org.encog.ml.ea.train.basic
Class TrainEA

java.lang.Object
  org.encog.ml.ea.train.basic.BasicEA
      org.encog.ml.ea.train.basic.TrainEA

All Implemented Interfaces:

Serializable, EncogShutdownTask, EvolutionaryAlgorithm, MLTrain, MultiThreadable

Direct Known Subclasses:

MLMethodGeneticAlgorithm.MLMethodGeneticAlgorithmHelper

public class TrainEA
extends BasicEA
implements MLTrain

Provides a MLTrain compatible class that can be used to train genomes.

See Also:

Serialized Form

Constructor Summary

TrainEA(Population thePopulation, CalculateScore theScoreFunction)
Create a trainer for a score function.

TrainEA(Population thePopulation, MLDataSet trainingData)
Create a trainer for training data.

Method Summary

void	addStrategy(Strategy strategy) Not supported, will throw an error.
boolean	canContinue()
void	finishTraining() Called when training is finished.
TrainingImplementationType	getImplementationType()
MLMethod	getMethod() Get the current best machine learning method from the training.
List<Strategy>	getStrategies() Returns an empty list, strategies are not supported.
MLDataSet	getTraining() Returns null, does not use a training set, rather uses a score function.
boolean	isTrainingDone()
void	iteration(int count) Perform the specified number of training iterations.
TrainingContinuation	pause() Pause the training to continue later.
void	resume(TrainingContinuation state) Resume training.
void	setError(double error) Not used.

Methods inherited from class org.encog.ml.ea.train.basic.BasicEA

addChild, addOperation, addScoreAdjuster, calculateScore, calculateScoreAdjustment, getBestComparator, getBestGenome, getChampMutation, getCODEC, getEliteRate, getError, getIteration, getMaxIndividualSize, getMaxOperationErrors, getMaxTries, getOldBestGenome, getOperators, getPopulation, getRandomNumberFactory, getRules, getScoreAdjusters, getScoreFunction, getSelection, getSelectionComparator, getShouldIgnoreExceptions, getSpeciation, getThreadCount, isValidationMode, iteration, performShutdownTask, reportError, setBestComparator, setChampMutation, setCODEC, setEliteRate, setIteration, setMaxOperationErrors, setMaxTries, setPopulation, setRandomNumberFactory, setRules, setSelection, setSelectionComparator, setShouldIgnoreExceptions, setSpeciation, setThreadCount, setValidationMode

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.encog.ml.train.MLTrain

getError, getIteration, iteration, setIteration

Constructor Detail

TrainEA

public TrainEA(Population thePopulation,
               CalculateScore theScoreFunction)

Create a trainer for a score function.

Parameters:

thePopulation - The population.

theScoreFunction - The score function.

TrainEA

public TrainEA(Population thePopulation,
               MLDataSet trainingData)

Create a trainer for training data.

Parameters:

thePopulation - The population.

trainingData - The training data.

Method Detail

setError

public void setError(double error)

Not used.

Specified by:

setError in interface MLTrain

Parameters:

error - Not used.

isTrainingDone

public boolean isTrainingDone()

Specified by:

isTrainingDone in interface MLTrain

Returns:

True if training can progress no further.

getImplementationType

public TrainingImplementationType getImplementationType()

Specified by:

getImplementationType in interface MLTrain

Returns:

The training implementation type.

iteration

public void iteration(int count)

Perform the specified number of training iterations. This is a basic implementation that just calls iteration the specified number of times. However, some training methods, particularly with the GPU, benefit greatly by calling with higher numbers than 1.

Specified by:

iteration in interface MLTrain

Parameters:

count - The number of training iterations.

pause

public TrainingContinuation pause()

Pause the training to continue later.

Specified by:

pause in interface MLTrain

Returns:

A training continuation object.

resume

public void resume(TrainingContinuation state)

Resume training.

Specified by:

resume in interface MLTrain

Parameters:

state - The training continuation object to use to continue.

addStrategy

public void addStrategy(Strategy strategy)

Not supported, will throw an error.

Specified by:

addStrategy in interface MLTrain

Parameters:

strategy - Not used.

canContinue

public boolean canContinue()

Specified by:

canContinue in interface MLTrain

Returns:

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

finishTraining

public void finishTraining()

Called when training is finished. This allows the EA to properly shut down.

Specified by:

finishTraining in interface EvolutionaryAlgorithm

Specified by:

finishTraining in interface MLTrain

Overrides:

finishTraining in class BasicEA

getMethod

public MLMethod getMethod()

Description copied from interface: MLTrain

Get the current best machine learning method from the training.

Specified by:

getMethod in interface MLTrain

Returns:

A network created for the best genome.

getTraining

public MLDataSet getTraining()

Returns null, does not use a training set, rather uses a score function.

Specified by:

getTraining in interface MLTrain

Returns:

null, not used.

getStrategies

public List<Strategy> getStrategies()

Returns an empty list, strategies are not supported.

Specified by:

getStrategies in interface MLTrain

Returns:

The strategies in use(none).