Uses of Class
weka.core.Instances

Packages that use Instances

weka.associations
weka.associations.tertius
weka.attributeSelection
weka.classifiers
weka.classifiers.bayes
weka.classifiers.evaluation
weka.classifiers.functions
weka.classifiers.functions.supportVector
weka.classifiers.lazy
weka.classifiers.lazy.kstar
weka.classifiers.meta
weka.classifiers.misc
weka.classifiers.rules
weka.classifiers.rules.part
weka.classifiers.trees
weka.classifiers.trees.adtree
weka.classifiers.trees.j48
weka.classifiers.trees.lmt
weka.classifiers.trees.m5
weka.clusterers
weka.core
weka.core.converters
weka.datagenerators
weka.experiment
weka.filters
weka.filters.supervised.attribute
weka.filters.supervised.instance
weka.filters.unsupervised.attribute
weka.filters.unsupervised.instance
weka.gui
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

Fields in weka.associations declared as Instances

protected Instances	Apriori.m_instances The instances (transactions) to be used for generating the association rules.
private Instances	Tertius.m_instances Instances used for the search.
private Instances	Tertius.m_parts Part instances for individual-based learning.

Methods in weka.associations that return Instances

private Instances

Apriori.removeMissingColumns(Instances instances) Removes columns that are all missing from the data

Methods in weka.associations with parameters of type Instances

private Instances	Apriori.removeMissingColumns(Instances instances) Removes columns that are all missing from the data
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.
private void	Apriori.findLargeItemSets(Instances instances) Method that finds all large itemsets for the given set of instances.
static FastVector	ItemSet.singletons(Instances instances) Converts the header info of the given set of instances into a set of item sets (singletons).
java.lang.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.
private Predicate	Tertius.buildPredicate(Instances instances, Attribute attr, boolean isClass) Build the predicate corresponding to an attribute.
void	Tertius.buildAssociations(Instances instances) Method that launches the search to find the rules with the highest confirmation.
abstract void	Associator.buildAssociations(Instances data) Generates an associator.

Uses of Instances in weka.associations.tertius

Subclasses of Instances in weka.associations.tertius

class

IndividualInstances

Fields in weka.associations.tertius declared as Instances

private Instances

IndividualInstance.m_parts

Methods in weka.associations.tertius that return Instances

Instances

IndividualInstance.getParts()

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

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

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

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.

IndividualInstance(Instance individual, Instances parts)

IndividualInstances(Instances individuals, Instances parts)

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

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

Uses of Instances in weka.attributeSelection

Fields in weka.attributeSelection declared as Instances

private Instances	AttributeSelection.m_trainInstances the instances to select attributes from
private Instances	WrapperSubsetEval.m_trainInstances training instances
private Instances	GainRatioAttributeEval.m_trainInstances The training instances
private Instances	ConsistencySubsetEval.m_trainInstances training instances
private Instances	SymmetricalUncertAttributeEval.m_trainInstances The training instances
private Instances	OneRAttributeEval.m_trainInstances The training instances
private Instances	ForwardSelection.m_Instances
private Instances	RaceSearch.m_Instances
private Instances	ClassifierSubsetEval.m_trainingInstances training instances
private Instances	ClassifierSubsetEval.m_holdOutInstances the instances to test on
private Instances	PrincipalComponents.m_trainInstances The data to transform analyse/transform
private Instances	PrincipalComponents.m_trainCopy Keep a copy for the class attribute (if set)
private Instances	PrincipalComponents.m_transformedFormat The header for the transformed data format
private Instances	PrincipalComponents.m_originalSpaceFormat The header for data transformed back to the original space
private Instances	RankSearch.m_Instances the training instances
private Instances	ReliefFAttributeEval.m_trainInstances The training instances
private Instances	CfsSubsetEval.m_trainInstances The training instances

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	AttributeTransformer.transformedHeader() Returns just the header for the transformed data (ie. an empty set of instances.
Instances	AttributeTransformer.transformedData() Returns the transformed data
Instances	PrincipalComponents.transformedHeader() Returns just the header for the transformed data (ie. an empty set of instances.
Instances	PrincipalComponents.transformedData() Gets the transformed training data.
private Instances	PrincipalComponents.setOutputFormatOriginal() Set up the header for the PC->original space dataset
private Instances	PrincipalComponents.setOutputFormat() Set the format for the transformed data

Methods in weka.attributeSelection with parameters of type Instances

abstract double	HoldOutSubsetEvaluator.evaluateSubset(java.util.BitSet subset, Instances holdOut) Evaluates a subset of attributes with respect to a set of instances.
void	InfoGainAttributeEval.buildEvaluator(Instances data) Initializes an information gain attribute evaluator.
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.
void	AttributeSelection.selectAttributesCVSplit(Instances split) Select attributes for a split of the data.
void	AttributeSelection.SelectAttributes(Instances data) Perform attribute selection on the supplied training instances.
static java.lang.String	AttributeSelection.SelectAttributes(ASEvaluation ASEvaluator, java.lang.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	WrapperSubsetEval.buildEvaluator(Instances data) Generates a attribute evaluator.
void	GainRatioAttributeEval.buildEvaluator(Instances data) Initializes a gain ratio attribute evaluator.
int[]	Ranker.search(ASEvaluation ASEval, Instances data) Kind of a dummy search algorithm.
void	ConsistencySubsetEval.buildEvaluator(Instances data) Generates a attribute evaluator.
java.lang.String	ConsistencySubsetEval.hashKey.toString(Instances t, int maxColWidth) Convert a hash entry to a string
void	SymmetricalUncertAttributeEval.buildEvaluator(Instances data) Initializes a symmetrical uncertainty attribute evaluator.
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.
abstract int[]	ASSearch.search(ASEvaluation ASEvaluator, Instances data) Searches the attribute subset/ranking space.
void	OneRAttributeEval.buildEvaluator(Instances data) Initializes a OneRAttribute attribute evaluator.
int[]	ForwardSelection.search(ASEvaluation ASEval, Instances data) Searches the attribute subset space by forward selection.
int[]	RaceSearch.search(ASEvaluation ASEval, Instances data) Searches the attribute subset space by racing cross validation errors of competing subsets
private int[]	RaceSearch.schemataRace(Instances data, java.util.Random random) Performs a schemata race---a series of races in parallel.
private int[]	RaceSearch.rankRace(Instances data, java.util.Random random) Performs a rank race---race consisting of no attributes, the top ranked attribute, the top two attributes etc.
private int[]	RaceSearch.hillclimbRace(Instances data, java.util.Random random) Performs a hill climbing race---all single attribute changes to a base subset are raced in parallel.
private double[]	RaceSearch.raceSubsets(char[][] raceSets, Instances data, boolean baseSetIncluded, java.util.Random random) Races the leave-one-out cross validation errors of a set of attribute subsets on a set of instances.
void	ClassifierSubsetEval.buildEvaluator(Instances data) Generates a attribute evaluator.
double	ClassifierSubsetEval.evaluateSubset(java.util.BitSet subset, Instances holdOut) Evaluates a subset of attributes with respect to a set of instances.
void	PrincipalComponents.buildEvaluator(Instances data) Initializes principal components and performs the analysis
private void	PrincipalComponents.buildAttributeConstructor(Instances data)
int[]	GeneticSearch.search(ASEvaluation ASEval, Instances data) Searches the attribute subset space using a genetic algorithm.
void	SVMAttributeEval.buildEvaluator(Instances data) Initializes the evaluator.
private int[]	SVMAttributeEval.rankBySVM(int classInd, Instances data) Get SVM-ranked attribute indexes (best to worst) selected for the class attribute indexed by classInd (one-vs-all).
int[]	RankSearch.search(ASEvaluation ASEval, Instances data) Ranks attributes using the specified attribute evaluator and then searches the ranking using the supplied subset evaluator.
void	ReliefFAttributeEval.buildEvaluator(Instances data) Initializes a ReliefF attribute evaluator.
abstract void	ASEvaluation.buildEvaluator(Instances data) Generates a attribute evaluator.
int[]	RandomSearch.search(ASEvaluation ASEval, Instances data) Searches the attribute subset space randomly.
void	ChiSquaredAttributeEval.buildEvaluator(Instances data) Initializes a chi-squared attribute evaluator.
void	CfsSubsetEval.buildEvaluator(Instances data) Generates a attribute evaluator.

Uses of Instances in weka.classifiers

Methods in weka.classifiers that return Instances

protected Instances	CheckClassifier.makeTestDataset(int seed, int numInstances, int numNominal, int numNumeric, int numClasses, boolean numericClass) Make a simple set of instances, which can later be modified for use in specific tests.
Instances	CostMatrix.applyCostMatrix(Instances data, java.util.Random random) Applies the cost matrix to a set of instances.

Methods in weka.classifiers with parameters of type Instances

protected boolean	CheckClassifier.testWRTZeroR(Classifier classifier, Evaluation evaluation, Instances train, Instances test) Determine whether the scheme performs worse than ZeroR during testing
protected void	CheckClassifier.compareDatasets(Instances data1, Instances data2) Compare two datasets to see if they differ.
protected void	CheckClassifier.addMissing(Instances data, int level, boolean predictorMissing, boolean classMissing) Add missing values to a dataset.
void	IterativeClassifier.initClassifier(Instances instances) Inits an iterative classifier.
Instances	CostMatrix.applyCostMatrix(Instances data, java.util.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, java.util.Random random) Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
void	Evaluation.crossValidateModel(java.lang.String classifierString, Instances data, int numFolds, java.lang.String[] options, java.util.Random random) Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
void	Evaluation.evaluateModel(Classifier classifier, Instances data) Evaluates the classifier on a given set of instances.
void	Evaluation.setPriors(Instances train) Sets the class prior probabilities
protected static java.lang.String	Evaluation.printClassifications(Classifier classifier, Instances train, java.lang.String testFileName, int classIndex, Range attributesToOutput) Prints the predictions for the given dataset into a String variable.

