cc.mallet.classify
Class MaxEntOptimizableByGE

java.lang.Object
  cc.mallet.classify.MaxEntOptimizableByGE

All Implemented Interfaces:

Optimizable, Optimizable.ByGradientValue

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

Author:

gdruck

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

MaxEntOptimizableByGE(InstanceList trainingList, java.util.HashMap<java.lang.Integer,double[]> constraints, MaxEnt initClassifier)

Method Summary

MaxEnt	getClassifier()
int	getNumParameters()
double	getParameter(int index)
void	getParameters(double[] buffer)
double	getValue()
void	getValueGradient(double[] buffer)
void	setGaussianPriorVariance(double variance) Sets the variance for Gaussian prior or equivalently the inverse of the weight of the L2 regularization term.
void	setParameter(int index, double value)
void	setParameters(double[] params)
void	setTemperature(double temp) Set the temperature, 1 / the exponent model predicted probabilities are raised to when computing model expectations.
void	setWeight(double weight) The weight of GE term in the objective function.

Methods inherited from class java.lang.Object

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

Constructor Detail

MaxEntOptimizableByGE

public MaxEntOptimizableByGE(InstanceList trainingList,
                             java.util.HashMap<java.lang.Integer,double[]> constraints,
                             MaxEnt initClassifier)

Parameters:

trainingList - List with unlabeled training instances.

constraints - Feature expectation constraints.

initClassifier - Initial classifier.

Method Detail

setGaussianPriorVariance

public void setGaussianPriorVariance(double variance)

Sets the variance for Gaussian prior or equivalently the inverse of the weight of the L2 regularization term.

Parameters:

variance - Gaussian prior variance.

setTemperature

public void setTemperature(double temp)

Set the temperature, 1 / the exponent model predicted probabilities are raised to when computing model expectations. As the temperature increases, model probabilities approach 1 for the maximum probability class, and 0 for other classes. DEFAULT: 1

Parameters:

temp - Temperature.

setWeight

public void setWeight(double weight)

The weight of GE term in the objective function.

Parameters:

weight - GE term weight.

getClassifier

public MaxEnt getClassifier()

getValue

public double getValue()

Specified by:

getValue in interface Optimizable.ByGradientValue

getValueGradient

public void getValueGradient(double[] buffer)

Specified by:

getValueGradient in interface Optimizable.ByGradientValue

getNumParameters

public int getNumParameters()

Specified by:

getNumParameters in interface Optimizable

getParameter

public double getParameter(int index)

Specified by:

getParameter in interface Optimizable

getParameters

public void getParameters(double[] buffer)

Specified by:

getParameters in interface Optimizable

setParameter

public void setParameter(int index,
                         double value)

Specified by:

setParameter in interface Optimizable

setParameters

public void setParameters(double[] params)

Specified by:

setParameters in interface Optimizable