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Java example source code file (GeometricDistribution.java)

This example Java source code file (GeometricDistribution.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

abstractintegerdistribution, geometricdistribution, outofrangeexception, override, well19937c

The GeometricDistribution.java Java example source code

/*
 * 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.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;

/**
 * Implementation of the geometric distribution.
 *
 * @see <a href="http://en.wikipedia.org/wiki/Geometric_distribution">Geometric distribution (Wikipedia)
 * @see <a href="http://mathworld.wolfram.com/GeometricDistribution.html">Geometric Distribution (MathWorld)
 * @since 3.3
 */
public class GeometricDistribution extends AbstractIntegerDistribution {

    /** Serializable version identifier. */
    private static final long serialVersionUID = 20130507L;
    /** The probability of success. */
    private final double probabilityOfSuccess;
    /** {@code log(p)} where p is the probability of success. */
    private final double logProbabilityOfSuccess;
    /** {@code log(1 - p)} where p is the probability of success. */
    private final double log1mProbabilityOfSuccess;

    /**
     * Create a geometric distribution with the given probability of success.
     * <p>
     * <b>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 p probability of success.
     * @throws OutOfRangeException if {@code p <= 0} or {@code p > 1}.
     */
    public GeometricDistribution(double p) {
        this(new Well19937c(), p);
    }

    /**
     * Creates a geometric distribution.
     *
     * @param rng Random number generator.
     * @param p Probability of success.
     * @throws OutOfRangeException if {@code p <= 0} or {@code p > 1}.
     */
    public GeometricDistribution(RandomGenerator rng, double p) {
        super(rng);

        if (p <= 0 || p > 1) {
            throw new OutOfRangeException(LocalizedFormats.OUT_OF_RANGE_LEFT, p, 0, 1);
        }

        probabilityOfSuccess = p;
        logProbabilityOfSuccess = FastMath.log(p);
        log1mProbabilityOfSuccess = FastMath.log1p(-p);
    }

    /**
     * Access the probability of success for this distribution.
     *
     * @return the probability of success.
     */
    public double getProbabilityOfSuccess() {
        return probabilityOfSuccess;
    }

    /** {@inheritDoc} */
    public double probability(int x) {
        if (x < 0) {
            return 0.0;
        } else {
            return FastMath.exp(log1mProbabilityOfSuccess * x) * probabilityOfSuccess;
        }
    }

    /** {@inheritDoc} */
    @Override
    public double logProbability(int x) {
        if (x < 0) {
            return Double.NEGATIVE_INFINITY;
        } else {
            return x * log1mProbabilityOfSuccess + logProbabilityOfSuccess;
        }
    }

    /** {@inheritDoc} */
    public double cumulativeProbability(int x) {
        if (x < 0) {
            return 0.0;
        } else {
            return -FastMath.expm1(log1mProbabilityOfSuccess * (x + 1));
        }
    }

    /**
     * {@inheritDoc}
     *
     * For probability parameter {@code p}, the mean is {@code (1 - p) / p}.
     */
    public double getNumericalMean() {
        return (1 - probabilityOfSuccess) / probabilityOfSuccess;
    }

    /**
     * {@inheritDoc}
     *
     * For probability parameter {@code p}, the variance is
     * {@code (1 - p) / (p * p)}.
     */
    public double getNumericalVariance() {
        return (1 - probabilityOfSuccess) / (probabilityOfSuccess * probabilityOfSuccess);
    }

    /**
     * {@inheritDoc}
     *
     * The lower bound of the support is always 0.
     *
     * @return lower bound of the support (always 0)
     */
    public int getSupportLowerBound() {
        return 0;
    }

    /**
     * {@inheritDoc}
     *
     * The upper bound of the support is infinite (which we approximate as
     * {@code Integer.MAX_VALUE}).
     *
     * @return upper bound of the support (always Integer.MAX_VALUE)
     */
    public int getSupportUpperBound() {
        return Integer.MAX_VALUE;
    }

    /**
     * {@inheritDoc}
     *
     * The support of this distribution is connected.
     *
     * @return {@code true}
     */
    public boolean isSupportConnected() {
        return true;
    }

    /**
     * {@inheritDoc}
     */
    @Override
    public int inverseCumulativeProbability(double p) throws OutOfRangeException {
        if (p < 0 || p > 1) {
            throw new OutOfRangeException(p, 0, 1);
        }
        if (p == 1) {
            return Integer.MAX_VALUE;
        }
        if (p == 0) {
            return 0;
        }
        return Math.max(0, (int) Math.ceil(FastMath.log1p(-p)/log1mProbabilityOfSuccess-1));
    }
}

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