Search Java Classes and Packages
#Cc.mallet.fst Classes and Interfaces - 121 results found.
| Name | Description | Type | Package | Framework |
| CachedDotTransitionIterator | TransitionIterator that caches dot products. | Class | cc.mallet.fst.semi_supervised.pr | Mallet |
| CacheStaleIndicator | Indicates when the value/gradient during training becomes stale. | Interface | cc.mallet.fst | Mallet |
| ConfidenceCorrectorEvaluator | Calculates the effectiveness of "constrained viterbi" in propagating corrections in one segment of a sequence to other | Class | cc.mallet.fst.confidence | Mallet |
| ConfidenceEvaluator | Class | cc.mallet.fst.confidence | Mallet | |
| ConfidenceEvaluator .EntityConfidence | a simple class to store a confidence score and whether or not this labeling is correct | Class | cc.mallet.fst.confidence.ConfidenceEvaluator | Mallet |
| ConstrainedForwardBackwardConfidenceEstimator | Estimates the confidence of a Segment extracted by a Transducer by performing a "constrained lattice" calculation. | Class | cc.mallet.fst.confidence | Mallet |
| ConstrainedViterbiTransducerCorrector | Corrects a subset of the Segments produced by a Transducer. | Class | cc.mallet.fst.confidence | Mallet |
| ConstraintsOptimizableByPR | Optimizable for E-step/I-projection in Posterior Regularization (PR). | Class | cc.mallet.fst.semi_supervised.pr | Mallet |
| CRF | Represents a CRF model. | Class | cc.mallet.fst | Mallet |
| CRF .Factors | A simple, transparent container to hold the parameters or sufficient statistics for the CRF. | Class | cc.mallet.fst.CRF | Mallet |
| CRF .State | See Also:Serialized Formprotected CRF. | Class | cc.mallet.fst.CRF | Mallet |
| CRF .TransitionIterator | Class | cc.mallet.fst.CRF | Mallet | |
| CRFCacheStaleIndicator | Indicates when the value/gradient becomes stale based on updates to CRF'sAuthor:Gaurav Chandalia | Class | cc.mallet.fst | Mallet |
| CRFOptimizableByBatchLabelLikelihood | easily parallelized. | Class | cc.mallet.fst | Mallet |
| CRFOptimizableByBatchLabelLikelihood .Factory | Constructor SummaryCRFOptimizableByBatchLabelLikelihood. | Class | cc.mallet.fst.CRFOptimizableByBatchLabelLikelihood | Mallet |
| CRFOptimizableByEntropyRegularization | A CRF objective function that is the entropy of the CRF's predictions on unlabeled data. | Class | cc.mallet.fst.semi_supervised | Mallet |
| CRFOptimizableByGE | Optimizable for CRF using Generalized Expectation constraints that consider either a single label or a pair of labels of a linear chain CRF. | Class | cc.mallet.fst.semi_supervised | Mallet |
| CRFOptimizableByGradientValues | A CRF objective function that is the sum of multiple objective functions that implement Optimizable. | Class | cc.mallet.fst | Mallet |
| CRFOptimizableByKL | M-step/M-projection for PR. | Class | cc.mallet.fst.semi_supervised.pr | Mallet |
| CRFOptimizableByLabelLikelihood | An objective function for CRFs that is the label likelihood plus a Gaussian or hyperbolic prior on parameters. | Class | cc.mallet.fst | Mallet |
| CRFOptimizableByLabelLikelihood .Factory | Constructor SummaryCRFOptimizableByLabelLikelihood. | Class | cc.mallet.fst.CRFOptimizableByLabelLikelihood | Mallet |
| CRFTrainerByEntropyRegularization | A CRF trainer that maximizes the log-likelihood plus a weighted entropy regularization term on unlabeled | Class | cc.mallet.fst.semi_supervised | Mallet |
| CRFTrainerByGE | Trains a CRF using Generalized Expectation constraints that consider either a single label or a pair of labels of a linear chain CRF. | Class | cc.mallet.fst.semi_supervised | Mallet |
| CRFTrainerByL1LabelLikelihood | CRF trainer that implements L1-regularization. | Class | cc.mallet.fst | Mallet |
| CRFTrainerByLabelLikelihood | Unlike ClassifierTrainer, TransducerTrainer is not "stateless" between calls to train. | Class | cc.mallet.fst | Mallet |
| CRFTrainerByLikelihoodAndGE | Nested Class SummaryNested classes/interfaces inherited from class cc. | Class | cc.mallet.fst.semi_supervised | Mallet |
| CRFTrainerByPR | Posterior regularization trainer. | Class | cc.mallet.fst.semi_supervised.pr | Mallet |
| CRFTrainerByStochasticGradient | Trains CRF by stochastic gradient. | Class | cc.mallet.fst | Mallet |
| CRFTrainerByThreadedLabelLikelihood | Class | cc.mallet.fst | Mallet | |
