Uses of Class weka.core.Instances (weka-dev 3.7.12 API)

Packages that use Instances
Package	Description
weka.associations
weka.attributeSelection
weka.classifiers
weka.classifiers.bayes
weka.classifiers.bayes.net
weka.classifiers.bayes.net.search
weka.classifiers.bayes.net.search.fixed
weka.classifiers.bayes.net.search.global
weka.classifiers.bayes.net.search.local
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.pmml.producer
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.json
weka.core.neighboursearch
weka.core.neighboursearch.balltrees
weka.core.neighboursearch.kdtrees
weka.core.pmml
weka.core.xml
weka.datagenerators
weka.datagenerators.classifiers.classification
weka.datagenerators.classifiers.regression
weka.datagenerators.clusterers
weka.estimators
weka.experiment
weka.filters
weka.filters.supervised.attribute
weka.filters.supervised.instance
weka.filters.unsupervised.attribute
weka.filters.unsupervised.instance
weka.filters.unsupervised.instance.subsetbyexpression
weka.gui
weka.gui.arffviewer
weka.gui.beans
weka.gui.boundaryvisualizer
weka.gui.experiment
weka.gui.explorer
weka.gui.streams
weka.gui.treevisualizer
weka.gui.visualize
weka.gui.visualize.plugins

Uses of Instances in weka.associations

Methods in weka.associations that return Instances
Modifier and Type	Method and Description
`static Instances`	LabeledItemSet.`divide(Instances instances, boolean invert)` Splits the class attribute away.
`Instances`	CARuleMiner.`getInstancesNoClass()` Gets the instances without the class attribute
`Instances`	Apriori.`getInstancesNoClass()` Gets the instances without the class atrribute.
`Instances`	CARuleMiner.`getInstancesOnlyClass()` Gets the class attribute and its values for all instances
`Instances`	Apriori.`getInstancesOnlyClass()` Gets only the class attribute of the instances.

Methods in weka.associations with parameters of type Instances
Modifier and Type	Method and Description
`void`	FPGrowth.`buildAssociations(Instances data)` Method that generates all large item sets with a minimum support, and from these all association rules with a minimum metric (i.e.
`void`	FilteredAssociator.`buildAssociations(Instances data)` Build the associator on the filtered data.
`void`	Associator.`buildAssociations(Instances data)` Generates an associator.
`void`	Apriori.`buildAssociations(Instances instances)` Method that generates all large itemsets with a minimum support, and from these all association rules with a minimum confidence.
`static Instances`	LabeledItemSet.`divide(Instances instances, boolean invert)` Splits the class attribute away.
`String`	AssociatorEvaluation.`evaluate(Associator associator, Instances data)` Evaluates the associator with the given commandline options and returns the evaluation string.
`ArrayList<Object>[]`	CARuleMiner.`mineCARs(Instances data)` Method for mining class association rules.
`ArrayList<Object>[]`	Apriori.`mineCARs(Instances data)` Method that mines all class association rules with minimum support and with a minimum confidence.
`static ArrayList<Object>`	ItemSet.`singletons(Instances instances)` Converts the header info of the given set of instances into a set of item sets (singletons).
`static ArrayList<Object>`	AprioriItemSet.`singletons(Instances instances, boolean treatZeroAsMissing)` Converts the header info of the given set of instances into a set of item sets (singletons).
`static ArrayList<Object>`	LabeledItemSet.`singletons(Instances instancesNoClass, Instances classes)` Converts the header info of the given set of instances into a set of item sets (singletons).
`String`	ItemSet.`toString(Instances instances)` Returns the contents of an item set as a string.
`String`	AprioriItemSet.`toString(Instances instances)` Returns the contents of an item set as a string.
`String`	ItemSet.`toString(Instances instances, char outerDelim, char innerDelim)` Returns the contents of an item set as a delimited string.
`static void`	ItemSet.`upDateCounters(ArrayList<Object> itemSets, Instances instances)` Updates counters for a set of item sets and a set of instances.
`static void`	LabeledItemSet.`upDateCounters(ArrayList<Object> itemSets, Instances instancesNoClass, Instances instancesClass)` Updates counter of a specific item set
`static void`	LabeledItemSet.`upDateCountersTreatZeroAsMissing(ArrayList<LabeledItemSet> itemSets, Instances instancesNoClass, Instances instancesClass)` Updates counter of a specific item set
`static void`	ItemSet.`upDateCountersTreatZeroAsMissing(ArrayList<Object> itemSets, Instances instances)` Updates counters for a set of item sets and a set of instances.

Uses of Instances in weka.attributeSelection

Methods in weka.attributeSelection that return Instances
Modifier and Type	Method and Description
`Instances`	PrincipalComponents.`getFilteredInputFormat()` Return the header of the training data after all filtering - i.e missing values and nominal to binary.
`Instances`	AttributeSelection.`reduceDimensionality(Instances in)` reduce the dimensionality of a set of instances to include only those attributes chosen by the last run of attribute selection.
`Instances`	PrincipalComponents.`transformedData(Instances data)` Gets the transformed training data.
`Instances`	AttributeTransformer.`transformedData(Instances data)` Transform the supplied data set (assumed to be the same format as the training data)
`Instances`	PrincipalComponents.`transformedHeader()` Returns just the header for the transformed data (ie.
`Instances`	AttributeTransformer.`transformedHeader()` Returns just the header for the transformed data (ie.

Methods in weka.attributeSelection with parameters of type Instances
Modifier and Type	Method and Description
`void`	WrapperSubsetEval.`buildEvaluator(Instances data)` Generates a attribute evaluator.
`void`	SymmetricalUncertAttributeEval.`buildEvaluator(Instances data)` Initializes a symmetrical uncertainty attribute evaluator.
`void`	ReliefFAttributeEval.`buildEvaluator(Instances data)` Initializes a ReliefF attribute evaluator.
`void`	PrincipalComponents.`buildEvaluator(Instances data)` Initializes principal components and performs the analysis
`void`	OneRAttributeEval.`buildEvaluator(Instances data)` Initializes a OneRAttribute attribute evaluator.
`void`	InfoGainAttributeEval.`buildEvaluator(Instances data)` Initializes an information gain attribute evaluator.
`void`	GainRatioAttributeEval.`buildEvaluator(Instances data)` Initializes a gain ratio attribute evaluator.
`void`	CorrelationAttributeEval.`buildEvaluator(Instances data)` Initializes an information gain attribute evaluator.
`void`	CfsSubsetEval.`buildEvaluator(Instances data)` Generates a attribute evaluator.
`abstract void`	ASEvaluation.`buildEvaluator(Instances data)` Generates a attribute evaluator.
`abstract double`	HoldOutSubsetEvaluator.`evaluateSubset(BitSet subset, Instances holdOut)` Evaluates a subset of attributes with respect to a set of instances.
`Instances`	AttributeSelection.`reduceDimensionality(Instances in)` reduce the dimensionality of a set of instances to include only those attributes chosen by the last run of attribute selection.
`int[]`	Ranker.`search(ASEvaluation ASEval, Instances data)` Kind of a dummy search algorithm.
`int[]`	GreedyStepwise.`search(ASEvaluation ASEval, Instances data)` Searches the attribute subset space by forward selection.
`int[]`	BestFirst.`search(ASEvaluation ASEval, Instances data)` Searches the attribute subset space by best first search
`abstract int[]`	ASSearch.`search(ASEvaluation ASEvaluator, Instances data)` Searches the attribute subset/ranking space.
`static String`	AttributeSelection.`SelectAttributes(ASEvaluation ASEvaluator, String[] options, Instances train)` Perform attribute selection with a particular evaluator and a set of options specifying search method and options for the search method and evaluator.
`void`	AttributeSelection.`SelectAttributes(Instances data)` Perform attribute selection on the supplied training instances.
`void`	AttributeSelection.`selectAttributesCVSplit(Instances split)` Select attributes for a split of the data.
`Instances`	PrincipalComponents.`transformedData(Instances data)` Gets the transformed training data.
`Instances`	AttributeTransformer.`transformedData(Instances data)` Transform the supplied data set (assumed to be the same format as the training data)

Uses of Instances in weka.classifiers

Methods in weka.classifiers that return Instances
Modifier and Type	Method and Description
`Instances`	CostMatrix.`applyCostMatrix(Instances data, Random random)` Applies the cost matrix to a set of instances.
`Instances`	Evaluation.`getHeader()` Returns the header of the underlying dataset.

Methods in weka.classifiers with parameters of type Instances
Modifier and Type	Method and Description
`Instances`	CostMatrix.`applyCostMatrix(Instances data, Random random)` Applies the cost matrix to a set of instances.
`void`	ParallelMultipleClassifiersCombiner.`buildClassifier(Instances data)` Stump method for building the classifiers
`void`	ParallelIteratedSingleClassifierEnhancer.`buildClassifier(Instances data)` Stump method for building the classifiers
`void`	IteratedSingleClassifierEnhancer.`buildClassifier(Instances data)` Stump method for building the classifiers.
`void`	Classifier.`buildClassifier(Instances data)` Generates a classifier.
`void`	Evaluation.`crossValidateModel(Classifier classifier, Instances data, int numFolds, Random random, Object... forPredictionsPrinting)` Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
`void`	Evaluation.`crossValidateModel(String classifierString, Instances data, int numFolds, String[] options, Random random)` Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
`double[]`	Evaluation.`evaluateModel(Classifier classifier, Instances data, Object... forPredictionsPrinting)` Evaluates the classifier on a given set of instances.
`void`	IterativeClassifier.`initializeClassifier(Instances instances)` Initializes an iterative classifier.
`void`	Evaluation.`setPriors(Instances train)` Sets the class prior probabilities.

Constructors in weka.classifiers with parameters of type Instances
Constructor and Description
`AggregateableEvaluation(Instances data)` Constructs a new AggregateableEvaluation object
`AggregateableEvaluation(Instances data, CostMatrix costMatrix)` Constructs a new AggregateableEvaluation object
`Evaluation(Instances data)`
`Evaluation(Instances data, CostMatrix costMatrix)`

Uses of Instances in weka.classifiers.bayes

Fields in weka.classifiers.bayes declared as Instances
Modifier and Type	Field and Description
`Instances`	BayesNet.`m_Instances` The dataset header for the purposes of printing out a semi-intelligible model

Methods in weka.classifiers.bayes with parameters of type Instances
Modifier and Type	Method and Description
`void`	NaiveBayesMultinomialUpdateable.`buildClassifier(Instances instances)` Generates the classifier.
`void`	NaiveBayesMultinomialText.`buildClassifier(Instances data)` Generates the classifier.
`void`	NaiveBayesMultinomial.`buildClassifier(Instances instances)` Generates the classifier.
`void`	NaiveBayes.`buildClassifier(Instances instances)` Generates the classifier.
`void`	BayesNet.`buildClassifier(Instances instances)` Generates the classifier.

Uses of Instances in weka.classifiers.bayes.net

Methods in weka.classifiers.bayes.net with parameters of type Instances
Modifier and Type	Method and Description
`void`	ParentSet.`addParent(int nParent, Instances _Instances)` Add parent to parent set and update internals (specifically the cardinality of the parent set)
`void`	ParentSet.`addParent(int nParent, int iParent, Instances _Instances)` Add parent to parent set at specific location and update internals (specifically the cardinality of the parent set)
`void`	ParentSet.`deleteLastParent(Instances _Instances)` Delete last added parent from parent set and update internals (specifically the cardinality of the parent set)
`int`	ParentSet.`deleteParent(int nParent, Instances _Instances)` delete node from parent set
`int`	ParentSet.`getFreshCardinalityOfParents(Instances _Instances)` returns cardinality of parents after recalculation
`static ADNode`	ADNode.`makeADTree(Instances instances)` create AD tree from set of instances
`static ADNode`	ADNode.`makeADTree(int iNode, ArrayList<Integer> nRecords, Instances instances)` create sub tree
`static VaryNode`	ADNode.`makeVaryNode(int iNode, ArrayList<Integer> nRecords, Instances instances)` create sub tree
`void`	EditableBayesNet.`setData(Instances instances)` Assuming a network structure is defined and we want to learn from data, the data set must be put if correct order first and possibly discretized/missing values filled in before proceeding to CPT learning.

Constructors in weka.classifiers.bayes.net with parameters of type Instances
Constructor and Description
`EditableBayesNet(Instances instances)` constructor, creates empty network with nodes based on the attributes in a data set

Uses of Instances in weka.classifiers.bayes.net.search

Methods in weka.classifiers.bayes.net.search with parameters of type Instances
Modifier and Type	Method and Description
`void`	SearchAlgorithm.`buildStructure(BayesNet bayesNet, Instances instances)` buildStructure determines the network structure/graph of the network.

