Uses of Class
cc.mallet.types.InstanceList

Packages that use InstanceList
cc.mallet.classify Classes for training and classifying instances. 
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.grmm.learning   
cc.mallet.grmm.learning.extract   
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.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
protected  InstanceList ClassifierTrainer.validationSet
           
 

Methods in cc.mallet.classify that return InstanceList
 InstanceList C45.Node.getInstances()
           
 InstanceList ClassifierTrainer.getValidationInstances()
           
 

Methods in cc.mallet.classify with parameters of type InstanceList
 java.util.ArrayList<Classification> Classifier.classify(InstanceList instances)
           
 double NaiveBayes.dataLogLikelihood(InstanceList ilist)
           
abstract  void ClassifierEvaluator.evaluateInstanceList(ClassifierTrainer trainer, InstanceList instances, java.lang.String description)
           
 void ClassifierAccuracyEvaluator.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)
           
 Optimizable.ByGradientValue RankMaxEntTrainer.getMaximizableTrainer(InstanceList ilist)
           
 Optimizable.ByGradientValue MCMaxEntTrainer.getMaximizableTrainer(InstanceList ilist)
           
 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)
          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.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)
          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)
           
 Winnow WinnowTrainer.train(InstanceList trainingList)
          Trains winnow on the instance list, updating weights according to errors
 MaxEnt RankMaxEntTrainer.train(InstanceList trainingSet)
           
 NaiveBayes NaiveBayesTrainer.train(InstanceList trainingList)
          Create a NaiveBayes classifier from a set of training data.
 NaiveBayes NaiveBayesEMTrainer.train(InstanceList trainingSet)
           
 MaxEnt MaxEntGETrainer.train(InstanceList trainingList)
           
 MCMaxEnt MCMaxEntTrainer.train(InstanceList trainingSet)
           
 Classifier FeatureSelectingClassifierTrainer.train(InstanceList trainingSet)
           
 DecisionTree DecisionTreeTrainer.train(InstanceList trainingList)
           
 MaxEnt MaxEntTrainer.train(InstanceList trainingSet)
           
 ConfidencePredictingClassifier ConfidencePredictingClassifierTrainer.train(InstanceList trainList)
           
 ClassifierEnsemble ClassifierEnsembleTrainer.train(InstanceList trainingSet)
           
 C45 C45Trainer.train(InstanceList trainingList)
           
 BalancedWinnow BalancedWinnowTrainer.train(InstanceList trainingList)
          Trains the classifier on the instance list, updating class weight vectors as appropriate
 BaggingClassifier BaggingTrainer.train(InstanceList trainingList)
           
 AdaBoost AdaBoostTrainer.train(InstanceList trainingList)
          Boosting method that resamples instances using their weights
abstract  C ClassifierTrainer.train(InstanceList trainingSet)
           
 AdaBoostM2 AdaBoostM2Trainer.train(InstanceList trainingList)
          Boosting method that resamples instances using their weights
 MaxEnt MaxEntGETrainer.train(InstanceList train, int numIterations)
           
 MaxEnt MaxEntTrainer.train(InstanceList trainingSet, int numIterations)
           
 C ClassifierTrainer.ByOptimization.train(InstanceList trainingSet, int numIterations)
           
 C ClassifierTrainer.ByActiveLearning.train(InstanceList trainingAndUnlabeledSet, Labeler labeler, int numLabelRequests)
           
 NaiveBayes NaiveBayesTrainer.trainIncremental(InstanceList trainingInstancesToAdd)
           
 C ClassifierTrainer.ByIncrements.trainIncremental(InstanceList trainingInstancesToAdd)
           
 

Constructors in cc.mallet.classify with parameters of type InstanceList
C45.Node(InstanceList ilist, C45.Node parent, int minNumInsts)
           
C45.Node(InstanceList ilist, C45.Node parent, int minNumInsts, int[] instIndices)
           
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)
           
DecisionTree.Node(InstanceList ilist, DecisionTree.Node parent, FeatureSelection fs)
           
MaxEntOptimizableByGE(InstanceList trainingList, java.util.HashMap<java.lang.Integer,double[]> constraints, MaxEnt initClassifier)
           
