PDL::GSL::RNG(3pm)

1RNG(3)                User Contributed Perl Documentation               RNG(3)
2
3
4

NAME

6       PDL::GSL::RNG - PDL interface to RNG and randist routines in GSL
7

DESCRIPTION

9       This is an interface to the rng and randist packages present in the GNU
10       Scientific Library.
11

SYNOPSIS

13          use PDL;
14          use PDL::GSL::RNG;
15
16          $rng = PDL::GSL::RNG->new('taus');
17
18          $rng->set_seed(time());
19
20          $a=zeroes(5,5,5)
21
22          $rng->get_uniform($a); # inplace
23
24          $b=$rng->get_uniform(3,4,5); # creates new pdl
25

FUNCTIONS

27   new
28       The new method initializes a new instance of the RNG.
29
30       The available RNGs are:
31
32        coveyou cmrg fishman18 fishman20 fishman2x gfsr4 knuthran
33        knuthran2 knuthran2002 lecuyer21 minstd mrg mt19937 mt19937_1999
34        mt19937_1998 r250 ran0 ran1 ran2 ran3 rand rand48 random128_bsd
35        random128_glibc2 random128_libc5 random256_bsd random256_glibc2
36        random256_libc5 random32_bsd random32_glibc2 random32_libc5
37        random64_bsd random64_glibc2 random64_libc5 random8_bsd
38        random8_glibc2 random8_libc5 random_bsd random_glibc2
39        random_libc5 randu ranf ranlux ranlux389 ranlxd1 ranlxd2 ranlxs0
40        ranlxs1 ranlxs2 ranmar slatec taus taus2 taus113 transputer tt800
41        uni uni32 vax waterman14 zuf default
42
43       The last one (default) uses the environment variable GSL_RNG_TYPE.
44
45       Note that only a few of these rngs are recommended for general use.
46       Please check the GSL documentation for more information.
47
48       Usage:
49
50          $blessed_ref = PDL::GSL::RNG->new($RNG_name);
51
52       Example:
53
54          $rng = PDL::GSL::RNG->new('taus');
55
56   set_seed
57       Sets the RNG seed.
58
59       Usage:
60
61          $rng->set_seed($integer);
62          # or
63          $rng = PDL::GSL::RNG->new('taus')->set_seed($integer);
64
65       Example:
66
67          $rng->set_seed(666);
68
69   min
70       Return the minimum value generable by this RNG.
71
72       Usage:
73
74          $integer = $rng->min();
75
76       Example:
77
78          $min = $rng->min(); $max = $rng->max();
79
80   max
81       Return the maximum value generable by the RNG.
82
83       Usage:
84
85          $integer = $rng->max();
86
87       Example:
88
89          $min = $rng->min(); $max = $rng->max();
90
91   name
92       Returns the name of the RNG.
93
94       Usage:
95
96          $string = $rng->name();
97
98       Example:
99
100          $name = $rng->name();
101
102   get
103       This function creates a piddle with given dimensions or accept an
104       existing piddle and fills it. get() returns integer values between a
105       minimum and a maximum specific to every RNG.
106
107       Usage:
108
109          $piddle = $rng->get($list_of_integers)
110          $rng->get($piddle);
111
112       Example:
113
114          $a = zeroes 5,6;
115          $o = $rng->get(10,10); $rng->get($a);
116
117   get_int
118       This function creates a piddle with given dimensions or accept an
119       existing piddle and fills it. get_int() returns integer values between
120       0 and $max.
121
122       Usage:
123
124          $piddle = $rng->get($max, $list_of_integers)
125          $rng->get($max, $piddle);
126
127       Example:
128
129          $a = zeroes 5,6; $max=100;
130          $o = $rng->get(10,10); $rng->get($a);
131
132   get_uniform
133       This function creates a piddle with given dimensions or accept an
134       existing piddle and fills it. get_uniform() returns values 0<=x<1,
135
136       Usage:
137
138          $piddle = $rng->get_uniform($list_of_integers)
139          $rng->get_uniform($piddle);
140
141       Example:
142
143          $a = zeroes 5,6; $max=100;
144          $o = $rng->get_uniform(10,10); $rng->get_uniform($a);
145
146   get_uniform_pos
147       This function creates a piddle with given dimensions or accept an
148       existing piddle and fills it. get_uniform_pos() returns values 0<x<1,
149
150       Usage:
151
152          $piddle = $rng->get_uniform_pos($list_of_integers)
153          $rng->get_uniform_pos($piddle);
154
155       Example:
156
157          $a = zeroes 5,6;
158          $o = $rng->get_uniform_pos(10,10); $rng->get_uniform_pos($a);
