Uses of Class
edu.umass.cs.mallet.base.types.InstanceList

Packages that use InstanceList

Package	Description
edu.umass.cs.mallet.base.classify
edu.umass.cs.mallet.base.classify.evaluate
edu.umass.cs.mallet.base.cluster
edu.umass.cs.mallet.base.extract
edu.umass.cs.mallet.base.fst
edu.umass.cs.mallet.base.fst.confidence
edu.umass.cs.mallet.base.pipe
edu.umass.cs.mallet.base.pipe.iterator
edu.umass.cs.mallet.base.topics
edu.umass.cs.mallet.base.types
edu.umass.cs.mallet.base.util
edu.umass.cs.mallet.projects.seg_plus_coref
edu.umass.cs.mallet.projects.seg_plus_coref.anaphora
edu.umass.cs.mallet.projects.seg_plus_coref.coreference
edu.umass.cs.mallet.projects.seg_plus_coref.ie

Uses of InstanceList in edu.umass.cs.mallet.base.classify

Methods in edu.umass.cs.mallet.base.classify that return InstanceList

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

Methods in edu.umass.cs.mallet.base.classify with parameters of type InstanceList

Modifier and Type	Method and Description
ArrayList	Classifier.classify(InstanceList instances)
boolean	ClassifierEvaluating.evaluate(Classifier classifier, boolean finishedTraining, int iteration, double cost, InstanceList training, InstanceList validation, InstanceList testing) Training will terminate if "false" is returned.
boolean	AccuracyEvaluator.evaluate(Classifier classifier, boolean finishedTraining, int iteration, double cost, InstanceList training, InstanceList validation, InstanceList testing)
double	Classifier.getAccuracy(InstanceList ilist)
double	Classifier.getF1(InstanceList ilist, int index) Calculate the F1-measure of the classifier on an instance list for a particular target index
double	Classifier.getF1(InstanceList ilist, Object entry) Calculate the F1-measure of the classifier on an instance list for a particular target entry
Maximizable.ByGradient	MaxEntTrainer.getMaximizableTrainer(InstanceList ilist)
Maximizable.ByGradient	MCMaxEntTrainer.getMaximizableTrainer(InstanceList ilist)
double	Classifier.getPrecision(InstanceList ilist, int index) Calculate the precision of the classifier on an instances list for a particular target index
double	Classifier.getPrecision(InstanceList ilist, Object entry) Calculate the precision of the classifier on an instance list for a particular target entry
double	Classifier.getRecall(InstanceList ilist, int index) Calculate the recall of the classifier on an instance list for a particular target index
double	Classifier.getRecall(InstanceList ilist, Object entry) Calculate the recall of the classifier on an instance list for a particular target entry
Classifier	IncrementalClassifierTrainer.incrementalTrain(InstanceList trainingSet) Return a new classifier tuned from an instanceList
Classifier	IncrementalClassifierTrainer.incrementalTrain(InstanceList trainingSet, InstanceList validationSet) Return a new classifier tuned using two arguments.
Classifier	IncrementalClassifierTrainer.incrementalTrain(InstanceList trainingSet, InstanceList validationSet, InstanceList testSet) Return a new classifier tuned using three arguments.
Classifier	IncrementalClassifierTrainer.incrementalTrain(InstanceList trainingSet, InstanceList validationSet, InstanceList testSet, ClassifierEvaluating evaluator) Return a new classifier tuned using four arguments.
Classifier	NaiveBayesTrainer.incrementalTrain(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier) Create a NaiveBayes classifier from a set of training data and the previous state of the trainer.
abstract Classifier	IncrementalClassifierTrainer.incrementalTrain(InstanceList trainingSet, InstanceList validationSet, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier) Return a new classifier tuned using the five arguments.
void	DecisionTree.induceFeatures(InstanceList ilist, boolean withFeatureShrinkage, boolean inducePerClassFeatures)
Classifier	ClassifierTrainer.train(InstanceList trainingSet)
Classifier	ClassifierTrainer.train(InstanceList trainingSet, InstanceList validationSet)
Classifier	ClassifierTrainer.train(InstanceList trainingSet, InstanceList validationSet, InstanceList testSet)
Classifier	ClassifierTrainer.train(InstanceList trainingSet, InstanceList validationSet, InstanceList testSet, ClassifierEvaluating evaluator)
Classifier	WinnowTrainer.train(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier) Trains winnow on the instance list, updating weights according to errors
Classifier	NaiveBayesTrainer.train(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier) Create a NaiveBayes classifier from a set of training data.
Classifier	MaxEntTrainer.train(InstanceList trainingSet, InstanceList validationSet, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier)
Classifier	MCMaxEntTrainer.train(InstanceList trainingSet, InstanceList validationSet, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier)
Classifier	FeatureSelectingClassifierTrainer.train(InstanceList trainingSet, InstanceList validationSet, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier)
Classifier	DecisionTreeTrainer.train(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier)
Classifier	ConfidencePredictingClassifierTrainer.train(InstanceList trainList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier)
abstract Classifier	ClassifierTrainer.train(InstanceList trainingSet, InstanceList validationSet, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier) Return a new classifier tuned using the three arguments.
Classifier	C45Trainer.train(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier)
Classifier	BalancedWinnowTrainer.train(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier) Trains the classifier on the instance list, updating class weight vectors as appropriate
Classifier	BaggingTrainer.train(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier)
Classifier	AdaBoostTrainer.train(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier) Boosting method that resamples instances using their weights
Classifier	AdaBoostM2Trainer.train(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier) Boosting method that resamples instances using their weights
Classifier	MaxEntTrainer.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, ClassifierEvaluating evaluator, int totalIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction) Trains a maximum entropy model using feature selection and feature induction (adding conjunctions of features as new features).
Classifier	MCMaxEntTrainer.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, ClassifierEvaluating evaluator, int totalIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction) Trains a maximum entropy model using feature selection and feature induction (adding conjunctions of features as new features).
Classifier	MaxEntTrainer.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, ClassifierEvaluating evaluator, MaxEnt maxent, int totalIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, String gainName) Like the other version of trainWithFeatureInduction, but allows some default options to be changed.
Classifier	MCMaxEntTrainer.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, ClassifierEvaluating evaluator, MCMaxEnt maxent, int totalIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, String gainName) Like the other version of trainWithFeatureInduction, but allows some default options to be changed.

