Randist.i

   1 %module Randist
   2 %include "typemaps.i"
   3
   4 void gsl_ran_dir_2d (const gsl_rng * r, double *OUTPUT, double *OUTPUT);
   5 void gsl_ran_dir_2d_trig_method (const gsl_rng * r, double *OUTPUT, double *OUTPUT);
   6 void gsl_ran_dir_3d (const gsl_rng * r, double *OUTPUT, double *OUTPUT, double *OUTPUT);
   7
   8 %typemap(in) void * {
   9     AV *tempav;
  10     I32 len;
  11     int i,x;
  12     SV  **tv;
  13
  14     if (!SvROK($input))
  15         croak("Argument $argnum is not a reference.");
  16     if (SvTYPE(SvRV($input)) != SVt_PVAV)
  17         croak("Argument $argnum is not an array.");
  18
  19     tempav = (AV*)SvRV($input);
  20     len = av_len(tempav);
  21     $1 = (int **) malloc((len+2)*sizeof(int *));
  22     for (i = 0; i <= len; i++) {
  23         tv = av_fetch(tempav, i, 0);
  24         x  = SvIV(*tv);
  25         memset((int*)($1+i), x , 1);
  26         //printf("curr = %d\n", (int)($1+i) );
  27     }
  28 };
  29 %typemap(freearg) void * {
  30     free($1);
  31 }
  32
  33 %{
  34     #include "/usr/local/include/gsl/gsl_randist.h"
  35 %}
  36 %include "/usr/local/include/gsl/gsl_randist.h"
  37
  38 %perlcode %{
  39
  40 our @EXPORT_OK = qw/gsl_ran_bernoulli gsl_ran_bernoulli_pdf gsl_ran_beta
  41                     gsl_ran_beta_pdf gsl_ran_binomial gsl_ran_binomial_knuth
  42                     gsl_ran_binomial_tpe gsl_ran_binomial_pdf gsl_ran_exponential
  43                     gsl_ran_exponential_pdf gsl_ran_exppow gsl_ran_exppow_pdf
  44                     gsl_ran_cauchy gsl_ran_cauchy_pdf gsl_ran_chisq
  45                     gsl_ran_chisq_pdf gsl_ran_dirichlet gsl_ran_dirichlet_pdf
  46                     gsl_ran_dirichlet_lnpdf gsl_ran_erlang gsl_ran_erlang_pdf
  47                     gsl_ran_fdist gsl_ran_fdist_pdf gsl_ran_flat
  48                     gsl_ran_flat_pdf gsl_ran_gamma gsl_ran_gamma_int
  49                     gsl_ran_gamma_pdf gsl_ran_gamma_mt gsl_ran_gamma_knuth
  50                     gsl_ran_gaussian gsl_ran_gaussian_ratio_method gsl_ran_gaussian_ziggurat
  51                     gsl_ran_gaussian_pdf gsl_ran_ugaussian gsl_ran_ugaussian_ratio_method
  52                     gsl_ran_ugaussian_pdf gsl_ran_gaussian_tail gsl_ran_gaussian_tail_pdf
  53                     gsl_ran_ugaussian_tail gsl_ran_ugaussian_tail_pdf gsl_ran_bivariate_gaussian
  54                     gsl_ran_bivariate_gaussian_pdf gsl_ran_landau gsl_ran_landau_pdf
  55                     gsl_ran_geometric gsl_ran_geometric_pdf gsl_ran_hypergeometric
  56                     gsl_ran_hypergeometric_pdf gsl_ran_gumbel1 gsl_ran_gumbel1_pdf
  57                     gsl_ran_gumbel2 gsl_ran_gumbel2_pdf gsl_ran_logistic
  58                     gsl_ran_logistic_pdf gsl_ran_lognormal gsl_ran_lognormal_pdf
  59                     gsl_ran_logarithmic gsl_ran_logarithmic_pdf gsl_ran_multinomial
  60                     gsl_ran_multinomial_pdf gsl_ran_multinomial_lnpdf
  61                     gsl_ran_negative_binomial gsl_ran_negative_binomial_pdf gsl_ran_pascal
  62                     gsl_ran_pascal_pdf gsl_ran_pareto gsl_ran_pareto_pdf
  63                     gsl_ran_poisson gsl_ran_poisson_array
  64                     gsl_ran_poisson_pdf gsl_ran_rayleigh gsl_ran_rayleigh_pdf
  65                     gsl_ran_rayleigh_tail gsl_ran_rayleigh_tail_pdf gsl_ran_tdist
  66                     gsl_ran_tdist_pdf gsl_ran_laplace gsl_ran_laplace_pdf
  67                     gsl_ran_levy gsl_ran_levy_skew gsl_ran_weibull
