/** * 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()); } }