Uses of Interface
weka.core.Instance

Packages that use Instance

Package	Description
weka.associations
weka.attributeSelection
weka.classifiers
weka.classifiers.bayes
weka.classifiers.bayes.net
weka.classifiers.bayes.net.estimate
weka.classifiers.evaluation
weka.classifiers.evaluation.output.prediction
weka.classifiers.functions
weka.classifiers.functions.supportVector
weka.classifiers.lazy
weka.classifiers.lazy.kstar
weka.classifiers.meta
weka.classifiers.misc
weka.classifiers.pmml.consumer
weka.classifiers.rules
weka.classifiers.rules.part
weka.classifiers.trees
weka.classifiers.trees.ht
weka.classifiers.trees.j48
weka.classifiers.trees.lmt
weka.classifiers.trees.m5
weka.clusterers
weka.core
weka.core.converters
weka.core.expressionlanguage.weka
weka.core.neighboursearch
weka.core.neighboursearch.balltrees
weka.core.pmml
weka.datagenerators
weka.datagenerators.classifiers.classification
weka.datagenerators.classifiers.regression
weka.datagenerators.clusterers
weka.experiment
weka.filters
weka.filters.supervised.attribute
weka.filters.supervised.instance
weka.filters.unsupervised.attribute
weka.filters.unsupervised.instance
weka.gui.beans
weka.gui.boundaryvisualizer
weka.gui.explorer
weka.gui.streams
weka.knowledgeflow.steps

Uses of Instance in weka.associations

Methods in weka.associations with parameters of type Instance

Modifier and Type	Method	Description
boolean	ItemSet.containedBy(Instance instance)	Checks if an instance contains an item set.
boolean	ItemSet.containedByTreatZeroAsMissing(Instance instance)	Checks if an instance contains an item set.
void	ItemSet.upDateCounter(Instance instance)	Updates counter of item set with respect to given transaction.
final void	LabeledItemSet.upDateCounter(Instance instanceNoClass, Instance instanceClass)	Updates counter of item set with respect to given transaction.
void	ItemSet.updateCounterTreatZeroAsMissing(Instance instance)	Updates counter of item set with respect to given transaction.
final void	LabeledItemSet.upDateCounterTreatZeroAsMissing(Instance instanceNoClass, Instance instanceClass)	Updates counter of item set with respect to given transaction.

Uses of Instance in weka.attributeSelection

Methods in weka.attributeSelection that return Instance

Modifier and Type	Method	Description
Instance	AttributeTransformer.convertInstance(Instance instance)	Transforms an instance in the format of the original data to the transformed space
Instance	PrincipalComponents.convertInstance(Instance instance)	Transform an instance in original (unormalized) format.
Instance	AttributeSelection.reduceDimensionality(Instance in)	reduce the dimensionality of a single instance to include only those attributes chosen by the last run of attribute selection.

Methods in weka.attributeSelection with parameters of type Instance

Modifier and Type	Method	Description
Instance	AttributeTransformer.convertInstance(Instance instance)	Transforms an instance in the format of the original data to the transformed space
Instance	PrincipalComponents.convertInstance(Instance instance)	Transform an instance in original (unormalized) format.
double	ClassifierSubsetEval.evaluateSubset(BitSet subset, Instance holdOut, boolean retrain)	Evaluates a subset of attributes with respect to a single instance.
abstract double	HoldOutSubsetEvaluator.evaluateSubset(BitSet subset, Instance holdOut, boolean retrain)	Evaluates a subset of attributes with respect to a single instance.
Instance	AttributeSelection.reduceDimensionality(Instance in)	reduce the dimensionality of a single instance to include only those attributes chosen by the last run of attribute selection.

Uses of Instance in weka.classifiers

Methods in weka.classifiers with parameters of type Instance

Modifier and Type	Method	Description
double	AbstractClassifier.classifyInstance(Instance instance)	Classifies the given test instance.
double	Classifier.classifyInstance(Instance instance)	Classifies the given test instance.
double[]	AbstractClassifier.distributionForInstance(Instance instance)	Predicts the class memberships for a given instance.
double[]	Classifier.distributionForInstance(Instance instance)	Predicts the class memberships for a given instance.
double	Evaluation.evaluateModelOnce(double[] dist, Instance instance)	Evaluates the supplied distribution on a single instance.
void	Evaluation.evaluateModelOnce(double prediction, Instance instance)	Evaluates the supplied prediction on a single instance.
double	Evaluation.evaluateModelOnce(Classifier classifier, Instance instance)	Evaluates the classifier on a single instance.
double	Evaluation.evaluateModelOnceAndRecordPrediction(double[] dist, Instance instance)	Evaluates the supplied distribution on a single instance.
double	Evaluation.evaluateModelOnceAndRecordPrediction(Classifier classifier, Instance instance)	Evaluates the classifier on a single instance and records the prediction.
double	Evaluation.evaluationForSingleInstance(double[] dist, Instance instance, boolean storePredictions)	Evaluates the supplied distribution on a single instance.
double[]	CostMatrix.expectedCosts(double[] classProbs, Instance inst)	Calculates the expected misclassification cost for each possible class value, given class probability estimates.
final double	CostMatrix.getElement(int rowIndex, int columnIndex, Instance inst)	Return the value of a cell as a double.
double	CostMatrix.getMaxCost(int classVal, Instance inst)	Gets the maximum cost for a particular class value.
double	ConditionalDensityEstimator.logDensity(Instance instance, double value)	Returns natural logarithm of density estimate for given value based on given instance.
double[][]	IntervalEstimator.predictIntervals(Instance inst, double confidenceLevel)	Returns an N * 2 array, where N is the number of prediction intervals.
void	UpdateableClassifier.updateClassifier(Instance instance)	Updates a classifier using the given instance.
void	Evaluation.updatePriors(Instance instance)	Updates the class prior probabilities or the mean respectively (when incrementally training).

Uses of Instance in weka.classifiers.bayes

Methods in weka.classifiers.bayes with parameters of type Instance

Modifier and Type	Method	Description
double[]	BayesNet.countsForInstance(Instance instance)	Calculates the counts for Dirichlet distribution for the class membership probabilities for the given test instance.
double[]	BayesNet.distributionForInstance(Instance instance)	Calculates the class membership probabilities for the given test instance.
double[]	NaiveBayes.distributionForInstance(Instance instance)	Calculates the class membership probabilities for the given test instance.
double[]	NaiveBayesMultinomial.distributionForInstance(Instance instance)	Calculates the class membership probabilities for the given test instance.
double[]	NaiveBayesMultinomialText.distributionForInstance(Instance instance)	Calculates the class membership probabilities for the given test instance.
void	BayesNet.updateClassifier(Instance instance)	Updates the classifier with the given instance.
void	NaiveBayes.updateClassifier(Instance instance)	Updates the classifier with the given instance.
void	NaiveBayesMultinomialText.updateClassifier(Instance instance)	Updates the classifier with the given instance.
void	NaiveBayesMultinomialUpdateable.updateClassifier(Instance instance)	Updates the classifier with information from one training instance.

Uses of Instance in weka.classifiers.bayes.net

Fields in weka.classifiers.bayes.net declared as Instance

Modifier and Type	Field	Description
Instance[]	ADNode.m_Instances	list of Instance children (either m_Instances or m_VaryNodes is instantiated)

Uses of Instance in weka.classifiers.bayes.net.estimate

Methods in weka.classifiers.bayes.net.estimate with parameters of type Instance

Modifier and Type	Method	Description
double[]	BayesNetEstimator.distributionForInstance(BayesNet bayesNet, Instance instance)	Calculates the class membership probabilities for the given test instance.
double[]	MultiNomialBMAEstimator.distributionForInstance(BayesNet bayesNet, Instance instance)	Calculates the class membership probabilities for the given test instance.
double[]	SimpleEstimator.distributionForInstance(BayesNet bayesNet, Instance instance)	Calculates the class membership probabilities for the given test instance.
void	BayesNetEstimator.updateClassifier(BayesNet bayesNet, Instance instance)	Updates the classifier with the given instance.
void	BMAEstimator.updateClassifier(BayesNet bayesNet, Instance instance)	Updates the classifier with the given instance.
void	MultiNomialBMAEstimator.updateClassifier(BayesNet bayesNet, Instance instance)	Updates the classifier with the given instance.
void	SimpleEstimator.updateClassifier(BayesNet bayesNet, Instance instance)	Updates the classifier with the given instance.

