Uses of Class
cc.mallet.types.InstanceList

Packages that use InstanceList

Package	Description
cc.mallet.classify	Classes for training and classifying instances.
cc.mallet.classify.constraints.ge
cc.mallet.classify.constraints.pr
cc.mallet.classify.evaluate	Classes for computing and displaying the quaility of a classification trial, including accuracy, precision, and confusion matrix.
cc.mallet.cluster	Unsupervised clustering of Instance objects within an InstanceList.
cc.mallet.cluster.iterator
cc.mallet.cluster.util
cc.mallet.extract	Unimplemented.
cc.mallet.fst	Transducers, including Conditional Random Fields (CRFs).
cc.mallet.fst.confidence
cc.mallet.fst.semi_supervised
cc.mallet.fst.semi_supervised.constraints
cc.mallet.fst.semi_supervised.pr
cc.mallet.fst.semi_supervised.pr.constraints
cc.mallet.fst.semi_supervised.tui
cc.mallet.pipe	Classes for processing arbitrary data into instances.
cc.mallet.pipe.iterator	Classes that generate instances from different kinds of input or data structures.
cc.mallet.regression
cc.mallet.regression.tui
cc.mallet.topics
cc.mallet.types	Fundamental MALLET types, including FeatureVector, Instance, Label etc.
cc.mallet.util	Miscellaneous utilities including command line processing, math functions, lexing, logging.

Uses of InstanceList in cc.mallet.classify

Fields in cc.mallet.classify declared as InstanceList

Modifier and Type	Field	Description
protected InstanceList	MaxEntOptimizableByGE.trainingList
protected InstanceList	ClassifierTrainer.validationSet

Methods in cc.mallet.classify that return InstanceList

Modifier and Type	Method	Description
InstanceList	C45.Node.getInstances()
InstanceList	ClassifierTrainer.getValidationInstances()

Methods in cc.mallet.classify with parameters of type InstanceList

Modifier and Type	Method	Description
java.util.ArrayList<Classification>	Classifier.classify(InstanceList instances)
double	NaiveBayes.dataLogLikelihood(InstanceList ilist)
void	ClassifierAccuracyEvaluator.evaluateInstanceList(ClassifierTrainer trainer, InstanceList instances, java.lang.String description)
abstract void	ClassifierEvaluator.evaluateInstanceList(ClassifierTrainer trainer, InstanceList instances, java.lang.String description)
double	Classifier.getAccuracy(InstanceList ilist)
double	Classifier.getAverageRank(InstanceList ilist)
double	Classifier.getF1(InstanceList ilist, int index)
double	Classifier.getF1(InstanceList ilist, Labeling labeling)
double	Classifier.getF1(InstanceList ilist, java.lang.Object labelEntry)
static double[][]	FeatureConstraintUtil.getFeatureLabelCounts(InstanceList list, boolean useValues)
Optimizable.ByGradientValue	MCMaxEntTrainer.getMaximizableTrainer(InstanceList ilist)
Optimizable.ByGradientValue	RankMaxEntTrainer.getMaximizableTrainer(InstanceList ilist)
Optimizable.ByGradientValue	MaxEntGERangeTrainer.getOptimizable(InstanceList trainingList)
Optimizable.ByGradientValue	MaxEntGETrainer.getOptimizable(InstanceList trainingList)
MaxEntOptimizableByLabelLikelihood	MaxEntTrainer.getOptimizable(InstanceList trainingSet)
MaxEntOptimizableByLabelLikelihood	MaxEntTrainer.getOptimizable(InstanceList trainingSet, MaxEnt initialClassifier)
Optimizer	MaxEntL1Trainer.getOptimizer(InstanceList trainingSet)
Optimizer	MaxEntTrainer.getOptimizer(InstanceList trainingSet)	This method is called by the train method.
double	Classifier.getPrecision(InstanceList ilist, int index)
double	Classifier.getPrecision(InstanceList ilist, Labeling labeling)
double	Classifier.getPrecision(InstanceList ilist, java.lang.Object labelEntry)
double	Classifier.getRecall(InstanceList ilist, int index)
double	Classifier.getRecall(InstanceList ilist, Labeling labeling)
double	Classifier.getRecall(InstanceList ilist, java.lang.Object labelEntry)
void	DecisionTree.induceFeatures(InstanceList ilist, boolean withFeatureShrinkage, boolean inducePerClassFeatures)
static java.util.HashMap<java.lang.Integer,java.util.ArrayList<java.lang.Integer>>	FeatureConstraintUtil.labelFeatures(InstanceList list, java.util.ArrayList<java.lang.Integer> features)
static java.util.HashMap<java.lang.Integer,java.util.ArrayList<java.lang.Integer>>	FeatureConstraintUtil.labelFeatures(InstanceList list, java.util.ArrayList<java.lang.Integer> features, boolean reject)	Label features using heuristic described in "Learning from Labeled Features using Generalized Expectation Criteria" Gregory Druck, Gideon Mann, Andrew McCallum.
double	NaiveBayes.labelLogLikelihood(InstanceList ilist)
static java.util.HashMap<java.lang.Integer,double[]>	FeatureConstraintUtil.readConstraintsFromFile(java.lang.String filename, InstanceList data)	Reads feature constraints from a file, whether they are stored using Strings or indices.
static java.util.HashMap<java.lang.Integer,double[]>	FeatureConstraintUtil.readConstraintsFromFileIndex(java.lang.String filename, InstanceList data)	Reads feature constraints stored using strings from a file.
static java.util.HashMap<java.lang.Integer,double[]>	FeatureConstraintUtil.readConstraintsFromFileString(java.lang.String filename, InstanceList data)	Reads feature constraints stored using strings from a file.
static java.util.HashMap<java.lang.Integer,double[][]>	FeatureConstraintUtil.readRangeConstraintsFromFile(java.lang.String filename, InstanceList data)	Reads range constraints stored using strings from a file.
static java.util.ArrayList<java.lang.Integer>	FeatureConstraintUtil.selectFeaturesByInfoGain(InstanceList list, int numFeatures)	Select features with the highest information gain.
static java.util.HashMap<java.lang.Integer,double[]>	FeatureConstraintUtil.setTargetsUsingData(InstanceList list, java.util.ArrayList<java.lang.Integer> features)
static java.util.HashMap<java.lang.Integer,double[]>	FeatureConstraintUtil.setTargetsUsingData(InstanceList list, java.util.ArrayList<java.lang.Integer> features, boolean normalize)
static java.util.HashMap<java.lang.Integer,double[]>	FeatureConstraintUtil.setTargetsUsingData(InstanceList list, java.util.ArrayList<java.lang.Integer> features, boolean useValues, boolean normalize)	Set target distributions using estimates from data.
static java.util.HashMap<java.lang.Integer,double[]>	FeatureConstraintUtil.setTargetsUsingFeatureVoting(java.util.HashMap<java.lang.Integer,java.util.ArrayList<java.lang.Integer>> labeledFeatures, InstanceList trainingData)	Set target distributions using feature voting heuristic described in "Learning from Labeled Features using Generalized Expectation Criteria" Gregory Druck, Gideon Mann, Andrew McCallum.
void	ClassifierTrainer.setValidationInstances(InstanceList validationSet)
AdaBoostM2	AdaBoostM2Trainer.train(InstanceList trainingList)	Boosting method that resamples instances using their weights
AdaBoost	AdaBoostTrainer.train(InstanceList trainingList)	Boosting method that resamples instances using their weights
BaggingClassifier	BaggingTrainer.train(InstanceList trainingList)
BalancedWinnow	BalancedWinnowTrainer.train(InstanceList trainingList)	Trains the classifier on the instance list, updating class weight vectors as appropriate
C45	C45Trainer.train(InstanceList trainingList)
ClassifierEnsemble	ClassifierEnsembleTrainer.train(InstanceList trainingSet)
C	ClassifierTrainer.ByActiveLearning.train(InstanceList trainingAndUnlabeledSet, Labeler labeler, int numLabelRequests)
C	ClassifierTrainer.ByOptimization.train(InstanceList trainingSet, int numIterations)
abstract C	ClassifierTrainer.train(InstanceList trainingSet)
ConfidencePredictingClassifier	ConfidencePredictingClassifierTrainer.train(InstanceList trainList)
DecisionTree	DecisionTreeTrainer.train(InstanceList trainingList)
Classifier	FeatureSelectingClassifierTrainer.train(InstanceList trainingSet)
MaxEnt	MaxEntGERangeTrainer.train(InstanceList trainingList)
MaxEnt	MaxEntGERangeTrainer.train(InstanceList train, int maxIterations)
MaxEnt	MaxEntGETrainer.train(InstanceList trainingList)
MaxEnt	MaxEntGETrainer.train(InstanceList train, int maxIterations)
MaxEnt	MaxEntPRTrainer.train(InstanceList trainingSet)
MaxEnt	MaxEntPRTrainer.train(InstanceList trainingSet, int maxIterations)
MaxEnt	MaxEntPRTrainer.train(InstanceList data, int minIterations, int maxIterations)
MaxEnt	MaxEntTrainer.train(InstanceList trainingSet)
MaxEnt	MaxEntTrainer.train(InstanceList trainingSet, int numIterations)
MCMaxEnt	MCMaxEntTrainer.train(InstanceList trainingSet)
MostFrequentClassifier	MostFrequentClassAssignmentTrainer.train(InstanceList trainingSet)	Create a MostFrequent classifier from a set of training data.
NaiveBayes	NaiveBayesEMTrainer.train(InstanceList trainingSet)
NaiveBayes	NaiveBayesTrainer.train(InstanceList trainingList)	Create a NaiveBayes classifier from a set of training data.
RandomClassifier	RandomAssignmentTrainer.train(InstanceList trainingList)	Create a Random classifier from a set of training data.
MaxEnt	RankMaxEntTrainer.train(InstanceList trainingSet)
Winnow	WinnowTrainer.train(InstanceList trainingList)	Trains winnow on the instance list, updating weights according to errors
C	ClassifierTrainer.ByIncrements.trainIncremental(InstanceList trainingInstancesToAdd)
NaiveBayes	NaiveBayesTrainer.trainIncremental(InstanceList trainingInstancesToAdd)

