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## Uses of Package
cc.mallet.fst

Packages that use cc.mallet.fst
cc.mallet.extract | Unimplemented.
cc.mallet.fst | Transducers, including Conditional Random Fields (CRFs).
cc.mallet.fst.confidence |
cc.mallet.fst.semi_supervised |
cc.mallet.pipe.iterator | Classes that generate instances from different kinds of input or data structures.

Classes in cc.mallet.fst used by cc.mallet.extract
CRF Represents a CRF model.

Classes in cc.mallet.fst used by cc.mallet.fst
CacheStaleIndicator Indicates when the value/gradient during training becomes stale.
CRF Represents a CRF model.
CRF.Factors A simple, transparent container to hold the parameters or sufficient statistics for the CRF.
CRF.State
CRF.TransitionIterator
CRFOptimizableByBatchLabelLikelihood Implements label likelihood gradient computations for batches of data, can be easily parallelized.
CRFOptimizableByLabelLikelihood An objective function for CRFs that is the label likelihood plus a Gaussian or hyperbolic prior on parameters.
CRFTrainerByLabelLikelihood Unlike ClassifierTrainer, TransducerTrainer is not "stateless" between calls to train.
CRFTrainerByValueGradients.OptimizableCRF An optimizable CRF that contains a collection of objective functions.
FeatureTransducer
FeatureTransducer.State
HMM A Hidden Markov Model.
HMM.State
MaxLattice The interface to classes implementing the Viterbi algorithm, finding the best sequence of states for a given input sequence.
MaxLatticeFactory
MEMM A Maximum Entropy Markov Model.
MEMM.State
MEMMTrainer.MEMMOptimizableByLabelLikelihood Represents the terms in the objective function.
Segment Represents a labelled chunk of a `Sequence` segmented by a `Transducer`, usually corresponding to some object extracted from an input `Sequence`.
SegmentationEvaluator
SumLattice Interface to perform forward-backward during training of a transducer.
SumLatticeDefault Default, full dynamic programming implementation of the Forward-Backward "Sum-(Product)-Lattice" algorithm
SumLatticeDefault.LatticeNode
SumLatticeFactory Provides factory methods to create inference engine for training a transducer.
SumLatticeScaling.LatticeNode
Transducer A base class for all sequence models, analogous to `classify.Classifier`.
Transducer.Incrementor Methods to be called by inference methods to indicate partial counts of sufficient statistics.
Transducer.State An abstract class used to represent the states of the transducer.
Transducer.TransitionIterator An abstract class to iterate over the states of the transducer.
TransducerEvaluator An abstract class to evaluate a transducer model.
TransducerTrainer An abstract class to train and evaluate a transducer model.
TransducerTrainer.ByIncrements
TransducerTrainer.ByInstanceIncrements
TransducerTrainer.ByOptimization

Classes in cc.mallet.fst used by cc.mallet.fst.confidence
Segment Represents a labelled chunk of a `Sequence` segmented by a `Transducer`, usually corresponding to some object extracted from an input `Sequence`.
SumLatticeDefault Default, full dynamic programming implementation of the Forward-Backward "Sum-(Product)-Lattice" algorithm
Transducer A base class for all sequence models, analogous to `classify.Classifier`.

Classes in cc.mallet.fst used by cc.mallet.fst.semi_supervised
CRF Represents a CRF model.
CRF.Factors A simple, transparent container to hold the parameters or sufficient statistics for the CRF.
SumLattice Interface to perform forward-backward during training of a transducer.
Transducer A base class for all sequence models, analogous to `classify.Classifier`.
Transducer.Incrementor Methods to be called by inference methods to indicate partial counts of sufficient statistics.
Transducer.State An abstract class used to represent the states of the transducer.
TransducerTrainer An abstract class to train and evaluate a transducer model.
TransducerTrainer.ByOptimization

Classes in cc.mallet.fst used by cc.mallet.pipe.iterator
Segment Represents a labelled chunk of a `Sequence` segmented by a `Transducer`, usually corresponding to some object extracted from an input `Sequence`.
Transducer A base class for all sequence models, analogous to `classify.Classifier`.

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