159
160   ran_shuffle
161       Shuffles values in piddle
162
163       Usage:
164
165          $rng->ran_shuffle($piddle);
166
167   ran_shuffle_vec
168       Shuffles values in piddle
169
170       Usage:
171
172          $rng->ran_shuffle_vec(@vec);
173
174   ran_choose
175       Chooses values from $inpiddle to $outpiddle.
176
177       Usage:
178
179          $rng->ran_choose($inpiddle,$outpiddle);
180
181   ran_choose_vec
182       Chooses $n values from @vec.
183
184       Usage:
185
186          @chosen = $rng->ran_choose_vec($n,@vec);
187
188   ran_gaussian
189       Fills output piddle with random values from Gaussian distribution with
190       mean zero and standard deviation $sigma.
191
192       Usage:
193
194        $piddle = $rng->ran_gaussian($sigma,[list of integers = output piddle dims]);
195        $rng->ran_gaussian($sigma, $output_piddle);
196
197       Example:
198
199         $o = $rng->ran_gaussian($sigma,10,10);
200         $rng->ran_gaussian($sigma,$a);
201
202   ran_gaussian_var
203       This method is similar to ran_gaussian except that it takes the
204       parameters of the distribution as a piddle and returns a piddle of
205       equal dimensions.
206
207       Usage:
208
209          $piddle = $rng->ran_gaussian_var($sigma_piddle);
210          $rng->ran_gaussian_var($sigma_piddle, $output_piddle);
211
212       Example:
213
214          $sigma_pdl = rvals zeroes 11,11;
215          $o = $rng->ran_gaussian_var($sigma_pdl);
216
217   ran_additive_gaussian
218       Add Gaussian noise of given sigma to a piddle.
219
220       Usage:
221
222          $rng->ran_additive_gaussian($sigma,$piddle);
223
224       Example:
225
226          $rng->ran_additive_gaussian(1,$image);
227
228   ran_bivariate_gaussian
229       Generates $n bivariate gaussian random deviates.
230
231       Usage:
232
233          $piddle = $rng->ran_bivariate_gaussian($sigma_x,$sigma_y,$rho,$n);
234
235       Example:
236
237          $o = $rng->ran_bivariate_gaussian(1,2,0.5,1000);
238
239   ran_poisson
240       Fills output piddle by with random integer values from the Poisson
241       distribution with mean $mu.
242
243       Usage:
244
245          $piddle = $rng->ran_poisson($mu,[list of integers = output piddle dims]);
246          $rng->ran_poisson($mu,$output_piddle);
247
248   ran_poisson_var
249       Similar to ran_poisson except that it takes the distribution parameters
250       as a piddle and returns a piddle of equal dimensions.
251
252       Usage:
253
254          $piddle = $rng->ran_poisson_var($mu_piddle);
255
256   ran_additive_poisson
257       Add Poisson noise of given $mu to a $piddle.
258
259       Usage:
260
261          $rng->ran_additive_poisson($mu,$piddle);
262
263       Example:
264
265          $rng->ran_additive_poisson(1,$image);
266
267   ran_feed_poisson
268       This method simulates shot noise, taking the values of piddle as values
269       for $mu to be fed in the poissonian RNG.
270
271       Usage:
272
273          $rng->ran_feed_poisson($piddle);
274
275       Example:
276
277          $rng->ran_feed_poisson($image);
278
279   ran_bernoulli
280       Fills output piddle with random values 0 or 1, the result of a
281       Bernoulli trial with probability $p.
282
283       Usage:
284
285          $piddle = $rng->ran_bernoulli($p,[list of integers = output piddle dims]);
286          $rng->ran_bernoulli($p,$output_piddle);
287
288   ran_bernoulli_var
289       Similar to ran_bernoulli except that it takes the distribution
290       parameters as a piddle and returns a piddle of equal dimensions.
291
292       Usage:
293
294          $piddle = $rng->ran_bernoulli_var($p_piddle);
295
296   ran_beta
297       Fills output piddle with random variates from the beta distribution
298       with parameters $a and $b.
299
300       Usage:
301
302          $piddle = $rng->ran_beta($a,$b,[list of integers = output piddle dims]);
303          $rng->ran_beta($a,$b,$output_piddle);
304
305   ran_beta_var
306       Similar to ran_beta except that it takes the distribution parameters as
307       a piddle and returns a piddle of equal dimensions.
308
309       Usage:
310
311          $piddle = $rng->ran_beta_var($a_piddle, $b_piddle);
312
313   ran_binomial
314       Fills output piddle with random integer values from the binomial
