/*
* 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.operator.learner.tree;
import com.rapidminer.example.set.SplittedExampleSet;
/**
* Calculates the Gini index for the given split.
*
* @author Ingo Mierswa
* @version $Id: GiniIndexCriterion.java,v 1.5 2008/05/09 19:22:52 ingomierswa Exp $
*/
public class GiniIndexCriterion extends AbstractCriterion {
private FrequencyCalculator calculator = new FrequencyCalculator();
public double getBenefit(SplittedExampleSet exampleSet) {
double[] totalWeights = calculator.getLabelWeights(exampleSet);
double totalWeight = calculator.getTotalWeight(totalWeights);
double totalEntropy = getGiniIndex(totalWeights, totalWeight);
double gain = 0;
for (int i = 0; i < exampleSet.getNumberOfSubsets(); i++) {
exampleSet.selectSingleSubset(i);
double[] partitionWeights = calculator.getLabelWeights(exampleSet);
double partitionWeight = calculator.getTotalWeight(partitionWeights);
gain += getGiniIndex(partitionWeights, partitionWeight) * partitionWeight / totalWeight;
}
return totalEntropy - gain;
}
private double getGiniIndex(double[] labelWeights, double totalWeight) {
double sum = 0.0d;
for (int i = 0; i < labelWeights.length; i++) {
double frequency = labelWeights[i] / totalWeight;
sum += frequency * frequency;
}
return 1.0d - sum;
}
public boolean supportsIncrementalCalculation() {
return true;
}
public double getIncrementalBenefit() {
double totalGiniEntropy = getGiniIndex(totalLabelWeights, totalWeight);
double gain = getGiniIndex(leftLabelWeights, leftWeight) * leftWeight / totalWeight;
gain += getGiniIndex(rightLabelWeights, rightWeight) * rightWeight / totalWeight;
return totalGiniEntropy - gain;
}
}