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.math.random; import java.io.Serializable; import java.security.MessageDigest; import java.security.NoSuchAlgorithmException; import java.security.NoSuchProviderException; import java.security.SecureRandom; import java.util.Collection; import org.apache.commons.math.MathException; import org.apache.commons.math.distribution.BetaDistributionImpl; import org.apache.commons.math.distribution.BinomialDistributionImpl; import org.apache.commons.math.distribution.CauchyDistributionImpl; import org.apache.commons.math.distribution.ChiSquaredDistributionImpl; import org.apache.commons.math.distribution.ContinuousDistribution; import org.apache.commons.math.distribution.FDistributionImpl; import org.apache.commons.math.distribution.GammaDistributionImpl; import org.apache.commons.math.distribution.HypergeometricDistributionImpl; import org.apache.commons.math.distribution.IntegerDistribution; import org.apache.commons.math.distribution.PascalDistributionImpl; import org.apache.commons.math.distribution.TDistributionImpl; import org.apache.commons.math.distribution.WeibullDistributionImpl; import org.apache.commons.math.distribution.ZipfDistributionImpl; import org.apache.commons.math.exception.MathInternalError; import org.apache.commons.math.exception.NotStrictlyPositiveException; import org.apache.commons.math.exception.NumberIsTooLargeException; import org.apache.commons.math.exception.util.LocalizedFormats; import org.apache.commons.math.util.FastMath; import org.apache.commons.math.util.MathUtils; /** * Implements the {@link RandomData} interface 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 generator based on {@link java.util.Random}. 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>RandomDataImpl</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>RandomDataImpl</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 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. * </ul> * </p> * * @version $Revision: 1061496 $ $Date: 2011-01-20 21:32:16 +0100 (jeu. 20 janv. 2011) $ */ public class RandomDataImpl implements RandomData, Serializable { /** Serializable version identifier */ private static final long serialVersionUID = -626730818244969716L; /** underlying random number generator */ private RandomGenerator rand = null; /** underlying secure random number generator */ private SecureRandom secRand = null; /** * Construct a RandomDataImpl. */ public RandomDataImpl() { } /** * 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 * @since 1.1 */ public RandomDataImpl(RandomGenerator rand) { super(); this.rand = rand; } /** * {@inheritDoc} * <p> * <strong>Algorithm Description:</strong> hex strings are generated using a * 2-step process. * <ol> * <li> * 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) { if (len <= 0) { throw new NotStrictlyPositiveException(LocalizedFormats.LENGTH, len); } // Get a random number generator RandomGenerator ran = getRan(); // Initialize output buffer StringBuilder outBuffer = new StringBuilder(); // Get int(len/2)+1 random bytes byte[] randomBytes = new byte[(len / 2) + 1]; ran.nextBytes(randomBytes); // Convert each byte to 2 hex digits for (int i = 0; i < randomBytes.length; i++) { Integer c = Integer.valueOf(randomBytes[i]); /* * Add 128 to byte value to make interval 0-255 before doing hex * conversion. This guarantees <= 2 hex digits from toHexString() * toHexString would otherwise add 2^32 to negative arguments. */ String hex = Integer.toHexString(c.intValue() + 128); // Make sure we add 2 hex digits for each byte if (hex.length() == 1) { hex = "0" + hex; } outBuffer.append(hex); } return outBuffer.toString().substring(0, len); } /** * Generate a random int value uniformly distributed between * <code>lower</code> and <code>upper</code>, inclusive. * * @param lower * the lower bound. * @param upper * the upper bound. * @return the random integer. * @throws NumberIsTooLargeException if {@code lower >= upper}. */ public int nextInt(int lower, int upper) { if (lower >= upper) { throw new NumberIsTooLargeException(LocalizedFormats.LOWER_BOUND_NOT_BELOW_UPPER_BOUND, lower, upper, false); } double r = getRan().nextDouble(); return (int) ((r * upper) + ((1.0 - r) * lower) + r); } /** * Generate a random long value uniformly distributed between * <code>lower</code> and <code>upper</code>, inclusive. * * @param lower * the lower bound. * @param upper * the