| /* |
| * 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.MessageDigest; |
| import java.security.NoSuchAlgorithmException; |
| import java.security.NoSuchProviderException; |
| import java.security.SecureRandom; |
| import java.util.Collection; |
| |
| import org.apache.commons.math3.distribution.BetaDistribution; |
| import org.apache.commons.math3.distribution.BinomialDistribution; |
| import org.apache.commons.math3.distribution.CauchyDistribution; |
| import org.apache.commons.math3.distribution.ChiSquaredDistribution; |
| import org.apache.commons.math3.distribution.ExponentialDistribution; |
| import org.apache.commons.math3.distribution.FDistribution; |
| import org.apache.commons.math3.distribution.GammaDistribution; |
| import org.apache.commons.math3.distribution.HypergeometricDistribution; |
| import org.apache.commons.math3.distribution.PascalDistribution; |
| import org.apache.commons.math3.distribution.PoissonDistribution; |
| import org.apache.commons.math3.distribution.TDistribution; |
| import org.apache.commons.math3.distribution.WeibullDistribution; |
| import org.apache.commons.math3.distribution.ZipfDistribution; |
| import org.apache.commons.math3.distribution.UniformIntegerDistribution; |
| import org.apache.commons.math3.exception.MathInternalError; |
| 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 org.apache.commons.math3.exception.util.LocalizedFormats; |
| import org.apache.commons.math3.util.MathArrays; |
| |
| /** |
| * 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 {@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>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 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> |
| * @since 3.1 |
| */ |
| public class RandomDataGenerator 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 RandomGenerator secRand = null; |
| |
| /** |
| * Construct a RandomDataGenerator, 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 RandomDataGenerator() { |
| } |
| |
| /** |
| * Construct a RandomDataGenerator 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) |
| */ |
| public RandomDataGenerator(RandomGenerator rand) { |
| this.rand = rand; |
| } |
| |
| /** |
| * {@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 { |
| if (len <= 0) { |
| throw new NotStrictlyPositiveException(LocalizedFormats.LENGTH, len); |
| } |
| |
| // Get a random number generator |
| RandomGenerator ran = getRandomGenerator(); |
| |
| // 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); |
| } |
| |
| /** {@inheritDoc} */ |
| public int nextInt(final int lower, final int upper) throws NumberIsTooLargeException { |
| return new UniformIntegerDistribution(getRandomGenerator(), lower, upper).sample(); |
| } |
| |
| /** {@inheritDoc} */ |
| public long nextLong(final long lower, final long upper) throws NumberIsTooLargeException { |
| if (lower >= upper) { |
| throw new NumberIsTooLargeException(LocalizedFormats.LOWER_BOUND_NOT_BELOW_UPPER_BOUND, |
| lower, upper, false); |
| } |
| final long max = (upper - lower) + 1; |
| if (max <= 0) { |
| // the range is too wide to fit in a positive long (larger than 2^63); as it covers |
| // more than half the long range, we use directly a simple rejection method |
| final RandomGenerator rng = getRandomGenerator(); |
| while (true) { |
| final long r = rng.nextLong(); |
| if (r >= lower && r <= upper) { |
| return r; |
| } |
| } |
| } else if (max < Integer.MAX_VALUE){ |
| // we can shift the range and generate directly a positive int |
| return lower + getRandomGenerator().nextInt((int) max); |
| } else { |
| // we can shift the range and generate directly a positive long |
| return lower + nextLong(getRandomGenerator(), max); |
| } |
| } |
| |
| /** |
| * Returns a pseudorandom, uniformly distributed {@code long} value |
| * between 0 (inclusive) and the specified value (exclusive), drawn from |
| * this random number generator's sequence. |
| * |
| * @param rng random generator to use |
| * @param n the bound on the random number to be returned. Must be |
| * positive. |
| * @return a pseudorandom, uniformly distributed {@code long} |
| * value between 0 (inclusive) and n (exclusive). |
| * @throws IllegalArgumentException if n is not positive. |
