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1   /*
2    * Licensed to the Apache Software Foundation (ASF) under one or more
3    * contributor license agreements.  See the NOTICE file distributed with
4    * this work for additional information regarding copyright ownership.
5    * The ASF licenses this file to You under the Apache License, Version 2.0
6    * (the "License"); you may not use this file except in compliance with
7    * the License.  You may obtain a copy of the License at
8    *
9    *      http://www.apache.org/licenses/LICENSE-2.0
10   *
11   * Unless required by applicable law or agreed to in writing, software
12   * distributed under the License is distributed on an "AS IS" BASIS,
13   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14   * See the License for the specific language governing permissions and
15   * limitations under the License.
16   */
17  
18  package org.apache.commons.math3.random;
19  
20  import java.io.Serializable;
21  import java.security.NoSuchAlgorithmException;
22  import java.security.NoSuchProviderException;
23  import java.util.Collection;
24  
25  import org.apache.commons.math3.distribution.IntegerDistribution;
26  import org.apache.commons.math3.distribution.RealDistribution;
27  import org.apache.commons.math3.exception.NotANumberException;
28  import org.apache.commons.math3.exception.NotFiniteNumberException;
29  import org.apache.commons.math3.exception.NotPositiveException;
30  import org.apache.commons.math3.exception.NotStrictlyPositiveException;
31  import org.apache.commons.math3.exception.MathIllegalArgumentException;
32  import org.apache.commons.math3.exception.NumberIsTooLargeException;
33  import org.apache.commons.math3.exception.OutOfRangeException;
34  
35  /**
36   * Generates random deviates and other random data using a {@link RandomGenerator}
37   * instance to generate non-secure data and a {@link java.security.SecureRandom}
38   * instance to provide data for the <code>nextSecureXxx</code> methods. If no
39   * <code>RandomGenerator</code> is provided in the constructor, the default is
40   * to use a {@link Well19937c} generator. To plug in a different
41   * implementation, either implement <code>RandomGenerator</code> directly or
42   * extend {@link AbstractRandomGenerator}.
43   * <p>
44   * Supports reseeding the underlying pseudo-random number generator (PRNG). The
45   * <code>SecurityProvider</code> and <code>Algorithm</code> used by the
46   * <code>SecureRandom</code> instance can also be reset.
47   * </p>
48   * <p>
49   * For details on the default PRNGs, see {@link java.util.Random} and
50   * {@link java.security.SecureRandom}.
51   * </p>
52   * <p>
53   * <strong>Usage Notes</strong>:
54   * <ul>
55   * <li>
56   * Instance variables are used to maintain <code>RandomGenerator</code> and
57   * <code>SecureRandom</code> instances used in data generation. Therefore, to
58   * generate a random sequence of values or strings, you should use just
59   * <strong>one</strong> <code>RandomDataGenerator</code> instance repeatedly.</li>
60   * <li>
61   * The "secure" methods are *much* slower. These should be used only when a
62   * cryptographically secure random sequence is required. A secure random
63   * sequence is a sequence of pseudo-random values which, in addition to being
64   * well-dispersed (so no subsequence of values is an any more likely than other
65   * subsequence of the the same length), also has the additional property that
66   * knowledge of values generated up to any point in the sequence does not make
67   * it any easier to predict subsequent values.</li>
68   * <li>
69   * When a new <code>RandomDataGenerator</code> is created, the underlying random
70   * number generators are <strong>not</strong> initialized. If you do not
71   * explicitly seed the default non-secure generator, it is seeded with the
72   * current time in milliseconds plus the system identity hash code on first use.
