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The Apache Commons Math project is a library of lightweight, self-contained mathematics and statistics components addressing the most common practical problems not immediately available in the Java programming language or commons-lang.

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/*
 * 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.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 Gumbel distribution.
 *
 * @see Gumbel Distribution (Wikipedia)
 * @see Gumbel Distribution (Mathworld)
 *
 * @since 3.4
 */
public class GumbelDistribution extends AbstractRealDistribution {

    /** Serializable version identifier. */
    private static final long serialVersionUID = 20141003;

    /**
     * Approximation of Euler's constant
     * see http://mathworld.wolfram.com/Euler-MascheroniConstantApproximations.html
     */
    private static final double EULER = FastMath.PI / (2 * FastMath.E);

    /** The location parameter. */
    private final double mu;
    /** The scale parameter. */
    private final double beta;

    /**
     * Build a new instance.
     * 

* Note: 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 beta scale parameter (must be positive) * @throws NotStrictlyPositiveException if {@code beta <= 0} */ public GumbelDistribution(double mu, double beta) { this(new Well19937c(), mu, beta); } /** * Build a new instance. * * @param rng Random number generator * @param mu location parameter * @param beta scale parameter (must be positive) * @throws NotStrictlyPositiveException if {@code beta <= 0} */ public GumbelDistribution(RandomGenerator rng, double mu, double beta) { super(rng); if (beta <= 0) { throw new NotStrictlyPositiveException(LocalizedFormats.SCALE, beta); } this.beta = beta; this.mu = mu; } /** * Access the location parameter, {@code mu}. * * @return the location parameter. */ public double getLocation() { return mu; } /** * Access the scale parameter, {@code beta}. * * @return the scale parameter. */ public double getScale() { return beta; } /** {@inheritDoc} */ public double density(double x) { final double z = (x - mu) / beta; final double t = FastMath.exp(-z); return FastMath.exp(-z - t) / beta; } /** {@inheritDoc} */ public double cumulativeProbability(double x) { final double z = (x - mu) / beta; return FastMath.exp(-FastMath.exp(-z)); } /** {@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 Double.NEGATIVE_INFINITY; } else if (p == 1) { return Double.POSITIVE_INFINITY; } return mu - FastMath.log(-FastMath.log(p)) * beta; } /** {@inheritDoc} */ public double getNumericalMean() { return mu + EULER * beta; } /** {@inheritDoc} */ public double getNumericalVariance() { return (MathUtils.PI_SQUARED) / 6.0 * (beta * beta); } /** {@inheritDoc} */ public double getSupportLowerBound() { return Double.NEGATIVE_INFINITY; } /** {@inheritDoc} */ public double getSupportUpperBound() { return Double.POSITIVE_INFINITY; } /** {@inheritDoc} */ public boolean isSupportLowerBoundInclusive() { return false; } /** {@inheritDoc} */ public boolean isSupportUpperBoundInclusive() { return false; } /** {@inheritDoc} */ public boolean isSupportConnected() { return true; } }





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