edu.umass.cs.mallet.base.fst

Class CRF

java.lang.Object

edu.umass.cs.mallet.base.fst.Transducer

edu.umass.cs.mallet.base.fst.CRF

All Implemented Interfaces:

Serializable

public class CRF
extends Transducer
implements Serializable

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
class	CRF.MinimizableCRF
class	CRF.State
protected class	CRF.TransitionIterator

Nested classes/interfaces inherited from class edu.umass.cs.mallet.base.fst.Transducer

Transducer.BeamLattice, Transducer.Lattice, Transducer.ViterbiLattice, Transducer.ViterbiPath, Transducer.ViterbiPath_NBest, Transducer.ViterbiPathBeam, Transducer.ViterbiPathBeamB, Transducer.ViterbiPathBeamFB, Transducer.ViterbiPathBeamKL

Field Summary

Fields

Modifier and Type	Field and Description
boolean	printGradient

Fields inherited from class edu.umass.cs.mallet.base.fst.Transducer

INFINITE_COST, inputPipe, outputPipe, ZERO_COST

Constructor Summary

Constructors

Constructor and Description
CRF(Alphabet inputAlphabet, Alphabet outputAlphabet)
CRF(Pipe inputPipe, Pipe outputPipe)

Method Summary

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)

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

Field Detail

printGradient

public boolean printGradient

Constructor Detail

CRF

public CRF(Pipe inputPipe,
           Pipe outputPipe)

CRF

public CRF(Alphabet inputAlphabet,
           Alphabet outputAlphabet)

Method Detail

getInputAlphabet

public Alphabet getInputAlphabet()

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()

getDefaultFeatureIndex

public int getDefaultFeatureIndex()

addState

public void addState(String name,
                     double initialCost,
                     double finalCost,
                     String[] destinationNames,
                     String[] labelNames,
                     String[] weightNames)

addState

public void addState(String name,
                     double initialCost,
                     double finalCost,
                     String[] destinationNames,
                     String[] labelNames)

addState

public void addState(String name,
                     String[] destinationNames)

addFullyConnectedStates

public void addFullyConnectedStates(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()

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. However, do create separate default feature for each transition, (which acts as an HMM-style transition probability).

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(String name)

setWeights

public void setWeights(int weightsIndex,
                       DenseVector transitionWeights)

setWeights

public void setWeights(String weightName,
                       DenseVector transitionWeights)

getWeightsName

public String getWeightsName(int weightIndex)

getWeights

public DenseVector getWeights(String weightName)

getWeights

public DenseVector getWeights(int weightIndex)

growWeightsDimensionToInputAlphabet

public void growWeightsDimensionToInputAlphabet()

Increase the size of the weights[] parameters to match (a new, larger) input Alphabet size

getWeightsIndex

public int getWeightsIndex(String weightName)

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 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(File f)

getMinimizableCRF

public CRF.MinimizableCRF getMinimizableCRF(InstanceList ilist)