001/*
002 * Licensed to the Apache Software Foundation (ASF) under one or more
003 * contributor license agreements.  See the NOTICE file distributed with
004 * this work for additional information regarding copyright ownership.
005 * The ASF licenses this file to You under the Apache License, Version 2.0
006 * (the "License"); you may not use this file except in compliance with
007 * the License.  You may obtain a copy of the License at
008 *
009 *      http://www.apache.org/licenses/LICENSE-2.0
010 *
011 * Unless required by applicable law or agreed to in writing, software
012 * distributed under the License is distributed on an "AS IS" BASIS,
013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
014 * See the License for the specific language governing permissions and
015 * limitations under the License.
016 */
017
018package org.apache.commons.math3.random;
019
020import java.io.Serializable;
021import java.security.NoSuchAlgorithmException;
022import java.security.NoSuchProviderException;
023import java.util.Collection;
024
025import org.apache.commons.math3.distribution.IntegerDistribution;
026import org.apache.commons.math3.distribution.RealDistribution;
027import org.apache.commons.math3.exception.NotANumberException;
028import org.apache.commons.math3.exception.NotFiniteNumberException;
029import org.apache.commons.math3.exception.NotPositiveException;
030import org.apache.commons.math3.exception.NotStrictlyPositiveException;
031import org.apache.commons.math3.exception.MathIllegalArgumentException;
032import org.apache.commons.math3.exception.NumberIsTooLargeException;
033import org.apache.commons.math3.exception.OutOfRangeException;
034
035/**
036 * Generates random deviates and other random data using a {@link RandomGenerator}
037 * instance to generate non-secure data and a {@link java.security.SecureRandom}
038 * instance to provide data for the <code>nextSecureXxx</code> methods. If no
039 * <code>RandomGenerator</code> is provided in the constructor, the default is
040 * to use a {@link Well19937c} generator. To plug in a different
041 * implementation, either implement <code>RandomGenerator</code> directly or
042 * extend {@link AbstractRandomGenerator}.
043 * <p>
044 * Supports reseeding the underlying pseudo-random number generator (PRNG). The
045 * <code>SecurityProvider</code> and <code>Algorithm</code> used by the
046 * <code>SecureRandom</code> instance can also be reset.
047 * </p>
048 * <p>
049 * For details on the default PRNGs, see {@link java.util.Random} and
050 * {@link java.security.SecureRandom}.
051 * </p>
052 * <p>
053 * <strong>Usage Notes</strong>:
054 * <ul>
055 * <li>
056 * Instance variables are used to maintain <code>RandomGenerator</code> and
057 * <code>SecureRandom</code> instances used in data generation. Therefore, to
058 * generate a random sequence of values or strings, you should use just
059 * <strong>one</strong> <code>RandomDataGenerator</code> instance repeatedly.</li>
060 * <li>
061 * The "secure" methods are *much* slower. These should be used only when a
062 * cryptographically secure random sequence is required. A secure random
063 * sequence is a sequence of pseudo-random values which, in addition to being
064 * well-dispersed (so no subsequence of values is an any more likely than other
065 * subsequence of the the same length), also has the additional property that
066 * knowledge of values generated up to any point in the sequence does not make
067 * it any easier to predict subsequent values.</li>
068 * <li>
069 * When a new <code>RandomDataGenerator</code> is created, the underlying random
070 * number generators are <strong>not</strong> initialized. If you do not
071 * explicitly seed the default non-secure generator, it is seeded with the
072 * current time in milliseconds plus the system identity hash code on first use.
