org.encog.ml.train.strategy

## Interface Strategy

- All Known Subinterfaces:
    EndTrainingStrategy
  All Known Implementing Classes:
    EarlyStoppingStrategy, EndIterationsStrategy, EndMaxErrorStrategy, EndMinutesStrategy, Greedy, HybridStrategy, RegularizationStrategy, RequiredImprovementStrategy, ResetStrategy, SimpleEarlyStoppingStrategy, 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

    Methods
    Modifier and Type | Method and Description
    --- | ---
    `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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