Uses of Class
weka.core.Instances

Packages that use Instances

weka.associations
weka.associations.gsp
weka.associations.tertius
weka.attributeSelection
weka.classifiers
weka.classifiers.bayes
weka.classifiers.bayes.blr
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.functions
weka.classifiers.functions.supportVector
weka.classifiers.lazy
weka.classifiers.lazy.kstar
weka.classifiers.meta
weka.classifiers.meta.nestedDichotomies
weka.classifiers.mi
weka.classifiers.mi.supportVector
weka.classifiers.misc
weka.classifiers.pmml.consumer
weka.classifiers.rules
weka.classifiers.rules.part
weka.classifiers.trees
weka.classifiers.trees.adtree
weka.classifiers.trees.ft
weka.classifiers.trees.j48
weka.classifiers.trees.lmt
weka.classifiers.trees.m5
weka.clusterers
weka.clusterers.forOPTICSAndDBScan.Databases
weka.core
weka.core.converters
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

Uses of Instances in weka.associations

Methods in weka.associations that return Instances

static Instances	LabeledItemSet.divide(Instances instances, boolean invert) Splits the class attribute away.
Instances	Apriori.getInstancesNoClass() Gets the instances without the class atrribute.
Instances	CARuleMiner.getInstancesNoClass() Gets the instances without the class attribute
Instances	PredictiveApriori.getInstancesNoClass() Gets the instances without the class attribute
Instances	Apriori.getInstancesOnlyClass() Gets only the class attribute of the instances.
Instances	CARuleMiner.getInstancesOnlyClass() Gets the class attribute and its values for all instances
Instances	PredictiveApriori.getInstancesOnlyClass() Gets the class attribute of all instances

Methods in weka.associations with parameters of type Instances

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.
void	Tertius.buildAssociations(Instances instances) Method that launches the search to find the rules with the highest confirmation.
void	FilteredAssociator.buildAssociations(Instances data) Build the associator on the filtered data.
void	PredictiveApriori.buildAssociations(Instances instances) Method that generates all large itemsets with a minimum support, and from these all association rules.
void	GeneralizedSequentialPatterns.buildAssociations(Instances data) Extracts all sequential patterns out of a given sequential data set and prints out the results.
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.
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.
RuleItem	RuleItem.generateRuleItem(ItemSet premise, ItemSet consequence, Instances instances, int genTime, int minRuleCount, double[] m_midPoints, Hashtable m_priors) Constructs a new RuleItem if the support of the given rule is above the support threshold.
TreeSet	RuleGeneration.generateRules(int numRules, double[] midPoints, Hashtable priors, double expectation, Instances instances, TreeSet best, int genTime) Generates all rules for an item set.
TreeSet	CaRuleGeneration.generateRules(int numRules, double[] midPoints, Hashtable priors, double expectation, Instances instances, TreeSet best, int genTime) Generates all rules for an item set.
FastVector[]	Apriori.mineCARs(Instances data) Method that mines all class association rules with minimum support and with a minimum confidence.
FastVector[]	CARuleMiner.mineCARs(Instances data) Method for mining class association rules.
FastVector[]	PredictiveApriori.mineCARs(Instances data) Method that mines the n best class association rules.
static FastVector	CaRuleGeneration.singleConsequence(Instances instances) generates a consequence of length 1 for a class association rule.
static FastVector	RuleGeneration.singleConsequence(Instances instances, int attNum, FastVector consequences) generates a consequence of length 1 for an association rule.
static FastVector	AprioriItemSet.singletons(Instances instances) Converts the header info of the given set of instances into a set of item sets (singletons).
static FastVector	ItemSet.singletons(Instances instances) Converts the header info of the given set of instances into a set of item sets (singletons).
static FastVector	CaRuleGeneration.singletons(Instances instances) Converts the header info of the given set of instances into a set of item sets (singletons).
static FastVector	LabeledItemSet.singletons(Instances instancesNoClass, Instances classes) Converts the header info of the given set of instances into a set of item sets (singletons).
String	AprioriItemSet.toString(Instances instances) Returns the contents of an item set as a string.
String	ItemSet.toString(Instances instances) Returns the contents of an item set as a string.
static void	ItemSet.upDateCounters(FastVector itemSets, Instances instances) Updates counters for a set of item sets and a set of instances.
static void	LabeledItemSet.upDateCounters(FastVector itemSets, Instances instancesNoClass, Instances instancesClass) Updates counter of a specific item set

Constructors in weka.associations with parameters of type Instances

PriorEstimation(Instances instances, int numRules, int numIntervals, boolean car)
Constructor

Uses of Instances in weka.associations.gsp

Methods in weka.associations.gsp with parameters of type Instances

static FastVector	Element.getOneElements(Instances instances) Returns all events of the given data set as Elements containing a single event.
static String	Sequence.setOfSequencesToString(FastVector setOfSequences, Instances dataSet, FastVector filterAttributes) Returns a String representation of a set of Sequences where the numeric value of each event/item is represented by its respective nominal value.
String	Element.toNominalString(Instances dataSet) Returns a String representation of an Element where the numeric value of each event/item is represented by its respective nominal value.
String	Sequence.toNominalString(Instances dataSet) Returns a String representation of a Sequences where the numeric value of each event/item is represented by its respective nominal value.

Uses of Instances in weka.associations.tertius

Subclasses of Instances in weka.associations.tertius

class

IndividualInstances

Methods in weka.associations.tertius that return Instances

Instances

IndividualInstance.getParts()

Methods in weka.associations.tertius with parameters of type Instances

void	Rule.upDate(Instances instances) Update the number of counter-instances of this rule in the dataset.
void	LiteralSet.upDate(Instances instances) Update the number of counter-instances of this set in the dataset.

Constructors in weka.associations.tertius with parameters of type Instances

Body(Instances instances)
Constructor storing the counter-instances.

Head(Instances instances)
Constructor storing the counter-instances.

IndividualInstance(Instance individual, Instances parts)

IndividualInstances(Instances individuals, Instances parts)

LiteralSet(Instances instances)
Constructor initializing the set of counter-instances to all the instances.

Rule(Instances instances, boolean repeatPredicate, int maxLiterals, boolean negBody, boolean negHead, boolean classRule, boolean horn)
Constructor for a rule when the counter-instances are stored, giving all the constraints applied to this rule.

