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# Java example source code file (PascalDistribution.java)

This example Java source code file (PascalDistribution.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.

## Java - Java tags/keywords

abstractintegerdistribution, notstrictlypositiveexception, outofrangeexception, override, pascaldistribution, well19937c

## The PascalDistribution.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
* 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
*
*
* Unless required by applicable law or agreed to in writing, software
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
*/
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.special.Beta;
import org.apache.commons.math3.util.CombinatoricsUtils;
import org.apache.commons.math3.util.FastMath;

/**
* <p>
* Implementation of the Pascal distribution. The Pascal distribution is a
* special case of the Negative Binomial distribution where the number of
* successes parameter is an integer.
* </p>
* <p>
* There are various ways to express the probability mass and distribution
* functions for the Pascal distribution. The present implementation represents
* the distribution of the number of failures before {@code r} successes occur.
* This is the convention adopted in e.g.
* <a href="http://mathworld.wolfram.com/NegativeBinomialDistribution.html">MathWorld,
* but <em>not in
* <a href="http://en.wikipedia.org/wiki/Negative_binomial_distribution">Wikipedia.
* </p>
* <p>
* For a random variable {@code X} whose values are distributed according to this
* distribution, the probability mass function is given by<br/>
* {@code P(X = k) = C(k + r - 1, r - 1) * p^r * (1 - p)^k,}<br/>
* where {@code r} is the number of successes, {@code p} is the probability of
* success, and {@code X} is the total number of failures. {@code C(n, k)} is
* the binomial coefficient ({@code n} choose {@code k}). The mean and variance
* of {@code X} are<br/>
* {@code E(X) = (1 - p) * r / p, var(X) = (1 - p) * r / p^2.}<br/>
* Finally, the cumulative distribution function is given by<br/>
* {@code P(X <= k) = I(p, r, k + 1)},
* where I is the regularized incomplete Beta function.
* </p>
*
* @see <a href="http://en.wikipedia.org/wiki/Negative_binomial_distribution">
* Negative binomial distribution (Wikipedia)</a>
* @see <a href="http://mathworld.wolfram.com/NegativeBinomialDistribution.html">
* Negative binomial distribution (MathWorld)</a>
* @since 1.2 (changed to concrete class in 3.0)
*/
public class PascalDistribution extends AbstractIntegerDistribution {
/** Serializable version identifier. */
private static final long serialVersionUID = 6751309484392813623L;
/** The number of successes. */
private final int numberOfSuccesses;
/** The probability of success. */
private final double probabilityOfSuccess;
/** The value of {@code log(p)}, where {@code p} is the probability of success,
* stored for faster computation. */
private final double logProbabilityOfSuccess;
/** The value of {@code log(1-p)}, where {@code p} is the probability of success,
* stored for faster computation. */
private final double log1mProbabilityOfSuccess;

/**
* Create a Pascal distribution with the given number of successes and
* 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
* needed for the created distribution, it is advised to pass {@code null}
* as random generator via the appropriate constructors to avoid the
*
* @param r Number of successes.
* @param p Probability of success.
* @throws NotStrictlyPositiveException if the number of successes is not positive
* @throws OutOfRangeException if the probability of success is not in the
* range {@code [0, 1]}.
*/
public PascalDistribution(int r, double p)
throws NotStrictlyPositiveException, OutOfRangeException {
this(new Well19937c(), r, p);
}

/**
* Create a Pascal distribution with the given number of successes and
* probability of success.
*
* @param rng Random number generator.
* @param r Number of successes.
* @param p Probability of success.
* @throws NotStrictlyPositiveException if the number of successes is not positive
* @throws OutOfRangeException if the probability of success is not in the
* range {@code [0, 1]}.
* @since 3.1
*/
public PascalDistribution(RandomGenerator rng,
int r,
double p)
throws NotStrictlyPositiveException, OutOfRangeException {
super(rng);

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

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

/**
* Access the number of successes for this distribution.
*
* @return the number of successes.
*/
public int getNumberOfSuccesses() {
return numberOfSuccesses;
}

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

/** {@inheritDoc} */
public double probability(int x) {
double ret;
if (x < 0) {
ret = 0.0;
} else {
ret = CombinatoricsUtils.binomialCoefficientDouble(x +
numberOfSuccesses - 1, numberOfSuccesses - 1) *
FastMath.pow(probabilityOfSuccess, numberOfSuccesses) *
FastMath.pow(1.0 - probabilityOfSuccess, x);
}
return ret;
}

/** {@inheritDoc} */
@Override
public double logProbability(int x) {
double ret;
if (x < 0) {
ret = Double.NEGATIVE_INFINITY;
} else {
ret = CombinatoricsUtils.binomialCoefficientLog(x +
numberOfSuccesses - 1, numberOfSuccesses - 1) +
logProbabilityOfSuccess * numberOfSuccesses +
log1mProbabilityOfSuccess * x;
}
return ret;
}

/** {@inheritDoc} */
public double cumulativeProbability(int x) {
double ret;
if (x < 0) {
ret = 0.0;
} else {
ret = Beta.regularizedBeta(probabilityOfSuccess,
numberOfSuccesses, x + 1.0);
}
return ret;
}

/**
* {@inheritDoc}
*
* For number of successes {@code r} and probability of success {@code p},
* the mean is {@code r * (1 - p) / p}.
*/
public double getNumericalMean() {
final double p = getProbabilityOfSuccess();
final double r = getNumberOfSuccesses();
return (r * (1 - p)) / p;
}

/**
* {@inheritDoc}
*
* For number of successes {@code r} and probability of success {@code p},
* the variance is {@code r * (1 - p) / p^2}.
*/
public double getNumericalVariance() {
final double p = getProbabilityOfSuccess();
final double r = getNumberOfSuccesses();
return r * (1 - p) / (p * p);
}

/**
* {@inheritDoc}
*
* The lower bound of the support is always 0 no matter the parameters.
*
* @return lower bound of the support (always 0)
*/
public int getSupportLowerBound() {
return 0;
}

/**
* {@inheritDoc}
*
* The upper bound of the support is always positive infinity no matter the
* parameters. Positive infinity is symbolized by {@code Integer.MAX_VALUE}.
*
* @return upper bound of the support (always {@code Integer.MAX_VALUE}
* for positive infinity)
*/
public int getSupportUpperBound() {
return Integer.MAX_VALUE;
}

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

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