Constructors in edu.umass.cs.mallet.base.classify with parameters of type InstanceList

Constructor and Description
Node(InstanceList ilist, C45.Node parent, int minNumInsts)
Node(InstanceList ilist, C45.Node parent, int minNumInsts, int[] instIndices)
Node(InstanceList ilist, DecisionTree.Node parent, FeatureSelection fs)
Trial(Classifier c, InstanceList ilist)

Uses of InstanceList in edu.umass.cs.mallet.base.classify.evaluate

Constructors in edu.umass.cs.mallet.base.classify.evaluate with parameters of type InstanceList

Constructor and Description
AccuracyCoverage(Classifier C, InstanceList ilist, int numBuckets, String title)
AccuracyCoverage(Classifier C, InstanceList ilist, String title)

Uses of InstanceList in edu.umass.cs.mallet.base.cluster

Methods in edu.umass.cs.mallet.base.cluster that return InstanceList

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

Methods in edu.umass.cs.mallet.base.cluster with parameters of type InstanceList

Modifier and Type	Method and Description
Clustering	KMeans.cluster(InstanceList instances) Cluster instances
abstract Clustering	Clusterer.cluster(InstanceList trainingSet) Return a clustering of the training set

Constructors in edu.umass.cs.mallet.base.cluster with parameters of type InstanceList

