/*
 * 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 org.apache.commons.math3.distribution.IntegerDistribution;
import org.apache.commons.math3.distribution.RealDistribution;
import org.apache.commons.math3.exception.MathIllegalArgumentException;
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 java.io.Serializable;
import java.security.NoSuchAlgorithmException;
import java.security.NoSuchProviderException;
import java.util.Collection;

/**
 * Generates random deviates and other random data 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>For details on the default PRNGs, see {@link java.util.Random} and {@link
 * java.security.SecureRandom}.
 *
 * <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>RandomDataGenerator
 *       </code> 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>When a new <code>RandomDataGenerator</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>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>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.
 * </ul>
 *
 * @deprecated to be removed in 4.0. Use {@link RandomDataGenerator} instead
 */
@Deprecated
public class RandomDataImpl implements RandomData, Serializable {

    /** Serializable version identifier */
    private static final long serialVersionUID = -626730818244969716L;

    /** RandomDataGenerator delegate */
    private final RandomDataGenerator delegate;

    /**
     * Construct a RandomDataImpl, 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.
     */
    public RandomDataImpl() {
        delegate = new RandomDataGenerator();
    }

    /**
     * Construct a RandomDataImpl using the supplied {@link RandomGenerator} as the source of
     * (non-secure) random data.
     *
     * @param rand the source of (non-secure) random data (may be null, resulting in the default
     *     generator)
     * @since 1.1
     */
    public RandomDataImpl(RandomGenerator rand) {
        delegate = new RandomDataGenerator(rand);
    }

    /**
     * @return the delegate object.
     * @deprecated To be removed in 4.0.
     */
    @Deprecated
    RandomDataGenerator getDelegate() {
        return delegate;
    }

    /**
     * {@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>Each binary byte is translated into 2 hex digits
     * </ol>
     *
     * @param len the desired string length.
     * @return the random string.
     * @throws NotStrictlyPositiveException if {@code len <= 0}.
     */
    public String nextHexString(int len) throws NotStrictlyPositiveException {
        return delegate.nextHexString(len);
    }

    /** {@inheritDoc} */
    public int nextInt(int lower, int upper) throws NumberIsTooLargeException {
        return delegate.nextInt(lower, upper);
    }

    /** {@inheritDoc} */
    public long nextLong(long lower, long upper) throws NumberIsTooLargeException {
        return delegate.nextLong(lower, upper);
    }

    /**
     * {@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>SHA-1 hash is applied to yield a 20-byte binary digest.
     *   <li>Each byte of the binary digest is converted to 2 hex digits.
     * </ol>
     */
    public String nextSecureHexString(int len) throws NotStrictlyPositiveException {
        return delegate.nextSecureHexString(len);
    }

    /** {@inheritDoc} */
    public int nextSecureInt(int lower, int upper) throws NumberIsTooLargeException {
        return delegate.nextSecureInt(lower, upper);
    }

    /** {@inheritDoc} */
    public long nextSecureLong(long lower, long upper) throws NumberIsTooLargeException {
        return delegate.nextSecureLong(lower, upper);
    }

    /**
     * {@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>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.
     * </ul>
     */
    public long nextPoisson(double mean) throws NotStrictlyPositiveException {
        return delegate.nextPoisson(mean);
    }

    /** {@inheritDoc} */
    public double nextGaussian(double mu, double sigma) throws NotStrictlyPositiveException {
        return delegate.nextGaussian(mu, sigma);
    }

    /**
     * {@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.
     */
    public double nextExponential(double mean) throws NotStrictlyPositiveException {
        return delegate.nextExponential(mean);
    }

    /**
     * {@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).
     */
    public double nextUniform(double lower, double upper)
            throws NumberIsTooLargeException, NotFiniteNumberException, NotANumberException {
        return delegate.nextUniform(lower, upper);
    }

    /**
     * {@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).
     *
     * @since 3.0
     */
    public double nextUniform(double lower, double upper, boolean lowerInclusive)
            throws NumberIsTooLargeException, NotFiniteNumberException, NotANumberException {
        return delegate.nextUniform(lower, upper, lowerInclusive);
    }

    /**
     * Generates a random value from the {@link
     * org.apache.commons.math3.distribution.BetaDistribution Beta Distribution}. This
     * implementation uses {@link #nextInversionDeviate(RealDistribution) inversion} to generate
     * random values.
     *
     * @param alpha first distribution shape parameter
     * @param beta second distribution shape parameter
     * @return random value sampled from the beta(alpha, beta) distribution
     * @since 2.2
     */
    public double nextBeta(double alpha, double beta) {
        return delegate.nextBeta(alpha, beta);
    }

