Mercurial > hg > octave-lyh
annotate src/DLD-FUNCTIONS/rand.cc @ 9647:54f45f883a53
optimize & extend randperm
| author | Jaroslav Hajek <highegg@gmail.com> |
|---|---|
| date | Wed, 16 Sep 2009 13:41:49 +0200 |
| parents | 610bf90fce2a |
| children | 09da0bd91412 |
| rev | line source |
|---|---|
| 2928 | 1 /* |
| 2 | |
| 7017 | 3 Copyright (C) 1996, 1997, 1998, 1999, 2000, 2002, 2003, 2005, 2006, |
| 8920 | 4 2007, 2008, 2009 John W. Eaton |
|
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|
5 Copyright (C) 2009 VZLU Prague |
| 2928 | 6 |
| 7 This file is part of Octave. | |
| 8 | |
| 9 Octave is free software; you can redistribute it and/or modify it | |
| 10 under the terms of the GNU General Public License as published by the | |
| 7016 | 11 Free Software Foundation; either version 3 of the License, or (at your |
| 12 option) any later version. | |
| 2928 | 13 |
| 14 Octave is distributed in the hope that it will be useful, but WITHOUT | |
| 15 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or | |
| 16 FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License | |
| 17 for more details. | |
| 18 | |
| 19 You should have received a copy of the GNU General Public License | |
| 7016 | 20 along with Octave; see the file COPYING. If not, see |
| 21 <http://www.gnu.org/licenses/>. | |
| 2928 | 22 |
| 23 */ | |
| 24 | |
| 25 #ifdef HAVE_CONFIG_H | |
| 26 #include <config.h> | |
| 27 #endif | |
| 28 | |
| 29 #include <ctime> | |
| 30 | |
| 31 #include <string> | |
| 32 | |
| 33 #include "f77-fcn.h" | |
| 34 #include "lo-mappers.h" | |
| 4307 | 35 #include "oct-rand.h" |
| 4153 | 36 #include "quit.h" |
| 2928 | 37 |
| 38 #include "defun-dld.h" | |
| 39 #include "error.h" | |
| 40 #include "gripes.h" | |
| 41 #include "oct-obj.h" | |
| 42 #include "unwind-prot.h" | |
| 43 #include "utils.h" | |
|
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44 #include "ov-re-mat.h" |
| 2928 | 45 |
| 6437 | 46 /* |
| 47 %!shared __random_statistical_tests__ | |
| 48 %! % Flag whether the statistical tests should be run in "make check" or not | |
| 49 %! __random_statistical_tests__ = 0; | |
| 50 */ | |
| 51 | |
| 4307 | 52 static octave_value |
| 5730 | 53 do_rand (const octave_value_list& args, int nargin, const char *fcn, |
| 54 bool additional_arg = false) | |
| 2928 | 55 { |
| 4307 | 56 octave_value retval; |
| 5730 | 57 NDArray a; |
| 58 int idx = 0; | |
| 59 dim_vector dims; | |
| 2928 | 60 |
| 5730 | 61 if (additional_arg) |
| 62 { | |
| 63 if (nargin == 0) | |
| 64 { | |
| 65 error ("%s: expecting at least one argument", fcn); | |
| 66 goto done; | |
| 67 } | |
| 68 else if (args(0).is_string()) | |
| 69 additional_arg = false; | |
| 70 else | |
| 71 { | |
| 72 a = args(0).array_value (); | |
| 73 if (error_state) | |
| 74 { | |
| 75 error ("%s: expecting scalar or matrix arguments", fcn); | |
| 76 goto done; | |
| 77 } | |
| 78 idx++; | |
| 79 nargin--; | |
| 80 } | |
| 81 } | |
| 2928 | 82 |
| 4543 | 83 switch (nargin) |
| 2928 | 84 { |
| 4543 | 85 case 0: |
| 86 { | |
| 5730 | 87 if (additional_arg) |
| 88 dims = a.dims (); | |
| 89 else | |
| 90 { | |
| 91 dims.resize (2); | |
| 4543 | 92 |
| 5730 | 93 dims(0) = 1; |
| 94 dims(1) = 1; | |
| 95 } | |
| 4543 | 96 goto gen_matrix; |
| 97 } | |
| 98 break; | |
| 2928 | 99 |
| 4543 | 100 case 1: |
| 101 { | |
| 5730 | 102 octave_value tmp = args(idx); |
| 4543 | 103 |
| 104 if (tmp.is_string ()) | |
| 105 { | |
| 106 std::string s_arg = tmp.string_value (); | |
| 2928 | 107 |
| 4543 | 108 if (s_arg == "dist") |
| 109 { | |
| 110 retval = octave_rand::distribution (); | |
| 111 } | |
| 5730 | 112 else if (s_arg == "seed") |
| 4543 | 113 { |
| 114 retval = octave_rand::seed (); | |
| 115 } | |
| 5730 | 116 else if (s_arg == "state" || s_arg == "twister") |
| 117 { | |
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118 retval = octave_rand::state (fcn); |
| 5730 | 119 } |
| 4543 | 120 else if (s_arg == "uniform") |
| 121 { | |
| 122 octave_rand::uniform_distribution (); | |
| 123 } | |
| 124 else if (s_arg == "normal") | |
| 125 { | |
| 126 octave_rand::normal_distribution (); | |
| 127 } | |
| 5730 | 128 else if (s_arg == "exponential") |
| 129 { | |
| 130 octave_rand::exponential_distribution (); | |
| 131 } | |
| 132 else if (s_arg == "poisson") | |
| 133 { | |
| 134 octave_rand::poisson_distribution (); | |
| 135 } | |
| 136 else if (s_arg == "gamma") | |
| 137 { | |
| 138 octave_rand::gamma_distribution (); | |
| 139 } | |
| 4543 | 140 else |
| 4664 | 141 error ("%s: unrecognized string argument", fcn); |
| 4543 | 142 } |
| 143 else if (tmp.is_scalar_type ()) | |
| 144 { | |
| 145 double dval = tmp.double_value (); | |
| 2928 | 146 |
| 4543 | 147 if (xisnan (dval)) |
| 148 { | |
| 4664 | 149 error ("%s: NaN is invalid a matrix dimension", fcn); |
| 4543 | 150 } |
| 151 else | |
| 152 { | |
| 153 dims.resize (2); | |
| 154 | |
| 5275 | 155 dims(0) = NINTbig (tmp.double_value ()); |
| 156 dims(1) = NINTbig (tmp.double_value ()); | |
| 2928 | 157 |
| 4543 | 158 if (! error_state) |
| 159 goto gen_matrix; | |
| 160 } | |
| 161 } | |
| 162 else if (tmp.is_range ()) | |
| 163 { | |
| 164 Range r = tmp.range_value (); | |
| 165 | |
| 166 if (r.all_elements_are_ints ()) | |
| 167 { | |
| 5275 | 168 octave_idx_type n = r.nelem (); |
| 4543 | 169 |
| 170 dims.resize (n); | |
| 171 | |
| 5275 | 172 octave_idx_type base = NINTbig (r.base ()); |
| 173 octave_idx_type incr = NINTbig (r.inc ()); | |
| 174 octave_idx_type lim = NINTbig (r.limit ()); | |
| 2928 | 175 |
| 4543 | 176 if (base < 0 || lim < 0) |
| 4664 | 177 error ("%s: all dimensions must be nonnegative", fcn); |
| 4543 | 178 else |
| 179 { | |
| 5275 | 180 for (octave_idx_type i = 0; i < n; i++) |
| 4543 | 181 { |
| 182 dims(i) = base; | |
| 183 base += incr; | |
| 184 } | |
| 2928 | 185 |
| 4543 | 186 goto gen_matrix; |
| 187 } | |
| 188 } | |
| 189 else | |
| 4664 | 190 error ("%s: expecting all elements of range to be integers", |
| 191 fcn); | |
| 4543 | 192 } |
| 193 else if (tmp.is_matrix_type ()) | |
| 194 { | |
| 195 Array<int> iv = tmp.int_vector_value (true); | |
