Adding =item markers to Eigen to make the POD more HTML-frienly
[Math-GSL.git] / RNG.i
blob1f90ac006733fdb6915134040c5d40464d79f189
1 %module "Math::GSL::RNG"
2 %{
3 #include "gsl/gsl_rng.h"
4 %}
5 %import "gsl/gsl_types.h"
7 %include "gsl/gsl_rng.h"
9 FILE *fopen(char *, char *);
10 int fclose(FILE *);
12 %perlcode %{
13 @EXPORT_OK = qw/ fopen fclose
14 gsl_rng_alloc gsl_rng_set gsl_rng_get gsl_rng_free gsl_rng_memcpy
15 gsl_rng_fwrite gsl_rng_fread gsl_rng_clone gsl_rng_max gsl_rng_min
16 gsl_rng_name gsl_rng_size gsl_rng_state gsl_rng_print_state gsl_rng_uniform gsl_rng_uniform_pos gsl_rng_uniform_int
17 $gsl_rng_default $gsl_rng_knuthran $gsl_rng_ran0 $gsl_rng_borosh13
18 $gsl_rng_coveyou $gsl_rng_cmrg $gsl_rng_fishman18 $gsl_rng_fishman20 $gsl_rng_fishman2x
19 $gsl_rng_gfsr4 $gsl_rng_knuthran $gsl_rng_knuthran2 $gsl_rng_knuthran2002 $gsl_rng_lecuyer21
20 $gsl_rng_minstd $gsl_rng_mrg $gsl_rng_mt19937 $gsl_rng_mt19937_1999 $gsl_rng_mt19937_1998
21 $gsl_rng_r250 $gsl_rng_ran0 $gsl_rng_ran1 $gsl_rng_ran2 $gsl_rng_ran3
22 $gsl_rng_rand $gsl_rng_rand48 $gsl_rng_random128_bsd $gsl_rng_random128_gli $gsl_rng_random128_lib
23 $gsl_rng_random256_bsd $gsl_rng_random256_gli $gsl_rng_random256_lib $gsl_rng_random32_bsd
24 $gsl_rng_random32_glib $gsl_rng_random32_libc $gsl_rng_random64_bsd $gsl_rng_random64_glib
25 $gsl_rng_random64_libc $gsl_rng_random8_bsd $gsl_rng_random8_glibc $gsl_rng_random8_libc5
26 $gsl_rng_random_bsd $gsl_rng_random_glibc2 $gsl_rng_random_libc5 $gsl_rng_randu
27 $gsl_rng_ranf $gsl_rng_ranlux $gsl_rng_ranlux389 $gsl_rng_ranlxd1 $gsl_rng_ranlxd2 $gsl_rng_ranlxs0
28 $gsl_rng_ranlxs1 $gsl_rng_ranlxs2 $gsl_rng_ranmar $gsl_rng_slatec $gsl_rng_taus $gsl_rng_taus2
29 $gsl_rng_taus113 $gsl_rng_transputer $gsl_rng_tt800 $gsl_rng_uni $gsl_rng_uni32 $gsl_rng_vax
30 $gsl_rng_waterman14 $gsl_rng_zuf
32 %EXPORT_TAGS = ( all => [ @EXPORT_OK ] );
34 =head1 NAME
36 Math::GSL::RNG - Random Number Generators
38 =head1 SYNOPSIS
40 use Math::GSL::RNG qw/:all/;
41 my $rng = Math::GSL::RNG->new;
42 my @random = map { $rng->get } (1..100);
44 =head2 Math::GSL::RNG->new($type, $seed)
46 my $rng = Math::GSL::RNG->new;
47 my $rng = Math::GSL::RNG->new($gsl_rng_knuthran,5);
49 Creates a new RNG object of type $type, seeded with $seed. Both of these
50 parameters are optional. The type $gsl_rng_default is used when no $type
51 is given.
