Statistics.i

   1 %module "Math::GSL::Statistics"
   2
   3 %include "typemaps.i"
   4 %include "gsl_typemaps.i"
   5
   6 %apply double *OUTPUT { double * min, double * max };
   7
   8 %apply int *OUTPUT { size_t * min_index, size_t * max_index };
   9
  10 %{
  11     #include "gsl/gsl_statistics_double.h"
  12     #include "gsl/gsl_statistics_int.h"
  13     #include "gsl/gsl_statistics_char.h"
  14 %}
  15
  16 %include "gsl/gsl_statistics_double.h"
  17 %include "gsl/gsl_statistics_int.h"
  18 %include "gsl/gsl_statistics_char.h"
  19
  20
  21 %perlcode %{
  22 @EXPORT_OK = qw/
  23                gsl_stats_mean
  24                gsl_stats_variance
  25                gsl_stats_sd
  26                gsl_stats_variance_with_fixed_mean
  27                gsl_stats_sd_with_fixed_mean
  28                gsl_stats_tss
  29                gsl_stats_tss_m
  30                gsl_stats_absdev
  31                gsl_stats_skew
  32                gsl_stats_kurtosis
  33                gsl_stats_lag1_autocorrelation
  34                gsl_stats_covariance
  35                gsl_stats_correlation
  36                gsl_stats_variance_m
  37                gsl_stats_sd_m
  38                gsl_stats_absdev_m
  39                gsl_stats_skew_m_sd
  40                gsl_stats_kurtosis_m_sd
  41                gsl_stats_lag1_autocorrelation_m
  42                gsl_stats_covariance_m
  43                gsl_stats_wmean
  44                gsl_stats_wvariance
  45                gsl_stats_wsd
  46                gsl_stats_wvariance_with_fixed_mean
  47                gsl_stats_wsd_with_fixed_mean
  48                gsl_stats_wtss
  49                gsl_stats_wtss_m
  50                gsl_stats_wabsdev
  51                gsl_stats_wskew
  52                gsl_stats_wkurtosis
  53                gsl_stats_wvariance_m
  54                gsl_stats_wsd_m
  55                gsl_stats_wabsdev_m
  56                gsl_stats_wskew_m_sd
  57                gsl_stats_wkurtosis_m_sd
  58                gsl_stats_pvariance
  59                gsl_stats_ttest
  60                gsl_stats_max
  61                gsl_stats_min
  62                gsl_stats_minmax
  63                gsl_stats_max_index
  64                gsl_stats_min_index
  65                gsl_stats_minmax_index
  66                gsl_stats_median_from_sorted_data
  67                gsl_stats_quantile_from_sorted_data
  68                /;
  69 our @EXPORT_int = qw/
  70                gsl_stats_int_mean
  71                gsl_stats_int_variance
  72                gsl_stats_int_sd
  73                gsl_stats_int_variance_with_fixed_mean
  74                gsl_stats_int_sd_with_fixed_mean
  75                gsl_stats_int_tss
  76                gsl_stats_int_tss_m
  77                gsl_stats_int_absdev
  78                gsl_stats_int_skew
  79                gsl_stats_int_kurtosis
  80                gsl_stats_int_lag1_autocorrelation
  81                gsl_stats_int_covariance
  82                gsl_stats_int_correlation
  83                gsl_stats_int_variance_m
  84                gsl_stats_int_sd_m
  85                gsl_stats_int_absdev_m
  86                gsl_stats_int_skew_m_sd
  87                gsl_stats_int_kurtosis_m_sd
  88                gsl_stats_int_lag1_autocorrelation_m
  89                gsl_stats_int_covariance_m
  90                gsl_stats_int_pvariance
  91                gsl_stats_int_ttest
  92                gsl_stats_int_max
  93                gsl_stats_int_min