    /**
     * Generates a random value from the {@link
     * org.apache.commons.math3.distribution.BinomialDistribution Binomial Distribution}. This
     * implementation uses {@link #nextInversionDeviate(RealDistribution) inversion} to generate
     * random values.
     *
     * @param numberOfTrials number of trials of the Binomial distribution
     * @param probabilityOfSuccess probability of success of the Binomial distribution
     * @return random value sampled from the Binomial(numberOfTrials, probabilityOfSuccess)
     *     distribution
     * @since 2.2
     */
    public int nextBinomial(int numberOfTrials, double probabilityOfSuccess) {
        return delegate.nextBinomial(numberOfTrials, probabilityOfSuccess);
    }

    /**
     * Generates a random value from the {@link
     * org.apache.commons.math3.distribution.CauchyDistribution Cauchy Distribution}. This
     * implementation uses {@link #nextInversionDeviate(RealDistribution) inversion} to generate
     * random values.
     *
     * @param median the median of the Cauchy distribution
     * @param scale the scale parameter of the Cauchy distribution
     * @return random value sampled from the Cauchy(median, scale) distribution
     * @since 2.2
     */
    public double nextCauchy(double median, double scale) {
        return delegate.nextCauchy(median, scale);
    }

    /**
     * Generates a random value from the {@link
     * org.apache.commons.math3.distribution.ChiSquaredDistribution ChiSquare Distribution}. This
     * implementation uses {@link #nextInversionDeviate(RealDistribution) inversion} to generate
     * random values.
     *
     * @param df the degrees of freedom of the ChiSquare distribution
     * @return random value sampled from the ChiSquare(df) distribution
     * @since 2.2
     */
    public double nextChiSquare(double df) {
        return delegate.nextChiSquare(df);
    }

    /**
     * Generates a random value from the {@link org.apache.commons.math3.distribution.FDistribution
     * F Distribution}. This implementation uses {@link #nextInversionDeviate(RealDistribution)
     * inversion} to generate random values.
     *
     * @param numeratorDf the numerator degrees of freedom of the F distribution
     * @param denominatorDf the denominator degrees of freedom of the F distribution
     * @return random value sampled from the F(numeratorDf, denominatorDf) distribution
     * @throws NotStrictlyPositiveException if {@code numeratorDf <= 0} or {@code denominatorDf <=
     *     0}.
     * @since 2.2
     */
    public double nextF(double numeratorDf, double denominatorDf)
            throws NotStrictlyPositiveException {
        return delegate.nextF(numeratorDf, denominatorDf);
    }

    /**
     * Generates a random value from the {@link
     * org.apache.commons.math3.distribution.GammaDistribution Gamma Distribution}.
     *
     * <p>This implementation uses the following algorithms:
     *
     * <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>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.
     *
     * @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}.
     * @since 2.2
     */
    public double nextGamma(double shape, double scale) throws NotStrictlyPositiveException {
        return delegate.nextGamma(shape, scale);
    }

    /**
     * Generates a random value from the {@link
     * org.apache.commons.math3.distribution.HypergeometricDistribution Hypergeometric
     * Distribution}. This implementation uses {@link #nextInversionDeviate(IntegerDistribution)
     * inversion} to generate random values.
     *
     * @param populationSize the population size of the Hypergeometric distribution
     * @param numberOfSuccesses number of successes in the population of the Hypergeometric
     *     distribution
     * @param sampleSize the sample size of the Hypergeometric distribution
     * @return random value sampled from the Hypergeometric(numberOfSuccesses, sampleSize)
     *     distribution
     * @throws NumberIsTooLargeException if {@code numberOfSuccesses > populationSize}, or {@code
     *     sampleSize > populationSize}.
     * @throws NotStrictlyPositiveException if {@code populationSize <= 0}.
     * @throws NotPositiveException if {@code numberOfSuccesses < 0}.
     * @since 2.2
     */
    public int nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize)
            throws NotPositiveException, NotStrictlyPositiveException, NumberIsTooLargeException {
        return delegate.nextHypergeometric(populationSize, numberOfSuccesses, sampleSize);
    }

    /**
     * Generates a random value from the {@link
     * org.apache.commons.math3.distribution.PascalDistribution Pascal Distribution}. This
     * implementation uses {@link #nextInversionDeviate(IntegerDistribution) inversion} to generate
     * random values.
     *
     * @param r the number of successes of the Pascal distribution
     * @param p the probability of success of the Pascal distribution
     * @return random value sampled from the Pascal(r, p) distribution
     * @since 2.2
     * @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 delegate.nextPascal(r, p);
    }

    /**
     * Generates a random value from the {@link org.apache.commons.math3.distribution.TDistribution
     * T Distribution}. This implementation uses {@link #nextInversionDeviate(RealDistribution)
     * inversion} to generate random values.
     *
     * @param df the degrees of freedom of the T distribution
     * @return random value from the T(df) distribution
     * @since 2.2
     * @throws NotStrictlyPositiveException if {@code df <= 0}
     */
    public double nextT(double df) throws NotStrictlyPositiveException {
        return delegate.nextT(df);
    }

