/*
* 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;
/**
* Cosine similarity that supports feature weights. If both vectors are empty or null vectors, NaN is returned.
*
* @author Michael Wurst
* @version $Id: CosineSimilarity.java,v 1.1 2008/08/05 09:40:31 stiefelolm Exp $
*/
public class CosineSimilarity extends SimilarityMeasure {
private static final long serialVersionUID = 2856052490402674777L;
public double calculateSimilarity(double[] value1, double[] value2) {
double sum = 0.0;
double sum1 = 0.0;
double sum2 = 0.0;
for (int i = 0; i < value1.length; i++) {
double v1 = value1[i];
double v2 = value2[i];
if ((!Double.isNaN(v1)) && (!Double.isNaN(v2))) {
sum += v2 * v1;
sum1 += v1 * v1;
sum2 += v2 * v2;
}
}
if ((sum1 > 0) && (sum2 > 0))
return sum / (Math.sqrt(sum1) * Math.sqrt(sum2));
else
return Double.NaN;
}
public double calculateDistance(double[] value1, double[] value2) {
return Math.acos(calculateSimilarity(value1, value2));
}
public void init(ExampleSet exampleSet) throws OperatorException {
Tools.onlyNumericalAttributes(exampleSet, "value based similarities");
}
}