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

E

E(Instances, boolean) - Method in class weka.clusterers.EM

The E step of the EM algorithm.

EAST_CONNECTOR - Static variable in class weka.gui.beans.BeanVisual

EDITOR_PROPERTIES - Static variable in class weka.gui.GenericObjectEditor

Contains the editor properties

EM - class weka.clusterers.EM.

Simple EM (expectation maximisation) class.

EM() - Constructor for class weka.clusterers.EM

Constructor.

EM_Init(Instances) - Method in class weka.clusterers.EM

Initialise estimators and storage.

EM_Report(Instances) - Method in class weka.clusterers.EM

verbose output for debugging

END - class weka.classifiers.meta.END.

Class for creating a committee of random classifiers.

END() - Constructor for class weka.classifiers.meta.END

Constructor.

ENTROPY - Static variable in interface weka.classifiers.bayes.Scoreable

EPSILON - Variable in class weka.classifiers.lazy.kstar.KStarCache.CacheTable

Accuracy value for equality

EPSILON - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants

EPSILON - Static variable in class weka.classifiers.misc.FLR

ERROR_SHAPE - Static variable in class weka.gui.visualize.Plot2D

EVAL_CROSS_VALIDATION - Static variable in class weka.classifiers.meta.ThresholdSelector

EVAL_TRAINING_SET - Static variable in class weka.classifiers.meta.ThresholdSelector

EVAL_TUNED_SPLIT - Static variable in class weka.classifiers.meta.ThresholdSelector

EXPLICIT - Static variable in class weka.associations.Tertius

Ways of handling missing values.

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)

Edge - class weka.gui.treevisualizer.Edge.

This class is used in conjunction with the Node class to form a tree structure.

Edge(String, String, String) - Constructor for class weka.gui.treevisualizer.Edge

This constructs an Edge with the specified label and parent , child serial tags.

EntropyBasedSplitCrit - class weka.classifiers.trees.j48.EntropyBasedSplitCrit.

"Abstract" class for computing splitting criteria based on the entropy of a class distribution.

EntropyBasedSplitCrit() - Constructor for class weka.classifiers.trees.j48.EntropyBasedSplitCrit

EntropySplitCrit - class weka.classifiers.trees.j48.EntropySplitCrit.

Class for computing the entropy for a given distribution.

EntropySplitCrit() - Constructor for class weka.classifiers.trees.j48.EntropySplitCrit

ErrorBasedMeritEvaluator - interface weka.attributeSelection.ErrorBasedMeritEvaluator.

Interface for evaluators that calculate the "merit" of attributes/subsets as the error of a learning scheme

Estimator - interface weka.estimators.Estimator.

Interface for probability estimators.

Evaluation - class weka.classifiers.Evaluation.

Class for evaluating machine learning models.

Evaluation(Instances) - Constructor for class weka.classifiers.Evaluation

Initializes all the counters for the evaluation.

Evaluation(Instances, CostMatrix) - Constructor for class weka.classifiers.Evaluation

Initializes all the counters for the evaluation and also takes a cost matrix as parameter.

EvaluationUtils - class weka.classifiers.evaluation.EvaluationUtils.

Contains utility functions for generating lists of predictions in various manners.

EvaluationUtils() - Constructor for class weka.classifiers.evaluation.EvaluationUtils

EventConstraints - interface weka.gui.beans.EventConstraints.

Interface for objects that want to be able to specify at any given time whether their current configuration allows a particular event to be generated.

ExhaustiveSearch - class weka.attributeSelection.ExhaustiveSearch.

Class for performing an exhaustive search.

ExhaustiveSearch() - Constructor for class weka.attributeSelection.ExhaustiveSearch

Constructor

Experiment - class weka.experiment.Experiment.

Holds all the necessary configuration information for a standard type experiment.

Experiment() - Constructor for class weka.experiment.Experiment

Experimenter - class weka.gui.experiment.Experimenter.

The main class for the experiment environment.

