org.encog.ml.train

Interface MLTrain

All Known Subinterfaces:

Train

All Known Implementing Classes:

Backpropagation, BaseBaumWelch, BasicTraining, BasicTrainSOM, CrossTraining, CrossValidationKFold, FreeformBackPropagation, FreeformPropagationTraining, FreeformResilientPropagation, LevenbergMarquardtTraining, ManhattanPropagation, MLMethodGeneticAlgorithm, MLMethodGeneticAlgorithm.MLMethodGeneticAlgorithmHelper, NelderMeadTraining, NeuralPSO, NeuralSimulatedAnnealing, Propagation, QuickPropagation, ResilientPropagation, ScaledConjugateGradient, SOMClusterCopyTraining, StochasticGradientDescent, SVDTraining, SVMSearchTrain, SVMTrain, TrainAdaline, TrainBasicPNN, TrainBaumWelch, TrainBaumWelchScaled, TrainBayesian, TrainEA, TrainGaussian, TrainInstar, TrainKMeans, TrainLinearRegression, TrainOutstar

public interface MLTrain

Defines a training method for a machine learning method. Most MLMethod objects need to be trained in some way before they are ready for use.

Method Summary

Modifier and Type	Method and Description
void	addStrategy(Strategy strategy) Training strategies can be added to improve the training results.
boolean	canContinue()
void	finishTraining() Should be called once training is complete and no more iterations are needed.
double	getError()
TrainingImplementationType	getImplementationType()
int	getIteration()
MLMethod	getMethod() Get the current best machine learning method from the training.
java.util.List<Strategy>	getStrategies()
MLDataSet	getTraining()
boolean	isTrainingDone()
void	iteration() Perform one iteration of training.
void	iteration(int count) Perform a number of training iterations.
TrainingContinuation	pause() Pause the training to continue later.
void	resume(TrainingContinuation state) Resume training.
void	setError(double error)
void	setIteration(int iteration) Set the current training iteration.

Method Detail

getImplementationType

TrainingImplementationType getImplementationType()

Returns:

The training implementation type.

isTrainingDone

boolean isTrainingDone()

Returns:

True if training can progress no further.

getTraining

MLDataSet getTraining()

Returns:

The training data to use.

iteration

void iteration()

Perform one iteration of training.

getError

double getError()

Returns:

Returns the training error. This value is calculated as the training data is evaluated by the iteration function. This has two important ramifications. First, the value returned by getError() is meaningless prior to a call to iteration. Secondly, the error is calculated BEFORE training is applied by the call to iteration. The timing of the error calculation is done for performance reasons.

finishTraining

void finishTraining()

Should be called once training is complete and no more iterations are needed. Calling iteration again will simply begin the training again, and require finishTraining to be called once the new training session is complete. It is particularly important to call finishTraining for multithreaded training techniques.

iteration

void iteration(int count)

Perform a number of training iterations.

Parameters:

count - The number of iterations to perform.

getIteration

int getIteration()

Returns:

The current training iteration.

canContinue

boolean canContinue()

Returns:

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

pause

TrainingContinuation pause()

Pause the training to continue later.

Returns:

A training continuation object.

resume

void resume(TrainingContinuation state)

Resume training.

Parameters:

state - The training continuation object to use to continue.

addStrategy

void addStrategy(Strategy strategy)

Training strategies can be added to improve the training results. There are a number to choose from, and several can be used at once.

Parameters:

strategy - The strategy to add.

getMethod

MLMethod getMethod()

Get the current best machine learning method from the training.

Returns:

The best machine learning method.

getStrategies

java.util.List<Strategy> getStrategies()

Returns:

The strategies to use.

setError

void setError(double error)

Parameters:

error - Set the current error rate. This is usually used by training strategies.

setIteration

void setIteration(int iteration)

Set the current training iteration.

Parameters:

iteration - Iteration.