All Packages Class Hierarchy
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Index of all Fields and Methods
- 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
-
- 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
-
- 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.
- 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
-
- 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
- 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
- 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
- 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
-
- 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
-
- 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.
- 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
- 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
-
- 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
-
- 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.
- 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
-
- 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.
- Queue().
Constructor for class weka.core.Queue
-
- quote(String).
Static method in class weka.core.Utils
- Quotes a string if it contains special characters.
- 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.
- 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
-
- 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.
- 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
-
- 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
-
- 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
- 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
- yStats.
Variable in class weka.experiment.PairedStats
- The stats associated with the data in column 2