Uses of Instances in weka.classifiers.bayes.net.search.fixed

Methods in weka.classifiers.bayes.net.search.fixed with parameters of type Instances
Modifier and Type	Method and Description
`void`	NaiveBayes.`buildStructure(BayesNet bayesNet, Instances instances)`
`void`	FromFile.`buildStructure(BayesNet bayesNet, Instances instances)`

Uses of Instances in weka.classifiers.bayes.net.search.global

Methods in weka.classifiers.bayes.net.search.global with parameters of type Instances
Modifier and Type	Method and Description
`void`	TAN.`buildStructure(BayesNet bayesNet, Instances instances)` buildStructure determines the network structure/graph of the network using the maximimum weight spanning tree algorithm of Chow and Liu
`void`	SimulatedAnnealing.`search(BayesNet bayesNet, Instances instances)`
`void`	K2.`search(BayesNet bayesNet, Instances instances)` search determines the network structure/graph of the network with the K2 algorithm, restricted by its initial structure (which can be an empty graph, or a Naive Bayes graph.

Uses of Instances in weka.classifiers.bayes.net.search.local

Methods in weka.classifiers.bayes.net.search.local with parameters of type Instances
Modifier and Type	Method and Description
`void`	TAN.`buildStructure(BayesNet bayesNet, Instances instances)` buildStructure determines the network structure/graph of the network using the maximimum weight spanning tree algorithm of Chow and Liu
`void`	LocalScoreSearchAlgorithm.`buildStructure(BayesNet bayesNet, Instances instances)` buildStructure determines the network structure/graph of the network with the K2 algorithm, restricted by its initial structure (which can be an empty graph, or a Naive Bayes graph.
`void`	SimulatedAnnealing.`search(BayesNet bayesNet, Instances instances)`
`void`	K2.`search(BayesNet bayesNet, Instances instances)` search determines the network structure/graph of the network with the K2 algorithm, restricted by its initial structure (which can be an empty graph, or a Naive Bayes graph.

Constructors in weka.classifiers.bayes.net.search.local with parameters of type Instances
Constructor and Description
`LocalScoreSearchAlgorithm(BayesNet bayesNet, Instances instances)` constructor

Uses of Instances in weka.classifiers.evaluation

Methods in weka.classifiers.evaluation that return Instances
Modifier and Type	Method and Description
`Instances`	ThresholdCurve.`getCurve(ArrayList<Prediction> predictions)` Calculates the performance stats for the default class and return results as a set of Instances.
`Instances`	MarginCurve.`getCurve(ArrayList<Prediction> predictions)` Calculates the cumulative margin distribution for the set of predictions, returning the result as a set of Instances.
`Instances`	CostCurve.`getCurve(ArrayList<Prediction> predictions)` Calculates the performance stats for the default class and return results as a set of Instances.
`Instances`	ThresholdCurve.`getCurve(ArrayList<Prediction> predictions, int classIndex)` Calculates the performance stats for the desired class and return results as a set of Instances.
`Instances`	CostCurve.`getCurve(ArrayList<Prediction> predictions, int classIndex)` Calculates the performance stats for the desired class and return results as a set of Instances.
`Instances`	Evaluation.`getHeader()` Returns the header of the underlying dataset.

Methods in weka.classifiers.evaluation with parameters of type Instances
Modifier and Type	Method and Description
`static double`	RegressionAnalysis.`calculateRSquared(Instances data, double ssr)` Returns the R-squared value for a linear regression model, where sum of squared residuals is already calculated.
`static double`	RegressionAnalysis.`calculateSSR(Instances data, Attribute chosen, double slope, double intercept)` Returns the sum of squared residuals of the simple linear regression model: y = a + bx.
`static double[]`	RegressionAnalysis.`calculateStdErrorOfCoef(Instances data, Attribute chosen, double slope, double intercept, int df)` Returns the standard errors of slope and intercept for a simple linear regression model: y = a + bx.
`static double[]`	RegressionAnalysis.`calculateStdErrorOfCoef(Instances data, boolean[] selected, double ssr, int n, int k)` Returns an array of the standard errors of the coefficients in a multiple linear regression.
`void`	Evaluation.`crossValidateModel(Classifier classifier, Instances data, int numFolds, Random random, Object... forPredictionsPrinting)` Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
`void`	Evaluation.`crossValidateModel(String classifierString, Instances data, int numFolds, String[] options, Random random)` Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
`double[]`	Evaluation.`evaluateModel(Classifier classifier, Instances data, Object... forPredictionsPrinting)` Evaluates the classifier on a given set of instances.
`ArrayList<Prediction>`	EvaluationUtils.`getCVPredictions(Classifier classifier, Instances data, int numFolds)` Generate a bunch of predictions ready for processing, by performing a cross-validation on the supplied dataset.
`static double`	ThresholdCurve.`getNPointPrecision(Instances tcurve, int n)` Calculates the n point precision result, which is the precision averaged over n evenly spaced (w.r.t recall) samples of the curve.
`static double`	ThresholdCurve.`getPRCArea(Instances tcurve)` Calculates the area under the precision-recall curve (AUPRC).
`static double`	ThresholdCurve.`getROCArea(Instances tcurve)` Calculates the area under the ROC curve as the Wilcoxon-Mann-Whitney statistic.
`ArrayList<Prediction>`	EvaluationUtils.`getTestPredictions(Classifier classifier, Instances test)` Generate a bunch of predictions ready for processing, by performing a evaluation on a test set assuming the classifier is already trained.
`static int`	ThresholdCurve.`getThresholdInstance(Instances tcurve, double threshold)` Gets the index of the instance with the closest threshold value to the desired target
`ArrayList<Prediction>`	EvaluationUtils.`getTrainTestPredictions(Classifier classifier, Instances train, Instances test)` Generate a bunch of predictions ready for processing, by performing a evaluation on a test set after training on the given training set.
`void`	Evaluation.`setPriors(Instances train)` Sets the class prior probabilities.

Constructors in weka.classifiers.evaluation with parameters of type Instances
Constructor and Description
`AggregateableEvaluation(Instances data)` Constructs a new AggregateableEvaluation object
`AggregateableEvaluation(Instances data, CostMatrix costMatrix)` Constructs a new AggregateableEvaluation object
`Evaluation(Instances data)` Initializes all the counters for the evaluation.
`Evaluation(Instances data, CostMatrix costMatrix)` Initializes all the counters for the evaluation and also takes a cost matrix as parameter.

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

Methods in weka.classifiers.evaluation.output.prediction that return Instances
Modifier and Type	Method and Description
`Instances`	AbstractOutput.`getHeader()` Returns the header of the dataset.

Methods in weka.classifiers.evaluation.output.prediction with parameters of type Instances
Modifier and Type	Method and Description
`void`	AbstractOutput.`print(Classifier classifier, Instances testset)` Prints the header, classifications and footer to the buffer.
`void`	AbstractOutput.`printClassifications(Classifier classifier, Instances testset)` Prints the classifications to the buffer.
`void`	AbstractOutput.`setHeader(Instances value)` Sets the header of the dataset.

Uses of Instances in weka.classifiers.functions

Methods in weka.classifiers.functions with parameters of type Instances
Modifier and Type	Method and Description
`void`	VotedPerceptron.`buildClassifier(Instances insts)` Builds the ensemble of perceptrons.
`void`	SMOreg.`buildClassifier(Instances instances)` Method for building the classifier.
`void`	SMO.`buildClassifier(Instances insts)` Method for building the classifier.
`void`	SimpleLogistic.`buildClassifier(Instances data)` Builds the logistic regression using LogitBoost.
`void`	SimpleLinearRegression.`buildClassifier(Instances insts)` Builds a simple linear regression model given the supplied training data.
`void`	SGDText.`buildClassifier(Instances data)` Method for building the classifier.
`void`	SGD.`buildClassifier(Instances data)` Method for building the classifier.
`void`	MultilayerPerceptron.`buildClassifier(Instances i)` Call this function to build and train a neural network for the training data provided.
`void`	Logistic.`buildClassifier(Instances train)` Builds the classifier
`void`	LinearRegression.`buildClassifier(Instances data)` Builds a regression model for the given data.
`void`	GaussianProcesses.`buildClassifier(Instances insts)` Method for building the classifier.
`String`	Logistic.`toPMML(Instances train)` Produce a PMML representation of this logistic model

Uses of Instances in weka.classifiers.functions.supportVector

Methods in weka.classifiers.functions.supportVector with parameters of type Instances
Modifier and Type	Method and Description
`void`	RegSMOImproved.`buildClassifier(Instances instances)` learn SVM parameters from data using Keerthi's SMO algorithm.
`void`	RegSMO.`buildClassifier(Instances instances)` learn SVM parameters from data using Smola's SMO algorithm.
`void`	RegOptimizer.`buildClassifier(Instances data)` learn SVM parameters from data.
`void`	StringKernel.`buildKernel(Instances data)` builds the kernel with the given data.
`void`	RBFKernel.`buildKernel(Instances data)` builds the kernel with the given data.
`void`	Puk.`buildKernel(Instances data)` builds the kernel with the given data.
`void`	Kernel.`buildKernel(Instances data)` builds the kernel with the given data
`void`	CachedKernel.`buildKernel(Instances data)` builds the kernel with the given data.
`String`	KernelEvaluation.`evaluate(Kernel kernel, Instances data)` Evaluates the Kernel with the given commandline options and returns the evaluation string.

Constructors in weka.classifiers.functions.supportVector with parameters of type Instances
Constructor and Description
`NormalizedPolyKernel(Instances dataset, int cacheSize, double exponent, boolean lowerOrder)` Creates a new `NormalizedPolyKernel` instance.
`PolyKernel(Instances data, int cacheSize, double exponent, boolean lowerOrder)` Creates a new `PolyKernel` instance.
`Puk(Instances data, int cacheSize, double omega, double sigma)` Constructor.
`RBFKernel(Instances data, int cacheSize, double gamma)` Constructor.
`StringKernel(Instances data, int cacheSize, int subsequenceLength, double lambda, boolean debug)` creates a new StringKernel object.

Uses of Instances in weka.classifiers.lazy

Methods in weka.classifiers.lazy that return Instances
Modifier and Type	Method and Description
`Instances`	IBk.`pruneToK(Instances neighbours, double[] distances, int k)` Prunes the list to contain the k nearest neighbors.

Methods in weka.classifiers.lazy with parameters of type Instances
Modifier and Type	Method and Description
`void`	LWL.`buildClassifier(Instances instances)` Generates the classifier.
`void`	KStar.`buildClassifier(Instances instances)` Generates the classifier.
`void`	IBk.`buildClassifier(Instances instances)` Generates the classifier.
`Instances`	IBk.`pruneToK(Instances neighbours, double[] distances, int k)` Prunes the list to contain the k nearest neighbors.

Uses of Instances in weka.classifiers.lazy.kstar

Constructors in weka.classifiers.lazy.kstar with parameters of type Instances
Constructor and 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 Instances in weka.classifiers.meta

Methods in weka.classifiers.meta with parameters of type Instances
Modifier and Type	Method and Description
`void`	Vote.`buildClassifier(Instances data)` Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
`void`	Stacking.`buildClassifier(Instances data)` Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
`void`	RegressionByDiscretization.`buildClassifier(Instances instances)` Generates the classifier.
`void`	RandomSubSpace.`buildClassifier(Instances data)` builds the classifier.
`void`	RandomizableFilteredClassifier.`buildClassifier(Instances data)` Build the classifier on the filtered data.
`void`	RandomCommittee.`buildClassifier(Instances data)` Builds the committee of randomizable classifiers.
`void`	MultiScheme.`buildClassifier(Instances data)` Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
`void`	MultiClassClassifierUpdateable.`buildClassifier(Instances insts)`
`void`	MultiClassClassifier.`buildClassifier(Instances insts)` Builds the classifiers.
`void`	LogitBoost.`buildClassifier(Instances data)` Method used to build the classifier.
`void`	IterativeClassifierOptimizer.`buildClassifier(Instances data)` Builds the classifier.
`void`	FilteredClassifier.`buildClassifier(Instances data)` Build the classifier on the filtered data.
`void`	CVParameterSelection.`buildClassifier(Instances instances)` Generates the classifier.
`void`	CostSensitiveClassifier.`buildClassifier(Instances data)` Builds the model of the base learner.
`void`	ClassificationViaRegression.`buildClassifier(Instances insts)` Builds the classifiers.
`void`	Bagging.`buildClassifier(Instances data)` Bagging method.
`void`	AttributeSelectedClassifier.`buildClassifier(Instances data)` Build the classifier on the dimensionally reduced data.
`void`	AdditiveRegression.`buildClassifier(Instances data)` Method used to build the classifier.
`void`	AdaBoostM1.`buildClassifier(Instances data)` Method used to build the classifier.
`double[][]`	LogitBoost.`distributionsForInstances(Instances insts)` Calculates the class membership probabilities for the given test instances.
`double[][]`	FilteredClassifier.`distributionsForInstances(Instances insts)` Batch scoring method.
`void`	RandomCommittee.`generatePartition(Instances data)` Builds the classifier to generate a partition.
`void`	FilteredClassifier.`generatePartition(Instances data)` Builds the classifier to generate a partition.
`void`	Bagging.`generatePartition(Instances data)` Builds the classifier to generate a partition.
`void`	LogitBoost.`initializeClassifier(Instances data)` Builds the boosted classifier
`void`	FilteredClassifier.`initializeClassifier(Instances data)` Initializes an iterative classifier.
`void`	AdditiveRegression.`initializeClassifier(Instances data)` Initialize classifier.
`void`	AdaBoostM1.`initializeClassifier(Instances data)` Initialize the classifier.

