/*
 *  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.tools.math.distribution;

import com.rapidminer.tools.Tools;


/**
 * This class represents a gaussian normal distribution.
 * 
 * @author Tobias Malbrecht, Sebastian Land
 */
public class EmpiricalNormalDistribution extends NormalDistribution implements EmpiricalDistribution, Comparable<EmpiricalNormalDistribution> {

	private static final long serialVersionUID = -1819042904676198636L;

	protected boolean recentlyUpdated;
	
	protected double sum;
	
	protected double squaredSum;
	
	protected double totalWeightSum;
	
	public EmpiricalNormalDistribution() {
 		super(Double.NaN, Double.MIN_VALUE);
 		sum = 0;
 		squaredSum = 0;
 		totalWeightSum = 0;
 		recentlyUpdated = true;
 		updateDistributionProperties();
	}
 	
 	public void update(double value, double weight) {
 		sum += weight * value;
 		squaredSum += weight * value * value;
 		totalWeightSum += weight;
 		recentlyUpdated = true;
 	}
 	
 	public void update(double value) {
 		sum += value;
 		squaredSum += value * value;
 		totalWeightSum += 1.0d;
 		recentlyUpdated = true;
 	}

 	public void update(EmpiricalNormalDistribution distribution) {
 		this.sum += distribution.sum;
 		this.squaredSum += distribution.squaredSum;
 		this.totalWeightSum += distribution.totalWeightSum;
 		recentlyUpdated = true;
 	}
 	
 	@Override
	public String getAttributeName() {
 		return null;
 	}
 	
	protected void updateDistributionProperties() {
		if (recentlyUpdated) {
			mean = sum / totalWeightSum;
			standardDeviation = totalWeightSum > 1 ? Math.sqrt((squaredSum - sum * sum / totalWeightSum) / (totalWeightSum - 1)) : Double.MIN_VALUE;
			recentlyUpdated = false;
		}
	}
 	
	@Override
	public double getProbability(double value) {
		updateDistributionProperties();
		return getProbability(mean, standardDeviation, value);
	}

	@Override
	public double getMean() {
		updateDistributionProperties();
		return mean;
	}
	
	@Override
	public double getStandardDeviation() {
		updateDistributionProperties();
		return standardDeviation;
	}
	
	@Override
	public double getVariance() {
		updateDistributionProperties();
		return standardDeviation * standardDeviation;
	}
	
	@Override
	public double getLowerBound() {
		updateDistributionProperties();
		return getLowerBound(mean, standardDeviation);
	}

	@Override
	public double getUpperBound() {
		updateDistributionProperties();
		return getUpperBound(mean, standardDeviation);
	}
	
	public double getTotalWeight() {
		return totalWeightSum;
	}
	
	@Override
	public String toString() {
		updateDistributionProperties();
		return ("Normal distribution --> mean: " + Tools.formatNumber(mean) + ", standard deviation: " + Tools.formatNumber(standardDeviation));
	}

	@Override
	public int getNumberOfParameters() {
		return 2;
	}

	@Override
	public String getParameterName(int index) {
		if (index == 0)
			return "mean";
		return "standard deviation";
	}

	@Override
	public double getParameterValue(int index) {
		updateDistributionProperties();
		if (index == 0)
			return mean;
		return standardDeviation;
	}
	
	public int compareTo(EmpiricalNormalDistribution otherDistribution) {
		return Double.compare(getMean(), otherDistribution.getMean());
	}
}