| /* |
| * 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.random; |
| |
| import java.io.Serializable; |
| import java.security.NoSuchAlgorithmException; |
| import java.security.NoSuchProviderException; |
| import java.util.Collection; |
| |
| import org.apache.commons.math3.distribution.IntegerDistribution; |
| import org.apache.commons.math3.distribution.RealDistribution; |
| import org.apache.commons.math3.exception.NotANumberException; |
| import org.apache.commons.math3.exception.NotFiniteNumberException; |
| import org.apache.commons.math3.exception.NotPositiveException; |
| import org.apache.commons.math3.exception.NotStrictlyPositiveException; |
| import org.apache.commons.math3.exception.MathIllegalArgumentException; |
| import org.apache.commons.math3.exception.NumberIsTooLargeException; |
| import org.apache.commons.math3.exception.OutOfRangeException; |
| |
| /** |
| * Generates random deviates and other random data using a {@link RandomGenerator} |
| * instance to generate non-secure data and a {@link java.security.SecureRandom} |
| * instance to provide data for the <code>nextSecureXxx</code> methods. If no |
| * <code>RandomGenerator</code> is provided in the constructor, the default is |
| * to use a {@link Well19937c} generator. To plug in a different |
| * implementation, either implement <code>RandomGenerator</code> directly or |
| * extend {@link AbstractRandomGenerator}. |
| * <p> |
| * Supports reseeding the underlying pseudo-random number generator (PRNG). The |
| * <code>SecurityProvider</code> and <code>Algorithm</code> used by the |
| * <code>SecureRandom</code> instance can also be reset. |
| * </p> |
| * <p> |
| * For details on the default PRNGs, see {@link java.util.Random} and |
| * {@link java.security.SecureRandom}. |
| * </p> |
| * <p> |
| * <strong>Usage Notes</strong>: |
| * <ul> |
| * <li> |
| * Instance variables are used to maintain <code>RandomGenerator</code> and |
| * <code>SecureRandom</code> instances used in data generation. Therefore, to |
| * generate a random sequence of values or strings, you should use just |
| * <strong>one</strong> <code>RandomDataGenerator</code> instance repeatedly.</li> |
| * <li> |
| * The "secure" methods are *much* slower. These should be used only when a |
| * cryptographically secure random sequence is required. A secure random |
| * sequence is a sequence of pseudo-random values which, in addition to being |
| * well-dispersed (so no subsequence of values is an any more likely than other |
| * subsequence of the the same length), also has the additional property that |
| * knowledge of values generated up to any point in the sequence does not make |
| * it any easier to predict subsequent values.</li> |
| * <li> |
| * When a new <code>RandomDataGenerator</code> is created, the underlying random |
| * number generators are <strong>not</strong> initialized. If you do not |
| * explicitly seed the default non-secure generator, it is seeded with the |
| * current time in milliseconds plus the system identity hash code on first use. |
| * The same holds for the secure generator. If you provide a <code>RandomGenerator</code> |
| * to the constructor, however, this generator is not reseeded by the constructor |
| * nor is it reseeded on first use.</li> |
| * <li> |
| * The <code>reSeed</code> and <code>reSeedSecure</code> methods delegate to the |
| * corresponding methods on the underlying <code>RandomGenerator</code> and |
| * <code>SecureRandom</code> instances. Therefore, <code>reSeed(long)</code> |
| * fully resets the initial state of the non-secure random number generator (so |
| * that reseeding with a specific value always results in the same subsequent |
| * random sequence); whereas reSeedSecure(long) does <strong>not</strong> |
| * reinitialize the secure random number generator (so secure sequences started |
| * with calls to reseedSecure(long) won't be identical).</li> |
| * <li> |
| * This implementation is not synchronized. The underlying <code>RandomGenerator</code> |
| * or <code>SecureRandom</code> instances are not protected by synchronization and |
| * are not guaranteed to be thread-safe. Therefore, if an instance of this class |
| * is concurrently utilized by multiple threads, it is the responsibility of |
| * client code to synchronize access to seeding and data generation methods. |
| * </li> |
| * </ul> |
| * </p> |
| * @deprecated to be removed in 4.0. Use {@link RandomDataGenerator} instead |
| */ |
| @Deprecated |
| public class RandomDataImpl implements RandomData, Serializable { |
