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D

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
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.
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.
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.attributeSelection.RaceSearch
Returns the tip text for this property
debugTipText(). Method in class weka.classifiers.AdditiveRegression
Returns the tip text for this property
debugTipText(). Method in class weka.filters.AttributeExpressionFilter
Returns the tip text for this property
decayTipText(). Method in class weka.classifiers.neural.NeuralNetwork
DecisionStump class weka.classifiers.DecisionStump.
Class for building and using a decision stump.
DecisionStump(). Constructor for class weka.classifiers.DecisionStump
DecisionTable class weka.classifiers.DecisionTable.
Class for building and using a simple decision table majority classifier.
DecisionTable.hashKey class weka.classifiers.DecisionTable.hashKey.
Class providing keys to the hash table
DecisionTable.hashKey(DecisionTable, double[]). Constructor for class weka.classifiers.DecisionTable.hashKey
Constructor for a hashKey
DecisionTable.hashKey(DecisionTable, Instance, int). Constructor for class weka.classifiers.DecisionTable.hashKey
Constructor for a hashKey
DecisionTable.Link class weka.classifiers.DecisionTable.Link.
Class for a node in a linked list.
DecisionTable.Link(DecisionTable, BitSet, double). Constructor for class weka.classifiers.DecisionTable.Link
The constructor.
DecisionTable.LinkedList class weka.classifiers.DecisionTable.LinkedList.
Class for handling a linked list.
DecisionTable.LinkedList(DecisionTable). Constructor for class weka.classifiers.DecisionTable.LinkedList
DecisionTable(). Constructor for class weka.classifiers.DecisionTable
Constructor for a DecisionTable
decompose(). Method in class weka.classifiers.BVDecompose
Carry out the bias-variance decomposition
DEFAULT_SHAPE_SIZE. Static variable in class weka.gui.visualize.Plot2D
del(int, Instance). Method in class weka.classifiers.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.
deleteAttributeAt(int). Method in class weka.core.Instances
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).
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.
deleteTrailingZerosAndDot(StringBuffer). Static method in class weka.classifiers.m5.M5Utils
Deletes the trailing zeros and decimal point in a stringBuffer
deleteWithMissing(Attribute). Method in class weka.core.Instances
Removes all instances with missing values for a particular attribute from the dataset.
deleteWithMissing(int). Method in class weka.core.Instances
Removes all instances with missing values for a particular attribute from the dataset.
deleteWithMissingClass(). Method in class weka.core.Instances
Removes all instances with a missing class value from the dataset.
delRange(int, Instances, int, int). Method in class weka.classifiers.j48.Distribution
Deletes all instances in given range from given bag.
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
Returns the density for an instance.
description(). Method in class weka.core.Option
Returns the option's description.
designatedClassTipText(). Method in class weka.classifiers.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.neural.NeuralConnection
Disconnects two units.
disconnectFromDatabase(). Method in class weka.experiment.DatabaseUtils
Closes the connection to the database.
DiscreteEstimator class weka.estimators.DiscreteEstimator.
Simple symbolic probability estimator based on symbol counts.
DiscreteEstimator(int, boolean). Constructor for class weka.estimators.DiscreteEstimator
Constructor
DiscretizeFilter class weka.filters.DiscretizeFilter.
An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
DiscretizeFilter(). Constructor for class weka.filters.DiscretizeFilter
Constructor - initialises the filter
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.j48.Distribution.
Class for handling a distribution of class values.
distribution(). Method in class weka.classifiers.evaluation.NominalPrediction
Gets the predicted probabilities
distribution(). Method in class weka.classifiers.j48.ClassifierSplitModel
Returns the distribution of class values induced by the model.
Distribution(Distribution). Constructor for class weka.classifiers.j48.Distribution
Creates distribution with only one bag by merging all bags of given distribution.
Distribution(Distribution, int). Constructor for class weka.classifiers.j48.Distribution
Creates distribution with two bags by merging all bags apart of the indicated one.
Distribution(double[][]). Constructor for class weka.classifiers.j48.Distribution
Creates and initializes a new distribution using the given array.
Distribution(Instances). Constructor for class weka.classifiers.j48.Distribution
Creates a distribution with only one bag according to instances in source.
Distribution(Instances, ClassifierSplitModel). Constructor for class weka.classifiers.j48.Distribution
Creates a distribution according to given instances and split model.
Distribution(int, int). Constructor for class weka.classifiers.j48.Distribution
Creates and initializes a new distribution.