Uses of Instances in weka.classifiers.misc

Methods in weka.classifiers.misc that return Instances
Modifier and Type	Method and Description
`Instances`	InputMappedClassifier.`getModelHeader(Instances defaultH)` Return the instance structure that the encapsulated model was built with.

Methods in weka.classifiers.misc with parameters of type Instances
Modifier and Type	Method and Description
`void`	SerializedClassifier.`buildClassifier(Instances data)` loads only the serialized classifier
`void`	InputMappedClassifier.`buildClassifier(Instances data)` Build the classifier
`Instances`	InputMappedClassifier.`getModelHeader(Instances defaultH)` Return the instance structure that the encapsulated model was built with.
`void`	InputMappedClassifier.`setModelHeader(Instances modelHeader)` Set the structure of the data used to create the model.
`void`	InputMappedClassifier.`setTestStructure(Instances testStructure)` Set the test structure (if known in advance) that we are likely to see.

Uses of Instances in weka.classifiers.pmml.consumer

Methods in weka.classifiers.pmml.consumer that return Instances
Modifier and Type	Method and Description
`Instances`	PMMLClassifier.`getDataDictionary()` Get the data dictionary.

Methods in weka.classifiers.pmml.consumer with parameters of type Instances
Modifier and Type	Method and Description
`void`	PMMLClassifier.`buildClassifier(Instances data)` Throw an exception - PMML models are pre-built.
`void`	PMMLClassifier.`mapToMiningSchema(Instances dataSet)` Map mining schema to incoming instances.

Constructors in weka.classifiers.pmml.consumer with parameters of type Instances
Constructor and Description
`GeneralRegression(Element model, Instances dataDictionary, MiningSchema miningSchema)` Constructs a GeneralRegression classifier.
`NeuralNetwork(Element model, Instances dataDictionary, MiningSchema miningSchema)`
`Regression(Element model, Instances dataDictionary, MiningSchema miningSchema)` Constructs a new PMML Regression.
`RuleSetModel(Element model, Instances dataDictionary, MiningSchema miningSchema)` Constructor for a RuleSetModel
`SupportVectorMachineModel(Element model, Instances dataDictionary, MiningSchema miningSchema)` Construct a new SupportVectorMachineModel encapsulating the information provided in the PMML document.
`TreeModel(Element model, Instances dataDictionary, MiningSchema miningSchema)`

Uses of Instances in weka.classifiers.pmml.producer

Methods in weka.classifiers.pmml.producer with parameters of type Instances
Modifier and Type	Method and Description
`static void`	AbstractPMMLProducerHelper.`addDataDictionary(Instances trainHeader, PMML pmml)` Adds a data dictionary to the supplied PMML object.
`static String[]`	AbstractPMMLProducerHelper.`getNameAndValueFromUnsupervisedNominalToBinaryDerivedAttribute(Instances train, Attribute derived)` Extracts the original attribute name and value from the name of a binary indicator attribute created by unsupervised NominalToBinary.
`static String`	LogisticProducerHelper.`toPMML(Instances train, Instances structureAfterFiltering, double[][] par, int numClasses)` Produce the PMML for a Logistic classifier

Uses of Instances in weka.classifiers.rules

Methods in weka.classifiers.rules that return Instances
Modifier and Type	Method and Description
`Instances`	RuleStats.`getData()` Get the data of the stats
`Instances[]`	RuleStats.`getFiltered(int index)` Get the data after filtering the given rule
`static Instances[]`	RuleStats.`partition(Instances data, int numFolds)` Patition the data into 2, first of which has (numFolds-1)/numFolds of the data and the second has 1/numFolds of the data
`static Instances`	RuleStats.`rmCoveredBySuccessives(Instances data, ArrayList<Rule> rules, int index)` Static utility function to count the data covered by the rules after the given index in the given rules, and then remove them.
`abstract Instances[]`	JRip.Antd.`splitData(Instances data, double defAcRt, double cla)`
`Instances[]`	JRip.NumericAntd.`splitData(Instances insts, double defAcRt, double cl)` Implements the splitData function.
`Instances[]`	JRip.NominalAntd.`splitData(Instances data, double defAcRt, double cl)` Implements the splitData function.
`static Instances`	RuleStats.`stratify(Instances data, int folds, Random rand)` Stratify the given data into the given number of bags based on the class values.

Methods in weka.classifiers.rules with parameters of type Instances
Modifier and Type	Method and Description
`void`	ZeroR.`buildClassifier(Instances instances)` Generates the classifier.
`void`	PART.`buildClassifier(Instances instances)` Generates the classifier.
`void`	OneR.`buildClassifier(Instances instances)` Generates the classifier.
`void`	JRip.`buildClassifier(Instances instances)` Builds Ripper in the order of class frequencies.
`void`	DecisionTable.`buildClassifier(Instances data)` Generates the classifier.
`void`	RuleStats.`countData(int index, Instances uncovered, double[][] prevRuleStats)` Count data from the position index in the ruleset assuming that given data are not covered by the rules in position 0...(index-1), and the statistics of these rules are provided. This procedure is typically useful when a temporary object of RuleStats is constructed in order to efficiently calculate the relative DL of rule in position index, thus all other stuff is not needed.
`abstract void`	Rule.`grow(Instances data)` Build this rule
`void`	JRip.RipperRule.`grow(Instances data)` Build one rule using the growing data
`weka.classifiers.rules.OneR.OneRRule`	OneR.`newNominalRule(Attribute attr, Instances data, int[] missingValueCounts)` Create a rule branching on this nominal attribute.
`weka.classifiers.rules.OneR.OneRRule`	OneR.`newNumericRule(Attribute attr, Instances data, int[] missingValueCounts)` Create a rule branching on this numeric attribute
`weka.classifiers.rules.OneR.OneRRule`	OneR.`newRule(Attribute attr, Instances data)` Create a rule branching on this attribute.
`static double`	RuleStats.`numAllConditions(Instances data)` Compute the number of all possible conditions that could appear in a rule of a given data.
`static Instances[]`	RuleStats.`partition(Instances data, int numFolds)` Patition the data into 2, first of which has (numFolds-1)/numFolds of the data and the second has 1/numFolds of the data
`void`	JRip.RipperRule.`prune(Instances pruneData, boolean useWhole)` Prune all the possible final sequences of the rule using the pruning data.
`static Instances`	RuleStats.`rmCoveredBySuccessives(Instances data, ArrayList<Rule> rules, int index)` Static utility function to count the data covered by the rules after the given index in the given rules, and then remove them.
`void`	RuleStats.`setData(Instances data)` Set the data of the stats, overwriting the old one if any
`abstract Instances[]`	JRip.Antd.`splitData(Instances data, double defAcRt, double cla)`
`Instances[]`	JRip.NumericAntd.`splitData(Instances insts, double defAcRt, double cl)` Implements the splitData function.
`Instances[]`	JRip.NominalAntd.`splitData(Instances data, double defAcRt, double cl)` Implements the splitData function.
`static Instances`	RuleStats.`stratify(Instances data, int folds, Random rand)` Stratify the given data into the given number of bags based on the class values.
`String`	DecisionTableHashKey.`toString(Instances t, int maxColWidth)` Convert a hash entry to a string

Constructors in weka.classifiers.rules with parameters of type Instances
Constructor and Description
`RuleStats(Instances data, ArrayList<Rule> rules)` Constructor that provides ruleset and data

Uses of Instances in weka.classifiers.rules.part

Methods in weka.classifiers.rules.part with parameters of type Instances
Modifier and Type	Method and Description
`void`	MakeDecList.`buildClassifier(Instances data)` Builds dec list.
`void`	ClassifierDecList.`buildDecList(Instances data, boolean leaf)` Builds the partial tree without hold out set.
`void`	C45PruneableDecList.`buildDecList(Instances data, boolean leaf)` Builds the partial tree without hold out set.
`void`	PruneableDecList.`buildDecList(Instances train, Instances test, boolean leaf)` Builds the partial tree with hold out set
`void`	ClassifierDecList.`buildRule(Instances data)` Method for building a pruned partial tree.
`void`	PruneableDecList.`buildRule(Instances train, Instances test)` Method for building a pruned partial tree.
`void`	ClassifierDecList.`cleanup(Instances justHeaderInfo)` Cleanup in order to save memory.

Uses of Instances in weka.classifiers.trees

Methods in weka.classifiers.trees with parameters of type Instances
Modifier and Type	Method and Description
`void`	REPTree.`buildClassifier(Instances data)` Builds classifier.
`void`	RandomTree.`buildClassifier(Instances data)` Builds classifier.
`void`	RandomForest.`buildClassifier(Instances data)` Builds a classifier for a set of instances.
`void`	LMT.`buildClassifier(Instances data)` Builds the classifier.
`void`	J48.`buildClassifier(Instances instances)` Generates the classifier.
`void`	HoeffdingTree.`buildClassifier(Instances data)` Builds the classifier.
`void`	DecisionStump.`buildClassifier(Instances instances)` Generates the classifier.
`void`	REPTree.`generatePartition(Instances data)` Builds the classifier to generate a partition.
`void`	RandomTree.`generatePartition(Instances data)` Builds the classifier to generate a partition.
`void`	RandomForest.`generatePartition(Instances data)` Builds the classifier to generate a partition.
`void`	J48.`generatePartition(Instances data)` Builds the classifier to generate a partition.

Uses of Instances in weka.classifiers.trees.ht

Constructors in weka.classifiers.trees.ht with parameters of type Instances
Constructor and Description
`NBNode(Instances header, double nbWeightThreshold)` Construct a new NBNode
`NBNodeAdaptive(Instances header, double nbWeightThreshold)` Constructor

Uses of Instances in weka.classifiers.trees.j48

Methods in weka.classifiers.trees.j48 that return Instances
Modifier and Type	Method and Description
`Instances[]`	ClassifierSplitModel.`split(Instances data)` Splits the given set of instances into subsets.

