/*
* Encog(tm) Core v3.0 - Java Version
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
* Copyright 2008-2011 Heaton Research, Inc.
*
* 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.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.neural.pattern;
import org.encog.engine.network.activation.ActivationFunction;
import org.encog.engine.network.activation.ActivationLinear;
import org.encog.mathutil.randomize.RangeRandomizer;
import org.encog.ml.MLMethod;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.layers.Layer;
/**
* Construct an ADALINE neural network.
*/
public class ADALINEPattern implements NeuralNetworkPattern {
/**
* The number of neurons in the input layer.
*/
private int inputNeurons;
/**
* The number of neurons in the output layer.
*/
private int outputNeurons;
/**
* Not used, the ADALINE has no hidden layers, this will throw an error.
*
* @param count
* The neuron count.
*/
public final void addHiddenLayer(final int count) {
throw new PatternError("An ADALINE network has no hidden layers.");
}
/**
* Clear out any parameters.
*/
public final void clear() {
this.inputNeurons = 0;
this.outputNeurons = 0;
}
/**
* Generate the network.
*
* @return The generated network.
*/
public final MLMethod generate() {
final BasicNetwork network = new BasicNetwork();
final Layer inputLayer = new BasicLayer(new ActivationLinear(), true,
this.inputNeurons);
final Layer outputLayer = new BasicLayer(new ActivationLinear(), false,
this.outputNeurons);
network.addLayer(inputLayer);
network.addLayer(outputLayer);
network.getStructure().finalizeStructure();
(new RangeRandomizer(-0.5, 0.5)).randomize(network);
return network;
}
/**
* Not used, ADALINE does not use custom activation functions.
*
* @param activation
* Not used.
*/
public final void setActivationFunction(final ActivationFunction activation) {
throw new PatternError( "A ADALINE network can't specify a custom activation function.");
}
/**
* Set the input neurons.
*
* @param count
* The number of neurons in the input layer.
*/
public final void setInputNeurons(final int count) {
this.inputNeurons = count;
}
/**
* Set the output neurons.
*
* @param count
* The number of neurons in the output layer.
*/
public final void setOutputNeurons(final int count) {
this.outputNeurons = count;
}
}