DistributionClassifier 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.ThresholdSelector
distributionClassifierTipText(). Method in class weka.classifiers.MultiClassClassifier
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.classifiers.DistributionClassifier
Predicts the class memberships for a given instance.
distributionForInstance(Instance). Method in class weka.classifiers.DecisionTable
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.DecisionStump
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.AdaBoostM1
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.ClassificationViaRegression
Returns the distribution for an instance.
distributionForInstance(Instance). Method in class weka.classifiers.AttributeSelectedClassifier
Classifies a given instance after attribute selection
distributionForInstance(Instance). Method in class weka.classifiers.Bagging
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.ThresholdSelector
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.KernelDensity
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.IBk
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.ZeroR
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.LogitBoost
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.HyperPipes
Classifies the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.Id3
Computes class distribution for instance using decision tree.
distributionForInstance(Instance). Method in class weka.classifiers.MultiClassClassifier
Returns the distribution for an instance.
distributionForInstance(Instance). Method in class weka.classifiers.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.NaiveBayes
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.SMO
Outputs the distribution for the given output.
distributionForInstance(Instance). Method in class weka.classifiers.Logistic
Computes the distribution for a given instance
distributionForInstance(Instance). Method in class weka.classifiers.VotedPerceptron
Outputs the distribution for the given output.
distributionForInstance(Instance). Method in class weka.classifiers.NaiveBayesSimple
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.DistributionMetaClassifier
Returns the distribution for an instance.
distributionForInstance(Instance). Method in class weka.classifiers.VFI
Classifies the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.FilteredClassifier
Classifies a given instance after filtering.
distributionForInstance(Instance). Method in class weka.classifiers.adtree.ADTree
Returns the class probability distribution for an instance.
distributionForInstance(Instance). Method in class weka.classifiers.j48.J48
Returns class probabilities for an instance.
distributionForInstance(Instance). Method in class weka.classifiers.j48.MakeDecList
Returns the class distribution for an instance.
distributionForInstance(Instance). Method in class weka.classifiers.j48.ClassifierDecList
Returns class probabilities for a weighted instance.
distributionForInstance(Instance). Method in class weka.classifiers.j48.PART
Returns class probabilities for an instance.
distributionForInstance(Instance). Method in class weka.classifiers.kstar.KStar
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance). Method in class weka.classifiers.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.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 distribution for an instance.
distributionForInstance(Instance, boolean). Method in class weka.classifiers.j48.ClassifierTree
Returns class probabilities for a weighted instance.
DistributionMetaClassifier class weka.classifiers.DistributionMetaClassifier.
Class that wraps up a Classifier and presents it as a DistributionClassifier for ease of programmatically handling Classifiers in general -- only the one predict method (distributionForInstance) need be worried about.
DistributionMetaClassifier(). Constructor for class weka.classifiers.DistributionMetaClassifier
Default constructor
DistributionMetaClassifier(Classifier). Constructor for class weka.classifiers.DistributionMetaClassifier
Creates a new DistributionMetaClassifier instance, specifying the Classifier to wrap around.
DistributionMetaClusterer class weka.clusterers.DistributionMetaClusterer.
Class that wraps up a Clusterer and presents it as a DistributionClusterer for ease of programmatically handling Clusterers in general -- only the one predict method (distributionForInstance) need be worried about.
DistributionMetaClusterer(). Constructor for class weka.clusterers.DistributionMetaClusterer
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
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.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
doubleToString(double, int). Static method in class weka.core.Utils
Rounds a double and converts it into String.
doubleToString(double, int, int). Static method in class weka.core.Utils
Rounds a double and converts it into a formatted decimal-justified String.
doubleToStringF(double, int, int). Static method in class weka.classifiers.m5.M5Utils
Rounds a double and converts it into a formatted right-justified String.
doubleToStringG(double, int, int). Static method in class weka.classifiers.m5.M5Utils
Rounds a double and converts it into a formatted right-justified String.
Drawable interface weka.core.Drawable.
Interface to something that can be drawn as a graph.
drawHighlight(Graphics, int, int). Method in class weka.classifiers.neural.NeuralConnection
Call this function to draw the node highlighted.
drawInputLines(Graphics, int, int). Method in class weka.classifiers.neural.NeuralConnection
Call this function to draw the nodes input connections.
drawNode(Graphics, int, int). Method in class weka.classifiers.neural.NeuralConnection
Call this function to draw the node.
drawOutputLines(Graphics, int, int). Method in class weka.classifiers.neural.NeuralConnection
Call this function to draw the nodes output connections.
dumpDistribution(). Method in class weka.classifiers.j48.Distribution
Prints distribution.
dumpLabel(int, Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
Prints label for subset index of instances (eg class).
dumpModel(Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
Prints the split model.
Dvector class weka.classifiers.m5.Dvector.
Class for handling a double vector.
Dvector(). Constructor for class weka.classifiers.m5.Dvector

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