Uses of Class
cc.mallet.fst.TransducerEvaluator

Packages that use TransducerEvaluator

cc.mallet.fst

Transducers, including Conditional Random Fields (CRFs).

Uses of TransducerEvaluator in cc.mallet.fst

Subclasses of TransducerEvaluator in cc.mallet.fst

class	CRFWriter
class	InstanceAccuracyEvaluator Reports the percentage of instances for which the entire predicted sequence was correct.
class	MultiSegmentationEvaluator
class	PerClassAccuracyEvaluator
class	SegmentationEvaluator
class	TokenAccuracyEvaluator Evaluates a transducer model based on predictions of individual tokens.
class	ViterbiWriter Prints the input instances along with the features and the true and predicted labels to a file.

Methods in cc.mallet.fst with parameters of type TransducerEvaluator

TransducerTrainer	TransducerTrainer.addEvaluator(TransducerEvaluator te)
void	CRF.evaluate(TransducerEvaluator eval, InstanceList testing) Deprecated.
TransducerTrainer	TransducerTrainer.removeEvaluator(TransducerEvaluator te)
static void	SimpleTagger.test(TransducerTrainer tt, TransducerEvaluator eval, InstanceList testing) Test a transducer on the given test data, evaluating accuracy with the given evaluator
boolean	HMM.train(InstanceList ilist, InstanceList validation, InstanceList testing, TransducerEvaluator eval)
boolean	MEMMTrainer.train(InstanceList training, InstanceList validation, InstanceList testing, TransducerEvaluator eval, int numIterations, int numIterationsPerProportion, double[] trainingProportions)
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.
boolean	CRFTrainerByLabelLikelihood.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions)
boolean	MEMMTrainer.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions, java.lang.String gainName)
boolean	CRFTrainerByLabelLikelihood.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions, java.lang.String gainName) Train a CRF using feature induction to generate conjunctions of features.

Method parameters in cc.mallet.fst with type arguments of type TransducerEvaluator

TransducerTrainer

TransducerTrainer.addEvaluators(java.util.Collection<TransducerEvaluator> tes)