073 * The same holds for the secure generator. If you provide a <code>RandomGenerator</code>
074 * to the constructor, however, this generator is not reseeded by the constructor
075 * nor is it reseeded on first use.</li>
076 * <li>
077 * The <code>reSeed</code> and <code>reSeedSecure</code> methods delegate to the
078 * corresponding methods on the underlying <code>RandomGenerator</code> and
079 * <code>SecureRandom</code> instances. Therefore, <code>reSeed(long)</code>
080 * fully resets the initial state of the non-secure random number generator (so
081 * that reseeding with a specific value always results in the same subsequent
082 * random sequence); whereas reSeedSecure(long) does <strong>not</strong>
083 * reinitialize the secure random number generator (so secure sequences started
084 * with calls to reseedSecure(long) won't be identical).</li>
085 * <li>
086 * This implementation is not synchronized. The underlying <code>RandomGenerator</code>
087 * or <code>SecureRandom</code> instances are not protected by synchronization and
088 * are not guaranteed to be thread-safe.  Therefore, if an instance of this class
089 * is concurrently utilized by multiple threads, it is the responsibility of
090 * client code to synchronize access to seeding and data generation methods.
091 * </li>
092 * </ul>
093 * </p>
094 * @deprecated to be removed in 4.0.  Use {@link RandomDataGenerator} instead
095 * @version $Id: RandomDataImpl.java 1499808 2013-07-04 17:00:42Z sebb $
096 */
097@Deprecated
098public class RandomDataImpl implements RandomData, Serializable {
099
100    /** Serializable version identifier */
101    private static final long serialVersionUID = -626730818244969716L;
102
103    /** RandomDataGenerator delegate */
104    private final RandomDataGenerator delegate;
105
106    /**
107     * Construct a RandomDataImpl, using a default random generator as the source
108     * of randomness.
109     *
110     * <p>The default generator is a {@link Well19937c} seeded
111     * with {@code System.currentTimeMillis() + System.identityHashCode(this))}.
112     * The generator is initialized and seeded on first use.</p>
113     */
114    public RandomDataImpl() {
115        delegate = new RandomDataGenerator();
116    }
117
118    /**
119     * Construct a RandomDataImpl using the supplied {@link RandomGenerator} as
120     * the source of (non-secure) random data.
121     *
122     * @param rand the source of (non-secure) random data
123     * (may be null, resulting in the default generator)
124     * @since 1.1
125     */
126    public RandomDataImpl(RandomGenerator rand) {
127        delegate = new RandomDataGenerator(rand);
128    }
129
130    /**
131     * @return the delegate object.
132     * @deprecated To be removed in 4.0.
133     */
134    @Deprecated
135    RandomDataGenerator getDelegate() {
136        return delegate;
137    }
138
139    /**
140     * {@inheritDoc}
141     * <p>
142     * <strong>Algorithm Description:</strong> hex strings are generated using a
143     * 2-step process.
144     * <ol>
145     * <li>{@code len / 2 + 1} binary bytes are generated using the underlying
146     * Random</li>
147     * <li>Each binary byte is translated into 2 hex digits</li>
148     * </ol>
149     * </p>
150     *
151     * @param len the desired string length.
152     * @return the random string.
153     * @throws NotStrictlyPositiveException if {@code len <= 0}.
154     */
155    public String nextHexString(int len) throws NotStrictlyPositiveException {
156        return delegate.nextHexString(len);
157    }
158
159    /** {@inheritDoc} */
160    public int nextInt(int lower, int upper) throws NumberIsTooLargeException {
161       return delegate.nextInt(lower, upper);
162    }
163
164    /** {@inheritDoc} */
165    public long nextLong(long lower, long upper) throws NumberIsTooLargeException {
166        return delegate.nextLong(lower, upper);
167    }
168
169    /**
170     * {@inheritDoc}
171     * <p>
172     * <strong>Algorithm Description:</strong> hex strings are generated in
173     * 40-byte segments using a 3-step process.
