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

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Neuroph 2.4 » org » neuroph » nnet » learning » OjaLearning.java
/**
 * 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.learning;

import org.neuroph.core.Connection;
import org.neuroph.core.NeuralNetwork;
import org.neuroph.core.Neuron;

/**
 * Oja learning rule wich is a modification of unsupervised hebbian learning.
 * @author Zoran Sevarac <sevarac@gmail.com>
 */
public class OjaLearning extends UnsupervisedHebbianLearning{

  /**
   * The class fingerprint that is set to indicate serialization
   * compatibility with a previous version of the class.
   */  
  private static final long serialVersionUID = 1L;

  /**
   * Creates an instance of OjaLearning algorithm
   */
  public OjaLearning() {
    super();
  }

  /**
   * Creates an instance of OjaLearning algorithm  for the specified 
   * neural network
   * 
   * @param neuralNetwork
     *                  neural network to train
   */  
  public OjaLearning(NeuralNetwork neuralNetwork) {
    super(neuralNetwork);
  }  
  
  /**
   * This method implements weights update procedure for the single neuron
   * 
   * @param neuron
   *            neuron to update weights
   */
  @Override
  protected void updateNeuronWeights(Neuron neuron) {
    double output = neuron.getOutput();
    for(Connection connection : neuron.getInputConnections()) {
      double input = connection.getInput();
      double weight = connection.getWeight().getValue();
      double deltaWeight = (input - output*weight) * output * this.learningRate;
      connection.getWeight().inc(deltaWeight);
    }
  }  
  
  
}
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