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

D

DATASET_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer

DATASET_FIELD_NAME - Static variable in class weka.experiment.RandomSplitResultProducer

DATE - Static variable in class weka.core.Attribute

Constant set for attributes with date values.

DATE - Static variable in class weka.experiment.DatabaseUtils

DDConditionalEstimator - class weka.estimators.DDConditionalEstimator.

Conditional probability estimator for a discrete domain conditional upon a discrete domain.

DDConditionalEstimator(int, int, boolean) - Constructor for class weka.estimators.DDConditionalEstimator

Constructor

DEFAULT_COLORS - Static variable in class weka.gui.boundaryvisualizer.BoundaryPanel

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

The default load factor for the hashtable

DEFAULT_NUM_PRECISION - Static variable in class weka.classifiers.bayes.NaiveBayes

The precision parameter used for numeric attributes

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

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

The default size of the hashtable

DEFAULT_WEIGHT - Static variable in class weka.core.converters.HierarchicalCostMatrix

The default weight of classes.

DEST_ARFF_TEXT - Static variable in class weka.gui.experiment.SimpleSetupPanel

DEST_CSV_TEXT - Static variable in class weka.gui.experiment.SimpleSetupPanel

DEST_DATABASE_TEXT - Static variable in class weka.gui.experiment.SimpleSetupPanel

The strings used to identify the combo box choices

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

DIRECTED - Static variable in interface weka.gui.graphvisualizer.GraphConstants

Types of Edges

DKConditionalEstimator - class weka.estimators.DKConditionalEstimator.

Conditional probability estimator for a discrete domain conditional upon a numeric domain.

DKConditionalEstimator(int, double) - Constructor for class weka.estimators.DKConditionalEstimator

Constructor

DNConditionalEstimator - class weka.estimators.DNConditionalEstimator.

Conditional probability estimator for a discrete domain conditional upon a numeric domain.

DNConditionalEstimator(int, double) - Constructor for class weka.estimators.DNConditionalEstimator

Constructor

DOUBLE - Static variable in class weka.experiment.DatabaseUtils

DOUBLE - Static variable in interface weka.gui.graphvisualizer.GraphConstants

Types of Edges

DRIVERS - Static variable in class weka.experiment.DatabaseUtils

Holds the jdbc drivers to be used (only to stop them being gc'ed)

DataGenerator - interface weka.gui.boundaryvisualizer.DataGenerator.

Interface to something that can generate new instances based on a set of input instances

DataSetEvent - class weka.gui.beans.DataSetEvent.

Event encapsulating a data set

DataSetEvent(Object, Instances) - Constructor for class weka.gui.beans.DataSetEvent

DataSink - interface weka.gui.beans.DataSink.

Indicator interface to something that can store instances to some destination

DataSource - interface weka.gui.beans.DataSource.

Interface to something that is capable of being a source for data - either batch or incremental data

DataSourceListener - interface weka.gui.beans.DataSourceListener.

Interface to something that can accept DataSetEvents

DataVisualizer - class weka.gui.beans.DataVisualizer.

Bean that encapsulates weka.gui.visualize.VisualizePanel

DataVisualizer() - Constructor for class weka.gui.beans.DataVisualizer

DataVisualizerBeanInfo - class weka.gui.beans.DataVisualizerBeanInfo.

Bean info class for the data visualizer

DataVisualizerBeanInfo() - Constructor for class weka.gui.beans.DataVisualizerBeanInfo

DatabaseConnectionDialog - class weka.gui.DatabaseConnectionDialog.

A dialog to enter URL, username and password for a database connection.

DatabaseConnectionDialog(Frame) - Constructor for class weka.gui.DatabaseConnectionDialog

Create database connection dialog.

DatabaseConnectionDialog(Frame, String, String) - Constructor for class weka.gui.DatabaseConnectionDialog

Create database connection dialog.

DatabaseResultListener - class weka.experiment.DatabaseResultListener.

DatabaseResultListener takes the results from a ResultProducer and submits them to a central database.

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

Sets up the database drivers

DatabaseResultProducer - class weka.experiment.DatabaseResultProducer.

DatabaseResultProducer examines a database and extracts out the results produced by the specified ResultProducer and submits them to the specified ResultListener.

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

Creates the DatabaseResultProducer, letting the parent constructor do it's thing.

DatabaseUtils - class weka.experiment.DatabaseUtils.

DatabaseUtils provides utility functions for accessing the experiment database.

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

Sets up the database drivers

DatasetListPanel - class weka.gui.experiment.DatasetListPanel.

This panel controls setting a list of datasets for an experiment to iterate over.

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

Creates the dataset list panel with the given experiment.

DatasetListPanel() - Constructor for class weka.gui.experiment.DatasetListPanel

Create the dataset list panel initially disabled.

DbConnectionDialog(String, String) - Method in class weka.gui.DatabaseConnectionDialog

Display the database connection dialog

DecisionStump - class weka.classifiers.trees.DecisionStump.

Class for building and using a decision stump.