Uses of Instances in weka.attributeSelection

Methods in weka.attributeSelection that return 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.
Instances	PrincipalComponents.transformedData(Instances data) Gets the transformed training data.
Instances	LatentSemanticAnalysis.transformedData(Instances data) Transform the supplied data set (assumed to be the same format as the 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	LatentSemanticAnalysis.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

void	SymmetricalUncertAttributeEval.buildEvaluator(Instances data) Initializes a symmetrical uncertainty attribute evaluator.
void	GainRatioAttributeEval.buildEvaluator(Instances data) Initializes a gain ratio attribute evaluator.
void	PrincipalComponents.buildEvaluator(Instances data) Initializes principal components and performs the analysis
void	FilteredAttributeEval.buildEvaluator(Instances data) Initializes a filtered attribute evaluator.
void	ChiSquaredAttributeEval.buildEvaluator(Instances data) Initializes a chi-squared attribute evaluator.
void	SVMAttributeEval.buildEvaluator(Instances data) Initializes the evaluator.
void	ConsistencySubsetEval.buildEvaluator(Instances data) Generates a attribute evaluator.
void	FilteredSubsetEval.buildEvaluator(Instances data) Initializes a filtered attribute evaluator.
void	InfoGainAttributeEval.buildEvaluator(Instances data) Initializes an information gain attribute evaluator.
abstract void	ASEvaluation.buildEvaluator(Instances data) Generates a attribute evaluator.
void	LatentSemanticAnalysis.buildEvaluator(Instances data) Initializes the singular values/vectors and performs the analysis
void	CfsSubsetEval.buildEvaluator(Instances data) Generates a attribute evaluator.
void	ClassifierSubsetEval.buildEvaluator(Instances data) Generates a attribute evaluator.
void	ReliefFAttributeEval.buildEvaluator(Instances data) Initializes a ReliefF attribute evaluator.
void	OneRAttributeEval.buildEvaluator(Instances data) Initializes a OneRAttribute attribute evaluator.
void	CostSensitiveASEvaluation.buildEvaluator(Instances data) Generates a attribute evaluator.
void	WrapperSubsetEval.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.
double	ClassifierSubsetEval.evaluateSubset(BitSet subset, Instances holdOut) Evaluates a subset of attributes with respect to a set of instances.
BitSet	LFSMethods.floatingForwardSearch(int cacheSize, BitSet startGroup, int[] ranking, int k, boolean incrementK, int maxStale, Instances data, SubsetEvaluator evaluator, boolean verbose) Performs linear floating forward selection ( the stopping criteria cannot be changed to a specific size value )
BitSet	LFSMethods.forwardSearch(int cacheSize, BitSet startGroup, int[] ranking, int k, boolean incrementK, int maxStale, int forceResultSize, Instances data, SubsetEvaluator evaluator, boolean verbose) Performs linear forward selection
int[]	LFSMethods.rankAttributes(Instances data, SubsetEvaluator evaluator, boolean verbose)
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[]	BestFirst.search(ASEvaluation ASEval, Instances data) Searches the attribute subset space by best first search
int[]	ExhaustiveSearch.search(ASEvaluation ASEval, Instances data) Searches the attribute subset space using an exhaustive search.
int[]	RandomSearch.search(ASEvaluation ASEval, Instances data) Searches the attribute subset space randomly.
int[]	RankSearch.search(ASEvaluation ASEval, Instances data) Ranks attributes using the specified attribute evaluator and then searches the ranking using the supplied subset evaluator.
int[]	SubsetSizeForwardSelection.search(ASEvaluation ASEval, Instances data) Searches the attribute subset space by subset size forward selection
abstract int[]	ASSearch.search(ASEvaluation ASEvaluator, Instances data) Searches the attribute subset/ranking space.
int[]	ScatterSearchV1.search(ASEvaluation ASEval, Instances data) Searches the attribute subset space using Scatter Search.
int[]	GreedyStepwise.search(ASEvaluation ASEval, Instances data) Searches the attribute subset space by forward selection.
int[]	Ranker.search(ASEvaluation ASEval, Instances data) Kind of a dummy search algorithm.
int[]	GeneticSearch.search(ASEvaluation ASEval, Instances data) Searches the attribute subset space using a genetic algorithm.
int[]	RaceSearch.search(ASEvaluation ASEval, Instances data) Searches the attribute subset space by racing cross validation errors of competing subsets
int[]	LinearForwardSelection.search(ASEvaluation ASEval, Instances data) Searches the attribute subset space by linear forward selection
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.
String	ConsistencySubsetEval.hashKey.toString(Instances t, int maxColWidth) Convert a hash entry to a string
Instances	PrincipalComponents.transformedData(Instances data) Gets the transformed training data.
Instances	LatentSemanticAnalysis.transformedData(Instances data) Transform the supplied data set (assumed to be the same format as the 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

Instances

CostMatrix.applyCostMatrix(Instances data, Random random) Applies the cost matrix to a set of instances.

Methods in weka.classifiers with parameters of type Instances

Instances	CostMatrix.applyCostMatrix(Instances data, Random random) Applies the cost matrix to a set of instances.
abstract void	Classifier.buildClassifier(Instances data) Generates a classifier.
void	IteratedSingleClassifierEnhancer.buildClassifier(Instances data) Stump method for building the classifiers.
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.initClassifier(Instances instances) Inits an iterative classifier.
static void	Evaluation.printClassifications(Classifier classifier, Instances train, ConverterUtils.DataSource testSource, int classIndex, Range attributesToOutput, boolean printDistribution, StringBuffer text) Prints the predictions for the given dataset into a supplied StringBuffer
static void	Evaluation.printClassifications(Classifier classifier, Instances train, ConverterUtils.DataSource testSource, int classIndex, Range attributesToOutput, StringBuffer predsText) Prints the predictions for the given dataset into a String variable.
void	Evaluation.setPriors(Instances train) Sets the class prior probabilities