| 196 | |
| 197 if (! error_state) | |
| 198 { | |
| 5275 | 199 octave_idx_type len = iv.length (); |
| 2928 | 200 |
| 4543 | 201 dims.resize (len); |
| 202 | |
| 5275 | 203 for (octave_idx_type i = 0; i < len; i++) |
| 4543 | 204 { |
| 5275 | 205 octave_idx_type elt = iv(i); |
| 4543 | 206 |
| 207 if (elt < 0) | |
| 208 { | |
| 4664 | 209 error ("%s: all dimensions must be nonnegative", fcn); |
| 4543 | 210 goto done; |
| 211 } | |
| 212 | |
| 213 dims(i) = iv(i); | |
| 214 } | |
| 2928 | 215 |
| 4543 | 216 goto gen_matrix; |
| 217 } | |
| 218 else | |
| 4664 | 219 error ("%s: expecting integer vector", fcn); |
| 4543 | 220 } |
| 221 else | |
| 222 { | |
| 223 gripe_wrong_type_arg ("rand", tmp); | |
| 224 return retval; | |
| 225 } | |
| 226 } | |
| 227 break; | |
| 228 | |
| 229 default: | |
| 230 { | |
| 5730 | 231 octave_value tmp = args(idx); |
| 4543 | 232 |
| 233 if (nargin == 2 && tmp.is_string ()) | |
| 234 { | |
| 5164 | 235 std::string ts = tmp.string_value (); |
| 236 | |
| 5730 | 237 if (ts == "seed") |
| 4543 | 238 { |
| 5782 | 239 if (args(idx+1).is_real_scalar ()) |
| 240 { | |
| 241 double d = args(idx+1).double_value (); | |
| 2928 | 242 |
| 5782 | 243 if (! error_state) |
| 244 octave_rand::seed (d); | |
| 245 } | |
| 246 else | |
| 247 error ("%s: seed must be a real scalar", fcn); | |
| 4543 | 248 } |
| 5730 | 249 else if (ts == "state" || ts == "twister") |
| 250 { | |
| 251 ColumnVector s = | |
| 252 ColumnVector (args(idx+1).vector_value(false, true)); | |
| 253 | |
| 254 if (! error_state) | |
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255 octave_rand::state (s, fcn); |
| 5730 | 256 } |
| 4543 | 257 else |
| 4664 | 258 error ("%s: unrecognized string argument", fcn); |
| 4543 | 259 } |
| 260 else | |
| 261 { | |
| 262 dims.resize (nargin); | |
| 263 | |
| 264 for (int i = 0; i < nargin; i++) | |
| 265 { | |
| 5760 | 266 dims(i) = args(idx+i).int_value (); |
| 4543 | 267 |
| 268 if (error_state) | |
| 269 { | |
| 4664 | 270 error ("%s: expecting integer arguments", fcn); |
| 4543 | 271 goto done; |
| 272 } | |
| 273 } | |
| 274 | |
| 275 goto gen_matrix; | |
| 276 } | |
| 277 } | |
| 278 break; | |
| 2928 | 279 } |
| 280 | |
| 4543 | 281 done: |
| 2928 | 282 |
| 283 return retval; | |
| 284 | |
| 285 gen_matrix: | |
| 286 | |
| 5355 | 287 dims.chop_trailing_singletons (); |
| 288 | |
| 5730 | 289 if (additional_arg) |
| 290 { | |
| 291 if (a.length() == 1) | |
| 292 return octave_rand::nd_array (dims, a(0)); | |
| 293 else | |
| 294 { | |
| 295 if (a.dims() != dims) | |
| 296 { | |
| 297 error ("%s: mismatch in argument size", fcn); | |
| 298 return retval; | |
| 299 } | |
| 300 octave_idx_type len = a.length (); | |
| 301 NDArray m (dims); | |
| 302 double *v = m.fortran_vec (); | |
| 303 for (octave_idx_type i = 0; i < len; i++) | |
| 304 v[i] = octave_rand::scalar (a(i)); | |
| 305 return m; | |
| 306 } | |
| 307 } | |
| 308 else | |
| 309 return octave_rand::nd_array (dims); | |
| 2928 | 310 } |
| 311 | |
| 4665 | 312 DEFUN_DLD (rand, args, , |
| 3369 | 313 "-*- texinfo -*-\n\ |
| 314 @deftypefn {Loadable Function} {} rand (@var{x})\n\ | |
| 315 @deftypefnx {Loadable Function} {} rand (@var{n}, @var{m})\n\ | |
| 5730 | 316 @deftypefnx {Loadable Function} {} rand (\"state\", @var{x})\n\ |
| 317 @deftypefnx {Loadable Function} {} rand (\"seed\", @var{x})\n\ | |
| 3369 | 318 Return a matrix with random elements uniformly distributed on the\n\ |
| 319 interval (0, 1). The arguments are handled the same as the arguments\n\ | |
| 5730 | 320 for @code{eye}.\n\ |
| 321 \n\ | |
| 322 You can query the state of the random number generator using the\n\ | |
| 3369 | 323 form\n\ |
| 2928 | 324 \n\ |
| 3369 | 325 @example\n\ |
| 5730 | 326 v = rand (\"state\")\n\ |
| 327 @end example\n\ | |
| 328 \n\ | |
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329 This returns a column vector @var{v} of length 625. Later, you can\n\ |
| 5730 | 330 restore the random number generator to the state @var{v}\n\ |
| 331 using the form\n\ | |
| 332 \n\ | |
| 333 @example\n\ | |
| 334 rand (\"state\", v)\n\ | |
| 3369 | 335 @end example\n\ |
| 336 \n\ | |
| 337 @noindent\n\ | |
| 5730 | 338 You may also initialize the state vector from an arbitrary vector of\n\ |
| 339 length <= 625 for @var{v}. This new state will be a hash based on the\n\ | |
| 5798 | 340 value of @var{v}, not @var{v} itself.\n\ |
| 5730 | 341 \n\ |
| 342 By default, the generator is initialized from @code{/dev/urandom} if it is\n\ | |
| 343 available, otherwise from cpu time, wall clock time and the current\n\ | |
| 344 fraction of a second.\n\ | |
| 345 \n\ | |
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346 To compute the pseudo-random sequence, @code{rand} uses the Mersenne\n\ |
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347 Twister with a period of @math{2^{19937}-1} (See M. Matsumoto and T. Nishimura,\n\ |
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348 @cite{Mersenne Twister: A 623-dimensionally equidistributed uniform pseudorandom number generator}, ACM Trans. on\n\ |
| 7001 | 349 Modeling and Computer Simulation Vol. 8, No. 1, January pp.3-30 1998,\n\ |
| 7171 | 350 @url{http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html}).\n\ |
| 6547 | 351 Do @strong{not} use for cryptography without securely hashing\n\ |
| 352 several returned values together, otherwise the generator state\n\ | |
| 353 can be learned after reading 624 consecutive values.\n\ | |
| 5730 | 354 \n\ |
| 7096 | 355 Older versions of Octave used a different random number generator.\n\ |
| 356 The new generator is used by default\n\ | |
| 5730 | 357 as it is significantly faster than the old generator, and produces\n\ |
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358 random numbers with a significantly longer cycle time. However, in\n\ |
| 5798 | 359 some circumstances it might be desirable to obtain the same random\n\ |
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360 sequences as used by the old generators. To do this the keyword\n\ |
