Package org.neuroph.nnet.learning

Source Code of org.neuroph.nnet.learning.SigmoidDeltaRule

/**
* 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.NeuralNetwork;
import org.neuroph.core.Neuron;
import org.neuroph.core.transfer.TransferFunction;

/**
* Delta rule learning algorithm for perceptrons with sigmoid (or any other diferentiable continuous) functions.
*
* TODO: Rename to DeltaRuleContinuous (ContinuousDeltaRule) or something like that, but that will break backward compatibility,
* posibly with backpropagation which is the most used
*
* @see LMS
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class SigmoidDeltaRule extends LMS {

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

  /**
   * Creates new SigmoidDeltaRule
   */
  public SigmoidDeltaRule() {
    super();
  }

  /**
   * This method implements weight update procedure for the whole network for
   * this learning rule
   *
   * @param patternError
   *            single pattern error vector
   */
  @Override
  protected void updateNetworkWeights(double[] patternError) {
    this.adjustOutputNeurons(patternError);
  }

  /**
   * This method implements weights update procedure for the output neurons
   *
   * @param patternError
   *            single pattern error vector
   */
  protected void adjustOutputNeurons(double[] patternError) {
    int i = 0;
    for(Neuron neuron : neuralNetwork.getOutputNeurons()) {
      double outputError = patternError[i];
      if (outputError == 0) {
        neuron.setError(0);
                                i++;
        continue;
      }
     
      TransferFunction transferFunction = neuron.getTransferFunction();
      double neuronInput = neuron.getNetInput();
      double delta = outputError * transferFunction.getDerivative(neuronInput);
      neuron.setError(delta);
      this.updateNeuronWeights(neuron);       
      i++;
    } // for       
  }

}
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