Constructors in weka.classifiers with parameters of type Instances

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.bayes

Fields in weka.classifiers.bayes declared as Instances

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

void	BayesNet.buildClassifier(Instances instances) Generates the classifier.
void	HNB.buildClassifier(Instances instances) Generates the classifier.
void	DMNBtext.buildClassifier(Instances data) Generates the classifier.
void	AODEsr.buildClassifier(Instances instances) Generates the classifier.
void	NaiveBayesMultinomialUpdateable.buildClassifier(Instances instances) Generates the classifier.
void	BayesianLogisticRegression.buildClassifier(Instances data) (1) Set the data to the class attribute m_Instances. (2)Call the method initialize() to initialize the values.
void	NaiveBayesMultinomial.buildClassifier(Instances instances) Generates the classifier.
void	NaiveBayesSimple.buildClassifier(Instances instances) Generates the classifier.
void	WAODE.buildClassifier(Instances instances) Generates the classifier.
void	AODE.buildClassifier(Instances instances) Generates the classifier.
void	NaiveBayes.buildClassifier(Instances instances) Generates the classifier.
void	ComplementNaiveBayes.buildClassifier(Instances instances) Generates the classifier.
double	BayesianLogisticRegression.getLoglikeliHood(double[] betas, Instances instances)
void	DMNBtext.DNBBinary.initClassifier(Instances instances)

Uses of Instances in weka.classifiers.bayes.blr

Methods in weka.classifiers.bayes.blr with parameters of type Instances

void	Prior.computelogLikelihood(double[] betas, Instances instances) Function computes the log-likelihood value: -sum{1 to n}{ln(1+exp(-Beta*x(i)*y(i))}
void	GaussianPriorImpl.computeLoglikelihood(double[] betas, Instances instances) This method calls the log-likelihood implemented in the Prior abstract class.
void	LaplacePriorImpl.computeLogLikelihood(double[] betas, Instances instances) Computes the log-likelihood values using the implementation in the Prior class.
double	GaussianPriorImpl.update(int j, Instances instances, double beta, double hyperparameter, double[] r, double deltaV) Update function specific to Laplace Prior.
double	Prior.update(int j, Instances instances, double beta, double hyperparameter, double[] r, double deltaV) Interface for the update functions for different types of priors.
double	LaplacePriorImpl.update(int j, Instances instances, double beta, double hyperparameter, double[] r, double deltaV) Update function specific to Laplace Prior.

Uses of Instances in weka.classifiers.bayes.net

Methods in weka.classifiers.bayes.net with parameters of type Instances

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, FastVector nRecords, Instances instances) create sub tree
static VaryNode	ADNode.makeVaryNode(int iNode, FastVector 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

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

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

void	FromFile.buildStructure(BayesNet bayesNet, Instances instances)
void	NaiveBayes.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

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	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.
void	SimulatedAnnealing.search(BayesNet bayesNet, Instances instances)

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

Methods in weka.classifiers.bayes.net.search.local with parameters of type Instances

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	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	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.
void	SimulatedAnnealing.search(BayesNet bayesNet, Instances instances)

Constructors in weka.classifiers.bayes.net.search.local with parameters of type Instances

LocalScoreSearchAlgorithm(BayesNet bayesNet, Instances instances)
constructor

Uses of Instances in weka.classifiers.evaluation

Methods in weka.classifiers.evaluation that return Instances

Instances	CostCurve.getCurve(FastVector predictions) Calculates the performance stats for the default class and return results as a set of Instances.
Instances	ThresholdCurve.getCurve(FastVector predictions) Calculates the performance stats for the default class and return results as a set of Instances.
Instances	MarginCurve.getCurve(FastVector predictions) Calculates the cumulative margin distribution for the set of predictions, returning the result as a set of Instances.
Instances	CostCurve.getCurve(FastVector predictions, int classIndex) Calculates the performance stats for the desired class and return results as a set of Instances.
Instances	ThresholdCurve.getCurve(FastVector predictions, int classIndex) Calculates the performance stats for the desired class and return results as a set of Instances.

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

FastVector	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.getROCArea(Instances tcurve) Calculates the area under the ROC curve as the Wilcoxon-Mann-Whitney statistic.
FastVector	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
FastVector	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.

Uses of Instances in weka.classifiers.functions

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

void	PLSClassifier.buildClassifier(Instances data) builds the classifier
void	LibLINEAR.buildClassifier(Instances insts) builds the classifier
void	Logistic.buildClassifier(Instances train) Builds the classifier
void	LinearRegression.buildClassifier(Instances data) Builds a regression model for the given data.
void	LeastMedSq.buildClassifier(Instances data) Build lms regression
void	GaussianProcesses.buildClassifier(Instances insts) Method for building the classifier.
void	SimpleLogistic.buildClassifier(Instances data) Builds the logistic regression using LogitBoost.
void	MultilayerPerceptron.buildClassifier(Instances i) Call this function to build and train a neural network for the training data provided.
void	LibSVM.buildClassifier(Instances insts) builds the classifier
void	VotedPerceptron.buildClassifier(Instances insts) Builds the ensemble of perceptrons.
void	IsotonicRegression.buildClassifier(Instances insts) Builds an isotonic regression model given the supplied training data.
void	SimpleLinearRegression.buildClassifier(Instances insts) Builds a simple linear regression model given the supplied training data.
void	SPegasos.buildClassifier(Instances data) Method for building the classifier.
void	Winnow.buildClassifier(Instances insts) Builds the classifier
void	SMOreg.buildClassifier(Instances instances) Method for building the classifier.
void	PaceRegression.buildClassifier(Instances data) Builds a pace regression model for the given data.
void	SMO.buildClassifier(Instances insts) Method for building the classifier.
void	RBFNetwork.buildClassifier(Instances instances) Builds the classifier
boolean	PaceRegression.checkForMissing(Instance instance, Instances model) Checks if an instance has a missing value.

Uses of Instances in weka.classifiers.functions.supportVector

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

void	RegSMO.buildClassifier(Instances instances) learn SVM parameters from data using Smola's SMO algorithm.
void	RegSMOImproved.buildClassifier(Instances instances) learn SVM parameters from data using Keerthi's SMO algorithm.
void	RegOptimizer.buildClassifier(Instances data) learn SVM parameters from data.
void	Puk.buildKernel(Instances data) builds the kernel with the given data.
void	CachedKernel.buildKernel(Instances data) builds the kernel with the given data.
void	Kernel.buildKernel(Instances data) builds the kernel with the given data
void	RBFKernel.buildKernel(Instances data) builds the kernel with the given data.
void	StringKernel.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

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

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

void	IB1.buildClassifier(Instances instances) Generates the classifier.
void	LWL.buildClassifier(Instances instances) Generates the classifier.
void	LBR.buildClassifier(Instances instances) For lazy learning, building classifier is only to prepare their inputs until classification time.
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

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 that return Instances

Instances

Bagging.resampleWithWeights(Instances data, Random random, boolean[] sampled) Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.

