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## 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
`CrossValidationKFold(MLTrain train, int k)` Construct a cross validation trainer.

Method Summary
`boolean` | `canContinue()`
`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, 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`

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 final boolean canContinue()

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

### pause

    public final TrainingContinuation pause()

  Pause the training to continue later.

  Returns:
    A training continuation object.

### resume

    public final void resume(TrainingContinuation state)

  Resume training.

  Parameters:
    `state` - The training continuation object to use to continue.

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