Uses of Class
cc.mallet.fst.CRF

Packages that use CRF

cc.mallet.extract	Unimplemented.
cc.mallet.fst	Transducers, including Conditional Random Fields (CRFs).
cc.mallet.fst.semi_supervised
cc.mallet.fst.semi_supervised.pr
cc.mallet.fst.semi_supervised.tui

Uses of CRF in cc.mallet.extract

Methods in cc.mallet.extract that return CRF

CRF	CRFExtractor.getCrf()

Constructors in cc.mallet.extract with parameters of type CRF

CRFExtractor(CRF crf)

CRFExtractor(CRF crf, Pipe tokpipe)

CRFExtractor(CRF crf, Pipe tokpipe, TokenizationFilter filter)

CRFExtractor(CRF crf, Pipe tokpipe, TokenizationFilter filter, java.lang.String backgroundTag)

Uses of CRF in cc.mallet.fst

Subclasses of CRF in cc.mallet.fst

class

MEMM A Maximum Entropy Markov Model.

Fields in cc.mallet.fst declared as CRF

protected CRF	CRFTrainerByStochasticGradient.crf
protected CRF	CRFOptimizableByLabelLikelihood.crf
protected CRF	CRFOptimizableByBatchLabelLikelihood.crf
protected CRF	CRFCacheStaleIndicator.crf

Methods in cc.mallet.fst that return CRF

CRF	CRFTrainerByValueGradients.getCRF()
CRF	CRFTrainerByThreadedLabelLikelihood.getCRF()
CRF	CRFTrainerByLabelLikelihood.getCRF()
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.

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

Optimizable.ByGradientValue	CRFOptimizableByLabelLikelihood.Factory.newCRFOptimizable(CRF crf, InstanceList trainingData)
Optimizable.ByCombiningBatchGradient	CRFOptimizableByBatchLabelLikelihood.Factory.newCRFOptimizable(CRF crf, InstanceList trainingData, int numBatches)
protected CRF.State	MEMM.newState(java.lang.String name, int index, double initialWeight, double finalWeight, java.lang.String[] destinationNames, java.lang.String[] labelNames, java.lang.String[][] weightNames, CRF crf)
protected CRF.State	CRF.newState(java.lang.String name, int index, double initialWeight, double finalWeight, java.lang.String[] destinationNames, java.lang.String[] labelNames, java.lang.String[][] weightNames, CRF crf)
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.

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

CRF.Factors(CRF crf)
Construct a new Factors with the same structure as the parameters of 'crf', but with values initialized to zero.

CRF.State(java.lang.String name, int index, double initialWeight, double finalWeight, java.lang.String[] destinationNames, java.lang.String[] labelNames, java.lang.String[][] weightNames, CRF crf)

CRF.TransitionIterator(CRF.State source, FeatureVectorSequence inputSeq, int inputPosition, java.lang.String output, CRF crf)

CRF.TransitionIterator(CRF.State source, FeatureVector fv, java.lang.String output, CRF crf)

CRF(CRF other)
Create a CRF whose states and weights are a copy of those from another CRF.

CRFCacheStaleIndicator(CRF crf)

CRFOptimizableByBatchLabelLikelihood(CRF crf, InstanceList ilist, int numBatches)

CRFOptimizableByGradientValues(CRF crf, Optimizable.ByGradientValue[] opts)

CRFOptimizableByLabelLikelihood(CRF crf, InstanceList ilist)

CRFTrainerByL1LabelLikelihood(CRF crf)

CRFTrainerByL1LabelLikelihood(CRF crf, double l1Weight)
Constructor for CRF trainer.

CRFTrainerByLabelLikelihood(CRF crf)

CRFTrainerByStochasticGradient(CRF crf, double learningRate)

CRFTrainerByStochasticGradient(CRF crf, InstanceList trainingSample)

CRFTrainerByThreadedLabelLikelihood(CRF crf, int numThreads)

CRFTrainerByValueGradients.OptimizableCRF(CRF crf, InstanceList ilist)

CRFTrainerByValueGradients(CRF crf, Optimizable.ByGradientValue[] optimizableByValueGradientObjects)

MEMM.State(java.lang.String name, int index, double initialCost, double finalCost, java.lang.String[] destinationNames, java.lang.String[] labelNames, java.lang.String[][] weightNames, CRF crf)

MEMM.TransitionIterator(MEMM.State source, FeatureVectorSequence inputSeq, int inputPosition, java.lang.String output, CRF memm)

MEMM.TransitionIterator(MEMM.State source, FeatureVector fv, java.lang.String output, CRF memm)

MEMM(CRF crf)

Uses of CRF in cc.mallet.fst.semi_supervised

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

protected CRF

CRFOptimizableByEntropyRegularization.crf

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

void	CRFOptimizableByGE.createReverseTransitionMatrices(CRF crf) Initializes data structures for mapping between a destination state and its source states / transition indices.

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

CRFOptimizableByEntropyRegularization(CRF crf, InstanceList ilist)
Initializes the structures (sets the scaling factor to 1.0).

CRFOptimizableByEntropyRegularization(CRF crf, InstanceList ilist, double scalingFactor)
Initializes the structures.

CRFOptimizableByGE(CRF crf, java.util.ArrayList<GEConstraint> constraints, InstanceList data, StateLabelMap map, int numThreads)

CRFOptimizableByGE(CRF crf, java.util.ArrayList<GEConstraint> constraints, InstanceList data, StateLabelMap map, int numThreads, double weight)

CRFTrainerByEntropyRegularization(CRF crf)

CRFTrainerByGE(CRF crf, java.util.ArrayList<GEConstraint> constraints)

CRFTrainerByGE(CRF crf, java.util.ArrayList<GEConstraint> constraints, int numThreads)

CRFTrainerByLikelihoodAndGE(CRF crf, java.util.ArrayList<GEConstraint> constraints, StateLabelMap map)

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

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

protected CRF	CRFOptimizableByKL.crf
protected CRF	ConstraintsOptimizableByPR.crf

Methods in cc.mallet.fst.semi_supervised.pr that return CRF

CRF	PRAuxiliaryModel.getBaseModel()

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

ConstraintsOptimizableByPR(CRF crf, InstanceList ilist, PRAuxiliaryModel model)

ConstraintsOptimizableByPR(CRF crf, InstanceList ilist, PRAuxiliaryModel model, int numThreads)

CRFOptimizableByKL(CRF crf, InstanceList trainingSet, PRAuxiliaryModel auxModel, double[][][][] cachedDots, int numThreads, double weight)

CRFTrainerByPR(CRF crf, java.util.ArrayList<PRConstraint> constraints)

CRFTrainerByPR(CRF crf, java.util.ArrayList<PRConstraint> constraints, int numThreads)

PRAuxiliaryModel(CRF baseModel, java.util.ArrayList<PRConstraint> constraints)

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

Methods in cc.mallet.fst.semi_supervised.tui that return CRF

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

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

static CRF	SimpleTaggerWithConstraints.trainGE(InstanceList training, InstanceList testing, java.util.ArrayList<GEConstraint> constraints, CRF crf, TransducerEvaluator eval, int iterations, double var, int resets) Create and train a CRF model from the given training data, optionally testing it on the given test data.
static CRF	SimpleTaggerWithConstraints.trainPR(InstanceList training, InstanceList testing, java.util.ArrayList<PRConstraint> constraints, CRF crf, TransducerEvaluator eval, int iterations, double var) Create and train a CRF model from the given training data, optionally testing it on the given test data.