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

void	MetaCost.buildClassifier(Instances data) Builds the model of the base learner.
void	ClassificationViaRegression.buildClassifier(Instances insts) Builds the classifiers.
void	AdditiveRegression.buildClassifier(Instances data) Build the classifier on the supplied data
void	FilteredClassifier.buildClassifier(Instances data) Build the classifier on the filtered data.
void	AdaBoostM1.buildClassifier(Instances data) Boosting method.
void	MultiBoostAB.buildClassifier(Instances training) Method for building this classifier.
void	Stacking.buildClassifier(Instances data) Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
void	ClassificationViaClustering.buildClassifier(Instances data) builds the classifier
void	OrdinalClassClassifier.buildClassifier(Instances insts) Builds the classifiers.
void	CVParameterSelection.buildClassifier(Instances instances) Generates the classifier.
void	RotationForest.buildClassifier(Instances data) builds the classifier.
void	MultiScheme.buildClassifier(Instances data) Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
void	GridSearch.buildClassifier(Instances data) builds the classifier
void	RegressionByDiscretization.buildClassifier(Instances instances) Generates the classifier.
void	MultiClassClassifier.buildClassifier(Instances insts) Builds the classifiers.
void	LogitBoost.buildClassifier(Instances data) Builds the boosted classifier
void	RandomSubSpace.buildClassifier(Instances data) builds the classifier.
void	Dagging.buildClassifier(Instances data) Bagging method.
void	END.buildClassifier(Instances data) Builds the committee of randomizable classifiers.
void	Vote.buildClassifier(Instances data) Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
void	AttributeSelectedClassifier.buildClassifier(Instances data) Build the classifier on the dimensionally reduced data.
void	RandomCommittee.buildClassifier(Instances data) Builds the committee of randomizable classifiers.
void	ThresholdSelector.buildClassifier(Instances instances) Generates the classifier.
void	Bagging.buildClassifier(Instances data) Bagging method.
void	CostSensitiveClassifier.buildClassifier(Instances data) Builds the model of the base learner.
void	Decorate.buildClassifier(Instances data) Build Decorate classifier
void	RacedIncrementalLogitBoost.buildClassifier(Instances data) Builds the classifier.
Instances	Bagging.resampleWithWeights(Instances data, Random random, boolean[] sampled) Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.

Uses of Instances in weka.classifiers.meta.nestedDichotomies

Methods in weka.classifiers.meta.nestedDichotomies with parameters of type Instances

void	ClassBalancedND.buildClassifier(Instances data) Builds tree recursively.
void	DataNearBalancedND.buildClassifier(Instances data) Builds tree recursively.
void	ND.buildClassifier(Instances data) Builds the classifier.
void	ND.buildClassifierForNode(weka.classifiers.meta.nestedDichotomies.ND.NDTree node, Instances data) Builds the classifier for one node.

Uses of Instances in weka.classifiers.mi

Methods in weka.classifiers.mi that return Instances

Instances

SimpleMI.transform(Instances train) Implements MITransform (3 type of transformation) 1.arithmatic average; 2.geometric centor; 3.merge minima and maxima attribute value together

Methods in weka.classifiers.mi with parameters of type Instances

void	MDD.buildClassifier(Instances train) Builds the classifier
void	MISVM.buildClassifier(Instances train) Builds the classifier
void	MILR.buildClassifier(Instances train) Builds the classifier
void	MINND.buildClassifier(Instances exs) As normal Nearest Neighbour algorithm does, it's lazy and simply records the exemplar information (i.e.
void	MIDD.buildClassifier(Instances train) Builds the classifier
void	MISMO.buildClassifier(Instances insts) Method for building the classifier.
void	SimpleMI.buildClassifier(Instances train) Builds the classifier
void	MIEMDD.buildClassifier(Instances train) Builds the classifier
void	MIBoost.buildClassifier(Instances exps) Builds the classifier
void	CitationKNN.buildClassifier(Instances train) Builds the classifier
void	MIOptimalBall.buildClassifier(Instances data) Builds the classifier
void	MIWrapper.buildClassifier(Instances data) Builds the classifier
void	MIOptimalBall.calculateDistance(Instances train) calculate the distances from each instance in a positive bag to each bag.
void	MIOptimalBall.findRadius(Instances train) Find the maximum radius for the optimal ball.
static double[]	SimpleMI.minimax(Instances data, int attIndex) Get the minimal and maximal value of a certain attribute in a certain data
Instance	MINND.preprocess(Instances data, int pos) Pre-process the given exemplar according to the other exemplars in the given exemplars.
Instances	SimpleMI.transform(Instances train) Implements MITransform (3 type of transformation) 1.arithmatic average; 2.geometric centor; 3.merge minima and maxima attribute value together

Uses of Instances in weka.classifiers.mi.supportVector

Methods in weka.classifiers.mi.supportVector with parameters of type Instances

void	MIRBFKernel.buildKernel(Instances data) builds the kernel with the given data.

Constructors in weka.classifiers.mi.supportVector with parameters of type Instances

MIPolyKernel(Instances data, int cacheSize, double exponent, boolean lowerOrder)
Creates a new MIPolyKernel instance.

MIRBFKernel(Instances data, int cacheSize, double gamma)
Constructor.

Uses of Instances in weka.classifiers.misc

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

void	SerializedClassifier.buildClassifier(Instances data) loads only the serialized classifier
void	VFI.buildClassifier(Instances instances) Generates the classifier.
void	HyperPipes.buildClassifier(Instances instances) Generates the classifier.

Uses of Instances in weka.classifiers.pmml.consumer

Methods in weka.classifiers.pmml.consumer that return Instances

Instances

PMMLClassifier.getDataDictionary() Get the data dictionary.

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

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

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.

