number of boost iterations
setTrainPercent(int). 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.
setUnpruned(boolean). Method in class weka.classifiers.j48.J48
Set the value of unpruned.
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".
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.
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.
setUseResampling(boolean). Method in class weka.classifiers.AdaBoostM1
Set resampling mode
setUseResampling(boolean). Method in class weka.classifiers.LogitBoost
Set resampling mode
setUseUnsmoothed(boolean). Method in class weka.classifiers.m5.M5Prime
Set the value of UseUnsmoothed.
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(int, double). Method in class weka.core.Instance
Sets a specific value in the instance to the given value (internal floating-point format).
setValue(int, String). Method in class weka.core.Instance
Sets a value of a nominal or string attribute to the given value.
setValue(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 of the first value used.
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.
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.
setXIndex(int). Method in class weka.gui.explorer.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.
setYIndex(int). Method in class weka.gui.explorer.VisualizePanel
Set the index of the attribute for the y axis
SFEntropyGain(). Method in class weka.classifiers.Evaluation
Returns the total SF, which is the null model entropy minus the scheme entropy.
SFMeanEntropyGain(). Method in class weka.classifiers.Evaluation
Returns the SF per instance, which is the null model entropy minus the scheme entropy, per instance.
SFMeanPriorEntropy(). Method in class weka.classifiers.Evaluation
Returns the entropy per instance for the null model
SFMeanSchemeEntropy(). Method in class weka.classifiers.Evaluation
Returns the entropy per instance for the scheme
SFPriorEntropy(). Method in class weka.classifiers.Evaluation
Returns the total entropy for the null model
SFSchemeEntropy(). Method in class weka.classifiers.Evaluation
Returns the total entropy for the scheme
shift(int, int, Instance). Method in class weka.classifiers.j48.Distribution
Shifts given instance from one bag to another one.
shiftRange(int, int, Instances, int, int). Method in class weka.classifiers.j48.Distribution
Shifts all instances in given range from one bag to another one.
showDialog(). Method in class weka.gui.ListSelectorDialog
Pops up the modal dialog and waits for cancel or a selection.
showDialog(). Method in class weka.gui.PropertySelectorDialog
Pops up the modal dialog and waits for cancel or a selection.
sigLevel. Variable in class weka.experiment.PairedStats
The significance level for comparisons
SimpleCLI(). Constructor for class weka.gui.SimpleCLI
Constructor
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.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(). Constructor for class weka.classifiers.SMO
smoothen(). Method in class weka.classifiers.m5.Node
Smoothens all unsmoothed formulae at the tree leaves under this node.
smoothenFormula(Node). Method in class weka.classifiers.m5.Node
Recursively smoothens the unsmoothed linear model at this node with the unsmoothed linear models at the nodes above this
smoothenValue(double, double, int, int). Static method in class weka.classifiers.m5.M5Utils
Returns the smoothed values according to the smoothing formula (np+kq)/(n+k)
smOrEq(double, double). Static method in class weka.core.Utils
Tests if a is smaller or equal to b.
sort(Attribute). Method in class weka.core.Instances
Sorts the instances based on an attribute.
sort(double[]). Static method in class weka.core.Utils
Sorts a given array of doubles in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
sort(int). Method in class weka.core.Instances
Sorts the instances based on an attribute.
sort(int[]). Static method in class weka.core.Utils
Sorts a given array of integers in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
sourceClass(int, Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
sourceExpression(int, Instances). Method in class weka.classifiers.j48.BinC45Split
Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances). Method in class weka.classifiers.j48.C45Split
Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
sourceExpression(int, Instances). Method in class weka.classifiers.j48.NoSplit
Returns a string containing java source code equivalent to the test made at this node.
