/**
* 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.SupervisedHebbianLearning;
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 supervised Hebbian learning algorithm.
* In order to work this network needs aditional bias neuron in input layer which is allways 1 in training set!
*
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class SupervisedHebbianNetwork 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 Supervised Hebbian Network net with specified
* number neurons in input and output layer
*
* @param inputNeuronsNum
* number of neurons in input layer
* @param outputNeuronsNum
* number of neurons in output layer
*/
public SupervisedHebbianNetwork(int inputNeuronsNum, int outputNeuronsNum) {
this.createNetwork(inputNeuronsNum, outputNeuronsNum,
TransferFunctionType.RAMP);
}
/**
* Creates an instance of Supervised Hebbian Network 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 SupervisedHebbianNetwork(int inputNeuronsNum, int outputNeuronsNum,
TransferFunctionType transferFunctionType) {
this.createNetwork(inputNeuronsNum, outputNeuronsNum,
transferFunctionType);
}
/**
*Creates an instance of Supervised Hebbian Network with specified number
* of neurons in input layer, 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("transferFunction", transferFunctionType);
neuronProperties.setProperty("transferFunction.slope", new Double(1));
neuronProperties.setProperty("transferFunction.yHigh", new Double(1));
neuronProperties.setProperty("transferFunction.xHigh", new Double(1));
neuronProperties.setProperty("transferFunction.yLow", new Double(-1));
neuronProperties.setProperty("transferFunction.xLow", new Double(-1));
// set network type code
this.setNetworkType(NeuralNetworkType.SUPERVISED_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 SupervisedHebbianLearning());
}
}