Java tutorial
/* * 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 real distribution. * * @see <a href="http://en.wikipedia.org/wiki/Uniform_distribution_(continuous)" * >Uniform distribution (continuous), at Wikipedia</a> * * @version $Id: UniformRealDistribution.java 1416643 2012-12-03 19:37:14Z tn $ * @since 3.0 */ public class UniformRealDistribution extends AbstractRealDistribution { /** Default inverse cumulative probability accuracy. */ public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY = 1e-9; /** Serializable version identifier. */ private static final long serialVersionUID = 20120109L; /** Lower bound of this distribution (inclusive). */ private final double lower; /** Upper bound of this distribution (exclusive). */ private final double upper; /** Inverse cumulative probability accuracy. */ private final double solverAbsoluteAccuracy; /** * Create a standard uniform real distribution with lower bound (inclusive) * equal to zero and upper bound (exclusive) equal to one. */ public UniformRealDistribution() { this(0, 1); } /** * Create a uniform real distribution using the given lower and upper * bounds. * * @param lower Lower bound of this distribution (inclusive). * @param upper Upper bound of this distribution (exclusive). * @throws NumberIsTooLargeException if {@code lower >= upper}. */ public UniformRealDistribution(double lower, double upper) throws NumberIsTooLargeException { this(lower, upper, DEFAULT_INVERSE_ABSOLUTE_ACCURACY); } /** * Create a uniform distribution. * * @param lower Lower bound of this distribution (inclusive). * @param upper Upper bound of this distribution (exclusive). * @param inverseCumAccuracy Inverse cumulative probability accuracy. * @throws NumberIsTooLargeException if {@code lower >= upper}. */ public UniformRealDistribution(double lower, double upper, double inverseCumAccuracy) throws NumberIsTooLargeException { this(new Well19937c(), lower, upper, inverseCumAccuracy); } /** * Creates a uniform distribution. * * @param rng Random number generator. * @param lower Lower bound of this distribution (inclusive). * @param upper Upper bound of this distribution (exclusive). * @param inverseCumAccuracy Inverse cumulative probability accuracy. * @throws NumberIsTooLargeException if {@code lower >= upper}. * @since 3.1 */ public UniformRealDistribution(RandomGenerator rng, double lower, double upper, double inverseCumAccuracy) throws NumberIsTooLargeException { super(rng); if (lower >= upper) { throw new NumberIsTooLargeException(LocalizedFormats.LOWER_BOUND_NOT_BELOW_UPPER_BOUND, lower, upper, false); } this.lower = lower; this.upper = upper; solverAbsoluteAccuracy = inverseCumAccuracy; } /** {@inheritDoc} */ public double density(double x) { if (x < lower || x > upper) { return 0.0; } return 1 / (upper - lower); } /** {@inheritDoc} */ public double cumulativeProbability(double x) { if (x <= lower) { return 0; } if (x >= upper) { return 1; } return (x - lower) / (upper - lower); } /** {@inheritDoc} */ @Override protected double getSolverAbsoluteAccuracy() { return solverAbsoluteAccuracy; } /** * {@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}, the * variance is {@code (upper - lower)^2 / 12}. */ public double getNumericalVariance() { double ul = upper - lower; return ul * ul / 12; } /** * {@inheritDoc} * * The lower bound of the support is equal to the lower bound parameter * of the distribution. * * @return lower bound of the support */ public double 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 double getSupportUpperBound() { return upper; } /** {@inheritDoc} */ public boolean isSupportLowerBoundInclusive() { return true; } /** {@inheritDoc} */ public boolean isSupportUpperBoundInclusive() { return true; } /** * {@inheritDoc} * * The support of this distribution is connected. * * @return {@code true} */ public boolean isSupportConnected() { return true; } /** {@inheritDoc} */ @Override public double sample() { final double u = random.nextDouble(); return u * upper + (1 - u) * lower; } }