/*
* RapidMiner
*
* Copyright (C) 2001-2014 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.jmysvm.kernel;
/**
* Gaussian Combination Kernel
*
* @author Ingo Mierswa
*/
public class KernelGaussianCombination extends Kernel {
private static final long serialVersionUID = 6080834703694525403L;
private double sigma1 = 1.0d;
private double sigma2 = 0.0d;
private double sigma3 = 2.0d;
/** Output as String */
@Override
public String toString() {
return ("gaussian_combination(s1=" + sigma1 + ",s2=" + sigma2 + ",s3=" + sigma3 + ")");
};
/** Class constructor. */
public KernelGaussianCombination() {}
public void setParameters(double sigma1, double sigma2, double sigma3) {
this.sigma1 = sigma1;
this.sigma2 = sigma2;
this.sigma3 = sigma3;
}
/** Calculates kernel value of vectors x and y. */
@Override
public double calculate_K(int[] x_index, double[] x_att, int[] y_index, double[] y_att) {
double norm2 = norm2(x_index, x_att, y_index, y_att);
double exp1 = sigma1 == 0.0d ? 0.0d : Math.exp((-1) * norm2 / sigma1);
double exp2 = sigma2 == 0.0d ? 0.0d : Math.exp((-1) * norm2 / sigma2);
double exp3 = sigma3 == 0.0d ? 0.0d : Math.exp((-1) * norm2 / sigma3);
return exp1 + exp2 - exp3;
}
@Override
public String getDistanceFormula(double[] x, String[] attributeConstructions) {
StringBuffer norm2Expression = new StringBuffer();
boolean first = true;
for (int i = 0; i < x.length; i++) {
double value = x[i];
String valueString = "(" + value + " - " + attributeConstructions[i] + ")";
if (first) {
norm2Expression.append(valueString + " * " + valueString);
} else {
norm2Expression.append(" + " + valueString + " * " + valueString);
}
first = false;
}
String exp1 = sigma1 == 0.0d ? "" : "exp(-1 * " + norm2Expression.toString() + " / " + sigma1 + ")";
String exp2 = sigma2 == 0.0d ? "" : "exp(-1 * " + norm2Expression.toString() + " / " + sigma2 + ")";
String exp3 = sigma3 == 0.0d ? "" : "exp(-1 * " + norm2Expression.toString() + " / " + sigma3 + ")";
StringBuffer result = new StringBuffer();
if (exp1.length() > 0) {
result.append(exp1);
}
if (exp2.length() > 0) {
if (result.length() > 0)
result.append(" + " + exp2);
else
result.append(exp2);
}
if (exp3.length() > 0) {
if (result.length() > 0)
result.append(" - " + exp3);
else
result.append("-" + exp3);
}
return result.toString();
}
}