Uses of Instance in weka.classifiers.evaluation

Methods in weka.classifiers.evaluation with parameters of type Instance

Modifier and Type	Method	Description
double	Evaluation.evaluateModelOnce(double[] dist, Instance instance)	Evaluates the supplied distribution on a single instance.
void	Evaluation.evaluateModelOnce(double prediction, Instance instance)	Evaluates the supplied prediction on a single instance.
double	Evaluation.evaluateModelOnce(Classifier classifier, Instance instance)	Evaluates the classifier on a single instance.
double	Evaluation.evaluateModelOnceAndRecordPrediction(double[] dist, Instance instance)	Evaluates the supplied distribution on a single instance.
double	Evaluation.evaluateModelOnceAndRecordPrediction(Classifier classifier, Instance instance)	Evaluates the classifier on a single instance and records the prediction.
double	Evaluation.evaluationForSingleInstance(double[] dist, Instance instance, boolean storePredictions)	Evaluates the supplied distribution on a single instance.
Prediction	EvaluationUtils.getPrediction(Classifier classifier, Instance test)	Generate a single prediction for a test instance given the pre-trained classifier.
void	Evaluation.updatePriors(Instance instance)	Updates the class prior probabilities or the mean respectively (when incrementally training).
void	InformationRetrievalEvaluationMetric.updateStatsForClassifier(double[] predictedDistribution, Instance instance)	Updates the statistics about a classifiers performance for the current test instance.
void	InformationTheoreticEvaluationMetric.updateStatsForClassifier(double[] predictedDistribution, Instance instance)	Updates the statistics about a classifiers performance for the current test instance.
void	StandardEvaluationMetric.updateStatsForClassifier(double[] predictedDistribution, Instance instance)	Updates the statistics about a classifiers performance for the current test instance.
void	InformationTheoreticEvaluationMetric.updateStatsForConditionalDensityEstimator(ConditionalDensityEstimator classifier, Instance classMissing, double classValue)	Updates stats for conditional density estimator based on current test instance.
void	IntervalBasedEvaluationMetric.updateStatsForIntervalEstimator(IntervalEstimator classifier, Instance classMissing, double classValue)	Updates stats for interval estimator based on current test instance.
void	InformationTheoreticEvaluationMetric.updateStatsForPredictor(double predictedValue, Instance instance)	Updates the statistics about a predictors performance for the current test instance.
void	StandardEvaluationMetric.updateStatsForPredictor(double predictedValue, Instance instance)	Updates the statistics about a predictors performance for the current test instance.

Uses of Instance in weka.classifiers.evaluation.output.prediction

Fields in weka.classifiers.evaluation.output.prediction declared as Instance

Modifier and Type	Field	Description
Instance	InMemory.PredictionContainer.instance	the instance.

Methods in weka.classifiers.evaluation.output.prediction with parameters of type Instance

Modifier and Type	Method	Description
void	AbstractOutput.printClassification(double[] dist, Instance inst, int index)	Prints the classification to the buffer.
void	AbstractOutput.printClassification(Classifier classifier, Instance inst, int index)	Prints the classification to the buffer.

Uses of Instance in weka.classifiers.functions

Methods in weka.classifiers.functions with parameters of type Instance

Modifier and Type	Method	Description
double	GaussianProcesses.classifyInstance(Instance inst)	Classifies a given instance.
double	LinearRegression.classifyInstance(Instance instance)	Classifies the given instance using the linear regression function.
double	SimpleLinearRegression.classifyInstance(Instance inst)	Generate a prediction for the supplied instance.
double	SMOreg.classifyInstance(Instance instance)	Classifies the given instance using the linear regression function.
double[]	Logistic.distributionForInstance(Instance instance)	Computes the distribution for a given instance
double[]	MultilayerPerceptron.distributionForInstance(Instance i)	Call this function to predict the class of an instance once a classification model has been built with the buildClassifier call.
double[]	SGD.distributionForInstance(Instance inst)	Computes the distribution for a given instance
double[]	SGDText.distributionForInstance(Instance inst)
double[]	SimpleLogistic.distributionForInstance(Instance inst)	Returns class probabilities for an instance.
double[]	SMO.distributionForInstance(Instance inst)	Estimates class probabilities for given instance.
double[]	VotedPerceptron.distributionForInstance(Instance inst)	Outputs the distribution for the given output.
double	GaussianProcesses.getStandardDeviation(Instance inst)	Gives standard deviation of the prediction at the given instance.
double	GaussianProcesses.logDensity(Instance inst, double value)	Returns natural logarithm of density estimate for given value based on given instance.
int[]	SMO.obtainVotes(Instance inst)	Returns an array of votes for the given instance.
double[][]	GaussianProcesses.predictIntervals(Instance inst, double confidenceLevel)	Computes a prediction interval for the given instance and confidence level.
double	SMO.BinarySMO.SVMOutput(int index, Instance inst)	Computes SVM output for given instance.
void	SGD.updateClassifier(Instance instance)	Updates the classifier with the given instance.
void	SGDText.updateClassifier(Instance instance)	Updates the classifier with the given instance.

Uses of Instance in weka.classifiers.functions.supportVector

Methods in weka.classifiers.functions.supportVector with parameters of type Instance

Modifier and Type	Method	Description
double	CachedKernel.eval(int id1, int id2, Instance inst1)	Implements the abstract function of Kernel using the cache.
abstract double	Kernel.eval(int id1, int id2, Instance inst1)	Computes the result of the kernel function for two instances.
double	PrecomputedKernelMatrixKernel.eval(int id1, int id2, Instance inst1)
double	StringKernel.eval(int id1, int id2, Instance inst1)	Computes the result of the kernel function for two instances.
double	RegOptimizer.SVMOutput(Instance inst)

Uses of Instance in weka.classifiers.lazy

Methods in weka.classifiers.lazy with parameters of type Instance

Modifier and Type	Method	Description
double[]	IBk.distributionForInstance(Instance instance)	Calculates the class membership probabilities for the given test instance.
double[]	KStar.distributionForInstance(Instance instance)	Calculates the class membership probabilities for the given test instance.
double[]	LWL.distributionForInstance(Instance instance)	Calculates the class membership probabilities for the given test instance.
void	IBk.updateClassifier(Instance instance)	Adds the supplied instance to the training set.
void	KStar.updateClassifier(Instance instance)	Adds the supplied instance to the training set
void	LWL.updateClassifier(Instance instance)	Adds the supplied instance to the training set.

