com.imsl.datamining.neural

Class EpochTrainer

java.lang.Object

com.imsl.datamining.neural.EpochTrainer

All Implemented Interfaces:

Trainer, Serializable

public class EpochTrainer
extends Object
implements Trainer, Serializable

Performs two-stage training using randomly selected training patterns in stage I. The EpochTrainer is a meta-trainer that combines two trainers. The first or Stage I trainer is used on a series of randomly selected subsets of the training patterns. For each subset, the weights are initialized to their initial values plus a random offset.

The best result from Stage I training is used as the initial guess for the second or Stage II trainer. The Stage II trainer uses the full set of training patterns. In EpochTrainer the second trainer is optional. If the Stage II trainer is null then the best result from Stage I is returned.

See Also:

Example, Serialized Form

Constructor Summary

Constructors

Constructor and Description
EpochTrainer(Trainer stage1Trainer) Creates a single stage EpochTrainer.
EpochTrainer(Trainer stage1Trainer, Trainer stage2Trainer) Creates a two-stage EpochTrainer.

Method Summary

Modifier and Type	Method and Description
int	getEpochSize() Returns the number of sample training patterns in each stage 1 epoch.
double[]	getErrorGradient() Returns the value of the gradient of the error function with respect to the weights.
int	getErrorStatus() Returns the training error status.
double	getErrorValue() Returns the value of the error function.
int	getNumberOfEpochs() Returns the number of epochs used during stage I training.
int	getNumberOfThreads() Gets the number of java.lang.Thread instances to use during stage I training.
Random	getRandom() Returns the random number generator used to perturb the stage 1 guesses.
protected com.imsl.datamining.neural.RandomSampleIndices	getRandomSampleIndicies() Gets the random number generators used to select random training patterns in stage 1.
protected Trainer	getStage1Trainer() Returns the stage 1 trainer.
protected Trainer	getStage2Trainer() Returns the stage 1 trainer.
protected int	incrementEpochCount() Increments the epoch counter.
void	setEpochSize(int epochSize) Sets the number of randomly selected training patterns in stage 1 epoch.
void	setNumberOfEpochs(int numberOfEpochs) Sets the number of epochs.
void	setNumberOfThreads(int numberOfThreads) Sets the number of java.lang.Thread instances to be used for parallel processing.
void	setRandom(Random random) Sets the random number generator used to perturb the initial stage 1 guesses.
void	setRandomSamples(Random randomA, Random randomB) Sets the random number generators used to select random training patterns in stage 1.
void	train(Network network, double[][] xData, double[][] yData) Trains the neural network using supplied training patterns.

Methods inherited from class java.lang.Object

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

Constructor Detail

EpochTrainer

public EpochTrainer(Trainer stage1Trainer)

Creates a single stage EpochTrainer. Stage 2 training is bypassed.

Parameters:

stage1Trainer - The Trainer used in stage I.

EpochTrainer

public EpochTrainer(Trainer stage1Trainer,
                    Trainer stage2Trainer)

Creates a two-stage EpochTrainer.

Parameters:

stage1Trainer - The stage I Trainer.

stage2Trainer - The stage II Trainer, or null if stage II is to be bypassed.

Method Detail

incrementEpochCount

protected int incrementEpochCount()

Increments the epoch counter.

getRandomSampleIndicies

protected com.imsl.datamining.neural.RandomSampleIndices getRandomSampleIndicies()

Gets the random number generators used to select random training patterns in stage 1.

Returns:

A RandomSampleIndicies containing the random number generators.

getStage1Trainer

protected Trainer getStage1Trainer()

Returns the stage 1 trainer.

Returns:

A Trainer containing the stage 1 trainer.

getStage2Trainer

protected Trainer getStage2Trainer()

Returns the stage 1 trainer.

Returns:

A Trainer containing the stage 2 trainer.

setNumberOfThreads

public void setNumberOfThreads(int numberOfThreads)

Sets the number of java.lang.Thread instances to be used for parallel processing.

Parameters:

numberOfThreads - an int specifying the number of java.lang.Thread instances to be used for parallel processing.

Default: numberOfThreads = 1.

getNumberOfThreads

public int getNumberOfThreads()

Gets the number of java.lang.Thread instances to use during stage I training.

Returns:

An int which contains the number of java.lang.Thread instances to use.

train

public void train(Network network,
                  double[][] xData,
                  double[][] yData)

Trains the neural network using supplied training patterns.

Specified by:

train in interface Trainer

Parameters:

network - The Network to be trained.

xData - A double matrix specifying the input training patterns. The number of columns in xData must equal the number of Nodes in the InputLayer.

yData - A double containing the output training patterns. The number of columns in yData must equal the number of Perceptrons in the OutputLayer.

Each row of xData and yData contains a training pattern. These number of rows in two arrays must be equal.

getErrorValue

public double getErrorValue()

Returns the value of the error function.

Specified by:

getErrorValue in interface Trainer

Returns:

A double containing final value of the error function from the last training. Before training, NaN is returned.

getErrorStatus

public int getErrorStatus()

Returns the training error status.

Specified by:

getErrorStatus in interface Trainer

Returns:

An int containing the error status from stage 2. If there is no stage 2 then the number of stage 1 epochs that returned a non-zero error status is returned.

getErrorGradient

public double[] getErrorGradient()

Returns the value of the gradient of the error function with respect to the weights.

Specified by:

getErrorGradient in interface Trainer

Returns:

A double array whose length is equal to the number of Network weights, containing the value of the gradient of the error function with respect to the weights. Before training, null is returned.

getRandom

public Random getRandom()

Returns the random number generator used to perturb the stage 1 guesses.

Returns:

The Random object used to generate stage 1 perturbations.

setRandom

public void setRandom(Random random)

Sets the random number generator used to perturb the initial stage 1 guesses.

Parameters:

random - The Random object used to set the random number generator.

setRandomSamples

public void setRandomSamples(Random randomA,
                             Random randomB)

Sets the random number generators used to select random training patterns in stage 1. The two random number generators should be independent.

Parameters:

randomA - A Random object which is the first random number generator.

randomB - A Random object which is the second random number generator, independent of randomA.

getNumberOfEpochs

public int getNumberOfEpochs()

Returns the number of epochs used during stage I training.

Returns:

An int which contains the number of epochs used during stage I training.

setNumberOfEpochs

public void setNumberOfEpochs(int numberOfEpochs)

Sets the number of epochs.

Parameters:

numberOfEpochs - An int which specifies the number of epochs to be used during stage I training. The default value is 10.

getEpochSize

public int getEpochSize()

Returns the number of sample training patterns in each stage 1 epoch.

Returns:

An int which contains the number of sample training patterns in each stage I epoch.

setEpochSize

public void setEpochSize(int epochSize)

Sets the number of randomly selected training patterns in stage 1 epoch.

Parameters:

epochSize - An int which specifies the number of sample training patterns in each stage I epoch. The default value is the number of observations in the training data.