Constructors in cc.mallet.classify with parameters of type InstanceList

Constructor	Description
ClassifierAccuracyEvaluator(InstanceList[] instances, java.lang.String[] descriptions)
ClassifierAccuracyEvaluator(InstanceList instanceList1, java.lang.String instanceListDescription1)
ClassifierAccuracyEvaluator(InstanceList instanceList1, java.lang.String instanceListDescription1, InstanceList instanceList2, java.lang.String instanceListDescription2)
ClassifierAccuracyEvaluator(InstanceList instanceList1, java.lang.String instanceListDescription1, InstanceList instanceList2, java.lang.String instanceListDescription2, InstanceList instanceList3, java.lang.String instanceListDescription3)
ClassifierEvaluator(InstanceList[] instanceLists, java.lang.String[] instanceListDescriptions)
ClassifierEvaluator(InstanceList instanceList1, java.lang.String instanceListDescription1)
ClassifierEvaluator(InstanceList instanceList1, java.lang.String instanceListDescription1, InstanceList instanceList2, java.lang.String instanceListDescription2)
ClassifierEvaluator(InstanceList instanceList1, java.lang.String instanceListDescription1, InstanceList instanceList2, java.lang.String instanceListDescription2, InstanceList instanceList3, java.lang.String instanceListDescription3)
ConfidencePredictingClassifierTrainer(ClassifierTrainer underlyingClassifierTrainer, InstanceList validationSet)
ConfidencePredictingClassifierTrainer(ClassifierTrainer underlyingClassifierTrainer, InstanceList validationSet, Pipe confidencePredictingPipe)
MaxEntOptimizableByGE(InstanceList trainingList, java.util.ArrayList<MaxEntGEConstraint> constraints, MaxEnt initClassifier)
MaxEntOptimizableByLabelDistribution(InstanceList trainingSet, MaxEnt initialClassifier)
MaxEntOptimizableByLabelLikelihood(InstanceList trainingSet, MaxEnt initialClassifier)
Node(InstanceList ilist, C45.Node parent, int minNumInsts)
Node(InstanceList ilist, C45.Node parent, int minNumInsts, int[] instIndices)
Node(InstanceList ilist, DecisionTree.Node parent, FeatureSelection fs)
PRAuxClassifierOptimizable(InstanceList trainingData, double[][] baseDistribution, PRAuxClassifier classifier)
Trial(Classifier c, InstanceList ilist)

Uses of InstanceList in cc.mallet.classify.constraints.ge

Methods in cc.mallet.classify.constraints.ge with parameters of type InstanceList

Modifier and Type	Method	Description
java.util.BitSet	MaxEntFLGEConstraints.preProcess(InstanceList data)
java.util.BitSet	MaxEntGEConstraint.preProcess(InstanceList data)
java.util.BitSet	MaxEntRangeL2FLGEConstraints.preProcess(InstanceList data)

Uses of InstanceList in cc.mallet.classify.constraints.pr

Methods in cc.mallet.classify.constraints.pr with parameters of type InstanceList

Modifier and Type	Method	Description
java.util.BitSet	MaxEntFLPRConstraints.preProcess(InstanceList data)
java.util.BitSet	MaxEntPRConstraint.preProcess(InstanceList data)

Uses of InstanceList in cc.mallet.classify.evaluate

Constructors in cc.mallet.classify.evaluate with parameters of type InstanceList

Constructor	Description
AccuracyCoverage(Classifier C, InstanceList ilist, int numBuckets, java.lang.String title)
AccuracyCoverage(Classifier C, InstanceList ilist, java.lang.String title)

Uses of InstanceList in cc.mallet.cluster

Fields in cc.mallet.cluster declared as InstanceList

Modifier and Type	Field	Description
protected InstanceList	Clustering.instances

Methods in cc.mallet.cluster that return InstanceList

Modifier and Type	Method	Description
InstanceList	Clustering.getCluster(int label)	Return an list of instances with a particular label.
InstanceList[]	Clustering.getClusters()	Returns an array of instance lists corresponding to clusters.
InstanceList	Clustering.getInstances()