Methods in weka.classifiers.trees.j48 with parameters of type Instances
Modifier and Type	Method and Description
`void`	Distribution.`addInstWithUnknown(Instances source, int attIndex)` Adds all instances with unknown values for given attribute, weighted according to frequency of instances in each bag.
`void`	Distribution.`addRange(int bagIndex, Instances source, int startIndex, int lastPlusOne)` Adds all instances in given range to given bag.
`void`	PruneableClassifierTree.`buildClassifier(Instances data)` Method for building a pruneable classifier tree.
`void`	NoSplit.`buildClassifier(Instances instances)` Creates a "no-split"-split for a given set of instances.
`void`	NBTreeSplit.`buildClassifier(Instances trainInstances)` Creates a NBTree-type split on the given data.
`void`	NBTreeNoSplit.`buildClassifier(Instances instances)` Build the no-split node
`void`	NBTreeClassifierTree.`buildClassifier(Instances data)` Method for building a naive bayes classifier tree
`void`	ClassifierTree.`buildClassifier(Instances data)` Method for building a classifier tree.
`abstract void`	ClassifierSplitModel.`buildClassifier(Instances instances)` Builds the classifier split model for the given set of instances.
`void`	C45Split.`buildClassifier(Instances trainInstances)` Creates a C4.5-type split on the given data.
`void`	C45PruneableClassifierTree.`buildClassifier(Instances data)` Method for building a pruneable classifier tree.
`void`	BinC45Split.`buildClassifier(Instances trainInstances)` Creates a C4.5-type split on the given data.
`void`	ClassifierTree.`buildTree(Instances data, boolean keepData)` Builds the tree structure.
`void`	ClassifierTree.`buildTree(Instances train, Instances test, boolean keepData)` Builds the tree structure with hold out set
`void`	ClassifierTree.`cleanup(Instances justHeaderInfo)` Cleanup in order to save memory.
`static double`	NBTreeNoSplit.`crossValidate(NaiveBayesUpdateable fullModel, Instances trainingSet, Random r)` Utility method for fast 5-fold cross validation of a naive bayes model
`void`	Distribution.`delRange(int bagIndex, Instances source, int startIndex, int lastPlusOne)` Deletes all instances in given range from given bag.
`String`	ClassifierSplitModel.`dumpLabel(int index, Instances data)` Prints label for subset index of instances (eg class).
`String`	ClassifierSplitModel.`dumpModel(Instances data)` Prints the split model.
`String`	NoSplit.`leftSide(Instances instances)` Does nothing because no condition has to be satisfied.
`String`	NBTreeSplit.`leftSide(Instances data)` Prints left side of condition..
`String`	NBTreeNoSplit.`leftSide(Instances instances)` Does nothing because no condition has to be satisfied.
`abstract String`	ClassifierSplitModel.`leftSide(Instances data)` Prints left side of condition satisfied by instances.
`String`	C45Split.`leftSide(Instances data)` Prints left side of condition..
`String`	BinC45Split.`leftSide(Instances data)` Prints left side of condition.
`double[][]`	C45Split.`minsAndMaxs(Instances data, double[][] minsAndMaxs, int index)` Returns the minsAndMaxs of the index.th subset.
`void`	ClassifierSplitModel.`resetDistribution(Instances data)` Sets distribution associated with model.
`void`	C45Split.`resetDistribution(Instances data)` Sets distribution associated with model.
`void`	BinC45Split.`resetDistribution(Instances data)` Sets distribution associated with model.
`String`	NoSplit.`rightSide(int index, Instances instances)` Does nothing because no condition has to be satisfied.
`String`	NBTreeSplit.`rightSide(int index, Instances data)` Prints the condition satisfied by instances in a subset.
`String`	NBTreeNoSplit.`rightSide(int index, Instances instances)` Does nothing because no condition has to be satisfied.
`abstract String`	ClassifierSplitModel.`rightSide(int index, Instances data)` Prints left side of condition satisfied by instances in subset index.
`String`	C45Split.`rightSide(int index, Instances data)` Prints the condition satisfied by instances in a subset.
`String`	BinC45Split.`rightSide(int index, Instances data)` Prints the condition satisfied by instances in a subset.
`ClassifierSplitModel`	NBTreeModelSelection.`selectModel(Instances data)` Selects NBTree-type split for the given dataset.
`abstract ClassifierSplitModel`	ModelSelection.`selectModel(Instances data)` Selects a model for the given dataset.
`ClassifierSplitModel`	C45ModelSelection.`selectModel(Instances data)` Selects C4.5-type split for the given dataset.
`ClassifierSplitModel`	BinC45ModelSelection.`selectModel(Instances data)` Selects C4.5-type split for the given dataset.
`ClassifierSplitModel`	NBTreeModelSelection.`selectModel(Instances train, Instances test)` Selects NBTree-type split for the given dataset.
`ClassifierSplitModel`	ModelSelection.`selectModel(Instances train, Instances test)` Selects a model for the given train data using the given test data
`ClassifierSplitModel`	C45ModelSelection.`selectModel(Instances train, Instances test)` Selects C4.5-type split for the given dataset.
`ClassifierSplitModel`	BinC45ModelSelection.`selectModel(Instances train, Instances test)` Selects C4.5-type split for the given dataset.
`void`	C45Split.`setSplitPoint(Instances allInstances)` Sets split point to greatest value in given data smaller or equal to old split point.
`void`	BinC45Split.`setSplitPoint(Instances allInstances)` Sets split point to greatest value in given data smaller or equal to old split point.
`void`	Distribution.`shiftRange(int from, int to, Instances source, int startIndex, int lastPlusOne)` Shifts all instances in given range from one bag to another one.
`String`	ClassifierSplitModel.`sourceClass(int index, Instances data)`
`String`	NoSplit.`sourceExpression(int index, Instances data)` Returns a string containing java source code equivalent to the test made at this node.
`String`	NBTreeSplit.`sourceExpression(int index, Instances data)` Returns a string containing java source code equivalent to the test made at this node.
`String`	NBTreeNoSplit.`sourceExpression(int index, Instances data)` Returns a string containing java source code equivalent to the test made at this node.
`abstract String`	ClassifierSplitModel.`sourceExpression(int index, Instances data)`
`String`	C45Split.`sourceExpression(int index, Instances data)` Returns a string containing java source code equivalent to the test made at this node.
`String`	BinC45Split.`sourceExpression(int index, Instances data)` Returns a string containing java source code equivalent to the test made at this node.
`Instances[]`	ClassifierSplitModel.`split(Instances data)` Splits the given set of instances into subsets.

Constructors in weka.classifiers.trees.j48 with parameters of type Instances
Constructor and Description
`BinC45ModelSelection(int minNoObj, Instances allData, boolean useMDLcorrection, boolean doNotMakeSplitPointActualValue)` Initializes the split selection method with the given parameters.
`C45ModelSelection(int minNoObj, Instances allData, boolean useMDLcorrection, boolean doNotMakeSplitPointActualValue)` Initializes the split selection method with the given parameters.
`Distribution(Instances source)` Creates a distribution with only one bag according to instances in source.
`Distribution(Instances source, ClassifierSplitModel modelToUse)` Creates a distribution according to given instances and split model.
`NBTreeModelSelection(int minNoObj, Instances allData)` Initializes the split selection method with the given parameters.

Uses of Instances in weka.classifiers.trees.lmt

Methods in weka.classifiers.trees.lmt with parameters of type Instances
Modifier and Type	Method and Description
`void`	SimpleLinearRegression.`buildClassifier(Instances insts)` Builds a simple linear regression model given the supplied training data.
`void`	ResidualSplit.`buildClassifier(Instances data)` Method not in use
`void`	LogisticBase.`buildClassifier(Instances data)` Builds the logistic regression model usiing LogitBoost.
`void`	LMTNode.`buildClassifier(Instances data)` Method for building a logistic model tree (only called for the root node).
`void`	ResidualSplit.`buildClassifier(Instances data, double[][] dataZs, double[][] dataWs)` Builds the split.
`void`	LMTNode.`buildTree(Instances data, SimpleLinearRegression[][] higherRegressions, double totalInstanceWeight, double higherNumParameters, Instances numericDataHeader)` Method for building the tree structure.
`String`	ResidualSplit.`leftSide(Instances data)` Returns name of splitting attribute (left side of condition).
`int`	LMTNode.`prune(double[] alphas, double[] errors, Instances test)` Method for performing one fold in the cross-validation of the cost-complexity parameter.
`String`	ResidualSplit.`rightSide(int index, Instances data)` Prints the condition satisfied by instances in a subset.
`ClassifierSplitModel`	ResidualModelSelection.`selectModel(Instances train)` Method not in use
`ClassifierSplitModel`	ResidualModelSelection.`selectModel(Instances data, double[][] dataZs, double[][] dataWs)` Selects split based on residuals for the given dataset.
`ClassifierSplitModel`	ResidualModelSelection.`selectModel(Instances train, Instances test)` Method not in use
`String`	ResidualSplit.`sourceExpression(int index, Instances data)` Method not in use

Uses of Instances in weka.classifiers.trees.m5

Methods in weka.classifiers.trees.m5 that return Instances
Modifier and Type	Method and Description
`Instances`	Rule.`notCoveredInstances()` Get the instances not covered by this rule

Methods in weka.classifiers.trees.m5 with parameters of type Instances
Modifier and Type	Method and Description
`void`	YongSplitInfo.`attrSplit(int attr, Instances inst)` Finds the best splitting point for an attribute in the instances
`void`	SplitEvaluate.`attrSplit(int attr, Instances inst)` Finds the best splitting point for an attribute in the instances
`void`	CorrelationSplitInfo.`attrSplit(int attr, Instances inst)` Finds the best splitting point for an attribute in the instances
`void`	RuleNode.`buildClassifier(Instances data)` Build this node (find an attribute and split point)
`void`	Rule.`buildClassifier(Instances data)` Generates a single rule or m5 model tree.
`void`	PreConstructedLinearModel.`buildClassifier(Instances instances)` Builds the classifier.
`void`	M5Base.`buildClassifier(Instances data)` Generates the classifier.
`String`	YongSplitInfo.`toString(Instances inst)` Converts the spliting information to string

Constructors in weka.classifiers.trees.m5 with parameters of type Instances
Constructor and Description
`Impurity(int partition, int attribute, Instances inst, int k)` Constructs an Impurity object containing the impurity values of partitioning the instances using an attribute
`Values(int low, int high, int attribute, Instances inst)` Constructs an object which stores some statistics of the instances such as sum, squared sum, variance, standard deviation

Uses of Instances in weka.clusterers

Methods in weka.clusterers that return Instances
Modifier and Type	Method and Description
`Instances`	Canopy.`getCanopies()` Get the canopies (cluster centers).
`Instances`	SimpleKMeans.`getClusterCentroids()` Gets the the cluster centroids.
`Instances`	FarthestFirst.`getClusterCentroids()` Get the centroids found by FarthestFirst
`Instances`	SimpleKMeans.`getClusterStandardDevs()` Gets the standard deviations of the numeric attributes in each cluster.

Methods in weka.clusterers with parameters of type Instances
Modifier and Type	Method and Description
`void`	SimpleKMeans.`buildClusterer(Instances data)` Generates a clusterer.
`void`	MakeDensityBasedClusterer.`buildClusterer(Instances data)` Builds a clusterer for a set of instances.
`void`	HierarchicalClusterer.`buildClusterer(Instances data)`
`void`	FilteredClusterer.`buildClusterer(Instances data)` Build the clusterer on the filtered data.
`void`	FarthestFirst.`buildClusterer(Instances data)` Generates a clusterer.
`void`	EM.`buildClusterer(Instances data)` Generates a clusterer.
`void`	Cobweb.`buildClusterer(Instances data)` Builds the clusterer.
`void`	Clusterer.`buildClusterer(Instances data)` Generates a clusterer.
`void`	Canopy.`buildClusterer(Instances data)`
`abstract void`	AbstractClusterer.`buildClusterer(Instances data)` Generates a clusterer.
`static double`	ClusterEvaluation.`crossValidateModel(DensityBasedClusterer clusterer, Instances data, int numFolds, Random random)` Perform a cross-validation for DensityBasedClusterer on a set of instances.
`static String`	ClusterEvaluation.`crossValidateModel(String clustererString, Instances data, int numFolds, String[] options, Random random)` Performs a cross-validation for a DensityBasedClusterer clusterer on a set of instances.
`void`	ClusterEvaluation.`evaluateClusterer(Instances test)` Evaluate the clusterer on a set of instances.
`void`	ClusterEvaluation.`evaluateClusterer(Instances test, String testFileName)` Evaluate the clusterer on a set of instances.
`void`	ClusterEvaluation.`evaluateClusterer(Instances test, String testFileName, boolean outputModel)` Evaluate the clusterer on a set of instances.
`void`	Canopy.`initializeDistanceFunction(Instances init)` Initialize the distance function (i.e set min/max values for numeric attributes) with the supplied instances.
`static String`	Canopy.`printCanopyAssignments(Instances dataPoints, List<long[]> canopyAssignments)` Print the supplied instances and their canopies
`void`	Canopy.`setCanopies(Instances canopies)` Set the canopies to use (replaces any learned by this clusterer already)

Uses of Instances in weka.core

Methods in weka.core that return Instances
Modifier and Type	Method and Description
`Instances`	Instance.`dataset()` Returns the dataset this instance has access to.
`Instances`	AbstractInstance.`dataset()` Returns the dataset this instance has access to.
`Instances`	TestInstances.`generate()` Generates a new dataset
`Instances`	TestInstances.`generate(String namePrefix)` generates a new dataset.
`Instances`	TestInstances.`getData()` returns the current dataset, can be null
`Instances`	AttributeLocator.`getData()` returns the underlying data
`Instances`	NormalizableDistance.`getInstances()` returns the instances currently set.
`Instances`	FilteredDistance.`getInstances()` returns the instances currently set.
`Instances`	DistanceFunction.`getInstances()` returns the instances currently set.
`Instances`	TestInstances.`getRelationalClassFormat()` returns the current strcuture of the relational class attribute, can be null
`Instances`	TestInstances.`getRelationalFormat(int index)` returns the format for the specified relational attribute, can be null
`static Instances`	Instances.`mergeInstances(Instances first, Instances second)` Merges two sets of Instances together.
`Instances`	CheckScheme.PostProcessor.`process(Instances data)` Provides a hook for derived classes to further modify the data.
`Instances`	Attribute.`relation()` Returns the header info for a relation-valued attribute, null if the attribute is not relation-valued.
`Instances`	Attribute.`relation(int valIndex)` Returns a value of a relation-valued attribute.
`Instances`	Instance.`relationalValue(Attribute att)` Returns the relational value of a relational attribute.
`Instances`	AbstractInstance.`relationalValue(Attribute att)` Returns the relational value of a relational attribute.
`Instances`	Instance.`relationalValue(int attIndex)` Returns the relational value of a relational attribute.
`Instances`	AbstractInstance.`relationalValue(int attIndex)` Returns the relational value of a relational attribute.
`Instances`	Instances.`resample(Random random)` Creates a new dataset of the same size using random sampling with replacement.
`Instances`	Instances.`resampleWithWeights(Random random)` Creates a new dataset of the same size using random sampling with replacement according to the current instance weights.
`Instances`	Instances.`resampleWithWeights(Random random, boolean representUsingWeights)` Creates a new dataset of the same size using random sampling with replacement according to the current instance weights.
`Instances`	Instances.`resampleWithWeights(Random random, boolean[] sampled)` Creates a new dataset of the same size using random sampling with replacement according to the current instance weights.
`Instances`	Instances.`resampleWithWeights(Random random, boolean[] sampled, boolean representUsingWeights)` Creates a new dataset of the same size using random sampling with replacement according to the current instance weights.
`Instances`	Instances.`resampleWithWeights(Random random, double[] weights)` Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
`Instances`	Instances.`resampleWithWeights(Random random, double[] weights, boolean[] sampled)` Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
`Instances`	Instances.`resampleWithWeights(Random random, double[] weights, boolean[] sampled, boolean representUsingWeights)` Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
`Instances`	Instances.`stringFreeStructure()` Create a copy of the structure if the data has string or relational attributes, "cleanses" string types (i.e.
`Instances`	Instances.`testCV(int numFolds, int numFold)` Creates the test set for one fold of a cross-validation on the dataset.
`Instances`	Instances.`trainCV(int numFolds, int numFold)` Creates the training set for one fold of a cross-validation on the dataset.
`Instances`	Instances.`trainCV(int numFolds, int numFold, Random random)` Creates the training set for one fold of a cross-validation on the dataset.

