| 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 |
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)
|
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)
|
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)
|
CRFOptimizableByLabelLikelihood(CRF crf,
InstanceList ilist)
|
CRFTrainerByLabelLikelihood(CRF crf)
|
CRFTrainerByStochasticGradient(CRF crf,
double learningRate)
|
CRFTrainerByStochasticGradient(CRF crf,
InstanceList trainingSample)
|
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)
|