DecisionStump() - Constructor for class weka.classifiers.trees.DecisionStump

DecisionTable - class weka.classifiers.rules.DecisionTable.

Class for building and using a simple decision table majority classifier.

DecisionTable() - Constructor for class weka.classifiers.rules.DecisionTable

Constructor for a DecisionTable

DecisionTable.Link - class weka.classifiers.rules.DecisionTable.Link.

Class for a node in a linked list.

DecisionTable.Link(BitSet, double) - Constructor for class weka.classifiers.rules.DecisionTable.Link

The constructor.

DecisionTable.LinkedList - class weka.classifiers.rules.DecisionTable.LinkedList.

Class for handling a linked list.

DecisionTable.LinkedList() - Constructor for class weka.classifiers.rules.DecisionTable.LinkedList

DecisionTable.hashKey - class weka.classifiers.rules.DecisionTable.hashKey.

Class providing keys to the hash table

DecisionTable.hashKey(Instance, int) - Constructor for class weka.classifiers.rules.DecisionTable.hashKey

Constructor for a hashKey

DecisionTable.hashKey(double[]) - Constructor for class weka.classifiers.rules.DecisionTable.hashKey

Constructor for a hashKey

Decorate - class weka.classifiers.meta.Decorate.

DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.

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

DeleteLastParent(Instances) - Method in class weka.classifiers.bayes.ParentSet

Delete last added parent from parent set and update internals (specifically the cardinality of the parent set)

DensityBasedClusterer - class weka.clusterers.DensityBasedClusterer.

Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie. a probability distribution).

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

DiscreteEstimator - class weka.estimators.DiscreteEstimator.

Simple symbolic probability estimator based on symbol counts.

DiscreteEstimator(int, boolean) - Constructor for class weka.estimators.DiscreteEstimator

Constructor

DiscreteEstimator(int, double) - Constructor for class weka.estimators.DiscreteEstimator

Constructor

DiscreteEstimatorBayes - class weka.classifiers.bayes.DiscreteEstimatorBayes.

Symbolic probability estimator based on symbol counts and a prior.

DiscreteEstimatorBayes(int, double) - Constructor for class weka.classifiers.bayes.DiscreteEstimatorBayes

Constructor

DiscreteFunction - class weka.classifiers.functions.pace.DiscreteFunction.

Class for handling discrete functions.

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

Constructs an empty discrete function

DiscreteFunction(DoubleVector) - Constructor for class weka.classifiers.functions.pace.DiscreteFunction

Constructs a discrete function with the point values provides and the function values are all 1/n.

DiscreteFunction(DoubleVector, DoubleVector) - Constructor for class weka.classifiers.functions.pace.DiscreteFunction

Constructs a discrete function with both the point values and function values provided.

Discretize - class weka.filters.supervised.attribute.Discretize.

An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.

Discretize() - Constructor for class weka.filters.supervised.attribute.Discretize

Constructor - initialises the filter

Discretize - class weka.filters.unsupervised.attribute.Discretize.

An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.

Discretize() - Constructor for class weka.filters.unsupervised.attribute.Discretize

Constructor - initialises the filter

Discretize(String) - Constructor for class weka.filters.unsupervised.attribute.Discretize

Another constructor

DistributeExperimentPanel - class weka.gui.experiment.DistributeExperimentPanel.

This panel enables an experiment to be distributed to multiple hosts; it also allows remote host names to be specified.

DistributeExperimentPanel() - Constructor for class weka.gui.experiment.DistributeExperimentPanel

Constructor

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

Creates the panel with the supplied initial experiment.

Distribution - class weka.classifiers.trees.j48.Distribution.

Class for handling a distribution of class values.

Distribution(int, int) - Constructor for class weka.classifiers.trees.j48.Distribution

Creates and initializes a new distribution.

Distribution(double[][]) - Constructor for class weka.classifiers.trees.j48.Distribution

Creates and initializes a new distribution using the given array.

Distribution(Instances) - Constructor for class weka.classifiers.trees.j48.Distribution

Creates a distribution with only one bag according to instances in source.

Distribution(Instances, ClassifierSplitModel) - Constructor for class weka.classifiers.trees.j48.Distribution

Creates a distribution according to given instances and split model.

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

Creates distribution with only one bag by merging all bags of given distribution.

Distribution(Distribution, int) - Constructor for class weka.classifiers.trees.j48.Distribution

Creates distribution with two bags by merging all bags apart of the indicated one.

DotParser - class weka.gui.graphvisualizer.DotParser.

This class parses input in DOT format, and builds the datastructures that are passed to it.

DotParser(Reader, FastVector, FastVector) - Constructor for class weka.gui.graphvisualizer.DotParser

Dot parser Constructor

DoubleVector - class weka.classifiers.functions.pace.DoubleVector.

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

Constructs a null vector.

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

Constructs an n-vector of zeros.

DoubleVector(int, double) - Constructor for class weka.classifiers.functions.pace.DoubleVector

Constructs a constant n-vector.

