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          $x=zeroes(5,5,5)
21
22          $rng->get_uniform($x); # inplace
23
24          $y=$rng->get_uniform(3,4,5); # creates new pdl
25

NOMENCLATURE

27       Throughout this documentation we strive to use the same variables that
28       are present in the original GSL documentation (see See Also).
29       Oftentimes those variables are called "a" and "b". Since good Perl
30       coding practices discourage the use of Perl variables $a and $b, here
31       we refer to Parameters "a" and "b" as $pa and $pb, respectively, and
32       Limits (of domain or integration) as $la and $lb.
33

FUNCTIONS

35   new
36       The new method initializes a new instance of the RNG.
37
38       The available RNGs are:
39
40        coveyou cmrg fishman18 fishman20 fishman2x gfsr4 knuthran
41        knuthran2 knuthran2002 lecuyer21 minstd mrg mt19937 mt19937_1999
42        mt19937_1998 r250 ran0 ran1 ran2 ran3 rand rand48 random128_bsd
43        random128_glibc2 random128_libc5 random256_bsd random256_glibc2
44        random256_libc5 random32_bsd random32_glibc2 random32_libc5
45        random64_bsd random64_glibc2 random64_libc5 random8_bsd
46        random8_glibc2 random8_libc5 random_bsd random_glibc2
47        random_libc5 randu ranf ranlux ranlux389 ranlxd1 ranlxd2 ranlxs0
48        ranlxs1 ranlxs2 ranmar slatec taus taus2 taus113 transputer tt800
49        uni uni32 vax waterman14 zuf default
50
51       The last one (default) uses the environment variable GSL_RNG_TYPE.
52
53       Note that only a few of these rngs are recommended for general use.
54       Please check the GSL documentation for more information.
55
56       Usage:
57
58          $blessed_ref = PDL::GSL::RNG->new($RNG_name);
59
60       Example:
61
62          $rng = PDL::GSL::RNG->new('taus');
63
64   set_seed
65       Sets the RNG seed.
66
67       Usage:
68
69          $rng->set_seed($integer);
70          # or
71          $rng = PDL::GSL::RNG->new('taus')->set_seed($integer);
72
73       Example:
74
75          $rng->set_seed(666);
76
77   min
78       Return the minimum value generable by this RNG.
79
80       Usage:
81
82          $integer = $rng->min();
83
84       Example:
85
86          $min = $rng->min(); $max = $rng->max();
87
88   max
89       Return the maximum value generable by the RNG.
90
91       Usage:
92
93          $integer = $rng->max();
94
95       Example:
96
97          $min = $rng->min(); $max = $rng->max();
98
99   name
100       Returns the name of the RNG.
101
102       Usage:
103
104          $string = $rng->name();
105
106       Example:
107
108          $name = $rng->name();
109
110   get
111       This function creates an ndarray with given dimensions or accepts an
112       existing ndarray and fills it. get() returns integer values between a
113       minimum and a maximum specific to every RNG.
114
115       Usage:
116
117          $ndarray = $rng->get($list_of_integers)
118          $rng->get($ndarray);
119
120       Example:
121
122          $x = zeroes 5,6;
123          $o = $rng->get(10,10); $rng->get($x);
124
125   get_int
126       This function creates an ndarray with given dimensions or accepts an
127       existing ndarray and fills it. get_int() returns integer values between
128       0 and $max.