Uses of Instance in weka.classifiers.lazy.kstar

Constructors in weka.classifiers.lazy.kstar with parameters of type Instance

Modifier	Constructor	Description
	KStarNominalAttribute(Instance test, Instance train, int attrIndex, Instances trainSet, int[][] randClassCol, KStarCache cache)	Constructor
	KStarNumericAttribute(Instance test, Instance train, int attrIndex, Instances trainSet, int[][] randClassCols, KStarCache cache)	Constructor

Uses of Instance in weka.classifiers.meta

Methods in weka.classifiers.meta with parameters of type Instance

Modifier and Type	Method	Description
double	AdditiveRegression.classifyInstance(Instance inst)	Classify an instance.
double	RegressionByDiscretization.classifyInstance(Instance instance)	Returns a predicted class for the test instance.
double	Vote.classifyInstance(Instance instance)	Classifies the given test instance.
double	WeightedInstancesHandlerWrapper.classifyInstance(Instance instance)	Classifies the given test instance.
double[]	AdaBoostM1.distributionForInstance(Instance instance)	Calculates the class membership probabilities for the given test instance.
double[]	AttributeSelectedClassifier.distributionForInstance(Instance instance)	Classifies a given instance after attribute selection
double[]	Bagging.distributionForInstance(Instance instance)	Calculates the class membership probabilities for the given test instance.
double[]	ClassificationViaRegression.distributionForInstance(Instance inst)	Returns the distribution for an instance.
double[]	CostSensitiveClassifier.distributionForInstance(Instance instance)	Returns class probabilities.
double[]	CVParameterSelection.distributionForInstance(Instance instance)	Predicts the class distribution for the given test instance.
double[]	FilteredClassifier.distributionForInstance(Instance instance)	Classifies a given instance after filtering.
double[]	IterativeClassifierOptimizer.distributionForInstance(Instance inst)	Returns the class distribution for an instance.
double[]	LogitBoost.distributionForInstance(Instance inst)	Calculates the class membership probabilities for the given test instance.
double[]	MultiClassClassifier.distributionForInstance(Instance inst)	Returns the distribution for an instance.
double[]	MultiClassClassifierUpdateable.distributionForInstance(Instance inst)	Returns the distribution for an instance.
double[]	MultiScheme.distributionForInstance(Instance instance)	Returns class probabilities.
double[]	RandomCommittee.distributionForInstance(Instance instance)	Calculates the class membership probabilities for the given test instance.
double[]	RandomSubSpace.distributionForInstance(Instance instance)	Calculates the class membership probabilities for the given test instance.
double[]	Stacking.distributionForInstance(Instance instance)	Returns estimated class probabilities for the given instance if the class is nominal and a one-element array containing the numeric prediction if the class is numeric.
double[]	Vote.distributionForInstance(Instance instance)	Classifies a given instance using the selected combination rule.
double[]	WeightedInstancesHandlerWrapper.distributionForInstance(Instance instance)	Calculates the class membership probabilities for the given test instance.
double[]	Bagging.getMembershipValues(Instance inst)	Computes an array that indicates leaf membership
double[]	FilteredClassifier.getMembershipValues(Instance inst)	Computes an array that has a value for each element in the partition.
double[]	RandomCommittee.getMembershipValues(Instance inst)	Computes an array that indicates leaf membership
double[]	MultiClassClassifier.individualPredictions(Instance inst)	Returns the individual predictions of the base classifiers for an instance.
double	RegressionByDiscretization.logDensity(Instance instance, double value)	Returns natural logarithm of density estimate for given value based on given instance.
double[][]	RegressionByDiscretization.predictIntervals(Instance instance, double confidenceLevel)	Returns an N * 2 array, where N is the number of prediction intervals.
void	MultiClassClassifierUpdateable.updateClassifier(Instance instance)	Updates the classifier with the given instance.

Uses of Instance in weka.classifiers.misc

Methods in weka.classifiers.misc that return Instance

Modifier and Type	Method	Description
Instance	InputMappedClassifier.constructMappedInstance(Instance incoming)

Methods in weka.classifiers.misc with parameters of type Instance

Modifier and Type	Method	Description
double	InputMappedClassifier.classifyInstance(Instance inst)
Instance	InputMappedClassifier.constructMappedInstance(Instance incoming)
double[]	InputMappedClassifier.distributionForInstance(Instance inst)
double[]	SerializedClassifier.distributionForInstance(Instance instance)	Calculates the class membership probabilities for the given test instance.

Uses of Instance in weka.classifiers.pmml.consumer

Methods in weka.classifiers.pmml.consumer with parameters of type Instance

Modifier and Type	Method	Description
double[]	GeneralRegression.distributionForInstance(Instance inst)	Classifies the given test instance.
double[]	NeuralNetwork.distributionForInstance(Instance inst)	Classifies the given test instance.
double[]	Regression.distributionForInstance(Instance inst)	Classifies the given test instance.
double[]	RuleSetModel.distributionForInstance(Instance inst)	Classifies the given test instance.
double[]	SupportVectorMachineModel.distributionForInstance(Instance inst)	Classifies the given test instance.
double[]	TreeModel.distributionForInstance(Instance inst)	Classifies the given test instance.

Uses of Instance in weka.classifiers.rules

Methods in weka.classifiers.rules with parameters of type Instance

Modifier and Type	Method	Description
double	OneR.classifyInstance(Instance inst)	Classifies a given instance.
double	PART.classifyInstance(Instance instance)	Classifies an instance.
double	ZeroR.classifyInstance(Instance instance)	Classifies a given instance.
abstract boolean	JRip.Antd.covers(Instance inst)
boolean	JRip.NominalAntd.covers(Instance inst)	Whether the instance is covered by this antecedent
boolean	JRip.NumericAntd.covers(Instance inst)	Whether the instance is covered by this antecedent
boolean	JRip.RipperRule.covers(Instance datum)	Whether the instance covered by this rule
abstract boolean	Rule.covers(Instance datum)	Whether the instance covered by this rule
double[]	DecisionTable.distributionForInstance(Instance instance)	Calculates the class membership probabilities for the given test instance.
double[]	JRip.distributionForInstance(Instance datum)	Classify the test instance with the rule learner and provide the class distributions
final double[]	PART.distributionForInstance(Instance instance)	Returns class probabilities for an instance.
double[]	ZeroR.distributionForInstance(Instance instance)	Calculates the class membership probabilities for the given test instance.

Constructors in weka.classifiers.rules with parameters of type Instance

Modifier	Constructor	Description
	DecisionTableHashKey(Instance t, int numAtts, boolean ignoreClass)	Constructor for a hashKey

Uses of Instance in weka.classifiers.rules.part

Methods in weka.classifiers.rules.part with parameters of type Instance

Modifier and Type	Method	Description
double	ClassifierDecList.classifyInstance(Instance instance)	Classifies an instance.
double	MakeDecList.classifyInstance(Instance instance)	Classifies an instance.
final double[]	ClassifierDecList.distributionForInstance(Instance instance)	Returns class probabilities for a weighted instance.
double[]	MakeDecList.distributionForInstance(Instance instance)	Returns the class distribution for an instance.
double	ClassifierDecList.weight(Instance instance)	Returns the weight a rule assigns to an instance.

Uses of Instance in weka.classifiers.trees

Methods in weka.classifiers.trees with parameters of type Instance

Modifier and Type	Method	Description
double	J48.classifyInstance(Instance instance)	Classifies an instance.
double	LMT.classifyInstance(Instance instance)	Classifies an instance.
double[]	DecisionStump.distributionForInstance(Instance instance)	Calculates the class membership probabilities for the given test instance.
double[]	HoeffdingTree.distributionForInstance(Instance inst)	Returns class probabilities for an instance.
final double[]	J48.distributionForInstance(Instance instance)	Returns class probabilities for an instance.
double[]	LMT.distributionForInstance(Instance instance)	Returns class probabilities for an instance.
double[]	RandomTree.distributionForInstance(Instance instance)	Computes class distribution of an instance using the tree.
double[]	REPTree.distributionForInstance(Instance instance)	Computes class distribution of an instance using the tree.
double[]	J48.getMembershipValues(Instance inst)	Computes an array that indicates node membership.
double[]	RandomTree.getMembershipValues(Instance instance)	Computes array that indicates node membership.
double[]	REPTree.getMembershipValues(Instance instance)	Computes array that indicates node membership.
void	HoeffdingTree.updateClassifier(Instance inst)	Updates the classifier with the given instance.