Methods in cc.mallet.cluster with parameters of type InstanceList

Modifier and Type	Method	Description
abstract Clustering	Clusterer.cluster(InstanceList trainingSet)	Return a clustering of an InstanceList
Clustering	HillClimbingClusterer.cluster(InstanceList instances)	While not converged, calls improveClustering to modify the current predicted Clustering.
Clustering	HillClimbingClusterer.cluster(InstanceList instances, int iterations, Clustering initialClustering)	While not converged, call improveClustering to modify the current predicted Clustering.
Clustering	KMeans.cluster(InstanceList instances)	Cluster instances
Clustering[]	HillClimbingClusterer.clusterKBest(InstanceList instances, int k)
Clustering[]	HillClimbingClusterer.clusterKBest(InstanceList instances, int iterations, Clustering initialClustering, int k)	Return the K most recent solutions.
abstract Clustering[]	KBestClusterer.clusterKBest(InstanceList trainingSet, int k)
Clustering	GreedyAgglomerative.initializeClustering(InstanceList instances)
abstract Clustering	HillClimbingClusterer.initializeClustering(InstanceList instances)

Constructors in cc.mallet.cluster with parameters of type InstanceList

Constructor	Description
Clustering(InstanceList instances, int numLabels, int[] labels)	Clustering constructor.

Uses of InstanceList in cc.mallet.cluster.iterator

Fields in cc.mallet.cluster.iterator declared as InstanceList

Modifier and Type	Field	Description
protected InstanceList	PairSampleIterator.instances

Uses of InstanceList in cc.mallet.cluster.util

Methods in cc.mallet.cluster.util that return InstanceList

Modifier and Type	Method	Description
static InstanceList	ClusterUtils.combineLists(InstanceList li, InstanceList lj)
static InstanceList	ClusterUtils.makeList(Instance i, Instance j)

Methods in cc.mallet.cluster.util with parameters of type InstanceList

Modifier and Type	Method	Description
static InstanceList	ClusterUtils.combineLists(InstanceList li, InstanceList lj)
static Clustering	ClusterUtils.createRandomClustering(InstanceList instances, Randoms random)
static Clustering	ClusterUtils.createSingletonClustering(InstanceList instances)	Initializes Clustering to one Instance per cluster.

Uses of InstanceList in cc.mallet.extract

Methods in cc.mallet.extract that return InstanceList

Modifier and Type	Method	Description
InstanceList	CRFExtractor.pipeInstances(java.util.Iterator<Instance> source)

Methods in cc.mallet.extract with parameters of type InstanceList

Modifier and Type	Method	Description
Extraction	CRFExtractor.extract(InstanceList ilist)	Assumes Instance.source contains the Tokenization object.

Uses of InstanceList in cc.mallet.fst

Fields in cc.mallet.fst declared as InstanceList

Modifier and Type	Field	Description
protected InstanceList[]	TransducerEvaluator.instanceLists
protected InstanceList	CRFOptimizableByBatchLabelLikelihood.trainingSet
protected InstanceList	CRFOptimizableByLabelLikelihood.trainingSet
protected InstanceList	ThreadedOptimizable.trainingSet	Data