SpecialFunctions(). Constructor for class weka.core.SpecialFunctions
split(Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
Splits the given set of instances into subsets.
split(Instances). Method in class weka.classifiers.m5.Node
Splits the node recursively, unless there are few instances or instances have similar values of the class attribute
SplitCriterion(). Constructor for class weka.classifiers.j48.SplitCriterion
splitCritValue(Distribution). Method in class weka.classifiers.j48.EntropySplitCrit
Computes entropy for given distribution.
splitCritValue(Distribution). Method in class weka.classifiers.j48.GainRatioSplitCrit
This method is a straightforward implementation of the gain ratio criterion for the given distribution.
splitCritValue(Distribution). Method in class weka.classifiers.j48.InfoGainSplitCrit
This method is a straightforward implementation of the information gain criterion for the given distribution.
splitCritValue(Distribution). Method in class weka.classifiers.j48.SplitCriterion
Computes result of splitting criterion for given distribution.
splitCritValue(Distribution, Distribution). Method in class weka.classifiers.j48.EntropySplitCrit
Computes entropy of test distribution with respect to training distribution.
splitCritValue(Distribution, Distribution). Method in class weka.classifiers.j48.SplitCriterion
Computes result of splitting criterion for given training and test distributions.
splitCritValue(Distribution, Distribution, Distribution). Method in class weka.classifiers.j48.SplitCriterion
Computes result of splitting criterion for given training and test distributions and given default distribution.
splitCritValue(Distribution, Distribution, int). Method in class weka.classifiers.j48.SplitCriterion
Computes result of splitting criterion for given training and test distributions and given number of classes.
splitCritValue(Distribution, double). Method in class weka.classifiers.j48.InfoGainSplitCrit
This method computes the information gain in the same way C4.5 does.
splitCritValue(Distribution, double, double). Method in class weka.classifiers.j48.GainRatioSplitCrit
This method computes the gain ratio in the same way C4.5 does.
splitCritValue(Distribution, double, double). Method in class weka.classifiers.j48.InfoGainSplitCrit
This method computes the information gain in the same way C4.5 does.
SplitDatasetFilter(). Constructor for class weka.filters.SplitDatasetFilter
splitEnt(Distribution). Method in class weka.classifiers.j48.EntropyBasedSplitCrit
Computes entropy after splitting without considering the class values.
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.
sqrSum(int, Instances). Static method in class weka.classifiers.m5.M5Utils
Returns the squared sum of the instances values of an attribute
Stacking(). Constructor for class weka.classifiers.Stacking
Statistics(). Constructor for class weka.core.Statistics
Stats(). Constructor for class weka.classifiers.j48.Stats
Stats(). Constructor for class weka.experiment.Stats
statusMessage(String). Method in interface weka.gui.Logger
Sends the supplied message to the status line.
statusMessage(String). Method in class weka.gui.LogPanel
Sends the supplied message to the status line.
statusMessage(String). Method in class weka.gui.SysErrLog
Sends the supplied message to the status line.
stdDev. Variable in class weka.experiment.Stats
The std deviation of values at the last calculateDerived() call
stdDev(int, Instances). Static method in class weka.classifiers.m5.M5Utils
Returns the standard deviation value of the instances values of an attribute
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.
stringValue(Attribute). Method in class weka.core.Instance
Returns the value of a nominal (or string) attribute for the instance.
stringValue(int). Method in class weka.core.Instance
Returns the value of a nominal (or string) attribute for the instance.
studentTConfidenceInterval(int, double, double). Static method in class weka.core.Statistics
Computes absolute size of half of a student-t confidence interval for given degrees of freedom, probability, and observed value.
sub(int, Instance). Method in class weka.classifiers.j48.Distribution
Subtracts given instance from given bag.
SubsetEvaluator(). Constructor for class weka.attributeSelection.SubsetEvaluator
subtract(Distribution). Method in class weka.classifiers.j48.Distribution
Subtracts the given distribution from this one.
subtract(double). Method in class weka.experiment.Stats
Removes a value to the observed values (no checking is done that the value being removed was actually added).
subtract(double, double). Method in class weka.experiment.PairedStats
Removes an observed pair of values.
subtract(ItemSet). Method in class weka.associations.ItemSet
Subtracts an item set from another one.
sum. Variable in class weka.experiment.Stats
The sum of values seen
sum(double[]). Static method in class weka.core.Utils
Computes the sum of the elements of an array of doubles.
sum(int, Instances). Static method in class weka.classifiers.m5.M5Utils
Returns the sum of the instances values of an attribute
sum(int[]). Static method in class weka.core.Utils
Computes the sum of the elements of an array of integers.
sumOfWeights(). Method in class weka.core.Instances
Computes the sum of all the instances' weights.
sumSq. Variable in class weka.experiment.Stats
The sum of values squared seen
support(). Method in class weka.associations.ItemSet
Outputs the support for an item set.
supportsCustomEditor(). Method in class weka.gui.CostMatrixEditor
Returns true because we do support a custom editor.
supportsCustomEditor(). Method in class weka.gui.FileEditor
Returns true because we do support a custom editor.
supportsCustomEditor(). Method in class weka.gui.GenericArrayEditor
Returns true because we do support a custom editor.
supportsCustomEditor(). Method in class weka.gui.GenericObjectEditor
Returns true because we do support a custom editor.
swap(int, int). Method in class weka.core.FastVector
Swaps two elements in the vector.