| CRFTrainerByValueGradients | A CRF trainer that can combine multiple objective functions, each represented by a Optmizable. | Class | cc.mallet.fst | Mallet |
| CRFWriter | Saves a trained model to specified filename. | Class | cc.mallet.fst | Mallet |
| EntropyLattice | Runs subsequence constrained forward-backward to compute the entropy of label Gideon Mann, Andrew McCallum | Class | cc.mallet.fst.semi_supervised | Mallet |
| FeatureTransducer | Class | cc.mallet.fst | Mallet | |
| FSTConstraintUtil | Expectation constraint utilities for fst package. | Class | cc.mallet.fst.semi_supervised | Mallet |
| GammaAverageConfidenceEstimator | Calculates the confidence in an extracted segment by taking the average of P(s_i | Class | cc.mallet.fst.confidence | Mallet |
| GammaProductConfidenceEstimator | Calculates the confidence in an extracted segment by taking the product of eP(s_i | Class | cc.mallet.fst.confidence | Mallet |
| GEConstraint | Interface for GE constraint that considers either one or two states. | Interface | cc.mallet.fst.semi_supervised.constraints | Mallet |
| GELattice | Runs the dynamic programming algorithm of [Mann and McCallum 08] for computing the gradient of a Generalized Expectation constraint that | Class | cc.mallet.fst.semi_supervised | Mallet |
| HMM | A Hidden Markov Model. | Class | cc.mallet.fst | Mallet |
| HMM .State | See Also:Serialized Formprotected HMM. | Class | cc.mallet.fst.HMM | Mallet |
| HMM .TransitionIterator | See Also:Serialized FormConstructor SummaryHMM. | Class | cc.mallet.fst.HMM | Mallet |
| HMMTrainerByLikelihood | Nested Class SummaryNested classes/interfaces inherited from class cc. | Class | cc.mallet.fst | Mallet |
| InstanceAccuracyEvaluator | Reports the percentage of instances for which the entire predicted sequence was Created: May 12, 2004 | Class | cc.mallet.fst | Mallet |
| InstanceWithConfidence | Helper class to store confidence of an Instance. | Class | cc.mallet.fst.confidence | Mallet |
| IsolatedSegmentTransducerCorrector | Corrects a subset of the Segments produced by a Transducer. | Class | cc.mallet.fst.confidence | Mallet |
| LabelDistributionEvaluator | Prints predicted and true label distribution. | Class | cc.mallet.fst | Mallet |
| MaxEntConfidenceEstimator | Estimates the confidence of a Segment extracted by a Transducer using a MaxEnt classifier to classify segments as "correct" or "incorrect. | Class | cc.mallet.fst.confidence | Mallet |
| MaxEntSequenceConfidenceEstimator | Estimates the confidence of a Sequence extracted by a Transducer using a MaxEnt classifier to classify Sequences as "correct" or "incorrect. | Class | cc.mallet.fst.confidence | Mallet |
| MaxLattice | The interface to classes implementing the Viterbi algorithm, finding the best sequence of states for a given input sequence. | Interface | cc.mallet.fst | Mallet |
| MaxLatticeDefault | Default, full dynamic programming version of the Viterbi "Max-(Product)-Lattice" algorithm. | Class | cc.mallet.fst | Mallet |
| MaxLatticeDefault .Factory | See Also:Serialized FormConstructor SummaryMaxLatticeDefault. | Class | cc.mallet.fst.MaxLatticeDefault | Mallet |
| MaxLatticeFactory | See Also:Serialized FormConstructor SummaryMaxLatticeFactory() | Class | cc.mallet.fst | Mallet |
| MEMM | A Maximum Entropy Markov Model. | Class | cc.mallet.fst | Mallet |
| MEMM .State | See Also:Serialized Formprotected MEMM. | Class | cc.mallet.fst.MEMM | Mallet |
| MEMM .TransitionIterator | See Also:Serialized FormFields inherited from class cc. | Class | cc.mallet.fst.MEMM | Mallet |
| MEMMTrainer | Trains and evaluates a MEMM. | Class | cc.mallet.fst | Mallet |
| MinSegmentConfidenceEstimator | Estimates the confidence of an entire sequence by the least confidence segment. | Class | cc.mallet.fst.confidence | Mallet |
| MultiSegmentationEvaluator | Evaluates a transducer model, computes the precision, recall and F1 scores; considers segments that span across multiple tokens. | Class | cc.mallet.fst | Mallet |
| NBestViterbiConfidenceEstimator | Estimates the confidence of an entire sequence by the probability that one of the the Viterbi paths rank 2->N is correct. | Class | cc.mallet.fst.confidence | Mallet |
| 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. | Class | cc.mallet.fst | Mallet |
| OneLabelGEConstraints | A set of constraints on distributions over single labels conditioned on the presence of input features. | Class | cc.mallet.fst.semi_supervised.constraints | Mallet |