Uses of Instance in weka.classifiers.trees.ht

Methods in weka.classifiers.trees.ht with parameters of type Instance

Modifier and Type	Method	Description
abstract String	Split.branchForInstance(Instance inst)	Returns the name of the branch that the supplied instance would go down
String	SplitNode.branchForInstance(Instance inst)	Return the branch that the supplied instance goes down
String	UnivariateNominalMultiwaySplit.branchForInstance(Instance inst)
String	UnivariateNumericBinarySplit.branchForInstance(Instance inst)
double[]	HNode.getDistribution(Instance inst, Attribute classAtt)	Return a class probability distribution computed from the frequency counts at this node
double[]	NBNode.getDistribution(Instance inst, Attribute classAtt)
double[]	NBNodeAdaptive.getDistribution(Instance inst, Attribute classAtt)
LeafNode	HNode.leafForInstance(Instance inst, SplitNode parent, String parentBranch)	Return the leaf that the supplied instance ends up at
LeafNode	SplitNode.leafForInstance(Instance inst, SplitNode parent, String parentBranch)
void	HNode.updateDistribution(Instance inst)	Update the class frequency distribution with the supplied instance
void	ActiveHNode.updateNode(Instance inst)
abstract void	HNode.updateNode(Instance inst)	Update the node with the supplied instance
void	InactiveHNode.updateNode(Instance inst)
void	LeafNode.updateNode(Instance inst)
void	NBNode.updateNode(Instance inst)
void	NBNodeAdaptive.updateNode(Instance inst)
void	SplitNode.updateNode(Instance inst)

Uses of Instance in weka.classifiers.trees.j48

Methods in weka.classifiers.trees.j48 with parameters of type Instance

Modifier and Type	Method	Description
final void	Distribution.add(int bagIndex, Instance instance)	Adds given instance to given bag.
final void	Distribution.addWeights(Instance instance, double[] weights)	Adds given instance to all bags weighting it according to given weights.
final double	ClassifierSplitModel.classifyInstance(Instance instance)	Classifies a given instance.
double	ClassifierTree.classifyInstance(Instance instance)	Classifies an instance.
final double	BinC45Split.classProb(int classIndex, Instance instance, int theSubset)	Gets class probability for instance.
final double	C45Split.classProb(int classIndex, Instance instance, int theSubset)	Gets class probability for instance.
double	ClassifierSplitModel.classProb(int classIndex, Instance instance, int theSubset)	Gets class probability for instance.
double	NBTreeNoSplit.classProb(int classIndex, Instance instance, int theSubset)	Return the probability for a class value
double	NBTreeSplit.classProb(int classIndex, Instance instance, int theSubset)	Return the probability for a class value
double	ClassifierSplitModel.classProbLaplace(int classIndex, Instance instance, int theSubset)	Gets class probability for instance.
final void	Distribution.del(int bagIndex, Instance instance)	Deletes given instance from given bag.
double[]	ClassifierTree.distributionForInstance(Instance instance, boolean useLaplace)	Returns class probabilities for a weighted instance.
double[]	ClassifierTree.getMembershipValues(Instance instance)	Computes a list that indicates node membership
final void	Distribution.shift(int from, int to, Instance instance)	Shifts given instance from one bag to another one.
final void	Distribution.sub(int bagIndex, Instance instance)	Subtracts given instance from given bag.
final double[]	BinC45Split.weights(Instance instance)	Returns weights if instance is assigned to more than one subset.
final double[]	C45Split.weights(Instance instance)	Returns weights if instance is assigned to more than one subset.
abstract double[]	ClassifierSplitModel.weights(Instance instance)	Returns weights if instance is assigned to more than one subset.
final double[]	NBTreeNoSplit.weights(Instance instance)	Always returns null because there is only one subset.
final double[]	NBTreeSplit.weights(Instance instance)	Returns weights if instance is assigned to more than one subset.
final double[]	NoSplit.weights(Instance instance)	Always returns null because there is only one subset.
final int	BinC45Split.whichSubset(Instance instance)	Returns index of subset instance is assigned to.
final int	C45Split.whichSubset(Instance instance)	Returns index of subset instance is assigned to.
abstract int	ClassifierSplitModel.whichSubset(Instance instance)	Returns index of subset instance is assigned to.
final int	NBTreeNoSplit.whichSubset(Instance instance)	Always returns 0 because only there is only one subset.
final int	NBTreeSplit.whichSubset(Instance instance)	Returns index of subset instance is assigned to.
final int	NoSplit.whichSubset(Instance instance)	Always returns 0 because only there is only one subset.

Uses of Instance in weka.classifiers.trees.lmt

Methods in weka.classifiers.trees.lmt with parameters of type Instance

Modifier and Type	Method	Description
double	SimpleLinearRegression.classifyInstance(Instance inst)	Generate a prediction for the supplied instance.
double[]	LMTNode.distributionForInstance(Instance instance)	Returns the class probabilities for an instance given by the logistic model tree.
double[]	LogisticBase.distributionForInstance(Instance instance)	Returns class probabilities for an instance.
double[]	LMTNode.modelDistributionForInstance(Instance instance)	Returns the class probabilities for an instance according to the logistic model at the node.
final double[]	ResidualSplit.weights(Instance instance)	Method not in use
final int	ResidualSplit.whichSubset(Instance instance)

Uses of Instance in weka.classifiers.trees.m5

Methods in weka.classifiers.trees.m5 with parameters of type Instance

Modifier and Type	Method	Description
double	M5Base.classifyInstance(Instance inst)	Calculates a prediction for an instance using a set of rules or an M5 model tree
double	PreConstructedLinearModel.classifyInstance(Instance inst)	Predicts the class of the supplied instance using the linear model.
double	Rule.classifyInstance(Instance instance)	Calculates a prediction for an instance using this rule or M5 model tree
double	RuleNode.classifyInstance(Instance inst)	Classify an instance using this node.

Uses of Instance in weka.clusterers

Methods in weka.clusterers with parameters of type Instance

Modifier and Type	Method	Description
void	Cobweb.addInstance(Instance newInstance)	Deprecated. updateClusterer(Instance) should be used instead
long[]	Canopy.assignCanopies(Instance inst)	Uses T1 distance to assign canopies to the supplied instance.
int	AbstractClusterer.clusterInstance(Instance instance)	Classifies a given instance.
int	Clusterer.clusterInstance(Instance instance)	Classifies a given instance.
int	Cobweb.clusterInstance(Instance instance)	Classifies a given instance.
int	FarthestFirst.clusterInstance(Instance instance)	Classifies a given instance.
int	HierarchicalClusterer.clusterInstance(Instance instance)
int	SimpleKMeans.clusterInstance(Instance instance)	Classifies a given instance.
double[]	AbstractClusterer.distributionForInstance(Instance instance)	Predicts the cluster memberships for a given instance.
double[]	AbstractDensityBasedClusterer.distributionForInstance(Instance instance)	Returns the cluster probability distribution for an instance.
double[]	Canopy.distributionForInstance(Instance instance)
double[]	Clusterer.distributionForInstance(Instance instance)	Predicts the cluster memberships for a given instance.
double[]	DensityBasedClusterer.distributionForInstance(Instance instance)	Returns the cluster probability distribution for an instance.
double[]	FilteredClusterer.distributionForInstance(Instance instance)	Classifies a given instance after filtering.
double[]	HierarchicalClusterer.distributionForInstance(Instance instance)
double	AbstractDensityBasedClusterer.logDensityForInstance(Instance instance)	Computes the density for a given instance.
double	DensityBasedClusterer.logDensityForInstance(Instance instance)	Computes the density for a given instance.
abstract double[]	AbstractDensityBasedClusterer.logDensityPerClusterForInstance(Instance instance)	Computes the log of the conditional density (per cluster) for a given instance.
double[]	DensityBasedClusterer.logDensityPerClusterForInstance(Instance instance)	Computes the log of the conditional density (per cluster) for a given instance.
double[]	EM.logDensityPerClusterForInstance(Instance inst)	Computes the log of the conditional density (per cluster) for a given instance.
double[]	MakeDensityBasedClusterer.logDensityPerClusterForInstance(Instance inst)	Computes the log of the conditional density (per cluster) for a given instance.
double[]	AbstractDensityBasedClusterer.logJointDensitiesForInstance(Instance inst)	Returns the logs of the joint densities for a given instance.
double[]	DensityBasedClusterer.logJointDensitiesForInstance(Instance inst)	Returns the logs of the joint densities for a given instance.
void	Canopy.updateClusterer(Instance newInstance)
void	Cobweb.updateClusterer(Instance newInstance)	Adds an instance to the clusterer.
void	UpdateableClusterer.updateClusterer(Instance newInstance)	Adds an instance to the clusterer.

