/**
* 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.nnet.learning.UnsupervisedHebbianLearning;
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;
/**
* Hebbian neural network with unsupervised Hebbian learning algorithm.
*
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class UnsupervisedHebbianNetwork extends NeuralNetwork {
/**
* The class fingerprint that is set to indicate serialization
* compatibility with a previous version of the class.
*/
private static final long serialVersionUID = 2L;
/**
* Creates an instance of Unsuervised Hebian net with specified number
* of neurons in input and output layer
*
* @param inputNeuronsNum
* number of neurons in input layer
* @param outputNeuronsNum
* number of neurons in output layer
*/
public UnsupervisedHebbianNetwork(int inputNeuronsNum, int outputNeuronsNum) {
this.createNetwork(inputNeuronsNum, outputNeuronsNum,
TransferFunctionType.LINEAR);
}
/**
* Creates an instance of Unsuervised Hebian net with specified number
* of neurons in input layer and output layer, and transfer function
*
* @param inputNeuronsNum
* number of neurons in input layer
* @param outputNeuronsNum
* number of neurons in output layer
* @param transferFunctionType
* transfer function type id
*/
public UnsupervisedHebbianNetwork(int inputNeuronsNum, int outputNeuronsNum,
TransferFunctionType transferFunctionType) {
this.createNetwork(inputNeuronsNum, outputNeuronsNum,
transferFunctionType);
}
/**
* Creates an instance of Unsuervised Hebian net with specified number
* of neurons in input layer and output layer, and transfer function
*
* @param inputNeuronsNum
* number of neurons in input layer
* @param outputNeuronsNum
* number of neurons in output layer
* @param transferFunctionType
* transfer function type
*/
private void createNetwork(int inputNeuronsNum, int outputNeuronsNum,
TransferFunctionType transferFunctionType) {
// init neuron properties
NeuronProperties neuronProperties = new NeuronProperties();
// neuronProperties.setProperty("bias", new Double(-Math
// .abs(Math.random() - 0.5))); // Hebbian network cann not work
// without bias
neuronProperties.setProperty("transferFunction", transferFunctionType);
neuronProperties.setProperty("transferFunction.slope", new Double(1));
// set network type code
this.setNetworkType(NeuralNetworkType.UNSUPERVISED_HEBBIAN_NET);
// createLayer input layer
Layer inputLayer = LayerFactory.createLayer(inputNeuronsNum,
neuronProperties);
this.addLayer(inputLayer);
// createLayer output layer
Layer outputLayer = LayerFactory.createLayer(outputNeuronsNum,
neuronProperties);
this.addLayer(outputLayer);
// createLayer full conectivity between input and output layer
ConnectionFactory.fullConnect(inputLayer, outputLayer);
// set input and output cells for this network
NeuralNetworkFactory.setDefaultIO(this);
// set appropriate learning rule for this network
this.setLearningRule(new UnsupervisedHebbianLearning());
//this.setLearningRule(new OjaLearning(this));
}
}