Methods in weka.core with parameters of type Instances
Modifier and Type	Method and Description
`int`	Attribute.`addRelation(Instances value)` Adds a relation to a relation-valued attribute.
`int`	EuclideanDistance.`closestPoint(Instance instance, Instances allPoints, int[] pointList)` Returns the index of the closest point to the current instance.
`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[][]`	BatchPredictor.`distributionsForInstances(Instances insts)` Batch scoring method
`boolean`	Instances.`equalHeaders(Instances dataset)` Checks if two headers are equivalent.
`String`	Instances.`equalHeadersMsg(Instances dataset)` Checks if two headers are equivalent.
`static Capabilities`	Capabilities.`forInstances(Instances data)` returns a Capabilities object specific for this data.
`static Capabilities`	Capabilities.`forInstances(Instances data, boolean multi)` returns a Capabilities object specific for this data.
`void`	PartitionGenerator.`generatePartition(Instances data)` Builds the classifier to generate a partition.
`Instance`	AlgVector.`getAsInstance(Instances model, Random random)` Gets the elements of the vector as an instance.
`static Instances`	Instances.`mergeInstances(Instances first, Instances second)` Merges two sets of Instances together.
`Instances`	CheckScheme.PostProcessor.`process(Instances data)` Provides a hook for derived classes to further modify the data.
`void`	Instance.`setDataset(Instances instances)` Sets the reference to the dataset.
`void`	AbstractInstance.`setDataset(Instances instances)` Sets the reference to the dataset.
`void`	NormalizableDistance.`setInstances(Instances insts)` Sets the instances.
`void`	FilteredDistance.`setInstances(Instances insts)` Sets the instances.
`void`	DistanceFunction.`setInstances(Instances insts)` Sets the instances.
`void`	TestInstances.`setRelationalClassFormat(Instances value)` sets the structure for the relational class attribute
`void`	TestInstances.`setRelationalFormat(int index, Instances value)` sets the structure for the bags for the relational attribute
`boolean`	Capabilities.`test(Instances data)` Tests the given data, whether it can be processed by the handler, given its capabilities.
`boolean`	Capabilities.`test(Instances data, int fromIndex, int toIndex)` Tests a certain range of attributes of the given data, whether it can be processed by the handler, given its capabilities.
`void`	Capabilities.`testWithFail(Instances data)` tests the given data by calling the test(Instances) method and throws an exception if the test fails.
`void`	Capabilities.`testWithFail(Instances data, int fromIndex, int toIndex)` tests the given data by calling the test(Instances,int,int) method and throws an exception if the test fails.

Constructors in weka.core with parameters of type Instances
Constructor and Description
`AlgVector(Instances format, Random random)` Constructs a vector using a given data format.
`Attribute(String attributeName, Instances header)` Constructor for relation-valued attributes.
`Attribute(String attributeName, Instances header, int index)` Constructor for a relation-valued attribute with a particular index.
`Attribute(String attributeName, Instances header, ProtectedProperties metadata)` Constructor for relation-valued attributes.
`AttributeLocator(Instances data, int type)` Initializes the AttributeLocator with the given data for the specified type of attribute.
`AttributeLocator(Instances data, int type, int[] indices)` initializes the AttributeLocator with the given data for the specified type of attribute.
`AttributeLocator(Instances data, int type, int fromIndex, int toIndex)` Initializes the AttributeLocator with the given data for the specified type of attribute.
`ChebyshevDistance(Instances data)` Constructs an Chebyshev Distance object and automatically initializes the ranges.
`EuclideanDistance(Instances data)` Constructs an Euclidean Distance object and automatically initializes the ranges.
`Instances(Instances dataset)` Constructor copying all instances and references to the header information from the given set of instances.
`Instances(Instances dataset, int capacity)` Constructor creating an empty set of instances.
`Instances(Instances source, int first, int toCopy)` Creates a new set of instances by copying a subset of another set.
`ManhattanDistance(Instances data)` Constructs an Manhattan Distance object and automatically initializes the ranges.
`MinkowskiDistance(Instances data)` Constructs an Minkowski Distance object and automatically initializes the ranges.
`NormalizableDistance(Instances data)` Initializes the distance function and automatically initializes the ranges.
`RelationalAttributeInfo(Instances header)` Constructs the information object based on the given parameter.
`RelationalLocator(Instances data)` Initializes the RelationalLocator with the given data.
`RelationalLocator(Instances data, int[] indices)` Initializes the RelationalLocator with the given data.
`RelationalLocator(Instances data, int fromIndex, int toIndex)` Initializes the RelationalLocator with the given data.
`StringLocator(Instances data)` initializes the StringLocator with the given data
`StringLocator(Instances data, int[] indices)` Initializes the AttributeLocator with the given data.
`StringLocator(Instances data, int fromIndex, int toIndex)` Initializes the StringLocator with the given data.

Uses of Instances in weka.core.converters

Methods in weka.core.converters that return Instances
Modifier and Type	Method and Description
`Instances`	ArffLoader.ArffReader.`getData()` Returns the data that was read
`Instances`	XRFFLoader.`getDataSet()` Return the full data set.
`Instances`	TextDirectoryLoader.`getDataSet()` Return the full data set.
`Instances`	SVMLightLoader.`getDataSet()` Return the full data set.
`Instances`	SerializedInstancesLoader.`getDataSet()` Return the full data set.
`Instances`	MatlabLoader.`getDataSet()` Return the full data set.
`Instances`	Loader.`getDataSet()` Return the full data set.
`Instances`	LibSVMLoader.`getDataSet()` Return the full data set.
`Instances`	JSONLoader.`getDataSet()` Return the full data set.
`Instances`	DatabaseLoader.`getDataSet()` Return the full data set in batch mode (header and all intances at once).
`Instances`	CSVLoader.`getDataSet()`
`Instances`	ConverterUtils.DataSource.`getDataSet()` returns the full dataset, can be null in case of an error.
`Instances`	C45Loader.`getDataSet()` Return the full data set.
`Instances`	ArffLoader.`getDataSet()` Return the full data set.
`abstract Instances`	AbstractLoader.`getDataSet()`
`Instances`	ConverterUtils.DataSource.`getDataSet(int classIndex)` returns the full dataset with the specified class index set, can be null in case of an error.
`Instances`	AbstractSaver.`getInstances()` Gets instances that should be stored.
`Instances`	XRFFLoader.`getStructure()` Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
`Instances`	TextDirectoryLoader.`getStructure()` Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
`Instances`	SVMLightLoader.`getStructure()` Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
`Instances`	SerializedInstancesLoader.`getStructure()` Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
`Instances`	MatlabLoader.`getStructure()` Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
`Instances`	Loader.`getStructure()` Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
`Instances`	LibSVMLoader.`getStructure()` Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
`Instances`	JSONLoader.`getStructure()` Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
`Instances`	DatabaseLoader.`getStructure()` Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
`Instances`	CSVLoader.`getStructure()`
`Instances`	ConverterUtils.DataSource.`getStructure()` returns the structure of the data.
`Instances`	C45Loader.`getStructure()` Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
`Instances`	ArffLoader.`getStructure()` Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
`Instances`	ArffLoader.ArffReader.`getStructure()` Returns the header format
`abstract Instances`	AbstractLoader.`getStructure()`
`Instances`	ConverterUtils.DataSource.`getStructure(int classIndex)` returns the structure of the data, with the defined class index.
`static Instances`	ConverterUtils.DataSource.`read(InputStream stream)` convencience method for loading a dataset in batch mode from a stream.
`static Instances`	ConverterUtils.DataSource.`read(Loader loader)` convencience method for loading a dataset in batch mode.
`static Instances`	ConverterUtils.DataSource.`read(String location)` convencience method for loading a dataset in batch mode.

Methods in weka.core.converters with parameters of type Instances
Modifier and Type	Method and Description
`Instance`	XRFFLoader.`getNextInstance(Instances structure)` XRFFLoader is unable to process a data set incrementally.
`Instance`	TextDirectoryLoader.`getNextInstance(Instances structure)` Process input directories/files incrementally.
`Instance`	SVMLightLoader.`getNextInstance(Instances structure)` SVMLightLoader 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`	MatlabLoader.`getNextInstance(Instances structure)` MatlabLoader 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`	LibSVMLoader.`getNextInstance(Instances structure)` LibSVmLoader is unable to process a data set incrementally.
`Instance`	JSONLoader.`getNextInstance(Instances structure)` JSONLoader is unable to process a data set incrementally.
`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`	CSVLoader.`getNextInstance(Instances structure)`
`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`	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.
`abstract Instance`	AbstractLoader.`getNextInstance(Instances structure)`
`boolean`	ConverterUtils.DataSource.`hasMoreElements(Instances structure)` returns whether there are more Instance objects in the data.
`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.
`void`	XRFFSaver.`setInstances(Instances instances)` Sets instances that should be stored.
`void`	SVMLightSaver.`setInstances(Instances instances)` Sets instances that should be stored.
`void`	Saver.`setInstances(Instances instances)` Sets the instances to be saved
`void`	LibSVMSaver.`setInstances(Instances instances)` Sets instances that should be stored.
`void`	JSONSaver.`setInstances(Instances instances)` Sets instances that should be stored.
`void`	AbstractSaver.`setInstances(Instances instances)` Sets instances that should be stored.
`int`	AbstractSaver.`setStructure(Instances headerInfo)` Sets the strcuture of the instances for the first step of incremental saving.
`void`	ConverterUtils.DataSink.`write(Instances data)` writes the given data either via the saver or to the defined output stream (depending on the constructor).
`static void`	ConverterUtils.DataSink.`write(OutputStream stream, Instances data)` writes the data to the given stream (always in ARFF format).
`static void`	ConverterUtils.DataSink.`write(Saver saver, Instances data)` writes the data via the given saver.
`static void`	ConverterUtils.DataSink.`write(String filename, Instances data)` writes the data to the given file.

Constructors in weka.core.converters with parameters of type Instances
Constructor and Description
`ArffLoader.ArffReader(Reader reader, Instances template, int lines, int capacity, boolean batch, String... fieldSepAndEnclosures)` Initializes the reader without reading the header according to the specified template.
`ArffLoader.ArffReader(Reader reader, Instances template, int lines, int capacity, String... fieldSepAndEnclosures)` Initializes the reader without reading the header according to the specified template.
`ArffLoader.ArffReader(Reader reader, Instances template, int lines, String... fieldSepAndEnclosures)` Reads the data without header according to the specified template.
`ConverterUtils.DataSource(Instances inst)` Initializes the datasource with the given dataset.

Uses of Instances in weka.core.json

Methods in weka.core.json that return Instances
Modifier and Type	Method and Description
`static Instances`	JSONInstances.`toHeader(JSONNode json)` Turns a JSON object, if possible, into an Instances object (only header).
`static Instances`	JSONInstances.`toInstances(JSONNode json)` Turns a JSON object, if possible, into an Instances object.