53 =cut
55 sub new {
56 my ($class, $type, $seed) = @_;
57 $type ||= $gsl_rng_default;
58 $seed ||= int 100*rand;
60 my $self = {};
61 my $rng = gsl_rng_alloc($type);
62 gsl_rng_set($rng, $seed);
64 $self->{_rng} = $rng;
65 bless $self, $class;
68 =head2 copy()
70 my $copy = $rng->copy;
72 Make a copy of a RNG object.
74 =cut
76 sub copy {
77 my ($self) = @_;
78 my $copy = Math::GSL::RNG->new;
79 $copy->{_rng} = gsl_rng_clone($self->{_rng});
81 return $copy;
84 =head2 free()
86 $rng->free();
88 Free memory associated with RNG object.
90 =cut
92 sub free {
93 my ($self) = @_;
94 gsl_rng_free($self->{_rng});
97 =head2 name()
99 my $name = $rng->name();
101 Get the name of the RNG object as a string.
103 =cut
105 sub name {
106 my ($self) = @_;
107 gsl_rng_name($self->{_rng});
110 =head2 get()
112 my $nextval = $rng->get();
114 Get the next random value from the RNG object.
116 =cut
118 sub get {
119 my ($self) = @_;
121 gsl_rng_get($self->{_rng});
124 __END__
127 =head1 DESCRIPTION
129 =over 1
131 =item gsl_rng_alloc($T) - This function returns a pointer to a newly-created instance of a random number generator of type $T. $T must be one of the constants below. The generator is automatically initialized with the default seed, $gsl_rng_default.
133 =item gsl_rng_set($r, $s) - This function initializes (or `seeds') the random number generator. If the generator is seeded with the same value of $s on two different runs, the same stream of random numbers will be generated by successive calls to the routines below. If different values of $s are supplied, then the generated streams of random numbers should be completely different. If the seed $s is zero then the standard seed from the original implementation is used instead. For example, the original Fortran source code for the ranlux generator used a seed of 314159265, and so choosing $s equal to zero reproduces this when using $gsl_rng_ranlux.
135 =item gsl_rng_get($r) - This function returns a random integer from the generator $r. The minimum and maximum values depend on the algorithm used, but all integers in the range [min,max] are equally likely. The values of min and max can determined using the auxiliary functions gsl_rng_max($r) and gsl_rng_min($r).
137 =item gsl_rng_free($r) - This function frees all the memory associated with the generator $r.
139 =item gsl_rng_memcpy($dest, $src) - This function copies the random number generator $src into the pre-existing generator $dest, making $dest into an exact copy of $src. The two generators must be of the same type.
141 =item gsl_rng_uniform($r) - This function returns a double precision floating point number uniformly distributed in the range [0,1). The range includes 0.0 but excludes 1.0. The value is typically obtained by dividing the result of gsl_rng_get($r) by gsl_rng_max($r) + 1.0 in double precision. Some generators compute this ratio internally so that they can provide floating point numbers with more than 32 bits of randomness (the maximum number of bits that can be portably represented in a single unsigned long int).
143 =item gsl_rng_uniform_pos($r) - This function returns a positive double precision floating point number uniformly distributed in the range (0,1), excluding both 0.0 and 1.0. The number is obtained by sampling the generator with the algorithm of gsl_rng_uniform until a non-zero value is obtained. You can use this function if you need to avoid a singularity at 0.0.
145 =item gsl_rng_uniform_int($r, $n) - This function returns a random integer from 0 to $n-1 inclusive by scaling down and/or discarding samples from the generator $r. All integers in the range [0,$n-1] are produced with equal probability. For generators with a non-zero minimum value an offset is applied so that zero is returned with the correct probability. Note that this function is designed for sampling from ranges smaller than the range of the underlying generator. The parameter $n must be less than or equal to the range of the generator $r. If $n is larger than the range of the generator then the function calls the error handler with an error code of $GSL_EINVAL and returns zero. In particular, this function is not intended for generating the full range of unsigned integer values [0,2^32-1]. Instead choose a generator with the maximal integer range and zero mimimum value, such as $gsl_rng_ranlxd1, $gsl_rng_mt19937 or $gsl_rng_taus, and sample it directly using gsl_rng_get. The range of each generator can be found using the auxiliary functions described in the next section.