SwapAttributeValuesFilter(). Constructor for class weka.filters.SwapAttributeValuesFilter
symmetricalUncertainty(double[][]). Static method in class weka.core.ContingencyTables
Calculates the symmetrical uncertainty for base 2.
SymmetricalUncertAttributeEval(). Constructor for class weka.attributeSelection.SymmetricalUncertAttributeEval
Constructor
synopsis(). Method in class weka.core.Option
Returns the option's synopsis.
SysErrLog(). Constructor for class weka.gui.SysErrLog

T

tableExists(String). Method in class weka.experiment.DatabaseUtils
Checks that a given table exists.
Tag(int, String). Constructor for class weka.core.Tag
TAGS_MODEL_TYPES. Static variable in class weka.classifiers.m5.M5Prime
TAGS_SELECTION. Static variable in class weka.attributeSelection.BestFirst
TAGS_SELECTION. Static variable in class weka.classifiers.LinearRegression
TAGS_WEIGHTING. Static variable in class weka.classifiers.IBk
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.
TimeSeriesDeltaFilter(). Constructor for class weka.filters.TimeSeriesDeltaFilter
TimeSeriesTranslateFilter(). Constructor for class weka.filters.TimeSeriesTranslateFilter
TIMESTAMP_FIELD_NAME. Static variable in class weka.experiment.CrossValidationResultProducer
TIMESTAMP_FIELD_NAME. Static variable in class weka.experiment.RandomSplitResultProducer
toClassDetailsString(). Method in class weka.classifiers.Evaluation
toClassDetailsString(String). Method in class weka.classifiers.Evaluation
For the following confusion matrix
 A B C
 5 1 0  A
 2 7 1  B
 1 1 9  C
 
Will print out a breakdown of the accuracy for each class, eg:
   TP    FP  Class
 0.85  0.14   A
 0.70  0.11   B
 0.82  0.06   C
 
Should be useful for ROC curves.
toCumulativeMarginDistributionString(). Method in class weka.classifiers.Evaluation
Output the cumulative margin distribution as a string suitable for input for gnuplot or similar package.
toInformationRetrievalStatisticsString(). Method in class weka.classifiers.Evaluation
Calls toInformationRetrievalStatisticsString() with a default title.
toInformationRetrievalStatisticsString(String). Method in class weka.classifiers.Evaluation
Outputs information retrieval statistics (precision, recall, f-measure) for two-class problems.
toMatrixString(). Method in class weka.classifiers.Evaluation
Calls toMatrixString() with a default title.
toMatrixString(String). Method in class weka.classifiers.Evaluation
Outputs the performance statistics as a classification confusion matrix.
toResultsString(). Method in class weka.attributeSelection.AttributeSelection
get a description of the attribute selection
toSource(String). Method in class weka.classifiers.AdaBoostM1
Returns the boosted model as Java source code.
toSource(String). Method in class weka.classifiers.j48.ClassifierTree
Returns source code for the tree as an if-then statement.
toSource(String). Method in class weka.classifiers.DecisionStump
Returns the decision tree as Java source code.
toSource(String). Method in class weka.classifiers.j48.J48
Returns tree as an if-then statement.
toSource(String). Method in class weka.classifiers.LogitBoost
Returns the boosted model as Java source code.
toSource(String). Method in interface weka.classifiers.Sourcable
Returns a string that describes the classifier as source.
toString(). Method in class weka.classifiers.AdaBoostM1
Returns description of the boosted classifier.
toString(). Method in class weka.associations.Apriori
Outputs the size of all the generated sets of itemsets and the rules.
toString(). Method in class weka.core.Attribute
Returns a description of this attribute in ARFF format.
toString(). Method in class weka.experiment.AveragingResultProducer
Gets a text descrption of the result producer.