174     * <ol>
175     * <li>
176     * 20 random bytes are generated using the underlying
177     * <code>SecureRandom</code>.</li>
178     * <li>
179     * SHA-1 hash is applied to yield a 20-byte binary digest.</li>
180     * <li>
181     * Each byte of the binary digest is converted to 2 hex digits.</li>
182     * </ol>
183     * </p>
184     */
185    public String nextSecureHexString(int len) throws NotStrictlyPositiveException {
186        return delegate.nextSecureHexString(len);
187    }
188
189    /**  {@inheritDoc} */
190    public int nextSecureInt(int lower, int upper) throws NumberIsTooLargeException {
191        return delegate.nextSecureInt(lower, upper);
192    }
193
194    /** {@inheritDoc} */
195    public long nextSecureLong(long lower, long upper) throws NumberIsTooLargeException {
196        return delegate.nextSecureLong(lower,upper);
197    }
198
199    /**
200     * {@inheritDoc}
201     * <p>
202     * <strong>Algorithm Description</strong>:
203     * <ul><li> For small means, uses simulation of a Poisson process
204     * using Uniform deviates, as described
205     * <a href="http://irmi.epfl.ch/cmos/Pmmi/interactive/rng7.htm"> here.</a>
206     * The Poisson process (and hence value returned) is bounded by 1000 * mean.</li>
207     *
208     * <li> For large means, uses the rejection algorithm described in <br/>
209     * Devroye, Luc. (1981).<i>The Computer Generation of Poisson Random Variables</i>
210     * <strong>Computing</strong> vol. 26 pp. 197-207.</li></ul></p>
211     */
212    public long nextPoisson(double mean) throws NotStrictlyPositiveException {
213        return delegate.nextPoisson(mean);
214    }
215
216    /** {@inheritDoc} */
217    public double nextGaussian(double mu, double sigma) throws NotStrictlyPositiveException {
218        return delegate.nextGaussian(mu,sigma);
219    }
220
221    /**
222     * {@inheritDoc}
223     *
224     * <p>
225     * <strong>Algorithm Description</strong>: Uses the Algorithm SA (Ahrens)
226     * from p. 876 in:
227     * [1]: Ahrens, J. H. and Dieter, U. (1972). Computer methods for
228     * sampling from the exponential and normal distributions.
229     * Communications of the ACM, 15, 873-882.
230     * </p>
231     */
232    public double nextExponential(double mean) throws NotStrictlyPositiveException {
233        return delegate.nextExponential(mean);
234    }
235
236    /**
237     * {@inheritDoc}
238     *
239     * <p>
240     * <strong>Algorithm Description</strong>: scales the output of
241     * Random.nextDouble(), but rejects 0 values (i.e., will generate another
242     * random double if Random.nextDouble() returns 0). This is necessary to
243     * provide a symmetric output interval (both endpoints excluded).
244     * </p>
245     */
246    public double nextUniform(double lower, double upper)
247        throws NumberIsTooLargeException, NotFiniteNumberException, NotANumberException {
248        return delegate.nextUniform(lower, upper);
249    }
250
251    /**
252     * {@inheritDoc}
253     *
254     * <p>
255     * <strong>Algorithm Description</strong>: if the lower bound is excluded,
256     * scales the output of Random.nextDouble(), but rejects 0 values (i.e.,
257     * will generate another random double if Random.nextDouble() returns 0).
258     * This is necessary to provide a symmetric output interval (both
259     * endpoints excluded).
260     * </p>
261     * @since 3.0
262     */
263    public double nextUniform(double lower, double upper, boolean lowerInclusive)
264        throws NumberIsTooLargeException, NotFiniteNumberException, NotANumberException {
265        return delegate.nextUniform(lower, upper, lowerInclusive);
266    }
267
268    /**
269     * Generates a random value from the {@link org.apache.commons.math3.distribution.BetaDistribution Beta Distribution}.
270     * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
271     * to generate random values.
272     *
273     * @param alpha first distribution shape parameter
274     * @param beta second distribution shape parameter
275     * @return random value sampled from the beta(alpha, beta) distribution
276     * @since 2.2
277     */
278    public double nextBeta(double alpha, double beta) {
279        return delegate.nextBeta(alpha, beta);
280    }
281
282    /**
283     * Generates a random value from the {@link org.apache.commons.math3.distribution.BinomialDistribution Binomial Distribution}.
284     * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
285     * to generate random values.
