/**
* 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;
/**
* Hopfield neural network.
* Notes: try to use [1, -1] activation levels, sgn as transfer function, or real numbers for activation
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class Hopfield 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 new Hopfield network with specified neuron number
*
* @param neuronsCount
* neurons number in Hopfied network
*/
public Hopfield(int neuronsCount) {
// init neuron settings for hopfield 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(neuronsCount, neuronProperties);
}
/**
* Creates new Hopfield network with specified neuron number and neuron
* properties
*
* @param neuronsCount
* neurons number in Hopfied network
* @param neuronProperties
* neuron properties
*/
public Hopfield(int neuronsCount, NeuronProperties neuronProperties) {
this.createNetwork(neuronsCount, neuronProperties);
}
/**
* Creates Hopfield network architecture
*
* @param neuronsCount
* neurons number in Hopfied network
* @param neuronProperties
* neuron properties
*/
private void createNetwork(int neuronsCount, NeuronProperties neuronProperties) {
// set network type
this.setNetworkType(NeuralNetworkType.HOPFIELD);
// createLayer neurons in layer
Layer layer = LayerFactory.createLayer(neuronsCount, neuronProperties);
// createLayer full connectivity in layer
ConnectionFactory.fullConnect(layer, 0.1);
// add layer to network
this.addLayer(layer);
// set input and output cells for this network
NeuralNetworkFactory.setDefaultIO(this);
// set Hopfield learning rule for this network
//this.setLearningRule(new HopfieldLearning(this));
this.setLearningRule(new BinaryHebbianLearning());
}
}