DistLogNormal.java
package nl.tudelft.simulation.jstats.distributions;
import nl.tudelft.simulation.jstats.streams.StreamInterface;
/**
* The LogNormal distribution. For more information on this distribution see
* <a href="https://mathworld.wolfram.com/LogNormalDistribution.html">
* https://mathworld.wolfram.com/LogNormalDistribution.html<br>
* The LogNormal distribution for random variable X is such that ln(X) ~ Normal(mu, sigma). </a>
* <p>
* Copyright (c) 2002-2024 Delft University of Technology, Jaffalaan 5, 2628 BX Delft, the Netherlands. All rights reserved. See
* for project information <a href="https://simulation.tudelft.nl/" target="_blank"> https://simulation.tudelft.nl</a>. The DSOL
* project is distributed under a three-clause BSD-style license, which can be found at
* <a href="https://https://simulation.tudelft.nl/dsol/docs/latest/license.html" target="_blank">
* https://https://simulation.tudelft.nl/dsol/docs/latest/license.html</a>.
* </p>
* @author <a href="https://www.linkedin.com/in/peterhmjacobs">Peter Jacobs </a>
* @author <a href="https://www.tudelft.nl/averbraeck">Alexander Verbraeck</a>
*/
public class DistLogNormal extends DistNormal
{
/** */
private static final long serialVersionUID = 1L;
/** the constant in the lognormal calculation: SQRT(2 * pi * sigma^2). */
private final double c2pisigma2;
/** the constant in the lognormal calculation: 2 * sigma^2. */
private final double c2sigma2;
/**
* Construct a new Lognormal distribution. The LogNormal distribution for random variable X is such that ln(X) ~ Normal(mu,
* sigma).
* @param stream StreamInterface; the random number stream
* @param normalMean double; the mean (mu) for the underlying normal distribution
* @param normalStDev double; the standard deviation (sigma) for the underlying normal distribution
* @throws IllegalArgumentException when normalStDev <= 0
*/
public DistLogNormal(final StreamInterface stream, final double normalMean, final double normalStDev)
{
super(stream, normalMean, normalStDev);
this.c2sigma2 = 2.0 * this.sigma * this.sigma;
this.c2pisigma2 = Math.sqrt(Math.PI * this.c2sigma2);
}
/** {@inheritDoc} */
@Override
public double draw()
{
double y = this.mu + this.sigma * super.nextGaussian();
return Math.exp(y);
}
/** {@inheritDoc} */
@Override
public double getProbabilityDensity(final double x)
{
if (x > 0.0)
{
double xminmu = Math.log(x) - this.mu;
return Math.exp(-1 * xminmu * xminmu / this.c2sigma2) / (x * this.c2pisigma2);
}
return 0.0;
}
/** {@inheritDoc} */
@Override
public double getCumulativeProbability(final double x)
{
if (x <= 0.0)
{
return 0.0;
}
return super.getCumulativeProbability(Math.log(x));
}
/** {@inheritDoc} */
@Override
public double getInverseCumulativeProbability(final double cumulativeProbability)
{
return Math.exp(super.getInverseCumulativeProbability(cumulativeProbability));
}
/** {@inheritDoc} */
@Override
public String toString()
{
return "LogNormal(" + this.mu + "," + this.sigma + ")";
}
}