Uses of Class
cc.mallet.fst.CRF

Packages that use CRF

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

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	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)

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)

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)