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

Uses of InstanceList in edu.umass.cs.mallet.base.extract

Methods in edu.umass.cs.mallet.base.extract that return InstanceList

Modifier and Type	Method and Description
InstanceList	CRFExtractor.pipeInstances(PipeInputIterator source)

Uses of InstanceList in edu.umass.cs.mallet.base.fst

Methods in edu.umass.cs.mallet.base.fst with parameters of type InstanceList

Modifier and Type	Method and Description
void	HMM.addFullyConnectedStatesForThreeQuarterLabels(InstanceList trainingSet)
void	CRF4.addFullyConnectedStatesForThreeQuarterLabels(InstanceList trainingSet)
void	CRF3.addFullyConnectedStatesForThreeQuarterLabels(InstanceList trainingSet)
String	HMM.addOrderNStates(InstanceList trainingSet, int[] orders, boolean[] defaults, String start, Pattern forbidden, Pattern allowed, boolean fullyConnected) Assumes that the HMM's output alphabet contains Strings.
String	CRF4.addOrderNStates(InstanceList trainingSet, int[] orders, boolean[] defaults, String start, Pattern forbidden, Pattern allowed, boolean fullyConnected) Assumes that the CRF's output alphabet contains Strings.
String	CRF3.addOrderNStates(InstanceList trainingSet, int[] orders, boolean[] defaults, String start, Pattern forbidden, 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	CRFByGISUpdate.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	CRF4.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	CRF3.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	CRF2.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	CRFByGISUpdate.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	CRF4.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	CRF3.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	CRF2.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	CRFByGISUpdate.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	CRF4.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	CRF3.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	CRF2.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	CRFByGISUpdate.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	CRF4.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	CRF3.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)
double	Transducer.averageTokenAccuracy(InstanceList ilist, String fileName)
void	MultiSegmentationEvaluator.batchTest(InstanceList data, ArrayList predictedSequences, String description, PrintStream viterbiOutputStream) Tests segmentation using an ArrayList of predicted Sequences instead of a Transducer.
boolean	TransducerEvaluator.evaluate(Transducer crf, boolean finishedTraining, int iteration, boolean converged, double cost, InstanceList training, InstanceList validation, InstanceList testing) Evaluates a Tranducers on a given training, validation, and testing set.
boolean	TokenAccuracyEvaluator.evaluate(Transducer crf, boolean finishedTraining, int iteration, boolean converged, double cost, InstanceList training, InstanceList validation, InstanceList testing)
boolean	SegmentationEvaluator.evaluate(Transducer model, boolean finishedTraining, int iteration, boolean converged, double cost, InstanceList training, InstanceList validation, InstanceList testing)
boolean	PerClassAccuracyEvaluator.evaluate(Transducer crf, boolean finishedTraining, int iteration, boolean converged, double cost, InstanceList training, InstanceList validation, InstanceList testing)
boolean	MultiSegmentationEvaluator.evaluate(Transducer model, boolean finishedTraining, int iteration, boolean converged, double cost, InstanceList training, InstanceList validation, InstanceList testing)
void	CRFByGISUpdate.evaluate(TransducerEvaluator eval, InstanceList testing) This method is deprecated.