| |
| /** Serializable version identifier */ |
| private static final long serialVersionUID = -626730818244969716L; |
| |
| /** RandomDataGenerator delegate */ |
| private final RandomDataGenerator delegate; |
| |
| /** |
| * Construct a RandomDataImpl, using a default random generator as the source |
| * of randomness. |
| * |
| * <p>The default generator is a {@link Well19937c} seeded |
| * with {@code System.currentTimeMillis() + System.identityHashCode(this))}. |
| * The generator is initialized and seeded on first use.</p> |
| */ |
| public RandomDataImpl() { |
| delegate = new RandomDataGenerator(); |
| } |
| |
| /** |
| * Construct a RandomDataImpl using the supplied {@link RandomGenerator} as |
| * the source of (non-secure) random data. |
| * |
| * @param rand the source of (non-secure) random data |
| * (may be null, resulting in the default generator) |
| * @since 1.1 |
| */ |
| public RandomDataImpl(RandomGenerator rand) { |
| delegate = new RandomDataGenerator(rand); |
| } |
| |
| /** |
| * @return the delegate object. |
| * @deprecated To be removed in 4.0. |
| */ |
| @Deprecated |
| RandomDataGenerator getDelegate() { |
| return delegate; |
| } |
| |
| /** |
| * {@inheritDoc} |
| * <p> |
| * <strong>Algorithm Description:</strong> hex strings are generated using a |
| * 2-step process. |
| * <ol> |
| * <li>{@code len / 2 + 1} binary bytes are generated using the underlying |
| * Random</li> |
| * <li>Each binary byte is translated into 2 hex digits</li> |
| * </ol> |
| * </p> |
| * |
| * @param len the desired string length. |
| * @return the random string. |
| * @throws NotStrictlyPositiveException if {@code len <= 0}. |
| */ |
| public String nextHexString(int len) throws NotStrictlyPositiveException { |
| return delegate.nextHexString(len); |
| } |
| |
| /** {@inheritDoc} */ |
| public int nextInt(int lower, int upper) throws NumberIsTooLargeException { |
| return delegate.nextInt(lower, upper); |
| } |
| |
| /** {@inheritDoc} */ |
| public long nextLong(long lower, long upper) throws NumberIsTooLargeException { |
| return delegate.nextLong(lower, upper); |
| } |
| |
| /** |
| * {@inheritDoc} |
| * <p> |
| * <strong>Algorithm Description:</strong> hex strings are generated in |
| * 40-byte segments using a 3-step process. |
| * <ol> |
| * <li> |
| * 20 random bytes are generated using the underlying |
| * <code>SecureRandom</code>.</li> |
| * <li> |
| * SHA-1 hash is applied to yield a 20-byte binary digest.</li> |
| * <li> |
| * Each byte of the binary digest is converted to 2 hex digits.</li> |
| * </ol> |
| * </p> |
| */ |
| public String nextSecureHexString(int len) throws NotStrictlyPositiveException { |
| return delegate.nextSecureHexString(len); |
| } |
| |
| /** {@inheritDoc} */ |
| public int nextSecureInt(int lower, int upper) throws NumberIsTooLargeException { |
| return delegate.nextSecureInt(lower, upper); |
| } |
| |
| /** {@inheritDoc} */ |
| public long nextSecureLong(long lower, long upper) throws NumberIsTooLargeException { |
| return delegate.nextSecureLong(lower,upper); |
| } |
| |
| /** |
| * {@inheritDoc} |
| * <p> |
| * <strong>Algorithm Description</strong>: |
| * <ul><li> For small means, uses simulation of a Poisson process |
| * using Uniform deviates, as described |
| * <a href="http://irmi.epfl.ch/cmos/Pmmi/interactive/rng7.htm"> here.</a> |
| * The Poisson process (and hence value returned) is bounded by 1000 * mean.</li> |
| * |
| * <li> For large means, uses the rejection algorithm described in <br/> |
| * Devroye, Luc. (1981).<i>The Computer Generation of Poisson Random Variables</i> |
| * <strong>Computing</strong> vol. 26 pp. 197-207.</li></ul></p> |
| */ |
| public long nextPoisson(double mean) throws NotStrictlyPositiveException { |
| return delegate.nextPoisson(mean); |
| } |
| |
| /** {@inheritDoc} */ |
| public double nextGaussian(double mu, double sigma) throws NotStrictlyPositiveException { |
| return delegate.nextGaussian(mu,sigma); |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * <p> |
| * <strong>Algorithm Description</strong>: Uses the Algorithm SA (Ahrens) |
| * from p. 876 in: |
| * [1]: Ahrens, J. H. and Dieter, U. (1972). Computer methods for |
| * sampling from the exponential and normal distributions. |
| * Communications of the ACM, 15, 873-882. |
| * </p> |
| */ |
| public double nextExponential(double mean) throws NotStrictlyPositiveException { |
| return delegate.nextExponential(mean); |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * <p> |
| * <strong>Algorithm Description</strong>: scales the output of |