  68                     gsl_ran_weibull_pdf gsl_ran_dir_2d gsl_ran_dir_2d_trig_method
  69                     gsl_ran_dir_3d gsl_ran_dir_nd gsl_ran_shuffle
  70                     gsl_ran_choose gsl_ran_sample
  71                     gsl_ran_discrete_t gsl_ran_discrete_free gsl_ran_discrete_preproc
  72                     gsl_ran_discrete gsl_ran_discrete_pdf
  73                 /;
  74
  75 our %EXPORT_TAGS = (
  76         all                 => [  @EXPORT_OK ],
  77         logarithmic         => [ gsl_ran_logarithmic , gsl_ran_logarithmic_pdf ],
  78         choose              => [ gsl_ran_choose ],
  79         exponential         => [ gsl_ran_exponential , gsl_ran_exponential_pdf ],
  80         gumbel1             => [ gsl_ran_gumbel1 , gsl_ran_gumbel1_pdf ],
  81         exppow              => [ gsl_ran_exppow , gsl_ran_exppow_pdf ],
  82         sample              => [ gsl_ran_sample ],
  83         logistic            => [ gsl_ran_logistic , gsl_ran_logistic_pdf ],
  84         gaussian            => [
  85                                  gsl_ran_gaussian , gsl_ran_gaussian_ratio_method , gsl_ran_gaussian_ziggurat,
  86                                  gsl_ran_gaussian_pdf , gsl_ran_gaussian_tail , gsl_ran_gaussian_tail_pdf
  87                                ],
  88         poisson             => [ gsl_ran_poisson , gsl_ran_poisson_array , gsl_ran_poisson_pdf ],
  89         binomial            => [ gsl_ran_binomial , gsl_ran_binomial_knuth , gsl_ran_binomial_tpe ,
  90                                  gsl_ran_binomial_pdf ],
  91         fdist               => [ gsl_ran_fdist , gsl_ran_fdist_pdf ],
  92         chisq               => [ gsl_ran_chisq , gsl_ran_chisq_pdf ],
  93         gamma               => [ gsl_ran_gamma , gsl_ran_gamma_int , gsl_ran_gamma_pdf , gsl_ran_gamma_mt , gsl_ran_gamma_knuth ],
  94         hypergeometric      => [ gsl_ran_hypergeometric , gsl_ran_hypergeometric_pdf ],
  95         dirichlet           => [ gsl_ran_dirichlet , gsl_ran_dirichlet_pdf , gsl_ran_dirichlet_lnpdf ],
  96         negative            => [ gsl_ran_negative_binomial , gsl_ran_negative_binomial_pdf ],
  97         flat                => [ gsl_ran_flat , gsl_ran_flat_pdf ],
  98         geometric           => [ gsl_ran_geometric , gsl_ran_geometric_pdf ],
  99         discrete            => [ gsl_ran_discrete , gsl_ran_discrete_pdf],
 100         tdist               => [ gsl_ran_tdist , gsl_ran_tdist_pdf ],
 101         ugaussian           => [ gsl_ran_ugaussian , gsl_ran_ugaussian_ratio_method , gsl_ran_ugaussian_pdf ,
 102                                  gsl_ran_ugaussian_tail , gsl_ran_ugaussian_tail_pdf ],
 103         rayleigh            => [ gsl_ran_rayleigh , gsl_ran_rayleigh_pdf , gsl_ran_rayleigh_tail ,
 104                                  gsl_ran_rayleigh_tail_pdf ],
 105         dir                 => [ gsl_ran_dir_2d , gsl_ran_dir_2d_trig_method , gsl_ran_dir_3d , gsl_ran_dir_nd ],
 106         pascal              => [ gsl_ran_pascal , gsl_ran_pascal_pdf ],
 107         gumbel2             => [ gsl_ran_gumbel2 , gsl_ran_gumbel2_pdf ],
 108         shuffle             => [ gsl_ran_shuffle ],
 109         landau              => [ gsl_ran_landau , gsl_ran_landau_pdf ],
 110         bernoulli           => [ gsl_ran_bernoulli , gsl_ran_bernoulli_pdf ],
 111         weibull             => [ gsl_ran_weibull , gsl_ran_weibull_pdf ],
 112         multinomial         => [ gsl_ran_multinomial , gsl_ran_multinomial_pdf , gsl_ran_multinomial_lnpdf ],