Experimenter(boolean) - Constructor for class weka.gui.experiment.Experimenter

Creates the experiment environment gui with no initial experiment

Explorer - class weka.gui.explorer.Explorer.

The main class for the Weka explorer.

Explorer() - Constructor for class weka.gui.explorer.Explorer

Creates the experiment environment gui with no initial experiment

ExponentialFormat - class weka.classifiers.functions.pace.ExponentialFormat.

ExponentialFormat() - Constructor for class weka.classifiers.functions.pace.ExponentialFormat

ExponentialFormat(int) - Constructor for class weka.classifiers.functions.pace.ExponentialFormat

ExponentialFormat(int, boolean) - Constructor for class weka.classifiers.functions.pace.ExponentialFormat

ExponentialFormat(int, int, boolean, boolean) - Constructor for class weka.classifiers.functions.pace.ExponentialFormat

ExtensionFileFilter - class weka.gui.ExtensionFileFilter.

Provides a file filter for FileChoosers that accepts or rejects files based on their extension.

ExtensionFileFilter(String, String) - Constructor for class weka.gui.ExtensionFileFilter

Creates the ExtensionFileFilter

ebEstimator - Static variable in class weka.classifiers.functions.PaceRegression

edgeAttrib(StreamTokenizer, GraphEdge) - Method in class weka.gui.graphvisualizer.DotParser

edgeStmt(StreamTokenizer, int) - Method in class weka.gui.graphvisualizer.DotParser

edgeStmt(String) - Method in class weka.gui.treevisualizer.TreeBuild

This will get the target of the edge.

edges - Variable in class weka.gui.graphvisualizer.GraphNode

The indices of nodes to which there are edges from this node, plus the type of edge

editableProperties() - Method in class weka.gui.PropertySheetPanel

Gets the number of editable properties for the current target.

eigenvalueDecomposition(double[][], double[]) - Method in class weka.core.Matrix

Performs Eigenvalue Decomposition using Householder QR Factorization This function is adapted from the CERN Jet Java libraries, for it the following copyright applies (see also, text on top of file) Copyright (C) 1999 CERN - European Organization for Nuclear Research.

element - Variable in class weka.associations.tertius.SimpleLinkedList.Entry

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.

eliminateColinearAttributesTipText() - Method in class weka.classifiers.functions.LinearRegression

Returns the tip text for this property

empiricalBayesEstimate(double) - Method in class weka.classifiers.functions.pace.NormalMixture

Returns the empirical Bayes estimate of a single value.

empiricalBayesEstimate(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture

Returns the empirical Bayes estimate of a vector.

empiricalProbability(DoubleVector, PaceMatrix) - Method in class weka.classifiers.functions.pace.MixtureDistribution

Computes the empirical probabilities of the data over a set of intervals.

empty() - Method in class weka.core.Queue

Checks if queue is empty.

encodedHierarchy - Variable in class weka.core.converters.ClassTreeParser

The encoded hierarchy currently to be parsed by this ClassTreeParser.

encodingFile - Variable in class weka.core.converters.ClassTreeFileParser

Keeps the name of file providing the hierarchy String.

endSearch() - Method in class weka.associations.Tertius

End the search by notifying to the waiting thread that it is finished.

entropicAutoBlendTipText() - Method in class weka.classifiers.lazy.KStar

Returns the tip text for this property

entropy(double[], double) - Method in class weka.classifiers.rules.ConjunctiveRule.Antd

Function used to calculate the entropy of given vector of values entropy = (1/sum)*{-sigma[i=1..P](Xi*log2(Xi)) + sum*log2(sum)} where P is the length of the vector

entropy(double[][], double[][]) - Method in class weka.classifiers.trees.lmt.ResidualSplit

Helper function to compute entropy from Z/W values.

entropy(double[]) - Static method in class weka.core.ContingencyTables

Computes the entropy of the given array.

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.

entropyGain() - Method in class weka.classifiers.trees.lmt.ResidualSplit

Computes entropy gain for current split.

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.

