org.apache.commons.statistics.distribution.LogNormalDistribution Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of virtdata-lib-curves4 Show documentation
Show all versions of virtdata-lib-curves4 Show documentation
Statistical sampling library for use in virtdata libraries, based
on apache commons math 4
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You 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.apache.commons.statistics.distribution;
import org.apache.commons.numbers.gamma.Erf;
import org.apache.commons.numbers.gamma.ErfDifference;
import org.apache.commons.rng.UniformRandomProvider;
import org.apache.commons.rng.sampling.distribution.ContinuousSampler;
import org.apache.commons.rng.sampling.distribution.LogNormalSampler;
import org.apache.commons.rng.sampling.distribution.ZigguratNormalizedGaussianSampler;
/**
* Implementation of the log-normal distribution.
*
*
* Parameters:
* {@code X} is log-normally distributed if its natural logarithm {@code log(X)}
* is normally distributed. The probability distribution function of {@code X}
* is given by (for {@code x > 0})
*
*
* {@code exp(-0.5 * ((ln(x) - m) / s)^2) / (s * sqrt(2 * pi) * x)}
*
*
* - {@code m} is the scale parameter: this is the mean of the
* normally distributed natural logarithm of this distribution,
* - {@code s} is the shape parameter: this is the standard
* deviation of the normally distributed natural logarithm of this
* distribution.
*
*/
public class LogNormalDistribution extends AbstractContinuousDistribution {
/** √(2 π) */
private static final double SQRT2PI = Math.sqrt(2 * Math.PI);
/** √(2) */
private static final double SQRT2 = Math.sqrt(2);
/** The scale parameter of this distribution. */
private final double scale;
/** The shape parameter of this distribution. */
private final double shape;
/** The value of {@code log(shape) + 0.5 * log(2*PI)} stored for faster computation. */
private final double logShapePlusHalfLog2Pi;
/**
* Creates a log-normal distribution.
*
* @param scale Scale parameter of this distribution.
* @param shape Shape parameter of this distribution.
* @throws IllegalArgumentException if {@code shape <= 0}.
*/
public LogNormalDistribution(double scale,
double shape) {
if (shape <= 0) {
throw new DistributionException(DistributionException.NEGATIVE, shape);
}
this.scale = scale;
this.shape = shape;
this.logShapePlusHalfLog2Pi = Math.log(shape) + 0.5 * Math.log(2 * Math.PI);
}
/**
* Returns the scale parameter of this distribution.
*
* @return the scale parameter
*/
public double getScale() {
return scale;
}
/**
* Returns the shape parameter of this distribution.
*
* @return the shape parameter
*/
public double getShape() {
return shape;
}
/**
* {@inheritDoc}
*
* For scale {@code m}, and shape {@code s} of this distribution, the PDF
* is given by
*
* - {@code 0} if {@code x <= 0},
* - {@code exp(-0.5 * ((ln(x) - m) / s)^2) / (s * sqrt(2 * pi) * x)}
* otherwise.
*
*/
@Override
public double density(double x) {
if (x <= 0) {
return 0;
}
final double x0 = Math.log(x) - scale;
final double x1 = x0 / shape;
return Math.exp(-0.5 * x1 * x1) / (shape * SQRT2PI * x);
}
/** {@inheritDoc}
*
* See documentation of {@link #density(double)} for computation details.
*/
@Override
public double logDensity(double x) {
if (x <= 0) {
return Double.NEGATIVE_INFINITY;
}
final double logX = Math.log(x);
final double x0 = logX - scale;
final double x1 = x0 / shape;
return -0.5 * x1 * x1 - (logShapePlusHalfLog2Pi + logX);
}
/**
* {@inheritDoc}
*
* For scale {@code m}, and shape {@code s} of this distribution, the CDF
* is given by
*
* - {@code 0} if {@code x <= 0},
* - {@code 0} if {@code ln(x) - m < 0} and {@code m - ln(x) > 40 * s}, as
* in these cases the actual value is within {@code Double.MIN_VALUE} of 0,
*
- {@code 1} if {@code ln(x) - m >= 0} and {@code ln(x) - m > 40 * s},
* as in these cases the actual value is within {@code Double.MIN_VALUE} of
* 1,
* - {@code 0.5 + 0.5 * erf((ln(x) - m) / (s * sqrt(2))} otherwise.
*
*/
@Override
public double cumulativeProbability(double x) {
if (x <= 0) {
return 0;
}
final double dev = Math.log(x) - scale;
if (Math.abs(dev) > 40 * shape) {
return dev < 0 ? 0.0d : 1.0d;
}
return 0.5 + 0.5 * Erf.value(dev / (shape * SQRT2));
}
/** {@inheritDoc} */
@Override
public double probability(double x0,
double x1) {
if (x0 > x1) {
throw new DistributionException(DistributionException.TOO_LARGE,
x0, x1);
}
if (x0 <= 0 || x1 <= 0) {
return super.probability(x0, x1);
}
final double denom = shape * SQRT2;
final double v0 = (Math.log(x0) - scale) / denom;
final double v1 = (Math.log(x1) - scale) / denom;
return 0.5 * ErfDifference.value(v0, v1);
}
/**
* {@inheritDoc}
*
* For scale {@code m} and shape {@code s}, the mean is
* {@code exp(m + s^2 / 2)}.
*/
@Override
public double getMean() {
double s = shape;
return Math.exp(scale + (s * s / 2));
}
/**
* {@inheritDoc}
*
* For scale {@code m} and shape {@code s}, the variance is
* {@code (exp(s^2) - 1) * exp(2 * m + s^2)}.
*/
@Override
public double getVariance() {
final double s = shape;
final double ss = s * s;
return (Math.expm1(ss)) * Math.exp(2 * scale + ss);
}
/**
* {@inheritDoc}
*
* The lower bound of the support is always 0 no matter the parameters.
*
* @return lower bound of the support (always 0)
*/
@Override
public double getSupportLowerBound() {
return 0;
}
/**
* {@inheritDoc}
*
* The upper bound of the support is always positive infinity
* no matter the parameters.
*
* @return upper bound of the support (always
* {@code Double.POSITIVE_INFINITY})
*/
@Override
public double getSupportUpperBound() {
return Double.POSITIVE_INFINITY;
}
/**
* {@inheritDoc}
*
* The support of this distribution is connected.
*
* @return {@code true}
*/
@Override
public boolean isSupportConnected() {
return true;
}
/** {@inheritDoc} */
@Override
public ContinuousDistribution.Sampler createSampler(final UniformRandomProvider rng) {
return new ContinuousDistribution.Sampler() {
/**
* Log normal distribution sampler.
*/
private final ContinuousSampler sampler =
new LogNormalSampler(new ZigguratNormalizedGaussianSampler(rng), scale, shape);
/**{@inheritDoc} */
@Override
public double sample() {
return sampler.sample();
}
};
}
}