286     *
287     * @param numberOfTrials number of trials of the Binomial distribution
288     * @param probabilityOfSuccess probability of success of the Binomial distribution
289     * @return random value sampled from the Binomial(numberOfTrials, probabilityOfSuccess) distribution
290     * @since 2.2
291     */
292    public int nextBinomial(int numberOfTrials, double probabilityOfSuccess) {
293        return delegate.nextBinomial(numberOfTrials, probabilityOfSuccess);
294    }
295
296    /**
297     * Generates a random value from the {@link org.apache.commons.math3.distribution.CauchyDistribution Cauchy Distribution}.
298     * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
299     * to generate random values.
300     *
301     * @param median the median of the Cauchy distribution
302     * @param scale the scale parameter of the Cauchy distribution
303     * @return random value sampled from the Cauchy(median, scale) distribution
304     * @since 2.2
305     */
306    public double nextCauchy(double median, double scale) {
307        return delegate.nextCauchy(median, scale);
308    }
309
310    /**
311     * Generates a random value from the {@link org.apache.commons.math3.distribution.ChiSquaredDistribution ChiSquare Distribution}.
312     * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
313     * to generate random values.
314     *
315     * @param df the degrees of freedom of the ChiSquare distribution
316     * @return random value sampled from the ChiSquare(df) distribution
317     * @since 2.2
318     */
319    public double nextChiSquare(double df) {
320       return delegate.nextChiSquare(df);
321    }
322
323    /**
324     * Generates a random value from the {@link org.apache.commons.math3.distribution.FDistribution F Distribution}.
325     * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
326     * to generate random values.
327     *
328     * @param numeratorDf the numerator degrees of freedom of the F distribution
329     * @param denominatorDf the denominator degrees of freedom of the F distribution
330     * @return random value sampled from the F(numeratorDf, denominatorDf) distribution
331     * @throws NotStrictlyPositiveException if
332     * {@code numeratorDf <= 0} or {@code denominatorDf <= 0}.
333     * @since 2.2
334     */
335    public double nextF(double numeratorDf, double denominatorDf) throws NotStrictlyPositiveException {
336        return delegate.nextF(numeratorDf, denominatorDf);
337    }
338
339    /**
340     * <p>Generates a random value from the
341     * {@link org.apache.commons.math3.distribution.GammaDistribution Gamma Distribution}.</p>
342     *
343     * <p>This implementation uses the following algorithms: </p>
344     *
345     * <p>For 0 < shape < 1: <br/>
346     * Ahrens, J. H. and Dieter, U., <i>Computer methods for
347     * sampling from gamma, beta, Poisson and binomial distributions.</i>
348     * Computing, 12, 223-246, 1974.</p>
349     *
350     * <p>For shape >= 1: <br/>
351     * Marsaglia and Tsang, <i>A Simple Method for Generating
352     * Gamma Variables.</i> ACM Transactions on Mathematical Software,
353     * Volume 26 Issue 3, September, 2000.</p>
354     *
355     * @param shape the median of the Gamma distribution
356     * @param scale the scale parameter of the Gamma distribution
357     * @return random value sampled from the Gamma(shape, scale) distribution
358     * @throws NotStrictlyPositiveException if {@code shape <= 0} or
359     * {@code scale <= 0}.
360     * @since 2.2
361     */
362    public double nextGamma(double shape, double scale) throws NotStrictlyPositiveException {
363        return delegate.nextGamma(shape, scale);
364    }
365
366    /**
367     * Generates a random value from the {@link org.apache.commons.math3.distribution.HypergeometricDistribution Hypergeometric Distribution}.
368     * This implementation uses {@link #nextInversionDeviate(IntegerDistribution) inversion}
369     * to generate random values.
370     *
371     * @param populationSize the population size of the Hypergeometric distribution
372     * @param numberOfSuccesses number of successes in the population of the Hypergeometric distribution
373     * @param sampleSize the sample size of the Hypergeometric distribution
374     * @return random value sampled from the Hypergeometric(numberOfSuccesses, sampleSize) distribution
375     * @throws NumberIsTooLargeException  if {@code numberOfSuccesses > populationSize},
376     * or {@code sampleSize > populationSize}.
