/*
 *  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.rules;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.set.SortedExampleSet;

/**
 * Find the best split point for numerical attributes according to accuracy. 
 * 
 * @author Ingo Mierswa
 * @version $Id: NumericalSplitter.java,v 1.9 2008/05/09 19:23:13 ingomierswa Exp $
 */
public class NumericalSplitter {
	
    private Criterion criterion;
    
    private double minValue = 0.5d;
    
    public NumericalSplitter() {
        this.criterion = new AccuracyCriterion();
    }
    
    public NumericalSplitter(Criterion criterion) {
        this.criterion = criterion;
    }
    
    public Split getBestSplit(ExampleSet inputSet, Attribute attribute, String labelName) {
        SortedExampleSet exampleSet = new SortedExampleSet((ExampleSet)inputSet.clone(), attribute, SortedExampleSet.INCREASING);
        
        Attribute labelAttribute = exampleSet.getAttributes().getLabel();
        int labelIndex = labelAttribute.getMapping().mapString(labelName);
        
        double oldLabel = Double.NaN;
        double bestSplit = Double.NaN;
        double lastValue = Double.NaN;
        double bestBenefit = Double.NEGATIVE_INFINITY;
        double bestTotalWeight = 0;
        int bestSplitType = Split.LESS_SPLIT;

        // initiating online counting of benefit: only 2 Datascans needed then
        criterion.reinitOnlineCounting(exampleSet);
        for (Example e : exampleSet) {
        	double currentValue = e.getValue(attribute);
        	double label = e.getValue(labelAttribute);            
        	if ((Double.isNaN(oldLabel)) || (oldLabel != label) && (lastValue != currentValue)) {
        		double splitValue = (lastValue + currentValue) / 2.0d;
       		
        		double[] benefits;
        		if (labelName == null) {
        			benefits = criterion.getOnlineBenefit(e);
        		} else {
        			benefits = criterion.getOnlineBenefit(e, labelIndex);
        		}
        		// online method returns both possible relations in one array(greater / smaller) in one array
                if ((benefits[0] > minValue) &&
                    (benefits[0] > 0) && (benefits[1] > 0) &&
                    ((benefits[0] > bestBenefit) || 
                    ((benefits[0] == bestBenefit) && (benefits[1] > bestTotalWeight)))) {
        			bestBenefit = benefits[0];
        			bestSplit = splitValue;
        			bestTotalWeight = benefits[1];
        			bestSplitType = Split.LESS_SPLIT;
        		}
                if ((benefits[2] > minValue) &&
                    (benefits[2] > 0) && (benefits[3] > 0) &&
                    ((benefits[2] > bestBenefit) || 
                    ((benefits[2] == bestBenefit) && (benefits[3] > bestTotalWeight)))) {
        			bestBenefit = benefits[2];
        			bestSplit = splitValue;
        			bestTotalWeight = benefits[3];
        			bestSplitType = Split.GREATER_SPLIT;
        		}
        		oldLabel = label;
        	}
            lastValue = currentValue;
        	criterion.update(e);
        }
        return new Split(bestSplit, new double[] { bestBenefit, bestTotalWeight }, bestSplitType);
    }
}