315       distribution, the number of successes in $n independent trials with
316       probability $p.
317
318       Usage:
319
320          $piddle = $rng->ran_binomial($p,$n,[list of integers = output piddle dims]);
321          $rng->ran_binomial($p,$n,$output_piddle);
322
323   ran_binomial_var
324       Similar to ran_binomial except that it takes the distribution
325       parameters as a piddle and returns a piddle of equal dimensions.
326
327       Usage:
328
329          $piddle = $rng->ran_binomial_var($p_piddle, $n_piddle);
330
331   ran_cauchy
332       Fills output piddle with random variates from the Cauchy distribution
333       with scale parameter $a.
334
335       Usage:
336
337          $piddle = $rng->ran_cauchy($a,[list of integers = output piddle dims]);
338          $rng->ran_cauchy($a,$output_piddle);
339
340   ran_cauchy_var
341       Similar to ran_cauchy except that it takes the distribution parameters
342       as a piddle and returns a piddle of equal dimensions.
343
344       Usage:
345
346          $piddle = $rng->ran_cauchy_var($a_piddle);
347
348   ran_chisq
349       Fills output piddle with random variates from the chi-squared
350       distribution with $nu degrees of freedom.
351
352       Usage:
353
354          $piddle = $rng->ran_chisq($nu,[list of integers = output piddle dims]);
355          $rng->ran_chisq($nu,$output_piddle);
356
357   ran_chisq_var
358       Similar to ran_chisq except that it takes the distribution parameters
359       as a piddle and returns a piddle of equal dimensions.
360
361       Usage:
362
363          $piddle = $rng->ran_chisq_var($nu_piddle);
364
365   ran_exponential
366       Fills output piddle with random variates from the exponential
367       distribution with mean $mu.
368
369       Usage:
370
371          $piddle = $rng->ran_exponential($mu,[list of integers = output piddle dims]);
372          $rng->ran_exponential($mu,$output_piddle);
373
374   ran_exponential_var
375       Similar to ran_exponential except that it takes the distribution
376       parameters as a piddle and returns a piddle of equal dimensions.
377
378       Usage:
379
380          $piddle = $rng->ran_exponential_var($mu_piddle);
381
382   ran_exppow
383       Fills output piddle with random variates from the exponential power
384       distribution with scale parameter $a and exponent $b.
385
386       Usage:
387
388          $piddle = $rng->ran_exppow($mu,$a,[list of integers = output piddle dims]);
389          $rng->ran_exppow($mu,$a,$output_piddle);
390
391   ran_exppow_var
392       Similar to ran_exppow except that it takes the distribution parameters
393       as a piddle and returns a piddle of equal dimensions.
394
395       Usage:
396
397          $piddle = $rng->ran_exppow_var($mu_piddle, $a_piddle);
398
399   ran_fdist
400       Fills output piddle with random variates from the F-distribution with
401       degrees of freedom $nu1 and $nu2.
402
403       Usage:
404
405          $piddle = $rng->ran_fdist($nu1, $nu2,[list of integers = output piddle dims]);
406          $rng->ran_fdist($nu1, $nu2,$output_piddle);
407
408   ran_fdist_var
409       Similar to ran_fdist except that it takes the distribution parameters
410       as a piddle and returns a piddle of equal dimensions.
411
412       Usage:
413
414          $piddle = $rng->ran_fdist_var($nu1_piddle, $nu2_piddle);
415
416   ran_flat
417       Fills output piddle with random variates from the flat (uniform)
418       distribution from $a to $b.
419
420       Usage:
421
422          $piddle = $rng->ran_flat($a,$b,[list of integers = output piddle dims]);
423          $rng->ran_flat($a,$b,$output_piddle);
424
425   ran_flat_var
426       Similar to ran_flat except that it takes the distribution parameters as
427       a piddle and returns a piddle of equal dimensions.
428
429       Usage:
430
431          $piddle = $rng->ran_flat_var($a_piddle, $b_piddle);
432
433   ran_gamma
434       Fills output piddle with random variates from the gamma distribution.
435
436       Usage:
437
438          $piddle = $rng->ran_gamma($a,$b,[list of integers = output piddle dims]);
439          $rng->ran_gamma($a,$b,$output_piddle);
440
441   ran_gamma_var
442       Similar to ran_gamma except that it takes the distribution parameters
443       as a piddle and returns a piddle of equal dimensions.
