public class CRF extends Transducer implements Serializable
| Modifier and Type | Class and Description |
|---|---|
class |
CRF.MinimizableCRF |
class |
CRF.State |
protected class |
CRF.TransitionIterator |
Transducer.BeamLattice, Transducer.Lattice, Transducer.ViterbiLattice, Transducer.ViterbiPath, Transducer.ViterbiPath_NBest, Transducer.ViterbiPathBeam, Transducer.ViterbiPathBeamB, Transducer.ViterbiPathBeamFB, Transducer.ViterbiPathBeamKL| Modifier and Type | Field and Description |
|---|---|
boolean |
printGradient |
INFINITE_COST, inputPipe, outputPipe, ZERO_COST| Constructor and Description |
|---|
CRF(Alphabet inputAlphabet,
Alphabet outputAlphabet) |
CRF(Pipe inputPipe,
Pipe outputPipe) |
| Modifier and Type | Method and Description |
|---|---|
void |
addFullyConnectedStates(String[] stateNames) |
void |
addFullyConnectedStatesForBiLabels()
Add as many states as there are labels, but don't create
separate observational-test-weights for each source-destination
pair of states---instead have all the incoming transitions to a
state share the same observational-feature-test weights.
|
void |
addFullyConnectedStatesForLabels() |
void |
addFullyConnectedStatesForTriLabels() |
void |
addSelfTransitioningStateForAllLabels(String name) |
void |
addState(String name,
double initialCost,
double finalCost,
String[] destinationNames,
String[] labelNames) |
void |
addState(String name,
double initialCost,
double finalCost,
String[] destinationNames,
String[] labelNames,
String[] weightNames) |
void |
addState(String name,
String[] destinationNames) |
void |
addStatesForBiLabelsConnectedAsIn(InstanceList trainingSet)
Add states to create a second-order Markov model on labels,
adding only those transitions the occur in the given
trainingSet.
|
void |
addStatesForHalfLabelsConnectedAsIn(InstanceList trainingSet)
Add as many states as there are labels, but don't create separate weights
for each source-destination pair of states.
|
void |
addStatesForLabelsConnectedAsIn(InstanceList trainingSet)
Add states to create a first-order Markov model on labels,
adding only those transitions the occur in the given
trainingSet.
|
void |
estimate() |
int |
getDefaultFeatureIndex() |
double |
getGaussianPriorVariance() |
Alphabet |
getInputAlphabet() |
CRF.MinimizableCRF |
getMinimizableCRF(InstanceList ilist) |
Alphabet |
getOutputAlphabet() |
double |
getParameter(int sourceStateIndex,
int destStateIndex,
int featureIndex,
double value) |
Transducer.State |
getState(int index) |
double |
getUseHyperbolicPriorSharpness() |
double |
getUseHyperbolicPriorSlope() |
DenseVector |
getWeights(int weightIndex) |
DenseVector |
getWeights(String weightName) |
int |
getWeightsIndex(String weightName) |
String |
getWeightsName(int weightIndex) |
void |
growWeightsDimensionToInputAlphabet()
Increase the size of the weights[] parameters to match (a new, larger)
input Alphabet size
|
Iterator |
initialStateIterator() |
boolean |
isTrainable() |
int |
numStates() |
void |
print() |
void |
reset() |
void |
setGaussianPriorVariance(double p) |
void |
setHyperbolicPriorSharpness(double p) |
void |
setHyperbolicPriorSlope(double p) |
void |
setParameter(int sourceStateIndex,
int destStateIndex,
int featureIndex,
double value) |
void |
setTrainable(boolean f) |
void |
setUseHyperbolicPrior(boolean f) |
void |
setWeights(int weightsIndex,
DenseVector transitionWeights) |
void |
setWeights(String weightName,
DenseVector transitionWeights) |
boolean |
train(InstanceList ilist) |
boolean |
train(InstanceList ilist,
InstanceList validation,
InstanceList testing) |
boolean |
train(InstanceList ilist,
InstanceList validation,
InstanceList testing,
TransducerEvaluator eval) |
boolean |
train(InstanceList ilist,
InstanceList validation,
InstanceList testing,
TransducerEvaluator eval,
int numIterations) |
boolean |
train(InstanceList training,
InstanceList validation,
InstanceList testing,
TransducerEvaluator eval,
int numIterations,
int numIterationsPerProportion,
double[] trainingProportions) |
boolean |
trainWithFeatureInduction(InstanceList trainingData,
InstanceList validationData,
InstanceList testingData,
TransducerEvaluator eval,
