Overview | Package | Class | Use | Tree | Deprecated | Index | Help
PREV NEXT | FRAMES NO FRAMES

## 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.fst.semi_supervised |
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)`
`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)`
`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` | `MaxEntTrainer.getOptimizer(InstanceList trainingSet)` This method is called by the train method.
`Optimizer` | `MaxEntL1Trainer.getOptimizer(InstanceList trainingSet)`
`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)`
`AdaBoost` | `AdaBoostTrainer.train(InstanceList trainingList)` Boosting method that resamples instances using their weights
`AdaBoostM2` | `AdaBoostM2Trainer.train(InstanceList trainingList)` Boosting method that resamples instances using their weights
`ConfidencePredictingClassifier` | `ConfidencePredictingClassifierTrainer.train(InstanceList trainList)`
`BaggingClassifier` | `BaggingTrainer.train(InstanceList trainingList)`
`C45` | `C45Trainer.train(InstanceList trainingList)`
`Winnow` | `WinnowTrainer.train(InstanceList trainingList)` Trains winnow on the instance list, updating `weights` according to errors
`abstract C` | `ClassifierTrainer.train(InstanceList trainingSet)`
`Classifier` | `FeatureSelectingClassifierTrainer.train(InstanceList trainingSet)`
`MaxEnt` | `MaxEntTrainer.train(InstanceList trainingSet)`
`NaiveBayes` | `NaiveBayesTrainer.train(InstanceList trainingList)` Create a NaiveBayes classifier from a set of training data.
`MaxEnt` | `RankMaxEntTrainer.train(InstanceList trainingSet)`
`NaiveBayes` | `NaiveBayesEMTrainer.train(InstanceList trainingSet)`
`MCMaxEnt` | `MCMaxEntTrainer.train(InstanceList trainingSet)`
`MaxEnt` | `MaxEntGETrainer.train(InstanceList trainingList)`
`ClassifierEnsemble` | `ClassifierEnsembleTrainer.train(InstanceList trainingSet)`
`DecisionTree` | `DecisionTreeTrainer.train(InstanceList trainingList)`
`BalancedWinnow` | `BalancedWinnowTrainer.train(InstanceList trainingList)` Trains the classifier on the instance list, updating class weight vectors as appropriate
`C` | `ClassifierTrainer.ByOptimization.train(InstanceList trainingSet, int numIterations)`
`MaxEnt` | `MaxEntTrainer.train(InstanceList trainingSet, int numIterations)`
`MaxEnt` | `MaxEntGETrainer.train(InstanceList train, int numIterations)`
`C` | `ClassifierTrainer.ByActiveLearning.train(InstanceList trainingAndUnlabeledSet, Labeler labeler, int numLabelRequests)`
`C` | `ClassifierTrainer.ByIncrements.trainIncremental(InstanceList trainingInstancesToAdd)`
`NaiveBayes` | `NaiveBayesTrainer.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
`abstract Clustering` | `Clusterer.cluster(InstanceList trainingSet)` Return a clustering of an InstanceList
`Clustering` | `KMeans.cluster(InstanceList instances)` Cluster instances
`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`.
`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.
`Clustering` | `GreedyAgglomerative.initializeClustering(InstanceList instances)`
`abstract Clustering` | `HillClimbingClusterer.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` | `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 `String`s.
`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 `String`s.
`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` | `SegmentationEvaluator.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` | `PerClassAccuracyEvaluator.evaluateInstanceList(TransducerTrainer tt, InstanceList data, java.lang.String description)`
`void` | `ViterbiWriter.evaluateInstanceList(TransducerTrainer transducerTrainer, InstanceList instances, java.lang.String description)`
`abstract void` | `TransducerEvaluator.evaluateInstanceList(TransducerTrainer transducer, InstanceList instances, java.lang.String description)`
`void` | `TokenAccuracyEvaluator.evaluateInstanceList(TransducerTrainer trainer, InstanceList instances, java.lang.String description)`
