/*
* 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.tools.math.similarity.numerical;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.Tools;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.tools.math.similarity.SimilarityMeasure;
/**
* Specialized similarity that takes the maximum product of two feature values. If this value is zero, the similarity is undefined. This similarity
* measure is used mainly with features extracted from cluster models.
*
* @author Michael Wurst
* @version $Id: MaxProductSimilarity.java,v 1.1 2008/08/05 09:40:31 stiefelolm Exp $
*/
public class MaxProductSimilarity extends SimilarityMeasure {
private static final long serialVersionUID = -7476444724888001751L;
public double calculateSimilarity(double[] value1, double[] value2) {
double max = Double.NEGATIVE_INFINITY;
for (int i = 0; i < value1.length; i++) {
if ((!Double.isNaN(value1[i])) && (!Double.isNaN(value2[i]))) {
double v = value2[i] * value1[i];
if (v > max)
max = v;
}
}
if (max > 0.0)
return max;
else
return Double.NaN;
}
public double calculateDistance(double[] value1, double[] value2) {
return -calculateSimilarity(value1, value2);
}
public void init(ExampleSet exampleSet) throws OperatorException {
Tools.onlyNumericalAttributes(exampleSet, "value based similarities");
}
}