source: Daodan/MSYS2/mingw32/include/c++/11.2.0/ext/random@ 1186

Last change on this file since 1186 was 1166, checked in by rossy, 3 years ago

Daodan: Replace MinGW build env with an up-to-date MSYS2 env
File size: 110.5 KB
Line 
1// Random number extensions -*- C++ -*-
2
3// Copyright (C) 2012-2021 Free Software Foundation, Inc.
4//
5// This file is part of the GNU ISO C++ Library. This library is free
6// software; you can redistribute it and/or modify it under the
7// terms of the GNU General Public License as published by the
8// Free Software Foundation; either version 3, or (at your option)
9// any later version.
10
11// This library is distributed in the hope that it will be useful,
12// but WITHOUT ANY WARRANTY; without even the implied warranty of
13// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
14// GNU General Public License for more details.
15
16// Under Section 7 of GPL version 3, you are granted additional
17// permissions described in the GCC Runtime Library Exception, version
18// 3.1, as published by the Free Software Foundation.
19
20// You should have received a copy of the GNU General Public License and
21// a copy of the GCC Runtime Library Exception along with this program;
22// see the files COPYING3 and COPYING.RUNTIME respectively. If not, see
23// <http://www.gnu.org/licenses/>.
24
25/** @file ext/random
26 * This file is a GNU extension to the Standard C++ Library.
27 */
28
29#ifndef _EXT_RANDOM
30#define _EXT_RANDOM 1
31
32#pragma GCC system_header
33
34#if __cplusplus < 201103L
35# include <bits/c++0x_warning.h>
36#else
37
38#include <random>
39#include <algorithm>
40#include <array>
41#include <ext/cmath>
42#ifdef __SSE2__
43# include <emmintrin.h>
44#endif
45
46#if defined(_GLIBCXX_USE_C99_STDINT_TR1) && defined(UINT32_C)
47
48namespace __gnu_cxx _GLIBCXX_VISIBILITY(default)
49{
50_GLIBCXX_BEGIN_NAMESPACE_VERSION
51
52#if __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__
53
54 /* Mersenne twister implementation optimized for vector operations.
55 *
56 * Reference: http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/
57 */
58 template<typename _UIntType, size_t __m,
59 size_t __pos1, size_t __sl1, size_t __sl2,
60 size_t __sr1, size_t __sr2,
61 uint32_t __msk1, uint32_t __msk2,
62 uint32_t __msk3, uint32_t __msk4,
63 uint32_t __parity1, uint32_t __parity2,
64 uint32_t __parity3, uint32_t __parity4>
65 class simd_fast_mersenne_twister_engine
66 {
67 static_assert(std::is_unsigned<_UIntType>::value, "template argument "
68 "substituting _UIntType not an unsigned integral type");
69 static_assert(__sr1 < 32, "first right shift too large");
70 static_assert(__sr2 < 16, "second right shift too large");
71 static_assert(__sl1 < 32, "first left shift too large");
72 static_assert(__sl2 < 16, "second left shift too large");
73
74 public:
75 typedef _UIntType result_type;
76
77 private:
78 static constexpr size_t m_w = sizeof(result_type) * 8;
79 static constexpr size_t _M_nstate = __m / 128 + 1;
80 static constexpr size_t _M_nstate32 = _M_nstate * 4;
81
82 static_assert(std::is_unsigned<_UIntType>::value, "template argument "
83 "substituting _UIntType not an unsigned integral type");
84 static_assert(__pos1 < _M_nstate, "POS1 not smaller than state size");
85 static_assert(16 % sizeof(_UIntType) == 0,
86 "UIntType size must divide 16");
87
88 template<typename _Sseq>
89 using _If_seed_seq
90 = typename std::enable_if<std::__detail::__is_seed_seq<
91 _Sseq, simd_fast_mersenne_twister_engine, result_type>::value
92 >::type;
93
94 public:
95 static constexpr size_t state_size = _M_nstate * (16
96 / sizeof(result_type));
97 static constexpr result_type default_seed = 5489u;
98
99 // constructors and member functions
100
101 simd_fast_mersenne_twister_engine()
102 : simd_fast_mersenne_twister_engine(default_seed)
103 { }
104
105 explicit
106 simd_fast_mersenne_twister_engine(result_type __sd)
107 { seed(__sd); }
108
109 template<typename _Sseq, typename = _If_seed_seq<_Sseq>>
110 explicit
111 simd_fast_mersenne_twister_engine(_Sseq& __q)