73   * The same holds for the secure generator. If you provide a <code>RandomGenerator</code>
74   * to the constructor, however, this generator is not reseeded by the constructor
75   * nor is it reseeded on first use.</li>
76   * <li>
77   * The <code>reSeed</code> and <code>reSeedSecure</code> methods delegate to the
78   * corresponding methods on the underlying <code>RandomGenerator</code> and
79   * <code>SecureRandom</code> instances. Therefore, <code>reSeed(long)</code>
80   * fully resets the initial state of the non-secure random number generator (so
81   * that reseeding with a specific value always results in the same subsequent
82   * random sequence); whereas reSeedSecure(long) does <strong>not</strong>
83   * reinitialize the secure random number generator (so secure sequences started
84   * with calls to reseedSecure(long) won't be identical).</li>
85   * <li>
86   * This implementation is not synchronized. The underlying <code>RandomGenerator</code>
87   * or <code>SecureRandom</code> instances are not protected by synchronization and
88   * are not guaranteed to be thread-safe.  Therefore, if an instance of this class
89   * is concurrently utilized by multiple threads, it is the responsibility of
90   * client code to synchronize access to seeding and data generation methods.
91   * </li>
92   * </ul>
93   * </p>
94   * @deprecated to be removed in 4.0.  Use {@link RandomDataGenerator} instead
95   */
96  @Deprecated
97  public class RandomDataImpl implements RandomData, Serializable {
98  
99      /** Serializable version identifier */
100     private static final long serialVersionUID = -626730818244969716L;
101 
102     /** RandomDataGenerator delegate */
103     private final RandomDataGenerator delegate;
104 
105     /**
106      * Construct a RandomDataImpl, using a default random generator as the source
107      * of randomness.
108      *
109      * <p>The default generator is a {@link Well19937c} seeded
110      * with {@code System.currentTimeMillis() + System.identityHashCode(this))}.
111      * The generator is initialized and seeded on first use.</p>
112      */
113     public RandomDataImpl() {
114         delegate = new RandomDataGenerator();
115     }
116 
117     /**
118      * Construct a RandomDataImpl using the supplied {@link RandomGenerator} as
119      * the source of (non-secure) random data.
120      *
121      * @param rand the source of (non-secure) random data
122      * (may be null, resulting in the default generator)
123      * @since 1.1
124      */
125     public RandomDataImpl(RandomGenerator rand) {
126         delegate = new RandomDataGenerator(rand);
127     }
128 
129     /**
130      * @return the delegate object.
131      * @deprecated To be removed in 4.0.
132      */
133     @Deprecated
134     RandomDataGenerator getDelegate() {
135         return delegate;
136     }
137 
138     /**
139      * {@inheritDoc}
140      * <p>
141      * <strong>Algorithm Description:</strong> hex strings are generated using a
142      * 2-step process.
143      * <ol>
144      * <li>{@code len / 2 + 1} binary bytes are generated using the underlying
145      * Random</li>
146      * <li>Each binary byte is translated into 2 hex digits</li>
147      * </ol>
148      * </p>
149      *
150      * @param len the desired string length.
151      * @return the random string.
152      * @throws NotStrictlyPositiveException if {@code len <= 0}.
153      */
154     public String nextHexString(int len) throws NotStrictlyPositiveException {
155         return delegate.nextHexString(len);
156     }
157 
158     /** {@inheritDoc} */
159     public int nextInt(int lower, int upper) throws NumberIsTooLargeException {
160        return delegate.nextInt(lower, upper);
161     }
162 
163     /** {@inheritDoc} */
164     public long nextLong(long lower, long upper) throws NumberIsTooLargeException {
165         return delegate.nextLong(lower, upper);
166     }
167 
168     /**
169      * {@inheritDoc}
170      * <p>
171      * <strong>Algorithm Description:</strong> hex strings are generated in
172      * 40-byte segments using a 3-step process.