Methods in cc.mallet.fst with parameters of type InstanceList

Modifier and Type	Method	Description
void	CRF.addFullyConnectedStatesForThreeQuarterLabels(InstanceList trainingSet)
void	HMM.addFullyConnectedStatesForThreeQuarterLabels(InstanceList trainingSet)
java.lang.String	CRF.addOrderNStates(InstanceList trainingSet, int[] orders, boolean[] defaults, java.lang.String start, java.util.regex.Pattern forbidden, java.util.regex.Pattern allowed, boolean fullyConnected)	Assumes that the CRF's output alphabet contains Strings.
java.lang.String	HMM.addOrderNStates(InstanceList trainingSet, int[] orders, boolean[] defaults, java.lang.String start, java.util.regex.Pattern forbidden, java.util.regex.Pattern allowed, boolean fullyConnected)	Assumes that the HMM's output alphabet contains Strings.
void	CRF.addStatesForBiLabelsConnectedAsIn(InstanceList trainingSet)	Add states to create a second-order Markov model on labels, adding only those transitions the occur in the given trainingSet.
void	HMM.addStatesForBiLabelsConnectedAsIn(InstanceList trainingSet)	Add states to create a second-order Markov model on labels, adding only those transitions the occur in the given trainingSet.
void	CRF.addStatesForHalfLabelsConnectedAsIn(InstanceList trainingSet)	Add as many states as there are labels, but don't create separate weights for each source-destination pair of states.
void	HMM.addStatesForHalfLabelsConnectedAsIn(InstanceList trainingSet)	Add as many states as there are labels, but don't create separate weights for each source-destination pair of states.
void	CRF.addStatesForLabelsConnectedAsIn(InstanceList trainingSet)	Add states to create a first-order Markov model on labels, adding only those transitions the occur in the given trainingSet.
void	HMM.addStatesForLabelsConnectedAsIn(InstanceList trainingSet)	Add states to create a first-order Markov model on labels, adding only those transitions the occur in the given trainingSet.
void	CRF.addStatesForThreeQuarterLabelsConnectedAsIn(InstanceList trainingSet)	Add as many states as there are labels, but don't create separate observational-test-weights for each source-destination pair of states---instead have all the incoming transitions to a state share the same observational-feature-test weights.
void	HMM.addStatesForThreeQuarterLabelsConnectedAsIn(InstanceList trainingSet)	Add as many states as there are labels, but don't create separate observational-test-weights for each source-destination pair of states---instead have all the incoming transitions to a state share the same observational-feature-test weights.
double	Transducer.averageTokenAccuracy(InstanceList ilist)	Runs inference across all the instances and returns the average token accuracy.
void	MultiSegmentationEvaluator.batchTest(InstanceList data, java.util.List<Sequence> predictedSequences, java.lang.String description, java.io.PrintStream viterbiOutputStream)	Tests segmentation using an ArrayList of predicted Sequences instead of a Transducer.
void	CRF.evaluate(TransducerEvaluator eval, InstanceList testing)	Deprecated.
void	CRFWriter.evaluateInstanceList(TransducerTrainer transducer, InstanceList instances, java.lang.String description)
void	InstanceAccuracyEvaluator.evaluateInstanceList(TransducerTrainer tt, InstanceList data, java.lang.String description)
void	LabelDistributionEvaluator.evaluateInstanceList(TransducerTrainer transducer, InstanceList instances, java.lang.String description)
void	MultiSegmentationEvaluator.evaluateInstanceList(TransducerTrainer tt, InstanceList data, java.lang.String description)
void	PerClassAccuracyEvaluator.evaluateInstanceList(TransducerTrainer tt, InstanceList data, java.lang.String description)
void	SegmentationEvaluator.evaluateInstanceList(TransducerTrainer tt, InstanceList data, java.lang.String description)
void	TokenAccuracyEvaluator.evaluateInstanceList(TransducerTrainer trainer, InstanceList instances, java.lang.String description)
abstract void	TransducerEvaluator.evaluateInstanceList(TransducerTrainer transducer, InstanceList instances, java.lang.String description)
void	ViterbiWriter.evaluateInstanceList(TransducerTrainer transducerTrainer, InstanceList instances, java.lang.String description)
protected void	CRFOptimizableByBatchLabelLikelihood.gatherConstraints(InstanceList ilist)	Set the constraints by running forward-backward with the output label sequence provided, thus restricting it to only those paths that agree with the label sequence.
protected void	CRFOptimizableByLabelLikelihood.gatherConstraints(InstanceList ilist)
CRFOptimizableByLabelLikelihood	CRFTrainerByLabelLikelihood.getOptimizableCRF(InstanceList trainingSet)
CRFOptimizableByBatchLabelLikelihood	CRFTrainerByThreadedLabelLikelihood.getOptimizableCRF(InstanceList trainingSet)
CRFTrainerByValueGradients.OptimizableCRF	CRFTrainerByValueGradients.getOptimizableCRF(InstanceList trainingSet)	Returns an optimizable CRF that contains a collection of objective functions.
MEMMTrainer.MEMMOptimizableByLabelLikelihood	MEMMTrainer.getOptimizableMEMM(InstanceList trainingSet)
Optimizer	CRFTrainerByL1LabelLikelihood.getOptimizer(InstanceList trainingSet)
Optimizer	CRFTrainerByLabelLikelihood.getOptimizer(InstanceList trainingSet)
Optimizer	CRFTrainerByThreadedLabelLikelihood.getOptimizer(InstanceList trainingSet)
Optimizer	CRFTrainerByValueGradients.getOptimizer(InstanceList trainingSet)	Returns a L-BFGS optimizer, creating if one doesn't exist.
void	CRF.induceFeaturesFor(InstanceList instances)	When the CRF has done feature induction, these new feature conjunctions must be created in the test or validation data in order for them to take effect.
Optimizable.ByCombiningBatchGradient	CRFOptimizableByBatchLabelLikelihood.Factory.newCRFOptimizable(CRF crf, InstanceList trainingData, int numBatches)
Optimizable.ByGradientValue	CRFOptimizableByLabelLikelihood.Factory.newCRFOptimizable(CRF crf, InstanceList trainingData)
Sequence[]	CRF.predict(InstanceList testing)	Deprecated.
void	CRFTrainerByStochasticGradient.setLearningRateByLikelihood(InstanceList trainingSample)	Automatically sets the learning rate to one that would be good
void	CRF.setWeightsDimensionAsIn(InstanceList trainingData)
void	CRF.setWeightsDimensionAsIn(InstanceList trainingData, boolean useSomeUnsupportedTrick)
static void	SimpleTagger.test(TransducerTrainer tt, TransducerEvaluator eval, InstanceList testing)	Test a transducer on the given test data, evaluating accuracy with the given evaluator
boolean	CRFTrainerByLabelLikelihood.train(InstanceList trainingSet, int numIterations)
boolean	CRFTrainerByLabelLikelihood.train(InstanceList training, int numIterationsPerProportion, double[] trainingProportions)	Train a CRF on various-sized subsets of the data.
boolean	CRFTrainerByStochasticGradient.train(InstanceList trainingSet, int numIterations)
boolean	CRFTrainerByStochasticGradient.train(InstanceList trainingSet, int numIterations, int numIterationsBetweenEvaluation)
boolean	CRFTrainerByThreadedLabelLikelihood.train(InstanceList trainingSet, int numIterations)
boolean	CRFTrainerByThreadedLabelLikelihood.train(InstanceList training, int numIterationsPerProportion, double[] trainingProportions)	Train a CRF on various-sized subsets of the data.
boolean	CRFTrainerByValueGradients.train(InstanceList trainingSet, int numIterations)	Trains a CRF until convergence or specified number of iterations, whichever is earlier.
boolean	CRFTrainerByValueGradients.train(InstanceList training, int numIterationsPerProportion, double[] trainingProportions)	Train a CRF on various-sized subsets of the data.
boolean	HMM.train(InstanceList ilist)	Trains a HMM without validation and evaluation.
boolean	HMM.train(InstanceList ilist, InstanceList validation, InstanceList testing)	Trains a HMM with evaluator set to null.
boolean	HMM.train(InstanceList ilist, InstanceList validation, InstanceList testing, TransducerEvaluator eval)
boolean	HMMTrainerByLikelihood.train(InstanceList trainingSet, int numIterations)
boolean	HMMTrainerByLikelihood.train(InstanceList trainingSet, InstanceList unlabeledSet, int numIterations)
boolean	MEMMTrainer.train(InstanceList training)	Trains a MEMM until convergence.
boolean	MEMMTrainer.train(InstanceList training, int numIterations)	Trains a MEMM for specified number of iterations or until convergence whichever occurs first; returns true if training converged within specified iterations.
boolean	MEMMTrainer.train(InstanceList training, InstanceList validation, InstanceList testing, TransducerEvaluator eval, int numIterations, int numIterationsPerProportion, double[] trainingProportions)	Not implemented yet.
boolean	NoopTransducerTrainer.train(InstanceList trainingSet)
boolean	NoopTransducerTrainer.train(InstanceList trainingSet, int numIterations)
boolean	ShallowTransducerTrainer.train(InstanceList trainingSet, int numIterations)	Deprecated.
static CRF	SimpleTagger.train(InstanceList training, InstanceList testing, TransducerEvaluator eval, int[] orders, java.lang.String defaultLabel, java.lang.String forbidden, java.lang.String allowed, boolean connected, int iterations, double var, CRF crf)	Create and train a CRF model from the given training data, optionally testing it on the given test data.
boolean	TransducerTrainer.train(InstanceList trainingSet)
abstract boolean	TransducerTrainer.train(InstanceList trainingSet, int numIterations)	Train the transducer associated with this TransducerTrainer.
boolean	CRFTrainerByLabelLikelihood.trainIncremental(InstanceList training)
boolean	CRFTrainerByStochasticGradient.trainIncremental(InstanceList trainingSet)
boolean	CRFTrainerByThreadedLabelLikelihood.trainIncremental(InstanceList training)
boolean	CRFTrainerByValueGradients.trainIncremental(InstanceList training)	Trains a CRF until convergence.
abstract boolean	TransducerTrainer.ByIncrements.trainIncremental(InstanceList incrementalTrainingSet)
boolean	CRFTrainerByLabelLikelihood.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions)
boolean	CRFTrainerByLabelLikelihood.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions, java.lang.String gainName)	Train a CRF using feature induction to generate conjunctions of features.
boolean	MEMMTrainer.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions, java.lang.String gainName)	Not implemented yet.