Methods in weka.core.json with parameters of type Instances
Modifier and Type	Method and Description
`static JSONNode`	JSONInstances.`toJSON(Instances inst)` Turns the Instances object into a JSON object.

Uses of Instances in weka.core.neighboursearch

Methods in weka.core.neighboursearch that return Instances
Modifier and Type	Method and Description
`Instances`	NearestNeighbourSearch.`getInstances()` returns the instances currently set.
`abstract Instances`	NearestNeighbourSearch.`kNearestNeighbours(Instance target, int k)` Returns k nearest instances in the current neighbourhood to the supplied instance.
`Instances`	LinearNNSearch.`kNearestNeighbours(Instance target, int kNN)` Returns k nearest instances in the current neighbourhood to the supplied instance.
`Instances`	KDTree.`kNearestNeighbours(Instance target, int k)` Returns the k nearest neighbours of the supplied instance.
`Instances`	FilteredNeighbourSearch.`kNearestNeighbours(Instance target, int k)` Returns the nearest neighbours for the given instance based on distance measured in the filtered space.
`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`	BallTree.`kNearestNeighbours(Instance target, int k)` Returns k nearest instances in the current neighbourhood to the supplied instance.

Methods in weka.core.neighboursearch with parameters of type Instances
Modifier and Type	Method and Description
`void`	KDTree.`assignSubToCenters(KDTreeNode node, Instances centers, int[] centList, int[] assignments)` Assigns instances of this node to center.
`void`	KDTree.`centerInstances(Instances centers, int[] assignments, double pc)` Assigns instances to centers using KDTree.
`void`	NearestNeighbourSearch.`setInstances(Instances insts)` Sets the instances.
`void`	LinearNNSearch.`setInstances(Instances insts)` Sets the instances comprising the current neighbourhood.
`void`	KDTree.`setInstances(Instances instances)` Builds the KDTree on the given set of instances.
`void`	FilteredNeighbourSearch.`setInstances(Instances data)` Sets the instances to build the filtering model from.
`void`	CoverTree.`setInstances(Instances instances)` Builds the Cover Tree on the given set of instances.
`void`	BallTree.`setInstances(Instances insts)` Builds the BallTree based on the given set of instances.

Constructors in weka.core.neighboursearch with parameters of type Instances
Constructor and Description
`BallTree(Instances insts)` Creates a new instance of BallTree.
`KDTree(Instances insts)` Creates a new instance of KDTree.
`LinearNNSearch(Instances insts)` Constructor that uses the supplied set of instances.
`NearestNeighbourSearch(Instances insts)` Constructor.

Uses of Instances in weka.core.neighboursearch.balltrees

Methods in weka.core.neighboursearch.balltrees with parameters of type Instances
Modifier and Type	Method and 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).
`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.
`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`	MiddleOutConstructor.`setInstances(Instances insts)` Sets the instances on which the tree is to be built.
`void`	BallTreeConstructor.`setInstances(Instances inst)` Sets the instances on which the tree is to be built.
`void`	BallSplitter.`setInstances(Instances inst)` Sets the training instances on which the tree is (or is to be) built.

Constructors in weka.core.neighboursearch.balltrees with parameters of type Instances
Constructor and Description
`BallSplitter(int[] instList, Instances insts, EuclideanDistance e)` Creates a new instance of BallSplitter.
`MedianDistanceFromArbitraryPoint(int[] instList, Instances insts, EuclideanDistance e)` Constructor.
`MedianOfWidestDimension(int[] instList, Instances insts, EuclideanDistance e)` Constructor.
`PointsClosestToFurthestChildren(int[] instList, Instances insts, EuclideanDistance e)` Constructor.

Uses of Instances in weka.core.neighboursearch.kdtrees

Methods in weka.core.neighboursearch.kdtrees with parameters of type Instances
Modifier and Type	Method and Description
`void`	KDTreeNodeSplitter.`setInstances(Instances inst)` Sets the training instances on which the tree is (or is to be) built.

Constructors in weka.core.neighboursearch.kdtrees with parameters of type Instances
Constructor and Description
`KDTreeNodeSplitter(int[] instList, Instances insts, EuclideanDistance e)` Creates a new instance of KDTreeNodeSplitter.

Uses of Instances in weka.core.pmml

Methods in weka.core.pmml that return Instances
Modifier and Type	Method and Description
`Instances`	MiningSchema.`getFieldsAsInstances()` Get the all the fields (both mining schema and derived) as Instances.
`Instances`	MiningSchema.`getMiningSchemaAsInstances()` Get the mining schema fields as an Instances object.

Methods in weka.core.pmml with parameters of type Instances
Modifier and Type	Method and Description
`static String`	PMMLFactory.`applyClassifier(PMMLModel model, Instances test)`
`void`	DerivedFieldMetaInfo.`setFieldDefs(Instances fields)` Upadate the field definitions for this derived field
`String`	PMMLProducer.`toPMML(Instances train)` Produce a PMML representation

Constructors in weka.core.pmml with parameters of type Instances
Constructor and Description
`MappingInfo(Instances dataSet, MiningSchema miningSchema, Logger log)`
`MiningSchema(Element model, Instances dataDictionary, weka.core.pmml.TransformationDictionary transDict)` Constructor for MiningSchema.

Uses of Instances in weka.core.xml

Methods in weka.core.xml that return Instances
Modifier and Type	Method and Description
`Instances`	XMLInstances.`getInstances()` returns the current instances, either the ones that were set or the ones that were generated from the XML structure.

Methods in weka.core.xml with parameters of type Instances
Modifier and Type	Method and Description
`void`	XMLInstances.`setInstances(Instances data)` builds up the XML structure based on the given data

Constructors in weka.core.xml with parameters of type Instances
Constructor and Description
`XMLInstances(Instances data)` generates the XML structure based on the given data

Uses of Instances in weka.datagenerators

Methods in weka.datagenerators that return Instances
Modifier and Type	Method and Description
`Instances`	DataGenerator.`defineDataFormat()` Initializes the format for the dataset produced.
`abstract Instances`	DataGenerator.`generateExamples()` Generates all examples of the dataset.
`Instances`	DataGenerator.`getDatasetFormat()` Gets the format of the dataset that is to be generated.

Methods in weka.datagenerators with parameters of type Instances
Modifier and Type	Method and Description
`void`	DataGenerator.`setDatasetFormat(Instances newFormat)` Sets the format of the dataset that is to be generated.

Constructors in weka.datagenerators with parameters of type Instances
Constructor and Description
`Test(int i, double s, Instances dataset)` Constructor
`Test(int i, double s, Instances dataset, boolean n)` Constructor

Uses of Instances in weka.datagenerators.classifiers.classification

Methods in weka.datagenerators.classifiers.classification that return Instances
Modifier and Type	Method and Description
`Instances`	RDG1.`defineDataFormat()` Initializes the format for the dataset produced.
`Instances`	RandomRBF.`defineDataFormat()` Initializes the format for the dataset produced.
`Instances`	LED24.`defineDataFormat()` Initializes the format for the dataset produced.
`Instances`	BayesNet.`defineDataFormat()` Initializes the format for the dataset produced.
`Instances`	Agrawal.`defineDataFormat()` Initializes the format for the dataset produced.
`Instances`	RDG1.`generateExamples()` Generate all examples of the dataset.
`Instances`	RandomRBF.`generateExamples()` Generates all examples of the dataset.
`Instances`	LED24.`generateExamples()` Generates all examples of the dataset.
`Instances`	BayesNet.`generateExamples()` Generates all examples of the dataset.
`Instances`	Agrawal.`generateExamples()` Generates all examples of the dataset.
`Instances`	RDG1.`generateExamples(int num, Random random, Instances format)` Generate all examples of the dataset.

Methods in weka.datagenerators.classifiers.classification with parameters of type Instances
Modifier and Type	Method and Description
`Instances`	RDG1.`generateExamples(int num, Random random, Instances format)` Generate all examples of the dataset.

Uses of Instances in weka.datagenerators.classifiers.regression

Methods in weka.datagenerators.classifiers.regression that return Instances
Modifier and Type	Method and Description
`Instances`	MexicanHat.`defineDataFormat()` Initializes the format for the dataset produced.
`Instances`	Expression.`defineDataFormat()` Initializes the format for the dataset produced.
`Instances`	MexicanHat.`generateExamples()` Generates all examples of the dataset.
`Instances`	Expression.`generateExamples()` Generates all examples of the dataset.

Uses of Instances in weka.datagenerators.clusterers

Methods in weka.datagenerators.clusterers that return Instances
Modifier and Type	Method and Description
`Instances`	SubspaceCluster.`defineDataFormat()` Initializes the format for the dataset produced.
`Instances`	BIRCHCluster.`defineDataFormat()` Initializes the format for the dataset produced.
`Instances`	SubspaceCluster.`generateExamples()` Generate all examples of the dataset.
`Instances`	BIRCHCluster.`generateExamples()` Generate all examples of the dataset.
`Instances`	BIRCHCluster.`generateExamples(Random random, Instances format)` Generate all examples of the dataset.

Methods in weka.datagenerators.clusterers with parameters of type Instances
Modifier and Type	Method and Description
`Instances`	BIRCHCluster.`generateExamples(Random random, Instances format)` Generate all examples of the dataset.

Uses of Instances in weka.estimators

Methods in weka.estimators that return Instances
Modifier and Type	Method and Description
`static Instances`	EstimatorUtils.`getInstancesFromClass(Instances data, int classIndex, double classValue)` Returns a dataset that contains of all instances of a certain class value.
`static Instances`	EstimatorUtils.`getInstancesFromValue(Instances data, int index, double v)` Returns a dataset that contains of all instances of a certain value for the given attribute.

Methods in weka.estimators with parameters of type Instances
Modifier and Type	Method and Description
`void`	Estimator.`addValues(Instances data, int attrIndex)` Initialize the estimator with a new dataset.
`void`	Estimator.`addValues(Instances data, int attrIndex, double min, double max, double factor)` Initialize the estimator with all values of one attribute of a dataset.
`void`	Estimator.`addValues(Instances data, int attrIndex, int classIndex, int classValue)` Initialize the estimator using only the instance of one class.
`void`	Estimator.`addValues(Instances data, int attrIndex, int classIndex, int classValue, double min, double max)` Initialize the estimator using only the instance of one class.
`static void`	Estimator.`buildEstimator(Estimator est, Instances instances, int attrIndex, int classIndex, int classValueIndex, boolean isIncremental)`
`static double`	EstimatorUtils.`findMinDistance(Instances inst, int attrIndex)` Find the minimum distance between values
`static Instances`	EstimatorUtils.`getInstancesFromClass(Instances data, int classIndex, double classValue)` Returns a dataset that contains of all instances of a certain class value.
`static Vector<Object>`	EstimatorUtils.`getInstancesFromClass(Instances data, int attrIndex, int classIndex, double classValue, Instances workData)` Returns a dataset that contains all instances of a certain class value.
`static Instances`	EstimatorUtils.`getInstancesFromValue(Instances data, int index, double v)` Returns a dataset that contains of all instances of a certain value for the given attribute.
`static int`	EstimatorUtils.`getMinMax(Instances inst, int attrIndex, double[] minMax)` Find the minimum and the maximum of the attribute and return it in the last parameter..
`static int`	CheckEstimator.`getMinMax(Instances inst, int attrIndex, double[] minMax)` Find the minimum and the maximum of the attribute and return it in the last parameter..
`void`	Estimator.`testCapabilities(Instances data, int attrIndex)` Test if the estimator can handle the data.

Uses of Instances in weka.experiment

Methods in weka.experiment that return Instances
Modifier and Type	Method and Description
`Instances`	Tester.`getInstances()` Get the value of Instances.
`Instances`	PairedTTester.`getInstances()` Get the value of Instances.
`Instances`	InstanceQuery.`retrieveInstances()` Makes a database query using the query set through the -Q option to convert a table into a set of instances
`static Instances`	InstanceQuery.`retrieveInstances(InstanceQueryAdapter adapter, ResultSet rs)`
`Instances`	InstanceQuery.`retrieveInstances(String query)` Makes a database query to convert a table into a set of instances

Methods in weka.experiment with parameters of type Instances
Modifier and Type	Method and Description
`Object[]`	SplitEvaluator.`getResult(Instances train, Instances test)` Gets the results for the supplied train and test datasets.
`Object[]`	RegressionSplitEvaluator.`getResult(Instances train, Instances test)` Gets the results for the supplied train and test datasets.
`Object[]`	DensityBasedClustererSplitEvaluator.`getResult(Instances train, Instances test)` Gets the results for the supplied train and test datasets.
`Object[]`	CostSensitiveClassifierSplitEvaluator.`getResult(Instances train, Instances test)` Gets the results for the supplied train and test datasets.
`Object[]`	ClassifierSplitEvaluator.`getResult(Instances train, Instances test)` Gets the results for the supplied train and test datasets.
`void`	Tester.`setInstances(Instances newInstances)` Set the value of Instances.
`void`	ResultProducer.`setInstances(Instances instances)` Sets the dataset that results will be obtained for.
`void`	RandomSplitResultProducer.`setInstances(Instances instances)` Sets the dataset that results will be obtained for.
`void`	PairedTTester.`setInstances(Instances newInstances)` Set the value of Instances.
`void`	LearningRateResultProducer.`setInstances(Instances instances)` Sets the dataset that results will be obtained for.
`void`	ExplicitTestsetResultProducer.`setInstances(Instances instances)` Sets the dataset that results will be obtained for.
`void`	DatabaseResultProducer.`setInstances(Instances instances)` Sets the dataset that results will be obtained for.
`void`	CrossValidationResultProducer.`setInstances(Instances instances)` Sets the dataset that results will be obtained for.
`void`	AveragingResultProducer.`setInstances(Instances instances)` Sets the dataset that results will be obtained for.