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

private Instances	ComplementNaiveBayes.header The instances header that'll be used in toString
Instances	BayesNet.m_Instances The dataset header for the purposes of printing out a semi-intelligible model
private Instances	AODE.m_Instances The dataset
(package private) Instances	NaiveBayesMultinomial.headerInfo
protected Instances	NaiveBayes.m_Instances The dataset header for the purposes of printing out a semi-intelligible model
private Instances	NaiveBayesSimple.m_Instances The instances used for training.

Methods in weka.classifiers.bayes 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.DeleteLastParent(Instances _Instances) Delete last added parent from parent set and update internals (specifically the cardinality of the parent set)
static VaryNode	ADNode.MakeVaryNode(int iNode, FastVector nRecords, Instances instances) create sub tree
static ADNode	ADNode.MakeADTree(int iNode, FastVector nRecords, Instances instances) create sub tree
static ADNode	ADNode.MakeADTree(Instances instances) create AD tree from set of instances
void	ComplementNaiveBayes.buildClassifier(Instances instances) Generates the classifier.
void	BayesNet.buildClassifier(Instances instances) Generates the classifier.
private double	BayesNet.CalcNodeScoreADTree(int nNode, Instances instances) helper function for CalcNodeScore above using the ADTree data structure
private double	BayesNet.CalcNodeScore(int nNode, Instances instances)
protected double	BayesNet.CalcScoreOfCounts(int[] nCounts, int nCardinality, int numValues, Instances instances) utility function used by CalcScore and CalcNodeScore to determine the score based on observed frequencies.
protected double	BayesNet.CalcScoreOfCounts2(int[][] nCounts, int nCardinality, int numValues, Instances instances)
void	AODE.buildClassifier(Instances instances) Generates the classifier.
void	NaiveBayesMultinomial.buildClassifier(Instances instances) Generates the classifier.
void	NaiveBayes.buildClassifier(Instances instances) Generates the classifier.
void	NaiveBayesSimple.buildClassifier(Instances instances) Generates the classifier.

Uses of Instances in weka.classifiers.evaluation

Methods in weka.classifiers.evaluation that return Instances

Instances	ThresholdCurve.getCurve(FastVector predictions) Calculates the performance stats for the default 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.
private Instances	ThresholdCurve.makeHeader()
Instances	CostCurve.getCurve(FastVector predictions) Calculates the performance stats for the default class and return results 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.
private Instances	CostCurve.makeHeader()
Instances	MarginCurve.getCurve(FastVector predictions) Calculates the cumulative margin distribution for the set of predictions, returning the result as a set of Instances.
private Instances	MarginCurve.makeHeader() Creates an Instances object with the attributes we will be calculating.

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.
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.
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 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.
static int	ThresholdCurve.getThresholdInstance(Instances tcurve, double threshold) Gets the index of the instance with the closest threshold value to the desired target

Uses of Instances in weka.classifiers.functions

Fields in weka.classifiers.functions declared as Instances

private Instances	MultilayerPerceptron.m_instances The training instances.
private Instances	SMOreg.m_data The training data.
private Instances	LeastMedSq.m_Data
private Instances	LeastMedSq.m_RLSData
private Instances	LeastMedSq.m_SubSample
private Instances	SMO.BinarySMO.m_data The training data.
private Instances	VotedPerceptron.m_Train The training instances
private Instances	Winnow.m_Train The training instances
private Instances	LinearRegression.m_TransformedData Variable for storing transformed training data.
(package private) Instances	PaceRegression.m_Model The model used

Methods in weka.classifiers.functions that return Instances

private Instances

MultilayerPerceptron.setClassType(Instances inst) This function sets what the m_numeric flag to represent the passed class it also performs the normalization of the attributes if applicable and sets up the info to normalize the class.

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

private Instances	MultilayerPerceptron.setClassType(Instances inst) This function sets what the m_numeric flag to represent the passed class it also performs the normalization of the attributes if applicable and sets up the info to normalize the class.
void	MultilayerPerceptron.buildClassifier(Instances i) Call this function to build and train a neural network for the training data provided.
void	SMOreg.buildClassifier(Instances insts) Method for building the classifier.
void	LeastMedSq.buildClassifier(Instances data) Build lms regression
private void	LeastMedSq.cleanUpData(Instances data) Cleans up data
private void	LeastMedSq.selectSubSample(Instances data) Produces a random sample from m_Data in m_SubSample
private java.lang.String	LeastMedSq.selectIndices(Instances data) Returns a string suitable for passing to RemoveRange consisting of m_samplesize indices.
private void	SMO.BinarySMO.fitLogistic(Instances insts, int cl1, int cl2, int numFolds, java.util.Random random) Fits logistic regression model to SVM outputs analogue to John Platt's method.
private void	SMO.BinarySMO.buildClassifier(Instances insts, int cl1, int cl2, boolean fitLogistic, int numFolds, int randomSeed) Method for building the binary classifier.
void	Logistic.buildClassifier(Instances train) Builds the classifier
void	SimpleLogistic.buildClassifier(Instances data) Builds the logistic regression using LogitBoost.
void	VotedPerceptron.buildClassifier(Instances insts) Builds the ensemble of perceptrons.
void	Winnow.buildClassifier(Instances insts) Builds the classifier
void	SimpleLinearRegression.buildClassifier(Instances insts) Builds a simple linear regression model given the supplied training data.
void	LinearRegression.buildClassifier(Instances data) Builds a regression model for the given data.
void	PaceRegression.buildClassifier(Instances data) Builds a pace regression model for the given data.
boolean	PaceRegression.checkForMissing(Instances data) Checks if instances have a missing value.
boolean	PaceRegression.checkForMissing(Instance instance, Instances model) Checks if an instance has a missing value.
boolean	PaceRegression.checkForNonBinary(Instances data) Checks if any of the nominal attributes is non-binary.
private double[][]	PaceRegression.getTransformedDataMatrix(Instances data, int classIndex) Transforms dataset into a two-dimensional array.
void	SMO.buildClassifier(Instances insts) Method for building the classifier.
void	RBFNetwork.buildClassifier(Instances instances) Builds the classifier

Uses of Instances in weka.classifiers.functions.supportVector

Fields in weka.classifiers.functions.supportVector declared as Instances

(package private) Instances

Kernel.m_data The dataset

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.

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

PolyKernel(Instances dataset, int cacheSize, double exponent, boolean lowerOrder)
Creates a new PolyKernel instance.

Uses of Instances in weka.classifiers.lazy

Fields in weka.classifiers.lazy declared as Instances

protected Instances	LWL.m_Train The training instances used for classification.
private Instances	IB1.m_Train The training instances used for classification.
protected Instances	KStar.m_Train The training instances used for classification.
protected Instances	LBR.m_Instances The set of instances used for current training.
protected Instances	IBk.m_Train The training instances used for classification.

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

void	LWL.buildClassifier(Instances instances) Generates the classifier.
void	IB1.buildClassifier(Instances instances) Generates the classifier.
void	KStar.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	IBk.buildClassifier(Instances instances) Generates the classifier.

Uses of Instances in weka.classifiers.lazy.kstar

Fields in weka.classifiers.lazy.kstar declared as Instances

protected Instances	KStarNumericAttribute.m_TrainSet The training instances used for classification.
protected Instances	KStarNominalAttribute.m_TrainSet The training instances used for classification.

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

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

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

Uses of Instances in weka.classifiers.meta

Fields in weka.classifiers.meta declared as Instances

protected Instances	LogitBoost.m_NumericClassData Dummy dataset with a numeric class
protected Instances	Stacking.m_MetaFormat Format for meta data
protected Instances	Stacking.m_BaseFormat Format for base data
protected Instances	AttributeSelectedClassifier.m_ReducedHeader The header of the dimensionally reduced data
protected Instances	FilteredClassifier.m_FilteredInstances The instance structure of the filtered instances
private Instances	MultiClassClassifier.m_TwoClassDataset A transformed dataset header used by the 1-against-1 method
protected Instances	RacedIncrementalLogitBoost.m_NumericClassData Dummy dataset with a numeric class
protected Instances	RacedIncrementalLogitBoost.m_validationSet The instances used for validation
protected Instances	RacedIncrementalLogitBoost.m_currentSet The instances currently in memory for training
(package private) Instances	TreeBasedMultiClassClassifier.m_Data

Methods in weka.classifiers.meta that return Instances

protected Instances	LogitBoost.selectWeightQuantile(Instances data, double quantile) Select only instances with weights that contribute to the specified quantile of the weight distribution
protected Instances	Stacking.metaFormat(Instances instances) Makes the format for the level-1 data.
protected Instances	Grading.metaFormat(Instances instances) Makes the format for the level-1 data.
private Instances	AdditiveRegression.residualReplace(Instances data, Classifier c, boolean useShrinkage) Replace the class values of the instances from the current iteration with residuals ater predicting with the supplied classifier.
Instances	Bagging.resampleWithWeights(Instances data, java.util.Random random, boolean[] sampled) Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
protected Instances	Decorate.generateArtificialData(int artSize, Instances data) Generate artificial training examples.
protected Instances	AdaBoostM1.selectWeightQuantile(Instances data, double quantile) Select only instances with weights that contribute to the specified quantile of the weight distribution