void	CRF4.evaluate(TransducerEvaluator eval, InstanceList testing) This method is deprecated.
void	CRF3.evaluate(TransducerEvaluator eval, InstanceList testing) This method is deprecated.
protected void	CRF_PL.MaximizableCRF_PL.gatherConstraints(InstanceList ilist)
protected void	CRF4.MaximizableCRF.gatherConstraints(InstanceList ilist)
void	CRF_PL.gatherTrainingSets(InstanceList training)
CRF4.MaximizableCRF	MEMM.getMaximizableCRF(InstanceList ilist)
CRF4.MaximizableCRF	CRF_PL.getMaximizableCRF(InstanceList ilist)
CRF4.MaximizableCRF	CRF4.getMaximizableCRF(InstanceList ilist)
CRFByGISUpdate.MinimizableCRF	CRFByGISUpdate.getMinimizableCRF(InstanceList ilist)
CRF3.MinimizableCRF	CRF3.getMinimizableCRF(InstanceList ilist)
CRF2.MinimizableCRF	CRF2.getMinimizableCRF(InstanceList ilist)
CRF.MinimizableCRF	CRF.getMinimizableCRF(InstanceList ilist)
void	CRF_PL.initializeTrainingFor(InstanceList training)
Sequence[]	CRFByGISUpdate.predict(InstanceList testing) This method is deprecated.
Sequence[]	CRF4.predict(InstanceList testing) This method is deprecated.
Sequence[]	CRF3.predict(InstanceList testing) This method is deprecated.
void	CRF3.priorCost(InstanceList trainingSet)
void	CRFByGISUpdate.setWeightsDimensionAsIn(InstanceList trainingData)
void	CRF4.setWeightsDimensionAsIn(InstanceList trainingData)
void	CRF3.setWeightsDimensionAsIn(InstanceList trainingData)
void	CRF2.setWeightsDimensionAsIn(InstanceList trainingData)
abstract void	TransducerEvaluator.test(Transducer transducer, InstanceList data, String description, PrintStream viterbiOutputStream)
void	TokenAccuracyEvaluator.test(Transducer model, InstanceList data, String description, PrintStream viterbiOutputStream)
void	SegmentationEvaluator.test(Transducer model, InstanceList data, String description, PrintStream viterbiOutputStream)
void	PerClassAccuracyEvaluator.test(Transducer model, InstanceList data, String description, PrintStream viterbiOutputStream)
void	MultiSegmentationEvaluator.test(Transducer model, InstanceList data, String description, PrintStream viterbiOutputStream)
void	InstanceAccuracyEvaluator.test(Transducer crf, InstanceList data, String description, PrintStream viterbiOutputStream)
static void	SimpleTagger.test(Transducer model, TransducerEvaluator eval, InstanceList testing) Test a transducer on the given test data, evaluating accuracy with the given evaluator
boolean	Transducer.train(InstanceList instances)
boolean	HMM.train(InstanceList ilist)
boolean	CRFByGISUpdate.train(InstanceList ilist)
boolean	CRF4.train(InstanceList ilist)
boolean	CRF3.train(InstanceList ilist)
boolean	CRF2.train(InstanceList ilist)
boolean	CRF.train(InstanceList ilist)
boolean	HMM.train(InstanceList ilist, InstanceList validation, InstanceList testing)
boolean	CRFByGISUpdate.train(InstanceList ilist, InstanceList validation, InstanceList testing)
boolean	CRF4.train(InstanceList ilist, InstanceList validation, InstanceList testing)
boolean	CRF3.train(InstanceList ilist, InstanceList validation, InstanceList testing)
boolean	CRF2.train(InstanceList ilist, InstanceList validation, InstanceList testing)
boolean	CRF.train(InstanceList ilist, InstanceList validation, InstanceList testing)
boolean	HMM.train(InstanceList ilist, InstanceList validation, InstanceList testing, TransducerEvaluator eval)
boolean	CRFByGISUpdate.train(InstanceList ilist, InstanceList validation, InstanceList testing, TransducerEvaluator eval)
boolean	CRF4.train(InstanceList ilist, InstanceList validation, InstanceList testing, TransducerEvaluator eval)
boolean	CRF3.train(InstanceList ilist, InstanceList validation, InstanceList testing, TransducerEvaluator eval)