377     * @throws NotStrictlyPositiveException if {@code populationSize <= 0}.
378     * @throws NotPositiveException  if {@code numberOfSuccesses < 0}.
379     * @since 2.2
380     */
381    public int nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize)
382        throws NotPositiveException, NotStrictlyPositiveException, NumberIsTooLargeException {
383        return delegate.nextHypergeometric(populationSize, numberOfSuccesses, sampleSize);
384    }
385
386    /**
387     * Generates a random value from the {@link org.apache.commons.math3.distribution.PascalDistribution Pascal Distribution}.
388     * This implementation uses {@link #nextInversionDeviate(IntegerDistribution) inversion}
389     * to generate random values.
390     *
391     * @param r the number of successes of the Pascal distribution
392     * @param p the probability of success of the Pascal distribution
393     * @return random value sampled from the Pascal(r, p) distribution
394     * @since 2.2
395     * @throws NotStrictlyPositiveException if the number of successes is not positive
396     * @throws OutOfRangeException if the probability of success is not in the
397     * range {@code [0, 1]}.
398     */
399    public int nextPascal(int r, double p)
400        throws NotStrictlyPositiveException, OutOfRangeException {
401        return delegate.nextPascal(r, p);
402    }
403
404    /**
405     * Generates a random value from the {@link org.apache.commons.math3.distribution.TDistribution T Distribution}.
406     * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
407     * to generate random values.
408     *
409     * @param df the degrees of freedom of the T distribution
410     * @return random value from the T(df) distribution
411     * @since 2.2
412     * @throws NotStrictlyPositiveException if {@code df <= 0}
413     */
414    public double nextT(double df) throws NotStrictlyPositiveException {
415        return delegate.nextT(df);
416    }
417
418    /**
419     * Generates a random value from the {@link org.apache.commons.math3.distribution.WeibullDistribution Weibull Distribution}.
420     * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
421     * to generate random values.
422     *
423     * @param shape the shape parameter of the Weibull distribution
424     * @param scale the scale parameter of the Weibull distribution
425     * @return random value sampled from the Weibull(shape, size) distribution
426     * @since 2.2
427     * @throws NotStrictlyPositiveException if {@code shape <= 0} or
428     * {@code scale <= 0}.
429     */
430    public double nextWeibull(double shape, double scale) throws NotStrictlyPositiveException {
431        return delegate.nextWeibull(shape, scale);
432    }
433
434    /**
435     * Generates a random value from the {@link org.apache.commons.math3.distribution.ZipfDistribution Zipf Distribution}.
436     * This implementation uses {@link #nextInversionDeviate(IntegerDistribution) inversion}
437     * to generate random values.
438     *
439     * @param numberOfElements the number of elements of the ZipfDistribution
440     * @param exponent the exponent of the ZipfDistribution
441     * @return random value sampled from the Zipf(numberOfElements, exponent) distribution
442     * @since 2.2
443     * @exception NotStrictlyPositiveException if {@code numberOfElements <= 0}
444     * or {@code exponent <= 0}.
445     */
446    public int nextZipf(int numberOfElements, double exponent) throws NotStrictlyPositiveException {
447        return delegate.nextZipf(numberOfElements, exponent);
448    }
449
450
451    /**
452     * Reseeds the random number generator with the supplied seed.
453     * <p>
454     * Will create and initialize if null.
455     * </p>
456     *
457     * @param seed
458     *            the seed value to use
459     */
460    public void reSeed(long seed) {
461        delegate.reSeed(seed);
462    }
463
464    /**
465     * Reseeds the secure random number generator with the current time in
466     * milliseconds.
467     * <p>
468     * Will create and initialize if null.
469     * </p>
470     */
471    public void reSeedSecure() {
472        delegate.reSeedSecure();
473    }
474
475    /**
476     * Reseeds the secure random number generator with the supplied seed.
477     * <p>
478     * Will create and initialize if null.
