| CRF |
Represents a CRF model.
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| CRF.Factors |
A simple, transparent container to hold the parameters or sufficient statistics for the CRF.
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| CRF.State |
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| CRF.TransitionIterator |
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| CRFCacheStaleIndicator |
Indicates when the value/gradient becomes stale based on updates to CRF's
parameters.
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| CRFOptimizableByBatchLabelLikelihood |
Implements label likelihood gradient computations for batches of data, can be
easily parallelized.
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| CRFOptimizableByBatchLabelLikelihood.Factory |
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| CRFOptimizableByGradientValues |
A CRF objective function that is the sum of multiple
objective functions that implement Optimizable.ByGradientValue.
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| CRFOptimizableByLabelLikelihood |
An objective function for CRFs that is the label likelihood plus a Gaussian or hyperbolic prior on parameters.
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| CRFOptimizableByLabelLikelihood.Factory |
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| CRFTrainerByL1LabelLikelihood |
CRF trainer that implements L1-regularization.
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| CRFTrainerByLabelLikelihood |
Unlike ClassifierTrainer, TransducerTrainer is not "stateless" between calls
to train.
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| CRFTrainerByStochasticGradient |
Trains CRF by stochastic gradient.
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| CRFTrainerByThreadedLabelLikelihood |
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| CRFTrainerByValueGradients |
A CRF trainer that can combine multiple objective functions, each represented
by a Optmizable.ByValueGradient.
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| CRFWriter |
Saves a trained model to specified filename.
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| FeatureTransducer |
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| HMM |
A Hidden Markov Model.
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| HMM.State |
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| HMM.TransitionIterator |
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| HMMTrainerByLikelihood |
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| InstanceAccuracyEvaluator |
Reports the percentage of instances for which the entire predicted sequence was
correct.
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| LabelDistributionEvaluator |
Prints predicted and true label distribution.
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| MaxLatticeDefault |
Default, full dynamic programming version of the Viterbi "Max-(Product)-Lattice" algorithm.
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| MaxLatticeDefault.Factory |
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| MaxLatticeFactory |
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| MEMM |
A Maximum Entropy Markov Model.
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| MEMM.State |
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| MEMM.TransitionIterator |
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| MEMMTrainer |
Trains and evaluates a MEMM. |
| MultiSegmentationEvaluator |
Evaluates a transducer model, computes the precision, recall and F1 scores;
considers segments that span across multiple tokens.
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| NoopTransducerTrainer |
A TransducerTrainer that does no training, but simply acts as a container for a Transducer;
for use in situations that require a TransducerTrainer, such as the TransducerEvaluator methods.
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| PerClassAccuracyEvaluator |
Determines the precision, recall and F1 on a per-class basis.
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| Segment |
Represents a labelled chunk of a Sequence segmented by a
Transducer, usually corresponding to some object extracted
from an input Sequence. |
| SegmentationEvaluator |
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| ShallowTransducerTrainer |
Deprecated.
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| SimpleTagger |
This class's main method trains, tests, or runs a generic CRF-based
sequence tagger.
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| SimpleTagger.SimpleTaggerSentence2FeatureVectorSequence |
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| SumLatticeBeam |
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| SumLatticeBeam.Factory |
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| SumLatticeConstrained |
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| SumLatticeDefault |
Default, full dynamic programming implementation of the Forward-Backward "Sum-(Product)-Lattice" algorithm
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| SumLatticeDefault.Factory |
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| SumLatticeFactory |
Provides factory methods to create inference engine for training a transducer.
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| SumLatticeScaling |
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| SumLatticeScaling.Factory |
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| ThreadedOptimizable |
An adaptor for optimizables based on batch values/gradients.
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| TokenAccuracyEvaluator |
Evaluates a transducer model based on predictions of individual tokens.
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| Transducer |
A base class for all sequence models, analogous to classify.Classifier.
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| Transducer.State |
An abstract class used to represent the states of the transducer.
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| Transducer.TransitionIterator |
An abstract class to iterate over the states of the transducer.
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| TransducerEvaluator |
An abstract class to evaluate a transducer model.
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| TransducerTrainer |
An abstract class to train and evaluate a transducer model.
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| TransducerTrainer.ByIncrements |
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| TransducerTrainer.ByInstanceIncrements |
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| ViterbiWriter |
Prints the input instances along with the features and the true and
predicted labels to a file.
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