boolean	CRF2.train(InstanceList ilist, InstanceList validation, InstanceList testing, TransducerEvaluator eval)
boolean	CRF.train(InstanceList ilist, InstanceList validation, InstanceList testing, TransducerEvaluator eval)
boolean	MEMM.train(InstanceList training, InstanceList validation, InstanceList testing, TransducerEvaluator eval, int numIterations)
boolean	CRF_PL.train(InstanceList training, InstanceList validation, InstanceList testing, TransducerEvaluator eval, int numIterations)
boolean	CRF4.train(InstanceList ilist, InstanceList validation, InstanceList testing, TransducerEvaluator eval, int numIterations)
boolean	CRF3.train(InstanceList ilist, InstanceList validation, InstanceList testing, TransducerEvaluator eval, int numIterations)
boolean	CRF2.train(InstanceList ilist, InstanceList validation, InstanceList testing, TransducerEvaluator eval, int numIterations)
boolean	CRF.train(InstanceList ilist, InstanceList validation, InstanceList testing, TransducerEvaluator eval, int numIterations)
boolean	MEMM.train(InstanceList training, InstanceList validation, InstanceList testing, TransducerEvaluator eval, int numIterations, int numIterationsPerProportion, double[] trainingProportions)
boolean	CRF_PL.train(InstanceList training, InstanceList validation, InstanceList testing, TransducerEvaluator eval, int numIterations, int numIterationsPerProportion, double[] trainingProportions)
boolean	CRF4.train(InstanceList training, InstanceList validation, InstanceList testing, TransducerEvaluator eval, int numIterations, int numIterationsPerProportion, double[] trainingProportions)
boolean	CRF3.train(InstanceList training, InstanceList validation, InstanceList testing, TransducerEvaluator eval, int numIterations, int numIterationsPerProportion, double[] trainingProportions)
boolean	CRF2.train(InstanceList training, InstanceList validation, InstanceList testing, TransducerEvaluator eval, int numIterations, int numIterationsPerProportion, double[] trainingProportions)
boolean	CRF.train(InstanceList training, InstanceList validation, InstanceList testing, TransducerEvaluator eval, int numIterations, int numIterationsPerProportion, double[] trainingProportions)
boolean	CRFByGISUpdate.train(InstanceList training, InstanceList validation, InstanceList testing, TransducerEvaluator eval, int numIterations, int numIterationsPerProportion, double[] trainingProportions, Minimizer.ByGISUpdate minimizer)
boolean	CRFByGISUpdate.train(InstanceList ilist, InstanceList validation, InstanceList testing, TransducerEvaluator eval, int numIterations, Minimizer.ByGISUpdate minimizer)
static CRF4	SimpleTagger.train(InstanceList training, InstanceList testing, TransducerEvaluator eval, int[] orders, String defaultLabel, String forbidden, String allowed, boolean connected, int iterations, double var, CRF4 crf) Create and train a CRF model from the given training data, optionally testing it on the given test data.
boolean	CRF4.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions)
boolean	CRF3.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions)
boolean	CRF2.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions)
boolean	CRF.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions)
boolean	CRFByGISUpdate.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions, Minimizer.ByGISUpdate minimizer)
boolean	MEMM.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions, String gainName)
boolean	CRF_PL.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions, String gainName)
boolean	CRF4.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions, String gainName)
boolean	CRF3.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions, String gainName)

