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## org.encog.neural.pattern
Class SOMPattern

    java.lang.Object
      org.encog.neural.pattern.SOMPattern

All Implemented Interfaces:
  NeuralNetworkPattern

    public class SOMPattern

extends Object
implements NeuralNetworkPattern

A self organizing map is a neural network pattern with an input and output layer. There is no hidden layer. The winning neuron, which is that neuron with the higest output is the winner, this winning neuron is often used to classify the input into a group.

Author:
  jheaton

Constructor Summary
`SOMPattern()`

Method Summary
`void` | `addHiddenLayer(int count)` Add a hidden layer.
`void` | `clear()` Clear out any hidden neurons.
`MLMethod` | `generate()` Generate the RSOM network.
`void` | `setActivationFunction(ActivationFunction activation)` Set the activation function.
`void` | `setInputNeurons(int count)` Set the input neuron count.
`void` | `setOutputNeurons(int count)` Set the output neuron count.

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

Constructor Detail

### SOMPattern

    public SOMPattern()

Method Detail

### addHiddenLayer

    public final void addHiddenLayer(int count)

  Add a hidden layer. SOM networks do not have hidden layers, so this will throw an error.

  Specified by:
    `addHiddenLayer` in interface `NeuralNetworkPattern`
  Parameters:
    `count` - The number of hidden neurons.

### clear

    public void clear()

  Clear out any hidden neurons.

  Specified by:
    `clear` in interface `NeuralNetworkPattern`

### generate

    public final MLMethod generate()

  Generate the RSOM network.

  Specified by:
    `generate` in interface `NeuralNetworkPattern`
  Returns:
    The neural network.

### setActivationFunction

    public final void setActivationFunction(ActivationFunction activation)

  Set the activation function. A SOM uses a linear activation function, so this method throws an error.

  Specified by:
    `setActivationFunction` in interface `NeuralNetworkPattern`
  Parameters:
    `activation` - The activation function to use.

### setInputNeurons

    public final void setInputNeurons(int count)

  Set the input neuron count.

  Specified by:
    `setInputNeurons` in interface `NeuralNetworkPattern`
  Parameters:
    `count` - The number of neurons.

### setOutputNeurons

    public final void setOutputNeurons(int count)

  Set the output neuron count.

  Specified by:
    `setOutputNeurons` in interface `NeuralNetworkPattern`
  Parameters:
    `count` - The number of neurons.

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