DoubleVector(double[]) - Constructor for class weka.classifiers.functions.pace.DoubleVector

Constructs a vector directly from a double array

Drawable - interface weka.core.Drawable.

Interface to something that can be drawn as a graph.

data - Variable in class weka.gui.graphvisualizer.GraphVisualizer.GraphVisualizerTableModel

dataDL(double, double, double, double, double) - Static method in class weka.classifiers.rules.RuleStats

The description length of data given the parameters of the data based on the ruleset.

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

Returns the tip text for this property

datapointSize - Variable in class weka.gui.visualize.MatrixPanel

This stores the size of the datapoint

dataset() - Method in class weka.core.Instance

Returns the dataset this instance has access to.

dataset(Instance) - Method in class weka.experiment.PairedTTester.Resultset

Returns a vector containing all instances belonging to one dataset.

datasetIntegrity(boolean, boolean, boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier

Checks whether the scheme alters the training dataset during training.

dchisq(double) - Static method in class weka.classifiers.functions.pace.Maths

Returns the density of the Chi-squared distribution.

dchisq(double, double) - Static method in class weka.classifiers.functions.pace.Maths

Returns the density of the noncentral Chi-squared distribution.

dchisq(double, DoubleVector) - Static method in class weka.classifiers.functions.pace.Maths

Returns the density of the noncentral Chi-squared distribution.

dchisqLog(double) - Static method in class weka.classifiers.functions.pace.Maths

Returns the log-density of the noncentral Chi-square distribution.

dchisqLog(double, double) - Static method in class weka.classifiers.functions.pace.Maths

Returns the log-density value of a noncentral Chi-square distribution.

dchisqLog(double, DoubleVector) - Static method in class weka.classifiers.functions.pace.Maths

Returns the log-density of a set of noncentral Chi-squared distributions.

debugTipText() - Method in class weka.attributeSelection.RaceSearch

Returns the tip text for this property

debugTipText() - Method in class weka.classifiers.Classifier

Returns the tip text for this property

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

Returns the tip text for this property

debugTipText() - Method in class weka.classifiers.functions.Logistic

Returns the tip text for this property

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

Returns the tip text for this property

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

Returns the tip text for this property

debugTipText() - Method in class weka.classifiers.meta.MultiScheme

Returns the tip text for this property

debugTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost

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

Returns the tip text for this property

debugTipText() - Method in class weka.classifiers.trees.RandomTree

Returns the tip text for this property

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

Returns the tip text for this property

decayTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron

decimal - Variable in class weka.classifiers.functions.pace.FloatingPointFormat

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

decimalDigits(double, boolean) - Method in class weka.classifiers.functions.pace.FlexibleDecimalFormat

decodeClassTree(String) - Method in class weka.core.converters.ClassTreeParser

Decodes the given code recursively.

decompose() - Method in class weka.classifiers.BVDecompose

Carry out the bias-variance decomposition

decompose() - Method in class weka.classifiers.BVDecomposeSegCVSub

Carry out the bias-variance decomposition using the sub-sampled cross-validation method.

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

The default class label

defaultClassifierString() - Method in class weka.classifiers.SingleClassifierEnhancer

String describing default classifier.

defaultClassifierString() - Method in class weka.classifiers.lazy.LWL

String describing default classifier.

defaultClassifierString() - Method in class weka.classifiers.meta.AdaBoostM1

String describing default classifier.

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

String describing default classifier.

defaultClassifierString() - Method in class weka.classifiers.meta.ClassificationViaRegression

String describing default classifier.

defaultClassifierString() - Method in class weka.classifiers.meta.END

String describing default classifier.

defaultClassifierString() - Method in class weka.classifiers.meta.LogitBoost

String describing default classifier.

defaultClassifierString() - Method in class weka.classifiers.meta.RandomCommittee

String describing default classifier.

defaultClassifierString() - Method in class weka.classifiers.meta.RegressionByDiscretization

String describing default classifier.

defaultWeightTipText() - Method in class weka.classifiers.functions.Winnow

Returns the tip text for this property

defineClusters(Random) - Method in class weka.datagenerators.BIRCHCluster

Defines the clusters

defineClustersGRID(Random) - Method in class weka.datagenerators.BIRCHCluster

Defines the clusters if pattern is GRID

defineClustersRANDOM(Random) - Method in class weka.datagenerators.BIRCHCluster

Defines the clusters if pattern is RANDOM

defineDataFormat() - Method in class weka.datagenerators.BIRCHCluster

Initializes the format for the dataset produced.

defineDataFormat() - Method in class weka.datagenerators.ClusterGenerator

Initializes the format for the dataset produced.

defineDataFormat() - Method in class weka.datagenerators.Generator

Initializes the format for the dataset produced.

defineDataFormat() - Method in class weka.datagenerators.RDG1

Initializes the format for the dataset produced.

defineDataset(Random) - Method in class weka.datagenerators.RDG1

Returns a dataset header.

defineIrrelevant(Random) - Method in class weka.datagenerators.RDG1

Defines randomly the attributes as irrelevant.