479     * </p>
480     *
481     * @param seed
482     *            the seed value to use
483     */
484    public void reSeedSecure(long seed) {
485        delegate.reSeedSecure(seed);
486    }
487
488    /**
489     * Reseeds the random number generator with
490     * {@code System.currentTimeMillis() + System.identityHashCode(this))}.
491     */
492    public void reSeed() {
493        delegate.reSeed();
494    }
495
496    /**
497     * Sets the PRNG algorithm for the underlying SecureRandom instance using
498     * the Security Provider API. The Security Provider API is defined in <a
499     * href =
500     * "http://java.sun.com/j2se/1.3/docs/guide/security/CryptoSpec.html#AppA">
501     * Java Cryptography Architecture API Specification & Reference.</a>
502     * <p>
503     * <strong>USAGE NOTE:</strong> This method carries <i>significant</i>
504     * overhead and may take several seconds to execute.
505     * </p>
506     *
507     * @param algorithm
508     *            the name of the PRNG algorithm
509     * @param provider
510     *            the name of the provider
511     * @throws NoSuchAlgorithmException
512     *             if the specified algorithm is not available
513     * @throws NoSuchProviderException
514     *             if the specified provider is not installed
515     */
516    public void setSecureAlgorithm(String algorithm, String provider)
517            throws NoSuchAlgorithmException, NoSuchProviderException {
518       delegate.setSecureAlgorithm(algorithm, provider);
519    }
520
521    /**
522     * {@inheritDoc}
523     *
524     * <p>
525     * Uses a 2-cycle permutation shuffle. The shuffling process is described <a
526     * href="http://www.maths.abdn.ac.uk/~igc/tch/mx4002/notes/node83.html">
527     * here</a>.
528     * </p>
529     */
530    public int[] nextPermutation(int n, int k)
531        throws NotStrictlyPositiveException, NumberIsTooLargeException {
532        return delegate.nextPermutation(n, k);
533    }
534
535    /**
536     * {@inheritDoc}
537     *
538     * <p>
539     * <strong>Algorithm Description</strong>: Uses a 2-cycle permutation
540     * shuffle to generate a random permutation of <code>c.size()</code> and
541     * then returns the elements whose indexes correspond to the elements of the
542     * generated permutation. This technique is described, and proven to
543     * generate random samples <a
544     * href="http://www.maths.abdn.ac.uk/~igc/tch/mx4002/notes/node83.html">
545     * here</a>
546     * </p>
547     */
548    public Object[] nextSample(Collection<?> c, int k)
549        throws NotStrictlyPositiveException, NumberIsTooLargeException {
550        return delegate.nextSample(c, k);
551    }
552
553    /**
554     * Generate a random deviate from the given distribution using the
555     * <a href="http://en.wikipedia.org/wiki/Inverse_transform_sampling"> inversion method.</a>
556     *
557     * @param distribution Continuous distribution to generate a random value from
558     * @return a random value sampled from the given distribution
559     * @throws MathIllegalArgumentException if the underlynig distribution throws one
560     * @since 2.2
561     * @deprecated use the distribution's sample() method
562     */
563    @Deprecated
564    public double nextInversionDeviate(RealDistribution distribution)
565        throws MathIllegalArgumentException {
566        return distribution.inverseCumulativeProbability(nextUniform(0, 1));
567
568    }
569
570    /**
571     * Generate a random deviate from the given distribution using the
572     * <a href="http://en.wikipedia.org/wiki/Inverse_transform_sampling"> inversion method.</a>
573     *
574     * @param distribution Integer distribution to generate a random value from
575     * @return a random value sampled from the given distribution
576     * @throws MathIllegalArgumentException if the underlynig distribution throws one
577     * @since 2.2
578     * @deprecated use the distribution's sample() method
579     */
580    @Deprecated
581    public int nextInversionDeviate(IntegerDistribution distribution)
582        throws MathIllegalArgumentException {
583        return distribution.inverseCumulativeProbability(nextUniform(0, 1));
584    }
585
586}