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Diffstat (limited to 'src/main/java/org/apache/commons/math3/random/RandomDataImpl.java')
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diff --git a/src/main/java/org/apache/commons/math3/random/RandomDataImpl.java b/src/main/java/org/apache/commons/math3/random/RandomDataImpl.java new file mode 100644 index 0000000..d5749e9 --- /dev/null +++ b/src/main/java/org/apache/commons/math3/random/RandomDataImpl.java @@ -0,0 +1,544 @@ +/* + * 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 org.apache.commons.math3.distribution.IntegerDistribution; +import org.apache.commons.math3.distribution.RealDistribution; +import org.apache.commons.math3.exception.MathIllegalArgumentException; +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.NumberIsTooLargeException; +import org.apache.commons.math3.exception.OutOfRangeException; + +import java.io.Serializable; +import java.security.NoSuchAlgorithmException; +import java.security.NoSuchProviderException; +import java.util.Collection; + +/** + * 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>For details on the default PRNGs, see {@link java.util.Random} and {@link + * java.security.SecureRandom}. + * + * <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>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>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>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>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. + * </ul> + * + * @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. + */ + 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>Each binary byte is translated into 2 hex digits + * </ol> + * + * @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>SHA-1 hash is applied to yield a 20-byte binary digest. + * <li>Each byte of the binary digest is converted to 2 hex digits. + * </ol> + */ + 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>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. + * </ul> + */ + 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. + */ + 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). + */ + 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). + * + * @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); + } + + /** + * Generates a random value from the {@link + * org.apache.commons.math3.distribution.GammaDistribution Gamma Distribution}. + * + * <p>This implementation uses the following algorithms: + * + * <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>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. + * + * @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. + * + * @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. + */ + public void reSeedSecure() { + delegate.reSeedSecure(); + } + + /** + * Reseeds the secure random number generator with the supplied seed. + * + * <p>Will create and initialize if null. + * + * @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. + * + * @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>. + */ + 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> + */ + 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)); + } +} |