Constructors in cc.mallet.fst with parameters of type InstanceList

Constructor	Description
CRFOptimizableByBatchLabelLikelihood(CRF crf, InstanceList ilist, int numBatches)
CRFOptimizableByLabelLikelihood(CRF crf, InstanceList ilist)
CRFTrainerByStochasticGradient(CRF crf, InstanceList trainingSample)
LabelDistributionEvaluator(InstanceList[] instanceLists, java.lang.String[] descriptions)
MEMMOptimizableByLabelLikelihood(MEMM memm, InstanceList trainingData)
MultiSegmentationEvaluator(InstanceList[] instanceLists, java.lang.String[] instanceListDescriptions, java.lang.Object[] segmentStartTags, java.lang.Object[] segmentContinueTags)
MultiSegmentationEvaluator(InstanceList instanceList1, java.lang.String description1, InstanceList instanceList2, java.lang.String description2, InstanceList instanceList3, java.lang.String description3, java.lang.Object[] segmentStartTags, java.lang.Object[] segmentContinueTags)
MultiSegmentationEvaluator(InstanceList instanceList1, java.lang.String description1, InstanceList instanceList2, java.lang.String description2, java.lang.Object[] segmentStartTags, java.lang.Object[] segmentContinueTags)
MultiSegmentationEvaluator(InstanceList instanceList1, java.lang.String description1, java.lang.Object[] segmentStartTags, java.lang.Object[] segmentContinueTags)
OptimizableCRF(CRF crf, InstanceList ilist)
PerClassAccuracyEvaluator(InstanceList[] instanceLists, java.lang.String[] descriptions)
PerClassAccuracyEvaluator(InstanceList i1, java.lang.String d1)
PerClassAccuracyEvaluator(InstanceList i1, java.lang.String d1, InstanceList i2, java.lang.String d2)
SegmentationEvaluator(InstanceList[] instanceLists, java.lang.String[] descriptions)
SegmentationEvaluator(InstanceList instanceList1, java.lang.String description1)
SegmentationEvaluator(InstanceList instanceList1, java.lang.String description1, InstanceList instanceList2, java.lang.String description2)
SegmentationEvaluator(InstanceList instanceList1, java.lang.String description1, InstanceList instanceList2, java.lang.String description2, InstanceList instanceList3, java.lang.String description3)
ThreadedOptimizable(Optimizable.ByCombiningBatchGradient optimizable, InstanceList trainingSet, int numFactors, CacheStaleIndicator cacheIndicator)	Initializes the optimizable and starts new threads.
TokenAccuracyEvaluator(InstanceList[] instanceLists, java.lang.String[] descriptions)
TokenAccuracyEvaluator(InstanceList instanceList1, java.lang.String description1)
TokenAccuracyEvaluator(InstanceList instanceList1, java.lang.String description1, InstanceList instanceList2, java.lang.String description2)
TokenAccuracyEvaluator(InstanceList instanceList1, java.lang.String description1, InstanceList instanceList2, java.lang.String description2, InstanceList instanceList3, java.lang.String description3)
TransducerEvaluator(InstanceList[] instanceLists, java.lang.String[] instanceListDescriptions)
ViterbiWriter(java.lang.String filenamePrefix, InstanceList[] instanceLists, java.lang.String[] descriptions)
ViterbiWriter(java.lang.String filenamePrefix, InstanceList instanceList1, java.lang.String description1)
ViterbiWriter(java.lang.String filenamePrefix, InstanceList instanceList1, java.lang.String description1, InstanceList instanceList2, java.lang.String description2)
ViterbiWriter(java.lang.String filenamePrefix, InstanceList instanceList1, java.lang.String description1, InstanceList instanceList2, java.lang.String description2, InstanceList instanceList3, java.lang.String description3)

Uses of InstanceList in cc.mallet.fst.confidence

Methods in cc.mallet.fst.confidence with parameters of type InstanceList

Modifier and Type	Method	Description
java.util.ArrayList	ConstrainedViterbiTransducerCorrector.correctLeastConfidentSegments(InstanceList ilist, java.lang.Object[] startTags, java.lang.Object[] continueTags)
java.util.ArrayList	ConstrainedViterbiTransducerCorrector.correctLeastConfidentSegments(InstanceList ilist, java.lang.Object[] startTags, java.lang.Object[] continueTags, boolean findIncorrect)	Returns an ArrayList of corrected Sequences.
java.util.ArrayList	IsolatedSegmentTransducerCorrector.correctLeastConfidentSegments(InstanceList ilist, java.lang.Object[] startTags, java.lang.Object[] continueTags)
java.util.ArrayList	TransducerCorrector.correctLeastConfidentSegments(InstanceList ilist, java.lang.Object[] startTags, java.lang.Object[] continueTags)
void	ConfidenceCorrectorEvaluator.evaluate(Transducer model, java.util.ArrayList predictions, InstanceList ilist, java.util.ArrayList correctedSegments, java.lang.String description, java.io.PrintStream outputStream, boolean errorsInUncorrected)	Only evaluates over sequences which contain errors.
java.util.ArrayList	ConstrainedViterbiTransducerCorrector.getLeastConfidentSegments(InstanceList ilist, java.lang.Object[] startTags, java.lang.Object[] continueTags)	Returns the least confident segments in ilist
InstanceWithConfidence[]	TransducerSequenceConfidenceEstimator.rankInstancesByConfidence(InstanceList ilist, java.lang.Object[] startTags, java.lang.Object[] continueTags)	Ranks all Sequencess in this InstanceList by confidence estimate.
PipedInstanceWithConfidence[]	MaxEntSequenceConfidenceEstimator.rankPipedInstancesByConfidence(InstanceList ilist, java.lang.Object[] startTags, java.lang.Object[] continueTags)
Segment[]	TransducerConfidenceEstimator.rankSegmentsByConfidence(InstanceList ilist, java.lang.Object[] startTags, java.lang.Object[] continueTags)	Ranks all Segments in this InstanceList by confidence estimate.
MaxEnt	MaxEntConfidenceEstimator.trainClassifier(InstanceList ilist, java.lang.String correct, java.lang.String incorrect)
MaxEnt	MaxEntSequenceConfidenceEstimator.trainClassifier(InstanceList ilist, java.lang.String correct, java.lang.String incorrect)	Train underlying classifier on ilist.

Uses of InstanceList in cc.mallet.fst.semi_supervised

Fields in cc.mallet.fst.semi_supervised declared as InstanceList

Modifier and Type	Field	Description
protected InstanceList	CRFOptimizableByEntropyRegularization.data

Methods in cc.mallet.fst.semi_supervised with parameters of type InstanceList

Modifier and Type	Method	Description
Optimizable.ByGradientValue	CRFTrainerByGE.getOptimizable(InstanceList unlabeled)
static java.util.HashMap<java.lang.Integer,double[][]>	FSTConstraintUtil.loadGEConstraints(java.io.Reader fileReader, InstanceList data)
boolean	CRFTrainerByEntropyRegularization.train(InstanceList trainingSet, int numIterations)
boolean	CRFTrainerByEntropyRegularization.train(InstanceList labeled, InstanceList unlabeled, int numIterations)	Performs CRF training with label likelihood and entropy regularization.
boolean	CRFTrainerByGE.train(InstanceList unlabeledSet, int numIterations)
boolean	CRFTrainerByLikelihoodAndGE.train(InstanceList trainingSet, int numIterations)
boolean	CRFTrainerByLikelihoodAndGE.train(InstanceList trainingSet, InstanceList unlabeledSet, int numIterations)