129
130       Usage:
131
132          $ndarray = $rng->get($max, $list_of_integers)
133          $rng->get($max, $ndarray);
134
135       Example:
136
137          $x = zeroes 5,6; $max=100;
138          $o = $rng->get(10,10); $rng->get($x);
139
140   get_uniform
141       This function creates an ndarray with given dimensions or accepts an
142       existing ndarray and fills it. get_uniform() returns values 0<=x<1,
143
144       Usage:
145
146          $ndarray = $rng->get_uniform($list_of_integers)
147          $rng->get_uniform($ndarray);
148
149       Example:
150
151          $x = zeroes 5,6; $max=100;
152          $o = $rng->get_uniform(10,10); $rng->get_uniform($x);
153
154   get_uniform_pos
155       This function creates an ndarray with given dimensions or accepts an
156       existing ndarray and fills it. get_uniform_pos() returns values 0<x<1,
157
158       Usage:
159
160          $ndarray = $rng->get_uniform_pos($list_of_integers)
161          $rng->get_uniform_pos($ndarray);
162
163       Example:
164
165          $x = zeroes 5,6;
166          $o = $rng->get_uniform_pos(10,10); $rng->get_uniform_pos($x);
167
168   ran_shuffle
169       Shuffles values in ndarray
170
171       Usage:
172
173          $rng->ran_shuffle($ndarray);
174
175   ran_shuffle_vec
176       Shuffles values in ndarray
177
178       Usage:
179
180          $rng->ran_shuffle_vec(@vec);
181
182   ran_choose
183       Chooses values from $inndarray to $outndarray.
184
185       Usage:
186
187          $rng->ran_choose($inndarray,$outndarray);
188
189   ran_choose_vec
190       Chooses $n values from @vec.
191
192       Usage:
193
194          @chosen = $rng->ran_choose_vec($n,@vec);
195
196   ran_gaussian
197       Fills output ndarray with random values from Gaussian distribution with
198       mean zero and standard deviation $sigma.
199
200       Usage:
201
202        $ndarray = $rng->ran_gaussian($sigma,[list of integers = output ndarray dims]);
203        $rng->ran_gaussian($sigma, $output_ndarray);
204
205       Example:
206
207         $o = $rng->ran_gaussian($sigma,10,10);
208         $rng->ran_gaussian($sigma,$o);
209
210   ran_gaussian_var
211       This method is similar to "ran_gaussian" except that it takes the
212       parameters of the distribution as an ndarray and returns an ndarray of
213       equal dimensions.
214
215       Usage:
216
217          $ndarray = $rng->ran_gaussian_var($sigma_ndarray);
218          $rng->ran_gaussian_var($sigma_ndarray, $output_ndarray);
219
220       Example:
221
222          $sigma_pdl = rvals zeroes 11,11;
223          $o = $rng->ran_gaussian_var($sigma_pdl);
224
225   ran_additive_gaussian
226       Add Gaussian noise of given sigma to an ndarray.
227
228       Usage:
229
230          $rng->ran_additive_gaussian($sigma,$ndarray);
231
232       Example:
233
234          $rng->ran_additive_gaussian(1,$image);
235
236   ran_bivariate_gaussian
237       Generates $n bivariate gaussian random deviates.
238
239       Usage:
240
241          $ndarray = $rng->ran_bivariate_gaussian($sigma_x,$sigma_y,$rho,$n);
242
243       Example:
244
245          $o = $rng->ran_bivariate_gaussian(1,2,0.5,1000);
246
247   ran_poisson
248       Fills output ndarray by with random integer values from the Poisson
249       distribution with mean $mu.
250
251       Usage:
252
253          $ndarray = $rng->ran_poisson($mu,[list of integers = output ndarray dims]);
254          $rng->ran_poisson($mu,$output_ndarray);
255
256   ran_poisson_var
257       Similar to "ran_poisson" except that it takes the distribution
258       parameters as an ndarray and returns an ndarray of equal dimensions.
259
260       Usage:
261
262          $ndarray = $rng->ran_poisson_var($mu_ndarray);
263
264   ran_additive_poisson
265       Add Poisson noise of given $mu to a $ndarray.
266
267       Usage:
268
269          $rng->ran_additive_poisson($mu,$ndarray);
270
271       Example:
272
273          $rng->ran_additive_poisson(1,$image);
274
275   ran_feed_poisson
276       This method simulates shot noise, taking the values of ndarray as
277       values for $mu to be fed in the poissonian RNG.