upper bound. * @return the random integer. * @throws NumberIsTooLargeException if {@code lower >= upper}. */ public long nextLong(long lower, long upper) { if (lower >= upper) { throw new NumberIsTooLargeException(LocalizedFormats.LOWER_BOUND_NOT_BELOW_UPPER_BOUND, lower, upper, false); } double r = getRan().nextDouble(); return (long) ((r * upper) + ((1.0 - r) * lower) + r); } /** * {@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> * * @param len * the length of the generated string * @return the random string * @throws NotStrictlyPositiveException if {@code len <= 0}. */ public String nextSecureHexString(int len) { if (len <= 0) { throw new NotStrictlyPositiveException(LocalizedFormats.LENGTH, len); } // Get SecureRandom and setup Digest provider SecureRandom secRan = getSecRan(); MessageDigest alg = null; try { alg = MessageDigest.getInstance("SHA-1"); } catch (NoSuchAlgorithmException ex) { // this should never happen throw new MathInternalError(ex); } alg.reset(); // Compute number of iterations required (40 bytes each) int numIter = (len / 40) + 1; StringBuilder outBuffer = new StringBuilder(); for (int iter = 1; iter < numIter + 1; iter++) { byte[] randomBytes = new byte[40]; secRan.nextBytes(randomBytes); alg.update(randomBytes); // Compute hash -- will create 20-byte binary hash byte hash[] = alg.digest(); // Loop over the hash, converting each byte to 2 hex digits for (int i = 0; i < hash.length; i++) { Integer c = Integer.valueOf(hash[i]); /* * Add 128 to byte value to make interval 0-255 This guarantees * <= 2 hex digits from toHexString() toHexString would * otherwise add 2^32 to negative arguments */ String hex = Integer.toHexString(c.intValue() + 128); // Keep strings uniform length -- guarantees 40 bytes if (hex.length() == 1) { hex = "0" + hex; } outBuffer.append(hex); } } return outBuffer.toString().substring(0, len); } /** * Generate a random int value uniformly distributed between * <code>lower</code> and <code>upper</code>, inclusive. This algorithm uses * a secure random number generator. * * @param lower * the lower bound. * @param upper * the upper bound. * @return the random integer. * @throws NumberIsTooLargeException if {@code lower >= upper}. */ public int nextSecureInt(int lower, int upper) { if (lower >= upper) { throw new NumberIsTooLargeException(LocalizedFormats.LOWER_BOUND_NOT_BELOW_UPPER_BOUND, lower, upper, false); } SecureRandom sec = getSecRan(); return lower + (int) (sec.nextDouble() * (upper - lower + 1)); } /** * Generate a random long value uniformly distributed between * <code>lower</code> and <code>upper</code>, inclusive. This algorithm uses * a secure random number generator. * * @param lower * the lower bound. * @param upper * the upper bound. * @return the random integer. * @throws NumberIsTooLargeException if {@code lower >= upper}. */ public long nextSecureLong(long lower, long upper) { if (lower >= upper) { throw new NumberIsTooLargeException(LocalizedFormats.LOWER_BOUND_NOT_BELOW_UPPER_BOUND, lower, upper, false); } SecureRandom sec = getSecRan(); return lower + (long) (sec.nextDouble() * (upper - lower + 1)); } /** * {@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> * * @param mean mean of the Poisson distribution. * @return the random Poisson value. * @throws NotStrictlyPositiveException if {@code mean <= 0}. */ public long nextPoisson(double mean) { if (mean <= 0) { throw new NotStrictlyPositiveException(LocalizedFormats.MEAN, mean); } final RandomGenerator generator = getRan(); final double pivot = 40.0d; if (mean < pivot) { double p = FastMath.exp(-mean); long n = 0; double r = 1.0d; double rnd = 1.0d; while (n < 1000 * mean) { rnd = generator.nextDouble(); r = r * rnd; if (r >= p) { n++; } else { return n; } } return n; } else { final double lambda = FastMath.floor(mean); final double lambdaFractional = mean - lambda; final double logLambda = FastMath.log(lambda); final double logLambdaFactorial = MathUtils.factorialLog((int) lambda); final long y2 = lambdaFractional < Double.MIN_VALUE ? 