MaxEntOptimizableByLabelDistribution(InstanceList trainingSet, MaxEnt initialClassifier)
           
MaxEntOptimizableByLabelLikelihood(InstanceList trainingSet, MaxEnt initialClassifier)
           
Trial(Classifier c, InstanceList ilist)
           
 

Uses of InstanceList in cc.mallet.classify.evaluate
 

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

Methods in cc.mallet.cluster that return InstanceList
 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
 Clustering KMeans.cluster(InstanceList instances)
          Cluster instances
 Clustering HillClimbingClusterer.cluster(InstanceList instances)
          While not converged, calls improveClustering to modify the current predicted Clustering.
abstract  Clustering Clusterer.cluster(InstanceList trainingSet)
          Return a clustering of an InstanceList
 Clustering HillClimbingClusterer.cluster(InstanceList instances, int iterations, Clustering initialClustering)
          While not converged, call improveClustering to modify the current predicted Clustering.
abstract  Clustering[] KBestClusterer.clusterKBest(InstanceList trainingSet, int k)
           
 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 HillClimbingClusterer.initializeClustering(InstanceList instances)
           
 Clustering GreedyAgglomerative.initializeClustering(InstanceList instances)
           
 

Constructors in cc.mallet.cluster with parameters of type InstanceList
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
protected  InstanceList PairSampleIterator.instances
           
 

Uses of InstanceList in cc.mallet.cluster.util
 

Methods in cc.mallet.cluster.util that return InstanceList
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
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
 InstanceList CRFExtractor.pipeInstances(java.util.Iterator<Instance> source)
           
 

Methods in cc.mallet.extract with parameters of type InstanceList
 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
protected  InstanceList[] TransducerEvaluator.instanceLists
           
protected  InstanceList ThreadedOptimizable.trainingSet
          Data
protected  InstanceList CRFOptimizableByLabelLikelihood.trainingSet
           
protected  InstanceList CRFOptimizableByBatchLabelLikelihood.trainingSet
           
 

Methods in cc.mallet.fst with parameters of type InstanceList
 void HMM.addFullyConnectedStatesForThreeQuarterLabels(InstanceList trainingSet)
           
 void CRF.addFullyConnectedStatesForThreeQuarterLabels(InstanceList trainingSet)
           
 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.
 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.
 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.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.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.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.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.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.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 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.
 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 ViterbiWriter.evaluateInstanceList(TransducerTrainer transducerTrainer, InstanceList instances, java.lang.String description)
           
 void TokenAccuracyEvaluator.evaluateInstanceList(TransducerTrainer trainer, InstanceList instances, java.lang.String description)
           
 void SegmentationEvaluator.evaluateInstanceList(TransducerTrainer tt, InstanceList data, java.lang.String description)
           
 void PerClassAccuracyEvaluator.evaluateInstanceList(TransducerTrainer tt, InstanceList data, java.lang.String description)
           
 void MultiSegmentationEvaluator.evaluateInstanceList(TransducerTrainer tt, InstanceList data, java.lang.String description)
           
 void LabelDistributionEvaluator.evaluateInstanceList(TransducerTrainer transducer, InstanceList instances, java.lang.String description)
           
 void InstanceAccuracyEvaluator.evaluateInstanceList(TransducerTrainer tt, InstanceList data, java.lang.String description)
           
 void CRFWriter.evaluateInstanceList(TransducerTrainer transducer, InstanceList instances, java.lang.String description)
           
abstract  void TransducerEvaluator.evaluateInstanceList(TransducerTrainer transducer, InstanceList instances, java.lang.String description)
           
protected  void CRFOptimizableByLabelLikelihood.gatherConstraints(InstanceList ilist)
           
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.
 CRFTrainerByValueGradients.OptimizableCRF CRFTrainerByValueGradients.getOptimizableCRF(InstanceList trainingSet)
          Returns an optimizable CRF that contains a collection of objective functions.
 CRFOptimizableByLabelLikelihood CRFTrainerByLabelLikelihood.getOptimizableCRF(InstanceList trainingSet)
           