| 5730 | 361 \"seed\" is used to specify that the old generators should be use,\n\ |
| 362 as in\n\ | |
| 2928 | 363 \n\ |
| 3369 | 364 @example\n\ |
| 5730 | 365 rand (\"seed\", val)\n\ |
| 3369 | 366 @end example\n\ |
| 367 \n\ | |
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368 which sets the seed of the generator to @var{val}. The seed of the\n\ |
| 5730 | 369 generator can be queried with\n\ |
| 370 \n\ | |
| 371 @example\n\ | |
| 372 s = rand (\"seed\")\n\ | |
| 373 @end example\n\ | |
| 374 \n\ | |
| 375 However, it should be noted that querying the seed will not cause\n\ | |
| 376 @code{rand} to use the old generators, only setting the seed will.\n\ | |
| 377 To cause @code{rand} to once again use the new generators, the\n\ | |
| 378 keyword \"state\" should be used to reset the state of the @code{rand}.\n\ | |
| 5798 | 379 @seealso{randn, rande, randg, randp}\n\ |
| 3369 | 380 @end deftypefn") |
| 2928 | 381 { |
| 4307 | 382 octave_value retval; |
| 2928 | 383 |
| 384 int nargin = args.length (); | |
| 385 | |
| 4543 | 386 retval = do_rand (args, nargin, "rand"); |
| 2928 | 387 |
| 388 return retval; | |
| 389 } | |
| 390 | |
| 8871 | 391 // FIXME -- The old generator (selected when "seed" is set) will not |
| 392 // work properly if compiled to use 64-bit integers. | |
| 393 | |
| 5730 | 394 /* |
| 395 %!test # 'state' can be a scalar | |
| 396 %! rand('state',12); x = rand(1,4); | |
| 397 %! rand('state',12); y = rand(1,4); | |
| 398 %! assert(x,y); | |
| 399 %!test # 'state' can be a vector | |
| 400 %! rand('state',[12,13]); x=rand(1,4); | |
| 401 %! rand('state',[12;13]); y=rand(1,4); | |
| 402 %! assert(x,y); | |
| 403 %!test # querying 'state' doesn't disturb sequence | |
| 404 %! rand('state',12); rand(1,2); x=rand(1,2); | |
| 405 %! rand('state',12); rand(1,2); | |
| 406 %! s=rand('state'); y=rand(1,2); | |
| 407 %! assert(x,y); | |
| 408 %! rand('state',s); z=rand(1,2); | |
| 409 %! assert(x,z); | |
| 410 %!test # 'seed' must be a scalar | |
| 411 %! rand('seed',12); x = rand(1,4); | |
| 412 %! rand('seed',12); y = rand(1,4); | |
| 413 %! assert(x,y); | |
| 414 %!error(rand('seed',[12,13])) | |
| 415 %!test # querying 'seed' returns a value which can be used later | |
| 416 %! s=rand('seed'); x=rand(1,2); | |
| 417 %! rand('seed',s); y=rand(1,2); | |
| 418 %! assert(x,y); | |
| 419 %!test # querying 'seed' doesn't disturb sequence | |
| 420 %! rand('seed',12); rand(1,2); x=rand(1,2); | |
| 421 %! rand('seed',12); rand(1,2); | |
| 422 %! s=rand('seed'); y=rand(1,2); | |
| 423 %! assert(x,y); | |
| 424 %! rand('seed',s); z=rand(1,2); | |
| 425 %! assert(x,z); | |
| 426 */ | |
| 427 | |
| 428 /* | |
| 429 %!test | |
| 6437 | 430 %! % Test fixed state |
| 431 %! rand("state",1); | |
| 6443 | 432 %! assert (rand(1,6), [0.1343642441124013 0.8474337369372327 0.763774618976614 0.2550690257394218 0.495435087091941 0.4494910647887382],1e-6); |
| 6437 | 433 %!test |
| 6443 | 434 %! % Test fixed seed |
| 6437 | 435 %! rand("seed",1); |
| 6443 | 436 %! assert (rand(1,6), [0.8668024251237512 0.9126510815694928 0.09366085007786751 0.1664607301354408 0.7408077004365623 0.7615650338120759],1e-6); |
| 5730 | 437 %!test |
| 6437 | 438 %! if (__random_statistical_tests__) |
| 439 %! % statistical tests may fail occasionally. | |
| 440 %! rand("state",12); | |
| 441 %! x = rand(100000,1); | |
| 442 %! assert(max(x)<1.); %*** Please report this!!! *** | |
| 443 %! assert(min(x)>0.); %*** Please report this!!! *** | |
| 444 %! assert(mean(x),0.5,0.0024); | |
| 445 %! assert(var(x),1/48,0.0632); | |
| 446 %! assert(skewness(x),0,0.012); | |
| 447 %! assert(kurtosis(x),-6/5,0.0094); | |
| 448 %! endif | |
| 449 %!test | |
| 450 %! if (__random_statistical_tests__) | |
| 451 %! % statistical tests may fail occasionally. | |
| 452 %! rand("seed",12); | |
| 453 %! x = rand(100000,1); | |
| 454 %! assert(max(x)<1.); %*** Please report this!!! *** | |
| 455 %! assert(min(x)>0.); %*** Please report this!!! *** | |
| 456 %! assert(mean(x),0.5,0.0024); | |
| 457 %! assert(var(x),1/48,0.0632); | |
| 458 %! assert(skewness(x),0,0.012); | |
| 459 %! assert(kurtosis(x),-6/5,0.0094); | |
| 460 %! endif | |
| 5730 | 461 */ |
| 462 | |
| 463 | |
| 4307 | 464 static std::string current_distribution = octave_rand::distribution (); |
| 465 | |
| 2928 | 466 static void |
| 467 reset_rand_generator (void *) | |
| 468 { | |
| 4307 | 469 octave_rand::distribution (current_distribution); |
| 2928 | 470 } |
| 471 | |
| 4665 | 472 DEFUN_DLD (randn, args, , |
| 3369 | 473 "-*- texinfo -*-\n\ |
| 474 @deftypefn {Loadable Function} {} randn (@var{x})\n\ | |
| 475 @deftypefnx {Loadable Function} {} randn (@var{n}, @var{m})\n\ | |
| 5730 | 476 @deftypefnx {Loadable Function} {} randn (\"state\", @var{x})\n\ |
| 477 @deftypefnx {Loadable Function} {} randn (\"seed\", @var{x})\n\ | |
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478 Return a matrix with normally distributed pseudo-random\n\ |
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479 elements having zero mean and variance one. The arguments are\n\ |
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480 handled the same as the arguments for @code{rand}.\n\ |
| 3369 | 481 \n\ |
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482 By default, @code{randn} uses the Marsaglia and Tsang ``Ziggurat technique'' to\n\ |
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483 transform from a uniform to a normal distribution. (G. Marsaglia and\n\ |
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484 W.W. Tsang, @cite{Ziggurat method for generating random variables},\n\ |
| 5730 | 485 J. Statistical Software, vol 5, 2000,\n\ |
| 486 @url{http://www.jstatsoft.org/v05/i08/})\n\ | |
| 2928 | 487 \n\ |
| 6547 | 488 @seealso{rand, rande, randg, randp}\n\ |
| 3369 | 489 @end deftypefn") |
| 2928 | 490 { |
| 4307 | 491 octave_value retval; |
| 2928 | 492 |
| 493 int nargin = args.length (); | |
| 494 | |
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495 unwind_protect::frame_id_t uwp_frame = unwind_protect::begin_frame (); |
| 2928 | 496 |