defineNumeric(Random) - Method in class weka.datagenerators.RDG1

Chooses randomly the attributes that get datatyp numeric.

del(int, Instance) - Method in class weka.classifiers.trees.j48.Distribution

Deletes given instance from given bag.

delRange(int, Instances, int, int) - Method in class weka.classifiers.trees.j48.Distribution

Deletes all instances in given range from given bag.

delete(int) - Method in class weka.classifiers.functions.supportVector.SMOset

Deletes an element from the set.

delete(int) - Method in class weka.core.Attribute

Removes a value of a nominal or string attribute.

delete() - Method in class weka.core.Instances

Removes all instances from the set.

delete(int) - Method in class weka.core.Instances

Removes an instance at the given position from the set.

deleteAttributeAt(int) - Method in class weka.core.ClassRemoveableInstances

Deletes an attribute at the given position (0 to numAttributes() - 1).

deleteAttributeAt(int) - Method in class weka.core.Instance

Deletes an attribute at the given position (0 to numAttributes() - 1).

deleteAttributeAt(int) - Method in class weka.core.Instances

Deletes an attribute at the given position (0 to numAttributes() - 1).

deleteConnectionPopup(Vector, int, int) - Method in class weka.gui.beans.KnowledgeFlow

Popup a menu giving choices for connections to delete (if any)

deleteItemSets(FastVector, int, int) - Static method in class weka.associations.ItemSet

Deletes all item sets that don't have minimum support.

deleteStringAttributes() - Method in class weka.core.Instances

Deletes all string attributes in the dataset.

deleteWithMissing(int) - Method in class weka.core.Instances

Removes all instances with missing values for a particular attribute from the dataset.

deleteWithMissing(Attribute) - 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.

delimiters - Variable in class weka.filters.unsupervised.attribute.StringToWordVector

Delimiters used in tokenization

delimitersTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector

Returns the tip text for this property

deltaTipText() - Method in class weka.associations.Apriori

Returns the tip text for this property

depth() - Method in interface weka.core.ClassHierarchy

Returns the depth of the Hierarchy.

depth() - Method in class weka.core.ClassTree

Returns the depth of this ClassTree.

depth() - Method in class weka.gui.HierarchyPropertyParser

Get the depth of the tree, i.e.

description() - Method in class weka.associations.tertius.Predicate

description() - Method in class weka.core.Option

Returns the option's description.

deselectColinearAttributes(boolean[], double[]) - Method in class weka.classifiers.functions.LinearRegression

Removes the attribute with the highest standardised coefficient greater than 1.5 from the selected attributes.

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

desiredSizeTipText() - Method in class weka.classifiers.meta.Decorate

Returns the tip text for this property

desiredWeightOfInstancesPerIntervalTipText() - Method in class weka.filters.unsupervised.attribute.Discretize

Returns the tip text for this property

dest - Variable in class weka.gui.graphvisualizer.GraphEdge

The index of target node in Nodes vector

destLbl - Variable in class weka.gui.graphvisualizer.GraphEdge

Label of target node

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

Responds to a change in the destination address.

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

Responds to a change in the destination type.

detectOverlapping(NNge.Exemplar) - Method in class weka.classifiers.rules.NNge

Returns true if ex overlaps any of the Exemplars in NNge's lists

determineAdditionalResultMeasures() - Method in class weka.experiment.Experiment

Iterate over the objects in the property array to determine what (if any) additional measures they support

determineBounds() - Method in class weka.gui.visualize.Plot2D

Determine the min and max values for axis and colouring attributes

determineBounds() - Method in class weka.gui.visualize.PlotData2D

Determine bounds for the current x,y and colouring indexes

determineColumnConstraints(ResultProducer) - Method in class weka.experiment.AveragingResultProducer

Determines if there are any constraints (imposed by the destination) on the result columns to be produced by resultProducers.

determineColumnConstraints(ResultProducer) - Method in class weka.experiment.CSVResultListener

Determines if there are any constraints (imposed by the destination) on the result columns to be produced by resultProducers.

determineColumnConstraints(ResultProducer) - Method in class weka.experiment.DatabaseResultListener

Determines if there are any constraints (imposed by the destination) on any additional measures produced by resultProducers.

determineColumnConstraints(ResultProducer) - Method in class weka.experiment.LearningRateResultProducer

Determines if there are any constraints (imposed by the destination) on the result columns to be produced by resultProducers.

determineColumnConstraints(ResultProducer) - Method in interface weka.experiment.ResultListener

Determines if there are any constraints (imposed by the destination) on additional result columns to be produced by resultProducers.

determineDictionary() - Method in class weka.filters.unsupervised.attribute.StringToWordVector

determineNumToSelectFromThreshold(double[][]) - Method in class weka.attributeSelection.ForwardSelection

determineNumToSelectFromThreshold(double[][]) - Method in class weka.attributeSelection.RaceSearch

determineNumToSelectFromThreshold(double[][]) - Method in class weka.attributeSelection.Ranker

determineSelectedRange() - Method in class weka.filters.unsupervised.attribute.StringToWordVector

determineTemplate(int) - Method in class weka.experiment.AveragingResultProducer

Simulates a run to collect the keys the sub-resultproducer could generate.