 MEMMTrainer.MEMMOptimizableByLabelLikelihood MEMMTrainer.getOptimizableMEMM(InstanceList trainingSet)
           
 Optimizer CRFTrainerByValueGradients.getOptimizer(InstanceList trainingSet)
          Returns a L-BFGS optimizer, creating if one doesn't exist.
 Optimizer CRFTrainerByLabelLikelihood.getOptimizer(InstanceList trainingSet)
           
 Optimizer CRFTrainerByL1LabelLikelihood.getOptimizer(InstanceList trainingSet)
           
 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.ByGradientValue CRFOptimizableByLabelLikelihood.Factory.newCRFOptimizable(CRF crf, InstanceList trainingData)
           
 Optimizable.ByCombiningBatchGradient CRFOptimizableByBatchLabelLikelihood.Factory.newCRFOptimizable(CRF crf, InstanceList trainingData, int numBatches)
           
 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 NoopTransducerTrainer.train(InstanceList trainingSet)
           
 boolean MEMMTrainer.train(InstanceList training)
          Trains a MEMM until convergence.
 boolean HMM.train(InstanceList ilist)
          Trains a HMM without validation and evaluation.
 boolean TransducerTrainer.train(InstanceList trainingSet)
           
 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 MEMMTrainer.train(InstanceList training, InstanceList validation, InstanceList testing, TransducerEvaluator eval, int numIterations, int numIterationsPerProportion, double[] trainingProportions)
          Not implemented yet.
 boolean HMMTrainerByLikelihood.train(InstanceList trainingSet, InstanceList unlabeledSet, int numIterations)
           
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 ShallowTransducerTrainer.train(InstanceList trainingSet, int numIterations)
          Deprecated.  
 boolean NoopTransducerTrainer.train(InstanceList trainingSet, int numIterations)
           
 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 HMMTrainerByLikelihood.train(InstanceList trainingSet, int numIterations)
           
 boolean CRFTrainerByValueGradients.train(InstanceList trainingSet, int numIterations)
          Trains a CRF until convergence or specified number of iterations, whichever is earlier.
 boolean CRFTrainerByStochasticGradient.train(InstanceList trainingSet, int numIterations)
           
 boolean CRFTrainerByLabelLikelihood.train(InstanceList trainingSet, int numIterations)
           
abstract  boolean TransducerTrainer.train(InstanceList trainingSet, int numIterations)
          Train the transducer associated with this TransducerTrainer.
 boolean CRFTrainerByValueGradients.train(InstanceList training, int numIterationsPerProportion, double[] trainingProportions)
          Train a CRF on various-sized subsets of the data.
 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, int numIterationsBetweenEvaluation)
           
 boolean CRFTrainerByValueGradients.trainIncremental(InstanceList training)
          Trains a CRF until convergence.
 boolean CRFTrainerByStochasticGradient.trainIncremental(InstanceList trainingSet)
           
 boolean CRFTrainerByLabelLikelihood.trainIncremental(InstanceList training)
           
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 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.
 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.
 

Constructors in cc.mallet.fst with parameters of type InstanceList
CRFOptimizableByBatchLabelLikelihood(CRF crf, InstanceList ilist, int numBatches)
           
CRFOptimizableByLabelLikelihood(CRF crf, InstanceList ilist)
           
CRFTrainerByStochasticGradient(CRF crf, InstanceList trainingSample)
           
CRFTrainerByValueGradients.OptimizableCRF(CRF crf, InstanceList ilist)
           
MEMMTrainer.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)
           
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
 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)
           
 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.
 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 MaxEntSequenceConfidenceEstimator.trainClassifier(InstanceList ilist, java.lang.String correct, java.lang.String incorrect)
          Train underlying classifier on ilist.
 MaxEnt MaxEntConfidenceEstimator.trainClassifier(InstanceList ilist, java.lang.String correct, java.lang.String incorrect)
           
 