Constructors in weka.clusterers with parameters of type Instance

Modifier	Constructor	Description
	CNode(int numAttributes, Instance leafInstance)	Creates a new leaf CNode instance.

Uses of Instance in weka.core

Classes in weka.core that implement Instance

Modifier and Type	Class	Description
class	AbstractInstance	Abstract class providing common functionality for the original instance implementations.
class	BinarySparseInstance	Class for storing a binary-data-only instance as a sparse vector.
class	DenseInstance	Class for handling an instance.
class	SparseInstance	Class for storing an instance as a sparse vector.

Methods in weka.core that return Instance

Modifier and Type	Method	Description
Instance	BinarySparseInstance.copy(double[] values)	Copies the instance but fills up its values based on the given array of doubles.
Instance	DenseInstance.copy(double[] values)	Copies the instance but fills up its values based on the given array of doubles.
Instance	Instance.copy(double[] values)	Copies the instance but fills up its values based on the given array of doubles.
Instance	SparseInstance.copy(double[] values)	Copies the instance but fills up its values based on the given array of doubles.
Instance	Instances.firstInstance()	Returns the first instance in the set.
Instance	Instances.get(int index)	Returns the instance at the given position.
Instance	AlgVector.getAsInstance(Instances model, Random random)	Gets the elements of the vector as an instance.
Instance	Instances.instance(int index)	Returns the instance at the given position.
Instance	Instances.lastInstance()	Returns the last instance in the set.
Instance	BinarySparseInstance.mergeInstance(Instance inst)	Merges this instance with the given instance and returns the result.
Instance	DenseInstance.mergeInstance(Instance inst)	Merges this instance with the given instance and returns the result.
Instance	Instance.mergeInstance(Instance inst)	Merges this instance with the given instance and returns the result.
Instance	SparseInstance.mergeInstance(Instance inst)	Merges this instance with the given instance and returns the result.
Instance	Instances.remove(int index)	Removes the instance at the given position.
Instance	Instances.set(int index, Instance instance)	Replaces the instance at the given position.
Instance	DictionaryBuilder.vectorizeInstance(Instance input)	Convert an input instance.
Instance	DictionaryBuilder.vectorizeInstance(Instance input, boolean retainStringAttValuesInMemory)	Convert an input instance.

Methods in weka.core that return types with arguments of type Instance

Modifier and Type	Method	Description
Enumeration<Instance>	Instances.enumerateInstances()	Returns an enumeration of all instances in the dataset.

Methods in weka.core with parameters of type Instance

Modifier and Type	Method	Description
void	Instances.add(int index, Instance instance)	Adds one instance at the given position in the list.
boolean	Instances.add(Instance instance)	Adds one instance to the end of the set.
boolean	Instances.checkInstance(Instance instance)	Checks if the given instance is compatible with this dataset.
int	EuclideanDistance.closestPoint(Instance instance, Instances allPoints, int[] pointList)	Returns the index of the closest point to the current instance.
int	InstanceComparator.compare(Instance inst1, Instance inst2)	compares the two instances, returns -1 if o1 is smaller than o2, 0 if equal and +1 if greater.
static void	RelationalLocator.copyRelationalValues(Instance instance, boolean instSrcCompat, Instances srcDataset, AttributeLocator srcLoc, Instances destDataset, AttributeLocator destLoc)	Takes relational values referenced by an Instance and copies them from a source dataset to a destination dataset.
static void	RelationalLocator.copyRelationalValues(Instance inst, Instances destDataset, AttributeLocator strAtts)	Copies relational values contained in the instance copied to a new dataset.
static void	StringLocator.copyStringValues(Instance instance, boolean instSrcCompat, Instances srcDataset, AttributeLocator srcLoc, Instances destDataset, AttributeLocator destLoc)	Takes string values referenced by an Instance and copies them from a source dataset to a destination dataset.
static void	StringLocator.copyStringValues(Instance inst, Instances destDataset, AttributeLocator strAtts)	Copies string values contained in the instance copied to a new dataset.
double	DistanceFunction.distance(Instance first, Instance second)	Calculates the distance between two instances.
double	DistanceFunction.distance(Instance first, Instance second, double cutOffValue)	Calculates the distance between two instances.
double	DistanceFunction.distance(Instance first, Instance second, double cutOffValue, PerformanceStats stats)	Calculates the distance between two instances.
double	DistanceFunction.distance(Instance first, Instance second, PerformanceStats stats)	Calculates the distance between two instances.
double	EuclideanDistance.distance(Instance first, Instance second)	Calculates the distance between two instances.
double	EuclideanDistance.distance(Instance first, Instance second, PerformanceStats stats)	Calculates the distance (or similarity) between two instances.
double	FilteredDistance.distance(Instance first, Instance second)	Calculates the distance between two instances.
double	FilteredDistance.distance(Instance first, Instance second, double cutOffValue)	Calculates the distance between two instances.
double	FilteredDistance.distance(Instance first, Instance second, double cutOffValue, PerformanceStats stats)	Calculates the distance between two instances.
double	FilteredDistance.distance(Instance first, Instance second, PerformanceStats stats)	Calculates the distance between two instances.
double	MinkowskiDistance.distance(Instance first, Instance second)	Calculates the distance between two instances.
double	MinkowskiDistance.distance(Instance first, Instance second, PerformanceStats stats)	Calculates the distance (or similarity) between two instances.
double	NormalizableDistance.distance(Instance first, Instance second)	Calculates the distance between two instances.
double	NormalizableDistance.distance(Instance first, Instance second, double cutOffValue)	Calculates the distance between two instances.
double	NormalizableDistance.distance(Instance first, Instance second, double cutOffValue, PerformanceStats stats)	Calculates the distance between two instances.
double	NormalizableDistance.distance(Instance first, Instance second, PerformanceStats stats)	Calculates the distance between two instances.
boolean	AbstractInstance.equalHeaders(Instance inst)	Tests if the headers of two instances are equivalent.
boolean	Instance.equalHeaders(Instance inst)	Tests if the headers of two instances are equivalent.
String	AbstractInstance.equalHeadersMsg(Instance inst)	Checks if the headers of two instances are equivalent.
String	Instance.equalHeadersMsg(Instance inst)	Checks if the headers of two instances are equivalent.
double[]	PartitionGenerator.getMembershipValues(Instance inst)	Computes an array that has a value for each element in the partition.
boolean	NormalizableDistance.inRanges(Instance instance, double[][] ranges)	Test if an instance is within the given ranges.
Instance	BinarySparseInstance.mergeInstance(Instance inst)	Merges this instance with the given instance and returns the result.
Instance	DenseInstance.mergeInstance(Instance inst)	Merges this instance with the given instance and returns the result.
Instance	Instance.mergeInstance(Instance inst)	Merges this instance with the given instance and returns the result.
Instance	SparseInstance.mergeInstance(Instance inst)	Merges this instance with the given instance and returns the result.
void	DictionaryBuilder.processInstance(Instance inst)	Process an instance by tokenizing string attributes and updating the dictionary.
Instance	Instances.set(int index, Instance instance)	Replaces the instance at the given position.
void	DistanceFunction.update(Instance ins)	Update the distance function (if necessary) for the newly added instance.
void	FilteredDistance.update(Instance ins)	Update the distance function (if necessary) for the newly added instance.
void	NormalizableDistance.update(Instance ins)	Update the distance function (if necessary) for the newly added instance.
void	NormalizableDistance.updateRanges(Instance instance)	Update the ranges with a new instance.
double[][]	NormalizableDistance.updateRanges(Instance instance, double[][] ranges)	Updates the ranges given a new instance.
void	NormalizableDistance.updateRanges(Instance instance, int numAtt, double[][] ranges)	Updates the minimum and maximum and width values for all the attributes based on a new instance.
void	NormalizableDistance.updateRangesFirst(Instance instance, int numAtt, double[][] ranges)	Used to initialize the ranges.
boolean	EuclideanDistance.valueIsSmallerEqual(Instance instance, int dim, double value)	Returns true if the value of the given dimension is smaller or equal the value to be compared with.
Instance	DictionaryBuilder.vectorizeInstance(Instance input)	Convert an input instance.
Instance	DictionaryBuilder.vectorizeInstance(Instance input, boolean retainStringAttValuesInMemory)	Convert an input instance.