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.

enumerateLiterals() - Method in class weka.associations.tertius.LiteralSet

Enumerate the literals contained in this set.

enumerateMeasures() - Method in class weka.classifiers.functions.SimpleLogistic

Returns an enumeration of the additional measure names

enumerateMeasures() - Method in class weka.classifiers.meta.AdditiveRegression

Returns an enumeration of the additional measure names

enumerateMeasures() - Method in class weka.classifiers.meta.AttributeSelectedClassifier

Returns an enumeration of the additional measure names

enumerateMeasures() - Method in class weka.classifiers.meta.Bagging

Returns an enumeration of the additional measure names.

enumerateMeasures() - Method in class weka.classifiers.misc.FLR

Returns an enumeration of the additional measure names

enumerateMeasures() - Method in class weka.classifiers.rules.DecisionTable

Returns an enumeration of the additional measure names

enumerateMeasures() - Method in class weka.classifiers.rules.JRip

Returns an enumeration of the additional measure names

enumerateMeasures() - Method in class weka.classifiers.rules.PART

Returns an enumeration of the additional measure names

enumerateMeasures() - Method in class weka.classifiers.rules.Ridor

Returns an enumeration of the additional measure names

enumerateMeasures() - Method in class weka.classifiers.trees.ADTree

Returns an enumeration of the additional measure names.

enumerateMeasures() - Method in class weka.classifiers.trees.J48

Returns an enumeration of the additional measure names

enumerateMeasures() - Method in class weka.classifiers.trees.LMT

Returns an enumeration of the additional measure names

enumerateMeasures() - Method in class weka.classifiers.trees.REPTree

Returns an enumeration of the additional measure names.

enumerateMeasures() - Method in class weka.classifiers.trees.RandomForest

Returns an enumeration of the additional measure names.

enumerateMeasures() - Method in class weka.classifiers.trees.m5.M5Base

Returns an enumeration of the additional measure names

enumerateMeasures() - Method in interface weka.core.AdditionalMeasureProducer

Returns an enumeration of the measure names.

enumerateMeasures() - Method in class weka.experiment.AveragingResultProducer

Returns an enumeration of any additional measure names that might be in the result producer

enumerateMeasures() - Method in class weka.experiment.ClassifierSplitEvaluator

Returns an enumeration of any additional measure names that might be in the classifier

enumerateMeasures() - Method in class weka.experiment.CrossValidationResultProducer

Returns an enumeration of any additional measure names that might be in the SplitEvaluator

enumerateMeasures() - Method in class weka.experiment.DatabaseResultProducer

Returns an enumeration of any additional measure names that might be in the result producer

enumerateMeasures() - Method in class weka.experiment.LearningRateResultProducer

Returns an enumeration of any additional measure names that might be in the result producer

enumerateMeasures() - Method in class weka.experiment.RandomSplitResultProducer

Returns an enumeration of any additional measure names that might be in the SplitEvaluator

enumerateMeasures() - Method in class weka.experiment.RegressionSplitEvaluator

Returns an enumeration of any additional measure names that might be in the classifier

enumerateRequests() - Method in class weka.gui.beans.AttributeSummarizer

Return an enumeration of actions that the user can ask this bean to perform

enumerateRequests() - Method in class weka.gui.beans.Classifier

Return an enumeration of requests that can be made by the user

enumerateRequests() - Method in class weka.gui.beans.ClassifierPerformanceEvaluator

Return an enumeration of user activated requests for this bean

enumerateRequests() - Method in class weka.gui.beans.CrossValidationFoldMaker

Return an enumeration of user requests

enumerateRequests() - Method in class weka.gui.beans.DataVisualizer

Describe enumerateRequests method here.

enumerateRequests() - Method in class weka.gui.beans.Filter

Return an enumeration of user requests

enumerateRequests() - Method in class weka.gui.beans.GraphViewer

Return an enumeration of user requests

enumerateRequests() - Method in class weka.gui.beans.Loader

Get a list of user requests

enumerateRequests() - Method in class weka.gui.beans.StripChart

Describe enumerateRequests method here.