Uses of InstanceList in cc.mallet.grmm.learning
 

Methods in cc.mallet.grmm.learning with parameters of type InstanceList
 int ACRF.Template.addSomeUnsupportedWeights(InstanceList training)
           
 java.util.List ACRF.bestAssignment(InstanceList lst)
           
protected  boolean DefaultAcrfTrainer.callEvaluator(ACRF acrf, InstanceList trainingList, InstanceList validationList, InstanceList testSet, int iter, ACRFEvaluator eval)
           
 void ACRF.MaximizableACRF.collectConstraints(InstanceList ilist)
           
static DefaultAcrfTrainer.TestResults DefaultAcrfTrainer.LogEvaluator.computeTestResults(InstanceList testList, java.util.List returnedList)
           
protected  Optimizable.ByGradientValue DefaultAcrfTrainer.createMaximizable(ACRF acrf, InstanceList trainingList)
           
 void ACRF.dumpUnrolledGraphs(InstanceList lst)
           
 boolean MultiSegmentationEvaluatorACRF.evaluate(ACRF acrf, int iter, InstanceList training, InstanceList validation, InstanceList testing)
           
 boolean DefaultAcrfTrainer.LogEvaluator.evaluate(ACRF acrf, int iter, InstanceList training, InstanceList validation, InstanceList testing)
           
 boolean DefaultAcrfTrainer.FileEvaluator.evaluate(ACRF acrf, int iter, InstanceList training, InstanceList validation, InstanceList testing)
           
 boolean AcrfSerialEvaluator.evaluate(ACRF acrf, int iter, InstanceList training, InstanceList validation, InstanceList testing)
           
abstract  boolean ACRFEvaluator.evaluate(ACRF acrf, int iter, InstanceList training, InstanceList validation, InstanceList testing)
          Evalutes the model in the middle of training.
 java.util.List ACRF.getBestLabels(InstanceList lst)
           
 Optimizable.ByGradientValue ACRF.getMaximizable(InstanceList ilst)
           
 boolean DefaultAcrfTrainer.incrementalTrain(ACRF acrf, InstanceList training, InstanceList validation, InstanceList testing, ACRFEvaluator eval, int numIter)
           
 boolean DefaultAcrfTrainer.incrementalTrain(ACRF acrf, InstanceList training, InstanceList validation, InstanceList testing, int numIter)
           
 int ACRF.Template.initWeights(InstanceList training)
          Initializes the weight vectors to the appropriate size for a set of training data.
 int ACRF.FixedFactorTemplate.initWeights(InstanceList training)
           
 boolean DefaultAcrfTrainer.someUnsupportedTrain(ACRF acrf, InstanceList trainingList, InstanceList validationList, InstanceList testSet, ACRFEvaluator eval, int numIter)
           
 void DefaultAcrfTrainer.test(ACRF acrf, InstanceList testing, ACRFEvaluator eval)
           
 void DefaultAcrfTrainer.test(ACRF acrf, InstanceList testing, ACRFEvaluator[] evals)
           
 void ACRFEvaluator.test(ACRF acrf, InstanceList data, java.lang.String description)
           
 void MultiSegmentationEvaluatorACRF.test(InstanceList gold, java.util.List returned, java.lang.String description)
           
 void DefaultAcrfTrainer.LogEvaluator.test(InstanceList testList, java.util.List returnedList, java.lang.String description)
           
 void DefaultAcrfTrainer.FileEvaluator.test(InstanceList testList, java.util.List returnedList, java.lang.String description)
           
 void AcrfSerialEvaluator.test(InstanceList gold, java.util.List returned, java.lang.String description)
           
abstract  void ACRFEvaluator.test(InstanceList gold, java.util.List returned, java.lang.String description)
           
 boolean DefaultAcrfTrainer.train(ACRF acrf, InstanceList training)
           
 boolean ACRFTrainer.train(ACRF acrf, InstanceList training)
           
 boolean DefaultAcrfTrainer.train(ACRF acrf, InstanceList training, ACRFEvaluator eval, int numIter)
           
 boolean ACRFTrainer.train(ACRF acrf, InstanceList training, ACRFEvaluator eval, int numIter)
           
 void DefaultAcrfTrainer.train(ACRF acrf, InstanceList training, InstanceList validation, InstanceList testing, ACRFEvaluator eval, double[] proportions, int iterPerProportion)
           
