| /* |
| * 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; |
| } |
| |
| } |