| 4543 | 497 // This relies on the fact that elements are popped from the unwind |
| 498 // stack in the reverse of the order they are pushed | |
| 499 // (i.e. current_distribution will be reset before calling | |
| 500 // reset_rand_generator()). | |
| 2928 | 501 |
| 4543 | 502 unwind_protect::add (reset_rand_generator, 0); |
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503 unwind_protect::protect_var (current_distribution); |
| 2928 | 504 |
| 4543 | 505 current_distribution = "normal"; |
| 2928 | 506 |
| 4543 | 507 octave_rand::distribution (current_distribution); |
| 2928 | 508 |
| 4543 | 509 retval = do_rand (args, nargin, "randn"); |
| 2928 | 510 |
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511 unwind_protect::run_frame (uwp_frame); |
| 2928 | 512 |
| 513 return retval; | |
| 514 } | |
| 515 | |
| 516 /* | |
| 5730 | 517 %!test |
| 6437 | 518 %! % Test fixed state |
| 519 %! randn("state",1); | |
| 6443 | 520 %! assert (randn(1,6), [-2.666521678978671 -0.7381719971724564 1.507903992673601 0.6019427189162239 -0.450661261143348 -0.7054431351574116],1e-6); |
| 6437 | 521 %!test |
| 6443 | 522 %! % Test fixed seed |
| 6437 | 523 %! randn("seed",1); |
| 6443 | 524 %! assert (randn(1,6), [-1.039402365684509 -1.25938892364502 0.1968704611063004 0.3874166905879974 -0.5976632833480835 -0.6615074276924133],1e-6); |
| 5730 | 525 %!test |
| 6437 | 526 %! if (__random_statistical_tests__) |
| 527 %! % statistical tests may fail occasionally. | |
| 528 %! randn("state",12); | |
| 529 %! x = randn(100000,1); | |
| 530 %! assert(mean(x),0,0.01); | |
| 531 %! assert(var(x),1,0.02); | |
| 532 %! assert(skewness(x),0,0.02); | |
| 533 %! assert(kurtosis(x),0,0.04); | |
| 534 %! endif | |
| 535 %!test | |
| 536 %! if (__random_statistical_tests__) | |
| 537 %! % statistical tests may fail occasionally. | |
| 538 %! randn("seed",12); | |
| 539 %! x = randn(100000,1); | |
| 540 %! assert(mean(x),0,0.01); | |
| 541 %! assert(var(x),1,0.02); | |
| 542 %! assert(skewness(x),0,0.02); | |
| 543 %! assert(kurtosis(x),0,0.04); | |
| 544 %! endif | |
| 5730 | 545 */ |
| 546 | |
| 547 DEFUN_DLD (rande, args, , | |
| 548 "-*- texinfo -*-\n\ | |
| 549 @deftypefn {Loadable Function} {} rande (@var{x})\n\ | |
| 550 @deftypefnx {Loadable Function} {} rande (@var{n}, @var{m})\n\ | |
| 551 @deftypefnx {Loadable Function} {} rande (\"state\", @var{x})\n\ | |
| 552 @deftypefnx {Loadable Function} {} rande (\"seed\", @var{x})\n\ | |
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553 Return a matrix with exponentially distributed random elements. The\n\ |
| 5730 | 554 arguments are handled the same as the arguments for @code{rand}.\n\ |
| 555 \n\ | |
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556 By default, @code{randn} uses the Marsaglia and Tsang ``Ziggurat technique'' to\n\ |
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557 transform from a uniform to a exponential distribution. (G. Marsaglia and\n\ |
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558 W.W. Tsang, @cite{Ziggurat method for generating random variables},\n\ |
| 5730 | 559 J. Statistical Software, vol 5, 2000,\n\ |
| 560 @url{http://www.jstatsoft.org/v05/i08/})\n\ | |
| 6547 | 561 @seealso{rand, randn, randg, randp}\n\ |
| 5730 | 562 @end deftypefn") |
| 563 { | |
| 564 octave_value retval; | |
| 565 | |
| 566 int nargin = args.length (); | |
| 567 | |
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568 unwind_protect::frame_id_t uwp_frame = unwind_protect::begin_frame (); |
| 5730 | 569 |
| 570 // This relies on the fact that elements are popped from the unwind | |
| 571 // stack in the reverse of the order they are pushed | |
| 572 // (i.e. current_distribution will be reset before calling | |
| 573 // reset_rand_generator()). | |
| 574 | |
| 575 unwind_protect::add (reset_rand_generator, 0); | |
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576 unwind_protect::protect_var (current_distribution); |
| 5730 | 577 |
| 578 current_distribution = "exponential"; | |
| 579 | |
| 580 octave_rand::distribution (current_distribution); | |
| 581 | |
| 582 retval = do_rand (args, nargin, "rande"); | |
| 583 | |
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584 unwind_protect::run_frame (uwp_frame); |
| 5730 | 585 |
| 586 return retval; | |
| 587 } | |
| 588 | |
| 589 /* | |
| 590 %!test | |
| 6437 | 591 %! % Test fixed state |
| 592 %! rande("state",1); | |
| 6443 | 593 %! assert (rande(1,6), [3.602973885835625 0.1386190677555021 0.6743112889616958 0.4512830847258422 0.7255744741233175 0.3415969205292291],1e-6); |
| 6437 | 594 %!test |
| 6443 | 595 %! % Test fixed seed |
| 6437 | 596 %! rande("seed",1); |
| 6443 | 597 %! assert (rande(1,6), [0.06492075175653866 1.717980206012726 0.4816154008731246 0.5231300676241517 0.103910739364359 1.668931916356087],1e-6); |
| 5730 | 598 %!test |
| 6437 | 599 %! if (__random_statistical_tests__) |
| 600 %! % statistical tests may fail occasionally | |
| 601 %! rande("state",1); | |
| 602 %! x = rande(100000,1); | |
| 603 %! assert(min(x)>0); % *** Please report this!!! *** | |
| 604 %! assert(mean(x),1,0.01); | |
| 605 %! assert(var(x),1,0.03); | |
| 606 %! assert(skewness(x),2,0.06); | |
| 607 %! assert(kurtosis(x),6,0.7); | |
| 608 %! endif | |
| 609 %!test | |
| 610 %! if (__random_statistical_tests__) | |
| 611 %! % statistical tests may fail occasionally | |
| 612 %! rande("seed",1); | |
| 613 %! x = rande(100000,1); | |
| 614 %! assert(min(x)>0); % *** Please report this!!! *** | |
| 615 %! assert(mean(x),1,0.01); | |
| 616 %! assert(var(x),1,0.03); | |
| 617 %! assert(skewness(x),2,0.06); | |
| 618 %! assert(kurtosis(x),6,0.7); | |
| 619 %! endif | |
| 5730 | 620 */ |
| 621 | |
| 622 DEFUN_DLD (randg, args, , | |
| 623 "-*- texinfo -*-\n\ | |
| 624 @deftypefn {Loadable Function} {} randg (@var{a}, @var{x})\n\ | |
| 625 @deftypefnx {Loadable Function} {} randg (@var{a}, @var{n}, @var{m})\n\ | |
| 626 @deftypefnx {Loadable Function} {} randg (\"state\", @var{x})\n\ | |
| 627 @deftypefnx {Loadable Function} {} randg (\"seed\", @var{x})\n\ | |
| 628 Return a matrix with @code{gamma(@var{a},1)} distributed random elements.\n\ | |
