cc.mallet.classify
Class MaxEntOptimizableByLabelDistribution

java.lang.Object
  cc.mallet.classify.MaxEntOptimizableByLabelDistribution

All Implemented Interfaces:

Optimizable, Optimizable.ByGradientValue

public class MaxEntOptimizableByLabelDistribution
extends java.lang.Object
implements Optimizable.ByGradientValue

Nested Class Summary

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

Constructor Summary

MaxEntOptimizableByLabelDistribution()

MaxEntOptimizableByLabelDistribution(InstanceList trainingSet, MaxEnt initialClassifier)

Method Summary

MaxEnt	getClassifier()
int	getNumParameters()
double	getParameter(int index)
void	getParameters(double[] buff)
double	getValue() Return the log probability of the training label distributions
int	getValueCalls() Counts how many times this trainer has computed the log probability of training labels.
void	getValueGradient(double[] buffer)
int	getValueGradientCalls() Counts how many times this trainer has computed the gradient of the log probability of training labels.
MaxEntOptimizableByLabelDistribution	setGaussianPriorVariance(double gaussianPriorVariance) Sets a parameter to prevent overtraining.
void	setParameter(int index, double v)
void	setParameters(double[] buff)
MaxEntOptimizableByLabelDistribution	useGaussianPrior()

Methods inherited from class java.lang.Object

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

Constructor Detail

MaxEntOptimizableByLabelDistribution

public MaxEntOptimizableByLabelDistribution()

MaxEntOptimizableByLabelDistribution

public MaxEntOptimizableByLabelDistribution(InstanceList trainingSet,
                                            MaxEnt initialClassifier)

Method Detail

getClassifier

public MaxEnt getClassifier()

getParameter

public double getParameter(int index)

Specified by:

getParameter in interface Optimizable

setParameter

public void setParameter(int index,
                         double v)

Specified by:

setParameter in interface Optimizable

getNumParameters

public int getNumParameters()

Specified by:

getNumParameters in interface Optimizable

getParameters

public void getParameters(double[] buff)

Specified by:

getParameters in interface Optimizable

setParameters

public void setParameters(double[] buff)

Specified by:

setParameters in interface Optimizable

getValue

public double getValue()

Return the log probability of the training label distributions

Specified by:

getValue in interface Optimizable.ByGradientValue

getValueGradient

public void getValueGradient(double[] buffer)

Specified by:

getValueGradient in interface Optimizable.ByGradientValue

getValueGradientCalls

public int getValueGradientCalls()

Counts how many times this trainer has computed the gradient of the log probability of training labels.

getValueCalls

public int getValueCalls()

Counts how many times this trainer has computed the log probability of training labels.

useGaussianPrior

public MaxEntOptimizableByLabelDistribution useGaussianPrior()

setGaussianPriorVariance

public MaxEntOptimizableByLabelDistribution setGaussianPriorVariance(double gaussianPriorVariance)

Sets a parameter to prevent overtraining. A smaller variance for the prior means that feature weights are expected to hover closer to 0, so extra evidence is required to set a higher weight.

Returns:

This trainer