toString(). Method in class weka.classifiers.Bagging
Returns description of the bagged classifier.
toString(). Method in class weka.attributeSelection.BestFirst
returns a description of the search as a String
toString(). Method in class weka.classifiers.BVDecompose
Returns description of the bias-variance decomposition results.
toString(). Method in class weka.attributeSelection.CfsSubsetEval
returns a string describing CFS
toString(). Method in class weka.classifiers.ClassificationViaRegression
Prints the classifiers.
toString(). Method in class weka.classifiers.j48.ClassifierDecList
Prints rules.
toString(). Method in class weka.experiment.ClassifierSplitEvaluator
Returns a text description of the split evaluator.
toString(). Method in class weka.classifiers.j48.ClassifierTree
Prints tree structure.
toString(). Method in class weka.clusterers.Cobweb
Returns a description of the clusterer as a string.
toString(). Method in class weka.attributeSelection.ConsistencySubsetEval
returns a description of the evaluator
toString(). Method in class weka.classifiers.CostSensitiveClassifier
Output a representation of this classifier
toString(). Method in class weka.experiment.CrossValidationResultProducer
Gets a text descrption of the result producer.
toString(). Method in class weka.classifiers.CVParameterSelection
Returns description of the cross-validated classifier.
toString(). Method in class weka.experiment.DatabaseResultProducer
Gets a text descrption of the result producer.
toString(). Method in class weka.estimators.DDConditionalEstimator
Display a representation of this estimator
toString(). Method in class weka.classifiers.DecisionStump
Returns a description of the classifier.
toString(). Method in class weka.classifiers.DecisionTable
Returns a description of the classifier.
toString(). Method in class weka.estimators.DiscreteEstimator
Display a representation of this estimator
toString(). Method in class weka.estimators.DKConditionalEstimator
Display a representation of this estimator
toString(). Method in class weka.estimators.DNConditionalEstimator
Display a representation of this estimator
toString(). Method in class weka.clusterers.EM
Outputs the generated clusters into a string.
toString(). Method in class weka.classifiers.m5.Errors
Converts the evaluation results of a model to a string
toString(). Method in class weka.attributeSelection.ExhaustiveSearch
prints a description of the search
toString(). Method in class weka.experiment.Experiment
Gets a string representation of the experiment configuration.
toString(). Method in class weka.classifiers.FilteredClassifier
Output a representation of this classifier
toString(). Method in class weka.attributeSelection.ForwardSelection
returns a description of the search.
toString(). Method in class weka.attributeSelection.GainRatioAttributeEval
Return a description of the evaluator
toString(). Method in class weka.attributeSelection.GeneticSearch
returns a description of the search
toString(). Method in class weka.classifiers.HyperPipes
Returns a description of this classifier.
toString(). Method in class weka.classifiers.IB1
Returns a description of this classifier.
toString(). Method in class weka.classifiers.IBk
Returns a description of this classifier.
toString(). Method in class weka.classifiers.Id3
Prints the decision tree using the private toString method from below.
toString(). Method in class weka.classifiers.m5.Impurity
Converts an Impurity object to a string
toString(). Method in class weka.attributeSelection.InfoGainAttributeEval
Describe the attribute evaluator
toString(). Method in class weka.core.Instance
Returns the description of one instance.
toString(). Method in class weka.core.Instances
Returns the dataset as a string in ARFF format.
toString(). Method in class weka.classifiers.j48.J48
Returns a description of the classifier.
toString(). Method in class weka.estimators.KDConditionalEstimator
Display a representation of this estimator
toString(). Method in class weka.classifiers.KernelDensity
Returns a description of the classifier.
toString(). Method in class weka.estimators.KernelEstimator
Display a representation of this estimator
toString(). Method in class weka.estimators.KKConditionalEstimator
Display a representation of this estimator
toString(). Method in class weka.classifiers.LinearRegression
Outputs the linear regression model as a string.
toString(). Method in class weka.classifiers.Logistic
Gets a string describing the classifier.
toString(). Method in class weka.classifiers.LogitBoost
Returns description of the boosted classifier.
toString(). Method in class weka.classifiers.LWR
Returns a description of this classifier.
toString(). Method in class weka.classifiers.m5.M5Prime
Converts the output of the training process into a string
toString(). Method in class weka.estimators.MahalanobisEstimator
Display a representation of this estimator
toString(). Method in class weka.classifiers.j48.MakeDecList
Outputs the classifier into a string.