| */ |
| private static long nextLong(final RandomGenerator rng, final long n) throws IllegalArgumentException { |
| if (n > 0) { |
| final byte[] byteArray = new byte[8]; |
| long bits; |
| long val; |
| do { |
| rng.nextBytes(byteArray); |
| bits = 0; |
| for (final byte b : byteArray) { |
| bits = (bits << 8) | (((long) b) & 0xffL); |
| } |
| bits &= 0x7fffffffffffffffL; |
| val = bits % n; |
| } while (bits - val + (n - 1) < 0); |
| return val; |
| } |
| throw new NotStrictlyPositiveException(n); |
| } |
| |
| /** |
| * {@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> |
| * @throws NotStrictlyPositiveException if {@code len <= 0} |
| */ |
| public String nextSecureHexString(int len) throws NotStrictlyPositiveException { |
| if (len <= 0) { |
| throw new NotStrictlyPositiveException(LocalizedFormats.LENGTH, len); |
| } |
| |
| // Get SecureRandom and setup Digest provider |
| final RandomGenerator 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); |
| } |
| |
| /** {@inheritDoc} */ |
| public int nextSecureInt(final int lower, final int upper) throws NumberIsTooLargeException { |
| return new UniformIntegerDistribution(getSecRan(), lower, upper).sample(); |
| } |
| |
| /** {@inheritDoc} */ |
| public long nextSecureLong(final long lower, final long upper) throws NumberIsTooLargeException { |
| if (lower >= upper) { |
| throw new NumberIsTooLargeException(LocalizedFormats.LOWER_BOUND_NOT_BELOW_UPPER_BOUND, |
| lower, upper, false); |
| } |
| final RandomGenerator rng = getSecRan(); |
| final long max = (upper - lower) + 1; |
| if (max <= 0) { |
| // the range is too wide to fit in a positive long (larger than 2^63); as it covers |
| // more than half the long range, we use directly a simple rejection method |
| while (true) { |
| final long r = rng.nextLong(); |
| if (r >= lower && r <= upper) { |
| return r; |
| } |
| } |
| } else if (max < Integer.MAX_VALUE){ |
| // we can shift the range and generate directly a positive int |
| return lower + rng.nextInt((int) max); |
| } else { |
| // we can shift the range and generate directly a positive long |
| return lower + nextLong(rng, max); |
| } |
| } |
| |
| /** |
| * {@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> |
| * @throws NotStrictlyPositiveException if {@code len <= 0} |
| */ |
| public long nextPoisson(double mean) throws NotStrictlyPositiveException { |
| return new PoissonDistribution(getRandomGenerator(), mean, |
| PoissonDistribution.DEFAULT_EPSILON, |
| PoissonDistribution.DEFAULT_MAX_ITERATIONS).sample(); |
| } |
| |
| /** {@inheritDoc} */ |
| public double nextGaussian(double mu, double sigma) throws NotStrictlyPositiveException { |
| if (sigma <= 0) { |
| throw new NotStrictlyPositiveException(LocalizedFormats.STANDARD_DEVIATION, sigma); |
| } |
| return sigma * getRandomGenerator().nextGaussian() + mu; |
| } |
| |
| /** |
| * {@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 new ExponentialDistribution(getRandomGenerator(), mean, |
| ExponentialDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY).sample(); |
| } |
| |
| /** |
| * <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}. |
| */ |
| public double nextGamma(double shape, double scale) throws NotStrictlyPositiveException { |
| return new GammaDistribution(getRandomGenerator(),shape, scale, |
| GammaDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY).sample(); |
| } |
| |
| /** |
| * Generates a random value from the {@link HypergeometricDistribution Hypergeometric Distribution}. |
| * |
| * @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}. |
| */ |
| public int nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize) throws NotPositiveException, NotStrictlyPositiveException, NumberIsTooLargeException { |
| return new HypergeometricDistribution(getRandomGenerator(),populationSize, |
| numberOfSuccesses, sampleSize).sample(); |
| } |
| |
| /** |
| * Generates a random value from the {@link PascalDistribution Pascal Distribution}. |
| * |
| * @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 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 new PascalDistribution(getRandomGenerator(), r, p).sample(); |
| } |
| |
| /** |
| * Generates a random value from the {@link TDistribution T Distribution}. |