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

protected Instances	LogitBoost.selectWeightQuantile(Instances data, double quantile) Select only instances with weights that contribute to the specified quantile of the weight distribution
void	LogitBoost.buildClassifier(Instances data) Builds the boosted classifier
private void	LogitBoost.performIteration(double[][] trainYs, double[][] trainFs, double[][] probs, Instances data, double origSumOfWeights) Performs one boosting iteration.
void	Stacking.buildClassifier(Instances data) Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
protected void	Stacking.generateMetaLevel(Instances newData, java.util.Random random) Generates the meta data
protected Instances	Stacking.metaFormat(Instances instances) Makes the format for the level-1 data.
private void	HND.buildLevelwiseClassifier(Instances data) Build levelwise NDs with respect to the specified hierarchy of classes.
void	HND.buildClassifier(Instances data) Builds the classifier for the given training data.
void	AttributeSelectedClassifier.buildClassifier(Instances data) Build the classifier on the dimensionally reduced data.
void	Vote.buildClassifier(Instances data) Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
void	FilteredClassifier.buildClassifier(Instances data) Build the classifier on the filtered data.
void	CostSensitiveClassifier.buildClassifier(Instances data) Builds the model of the base learner.
void	MultiScheme.buildClassifier(Instances data) Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
void	RandomCommittee.buildClassifier(Instances data) Builds the committee of randomizable classifiers.
protected void	CVParameterSelection.findParamsByCrossValidation(int depth, Instances trainData, java.util.Random random) Finds the best parameter combination.
void	CVParameterSelection.buildClassifier(Instances instances) Generates the classifier.
void	MultiClassClassifier.buildClassifier(Instances insts) Builds the classifiers.
protected void	Grading.generateMetaLevel(Instances newData, java.util.Random random) Generates the meta data
protected Instances	Grading.metaFormat(Instances instances) Makes the format for the level-1 data.
void	ND.buildClassifier(Instances data) Builds the classifier.
void	ND.buildClassifierForNode(ND.NDTree node, Instances data) Builds the classifier for one node.
protected Classifier[]	RacedIncrementalLogitBoost.Committee.boost(Instances data)
void	ClassificationViaRegression.buildClassifier(Instances insts) Builds the classifiers.
void	RegressionByDiscretization.buildClassifier(Instances instances) Generates the classifier.
protected void	StackingC.generateMetaLevel(Instances newData, java.util.Random random) Method that builds meta level.
void	MetaCost.buildClassifier(Instances data) Builds the model of the base learner.
void	AdditiveRegression.buildClassifier(Instances data) Build the classifier on the supplied data
private Instances	AdditiveRegression.residualReplace(Instances data, Classifier c, boolean useShrinkage) Replace the class values of the instances from the current iteration with residuals ater predicting with the supplied classifier.
void	END.buildClassifier(Instances data) Builds the committee of randomizable classifiers.
void	RacedIncrementalLogitBoost.buildClassifier(Instances data) Builds the classifier.
Instances	Bagging.resampleWithWeights(Instances data, java.util.Random random, boolean[] sampled) Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
void	Bagging.buildClassifier(Instances data) Bagging method.
void	OrdinalClassClassifier.buildClassifier(Instances insts) Builds the classifiers.
void	MultiBoostAB.buildClassifier(Instances training) Method for building this classifier.
protected void	MultiBoostAB.setWeights(Instances training, double reweight) Sets the weights for the next iteration.
void	Decorate.buildClassifier(Instances data) Build Decorate classifier
protected void	Decorate.computeStats(Instances data) Compute and store statistics required for generating artificial data.
protected Instances	Decorate.generateArtificialData(int artSize, Instances data) Generate artificial training examples.
protected void	Decorate.labelData(Instances artData) Labels the artificially generated data.
protected void	Decorate.removeInstances(Instances data, int numRemove) Removes a specified number of instances from the given set of instances.
protected void	Decorate.addInstances(Instances data, Instances newData) Add new instances to the given set of instances.
protected double	Decorate.computeError(Instances data) Computes the error in classification on the given data.
protected Instances	AdaBoostM1.selectWeightQuantile(Instances data, double quantile) Select only instances with weights that contribute to the specified quantile of the weight distribution
void	AdaBoostM1.buildClassifier(Instances data) Boosting method.
protected void	AdaBoostM1.buildClassifierUsingResampling(Instances data) Boosting method.
protected void	AdaBoostM1.setWeights(Instances training, double reweight) Sets the weights for the next iteration.
protected void	AdaBoostM1.buildClassifierWithWeights(Instances data) Boosting method.
private void	TreeBasedMultiClassClassifier.generateClassifierForNode(Instances data, Range classes, java.util.Random rand, Classifier classifier, boolean random, boolean ordinal) Generates a classifier for the current node and proceeds recursively
void	TreeBasedMultiClassClassifier.buildClassifier(Instances data) Builds tree recursively
protected FastVector	ThresholdSelector.getPredictions(Instances instances, int mode, int numFolds) Collects the classifier predictions using the specified evaluation method.
void	ThresholdSelector.buildClassifier(Instances instances) Generates the classifier.
private boolean	ThresholdSelector.checkForInstance(Instances data) Checks whether instance of designated class is in subset.

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

MultiClassClassifier.RandomCode(int numClasses, int numCodes, Instances data)

Uses of Instances in weka.classifiers.misc

Fields in weka.classifiers.misc declared as Instances

protected Instances	VFI.m_Instances The training data
protected Instances	HyperPipes.m_Instances The structure of the training data

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

void	VFI.buildClassifier(Instances instances) Generates the classifier.
void	FLR.buildClassifier(Instances data) Builds the FLR Classifier
void	FLR.setBounds(Instances data) Sets the metric space from the training set using the min-max stats, in case -B option is not used.
void	HyperPipes.buildClassifier(Instances instances) Generates the classifier.

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

HyperPipes.HyperPipe(Instances instances)
Creates the HyperPipe as the n-dimensional parallel-piped with minimum volume containing all the points in pointSet.

Uses of Instances in weka.classifiers.rules

Subclasses of Instances in weka.classifiers.rules

private class

NNge.Exemplar Implements Exemplar as used by NNge : parallel axis hyperrectangle.

Fields in weka.classifiers.rules declared as Instances

private Instances	NNge.m_Train An empty instances to keep the headers, the classIndex, etc...
private Instances	Prism.PrismRule.m_instances The instance
private Instances	RuleStats.m_Data The data on which the stats calculation is based
private Instances	DecisionTable.m_theInstances Holds the training instances

Methods in weka.classifiers.rules that return Instances

abstract Instances[]	ConjunctiveRule.Antd.splitData(Instances data, double defInfo)
private Instances[]	ConjunctiveRule.computeInfoGain(Instances instances, double defInfo, ConjunctiveRule.Antd antd) Compute the best information gain for the specified antecedent
Instances[]	ConjunctiveRule.NumericAntd.splitData(Instances insts, double defInfo) Implements the splitData function.
Instances[]	ConjunctiveRule.NominalAntd.splitData(Instances data, double defInfo) Implements the splitData function.
Instances	Prism.PrismRule.coveredBy(Instances data) Returns the set of instances that are covered by this rule.
Instances	Prism.PrismRule.notCoveredBy(Instances data) Returns the set of instances that are not covered by this rule.
Instances	RuleStats.getData() Get the data of the stats
Instances[]	RuleStats.getFiltered(int index) Get the data after filtering the given rule
private Instances[]	RuleStats.computeSimpleStats(int index, Instances insts, double[] stats, double[] dist) Find all the instances in the dataset covered/not covered by the rule in given index, and the correponding simple statistics and predicted class distributions are stored in the given double array, which can be obtained by getSimpleStats() and getDistributions().
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, java.util.Random rand) Stratify the given data into the given number of bags based on the class values.
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
private Instances	JRip.RipperRule.computeInfoGain(Instances instances, double defAcRt, JRip.Antd antd) Compute the best information gain for the specified antecedent
Instances[]	Ridor.RidorRule.coveredByRule(Instances insts) Find all the instances in the dataset covered by this rule.
private Instances	Ridor.RidorRule.computeInfoGain(Instances instances, double defAcRt, Ridor.Antd antd) Compute the best information gain for the specified antecedent
abstract Instances[]	Ridor.Antd.splitData(Instances data, double defAcRt, double cla)
Instances[]	Ridor.NumericAntd.splitData(Instances insts, double defAcRt, double cl) Implements the splitData function.
protected Instances	JRip.rulesetForOneClass(double expFPRate, Instances data, double classIndex, double defDL) Build a ruleset for the given class according to the given data
abstract Instances[]	JRip.Antd.splitData(Instances data, double defAcRt, double cla)
Instances[]	JRip.NumericAntd.splitData(Instances insts, double defAcRt, double cl) Implements the splitData function.
Instances[]	JRip.NominalAntd.splitData(Instances data, double defAcRt, double cl) Implements the splitData function.
Instances[]	Ridor.NominalAntd.splitData(Instances data, double defAcRt, double cl) Implements the splitData function.
private Instances	Ridor.Ridor_node.append(Instances data1, Instances data2) Private function to combine two data
private Instances[][]	Ridor.Ridor_node.divide(Ridor.RidorRule rule, Instances[] dataByClass) Builds an array of data according to their true class label Each bag of data is filtered through the rule specified and is totally covered by this rule.