Constructors in cc.mallet.fst.semi_supervised with parameters of type InstanceList

Constructor	Description
CRFOptimizableByEntropyRegularization(CRF crf, InstanceList ilist)	Initializes the structures (sets the scaling factor to 1.0).
CRFOptimizableByEntropyRegularization(CRF crf, InstanceList ilist, double scalingFactor)	Initializes the structures.
CRFOptimizableByGE(CRF crf, java.util.ArrayList<GEConstraint> constraints, InstanceList data, StateLabelMap map, int numThreads)
CRFOptimizableByGE(CRF crf, java.util.ArrayList<GEConstraint> constraints, InstanceList data, StateLabelMap map, int numThreads, double weight)

Uses of InstanceList in cc.mallet.fst.semi_supervised.constraints

Methods in cc.mallet.fst.semi_supervised.constraints with parameters of type InstanceList

Modifier and Type	Method	Description
java.util.BitSet	GEConstraint.preProcess(InstanceList data)
java.util.BitSet	OneLabelGEConstraints.preProcess(InstanceList data)
java.util.BitSet	OneLabelL2RangeGEConstraints.preProcess(InstanceList data)
java.util.BitSet	SelfTransitionGEConstraint.preProcess(InstanceList data)
java.util.BitSet	TwoLabelGEConstraints.preProcess(InstanceList data)

Uses of InstanceList in cc.mallet.fst.semi_supervised.pr

Fields in cc.mallet.fst.semi_supervised.pr declared as InstanceList

Modifier and Type	Field	Description
protected InstanceList	ConstraintsOptimizableByPR.trainingSet
protected InstanceList	CRFOptimizableByKL.trainingSet

Methods in cc.mallet.fst.semi_supervised.pr with parameters of type InstanceList

Modifier and Type	Method	Description
boolean	CRFTrainerByPR.train(InstanceList train, int numIterations)
boolean	CRFTrainerByPR.train(InstanceList train, int minIter, int maxIter)
boolean	CRFTrainerByPR.train(InstanceList train, int minIter, int maxIter, int maxIterPerStep)

Constructors in cc.mallet.fst.semi_supervised.pr with parameters of type InstanceList

Constructor	Description
ConstraintsOptimizableByPR(CRF crf, InstanceList ilist, PRAuxiliaryModel model)
ConstraintsOptimizableByPR(CRF crf, InstanceList ilist, PRAuxiliaryModel model, int numThreads)
CRFOptimizableByKL(CRF crf, InstanceList trainingSet, PRAuxiliaryModel auxModel, double[][][][] cachedDots, int numThreads, double weight)

Uses of InstanceList in cc.mallet.fst.semi_supervised.pr.constraints

Methods in cc.mallet.fst.semi_supervised.pr.constraints with parameters of type InstanceList

Modifier and Type	Method	Description
java.util.BitSet	OneLabelL2IndPRConstraints.preProcess(InstanceList data)
java.util.BitSet	OneLabelL2PRConstraints.preProcess(InstanceList data)
java.util.BitSet	PRConstraint.preProcess(InstanceList data)

Uses of InstanceList in cc.mallet.fst.semi_supervised.tui

Methods in cc.mallet.fst.semi_supervised.tui with parameters of type InstanceList

Modifier and Type	Method	Description
static CRF	SimpleTaggerWithConstraints.getCRF(InstanceList training, int[] orders, java.lang.String defaultLabel, java.lang.String forbidden, java.lang.String allowed, boolean connected)
static void	SimpleTaggerWithConstraints.test(TransducerTrainer tt, TransducerEvaluator eval, InstanceList testing)	Test a transducer on the given test data, evaluating accuracy with the given evaluator
static CRF	SimpleTaggerWithConstraints.trainGE(InstanceList training, InstanceList testing, java.util.ArrayList<GEConstraint> constraints, CRF crf, TransducerEvaluator eval, int iterations, double var, int resets)	Create and train a CRF model from the given training data, optionally testing it on the given test data.
static CRF	SimpleTaggerWithConstraints.trainPR(InstanceList training, InstanceList testing, java.util.ArrayList<PRConstraint> constraints, CRF crf, TransducerEvaluator eval, int iterations, double var)	Create and train a CRF model from the given training data, optionally testing it on the given test data.

Uses of InstanceList in cc.mallet.pipe

Methods in cc.mallet.pipe that return InstanceList

Modifier and Type	Method	Description
static InstanceList	AddClassifierTokenPredictions.convert(InstanceList ilist, Noop alphabetsPipe)	Converts each instance containing a FeatureVectorSequence to multiple instances, each containing an AugmentableFeatureVector as data.
static InstanceList	AddClassifierTokenPredictions.convert(Instance inst, Noop alphabetsPipe)

Methods in cc.mallet.pipe with parameters of type InstanceList

Modifier and Type	Method	Description
static InstanceList	AddClassifierTokenPredictions.convert(InstanceList ilist, Noop alphabetsPipe)	Converts each instance containing a FeatureVectorSequence to multiple instances, each containing an AugmentableFeatureVector as data.

Constructors in cc.mallet.pipe with parameters of type InstanceList

Constructor	Description
AddClassifierTokenPredictions(AddClassifierTokenPredictions.TokenClassifiers tokenClassifiers, int[] predRanks2add, boolean binary, InstanceList testList)
AddClassifierTokenPredictions(InstanceList trainList)
AddClassifierTokenPredictions(InstanceList trainList, InstanceList testList)
TokenClassifiers(ClassifierTrainer trainer, InstanceList trainList, int randSeed, int numCV)
TokenClassifiers(InstanceList trainList)	Train a token classifier using the given Instances with 5-fold cross validation
TokenClassifiers(InstanceList trainList, int randSeed, int numCV)

Uses of InstanceList in cc.mallet.pipe.iterator

Methods in cc.mallet.pipe.iterator that return InstanceList

Modifier and Type	Method	Description
static InstanceList	DBInstanceIterator.getInstances(java.lang.String dbName)

Constructors in cc.mallet.pipe.iterator with parameters of type InstanceList

Constructor	Description
SegmentIterator(Transducer model, InstanceList ilist, java.lang.Object[] segmentStartTags, java.lang.Object[] segmentContinueTags)	NOTE!: Assumes that segmentStartTags[i] corresponds to segmentContinueTags[i].
SegmentIterator(InstanceList ilist, java.lang.Object[] startTags, java.lang.Object[] inTags, java.util.ArrayList predictions)	Useful when no Transduce is specified.

Uses of InstanceList in cc.mallet.regression

Constructors in cc.mallet.regression with parameters of type InstanceList

Constructor	Description
CoordinateDescent(InstanceList data, double l1Weight)
LeastSquares(InstanceList data)
LeastSquares(InstanceList data, double regularization)
LinearRegressionTrainer(InstanceList data)

Uses of InstanceList in cc.mallet.regression.tui

Constructors in cc.mallet.regression.tui with parameters of type InstanceList

Constructor	Description
Regression(InstanceList data, double regularization)

Uses of InstanceList in cc.mallet.topics

Fields in cc.mallet.topics declared as InstanceList

Modifier and Type	Field	Description
InstanceList	RTopicModel.instances
InstanceList	WordEmbeddingCallable.instances
protected InstanceList	LDAHyper.testing	Deprecated.

