cc.mallet.topics

Class DMROptimizable

java.lang.Object

cc.mallet.topics.DMROptimizable

All Implemented Interfaces:

Optimizable, Optimizable.ByGradientValue

public class DMROptimizable
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

Constructors

Constructor and Description
DMROptimizable()
DMROptimizable(InstanceList instances, MaxEnt initialClassifier)

Method Summary

Modifier and Type	Method and Description
MaxEnt	getClassifier()
int	getNumParameters()
double	getParameter(int index)
void	getParameters(double[] buff)
double	getValue() The log probability of the observed count vectors given the features.
void	getValueGradient(double[] buffer)
void	setInterceptGaussianPriorVariance(double sigmaSquared) Set the variance for the default features (aka intercept terms), generally larger than the variance for the regular features.
void	setParameter(int index, double v)
void	setParameters(double[] buff)
void	setRegularGaussianPriorVariance(double sigmaSquared) Set the variance for regular (non default) features, generally smaller than the variance for the default features.

Methods inherited from class java.lang.Object

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

Constructor Detail

DMROptimizable

public DMROptimizable()

DMROptimizable

public DMROptimizable(InstanceList instances,
                      MaxEnt initialClassifier)

Method Detail

setInterceptGaussianPriorVariance

public void setInterceptGaussianPriorVariance(double sigmaSquared)

Set the variance for the default features (aka intercept terms), generally larger than the variance for the regular features.

setRegularGaussianPriorVariance

public void setRegularGaussianPriorVariance(double sigmaSquared)

Set the variance for regular (non default) features, generally smaller than the variance for the default features.

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()

The log probability of the observed count vectors given the features.

Specified by:

getValue in interface Optimizable.ByGradientValue

getValueGradient

public void getValueGradient(double[] buffer)

Specified by:

getValueGradient in interface Optimizable.ByGradientValue