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

void	NNge.buildClassifier(Instances data) Generates a classifier.
abstract Instances[]	ConjunctiveRule.Antd.splitData(Instances data, double defInfo)
void	ConjunctiveRule.buildClassifier(Instances instances) Builds a single rule learner with REP dealing with nominal classes or numeric classes.
private void	ConjunctiveRule.grow(Instances data) Build one rule using the growing data
private Instances[]	ConjunctiveRule.computeInfoGain(Instances instances, double defInfo, ConjunctiveRule.Antd antd) Compute the best information gain for the specified antecedent
private void	ConjunctiveRule.prune(Instances pruneData) Prune the rule using the pruning data.
private double	ConjunctiveRule.computeAccu(Instances data, int clas) Private function to compute number of accurate instances based on the specified predicted class
private double	ConjunctiveRule.meanSquaredError(Instances data, double mean) Private function to compute the squared error of the specified data and the specified mean
Instances[]	ConjunctiveRule.NumericAntd.splitData(Instances insts, double defInfo) Implements the splitData function.
Instances[]	ConjunctiveRule.NominalAntd.splitData(Instances data, double defInfo) Implements the splitData function.
Instances	Prism.PrismRule.coveredBy(Instances data) Returns the set of instances that are covered by this rule.
Instances	Prism.PrismRule.notCoveredBy(Instances data) Returns the set of instances that are not covered by this rule.
void	RuleStats.setData(Instances data) Set the data of the stats, overwriting the old one if any
static double	RuleStats.numAllConditions(Instances data) Compute the number of all possible conditions that could appear in a rule of a given data.
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...
private Instances[]	RuleStats.computeSimpleStats(int index, Instances insts, double[] stats, double[] dist) Find all the instances in the dataset covered/not covered by the rule in given index, and the correponding simple statistics and predicted class distributions are stored in the given double array, which can be obtained by getSimpleStats() and getDistributions().
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, java.util.Random rand) Stratify the given data into the given number of bags based on the class values.
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
private double	JRip.RipperRule.computeDefAccu(Instances data) Private function to compute default number of accurate instances in the specified data for the consequent of the rule
void	JRip.RipperRule.grow(Instances data) Build one rule using the growing data
private Instances	JRip.RipperRule.computeInfoGain(Instances instances, double defAcRt, JRip.Antd antd) Compute the best information gain for the specified antecedent
void	JRip.RipperRule.prune(Instances pruneData, boolean useWhole) Prune all the possible final sequences of the rule using the pruning data.
void	OneR.buildClassifier(Instances instances) Generates the classifier.
OneR.OneRRule	OneR.newRule(Attribute attr, Instances data) Create a rule branching on this attribute.
OneR.OneRRule	OneR.newNominalRule(Attribute attr, Instances data, int[] missingValueCounts) Create a rule branching on this nominal attribute.
OneR.OneRRule	OneR.newNumericRule(Attribute attr, Instances data, int[] missingValueCounts) Create a rule branching on this numeric attribute
abstract void	Rule.grow(Instances data) Build this rule
void	Prism.buildClassifier(Instances data) Generates the classifier.
private static boolean	Prism.contains(Instances E, int C) Does E contain any examples in the class C?
void	ZeroR.buildClassifier(Instances instances) Generates the classifier.
void	Ridor.RidorRule.buildClassifier(Instances instances) Builds a single rule learner with REP dealing with 2 classes.
Instances[]	Ridor.RidorRule.coveredByRule(Instances insts) Find all the instances in the dataset covered by this rule.
private void	Ridor.RidorRule.grow(Instances data) Build one rule using the growing data
private Instances	Ridor.RidorRule.computeInfoGain(Instances instances, double defAcRt, Ridor.Antd antd) Compute the best information gain for the specified antecedent
private void	Ridor.RidorRule.prune(Instances pruneData) Prune the rule using the pruning data and update the worth parameters for this rule The accuracy rate is used to prune the rule.
private double	Ridor.RidorRule.computeDefAccu(Instances data) Private function to compute default number of accurate instances in the specified data for m_Class
abstract Instances[]	Ridor.Antd.splitData(Instances data, double defAcRt, double cla)
Instances[]	Ridor.NumericAntd.splitData(Instances insts, double defAcRt, double cl) Implements the splitData function.
void	JRip.buildClassifier(Instances instances) Builds Ripper in the order of class frequencies.
protected Instances	JRip.rulesetForOneClass(double expFPRate, Instances data, double classIndex, double defDL) Build a ruleset for the given class according to the given data
abstract Instances[]	JRip.Antd.splitData(Instances data, double defAcRt, double cla)
Instances[]	JRip.NumericAntd.splitData(Instances insts, double defAcRt, double cl) Implements the splitData function.
Instances[]	JRip.NominalAntd.splitData(Instances data, double defAcRt, double cl) Implements the splitData function.
java.lang.String	DecisionTable.hashKey.toString(Instances t, int maxColWidth) Convert a hash entry to a string
Instances[]	Ridor.NominalAntd.splitData(Instances data, double defAcRt, double cl) Implements the splitData function.
(package private) double	DecisionTable.classifyFoldCV(Instances fold, int[] fs) Calculates the accuracy on a test fold for internal cross validation of feature sets
void	DecisionTable.buildClassifier(Instances data) Generates the classifier.
void	PART.buildClassifier(Instances instances) Generates the classifier.
void	Ridor.Ridor_node.findRules(Instances[] dataByClass, int lvl) Builds a ripple-down manner rule learner.
private double	Ridor.Ridor_node.buildRuleset(Instances insts, double classCount, java.util.Vector ruleset) Private function to build a rule set and return the weighted avg of accuracy rate of rules in the set.
private Instances	Ridor.Ridor_node.append(Instances data1, Instances data2) Private function to combine two data
private Instances[][]	Ridor.Ridor_node.divide(Ridor.RidorRule rule, Instances[] dataByClass) Builds an array of data according to their true class label Each bag of data is filtered through the rule specified and is totally covered by this rule.
void	Ridor.buildClassifier(Instances instances) Builds a ripple-down manner rule learner.

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

NNge.Exemplar(NNge nnge, Instances inst, int size, double classV)
Build a new empty Exemplar

Prism.PrismRule(Instances data, int cl)
Constructor that takes instances and the classification.

OneR.OneRRule(Instances data, Attribute attribute)
Constructor for nominal attribute.

OneR.OneRRule(Instances data, Attribute attribute, int nBreaks)
Constructor for numeric attribute.

RuleStats(Instances data, FastVector rules)
Constructor that provides ruleset and data

Uses of Instances in weka.classifiers.rules.part

Fields in weka.classifiers.rules.part declared as Instances

protected Instances

ClassifierDecList.m_train The training instances.

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

void	C45PruneableDecList.buildDecList(Instances data, boolean leaf) Builds the partial tree without hold out set.
protected ClassifierDecList	C45PruneableDecList.getNewDecList(Instances data, boolean leaf) Returns a newly created tree.
void	MakeDecList.buildClassifier(Instances data) Builds dec list.
void	PruneableDecList.buildRule(Instances train, Instances test) Method for building a pruned partial tree.
void	PruneableDecList.buildDecList(Instances train, Instances test, boolean leaf) Builds the partial tree with hold out set
protected ClassifierDecList	PruneableDecList.getNewDecList(Instances train, Instances test, boolean leaf) Returns a newly created tree.
void	ClassifierDecList.buildRule(Instances data) Method for building a pruned partial tree.
void	ClassifierDecList.buildDecList(Instances data, boolean leaf) Builds the partial tree without hold out set.
void	ClassifierDecList.cleanup(Instances justHeaderInfo) Cleanup in order to save memory.
protected ClassifierDecList	ClassifierDecList.getNewDecList(Instances train, boolean leaf) Returns a newly created tree.

Uses of Instances in weka.classifiers.trees

Fields in weka.classifiers.trees declared as Instances

protected Instances	REPTree.Tree.m_Info The header information (for printing the tree).
protected Instances	ADTree.m_trainInstances The instances used to train the tree
protected Instances	ADTree.m_search_bestPathPosInstances The positive instances that apply to the best path found so far
protected Instances	ADTree.m_search_bestPathNegInstances The negative instances that apply to the best path found so far
protected Instances	RandomTree.m_Info The header information.
Instances	UserClassifier.TreeClass.m_training
private Instances	DecisionStump.m_Instances The instances used for training.

Methods in weka.classifiers.trees that return Instances

private Instances[]

Id3.splitData(Instances data, Attribute att) Splits a dataset according to the values of a nominal attribute.

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

void	LMT.buildClassifier(Instances data) Builds the classifier.
void	REPTree.buildClassifier(Instances data) Builds classifier.
protected void	REPTree.Tree.buildTree(int[][] sortedIndices, double[][] weights, Instances data, double totalWeight, double[] classProbs, Instances header, double minNum, double minVariance, int depth, int maxDepth) Recursively generates a tree.
protected void	REPTree.Tree.splitData(int[][][] subsetIndices, double[][][] subsetWeights, int att, double splitPoint, int[][] sortedIndices, double[][] weights, Instances data) Splits instances into subsets.
protected double	REPTree.Tree.distribution(double[][] props, double[][][] dists, int att, int[] sortedIndices, double[] weights, double[][] subsetWeights, Instances data) Computes class distribution for an attribute.
protected double	REPTree.Tree.numericDistribution(double[][] props, double[][][] dists, int att, int[] sortedIndices, double[] weights, double[][] subsetWeights, Instances data, double[] vals) Computes class distribution for an attribute.
protected void	REPTree.Tree.insertHoldOutSet(Instances data) Inserts hold-out set into tree.
protected void	REPTree.Tree.backfitHoldOutSet(Instances data) Inserts hold-out set into tree.
void	ADTree.initClassifier(Instances instances) Sets up the tree ready to be trained, using two-class optimized method.
private void	ADTree.searchForBestTestSingle(PredictionNode currentNode, Instances posInstances, Instances negInstances) Recursive function that carries out search for the best test (splitter) to add to this part of the tree, by aiming to minimize the Z value.
private void	ADTree.goDownAllPathsSingle(PredictionNode currentNode, Instances posInstances, Instances negInstances) Continues single (two-class optimized) search by investigating every node in the subtree under currentNode.
private void	ADTree.goDownHeaviestPathSingle(PredictionNode currentNode, Instances posInstances, Instances negInstances) Continues single (two-class optimized) search by investigating only the path with the most heavily weighted instances.
private void	ADTree.goDownZpurePathSingle(PredictionNode currentNode, Instances posInstances, Instances negInstances) Continues single (two-class optimized) search by investigating only the path with the best Z-pure value at each branch.
private void	ADTree.goDownRandomPathSingle(PredictionNode currentNode, Instances posInstances, Instances negInstances) Continues single (two-class optimized) search by investigating a random path.
private void	ADTree.evaluateNominalSplitSingle(int attIndex, PredictionNode currentNode, Instances posInstances, Instances negInstances) Investigates the option of introducing a nominal split under currentNode.
private void	ADTree.evaluateNumericSplitSingle(int attIndex, PredictionNode currentNode, Instances posInstances, Instances negInstances, Instances allInstances) Investigates the option of introducing a two-way numeric split under currentNode.
private double	ADTree.calcPredictionValue(Instances posInstances, Instances negInstances) Calculates the prediction value used for a particular set of instances.
private double	ADTree.calcZpure(Instances posInstances, Instances negInstances) Calculates the Z-pure value for a particular set of instances.
private void	ADTree.updateWeights(Instances posInstances, Instances negInstances, double predictionValue) Updates the weights of instances that are influenced by a new prediction value.
private double[]	ADTree.findLowestZNominalSplit(Instances posInstances, Instances negInstances, int attIndex) Finds the nominal attribute value to split on that results in the lowest Z-value.
private double[]	ADTree.attributeValueWeights(Instances instances, int attIndex) Simultanously sum the weights of all attribute values for all instances.
private double[]	ADTree.findLowestZNumericSplit(Instances instances, int attIndex) Finds the numeric split-point that results in the lowest Z-value.
protected void	ADTree.graphTraverse(PredictionNode currentNode, java.lang.StringBuffer text, int splitOrder, int predOrder, Instances instances) Traverses the tree, graphing each node.
void	ADTree.buildClassifier(Instances instances) Builds a classifier for a set of instances.
void	RandomForest.buildClassifier(Instances data) Builds a classifier for a set of instances.
void	J48.buildClassifier(Instances instances) Generates the classifier.
void	RandomTree.buildClassifier(Instances data) Builds classifier.
protected void	RandomTree.buildTree(int[][] sortedIndices, double[][] weights, Instances data, double[] classProbs, Instances header, double minNum, boolean debug, int[] attIndicesWindow, java.util.Random random) Recursively generates a tree.
protected void	RandomTree.splitData(int[][][] subsetIndices, double[][][] subsetWeights, int att, double splitPoint, int[][] sortedIndices, double[][] weights, double[][] dist, Instances data) Splits instances into subsets.
protected double	RandomTree.distribution(double[][] props, double[][][] dists, int att, int[] sortedIndices, double[] weights, Instances data) Computes class distribution for an attribute.
void	Id3.buildClassifier(Instances data) Builds Id3 decision tree classifier.
private void	Id3.makeTree(Instances data) Method building Id3 tree.
private double	Id3.computeInfoGain(Instances data, Attribute att) Computes information gain for an attribute.
private double	Id3.computeEntropy(Instances data) Computes the entropy of a dataset.
private Instances[]	Id3.splitData(Instances data, Attribute att) Splits a dataset according to the values of a nominal attribute.
void	UserClassifier.buildClassifier(Instances i) Call this function to build a decision tree for the training data provided.
void	DecisionStump.buildClassifier(Instances instances) Generates the classifier.