| OneLabelKLGEConstraints | A set of constraints on distributions over consecutive labels conditioned an input features. | Class | cc.mallet.fst.semi_supervised.constraints | Mallet |
| OneLabelL2GEConstraints | A set of constraints on distributions over consecutive labels conditioned an input features. | Class | cc.mallet.fst.semi_supervised.constraints | Mallet |
| OneLabelL2IndPRConstraints | A set of constraints on individual input feature label pairs. | Class | cc.mallet.fst.semi_supervised.pr.constraints | Mallet |
| OneLabelL2PRConstraints | A set of constraints on distributions over single labels conditioned on the presence of input features. | Class | cc.mallet.fst.semi_supervised.pr.constraints | Mallet |
| OneLabelL2RangeGEConstraints | A set of constraints on individual input feature label pairs. | Class | cc.mallet.fst.semi_supervised.constraints | Mallet |
| PerClassAccuracyEvaluator | Determines the precision, recall and F1 on a per-class basis. | Class | cc.mallet.fst | Mallet |
| PipedInstanceWithConfidence | Helper class to store confidence of an Instance. | Class | cc.mallet.fst.confidence | Mallet |
| PRAuxiliaryModel | Auxiliar model (q) for E-step/I-projection in Posterior Regularization (PR). | Class | cc.mallet.fst.semi_supervised.pr | Mallet |
| PRConstraint | Interface for PR constraint that considers either one or two states. | Interface | cc.mallet.fst.semi_supervised.pr.constraints | Mallet |
| QBCSequenceConfidenceEstimator | Estimates the confidence of an entire sequence by the "disagreement" among a committee of CRFs. | Class | cc.mallet.fst.confidence | Mallet |
| RandomConfidenceEstimator | Randomly assigns values between 0-1 to the confidence of a Segment. | Class | cc.mallet.fst.confidence | Mallet |
| RandomSequenceConfidenceEstimator | Estimates the confidence of an entire sequence randomly. | Class | cc.mallet.fst.confidence | Mallet |
| Segment | Represents a labelled chunk of a Sequence segmented by a Transducer, usually corresponding to some object extracted | Class | cc.mallet.fst | Mallet |
| SegmentationEvaluator | Fields inherited from class cc. | Class | cc.mallet.fst | Mallet |
| SegmentProductConfidenceEstimator | Estimates the confidence of an entire sequence by combining the output of a segment confidence estimator for each segment. | Class | cc.mallet.fst.confidence | Mallet |
| SelfTransitionGEConstraint | GE Constraint on the probability of self-transitions in the FST. | Class | cc.mallet.fst.semi_supervised.constraints | Mallet |
| SequenceConfidenceInstance | Stores a Sequence and a PropertyList, used when extracting features from a Sequence in a pipe for confidence prediction | Class | cc.mallet.fst.confidence | Mallet |
| ShallowTransducerTrainer | Wraps around an already trained Transducer model. | Class | cc.mallet.fst | Mallet |
| SimpleTagger | This class's main method trains, tests, or runs a generic CRF-based Training and test files consist of blocks of lines, one block for each instance, | Class | cc.mallet.fst | Mallet |
| SimpleTagger .SimpleTaggerSentence2FeatureVectorSequence | Converts an external encoding of a sequence of elements with binary features to a FeatureVectorSequence. | Class | cc.mallet.fst.SimpleTagger | Mallet |
| SimpleTaggerWithConstraints | Version of SimpleTagger that trains CRFs with expectation constraints rather than labeled data. | Class | cc.mallet.fst.semi_supervised.tui | Mallet |
| StateLabelMap | Maps states in the lattice to labels. | Class | cc.mallet.fst.semi_supervised | Mallet |
| SumLattice | Interface to perform forward-backward during training of a transducer. | Interface | cc.mallet.fst | Mallet |
| SumLatticeBeam | Class | cc.mallet.fst | Mallet | |
| SumLatticeBeam .Factory | See Also:Serialized FormConstructor SummarySumLatticeBeam. | Class | cc.mallet.fst.SumLatticeBeam | Mallet |
| SumLatticeConstrained | Nested Class SummaryNested classes/interfaces inherited from class cc. | Class | cc.mallet.fst | Mallet |
| SumLatticeDefault | Class | cc.mallet.fst | Mallet | |
| SumLatticeDefault .Factory | See Also:Serialized FormConstructor SummarySumLatticeDefault. | Class | cc.mallet.fst.SumLatticeDefault | Mallet |
| SumLatticeDefaultCachedDot | Class | cc.mallet.fst.semi_supervised.pr | Mallet | |
