/**
* 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.BiasNeuron;
import org.neuroph.nnet.learning.LMS;
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;
/**
* Adaline neural network architecture with LMS learning rule.
* Uses bias input, bipolar inputs [-1, 1] and ramp transfer function
* It can be also created using binary inputs and linear transfer function,
* but that dont works for some problems.
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class Adaline 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 Adaline network with specified number of neurons in input
* layer
*
* @param inputNeuronsCount
* number of neurons in input layer
*/
public Adaline(int inputNeuronsCount) {
this.createNetwork(inputNeuronsCount);
}
/**
* Creates adaline network architecture with specified number of input neurons
*
* @param inputNeuronsCount
* number of neurons in input layer
*/
private void createNetwork(int inputNeuronsCount) {
// set network type code
this.setNetworkType(NeuralNetworkType.ADALINE);
// create input layer neuron settings for this network
NeuronProperties inNeuronProperties = new NeuronProperties();
inNeuronProperties.setProperty("transferFunction", TransferFunctionType.LINEAR);
// createLayer input layer with specified number of neurons
Layer inputLayer = LayerFactory.createLayer(inputNeuronsCount, inNeuronProperties);
inputLayer.addNeuron(new BiasNeuron()); // add bias neuron (always 1, and it will act as bias input for output neuron)
this.addLayer(inputLayer);
// create output layer neuron settings for this network
NeuronProperties outNeuronProperties = new NeuronProperties();
outNeuronProperties.setProperty("transferFunction", TransferFunctionType.RAMP);
outNeuronProperties.setProperty("transferFunction.slope", new Double(1));
outNeuronProperties.setProperty("transferFunction.yHigh", new Double(1));
outNeuronProperties.setProperty("transferFunction.xHigh", new Double(1));
outNeuronProperties.setProperty("transferFunction.yLow", new Double(-1));
outNeuronProperties.setProperty("transferFunction.xLow", new Double(-1));
// createLayer output layer (only one neuron)
Layer outputLayer = LayerFactory.createLayer(1, outNeuronProperties);
this.addLayer(outputLayer);
// createLayer full conectivity between input and output layer
ConnectionFactory.fullConnect(inputLayer, outputLayer);
// set input and output cells for network
NeuralNetworkFactory.setDefaultIO(this);
// set LMS learning rule for this network
this.setLearningRule(new LMS());
}
}