  94                gsl_stats_int_minmax
  95                gsl_stats_int_max_index
  96                gsl_stats_int_min_index
  97                gsl_stats_int_minmax_index
  98                gsl_stats_int_median_from_sorted_data
  99                gsl_stats_int_quantile_from_sorted_data
 100                /;
 101 our @EXPORT_char = qw/
 102                gsl_stats_char_mean
 103                gsl_stats_char_variance
 104                gsl_stats_char_sd
 105                gsl_stats_char_variance_with_fixed_mean
 106                gsl_stats_char_sd_with_fixed_mean
 107                gsl_stats_char_tss
 108                gsl_stats_char_tss_m
 109                gsl_stats_char_absdev
 110                gsl_stats_char_skew
 111                gsl_stats_char_kurtosis
 112                gsl_stats_char_lag1_autocorrelation
 113                gsl_stats_char_covariance
 114                gsl_stats_char_correlation
 115                gsl_stats_char_variance_m
 116                gsl_stats_char_sd_m
 117                gsl_stats_char_absdev_m
 118                gsl_stats_char_skew_m_sd
 119                gsl_stats_char_kurtosis_m_sd
 120                gsl_stats_char_lag1_autocorrelation_m
 121                gsl_stats_char_covariance_m
 122                gsl_stats_char_pvariance
 123                gsl_stats_char_ttest
 124                gsl_stats_char_max
 125                gsl_stats_char_min
 126                gsl_stats_char_minmax
 127                gsl_stats_char_max_index
 128                gsl_stats_char_min_index
 129                gsl_stats_char_minmax_index
 130                gsl_stats_char_median_from_sorted_data
 131                gsl_stats_char_quantile_from_sorted_data
 132              /;
 133 push @EXPORT_OK, @EXPORT_int, @EXPORT_char;
 134 %EXPORT_TAGS = (
 135                 all => \@EXPORT_OK,
 136                 int => \@EXPORT_int,
 137                 char => \@EXPORT_char
 138                );
 139 __END__
 140
 141 =head1 NAME
 142
 143 Math::GSL::Statistics - Statistical functions
 144
 145 =head1 SYNOPSIS
 146
 147 use Math::GSL::Statistics qw /:all/;
 148
 149 =head1 DESCRIPTION
 150
 151 Here is a list of all the functions in this module :
 152
 153 =over 2
 154
 155 =item * C<gsl_stats_mean($data, $stride, $n)> - This function returns the arithmetic mean of the array reference $data, a dataset of length $n with stride $stride. The arithmetic mean, or sample mean, is denoted by \Hat\mu and defined as, \Hat\mu = (1/N) \sum x_i where x_i are the elements of the dataset $data. For samples drawn from a gaussian distribution the variance of \Hat\mu is \sigma^2 / N.
 156
 157 =item * C<gsl_stats_variance($data, $stride, $n)>
 158
 159 =item * C<gsl_stats_sd($data, $stride, $n)>
 160
 161 =item * C<gsl_stats_sd_m($data, $stride, $n, $mean)>
 162
 163 =item * C<gsl_stats_variance_with_fixed_mean($data, $stride, $n, $mean)>
 164
 165 =item * C<gsl_stats_sd_with_fixed_mean($data, $stride, $n, $mean)>
 166
 167 =item * C<gsl_stats_tss($data, $stride, $n)>
 168
 169 =item * C<gsl_stats_tss_m($data, $stride, $n, $mean)>
 170
 171 =item * C<gsl_stats_absdev($data, $stride, $n)>
 172
 173 =item * C<gsl_stats_skew($data, $stride, $n)>
 174
 175 =item * C<gsl_stats_skew_m_sd($data, $stride, $n, $mean, $sd)>
 176
 177 =item * C<gsl_stats_kurtosis($data, $stride, $n)>
 178
 179 =item * C<gsl_stats_kurtosis_m_sd($data, $stride, $n, $mean, $sd)>
 180
 181 =item * C<gsl_stats_lag1_autocorrelation($data, $stride, $n)>
 182