enumerateRequests() - Method in class weka.gui.beans.TextViewer

Get a list of user requests

enumerateRequests() - Method in class weka.gui.beans.TrainTestSplitMaker

Get list of user requests

enumerateRequests() - Method in interface weka.gui.beans.UserRequestAcceptor

Get a list of performable requests

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.

epsTipText() - Method in class weka.classifiers.functions.SMOreg

Returns the tip text for this property

epsilonParameterTipText() - Method in class weka.attributeSelection.SVMAttributeEval

Returns a tip text for this property suitable for display in the GUI

epsilonTipText() - Method in class weka.classifiers.functions.SMO

Returns the tip text for this property

epsilonTipText() - Method in class weka.classifiers.functions.SMOreg

Returns the tip text for this property

eq(double, double) - Static method in class weka.core.Utils

Tests if a is equal to b.

equal(FastVector, FastVector) - Method in class weka.core.Optimization

Check whether the two integer vectors equal to each other Two integer vectors are equal if all the elements are the same, regardless of the order of the elements

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.

equalTo(Splitter) - Method in class weka.classifiers.trees.adtree.Splitter

Tests whether two splitters are equivalent.

equalTo(Splitter) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit

Tests whether two splitters are equivalent.

equalTo(Splitter) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit

Tests whether two splitters are equivalent.

equalTo(Test) - Method in class weka.datagenerators.Test

Compares the test with the test that is given as parameter.

equals(Object) - Method in class weka.associations.ItemSet

Tests if two item sets are equal.

equals(Object) - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey

Tests if two instances are equal

equals(Object) - Method in class weka.classifiers.Evaluation

Tests whether the current evaluation object is equal to another evaluation object

equals(Object) - Method in class weka.classifiers.rules.DecisionTable.hashKey

Tests if two instances are equal

equals(Object) - Method in class weka.core.Attribute

Tests if given attribute is equal to this attribute.

equals(Object) - Method in class weka.core.SelectedTag

Returns true if this SelectedTag equals another object

equals(Object) - Method in class weka.core.SerializedObject

equals(Object) - Method in class weka.gui.graphvisualizer.GraphEdge

equals(Object) - Method in class weka.gui.graphvisualizer.GraphNode

Returns true if passed in argument is an instance of GraphNode and is equal to this node.

equivalentTipText() - Method in class weka.associations.Tertius

Returns the tip text for this property.

equivalentTo(Rule) - Method in class weka.associations.tertius.Rule

Test if this rule is equivalent to another rule.

errms(StreamTokenizer, String) - Method in class weka.core.Instances

Throws error message with line number and last token read.

errms(StreamTokenizer, String) - Static method in class weka.core.converters.ConverterUtils

Throws error message with line number and last token read.

error() - Method in class weka.classifiers.evaluation.NumericPrediction

Calculates the prediction error.

errorFunction(double) - Static method in class weka.core.Statistics

Returns the error function of the normal distribution.

errorFunctionComplemented(double) - Static method in class weka.core.Statistics

Returns the complementary Error function of the normal distribution.

errorOnProbabilitiesTipText() - Method in class weka.classifiers.functions.SimpleLogistic

Returns the tip text for this property

errorOnProbabilitiesTipText() - Method in class weka.classifiers.trees.LMT

Returns the tip text for this property

errorRate() - Method in class weka.classifiers.Evaluation

Returns the estimated error rate or the root mean squared error (if the class is numeric).

errorRate() - Method in class weka.classifiers.evaluation.ConfusionMatrix

Returns the estimated error rate.

errorValue(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd

Call this to get the error value of this unit, which in this case is the difference between the predicted class, and the actual class.

errorValue(NeuralNode) - Method in class weka.classifiers.functions.neural.LinearUnit

This function calculates what the error value should be.

errorValue(boolean) - Method in class weka.classifiers.functions.neural.NeuralConnection

Call this to get the error value of this unit.

