mirror of https://github.com/jlizier/jidt
615 lines
25 KiB
Java
615 lines
25 KiB
Java
/*
|
|
* Java Information Dynamics Toolkit (JIDT)
|
|
* Copyright (C) 2017, Joseph T. Lizier
|
|
*
|
|
* This program is free software: you can redistribute it and/or modify
|
|
* it under the terms of the GNU General Public License as published by
|
|
* the Free Software Foundation, either version 3 of the License, or
|
|
* (at your option) any later version.
|
|
*
|
|
* This program is distributed in the hope that it will be useful,
|
|
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
* GNU General Public License for more details.
|
|
*
|
|
* You should have received a copy of the GNU General Public License
|
|
* along with this program. If not, see <http://www.gnu.org/licenses/>.
|
|
*/
|
|
|
|
/*
|
|
* This class was originally distributed as part of the Apache Commons
|
|
* Math3 library (3.6.1), under the Apache License Version 2.0, which is
|
|
* copied below. This Apache 2 software is now included as a derivative
|
|
* work in the GPLv3 licensed JIDT project, as per:
|
|
* http://www.apache.org/licenses/GPL-compatibility.html
|
|
*
|
|
* The original Apache source code has been modified as follows:
|
|
* -- We have modified package names to sit inside the JIDT structure.
|
|
*/
|
|
|
|
/*
|
|
* 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 infodynamics.utils.commonsmath3.random;
|
|
|
|
import java.io.Serializable;
|
|
import java.security.NoSuchAlgorithmException;
|
|
import java.security.NoSuchProviderException;
|
|
import java.util.Collection;
|
|
|
|
import infodynamics.utils.commonsmath3.distribution.IntegerDistribution;
|
|
import infodynamics.utils.commonsmath3.distribution.RealDistribution;
|
|
import infodynamics.utils.commonsmath3.exception.NotANumberException;
|
|
import infodynamics.utils.commonsmath3.exception.NotFiniteNumberException;
|
|
import infodynamics.utils.commonsmath3.exception.NotPositiveException;
|
|
import infodynamics.utils.commonsmath3.exception.NotStrictlyPositiveException;
|
|
import infodynamics.utils.commonsmath3.exception.MathIllegalArgumentException;
|
|
import infodynamics.utils.commonsmath3.exception.NumberIsTooLargeException;
|
|
import infodynamics.utils.commonsmath3.exception.OutOfRangeException;
|
|
|
|
/**
|
|
* 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>
|
|
* <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>RandomDataGenerator</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>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>
|
|
* <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>
|
|
* @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.</p>
|
|
*/
|
|
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>
|
|
* <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 {
|
|
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>
|
|
* <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>
|
|
*/
|
|
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>
|
|
*
|
|
* <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>
|
|
*/
|
|
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.
|
|
* </p>
|
|
*/
|
|
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).
|
|
* </p>
|
|
*/
|
|
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).
|
|
* </p>
|
|
* @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 infodynamics.utils.commonsmath3.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 infodynamics.utils.commonsmath3.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 infodynamics.utils.commonsmath3.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 infodynamics.utils.commonsmath3.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 infodynamics.utils.commonsmath3.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);
|
|
}
|
|
|
|
/**
|
|
* <p>Generates a random value from the
|
|
* {@link infodynamics.utils.commonsmath3.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}.
|
|
* @since 2.2
|
|
*/
|
|
public double nextGamma(double shape, double scale) throws NotStrictlyPositiveException {
|
|
return delegate.nextGamma(shape, scale);
|
|
}
|
|
|
|
/**
|
|
* Generates a random value from the {@link infodynamics.utils.commonsmath3.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 infodynamics.utils.commonsmath3.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 infodynamics.utils.commonsmath3.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 infodynamics.utils.commonsmath3.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 infodynamics.utils.commonsmath3.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.
|
|
* </p>
|
|
*
|
|
* @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.
|
|
* </p>
|
|
*/
|
|
public void reSeedSecure() {
|
|
delegate.reSeedSecure();
|
|
}
|
|
|
|
/**
|
|
* 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) {
|
|
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.
|
|
* </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 {
|
|
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>.
|
|
* </p>
|
|
*/
|
|
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>
|
|
* </p>
|
|
*/
|
|
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));
|
|
}
|
|
|
|
}
|