Uses of Instances in weka.classifiers.rules

Methods in weka.classifiers.rules that return Instances

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, FastVector 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.
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

void	Ridor.buildClassifier(Instances instances) Builds a ripple-down manner rule learner.
void	DTNB.buildClassifier(Instances data) Generates the classifier.
void	ConjunctiveRule.buildClassifier(Instances instances) Builds a single rule learner with REP dealing with nominal classes or numeric classes.
void	JRip.buildClassifier(Instances instances) Builds Ripper in the order of class frequencies.
void	DecisionTable.buildClassifier(Instances data) Generates the classifier.
void	PART.buildClassifier(Instances instances) Generates the classifier.
void	Prism.buildClassifier(Instances data) Generates the classifier.
void	NNge.buildClassifier(Instances data) Generates a classifier.
void	ZeroR.buildClassifier(Instances instances) Generates the classifier.
void	OneR.buildClassifier(Instances instances) 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
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
static Instances	RuleStats.rmCoveredBySuccessives(Instances data, FastVector 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
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

RuleStats(Instances data, FastVector 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

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

void	RandomTree.backfitData(Instances data) Backfits the given data into the tree.
void	DecisionStump.buildClassifier(Instances instances) Generates the classifier.
void	FT.buildClassifier(Instances data) Builds the classifier.
void	ADTree.buildClassifier(Instances instances) Builds a classifier for a set of instances.
void	J48graft.buildClassifier(Instances instances) Generates the classifier.
void	LADTree.buildClassifier(Instances instances) Builds a classifier for a set of instances.
void	Id3.buildClassifier(Instances data) Builds Id3 decision tree classifier.
void	REPTree.buildClassifier(Instances data) Builds classifier.
void	RandomForest.buildClassifier(Instances data) Builds a classifier for a set of instances.
void	NBTree.buildClassifier(Instances instances) Generates the classifier.
void	BFTree.buildClassifier(Instances data) Method for building a BestFirst decision tree classifier.
void	UserClassifier.buildClassifier(Instances i) Call this function to build a decision tree for the training data provided.
void	SimpleCart.buildClassifier(Instances data) Build the classifier.
void	RandomTree.buildClassifier(Instances data) Builds classifier.
void	J48.buildClassifier(Instances instances) Generates the classifier.
void	LMT.buildClassifier(Instances data) Builds the classifier.
void	ADTree.initClassifier(Instances instances) Sets up the tree ready to be trained, using two-class optimized method.
void	LADTree.initClassifier(Instances instances) Sets up the tree ready to be trained.
int	LADTree.predictiveError(Instances test)
int	SimpleCart.prune(double[] alphas, double[] errors, Instances test) Method for performing one fold in the cross-validation of minimal cost-complexity pruning.

Uses of Instances in weka.classifiers.trees.adtree

Subclasses of Instances in weka.classifiers.trees.adtree

class

ReferenceInstances Simple class that extends the Instances class making it possible to create subsets of instances that reference their source set.

Methods in weka.classifiers.trees.adtree with parameters of type Instances

String	TwoWayNominalSplit.attributeString(Instances dataset) Gets the string describing the attributes the split depends on.
String	TwoWayNumericSplit.attributeString(Instances dataset) Gets the string describing the attributes the split depends on.
abstract String	Splitter.attributeString(Instances dataset) Gets the string describing the attributes the split depends on.
String	TwoWayNominalSplit.comparisonString(int branchNum, Instances dataset) Gets the string describing the comparision the split depends on for a particular branch.
String	TwoWayNumericSplit.comparisonString(int branchNum, Instances dataset) Gets the string describing the comparision the split depends on for a particular branch.
abstract String	Splitter.comparisonString(int branchNum, Instances dataset) Gets the string describing the comparision the split depends on for a particular branch.
ReferenceInstances	TwoWayNominalSplit.instancesDownBranch(int branch, Instances instances) Gets the subset of instances that apply to a particluar branch of the split.
ReferenceInstances	TwoWayNumericSplit.instancesDownBranch(int branch, Instances instances) Gets the subset of instances that apply to a particluar branch of the split.
abstract ReferenceInstances	Splitter.instancesDownBranch(int branch, Instances sourceInstances) Gets the subset of instances that apply to a particluar branch of the split.

Constructors in weka.classifiers.trees.adtree with parameters of type Instances

ReferenceInstances(Instances dataset, int capacity)
Creates an empty set of instances.

Uses of Instances in weka.classifiers.trees.ft

Methods in weka.classifiers.trees.ft with parameters of type Instances

void	FTInnerNode.buildClassifier(Instances data) Method for building a Functional Inner tree (only called for the root node).
void	FTLeavesNode.buildClassifier(Instances data) Method for building a Functional Leaves tree (only called for the root node).
void	FTNode.buildClassifier(Instances data) Method for building a Functional tree (only called for the root node).
abstract void	FTtree.buildClassifier(Instances data) Method for building a Functional Tree (only called for the root node).
void	FTInnerNode.buildTree(Instances data, SimpleLinearRegression[][] higherRegressions, double totalInstanceWeight, double higherNumParameters) Method for building the tree structure.
void	FTLeavesNode.buildTree(Instances data, SimpleLinearRegression[][] higherRegressions, double totalInstanceWeight, double higherNumParameters) Method for building the tree structure.
void	FTNode.buildTree(Instances data, SimpleLinearRegression[][] higherRegressions, double totalInstanceWeight, double higherNumParameters) Method for building the tree structure.
abstract void	FTtree.buildTree(Instances data, SimpleLinearRegression[][] higherRegressions, double totalInstanceWeight, double higherNumParameters) Abstract method for building the tree structure.

Uses of Instances in weka.classifiers.trees.j48

Methods in weka.classifiers.trees.j48 that return Instances

Instances[]

ClassifierSplitModel.split(Instances data) Splits the given set of instances into subsets.

