/*
 * 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.
 */

/*
 * Portions Copyright (c) Microsoft Corporation
 */

package com.azure.cosmos.implementation.apachecommons.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 com.azure.cosmos.implementation.apachecommons.math.distribution.BetaDistribution;
import com.azure.cosmos.implementation.apachecommons.math.distribution.BinomialDistribution;
import com.azure.cosmos.implementation.apachecommons.math.distribution.CauchyDistribution;
import com.azure.cosmos.implementation.apachecommons.math.distribution.ChiSquaredDistribution;
import com.azure.cosmos.implementation.apachecommons.math.distribution.ExponentialDistribution;
import com.azure.cosmos.implementation.apachecommons.math.distribution.FDistribution;
import com.azure.cosmos.implementation.apachecommons.math.distribution.GammaDistribution;
import com.azure.cosmos.implementation.apachecommons.math.distribution.HypergeometricDistribution;
import com.azure.cosmos.implementation.apachecommons.math.distribution.PascalDistribution;
import com.azure.cosmos.implementation.apachecommons.math.distribution.PoissonDistribution;
import com.azure.cosmos.implementation.apachecommons.math.distribution.TDistribution;
import com.azure.cosmos.implementation.apachecommons.math.distribution.WeibullDistribution;
import com.azure.cosmos.implementation.apachecommons.math.distribution.ZipfDistribution;
import com.azure.cosmos.implementation.apachecommons.math.distribution.UniformIntegerDistribution;
import com.azure.cosmos.implementation.apachecommons.math.exception.MathInternalError;
import com.azure.cosmos.implementation.apachecommons.math.exception.NotANumberException;
import com.azure.cosmos.implementation.apachecommons.math.exception.NotFiniteNumberException;
import com.azure.cosmos.implementation.apachecommons.math.exception.NotPositiveException;
import com.azure.cosmos.implementation.apachecommons.math.exception.NotStrictlyPositiveException;
import com.azure.cosmos.implementation.apachecommons.math.exception.NumberIsTooLargeException;
import com.azure.cosmos.implementation.apachecommons.math.exception.OutOfRangeException;
import com.azure.cosmos.implementation.apachecommons.math.exception.util.LocalizedFormats;
import com.azure.cosmos.implementation.apachecommons.math.util.MathArrays;

/**
 * Implements the {@link RandomData} interface using a {@link RandomGenerator}
 * instance to generate non-secure data and a {@link 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 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 com.azure.cosmos.implementation.apachecommons.math.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;
    }
}