| * Random.nextDouble(), but rejects 0 values (i.e., will generate another |
| * random double if Random.nextDouble() returns 0). This is necessary to |
| * provide a symmetric output interval (both endpoints excluded). |
| * </p> |
| */ |
| public double nextUniform(double lower, double upper) |
| throws NumberIsTooLargeException, NotFiniteNumberException, NotANumberException { |
| return delegate.nextUniform(lower, upper); |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * <p> |
| * <strong>Algorithm Description</strong>: if the lower bound is excluded, |
| * scales the output of Random.nextDouble(), but rejects 0 values (i.e., |
| * will generate another random double if Random.nextDouble() returns 0). |
| * This is necessary to provide a symmetric output interval (both |
| * endpoints excluded). |
| * </p> |
| * @since 3.0 |
| */ |
| public double nextUniform(double lower, double upper, boolean lowerInclusive) |
| throws NumberIsTooLargeException, NotFiniteNumberException, NotANumberException { |
| return delegate.nextUniform(lower, upper, lowerInclusive); |
| } |
| |
| /** |
| * Generates a random value from the {@link org.apache.commons.math3.distribution.BetaDistribution Beta Distribution}. |
| * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion} |
| * to generate random values. |
| * |
| * @param alpha first distribution shape parameter |
| * @param beta second distribution shape parameter |
| * @return random value sampled from the beta(alpha, beta) distribution |
| * @since 2.2 |
| */ |
| public double nextBeta(double alpha, double beta) { |
| return delegate.nextBeta(alpha, beta); |
| } |
| |
| /** |
| * Generates a random value from the {@link org.apache.commons.math3.distribution.BinomialDistribution Binomial Distribution}. |
| * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion} |
| * to generate random values. |
| * |
| * @param numberOfTrials number of trials of the Binomial distribution |
| * @param probabilityOfSuccess probability of success of the Binomial distribution |
| * @return random value sampled from the Binomial(numberOfTrials, probabilityOfSuccess) distribution |
| * @since 2.2 |
| */ |
| public int nextBinomial(int numberOfTrials, double probabilityOfSuccess) { |
| return delegate.nextBinomial(numberOfTrials, probabilityOfSuccess); |
| } |
| |
| /** |
| * Generates a random value from the {@link org.apache.commons.math3.distribution.CauchyDistribution Cauchy Distribution}. |
| * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion} |
| * to generate random values. |
| * |
| * @param median the median of the Cauchy distribution |
| * @param scale the scale parameter of the Cauchy distribution |
| * @return random value sampled from the Cauchy(median, scale) distribution |
| * @since 2.2 |
| */ |
| public double nextCauchy(double median, double scale) { |
| return delegate.nextCauchy(median, scale); |
| } |
| |
| /** |
| * Generates a random value from the {@link org.apache.commons.math3.distribution.ChiSquaredDistribution ChiSquare Distribution}. |
| * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion} |
| * to generate random values. |
| * |
| * @param df the degrees of freedom of the ChiSquare distribution |
| * @return random value sampled from the ChiSquare(df) distribution |
| * @since 2.2 |
| */ |
| public double nextChiSquare(double df) { |
| return delegate.nextChiSquare(df); |
| } |
| |
| /** |
| * Generates a random value from the {@link org.apache.commons.math3.distribution.FDistribution F Distribution}. |
| * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion} |
| * to generate random values. |
| * |
| * @param numeratorDf the numerator degrees of freedom of the F distribution |
| * @param denominatorDf the denominator degrees of freedom of the F distribution |
| * @return random value sampled from the F(numeratorDf, denominatorDf) distribution |
| * @throws NotStrictlyPositiveException if |
| * {@code numeratorDf <= 0} or {@code denominatorDf <= 0}. |
| * @since 2.2 |
| */ |
| public double nextF(double numeratorDf, double denominatorDf) throws NotStrictlyPositiveException { |
| return delegate.nextF(numeratorDf, denominatorDf); |
| } |
| |
| /** |
| * <p>Generates a random value from the |
| * {@link org.apache.commons.math3.distribution.GammaDistribution Gamma Distribution}.</p> |
| * |
| * <p>This implementation uses the following algorithms: </p> |
| * |
| * <p>For 0 < shape < 1: <br/> |
| * Ahrens, J. H. and Dieter, U., <i>Computer methods for |
| * sampling from gamma, beta, Poisson and binomial distributions.</i> |