errorValue(NeuralNode) - Method in interface weka.classifiers.functions.neural.NeuralMethod

This function calculates what the error value should be.

errorValue(boolean) - Method in class weka.classifiers.functions.neural.NeuralNode

Call this to get the error value of this unit.

errorValue(NeuralNode) - Method in class weka.classifiers.functions.neural.SigmoidUnit

This function calculates what the error value should be.

errorsForLeaf() - Method in class weka.classifiers.rules.part.PruneableDecList

Computes estimated errors for leaf.

errorsForLeaf() - Method in class weka.classifiers.trees.j48.PruneableClassifierTree

Computes estimated errors for leaf.

errorsForTree() - Method in class weka.classifiers.rules.part.PruneableDecList

Computes error estimate for tree.

errorsForTree() - Method in class weka.classifiers.trees.j48.PruneableClassifierTree

Computes estimated errors for tree.

estimateAccuracy(BitSet, int) - Method in class weka.classifiers.rules.DecisionTable

Evaluates a feature subset by cross validation

estimateCPTs() - Method in class weka.classifiers.bayes.BayesNet

estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.

estimate_priors(Instances) - Method in class weka.clusterers.EM

calculate prior probabilites for the clusters

estimatorTipText() - Method in class weka.classifiers.functions.PaceRegression

Returns the tip text for this property

eval(int, int, Instance) - Method in class weka.classifiers.functions.supportVector.Kernel

Computes the result of the kernel function for two instances.

eval(int, int, Instance) - Method in class weka.classifiers.functions.supportVector.NormalizedPolyKernel

Redefines the eval function of PolyKernel.

eval(int, int, Instance) - Method in class weka.classifiers.functions.supportVector.PolyKernel

Implements the abstract function of Kernel.

eval(int, int, Instance) - Method in class weka.classifiers.functions.supportVector.RBFKernel

Implements the abstract function of Kernel.

evalUsingTrainingDataTipText() - Method in class weka.attributeSelection.OneRAttributeEval

Returns a string for this option suitable for display in the gui as a tip text

evaluateAttribute(int) - Method in class weka.attributeSelection.AttributeEvaluator

evaluates an individual attribute

evaluateAttribute(int) - Method in class weka.attributeSelection.ChiSquaredAttributeEval

evaluates an individual attribute by measuring its chi-squared value.

evaluateAttribute(int) - Method in class weka.attributeSelection.GainRatioAttributeEval

evaluates an individual attribute by measuring the gain ratio of the class given the attribute.

evaluateAttribute(int) - Method in class weka.attributeSelection.InfoGainAttributeEval

evaluates an individual attribute by measuring the amount of information gained about the class given the attribute.

evaluateAttribute(int) - Method in class weka.attributeSelection.OneRAttributeEval

evaluates an individual attribute by measuring the amount of information gained about the class given the attribute.

evaluateAttribute(int) - Method in class weka.attributeSelection.PrincipalComponents

Evaluates the merit of a transformed attribute.

evaluateAttribute(int) - Method in class weka.attributeSelection.ReliefFAttributeEval

Evaluates an individual attribute using ReliefF's instance based approach.

evaluateAttribute(int) - Method in class weka.attributeSelection.SVMAttributeEval

Evaluates an attribute by returning the rank of the square of its coefficient in a linear support vector machine.

evaluateAttribute(int) - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval

evaluates an individual attribute by measuring the symmetrical uncertainty between it and the class.

evaluateClusterer(Instances) - Method in class weka.clusterers.ClusterEvaluation

Evaluate the clusterer on a set of instances.

evaluateClusterer(Clusterer, String[]) - Static method in class weka.clusterers.ClusterEvaluation

Evaluates a clusterer with the options given in an array of strings.

evaluateClustersWithRespectToClass(Instances) - Method in class weka.clusterers.ClusterEvaluation

Evaluates cluster assignments with respect to actual class labels.

evaluateExpression(double[]) - Method in class weka.filters.unsupervised.attribute.AddExpression

