edu.umass.cs.mallet.base.fst
Class CRF2
java.lang.Object
edu.umass.cs.mallet.base.fst.Transducer
edu.umass.cs.mallet.base.fst.CRF2
- All Implemented Interfaces:
- java.io.Serializable
- public class CRF2
- extends Transducer
- implements java.io.Serializable
- See Also:
- Serialized Form
Method Summary |
void |
addFullyConnectedStates(java.lang.String[] stateNames)
|
void |
addFullyConnectedStatesForBiLabels()
|
void |
addFullyConnectedStatesForLabels()
|
void |
addFullyConnectedStatesForTriLabels()
|
void |
addSelfTransitioningStateForAllLabels(java.lang.String name)
|
void |
addState(java.lang.String name,
double initialCost,
double finalCost,
java.lang.String[] destinationNames,
java.lang.String[] labelNames)
|
void |
addState(java.lang.String name,
double initialCost,
double finalCost,
java.lang.String[] destinationNames,
java.lang.String[] labelNames,
java.lang.String[] weightNames)
|
void |
addState(java.lang.String name,
java.lang.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()
|
double |
getGaussianPriorVariance()
|
Alphabet |
getInputAlphabet()
Create a new CRF sharing Alphabet and other attributes, but possibly
having a larger weights array. |
CRF2.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()
|
SparseVector |
getWeights(int weightIndex)
|
SparseVector |
getWeights(java.lang.String weightName)
|
int |
getWeightsIndex(java.lang.String weightName)
Increase the size of the weights[] parameters to match (a new, larger)
input Alphabet size |
java.lang.String |
getWeightsName(int weightIndex)
|
java.util.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,
SparseVector transitionWeights)
|
void |
setWeights(java.lang.String weightName,
SparseVector transitionWeights)
|
void |
setWeightsDimensionAsIn(InstanceList trainingData)
|
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(java.io.File f)
|
Methods inherited from class edu.umass.cs.mallet.base.fst.Transducer |
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, viterbiPathBeamKL |
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
printGradient
public boolean printGradient
CRF2
public CRF2(Pipe inputPipe,
Pipe outputPipe)
CRF2
public CRF2(Alphabet inputAlphabet,
Alphabet outputAlphabet)
getInputAlphabet
public Alphabet getInputAlphabet()
- Create a new CRF sharing Alphabet and other attributes, but possibly
having a larger weights array.
getOutputAlphabet
public Alphabet getOutputAlphabet()
setUseHyperbolicPrior
public void setUseHyperbolicPrior(boolean f)
setHyperbolicPriorSlope
public void setHyperbolicPriorSlope(double p)
setHyperbolicPriorSharpness
public void setHyperbolicPriorSharpness(double p)
getUseHyperbolicPriorSlope
public double getUseHyperbolicPriorSlope()
getUseHyperbolicPriorSharpness
public double getUseHyperbolicPriorSharpness()
setGaussianPriorVariance
public void setGaussianPriorVariance(double p)
getGaussianPriorVariance
public double getGaussianPriorVariance()
addState
public void addState(java.lang.String name,
double initialCost,
double finalCost,
java.lang.String[] destinationNames,
java.lang.String[] labelNames,
java.lang.String[] weightNames)
addState
public void addState(java.lang.String name,
double initialCost,
double finalCost,
java.lang.String[] destinationNames,
java.lang.String[] labelNames)
addState
public void addState(java.lang.String name,
java.lang.String[] destinationNames)
addFullyConnectedStates
public void addFullyConnectedStates(java.lang.String[] stateNames)
addFullyConnectedStatesForLabels
public void addFullyConnectedStatesForLabels()
addStatesForLabelsConnectedAsIn
public 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.
addStatesForHalfLabelsConnectedAsIn
public 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. Instead have all the incoming
transitions to a state share the same weights.
addFullyConnectedStatesForBiLabels
public void addFullyConnectedStatesForBiLabels()
addStatesForBiLabelsConnectedAsIn
public 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.
addFullyConnectedStatesForTriLabels
public void addFullyConnectedStatesForTriLabels()
addSelfTransitioningStateForAllLabels
public void addSelfTransitioningStateForAllLabels(java.lang.String name)
setWeights
public void setWeights(int weightsIndex,
SparseVector transitionWeights)
setWeights
public void setWeights(java.lang.String weightName,
SparseVector transitionWeights)
getWeightsName
public java.lang.String getWeightsName(int weightIndex)
getWeights
public SparseVector getWeights(java.lang.String weightName)
getWeights
public SparseVector getWeights(int weightIndex)
setWeightsDimensionAsIn
public void setWeightsDimensionAsIn(InstanceList trainingData)
getWeightsIndex
public int getWeightsIndex(java.lang.String weightName)
- Increase the size of the weights[] parameters to match (a new, larger)
input Alphabet size
numStates
public int numStates()
- Specified by:
numStates
in class Transducer
getState
public Transducer.State getState(int index)
- Specified by:
getState
in class Transducer
initialStateIterator
public java.util.Iterator initialStateIterator()
- Specified by:
initialStateIterator
in class Transducer
isTrainable
public boolean isTrainable()
- Overrides:
isTrainable
in class Transducer
setTrainable
public void setTrainable(boolean f)
- Overrides:
setTrainable
in class Transducer
setParameter
public void setParameter(int sourceStateIndex,
int destStateIndex,
int featureIndex,
double value)
getParameter
public double getParameter(int sourceStateIndex,
int destStateIndex,
int featureIndex,
double value)
reset
public void reset()
estimate
public void estimate()
print
public void print()
- Overrides:
print
in class Transducer
train
public boolean train(InstanceList ilist)
- Overrides:
train
in class Transducer
train
public boolean train(InstanceList ilist,
InstanceList validation,
InstanceList testing)
train
public boolean train(InstanceList ilist,
InstanceList validation,
InstanceList testing,
TransducerEvaluator eval)
train
public boolean train(InstanceList ilist,
InstanceList validation,
InstanceList testing,
TransducerEvaluator eval,
int numIterations)
train
public boolean train(InstanceList training,
InstanceList validation,
InstanceList testing,
TransducerEvaluator eval,
int numIterations,
int numIterationsPerProportion,
double[] trainingProportions)
trainWithFeatureInduction
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)
write
public void write(java.io.File f)
getMinimizableCRF
public CRF2.MinimizableCRF getMinimizableCRF(InstanceList ilist)