/**
* 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.Neuron;
import org.neuroph.core.input.Difference;
import org.neuroph.core.input.Intensity;
import org.neuroph.core.transfer.Linear;
import org.neuroph.nnet.learning.KohonenLearning;
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;
/**
* Kohonen neural network.
*
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class Kohonen 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 Kohonen network with specified number of neurons in input and
* map layer
*
* @param inputNeuronsCount
* number of neurons in input layer
* @param outputNeuronsCount
* number of neurons in output layer
*/
public Kohonen(int inputNeuronsCount, int outputNeuronsCount) {
this.createNetwork(inputNeuronsCount, outputNeuronsCount);
}
/**
* Creates Kohonen network architecture with specified number of neurons in
* input and map layer
*
* @param inputNeuronsCount
* number of neurons in input layer
* @param outputNeuronsCount
* number of neurons in output layer
*/
private void createNetwork(int inputNeuronsCount, int outputNeuronsCount) {
// specify input neuron properties (use default: weighted sum input with
// linear transfer)
NeuronProperties inputNeuronProperties = new NeuronProperties();
// specify map neuron properties
NeuronProperties outputNeuronProperties = new NeuronProperties(
Difference.class, // weights function
Intensity.class, // summing function
Linear.class, // transfer function
Neuron.class // neuron type
);
// set network type
this.setNetworkType(NeuralNetworkType.KOHONEN);
// createLayer input layer
Layer inLayer = LayerFactory.createLayer(inputNeuronsCount,
inputNeuronProperties);
this.addLayer(inLayer);
// createLayer map layer
Layer mapLayer = LayerFactory.createLayer(outputNeuronsCount,
outputNeuronProperties);
this.addLayer(mapLayer);
// createLayer full connectivity between input and output layer
ConnectionFactory.fullConnect(inLayer, mapLayer);
// set network input and output cells
NeuralNetworkFactory.setDefaultIO(this);
this.setLearningRule(new KohonenLearning());
}
}