278
279       Usage:
280
281          $rng->ran_feed_poisson($ndarray);
282
283       Example:
284
285          $rng->ran_feed_poisson($image);
286
287   ran_bernoulli
288       Fills output ndarray with random values 0 or 1, the result of a
289       Bernoulli trial with probability $p.
290
291       Usage:
292
293          $ndarray = $rng->ran_bernoulli($p,[list of integers = output ndarray dims]);
294          $rng->ran_bernoulli($p,$output_ndarray);
295
296   ran_bernoulli_var
297       Similar to "ran_bernoulli" except that it takes the distribution
298       parameters as an ndarray and returns an ndarray of equal dimensions.
299
300       Usage:
301
302          $ndarray = $rng->ran_bernoulli_var($p_ndarray);
303
304   ran_beta
305       Fills output ndarray with random variates from the beta distribution
306       with parameters $pa and $pb.
307
308       Usage:
309
310          $ndarray = $rng->ran_beta($pa,$pb,[list of integers = output ndarray dims]);
311          $rng->ran_beta($pa,$pb,$output_ndarray);
312
313   ran_beta_var
314       Similar to "ran_beta" except that it takes the distribution parameters
315       as an ndarray and returns an ndarray of equal dimensions.
316
317       Usage:
318
319          $ndarray = $rng->ran_beta_var($a_ndarray, $b_ndarray);
320
321   ran_binomial
322       Fills output ndarray with random integer values from the binomial
323       distribution, the number of successes in $n independent trials with
324       probability $p.
325
326       Usage:
327
328          $ndarray = $rng->ran_binomial($p,$n,[list of integers = output ndarray dims]);
329          $rng->ran_binomial($p,$n,$output_ndarray);
330
331   ran_binomial_var
332       Similar to "ran_binomial" except that it takes the distribution
333       parameters as an ndarray and returns an ndarray of equal dimensions.
334
335       Usage:
336
337          $ndarray = $rng->ran_binomial_var($p_ndarray, $n_ndarray);
338
339   ran_cauchy
340       Fills output ndarray with random variates from the Cauchy distribution
341       with scale parameter $pa.
342
343       Usage:
344
345          $ndarray = $rng->ran_cauchy($pa,[list of integers = output ndarray dims]);
346          $rng->ran_cauchy($pa,$output_ndarray);
347
348   ran_cauchy_var
349       Similar to "ran_cauchy" except that it takes the distribution
350       parameters as an ndarray and returns an ndarray of equal dimensions.
351
352       Usage:
353
354          $ndarray = $rng->ran_cauchy_var($a_ndarray);
355
356   ran_chisq
357       Fills output ndarray with random variates from the chi-squared
358       distribution with $nu degrees of freedom.
359
360       Usage:
361
362          $ndarray = $rng->ran_chisq($nu,[list of integers = output ndarray dims]);
363          $rng->ran_chisq($nu,$output_ndarray);
364
365   ran_chisq_var
366       Similar to "ran_chisq" except that it takes the distribution parameters
367       as an ndarray and returns an ndarray of equal dimensions.
368
369       Usage:
370
371          $ndarray = $rng->ran_chisq_var($nu_ndarray);
372
373   ran_exponential
374       Fills output ndarray with random variates from the exponential
375       distribution with mean $mu.
376
377       Usage:
378
379          $ndarray = $rng->ran_exponential($mu,[list of integers = output ndarray dims]);
380          $rng->ran_exponential($mu,$output_ndarray);
381
382   ran_exponential_var
383       Similar to "ran_exponential" except that it takes the distribution
384       parameters as an ndarray and returns an ndarray of equal dimensions.
385
386       Usage:
387
388          $ndarray = $rng->ran_exponential_var($mu_ndarray);
389
390   ran_exppow
391       Fills output ndarray with random variates from the exponential power
392       distribution with scale parameter $pa and exponent $pb.