 183 =item * C<gsl_stats_lag1_autocorrelation_m($data, $stride, $n, $mean)>
 184
 185 =item * C<gsl_stats_covariance($data1, $stride1, $data2, $stride2, $n)>
 186
 187 =item * C<gsl_stats_covariance_m($data1, $stride1, $data2, $stride2, $n, $mean1, $mean2)>
 188
 189 =item * C<gsl_stats_correlation($data1, $stride1, $data2, $stride2, $n)>
 190
 191 =item * C<gsl_stats_variance_m($data, $stride, $n, $mean)>
 192
 193 =item * C<gsl_stats_absdev_m >
 194
 195 =item * C<gsl_stats_wmean($w, $wstride, $data, $stride, $n)> - This function returns the weighted mean of the dataset $data array reference with stride $stride and length $n, using the set of weights $w, which is an array reference, with stride $wstride and length $n. The weighted mean is defined as, \Hat\mu = (\sum w_i x_i) / (\sum w_i)
 196
 197 =item * C<gsl_stats_wvariance($w, $wstride, $data, $stride, $n)>
 198
 199 =item * C<gsl_stats_wsd($w, $wstride, $data, $stride, $n)>
 200
 201 =item * C<gsl_stats_wsd_m($w, $wstride, $data, $stride, $n, $wmean)>
 202
 203 =item * C<gsl_stats_wvariance_with_fixed_mean($w, $wstride, $data, $stride, $n, $mean)>
 204
 205 =item * C<gsl_stats_wsd_with_fixed_mean($w, $wstride, $data, $stride, $n, $mean)>
 206
 207 =item * C<gsl_stats_wtss($w, $wstride, $data, $stride, $n)>
 208
 209 =item * C<gsl_stats_wtss_m($w, $wstride, $data, $stride, $n, $wmean)>
 210
 211 =item * C<gsl_stats_wabsdev($w, $wstride, $data, $stride, $n)>
 212
 213 =item * C<gsl_stats_wabsdev_m($w, $wstride, $data, $stride, $n, $wmean)>
 214
 215 =item * C<gsl_stats_wskew($w, $wstride, $data, $stride, $n)>
 216
 217 =item * C<gsl_stats_wskew_m_sd($w, $wstride, $data, $stride, $n, $wmean, $wsd)>
 218
 219 =item * C<gsl_stats_wkurtosis($w, $wstride, $data, $stride, $n)>
 220
 221 =item * C<gsl_stats_wvariance_m($w, $wstride, $data, $stride, $n, $wmean, $wsd)>
 222
 223 =item * C<gsl_stats_wkurtosis_m_sd($w, $wstride, $data, $stride, $n, $wmean, $wsd)>
 224
 225 =item * C<gsl_stats_pvariance >
 226
 227 =item * C<gsl_stats_ttest >
 228
 229 =item * C<gsl_stats_max($data, $stride, $n)> - This function returns the maximum value in the $data array reference, a dataset of length $n with stride $stride. The maximum value is defined as the value of the element x_i which satisfies x_i >= x_j for all j. If you want instead to find the element with the largest absolute magnitude you will need to apply fabs or abs to your data before calling this function.
 230
 231 =item * C<gsl_stats_min($data, $stride, $n)>
 232
 233 =item * C<gsl_stats_minmax($data, $stride, $n)> - This function finds both the minimum and maximum values in $data, which is an array reference, in a single pass and returns them in this order.
 234
 235 =item * C<gsl_stats_max_index($data, $stride, $n)>
 236
 237 =item * C<gsl_stats_min_index($data, $stride, $n)>
 238
 239 =item * C<gsl_stats_minmax_index($data, $stride, $n)>
 240
 241 =item * C<gsl_stats_median_from_sorted_data($data, $stride, $n)>
 242
 243 =item * C<gsl_stats_quantile_from_sorted_data($data, $stride, $n, $f)>
 244
 245 =back
 246
 247 The following function are simply variants for int and char of the last functions:
 248
 249 =over 4
 250
 251 =item * C<gsl_stats_int_mean >
 252
 253 =item * C<gsl_stats_int_variance >
 254
 255 =item * C<gsl_stats_int_sd >
 256
 257 =item * C<gsl_stats_int_variance_with_fixed_mean >
 258
 259 =item * C<gsl_stats_int_sd_with_fixed_mean >
 260
 261 =item * C<gsl_stats_int_tss >
 262
 263 =item * C<gsl_stats_int_tss_m >
 264
 265 =item * C<gsl_stats_int_absdev >
