Hopfield.java :  » Net » Neuroph-2.4 » org » neuroph » nnet » Java Open Source

/**
 * Copyright 2010 Neuroph Project http://neuroph.sourceforge.net
 *
 * 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.
 */

package org.neuroph.nnet;

import org.neuroph.core.Layer;
import org.neuroph.core.NeuralNetwork;
import org.neuroph.nnet.comp.InputOutputNeuron;
import org.neuroph.nnet.learning.BinaryHebbianLearning;
import org.neuroph.util.ConnectionFactory;
import org.neuroph.util.LayerFactory;
import org.neuroph.util.NeuralNetworkFactory;
import org.neuroph.util.NeuralNetworkType;
import org.neuroph.util.NeuronProperties;
import org.neuroph.util.TransferFunctionType;

/**
 * Hopfield neural network.
 * Notes: try to use [1, -1] activation levels, sgn as transfer function, or real numbers for activation
 * @author Zoran Sevarac <sevarac@gmail.com>
 */

public class Hopfield extends NeuralNetwork {
  
  /**
   * The class fingerprint that is set to indicate serialization
   * compatibility with a previous version of the class.
   */  
  private static final long serialVersionUID = 2L;

  /**
   * Creates new Hopfield network with specified neuron number
   * 
   * @param neuronsCount
   *            neurons number in Hopfied network
   */
  public Hopfield(int neuronsCount) {

    // init neuron settings for hopfield network
    NeuronProperties neuronProperties = new NeuronProperties();
    neuronProperties.setProperty("neuronType", InputOutputNeuron.class);
    neuronProperties.setProperty("bias", new Double(0));
    neuronProperties.setProperty("transferFunction", TransferFunctionType.STEP);
    neuronProperties.setProperty("transferFunction.yHigh", new Double(1));
    neuronProperties.setProperty("transferFunction.yLow", new Double(0));

    this.createNetwork(neuronsCount, neuronProperties);
  }

  /**
   * Creates new Hopfield network with specified neuron number and neuron
   * properties
   * 
   * @param neuronsCount
   *            neurons number in Hopfied network
   * @param neuronProperties
   *            neuron properties
   */
  public Hopfield(int neuronsCount, NeuronProperties neuronProperties) {
    this.createNetwork(neuronsCount, neuronProperties);
  }

  /**
   * Creates Hopfield network architecture
   * 
   * @param neuronsCount
   *            neurons number in Hopfied network
   * @param neuronProperties
   *            neuron properties
   */
  private void createNetwork(int neuronsCount, NeuronProperties neuronProperties) {

    // set network type
    this.setNetworkType(NeuralNetworkType.HOPFIELD);

    // createLayer neurons in layer
    Layer layer = LayerFactory.createLayer(neuronsCount, neuronProperties);

    // createLayer full connectivity in layer
    ConnectionFactory.fullConnect(layer, 0.1);

    // add layer to network
    this.addLayer(layer);

    // set input and output cells for this network
    NeuralNetworkFactory.setDefaultIO(this);

    // set Hopfield learning rule for this network
    //this.setLearningRule(new HopfieldLearning(this));  
    this.setLearningRule(new BinaryHebbianLearning(this));      
  }

}