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## org.encog.neural.networks.training.propagation.manhattan
Class ManhattanPropagation

    java.lang.Object
      org.encog.ml.train.BasicTraining
          org.encog.neural.networks.training.propagation.Propagation
              org.encog.neural.networks.training.propagation.manhattan.ManhattanPropagation

All Implemented Interfaces:
  MLTrain, LearningRate, Train, MultiThreadable

    public class ManhattanPropagation

extends Propagation
implements LearningRate

One problem that the backpropagation technique has is that the magnitude of the partial derivative may be calculated too large or too small. The Manhattan update algorithm attempts to solve this by using the partial derivative to only indicate the sign of the update to the weight matrix. The actual amount added or subtracted from the weight matrix is obtained from a simple constant. This constant must be adjusted based on the type of neural network being trained. In general, start with a higher constant and decrease it as needed. The Manhattan update algorithm can be thought of as a simplified version of the resilient algorithm. The resilient algorithm uses more complex techniques to determine the update value.

Author:
  jheaton

Constructor Summary
`ManhattanPropagation(ContainsFlat network, MLDataSet training, double learnRate)` Construct a Manhattan propagation training object.

Method Summary
`boolean` | `canContinue()` This training type does not support training continue.
`double` | `getLearningRate()`
`TrainingContinuation` | `pause()` This training type does not support training continue.
`void` | `resume(TrainingContinuation state)` This training type does not support training continue.
`void` | `setLearningRate(double rate)` Set the learning rate.

Methods inherited from class org.encog.neural.networks.training.propagation.Propagation
`finishTraining, fixFlatSpot, getCurrentFlatNetwork, getFlatTraining, getMethod, getThreadCount, iteration, iteration, setErrorFunction, setFlatTraining, setThreadCount`

Methods inherited from class org.encog.ml.train.BasicTraining
`addStrategy, getError, getImplementationType, getIteration, getStrategies, getTraining, isTrainingDone, postIteration, preIteration, setError, setIteration, setTraining`

Methods inherited from class java.lang.Object
`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Methods inherited from interface org.encog.ml.train.MLTrain
`addStrategy, getError, getImplementationType, getIteration, getStrategies, getTraining, isTrainingDone, setError, setIteration`

Constructor Detail

### ManhattanPropagation

    public ManhattanPropagation(ContainsFlat network,
                                MLDataSet training,
                                double learnRate)

  Construct a Manhattan propagation training object.

  Parameters:
    `network` - The network to train.
    `training` - The training data to use.
    `learnRate` - The learning rate.

Method Detail

### getLearningRate

    public final double getLearningRate()

  Specified by:
    `getLearningRate` in interface `LearningRate`
  Returns:
    The learning rate that was specified in the constructor.

### setLearningRate

    public final void setLearningRate(double rate)

  Set the learning rate.

  Specified by:
    `setLearningRate` in interface `LearningRate`
  Parameters:
    `rate` - The new learning rate.

### canContinue

    public final boolean canContinue()

  This training type does not support training continue.

  Specified by:
    `canContinue` in interface `MLTrain`
  Returns:
    Always returns false.

### pause

    public final TrainingContinuation pause()

  This training type does not support training continue.

  Specified by:
    `pause` in interface `MLTrain`
  Returns:
    Always returns null.

### resume

    public final void resume(TrainingContinuation state)

  This training type does not support training continue.

  Specified by:
    `resume` in interface `MLTrain`
  Parameters:
    `state` - Not used.

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