/*
* $Id$
* This file is a part of the Arakhne Foundation Classes, http://www.arakhne.org/afc
*
* Copyright (c) 2000-2012 Stephane GALLAND.
* Copyright (c) 2005-10, Multiagent Team, Laboratoire Systemes et Transports,
* Universite de Technologie de Belfort-Montbeliard.
* Copyright (c) 2013-2016 The original authors, and other authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.arakhne.afc.math.stochastic;
import java.util.Map;
import java.util.Random;
import org.eclipse.xtext.xbase.lib.Pure;
/**
* Law that representes a gaussian density.
*
* <p>Reference:
* <a href="http://en.wikipedia.org/wiki/Log-normal_distribution">Log-Normal Distribution</a>.
*
* <p>This class uses the gaussian random number distribution provided by {@link Random}.
*
* @author $Author: cbohrhauer$
* @version $FullVersion$
* @mavengroupid $GroupId$
* @mavenartifactid $ArtifactId$
* @since 13.0
*/
public class LogNormalStochasticLaw extends StochasticLaw {
private static final double SQRT2PI = Math.sqrt(2. * Math.PI);
private double mean;
private double standardDeviation;
/**
* Construct a law with the following parameters.
* <ul>
* <li><code>mean</code></li>
* <li><code>standardDeviation</code></li>
* </ul>
*
* @param parameters is the set of accepted paramters.
* @throws LawParameterNotFoundException if the list of parameters does not permits to create the law.
* @throws OutsideDomainException when standardDevisition is negative or nul.
*/
public LogNormalStochasticLaw(Map<String, String> parameters) throws OutsideDomainException, LawParameterNotFoundException {
this.mean = paramFloat("mean", parameters); //$NON-NLS-1$
this.standardDeviation = paramFloat("standardDeviation", parameters); //$NON-NLS-1$
if (this.standardDeviation <= 0) {
throw new OutsideDomainException(this.standardDeviation);
}
}
/**
* @param mean1 is the mean of the normal distribution.
* @param standardDeviation is the standard deviation associated to the nromal distribution.
* @throws OutsideDomainException when standardDevisition is negative or nul.
*/
public LogNormalStochasticLaw(double mean1, double standardDeviation) throws OutsideDomainException {
if (standardDeviation <= 0) {
throw new OutsideDomainException(standardDeviation);
}
this.mean = mean1;
this.standardDeviation = standardDeviation;
}
/** Replies a random value that respect
* the current stochastic law.
*
* @param mean is the mean of the normal distribution.
* @param standardDeviation is the standard deviation associated to the nromal distribution.
* @return a value depending of the stochastic law parameters
* @throws MathException when error in the math definition.
*/
@Pure
public static double random(double mean, double standardDeviation) throws MathException {
return StochasticGenerator.generateRandomValue(new LogNormalStochasticLaw(mean, standardDeviation));
}
@Pure
@Override
public String toString() {
final StringBuilder b = new StringBuilder();
b.append("LOGNORMAL(mean="); //$NON-NLS-1$
b.append(this.mean);
b.append(";deviation="); //$NON-NLS-1$
b.append(this.standardDeviation);
b.append(')');
return b.toString();
}
@Pure
@Override
public double f(double x) throws MathException {
if (x <= 0) {
throw new OutsideDomainException(x);
}
double ex = Math.log(x) - this.mean;
ex = ex * ex;
return Math.exp((-ex) / (2. * this.standardDeviation * this.standardDeviation))
/ (x * this.standardDeviation * SQRT2PI);
}
@Pure
@Override
public MathFunctionRange[] getRange() {
return new MathFunctionRange[] {new MathFunctionRange(0, false, Double.POSITIVE_INFINITY, false) };
}
/** Replies the x according to the value of the distribution function.
*
* @param u is a value given by the uniform random variable generator {@code U(0, 1)}.
* @return {@code F<sup>-1</sup>(u)}
* @throws MathException in case {@code F<sup>-1</sup>(u)} could not be computed
*/
@Pure
@Override
public double inverseF(double u) throws MathException {
return Math.exp(this.standardDeviation * u + this.mean);
}
/** Replies the x according to the value of the inverted
* cummulative distribution function {@code F<sup>-1</sup>(u)}
* where {@code u = U(0, 1)}.
*
* @param u is the uniform random variable generator {@code U(0, 1)}.
* @return {@code F<sup>-1</sup>(u)}
* @throws MathException in case {@code F<sup>-1</sup>(u)} could not be computed
*/
@Override
protected final double inverseF(Random u) throws MathException {
final double uvalue = (u.nextGaussian() + 1) / 2.;
return inverseF(uvalue);
}
}