147 =item gsl_rng_fwrite($stream, $r) - This function writes the random number state of the random number generator $r to the stream $stream (opened with the fopen function) in binary format. The return value is 0 for success and $GSL_EFAILED if there was a problem writing to the file. Since the data is written in the native binary format it may not be portable between different architectures.
149 =item gsl_rng_fread($stream, $r) - This function reads the random number state into the random number generator $r from the open stream $stream (opened with the fopen function) in binary format. The random number generator $r must be preinitialized with the correct random number generator type since type information is not saved. The return value is 0 for success and $GSL_EFAILED if there was a problem reading from the file. The data is assumed to have been written in the native binary format on the same architecture.
151 =item gsl_rng_clone($r) - This function returns a pointer to a newly created generator which is an exact copy of the generator $r.
153 =item gsl_rng_max($r) - This function returns the largest value that gsl_rng_get can return.
155 =item gsl_rng_min($r) - gsl_rng_min returns the smallest value that gsl_rng_get can return. Usually this value is zero. There are some generators with algorithms that cannot return zero, and for these generators the minimum value is 1.
157 =item gsl_rng_name($r) - This function returns a pointer to the name of the generator. For example,
159 =over
161 =item print "r is a " . gsl_rng_name($r) . "generator\n";
163 =item would print something like r is a 'taus' generator.
165 =back
167 =item gsl_rng_size($r) - This function returns the size of the state of generator $r. You can use this information to access the state directly.
169 =item gsl_rng_state($r) - This function returns a pointer to the state of generator $r. You can use this information to access the state directly.
171 =item gsl_rng_print_state($r)
173 =back
175 =head1 Random Number Generator Types
177 =over 1
179 =item $gsl_rng_default
181 =item $gsl_rng_knuthran
183 =item $gsl_rng_ran0
185 =item $gsl_rng_borosh13
187 =item $gsl_rng_coveyou
189 =item $gsl_rng_cmrg
191 =item $gsl_rng_fishman18
193 =item $gsl_rng_fishman20
195 =item $gsl_rng_fishman2x - This is the L'Ecuyer–Fishman random number generator. It is taken from Knuth's Seminumerical Algorithms, 3rd Ed., page 108. Its sequence is, z_{n+1} = (x_n - y_n) mod m with m = 2^31 - 1. x_n and y_n are given by the fishman20 and lecuyer21 algorithms. The seed specifies the initial value, x_1.
197 =item $gsl_rng_gfsr4
199 =item $gsl_rng_knuthran
201 =item $gsl_rng_knuthran2
203 =item $gsl_rng_knuthran2002
205 =item $gsl_rng_lecuyer21
207 =item $gsl_rng_minstd
209 =item $gsl_rng_mrg
211 =item $gsl_rng_mt19937
213 =item $gsl_rng_mt19937_1999
215 =item $gsl_rng_mt19937_1998
217 =item $gsl_rng_r250
219 =item $gsl_rng_ran0
221 =item $gsl_rng_ran1
223 =item $gsl_rng_ran2
225 =item $gsl_rng_ran3
227 =item $gsl_rng_rand - This is the BSD rand generator. Its sequence is x_{n+1} = (a x_n + c) mod m with a = 1103515245, c = 12345 and m = 2^31. The seed specifies the initial value, x_1. The period of this generator is 2^31, and it uses 1 word of storage per generator.