Constructors in weka.core with parameters of type Instance

Modifier	Constructor	Description
	AlgVector(Instance instance)	Constructs a vector using an instance.
	BinarySparseInstance(Instance instance)	Constructor that generates a sparse instance from the given instance.
	DenseInstance(Instance instance)	Constructor that copies the attribute values and the weight from the given instance.
	SparseInstance(Instance instance)	Constructor that generates a sparse instance from the given instance.

Uses of Instance in weka.core.converters

Methods in weka.core.converters that return Instance

Modifier and Type	Method	Description
abstract Instance	AbstractLoader.getNextInstance(Instances structure)
Instance	ArffLoader.getNextInstance(Instances structure)	Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
Instance	C45Loader.getNextInstance(Instances structure)	Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
Instance	CSVLoader.getNextInstance(Instances structure)
Instance	DatabaseLoader.getNextInstance(Instances structure)	Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
Instance	JSONLoader.getNextInstance(Instances structure)	JSONLoader is unable to process a data set incrementally.
Instance	LibSVMLoader.getNextInstance(Instances structure)	LibSVmLoader is unable to process a data set incrementally.
Instance	Loader.getNextInstance(Instances structure)	Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
Instance	MatlabLoader.getNextInstance(Instances structure)	MatlabLoader is unable to process a data set incrementally.
Instance	SerializedInstancesLoader.getNextInstance(Instances structure)	Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
Instance	SVMLightLoader.getNextInstance(Instances structure)	SVMLightLoader is unable to process a data set incrementally.
Instance	TextDirectoryLoader.getNextInstance(Instances structure)	Process input directories/files incrementally.
Instance	XRFFLoader.getNextInstance(Instances structure)	XRFFLoader is unable to process a data set incrementally.
Instance	ConverterUtils.DataSource.nextElement(Instances dataset)	returns the next element and sets the specified dataset, null if none available.
Instance	ArffLoader.ArffReader.readInstance(Instances structure)	Reads a single instance using the tokenizer and returns it.
Instance	ArffLoader.ArffReader.readInstance(Instances structure, boolean flag)	Reads a single instance using the tokenizer and returns it.

Methods in weka.core.converters with parameters of type Instance

Modifier and Type	Method	Description
void	AbstractSaver.writeIncremental(Instance i)	Method for incremental saving.
void	ArffSaver.writeIncremental(Instance inst)	Saves an instances incrementally.
void	C45Saver.writeIncremental(Instance inst)	Saves an instances incrementally.
void	CSVSaver.writeIncremental(Instance inst)	Saves an instances incrementally.
void	DatabaseSaver.writeIncremental(Instance inst)	Saves an instances incrementally.
void	DictionarySaver.writeIncremental(Instance inst)
void	LibSVMSaver.writeIncremental(Instance inst)	Saves an instances incrementally.
void	MatlabSaver.writeIncremental(Instance inst)	Saves an instances incrementally.
void	Saver.writeIncremental(Instance inst)	Writes to a destination in incremental mode.
void	SVMLightSaver.writeIncremental(Instance inst)	Saves an instances incrementally.

Uses of Instance in weka.core.expressionlanguage.weka

Methods in weka.core.expressionlanguage.weka with parameters of type Instance

Modifier and Type	Method	Description
void	InstancesHelper.setInstance(Instance instance)	Sets the current instance to be the supplied instance

Uses of Instance in weka.core.neighboursearch

Methods in weka.core.neighboursearch that return Instance

Modifier and Type	Method	Description
Instance	BallTree.nearestNeighbour(Instance target)	Returns the nearest instance in the current neighbourhood to the supplied instance.
Instance	CoverTree.nearestNeighbour(Instance target)	Returns the NN instance of a given target instance, from among the previously supplied training instances.
Instance	FilteredNeighbourSearch.nearestNeighbour(Instance target)	Returns the nearest neighbour for the given instance based on distance measured in the filtered space.
Instance	KDTree.nearestNeighbour(Instance target)	Returns the nearest neighbour of the supplied target instance.
Instance	LinearNNSearch.nearestNeighbour(Instance target)	Returns the nearest instance in the current neighbourhood to the supplied instance.
abstract Instance	NearestNeighbourSearch.nearestNeighbour(Instance target)	Returns the nearest instance in the current neighbourhood to the supplied instance.
Instance	CoverTree.CoverTreeNode.p()	Returns the instance represented by the node.

Methods in weka.core.neighboursearch with parameters of type Instance

Modifier and Type	Method	Description
void	BallTree.addInstanceInfo(Instance ins)	Adds the given instance's info.
void	CoverTree.addInstanceInfo(Instance ins)	Adds the given instance info.
void	FilteredNeighbourSearch.addInstanceInfo(Instance ins)	Updates the instance info in the underlying search method, once the instance has been filtered.
void	KDTree.addInstanceInfo(Instance instance)	Adds one instance to KDTree loosly.
void	LinearNNSearch.addInstanceInfo(Instance ins)	Adds the given instance info.
void	NearestNeighbourSearch.addInstanceInfo(Instance ins)	Adds information from the given instance without modifying the datastructure a lot.
Instances	BallTree.kNearestNeighbours(Instance target, int k)	Returns k nearest instances in the current neighbourhood to the supplied instance.
Instances	CoverTree.kNearestNeighbours(Instance target, int k)	Returns k-NNs of a given target instance, from among the previously supplied training instances (supplied through setInstances method) P.S.: May return more than k-NNs if more one instances have the same distance to the target as the kth NN.
Instances	FilteredNeighbourSearch.kNearestNeighbours(Instance target, int k)	Returns the nearest neighbours for the given instance based on distance measured in the filtered space.
Instances	KDTree.kNearestNeighbours(Instance target, int k)	Returns the k nearest neighbours of the supplied instance.
Instances	LinearNNSearch.kNearestNeighbours(Instance target, int kNN)	Returns k nearest instances in the current neighbourhood to the supplied instance.
abstract Instances	NearestNeighbourSearch.kNearestNeighbours(Instance target, int k)	Returns k nearest instances in the current neighbourhood to the supplied instance.
Instance	BallTree.nearestNeighbour(Instance target)	Returns the nearest instance in the current neighbourhood to the supplied instance.
Instance	CoverTree.nearestNeighbour(Instance target)	Returns the NN instance of a given target instance, from among the previously supplied training instances.
Instance	FilteredNeighbourSearch.nearestNeighbour(Instance target)	Returns the nearest neighbour for the given instance based on distance measured in the filtered space.
Instance	KDTree.nearestNeighbour(Instance target)	Returns the nearest neighbour of the supplied target instance.
Instance	LinearNNSearch.nearestNeighbour(Instance target)	Returns the nearest instance in the current neighbourhood to the supplied instance.
abstract Instance	NearestNeighbourSearch.nearestNeighbour(Instance target)	Returns the nearest instance in the current neighbourhood to the supplied instance.
void	BallTree.update(Instance ins)	Adds one instance to the BallTree.
void	CoverTree.update(Instance ins)	Adds an instance to the cover tree.
void	FilteredNeighbourSearch.update(Instance ins)	Updates ranges based on the given instance, once it has been filtered.
void	KDTree.update(Instance instance)	Adds one instance to the KDTree.
void	LinearNNSearch.update(Instance ins)	Updates the LinearNNSearch to cater for the new added instance.
abstract void	NearestNeighbourSearch.update(Instance ins)	Updates the NearNeighbourSearch algorithm for the new added instance.

