/*
 *  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.operator.learner.tree.criterions;

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

/**
 * Calculates the accuracies for the given split if the children 
 * predict the majority classes.
 * 
 * @author Ingo Mierswa
 */
public class AccuracyCriterion 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) {
    	double sum = 0.0d;
    	for (int v = 0; v < weightCounts.length; v++) {
            int maxIndex = -1;
            double maxValue = Double.NEGATIVE_INFINITY;
            double currentSum = 0.0d;
            for (int l = 0; l < weightCounts[v].length; l++) {
            	if (weightCounts[v][l] > maxValue) {
            		maxIndex = l;
            		maxValue = weightCounts[v][l];
            	}
            	currentSum += weightCounts[v][l];
            }
            sum += weightCounts[v][maxIndex] / currentSum;
        }
        return sum;
    }
    
    @Override
	public boolean supportsIncrementalCalculation() {
    	return true;
    }
    
 	@Override
	public double getIncrementalBenefit() {
		int maxIndex = -1;
		double maxValue = Double.NEGATIVE_INFINITY;
		double currentSum = 0.0d;
		for (int j = 0; j < leftLabelWeights.length; j++) {
			if (leftLabelWeights[j] > maxValue) {
				maxIndex = j;
				maxValue = leftLabelWeights[j];
			}
			currentSum += leftLabelWeights[j];
		}
		double sum = leftLabelWeights[maxIndex] / currentSum;
		maxIndex = -1;
		maxValue = Double.NEGATIVE_INFINITY;
		currentSum = 0.0d;
		for (int j = 0; j < rightLabelWeights.length; j++) {
			if (rightLabelWeights[j] > maxValue) {
				maxIndex = j;
				maxValue = rightLabelWeights[j];
			}
			currentSum += rightLabelWeights[j];
		}
		return sum + rightLabelWeights[maxIndex] / currentSum;
	}
}