determineThreshFromNumToSelect(double[][]) - Method in class weka.attributeSelection.Ranker

difference(int, double, double) - Method in class weka.attributeSelection.ReliefFAttributeEval

Computes the difference between two given attribute values.

difference(int, double, double) - Method in class weka.classifiers.lazy.IBk

Computes the difference between two given attribute values.

difference(int, double, double) - Method in class weka.classifiers.lazy.LWL

Computes the difference between two given attribute values.

difference(int, double, double) - Method in class weka.clusterers.FarthestFirst

Computes the difference between two given attribute values.

difference(int, double, double) - Method in class weka.clusterers.SimpleKMeans

Computes the difference between two given attribute values.

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

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

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

directionTipText() - Method in class weka.attributeSelection.BestFirst

Returns the tip text for this property

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

Get the value of equivalent.

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

Get the value of partFile.

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

Get the value of sameClause.

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

Get the value of subsumption.

disabled_setEquivalent(boolean) - Method in class weka.associations.Tertius

Set the value of equivalent.

disabled_setPartFile(File) - Method in class weka.associations.Tertius

Set the value of partFile.

disabled_setSameClause(boolean) - Method in class weka.associations.Tertius

Set the value of sameClause.

disabled_setSubsumption(boolean) - Method in class weka.associations.Tertius

Set the value of subsumption.

disconnect(NeuralConnection, NeuralConnection) - Static method in class weka.classifiers.functions.neural.NeuralConnection

Disconnects two units.

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

Closes the connection to the database.

disconnectInput(NeuralConnection, int) - Method in class weka.classifiers.functions.neural.NeuralConnection

This will disconnect the input with the specific connection number From this node (only on this end however).

disconnectInput(NeuralConnection, int) - Method in class weka.classifiers.functions.neural.NeuralNode

This will disconnect the input with the specific connection number From this node (only on this end however).

disconnectOutput(NeuralConnection, int) - Method in class weka.classifiers.functions.neural.NeuralConnection

This will disconnect the output with the specific connection number From this node (only on this end however).

disconnectionNotification(String, Object) - Method in class weka.gui.beans.AbstractDataSink

Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name

disconnectionNotification(String, Object) - Method in class weka.gui.beans.AbstractEvaluator

Notify this object that it has been deregistered as a listener with a source with respect to the supplied event named

disconnectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTestSetProducer

Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name

disconnectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer

Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name

disconnectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTrainingSetProducer

Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name

disconnectionNotification(String, Object) - Method in interface weka.gui.beans.BeanCommon

Notify this object that it has been deregistered as a listener with a source for named event.

disconnectionNotification(String, Object) - Method in class weka.gui.beans.ClassAssigner

Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name

disconnectionNotification(String, Object) - Method in class weka.gui.beans.Classifier

Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name

disconnectionNotification(String, Object) - Method in class weka.gui.beans.Filter

Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name

disconnectionNotification(String, Object) - Method in class weka.gui.beans.PredictionAppender

Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name

disconnectionNotification(String, Object) - Method in class weka.gui.beans.StripChart

Notify this object that it has been deregistered as a listener with a source for named event.

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

Debuggage function Compute and display bLow, lUp and so on...

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

Returns the tip text for this property

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

Debuggage function.

distGroup - Variable in class weka.gui.visualize.MatrixPanel

Button group for subsampling radio buttons

distance(Instance, Instance) - Method in class weka.attributeSelection.ReliefFAttributeEval

Calculates the distance between two instances

distance(Instance, Instance) - Method in class weka.classifiers.lazy.IB1

Calculates the distance between two instances

distance(Instance, Instance) - Method in class weka.classifiers.lazy.IBk

Calculates the distance between two instances

distance(Instance, Instance) - Method in class weka.classifiers.lazy.LWL

Calculates the distance between two instances

distance(Instance, Instance) - Method in class weka.clusterers.FarthestFirst

Calculates the distance between two instances

distance(Instance, Instance) - Method in class weka.clusterers.SimpleKMeans

Calculates the distance between two instances

distance(Instance, Instance) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator

Calculates the distance between two instances

distanceWeightingTipText() - Method in class weka.classifiers.lazy.IBk

Returns the tip text for this property

distinctCount - Variable in class weka.core.AttributeStats

The number of distinct values

distributedExperimentSelected() - Method in class weka.gui.experiment.DistributeExperimentPanel

Returns true if the distribute experiment checkbox is selected

distribution() - Method in class weka.classifiers.evaluation.NominalPrediction

Gets the predicted probabilities

distribution(double[][], double[][][], int, int[], double[], double[][], Instances) - Method in class weka.classifiers.trees.REPTree.Tree

Computes class distribution for an attribute.