0 : nextPoisson(lambdaFractional); final double delta = FastMath.sqrt(lambda * FastMath.log(32 * lambda / FastMath.PI + 1)); final double halfDelta = delta / 2; final double twolpd = 2 * lambda + delta; final double a1 = FastMath.sqrt(FastMath.PI * twolpd) * FastMath.exp(1 / 8 * lambda); final double a2 = (twolpd / delta) * FastMath.exp(-delta * (1 + delta) / twolpd); final double aSum = a1 + a2 + 1; final double p1 = a1 / aSum; final double p2 = a2 / aSum; final double c1 = 1 / (8 * lambda); double x = 0; double y = 0; double v = 0; int a = 0; double t = 0; double qr = 0; double qa = 0; for (;;) { final double u = nextUniform(0.0, 1); if (u <= p1) { final double n = nextGaussian(0d, 1d); x = n * FastMath.sqrt(lambda + halfDelta) - 0.5d; if (x > delta || x < -lambda) { continue; } y = x < 0 ? FastMath.floor(x) : FastMath.ceil(x); final double e = nextExponential(1d); v = -e - (n * n / 2) + c1; } else { if (u > p1 + p2) { y = lambda; break; } else { x = delta + (twolpd / delta) * nextExponential(1d); y = FastMath.ceil(x); v = -nextExponential(1d) - delta * (x + 1) / twolpd; } } a = x < 0 ? 1 : 0; t = y * (y + 1) / (2 * lambda); if (v < -t && a == 0) { y = lambda + y; break; } qr = t * ((2 * y + 1) / (6 * lambda) - 1); qa = qr - (t * t) / (3 * (lambda + a * (y + 1))); if (v < qa) { y = lambda + y; break; } if (v > qr) { continue; } if (v < y * logLambda - MathUtils.factorialLog((int) (y + lambda)) + logLambdaFactorial) { y = lambda + y; break; } } return y2 + (long) y; } } /** * Generate a random value from a Normal (a.k.a. Gaussian) distribution with * the given mean, <code>mu</code> and the given standard deviation, * <code>sigma</code>. * * @param mu * the mean of the distribution * @param sigma * the standard deviation of the distribution * @return the random Normal value * @throws NotStrictlyPositiveException if {@code sigma <= 0}. */ public double nextGaussian(double mu, double sigma) { if (sigma <= 0) { throw new NotStrictlyPositiveException(LocalizedFormats.STANDARD_DEVIATION, sigma); } return sigma * getRan().nextGaussian() + mu; } /** * Returns a random value from an Exponential distribution with the given * mean. * <p> * <strong>Algorithm Description</strong>: Uses the <a * href="http://www.jesus.ox.ac.uk/~clifford/a5/chap1/node5.html"> Inversion * Method</a> to generate exponentially distributed random values from * uniform deviates. * </p> * * @param mean the mean of the distribution * @return the random Exponential value * @throws NotStrictlyPositiveException if {@code mean <= 0}. */ public double nextExponential(double mean) { if (mean <= 0.0) { throw new NotStrictlyPositiveException(LocalizedFormats.MEAN, mean); } final RandomGenerator generator = getRan(); double unif = generator.nextDouble(); while (unif == 0.0d) { unif = generator.nextDouble(); } return -mean * FastMath.log(unif); } /** * {@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> * * @param lower * the lower bound. * @param upper * the upper bound. * @return a uniformly distributed random value from the interval (lower, * upper) * @throws NumberIsTooLargeException if {@code lower >= upper}. */ public double nextUniform(double lower, double upper) { if (lower >= upper) { throw new NumberIsTooLargeException(LocalizedFormats.LOWER_BOUND_NOT_BELOW_UPPER_BOUND, lower, upper, false); } final RandomGenerator generator = getRan(); // ensure nextDouble() isn't 0.0 double u = generator.nextDouble(); while (u <= 0.0) { u = generator.nextDouble(); } return lower + u * (upper - lower); } /** * Generates a random value from the {@link BetaDistributionImpl Beta Distribution}. * This implementation uses {@link #nextInversionDeviate(ContinuousDistribution) 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 * @throws MathException if an error occurs generating the random value * @since 2.2 */ public double nextBeta(double alpha, double beta) throws MathException { return nextInversionDeviate(new BetaDistributionImpl(alpha, beta)); } /** * Generates a random value from the {@link BinomialDistributionImpl Binomial Distribution}. * This implementation uses {@link #nextInversionDeviate(ContinuousDistribution) 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 * @throws MathException if an error occurs generating the random value * @since 2.2 */ public int nextBinomial(int numberOfTrials, double probabilityOfSuccess) throws MathException { return nextInversionDeviate(new BinomialDistributionImpl(numberOfTrials, probabilityOfSuccess)); } /** * Generates a random value from the {@link CauchyDistributionImpl Cauchy Distribution}. * This implementation uses {@link #nextInversionDeviate(ContinuousDistribution) 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 * @throws MathException if an error occurs generating the random value * @since 2.2 */ public double nextCauchy(double