/**
* 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.ThresholdNeuron;
import org.neuroph.nnet.learning.BinaryDeltaRule;
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;
/**
* Perceptron neural network with some LMS based learning algorithm.
*
* @see org.neuroph.nnet.learning.PerceptronLearning
* @see org.neuroph.nnet.learning.BinaryDeltaRule
* @see org.neuroph.nnet.learning.SigmoidDeltaRule
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class Perceptron extends NeuralNetwork {
/**
* The class fingerprint that is set to indicate serialization
* compatibility with a previous version of the class.
*/
private static final long serialVersionUID = 1L;
/**
* Creates new Perceptron with specified number of neurons in input and
* output layer, with Step trqansfer function
*
* @param inputNeuronsCount
* number of neurons in input layer
* @param outputNeuronsCount
* number of neurons in output layer
*/
public Perceptron(int inputNeuronsCount, int outputNeuronsCount) {
this.createNetwork(inputNeuronsCount, outputNeuronsCount, TransferFunctionType.STEP);
}
/**
* Creates new Perceptron with specified number of neurons in input and
* output layer, and specified transfer function
*
* @param inputNeuronsCount
* number of neurons in input layer
* @param outputNeuronsCount
* number of neurons in output layer
* @param transferFunctionType
* transfer function type
*/
public Perceptron(int inputNeuronsCount, int outputNeuronsCount, TransferFunctionType transferFunctionType) {
this.createNetwork(inputNeuronsCount, outputNeuronsCount, transferFunctionType);
}
/**
* Creates perceptron architecture with specified number of neurons in input
* and output layer, specified transfer function
*
* @param inputNeuronsCount
* number of neurons in input layer
* @param outputNeuronsCount
* number of neurons in output layer
* @param transferFunctionType
* neuron transfer function type
*/
private void createNetwork(int inputNeuronsCount, int outputNeuronsCount, TransferFunctionType transferFunctionType) {
// set network type
this.setNetworkType(NeuralNetworkType.PERCEPTRON);
// init neuron settings for input layer
NeuronProperties inputNeuronProperties = new NeuronProperties();
inputNeuronProperties.setProperty("transferFunction", TransferFunctionType.LINEAR);
// create input layer
Layer inputLayer = LayerFactory.createLayer(inputNeuronsCount, inputNeuronProperties);
this.addLayer(inputLayer);
NeuronProperties outputNeuronProperties = new NeuronProperties();
outputNeuronProperties.setProperty("neuronType", ThresholdNeuron.class);
outputNeuronProperties.setProperty("thresh", new Double(Math.abs(Math.random())));
outputNeuronProperties.setProperty("transferFunction", transferFunctionType);
// for sigmoid and tanh transfer functions set slope propery
outputNeuronProperties.setProperty("transferFunction.slope", new Double(1));
// createLayer output layer
Layer outputLayer = LayerFactory.createLayer(outputNeuronsCount, outputNeuronProperties);
this.addLayer(outputLayer);
// create full conectivity between input and output layer
ConnectionFactory.fullConnect(inputLayer, outputLayer);
// set input and output cells for this network
NeuralNetworkFactory.setDefaultIO(this);
this.setLearningRule(new BinaryDeltaRule());
// set appropriate learning rule for this network
// if (transferFunctionType == TransferFunctionType.STEP) {
// this.setLearningRule(new BinaryDeltaRule(this));
// } else if (transferFunctionType == TransferFunctionType.SIGMOID) {
// this.setLearningRule(new SigmoidDeltaRule(this));
// } else if (transferFunctionType == TransferFunctionType.TANH) {
// this.setLearningRule(new SigmoidDeltaRule(this));
// } else {
// this.setLearningRule(new PerceptronLearning(this));
// }
}
}