Package cc.mallet.classify

Class MCMaxEntTrainer

java.lang.Object

cc.mallet.classify.ClassifierTrainer<MCMaxEnt>

cc.mallet.classify.MCMaxEntTrainer

All Implemented Interfaces:

Boostable, java.io.Serializable

public class MCMaxEntTrainer
extends ClassifierTrainer<MCMaxEnt>
implements Boostable, java.io.Serializable

The trainer for a Maximum Entropy classifier.

Author:

Andrew McCallum mccallum@cs.umass.edu

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class cc.mallet.classify.ClassifierTrainer

ClassifierTrainer.ByActiveLearning<C extends Classifier>, ClassifierTrainer.ByIncrements<C extends Classifier>, ClassifierTrainer.ByInstanceIncrements<C extends Classifier>, ClassifierTrainer.ByOptimization<C extends Classifier>, ClassifierTrainer.Factory<CT extends ClassifierTrainer<? extends Classifier>>

Field Summary

Fields

Modifier and Type	Field	Description
static java.lang.String	EXP_GAIN
static java.lang.String	GRADIENT_GAIN
static java.lang.String	INFORMATION_GAIN

Fields inherited from class cc.mallet.classify.ClassifierTrainer

finishedTraining, validationSet

Constructor Summary

Constructors

Constructor	Description
MCMaxEntTrainer()
MCMaxEntTrainer(boolean useHyperbolicPrior)
MCMaxEntTrainer(double gaussianPriorVariance)	Constructs a trainer with a parameter to avoid overtraining.
MCMaxEntTrainer(double gaussianPriorVariance, boolean useMultiConditionalTraining)
MCMaxEntTrainer(double hyperbolicPriorSlope, double hyperbolicPriorSharpness)
MCMaxEntTrainer(MCMaxEnt initialClassifier)
MCMaxEntTrainer(CommandOption.List col)

Method Summary

Modifier and Type	Method	Description
MCMaxEnt	getClassifier()
static CommandOption.List	getCommandOptionList()
Optimizable.ByGradientValue	getMaximizableTrainer(InstanceList ilist)
int	getValueCalls()	Counts how many times this trainer has computed the log probability of training labels.
int	getValueGradientCalls()	Counts how many times this trainer has computed the gradient of the log probability of training labels.
MCMaxEntTrainer	setGaussianPriorVariance(double gaussianPriorVariance)	Sets a parameter to prevent overtraining.
MCMaxEntTrainer	setHyperbolicPriorSharpness(double hyperbolicPriorSharpness)
MCMaxEntTrainer	setHyperbolicPriorSlope(double hyperbolicPriorSlope)
MCMaxEntTrainer	setNumIterations(int i)	Specifies the maximum number of iterations to run during a single call to train or trainWithFeatureInduction.
MCMaxEntTrainer	setUseHyperbolicPrior(boolean useHyperbolicPrior)
java.lang.String	toString()
MCMaxEnt	train(InstanceList trainingSet)

Methods inherited from class cc.mallet.classify.ClassifierTrainer

getValidationInstances, isFinishedTraining, setValidationInstances

Methods inherited from class java.lang.Object

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

Field Detail

EXP_GAIN

public static final java.lang.String EXP_GAIN

See Also:

Constant Field Values

GRADIENT_GAIN

public static final java.lang.String GRADIENT_GAIN

See Also:

Constant Field Values

INFORMATION_GAIN

public static final java.lang.String INFORMATION_GAIN

See Also:

Constant Field Values

Constructor Detail

MCMaxEntTrainer

public MCMaxEntTrainer(CommandOption.List col)

MCMaxEntTrainer

public MCMaxEntTrainer(MCMaxEnt initialClassifier)

MCMaxEntTrainer

public MCMaxEntTrainer()

MCMaxEntTrainer

public MCMaxEntTrainer(boolean useHyperbolicPrior)

MCMaxEntTrainer

public MCMaxEntTrainer(double gaussianPriorVariance)

Constructs a trainer with a parameter to avoid overtraining. 1.0 is usually a reasonable default value.

MCMaxEntTrainer

public MCMaxEntTrainer(double gaussianPriorVariance,
                       boolean useMultiConditionalTraining)

MCMaxEntTrainer

public MCMaxEntTrainer(double hyperbolicPriorSlope,
                       double hyperbolicPriorSharpness)

Method Detail

getCommandOptionList

public static CommandOption.List getCommandOptionList()

getMaximizableTrainer

public Optimizable.ByGradientValue getMaximizableTrainer(InstanceList ilist)

setNumIterations

public MCMaxEntTrainer setNumIterations(int i)

Specifies the maximum number of iterations to run during a single call to train or trainWithFeatureInduction. Not currently functional.

Returns:

This trainer

setUseHyperbolicPrior

public MCMaxEntTrainer setUseHyperbolicPrior(boolean useHyperbolicPrior)

setGaussianPriorVariance

public MCMaxEntTrainer 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

setHyperbolicPriorSlope

public MCMaxEntTrainer setHyperbolicPriorSlope(double hyperbolicPriorSlope)

setHyperbolicPriorSharpness

public MCMaxEntTrainer setHyperbolicPriorSharpness(double hyperbolicPriorSharpness)

getClassifier

public MCMaxEnt getClassifier()

Specified by:

getClassifier in class ClassifierTrainer<MCMaxEnt>

train

public MCMaxEnt train(InstanceList trainingSet)

Specified by:

train in class ClassifierTrainer<MCMaxEnt>

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.

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object