/**
* 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.comp.InputOutputNeuron;
import org.neuroph.nnet.learning.BinaryHebbianLearning;
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;
/**
* Bidirectional Associative Memory
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class BAM extends NeuralNetwork {
private static final long serialVersionUID = 1L;
/**
* Creates an instance of BAM network with specified number of neurons
* in input and output layers.
*
* @param inputNeuronsCount
* number of neurons in input layer
* @param outputNeuronsCount
* number of neurons in output layer
*/
public BAM(int inputNeuronsCount, int outputNeuronsCount) {
// init neuron settings for BAM network
NeuronProperties neuronProperties = new NeuronProperties();
neuronProperties.setProperty("neuronType", InputOutputNeuron.class);
neuronProperties.setProperty("bias", new Double(0));
neuronProperties.setProperty("transferFunction", TransferFunctionType.STEP);
neuronProperties.setProperty("transferFunction.yHigh", new Double(1));
neuronProperties.setProperty("transferFunction.yLow", new Double(0));
this.createNetwork(inputNeuronsCount, outputNeuronsCount, neuronProperties);
}
/**
* Creates BAM network architecture
*
* @param inputNeuronsCount
* number of neurons in input layer
* @param outputNeuronsCount
* number of neurons in output layer
* @param neuronProperties
* neuron properties
*/
private void createNetwork(int inputNeuronsCount, int outputNeuronsCount, NeuronProperties neuronProperties) {
// set network type
this.setNetworkType(NeuralNetworkType.BAM);
// create input layer
Layer inputLayer = LayerFactory.createLayer(inputNeuronsCount, neuronProperties);
// add input layer to network
this.addLayer(inputLayer);
// create output layer
Layer outputLayer = LayerFactory.createLayer(outputNeuronsCount, neuronProperties);
// add output layer to network
this.addLayer(outputLayer);
// create full connectivity from in to out layer
ConnectionFactory.fullConnect(inputLayer, outputLayer);
// create full connectivity from out to in layer
ConnectionFactory.fullConnect(outputLayer, inputLayer);
// set input and output cells for this network
NeuralNetworkFactory.setDefaultIO(this);
// set Hebbian learning rule for this network
this.setLearningRule(new BinaryHebbianLearning());
}
}