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

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	NBTreeNoSplit.buildClassifier(Instances instances) Build the no-split node
void	NBTreeClassifierTree.buildClassifier(Instances data) Method for building a naive bayes 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.
abstract void	ClassifierSplitModel.buildClassifier(Instances instances) Builds the classifier split model for the given set of instances.
void	ClassifierTree.buildClassifier(Instances data) Method for building a classifier tree.
void	PruneableClassifierTree.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	C45PruneableClassifierTree.buildClassifier(Instances data) Method for building a pruneable classifier tree.
void	C45PruneableClassifierTreeG.buildClassifier(Instances data) Method for building a pruneable classifier tree.
void	GraftSplit.buildClassifier(Instances data) builds m_graftdistro using the passed data
void	C45Split.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	GraftSplit.deleteGraftedCases(Instances data) deletes the cases in data that belong to leaf pointed to by the test (i.e.
void	Distribution.delRange(int bagIndex, Instances source, int startIndex, int lastPlusOne) Deletes all instances in given range from given bag.
void	C45PruneableClassifierTreeG.doGrafting(Instances data) Initializes variables for grafting.
String	ClassifierSplitModel.dumpLabel(int index, Instances data) Prints label for subset index of instances (eg class).
String	GraftSplit.dumpLabelG(int index, Instances data) Prints label for subset index of instances (eg class).
String	ClassifierSplitModel.dumpModel(Instances data) Prints the split model.
String	NBTreeNoSplit.leftSide(Instances instances) Does nothing because no condition has to be satisfied.
String	NoSplit.leftSide(Instances instances) Does nothing because no condition has to be satisfied.
String	NBTreeSplit.leftSide(Instances data) Prints left side of condition..
abstract String	ClassifierSplitModel.leftSide(Instances data) Prints left side of condition satisfied by instances.
String	BinC45Split.leftSide(Instances data) Prints left side of condition.
String	GraftSplit.leftSide(Instances data) Prints left side of condition satisfied by instances.
String	C45Split.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	BinC45Split.resetDistribution(Instances data) Sets distribution associated with model.
void	C45Split.resetDistribution(Instances data) Sets distribution associated with model.
String	NBTreeNoSplit.rightSide(int index, Instances instances) Does nothing because no condition has to be satisfied.
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.
abstract String	ClassifierSplitModel.rightSide(int index, Instances data) Prints left side of condition satisfied by instances in subset index.
String	BinC45Split.rightSide(int index, Instances data) Prints the condition satisfied by instances in a subset.
String	GraftSplit.rightSide(int index, Instances data) Prints condition satisfied by instances in subset index.
String	C45Split.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.
ClassifierSplitModel	BinC45ModelSelection.selectModel(Instances data) Selects C4.5-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	NBTreeModelSelection.selectModel(Instances train, Instances test) Selects NBTree-type split for the given dataset.
ClassifierSplitModel	BinC45ModelSelection.selectModel(Instances train, Instances test) Selects C4.5-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.
void	BinC45Split.setSplitPoint(Instances allInstances) Sets split point to greatest value in given data smaller or equal to old split point.
void	C45Split.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	NBTreeNoSplit.sourceExpression(int index, Instances data) Returns a string containing java source code equivalent to the test made at this node.
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.
abstract String	ClassifierSplitModel.sourceExpression(int index, Instances data)
String	BinC45Split.sourceExpression(int index, Instances data) Returns a string containing java source code equivalent to the test made at this node.
String	GraftSplit.sourceExpression(int index, Instances data) Returns a string containing java source code equivalent to the test made at this node.
String	C45Split.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.
String	GraftSplit.toString(Instances data) method for returning information about this GraftSplit

Constructors in weka.classifiers.trees.j48 with parameters of type Instances

BinC45ModelSelection(int minNoObj, Instances allData)
Initializes the split selection method with the given parameters.

C45ModelSelection(int minNoObj, Instances allData)
Initializes the split selection method with the given parameters.

C45PruneableClassifierTreeG(ModelSelection toSelectLocModel, Instances data, ClassifierSplitModel gs, boolean prune, float cf, boolean raise, boolean isLeaf, boolean relabel, boolean cleanup)
Constructor for pruneable tree structure.

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

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) Method not in use
void	ResidualSplit.buildClassifier(Instances data, double[][] dataZs, double[][] dataWs) Builds the split.
void	LMTNode.buildTree(Instances data, SimpleLinearRegression[][] higherRegressions, double totalInstanceWeight, double higherNumParameters) 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

Instances

Rule.notCoveredInstances() Get the instances not covered by this rule

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

void	YongSplitInfo.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	SplitEvaluate.attrSplit(int attr, Instances inst) Finds the best splitting point for an attribute in the instances
void	M5Base.buildClassifier(Instances data) Generates the classifier.
void	Rule.buildClassifier(Instances data) Generates a single rule or m5 model tree.
void	RuleNode.buildClassifier(Instances data) Build this node (find an attribute and split point)
void	PreConstructedLinearModel.buildClassifier(Instances instances) Builds the classifier.
String	YongSplitInfo.toString(Instances inst) Converts the spliting information to string

Constructors in weka.classifiers.trees.m5 with parameters of type Instances

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

Instances	XMeans.getClusterCenters() Return the centers of the clusters as an Instances object
Instances	SimpleKMeans.getClusterCentroids() Gets the the cluster centroids
Instances	SimpleKMeans.getClusterStandardDevs() Gets the standard deviations of the numeric attributes in each cluster

Methods in weka.clusterers with parameters of type Instances

void	Cobweb.buildClusterer(Instances data) Builds the clusterer.
void	sIB.buildClusterer(Instances data) Generates a clusterer.
abstract void	AbstractClusterer.buildClusterer(Instances data) Generates a clusterer.
void	EM.buildClusterer(Instances data) Generates a clusterer.
void	FarthestFirst.buildClusterer(Instances data) Generates a clusterer.
void	CLOPE.buildClusterer(Instances data) Generate Clustering via CLOPE
void	FilteredClusterer.buildClusterer(Instances data) Build the clusterer on the filtered data.
void	HierarchicalClusterer.buildClusterer(Instances data)
void	MakeDensityBasedClusterer.buildClusterer(Instances data) Builds a clusterer for a set of instances.
void	DBScan.buildClusterer(Instances instances) Generate Clustering via DBScan
void	XMeans.buildClusterer(Instances data) Generates the X-Means clusterer.
void	OPTICS.buildClusterer(Instances instances) Generate Clustering via OPTICS
void	SimpleKMeans.buildClusterer(Instances data) Generates a clusterer.
void	Clusterer.buildClusterer(Instances data) Generates a clusterer.
boolean	XMeans.checkForNominalAttributes(Instances data) Checks for nominal attributes in the dataset.
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.
Database	DBScan.databaseForName(String database_Type, Instances instances) Returns a new Class-Instance of the specified database
Database	OPTICS.databaseForName(String database_Type, Instances instances) Returns a new Class-Instance of the specified database
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.
Instance	XMeans.getNextDebugVectorsInstance(Instances model) Read an instance from debug vectors file.