Evaluate the expression using the supplied array of attribute values.

evaluateGradient(double[]) - Method in class weka.classifiers.functions.Logistic.OptEng

Evaluate Jacobian vector

evaluateGradient(double[]) - Method in class weka.core.Optimization

evaluateHessian(double[], int) - Method in class weka.core.Optimization

evaluateModel(String, String[]) - Static method in class weka.classifiers.Evaluation

Evaluates a classifier with the options given in an array of strings.

evaluateModel(Classifier, String[]) - Static method in class weka.classifiers.Evaluation

Evaluates a classifier with the options given in an array of strings.

evaluateModel(Classifier, Instances) - Method in class weka.classifiers.Evaluation

Evaluates the classifier on a given set of instances.

evaluateModelOnce(Classifier, Instance) - Method in class weka.classifiers.Evaluation

Evaluates the classifier on a single instance.

evaluateModelOnce(double[], Instance) - Method in class weka.classifiers.Evaluation

Evaluates the supplied distribution on a single instance.

evaluateModelOnce(double, Instance) - Method in class weka.classifiers.Evaluation

Evaluates the supplied prediction on a single instance.

evaluateNominalSplitSingle(int, PredictionNode, Instances, Instances) - Method in class weka.classifiers.trees.ADTree

Investigates the option of introducing a nominal split under currentNode.

evaluateNumericSplitSingle(int, PredictionNode, Instances, Instances, Instances) - Method in class weka.classifiers.trees.ADTree

Investigates the option of introducing a two-way numeric split under currentNode.

evaluatePopulation(SubsetEvaluator) - Method in class weka.attributeSelection.GeneticSearch

evaluates an entire population.

evaluateProbability(double[]) - Method in class weka.classifiers.functions.Logistic

Compute the posterior distribution using optimized parameter values and the testing instance.

evaluateSubset(BitSet) - Method in class weka.attributeSelection.CfsSubsetEval

evaluates a subset of attributes

evaluateSubset(BitSet) - Method in class weka.attributeSelection.ClassifierSubsetEval

Evaluates a subset of attributes

evaluateSubset(BitSet, Instances) - Method in class weka.attributeSelection.ClassifierSubsetEval

Evaluates a subset of attributes with respect to a set of instances.

evaluateSubset(BitSet, Instance, boolean) - Method in class weka.attributeSelection.ClassifierSubsetEval

Evaluates a subset of attributes with respect to a single instance.

evaluateSubset(BitSet) - Method in class weka.attributeSelection.ConsistencySubsetEval

Evaluates a subset of attributes

evaluateSubset(BitSet, Instances) - Method in class weka.attributeSelection.HoldOutSubsetEvaluator

Evaluates a subset of attributes with respect to a set of instances.

evaluateSubset(BitSet, Instance, boolean) - Method in class weka.attributeSelection.HoldOutSubsetEvaluator

Evaluates a subset of attributes with respect to a single instance.

evaluateSubset(BitSet) - Method in class weka.attributeSelection.SubsetEvaluator

evaluates a subset of attributes

evaluateSubset(BitSet) - Method in class weka.attributeSelection.WrapperSubsetEval

Evaluates a subset of attributes

evaluationModeTipText() - Method in class weka.classifiers.meta.ThresholdSelector

evaluatorTipText() - Method in class weka.classifiers.meta.AttributeSelectedClassifier

Returns the tip text for this property

evaluatorTipText() - Method in class weka.filters.supervised.attribute.AttributeSelection

Returns the tip text for this property

eventGeneratable(String) - Method in class weka.gui.beans.ClassAssigner

Returns true, if at the current time, the named event could be generated.

eventGeneratable(EventSetDescriptor) - Method in class weka.gui.beans.Classifier

Returns true, if at the current time, the event described by the supplied event descriptor could be generated.

eventGeneratable(String) - Method in class weka.gui.beans.Classifier

Returns true, if at the current time, the named event could be generated.

eventGeneratable(String) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator

Returns true, if at the current time, the named event could be generated.

eventGeneratable(String) - Method in class weka.gui.beans.CrossValidationFoldMaker

Returns true, if at the current time, the named event could be generated.

eventGeneratable(String) - Method in interface weka.gui.beans.EventConstraints

Returns true if, at the current time, the named event could be generated.

eventGeneratable(String) - Method in class weka.gui.beans.Filter

Returns true, if at the current time, the named event could be generated.

eventGeneratable(String) - Method in class weka.gui.beans.IncrementalClassifierEvaluator

Returns true, if at the current time, the named event could be generated.

eventGeneratable(String) - Method in class weka.gui.beans.Loader

Returns true if the named event can be generated at this time

eventGeneratable(String) - Method in class weka.gui.beans.PredictionAppender

Returns true, if at the current time, the named event could be generated.

eventGeneratable(String) - Method in class weka.gui.beans.TestSetMaker

Returns true, if at the current time, the named event could be generated.

eventGeneratable(String) - Method in class weka.gui.beans.TrainTestSplitMaker

Returns true, if at the current time, the named event could be generated.

eventGeneratable(String) - Method in class weka.gui.beans.TrainingSetMaker

Returns true, if at the current time, the named event could be generated.

examineExample(int) - Method in class weka.classifiers.functions.SMO.BinarySMO

Examines instance.

examineExample(int) - Method in class weka.classifiers.functions.SMOreg

Examines instance.

excepts - Variable in class weka.classifiers.rules.Ridor.Ridor_node

The exceptions of the exception rules

exclusiveTipText() - Method in class weka.classifiers.rules.ConjunctiveRule

Returns the tip text for this property

execute(String) - Method in class weka.experiment.DatabaseUtils

Executes a SQL query.

execute() - Method in class weka.experiment.RemoteExperimentSubTask

Run the experiment

execute() - Method in interface weka.experiment.Task

Execute this task.

execute() - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask

Perform the sub task

executeTask(Task) - Method in interface weka.experiment.Compute

Execute a task

executeTask(Task) - Method in class weka.experiment.RemoteEngine

Takes a task object and queues it for execution

exp - Variable in class weka.classifiers.functions.pace.ExponentialFormat

exp - Variable in class weka.classifiers.functions.pace.FlexibleDecimalFormat

expDecimalDigits - Variable in class weka.classifiers.functions.pace.FlexibleDecimalFormat

expIntDigits - Variable in class weka.classifiers.functions.pace.FlexibleDecimalFormat

expParamChanged() - Method in class weka.gui.experiment.SimpleSetupPanel

Responds to a change in the experiment parameter.

expTypeChanged() - Method in class weka.gui.experiment.SimpleSetupPanel

Responds to a change in the experiment type.

expectedCosts(double[]) - Method in class weka.classifiers.CostMatrix

Calculates the expected misclassification cost for each possible class value, given class probability estimates.

expectedResultsPerAverageTipText() - Method in class weka.experiment.AveragingResultProducer

Returns the tip text for this property

experimentIndexExists() - Method in class weka.experiment.DatabaseUtils

Returns true if the experiment index exists.

exponentTipText() - Method in class weka.classifiers.functions.SMO

Returns the tip text for this property

exponentTipText() - Method in class weka.classifiers.functions.SMOreg

Returns the tip text for this property

exponentTipText() - Method in class weka.classifiers.functions.VotedPerceptron

Returns the tip text for this property

expressionTipText() - Method in class weka.filters.unsupervised.attribute.AddExpression

Returns the tip text for this property

extendColourMap() - Method in class weka.gui.visualize.AttributePanel

Adds more colours to the colour list

extendColourMap() - Method in class weka.gui.visualize.ClassPanel

Extends the list of colours if a new attribute with more values than the previous one is chosen

extendColourMap(int) - Method in class weka.gui.visualize.Plot2D

Add more colours to the colour map

extpad - Variable in class weka.gui.visualize.MatrixPanel.Plot