/**
* Copyright (C) 2001-2017 by RapidMiner and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapidminer.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.tools.math.kernels.Kernel;
/**
* This function must be maximized for the search for an optimal hyperplane for classification.
*
* @author Ingo Mierswa 15:35:48 ingomierswa Exp $
*/
public class ClassificationOptimizationFunction implements OptimizationFunction {
private boolean multiobjective;
public ClassificationOptimizationFunction(boolean multiobjective) {
this.multiobjective = multiobjective;
}
@Override
public double[] getFitness(double[] alphas, double[] ys, Kernel kernel) {
double sum = 0.0d;
double alphaLabelSum = 0.0d;
int numberSV = 0;
for (int i = 0; i < ys.length; i++) {
sum += alphas[i];
alphaLabelSum += ys[i] * alphas[i];
if (alphas[i] > 0) {
numberSV++;
}
}
double matrixSum = 0.0d;
for (int i = 0; i < ys.length; i++) {
if (alphas[i] == 0.0d) {
continue;
}
for (int j = 0; j < ys.length; j++) {
if (alphas[j] == 0.0d) {
continue;
}
matrixSum += (alphas[i] * alphas[j] * ys[i] * ys[j] * kernel.getDistance(i, j));
}
}
alphaLabelSum = -Math.abs(alphaLabelSum);
if (multiobjective) {
// return new double[] { sum, -matrixSum };
return new double[] { sum, -matrixSum, alphaLabelSum };
// return new double[] { sum, (sum - 0.5d * matrixSum) };
} else {
return new double[] { sum - 0.5d * matrixSum };
}
}
}