/* * 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.networks.training.simple; import org.encog.engine.util.ErrorCalculation; import org.encog.neural.NeuralNetworkError; import org.encog.neural.data.NeuralData; import org.encog.neural.data.NeuralDataPair; import org.encog.neural.data.NeuralDataSet; import org.encog.neural.networks.BasicNetwork; import org.encog.neural.networks.layers.Layer; import org.encog.neural.networks.structure.FlatUpdateNeeded; import org.encog.neural.networks.synapse.Synapse; import org.encog.neural.networks.training.BasicTraining; import org.encog.neural.networks.training.LearningRate; /** * Train an ADALINE neural network. */ public class TrainAdaline extends BasicTraining implements LearningRate { /** * The network to train. */ private final BasicNetwork network; /** * The synapse to train. */ private final Synapse synapse; /** * The training data to use. */ private final NeuralDataSet training; /** * The learning rate. */ private double learningRate; /** * Construct an ADALINE trainer. * * @param network * The network to train. * @param training * The training data. * @param learningRate * The learning rate. */ public TrainAdaline(final BasicNetwork network, final NeuralDataSet training, final double learningRate) { if (network.getStructure().getLayers().size() > 2) { throw new NeuralNetworkError( "An ADALINE network only has two layers."); } this.network = network; final Layer input = network.getLayer(BasicNetwork.TAG_INPUT); this.synapse = input.getNext().get(0); this.training = training; this.learningRate = learningRate; } /** * @return The learning rate. */ public double getLearningRate() { return this.learningRate; } /** * @return The network being trained. */ public BasicNetwork getNetwork() { return this.network; } /** * Perform a training iteration. */ public void iteration() { final ErrorCalculation errorCalculation = new ErrorCalculation(); final Layer inputLayer = this.network.getLayer(BasicNetwork.TAG_INPUT); final Layer outputLayer = this.network .getLayer(BasicNetwork.TAG_OUTPUT); for (final NeuralDataPair pair : this.training) { // calculate the error final NeuralData output = this.network.compute(pair.getInput()); for (int currentAdaline = 0; currentAdaline < output.size(); currentAdaline++) { final double diff = pair.getIdeal().getData(currentAdaline) - output.getData(currentAdaline); // weights for (int i = 0; i < inputLayer.getNeuronCount(); i++) { final double input = pair.getInput().getData(i); this.synapse.getMatrix().add(i, currentAdaline, this.learningRate * diff * input); } // bias double t = outputLayer.getBiasWeight(currentAdaline); t += this.learningRate * diff; outputLayer.setBiasWeight(currentAdaline, t); } this.network.getStructure().setFlatUpdate(FlatUpdateNeeded.Flatten); errorCalculation.updateError(output.getData(), pair.getIdeal().getData()); } // set the global error setError(errorCalculation.calculate()); } /** * Set the learning rate. * * @param rate * The new learning rate. */ public void setLearningRate(final double rate) { this.learningRate = rate; } }