393
394       Usage:
395
396          $ndarray = $rng->ran_exppow($pa,$pb,[list of integers = output ndarray dims]);
397          $rng->ran_exppow($pa,$pb,$output_ndarray);
398
399   ran_exppow_var
400       Similar to "ran_exppow" except that it takes the distribution
401       parameters as an ndarray and returns an ndarray of equal dimensions.
402
403       Usage:
404
405          $ndarray = $rng->ran_exppow_var($a_ndarray, $b_ndarray);
406
407   ran_fdist
408       Fills output ndarray with random variates from the F-distribution with
409       degrees of freedom $nu1 and $nu2.
410
411       Usage:
412
413          $ndarray = $rng->ran_fdist($nu1, $nu2,[list of integers = output ndarray dims]);
414          $rng->ran_fdist($nu1, $nu2,$output_ndarray);
415
416   ran_fdist_var
417       Similar to "ran_fdist" except that it takes the distribution parameters
418       as an ndarray and returns an ndarray of equal dimensions.
419
420       Usage:
421
422          $ndarray = $rng->ran_fdist_var($nu1_ndarray, $nu2_ndarray);
423
424   ran_flat
425       Fills output ndarray with random variates from the flat (uniform)
426       distribution from $la to $lb.
427
428       Usage:
429
430          $ndarray = $rng->ran_flat($la,$lb,[list of integers = output ndarray dims]);
431          $rng->ran_flat($la,$lb,$output_ndarray);
432
433   ran_flat_var
434       Similar to "ran_flat" except that it takes the distribution parameters
435       as an ndarray and returns an ndarray of equal dimensions.
436
437       Usage:
438
439          $ndarray = $rng->ran_flat_var($a_ndarray, $b_ndarray);
440
441   ran_gamma
442       Fills output ndarray with random variates from the gamma distribution.
443
444       Usage:
445
446          $ndarray = $rng->ran_gamma($pa,$pb,[list of integers = output ndarray dims]);
447          $rng->ran_gamma($pa,$pb,$output_ndarray);
448
449   ran_gamma_var
450       Similar to "ran_gamma" except that it takes the distribution parameters
451       as an ndarray and returns an ndarray of equal dimensions.
452
453       Usage:
454
455          $ndarray = $rng->ran_gamma_var($a_ndarray, $b_ndarray);
456
457   ran_geometric
458       Fills output ndarray with random integer values from the geometric
459       distribution, the number of independent trials with probability $p
460       until the first success.
461
462       Usage:
463
464          $ndarray = $rng->ran_geometric($p,[list of integers = output ndarray dims]);
465          $rng->ran_geometric($p,$output_ndarray);
466
467   ran_geometric_var
468       Similar to "ran_geometric" except that it takes the distribution
469       parameters as an ndarray and returns an ndarray of equal dimensions.
470
471       Usage:
472
473          $ndarray = $rng->ran_geometric_var($p_ndarray);
474
475   ran_gumbel1
476       Fills output ndarray with random variates from the Type-1 Gumbel
477       distribution.
478
479       Usage:
480
481          $ndarray = $rng->ran_gumbel1($pa,$pb,[list of integers = output ndarray dims]);
482          $rng->ran_gumbel1($pa,$pb,$output_ndarray);
483
484   ran_gumbel1_var
485       Similar to "ran_gumbel1" except that it takes the distribution
486       parameters as an ndarray and returns an ndarray of equal dimensions.
487
488       Usage:
489
490          $ndarray = $rng->ran_gumbel1_var($a_ndarray, $b_ndarray);
491
492   ran_gumbel2
493       Fills output ndarray with random variates from the Type-2 Gumbel
494       distribution.
495
496       Usage:
497
498          $ndarray = $rng->ran_gumbel2($pa,$pb,[list of integers = output ndarray dims]);
499          $rng->ran_gumbel2($pa,$pb,$output_ndarray);
500
501   ran_gumbel2_var
502       Similar to "ran_gumbel2" except that it takes the distribution
503       parameters as an ndarray and returns an ndarray of equal dimensions.