distribution(double[][], double[][][], int, int[], double[], Instances) - Method in class weka.classifiers.trees.RandomTree

Computes class distribution for an attribute.

distribution() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel

Returns the distribution of class values induced by the model.

distributionForInstance(Instance) - Method in class weka.classifiers.Classifier

Predicts the class memberships for a given instance.

distributionForInstance(Instance) - Method in class weka.classifiers.bayes.AODE

Calculates the class membership probabilities for the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.bayes.BayesNet

Calculates the class membership probabilities for the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.bayes.NaiveBayes

Calculates the class membership probabilities for the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.bayes.NaiveBayesMultinomial

Calculates the class membership probabilities for the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.bayes.NaiveBayesSimple

Calculates the class membership probabilities for the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.functions.Logistic

Computes the distribution for a given instance

distributionForInstance(Instance) - Method in class weka.classifiers.functions.MultilayerPerceptron

Call this function to predict the class of an instance once a classification model has been built with the buildClassifier call.

distributionForInstance(Instance) - Method in class weka.classifiers.functions.RBFNetwork

Computes the distribution for a given instance

distributionForInstance(Instance) - Method in class weka.classifiers.functions.SMO

Estimates class probabilities for given instance.

distributionForInstance(Instance) - Method in class weka.classifiers.functions.SimpleLogistic

Returns class probabilities for an instance.

distributionForInstance(Instance) - Method in class weka.classifiers.functions.VotedPerceptron

Outputs the distribution for the given output.

distributionForInstance(Instance) - Method in class weka.classifiers.lazy.IBk

Calculates the class membership probabilities for the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.lazy.KStar

Calculates the class membership probabilities for the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.lazy.LBR

Calculates the class membership probabilities for the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.lazy.LWL

Calculates the class membership probabilities for the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.meta.AdaBoostM1

Calculates the class membership probabilities for the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.meta.AttributeSelectedClassifier

Classifies a given instance after attribute selection

distributionForInstance(Instance) - Method in class weka.classifiers.meta.Bagging

Calculates the class membership probabilities for the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.meta.CVParameterSelection

Predicts the class distribution for the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.meta.ClassificationViaRegression

Returns the distribution for an instance.

distributionForInstance(Instance) - Method in class weka.classifiers.meta.CostSensitiveClassifier

Returns class probabilities.

distributionForInstance(Instance) - Method in class weka.classifiers.meta.Decorate

Calculates the class membership probabilities for the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.meta.END

Calculates the class membership probabilities for the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.meta.FilteredClassifier

Classifies a given instance after filtering.

distributionForInstance(Instance) - Method in class weka.classifiers.meta.Grading

Returns class probabilities for a given instance using the stacked classifier.

distributionForInstance(Instance) - Method in class weka.classifiers.meta.HND

Returns a class probability estimation for the given instance.

distributionForInstance(Instance, Map, double) - Method in class weka.classifiers.meta.HND

Computes levelwise class probability estimation for the given instance and fills in the respective values to the classDistribution, using the factor (accumulated class probability for the respective superclass).

distributionForInstance(Instance) - Method in class weka.classifiers.meta.LogitBoost

Calculates the class membership probabilities for the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.meta.MultiClassClassifier

Returns the distribution for an instance.

distributionForInstance(Instance) - Method in class weka.classifiers.meta.MultiScheme

Returns class probabilities.

distributionForInstance(Instance) - Method in class weka.classifiers.meta.ND

Predicts the class distribution for a given instance

distributionForInstance(Instance, ND.NDTree) - Method in class weka.classifiers.meta.ND

Predicts the class distribution for a given instance

distributionForInstance(Instance) - Method in class weka.classifiers.meta.OrdinalClassClassifier

Returns the distribution for an instance.

distributionForInstance(double[]) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee

distributionForInstance(Instance) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee

distributionForInstance(Instance) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost

Computes class distribution of an instance using the best committee.

distributionForInstance(Instance) - Method in class weka.classifiers.meta.RandomCommittee

Calculates the class membership probabilities for the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.meta.Stacking

Returns class probabilities.

distributionForInstance(Instance) - Method in class weka.classifiers.meta.StackingC

Classifies a given instance using the stacked classifier.

distributionForInstance(Instance) - Method in class weka.classifiers.meta.ThresholdSelector

Calculates the class membership probabilities for the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.meta.TreeBasedMultiClassClassifier

Predicts the class distribution for a given instance * * @param inst the (multi-class) instance to be classified

distributionForInstance(Instance) - Method in class weka.classifiers.meta.Vote

Classifies a given instance using the selected classifier.

distributionForInstance(Instance) - Method in class weka.classifiers.misc.HyperPipes

Classifies the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.misc.VFI

Classifies the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.rules.ConjunctiveRule

Computes class distribution for the given instance.

distributionForInstance(Instance) - Method in class weka.classifiers.rules.DecisionTable

Calculates the class membership probabilities for the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.rules.JRip

Classify the test instance with the rule learner and provide the class distributions

distributionForInstance(Instance) - Method in class weka.classifiers.rules.PART

Returns class probabilities for an instance.