 266
 267 =item * C<gsl_stats_int_skew >
 268
 269 =item * C<gsl_stats_int_kurtosis >
 270
 271 =item * C<gsl_stats_int_lag1_autocorrelation >
 272
 273 =item * C<gsl_stats_int_covariance >
 274
 275 =item * C<gsl_stats_int_correlation >
 276
 277 =item * C<gsl_stats_int_variance_m >
 278
 279 =item * C<gsl_stats_int_sd_m >
 280
 281 =item * C<gsl_stats_int_absdev_m >
 282
 283 =item * C<gsl_stats_int_skew_m_sd >
 284
 285 =item * C<gsl_stats_int_kurtosis_m_sd >
 286
 287 =item * C<gsl_stats_int_lag1_autocorrelation_m >
 288
 289 =item * C<gsl_stats_int_covariance_m >
 290
 291 =item * C<gsl_stats_int_pvariance >
 292
 293 =item * C<gsl_stats_int_ttest >
 294
 295 =item * C<gsl_stats_int_max >
 296
 297 =item * C<gsl_stats_int_min >
 298
 299 =item * C<gsl_stats_int_minmax >
 300
 301 =item * C<gsl_stats_int_max_index >
 302
 303 =item * C<gsl_stats_int_min_index >
 304
 305 =item * C<gsl_stats_int_minmax_index >
 306
 307 =item * C<gsl_stats_int_median_from_sorted_data >
 308
 309 =item * C<gsl_stats_int_quantile_from_sorted_data >
 310
 311 =item * C<gsl_stats_char_mean >
 312
 313 =item * C<gsl_stats_char_variance >
 314
 315 =item * C<gsl_stats_char_sd >
 316
 317 =item * C<gsl_stats_char_variance_with_fixed_mean >
 318
 319 =item * C<gsl_stats_char_sd_with_fixed_mean >
 320
 321 =item * C<gsl_stats_char_tss >
 322
 323 =item * C<gsl_stats_char_tss_m >
 324
 325 =item * C<gsl_stats_char_absdev >
 326
 327 =item * C<gsl_stats_char_skew >
 328
 329 =item * C<gsl_stats_char_kurtosis >
 330
 331 =item * C<gsl_stats_char_lag1_autocorrelation >
 332
 333 =item * C<gsl_stats_char_covariance >
 334
 335 =item * C<gsl_stats_char_correlation >
 336
 337 =item * C<gsl_stats_char_variance_m >
 338
 339 =item * C<gsl_stats_char_sd_m >
 340
 341 =item * C<gsl_stats_char_absdev_m >
 342
 343 =item * C<gsl_stats_char_skew_m_sd >
 344
 345 =item * C<gsl_stats_char_kurtosis_m_sd >
 346
 347 =item * C<gsl_stats_char_lag1_autocorrelation_m >
 348
 349 =item * C<gsl_stats_char_covariance_m >
 350
 351 =item * C<gsl_stats_char_pvariance >
 352
 353 =item * C<gsl_stats_char_ttest >
 354
 355 =item * C<gsl_stats_char_max >
 356
 357 =item * C<gsl_stats_char_min >
 358
 359 =item * C<gsl_stats_char_minmax >
 360
 361 =item * C<gsl_stats_char_max_index >
 362
 363 =item * C<gsl_stats_char_min_index >
 364
 365 =item * C<gsl_stats_char_minmax_index >
 366
 367 =item * C<gsl_stats_char_median_from_sorted_data >
 368
 369 =item * C<gsl_stats_char_quantile_from_sorted_data >
 370
 371 =back
 372
 373 You have to add the functions you want to use inside the qw /put_funtion_here /.
 374 You can also write use Math::GSL::Randist qw/:all/; to use all avaible functions of the module.
 375 Other tags are also avaible, here is a complete list of all tags for this module :
 376
 377 =over
 378
 379 =item all
 380
 381 =item int
 382
 383 =item char
 384
 385 =back
 386
 387 For more informations on the functions, we refer you to the GSL offcial
 388 documentation: L<http://www.gnu.org/software/gsl/manual/html_node/>
 389
 390  Tip : search on google: site:http://www.gnu.org/software/gsl/manual/html_node/ name_of_the_function_you_want
 391
 392
 393 =head1 AUTHORS
 394
 395 Jonathan Leto <jonathan@leto.net> and Thierry Moisan <thierry.moisan@gmail.com>
 396
 397 =head1 COPYRIGHT AND LICENSE
 398
 399 Copyright (C) 2008 Jonathan Leto and Thierry Moisan
 400
 401 This program is free software; you can redistribute it and/or modify it
 402 under the same terms as Perl itself.
 403
 404 =cut
 405
 406
 407 %}