| * Computing, 12, 223-246, 1974.</p> |
| * |
| * <p>For shape >= 1: <br/> |
| * Marsaglia and Tsang, <i>A Simple Method for Generating |
| * Gamma Variables.</i> ACM Transactions on Mathematical Software, |
| * Volume 26 Issue 3, September, 2000.</p> |
| * |
| * @param shape the median of the Gamma distribution |
| * @param scale the scale parameter of the Gamma distribution |
| * @return random value sampled from the Gamma(shape, scale) distribution |
| * @throws NotStrictlyPositiveException if {@code shape <= 0} or |
| * {@code scale <= 0}. |
| * @since 2.2 |
| */ |
| public double nextGamma(double shape, double scale) throws NotStrictlyPositiveException { |
| return delegate.nextGamma(shape, scale); |
| } |
| |
| /** |
| * Generates a random value from the {@link org.apache.commons.math3.distribution.HypergeometricDistribution Hypergeometric Distribution}. |
| * This implementation uses {@link #nextInversionDeviate(IntegerDistribution) inversion} |
| * to generate random values. |
| * |
| * @param populationSize the population size of the Hypergeometric distribution |
| * @param numberOfSuccesses number of successes in the population of the Hypergeometric distribution |
| * @param sampleSize the sample size of the Hypergeometric distribution |
| * @return random value sampled from the Hypergeometric(numberOfSuccesses, sampleSize) distribution |
| * @throws NumberIsTooLargeException if {@code numberOfSuccesses > populationSize}, |
| * or {@code sampleSize > populationSize}. |
| * @throws NotStrictlyPositiveException if {@code populationSize <= 0}. |
| * @throws NotPositiveException if {@code numberOfSuccesses < 0}. |
| * @since 2.2 |
| */ |
| public int nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize) |
| throws NotPositiveException, NotStrictlyPositiveException, NumberIsTooLargeException { |
| return delegate.nextHypergeometric(populationSize, numberOfSuccesses, sampleSize); |
| } |
| |
| /** |
| * Generates a random value from the {@link org.apache.commons.math3.distribution.PascalDistribution Pascal Distribution}. |
| * This implementation uses {@link #nextInversionDeviate(IntegerDistribution) inversion} |
| * to generate random values. |
| * |
| * @param r the number of successes of the Pascal distribution |
| * @param p the probability of success of the Pascal distribution |
| * @return random value sampled from the Pascal(r, p) distribution |
| * @since 2.2 |
| * @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 int nextPascal(int r, double p) |
| throws NotStrictlyPositiveException, OutOfRangeException { |
| return delegate.nextPascal(r, p); |
| } |
| |
| /** |
| * Generates a random value from the {@link org.apache.commons.math3.distribution.TDistribution T Distribution}. |
| * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion} |
| * to generate random values. |
| * |
| * @param df the degrees of freedom of the T distribution |
| * @return random value from the T(df) distribution |
| * @since 2.2 |
| * @throws NotStrictlyPositiveException if {@code df <= 0} |
| */ |
| public double nextT(double df) throws NotStrictlyPositiveException { |
| return delegate.nextT(df); |
| } |
| |
| /** |
| * Generates a random value from the {@link org.apache.commons.math3.distribution.WeibullDistribution Weibull Distribution}. |
| * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion} |
| * to generate random values. |
| * |
| * @param shape the shape parameter of the Weibull distribution |
| * @param scale the scale parameter of the Weibull distribution |
| * @return random value sampled from the Weibull(shape, size) distribution |
| * @since 2.2 |
| * @throws NotStrictlyPositiveException if {@code shape <= 0} or |
| * {@code scale <= 0}. |
| */ |
| public double nextWeibull(double shape, double scale) throws NotStrictlyPositiveException { |
| return delegate.nextWeibull(shape, scale); |
| } |
| |
| /** |
| * Generates a random value from the {@link org.apache.commons.math3.distribution.ZipfDistribution Zipf Distribution}. |
| * This implementation uses {@link #nextInversionDeviate(IntegerDistribution) inversion} |
| * to generate random values. |
| * |
| * @param numberOfElements the number of elements of the ZipfDistribution |
| * @param exponent the exponent of the ZipfDistribution |
| * @return random value sampled from the Zipf(numberOfElements, exponent) distribution |
| * @since 2.2 |
| * @exception NotStrictlyPositiveException if {@code numberOfElements <= 0} |
| * or {@code exponent <= 0}. |
| */ |
| public int nextZipf(int numberOfElements, double exponent) throws NotStrictlyPositiveException { |
