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## 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 |

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

Methods in cc.mallet.fst that return CRF
`CRF` | `CRFTrainerByLabelLikelihood.getCRF()`
`CRF` | `CRFTrainerByValueGradients.getCRF()`
`CRF` | `CRFTrainerByThreadedLabelLikelihood.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` | `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)`
`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)`
`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` | `CRFOptimizableByGECriteria.crf`
`protected CRF` | `CRFOptimizableByEntropyRegularization.crf`

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.
`CRFOptimizableByGECriteria(GECriteria geCriteria, CRF crf, InstanceList ilist, int numThreads)` Initializes the structures.
`CRFTrainerByEntropyRegularization(CRF crf)`
`CRFTrainerByGE(CRF crf, java.util.HashMap<java.lang.Integer,GECriterion> constraints)`
`CRFTrainerByGE(CRF crf, java.util.HashMap<java.lang.Integer,GECriterion> constraints, int numThreads)`

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