org.encog.neural.networks.training.anneal

Class NeuralSimulatedAnnealing

java.lang.Object

org.encog.ml.train.BasicTraining

org.encog.neural.networks.training.anneal.NeuralSimulatedAnnealing

All Implemented Interfaces:

MLTrain

public class NeuralSimulatedAnnealing
extends BasicTraining

This class implements a simulated annealing training algorithm for neural networks. It is based on the generic SimulatedAnnealing class. It is used in the same manner as any other training class that implements the Train interface. There are essentially two ways you can make use of this class. Either way, you will need a score object. The score object tells the simulated annealing algorithm how well suited a neural network is. If you would like to use simulated annealing with a training set you should make use TrainingSetScore class. This score object uses a training set to score your neural network. If you would like to be more abstract, and not use a training set, you can create your own implementation of the CalculateScore method. This class can then score the networks any way that you like.

Field Summary

Fields

Modifier and Type	Field and Description
static double	CUT The cutoff for random data.

Constructor Summary

Constructors

Constructor and Description
NeuralSimulatedAnnealing(MLEncodable network, CalculateScore calculateScore, double startTemp, double stopTemp, int cycles) Construct a simulated annleaing trainer for a encodable MLMethod.

Method Summary

Methods

Modifier and Type	Method and Description
boolean	canContinue()
double[]	getArray() Get the network as an array of doubles.
double[]	getArrayCopy()
CalculateScore	getCalculateScore()
MLEncodable	getMethod() Get the current best machine learning method from the training.
void	iteration() Perform one iteration of simulated annealing.
TrainingContinuation	pause() Pause the training to continue later.
void	putArray(double[] array) Convert an array of doubles to the current best network.
void	randomize() Randomize the weights and bias values.
void	resume(TrainingContinuation state) Resume training.

Methods inherited from class org.encog.ml.train.BasicTraining

addStrategy, finishTraining, getError, getImplementationType, getIteration, getStrategies, getTraining, isTrainingDone, iteration, postIteration, preIteration, setError, setIteration, setTraining

Methods inherited from class java.lang.Object

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

Field Detail

CUT

public static final double CUT

The cutoff for random data.

See Also:

Constant Field Values

Constructor Detail

NeuralSimulatedAnnealing

public NeuralSimulatedAnnealing(MLEncodable network,
                        CalculateScore calculateScore,
                        double startTemp,
                        double stopTemp,
                        int cycles)

Construct a simulated annleaing trainer for a encodable MLMethod.

Parameters:

network - The neural network to be trained.

calculateScore - Used to calculate the score for a MLMethod.

startTemp - The starting temperature.

stopTemp - The ending temperature.

cycles - The number of cycles in a training iteration.

Method Detail

canContinue

public boolean canContinue()

Returns:

True if the training can be paused, and later continued.

getArray

public double[] getArray()

Get the network as an array of doubles.

Returns:

The network as an array of doubles.

getArrayCopy

public double[] getArrayCopy()

Returns:

A copy of the annealing array.

getCalculateScore

public CalculateScore getCalculateScore()

Returns:

The object used to calculate the score.

getMethod

public MLEncodable getMethod()

Get the current best machine learning method from the training.

Returns:

The best machine learningm method.

iteration

public void iteration()

Perform one iteration of simulated annealing.

pause

public TrainingContinuation pause()

Description copied from interface: MLTrain

Pause the training to continue later.

Returns:

A training continuation object.

putArray

public void putArray(double[] array)

Convert an array of doubles to the current best network.

Parameters:

array - An array.

randomize

public void randomize()

Randomize the weights and bias values. This function does most of the work of the class. Each call to this class will randomize the data according to the current temperature. The higher the temperature the more randomness.

resume

public void resume(TrainingContinuation state)

Resume training.

Parameters:

state - The training continuation object to use to continue.