112 { seed(__q); }
113
114 void
115 seed(result_type __sd = default_seed);
116
117 template<typename _Sseq>
118 _If_seed_seq<_Sseq>
119 seed(_Sseq& __q);
120
121 static constexpr result_type
122 min()
123 { return 0; }
124
125 static constexpr result_type
126 max()
127 { return std::numeric_limits<result_type>::max(); }
128
129 void
130 discard(unsigned long long __z);
131
132 result_type
133 operator()()
134 {
135 if (__builtin_expect(_M_pos >= state_size, 0))
136 _M_gen_rand();
137
138 return _M_stateT[_M_pos++];
139 }
140
141 template<typename _UIntType_2, size_t __m_2,
142 size_t __pos1_2, size_t __sl1_2, size_t __sl2_2,
143 size_t __sr1_2, size_t __sr2_2,
144 uint32_t __msk1_2, uint32_t __msk2_2,
145 uint32_t __msk3_2, uint32_t __msk4_2,
146 uint32_t __parity1_2, uint32_t __parity2_2,
147 uint32_t __parity3_2, uint32_t __parity4_2>
148 friend bool
149 operator==(const simd_fast_mersenne_twister_engine<_UIntType_2,
150 __m_2, __pos1_2, __sl1_2, __sl2_2, __sr1_2, __sr2_2,
151 __msk1_2, __msk2_2, __msk3_2, __msk4_2,
152 __parity1_2, __parity2_2, __parity3_2, __parity4_2>& __lhs,
153 const simd_fast_mersenne_twister_engine<_UIntType_2,
154 __m_2, __pos1_2, __sl1_2, __sl2_2, __sr1_2, __sr2_2,
155 __msk1_2, __msk2_2, __msk3_2, __msk4_2,
156 __parity1_2, __parity2_2, __parity3_2, __parity4_2>& __rhs);
157
158 template<typename _UIntType_2, size_t __m_2,
159 size_t __pos1_2, size_t __sl1_2, size_t __sl2_2,
160 size_t __sr1_2, size_t __sr2_2,
161 uint32_t __msk1_2, uint32_t __msk2_2,
162 uint32_t __msk3_2, uint32_t __msk4_2,
163 uint32_t __parity1_2, uint32_t __parity2_2,
164 uint32_t __parity3_2, uint32_t __parity4_2,
165 typename _CharT, typename _Traits>
166 friend std::basic_ostream<_CharT, _Traits>&
167 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
168 const __gnu_cxx::simd_fast_mersenne_twister_engine
169 <_UIntType_2,
170 __m_2, __pos1_2, __sl1_2, __sl2_2, __sr1_2, __sr2_2,
171 __msk1_2, __msk2_2, __msk3_2, __msk4_2,
172 __parity1_2, __parity2_2, __parity3_2, __parity4_2>& __x);
173
174 template<typename _UIntType_2, size_t __m_2,
175 size_t __pos1_2, size_t __sl1_2, size_t __sl2_2,
176 size_t __sr1_2, size_t __sr2_2,
177 uint32_t __msk1_2, uint32_t __msk2_2,
178 uint32_t __msk3_2, uint32_t __msk4_2,
179 uint32_t __parity1_2, uint32_t __parity2_2,
180 uint32_t __parity3_2, uint32_t __parity4_2,
181 typename _CharT, typename _Traits>
182 friend std::basic_istream<_CharT, _Traits>&
183 operator>>(std::basic_istream<_CharT, _Traits>& __is,
184 __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType_2,
185 __m_2, __pos1_2, __sl1_2, __sl2_2, __sr1_2, __sr2_2,
186 __msk1_2, __msk2_2, __msk3_2, __msk4_2,
187 __parity1_2, __parity2_2, __parity3_2, __parity4_2>& __x);
188
189 private:
190 union
191 {
192#ifdef __SSE2__
193 __m128i _M_state[_M_nstate];
194#endif
195#ifdef __ARM_NEON
196#ifdef __aarch64__
197 __Uint32x4_t _M_state[_M_nstate];
198#endif
199#endif
200 uint32_t _M_state32[_M_nstate32];
201 result_type _M_stateT[state_size];
202 } __attribute__ ((__aligned__ (16)));
203 size_t _M_pos;
204
205 void _M_gen_rand(void);
206 void _M_period_certification();
207 };
208
209
210 template<typename _UIntType, size_t __m,
211 size_t __pos1, size_t __sl1, size_t __sl2,
212 size_t __sr1, size_t __sr2,
213 uint32_t __msk1, uint32_t __msk2,
214 uint32_t __msk3, uint32_t __msk4,
215 uint32_t __parity1, uint32_t __parity2,
216 uint32_t __parity3, uint32_t __parity4>
217 inline bool
218 operator!=(const __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType,
219 __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3,
220 __msk4, __parity1, __parity2, __parity3, __parity4>& __lhs,
221 const __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType,
222 __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3,
223 __msk4, __parity1, __parity2, __parity3, __parity4>& __rhs)
224 { return !(__lhs == __rhs); }
225
226
227 /* Definitions for the SIMD-oriented Fast Mersenne Twister as defined
228 * in the C implementation by Daito and Matsumoto, as both a 32-bit
229 * and 64-bit version.
