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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.NumberIsTooLargeException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.random.Well19937c;

/**
 * Implementation of the uniform integer distribution.
 *
 * @see Uniform distribution (discrete), at Wikipedia
 *
 * @since 3.0
 */
public class UniformIntegerDistribution extends AbstractIntegerDistribution {
    /** Serializable version identifier. */
    private static final long serialVersionUID = 20120109L;
    /** Lower bound (inclusive) of this distribution. */
    private final int lower;
    /** Upper bound (inclusive) of this distribution. */
    private final int upper;

    /**
     * Creates a new uniform integer distribution using the given lower and
     * upper bounds (both inclusive).
     * 

* 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 lower Lower bound (inclusive) of this distribution. * @param upper Upper bound (inclusive) of this distribution. * @throws NumberIsTooLargeException if {@code lower >= upper}. */ public UniformIntegerDistribution(int lower, int upper) throws NumberIsTooLargeException { this(new Well19937c(), lower, upper); } /** * Creates a new uniform integer distribution using the given lower and * upper bounds (both inclusive). * * @param rng Random number generator. * @param lower Lower bound (inclusive) of this distribution. * @param upper Upper bound (inclusive) of this distribution. * @throws NumberIsTooLargeException if {@code lower > upper}. * @since 3.1 */ public UniformIntegerDistribution(RandomGenerator rng, int lower, int upper) throws NumberIsTooLargeException { super(rng); if (lower > upper) { throw new NumberIsTooLargeException( LocalizedFormats.LOWER_BOUND_NOT_BELOW_UPPER_BOUND, lower, upper, true); } this.lower = lower; this.upper = upper; } /** {@inheritDoc} */ public double probability(int x) { if (x < lower || x > upper) { return 0; } return 1.0 / (upper - lower + 1); } /** {@inheritDoc} */ public double cumulativeProbability(int x) { if (x < lower) { return 0; } if (x > upper) { return 1; } return (x - lower + 1.0) / (upper - lower + 1.0); } /** * {@inheritDoc} * * For lower bound {@code lower} and upper bound {@code upper}, the mean is * {@code 0.5 * (lower + upper)}. */ public double getNumericalMean() { return 0.5 * (lower + upper); } /** * {@inheritDoc} * * For lower bound {@code lower} and upper bound {@code upper}, and * {@code n = upper - lower + 1}, the variance is {@code (n^2 - 1) / 12}. */ public double getNumericalVariance() { double n = upper - lower + 1; return (n * n - 1) / 12.0; } /** * {@inheritDoc} * * The lower bound of the support is equal to the lower bound parameter * of the distribution. * * @return lower bound of the support */ public int getSupportLowerBound() { return lower; } /** * {@inheritDoc} * * The upper bound of the support is equal to the upper bound parameter * of the distribution. * * @return upper bound of the support */ public int getSupportUpperBound() { return upper; } /** * {@inheritDoc} * * The support of this distribution is connected. * * @return {@code true} */ public boolean isSupportConnected() { return true; } /** {@inheritDoc} */ @Override public int sample() { final int max = (upper - lower) + 1; if (max <= 0) { // The range is too wide to fit in a positive int (larger // than 2^31); as it covers more than half the integer range, // we use a simple rejection method. while (true) { final int r = random.nextInt(); if (r >= lower && r <= upper) { return r; } } } else { // We can shift the range and directly generate a positive int. return lower + random.nextInt(max); } } }





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