/*
* RapidMiner
*
* Copyright (C) 2001-2008 by Rapid-I and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapid-i.com
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License
* along with this program. If not, see http://www.gnu.org/licenses/.
*/
package com.rapidminer.operator.learner.functions.kernel.evosvm;
import com.rapidminer.operator.learner.functions.kernel.functions.Kernel;
/**
* This function must be maximized for the search for an optimal hyperplane for
* regression.
*
* @author Ingo Mierswa
* @version $Id: RegressionOptimizationFunction.java,v 1.2 2006/03/21 15:35:48
* ingomierswa Exp $
*/
public class RegressionOptimizationFunction implements OptimizationFunction {
private double epsilon;
public RegressionOptimizationFunction(double epsilon) {
this.epsilon = epsilon;
}
public double[] getFitness(double[] alphas, double[] ys, Kernel kernel) {
int offset = ys.length;
double matrixSum = 0.0d;
for (int i = 0; i < ys.length; i++) {
for (int j = 0; j < ys.length; j++) {
matrixSum += (alphas[i] - alphas[i + offset]) * (alphas[j] - alphas[j + offset]) * kernel.getDistance(i, j);
}
}
double alphaSum = 0.0d;
for (int i = 0; i < ys.length; i++) {
alphaSum += (alphas[i] + alphas[i + offset]);
}
double labelSum = 0.0d;
for (int i = 0; i < ys.length; i++) {
labelSum += ys[i] * (alphas[i] - alphas[i + offset]);
}
return new double[] { ((-0.5d * matrixSum) - (epsilon * alphaSum) + labelSum), 0.0d };
}
}