| 629 The arguments are handled the same as the arguments for @code{rand},\n\ | |
| 630 except for the argument @var{a}.\n\ | |
| 631 \n\ | |
| 632 This can be used to generate many distributions:\n\ | |
| 633 \n\ | |
| 634 @table @asis\n\ | |
| 6547 | 635 @item @code{gamma (a, b)} for @code{a > -1}, @code{b > 0}\n\ |
| 5730 | 636 @example\n\ |
| 6547 | 637 r = b * randg (a)\n\ |
| 5730 | 638 @end example\n\ |
| 6547 | 639 @item @code{beta (a, b)} for @code{a > -1}, @code{b > -1}\n\ |
| 5730 | 640 @example\n\ |
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641 @group\n\ |
| 6547 | 642 r1 = randg (a, 1)\n\ |
| 643 r = r1 / (r1 + randg (b, 1))\n\ | |
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644 @end group\n\ |
| 5730 | 645 @end example\n\ |
| 6547 | 646 @item @code{Erlang (a, n)}\n\ |
| 5730 | 647 @example\n\ |
| 6547 | 648 r = a * randg (n)\n\ |
| 5730 | 649 @end example\n\ |
| 6547 | 650 @item @code{chisq (df)} for @code{df > 0}\n\ |
| 5730 | 651 @example\n\ |
| 6547 | 652 r = 2 * randg (df / 2)\n\ |
| 5730 | 653 @end example\n\ |
| 654 @item @code{t(df)} for @code{0 < df < inf} (use randn if df is infinite)\n\ | |
| 655 @example\n\ | |
| 6547 | 656 r = randn () / sqrt (2 * randg (df / 2) / df)\n\ |
| 5730 | 657 @end example\n\ |
| 6547 | 658 @item @code{F (n1, n2)} for @code{0 < n1}, @code{0 < n2}\n\ |
| 5730 | 659 @example\n\ |
| 7096 | 660 @group\n\ |
| 661 ## r1 equals 1 if n1 is infinite\n\ | |
| 662 r1 = 2 * randg (n1 / 2) / n1\n\ | |
| 663 ## r2 equals 1 if n2 is infinite\n\ | |
| 664 r2 = 2 * randg (n2 / 2) / n2\n\ | |
| 5730 | 665 r = r1 / r2\n\n\ |
| 7096 | 666 @end group\n\ |
| 5730 | 667 @end example\n\ |
| 668 @item negative @code{binomial (n, p)} for @code{n > 0}, @code{0 < p <= 1}\n\ | |
| 669 @example\n\ | |
| 6547 | 670 r = randp ((1 - p) / p * randg (n))\n\ |
| 5730 | 671 @end example\n\ |
| 6547 | 672 @item non-central @code{chisq (df, L)}, for @code{df >= 0} and @code{L > 0}\n\ |
| 5730 | 673 (use chisq if @code{L = 0})\n\ |
| 674 @example\n\ | |
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675 @group\n\ |
| 6547 | 676 r = randp (L / 2)\n\ |
| 677 r(r > 0) = 2 * randg (r(r > 0))\n\ | |
| 678 r(df > 0) += 2 * randg (df(df > 0)/2)\n\ | |
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679 @end group\n\ |
| 5730 | 680 @end example\n\ |
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681 @item @code{Dirichlet (a1, @dots{} ak)}\n\ |
| 5730 | 682 @example\n\ |
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683 @group\n\ |
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684 r = (randg (a1), @dots{}, randg (ak))\n\ |
| 6547 | 685 r = r / sum (r)\n\ |
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686 @end group\n\ |
| 5730 | 687 @end example\n\ |
| 688 @end table\n\ | |
| 6547 | 689 @seealso{rand, randn, rande, randp}\n\ |
| 5730 | 690 @end deftypefn") |
| 691 { | |
| 692 octave_value retval; | |
| 693 | |
| 694 int nargin = args.length (); | |
| 695 | |
| 696 if (nargin < 1) | |
| 697 error ("randg: insufficient arguments"); | |
| 698 else | |
| 699 { | |
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700 unwind_protect::frame_id_t uwp_frame = unwind_protect::begin_frame (); |
| 5730 | 701 |
| 702 // This relies on the fact that elements are popped from the unwind | |
| 703 // stack in the reverse of the order they are pushed | |
| 704 // (i.e. current_distribution will be reset before calling | |
| 705 // reset_rand_generator()). | |
| 706 | |
| 707 unwind_protect::add (reset_rand_generator, 0); | |
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708 unwind_protect::protect_var (current_distribution); |
| 5730 | 709 |
| 710 current_distribution = "gamma"; | |
| 711 | |
| 712 octave_rand::distribution (current_distribution); | |
| 713 | |
| 714 retval = do_rand (args, nargin, "randg", true); | |
| 715 | |
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716 unwind_protect::run_frame (uwp_frame); |
| 5730 | 717 } |
| 718 | |
| 719 return retval; | |
| 720 } | |
| 721 | |
| 722 /* | |
| 723 %!test | |
| 6437 | 724 %! randg("state",12) |
| 725 %!assert(randg([-inf,-1,0,inf,nan]),[nan,nan,nan,nan,nan]) % *** Please report | |
| 726 | |
| 727 | |
| 728 %!test | |
| 729 %! % Test fixed state | |
| 730 %! randg("state",1); | |
| 6443 | 731 %! assert (randg(0.1,1,6), [0.0103951513331241 8.335671459898252e-05 0.00138691397249762 0.000587308416993855 0.495590518784736 2.3921917414795e-12],1e-6); |
| 6437 | 732 %!test |
| 733 %! % Test fixed state | |
| 734 %! randg("state",1); | |
| 6443 | 735 %! assert (randg(0.95,1,6), [3.099382433255327 0.3974529788871218 0.644367450750855 1.143261091802246 1.964111762696822 0.04011915547957939],1e-6); |
| 6437 | 736 %!test |
| 737 %! % Test fixed state | |
| 738 %! randg("state",1); | |
| 6443 | 739 %! assert (randg(1,1,6), [0.2273389379645993 1.288822625058359 0.2406335209340746 1.218869553370733 1.024649860162554 0.09631230343599533],1e-6); |
| 6437 | 740 %!test |
| 741 %! % Test fixed state | |
| 742 %! randg("state",1); | |
| 6443 | 743 %! assert (randg(10,1,6), [3.520369644331133 15.15369864472106 8.332112081991205 8.406211067432674 11.81193475187611 10.88792728177059],1e-5); |
| 6437 | 744 %!test |
| 745 %! % Test fixed state | |
| 746 %! randg("state",1); | |
| 6443 | 747 %! assert (randg(100,1,6), [75.34570255262264 115.4911985594699 95.23493031356388 95.48926019250911 106.2397448229803 103.4813150404118],1e-4); |
| 6437 | 748 %!test |
| 749 %! % Test fixed seed | |
| 750 %! randg("seed",1); | |
| 6443 | 751 %! assert (randg(0.1,1,6), [0.07144210487604141 0.460641473531723 0.4749028384685516 0.06823389977216721 0.000293838675133884 1.802567535340305e-12],1e-6); |
| 6437 | 752 %!test |
| 753 %! % Test fixed seed | |
| 754 %! randg("seed",1); | |
| 6443 | 755 %! assert (randg(0.95,1,6), [1.664905071258545 1.879976987838745 1.905677795410156 0.9948706030845642 0.5606933236122131 0.0766092911362648],1e-6); |
| 6437 | 756 %!test |
| 757 %! % Test fixed seed | |
| 758 %! randg("seed",1); | |