Uses of Instances in weka.clusterers.forOPTICSAndDBScan.Databases

Methods in weka.clusterers.forOPTICSAndDBScan.Databases that return Instances

Instances	Database.getInstances() Returns the original instances delivered from WEKA
Instances	SequentialDatabase.getInstances() Returns the original instances delivered from WEKA

Constructors in weka.clusterers.forOPTICSAndDBScan.Databases with parameters of type Instances

SequentialDatabase(Instances instances)
Constructs a new sequential database and holds the original instances

Uses of Instances in weka.core

Methods in weka.core that return Instances

Instances	Instance.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	AttributeLocator.getData() returns the underlying data
Instances	TestInstances.getData() returns the current dataset, can be null
Instances	NormalizableDistance.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	Instance.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, double[] weights) 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

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.
boolean	Instances.equalHeaders(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.
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	NormalizableDistance.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

AbstractStringDistanceFunction(Instances data)
Constructor that sets the data

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.

EditDistance(Instances data)

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.

NormalizableDistance(Instances data)
Initializes the distance function and automatically initializes the ranges.

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

Instances	ArffLoader.ArffReader.getData() Returns the data that was read
Instances	DatabaseLoader.getDataSet() Return the full data set in batch mode (header and all intances at once).
Instances	LibSVMLoader.getDataSet() Return the full data set.
Instances	ArffLoader.getDataSet() Return the full data set.
Instances	Loader.getDataSet() Return the full data set.
Instances	C45Loader.getDataSet() Return the full data set.
Instances	XRFFLoader.getDataSet() Return the full data set.
Instances	TextDirectoryLoader.getDataSet() Return the full data set.
Instances	SVMLightLoader.getDataSet() Return the full data set.
abstract Instances	AbstractLoader.getDataSet()
Instances	CSVLoader.getDataSet() Return the full data set.
Instances	ConverterUtils.DataSource.getDataSet() returns the full dataset, can be null in case of an error.
Instances	SerializedInstancesLoader.getDataSet() Return the full data set.
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	DatabaseLoader.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	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
Instances	Loader.getStructure() Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
Instances	C45Loader.getStructure() Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
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.
abstract Instances	AbstractLoader.getStructure()
Instances	CSVLoader.getStructure() Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
Instances	ConverterUtils.DataSource.getStructure() returns the structure of the data.
Instances	SerializedInstancesLoader.getStructure() Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
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

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	LibSVMLoader.getNextInstance(Instances structure) LibSVmLoader is unable to process a data set incrementally.
Instance	ArffLoader.getNextInstance(Instances structure) Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
Instance	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	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	XRFFLoader.getNextInstance(Instances structure) XRFFLoader is unable to process a data set incrementally.
Instance	TextDirectoryLoader.getNextInstance(Instances structure) TextDirectoryLoader is unable to process a data set incrementally.
Instance	SVMLightLoader.getNextInstance(Instances structure) SVMLightLoader is unable to process a data set incrementally.
abstract Instance	AbstractLoader.getNextInstance(Instances structure)
Instance	CSVLoader.getNextInstance(Instances structure) CSVLoader 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.
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	AbstractSaver.setInstances(Instances instances) Sets instances that should be stored.
void	LibSVMSaver.setInstances(Instances instances) Sets instances that should be stored.
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
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

ArffLoader.ArffReader(Reader reader, Instances template, int lines)
Reads the data without header according to the specified template.

ArffLoader.ArffReader(Reader reader, Instances template, int lines, int capacity)
Initializes the reader without reading the header according to the specified template.

ConverterUtils.DataSource(Instances inst)
Initializes the datasource with the given dataset.

Uses of Instances in weka.core.neighboursearch

Methods in weka.core.neighboursearch that return Instances

Instances	NearestNeighbourSearch.getInstances() returns the instances currently set.
Instances	KDTree.kNearestNeighbours(Instance target, int k) Returns the k nearest neighbours of the supplied instance.
Instances	LinearNNSearch.kNearestNeighbours(Instance target, int kNN) Returns k nearest instances in the current neighbourhood to the supplied instance.
abstract Instances	NearestNeighbourSearch.kNearestNeighbours(Instance target, int k) Returns k nearest instances in the current neighbourhood to the supplied instance.
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

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	KDTree.setInstances(Instances instances) Builds the KDTree on the given set of instances.
void	LinearNNSearch.setInstances(Instances insts) Sets the instances comprising the current neighbourhood.
void	NearestNeighbourSearch.setInstances(Instances insts) Sets the instances.
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

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

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	BallSplitter.setInstances(Instances inst) Sets the training instances on which the tree is (or is to be) built.
void	BallTreeConstructor.setInstances(Instances inst) Sets the instances on which the tree is to be built.
void	MiddleOutConstructor.setInstances(Instances insts) Sets the instances on which the tree is to be built.

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

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

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

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

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

static String

PMMLFactory.applyClassifier(PMMLModel model, Instances test)

Constructors in weka.core.pmml with parameters of type Instances

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

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

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

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

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

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

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

Instances	LED24.defineDataFormat() Initializes the format for the dataset produced.
Instances	BayesNet.defineDataFormat() Initializes the format for the dataset produced.
Instances	RandomRBF.defineDataFormat() Initializes the format for the dataset produced.
Instances	RDG1.defineDataFormat() Initializes the format for the dataset produced.
Instances	Agrawal.defineDataFormat() Initializes the format for the dataset produced.
Instances	LED24.generateExamples() Generates all examples of the dataset.
Instances	BayesNet.generateExamples() Generates all examples of the dataset.
Instances	RandomRBF.generateExamples() Generates all examples of the dataset.
Instances	RDG1.generateExamples() Generate 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

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

Instances	Expression.defineDataFormat() Initializes the format for the dataset produced.
Instances	MexicanHat.defineDataFormat() Initializes the format for the dataset produced.
Instances	Expression.generateExamples() Generates all examples of the dataset.
Instances	MexicanHat.generateExamples() Generates all examples of the dataset.