Uses of Instance in weka.core.neighboursearch.balltrees

Methods in weka.core.neighboursearch.balltrees that return Instance

Modifier and Type	Method	Description
static Instance	BallNode.calcCentroidPivot(int[] instList, Instances insts)	Calculates the centroid pivot of a node.
static Instance	BallNode.calcCentroidPivot(int start, int end, int[] instList, Instances insts)	Calculates the centroid pivot of a node.
static Instance	BallNode.calcPivot(BallNode child1, BallNode child2, Instances insts)	Calculates the centroid pivot of a node based on its two child nodes (if merging two nodes).
Instance	BottomUpConstructor.calcPivot(weka.core.neighboursearch.balltrees.BottomUpConstructor.TempNode node1, weka.core.neighboursearch.balltrees.BottomUpConstructor.TempNode node2, Instances insts)	Calculates the centroid pivot of a node based on its two child nodes.
Instance	MiddleOutConstructor.calcPivot(weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList list1, weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList list2, Instances insts)	Calculates the centroid pivot of a node based on the list of points that it contains (tbe two lists of its children are provided).
Instance	MiddleOutConstructor.calcPivot(weka.core.neighboursearch.balltrees.MiddleOutConstructor.TempNode node1, weka.core.neighboursearch.balltrees.MiddleOutConstructor.TempNode node2, Instances insts)	/** Calculates the centroid pivot of a node based on its two child nodes (if merging two nodes).
Instance	BallNode.getPivot()	Returns the pivot/centre of the node's ball.

Methods in weka.core.neighboursearch.balltrees with parameters of type Instance

Modifier and Type	Method	Description
abstract int[]	BallTreeConstructor.addInstance(BallNode node, Instance inst)	Adds an instance to the ball tree.
int[]	BottomUpConstructor.addInstance(BallNode node, Instance inst)	Adds an instance to the ball tree.
int[]	MiddleOutConstructor.addInstance(BallNode node, Instance inst)	Adds an instance to the tree.
int[]	TopDownConstructor.addInstance(BallNode node, Instance inst)	Adds an instance to the ball tree.
static double	BallNode.calcRadius(int[] instList, Instances insts, Instance pivot, DistanceFunction distanceFunction)	Calculates the radius of node.
static double	BallNode.calcRadius(int start, int end, int[] instList, Instances insts, Instance pivot, DistanceFunction distanceFunction)	Calculates the radius of a node.
static double	BallNode.calcRadius(BallNode child1, BallNode child2, Instance pivot, DistanceFunction distanceFunction)	Calculates the radius of a node based on its two child nodes (if merging two nodes).
double	MiddleOutConstructor.calcRadius(weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList list1, weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList list2, Instance pivot, Instances insts)	Calculates the radius of a node based on the list of points that it contains (the two lists of its children are provided).
void	BallNode.setPivot(Instance pivot)	Sets the pivot/centre of this nodes ball.

Constructors in weka.core.neighboursearch.balltrees with parameters of type Instance

Modifier	Constructor	Description
	BallNode(int start, int end, int nodeNumber, Instance pivot, double radius)	Creates a new instance of BallNode.

Uses of Instance in weka.core.pmml

Methods in weka.core.pmml with parameters of type Instance

Modifier and Type	Method	Description
double[]	MappingInfo.instanceToSchema(Instance inst, MiningSchema miningSchema)	Convert an Instance to an array of values that matches the format of the mining schema.

Uses of Instance in weka.datagenerators

Methods in weka.datagenerators that return Instance

Modifier and Type	Method	Description
abstract Instance	DataGenerator.generateExample()	Generates one example of the dataset.

Methods in weka.datagenerators with parameters of type Instance

Modifier and Type	Method	Description
boolean	Test.passesTest(Instance inst)	Determines whether an instance passes the test.

Uses of Instance in weka.datagenerators.classifiers.classification

Methods in weka.datagenerators.classifiers.classification that return Instance

Modifier and Type	Method	Description
Instance	Agrawal.generateExample()	Generates one example of the dataset.
Instance	BayesNet.generateExample()	Generates one example of the dataset.
Instance	LED24.generateExample()	Generates one example of the dataset.
Instance	RandomRBF.generateExample()	Generates one example of the dataset.
Instance	RDG1.generateExample()	Generate an example of the dataset dataset.

Uses of Instance in weka.datagenerators.classifiers.regression

Methods in weka.datagenerators.classifiers.regression that return Instance

Modifier and Type	Method	Description
Instance	Expression.generateExample()	Generates one example of the dataset.
Instance	MexicanHat.generateExample()	Generates one example of the dataset.

Uses of Instance in weka.datagenerators.clusterers

Methods in weka.datagenerators.clusterers that return Instance

Modifier and Type	Method	Description
Instance	BIRCHCluster.generateExample()	Generate an example of the dataset.
Instance	SubspaceCluster.generateExample()	Generate an example of the dataset.

Uses of Instance in weka.experiment

Methods in weka.experiment with parameters of type Instance

Modifier and Type	Method	Description
PairedStats	PairedCorrectedTTester.calculateStatistics(Instance datasetSpecifier, int resultset1Index, int resultset2Index, int comparisonColumn)	Computes a paired t-test comparison for a specified dataset between two resultsets.
PairedStats	PairedTTester.calculateStatistics(Instance datasetSpecifier, int resultset1Index, int resultset2Index, int comparisonColumn)	Computes a paired t-test comparison for a specified dataset between two resultsets.
PairedStats	Tester.calculateStatistics(Instance datasetSpecifier, int resultset1Index, int resultset2Index, int comparisonColumn)	Computes a paired t-test comparison for a specified dataset between two resultsets.

Uses of Instance in weka.filters

Methods in weka.filters that return Instance

Modifier and Type	Method	Description
Instance	Filter.output()	Output an instance after filtering and remove from the output queue.
Instance	Filter.outputPeek()	Output an instance after filtering but do not remove from the output queue.

Methods in weka.filters with parameters of type Instance

Modifier and Type	Method	Description
boolean	AllFilter.input(Instance instance)	Input an instance for filtering.
boolean	Filter.input(Instance instance)	Input an instance for filtering.
boolean	RenameRelation.input(Instance instance)
boolean	SimpleBatchFilter.input(Instance instance)	Input an instance for filtering.
boolean	SimpleStreamFilter.input(Instance instance)	Input an instance for filtering.

Uses of Instance in weka.filters.supervised.attribute

Methods in weka.filters.supervised.attribute with parameters of type Instance

Modifier and Type	Method	Description
boolean	AttributeSelection.input(Instance instance)	Input an instance for filtering.
boolean	ClassConditionalProbabilities.input(Instance inst)
boolean	ClassOrder.input(Instance instance)	Input an instance for filtering.
boolean	Discretize.input(Instance instance)	Input an instance for filtering.
boolean	NominalToBinary.input(Instance instance)	Input an instance for filtering.
boolean	PartitionMembership.input(Instance instance)	Input an instance for filtering.

Uses of Instance in weka.filters.supervised.instance

Methods in weka.filters.supervised.instance with parameters of type Instance

Modifier and Type	Method	Description
boolean	Resample.input(Instance instance)	Input an instance for filtering.
boolean	SpreadSubsample.input(Instance instance)	Input an instance for filtering.
boolean	StratifiedRemoveFolds.input(Instance instance)	Input an instance for filtering.