| SumLatticeFactory | Provides factory methods to create inference engine for training a transducer. | Class | cc.mallet.fst | Mallet |
| SumLatticeKL | Lattice for M-step/M-projection in PR. | Class | cc.mallet.fst.semi_supervised.pr | Mallet |
| SumLatticePR | Lattice for E-step/I-projection in PR. | Class | cc.mallet.fst.semi_supervised.pr | Mallet |
| SumLatticeScaling | Class | cc.mallet.fst | Mallet | |
| SumLatticeScaling .Factory | See Also:Serialized FormConstructor SummarySumLatticeScaling. | Class | cc.mallet.fst.SumLatticeScaling | Mallet |
| TestCRF | Tests for CRF training. | Class | cc.mallet.fst.tests | Mallet |
| TestCRF .TestCRFTokenSequenceRemoveSpaces | See Also:Serialized FormConstructor SummaryTestCRF. | Class | cc.mallet.fst.tests.TestCRF | Mallet |
| TestFeatureTransducer | Constructor SummaryTestFeatureTransducer(java. | Class | cc.mallet.fst.tests | Mallet |
| TestMEMM | Tests for MEMM training. | Class | cc.mallet.fst.tests | Mallet |
| TestMEMM .TestMEMMTokenSequenceRemoveSpaces | See Also:Serialized FormConstructor SummaryTestMEMM. | Class | cc.mallet.fst.tests.TestMEMM | Mallet |
| TestSumNegLogProb2 | Constructor SummaryTestSumNegLogProb2(java. | Class | cc.mallet.fst.tests | Mallet |
| ThreadedOptimizable | An adaptor for optimizables based on batch values/gradients. | Class | cc.mallet.fst | Mallet |
| TokenAccuracyEvaluator | Evaluates a transducer model based on predictions of individual tokens. | Class | cc.mallet.fst | Mallet |
| Transducer | A base class for all sequence models, analogous to classify. | Class | cc.mallet.fst | Mallet |
| Transducer .Incrementor | Methods to be called by inference methods to indicate partial counts of sufficient statistics. | Interface | cc.mallet.fst.Transducer | Mallet |
| Transducer .State | An abstract class used to represent the states of the transducer. | Class | cc.mallet.fst.Transducer | Mallet |
| Transducer .TransitionIterator | An abstract class to iterate over the states of the transducer. | Class | cc.mallet.fst.Transducer | Mallet |
| TransducerConfidenceEstimator | Abstract class that estimates the confidence of a Segment extracted by a Transducer. | Class | cc.mallet.fst.confidence | Mallet |
| TransducerCorrector | Interface for transducerCorrectors, which correct a subset of the Segments produced by a Transducer. | Interface | cc.mallet.fst.confidence | Mallet |
| TransducerEvaluator | An abstract class to evaluate a transducer model. | Class | cc.mallet.fst | Mallet |
| TransducerSequenceConfidenceEstimator | Abstract class that estimates the confidence of a Sequence extracted by a Transducer. | Class | cc.mallet.fst.confidence | Mallet |
| TransducerTrainer | An abstract class to train and evaluate a transducer model. | Class | cc.mallet.fst | Mallet |
| TransducerTrainer .ByIncrements | Nested Class SummaryNested classes/interfaces inherited from class cc. | Class | cc.mallet.fst.TransducerTrainer | Mallet |
| TransducerTrainer .ByInstanceIncrements | Nested Class SummaryNested classes/interfaces inherited from class cc. | Class | cc.mallet.fst.TransducerTrainer | Mallet |
| TransducerTrainer .ByOptimization | Interface | cc.mallet.fst.TransducerTrainer | Mallet | |
| TwoLabelGEConstraints | A set of constraints on distributions over pairs of consecutive labels conditioned on the presence of input features. | Class | cc.mallet.fst.semi_supervised.constraints | Mallet |
| TwoLabelKLGEConstraints | A set of constraints on distributions over consecutive labels conditioned an input features. | Class | cc.mallet.fst.semi_supervised.constraints | Mallet |
| TwoLabelL2GEConstraints | A set of constraints on distributions over consecutive labels conditioned an input features. | Class | cc.mallet.fst.semi_supervised.constraints | Mallet |
| ViterbiConfidenceEstimator | Estimates the confidence of an entire sequence by the probability of the Viterbi path normalized by the probabliity of the entire | Class | cc.mallet.fst.confidence | Mallet |
| ViterbiRatioConfidenceEstimator | Estimates the confidence of an entire sequence by the ration of the probabilities of the first and second best Viterbi paths. | Class | cc.mallet.fst.confidence | Mallet |
| ViterbiWriter | Prints the input instances along with the features and the true and predicted labels to a file. | Class | cc.mallet.fst | Mallet |