`void` | `CRFWriter.evaluateInstanceList(TransducerTrainer transducer, InstanceList instances, java.lang.String description)`
`void` | `MultiSegmentationEvaluator.evaluateInstanceList(TransducerTrainer tt, InstanceList data, 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.
`CRFOptimizableByLabelLikelihood` | `CRFTrainerByLabelLikelihood.getOptimizableCRF(InstanceList trainingSet)`
`CRFTrainerByValueGradients.OptimizableCRF` | `CRFTrainerByValueGradients.getOptimizableCRF(InstanceList trainingSet)` Returns an optimizable CRF that contains a collection of objective functions.
`CRFOptimizableByBatchLabelLikelihood` | `CRFTrainerByThreadedLabelLikelihood.getOptimizableCRF(InstanceList trainingSet)`
`MEMMTrainer.MEMMOptimizableByLabelLikelihood` | `MEMMTrainer.getOptimizableMEMM(InstanceList trainingSet)`
`Optimizer` | `CRFTrainerByLabelLikelihood.getOptimizer(InstanceList trainingSet)`
`Optimizer` | `CRFTrainerByL1LabelLikelihood.getOptimizer(InstanceList trainingSet)`
`Optimizer` | `CRFTrainerByValueGradients.getOptimizer(InstanceList trainingSet)` Returns a L-BFGS optimizer, creating if one doesn't exist.
`Optimizer` | `CRFTrainerByThreadedLabelLikelihood.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` | `TransducerTrainer.train(InstanceList trainingSet)`
`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` | `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` | `CRFTrainerByLabelLikelihood.train(InstanceList trainingSet, int numIterations)`
`boolean` | `CRFTrainerByStochasticGradient.train(InstanceList trainingSet, int numIterations)`
`boolean` | `NoopTransducerTrainer.train(InstanceList trainingSet, int numIterations)`
`boolean` | `ShallowTransducerTrainer.train(InstanceList trainingSet, int numIterations)` Deprecated.
`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` | `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.
`abstract boolean` | `TransducerTrainer.train(InstanceList trainingSet, int numIterations)` Train the transducer associated with this TransducerTrainer.
`boolean` | `CRFTrainerByThreadedLabelLikelihood.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` | `CRFTrainerByValueGradients.train(InstanceList training, int numIterationsPerProportion, double[] trainingProportions)` Train a CRF on various-sized subsets of the data.
`boolean` | `CRFTrainerByThreadedLabelLikelihood.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` | `CRFTrainerByLabelLikelihood.trainIncremental(InstanceList training)`
`boolean` | `CRFTrainerByStochasticGradient.trainIncremental(InstanceList trainingSet)`
`boolean` | `CRFTrainerByValueGradients.trainIncremental(InstanceList training)` Trains a CRF until convergence.
`abstract boolean` | `TransducerTrainer.ByIncrements.trainIncremental(InstanceList incrementalTrainingSet)`
`boolean` | `CRFTrainerByThreadedLabelLikelihood.trainIncremental(InstanceList training)`
`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
`CRFOptimizableByBatchLabelLikelihood(CRF crf, InstanceList ilist, int numBatches)`
`CRFOptimizableByLabelLikelihood(CRF crf, InstanceList ilist)`
`CRFTrainerByStochasticGradient(CRF crf, InstanceList trainingSample)`
`CRFTrainerByValueGradients.OptimizableCRF(CRF crf, InstanceList ilist)`
`LabelDistributionEvaluator(InstanceList[] instanceLists, java.lang.String[] descriptions)`
`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` | `ConstrainedViterbiTransducerCorrector.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` | `IsolatedSegmentTransducerCorrector.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 `Sequences`s 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 `Segment`s 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
`protected InstanceList` | `CRFOptimizableByGECriteria.data`
`protected InstanceList` | `CRFOptimizableByEntropyRegularization.data`