Uses of Instance in weka.filters.unsupervised.attribute

Methods in weka.filters.unsupervised.attribute that return Instance

Modifier and Type	Method	Description
Instance	RemoveType.output()	Output an instance after filtering and remove from the output queue.
Instance	RemoveType.outputPeek()	Output an instance after filtering but do not remove from the output queue.

Methods in weka.filters.unsupervised.attribute with parameters of type Instance

Modifier and Type	Method	Description
boolean	AbstractTimeSeries.input(Instance instance)	Input an instance for filtering.
boolean	Add.input(Instance instance)	Input an instance for filtering.
boolean	AddCluster.input(Instance instance)	Input an instance for filtering.
boolean	AddExpression.input(Instance instance)	Input an instance for filtering.
boolean	AddID.input(Instance instance)	Input an instance for filtering.
boolean	AddNoise.input(Instance instance)	Input an instance for filtering.
boolean	AddUserFields.input(Instance instance)	Input an instance for filtering.
boolean	AddValues.input(Instance instance)	Input an instance for filtering.
boolean	Center.input(Instance instance)	Input an instance for filtering.
boolean	ChangeDateFormat.input(Instance instance)	Input an instance for filtering.
boolean	ClusterMembership.input(Instance instance)	Input an instance for filtering.
boolean	Copy.input(Instance instance)	Input an instance for filtering.
boolean	Discretize.input(Instance instance)	Input an instance for filtering.
boolean	FirstOrder.input(Instance instance)	Input an instance for filtering.
boolean	MakeIndicator.input(Instance instance)	Input an instance for filtering.
boolean	MathExpression.input(Instance instance)	Input an instance for filtering.
boolean	MergeManyValues.input(Instance instance)	Input an instance for filtering.
boolean	MergeTwoValues.input(Instance instance)	Input an instance for filtering.
boolean	NominalToBinary.input(Instance instance)	Input an instance for filtering.
boolean	NominalToString.input(Instance instance)	Input an instance for filtering.
boolean	Normalize.input(Instance instance)	Input an instance for filtering.
boolean	NumericToBinary.input(Instance instance)	Input an instance for filtering.
boolean	NumericTransform.input(Instance instance)	Input an instance for filtering.
boolean	Obfuscate.input(Instance instance)	Input an instance for filtering.
boolean	OrdinalToNumeric.input(Instance inst)	Input an instance for filtering.
boolean	PrincipalComponents.input(Instance instance)	Input an instance for filtering.
boolean	RandomProjection.input(Instance instance)	Input an instance for filtering.
boolean	Remove.input(Instance instance)	Input an instance for filtering.
boolean	RemoveType.input(Instance instance)	Input an instance for filtering.
boolean	RemoveUseless.input(Instance instance)	Input an instance for filtering.
boolean	RenameNominalValues.input(Instance instance)	Input an instance for filtering.
boolean	Reorder.input(Instance instance)	Input an instance for filtering.
boolean	ReplaceMissingValues.input(Instance instance)	Input an instance for filtering.
boolean	ReplaceMissingWithUserConstant.input(Instance inst)
boolean	Standardize.input(Instance instance)	Input an instance for filtering.
boolean	StringToNominal.input(Instance instance)	Input an instance for filtering.
boolean	StringToWordVector.input(Instance instance)	Input an instance for filtering.
boolean	SwapValues.input(Instance instance)	Input an instance for filtering.

Uses of Instance in weka.filters.unsupervised.instance

Methods in weka.filters.unsupervised.instance with parameters of type Instance

Modifier and Type	Method	Description
boolean	NonSparseToSparse.input(Instance instance)	Input an instance for filtering.
boolean	Randomize.input(Instance instance)	Input an instance for filtering.
boolean	RemoveDuplicates.input(Instance instance)	Input an instance for filtering.
boolean	RemoveFolds.input(Instance instance)	Input an instance for filtering.
boolean	RemoveFrequentValues.input(Instance instance)	Input an instance for filtering.
boolean	RemoveMisclassified.input(Instance instance)	Input an instance for filtering.
boolean	RemovePercentage.input(Instance instance)	Input an instance for filtering.
boolean	RemoveRange.input(Instance instance)	Input an instance for filtering.
boolean	RemoveWithValues.input(Instance instance)	Input an instance for filtering.
boolean	Resample.input(Instance instance)	Input an instance for filtering.
boolean	ReservoirSample.input(Instance instance)	Input an instance for filtering.
boolean	SparseToNonSparse.input(Instance instance)	Input an instance for filtering.
boolean	SubsetByExpression.input(Instance instance)	Input an instance for filtering.

Uses of Instance in weka.gui.beans

Methods in weka.gui.beans that return Instance

Modifier and Type	Method	Description
Instance	IncrementalClassifierEvent.getCurrentInstance()	Get the current instance
Instance	InstanceEvent.getInstance()	Get the instance
Instance	SubstringLabelerRules.makeOutputInstance(Instance inputI, boolean batch)	Process and input instance and return an output instance
Instance	SubstringReplacerRules.makeOutputInstance(Instance inputI)	Make an output instance given an input one

Methods in weka.gui.beans with parameters of type Instance

Modifier and Type	Method	Description
String	SubstringLabelerRules.SubstringLabelerMatchRule.apply(Instance inst)	Apply this rule to the supplied instance
void	SubstringReplacerRules.SubstringReplacerMatchRule.apply(Instance inst)	Apply this rule to the supplied instance
void	SubstringReplacerRules.applyRules(Instance inst)
Instance	SubstringLabelerRules.makeOutputInstance(Instance inputI, boolean batch)	Process and input instance and return an output instance
Instance	SubstringReplacerRules.makeOutputInstance(Instance inputI)	Make an output instance given an input one
void	IncrementalClassifierEvent.setCurrentInstance(Instance i)	Set the current instance for this event
void	InstanceEvent.setInstance(Instance i)	Set the instance

Constructors in weka.gui.beans with parameters of type Instance

Modifier	Constructor	Description
	IncrementalClassifierEvent(Object source, Classifier scheme, Instance currentI, int status)	Creates a new IncrementalClassifierEvent instance.
	InstanceEvent(Object source, Instance instance, int status)	Creates a new InstanceEvent instance that encapsulates a single instance only.

Uses of Instance in weka.gui.boundaryvisualizer

Methods in weka.gui.boundaryvisualizer with parameters of type Instance

Modifier and Type	Method	Description
void	BoundaryPanel.addTrainingInstance(Instance instance)	Adds a training instance to the visualization dataset.

Uses of Instance in weka.gui.explorer

Methods in weka.gui.explorer with parameters of type Instance

Modifier and Type	Method	Description
void	ClassifierErrorsPlotInstances.process(Instance toPredict, Classifier classifier, Evaluation eval)	Process a classifier's prediction for an instance and update a set of plotting instances and additional plotting info.

Uses of Instance in weka.gui.streams

Methods in weka.gui.streams that return Instance

Modifier and Type	Method	Description
Instance	InstanceJoiner.outputPeek()	Output an instance after filtering but do not remove from the output queue.
Instance	InstanceLoader.outputPeek()
Instance	InstanceProducer.outputPeek()

Methods in weka.gui.streams with parameters of type Instance

Modifier and Type	Method	Description
void	InstanceCounter.input(Instance instance)
boolean	InstanceJoiner.input(Instance instance)
void	InstanceSavePanel.input(Instance instance)
void	InstanceTable.input(Instance instance)
void	InstanceViewer.input(Instance instance)

Uses of Instance in weka.knowledgeflow.steps

Methods in weka.knowledgeflow.steps with parameters of type Instance

Modifier and Type	Method	Description
boolean	FlowByExpression.BracketNode.evaluate(Instance inst, boolean result)
boolean	FlowByExpression.ExpressionClause.evaluate(Instance inst, boolean result)
abstract boolean	FlowByExpression.ExpressionNode.evaluate(Instance inst, boolean result)	Evaluate this node and combine with the result so far