home | career | drupal | java | mac | mysql | perl | scala | uml | unix  

Java example source code file (RandomDataGenerator.java)

This example Java source code file (RandomDataGenerator.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

hypergeometricdistribution, integer, nosuchalgorithmexception, notanumberexception, notfinitenumberexception, notstrictlypositiveexception, numberistoolargeexception, object, randomdatagenerator, randomgenerator, security, string, stringbuilder, uniformintegerdistribution, util, zipfdistribution

The RandomDataGenerator.java Java example source code

/*
 * 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 methods. If no
 * <code>RandomGenerator is provided in the constructor, the default is
 * to use a {@link Well19937c} generator. To plug in a different
 * implementation, either implement <code>RandomGenerator directly or
 * extend {@link AbstractRandomGenerator}.
 * <p>
 * Supports reseeding the underlying pseudo-random number generator (PRNG). The
 * <code>SecurityProvider and Algorithm used by the
 * <code>SecureRandom 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:
 * <ul>
 * <li>
 * Instance variables are used to maintain <code>RandomGenerator and
 * <code>SecureRandom instances used in data generation. Therefore, to
 * generate a random sequence of values or strings, you should use just
 * <strong>one RandomDataImpl instance repeatedly.
 * <li>
 * The "secure" methods are *much* slower. These should be used only when a
 * cryptographically secure random sequence is required. A secure random
 * sequence is a sequence of pseudo-random values which, in addition to being
 * well-dispersed (so no subsequence of values is an any more likely than other
 * subsequence of the the same length), also has the additional property that
 * knowledge of values generated up to any point in the sequence does not make
 * it any easier to predict subsequent values.</li>
 * <li>
 * When a new <code>RandomDataImpl is created, the underlying random
 * number generators are <strong>not 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
 * to the constructor, however, this generator is not reseeded by the constructor
 * nor is it reseeded on first use.</li>
 * <li>
 * The <code>reSeed and reSeedSecure methods delegate to the
 * corresponding methods on the underlying <code>RandomGenerator and
 * <code>SecureRandom instances. Therefore, reSeed(long)
 * 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
 * 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
 * or <code>SecureRandom 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: 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
     * </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: 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.
     * <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:
     * <ul>
  • 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. * The Poisson process (and hence value returned) is bounded by 1000 * mean.</li> * * <li> For large means, uses the rejection algorithm described in
    * Devroye, Luc. (1981).<i>The Computer Generation of Poisson Random Variables * <strong>Computing vol. 26 pp. 197-207.
  • * @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: 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>For 0 < shape < 1:
    * 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:
    * 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: 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: 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: This method carries significant * 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; } }

    Other Java examples (source code examples)

    Here is a short list of links related to this Java RandomDataGenerator.java source code file:



    my book on functional programming

     

    new blog posts

     

    Copyright 1998-2021 Alvin Alexander, alvinalexander.com
    All Rights Reserved.

    A percentage of advertising revenue from
    pages under the /java/jwarehouse URI on this website is
    paid back to open source projects.