All Packages  Class Hierarchy

D

D_CONVCHCLOSER. Static variable in class weka.clusterers.XMeans

D_CURR. Static variable in class weka.clusterers.XMeans

D_FOLLOWSPLIT. Static variable in class weka.clusterers.XMeans

D_GENERAL. Static variable in class weka.clusterers.XMeans

D_ITERCOUNT. Static variable in class weka.clusterers.XMeans

D_KDTREE. Static variable in class weka.clusterers.XMeans

D_METH_MISUSE. Static variable in class weka.clusterers.XMeans

D_PRINTCENTERS. Static variable in class weka.clusterers.XMeans

D_RANDOMVEKTOR. Static variable in class weka.clusterers.XMeans

DatabaseResultListener class weka.experiment.DatabaseResultListener.

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

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

Sets up the database drivers

DatabaseResultProducer class weka.experiment.DatabaseResultProducer.

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

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

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

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

Returns the tip text for this property

DatabaseUtils class weka.experiment.DatabaseUtils.

DatabaseUtils provides utility functions for accessing the experiment database.

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

Sets up the database drivers

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.

DataGenerator interface weka.gui.boundaryvisualizer.DataGenerator.

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

DATASET_FIELD_NAME. Static variable in class weka.experiment.CrossValidationResultProducer

DATASET_FIELD_NAME. Static variable in class weka.experiment.RandomSplitResultProducer

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

Returns the dataset this instance has access to.

DataSetEvent class weka.gui.beans.DataSetEvent.

Event encapsulating a data set

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

DatasetListPanel class weka.gui.experiment.DatasetListPanel.

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

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

Create the dataset list panel initially disabled.

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

Creates the dataset list panel with the given experiment.

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

DATE. Static variable in class weka.core.Attribute

Constant set for attributes with date values.

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.

DDConditionalEstimator class weka.estimators.DDConditionalEstimator.

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

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

Constructor

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

Returns the tip text for this property

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

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.RacedIncrementalLogitBoost

decayTipText(). Method in class weka.classifiers.functions.neural.NeuralNetwork

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

Class providing keys to the hash table

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

Constructor for a hashKey

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

Constructor for a hashKey

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

Class for a node in a linked list.

DecisionTable.Link(DecisionTable, 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(DecisionTable). Constructor for class weka.classifiers.rules.DecisionTable.LinkedList

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

Constructor for a DecisionTable

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

Carry out the bias-variance decomposition

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

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

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

Deletes given instance from given bag.

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

Removes all instances from the set.

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

Removes an instance at the given position from the set.

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

Deletes an element 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).

deleteInstance(Instance). Method in class weka.core.KDTree

Deletes one instance in the KDTree.

deleteInstance(int). Method in class weka.core.KDTree

Deletes one instance in the KDTree.

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

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

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)

deleteOneIndex(int). Method in class weka.core.DynamicArrayOfPosInt

Deletes an entry.

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

Deletes all string attributes in the dataset.

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.trees.j48.Distribution

Deletes all instances in given range from given bag.

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

Returns the tip text for this property

densityForInstance(Instance). Method in class weka.clusterers.DistributionClusterer

Computes the density for a given instance.

densityForInstance(Instance). Method in class weka.clusterers.EM

Computes the density for a given instance.

densityForInstance(Instance). Method in class weka.clusterers.DistributionMetaClusterer

Computes the density for a given instance.

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

Get the depth of the tree, i.e.

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

Returns the option's description.

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

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

Determine the min and max values for axis and colouring attributes

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

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

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

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.

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.

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

differencesProbability. Variable in class weka.experiment.PairedStats

The probability of obtaining the observed differences

differencesSignificance. Variable in class weka.experiment.PairedStats

A significance indicator: 0 if the differences are not significant > 0 if x significantly greater than y < 0 if x significantly less than y

differencesStats. Variable in class weka.experiment.PairedStats

The stats associated with the paired differences

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

Returns the tip text for this property

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

Disconnects two units.

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

Closes the connection to the database.

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.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.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.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 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.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.StripChart

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 interface weka.gui.beans.BeanCommon

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

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

Constructor - initialises the filter

distance(Instance, Instance). Method in class weka.core.DistanceFunction

Calculates the distance (or similarity) between two instances.

distance(Instance, Instance). Method in class weka.core.EuclideanDistance

Calculates the distance (or similarity) between two instances.

DistanceFunction class weka.core.DistanceFunction.

Abstract class to implement a distance function.

DistanceFunction(). Constructor for class weka.core.DistanceFunction

Constructs a distance function object.

DistanceFunction(Instances). Constructor for class weka.core.DistanceFunction

Constructs a distance function object.

DistanceFunction(Instances, double[][]). Constructor for class weka.core.DistanceFunction

Constructs a distance function object.

distinctCount. Variable in class weka.core.AttributeStats

The number of distinct values

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

Returns true if the distribute experiment checkbox is selected

DistributeExperimentPanel class weka.gui.experiment.DistributeExperimentPanel.