toString(). Method in class weka.core.Matrix
Converts a matrix to a string
toString(). Method in class weka.classifiers.MultiClassClassifier
Prints the classifiers.
toString(). Method in class weka.classifiers.MultiScheme
Output a representation of this classifier
toString(). Method in class weka.classifiers.NaiveBayes
Returns a description of the classifier.
toString(). Method in class weka.classifiers.NaiveBayesSimple
Returns a description of the classifier.
toString(). Method in class weka.estimators.NDConditionalEstimator
Display a representation of this estimator
toString(). Method in class weka.estimators.NNConditionalEstimator
Display a representation of this estimator
toString(). Method in class weka.estimators.NormalEstimator
Display a representation of this estimator
toString(). Method in class weka.classifiers.OneR
Returns a description of the classifier
toString(). Method in class weka.attributeSelection.OneRAttributeEval
Return a description of the evaluator
toString(). Method in class weka.experiment.PairedStats
Returns statistics on the paired comparison.
toString(). Method in class weka.classifiers.j48.PART
Returns a description of the classifier
toString(). Method in class weka.estimators.PoissonEstimator
Display a representation of this estimator
toString(). Method in class weka.classifiers.Prism
Prints a description of the classifier.
toString(). Method in class weka.experiment.PropertyNode
Returns a string description of this property.
toString(). Method in class weka.core.Queue
Produces textual description of queue.
toString(). Method in class weka.attributeSelection.RandomSearch
prints a description of the search
toString(). Method in class weka.experiment.RandomSplitResultProducer
Gets a text descrption of the result producer.
toString(). Method in class weka.core.Range
Constructs a representation of the current range.
toString(). Method in class weka.attributeSelection.Ranker
returns a description of the search as a String
toString(). Method in class weka.classifiers.RegressionByDiscretization
Returns a description of the classifier.
toString(). Method in class weka.experiment.RegressionSplitEvaluator
Returns a text description of the split evaluator.
toString(). Method in class weka.attributeSelection.ReliefFAttributeEval
Return a description of the ReliefF attribute evaluator.
toString(). Method in class weka.classifiers.SMO
Prints out the classifier.
toString(). Method in class weka.classifiers.Stacking
Output a representation of this classifier
toString(). Method in class weka.experiment.Stats
Returns a string summarising the stats so far.
toString(). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Return a description of the evaluator
toString(). Method in class weka.classifiers.m5.Values
Converts the stats to a string
toString(). Method in class weka.classifiers.VotedPerceptron
Returns textual description of classifier.
toString(). Method in class weka.attributeSelection.WrapperSubsetEval
Returns a string describing the wrapper
toString(). Method in class weka.classifiers.ZeroR
Returns a description of the classifier.
toString(Attribute). Method in class weka.core.Instance
Returns the description of one value of the instance as a string.
toString(double, double, String, String). Method in class weka.classifiers.m5.Measures
Converts the performance measures to a string
toString(Instances). Method in class weka.associations.ItemSet
Returns the contents of an item set as a string.
toString(Instances). Method in class weka.classifiers.m5.Options
Prints information stored in an 'Options' object, basically containing command line options
toString(Instances). Method in class weka.classifiers.m5.SplitInfo
Converts the spliting information to string
toString(Instances, int). Method in class weka.classifiers.m5.Function
Converts a function to a string
toString(int). Method in class weka.core.Instance
Returns the description of one value of the instance as a string.
toString(int, int, int, int). Method in class weka.classifiers.m5.Matrix
Converts a matrix to a string
toString(int[], int, int). Static method in class weka.classifiers.m5.Ivector
Converts a string
toSummaryString(). Method in class weka.classifiers.CVParameterSelection
toSummaryString(). Method in class weka.classifiers.Evaluation
Calls toSummaryString() with no title and no complexity stats
toSummaryString(). Method in class weka.core.Instances
Generates a string summarizing the set of instances.
toSummaryString(). Method in class weka.classifiers.j48.J48
Returns a superconcise version of the model
toSummaryString(). Method in class weka.classifiers.j48.PART
Returns a superconcise version of the model
toSummaryString(). Method in interface weka.core.Summarizable
Returns a string that summarizes the object.
toSummaryString(boolean). Method in class weka.classifiers.Evaluation
Calls toSummaryString() with a default title.
toSummaryString(String, boolean). Method in class weka.classifiers.Evaluation
Outputs the performance statistics in summary form.
total(). Method in class weka.classifiers.j48.Distribution
Returns total number of (possibly fractional) instances.
trainCV(int, int). Method in class weka.core.Instances
Creates the training set for one fold of a cross-validation on the dataset.