median, double scale) throws MathException { return nextInversionDeviate(new CauchyDistributionImpl(median, scale)); } /** * Generates a random value from the {@link ChiSquaredDistributionImpl ChiSquare Distribution}. * This implementation uses {@link #nextInversionDeviate(ContinuousDistribution) inversion} * to generate random values. * * @param df the degrees of freedom of the ChiSquare distribution * @return random value sampled from the ChiSquare(df) distribution * @throws MathException if an error occurs generating the random value * @since 2.2 */ public double nextChiSquare(double df) throws MathException { return nextInversionDeviate(new ChiSquaredDistributionImpl(df)); } /** * Generates a random value from the {@link FDistributionImpl F Distribution}. * This implementation uses {@link #nextInversionDeviate(ContinuousDistribution) 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 MathException if an error occurs generating the random value * @since 2.2 */ public double nextF(double numeratorDf, double denominatorDf) throws MathException { return nextInversionDeviate(new FDistributionImpl(numeratorDf, denominatorDf)); } /** * Generates a random value from the {@link GammaDistributionImpl Gamma Distribution}. * This implementation uses {@link #nextInversionDeviate(ContinuousDistribution) inversion} * to generate random values. * * @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 MathException if an error occurs generating the random value * @since 2.2 */ public double nextGamma(double shape, double scale) throws MathException { return nextInversionDeviate(new GammaDistributionImpl(shape, scale)); } /** * Generates a random value from the {@link HypergeometricDistributionImpl 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 MathException if an error occurs generating the random value * @since 2.2 */ public int nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize) throws MathException { return nextInversionDeviate( new HypergeometricDistributionImpl(populationSize, numberOfSuccesses, sampleSize)); } /** * Generates a random value from the {@link PascalDistributionImpl 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 * @throws MathException if an error occurs generating the random value * @since 2.2 */ public int nextPascal(int r, double p) throws MathException { return nextInversionDeviate(new PascalDistributionImpl(r, p)); } /** * Generates a random value from the {@link TDistributionImpl T Distribution}. * This implementation uses {@link #nextInversionDeviate(ContinuousDistribution) inversion} * to generate random values. * * @param df the degrees of freedom of the T distribution * @return random value from the T(df) distribution * @throws MathException if an error occurs generating the random value * @since 2.2 */ public double nextT(double df) throws MathException { return nextInversionDeviate(new TDistributionImpl(df)); } /** * Generates a random value from the {@link WeibullDistributionImpl Weibull Distribution}. * This implementation uses {@link #nextInversionDeviate(ContinuousDistribution) 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 * @throws MathException if an error occurs generating the random value * @since 2.2 */ public double nextWeibull(double shape, double scale) throws MathException { return nextInversionDeviate(new WeibullDistributionImpl(shape, scale)); } /** * Generates a random value from the {@link ZipfDistributionImpl 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 * @throws MathException if an error occurs generating the random value * @since 2.2 */ public int nextZipf(int numberOfElements, double exponent) throws MathException { return nextInversionDeviate(new ZipfDistributionImpl(numberOfElements, exponent)); } /** * Returns the RandomGenerator used to generate non-secure random data. * <p> * Creates and initializes a default generator if null. * </p> * * @return the Random used to generate random data * @since 1.1 */ private RandomGenerator getRan() { if (rand == null) { rand = new JDKRandomGenerator(); rand.setSeed(System.currentTimeMillis()); } return rand; } /** * Returns the SecureRandom used to generate secure random data. * <p> * Creates and initializes if null. * </p> * * @return the SecureRandom used to generate secure random data */ private SecureRandom getSecRan() { if (secRand == null) { secRand = new SecureRandom(); secRand.setSeed(System.currentTimeMillis()); } return