444
445       Usage:
446
447          $piddle = $rng->ran_gamma_var($a_piddle, $b_piddle);
448
449   ran_geometric
450       Fills output piddle with random integer values from the geometric
451       distribution, the number of independent trials with probability $p
452       until the first success.
453
454       Usage:
455
456          $piddle = $rng->ran_geometric($p,[list of integers = output piddle dims]);
457          $rng->ran_geometric($p,$output_piddle);
458
459   ran_geometric_var
460       Similar to ran_geometric except that it takes the distribution
461       parameters as a piddle and returns a piddle of equal dimensions.
462
463       Usage:
464
465          $piddle = $rng->ran_geometric_var($p_piddle);
466
467   ran_gumbel1
468       Fills output piddle with random variates from the Type-1 Gumbel
469       distribution.
470
471       Usage:
472
473          $piddle = $rng->ran_gumbel1($a,$b,[list of integers = output piddle dims]);
474          $rng->ran_gumbel1($a,$b,$output_piddle);
475
476   ran_gumbel1_var
477       Similar to ran_gumbel1 except that it takes the distribution parameters
478       as a piddle and returns a piddle of equal dimensions.
479
480       Usage:
481
482          $piddle = $rng->ran_gumbel1_var($a_piddle, $b_piddle);
483
484   ran_gumbel2
485       Fills output piddle with random variates from the Type-2 Gumbel
486       distribution.
487
488       Usage:
489
490          $piddle = $rng->ran_gumbel2($a,$b,[list of integers = output piddle dims]);
491          $rng->ran_gumbel2($a,$b,$output_piddle);
492
493   ran_gumbel2_var
494       Similar to ran_gumbel2 except that it takes the distribution parameters
495       as a piddle and returns a piddle of equal dimensions.
496
497       Usage:
498
499          $piddle = $rng->ran_gumbel2_var($a_piddle, $b_piddle);
500
501   ran_hypergeometric
502       Fills output piddle with random integer values from the hypergeometric
503       distribution.  If a population contains $n1 elements of type 1 and $n2
504       elements of type 2 then the hypergeometric distribution gives the
505       probability of obtaining $x elements of type 1 in $t samples from the
506       population without replacement.
507
508       Usage:
509
510          $piddle = $rng->ran_hypergeometric($n1, $n2, $t,[list of integers = output piddle dims]);
511          $rng->ran_hypergeometric($n1, $n2, $t,$output_piddle);
512
513   ran_hypergeometric_var
514       Similar to ran_hypergeometric except that it takes the distribution
515       parameters as a piddle and returns a piddle of equal dimensions.
516
517       Usage:
518
519          $piddle = $rng->ran_hypergeometric_var($n1_piddle, $n2_piddle, $t_piddle);
520
521   ran_laplace
522       Fills output piddle with random variates from the Laplace distribution
523       with width $a.
524
525       Usage:
526
527          $piddle = $rng->ran_laplace($a,[list of integers = output piddle dims]);
528          $rng->ran_laplace($a,$output_piddle);
529
530   ran_laplace_var
531       Similar to ran_laplace except that it takes the distribution parameters
532       as a piddle and returns a piddle of equal dimensions.
533
534       Usage:
535
536          $piddle = $rng->ran_laplace_var($a_piddle);
537
538   ran_levy
539       Fills output piddle with random variates from the Levy symmetric stable
540       distribution with scale $c and exponent $alpha.
541
542       Usage:
543
544          $piddle = $rng->ran_levy($mu,$a,[list of integers = output piddle dims]);
545          $rng->ran_levy($mu,$a,$output_piddle);
546
547   ran_levy_var
548       Similar to ran_levy except that it takes the distribution parameters as
549       a piddle and returns a piddle of equal dimensions.
550
551       Usage:
552
553          $piddle = $rng->ran_levy_var($mu_piddle, $a_piddle);
554
555   ran_logarithmic
556       Fills output piddle with random integer values from the logarithmic
557       distribution.
558
559       Usage:
560
561          $piddle = $rng->ran_logarithmic($p,[list of integers = output piddle dims]);
562          $rng->ran_logarithmic($p,$output_piddle);
563
564   ran_logarithmic_var
565       Similar to ran_logarithmic except that it takes the distribution
566       parameters as a piddle and returns a piddle of equal dimensions.
567
568       Usage:
569