173      * <ol>
174      * <li>
175      * 20 random bytes are generated using the underlying
176      * <code>SecureRandom</code>.</li>
177      * <li>
178      * SHA-1 hash is applied to yield a 20-byte binary digest.</li>
179      * <li>
180      * Each byte of the binary digest is converted to 2 hex digits.</li>
181      * </ol>
182      * </p>
183      */
184     public String nextSecureHexString(int len) throws NotStrictlyPositiveException {
185         return delegate.nextSecureHexString(len);
186     }
187 
188     /**  {@inheritDoc} */
189     public int nextSecureInt(int lower, int upper) throws NumberIsTooLargeException {
190         return delegate.nextSecureInt(lower, upper);
191     }
192 
193     /** {@inheritDoc} */
194     public long nextSecureLong(long lower, long upper) throws NumberIsTooLargeException {
195         return delegate.nextSecureLong(lower,upper);
196     }
197 
198     /**
199      * {@inheritDoc}
200      * <p>
201      * <strong>Algorithm Description</strong>:
202      * <ul><li> For small means, uses simulation of a Poisson process
203      * using Uniform deviates, as described
204      * <a href="http://irmi.epfl.ch/cmos/Pmmi/interactive/rng7.htm"> here.</a>
205      * The Poisson process (and hence value returned) is bounded by 1000 * mean.</li>
206      *
207      * <li> For large means, uses the rejection algorithm described in <br/>
208      * Devroye, Luc. (1981).<i>The Computer Generation of Poisson Random Variables</i>
209      * <strong>Computing</strong> vol. 26 pp. 197-207.</li></ul></p>
210      */
211     public long nextPoisson(double mean) throws NotStrictlyPositiveException {
212         return delegate.nextPoisson(mean);
213     }
214 
215     /** {@inheritDoc} */
216     public double nextGaussian(double mu, double sigma) throws NotStrictlyPositiveException {
217         return delegate.nextGaussian(mu,sigma);
218     }
219 
220     /**
221      * {@inheritDoc}
222      *
223      * <p>
224      * <strong>Algorithm Description</strong>: Uses the Algorithm SA (Ahrens)
225      * from p. 876 in:
226      * [1]: Ahrens, J. H. and Dieter, U. (1972). Computer methods for
227      * sampling from the exponential and normal distributions.
228      * Communications of the ACM, 15, 873-882.
229      * </p>
230      */
231     public double nextExponential(double mean) throws NotStrictlyPositiveException {
232         return delegate.nextExponential(mean);
233     }
234 
235     /**
236      * {@inheritDoc}
237      *
238      * <p>
239      * <strong>Algorithm Description</strong>: scales the output of
240      * Random.nextDouble(), but rejects 0 values (i.e., will generate another
241      * random double if Random.nextDouble() returns 0). This is necessary to
242      * provide a symmetric output interval (both endpoints excluded).
243      * </p>
244      */
245     public double nextUniform(double lower, double upper)
246         throws NumberIsTooLargeException, NotFiniteNumberException, NotANumberException {
247         return delegate.nextUniform(lower, upper);
248     }
249 
250     /**
251      * {@inheritDoc}
252      *
253      * <p>
254      * <strong>Algorithm Description</strong>: if the lower bound is excluded,
255      * scales the output of Random.nextDouble(), but rejects 0 values (i.e.,
256      * will generate another random double if Random.nextDouble() returns 0).
257      * This is necessary to provide a symmetric output interval (both
258      * endpoints excluded).
259      * </p>
260      * @since 3.0
261      */
262     public double nextUniform(double lower, double upper, boolean lowerInclusive)
263         throws NumberIsTooLargeException, NotFiniteNumberException, NotANumberException {
264         return delegate.nextUniform(lower, upper, lowerInclusive);
265     }
266 
267     /**
268      * Generates a random value from the {@link org.apache.commons.math3.distribution.BetaDistribution Beta Distribution}.
269      * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
270      * to generate random values.
271      *
272      * @param alpha first distribution shape parameter
273      * @param beta second distribution shape parameter
274      * @return random value sampled from the beta(alpha, beta) distribution
275      * @since 2.2
276      */
277     public double nextBeta(double alpha, double beta) {
278         return delegate.nextBeta(alpha, beta);
279     }
280 
281     /**
282      * Generates a random value from the {@link org.apache.commons.math3.distribution.BinomialDistribution Binomial Distribution}.
283      * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
284      * to generate random values.