Constructors in edu.umass.cs.mallet.base.fst with parameters of type InstanceList

Constructor and Description
MaximizableCRF_PL(InstanceList trainingData, CRF_PL memm)
MaximizableCRF(InstanceList ilist, CRF4 crf)
MaximizableMEMM(InstanceList trainingData, MEMM memm)
MinimizableCRF(InstanceList ilist, CRF crf)
MinimizableCRF(InstanceList ilist, CRF2 crf)
MinimizableCRF(InstanceList ilist, CRF3 crf)
MinimizableCRF(InstanceList ilist, CRFByGISUpdate crf)

Uses of InstanceList in edu.umass.cs.mallet.base.fst.confidence

Methods in edu.umass.cs.mallet.base.fst.confidence with parameters of type InstanceList

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

Uses of InstanceList in edu.umass.cs.mallet.base.pipe

Methods in edu.umass.cs.mallet.base.pipe that return InstanceList

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

Methods in edu.umass.cs.mallet.base.pipe with parameters of type InstanceList

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

Constructors in edu.umass.cs.mallet.base.pipe with parameters of type InstanceList

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

Uses of InstanceList in edu.umass.cs.mallet.base.pipe.iterator

Constructors in edu.umass.cs.mallet.base.pipe.iterator with parameters of type InstanceList

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

Uses of InstanceList in edu.umass.cs.mallet.base.topics

Methods in edu.umass.cs.mallet.base.topics with parameters of type InstanceList

Modifier and Type	Method and Description
void	TopicalNGrams.estimate(InstanceList documents, int numIterations, int showTopicsInterval, int outputModelInterval, String outputModelFilename, Random r)
void	LDA.estimate(InstanceList documents, int numIterations, int showTopicsInterval, int outputModelInterval, String outputModelFilename, Random r)

Uses of InstanceList in edu.umass.cs.mallet.base.types

Subclasses of InstanceList in edu.umass.cs.mallet.base.types

Modifier and Type	Class and Description
class	PagedInstanceList xxx .split() methods still unreliable An InstanceList which avoids OutOfMemoryErrors by saving Instances to disk when there is not enough memory to create a new Instance.

Methods in edu.umass.cs.mallet.base.types that return InstanceList

Modifier and Type	Method and Description
InstanceList	PagedInstanceList.cloneEmpty()
InstanceList	InstanceList.cloneEmpty()
InstanceList	InvertedIndex.getInstanceList()
static InstanceList	PagedInstanceList.load(File file) Constructs a new InstanceList, deserialized from file.
static InstanceList	InstanceList.load(File file) Constructs a new InstanceList, deserialized from file.
InstanceList[]	InstanceList.CrossValidationIterator.nextSplit() Returns the next training/testing split.
InstanceList[]	InstanceList.CrossValidationIterator.nextSplit(int numTrainFolds) Returns the next split, given the number of folds you want in the training data.
InstanceList	InstanceList.sampleWithInstanceWeights(Random r) Returns an InstanceList of the same size, where the instances come from the random sampling (with replacement) of this list using the instance weights.
InstanceList	PagedInstanceList.sampleWithReplacement(Random r, int numSamples) Overridden to add samples in original order to reduce thrashing.
InstanceList	InstanceList.sampleWithReplacement(Random r, int numSamples)
InstanceList	PagedInstanceList.sampleWithWeights(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.sampleWithWeights(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	InstanceList.shallowClone()
InstanceList[]	PagedInstanceList.split(double[] proportions)
InstanceList[]	InstanceList.split(double[] proportions)
InstanceList[]	PagedInstanceList.split(Random r, double[] proportions) Shuffles the elements of this list among several smaller lists.
InstanceList[]	InstanceList.split(Random r, double[] proportions) Shuffles the elements of this list among several smaller lists.
InstanceList[]	PagedInstanceList.splitByModulo(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[]	InstanceList.splitByModulo(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[]	InstanceList.splitInOrder(double[] proportions) Chops this list into several sequential sublists.
InstanceList	InstanceList.subList(int start, int end)

Methods in edu.umass.cs.mallet.base.types with parameters of type InstanceList

Modifier and Type	Method and Description
void	InstanceList.add(InstanceList ilist) Adds to this list each instance in the input list.
protected static 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)
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	RankedFeatureVector.Factory.newRankedFeatureVector(InstanceList ilist)
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.PerLabelFactory.newRankedFeatureVectors(InstanceList ilist)
RankedFeatureVector[]	PerLabelInfoGain.Factory.newRankedFeatureVectors(InstanceList ilist)
RankedFeatureVector[]	PerLabelFeatureCounts.Factory.newRankedFeatureVectors(InstanceList ilist)
void	FeatureSelector.selectFeaturesFor(InstanceList ilist, InstanceList validationList)
void	FeatureSelector.selectFeaturesForAllLabels(InstanceList ilist, InstanceList validationList)
void	FeatureSelector.selectFeaturesForPerLabel(InstanceList ilist, InstanceList validationList)
static int[]	GainRatio.sortInstances(InstanceList ilist, int[] instIndices, int featureIndex)

