smile.classification

Class Maxent.Trainer

java.lang.Object

smile.classification.ClassifierTrainer<int[]>

smile.classification.Maxent.Trainer

Enclosing class:

Maxent

public static class Maxent.Trainer
extends ClassifierTrainer<int[]>

Trainer for maximum entropy classifier.

Constructor Summary

Constructors

Constructor and Description
Maxent.Trainer(int p) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
void	setMaxNumIteration(int maxIter) Sets the maximum number of BFGS stopping iterations.
void	setRegularizationFactor(double lambda) Sets the regularization factor.
void	setTolerance(double tol) Sets the tolerance for BFGS stopping iterations.
Maxent	train(int[][] x, int[] y) Learns a classifier with given training data.

Methods inherited from class smile.classification.ClassifierTrainer

setAttributes

Methods inherited from class java.lang.Object

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

Constructor Detail

Maxent.Trainer

public Maxent.Trainer(int p)

Constructor.

Parameters:

p - the dimension of feature space.

Method Detail

setRegularizationFactor

public void setRegularizationFactor(double lambda)

Sets the regularization factor. λ > 0 gives a "regularized" estimate of linear weights which often has superior generalization performance, especially when the dimensionality is high.

Parameters:

lambda - regularization factor.

setTolerance

public void setTolerance(double tol)

Sets the tolerance for BFGS stopping iterations.

Parameters:

tol - tolerance for stopping iterations.

setMaxNumIteration

public void setMaxNumIteration(int maxIter)

Sets the maximum number of BFGS stopping iterations.

Parameters:

maxIter - maximum number of iterations.

train

public Maxent train(int[][] x,
           int[] y)

Description copied from class: ClassifierTrainer

Learns a classifier with given training data.

Specified by:

train in class ClassifierTrainer<int[]>

Parameters:

x - the training instances.

y - the training labels.

Returns:

a trained classifier.