/**
* Copyright (C) 2001-2017 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 = false;
}
@Override
public void update(double value, double weight) {
sum += weight * value;
squaredSum += weight * value * value;
totalWeightSum += weight;
recentlyUpdated = true;
}
@Override
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);
}
@Override
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;
}
@Override
public int compareTo(EmpiricalNormalDistribution otherDistribution) {
return Double.compare(getMean(), otherDistribution.getMean());
}
}