/**
* 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());
}
}