504
505       Usage:
506
507          $ndarray = $rng->ran_gumbel2_var($a_ndarray, $b_ndarray);
508
509   ran_hypergeometric
510       Fills output ndarray with random integer values from the hypergeometric
511       distribution.  If a population contains $n1 elements of type 1 and $n2
512       elements of type 2 then the hypergeometric distribution gives the
513       probability of obtaining $x elements of type 1 in $t samples from the
514       population without replacement.
515
516       Usage:
517
518          $ndarray = $rng->ran_hypergeometric($n1, $n2, $t,[list of integers = output ndarray dims]);
519          $rng->ran_hypergeometric($n1, $n2, $t,$output_ndarray);
520
521   ran_hypergeometric_var
522       Similar to "ran_hypergeometric" except that it takes the distribution
523       parameters as an ndarray and returns an ndarray of equal dimensions.
524
525       Usage:
526
527          $ndarray = $rng->ran_hypergeometric_var($n1_ndarray, $n2_ndarray, $t_ndarray);
528
529   ran_laplace
530       Fills output ndarray with random variates from the Laplace distribution
531       with width $pa.
532
533       Usage:
534
535          $ndarray = $rng->ran_laplace($pa,[list of integers = output ndarray dims]);
536          $rng->ran_laplace($pa,$output_ndarray);
537
538   ran_laplace_var
539       Similar to "ran_laplace" except that it takes the distribution
540       parameters as an ndarray and returns an ndarray of equal dimensions.
541
542       Usage:
543
544          $ndarray = $rng->ran_laplace_var($a_ndarray);
545
546   ran_levy
547       Fills output ndarray with random variates from the Levy symmetric
548       stable distribution with scale $c and exponent $alpha.
549
550       Usage:
551
552          $ndarray = $rng->ran_levy($mu,$x,[list of integers = output ndarray dims]);
553          $rng->ran_levy($mu,$x,$output_ndarray);
554
555   ran_levy_var
556       Similar to "ran_levy" except that it takes the distribution parameters
557       as an ndarray and returns an ndarray of equal dimensions.
558
559       Usage:
560
561          $ndarray = $rng->ran_levy_var($mu_ndarray, $a_ndarray);
562
563   ran_logarithmic
564       Fills output ndarray with random integer values from the logarithmic
565       distribution.
566
567       Usage:
568
569          $ndarray = $rng->ran_logarithmic($p,[list of integers = output ndarray dims]);
570          $rng->ran_logarithmic($p,$output_ndarray);
571
572   ran_logarithmic_var
573       Similar to "ran_logarithmic" except that it takes the distribution
574       parameters as an ndarray and returns an ndarray of equal dimensions.
575
576       Usage:
577
578          $ndarray = $rng->ran_logarithmic_var($p_ndarray);
579
580   ran_logistic
581       Fills output ndarray with random random variates from the logistic
582       distribution.
583
584       Usage:
585
586          $ndarray = $rng->ran_logistic($m,[list of integers = output ndarray dims]u)
587          $rng->ran_logistic($m,$output_ndarray)
588
589   ran_logistic_var
590       Similar to "ran_logistic" except that it takes the distribution
591       parameters as an ndarray and returns an ndarray of equal dimensions.
592
593       Usage:
594
595          $ndarray = $rng->ran_logistic_var($m_ndarray);
596
597   ran_lognormal
598       Fills output ndarray with random variates from the lognormal
599       distribution with parameters $mu (location) and $sigma (scale).
600
601       Usage:
602
603          $ndarray = $rng->ran_lognormal($mu,$sigma,[list of integers = output ndarray dims]);
604          $rng->ran_lognormal($mu,$sigma,$output_ndarray);
605
606   ran_lognormal_var
607       Similar to "ran_lognormal" except that it takes the distribution
608       parameters as an ndarray and returns an ndarray of equal dimensions.