 113         beta                => [ gsl_ran_beta , gsl_ran_beta_pdf ],
 114         lognormal           => [ gsl_ran_lognormal , gsl_ran_lognormal_pdf ],
 115         laplace             => [ gsl_ran_laplace , gsl_ran_laplace_pdf ],
 116         erlang              => [ gsl_ran_erlang , gsl_ran_erlang_pdf ],
 117         cauchy              => [ gsl_ran_cauchy , gsl_ran_cauchy_pdf ],
 118         levy                => [ gsl_ran_levy , gsl_ran_levy_skew ],
 119         bivariate           => [ gsl_ran_bivariate_gaussian , gsl_ran_bivariate_gaussian_pdf ],
 120         pareto              => [ gsl_ran_pareto , gsl_ran_pareto_pdf ]
 121 );
 122
 123 __END__
 124
 125 =head1 NAME
 126
 127 Math::GSL::Randist - Probability Distributions
 128
 129 =head1 SYPNOPSIS
 130
 131 use Math::GSL::Randist qw/:all/;
 132
 133 =head1 DESCRIPTION
 134
 135  Here is a list of all the functions included in this module :
 136
 137 =over
 138
 139 =item gsl_ran_bernoulli($r, $p) - This function returns either 0 or 1, the result of a Bernoulli trial with probability $p. The probability distribution for a Bernoulli trial is, p(0) = 1 - $p and  p(1) = $p. $r is a gsl_rng structure.
 140
 141 =item gsl_ran_bernoulli_pdf($k, $p) - This function computes the probability p($k) of obtaining $k from a Bernoulli distribution with probability parameter $p, using the formula given above.
 142
 143 =item gsl_ran_beta($r, $a, $b) - This function returns a random variate from the beta distribution. The distribution function is, p($x) dx = {Gamma($a+$b) \ Gamma($a) Gamma($b)} $x**{$a-1} (1-$x)**{$b-1} dx for 0 <= $x <= 1.$r is a gsl_rng structure.
 144
 145 =item gsl_ran_beta_pdf($x, $a, $b) - This function computes the probability density p($x) at $x for a beta distribution with parameters $a and $b, using the formula given above.
 146
 147 =item gsl_ran_binomial($k, $p, $n) - This function returns a random integer from the binomial distribution, the number of successes in n independent trials with probability $p. The probability distribution for binomial variates is p($k) = {$n! \ $k! ($n-$k)! } $p**$k (1-$p)^{$n-$k} for 0 <= $k <= $n.
 148
 149 =item gsl_ran_binomial_knuth
 150
 151 =item gsl_ran_binomial_tpe
 152
 153 =item gsl_ran_binomial_pdf($k, $p, $n) - This function computes the probability p($k) of obtaining $k from a binomial distribution with parameters $p and $n, using the formula given above.
 154
 155 =item gsl_ran_exponential($r, $mu) - This function returns a random variate from the exponential distribution with mean $mu. The distribution is, p($x) dx = {1 \ $mu} exp(-$x/$mu) dx for $x >= 0. $r is a gsl_rng structure.
 156
 157 =item gsl_ran_exponential_pdf($x, $mu) - This function computes the probability density p($x) at $x for an exponential distribution with mean $mu, using the formula given above.
 158
 159 =item gsl_ran_exppow($r, $a, $b) - This function returns a random variate from the exponential power distribution with scale parameter $a and exponent $b. The distribution is, p(x) dx = {1 / 2 $a Gamma(1+1/$b)} exp(-|$x/$a|**$b) dx for $x >= 0. For $b = 1 this reduces to the Laplace distribution. For $b = 2 it has the same form as a gaussian distribution, but with $a = sqrt(2) sigma. $r is a gsl_rng structure.
 160
 161 =item gsl_ran_exppow_pdf($x, $a, $b) - This function computes the probability density p($x) at $x for an exponential power distribution with scale parameter $a and exponent $b, using the formula given above.