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]
treeToString(int, double). Method in class weka.classifiers.m5.Node
Converts the tree under this node to a string
trimToSize(). Method in class weka.core.FastVector
Sets the vector's capacity to its size.
truePositives(int). Method in class weka.classifiers.Evaluation
Calculate the true positive rate with respect to a particular class.
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

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).
UnsupervisedAttributeEvaluator(). Constructor for class weka.attributeSelection.UnsupervisedAttributeEvaluator
UnsupervisedSubsetEvaluator(). Constructor for class weka.attributeSelection.UnsupervisedSubsetEvaluator
updateClassifier(Instance). Method in class weka.classifiers.HyperPipes
Updates the classifier.
updateClassifier(Instance). Method in class weka.classifiers.IB1
Updates the classifier.
updateClassifier(Instance). Method in class weka.classifiers.IBk
Adds the supplied instance to the training set
updateClassifier(Instance). Method in class weka.classifiers.LWR
Adds the supplied instance to the training set
updateClassifier(Instance). Method in class weka.classifiers.NaiveBayes
Updates the classifier with the given instance.
updateClassifier(Instance). Method in interface weka.classifiers.UpdateableClassifier
Updates a classifier using the given instance.
upDateCounter(Instance). Method in class weka.associations.ItemSet
Updates counter of item set with respect to given transaction.
upDateCounters(FastVector, Instances). Static method in class weka.associations.ItemSet
Updates counters for a set of item sets and a set of instances.
updatePriors(Instance). Method in class weka.classifiers.Evaluation
Updates the class prior probabilities (when incrementally training)
updateResult(String). Method in class weka.gui.ResultHistoryPanel
Tells any component currently displaying the named result that the contents of the result text in the StringBuffer have been updated.
useFilter(Instances, Filter). Static method in class weka.filters.Filter
Filters an entire set of instances through a filter and returns the new set.
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
value. Variable in class weka.experiment.PropertyNode
The current property value
value(Attribute). Method in class weka.core.Instance
Returns an instance's attribute value in internal format.
value(int). Method in class weka.core.Attribute
Returns a value of a nominal or string attribute.
value(int). Method in class weka.core.Instance
Returns an instance's attribute value in internal format.
valueNode(). Method in class weka.classifiers.m5.Node
Takes a constant value as the function at the node
Values(int, int, int, Instances). Constructor for class weka.classifiers.m5.Values
Constructs an object which stores some statistics of the instances such as sum, squared sum, variance, standard deviation
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
VisualizePanel(). Constructor for class weka.gui.explorer.VisualizePanel
Constructor
VotedPerceptron(). Constructor for class weka.classifiers.VotedPerceptron

W

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.
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
weights(Instance). Method in class weka.classifiers.j48.BinC45Split
Returns weights if instance is assigned to more than one subset.
weights(Instance). Method in class weka.classifiers.j48.C45Split
Returns weights if instance is assigned to more than one subset.
weights(Instance). Method in class weka.classifiers.j48.ClassifierSplitModel
Returns weights if instance is assigned to more than one subset.
weights(Instance). Method in class weka.classifiers.j48.NoSplit
Always returns null because there is only one subset.
whichSubset(Instance). Method in class weka.classifiers.j48.BinC45Split
Returns index of subset instance is assigned to.
whichSubset(Instance). Method in class weka.classifiers.j48.C45Split
Returns index of subset instance is assigned to.
whichSubset(Instance). Method in class weka.classifiers.j48.ClassifierSplitModel
Returns index of subset instance is assigned to.
whichSubset(Instance). Method in class weka.classifiers.j48.NoSplit
Always returns 0 because only there is only one subset.
WrapperSubsetEval(). Constructor for class weka.attributeSelection.WrapperSubsetEval
Constructor.
write(Writer). Method in class weka.core.Matrix
Writes out a matrix

X

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
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