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## org.encog.neural.thermal
Class HopfieldNetwork

    java.lang.Object
      org.encog.ml.BasicML
          org.encog.neural.thermal.ThermalNetwork
              org.encog.neural.thermal.HopfieldNetwork

All Implemented Interfaces:
  Serializable, MLAutoAssocation, MLInput, MLInputOutput, MLMethod, MLOutput, MLProperties, MLRegression, MLResettable

    public class HopfieldNetwork

extends ThermalNetwork

Implements a Hopfield network.

See Also:
  Serialized Form

Constructor Summary
`HopfieldNetwork()` Default constructor.
`HopfieldNetwork(int neuronCount)` Construct a Hopfield with the specified neuron count.

Method Summary
`void` | `addPattern(MLData pattern)` Train the neural network for the specified pattern.
`MLData` | `compute(MLData input)` Note: for Hopfield networks, you will usually want to call the "run" method to compute the output.
`int` | `getInputCount()`
`int` | `getOutputCount()`
`void` | `run()` Perform one Hopfield iteration.
`int` | `runUntilStable(int max)` Run the network until it becomes stable and does not change from more runs.
`void` | `updateProperties()` Update any objeccts when a property changes.

Methods inherited from class org.encog.neural.thermal.ThermalNetwork
`addWeight, calculateEnergy, clear, getCurrentState, getNeuronCount, getWeight, getWeights, init, reset, reset, setCurrentState, setCurrentState, setNeuronCount, setWeight, setWeights`

Methods inherited from class org.encog.ml.BasicML
`getProperties, getPropertyDouble, getPropertyLong, getPropertyString, setProperty, setProperty, setProperty`

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

Constructor Detail

### HopfieldNetwork

    public HopfieldNetwork()

  Default constructor.

### HopfieldNetwork

    public HopfieldNetwork(int neuronCount)

  Construct a Hopfield with the specified neuron count.

  Parameters:
    `neuronCount` - The neuron count.

Method Detail

### addPattern

    public final void addPattern(MLData pattern)

  Train the neural network for the specified pattern. The neural network can be trained for more than one pattern. To do this simply call the train method more than once.

  Parameters:
    `pattern` - The pattern to train for.

### compute

    public final MLData compute(MLData input)

  Note: for Hopfield networks, you will usually want to call the "run" method to compute the output. This method can be used to copy the input data to the current state. A single iteration is then run, and the new current state is returned.

  Parameters:
    `input` - The input pattern.
  Returns:
    The new current state.

### getInputCount

    public final int getInputCount()

  Returns:
    The input.

### getOutputCount

    public final int getOutputCount()

  Returns:
    The output count.

### run

    public final void run()

  Perform one Hopfield iteration.

### runUntilStable

    public final int runUntilStable(int max)

  Run the network until it becomes stable and does not change from more runs.

  Parameters:
    `max` - The maximum number of cycles to run before giving up.
  Returns:
    The number of cycles that were run.

### updateProperties

    public void updateProperties()

  Update any objeccts when a property changes.

  Specified by:
    `updateProperties` in interface `MLProperties`
  Specified by:
    `updateProperties` in class `BasicML`

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