 162
 163 =item gsl_ran_cauchy
 164
 165 =item gsl_ran_cauchy_pdf
 166
 167 =item gsl_ran_chisq
 168
 169 =item gsl_ran_chisq_pdf
 170
 171 =item gsl_ran_dirichlet
 172
 173 =item gsl_ran_dirichlet_pdf
 174
 175 =item gsl_ran_dirichlet_lnpdf
 176
 177 =item gsl_ran_erlang
 178
 179 =item gsl_ran_erlang_pdf
 180
 181 =item gsl_ran_fdist
 182
 183 =item gsl_ran_fdist_pdf
 184
 185 =item gsl_ran_flat
 186
 187 =item gsl_ran_flat_pdf
 188
 189 =item gsl_ran_gamma
 190
 191 =item gsl_ran_gamma_int
 192
 193 =item gsl_ran_gamma_pdf
 194
 195 =item gsl_ran_gamma_mt
 196
 197 =item gsl_ran_gamma_knuth
 198
 199 =item gsl_ran_gaussian
 200
 201 =item gsl_ran_gaussian_ratio_method
 202
 203 =item gsl_ran_gaussian_ziggurat
 204
 205 =item gsl_ran_gaussian_pdf
 206
 207 =item gsl_ran_ugaussian
 208
 209 =item gsl_ran_ugaussian_ratio_method
 210
 211 =item gsl_ran_ugaussian_pdf
 212
 213 =item gsl_ran_gaussian_tail
 214
 215 =item gsl_ran_gaussian_tail_pdf
 216
 217 =item gsl_ran_ugaussian_tail
 218
 219 =item gsl_ran_ugaussian_tail_pdf
 220
 221 =item gsl_ran_bivariate_gaussian
 222
 223 =item gsl_ran_bivariate_gaussian_pdf
 224
 225 =item gsl_ran_landau
 226
 227 =item gsl_ran_landau_pdf
 228
 229 =item gsl_ran_geometric
 230
 231 =item gsl_ran_geometric_pdf
 232
 233 =item gsl_ran_hypergeometric
 234
 235 =item gsl_ran_hypergeometric_pdf
 236
 237 =item gsl_ran_gumbel1
 238
 239 =item gsl_ran_gumbel1_pdf
 240
 241 =item gsl_ran_gumbel2
 242
 243 =item gsl_ran_gumbel2_pdf
 244
 245 =item gsl_ran_logistic
 246
 247 =item gsl_ran_logistic_pdf
 248
 249 =item gsl_ran_lognormal
 250
 251 =item gsl_ran_lognormal_pdf
 252
 253 =item gsl_ran_logarithmic
 254
 255 =item gsl_ran_logarithmic_pdf
 256
 257 =item gsl_ran_multinomial
 258
 259 =item gsl_ran_multinomial_pdf
 260
 261 =item gsl_ran_multinomial_lnpdf
 262
 263 =item gsl_ran_negative_binomial
 264
 265 =item gsl_ran_negative_binomial_pdf
 266
 267 =item gsl_ran_pascal($r, $p, $n) - This function returns a random integer from the Pascal distribution. The Pascal distribution is simply a negative binomial distribution with an integer value of $n. p($k) = {($n + $k - 1)! \ $k! ($n - 1)! } $p**$n (1-$p)**$k for $k >= 0. $r is gsl_rng structure
 268
 269 =item gsl_ran_pascal_pdf($k, $p, $n) - This function computes the probability p($k) of obtaining $k from a Pascal distribution with parameters $p and $n, using the formula given above.
 270
 271 =item gsl_ran_pareto($r, $a, $b) - This function returns a random variate from the Pareto distribution of order $a. The distribution function is p($x) dx = ($a/$b) / ($x/$b)^{$a+1} dx for $x >= $b. $r is a gsl_rng structure
 272
 273 =item gsl_ran_pareto_pdf($x, $a, $b) - This function computes the probability density p(x) at x for a Pareto distribution with exponent a and scale b, using the formula given above.
 274
 275 =item gsl_ran_poisson($r, $mu) -This function returns a random integer from the Poisson distribution with mean $mu. $r is a gsl_rng structure. The probability distribution for Poisson variates is, p(k) = {$mu**$k \ $k!} exp(-$mu) for $k >= 0.
 276
 277 =item gsl_ran_poisson_array
 278
 279 =item gsl_ran_poisson_pdf($k, $mu)- This function computes the probability p($k) of obtaining $k from a Poisson distribution with mean $mu, using the formula given above.
 280
 281 =item gsl_ran_rayleigh
 282
 283 =item gsl_ran_rayleigh_pdf
 284
 285 =item gsl_ran_rayleigh_tail
 286
 287 =item gsl_ran_rayleigh_tail_pdf
 288
 289 =item gsl_ran_tdist
 290
 291 =item gsl_ran_tdist_pdf
 292
 293 =item gsl_ran_laplace
 294
 295 =item gsl_ran_laplace_pdf
 296
 297 =item gsl_ran_levy
 298
 299 =item gsl_ran_levy_skew
 300
 301 =item gsl_ran_weibull
 302
 303 =item gsl_ran_weibull_pdf
 304
 305 =item gsl_ran_dir_2d($r) - This function returns two values. The first is $x and the second is $y of a random direction vector v = ($x,$y) in two dimensions. The vector is normalized such that |v|^2 = $x^2 + $y^2 = 1.