285      *
286      * @param numberOfTrials number of trials of the Binomial distribution
287      * @param probabilityOfSuccess probability of success of the Binomial distribution
288      * @return random value sampled from the Binomial(numberOfTrials, probabilityOfSuccess) distribution
289      * @since 2.2
290      */
291     public int nextBinomial(int numberOfTrials, double probabilityOfSuccess) {
292         return delegate.nextBinomial(numberOfTrials, probabilityOfSuccess);
293     }
294 
295     /**
296      * Generates a random value from the {@link org.apache.commons.math3.distribution.CauchyDistribution Cauchy Distribution}.
297      * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
298      * to generate random values.
299      *
300      * @param median the median of the Cauchy distribution
301      * @param scale the scale parameter of the Cauchy distribution
302      * @return random value sampled from the Cauchy(median, scale) distribution
303      * @since 2.2
304      */
305     public double nextCauchy(double median, double scale) {
306         return delegate.nextCauchy(median, scale);
307     }
308 
309     /**
310      * Generates a random value from the {@link org.apache.commons.math3.distribution.ChiSquaredDistribution ChiSquare Distribution}.
311      * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
312      * to generate random values.
313      *
314      * @param df the degrees of freedom of the ChiSquare distribution
315      * @return random value sampled from the ChiSquare(df) distribution
316      * @since 2.2
317      */
318     public double nextChiSquare(double df) {
319        return delegate.nextChiSquare(df);
320     }
321 
322     /**
323      * Generates a random value from the {@link org.apache.commons.math3.distribution.FDistribution F Distribution}.
324      * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
325      * to generate random values.
326      *
327      * @param numeratorDf the numerator degrees of freedom of the F distribution
328      * @param denominatorDf the denominator degrees of freedom of the F distribution
329      * @return random value sampled from the F(numeratorDf, denominatorDf) distribution
330      * @throws NotStrictlyPositiveException if
331      * {@code numeratorDf <= 0} or {@code denominatorDf <= 0}.
332      * @since 2.2
333      */
334     public double nextF(double numeratorDf, double denominatorDf) throws NotStrictlyPositiveException {
335         return delegate.nextF(numeratorDf, denominatorDf);
336     }
337 
338     /**
339      * <p>Generates a random value from the
340      * {@link org.apache.commons.math3.distribution.GammaDistribution Gamma Distribution}.</p>
341      *
342      * <p>This implementation uses the following algorithms: </p>
343      *
344      * <p>For 0 < shape < 1: <br/>
345      * Ahrens, J. H. and Dieter, U., <i>Computer methods for
346      * sampling from gamma, beta, Poisson and binomial distributions.</i>
347      * Computing, 12, 223-246, 1974.</p>
348      *
349      * <p>For shape >= 1: <br/>
350      * Marsaglia and Tsang, <i>A Simple Method for Generating
351      * Gamma Variables.</i> ACM Transactions on Mathematical Software,
352      * Volume 26 Issue 3, September, 2000.</p>
353      *
354      * @param shape the median of the Gamma distribution
355      * @param scale the scale parameter of the Gamma distribution
356      * @return random value sampled from the Gamma(shape, scale) distribution
357      * @throws NotStrictlyPositiveException if {@code shape <= 0} or
358      * {@code scale <= 0}.
359      * @since 2.2
360      */
361     public double nextGamma(double shape, double scale) throws NotStrictlyPositiveException {
362         return delegate.nextGamma(shape, scale);
363     }
364 
365     /**
366      * Generates a random value from the {@link org.apache.commons.math3.distribution.HypergeometricDistribution Hypergeometric Distribution}.
367      * This implementation uses {@link #nextInversionDeviate(IntegerDistribution) inversion}
368      * to generate random values.
369      *
370      * @param populationSize the population size of the Hypergeometric distribution
371      * @param numberOfSuccesses number of successes in the population of the Hypergeometric distribution
372      * @param sampleSize the sample size of the Hypergeometric distribution
373      * @return random value sampled from the Hypergeometric(numberOfSuccesses, sampleSize) distribution
374      * @throws NumberIsTooLargeException  if {@code numberOfSuccesses > populationSize},
375      * or {@code sampleSize > populationSize}.