    /**
     * Generates a random value from the {@link
     * org.apache.commons.math3.distribution.WeibullDistribution Weibull Distribution}. This
     * implementation uses {@link #nextInversionDeviate(RealDistribution) inversion} to generate
     * random values.
     *
     * @param shape the shape parameter of the Weibull distribution
     * @param scale the scale parameter of the Weibull distribution
     * @return random value sampled from the Weibull(shape, size) distribution
     * @since 2.2
     * @throws NotStrictlyPositiveException if {@code shape <= 0} or {@code scale <= 0}.
     */
    public double nextWeibull(double shape, double scale) throws NotStrictlyPositiveException {
        return delegate.nextWeibull(shape, scale);
    }

    /**
     * Generates a random value from the {@link
     * org.apache.commons.math3.distribution.ZipfDistribution Zipf Distribution}. This
     * implementation uses {@link #nextInversionDeviate(IntegerDistribution) inversion} to generate
     * random values.
     *
     * @param numberOfElements the number of elements of the ZipfDistribution
     * @param exponent the exponent of the ZipfDistribution
     * @return random value sampled from the Zipf(numberOfElements, exponent) distribution
     * @since 2.2
     * @exception NotStrictlyPositiveException if {@code numberOfElements <= 0} or {@code exponent
     *     <= 0}.
     */
    public int nextZipf(int numberOfElements, double exponent) throws NotStrictlyPositiveException {
        return delegate.nextZipf(numberOfElements, exponent);
    }

    /**
     * Reseeds the random number generator with the supplied seed.
     *
     * <p>Will create and initialize if null.
     *
     * @param seed the seed value to use
     */
    public void reSeed(long seed) {
        delegate.reSeed(seed);
    }

    /**
     * Reseeds the secure random number generator with the current time in milliseconds.
     *
     * <p>Will create and initialize if null.
     */
    public void reSeedSecure() {
        delegate.reSeedSecure();
    }

    /**
     * Reseeds the secure random number generator with the supplied seed.
     *
     * <p>Will create and initialize if null.
     *
     * @param seed the seed value to use
     */
    public void reSeedSecure(long seed) {
        delegate.reSeedSecure(seed);
    }

    /**
     * Reseeds the random number generator with {@code System.currentTimeMillis() +
     * System.identityHashCode(this))}.
     */
    public void reSeed() {
        delegate.reSeed();
    }

    /**
     * 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.
     *
     * @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 {
        delegate.setSecureAlgorithm(algorithm, provider);
    }

    /**
     * {@inheritDoc}
     *
     * <p>Uses a 2-cycle permutation shuffle. The shuffling process is described <a
     * href="http://www.maths.abdn.ac.uk/~igc/tch/mx4002/notes/node83.html">here</a>.
     */
    public int[] nextPermutation(int n, int k)
            throws NotStrictlyPositiveException, NumberIsTooLargeException {
        return delegate.nextPermutation(n, k);
    }

    /**
     * {@inheritDoc}
     *
     * <p><strong>Algorithm Description</strong>: Uses a 2-cycle permutation shuffle to generate a
     * random permutation of <code>c.size()</code> and then returns the elements whose indexes
     * correspond to the elements of the generated permutation. This technique is described, and
     * proven to generate random samples <a
     * href="http://www.maths.abdn.ac.uk/~igc/tch/mx4002/notes/node83.html">here</a>
     */
    public Object[] nextSample(Collection<?> c, int k)
            throws NotStrictlyPositiveException, NumberIsTooLargeException {
        return delegate.nextSample(c, k);
    }

    /**
     * Generate a random deviate from the given distribution using the <a
     * href="http://en.wikipedia.org/wiki/Inverse_transform_sampling">inversion method.</a>
     *
     * @param distribution Continuous distribution to generate a random value from
     * @return a random value sampled from the given distribution
     * @throws MathIllegalArgumentException if the underlynig distribution throws one
     * @since 2.2
     * @deprecated use the distribution's sample() method
     */
    @Deprecated
    public double nextInversionDeviate(RealDistribution distribution)
            throws MathIllegalArgumentException {
        return distribution.inverseCumulativeProbability(nextUniform(0, 1));
    }

    /**
     * Generate a random deviate from the given distribution using the <a
     * href="http://en.wikipedia.org/wiki/Inverse_transform_sampling">inversion method.</a>
     *
     * @param distribution Integer distribution to generate a random value from
     * @return a random value sampled from the given distribution
     * @throws MathIllegalArgumentException if the underlynig distribution throws one
     * @since 2.2
     * @deprecated use the distribution's sample() method
     */
    @Deprecated
    public int nextInversionDeviate(IntegerDistribution distribution)
            throws MathIllegalArgumentException {
        return distribution.inverseCumulativeProbability(nextUniform(0, 1));
    }
}