secRand; } /** * 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) { if (rand == null) { rand = new JDKRandomGenerator(); } rand.setSeed(seed); } /** * Reseeds the secure random number generator with the current time in * milliseconds. * <p> * Will create and initialize if null. * </p> */ public void reSeedSecure() { if (secRand == null) { secRand = new SecureRandom(); } secRand.setSeed(System.currentTimeMillis()); } /** * 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) { if (secRand == null) { secRand = new SecureRandom(); } secRand.setSeed(seed); } /** * Reseeds the random number generator with the current time in * milliseconds. */ public void reSeed() { if (rand == null) { rand = new JDKRandomGenerator(); } rand.setSeed(System.currentTimeMillis()); } /** * 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 { secRand = SecureRandom.getInstance(algorithm, provider); } /** * Generates an integer array of length <code>k</code> whose entries are * selected randomly, without repetition, from the integers * <code>0 through n-1</code> (inclusive). * <p> * Generated arrays represent permutations of <code>n</code> taken * <code>k</code> at a time. * </p> * <p> * <strong>Preconditions:</strong> * <ul> * <li> <code>k <= n</code></li> * <li> <code>n > 0</code></li> * </ul> * If the preconditions are not met, an IllegalArgumentException is thrown. * </p> * <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> * * @param n * domain of the permutation (must be positive) * @param k * size of the permutation (must satisfy 0 < k <= n). * @return the random permutation as an int array * @throws NumberIsTooLargeException if {@code k > n}. * @throws NotStrictlyPositiveException if {@code k <= 0}. */ public int[] nextPermutation(int n, int k) { if (k > n) { throw new NumberIsTooLargeException(LocalizedFormats.PERMUTATION_EXCEEDS_N, k, n, true); } if (k == 0) { throw new NotStrictlyPositiveException(LocalizedFormats.PERMUTATION_SIZE, k); } int[] index = getNatural(n); shuffle(index, n - k); int[] result = new int[k]; for (int i = 0; i < k; i++) { result[i] = index[n - i - 1]; } return result; } /** * Uses a 2-cycle permutation shuffle to generate a random permutation. * <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> * * @param c * Collection to sample from. * @param k * sample size. * @return the random sample. * @throws NumberIsTooLargeException if {@code k > c.size()}. * @throws NotStrictlyPositiveException if {@code k <= 0}. */ public Object[] nextSample(Collection<?> c, int k) { int len = c.size(); if (k > len) { throw new NumberIsTooLargeException(LocalizedFormats.SAMPLE_SIZE_EXCEEDS_COLLECTION_SIZE, k, len, true); } if (k <= 0) { throw new NotStrictlyPositiveException(LocalizedFormats.NUMBER_OF_SAMPLES, k); } Object[] objects = c.toArray(); int[] index = nextPermutation(len, k); Object[] result = new Object[k]; for (int i = 0; i < k; i++) { result[i] = objects[index[i]]; } return result; } /** * 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 MathException if an error occurs computing the inverse cumulative distribution function * @since 2.2 */ public double nextInversionDeviate(ContinuousDistribution distribution) throws MathException { 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 MathException if an error occurs computing the inverse cumulative distribution function * @since 2.2 */ public int nextInversionDeviate(IntegerDistribution distribution) throws MathException { final double target = nextUniform(0, 1); final int glb = distribution.inverseCumulativeProbability(target); if (distribution.cumulativeProbability(glb) == 1.0d) { // No mass above return glb; } else { return glb + 1; } } // ------------------------Private methods---------------------------------- /** * Uses a 2-cycle permutation shuffle to randomly re-order the last elements * of list. * * @param list * list to be shuffled * @param end * element past which shuffling begins */ private void shuffle(int[] list, int end) { int target = 0; for (int i = list.length - 1; i >= end; i--) { if (i == 0) { target = 0; } else { target = nextInt(0, i); } int temp = list[target]; list[target] = list[i]; list[i] = temp; } } /** * Returns an array representing n. * * @param n * the natural number to represent * @return array with entries = elements of n */ private int[] getNatural(int n) { int[] natural = new int[n]; for (int i = 0; i < n; i++) { natural[i] = i; } return natural; } }