376      * @throws NotStrictlyPositiveException if {@code populationSize <= 0}.
377      * @throws NotPositiveException  if {@code numberOfSuccesses < 0}.
378      * @since 2.2
379      */
380     public int nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize)
381         throws NotPositiveException, NotStrictlyPositiveException, NumberIsTooLargeException {
382         return delegate.nextHypergeometric(populationSize, numberOfSuccesses, sampleSize);
383     }
384 
385     /**
386      * Generates a random value from the {@link org.apache.commons.math3.distribution.PascalDistribution Pascal Distribution}.
387      * This implementation uses {@link #nextInversionDeviate(IntegerDistribution) inversion}
388      * to generate random values.
389      *
390      * @param r the number of successes of the Pascal distribution
391      * @param p the probability of success of the Pascal distribution
392      * @return random value sampled from the Pascal(r, p) distribution
393      * @since 2.2
394      * @throws NotStrictlyPositiveException if the number of successes is not positive
395      * @throws OutOfRangeException if the probability of success is not in the
396      * range {@code [0, 1]}.
397      */
398     public int nextPascal(int r, double p)
399         throws NotStrictlyPositiveException, OutOfRangeException {
400         return delegate.nextPascal(r, p);
401     }
402 
403     /**
404      * Generates a random value from the {@link org.apache.commons.math3.distribution.TDistribution T Distribution}.
405      * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
406      * to generate random values.
407      *
408      * @param df the degrees of freedom of the T distribution
409      * @return random value from the T(df) distribution
410      * @since 2.2
411      * @throws NotStrictlyPositiveException if {@code df <= 0}
412      */
413     public double nextT(double df) throws NotStrictlyPositiveException {
414         return delegate.nextT(df);
415     }
416 
417     /**
418      * Generates a random value from the {@link org.apache.commons.math3.distribution.WeibullDistribution Weibull Distribution}.
419      * This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
420      * to generate random values.
421      *
422      * @param shape the shape parameter of the Weibull distribution
423      * @param scale the scale parameter of the Weibull distribution
424      * @return random value sampled from the Weibull(shape, size) distribution
425      * @since 2.2
426      * @throws NotStrictlyPositiveException if {@code shape <= 0} or
427      * {@code scale <= 0}.
428      */
429     public double nextWeibull(double shape, double scale) throws NotStrictlyPositiveException {
430         return delegate.nextWeibull(shape, scale);
431     }
432 
433     /**
434      * Generates a random value from the {@link org.apache.commons.math3.distribution.ZipfDistribution Zipf Distribution}.
435      * This implementation uses {@link #nextInversionDeviate(IntegerDistribution) inversion}
436      * to generate random values.
437      *
438      * @param numberOfElements the number of elements of the ZipfDistribution
439      * @param exponent the exponent of the ZipfDistribution
440      * @return random value sampled from the Zipf(numberOfElements, exponent) distribution
441      * @since 2.2
442      * @exception NotStrictlyPositiveException if {@code numberOfElements <= 0}
443      * or {@code exponent <= 0}.
444      */
445     public int nextZipf(int numberOfElements, double exponent) throws NotStrictlyPositiveException {
446         return delegate.nextZipf(numberOfElements, exponent);
447     }
448 
449 
450     /**
451      * Reseeds the random number generator with the supplied seed.
452      * <p>
453      * Will create and initialize if null.
454      * </p>
455      *
456      * @param seed
457      *            the seed value to use
458      */
459     public void reSeed(long seed) {
460         delegate.reSeed(seed);
461     }
462 
463     /**
464      * Reseeds the secure random number generator with the current time in
465      * milliseconds.
466      * <p>
467      * Will create and initialize if null.
468      * </p>
469      */
470     public void reSeedSecure() {
471         delegate.reSeedSecure();
472     }
473 
474     /**
475      * Reseeds the secure random number generator with the supplied seed.
476      * <p>
477      * Will create and initialize if null.