| 6443 | 759 %! assert (randg(1,1,6), [0.03512085229158401 0.6488978862762451 0.8114678859710693 0.1666885763406754 1.60791552066803 1.90356981754303],1e-6); |
| 6437 | 760 %!test |
| 761 %! % Test fixed seed | |
| 762 %! randg("seed",1); | |
| 6443 | 763 %! assert (randg(10,1,6), [6.566435813903809 10.11648464202881 10.73162078857422 7.747178077697754 6.278522491455078 6.240195751190186],1e-5); |
| 6437 | 764 %!test |
| 765 %! % Test fixed seed | |
| 766 %! randg("seed",1); | |
| 6443 | 767 %! assert (randg(100,1,6), [89.40208435058594 101.4734725952148 103.4020004272461 93.62763214111328 88.33104705810547 88.1871337890625],1e-4); |
| 6437 | 768 %!test |
| 769 %! if (__random_statistical_tests__) | |
| 770 %! % statistical tests may fail occasionally. | |
| 771 %! randg("state",12) | |
| 772 %! a=0.1; x = randg(a,100000,1); | |
| 773 %! assert(mean(x), a, 0.01); | |
| 774 %! assert(var(x), a, 0.01); | |
| 775 %! assert(skewness(x),2/sqrt(a), 1.); | |
| 776 %! assert(kurtosis(x),6/a, 50.); | |
| 777 %! endif | |
| 778 %!test | |
| 779 %! if (__random_statistical_tests__) | |
| 780 %! % statistical tests may fail occasionally. | |
| 781 %! randg("state",12) | |
| 782 %! a=0.95; x = randg(a,100000,1); | |
| 783 %! assert(mean(x), a, 0.01); | |
| 784 %! assert(var(x), a, 0.04); | |
| 785 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 786 %! assert(kurtosis(x),6/a, 2.); | |
| 787 %! endif | |
| 788 %!test | |
| 789 %! if (__random_statistical_tests__) | |
| 790 %! % statistical tests may fail occasionally. | |
| 791 %! randg("state",12) | |
| 792 %! a=1; x = randg(a,100000,1); | |
| 793 %! assert(mean(x),a, 0.01); | |
| 794 %! assert(var(x),a, 0.04); | |
| 795 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 796 %! assert(kurtosis(x),6/a, 2.); | |
| 797 %! endif | |
| 798 %!test | |
| 799 %! if (__random_statistical_tests__) | |
| 800 %! % statistical tests may fail occasionally. | |
| 801 %! randg("state",12) | |
| 802 %! a=10; x = randg(a,100000,1); | |
| 803 %! assert(mean(x), a, 0.1); | |
| 804 %! assert(var(x), a, 0.5); | |
| 805 %! assert(skewness(x),2/sqrt(a), 0.1); | |
| 806 %! assert(kurtosis(x),6/a, 0.5); | |
| 807 %! endif | |
| 808 %!test | |
| 809 %! if (__random_statistical_tests__) | |
| 810 %! % statistical tests may fail occasionally. | |
| 811 %! randg("state",12) | |
| 812 %! a=100; x = randg(a,100000,1); | |
| 813 %! assert(mean(x), a, 0.2); | |
| 814 %! assert(var(x), a, 2.); | |
| 815 %! assert(skewness(x),2/sqrt(a), 0.05); | |
| 816 %! assert(kurtosis(x),6/a, 0.2); | |
| 817 %! endif | |
| 818 %!test | |
| 819 %! randg("seed",12) | |
| 5730 | 820 %!assert(randg([-inf,-1,0,inf,nan]),[nan,nan,nan,nan,nan]) % *** Please report |
| 821 %!test | |
| 6437 | 822 %! if (__random_statistical_tests__) |
| 823 %! % statistical tests may fail occasionally. | |
| 824 %! randg("seed",12) | |
| 825 %! a=0.1; x = randg(a,100000,1); | |
| 826 %! assert(mean(x), a, 0.01); | |
| 827 %! assert(var(x), a, 0.01); | |
| 828 %! assert(skewness(x),2/sqrt(a), 1.); | |
| 829 %! assert(kurtosis(x),6/a, 50.); | |
| 830 %! endif | |
| 5730 | 831 %!test |
| 6437 | 832 %! if (__random_statistical_tests__) |
| 833 %! % statistical tests may fail occasionally. | |
| 834 %! randg("seed",12) | |
| 835 %! a=0.95; x = randg(a,100000,1); | |
| 836 %! assert(mean(x), a, 0.01); | |
| 837 %! assert(var(x), a, 0.04); | |
| 838 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 839 %! assert(kurtosis(x),6/a, 2.); | |
| 840 %! endif | |
| 5730 | 841 %!test |
| 6437 | 842 %! if (__random_statistical_tests__) |
| 843 %! % statistical tests may fail occasionally. | |
| 844 %! randg("seed",12) | |
| 845 %! a=1; x = randg(a,100000,1); | |
| 846 %! assert(mean(x),a, 0.01); | |
| 847 %! assert(var(x),a, 0.04); | |
| 848 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 849 %! assert(kurtosis(x),6/a, 2.); | |
| 850 %! endif | |
| 5730 | 851 %!test |
| 6437 | 852 %! if (__random_statistical_tests__) |
| 853 %! % statistical tests may fail occasionally. | |
| 854 %! randg("seed",12) | |
| 855 %! a=10; x = randg(a,100000,1); | |
| 856 %! assert(mean(x), a, 0.1); | |
| 857 %! assert(var(x), a, 0.5); | |
| 858 %! assert(skewness(x),2/sqrt(a), 0.1); | |
| 859 %! assert(kurtosis(x),6/a, 0.5); | |
| 860 %! endif | |
| 5730 | 861 %!test |
| 6437 | 862 %! if (__random_statistical_tests__) |
| 863 %! % statistical tests may fail occasionally. | |
| 864 %! randg("seed",12) | |
| 865 %! a=100; x = randg(a,100000,1); | |
| 866 %! assert(mean(x), a, 0.2); | |
| 867 %! assert(var(x), a, 2.); | |
| 868 %! assert(skewness(x),2/sqrt(a), 0.05); | |
| 869 %! assert(kurtosis(x),6/a, 0.2); | |
| 870 %! endif | |
| 5730 | 871 */ |
| 872 | |
| 873 | |
| 874 DEFUN_DLD (randp, args, , | |
| 875 "-*- texinfo -*-\n\ | |
| 876 @deftypefn {Loadable Function} {} randp (@var{l}, @var{x})\n\ | |
| 877 @deftypefnx {Loadable Function} {} randp (@var{l}, @var{n}, @var{m})\n\ | |
| 878 @deftypefnx {Loadable Function} {} randp (\"state\", @var{x})\n\ | |
| 879 @deftypefnx {Loadable Function} {} randp (\"seed\", @var{x})\n\ | |
|
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880 Return a matrix with Poisson distributed random elements with mean value parameter given by the first argument, @var{l}. The arguments\n\ |
| 5730 | 881 are handled the same as the arguments for @code{rand}, except for the\n\ |
| 882 argument @var{l}.\n\ | |
| 883 \n\ | |
| 884 Five different algorithms are used depending on the range of @var{l}\n\ | |
| 885 and whether or not @var{l} is a scalar or a matrix.\n\ | |
| 886 \n\ | |
| 887 @table @asis\n\ | |
| 888 @item For scalar @var{l} <= 12, use direct method.\n\ | |
| 889 Press, et al., 'Numerical Recipes in C', Cambridge University Press, 1992.\n\ | |
| 890 @item For scalar @var{l} > 12, use rejection method.[1]\n\ | |
| 891 Press, et al., 'Numerical Recipes in C', Cambridge University Press, 1992.\n\ | |
| 892 @item For matrix @var{l} <= 10, use inversion method.[2]\n\ | |
| 893 Stadlober E., et al., WinRand source code, available via FTP.\n\ | |
| 894 @item For matrix @var{l} > 10, use patchwork rejection method.\n\ | |