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

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

Constructor

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

Creates the panel with the supplied initial experiment.

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

Class for handling a distribution of class values.

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

Returns the distribution of class values induced by the model.

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

Gets the predicted probabilities

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.

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

Creates and initializes a new distribution.

DistributionClassifier class weka.classifiers.DistributionClassifier.

Abstract classification model that produces (for each test instance) an estimate of the membership in each class (ie.

DistributionClassifier(). Constructor for class weka.classifiers.DistributionClassifier

distributionClassifierTipText(). Method in class weka.classifiers.meta.ThresholdSelector

distributionClassifierTipText(). Method in class weka.classifiers.meta.MultiClassClassifier

distributionClassifierTipText(). Method in class weka.classifiers.meta.OrdinalClassClassifier

DistributionClusterer class weka.clusterers.DistributionClusterer.

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

DistributionClusterer(). Constructor for class weka.clusterers.DistributionClusterer

distributionForInstance(Instance). Method in class weka.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.clusterers.DistributionMetaClusterer

Returns the cluster probability distribution for an instance.

distributionForInstance(Instance). Method in class weka.classifiers.DistributionClassifier

Predicts the class memberships for a given instance.

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.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.lazy.kstar.KStar

Calculates the class membership probabilities 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.DistributionMetaClassifier

Returns the class probability distribution for an instance.

distributionForInstance(Instance). Method in class weka.classifiers.meta.MultiBoostAB

Calculates the class membership probabilities for the given test instance.

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

Classifies a given instance using the selected classifier.

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.ThresholdSelector

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.AdaBoostM1

Calculates the class membership probabilities for the given test instance.

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

Returns the distribution for an 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.LogitBoost

Calculates the class membership probabilities for the given test instance.

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.RacedIncrementalLogitBoost

Computes class distribution of an instance using the best committee.

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

Classifies a given instance after filtering.

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.bayes.BayesNet

Calculates the class membership probabilities for the given test 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.NaiveBayesSimple

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.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.ConjunctiveRule

Computes class distribution for the given 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.PART

Returns class probabilities for an 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.DecisionStump

Calculates the class membership probabilities for the given test instance.

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). 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.Id3

Computes class distribution for instance using decision tree.

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.j48.J48

Returns class probabilities for an instance.

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

Returns the class probability distribution 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.functions.Logistic

Computes the distribution for a given instance

distributionForInstance(Instance). Method in class weka.classifiers.functions.neural.NeuralNetwork

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

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

Estimates class probabilities for given instance.

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

Returns class probabilities for a weighted instance.

DistributionMetaClassifier class weka.classifiers.meta.DistributionMetaClassifier.

Class for wrapping a Classifier to make it return a distribution.

DistributionMetaClassifier(). Constructor for class weka.classifiers.meta.DistributionMetaClassifier

Default constructor.

DistributionMetaClassifier(Classifier). Constructor for class weka.classifiers.meta.DistributionMetaClassifier

Contructs a DistributionMetaClassifier wrapping a given Classifier.

DistributionMetaClusterer class weka.clusterers.DistributionMetaClusterer.

Class for wrapping a Clusterer to make it return a distribution.

DistributionMetaClusterer(). Constructor for class weka.clusterers.DistributionMetaClusterer

Default constructor.

DistributionMetaClusterer(Clusterer). Constructor for class weka.clusterers.DistributionMetaClusterer

Contructs a DistributionMetaClusterer wrapping a given Clusterer.

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

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

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

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.

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

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

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

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

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

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

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

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.CrossValidationResultProducer

Gets the results for a specified run number.

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

Gets the results for a specified run number.

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

Gets the results for a specified run number.

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

Gets the results for a specified run number.

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

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.CrossValidationResultProducer

Gets the keys for a specified run number.

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

Gets the keys for a specified run number.

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

Gets the keys for a specified run number.

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

Gets the keys for a specified run number.

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

Begin the tests, reporting results to System.out

dotMultiply(AlgVector). Method in class weka.core.AlgVector

Returns the inner (or dot) product of two vectors

dotMultiply(AlgVector). Method in class weka.clusterers.AlgVector

Returns the inner (or dot) product of two vectors

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.

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

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

Constructs a null vector.

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

Constructs a vector directly from a double array

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.

Drawable interface weka.core.Drawable.

Interface to something that can be drawn as a graph.

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.

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

Call this function to draw the node.

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

Call this function to draw the nodes output connections.

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.

DynamicArrayOfPosInt class weka.core.DynamicArrayOfPosInt.

Implements a dynamic array of positive integers.

DynamicArrayOfPosInt(). Constructor for class weka.core.DynamicArrayOfPosInt

Constructor

DynamicArrayOfPosInt(int). Constructor for class weka.core.DynamicArrayOfPosInt

Constructor with size larger than 1.

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