230 */
231 typedef simd_fast_mersenne_twister_engine<uint32_t, 607, 2,
232 15, 3, 13, 3,
233 0xfdff37ffU, 0xef7f3f7dU,
234 0xff777b7dU, 0x7ff7fb2fU,
235 0x00000001U, 0x00000000U,
236 0x00000000U, 0x5986f054U>
237 sfmt607;
238
239 typedef simd_fast_mersenne_twister_engine<uint64_t, 607, 2,
240 15, 3, 13, 3,
241 0xfdff37ffU, 0xef7f3f7dU,
242 0xff777b7dU, 0x7ff7fb2fU,
243 0x00000001U, 0x00000000U,
244 0x00000000U, 0x5986f054U>
245 sfmt607_64;
246
247
248 typedef simd_fast_mersenne_twister_engine<uint32_t, 1279, 7,
249 14, 3, 5, 1,
250 0xf7fefffdU, 0x7fefcfffU,
251 0xaff3ef3fU, 0xb5ffff7fU,
252 0x00000001U, 0x00000000U,
253 0x00000000U, 0x20000000U>
254 sfmt1279;
255
256 typedef simd_fast_mersenne_twister_engine<uint64_t, 1279, 7,
257 14, 3, 5, 1,
258 0xf7fefffdU, 0x7fefcfffU,
259 0xaff3ef3fU, 0xb5ffff7fU,
260 0x00000001U, 0x00000000U,
261 0x00000000U, 0x20000000U>
262 sfmt1279_64;
263
264
265 typedef simd_fast_mersenne_twister_engine<uint32_t, 2281, 12,
266 19, 1, 5, 1,
267 0xbff7ffbfU, 0xfdfffffeU,
268 0xf7ffef7fU, 0xf2f7cbbfU,
269 0x00000001U, 0x00000000U,
270 0x00000000U, 0x41dfa600U>
271 sfmt2281;
272
273 typedef simd_fast_mersenne_twister_engine<uint64_t, 2281, 12,
274 19, 1, 5, 1,
275 0xbff7ffbfU, 0xfdfffffeU,
276 0xf7ffef7fU, 0xf2f7cbbfU,
277 0x00000001U, 0x00000000U,
278 0x00000000U, 0x41dfa600U>
279 sfmt2281_64;
280
281
282 typedef simd_fast_mersenne_twister_engine<uint32_t, 4253, 17,
283 20, 1, 7, 1,
284 0x9f7bffffU, 0x9fffff5fU,
285 0x3efffffbU, 0xfffff7bbU,
286 0xa8000001U, 0xaf5390a3U,
287 0xb740b3f8U, 0x6c11486dU>
288 sfmt4253;
289
290 typedef simd_fast_mersenne_twister_engine<uint64_t, 4253, 17,
291 20, 1, 7, 1,
292 0x9f7bffffU, 0x9fffff5fU,
293 0x3efffffbU, 0xfffff7bbU,
294 0xa8000001U, 0xaf5390a3U,
295 0xb740b3f8U, 0x6c11486dU>
296 sfmt4253_64;
297
298
299 typedef simd_fast_mersenne_twister_engine<uint32_t, 11213, 68,
300 14, 3, 7, 3,
301 0xeffff7fbU, 0xffffffefU,
302 0xdfdfbfffU, 0x7fffdbfdU,
303 0x00000001U, 0x00000000U,
304 0xe8148000U, 0xd0c7afa3U>
305 sfmt11213;
306
307 typedef simd_fast_mersenne_twister_engine<uint64_t, 11213, 68,
308 14, 3, 7, 3,
309 0xeffff7fbU, 0xffffffefU,
310 0xdfdfbfffU, 0x7fffdbfdU,
311 0x00000001U, 0x00000000U,
312 0xe8148000U, 0xd0c7afa3U>
313 sfmt11213_64;
314
315
316 typedef simd_fast_mersenne_twister_engine<uint32_t, 19937, 122,
317 18, 1, 11, 1,