| 895 Stadlober E., et al., WinRand source code, available via FTP, or\n\ | |
| 896 H. Zechner, 'Efficient sampling from continuous and discrete\n\ | |
|
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897 unimodal distributions', Doctoral Dissertation, 156pp., Technical\n\ |
| 5730 | 898 University Graz, Austria, 1994.\n\ |
| 899 @item For @var{l} > 1e8, use normal approximation.\n\ | |
| 900 L. Montanet, et al., 'Review of Particle Properties', Physical Review\n\ | |
| 901 D 50 p1284, 1994\n\ | |
| 902 @end table\n\ | |
| 6547 | 903 @seealso{rand, randn, rande, randg}\n\ |
| 5730 | 904 @end deftypefn") |
| 905 { | |
| 906 octave_value retval; | |
| 907 | |
| 908 int nargin = args.length (); | |
| 909 | |
| 910 if (nargin < 1) | |
| 911 error ("randp: insufficient arguments"); | |
| 912 else | |
| 913 { | |
|
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914 unwind_protect::frame_id_t uwp_frame = unwind_protect::begin_frame (); |
| 5730 | 915 |
| 916 // This relies on the fact that elements are popped from the unwind | |
| 917 // stack in the reverse of the order they are pushed | |
| 918 // (i.e. current_distribution will be reset before calling | |
| 919 // reset_rand_generator()). | |
| 920 | |
| 921 unwind_protect::add (reset_rand_generator, 0); | |
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922 unwind_protect::protect_var (current_distribution); |
| 5730 | 923 |
| 924 current_distribution = "poisson"; | |
| 925 | |
| 926 octave_rand::distribution (current_distribution); | |
| 927 | |
| 928 retval = do_rand (args, nargin, "randp", true); | |
| 929 | |
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930 unwind_protect::run_frame (uwp_frame); |
| 5730 | 931 } |
| 932 | |
| 933 return retval; | |
| 934 } | |
| 935 | |
| 936 /* | |
| 937 %!test | |
| 6437 | 938 %! randp("state",12) |
| 939 %!assert(randp([-inf,-1,0,inf,nan]),[nan,nan,0,nan,nan]); % *** Please report | |
| 940 %!test | |
| 941 %! % Test fixed state | |
| 942 %! randp("state",1); | |
| 943 %! assert(randp(5,1,6),[5 5 3 7 7 3]) | |
| 944 %!test | |
| 945 %! % Test fixed state | |
| 946 %! randp("state",1); | |
| 947 %! assert(randp(15,1,6),[13 15 8 18 18 15]) | |
| 948 %!test | |
| 949 %! % Test fixed state | |
| 950 %! randp("state",1); | |
| 7421 | 951 %! assert(randp(1e9,1,6),[999915677 999976657 1000047684 1000019035 999985749 999977692],-1e-6) |
| 6437 | 952 %!test |
| 953 %! % Test fixed state | |
| 954 %! randp("seed",1); | |
| 6449 | 955 %! %%assert(randp(5,1,6),[8 2 3 6 6 8]) |
| 956 %! assert(randp(5,1,5),[8 2 3 6 6]) | |
| 6437 | 957 %!test |
| 958 %! % Test fixed state | |
| 959 %! randp("seed",1); | |
| 960 %! assert(randp(15,1,6),[15 16 12 10 10 12]) | |
| 961 %!test | |
| 962 %! % Test fixed state | |
| 963 %! randp("seed",1); | |
| 7421 | 964 %! assert(randp(1e9,1,6),[1000006208 1000012224 999981120 999963520 999963072 999981440],-1e-6) |
| 6437 | 965 %!test |
| 966 %! if (__random_statistical_tests__) | |
| 967 %! % statistical tests may fail occasionally. | |
| 968 %! randp("state",12) | |
| 969 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 970 %! x = randp(a(1),100000,1); | |
| 971 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 972 %! assert(mean(x),a(1),a(2)); | |
| 973 %! assert(var(x),a(1),0.02*a(1)); | |
| 974 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 975 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 976 %! endfor | |
| 977 %! endif | |
| 978 %!test | |
| 979 %! if (__random_statistical_tests__) | |
| 980 %! % statistical tests may fail occasionally. | |
| 981 %! randp("state",12) | |
| 982 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 983 %! x = randp(a(1)*ones(100000,1),100000,1); | |
| 984 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 985 %! assert(mean(x),a(1),a(2)); | |
| 986 %! assert(var(x),a(1),0.02*a(1)); | |
| 987 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 988 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 989 %! endfor | |
| 990 %! endif | |
| 991 %!test | |
| 992 %! randp("seed",12) | |
| 5730 | 993 %!assert(randp([-inf,-1,0,inf,nan]),[nan,nan,0,nan,nan]); % *** Please report |
| 994 %!test | |
| 6449 | 995 %! if (__random_statistical_tests__) |
| 996 %! % statistical tests may fail occasionally. | |
| 997 %! randp("seed",12) | |
| 998 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 999 %! x = randp(a(1),100000,1); | |
| 1000 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 1001 %! assert(mean(x),a(1),a(2)); | |
| 1002 %! assert(var(x),a(1),0.02*a(1)); | |
| 1003 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 1004 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 1005 %! endfor | |
| 1006 %! endif | |
| 5730 | 1007 %!test |
| 6449 | 1008 %! if (__random_statistical_tests__) |
| 1009 %! % statistical tests may fail occasionally. | |
| 1010 %! randp("seed",12) | |
| 1011 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 1012 %! x = randp(a(1)*ones(100000,1),100000,1); | |
| 1013 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 1014 %! assert(mean(x),a(1),a(2)); | |
| 1015 %! assert(var(x),a(1),0.02*a(1)); | |
| 1016 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 1017 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 1018 %! endfor | |
| 1019 %! endif | |
| 5730 | 1020 */ |
| 1021 | |
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1022 DEFUN_DLD (randperm, args, , |
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1023 "-*- texinfo -*-\n\ |
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1024 @deftypefn {Loadable Function} {} randperm (@var{n})\n\ |
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1025 @deftypefnx {Loadable Function} {} randperm (@var{n}, @var{m})\n\ |
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1026 Return a row vector containing a random permutation of @code{1:@var{n}}.\n\ |
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1027 If @var{m} is supplied, return @var{m} permutations,\n\ |
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1028 one in each row of a NxM matrix. The complexity is O(M*N) in both time and\n\ |