Constructors in edu.umass.cs.mallet.base.types with parameters of type InstanceList

Constructor and Description
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)
PerLabelFeatureCounts(InstanceList ilist)
PerLabelInfoGain(InstanceList ilist)

Uses of InstanceList in edu.umass.cs.mallet.base.util

Methods in edu.umass.cs.mallet.base.util with parameters of type InstanceList

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

Uses of InstanceList in edu.umass.cs.mallet.projects.seg_plus_coref

Methods in edu.umass.cs.mallet.projects.seg_plus_coref with parameters of type InstanceList

Modifier and Type	Method and Description
protected static void	BaseTUICRF.writeOutput(Transducer crf, InstanceList testing)
protected static void	BaseTUICRF.writeOutput(Transducer crf, InstanceList testing, String num)

Uses of InstanceList in edu.umass.cs.mallet.projects.seg_plus_coref.anaphora

Methods in edu.umass.cs.mallet.projects.seg_plus_coref.anaphora with parameters of type InstanceList

Modifier and Type	Method and Description
static Set	MentionPairIterator.partitionIntoDocumentInstances(InstanceList allInstances)

Constructors in edu.umass.cs.mallet.projects.seg_plus_coref.anaphora with parameters of type InstanceList

Constructor and Description
AccuracyCoverage(Classifier classifier, InstanceList instances)

Uses of InstanceList in edu.umass.cs.mallet.projects.seg_plus_coref.coreference

Methods in edu.umass.cs.mallet.projects.seg_plus_coref.coreference that return InstanceList

Modifier and Type	Method and Description
static InstanceList	CitationUtils.makePairs(Pipe instancePipe, ArrayList nodes)
protected static InstanceList	BenTUISGD.makePairs(Pipe instancePipe, ArrayList nodes)
protected static InstanceList	BenTUI1.makePairs(Pipe instancePipe, ArrayList nodes)
protected static InstanceList	BenCitationTUINoSeg.makePairs(Pipe instancePipe, ArrayList nodes)
protected static InstanceList	BenCitationTUI2.makePairs(Pipe instancePipe, ArrayList nodes)
static InstanceList	CitationUtils.makePairs(Pipe instancePipe, ArrayList nodes, double negativeProb)
static InstanceList	CitationUtils.makePairs(Pipe instancePipe, ArrayList nodes, List pairs)
protected static InstanceList	BenTUISGD.makePairs(Pipe instancePipe, ArrayList nodes, List pairs)
protected static InstanceList	BenTUI1.makePairs(Pipe instancePipe, ArrayList nodes, List pairs)
protected static InstanceList	BenCitationTUINoSeg.makePairs(Pipe instancePipe, ArrayList nodes, List pairs)
protected static InstanceList	BenCitationTUI2.makePairs(Pipe instancePipe, ArrayList nodes, List pairs)

Methods in edu.umass.cs.mallet.projects.seg_plus_coref.coreference with parameters of type InstanceList

