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## org.encog.ml.train
Interface MLTrain

All Known Subinterfaces:
  Train

All Known Implementing Classes:
  Backpropagation, BasicTraining, BasicTrainSOM, CrossTraining, CrossValidationKFold, LevenbergMarquardtTraining, ManhattanPropagation, NEATTraining, NeuralGeneticAlgorithm, NeuralSimulatedAnnealing, Propagation, QuickPropagation, ResilientPropagation, ScaledConjugateGradient, SOMClusterCopyTraining, SVDTraining, SVMSearchTrain, SVMTrain, TrainAdaline, TrainBasicPNN, TrainInstar, 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
`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()` Get the current error percent from the training.
`TrainingImplementationType` | `getImplementationType()`
`int` | `getIteration()`
`MLMethod` | `getMethod()` Get the current best machine learning method from the training.
`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()

  Get the current error percent from the training.

  Returns:
    The current error.

### 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 learningm method.

### getStrategies

    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.

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