int numIterations,
int numIterationsBetweenFeatureInductions,
int numFeatureInductions,
int numFeaturesPerFeatureInduction,
double trueLabelProbThreshold,
boolean clusteredFeatureInduction,
double[] trainingProportions) |
void |
write(File f) |
averageTokenAccuracy, averageTokenAccuracy, canIterateAllTransitions, forwardBackward, forwardBackward, forwardBackward, forwardBackward, forwardBackward, forwardBackward, forwardBackward, forwardBackward, forwardBackwardBeam, forwardBackwardBeam, forwardBackwardBeam, forwardBackwardBeam, forwardBackwardBeam, forwardBackwardBeam, forwardBackwardBeam, forwardBackwardBeam, generatePath, getBeamWidth, getInputPipe, getNstatesExpl, getOutputPipe, getViterbiLattice, incIter, isGenerative, pipe, setBeamWidth, setCurIter, setKLeps, setRmin, setUseForwardBackwardBeam, stateIndexOfString, sumNegLogProb, transduce, viterbiPath_NBest, viterbiPath_NBest, viterbiPath, viterbiPath, viterbiPath, viterbiPathBeam, viterbiPathBeam, viterbiPathBeam, viterbiPathBeamB, viterbiPathBeamB, viterbiPathBeamB, viterbiPathBeamB, viterbiPathBeamFB, viterbiPathBeamFB, viterbiPathBeamFB, viterbiPathBeamFB, viterbiPathBeamKL, viterbiPathBeamKL, viterbiPathBeamKLpublic Alphabet getInputAlphabet()
public Alphabet getOutputAlphabet()
public void setUseHyperbolicPrior(boolean f)
public void setHyperbolicPriorSlope(double p)
public void setHyperbolicPriorSharpness(double p)
public double getUseHyperbolicPriorSlope()
public double getUseHyperbolicPriorSharpness()
public void setGaussianPriorVariance(double p)
public double getGaussianPriorVariance()
public int getDefaultFeatureIndex()
public void addState(String name, double initialCost, double finalCost, String[] destinationNames, String[] labelNames, String[] weightNames)
public void addState(String name, double initialCost, double finalCost, String[] destinationNames, String[] labelNames)
public void addFullyConnectedStates(String[] stateNames)
public void addFullyConnectedStatesForLabels()
public void addStatesForLabelsConnectedAsIn(InstanceList trainingSet)
public void addStatesForHalfLabelsConnectedAsIn(InstanceList trainingSet)
public void addFullyConnectedStatesForBiLabels()
public void addStatesForBiLabelsConnectedAsIn(InstanceList trainingSet)
public void addFullyConnectedStatesForTriLabels()
public void addSelfTransitioningStateForAllLabels(String name)
public void setWeights(int weightsIndex,
DenseVector transitionWeights)
public void setWeights(String weightName, DenseVector transitionWeights)
public String getWeightsName(int weightIndex)
public DenseVector getWeights(String weightName)
public DenseVector getWeights(int weightIndex)
public void growWeightsDimensionToInputAlphabet()
public int getWeightsIndex(String weightName)
public int numStates()
numStates in class Transducerpublic Transducer.State getState(int index)
getState in class Transducerpublic Iterator initialStateIterator()
initialStateIterator in class Transducerpublic boolean isTrainable()
isTrainable in class Transducerpublic void setTrainable(boolean f)
setTrainable in class Transducerpublic void setParameter(int sourceStateIndex,
int destStateIndex,
int featureIndex,
double value)
public double getParameter(int sourceStateIndex,
int destStateIndex,
int featureIndex,
double value)
public void reset()
public void estimate()
public void print()
print in class Transducerpublic boolean train(InstanceList ilist)
train in class Transducerpublic boolean train(InstanceList ilist, InstanceList validation, InstanceList testing)
public boolean train(InstanceList ilist, InstanceList validation, InstanceList testing, TransducerEvaluator eval)
public boolean train(InstanceList ilist, InstanceList validation, InstanceList testing, TransducerEvaluator eval, int numIterations)
public boolean train(InstanceList training, InstanceList validation, InstanceList testing, TransducerEvaluator eval, int numIterations, int numIterationsPerProportion, double[] trainingProportions)
public boolean trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions)
public void write(File f)
public CRF.MinimizableCRF getMinimizableCRF(InstanceList ilist)
Copyright © 2019 JULIE Lab, Germany. All rights reserved.