 boolean DefaultAcrfTrainer.train(ACRF acrf, InstanceList trainingList, InstanceList validationList, InstanceList testSet, ACRFEvaluator eval, int numIter)
           
 boolean ACRFTrainer.train(ACRF acrf, InstanceList training, InstanceList validation, InstanceList testing, ACRFEvaluator eval, int numIter)
           
 boolean DefaultAcrfTrainer.train(ACRF acrf, InstanceList trainingList, InstanceList validationList, InstanceList testSet, ACRFEvaluator eval, int numIter, Optimizable.ByGradientValue macrf)
           
 boolean ACRFTrainer.train(ACRF acrf, InstanceList training, InstanceList validation, InstanceList testing, ACRFEvaluator eval, int numIter, Optimizable.ByGradientValue macrf)
           
 boolean DefaultAcrfTrainer.train(ACRF acrf, InstanceList training, InstanceList validation, InstanceList testing, int numIter)
           
 boolean ACRFTrainer.train(ACRF acrf, InstanceList training, InstanceList validation, InstanceList testing, int numIter)
           
 boolean DefaultAcrfTrainer.train(ACRF acrf, InstanceList training, int numIter)
           
 boolean ACRFTrainer.train(ACRF acrf, InstanceList training, int numIter)
           
 

Constructors in cc.mallet.grmm.learning with parameters of type InstanceList
ACRF.MaximizableACRF(InstanceList ilist)
           
 

Uses of InstanceList in cc.mallet.grmm.learning.extract
 

Fields in cc.mallet.grmm.learning.extract declared as InstanceList
protected  InstanceList ACRFExtractorTrainer.testing
           
protected  InstanceList ACRFExtractorTrainer.training
           
 

Methods in cc.mallet.grmm.learning.extract that return InstanceList
 InstanceList ACRFExtractorTrainer.getTestingData()
           
 InstanceList ACRFExtractorTrainer.getTrainingData()
           
 

Methods in cc.mallet.grmm.learning.extract with parameters of type InstanceList
 Extraction ACRFExtractor.extract(InstanceList testing)
           
 ACRFExtractorTrainer ACRFExtractorTrainer.setData(InstanceList training, InstanceList testing)
           
 

Uses of InstanceList in cc.mallet.pipe
 

Methods in cc.mallet.pipe that return InstanceList
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
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
AddClassifierTokenPredictions.TokenClassifiers(ClassifierTrainer trainer, InstanceList trainList, int randSeed, int numCV)
           
AddClassifierTokenPredictions.TokenClassifiers(InstanceList trainList)
          Train a token classifier using the given Instances with 5-fold cross validation
AddClassifierTokenPredictions.TokenClassifiers(InstanceList trainList, int randSeed, int numCV)
           
AddClassifierTokenPredictions(AddClassifierTokenPredictions.TokenClassifiers tokenClassifiers, int[] predRanks2add, boolean binary, InstanceList testList)
           
AddClassifierTokenPredictions(InstanceList trainList)
           
AddClassifierTokenPredictions(InstanceList trainList, InstanceList testList)
           
 

Uses of InstanceList in cc.mallet.pipe.iterator
 

Constructors in cc.mallet.pipe.iterator with parameters of type InstanceList
SegmentIterator(InstanceList ilist, java.lang.Object[] startTags, java.lang.Object[] inTags, java.util.ArrayList predictions)
          Useful when no Transduce is specified.
SegmentIterator(Transducer model, InstanceList ilist, java.lang.Object[] segmentStartTags, java.lang.Object[] segmentContinueTags)
          NOTE!: Assumes that segmentStartTags[i] corresponds to segmentContinueTags[i].
 

Uses of InstanceList in cc.mallet.topics
 

Fields in cc.mallet.topics declared as InstanceList
protected  InstanceList LDAHyper.testing
           
 

Methods in cc.mallet.topics that return InstanceList
 InstanceList LDA.getInstanceList()
          Deprecated.  
 