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

UserClassifier.TreeClass(FastVector r, int a1, int a2, int id, double w, Instances i, UserClassifier.TreeClass p)
Constructs a TreeClass node with all the important information.

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

abstract ReferenceInstances	Splitter.instancesDownBranch(int branch, Instances sourceInstances) Gets the subset of instances that apply to a particluar branch of the split.
abstract java.lang.String	Splitter.attributeString(Instances dataset) Gets the string describing the attributes the split depends on.
abstract java.lang.String	Splitter.comparisonString(int branchNum, Instances dataset) Gets the string describing the comparision the split depends on for a particular branch. i.e. the right hand side of the description of the split.
ReferenceInstances	TwoWayNominalSplit.instancesDownBranch(int branch, Instances instances) Gets the subset of instances that apply to a particluar branch of the split.
java.lang.String	TwoWayNominalSplit.attributeString(Instances dataset) Gets the string describing the attributes the split depends on.
java.lang.String	TwoWayNominalSplit.comparisonString(int branchNum, Instances dataset) Gets the string describing the comparision the split depends on for a particular branch. i.e. the right hand side of the description of the split.
ReferenceInstances	TwoWayNumericSplit.instancesDownBranch(int branch, Instances instances) Gets the subset of instances that apply to a particluar branch of the split.
java.lang.String	TwoWayNumericSplit.attributeString(Instances dataset) Gets the string describing the attributes the split depends on.
java.lang.String	TwoWayNumericSplit.comparisonString(int branchNum, Instances dataset) Gets the string describing the comparision the split depends on for a particular branch. i.e. the right hand side of the description 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.j48

Fields in weka.classifiers.trees.j48 declared as Instances

private Instances	C45ModelSelection.m_allData All the training data
protected Instances	ClassifierTree.m_train The training instances.
private Instances	BinC45ModelSelection.m_allData The FULL training dataset.

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

ClassifierSplitModel	C45ModelSelection.selectModel(Instances data) Selects C4.5-type split for the given dataset.
ClassifierSplitModel	C45ModelSelection.selectModel(Instances train, Instances test) Selects C4.5-type split for the given dataset.
abstract void	ClassifierSplitModel.buildClassifier(Instances instances) Builds the classifier split model for the given set of instances.
abstract java.lang.String	ClassifierSplitModel.leftSide(Instances data) Prints left side of condition satisfied by instances.
abstract java.lang.String	ClassifierSplitModel.rightSide(int index, Instances data) Prints left side of condition satisfied by instances in subset index.
java.lang.String	ClassifierSplitModel.dumpLabel(int index, Instances data) Prints label for subset index of instances (eg class).
java.lang.String	ClassifierSplitModel.sourceClass(int index, Instances data)
abstract java.lang.String	ClassifierSplitModel.sourceExpression(int index, Instances data)
java.lang.String	ClassifierSplitModel.dumpModel(Instances data) Prints the split model.
void	ClassifierSplitModel.resetDistribution(Instances data) Sets distribution associated with model.
Instances[]	ClassifierSplitModel.split(Instances data) Splits the given set of instances into subsets.
void	C45PruneableClassifierTree.buildClassifier(Instances data) Method for building a pruneable classifier tree.
protected ClassifierTree	C45PruneableClassifierTree.getNewTree(Instances data) Returns a newly created tree.
private double	C45PruneableClassifierTree.getEstimatedErrorsForBranch(Instances data) Computes estimated errors for one branch.
private void	C45PruneableClassifierTree.newDistribution(Instances data) Computes new distributions of instances for nodes in tree.
void	C45Split.buildClassifier(Instances trainInstances) Creates a C4.5-type split on the given data.
private void	C45Split.handleEnumeratedAttribute(Instances trainInstances) Creates split on enumerated attribute.
private void	C45Split.handleNumericAttribute(Instances trainInstances) Creates split on numeric attribute.
java.lang.String	C45Split.leftSide(Instances data) Prints left side of condition..
java.lang.String	C45Split.rightSide(int index, Instances data) Prints the condition satisfied by instances in a subset.
java.lang.String	C45Split.sourceExpression(int index, Instances data) Returns a string containing java source code equivalent to the test made at this node.
void	C45Split.setSplitPoint(Instances allInstances) Sets split point to greatest value in given data smaller or equal to old split point.
double[][]	C45Split.minsAndMaxs(Instances data, double[][] minsAndMaxs, int index) Returns the minsAndMaxs of the index.th subset.
void	C45Split.resetDistribution(Instances data) Sets distribution associated with model.
void	BinC45Split.buildClassifier(Instances trainInstances) Creates a C4.5-type split on the given data.
private void	BinC45Split.handleEnumeratedAttribute(Instances trainInstances) Creates split on enumerated attribute.
private void	BinC45Split.handleNumericAttribute(Instances trainInstances) Creates split on numeric attribute.
java.lang.String	BinC45Split.leftSide(Instances data) Prints left side of condition..
java.lang.String	BinC45Split.rightSide(int index, Instances data) Prints the condition satisfied by instances in a subset.
java.lang.String	BinC45Split.sourceExpression(int index, Instances data) Returns a string containing java source code equivalent to the test made at this node.
void	BinC45Split.setSplitPoint(Instances allInstances) Sets split point to greatest value in given data smaller or equal to old split point.
void	BinC45Split.resetDistribution(Instances data) Sets distribution associated with model.
abstract ClassifierSplitModel	ModelSelection.selectModel(Instances data) Selects a model for the given dataset.
ClassifierSplitModel	ModelSelection.selectModel(Instances train, Instances test) Selects a model for the given train data using the given test data
void	ClassifierTree.buildClassifier(Instances data) Method for building a classifier tree.
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.
protected ClassifierTree	ClassifierTree.getNewTree(Instances data) Returns a newly created tree.
protected ClassifierTree	ClassifierTree.getNewTree(Instances train, Instances test) Returns a newly created tree.
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	Distribution.delRange(int bagIndex, Instances source, int startIndex, int lastPlusOne) Deletes all instances in given range from given bag.
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.
void	NoSplit.buildClassifier(Instances instances) Creates a "no-split"-split for a given set of instances.
java.lang.String	NoSplit.leftSide(Instances instances) Does nothing because no condition has to be satisfied.
java.lang.String	NoSplit.rightSide(int index, Instances instances) Does nothing because no condition has to be satisfied.
java.lang.String	NoSplit.sourceExpression(int index, Instances data) Returns a string containing java source code equivalent to the test made at this node.
ClassifierSplitModel	BinC45ModelSelection.selectModel(Instances data) Selects C4.5-type split for the given dataset.
ClassifierSplitModel	BinC45ModelSelection.selectModel(Instances train, Instances test) Selects C4.5-type split for the given dataset.
void	PruneableClassifierTree.buildClassifier(Instances data) Method for building a pruneable classifier tree.
protected ClassifierTree	PruneableClassifierTree.getNewTree(Instances train, Instances test) Returns a newly created tree.

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

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

Distribution(Instances source)
Creates a distribution with only one bag according to instances in source.

Distribution(Instances source, ClassifierSplitModel modelToUse)
Creates a distribution according to given instances and split model.

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

Uses of Instances in weka.classifiers.trees.lmt

Fields in weka.classifiers.trees.lmt declared as Instances

protected Instances	LogisticBase.m_numericDataHeader Header-only version of the numeric version of the training data
protected Instances	LogisticBase.m_numericData Numeric version of the training data.
protected Instances	LogisticBase.m_train Training data
protected Instances	ResidualSplit.m_data The set of instances

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

protected Instances	LMTNode.getNumericData(Instances train) Returns a numeric version of a set of instances.
protected Instances	LogisticBase.getNumericData(Instances data) Converts training data to numeric version.