Methods in cc.mallet.topics that return InstanceList

Modifier and Type	Method	Description
InstanceList	LDA.getInstanceList()	Deprecated.

Methods in cc.mallet.topics with parameters of type InstanceList

Modifier and Type	Method	Description
void	LDA.addDocuments(InstanceList additionalDocuments, int numIterations, int showTopicsInterval, int outputModelInterval, java.lang.String outputModelFilename, Randoms r)	Deprecated.
static void	RTopicModel.addInstance(InstanceList instances, java.lang.String id, java.lang.String text)	This is a helper method that simplifies class casting from rJava.
void	DMRTopicModel.addInstances(InstanceList training)
void	LabeledLDA.addInstances(InstanceList training)
void	LDAHyper.addInstances(InstanceList training)	Deprecated.
void	LDAHyper.addInstances(InstanceList training, java.util.List<LabelSequence> topics)	Deprecated.
void	NPTopicModel.addInstances(InstanceList training, int initialTopics)
void	ParallelTopicModel.addInstances(InstanceList training)
void	PolylingualTopicModel.addInstances(InstanceList[] training)
static void	RTopicModel.addInstances(InstanceList instances, java.lang.String[] ids, java.lang.String[] texts)
void	SimpleLDA.addInstances(InstanceList training)
void	WeightedTopicModel.addInstances(InstanceList training)
void	WordEmbeddings.countWords(InstanceList instances, double samplingFactor)
double	HierarchicalLDA.empiricalLikelihood(int numSamples, InstanceList testing)	For use with empirical likelihood evaluation: sample a path through the tree, then sample a multinomial over topics in that path, then return a weighted sum of words.
double	LDAHyper.empiricalLikelihood(int numSamples, InstanceList testing)	Deprecated.
void	HierarchicalPAM.estimate(InstanceList documents, InstanceList testing, int numIterations, int showTopicsInterval, int outputModelInterval, int optimizeInterval, java.lang.String outputModelFilename, Randoms r)
void	LDA.estimate(InstanceList documents, int numIterations, int showTopicsInterval, int outputModelInterval, java.lang.String outputModelFilename, Randoms r)	Deprecated.
void	PAM4L.estimate(InstanceList documents, int numIterations, int optimizeInterval, int showTopicsInterval, int outputModelInterval, java.lang.String outputModelFilename, Randoms r)
void	TopicalNGrams.estimate(InstanceList documents, int numIterations, int showTopicsInterval, int outputModelInterval, java.lang.String outputModelFilename, Randoms r)
double	MarginalProbEstimator.evaluateLeftToRight(InstanceList testing, int numParticles, boolean usingResampling, java.io.PrintStream docProbabilityStream)
void	LDAStream.inferenceWithTheta(int maxIteration, InstanceList theta)
void	HierarchicalLDA.initialize(InstanceList instances, InstanceList testing, int numLevels, Randoms random)
void	RTopicModel.loadDocuments(InstanceList instances)
void	LDAHyper.setTestingInstances(InstanceList testing)	Deprecated. Held-out instances for empirical likelihood calculation
void	WordEmbeddings.train(InstanceList instances, int numThreads, int numSamples)
void	TopicInferencer.writeInferredDistributions(InstanceList instances, java.io.File distributionsFile, int numIterations, int thinning, int burnIn, double threshold, int max)	Infer topics for the provided instances and write distributions to the provided file.

Constructors in cc.mallet.topics with parameters of type InstanceList

Constructor	Description
DMROptimizable(InstanceList instances, MaxEnt initialClassifier)
NonNegativeMatrixFactorization(InstanceList instances, int numFactors, boolean idfWeighting)
NonNegativeMatrixFactorization(InstanceList instances, int numFactors, boolean idfWeighting, Randoms random)
WordEmbeddingCallable(WordEmbeddings model, InstanceList instances, int numSamples, int numThreads, int threadID)

Uses of InstanceList in cc.mallet.types

Subclasses of InstanceList in cc.mallet.types

Modifier and Type	Class	Description
class	MultiInstanceList	An implementation of InstanceList that logically combines multiple instance lists so that they appear as one list without copying the original lists.
class	PagedInstanceList	An InstanceList which avoids OutOfMemoryErrors by saving Instances to disk when there is not enough memory to create a new Instance.

Methods in cc.mallet.types that return InstanceList

Modifier and Type	Method	Description
InstanceList	InstanceList.cloneEmpty()
InstanceList	MultiInstanceList.cloneEmpty()
InstanceList	PagedInstanceList.cloneEmpty()
protected InstanceList	InstanceList.cloneEmptyInto(InstanceList ret)
protected InstanceList	MultiInstanceList.cloneEmptyInto(InstanceList ret)
InstanceList	InvertedIndex.getInstanceList()
static InstanceList	InstanceList.load(java.io.File file)	Constructs a new InstanceList, deserialized from file.
static InstanceList	PagedInstanceList.load(java.io.File file)	Constructs a new InstanceList, deserialized from file.
InstanceList[]	CrossValidationIterator.next()	Returns the next training/testing split.
InstanceList[]	InstanceList.CrossValidationIterator.next()
InstanceList[]	CrossValidationIterator.nextSplit()	Returns the next training/testing split.
InstanceList[]	CrossValidationIterator.nextSplit(int numTrainFolds)	Returns the next training/testing split.
InstanceList[]	InstanceList.CrossValidationIterator.nextSplit()	Returns the next training/testing split.
InstanceList[]	InstanceList.CrossValidationIterator.nextSplit(int numTrainFolds)	Returns the next split, given the number of folds you want in the training data.
InstanceList	InstanceList.sampleWithInstanceWeights(java.util.Random r)	Deprecated.
InstanceList	InstanceList.sampleWithReplacement(java.util.Random r, int numSamples)
InstanceList	InstanceList.sampleWithWeights(java.util.Random r, double[] weights)	Returns an InstanceList of the same size, where the instances come from the random sampling (with replacement) of this list using the given weights.
InstanceList	InstanceList.shallowClone()
InstanceList	MultiInstanceList.shallowClone()
InstanceList	PagedInstanceList.shallowClone()
InstanceList[]	InstanceList.split(double[] proportions)
InstanceList[]	InstanceList.split(java.util.Random r, double[] proportions)	Shuffles the elements of this list among several smaller lists.
InstanceList[]	MultiInstanceList.split(double[] proportions)
InstanceList[]	MultiInstanceList.split(java.util.Random r, double[] proportions)
InstanceList[]	PagedInstanceList.split(java.util.Random r, double[] proportions)	Shuffles the elements of this list among several smaller lists.
InstanceList[]	InstanceList.splitInOrder(double[] proportions)	Chops this list into several sequential sublists.
InstanceList[]	InstanceList.splitInOrder(int[] counts)
InstanceList[]	MultiInstanceList.splitInOrder(double[] proportions)
InstanceList[]	MultiInstanceList.splitInOrder(int[] counts)
InstanceList[]	InstanceList.splitInTwoByModulo(int m)	Returns a pair of new lists such that the first list in the pair contains every mth element of this list, starting with the first.
InstanceList[]	MultiInstanceList.splitInTwoByModulo(int m)
InstanceList[]	InstanceList.stratifiedSplit(java.util.Random r, double[] proportions)	Shuffles the elements of this list among several smaller lists, each sublist having a number of elements proportional to the amount given in the array.
InstanceList[]	InstanceList.stratifiedSplitInOrder(double[] proportions)	Chops this list into several sequential sublists, where each sublist contains an (approximately) equal proportion of each target label.
InstanceList	InstanceList.subList(double proportion)
InstanceList	InstanceList.subList(int start, int end)
InstanceList	MultiInstanceList.subList(double proportion)
InstanceList	MultiInstanceList.subList(int start, int end)