Modifier and Type	Method and Description
Collection	CorefClusterAdv.absoluteCluster(InstanceList ilist, List mentions)
Collection	CorefCluster.absoluteCluster(InstanceList ilist, List mentions)
Collection[]	MultipleCorefClusterer.clusterMentions(InstanceList[] ilists, List[] mentions, int optimalBest, boolean stochastic) Returns a list of collections representing the clustering of "ilists"
Collection	CorefClusterAdv.clusterMentions(InstanceList ilist, List mentions)
Collection	CorefCluster2.clusterMentions(InstanceList ilist, List mentions)
Collection	CorefCluster.clusterMentions(InstanceList ilist, List mentions)
Collection	CorefClusterAdv.clusterMentions(InstanceList ilist, List mentions, int optimalNBest, boolean stochastic)
salvo.jesus.graph.WeightedGraph	CorefClusterAdv.createGraph(InstanceList ilist, List mentions)
salvo.jesus.graph.WeightedGraph	CorefClusterAdv.createGraph(InstanceList ilist, List mentions, salvo.jesus.graph.WeightedGraph graph)
salvo.jesus.graph.WeightedGraph	CorefClusterAdv.createGraph(InstanceList ilist, List mentions, salvo.jesus.graph.WeightedGraph graph, MaxEnt classifier)
List	CorefClusterAdv.createPseudoEdges(InstanceList instances, Map map)
Collection	CorefClusterAdv.createPseudoVertices(InstanceList instances, List mentions, HashMap map)
double	CorefClusterAdv.evaluatePartitioningExternal(InstanceList ilist, List mentions, Collection collection)
double	CorefClusterAdv.evaluatePartitioningExternal(InstanceList ilist, List mentions, Collection collection, int nBestList)
protected int[]	ComputeUpperBound1.indexListSearch_approximate(InstanceList instancelist, ArrayList nbestlists)
protected int[]	ComputeUpperBound1.indexListSearch_exaustive(InstanceList instancelist, ArrayList nbestlists, int N)
void	MultipleCorefClusterer.setIndices(InstanceList[] ilists) Sets the mapping from citation type to index
Collection	SGDLearner.test(InstanceList testPairs, List tMentions)
void	CorefClusterAdv.testClassifier(InstanceList tlist)
void	CorefClusterAdv.testClassifier(InstanceList tlist, MaxEnt classifier)
void	MultipleCorefClusterer.testClassifiers(InstanceList[] ilists)
void	CorefClusterAdv.train(InstanceList ilist)
void	CorefCluster.train(InstanceList ilist)
void	MultipleCorefClusterer.train(InstanceList[] ilists) Train the underlying classifiers with "ilists" as trainingData
void	SGDLearner.train(InstanceList instPairs, List mentions)
void	CorefCluster2.train(InstanceList ilist, List mentions)
MaxEnt	CorefClusterAdv.trainClassifier(InstanceList ilist)
Collection	CorefClusterAdv.typicalClusterAdv(InstanceList ilist, List mentions)
protected double	ComputeUpperBound1.weightOfConfig(int[] indexList, InstanceList instancelist, ArrayList nbestlists)

Constructors in edu.umass.cs.mallet.projects.seg_plus_coref.coreference with parameters of type InstanceList

Constructor and Description
PseudoVertex(InstanceList instances, Object mention)

Uses of InstanceList in edu.umass.cs.mallet.projects.seg_plus_coref.ie

Methods in edu.umass.cs.mallet.projects.seg_plus_coref.ie that return InstanceList

Modifier and Type	Method and Description
InstanceList	TUI_CorefIE.AllClusterSegmentation.getCluster(Instance inst)

Methods in edu.umass.cs.mallet.projects.seg_plus_coref.ie with parameters of type InstanceList

Modifier and Type	Method and Description
boolean	MultiSegmentationEvaluator.evaluate(Transducer crf, boolean finishedTraining, int iteration, boolean converged, double cost, InstanceList training, InstanceList validation, InstanceList testing)
boolean	IEEvaluator.evaluate(Transducer crf, boolean finishedTraining, int iteration, boolean converged, double cost, InstanceList training, InstanceList validation, InstanceList testing)
void	IEEvaluator.printFeatures(InstanceList training)
void	MultiSegmentationEvaluator.test(Transducer transducer, InstanceList data, String description, PrintStream viterbiOutputStream)
void	IEEvaluator.test(Transducer transducer, InstanceList data, String description, PrintStream viterbiOutputStream)
void	FieldF1Evaluator.test(Transducer transducer, InstanceList data, String description, PrintStream viterbiOutputStream)

Constructors in edu.umass.cs.mallet.projects.seg_plus_coref.ie with parameters of type InstanceList

Constructor and Description
AllClusterSegmentation(InstanceList clusterlist, Pipe pipe) make an AllClusterSegmentation from the true segmentation of an instancelist
ClusterListIterator(InstanceList instList)