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

ClassifierSplitModel	ResidualModelSelection.selectModel(Instances data, double[][] dataZs, double[][] dataWs) Selects split based on residuals for the given dataset.
ClassifierSplitModel	ResidualModelSelection.selectModel(Instances train) Method not in use
ClassifierSplitModel	ResidualModelSelection.selectModel(Instances train, Instances test) Method not in use
void	LMTNode.buildClassifier(Instances data) Method for building a logistic model tree (only called for the root node).
void	LMTNode.buildTree(Instances data, SimpleLinearRegression[][] higherRegressions, double totalInstanceWeight) Method for building the tree structure.
int	LMTNode.prune(double[] alphas, double[] errors, Instances test) Method for performing one fold in the cross-validation of the cost-complexity parameter.
protected int	LMTNode.tryLogistic(Instances data) Determines the optimum number of LogitBoost iterations to perform by building a standalone logistic regression function on the training data.
protected Instances	LMTNode.getNumericData(Instances train) Returns a numeric version of a set of instances.
void	LogisticBase.buildClassifier(Instances data) Builds the logistic regression model usiing LogitBoost.
protected int	LogisticBase.performBoosting(Instances train, Instances test, double[] error, int maxIterations) Runs LogitBoost on a training set and monitors the error on a test set.
protected double	LogisticBase.getErrorRate(Instances data) Returns the misclassification error of the current model on a set of instances.
protected double	LogisticBase.getMeanAbsoluteError(Instances data) Returns the error of the probability estimates for the current model on a set of instances.
protected boolean	LogisticBase.performIteration(int iteration, double[][] trainYs, double[][] trainFs, double[][] probs, Instances trainNumeric) Performs a single iteration of LogitBoost, and updates the model accordingly.
protected Instances	LogisticBase.getNumericData(Instances data) Converts training data to numeric version.
protected double[][]	LogisticBase.getYs(Instances data) Computes the Y-values (actual class probabilities) for a set of instances.
protected double[][]	LogisticBase.getFs(Instances data) Computes the F-values for a set of instances.
void	ResidualSplit.buildClassifier(Instances data, double[][] dataZs, double[][] dataWs) Builds the split.
java.lang.String	ResidualSplit.leftSide(Instances data) Returns name of splitting attribute (left side of condition).
java.lang.String	ResidualSplit.rightSide(int index, Instances data) Prints the condition satisfied by instances in a subset.
void	ResidualSplit.buildClassifier(Instances data) Method not in use
java.lang.String	ResidualSplit.sourceExpression(int index, Instances data) Method not in use

Uses of Instances in weka.classifiers.trees.m5

Fields in weka.classifiers.trees.m5 declared as Instances

private Instances	RuleNode.m_instances instances reaching this node
private Instances	PreConstructedLinearModel.m_instancesHeader
private Instances	Rule.m_instances the instances covered by this rule
private Instances	Rule.m_covered the instances covered by this rule
private Instances	Rule.m_notCovered the instances not covered by this rule
private Instances	M5Base.m_instances the instances covered by the tree/rules

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	RuleNode.buildClassifier(Instances data) Build this node (find an attribute and split point)
void	PreConstructedLinearModel.buildClassifier(Instances instances) Builds the classifier.
void	SplitEvaluate.attrSplit(int attr, Instances inst) Finds the best splitting point for an attribute in the instances
java.lang.String	YongSplitInfo.toString(Instances inst) Converts the spliting information to string
void	YongSplitInfo.attrSplit(int attr, Instances inst) Finds the best splitting point for an attribute in the instances
void	Rule.buildClassifier(Instances data) Generates a single rule or m5 model tree.
protected static double	Rule.stdDev(int attr, Instances inst) Returns the standard deviation value of the supplied attribute index.
protected static double	Rule.absDev(int attr, Instances inst) Returns the absolute deviation value of the supplied attribute index.
void	M5Base.buildClassifier(Instances data) Generates the classifier.
void	CorrelationSplitInfo.attrSplit(int attr, Instances inst) Finds the best splitting point for an attribute in the instances

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

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

Impurity(int partition, int attribute, Instances inst, int k)
Constructs an Impurity object containing the impurity values of partitioning the instances using an attribute

Uses of Instances in weka.clusterers

Fields in weka.clusterers declared as Instances

private Instances	MakeDensityBasedClusterer.m_theInstances holds training instances header information
protected Instances	FarthestFirst.m_instances training instances, not necessary to keep, could be replaced by m_ClusterCentroids where needed for header info
protected Instances	FarthestFirst.m_ClusterCentroids holds the cluster centroids
private Instances	EM.m_theInstances training instances
private Instances	ClusterEvaluation.m_trainInstances the instances to cluster
protected Instances	Cobweb.CNode.m_clusterInstances Instances at this node
private Instances	SimpleKMeans.m_ClusterCentroids holds the cluster centroids
private Instances	SimpleKMeans.m_ClusterStdDevs Holds the standard deviations of attributes in each cluster

Methods in weka.clusterers with parameters of type Instances

void	MakeDensityBasedClusterer.buildClusterer(Instances data) Builds a clusterer for a set of instances.
void	FarthestFirst.buildClusterer(Instances data) Generates a clusterer.
protected void	FarthestFirst.updateMinDistance(double[] minDistance, boolean[] selected, Instances data, Instance center)
protected void	FarthestFirst.initMinMax(Instances data)
private void	EM.EM_Init(Instances inst) Initialise estimators and storage.
private void	EM.estimate_priors(Instances inst) calculate prior probabilites for the clusters
private void	EM.M(Instances inst) The M step of the EM algorithm.
private double	EM.E(Instances inst, boolean change_weights) The E step of the EM algorithm.
private void	EM.EM_Report(Instances inst) verbose output for debugging
void	EM.buildClusterer(Instances data) Generates a clusterer.
private double	EM.iterate(Instances inst, boolean report) iterates the E and M steps until the log likelihood of the data converges.
void	ClusterEvaluation.evaluateClusterer(Instances test) Evaluate the clusterer on a set of instances.
private void	ClusterEvaluation.evaluateClustersWithRespectToClass(Instances inst) Evaluates cluster assignments with respect to actual class labels.
private java.lang.String	ClusterEvaluation.toMatrixString(int[][] counts, int[] clusterTotals, Instances inst) Returns a "confusion" style matrix of classes to clusters assignments
static java.lang.String	ClusterEvaluation.crossValidateModel(java.lang.String clustererString, Instances data, int numFolds, java.lang.String[] options, java.util.Random random) Performs a cross-validation for a distribution clusterer on a set of instances.
private static java.lang.String	ClusterEvaluation.printClusterings(Clusterer clusterer, Instances train, java.lang.String testFileName, Range attributesToOutput) Print the cluster assignments for either the training or the testing data.
void	Cobweb.buildClusterer(Instances data) Builds the clusterer.
void	SimpleKMeans.buildClusterer(Instances data) Generates a clusterer.
abstract void	Clusterer.buildClusterer(Instances data) Generates a clusterer.

Uses of Instances in weka.core

Subclasses of Instances in weka.core

class

ClassRemoveableInstances

Fields in weka.core declared as Instances

protected Instances

Instance.m_Dataset The dataset the instance has access to.

Methods in weka.core that return Instances

Instances	ClassHierarchy.selectCoveredClasses(Instances data) Returns the part of data covered by this hierarchy.
Instances	ClassHierarchy.mergeClasses(Instances instances) Returns a new Instances with classes merged to superclasses according to the superclasses of this hierarchy.
Instances	Instance.dataset() Returns the dataset this instance has access to.
Instances	ClassTree.selectCoveredClasses(Instances data) Returns the part of data covered by this hierarchy.
Instances[]	ClassTree.splitForSuperClasses(Instances data) Returns an array of Instances by splitting the given Instances with respect to the current superclasses.
Instances	ClassTree.mergeClasses(Instances instances) Returns a new Instances with classes merged to superclasses according to the superclasses of this ClassTree.
Instances	Instances.stringFreeStructure() Create a copy of the structure, but "cleanse" string types (i.e.
Instances	Instances.resample(java.util.Random random) Creates a new dataset of the same size using random sampling with replacement.
Instances	Instances.resampleWithWeights(java.util.Random random) Creates a new dataset of the same size using random sampling with replacement according to the current instance weights.
Instances	Instances.resampleWithWeights(java.util.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.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, java.util.Random random) Creates the training set for one fold of a cross-validation on the dataset.
static Instances	Instances.mergeInstances(Instances first, Instances second) Merges two sets of Instances together.

Methods in weka.core with parameters of type Instances

java.util.Map	ClassHierarchy.getChildren(Instances instanceInfo) Returns the childrenHierarchies as Map.
Instances	ClassHierarchy.selectCoveredClasses(Instances data) Returns the part of data covered by this hierarchy.
Instances	ClassHierarchy.mergeClasses(Instances instances) Returns a new Instances with classes merged to superclasses according to the superclasses of this hierarchy.
void	Instance.setDataset(Instances instances) Sets the reference to the dataset.
java.util.Map	ClassTree.getChildren(Instances instanceInfo) Returns the childrenHierarchies as Map.
Instances	ClassTree.selectCoveredClasses(Instances data) Returns the part of data covered by this hierarchy.
private java.lang.String	ClassTree.createKey(Instances instanceInfo) Provides a String as unique key for a ClassTree with respect to the given Instances.
Instances[]	ClassTree.splitForSuperClasses(Instances data) Returns an array of Instances by splitting the given Instances with respect to the current superclasses.
Instances	ClassTree.mergeClasses(Instances instances) Returns a new Instances with classes merged to superclasses according to the superclasses of this ClassTree.
boolean	Instances.equalHeaders(Instances dataset) Checks if two headers are equivalent.
private void	Instances.copyInstances(int from, Instances dest, int num) Copies instances from one set to the end of another one.
static Instances	Instances.mergeInstances(Instances first, Instances second) Merges two sets of Instances together.

Constructors in weka.core with parameters of type Instances

ClassRemoveableInstances(Instances dataset)

ClassRemoveableInstances(Instances dataset, int capacity)

ClassRemoveableInstances(Instances source, int first, int toCopy)

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.