609
610       Usage:
611
612          $ndarray = $rng->ran_lognormal_var($mu_ndarray, $sigma_ndarray);
613
614   ran_negative_binomial
615       Fills output ndarray with random integer values from the negative
616       binomial distribution, the number of failures occurring before $n
617       successes in independent trials with probability $p of success. Note
618       that $n is not required to be an integer.
619
620       Usage:
621
622          $ndarray = $rng->ran_negative_binomial($p,$n,[list of integers = output ndarray dims]);
623          $rng->ran_negative_binomial($p,$n,$output_ndarray);
624
625   ran_negative_binomial_var
626       Similar to "ran_negative_binomial" except that it takes the
627       distribution parameters as an ndarray and returns an ndarray of equal
628       dimensions.
629
630       Usage:
631
632          $ndarray = $rng->ran_negative_binomial_var($p_ndarray, $n_ndarray);
633
634   ran_pareto
635       Fills output ndarray with random variates from the Pareto distribution
636       of order $pa and scale $lb.
637
638       Usage:
639
640          $ndarray = $rng->ran_pareto($pa,$lb,[list of integers = output ndarray dims]);
641          $rng->ran_pareto($pa,$lb,$output_ndarray);
642
643   ran_pareto_var
644       Similar to "ran_pareto" except that it takes the distribution
645       parameters as an ndarray and returns an ndarray of equal dimensions.
646
647       Usage:
648
649          $ndarray = $rng->ran_pareto_var($a_ndarray, $b_ndarray);
650
651   ran_pascal
652       Fills output ndarray with random integer values from the Pascal
653       distribution.  The Pascal distribution is simply a negative binomial
654       distribution (see "ran_negative_binomial") with an integer value of $n.
655
656       Usage:
657
658          $ndarray = $rng->ran_pascal($p,$n,[list of integers = output ndarray dims]);
659          $rng->ran_pascal($p,$n,$output_ndarray);
660
661   ran_pascal_var
662       Similar to "ran_pascal" except that it takes the distribution
663       parameters as an ndarray and returns an ndarray of equal dimensions.
664
665       Usage:
666
667          $ndarray = $rng->ran_pascal_var($p_ndarray, $n_ndarray);
668
669   ran_rayleigh
670       Fills output ndarray with random variates from the Rayleigh
671       distribution with scale parameter $sigma.
672
673       Usage:
674
675          $ndarray = $rng->ran_rayleigh($sigma,[list of integers = output ndarray dims]);
676          $rng->ran_rayleigh($sigma,$output_ndarray);
677
678   ran_rayleigh_var
679       Similar to "ran_rayleigh" except that it takes the distribution
680       parameters as an ndarray and returns an ndarray of equal dimensions.
681
682       Usage:
683
684          $ndarray = $rng->ran_rayleigh_var($sigma_ndarray);
685
686   ran_rayleigh_tail
687       Fills output ndarray with random variates from the tail of the Rayleigh
688       distribution with scale parameter $sigma and a lower limit of $la.
689
690       Usage:
691
692          $ndarray = $rng->ran_rayleigh_tail($la,$sigma,[list of integers = output ndarray dims]);
693          $rng->ran_rayleigh_tail($x,$sigma,$output_ndarray);
694
695   ran_rayleigh_tail_var
696       Similar to "ran_rayleigh_tail" except that it takes the distribution
697       parameters as an ndarray and returns an ndarray of equal dimensions.
698
699       Usage:
700
701          $ndarray = $rng->ran_rayleigh_tail_var($a_ndarray, $sigma_ndarray);
702
703   ran_tdist
704       Fills output ndarray with random variates from the t-distribution (AKA
705       Student's t-distribution) with $nu degrees of freedom.
706
707       Usage:
708
709          $ndarray = $rng->ran_tdist($nu,[list of integers = output ndarray dims]);
710          $rng->ran_tdist($nu,$output_ndarray);
711
712   ran_tdist_var
713       Similar to "ran_tdist" except that it takes the distribution parameters
714       as an ndarray and returns an ndarray of equal dimensions.