Methods in cc.mallet.types with parameters of type InstanceList

Modifier and Type	Method	Description
protected static java.lang.Object[]	GainRatio.calcGainRatios(InstanceList ilist, int[] instIndices, int minNumInsts)	Calculates gain ratios for all (feature, split point) pairs snd returns array of:
static double[][]	PerLabelInfoGain.calcPerLabelInfoGains(InstanceList ilist)
protected InstanceList	InstanceList.cloneEmptyInto(InstanceList ret)
protected InstanceList	MultiInstanceList.cloneEmptyInto(InstanceList ret)
static GainRatio	GainRatio.createGainRatio(InstanceList ilist)	Constructs a GainRatio object.
static GainRatio	GainRatio.createGainRatio(InstanceList ilist, int[] instIndices, int minNumInsts)	Constructs a GainRatio object
void	FeatureInducer.induceFeaturesFor(InstanceList ilist, boolean withFeatureShrinkage, boolean addPerClassFeatures)
PartiallyRankedFeatureVector	PartiallyRankedFeatureVector.Factory.newPartiallyRankedFeatureVector(InstanceList ilist, LabelVector[] posteriors)
PartiallyRankedFeatureVector[]	PartiallyRankedFeatureVector.PerLabelFactory.newPartiallyRankedFeatureVectors(InstanceList ilist, LabelVector[] posteriors)
RankedFeatureVector	BiNormalSeparation.Factory.newRankedFeatureVector(InstanceList instanceList)	Create a new feature ranking for the given instance list.
RankedFeatureVector	ExpGain.Factory.newRankedFeatureVector(InstanceList ilist)
RankedFeatureVector	FeatureCounts.Factory.newRankedFeatureVector(InstanceList instances)
RankedFeatureVector	GradientGain.Factory.newRankedFeatureVector(InstanceList ilist)
RankedFeatureVector	InfoGain.Factory.newRankedFeatureVector(InstanceList ilist)
RankedFeatureVector	RankedFeatureVector.Factory.newRankedFeatureVector(InstanceList ilist)
RankedFeatureVector[]	PerLabelFeatureCounts.Factory.newRankedFeatureVectors(InstanceList ilist)
RankedFeatureVector[]	PerLabelInfoGain.Factory.newRankedFeatureVectors(InstanceList ilist)
RankedFeatureVector[]	RankedFeatureVector.PerLabelFactory.newRankedFeatureVectors(InstanceList ilist)
void	FeatureSelector.selectFeaturesFor(InstanceList ilist)
void	FeatureSelector.selectFeaturesForAllLabels(InstanceList ilist)
void	FeatureSelector.selectFeaturesForPerLabel(InstanceList ilist)
static int[]	GainRatio.sortInstances(InstanceList ilist, int[] instIndices, int featureIndex)

Constructors in cc.mallet.types with parameters of type InstanceList

Constructor	Description
BiNormalSeparation(InstanceList ilist)	Create a new feature ranking for the given instance list.
CrossValidationIterator(InstanceList ilist, int _nfolds)	Constructs a new n-fold cross-validation iterator
CrossValidationIterator(InstanceList ilist, int nfolds, java.util.Random r)	Constructs a new n-fold cross-validation iterator
ExpGain(InstanceList ilist, Classification[] classifications, double gaussianPriorVariance)
ExpGain(InstanceList ilist, LabelVector[] classifications, double gaussianPriorVariance)
FeatureCounts(InstanceList instances)
FeatureInducer(RankedFeatureVector.Factory ranker, InstanceList ilist, int numNewFeatures)
FeatureInducer(RankedFeatureVector.Factory ranker, InstanceList ilist, int numNewFeatures, int beam1, int beam2)
GradientGain(InstanceList ilist, Classification[] classifications)
GradientGain(InstanceList ilist, LabelVector[] classifications)
InfoGain(InstanceList ilist)
InvertedIndex(InstanceList ilist)
KLGain(InstanceList ilist, Classification[] classifications)
KLGain(InstanceList ilist, LabelVector[] classifications)
MultiInstanceList(InstanceList[] lists)	Constructs a MultiInstanceList with an array of InstanceList
PerLabelFeatureCounts(InstanceList ilist)
PerLabelInfoGain(InstanceList ilist)

Constructor parameters in cc.mallet.types with type arguments of type InstanceList

Constructor	Description
MultiInstanceList(java.util.List<InstanceList> lists)	Constructs a MultiInstanceList with a List of InstanceList

Uses of InstanceList in cc.mallet.util

Methods in cc.mallet.util with parameters of type InstanceList

Modifier and Type	Method	Description
static SparseVector	VectorStats.mean(InstanceList instances)	Returns a SparseVector whose entries (taken from the union of those in the instances) are the expected values of those in the InstanceList.
static SparseVector	VectorStats.mean(InstanceList instances, int numIndices)	Returns a SparseVector whose entries (dense with the given number of indices) are the expected values of those in the InstanceList.
static SparseVector	VectorStats.mean(InstanceList instances, int[] indices)	Returns a SparseVector whose entries (the given indices) are the expected values of those in the InstanceList.
static SparseVector	VectorStats.stddev(InstanceList instances)	Square root of unbiased variance.
static SparseVector	VectorStats.stddev(InstanceList instances, boolean unbiased)	Square root of variance.
static SparseVector	VectorStats.stddev(InstanceList instances, SparseVector mean)	Square root of unbiased variance of instances having the given mean
static SparseVector	VectorStats.stddev(InstanceList instances, SparseVector mean, boolean unbiased)	Square root of variance.
static SparseVector	VectorStats.variance(InstanceList instances)	Returns unbiased variance
static SparseVector	VectorStats.variance(InstanceList instances, boolean unbiased)	Returns a SparseVector whose entries (taken from the union of those in the instances) are the variance of those in the InstanceList.
static SparseVector	VectorStats.variance(InstanceList instances, SparseVector mean)	Returns unbiased variance of instances having the given mean.
static SparseVector	VectorStats.variance(InstanceList instances, SparseVector mean, boolean unbiased)	Returns a SparseVector whose entries (taken from the mean argument) are the variance of those in the InstanceList.

Constructors in cc.mallet.util with parameters of type InstanceList

Constructor	Description
FeatureCooccurrenceCounter(InstanceList instances)
FeatureCountTool(InstanceList instances)