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## org.encog.ml.train.strategy
Interface Strategy

All Known Subinterfaces:
  EndTrainingStrategy

All Known Implementing Classes:
  EndIterationsStrategy, EndMaxErrorStrategy, EndMinutesStrategy, Greedy, HybridStrategy, RequiredImprovementStrategy, ResetStrategy, SmartLearningRate, SmartMomentum, StopTrainingStrategy

    public interface Strategy

Training strategies can be added to training algorithms. Training strategies allow different additional logic to be added to an existing training algorithm. There are a number of different training strategies that can perform various tasks, such as adjusting the learning rate or momentum, or terminating training when improvement diminishes. Other strategies are provided as well.

Author:
  jheaton

Method Summary
`void` | `init(MLTrain train)` Initialize this strategy.
`void` | `postIteration()` Called just after a training iteration.
`void` | `preIteration()` Called just before a training iteration.

Method Detail

### init

    void init(MLTrain train)

  Initialize this strategy.

  Parameters:
    `train` - The training algorithm.

### preIteration

    void preIteration()

  Called just before a training iteration.

### postIteration

    void postIteration()

  Called just after a training iteration.

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