715
716       Usage:
717
718          $ndarray = $rng->ran_tdist_var($nu_ndarray);
719
720   ran_ugaussian_tail
721       Fills output ndarray with random variates from the upper tail of a
722       Gaussian distribution with "standard deviation = 1" (AKA unit Gaussian
723       distribution).
724
725       Usage:
726
727          $ndarray = $rng->ran_ugaussian_tail($tail,[list of integers = output ndarray dims]);
728          $rng->ran_ugaussian_tail($tail,$output_ndarray);
729
730   ran_ugaussian_tail_var
731       Similar to "ran_ugaussian_tail" except that it takes the distribution
732       parameters as an ndarray and returns an ndarray of equal dimensions.
733
734       Usage:
735
736          $ndarray = $rng->ran_ugaussian_tail_var($tail_ndarray);
737
738   ran_weibull
739       Fills output ndarray with random variates from the Weibull distribution
740       with scale $pa and exponent $pb. (Some literature uses "lambda" for $pa
741       and "k" for $pb.)
742
743       Usage:
744
745          $ndarray = $rng->ran_weibull($pa,$pb,[list of integers = output ndarray dims]);
746          $rng->ran_weibull($pa,$pb,$output_ndarray);
747
748   ran_weibull_var
749       Similar to "ran_weibull" except that it takes the distribution
750       parameters as an ndarray and returns an ndarray of equal dimensions.
751
752       Usage:
753
754          $ndarray = $rng->ran_weibull_var($a_ndarray, $b_ndarray);
755
756   ran_dir
757       Returns $n random vectors in $ndim dimensions.
758
759       Usage:
760
761          $ndarray = $rng->ran_dir($ndim,$n);
762
763       Example:
764
765          $o = $rng->ran_dir($ndim,$n);
766
767   ran_discrete_preproc
768       This method returns a handle that must be used when calling
769       "ran_discrete". You specify the probability of the integer number that
770       are returned by "ran_discrete".
771
772       Usage:
773
774          $discrete_dist_handle = $rng->ran_discrete_preproc($double_ndarray_prob);
775
776       Example:
777
778          $prob = pdl [0.1,0.3,0.6];
779          $ddh = $rng->ran_discrete_preproc($prob);
780          $o = $rng->ran_discrete($discrete_dist_handle,100);
781
782   ran_discrete
783       Is used to get the desired samples once a proper handle has been
784       enstablished (see ran_discrete_preproc()).
785
786       Usage:
787
788          $ndarray = $rng->ran_discrete($discrete_dist_handle,$num);
789
790       Example:
791
792          $prob = pdl [0.1,0.3,0.6];
793          $ddh = $rng->ran_discrete_preproc($prob);
794          $o = $rng->ran_discrete($discrete_dist_handle,100);
795
796   ran_ver
797       Returns an ndarray with $n values generated by the Verhulst map from
798       $x0 and parameter $r.
799
800       Usage:
801
802          $rng->ran_ver($x0, $r, $n);
803
804   ran_caos
805       Returns values from Verhuls map with "$r=4.0" and randomly chosen $x0.
806       The values are scaled by $m.
807
808       Usage:
809
810          $rng->ran_caos($m,$n);
811

BUGS

813       Feedback is welcome. Log bugs in the PDL bug database (the database is
814       always linked from <http://pdl.perl.org/>).
815

AUTHOR

823       This file copyright (C) 1999 Christian Pellegrin
824       <chri@infis.univ.trieste.it> Docs mangled by C. Soeller. All rights
825       reserved. There is no warranty. You are allowed to redistribute this
826       software / documentation under certain conditions. For details, see the
827       file COPYING in the PDL distribution. If this file is separated from
828       the PDL distribution, the copyright notice should be included in the
829       file.
830
831       The GSL RNG and randist modules were written by James Theiler.
832
833
834
835perl v5.36.0                      2023-01-20                            RNG(3)