Methods in cc.mallet.topics with parameters of type InstanceList
 void LDA.addDocuments(InstanceList additionalDocuments, int numIterations, int showTopicsInterval, int outputModelInterval, java.lang.String outputModelFilename, Randoms r)
          Deprecated.  
 void LDAHyper.addInstances(InstanceList training)
           
 void ParallelTopicModel.addInstances(InstanceList training)
           
 void LDAHyper.addInstances(InstanceList training, java.util.List<LabelSequence> topics)
           
 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)
           
 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)
           
 void LDA.estimate(InstanceList documents, int numIterations, int showTopicsInterval, int outputModelInterval, java.lang.String outputModelFilename, Randoms r)
          Deprecated.  
 void LDAStream.inferenceWithTheta(int maxIteration, InstanceList theta)
           
 void HierarchicalLDA.initialize(InstanceList instances, InstanceList testing, int numLevels, Randoms random)
           
 void LDAHyper.setTestingInstances(InstanceList testing)
          Held-out instances for empirical likelihood calculation
 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
DMROptimizable(InstanceList instances, MaxEnt initialClassifier)
           
 

Uses of InstanceList in cc.mallet.types
 

Subclasses of InstanceList in cc.mallet.types
 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
 InstanceList PagedInstanceList.cloneEmpty()
           
 InstanceList MultiInstanceList.cloneEmpty()
           
 InstanceList InstanceList.cloneEmpty()
           
protected  InstanceList MultiInstanceList.cloneEmptyInto(InstanceList ret)
           
protected  InstanceList InstanceList.cloneEmptyInto(InstanceList ret)
           
 InstanceList InvertedIndex.getInstanceList()
           
static InstanceList PagedInstanceList.load(java.io.File file)
          Constructs a new InstanceList, deserialized from file.
static InstanceList InstanceList.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[] InstanceList.CrossValidationIterator.nextSplit()
          Returns the next training/testing split.
 InstanceList[] CrossValidationIterator.nextSplit(int numTrainFolds)
          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 PagedInstanceList.shallowClone()
           
 InstanceList MultiInstanceList.shallowClone()
           
 InstanceList InstanceList.shallowClone()
           
 InstanceList[] MultiInstanceList.split(double[] proportions)
           
 InstanceList[] InstanceList.split(double[] proportions)
           
 InstanceList[] PagedInstanceList.split(java.util.Random r, double[] proportions)
          Shuffles the elements of this list among several smaller lists.
 InstanceList[] MultiInstanceList.split(java.util.Random r, double[] proportions)
           
 InstanceList[] InstanceList.split(java.util.Random r, double[] proportions)
          Shuffles the elements of this list among several smaller lists.
 InstanceList[] MultiInstanceList.splitInOrder(double[] proportions)
           
 InstanceList[] InstanceList.splitInOrder(double[] proportions)
          Chops this list into several sequential sublists.
 InstanceList[] MultiInstanceList.splitInOrder(int[] counts)
           
 InstanceList[] InstanceList.splitInOrder(int[] counts)
           
 InstanceList[] MultiInstanceList.splitInTwoByModulo(int m)
           
 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.subList(double proportion)
           
 InstanceList InstanceList.subList(double proportion)
           
 InstanceList MultiInstanceList.subList(int start, int end)
           
 InstanceList InstanceList.subList(int start, int end)
           
 

Methods in cc.mallet.types with parameters of type InstanceList
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 MultiInstanceList.cloneEmptyInto(InstanceList ret)
           
protected  InstanceList InstanceList.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 InfoGain.Factory.newRankedFeatureVector(InstanceList ilist)
           
 RankedFeatureVector GradientGain.Factory.newRankedFeatureVector(InstanceList ilist)
           
 RankedFeatureVector FeatureCounts.Factory.newRankedFeatureVector(InstanceList ilist)
           
 RankedFeatureVector ExpGain.Factory.newRankedFeatureVector(InstanceList ilist)
           
 RankedFeatureVector RankedFeatureVector.Factory.newRankedFeatureVector(InstanceList ilist)
           
 RankedFeatureVector[] PerLabelInfoGain.Factory.newRankedFeatureVectors(InstanceList ilist)
           
 RankedFeatureVector[] PerLabelFeatureCounts.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
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 ilist)
           
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
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
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.