Uses of Instances in weka.filters

Methods in weka.filters that return Instances
Modifier and Type	Method and Description
`Instances`	Filter.`getOutputFormat()` Gets the format of the output instances.
`static Instances`	Filter.`useFilter(Instances data, Filter filter)` Filters an entire set of instances through a filter and returns the new set.

Methods in weka.filters with parameters of type Instances
Modifier and Type	Method and Description
`Capabilities`	Filter.`getCapabilities(Instances data)` Returns the Capabilities of this filter, customized based on the data.
`boolean`	SimpleFilter.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	Filter.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	AllFilter.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`String`	Sourcable.`toSource(String className, Instances data)` Returns a string that describes the filter as source.
`String`	AllFilter.`toSource(String className, Instances data)` Returns a string that describes the filter as source.
`static Instances`	Filter.`useFilter(Instances data, Filter filter)` Filters an entire set of instances through a filter and returns the new set.
`static String`	Filter.`wekaStaticWrapper(Sourcable filter, String className, Instances input, Instances output)` generates source code from the filter

Uses of Instances in weka.filters.supervised.attribute

Methods in weka.filters.supervised.attribute with parameters of type Instances
Modifier and Type	Method and Description
`boolean`	PartitionMembership.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	NominalToBinary.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	Discretize.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	ClassOrder.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.

Uses of Instances in weka.filters.supervised.instance

Methods in weka.filters.supervised.instance with parameters of type Instances
Modifier and Type	Method and Description
`boolean`	StratifiedRemoveFolds.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	SpreadSubsample.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	Resample.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.

Uses of Instances in weka.filters.unsupervised.attribute

Methods in weka.filters.unsupervised.attribute that return Instances
Modifier and Type	Method and Description
`Instances`	PotentialClassIgnorer.`getOutputFormat()` Gets the format of the output instances.

Methods in weka.filters.unsupervised.attribute with parameters of type Instances
Modifier and Type	Method and Description
`void`	AddNoise.`addNoise(Instances instances, int seed, int percent, int attIndex, boolean useMissing)` add noise to the dataset a given percentage of the instances are changed in the way, that a set of instances are randomly selected using seed.
`Capabilities`	ClusterMembership.`getCapabilities(Instances data)` Returns the Capabilities of this filter, makes sure that the class is never set (for the clusterer).
`Capabilities`	AddCluster.`getCapabilities(Instances data)` Returns the Capabilities of this filter, makes sure that the class is never set (for the clusterer).
`void`	KernelFilter.`initFilter(Instances instances)` initializes the filter with the given dataset, i.e., the kernel gets built.
`boolean`	TimeSeriesTranslate.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	TimeSeriesDelta.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	SwapValues.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	StringToWordVector.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	StringToNominal.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	Standardize.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	ReplaceMissingWithUserConstant.`setInputFormat(Instances instanceInfo)`
`boolean`	ReplaceMissingValues.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	Reorder.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	RenameNominalValues.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	RemoveUseless.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	RemoveType.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	Remove.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	RandomProjection.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	PrincipalComponents.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	PotentialClassIgnorer.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	PKIDiscretize.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	Obfuscate.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	NumericTransform.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	NumericToBinary.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	Normalize.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	NominalToString.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	NominalToBinary.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	MergeTwoValues.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	MergeManyValues.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	MathExpression.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	MakeIndicator.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	FirstOrder.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	Discretize.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	Copy.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	ClusterMembership.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	ChangeDateFormat.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	Center.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	AddValues.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	AddUserFields.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	AddNoise.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	AddID.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	AddExpression.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	AddCluster.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	Add.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	AbstractTimeSeries.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`String`	Standardize.`toSource(String className, Instances data)` Returns a string that describes the filter as source.
`String`	ReplaceMissingValues.`toSource(String className, Instances data)` Returns a string that describes the filter as source.
`String`	Normalize.`toSource(String className, Instances data)` Returns a string that describes the filter as source.
`String`	Center.`toSource(String className, Instances data)` Returns a string that describes the filter as source.

Uses of Instances in weka.filters.unsupervised.instance

Methods in weka.filters.unsupervised.instance with parameters of type Instances
Modifier and Type	Method and Description
`void`	RemoveFrequentValues.`determineValues(Instances inst)` determines the values to retain, it is always at least 1 and up to the maximum number of distinct values
`boolean`	SparseToNonSparse.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	ReservoirSample.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	Resample.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	RemoveWithValues.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	RemoveRange.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	RemovePercentage.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	RemoveMisclassified.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	RemoveFrequentValues.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	RemoveFolds.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	Randomize.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`boolean`	NonSparseToSparse.`setInputFormat(Instances instanceInfo)` Sets the format of the input instances.

Uses of Instances in weka.filters.unsupervised.instance.subsetbyexpression

Methods in weka.filters.unsupervised.instance.subsetbyexpression that return Instances
Modifier and Type	Method and Description
`static Instances`	Parser.`filter(String expression, Instances input)` Filters the input dataset against the provided expression.

Methods in weka.filters.unsupervised.instance.subsetbyexpression with parameters of type Instances
Modifier and Type	Method and Description
`static Instances`	Parser.`filter(String expression, Instances input)` Filters the input dataset against the provided expression.

Uses of Instances in weka.gui

Methods in weka.gui that return Instances
Modifier and Type	Method and Description
`Instances`	ViewerDialog.`getInstances()` returns the currently displayed instances
`Instances`	SetInstancesPanel.`getInstances()` Gets the set of instances currently held by the panel.

Methods in weka.gui with parameters of type Instances
Modifier and Type	Method and Description
`void`	ViewerDialog.`setInstances(Instances inst)` sets the instances to display
`void`	SetInstancesPanel.`setInstances(Instances i)` Updates the set of instances that is currently held by the panel.
`void`	InstancesSummaryPanel.`setInstances(Instances inst)` Tells the panel to use a new set of instances.
`void`	AttributeVisualizationPanel.`setInstances(Instances newins)` Sets the instances for use
`void`	AttributeSummaryPanel.`setInstances(Instances inst)` Tells the panel to use a new set of instances.
`void`	AttributeSelectionPanel.`setInstances(Instances newInstances)` Sets the instances who's attribute names will be displayed.
`void`	AttributeListPanel.`setInstances(Instances newInstances)` Sets the instances who's attribute names will be displayed.
`int`	ViewerDialog.`showDialog(Instances inst)` Pops up the modal dialog and waits for Cancel or OK.

Uses of Instances in weka.gui.arffviewer

Methods in weka.gui.arffviewer that return Instances
Modifier and Type	Method and Description
`Instances`	ArffTableModel.`getInstances()` returns the data
`Instances`	ArffSortedTableModel.`getInstances()` returns the data
`Instances`	ArffPanel.`getInstances()` returns the instances of the panel, if none then NULL

Methods in weka.gui.arffviewer with parameters of type Instances
Modifier and Type	Method and Description
`void`	ArffTableModel.`setInstances(Instances data)` sets the data
`void`	ArffSortedTableModel.`setInstances(Instances data)` sets the data
`void`	ArffPanel.`setInstances(Instances data)` displays the given instances, i.e.

Constructors in weka.gui.arffviewer with parameters of type Instances
Constructor and Description
`ArffPanel(Instances data)` initializes the panel with the given data
`ArffSortedTableModel(Instances data)` initializes the sorter w/o a model, but uses the given data to create a model from that
`ArffTableModel(Instances data)` initializes the model with the given data

Uses of Instances in weka.gui.beans

Methods in weka.gui.beans that return Instances
Modifier and Type	Method and Description
`Instances`	Sorter.`getConnectedFormat()` Returns the structure of the incoming instances (if any)
`Instances`	FlowByExpression.`getConnectedFormat()` Returns the structure of the incoming instances (if any)
`Instances`	ClassValuePicker.`getConnectedFormat()` Returns the structure of the incoming instances (if any)
`Instances`	ClassAssigner.`getConnectedFormat()` Returns the structure of the incoming instances (if any)
`Instances`	DataSetEvent.`getDataSet()` Return the instances of the data set
`Instances`	SubstringLabelerRules.`getInputStructure()` Get the input structure
`Instances`	SubstringLabelerRules.`getOutputStructure()` Get the output structure
`Instances`	InstanceEvent.`getStructure()` Get the instances structure (may be null if this is not a FORMAT_AVAILABLE event)
`Instances`	IncrementalClassifierEvent.`getStructure()` Get the instances structure (may be null if this is not a NEW_BATCH event)
`Instances`	TrainTestSplitMaker.`getStructure(String eventName)` Get the structure of the output encapsulated in the named event.
`Instances`	TrainingSetMaker.`getStructure(String eventName)` Get the structure of the output encapsulated in the named event.
`Instances`	TestSetMaker.`getStructure(String eventName)` Get the structure of the output encapsulated in the named event.
`Instances`	StructureProducer.`getStructure(String eventName)` Get the structure of the output encapsulated in the named event.
`Instances`	Sorter.`getStructure(String eventName)` Get the structure of the output encapsulated in the named event.
`Instances`	Loader.`getStructure(String eventName)` Get the structure of the output encapsulated in the named event.
`Instances`	Join.`getStructure(String eventName)` Get the output instances structure given an input event type
`Instances`	FlowByExpression.`getStructure(String eventName)` Get the structure of the output encapsulated in the named event.
`Instances`	CrossValidationFoldMaker.`getStructure(String eventName)` Get the structure of the output encapsulated in the named event.
`Instances`	ClassValuePicker.`getStructure(String eventName)`
`Instances`	ClassAssigner.`getStructure(String eventName)` Get the structure of the output encapsulated in the named event.
`Instances`	Associator.`getStructure(String eventName)` Get the structure of the output encapsulated in the named event.
`Instances`	TestSetEvent.`getTestSet()` Get the test set instances
`Instances`	TrainingSetEvent.`getTrainingSet()` Get the training instances

Methods in weka.gui.beans with parameters of type Instances
Modifier and Type	Method and Description
`void`	SubstringReplacerRules.SubstringReplacerMatchRule.`init(Environment env, Instances structure)` Initialize this match replace rule by substituting any environment variables in the attributes, match and replace strings.
`void`	SubstringLabelerRules.SubstringLabelerMatchRule.`init(Environment env, Instances structure)` Initialize this match rule by substituting any environment variables in the attributes, match and label strings.
`static List<SubstringReplacerRules.SubstringReplacerMatchRule>`	SubstringReplacerRules.`matchRulesFromInternal(String matchReplaceDetails, Instances inputStructure, String statusMessagePrefix, Logger log, Environment env)` Get a list of match rules from an internally encoded match specification
`static List<SubstringLabelerRules.SubstringLabelerMatchRule>`	SubstringLabelerRules.`matchRulesFromInternal(String matchDetails, Instances inputStructure, String statusMessagePrefix, Logger log, Environment env)` Get a list of match rules from an internally encoded match specification
`static void`	SerializedModelSaver.`saveBinary(File saveTo, Object model, Instances header)` Save a model in binary form.
`static void`	SerializedModelSaver.`saveKOML(File saveTo, Object model, Instances header)` Save a model in KOML deep object serialized XML form.
`static void`	SerializedModelSaver.`saveXStream(File saveTo, Object model, Instances header)` Save a model in XStream deep object serialized XML form.
`void`	SQLViewerPerspective.`setInstances(Instances insts)` Set instances (if the perspective accepts them)
`void`	ScatterPlotMatrix.`setInstances(Instances inst)` Set instances for this bean.
`void`	KnowledgeFlowApp.KFPerspective.`setInstances(Instances insts)` Set instances (if the perspective accepts them)
`void`	KnowledgeFlowApp.MainKFPerspective.`setInstances(Instances insts)`
`void`	DataVisualizer.`setInstances(Instances inst)` Set instances for this bean.
`void`	AttributeSummarizer.`setInstances(Instances inst)` Set instances for this bean.
`void`	InstanceEvent.`setStructure(Instances structure)` Set the instances structure
`void`	IncrementalClassifierEvent.`setStructure(Instances structure)` Set the instances structure

