/*
* 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;
}
}