/*
* RapidMiner
*
* Copyright (C) 2001-2011 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.tools.math.kernels;
import com.rapidminer.tools.Tools;
/**
* Returns the value of the Gaussian combination kernel of both examples.
*
* @author Ingo Mierswa
*/
public class GaussianCombinationKernel extends Kernel {
private static final long serialVersionUID = 542405909968243049L;
/** The parameter sigma1 of the Gaussian combination kernel. */
private double sigma1 = 1.0d;
/** The parameter sigma2 of the Gaussian combination kernel. */
private double sigma2 = 0.0d;
/** The parameter sigma3 of the Gaussian combination kernel. */
private double sigma3 = 2.0d;
@Override
public int getType() {
return KERNEL_GAUSSIAN_COMBINATION;
}
public void setSigma1(double sigma1) {
this.sigma1 = sigma1;
}
public void setSigma2(double sigma2) {
this.sigma2 = sigma2;
}
public void setSigma3(double sigma3) {
this.sigma3 = sigma3;
}
public double getSigma1() {
return sigma1;
}
public double getSigma2() {
return sigma2;
}
public double getSigma3() {
return sigma3;
}
/** Calculates kernel value of vectors x and y. */
@Override
public double calculateDistance(double[] x1, double[] x2) {
double norm2 = norm2(x1, x2);
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();
}
@Override
public String toString() {
return "GaussianCombination Kernel with" + Tools.getLineSeparator() +
" sigma1: " + Tools.formatNumber(getSigma1()) + Tools.getLineSeparator() +
" sigma2: " + Tools.formatNumber(getSigma2()) + Tools.getLineSeparator() +
" sigma3: " + Tools.formatNumber(getSigma3()) + Tools.getLineSeparator();
}
}