478      * </p>
479      *
480      * @param seed
481      *            the seed value to use
482      */
483     public void reSeedSecure(long seed) {
484         delegate.reSeedSecure(seed);
485     }
486 
487     /**
488      * Reseeds the random number generator with
489      * {@code System.currentTimeMillis() + System.identityHashCode(this))}.
490      */
491     public void reSeed() {
492         delegate.reSeed();
493     }
494 
495     /**
496      * Sets the PRNG algorithm for the underlying SecureRandom instance using
497      * the Security Provider API. The Security Provider API is defined in <a
498      * href =
499      * "http://java.sun.com/j2se/1.3/docs/guide/security/CryptoSpec.html#AppA">
500      * Java Cryptography Architecture API Specification & Reference.</a>
501      * <p>
502      * <strong>USAGE NOTE:</strong> This method carries <i>significant</i>
503      * overhead and may take several seconds to execute.
504      * </p>
505      *
506      * @param algorithm
507      *            the name of the PRNG algorithm
508      * @param provider
509      *            the name of the provider
510      * @throws NoSuchAlgorithmException
511      *             if the specified algorithm is not available
512      * @throws NoSuchProviderException
513      *             if the specified provider is not installed
514      */
515     public void setSecureAlgorithm(String algorithm, String provider)
516             throws NoSuchAlgorithmException, NoSuchProviderException {
517        delegate.setSecureAlgorithm(algorithm, provider);
518     }
519 
520     /**
521      * {@inheritDoc}
522      *
523      * <p>
524      * Uses a 2-cycle permutation shuffle. The shuffling process is described <a
525      * href="http://www.maths.abdn.ac.uk/~igc/tch/mx4002/notes/node83.html">
526      * here</a>.
527      * </p>
528      */
529     public int[] nextPermutation(int n, int k)
530         throws NotStrictlyPositiveException, NumberIsTooLargeException {
531         return delegate.nextPermutation(n, k);
532     }
533 
534     /**
535      * {@inheritDoc}
536      *
537      * <p>
538      * <strong>Algorithm Description</strong>: Uses a 2-cycle permutation
539      * shuffle to generate a random permutation of <code>c.size()</code> and
540      * then returns the elements whose indexes correspond to the elements of the
541      * generated permutation. This technique is described, and proven to
542      * generate random samples <a
543      * href="http://www.maths.abdn.ac.uk/~igc/tch/mx4002/notes/node83.html">
544      * here</a>
545      * </p>
546      */
547     public Object[] nextSample(Collection<?> c, int k)
548         throws NotStrictlyPositiveException, NumberIsTooLargeException {
549         return delegate.nextSample(c, k);
550     }
551 
552     /**
553      * Generate a random deviate from the given distribution using the
554      * <a href="http://en.wikipedia.org/wiki/Inverse_transform_sampling"> inversion method.</a>
555      *
556      * @param distribution Continuous distribution to generate a random value from
557      * @return a random value sampled from the given distribution
558      * @throws MathIllegalArgumentException if the underlynig distribution throws one
559      * @since 2.2
560      * @deprecated use the distribution's sample() method
561      */
562     @Deprecated
563     public double nextInversionDeviate(RealDistribution distribution)
564         throws MathIllegalArgumentException {
565         return distribution.inverseCumulativeProbability(nextUniform(0, 1));
566 
567     }
568 
569     /**
570      * Generate a random deviate from the given distribution using the
571      * <a href="http://en.wikipedia.org/wiki/Inverse_transform_sampling"> inversion method.</a>
572      *
573      * @param distribution Integer distribution to generate a random value from
574      * @return a random value sampled from the given distribution
575      * @throws MathIllegalArgumentException if the underlynig distribution throws one
576      * @since 2.2
577      * @deprecated use the distribution's sample() method
578      */
579     @Deprecated
580     public int nextInversionDeviate(IntegerDistribution distribution)
581         throws MathIllegalArgumentException {
582         return distribution.inverseCumulativeProbability(nextUniform(0, 1));
583     }
584 
585 }