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 * 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
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 *      http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.commons.math3.distribution;

import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.exception.OutOfRangeException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.random.Well19937c;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.MathUtils;

This class implements the Logistic distribution.
See Also:
Since:3.4
/** * This class implements the Logistic distribution. * * @see <a href="http://en.wikipedia.org/wiki/Logistic_distribution">Logistic Distribution (Wikipedia)</a> * @see <a href="http://mathworld.wolfram.com/LogisticDistribution.html">Logistic Distribution (Mathworld)</a> * * @since 3.4 */
public class LogisticDistribution extends AbstractRealDistribution {
Serializable version identifier.
/** Serializable version identifier. */
private static final long serialVersionUID = 20141003;
The location parameter.
/** The location parameter. */
private final double mu;
The scale parameter.
/** The scale parameter. */
private final double s;
Build a new instance.

Note: this constructor will implicitly create an instance of Well19937c as random generator to be used for sampling only (see AbstractRealDistribution.sample() and AbstractRealDistribution.sample(int)). In case no sampling is needed for the created distribution, it is advised to pass null as random generator via the appropriate constructors to avoid the additional initialisation overhead.

Params:
  • mu – location parameter
  • s – scale parameter (must be positive)
Throws:
/** * Build a new instance. * <p> * <b>Note:</b> this constructor will implicitly create an instance of * {@link Well19937c} as random generator to be used for sampling only (see * {@link #sample()} and {@link #sample(int)}). In case no sampling is * needed for the created distribution, it is advised to pass {@code null} * as random generator via the appropriate constructors to avoid the * additional initialisation overhead. * * @param mu location parameter * @param s scale parameter (must be positive) * @throws NotStrictlyPositiveException if {@code beta <= 0} */
public LogisticDistribution(double mu, double s) { this(new Well19937c(), mu, s); }
Build a new instance.
Params:
  • rng – Random number generator
  • mu – location parameter
  • s – scale parameter (must be positive)
Throws:
/** * Build a new instance. * * @param rng Random number generator * @param mu location parameter * @param s scale parameter (must be positive) * @throws NotStrictlyPositiveException if {@code beta <= 0} */
public LogisticDistribution(RandomGenerator rng, double mu, double s) { super(rng); if (s <= 0.0) { throw new NotStrictlyPositiveException(LocalizedFormats.NOT_POSITIVE_SCALE, s); } this.mu = mu; this.s = s; }
Access the location parameter, mu.
Returns:the location parameter.
/** * Access the location parameter, {@code mu}. * * @return the location parameter. */
public double getLocation() { return mu; }
Access the scale parameter, s.
Returns:the scale parameter.
/** * Access the scale parameter, {@code s}. * * @return the scale parameter. */
public double getScale() { return s; }
{@inheritDoc}
/** {@inheritDoc} */
public double density(double x) { double z = (x - mu) / s; double v = FastMath.exp(-z); return 1 / s * v / ((1.0 + v) * (1.0 + v)); }
{@inheritDoc}
/** {@inheritDoc} */
public double cumulativeProbability(double x) { double z = 1 / s * (x - mu); return 1.0 / (1.0 + FastMath.exp(-z)); }
{@inheritDoc}
/** {@inheritDoc} */
@Override public double inverseCumulativeProbability(double p) throws OutOfRangeException { if (p < 0.0 || p > 1.0) { throw new OutOfRangeException(p, 0.0, 1.0); } else if (p == 0) { return 0.0; } else if (p == 1) { return Double.POSITIVE_INFINITY; } return s * Math.log(p / (1.0 - p)) + mu; }
{@inheritDoc}
/** {@inheritDoc} */
public double getNumericalMean() { return mu; }
{@inheritDoc}
/** {@inheritDoc} */
public double getNumericalVariance() { return (MathUtils.PI_SQUARED / 3.0) * (1.0 / (s * s)); }
{@inheritDoc}
/** {@inheritDoc} */
public double getSupportLowerBound() { return Double.NEGATIVE_INFINITY; }
{@inheritDoc}
/** {@inheritDoc} */
public double getSupportUpperBound() { return Double.POSITIVE_INFINITY; }
{@inheritDoc}
/** {@inheritDoc} */
public boolean isSupportLowerBoundInclusive() { return false; }
{@inheritDoc}
/** {@inheritDoc} */
public boolean isSupportUpperBoundInclusive() { return false; }
{@inheritDoc}
/** {@inheritDoc} */
public boolean isSupportConnected() { return true; } }