| return delegate.nextZipf(numberOfElements, exponent); |
| } |
| |
| |
| /** |
| * Reseeds the random number generator with the supplied seed. |
| * <p> |
| * Will create and initialize if null. |
| * </p> |
| * |
| * @param seed |
| * the seed value to use |
| */ |
| public void reSeed(long seed) { |
| delegate.reSeed(seed); |
| } |
| |
| /** |
| * Reseeds the secure random number generator with the current time in |
| * milliseconds. |
| * <p> |
| * Will create and initialize if null. |
| * </p> |
| */ |
| public void reSeedSecure() { |
| delegate.reSeedSecure(); |
| } |
| |
| /** |
| * Reseeds the secure random number generator with the supplied seed. |
| * <p> |
| * Will create and initialize if null. |
| * </p> |
| * |
| * @param seed |
| * the seed value to use |
| */ |
| public void reSeedSecure(long seed) { |
| delegate.reSeedSecure(seed); |
| } |
| |
| /** |
| * Reseeds the random number generator with |
| * {@code System.currentTimeMillis() + System.identityHashCode(this))}. |
| */ |
| public void reSeed() { |
| delegate.reSeed(); |
| } |
| |
| /** |
| * Sets the PRNG algorithm for the underlying SecureRandom instance using |
| * the Security Provider API. The Security Provider API is defined in <a |
| * href = |
| * "http://java.sun.com/j2se/1.3/docs/guide/security/CryptoSpec.html#AppA"> |
| * Java Cryptography Architecture API Specification & Reference.</a> |
| * <p> |
| * <strong>USAGE NOTE:</strong> This method carries <i>significant</i> |
| * overhead and may take several seconds to execute. |
| * </p> |
| * |
| * @param algorithm |
| * the name of the PRNG algorithm |
| * @param provider |
| * the name of the provider |
| * @throws NoSuchAlgorithmException |
| * if the specified algorithm is not available |
| * @throws NoSuchProviderException |
| * if the specified provider is not installed |
| */ |
| public void setSecureAlgorithm(String algorithm, String provider) |
| throws NoSuchAlgorithmException, NoSuchProviderException { |
| delegate.setSecureAlgorithm(algorithm, provider); |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * <p> |
| * Uses a 2-cycle permutation shuffle. The shuffling process is described <a |
| * href="http://www.maths.abdn.ac.uk/~igc/tch/mx4002/notes/node83.html"> |
| * here</a>. |
| * </p> |
| */ |
| public int[] nextPermutation(int n, int k) |
| throws NotStrictlyPositiveException, NumberIsTooLargeException { |
| return delegate.nextPermutation(n, k); |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * <p> |
| * <strong>Algorithm Description</strong>: Uses a 2-cycle permutation |
| * shuffle to generate a random permutation of <code>c.size()</code> and |
| * then returns the elements whose indexes correspond to the elements of the |
| * generated permutation. This technique is described, and proven to |
| * generate random samples <a |
| * href="http://www.maths.abdn.ac.uk/~igc/tch/mx4002/notes/node83.html"> |
| * here</a> |
| * </p> |
| */ |
| public Object[] nextSample(Collection<?> c, int k) |
| throws NotStrictlyPositiveException, NumberIsTooLargeException { |
| return delegate.nextSample(c, k); |
| } |
| |
| /** |
| * Generate a random deviate from the given distribution using the |
| * <a href="http://en.wikipedia.org/wiki/Inverse_transform_sampling"> inversion method.</a> |
| * |
| * @param distribution Continuous distribution to generate a random value from |
| * @return a random value sampled from the given distribution |
| * @throws MathIllegalArgumentException if the underlynig distribution throws one |
| * @since 2.2 |
| * @deprecated use the distribution's sample() method |
| */ |
| @Deprecated |
| public double nextInversionDeviate(RealDistribution distribution) |
| throws MathIllegalArgumentException { |
| return distribution.inverseCumulativeProbability(nextUniform(0, 1)); |
| |
| } |
| |
| /** |
| * Generate a random deviate from the given distribution using the |
| * <a href="http://en.wikipedia.org/wiki/Inverse_transform_sampling"> inversion method.</a> |
| * |
| * @param distribution Integer distribution to generate a random value from |
| * @return a random value sampled from the given distribution |
| * @throws MathIllegalArgumentException if the underlynig distribution throws one |
| * @since 2.2 |
| * @deprecated use the distribution's sample() method |
| */ |
| @Deprecated |
| public int nextInversionDeviate(IntegerDistribution distribution) |
| throws MathIllegalArgumentException { |
| return distribution.inverseCumulativeProbability(nextUniform(0, 1)); |
| } |
| |
| } |
