/* * Encog(tm) Core v2.5 - Java Version * http://www.heatonresearch.com/encog/ * http://code.google.com/p/encog-java/ * Copyright 2008-2010 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.neural.networks.BasicNetwork; import org.encog.neural.networks.layers.BasicLayer; import org.encog.neural.networks.layers.Layer; import org.slf4j.Logger; import org.slf4j.LoggerFactory; /** * 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; /** * The logging object. */ private final Logger logger = LoggerFactory.getLogger(this.getClass()); /** * Not used, the ADALINE has no hidden layers, this will throw an error. * * @param count * The neuron count. */ public void addHiddenLayer(final int count) { final String str = "An ADALINE network has no hidden layers."; if (this.logger.isErrorEnabled()) { this.logger.error(str); } throw new PatternError(str); } /** * Clear out any parameters. */ public void clear() { this.inputNeurons = 0; this.outputNeurons = 0; } /** * Generate the network. * * @return The generated network. */ public BasicNetwork generate() { final BasicNetwork network = new BasicNetwork(); int y = PatternConst.START_Y; final Layer inputLayer = new BasicLayer(new ActivationLinear(), false, this.inputNeurons); final Layer outputLayer = new BasicLayer(new ActivationLinear(), true, this.outputNeurons); network.addLayer(inputLayer); network.addLayer(outputLayer); network.getStructure().finalizeStructure(); (new RangeRandomizer(-0.5, 0.5)).randomize(network); inputLayer.setX(PatternConst.START_X); inputLayer.setY(y); y += PatternConst.INC_Y; outputLayer.setX(PatternConst.START_X); outputLayer.setY(y); return network; } /** * Not used, ADALINE does not use custom activation functions. * * @param activation * Not used. */ public void setActivationFunction(final ActivationFunction activation) { final String str = "A ADALINE network can't specify a custom activation function."; if (this.logger.isErrorEnabled()) { this.logger.error(str); } throw new PatternError(str); } /** * Set the input neurons. * * @param count * The number of neurons in the input layer. */ public void setInputNeurons(final int count) { this.inputNeurons = count; } /** * Set the output neurons. * * @param count * The number of neurons in the output layer. */ public void setOutputNeurons(final int count) { this.outputNeurons = count; } }