570          $piddle = $rng->ran_logarithmic_var($p_piddle);
571
572   ran_logistic
573       Fills output piddle with random random variates from the logistic
574       distribution.
575
576       Usage:
577
578          $piddle = $rng->ran_logistic($m,[list of integers = output piddle dims]u)
579          $rng->ran_logistic($m,$output_piddle)
580
581   ran_logistic_var
582       Similar to ran_logistic except that it takes the distribution
583       parameters as a piddle and returns a piddle of equal dimensions.
584
585       Usage:
586
587          $piddle = $rng->ran_logistic_var($m_piddle);
588
589   ran_lognormal
590       Fills output piddle with random variates from the lognormal
591       distribution with parameters $mu (location) and $sigma (scale).
592
593       Usage:
594
595          $piddle = $rng->ran_lognormal($mu,$sigma,[list of integers = output piddle dims]);
596          $rng->ran_lognormal($mu,$sigma,$output_piddle);
597
598   ran_lognormal_var
599       Similar to ran_lognormal except that it takes the distribution
600       parameters as a piddle and returns a piddle of equal dimensions.
601
602       Usage:
603
604          $piddle = $rng->ran_lognormal_var($mu_piddle, $sigma_piddle);
605
606   ran_negative_binomial
607       Fills output piddle with random integer values from the negative
608       binomial distribution, the number of failures occurring before $n
609       successes in independent trials with probability $p of success. Note
610       that $n is not required to be an integer.
611
612       Usage:
613
614          $piddle = $rng->ran_negative_binomial($p,$n,[list of integers = output piddle dims]);
615          $rng->ran_negative_binomial($p,$n,$output_piddle);
616
617   ran_negative_binomial_var
618       Similar to ran_negative_binomial except that it takes the distribution
619       parameters as a piddle and returns a piddle of equal dimensions.
620
621       Usage:
622
623          $piddle = $rng->ran_negative_binomial_var($p_piddle, $n_piddle);
624
625   ran_pareto
626       Fills output piddle with random variates from the Pareto distribution
627       of order $a and scale $b.
628
629       Usage:
630
631          $piddle = $rng->ran_pareto($a,$b,[list of integers = output piddle dims]);
632          $rng->ran_pareto($a,$b,$output_piddle);
633
634   ran_pareto_var
635       Similar to ran_pareto except that it takes the distribution parameters
636       as a piddle and returns a piddle of equal dimensions.
637
638       Usage:
639
640          $piddle = $rng->ran_pareto_var($a_piddle, $b_piddle);
641
642   ran_pascal
643       Fills output piddle with random integer values from the Pascal
644       distribution.  The Pascal distribution is simply a negative binomial
645       distribution (see ran_negative_binomial) with an integer value of $n.
646
647       Usage:
648
649          $piddle = $rng->ran_pascal($p,$n,[list of integers = output piddle dims]);
650          $rng->ran_pascal($p,$n,$output_piddle);
651
652   ran_pascal_var
653       Similar to ran_pascal except that it takes the distribution parameters
654       as a piddle and returns a piddle of equal dimensions.
655
656       Usage:
657
658          $piddle = $rng->ran_pascal_var($p_piddle, $n_piddle);
659
660   ran_rayleigh
661       Fills output piddle with random variates from the Rayleigh distribution
662       with scale parameter $sigma.
663
664       Usage:
665
666          $piddle = $rng->ran_rayleigh($sigma,[list of integers = output piddle dims]);
667          $rng->ran_rayleigh($sigma,$output_piddle);
668
669   ran_rayleigh_var
670       Similar to ran_rayleigh except that it takes the distribution
671       parameters as a piddle and returns a piddle of equal dimensions.
672
673       Usage:
674
675          $piddle = $rng->ran_rayleigh_var($sigma_piddle);
676
677   ran_rayleigh_tail
678       Fills output piddle with random variates from the tail of the Rayleigh
679       distribution with scale parameter $sigma and a lower limit of $a.
680
681       Usage:
682
683          $piddle = $rng->ran_rayleigh_tail($a,$sigma,[list of integers = output piddle dims]);
684          $rng->ran_rayleigh_tail($a,$sigma,$output_piddle);
685
686   ran_rayleigh_tail_var
687       Similar to ran_rayleigh_tail except that it takes the distribution