Uses of Instances in weka.datagenerators.clusterers

Methods in weka.datagenerators.clusterers that return Instances

Instances	BIRCHCluster.defineDataFormat() Initializes the format for the dataset produced.
Instances	SubspaceCluster.defineDataFormat() Initializes the format for the dataset produced.
Instances	BIRCHCluster.generateExamples() Generate all examples of the dataset.
Instances	SubspaceCluster.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

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

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

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	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

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
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

Object[]	RegressionSplitEvaluator.getResult(Instances train, Instances test) Gets the results for the supplied train and test datasets.
Object[]	SplitEvaluator.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[]	DensityBasedClustererSplitEvaluator.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	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.
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	DatabaseResultProducer.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

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

Capabilities	Filter.getCapabilities(Instances data) Returns the Capabilities of this filter, customized based on the data.
boolean	AllFilter.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	SimpleFilter.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Filter.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

boolean	NominalToBinary.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	ClassOrder.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Discretize.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

boolean	Resample.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
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	SMOTE.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

Instances

PotentialClassIgnorer.getOutputFormat() Gets the format of the output instances.

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

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	AddValues.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	PKIDiscretize.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	RemoveType.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	ClusterMembership.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	PrincipalComponents.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Obfuscate.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	AddExpression.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	FirstOrder.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	AbstractTimeSeries.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Remove.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	NumericToBinary.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	PotentialClassIgnorer.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	MergeTwoValues.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	NominalToBinary.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Copy.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	StringToNominal.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	NumericTransform.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	TimeSeriesDelta.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	NominalToString.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	MathExpression.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	AddNoise.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	PropositionalToMultiInstance.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	RandomProjection.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	TimeSeriesTranslate.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	ChangeDateFormat.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Standardize.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Normalize.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	MultiInstanceToPropositional.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Reorder.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Center.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	MakeIndicator.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	AddCluster.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	AddID.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	RemoveUseless.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Discretize.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Add.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	StringToWordVector.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	ReplaceMissingValues.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	SwapValues.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	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.
String	ReplaceMissingValues.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

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	RemoveMisclassified.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	ReservoirSample.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	RemovePercentage.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	Randomize.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	NonSparseToSparse.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	RemoveFolds.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	RemoveRange.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	RemoveFrequentValues.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Normalize.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	SparseToNonSparse.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

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

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

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

void	ViewerDialog.setInstances(Instances inst) sets the instances to display
void	InstancesSummaryPanel.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	AttributeSummaryPanel.setInstances(Instances inst) Tells the panel to use a new set of instances.
void	AttributeVisualizationPanel.setInstances(Instances newins) Sets the instances for use
void	AttributeListPanel.setInstances(Instances newInstances) Sets the instances who's attribute names will be displayed.
void	SetInstancesPanel.setInstances(Instances i) Updates the set of instances that is currently held by the panel
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

Instances	ArffTableModel.getInstances() returns the data
Instances	ArffPanel.getInstances() returns the instances of the panel, if none then NULL
Instances	ArffSortedTableModel.getInstances() returns the data

Methods in weka.gui.arffviewer with parameters of type Instances

void	ArffTableModel.setInstances(Instances data) sets the data
void	ArffPanel.setInstances(Instances data) displays the given instances, i.e.
void	ArffSortedTableModel.setInstances(Instances data) sets the data

Constructors in weka.gui.arffviewer with parameters of type Instances

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

Instances	ClassAssigner.getConnectedFormat() Returns the structure of the incoming instances (if any)
Instances	ClassValuePicker.getConnectedFormat() Returns the structure of the incoming instances (if any)
Instances	DataSetEvent.getDataSet() Return the instances of the data set
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	StructureProducer.getStructure(String eventName) Get the structure of the output encapsulated in the named event.
Instances	ClassAssigner.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	ClassValuePicker.getStructure(String eventName)
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

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	ScatterPlotMatrix.setInstances(Instances inst) Set instances for this bean.
void	AttributeSummarizer.setInstances(Instances inst) Set instances for this bean.
void	DataVisualizer.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

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

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.

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

Instances

BoundaryVisualizer.getInstances() Get the training instances

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

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	BoundaryVisualizer.setInstances(Instances inst) Set the training instances
void	RemoteBoundaryVisualizerSubTask.setInstances(Instances i) Set the training data
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

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

Instances	DataGeneratorPanel.getInstances() returns the generated instances, null if the process was cancelled.
Instances	PreprocessPanel.getInstances() Gets the working set of instances.
static Instances	ClassifierPanel.setUpVisualizableInstances(Instances trainInstances) Sets up the structure for the visualizable instances.

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

static void	ClassifierPanel.processClassifierPrediction(Instance toPredict, Classifier classifier, Evaluation eval, Instances plotInstances, FastVector plotShape, FastVector plotSize) Process a classifier's prediction for an instance and update a set of plotting instances and additional plotting info.
void	PreprocessPanel.saveInstancesToFile(AbstractFileSaver saver, Instances inst) saves the data with the specified saver
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	ClustererPanel.setInstances(Instances inst) Tells the panel to use a new set of instances.
void	Explorer.ExplorerPanel.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	PreprocessPanel.setInstances(Instances inst) Tells the panel to use a new base set of instances.
static Instances	ClassifierPanel.setUpVisualizableInstances(Instances trainInstances) Sets up the structure for the visualizable instances.
static PlotData2D	ClustererPanel.setUpVisualizableInstances(Instances testInstances, ClusterEvaluation eval) Sets up the structure for the visualizable instances.

Uses of Instances in weka.gui.streams

Methods in weka.gui.streams that return Instances

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

void	InstanceTable.inputFormat(Instances instanceInfo)
void	InstanceSavePanel.inputFormat(Instances instanceInfo)
void	InstanceCounter.inputFormat(Instances instanceInfo)
void	InstanceViewer.inputFormat(Instances instanceInfo)
boolean	InstanceJoiner.inputFormat(Instances instanceInfo) Sets the format of the input instances.

Uses of Instances in weka.gui.treevisualizer

Methods in weka.gui.treevisualizer that return Instances

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

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 with parameters of type Instances

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	AttributePanel.setInstances(Instances ins) This sets the instances to be drawn into the attribute panel
void	ClassPanel.setInstances(Instances insts) Set the instances.
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.

Constructors in weka.gui.visualize with parameters of type Instances

PlotData2D(Instances insts)
Construct a new PlotData2D using the supplied instances

VisualizePanelEvent(FastVector ar, Instances i, Instances i2, int at1, int at2)
This constructor creates the event with all the parameters set.