Uses of Instances in weka.core.converters

Fields in weka.core.converters declared as Instances

protected Instances	ArffLoader.m_structure Holds the determined structure (header) of the data set.
protected Instances	CSVLoader.m_structure Holds the determined structure (header) of the data set.
protected Instances	SerializedInstancesLoader.m_Dataset Holds the structure (header) of the data set.
protected Instances	C45Loader.m_structure Holds the determined structure (header) of the data set.

Methods in weka.core.converters that return 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.getDataSet() Return the full data set.
Instances	CSVLoader.getStructure() Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
Instances	CSVLoader.getDataSet() Return the full data set.
Instances	Loader.getStructure() Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
Instances	Loader.getDataSet() Return the full data set.
abstract Instances	TreeLoader.getStructure()
abstract Instances	TreeLoader.getDataSet()
Instances	SerializedInstancesLoader.getStructure() Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
Instances	SerializedInstancesLoader.getDataSet() Return the full data set.
Instances	C45Loader.getStructure() Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
Instances	C45Loader.getDataSet() Return the full data set.
abstract Instances	AbstractLoader.getStructure()
abstract Instances	AbstractLoader.getDataSet()

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

void	ClassTreeParser.init(Instances instanceInfo) Inits the ClassTreeParser.
void	ClassHierarchyParser.init(Instances instanceInfo) Initiation of a ClassHierarchyParser should enable it to validate a given encoded hierarchy.
void	AbstractClassHierarchyParser.init(Instances instanceInfo) This init-method checks only if the class-attribute is nominal.

Uses of Instances in weka.datagenerators

Fields in weka.datagenerators declared as Instances

private Instances	BIRCHCluster.m_DatasetFormat
private Instances	ClusterGenerator.m_Format
private Instances	RDG1.m_DatasetFormat
private Instances	Generator.m_Format
(package private) Instances	Test.m_Dataset

Methods in weka.datagenerators that return Instances

Instances	BIRCHCluster.getDatasetFormat() Gets the dataset format.
Instances	BIRCHCluster.defineDataFormat() Initializes the format for the dataset produced.
Instances	BIRCHCluster.generateExamples() Generate all examples of the dataset.
Instances	BIRCHCluster.generateExamples(java.util.Random random, Instances format) Generate all examples of the dataset.
(package private) abstract Instances	ClusterGenerator.defineDataFormat() Initializes the format for the dataset produced.
(package private) abstract Instances	ClusterGenerator.generateExamples() Generates all examples of the dataset.
protected Instances	ClusterGenerator.getFormat() Gets the format of the dataset that is to be generated.
Instances	RDG1.getDatasetFormat() Gets the dataset format.
Instances	RDG1.defineDataFormat() Initializes the format for the dataset produced.
Instances	RDG1.generateExamples() Generate all examples of the dataset.
Instances	RDG1.generateExamples(int num, java.util.Random random, Instances format) Generate all examples of the dataset.
private Instances	RDG1.defineDataset(java.util.Random random) Returns a dataset header.
private Instances	RDG1.voteDataset(Instances dataset) Resets the class values of all instances using voting.
(package private) abstract Instances	Generator.defineDataFormat() Initializes the format for the dataset produced.
(package private) abstract Instances	Generator.generateExamples() Generates all examples of the dataset.
protected Instances	Generator.getFormat() Gets the format of the dataset that is to be generated.

Methods in weka.datagenerators with parameters of type Instances

void	BIRCHCluster.setDatasetFormat(Instances newDatasetFormat) Sets the dataset format.
Instances	BIRCHCluster.generateExamples(java.util.Random random, Instances format) Generate all examples of the dataset.
private Instance	BIRCHCluster.generateInstance(Instances format, java.util.Random randomG, double stdDev, double[] center, java.lang.String cName) Generate an example of the dataset.
protected void	ClusterGenerator.setFormat(Instances newFormat) Sets the format of the dataset that is to be generated.
void	RDG1.setDatasetFormat(Instances newDatasetFormat) Sets the dataset format.
Instances	RDG1.generateExamples(int num, java.util.Random random, Instances format) Generate all examples of the dataset.
private Instance	RDG1.generateExample(java.util.Random random, Instances format) Generates an example with its classvalue set to missing and binds it to the datasets.
private Instances	RDG1.voteDataset(Instances dataset) Resets the class values of all instances using voting.
protected void	Generator.setFormat(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.experiment

Fields in weka.experiment declared as Instances

protected Instances	DatabaseResultProducer.m_Instances The dataset of interest
protected Instances	CrossValidationResultProducer.m_Instances The dataset of interest
protected Instances	RandomSplitResultProducer.m_Instances The dataset of interest
protected Instances	AveragingResultProducer.m_Instances The dataset of interest
protected Instances	Experiment.m_CurrentInstances The dataset currently being used
protected Instances	LearningRateResultProducer.m_Instances The dataset of interest
protected Instances	PairedTTester.m_Instances The set of instances we will analyse

Methods in weka.experiment that return 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(java.lang.String query) Makes a database query to convert a table into a set of instances
Instances	PairedTTester.getInstances() Get the value of Instances.

Methods in weka.experiment with parameters of type Instances

java.lang.Object[]	CostSensitiveClassifierSplitEvaluator.getResult(Instances train, Instances test) Gets the results for the supplied train and test datasets.
void	DatabaseResultProducer.setInstances(Instances instances) Sets the dataset that results will be obtained for.
void	CrossValidationResultProducer.setInstances(Instances instances) Sets the dataset that results will be obtained for.
void	RandomSplitResultProducer.setInstances(Instances instances) Sets the dataset that results will be obtained for.
java.lang.Object[]	SplitEvaluator.getResult(Instances train, Instances test) Gets the results for the supplied train and test datasets.
void	ResultProducer.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.
java.lang.Object[]	ClassifierSplitEvaluator.getResult(Instances train, Instances test) Gets the results for the supplied train and test datasets.
void	LearningRateResultProducer.setInstances(Instances instances) Sets the dataset that results will be obtained for.
void	PairedTTester.setInstances(Instances newInstances) Set the value of Instances.
java.lang.Object[]	RegressionSplitEvaluator.getResult(Instances train, Instances test) Gets the results for the supplied train and test datasets.

Uses of Instances in weka.filters

Fields in weka.filters declared as Instances

private Instances	Filter.m_OutputFormat The output format for instances
private Instances	Filter.m_InputFormat The input format for instances

Methods in weka.filters that return Instances

protected Instances	Filter.getInputFormat() Gets the currently set inputformat instances.
protected Instances	Filter.inputFormatPeek() Returns a reference to the current input format without copying it.
protected Instances	Filter.outputFormatPeek() Returns a reference to the current output format without copying it.
Instances	Filter.outputFormat() Deprecated. use getOutputFormat() instead.
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

protected void	Filter.setOutputFormat(Instances outputFormat) Sets the format of output instances.
private void	Filter.copyStringValues(Instance inst, Instances destDataset, int[] strAtts) Copies string values contained in the instance copied to a new dataset.
protected void	Filter.copyStringValues(Instance instance, boolean instSrcCompat, Instances srcDataset, Instances destDataset) Takes string values referenced by an Instance and copies them from a source dataset to a destination dataset.
protected void	Filter.copyStringValues(Instance instance, boolean instSrcCompat, Instances srcDataset, int[] srcStrAtts, Instances destDataset, int[] destStrAtts) Takes string values referenced by an Instance and copies them from a source dataset to a destination dataset.
boolean	Filter.inputFormat(Instances instanceInfo) Deprecated. use setInputFormat(Instances) instead.
boolean	Filter.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
protected int[]	Filter.getStringIndices(Instances insts) Gets an array containing the indices of all string attributes.
static Instances	Filter.useFilter(Instances data, Filter filter) Filters an entire set of instances through a filter and returns the new set.
boolean	AllFilter.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	NullFilter.setInputFormat(Instances instanceInfo) Sets the format of the input instances.

Uses of Instances in weka.filters.supervised.attribute

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

boolean	ClassOrder.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	NominalToBinary.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Discretize.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
protected void	Discretize.calculateCutPointsByMDL(int index, Instances data) Set cutpoints for a single attribute using MDL.
private double[]	Discretize.cutPointsForSubset(Instances instances, int attIndex, int first, int lastPlusOne) Selects cutpoints for sorted subset.

Uses of Instances in weka.filters.supervised.instance

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

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

boolean	RemoveType.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Add.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	NominalToBinary.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	ReplaceMissingValues.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	AddNoise.setInputFormat(Instances instanceInfo) Sets the format of the input 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.
boolean	Standardize.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	StringToNominal.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	PKIDiscretize.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Normalize.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Copy.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	TimeSeriesDelta.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	RandomProjection.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	AddCluster.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	ClusterMembership.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	MakeIndicator.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	NumericToBinary.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Discretize.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Obfuscate.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	RemoveUseless.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	NumericTransform.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	TimeSeriesTranslate.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	SwapValues.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	AddExpression.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	FirstOrder.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	StringToWordVector.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Remove.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	AbstractTimeSeries.setInputFormat(Instances instanceInfo) Sets the format of the input instances.

Uses of Instances in weka.filters.unsupervised.instance

Methods in weka.filters.unsupervised.instance that return Instances

private Instances	RemoveMisclassified.cleanseTrain(Instances data) Cleanses the data based on misclassifications when used training data.
private Instances	RemoveMisclassified.cleanseCross(Instances data) Cleanses the data based on misclassifications when performing cross-validation.

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

boolean	SparseToNonSparse.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Resample.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	RemoveRange.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	RemoveWithValues.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	RemoveFolds.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	RemovePercentage.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	NonSparseToSparse.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	Randomize.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
boolean	RemoveMisclassified.setInputFormat(Instances instanceInfo) Sets the format of the input instances.
private Instances	RemoveMisclassified.cleanseTrain(Instances data) Cleanses the data based on misclassifications when used training data.
private Instances	RemoveMisclassified.cleanseCross(Instances data) Cleanses the data based on misclassifications when performing cross-validation.