688       parameters as a piddle and returns a piddle of equal dimensions.
689
690       Usage:
691
692          $piddle = $rng->ran_rayleigh_tail_var($a_piddle, $sigma_piddle);
693
694   ran_tdist
695       Fills output piddle with random variates from the t-distribution (AKA
696       Student's t-distribution) with $nu degrees of freedom.
697
698       Usage:
699
700          $piddle = $rng->ran_tdist($nu,[list of integers = output piddle dims]);
701          $rng->ran_tdist($nu,$output_piddle);
702
703   ran_tdist_var
704       Similar to ran_tdist except that it takes the distribution parameters
705       as a piddle and returns a piddle of equal dimensions.
706
707       Usage:
708
709          $piddle = $rng->ran_tdist_var($nu_piddle);
710
711   ran_ugaussian_tail
712       Fills output piddle with random variates from the upper tail of a
713       Gaussian distribution with "standard deviation = 1" (AKA unit Gaussian
714       distribution).
715
716       Usage:
717
718          $piddle = $rng->ran_ugaussian_tail($tail,[list of integers = output piddle dims]);
719          $rng->ran_ugaussian_tail($tail,$output_piddle);
720
721   ran_ugaussian_tail_var
722       Similar to ran_ugaussian_tail except that it takes the distribution
723       parameters as a piddle and returns a piddle of equal dimensions.
724
725       Usage:
726
727          $piddle = $rng->ran_ugaussian_tail_var($tail_piddle);
728
729   ran_weibull
730       Fills output piddle with random variates from the Weibull distribution.
731
732       Usage:
733
734          $piddle = $rng->ran_weibull($mu,$a,[list of integers = output piddle dims]);
735          $rng->ran_weibull($mu,$a,$output_piddle);
736
737   ran_weibull_var
738       Similar to ran_weibull except that it takes the distribution parameters
739       as a piddle and returns a piddle of equal dimensions.
740
741       Usage:
742
743          $piddle = $rng->ran_weibull_var($mu_piddle, $a_piddle);
744
745   ran_dir
746       Returns $n random vectors in $ndim dimensions.
747
748       Usage:
749
750          $piddle = $rng->ran_dir($ndim,$n);
751
752       Example:
753
754          $o = $rng->ran_dir($ndim,$n);
755
756   ran_discrete_preproc
757       This method returns a handle that must be used when calling
758       ran_discrete. You specify the probability of the integer number that
759       are returned by ran_discrete.
760
761       Usage:
762
763          $discrete_dist_handle = $rng->ran_discrete_preproc($double_piddle_prob);
764
765       Example:
766
767          $prob = pdl [0.1,0.3,0.6];
768          $ddh = $rng->ran_discrete_preproc($prob);
769          $o = $rng->ran_discrete($discrete_dist_handle,100);
770
771   ran_discrete
772       Is used to get the desired samples once a proper handle has been
773       enstablished (see ran_discrete_preproc()).
774
775       Usage:
776
777          $piddle = $rng->ran_discrete($discrete_dist_handle,$num);
778
779       Example:
780
781          $prob = pdl [0.1,0.3,0.6];
782          $ddh = $rng->ran_discrete_preproc($prob);
783          $o = $rng->ran_discrete($discrete_dist_handle,100);
784
785   ran_ver
786       Returns a piddle with $n values generated by the Verhulst map from $x0
787       and parameter $r.
788
789       Usage:
790
791          $rng->ran_ver($x0, $r, $n);
792
793   ran_caos
794       Returns values from Verhuls map with "$r=4.0" and randomly chosen $x0.
795       The values are scaled by $m.
796
797       Usage:
798
799          $rng->ran_caos($m,$n);
800

BUGS

802       Feedback is welcome. Log bugs in the PDL bug database (the database is
803       always linked from <http://pdl.perl.org/>).
804

AUTHOR

812       This file copyright (C) 1999 Christian Pellegrin
813       <chri@infis.univ.trieste.it> Docs mangled by C. Soeller. All rights
814       reserved. There is no warranty. You are allowed to redistribute this
815       software / documentation under certain conditions. For details, see the
816       file COPYING in the PDL distribution. If this file is separated from
817       the PDL distribution, the copyright notice should be included in the
818       file.
819
820       The GSL RNG and randist modules were written by James Theiler.
821
822
823
824perl v5.30.0                      2019-09-05                            RNG(3)

NAME

DESCRIPTION

SYNOPSIS

FUNCTIONS

BUGS

SEE ALSO

AUTHOR