backpropagationmodel.java

利用Java实现的神经网络工具箱

/*
 * $RCSfile: BackPropagationModel.java,v $
 * $Revision: 1.5 $
 * $Date: 2005/05/08 02:16:28 $
 *
 * NeuralNetworkToolkit
 * Copyright (C) 2004 Universidade de Brasília
 *
 * This file is part of NeuralNetworkToolkit.
 *
 * NeuralNetworkToolkit is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 2 of the License, or
 * (at your option) any later version.
 *
 * NeuralNetworkToolkit is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with NeuralNetworkToolkit; if not, write to the Free Software
 * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA - 02111-1307 - USA.
 */
package neuralnetworktoolkit.methods.gradientbased.backpropagation;

import neuralnetworktoolkit.StatisticalResults;
import neuralnetworktoolkit.methods.gradientbased.GradientBasedMethod;
import neuralnetworktoolkit.neuralnetwork.*;

/**
 * 
 * 
 * @version $Revision: 1.5 $ - $Date: 2005/05/08 02:16:28 $
 * 
 * @author <a href="mailto:[e-mail]">Hugo Iver V. Gonçalves</a>
 * @author <a href="mailto:[e-mail]">Rodrigo C. M. Coimbra</a>
 */
public abstract class BackPropagationModel extends GradientBasedMethod {

	
	/** Maximum number of epochs for training. */
	public static final int MAX_EPOCHS = 10000000;
			
	/** Iteration condition. */
	protected boolean goAhead = true;
	
	protected StatisticalResults results;
	
	protected double inicio, fim;
	protected int inputSize;
	protected int outputSize;
	protected int numberOfEpochs;
	protected double totalErrorEnergy;
	protected double instantaneousError;
	protected double learningRate;
	protected double[][] deltaW;
	protected double[][] instantDeltaW; 
	protected double[] derivativesVector;
	protected double errorGoal;
	protected int maximumNumberOfEpochs;
	protected int numberOfSynapses;
	
	public BackPropagationModel() {
		this.inputSize = 0;
		this.outputSize = 0;
		this.numberOfEpochs = 0;
		this.totalErrorEnergy = 500;
		this.instantaneousError = 0;
		this.errorGoal = 0;
		this.maximumNumberOfEpochs = OnLineBackPropagation.MAX_EPOCHS;
		this.results = new StatisticalResults();
		
	}
	
	/**
	 * Calculates total error.
	 * 
	 * @param neuralNetwork
	 *            Neural network to calculate error.
	 * @param instanceSet
	 *            Training instances set.
	 * @param outputs
	 *            Network expected outputs.
	 * 
	 * @return Total error calculated.
	 */
	public double calculateTotalError(INeuralNetwork neuralNetwork,
			double[][] instanceSet, double[][] outputs) {
		
		double error = 0;
		double actualOutput = 0;
		
		for (int i = 0; i < instanceSet.length; i++) {
			actualOutput = 0;
			neuralNetwork.inputLayerSetup(instanceSet[i]);
			neuralNetwork.propagateInput();
			actualOutput = neuralNetwork.retrieveFinalResults()[0];
			error = error + (outputs[i][0] - actualOutput)
					* (outputs[i][0] - actualOutput);
		}
		return error;
		
	} //calculateTotalError()
	
	/**
	 * Calculates neuron deltas.
	 * 
	 * @param neuralNetwork
	 *            Neural network to calculate deltas.
	 * @param outputs
	 *            Network expected outputs.
	 */
	public void calculateNeuronDeltas(INeuralNetwork neuralNetwork,
			double[] outputs) {
		
		double inputValue = 0;
		double wXdelta = 0;
		
		
		if (neuralNetwork.isDynamic() == false) {
			if (neuralNetwork.isMultiConexed() == true) {
				
				ILayer l1 = neuralNetwork.getLayer(neuralNetwork
						.getNetworkSize() - 1);
				ILayer l2 = neuralNetwork.getLayer(neuralNetwork
						.getNetworkSize() - 2);
				for (int k = 0; k < neuralNetwork.getLayer(
						neuralNetwork.getNetworkSize() - 1).getLayerSize(); k++) {
					inputValue = 0;
					// Calculates the inputValue for the Output Layer.
					for (int j = 0; j < neuralNetwork.getLayer(
							neuralNetwork.getNetworkSize() - 2).getLayerSize(); j++) {
						inputValue = inputValue
								+ l2.getNeuron(j).getOutputValue()
								* l1.getWeight(j, k);
					}
					inputValue = inputValue + l1.getBias(k);
					l1.getNeuron(k).setDelta(
							-l1.getNeuron(k).getActivationFunction()
									.functionDerivative(inputValue)
									* (outputs[k] - l1.getNeuron(k)
											.getOutputValue()));
				}
				for (int i = (neuralNetwork.getNetworkSize() - 2); i >= 0; i--) {
					
					for (int j = 0; j < neuralNetwork.getLayer(i)
							.getLayerSize(); j++) {
						inputValue = 0;
						wXdelta = 0;
						// Calculates the respective weight times delta of the
						// forward layer.
						for (int k = 0; k < neuralNetwork.getLayer(i + 1)
								.getLayerSize(); k++) {
							wXdelta = wXdelta
									+ neuralNetwork.getLayer(i + 1).getWeight(
											j, k)
									* neuralNetwork.getLayer(i + 1)
											.getNeuron(k).getDelta();
						}
						if (i > 0) {
							
							for (int a = 0; a < neuralNetwork.getLayer(i - 1)
									.getLayerSize(); a++) {
								inputValue = inputValue
										+ neuralNetwork.getLayer(i - 1)
												.getNeuron(a).getOutputValue()
										* neuralNetwork.getLayer(i).getWeight(
												a, j);
							}
							inputValue = inputValue
									+ neuralNetwork.getLayer(i).getBias(j);
						} else {
							
							for (int a = 0; a < neuralNetwork
									.getStaticInputValues().length; a++) {
								
								inputValue = inputValue
										+ neuralNetwork.getStaticInputValues()[a]
										* neuralNetwork.getLayer(i).getWeight(
												a, j);
							}
							inputValue = inputValue
									+ neuralNetwork.getLayer(i).getBias(j);
						}
						l1 = neuralNetwork.getLayer(i);
						l1.getNeuron(j).setDelta(
								l1.getNeuron(j).getActivationFunction()
										.functionDerivative(inputValue)
										* wXdelta);
					}
				}
			} else {
				// TODO implement this case.
			}
		} else {
			// TODO Implement this case.
			if (neuralNetwork.isMultiConexed() == true) {
				// TODO Implement this.
			} else {
				// TODO Implement this.
			}
		}
	} //calculateNeuronDeltas()

} //BackPropagationModel