Package org.encog.neural.pattern

Source Code of org.encog.neural.pattern.ElmanPattern

/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
*     http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*  
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.neural.pattern;

import org.encog.engine.network.activation.ActivationFunction;
import org.encog.ml.MLMethod;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;

/**
* This class is used to generate an Elman style recurrent neural network. This
* network type consists of three regular layers, an input output and hidden
* layer. There is also a context layer which accepts output from the hidden
* layer and outputs back to the hidden layer. This makes it a recurrent neural
* network.
*
* The Elman neural network is useful for temporal input data. The specified
* activation function will be used on all layers. The Elman neural network is
* similar to the Jordan neural network.
*
* @author jheaton
*
*/
public class ElmanPattern implements NeuralNetworkPattern {

  /**
   * The number of input neurons.
   */
  private int inputNeurons;

  /**
   * The number of output neurons.
   */
  private int outputNeurons;

  /**
   * The number of hidden neurons.
   */
  private int hiddenNeurons;

  /**
   * The activation function.
   */
  private ActivationFunction activation;

  /**
   * Create an object to generate Elman neural networks.
   */
  public ElmanPattern() {
    this.inputNeurons = -1;
    this.outputNeurons = -1;
    this.hiddenNeurons = -1;
  }

  /**
   * Add a hidden layer with the specified number of neurons.
   *
   * @param count
   *            The number of neurons in this hidden layer.
   */
  @Override
  public void addHiddenLayer(final int count) {
    if (this.hiddenNeurons != -1) {
      throw new PatternError(
          "An Elman neural network should have only one hidden layer.");
    }

    this.hiddenNeurons = count;

  }

  /**
   * Clear out any hidden neurons.
   */
  @Override
  public void clear() {
    this.hiddenNeurons = -1;
  }

  /**
   * Generate the Elman neural network.
   *
   * @return The Elman neural network.
   */
  @Override
  public MLMethod generate() {
    BasicLayer hidden, input;

    final BasicNetwork network = new BasicNetwork();
    network.addLayer(input = new BasicLayer(this.activation, true,
        this.inputNeurons));
    network.addLayer(hidden = new BasicLayer(this.activation, true,
        this.hiddenNeurons));
    network.addLayer(new BasicLayer(null, false, this.outputNeurons));
    input.setContextFedBy(hidden);
    network.getStructure().finalizeStructure();
    network.reset();
    return network;
  }

  /**
   * Set the activation function to use on each of the layers.
   *
   * @param activation
   *            The activation function.
   */
  @Override
  public void setActivationFunction(final ActivationFunction activation) {
    this.activation = activation;
  }

  /**
   * Set the number of input neurons.
   *
   * @param count
   *            Neuron count.
   */
  @Override
  public void setInputNeurons(final int count) {
    this.inputNeurons = count;
  }

  /**
   * Set the number of output neurons.
   *
   * @param count
   *            Neuron count.
   */
  @Override
  public void setOutputNeurons(final int count) {
    this.outputNeurons = count;
  }

}
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