cc.mallet.fst
Class CRFOptimizableByLabelLikelihood

java.lang.Object
  cc.mallet.fst.CRFOptimizableByLabelLikelihood

All Implemented Interfaces:

Optimizable, Optimizable.ByGradientValue, java.io.Serializable

Direct Known Subclasses:

MEMMTrainer.MEMMOptimizableByLabelLikelihood

public class CRFOptimizableByLabelLikelihood
extends java.lang.Object
implements Optimizable.ByGradientValue, java.io.Serializable

An objective function for CRFs that is the label likelihood plus a Gaussian or hyperbolic prior on parameters.

See Also:

Serialized Form

Nested Class Summary

static class

CRFOptimizableByLabelLikelihood.Factory

Nested classes/interfaces inherited from interface cc.mallet.optimize.Optimizable

Optimizable.ByBatchGradient, Optimizable.ByCombiningBatchGradient, Optimizable.ByGISUpdate, Optimizable.ByGradient, Optimizable.ByGradientValue, Optimizable.ByHessian, Optimizable.ByValue, Optimizable.ByVotedPerceptron

Field Summary

protected double[]	cachedGradient
protected double	cachedValue
protected CRF.Factors	constraints
protected CRF	crf
protected CRF.Factors	expectations
protected java.util.BitSet	infiniteValues
protected InstanceList	trainingSet

Constructor Summary

CRFOptimizableByLabelLikelihood(CRF crf, InstanceList ilist)

Method Summary

protected void	gatherConstraints(InstanceList ilist)
protected double	getExpectationValue()
double	getGaussianPriorVariance()
int	getNumParameters()
double	getParameter(int index)
void	getParameters(double[] buffer)
double	getUseHyperbolicPriorSharpness()
double	getUseHyperbolicPriorSlope()
double	getValue() Returns the log probability of the training sequence labels and the prior over parameters.
void	getValueGradient(double[] buffer)
void	setGaussianPriorVariance(double p)
void	setHyperbolicPriorSharpness(double p)
void	setHyperbolicPriorSlope(double p)
void	setParameter(int index, double value)
void	setParameters(double[] buff)
void	setUseHyperbolicPrior(boolean f)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

trainingSet

protected InstanceList trainingSet

cachedValue

protected double cachedValue

cachedGradient

protected double[] cachedGradient

infiniteValues

protected java.util.BitSet infiniteValues

crf

protected CRF crf

constraints

protected CRF.Factors constraints

expectations

protected CRF.Factors expectations

Constructor Detail

CRFOptimizableByLabelLikelihood

public CRFOptimizableByLabelLikelihood(CRF crf,
                                       InstanceList ilist)

Method Detail

gatherConstraints

protected void gatherConstraints(InstanceList ilist)

getNumParameters

public int getNumParameters()

Specified by:

getNumParameters in interface Optimizable

getParameters

public void getParameters(double[] buffer)

Specified by:

getParameters in interface Optimizable

getParameter

public double getParameter(int index)

Specified by:

getParameter in interface Optimizable

setParameters

public void setParameters(double[] buff)

Specified by:

setParameters in interface Optimizable

setParameter

public void setParameter(int index,
                         double value)

Specified by:

setParameter in interface Optimizable

getExpectationValue

protected double getExpectationValue()

getValue

public double getValue()

Returns the log probability of the training sequence labels and the prior over parameters.

Specified by:

getValue in interface Optimizable.ByGradientValue

getValueGradient

public void getValueGradient(double[] buffer)

Specified by:

getValueGradient in interface Optimizable.ByGradientValue

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()