/*
 *  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.tree.criterions;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.learner.tree.FrequencyCalculator;

/**
 * Calculates the Gini index for the given split.
 * 
 * @author Ingo Mierswa
 */
public class GiniIndexCriterion extends AbstractCriterion {
    
    private FrequencyCalculator frequencyCalculator = new FrequencyCalculator();
    
    public double getNominalBenefit(ExampleSet exampleSet, Attribute attribute) {    	
    	double[][] weightCounts =  frequencyCalculator.getNominalWeightCounts(exampleSet, attribute);
    	return getBenefit(weightCounts);
    } 
    
    public double getNumericalBenefit(ExampleSet exampleSet, Attribute attribute, double splitValue) {    	
    	double[][] weightCounts =  frequencyCalculator.getNumericalWeightCounts(exampleSet, attribute, splitValue);
    	return getBenefit(weightCounts);
    }     
    
    public double getBenefit(double[][] weightCounts) {
    	// calculate information amount WITHOUT this attribute
    	double[] classWeights = new double[weightCounts[0].length];
    	for (int l = 0; l < classWeights.length; l++) {
        	for (int v = 0; v < weightCounts.length; v++) {
        		classWeights[l] += weightCounts[v][l];
    		}
    	}
    	
        double totalClassWeight = frequencyCalculator.getTotalWeight(classWeights);
        
        double totalEntropy = getGiniIndex(classWeights, totalClassWeight);
        
        double gain = 0;
        for (int v = 0; v < weightCounts.length; v++) {
            double[] partitionWeights = weightCounts[v];
            double partitionWeight = frequencyCalculator.getTotalWeight(partitionWeights);
            gain += getGiniIndex(partitionWeights, partitionWeight) * partitionWeight / totalClassWeight;
        }
        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;
    }
    
    @Override
	public boolean supportsIncrementalCalculation() {
    	return true;
    }
    
	@Override
	public double getIncrementalBenefit() {
		double totalGiniEntropy = getGiniIndex(totalLabelWeights, totalWeight);
		double gain = getGiniIndex(leftLabelWeights, leftWeight) * leftWeight / totalWeight;
		gain += getGiniIndex(rightLabelWeights, rightWeight) * rightWeight / totalWeight;
		return totalGiniEntropy - gain;
	}
}