| * |
| * @param df the degrees of freedom of the T distribution |
| * @return random value from the T(df) distribution |
| * @throws NotStrictlyPositiveException if {@code df <= 0} |
| */ |
| public double nextT(double df) throws NotStrictlyPositiveException { |
| return new TDistribution(getRandomGenerator(), df, |
| TDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY).sample(); |
| } |
| |
| /** |
| * Generates a random value from the {@link WeibullDistribution Weibull Distribution}. |
| * |
| * @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 NotStrictlyPositiveException if {@code shape <= 0} or |
| * {@code scale <= 0}. |
| */ |
| public double nextWeibull(double shape, double scale) throws NotStrictlyPositiveException { |
| return new WeibullDistribution(getRandomGenerator(), shape, scale, |
| WeibullDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY).sample(); |
| } |
| |
| /** |
| * Generates a random value from the {@link ZipfDistribution Zipf Distribution}. |
| * |
| * @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 |
| * @exception NotStrictlyPositiveException if {@code numberOfElements <= 0} |
| * or {@code exponent <= 0}. |
| */ |
| public int nextZipf(int numberOfElements, double exponent) throws NotStrictlyPositiveException { |
| return new ZipfDistribution(getRandomGenerator(), numberOfElements, exponent).sample(); |
| } |
| |
| /** |
| * Generates a random value from the {@link BetaDistribution Beta Distribution}. |
| * |
| * @param alpha first distribution shape parameter |
| * @param beta second distribution shape parameter |
| * @return random value sampled from the beta(alpha, beta) distribution |
| */ |
| public double nextBeta(double alpha, double beta) { |
| return new BetaDistribution(getRandomGenerator(), alpha, beta, |
| BetaDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY).sample(); |
| } |
| |
| /** |
| * Generates a random value from the {@link BinomialDistribution Binomial Distribution}. |
| * |
| * @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 |
| */ |
| public int nextBinomial(int numberOfTrials, double probabilityOfSuccess) { |
| return new BinomialDistribution(getRandomGenerator(), numberOfTrials, probabilityOfSuccess).sample(); |
| } |
| |
| /** |
| * Generates a random value from the {@link CauchyDistribution Cauchy Distribution}. |
| * |
| * @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 |
| */ |
| public double nextCauchy(double median, double scale) { |
| return new CauchyDistribution(getRandomGenerator(), median, scale, |
| CauchyDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY).sample(); |
| } |
| |
| /** |
| * Generates a random value from the {@link ChiSquaredDistribution ChiSquare Distribution}. |
| * |
| * @param df the degrees of freedom of the ChiSquare distribution |
| * @return random value sampled from the ChiSquare(df) distribution |
| */ |
| public double nextChiSquare(double df) { |
| return new ChiSquaredDistribution(getRandomGenerator(), df, |
| ChiSquaredDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY).sample(); |
| } |
| |
| /** |
| * Generates a random value from the {@link FDistribution F Distribution}. |
| * |
| * @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}. |
| */ |
| public double nextF(double numeratorDf, double denominatorDf) throws NotStrictlyPositiveException { |
| return new FDistribution(getRandomGenerator(), numeratorDf, denominatorDf, |
| FDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY).sample(); |
| } |
| |
| /** |
| * {@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> |
| * @throws NumberIsTooLargeException if {@code lower >= upper} |
| * @throws NotFiniteNumberException if one of the bounds is infinite |
| * @throws NotANumberException if one of the bounds is NaN |
| */ |
| public double nextUniform(double lower, double upper) |
| throws NumberIsTooLargeException, NotFiniteNumberException, NotANumberException { |
| return nextUniform(lower, upper, false); |
| } |
| |
| /** |
| * {@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> |
| * |
| * @throws NumberIsTooLargeException if {@code lower >= upper} |
| * @throws NotFiniteNumberException if one of the bounds is infinite |
| * @throws NotANumberException if one of the bounds is NaN |
