euclideannorm1.java

利用Java实现的神经网络工具箱

/*
 * $RCSfile: EuclideanNorm1.java,v $
 * $Revision: 1.2 $
 * $Date: 2004/10/24 03:05:29 $
 *
 * 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.normalization;

/**
 * Euclidean Norm 1 normalization.<br>
 * <br>
 * Implementation according the description avaiable in
 * <a href="http://www.ivorix.com/en/products/tech/norm/">
 * http://www.ivorix.com/en/products/tech/norm/</a>.
 * 
 * @version $Revision: 1.2 $ - $Date: 2004/10/24 03:05:29 $
 * 
 * @author <a href="mailto:[e-mail]">Rodrigo C. M. Coimbra</a>
 * @author <a href="mailto:[e-mail]">Hugo Iver V. Gonçalves</a>
 */
public class EuclideanNorm1 extends Normalization {
	private double[] quadraticSum;

	/* 
	 * @see neuralnetworktoolkit.normalization.INormalization#normalize(double[][])
	 */
	public double[][] normalize(double[][] originalData) {
		double[][] normalizedData;
		
		if (quadraticSum == null) {
			setupParameters(originalData);
			
		}
		normalizedData = transform(originalData);
		
		return normalizedData;
		
	} //normalize()

	/* 
	 * @see neuralnetworktoolkit.normalization.INormalization#unnormalize(double[][])
	 */
	public double[][] unnormalize(double[][] normalizedData) {
		double[][] originalData;
		
		originalData = new double[normalizedData.length][normalizedData[0].length];
		
		for (int j = 0; j < normalizedData[0].length; j++) {
			for (int i = 0; i < normalizedData.length; i++) {
				originalData[i][j] = normalizedData[i][j] * quadraticSum[j]; 
				
			}

		}
		return originalData;
		
	} //unnormalize()

	/* 
	 * @see neuralnetworktoolkit.normalization.INormalization#setupParameters(double[][])
	 */
	public void setupParameters(double[][] originalData) {
		computeQuadraticSum(originalData);

	} //setupParameters()
	
	/* 
	 * @see neuralnetworktoolkit.normalization.Normalization#transform(double[][])
	 */
	protected double[][] transform(double[][] originalData) {
		double[][] normalizedData;
		
		normalizedData = new double[originalData.length][originalData[0].length];
		
		for (int j = 0; j < originalData[0].length; j++) {
			for (int i = 0; i < originalData.length; i++) {
				normalizedData[i][j] = originalData[i][j] / quadraticSum[j];

			}

		}
		return normalizedData;
		
	} //transform()
	
	/**
	 * @param originalData
	 */
	private void computeQuadraticSum(double[][] originalData) {
		quadraticSum = new double[originalData[0].length];
		
		for (int j = 0; j < originalData[0].length; j++) {
			for (int i = 0; i < originalData.length; i++) {
				quadraticSum[j] += originalData[i][j] * originalData[i][j];

			}
						
		}
		
	} //computeQuadraticSum()

	/**
	 * @return Returns the quadraticSum.
	 */
	public double[] getQuadraticSum() {
		return quadraticSum;
		
	} //getQuadraticSum()
	
} //EuclideanNorm1