Uses of Instances in weka.gui

Fields in weka.gui declared as Instances

protected Instances	AttributeListPanel.AttributeTableModel.m_Instances The instances who's attribute structure we are reporting
protected Instances	AttributeSelectionPanel.AttributeTableModel.m_Instances The instances who's attribute structure we are reporting
protected Instances	AttributeSummaryPanel.m_Instances The instances we're playing with
protected Instances	SetInstancesPanel.m_Instances The current set of instances loaded
(package private) Instances	AttributeVisualizationPanel.m_data
protected Instances	InstancesSummaryPanel.m_Instances The instances we're playing with

Methods in weka.gui that return Instances

Instances

SetInstancesPanel.getInstances() Gets the set of instances currently held by the panel

Methods in weka.gui with parameters of type Instances

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

Constructors in weka.gui with parameters of type Instances

AttributeListPanel.AttributeTableModel(Instances instances)
Creates the tablemodel with the given set of instances.

AttributeSelectionPanel.AttributeTableModel(Instances instances)
Creates the tablemodel with the given set of instances.

Uses of Instances in weka.gui.beans

Fields in weka.gui.beans declared as Instances

protected Instances	DataVisualizer.m_visualizeDataSet
private Instances	Loader.m_dataSet Holds the instances loaded
private Instances	Classifier.m_trainingSet Holds training instances for batch training.
private Instances	Classifier.m_testingSet
private Instances	Filter.m_trainingSet
private Instances	Filter.m_testingSet
protected Instances	TestSetEvent.m_testSet The test set instances
protected Instances	PredictionAppender.m_incrementalStructure
protected Instances	TrainingSetEvent.m_trainingSet The training instances
protected Instances	BatchClassifierEvent.m_testSet Instances that can be used for testing the classifier
private Instances	DataSetEvent.m_dataSet

Methods in weka.gui.beans that return Instances

Instances	TestSetEvent.getTestSet() Get the test set instances
private Instances	PredictionAppender.makeDataSetProbabilities(Instances format, Classifier classifier, java.lang.String relationNameModifier)
private Instances	PredictionAppender.makeDataSetClass(Instances format, Classifier classifier, java.lang.String relationNameModifier)
Instances	TrainingSetEvent.getTrainingSet() Get the training instances
Instances	BatchClassifierEvent.getTestSet() Get the test set
Instances	DataSetEvent.getDataSet() Return the instances of the data set

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

private void	ClassAssigner.assignClass(Instances dataSet)
void	DataVisualizer.setInstances(Instances inst) Set instances for this bean.
private Instances	PredictionAppender.makeDataSetProbabilities(Instances format, Classifier classifier, java.lang.String relationNameModifier)
private Instances	PredictionAppender.makeDataSetClass(Instances format, Classifier classifier, java.lang.String relationNameModifier)
void	ScatterPlotMatrix.setInstances(Instances inst) Set instances for this bean.
void	AttributeSummarizer.setInstances(Instances inst) Set instances for this bean.

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

TestSetEvent(java.lang.Object source, Instances testSet)

TrainingSetEvent(java.lang.Object source, Instances trainSet)
Creates a new TrainingSetEvent

BatchClassifierEvent(java.lang.Object source, Classifier scheme, Instances tstI, int setNum, int maxSetNum)
Creates a new BatchClassifierEvent instance.

DataSetEvent(java.lang.Object source, Instances dataSet)

Uses of Instances in weka.gui.boundaryvisualizer

Fields in weka.gui.boundaryvisualizer declared as Instances

private Instances	RemoteBoundaryVisualizerSubTask.m_trainingData
protected Instances	BoundaryPanel.m_trainingData
private Instances	BoundaryVisualizer.m_trainingInstances
private Instances	KDDataGenerator.m_instances

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	DataGenerator.buildGenerator(Instances inputInstances) Build the data generator
void	RemoteBoundaryVisualizerSubTask.setInstances(Instances i) Set the training data
void	BoundaryPanel.setTrainingData(Instances trainingData) Set the training data to use
void	BoundaryVisualizer.setInstances(Instances inst) Set the training instances
void	KDDataGenerator.buildGenerator(Instances inputInstances) Initialize the generator using the supplied instances

Uses of Instances in weka.gui.experiment

Fields in weka.gui.experiment declared as Instances

protected Instances

ResultsPanel.m_Instances The instances we're extracting results from

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

Fields in weka.gui.explorer declared as Instances

protected Instances	ClustererPanel.m_Instances The main set of instances we're playing with
protected Instances	ClustererPanel.m_TestInstances The user-supplied test set (if any)
protected Instances	ClustererPanel.m_TestInstancesCopy The user supplied test set after preprocess filters have been applied
protected Instances	ClassifierPanel.m_Instances The main set of instances we're playing with
protected Instances	ClassifierPanel.m_TestInstances The user-supplied test set (if any)
protected Instances	AssociationsPanel.m_Instances The main set of instances we're playing with
protected Instances	AssociationsPanel.m_TestInstances The user-supplied test set (if any)
protected Instances	PreprocessPanel.m_Instances The working instances
protected Instances	AttributeSelectionPanel.m_Instances The main set of instances we're playing with

Methods in weka.gui.explorer that return Instances

private Instances	ClustererPanel.removeClass(Instances inst)
private Instances	ClustererPanel.removeIgnoreCols(Instances inst)
private Instances	ClustererPanel.removeIgnoreCols(Instances inst, int[] toIgnore)
private Instances	ClassifierPanel.setUpVisualizableInstances(Instances trainInstances) Sets up the structure for the visualizable instances.
Instances	PreprocessPanel.getInstances() Gets the working set of instances.

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

void	ClustererPanel.setInstances(Instances inst) Tells the panel to use a new set of instances.
static PlotData2D	ClustererPanel.setUpVisualizableInstances(Instances testInstances, ClusterEvaluation eval) Sets up the structure for the visualizable instances.
private Instances	ClustererPanel.removeClass(Instances inst)
private Instances	ClustererPanel.removeIgnoreCols(Instances inst)
private Instances	ClustererPanel.removeIgnoreCols(Instances inst, int[] toIgnore)
protected void	ClustererPanel.saveClusterer(java.lang.String name, Clusterer clusterer, Instances trainHeader, int[] ignoredAtts) Saves the currently selected clusterer
protected void	ClustererPanel.reevaluateModel(java.lang.String name, Clusterer clusterer, Instances trainHeader, int[] ignoredAtts) Re-evaluates the named clusterer with the current test set.
void	ClassifierPanel.setInstances(Instances inst) Tells the panel to use a new set of instances.
private void	ClassifierPanel.processClassifierPrediction(Instance toPredict, Classifier classifier, Evaluation eval, FastVector predictions, 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. plotInfo for nominal class datasets holds shape types (actual data points have automatic shape type assignment; classifier error data points have box shape type).
private Instances	ClassifierPanel.setUpVisualizableInstances(Instances trainInstances) Sets up the structure for the visualizable instances.
protected void	ClassifierPanel.saveClassifier(java.lang.String name, Classifier classifier, Instances trainHeader) Saves the currently selected classifier
protected void	ClassifierPanel.reevaluateModel(java.lang.String name, Classifier classifier, Instances trainHeader) Re-evaluates the named classifier with the current test set.
void	AssociationsPanel.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.
protected void	PreprocessPanel.saveInstancesToFile(java.io.File f, Instances inst) Saves the current instances to the supplied file.
void	AttributeSelectionPanel.setInstances(Instances inst) Tells the panel to use a new set of instances.
protected void	AttributeSelectionPanel.visualizeTransformedData(Instances ti) Popup a visualize panel for viewing transformed data

Uses of Instances in weka.gui.streams

Fields in weka.gui.streams declared as Instances

private Instances	InstanceTable.m_Instances
protected Instances	InstanceJoiner.m_InputFormat The input format for instances
private Instances	InstanceLoader.m_OutputInstances

Methods in weka.gui.streams that return Instances

Instances	InstanceProducer.outputFormat()
Instances	InstanceJoiner.outputFormat() Gets the format of the output instances.
Instances	InstanceLoader.outputFormat()

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

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

Uses of Instances in weka.gui.treevisualizer

Fields in weka.gui.treevisualizer declared as Instances

private Instances

Node.m_theData An Instances variable generated from the data.

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

Fields in weka.gui.visualize declared as Instances

protected Instances	MatrixPanel.m_data The dataset for which this panel will display the plot matrix for
protected Instances	Plot2D.m_plotInstances The instances to be plotted
protected Instances	VisualizePanel.PlotPanel.m_plotInstances The instances from the master plot
protected Instances	PlotData2D.m_plotInstances The instances
protected Instances	AttributePanel.m_plotInstances The instances to be plotted
private Instances	VisualizePanelEvent.m_inst The instances that fall inside the shapes described in m_values.
private Instances	VisualizePanelEvent.m_inst2 The instances that fall outside the shapes described in m_values.
private Instances	ClassPanel.m_Instances Instances being plotted

Methods in weka.gui.visualize that return Instances

Instances	VisualizePanel.getInstances() Get the master plot's instances
Instances	PlotData2D.getPlotInstances() Returns the instances for this plot
Instances	VisualizePanelEvent.getInstances1()
Instances	VisualizePanelEvent.getInstances2()

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

void	MatrixPanel.setInstances(Instances newInst) This method changes the Instances object of this class to a new one.
void	Plot2D.setInstances(Instances inst) Sets the master plot from a set of instances
protected void	VisualizePanel.newColorAttribute(int a, Instances i) Sets the Colors in use for a different attrib if it is not a nominal attrib and or does not have more possible values then this will do nothing.
void	VisualizePanel.setInstances(Instances inst) Tells the panel to use a new set of instances.
void	VisualizePanel.setUpComboBoxes(Instances inst)
private void	VisualizePanel.PlotPanel.plotReset(Instances inst, int cIndex) Reset the visualize panel's buttons and the plot panels instances
void	ThresholdVisualizePanel.setUpComboBoxes(Instances inst) This overloads VisualizePanel's setUpComboBoxes to add ActionListeners to watch for when the X/Y Axis comboboxes are changed.
void	AttributePanel.setInstances(Instances ins) This sets the instances to be drawn into the attribute panel
void	ClassPanel.setInstances(Instances insts) Set the instances.

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.