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1029 memory. The randomization is performed using rand().\n\ |
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1030 All permutations are equally likely.\n\ |
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1031 @seealso{perms}\n\ |
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1032 @end deftypefn") |
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1033 { |
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1034 int nargin = args.length (); |
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1035 octave_value retval; |
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1036 |
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1037 if (nargin == 1 || nargin == 2) |
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1038 { |
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1039 octave_idx_type n, m; |
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1040 |
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1041 if (nargin == 2) |
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1042 m = args(1).idx_type_value (true); |
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1043 else |
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1044 m = 1; |
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1045 |
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1046 n = args(0).idx_type_value (true); |
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1047 |
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1048 if (m < 0 || n < 0) |
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1049 error ("randperm: m and n must be non-negative"); |
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1050 |
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1051 if (! error_state) |
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1052 { |
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1053 // Generate random numbers. |
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1054 NDArray r = octave_rand::nd_array (dim_vector (m, n)); |
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1055 |
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1056 // Create transposed to allow faster access. |
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1057 Array<octave_idx_type> idx (dim_vector (n, m)); |
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1058 |
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1059 double *rvec = r.fortran_vec (); |
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1060 |
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1061 octave_idx_type *ivec = idx.fortran_vec (); |
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1062 |
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1063 // Perform the Knuth shuffle. |
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1064 for (octave_idx_type j = 0; j < m; j++) |
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1065 { |
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1066 for (octave_idx_type i = 0; i < n; i++) |
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1067 ivec[i] = i; |
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1068 |
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1069 for (octave_idx_type i = 0; i < n; i++) |
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1070 { |
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1071 octave_idx_type k = i + floor (rvec[i] * (n - i)); |
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1072 std::swap (ivec[i], ivec[k]); |
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1073 } |
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1074 |
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1075 ivec += n; |
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1076 rvec += n; |
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1077 } |
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1078 |
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1079 // Transpose. |
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1080 idx = idx.transpose (); |
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1081 |
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1082 // Re-fetch the pointers. |
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1083 ivec = idx.fortran_vec (); |
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1084 rvec = r.fortran_vec (); |
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1085 |
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1086 // Convert to doubles, reusing r. |
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1087 for (octave_idx_type i = 0, l = m*n; i < l; i++) |
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1088 rvec[i] = ivec[i] + 1; |
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1089 |
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1090 // Now create an array object with a cached idx_vector. |
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1091 retval = new octave_matrix (r, idx_vector (idx)); |
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1092 } |
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1093 } |
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1094 else |
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1095 print_usage (); |
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1096 |
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1097 return retval; |
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1098 } |
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1099 |
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1100 /* |
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1101 %!assert(sort(randperm(20)),1:20) |
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1102 %!assert(sort(randperm(20,50),2),repmat(1:20,50,1)) |
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1103 */ |
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1104 |
| 5730 | 1105 /* |
| 2928 | 1106 ;;; Local Variables: *** |
| 1107 ;;; mode: C++ *** | |
| 1108 ;;; End: *** | |
| 1109 */ |