Method parameters in weka.gui.beans with type arguments of type Instances
Modifier and Type	Method and Description
`BufferedImage`	WekaOffscreenChartRenderer.`renderHistogram(int width, int height, List<Instances> series, String attToPlot, List<String> optionalArgs)` Render histogram(s) (numeric attribute) or pie chart (nominal attribute).
`BufferedImage`	OffscreenChartRenderer.`renderHistogram(int width, int height, List<Instances> series, String attsToPlot, List<String> optionalArgs)` Render histogram(s) (numeric attribute) or bar chart(s) (nominal attribute).
`BufferedImage`	WekaOffscreenChartRenderer.`renderXYLineChart(int width, int height, List<Instances> series, String xAxis, String yAxis, List<String> optionalArgs)` Render an XY line chart
`BufferedImage`	OffscreenChartRenderer.`renderXYLineChart(int width, int height, List<Instances> series, String xAxis, String yAxis, List<String> optionalArgs)` Render an XY line chart
`BufferedImage`	WekaOffscreenChartRenderer.`renderXYScatterPlot(int width, int height, List<Instances> series, String xAxis, String yAxis, List<String> optionalArgs)` Render an XY scatter plot
`BufferedImage`	OffscreenChartRenderer.`renderXYScatterPlot(int width, int height, List<Instances> series, String xAxis, String yAxis, List<String> optionalArgs)` Render an XY scatter plot

Constructors in weka.gui.beans with parameters of type Instances
Constructor and Description
`DataSetEvent(Object source, Instances dataSet)`
`IncrementalClassifierEvent(Object source, Classifier scheme, Instances structure)` Creates a new incremental classifier event that encapsulates header information and classifier.
`InstanceEvent(Object source, Instances structure)` Creates a new `InstanceEvent` instance which encapsulates header information only.
`SubstringLabelerRules(String matchDetails, String newAttName, boolean consumeNonMatching, boolean nominalBinary, Instances inputStructure, String statusMessagePrefix, Logger log, Environment env)` Constructor
`SubstringLabelerRules(String matchDetails, String newAttName, Instances inputStructure)` Constructor.
`SubstringReplacerRules(String matchDetails, Instances inputStructure)` Constructor.
`SubstringReplacerRules(String matchDetails, Instances inputStructure, String statusMessagePrefix, Logger log, Environment env)` Constructor
`TestSetEvent(Object source, Instances testSet)` Creates a new `TestSetEvent`
`TestSetEvent(Object source, Instances testSet, int setNum, int maxSetNum)` Creates a new `TestSetEvent`
`TestSetEvent(Object source, Instances testSet, int runNum, int maxRunNum, int setNum, int maxSetNum)` Creates a new `TestSetEvent`
`TrainingSetEvent(Object source, Instances trainSet)` Creates a new `TrainingSetEvent`
`TrainingSetEvent(Object source, Instances trainSet, int setNum, int maxSetNum)` Creates a new `TrainingSetEvent`
`TrainingSetEvent(Object source, Instances trainSet, int runNum, int maxRunNum, int setNum, int maxSetNum)` Creates a new `TrainingSetEvent`

Uses of Instances in weka.gui.boundaryvisualizer

Methods in weka.gui.boundaryvisualizer that return Instances
Modifier and Type	Method and Description
`Instances`	BoundaryVisualizer.`getInstances()` Get the training instances

Methods in weka.gui.boundaryvisualizer with parameters of type Instances
Modifier and Type	Method and Description
`void`	KDDataGenerator.`buildGenerator(Instances inputInstances)` Initialize the generator using the supplied instances
`void`	DataGenerator.`buildGenerator(Instances inputInstances)` Build the data generator
`static void`	BoundaryVisualizer.`createNewVisualizerWindow(Classifier classifier, Instances instances)` Creates a new GUI window with all of the BoundaryVisualizer trappings,
`void`	RemoteBoundaryVisualizerSubTask.`setInstances(Instances i)` Set the training data
`void`	BoundaryVisualizer.`setInstances(Instances inst)` Set the training instances
`void`	BoundaryPanel.`setTrainingData(Instances trainingData)` Set the training data to use

Uses of Instances in weka.gui.experiment

Methods in weka.gui.experiment with parameters of type Instances
Modifier and Type	Method and Description
`void`	ResultsPanel.`setInstances(Instances newInstances)` Sets up the panel with a new set of instances, attempting to guess the correct settings for various columns.

Uses of Instances in weka.gui.explorer

Methods in weka.gui.explorer that return Instances
Modifier and Type	Method and Description
`Instances`	PreprocessPanel.`getInstances()` Gets the working set of instances.
`Instances`	DataGeneratorPanel.`getInstances()` returns the generated instances, null if the process was cancelled.
`Instances`	AbstractPlotInstances.`getInstances()` Returns the training data.
`Instances`	AbstractPlotInstances.`getPlotInstances()` Returns the generated plot instances.

Methods in weka.gui.explorer with parameters of type Instances
Modifier and Type	Method and Description
`void`	ClassifierErrorsPlotInstances.`process(Instances batch, double[][] predictions, Evaluation eval)`
`void`	PreprocessPanel.`saveInstancesToFile(AbstractFileSaver saver, Instances inst)` saves the data with the specified saver
`void`	PreprocessPanel.`setInstances(Instances inst)` Tells the panel to use a new base set of instances.
`void`	Explorer.ExplorerPanel.`setInstances(Instances inst)` Tells the panel to use a new set of instances.
`void`	ClustererPanel.`setInstances(Instances inst)` Tells the panel to use a new set of instances.
`void`	ClassifierPanel.`setInstances(Instances inst)` Tells the panel to use a new set of instances.
`void`	AttributeSelectionPanel.`setInstances(Instances inst)` Tells the panel to use a new set of instances.
`void`	AssociationsPanel.`setInstances(Instances inst)` Tells the panel to use a new set of instances.
`void`	AbstractPlotInstances.`setInstances(Instances value)` Sets the instances that are the basis for the plot instances.

Uses of Instances in weka.gui.streams

Methods in weka.gui.streams that return Instances
Modifier and Type	Method and Description
`Instances`	InstanceProducer.`outputFormat()`
`Instances`	InstanceLoader.`outputFormat()`
`Instances`	InstanceJoiner.`outputFormat()` Gets the format of the output instances.

Methods in weka.gui.streams with parameters of type Instances
Modifier and Type	Method and Description
`void`	InstanceViewer.`inputFormat(Instances instanceInfo)`
`void`	InstanceTable.`inputFormat(Instances instanceInfo)`
`void`	InstanceSavePanel.`inputFormat(Instances instanceInfo)`
`boolean`	InstanceJoiner.`inputFormat(Instances instanceInfo)` Sets the format of the input instances.
`void`	InstanceCounter.`inputFormat(Instances instanceInfo)`

Uses of Instances in weka.gui.treevisualizer

Methods in weka.gui.treevisualizer that return Instances
Modifier and Type	Method and Description
`Instances`	Node.`getInstances()` This will return the Instances object related to this node.

Uses of Instances in weka.gui.visualize

Methods in weka.gui.visualize that return Instances
Modifier and Type	Method and Description
`Instances`	VisualizePanel.`getInstances()` Get the master plot's instances
`Instances`	VisualizePanelEvent.`getInstances1()`
`Instances`	VisualizePanelEvent.`getInstances2()`
`Instances`	PlotData2D.`getPlotInstances()` Returns the instances for this plot

Methods in weka.gui.visualize that return types with arguments of type Instances
Modifier and Type	Method and Description
`Vector<Instances>`	InstanceInfoFrame.`getInfoData()` Returns the underlying data.
`Vector<Instances>`	InstanceInfo.`getInfoData()` Returns the underlying data.

Methods in weka.gui.visualize with parameters of type Instances
Modifier and Type	Method and Description
`void`	VisualizePanel.`setInstances(Instances inst)` Tells the panel to use a new set of instances.
`void`	Plot2D.`setInstances(Instances inst)` Sets the master plot from a set of instances
`void`	MatrixPanel.`setInstances(Instances newInst)` This method changes the Instances object of this class to a new one.
`void`	ClassPanel.`setInstances(Instances insts)` Set the instances.
`void`	AttributePanel.`setInstances(Instances ins)` This sets the instances to be drawn into the attribute panel
`void`	VisualizePanel.`setUpComboBoxes(Instances inst)` initializes the comboboxes based on the data
`void`	ThresholdVisualizePanel.`setUpComboBoxes(Instances inst)` This overloads VisualizePanel's setUpComboBoxes to add ActionListeners to watch for when the X/Y Axis comboboxes are changed.

Method parameters in weka.gui.visualize with type arguments of type Instances
Modifier and Type	Method and Description
`void`	InstanceInfoFrame.`setInfoData(Vector<Instances> data)` Sets the underlying data.
`void`	InstanceInfo.`setInfoData(Vector<Instances> data)` Sets the underlying data.

Constructors in weka.gui.visualize with parameters of type Instances
Constructor and Description
`PlotData2D(Instances insts)` Construct a new PlotData2D using the supplied instances
`VisualizePanelEvent(ArrayList<ArrayList<Double>> ar, Instances i, Instances i2, int at1, int at2)` This constructor creates the event with all the parameters set.

Uses of Instances in weka.gui.visualize.plugins

Methods in weka.gui.visualize.plugins with parameters of type Instances
Modifier and Type	Method and Description
`JMenuItem`	ErrorVisualizePlugin.`getVisualizeMenuItem(Instances predInst)` Get a JMenu or JMenuItem which contain action listeners that perform the visualization of the classifier errors.

Uses of Classweka.core.Instances

Uses of Instances in weka.associations

Uses of Instances in weka.attributeSelection

Uses of Instances in weka.classifiers

Uses of Instances in weka.classifiers.bayes

Uses of Instances in weka.classifiers.bayes.net

Uses of Instances in weka.classifiers.bayes.net.search

Uses of Instances in weka.classifiers.bayes.net.search.fixed

Uses of Instances in weka.classifiers.bayes.net.search.global

Uses of Instances in weka.classifiers.bayes.net.search.local

Uses of Instances in weka.classifiers.evaluation

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

Uses of Instances in weka.classifiers.functions

Uses of Instances in weka.classifiers.functions.supportVector

Uses of Instances in weka.classifiers.lazy

Uses of Instances in weka.classifiers.lazy.kstar

Uses of Instances in weka.classifiers.meta

Uses of Instances in weka.classifiers.misc

Uses of Instances in weka.classifiers.pmml.consumer

Uses of Instances in weka.classifiers.pmml.producer

Uses of Instances in weka.classifiers.rules

Uses of Instances in weka.classifiers.rules.part

Uses of Instances in weka.classifiers.trees

Uses of Instances in weka.classifiers.trees.ht

Uses of Instances in weka.classifiers.trees.j48

Uses of Instances in weka.classifiers.trees.lmt

Uses of Instances in weka.classifiers.trees.m5

Uses of Instances in weka.clusterers

Uses of Instances in weka.core

Uses of Instances in weka.core.converters

Uses of Instances in weka.core.json

Uses of Instances in weka.core.neighboursearch

Uses of Instances in weka.core.neighboursearch.balltrees

Uses of Instances in weka.core.neighboursearch.kdtrees

Uses of Instances in weka.core.pmml

Uses of Instances in weka.core.xml

Uses of Instances in weka.datagenerators

Uses of Instances in weka.datagenerators.classifiers.classification

Uses of Instances in weka.datagenerators.classifiers.regression

Uses of Instances in weka.datagenerators.clusterers

Uses of Instances in weka.estimators

Uses of Instances in weka.experiment

Uses of Instances in weka.filters

Uses of Instances in weka.filters.supervised.attribute

Uses of Instances in weka.filters.supervised.instance

Uses of Instances in weka.filters.unsupervised.attribute

Uses of Instances in weka.filters.unsupervised.instance

Uses of Instances in weka.filters.unsupervised.instance.subsetbyexpression

Uses of Instances in weka.gui

Uses of Instances in weka.gui.arffviewer

Uses of Instances in weka.gui.beans

Uses of Instances in weka.gui.boundaryvisualizer

Uses of Instances in weka.gui.experiment

Uses of Instances in weka.gui.explorer

Uses of Instances in weka.gui.streams

Uses of Instances in weka.gui.treevisualizer

Uses of Instances in weka.gui.visualize

Uses of Instances in weka.gui.visualize.plugins

Uses of Class
weka.core.Instances