229 =item $gsl_rng_rand48
231 =item $gsl_rng_random128_bsd
233 =item $gsl_rng_random128_gli
235 =item $gsl_rng_random128_lib
237 =item $gsl_rng_random256_bsd
239 =item $gsl_rng_random256_gli
241 =item $gsl_rng_random256_lib
243 =item $gsl_rng_random32_bsd
245 =item $gsl_rng_random32_glib
247 =item $gsl_rng_random32_libc
249 =item $gsl_rng_random64_bsd
251 =item $gsl_rng_random64_glib
253 =item $gsl_rng_random64_libc
255 =item $gsl_rng_random8_bsd
257 =item $gsl_rng_random8_glibc
259 =item $gsl_rng_random8_libc5
261 =item $gsl_rng_random_bsd
263 =item $gsl_rng_random_glibc2
265 =item $gsl_rng_random_libc5
267 =item $gsl_rng_randu
269 =item $gsl_rng_ranf
271 =item $gsl_rng_ranlux
273 =item $gsl_rng_ranlux389
275 =item $gsl_rng_ranlxd1
277 =item $gsl_rng_ranlxd2
279 =item $gsl_rng_ranlxs0
281 =item $gsl_rng_ranlxs1
283 =item $gsl_rng_ranlxs2
285 =item $gsl_rng_ranmar - This is the RANMAR lagged-fibonacci generator of Marsaglia, Zaman and Tsang. It is a 24-bit generator, originally designed for single-precision IEEE floating point numbers. It was included in the CERNLIB high-energy physics library.
287 =item $gsl_rng_slatec - This is the SLATEC random number generator RAND. It is ancient. The original source code is available from NETLIB.
289 =item $gsl_rng_taus
291 =item $gsl_rng_taus2
293 =item $gsl_rng_taus113
295 =item $gsl_rng_transputer
297 =item $gsl_rng_tt800
299 =item $gsl_rng_uni
301 =item $gsl_rng_uni32
303 =item $gsl_rng_vax - This is the VAX generator MTH$RANDOM. Its sequence is, x_{n+1} = (a x_n + c) mod m with a = 69069, c = 1 and m = 2^32. The seed specifies the initial value, x_1. The period of this generator is 2^32 and it uses 1 word of storage per generator.
305 =item $gsl_rng_waterman14
307 =item $gsl_rng_zuf - This is the ZUFALL lagged Fibonacci series generator of Peterson. Its sequence is,
309 =over
311 =item t = u_{n-273} + u_{n-607}
313 =item u_n = t - floor(t)
315 =back
317 The original source code is available from NETLIB. For more information see,
319 * W. Petersen, “Lagged Fibonacci Random Number Generators for the NEC SX-3”, International Journal of High Speed Computing (1994).
321 =back
323 For more informations on the functions, we refer you to the GSL offcial documentation:
325 L<http://www.gnu.org/software/gsl/manual/html_node/>
327 Tip : search on google: site:http://www.gnu.org/software/gsl/manual/html_node/ name_of_the_function_you_want
329 =head1 EXAMPLES
331 The following example will print out a list a random integers between certain
332 minimum and maximum values. The command line arguments are first the number of
333 random numbers wanted, the minimum and then maximum. The defaults are 10, 0 and
334 100, respectively.
336 use Math::GSL::RNG qw/:all/;
337 my $seed = int rand(100);
338 my $rng = Math::GSL::RNG->new($gsl_rng_knuthran, $seed );
339 my ($num,$min,$max) = @ARGV;
340 $num ||= 10;
341 $min ||= 0;
342 $max ||= 100;
343 print join "\n", map { $min + $rng->get % ($max-$min+1) } (1..$num);
344 print "\n";
346 The C<$seed> argument is optional but encouraged. This program is available in
347 the B<examples/> directory that comes with the source of this module.
349 If you would like a series of random non-integer numbers, then you can generate one "scaling factor"
350 and multiple by that, such as
352 use Math::GSL::RNG qw/:all/;
353 my $scale= rand(10);
354 my $seed = int rand(100);
355 my $rng = Math::GSL::RNG->new($gsl_rng_knuthran, $seed );
356 my ($num,$min,$max) = (10,0,100);
357 print join "\n", map { $scale*($min + $rng->get % ($max-$min+1)) } (1..$num);
358 print "\n";
360 =head1 AUTHORS
362 Jonathan Leto <jonathan@leto.net> and Thierry Moisan <thierry.moisan@gmail.com>
364 =head1 COPYRIGHT AND LICENSE
366 Copyright (C) 2008 Jonathan Leto and Thierry Moisan
368 This program is free software; you can redistribute it and/or modify it
369 under the same terms as Perl itself.
371 =cut