Package org.neuroph.nnet

Source Code of org.neuroph.nnet.CompetitiveNetwork

/**
* 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.core.input.Sum;
import org.neuroph.core.input.WeightedInput;
import org.neuroph.nnet.comp.CompetitiveLayer;
import org.neuroph.nnet.comp.CompetitiveNeuron;
import org.neuroph.nnet.learning.CompetitiveLearning;
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;

/**
* Two layer neural network with competitive learning rule.
*
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class CompetitiveNetwork extends NeuralNetwork {
 
  /**
   * 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 competitive network with specified neuron number
   *
   * @param inputNeuronsCount
   *            number of input neurons
         * @param outputNeuronsCount
         *            number of output neurons
   */
  public CompetitiveNetwork(int inputNeuronsCount, int outputNeuronsCount) {
    this.createNetwork(inputNeuronsCount, outputNeuronsCount);
  }

  /**
   * Creates Competitive network architecture
   *
   * @param inputNeuronsCount
   *            input neurons number
         * @param outputNeuronsCount
         *            output neurons number
   * @param neuronProperties
   *            neuron properties
   */
  private void createNetwork(int inputNeuronsCount, int outputNeuronsCount) {
    // set network type
    this.setNetworkType(NeuralNetworkType.COMPETITIVE);

    // createLayer input layer
    Layer inputLayer = LayerFactory.createLayer(inputNeuronsCount, new NeuronProperties());
    this.addLayer(inputLayer);

    // createLayer properties for neurons in output layer
    NeuronProperties neuronProperties = new NeuronProperties();
    neuronProperties.setProperty("neuronType", CompetitiveNeuron.class);
    neuronProperties.setProperty("weightsFunction",  WeightedInput.class);
    neuronProperties.setProperty("summingFunction", Sum.class);
    neuronProperties.setProperty("transferFunction",TransferFunctionType.RAMP);

    // createLayer full connectivity in competitive layer
    CompetitiveLayer competitiveLayer = new CompetitiveLayer(outputNeuronsCount, neuronProperties);

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

    double competitiveWeight = -(1 / (double) outputNeuronsCount);
    // createLayer full connectivity within competitive layer
    ConnectionFactory.fullConnect(competitiveLayer, competitiveWeight, 1);

    // createLayer full connectivity from input to competitive layer
    ConnectionFactory.fullConnect(inputLayer, competitiveLayer);

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

    this.setLearningRule(new CompetitiveLearning());
  }

}
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