org.encog.neural.networks.training.cross

Class CrossValidationKFold

java.lang.Object

org.encog.ml.train.BasicTraining

org.encog.neural.networks.training.cross.CrossTraining

org.encog.neural.networks.training.cross.CrossValidationKFold

All Implemented Interfaces:

MLTrain

public class CrossValidationKFold
extends CrossTraining

Train using K-Fold cross validation. Each iteration will train a number of times equal to the number of folds - 1. Each of these sub iterations will train all of the data minus the fold. The fold is used to validate. Therefore, you are seeing an error that reflects data that was not always used as part of training. This should give you a better error result based on how the network will perform on non-trained data.(validation). The cross validation trainer must be provided with some other sort of trainer, perhaps RPROP, to actually perform the training. The training data must be the FoldedDataSet. The folded dataset can wrap most other training sets.

Constructor Summary

Constructors

Constructor and Description
CrossValidationKFold(MLTrain train, int k) Construct a cross validation trainer.

Method Summary

Modifier and Type	Method and Description
boolean	canContinue()
int	getIteration()
void	iteration() Perform one iteration.
TrainingContinuation	pause() Pause the training to continue later.
void	resume(TrainingContinuation state) Resume training.

Methods inherited from class org.encog.neural.networks.training.cross.CrossTraining

getFolded, getMethod

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

addStrategy, finishTraining, getError, getImplementationType, 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

Constructor Detail

CrossValidationKFold

public CrossValidationKFold(MLTrain train,
                            int k)

Construct a cross validation trainer.

Parameters:

train - The training

k - The number of folds.

Method Detail

iteration

public void iteration()

Perform one iteration.

canContinue

public boolean canContinue()

Returns:

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

pause

public TrainingContinuation pause()

Pause the training to continue later.

Returns:

A training continuation object.

resume

public void resume(TrainingContinuation state)

Resume training.

Parameters:

state - The training continuation object to use to continue.

getIteration

public int getIteration()

Specified by:

getIteration in interface MLTrain

Overrides:

getIteration in class BasicTraining

Returns:

the iteration