 306
 307 =item gsl_ran_dir_2d_trig_method($r)  - This function returns two values. The first is $x and the second is $y of a random direction vector v = ($x,$y) in two dimensions. The vector is normalized such that |v|^2 = $x^2 + $y^2 = 1.
 308
 309 =item gsl_ran_dir_3d
 310
 311 =item gsl_ran_dir_nd
 312
 313 =item gsl_ran_shuffle
 314
 315 =item gsl_ran_choose
 316
 317 =item gsl_ran_sample
 318
 319 =item gsl_ran_discrete_preproc
 320
 321 =item gsl_ran_discrete($r, $g) - After gsl_ran_discrete_preproc has been called, you use this function to get the discrete random numbers. $r is a gsl_rng structure and $g is a gsl_ran_discrete structure
 322 =item gsl_ran_discrete_pdf($k, $g) - Returns the probability P[$k] of observing the variable $k. Since P[$k] is not stored as part of the lookup table, it must be recomputed; this computation takes O(K), so if K is large and you care about the original array P[$k] used to create the lookup table, then you should just keep this original array P[$k] around. $r is a gsl_rng structure and $g is a gsl_ran_discrete structure
 323
 324 =item gsl_ran_discrete_free($g) - De-allocates the gsl_ran_discrete pointed to by g.
 325
 326 =back
 327
 328  You have to add the functions you want to use inside the qw /put_funtion_here /.
 329  You can also write use Math::GSL::Randist qw/:name_of_tag/; to use all avaible functions of the module.
 330  Other tags are also avaible, here is a complete list of all tags for this module :
 331
 332 =over
 333
 334 =item logarithmic
 335
 336 =item choose
 337
 338 =item exponential
 339
 340 =item gumbel1
 341
 342 =item exppow
 343
 344 =item sample
 345
 346 =item logistic
 347
 348 =item gaussian
 349
 350 =item poisson
 351
 352 =item binomial
 353
 354 =item fdist
 355
 356 =item chisq
 357
 358 =item gamma
 359
 360 =item hypergeometric
 361
 362 =item dirichlet
 363
 364 =item negative
 365
 366 =item flat
 367
 368 =item geometric
 369
 370 =item discrete
 371
 372 =item tdist
 373
 374 =item ugaussian
 375
 376 =item rayleigh
 377
 378 =item dir
 379
 380 =item pascal
 381
 382 =item gumbel2
 383
 384 =item shuffle
 385
 386 =item landau
 387
 388 =item bernoulli
 389
 390 =item weibull
 391
 392 =item multinomial
 393
 394 =item beta
 395
 396 =item lognormal
 397
 398 =item laplace
 399
 400 =item erlang
 401
 402 =item cauchy
 403
 404 =item levy
 405
 406 =item bivariate
 407
 408 =item pareto
 409
 410 =back
 411
 412  For example the beta tag contains theses functions : gsl_ran_beta, gsl_ran_beta_pdf.
 413
 414  For more informations on the functions, we refer you to the GSL offcial documentation: http://www.gnu.org/software/gsl/manual/html_node/
 415  Tip : search on google: site:http://www.gnu.org/software/gsl/manual/html_node/ name_of_the_function_you_want
 416
 417  You might also want to write
 418  use Math::GSL::RNG qw/:all/;
 419  since a lot of the functions of Math::GSL::Randist take as argument a structure that is created by Math::GSL::RNG.
 420  Refer to Math::GSL::RNG documentation to see how to create such a structure.
 421
 422  Math::GSL::CDF also contains a structure named gsl_ran_discrete_t. An example is given in the EXAMPLES part on how to use the function related to this structure.
 423
 424
 425 =head1 EXAMPLES
 426
 427     use Math::GSL::Randist qw/:all/;
 428     print gsl_ran_exponential_pdf(5,2) . "\n";
 429
 430     use Math::GSL::Randist qw/:all/;
 431     $x= Math::GSL::gsl_ran_discrete_t::new;
 432
 433
 434 =head1 AUTHOR
 435
 436 Jonathan Leto <jonathan@leto.net> and Thierry Moisan <thierry.moisan@gmail.com>
 437
 438 =head1 COPYRIGHT AND LICENSE
 439
 440 Copyright (C) 2008 Jonathan Leto and Thierry Moisan
 441
 442 This program is free software; you can redistribute it and/or modify it
 443 under the same terms as Perl itself.
 444
 445 =cut
 446 %}