distributionForInstance(Instance) - Method in class weka.classifiers.rules.ZeroR

Calculates the class membership probabilities for the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.rules.part.ClassifierDecList

Returns class probabilities for a weighted instance.

distributionForInstance(Instance) - Method in class weka.classifiers.rules.part.MakeDecList

Returns the class distribution for an instance.

distributionForInstance(Instance) - Method in class weka.classifiers.trees.ADTree

Returns the class probability distribution for an instance.

distributionForInstance(Instance) - Method in class weka.classifiers.trees.DecisionStump

Calculates the class membership probabilities for the given test instance.

distributionForInstance(Instance) - Method in class weka.classifiers.trees.Id3

Computes class distribution for instance using decision tree.

distributionForInstance(Instance) - Method in class weka.classifiers.trees.J48

Returns class probabilities for an instance.

distributionForInstance(Instance) - Method in class weka.classifiers.trees.LMT

Returns class probabilities for an instance.

distributionForInstance(Instance) - Method in class weka.classifiers.trees.REPTree.Tree

Computes class distribution of an instance using the tree.

distributionForInstance(Instance) - Method in class weka.classifiers.trees.REPTree

Computes class distribution of an instance using the tree.

distributionForInstance(Instance) - Method in class weka.classifiers.trees.RandomForest

Returns the class probability distribution for an instance.

distributionForInstance(Instance) - Method in class weka.classifiers.trees.RandomTree

Computes class distribution of an instance using the decision tree.

distributionForInstance(Instance) - Method in class weka.classifiers.trees.UserClassifier

Call this function to get a double array filled with the probability of how likely each class type is the class of the instance.

distributionForInstance(Instance, boolean) - Method in class weka.classifiers.trees.j48.ClassifierTree

Returns class probabilities for a weighted instance.

distributionForInstance(Instance) - Method in class weka.classifiers.trees.lmt.LMTNode

Returns the class probabilities for an instance given by the logistic model tree.

distributionForInstance(Instance) - Method in class weka.classifiers.trees.lmt.LogisticBase

Returns class probabilities for an instance.

distributionForInstance(Instance) - Method in class weka.clusterers.Clusterer

Predicts the cluster memberships for a given instance.

distributionForInstance(Instance) - Method in class weka.clusterers.DensityBasedClusterer

Returns the cluster probability distribution for an instance.

distributionSpreadTipText() - Method in class weka.filters.supervised.instance.SpreadSubsample

Returns the tip text for this property

distributionTipText() - Method in class weka.filters.unsupervised.attribute.RandomProjection

Returns the tip text for this property

distributionsByOriginalIndex(double[]) - Method in class weka.filters.supervised.attribute.ClassOrder

Convert the given class distribution back to the distributions with the original internal class index

divide(Ridor.RidorRule, Instances[]) - Method in class weka.classifiers.rules.Ridor.Ridor_node

Builds an array of data according to their true class label Each bag of data is filtered through the rule specified and is totally covered by this rule.

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

Divided by another DoubleVector element by element

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

Divided by another DoubleVector element by element in place

dnorm(double) - Static method in class weka.classifiers.functions.pace.Maths

Returns the density of the standard normal.

dnorm(double, double, double) - Static method in class weka.classifiers.functions.pace.Maths

Returns the density value of a standard normal.

dnorm(double, DoubleVector, double) - Static method in class weka.classifiers.functions.pace.Maths

Returns the density values of a set of normal distributions with different means.

dnormLog(double) - Static method in class weka.classifiers.functions.pace.Maths

Returns the log-density of the standard normal.

dnormLog(double, double, double) - Static method in class weka.classifiers.functions.pace.Maths

Returns the log-density value of a standard normal.

dnormLog(double, DoubleVector, double) - Static method in class weka.classifiers.functions.pace.Maths

Returns the log-density values of a set of normal distributions with different means.

doAverageResult(Object[]) - Method in class weka.experiment.AveragingResultProducer

Asks the resultlistener whether an average result is required, and if so, calculates it.

doEM() - Method in class weka.clusterers.EM

Perform the EM algorithm

doHistory(KeyEvent) - Method in class weka.gui.SimpleCLI

Changes the currently displayed command line when certain keys are pressed.

doLayout() - Method in class weka.gui.beans.KnowledgeFlow.BeanLayout

doPopup(String) - Method in class weka.gui.beans.GraphViewer

doPopup(Point, BeanInstance, int, int) - Method in class weka.gui.beans.KnowledgeFlow

Popup a context sensitive menu for the bean component

doRegression(boolean[]) - Method in class weka.classifiers.functions.LinearRegression

Calculate a linear regression using the selected attributes

doRun(int) - Method in class weka.experiment.AveragingResultProducer

Gets the results for a specified run number.

doRun(int) - Method in class weka.experiment.CrossValidationResultProducer

Gets the results for a specified run number.

doRun(int) - Method in class weka.experiment.DatabaseResultProducer

Gets the results for a specified run number.