| */ |
| public double nextUniform(double lower, double upper, boolean lowerInclusive) |
| throws NumberIsTooLargeException, NotFiniteNumberException, NotANumberException { |
| |
| if (lower >= upper) { |
| throw new NumberIsTooLargeException(LocalizedFormats.LOWER_BOUND_NOT_BELOW_UPPER_BOUND, |
| lower, upper, false); |
| } |
| |
| if (Double.isInfinite(lower)) { |
| throw new NotFiniteNumberException(LocalizedFormats.INFINITE_BOUND, lower); |
| } |
| if (Double.isInfinite(upper)) { |
| throw new NotFiniteNumberException(LocalizedFormats.INFINITE_BOUND, upper); |
| } |
| |
| if (Double.isNaN(lower) || Double.isNaN(upper)) { |
| throw new NotANumberException(); |
| } |
| |
| final RandomGenerator generator = getRandomGenerator(); |
| |
| // ensure nextDouble() isn't 0.0 |
| double u = generator.nextDouble(); |
| while (!lowerInclusive && u <= 0.0) { |
| u = generator.nextDouble(); |
| } |
| |
| return u * upper + (1.0 - u) * lower; |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * This method calls {@link MathArrays#shuffle(int[],RandomGenerator) |
| * MathArrays.shuffle} in order to create a random shuffle of the set |
| * of natural numbers {@code { 0, 1, ..., n - 1 }}. |
| * |
| * @throws NumberIsTooLargeException if {@code k > n}. |
| * @throws NotStrictlyPositiveException if {@code k <= 0}. |
| */ |
| public int[] nextPermutation(int n, int k) |
| throws NumberIsTooLargeException, NotStrictlyPositiveException { |
| 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 = MathArrays.natural(n); |
| MathArrays.shuffle(index, getRandomGenerator()); |
| |
| // Return a new array containing the first "k" entries of "index". |
| return MathArrays.copyOf(index, k); |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * This method calls {@link #nextPermutation(int,int) nextPermutation(c.size(), k)} |
| * in order to sample the collection. |
| */ |
| public Object[] nextSample(Collection<?> c, int k) throws NumberIsTooLargeException, NotStrictlyPositiveException { |
| |
| 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; |
| } |
| |
| |
| |
| /** |
| * 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) { |
| getRandomGenerator().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() { |
| getSecRan().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) { |
| getSecRan().setSeed(seed); |
| } |
| |
| /** |
| * Reseeds the random number generator with |
| * {@code System.currentTimeMillis() + System.identityHashCode(this))}. |
| */ |
| public void reSeed() { |
| getRandomGenerator().setSeed(System.currentTimeMillis() + System.identityHashCode(this)); |
| } |
| |
| /** |
| * 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 = RandomGeneratorFactory.createRandomGenerator(SecureRandom.getInstance(algorithm, provider)); |
| } |
| |
| /** |
| * Returns the RandomGenerator used to generate non-secure random data. |
| * <p> |
| * Creates and initializes a default generator if null. Uses a {@link Well19937c} |
| * generator with {@code System.currentTimeMillis() + System.identityHashCode(this))} |
| * as the default seed. |
| * </p> |
| * |
| * @return the Random used to generate random data |
| * @since 3.2 |
| */ |
| public RandomGenerator getRandomGenerator() { |
| if (rand == null) { |
| initRan(); |
| } |
| return rand; |
| } |
| |
| /** |
| * Sets the default generator to a {@link Well19937c} generator seeded with |
| * {@code System.currentTimeMillis() + System.identityHashCode(this))}. |
| */ |
| private void initRan() { |
| rand = new Well19937c(System.currentTimeMillis() + System.identityHashCode(this)); |
| } |
| |
| /** |
| * Returns the SecureRandom used to generate secure random data. |
| * <p> |
| * Creates and initializes if null. Uses |
| * {@code System.currentTimeMillis() + System.identityHashCode(this)} as the default seed. |
| * </p> |
| * |
| * @return the SecureRandom used to generate secure random data, wrapped in a |
| * {@link RandomGenerator}. |
| */ |
| private RandomGenerator getSecRan() { |
| if (secRand == null) { |
| secRand = RandomGeneratorFactory.createRandomGenerator(new SecureRandom()); |
| secRand.setSeed(System.currentTimeMillis() + System.identityHashCode(this)); |
| } |
| return secRand; |
| } |
| } |
