/*
 *  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.tools.math;

import java.util.Comparator;


/**
 * Helper class for finding thresholds for cost sensitive learning or
 * calculating the AUC performance criterion.
 * 
 * @author Martin Scholz, Ingo Mierswa
 *          ingomierswa Exp $
 */
public class WeightedConfidenceAndLabel implements Comparable {

	public static class WCALComparator implements Comparator<WeightedConfidenceAndLabel> {
		private ROCBias method;
		public WCALComparator(ROCBias method) {
			this.method = method;
		}
		@Override
		public int compare(WeightedConfidenceAndLabel o1, WeightedConfidenceAndLabel o2) {
			int compi = (-1) * Double.compare(o1.confidence, o2.confidence);
			if (compi == 0) {
				switch (method) {
				case OPTIMISTIC:
					return -Double.compare(o1.label, o2.label);
				case PESSIMISTIC:
				case NEUTRAL:
				default:
					return Double.compare(o1.label, o2.label);
				}
			} else {
				return compi;
			}
		}
		
	}

	private final double confidence, label, prediction;

	private double weight = 1.0d;

	public WeightedConfidenceAndLabel(double confidence, double label, double prediction) {
		this(confidence, label, prediction, 1.0d);
	}

	public WeightedConfidenceAndLabel(double confidence, double label, double prediction, double weight) {
		this.confidence = confidence;
		this.label = label;
		this.prediction = prediction;
		this.weight = weight;
	}

	public int compareTo(Object obj) {
		// We need to sort the examples by *decreasing* confidence:
		int compi = (-1) * Double.compare(this.confidence, ((WeightedConfidenceAndLabel) obj).confidence);
		if (compi == 0)
			return -Double.compare(this.label, ((WeightedConfidenceAndLabel) obj).label);
		else
			return compi;
	}

	@Override
	public boolean equals(Object o) {
		if (!(o instanceof WeightedConfidenceAndLabel)) {
			return false;
		} else {
			WeightedConfidenceAndLabel l = (WeightedConfidenceAndLabel)o;
			return (this.label == l.label) && (this.confidence == l.confidence);
		}
	}
	
	@Override
	public int hashCode() {
		return Double.valueOf(this.label).hashCode() ^ Double.valueOf(this.confidence).hashCode();
	}
	
	public double getLabel() {
		return this.label;
	}

	public double getPrediction() {
		return this.prediction;
	}
	
	public double getConfidence() {
		return this.confidence;
	}

	public double getWeight() {
		return weight;
	}
	
	@Override
	public String toString() {
		return "conf: " + confidence + ", label: " + label + ", weight: " + weight;
	}
}