doRun(int) - Method in class weka.experiment.LearningRateResultProducer

Gets the results for a specified run number.

doRun(int) - Method in class weka.experiment.RandomSplitResultProducer

Gets the results for a specified run number.

doRun(int) - Method in interface weka.experiment.ResultProducer

Gets the results for a specified run number.

doRunKeys(int) - Method in class weka.experiment.AveragingResultProducer

Gets the keys for a specified run number.

doRunKeys(int) - Method in class weka.experiment.CrossValidationResultProducer

Gets the keys for a specified run number.

doRunKeys(int) - Method in class weka.experiment.DatabaseResultProducer

Gets the keys for a specified run number.

doRunKeys(int) - Method in class weka.experiment.LearningRateResultProducer

Gets the keys for a specified run number.

doRunKeys(int) - Method in class weka.experiment.RandomSplitResultProducer

Gets the keys for a specified run number.

doRunKeys(int) - Method in interface weka.experiment.ResultProducer

Gets the keys for a specified run number.

doTests() - Method in class weka.classifiers.CheckClassifier

Begin the tests, reporting results to System.out

docCount - Variable in class weka.filters.unsupervised.attribute.StringToWordVector.Count

docsCounts - Variable in class weka.filters.unsupervised.attribute.StringToWordVector

Contains the number of documents (instances) a particular word appears in.

doesntUseTestClassVal(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier

Checks whether the classifier erroneously uses the class value of test instances (if provided).

done() - Method in interface weka.classifiers.IterativeClassifier

Signal end of iterating, useful for any house-keeping/cleanup

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

Frees memory that is no longer needed for a final model - will no longer be able to increment the classifier after calling this.

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

Calculates a dot product between two instances

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

Calculates a dot product between two instances

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.

drawArrow(Graphics, int, int, int, int) - Method in class weka.gui.graphvisualizer.GraphVisualizer.GraphPanel

This method draws an arrow on a line from (x1,y1) to (x2,y2).

drawDataPoint(double, double, double, double, int, int, Graphics) - Static method in class weka.gui.visualize.Plot2D

Draws a data point at a given set of panel coordinates at a given size and connects a line to the previous point.

drawDataPoint(double, double, int, int, Graphics) - Static method in class weka.gui.visualize.Plot2D

Draws a data point at a given set of panel coordinates at a given size.

drawDiamond(Graphics, double, double, int) - Static method in class weka.gui.visualize.Plot2D

Draws a diamond.

drawHighlight(Graphics, int, int) - Method in class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd

Call this function to draw the node highlighted.

drawHighlight(Graphics, int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection

Call this function to draw the node highlighted.

drawInputLines(Graphics, int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection

Call this function to draw the nodes input connections.

drawLine(int, Graphics) - Method in class weka.gui.treevisualizer.TreeVisualizer

Determines the attributes of the edge and draws it.

drawNode(Graphics, int, int) - Method in class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd

This will draw the node id to the graphics context.

drawNode(Graphics, int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection

Call this function to draw the node.

drawNode(int, Graphics) - Method in class weka.gui.treevisualizer.TreeVisualizer

Determines the attributes of the node and draws it.

drawOutputLines(Graphics, int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection

Call this function to draw the nodes output connections.

drawPlus(Graphics, double, double, int) - Static method in class weka.gui.visualize.Plot2D

Draws a plus.

drawShapes(Graphics) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel

This will draw the shapes created onto the panel.

drawText(int, int, int, boolean, Graphics) - Method in class weka.gui.treevisualizer.TreeVisualizer

Draws the text for either an Edge or a Node.

drawTriangleDown(Graphics, double, double, int) - Static method in class weka.gui.visualize.Plot2D

Draws an triangle (point at bottom).

drawTriangleUp(Graphics, double, double, int) - Static method in class weka.gui.visualize.Plot2D

Draws an triangle (point at top).

drawX(Graphics, double, double, int) - Static method in class weka.gui.visualize.Plot2D

Draws an X.

dumpData() - Method in class weka.clusterers.Cobweb.CNode

Returns the instances at this node as a string.

dumpDecList(StringBuffer) - Method in class weka.classifiers.rules.part.ClassifierDecList

Help method for printing tree structure.

dumpDistribution() - Method in class weka.classifiers.trees.j48.Distribution

Prints distribution.

dumpLabel(int, Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel

Prints label for subset index of instances (eg class).

dumpModel(Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel

Prints the split model.

dumpTree(int, StringBuffer) - Method in class weka.classifiers.rules.part.ClassifierDecList

Dumps the partial tree (only used for debugging)

dumpTree(int, StringBuffer) - Method in class weka.classifiers.trees.j48.ClassifierTree

Help method for printing tree structure.

dumpTree(int, StringBuffer) - Method in class weka.classifiers.trees.lmt.LMTNode

Help method for printing tree structure.

dumpTree(int, StringBuffer) - Method in class weka.clusterers.Cobweb.CNode

Recursively build a string representation of the Cobweb tree