Methods in cc.mallet.fst.semi_supervised with parameters of type InstanceList
`void` | `GECriteria.calculateExpectations(InstanceList ilist, Transducer transducer, java.util.Map<java.lang.Integer,SumLattice> lattices)` Calculates the model expectation of all feature constraints.
`void` | `GECriteria.setConstraintBits(InstanceList ilist, int start, int end)` Sets a bit for each instance if it has at least one feature constraint (anywhere in the sequence).
`boolean` | `CRFTrainerByEntropyRegularization.train(InstanceList labeled, InstanceList unlabeled, int numIterations)` Performs CRF training with label likelihood and entropy regularization.
`boolean` | `CRFTrainerByEntropyRegularization.train(InstanceList trainingSet, int numIterations)`
`boolean` | `CRFTrainerByGE.train(InstanceList unlabeledSet, int numIterations)`

Constructors in cc.mallet.fst.semi_supervised with parameters of type InstanceList
`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.
`CRFOptimizableByGECriteria(GECriteria geCriteria, CRF crf, InstanceList ilist, int numThreads)` Initializes the structures.

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` | `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)`
`boolean` | `MultiSegmentationEvaluatorACRF.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` | `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)`
`void` | `MultiSegmentationEvaluatorACRF.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` Deprecated.

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` | `ParallelTopicModel.addInstances(InstanceList training)`
`void` | `SimpleLDA.addInstances(InstanceList training)`
`void` | `LDAHyper.addInstances(InstanceList training)` Deprecated.
`void` | `PolylingualTopicModel.addInstances(InstanceList[] training)`
`void` | `NPTopicModel.addInstances(InstanceList training, int initialTopics)`
`void` | `LDAHyper.addInstances(InstanceList training, java.util.List<LabelSequence> topics)` Deprecated.
`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` | `PAM4L.estimate(InstanceList documents, int numIterations, int optimizeInterval, 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` | `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` | `LDAHyper.setTestingInstances(InstanceList testing)` Deprecated. 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` | `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[]` | `InstanceList.CrossValidationIterator.next()`
`InstanceList[]` | `CrossValidationIterator.next()` Returns the next training/testing split.
`InstanceList[]` | `InstanceList.CrossValidationIterator.nextSplit()` Returns the next training/testing split.
`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[]` | `CrossValidationIterator.nextSplit(int numTrainFolds)` Returns the next training/testing split.
`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[]` | `MultiInstanceList.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(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[]` | `MultiInstanceList.splitInOrder(double[] proportions)`
`InstanceList[]` | `InstanceList.splitInOrder(int[] counts)`
`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 `m`th element of this list, starting with the first.
`InstanceList[]` | `MultiInstanceList.splitInTwoByModulo(int m)`
`InstanceList` | `InstanceList.subList(double proportion)`
`InstanceList` | `MultiInstanceList.subList(double proportion)`
`InstanceList` | `InstanceList.subList(int start, int end)`
`InstanceList` | `MultiInstanceList.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` | `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` | `FeatureCounts.Factory.newRankedFeatureVector(InstanceList ilist)`
`RankedFeatureVector` | `GradientGain.Factory.newRankedFeatureVector(InstanceList ilist)`
`RankedFeatureVector` | `RankedFeatureVector.Factory.newRankedFeatureVector(InstanceList ilist)`
`RankedFeatureVector` | `ExpGain.Factory.newRankedFeatureVector(InstanceList ilist)`
`RankedFeatureVector` | `InfoGain.Factory.newRankedFeatureVector(InstanceList ilist)`
`RankedFeatureVector[]` | `PerLabelInfoGain.Factory.newRankedFeatureVectors(InstanceList ilist)`
`RankedFeatureVector[]` | `RankedFeatureVector.PerLabelFactory.newRankedFeatureVectors(InstanceList ilist)`
`RankedFeatureVector[]` | `PerLabelFeatureCounts.Factory.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`.

Overview | Package | Class | Use | Tree | Deprecated | Index | Help
PREV NEXT | FRAMES NO FRAMES