318 0xdfffffefU, 0xddfecb7fU,
319 0xbffaffffU, 0xbffffff6U,
320 0x00000001U, 0x00000000U,
321 0x00000000U, 0x13c9e684U>
322 sfmt19937;
323
324 typedef simd_fast_mersenne_twister_engine<uint64_t, 19937, 122,
325 18, 1, 11, 1,
326 0xdfffffefU, 0xddfecb7fU,
327 0xbffaffffU, 0xbffffff6U,
328 0x00000001U, 0x00000000U,
329 0x00000000U, 0x13c9e684U>
330 sfmt19937_64;
331
332
333 typedef simd_fast_mersenne_twister_engine<uint32_t, 44497, 330,
334 5, 3, 9, 3,
335 0xeffffffbU, 0xdfbebfffU,
336 0xbfbf7befU, 0x9ffd7bffU,
337 0x00000001U, 0x00000000U,
338 0xa3ac4000U, 0xecc1327aU>
339 sfmt44497;
340
341 typedef simd_fast_mersenne_twister_engine<uint64_t, 44497, 330,
342 5, 3, 9, 3,
343 0xeffffffbU, 0xdfbebfffU,
344 0xbfbf7befU, 0x9ffd7bffU,
345 0x00000001U, 0x00000000U,
346 0xa3ac4000U, 0xecc1327aU>
347 sfmt44497_64;
348
349
350 typedef simd_fast_mersenne_twister_engine<uint32_t, 86243, 366,
351 6, 7, 19, 1,
352 0xfdbffbffU, 0xbff7ff3fU,
353 0xfd77efffU, 0xbf9ff3ffU,
354 0x00000001U, 0x00000000U,
355 0x00000000U, 0xe9528d85U>
356 sfmt86243;
357
358 typedef simd_fast_mersenne_twister_engine<uint64_t, 86243, 366,
359 6, 7, 19, 1,
360 0xfdbffbffU, 0xbff7ff3fU,
361 0xfd77efffU, 0xbf9ff3ffU,
362 0x00000001U, 0x00000000U,
363 0x00000000U, 0xe9528d85U>
364 sfmt86243_64;
365
366
367 typedef simd_fast_mersenne_twister_engine<uint32_t, 132049, 110,
368 19, 1, 21, 1,
369 0xffffbb5fU, 0xfb6ebf95U,
370 0xfffefffaU, 0xcff77fffU,
371 0x00000001U, 0x00000000U,
372 0xcb520000U, 0xc7e91c7dU>
373 sfmt132049;
374
375 typedef simd_fast_mersenne_twister_engine<uint64_t, 132049, 110,
376 19, 1, 21, 1,
377 0xffffbb5fU, 0xfb6ebf95U,
378 0xfffefffaU, 0xcff77fffU,
379 0x00000001U, 0x00000000U,
380 0xcb520000U, 0xc7e91c7dU>
381 sfmt132049_64;
382
383
384 typedef simd_fast_mersenne_twister_engine<uint32_t, 216091, 627,
385 11, 3, 10, 1,
386 0xbff7bff7U, 0xbfffffffU,
387 0xbffffa7fU, 0xffddfbfbU,
388 0xf8000001U, 0x89e80709U,
389 0x3bd2b64bU, 0x0c64b1e4U>
390 sfmt216091;
391
392 typedef simd_fast_mersenne_twister_engine<uint64_t, 216091, 627,
393 11, 3, 10, 1,
394 0xbff7bff7U, 0xbfffffffU,
395 0xbffffa7fU, 0xffddfbfbU,
396 0xf8000001U, 0x89e80709U,
397 0x3bd2b64bU, 0x0c64b1e4U>
398 sfmt216091_64;
399
400#endif // __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__
401
402 /**
403 * @brief A beta continuous distribution for random numbers.
404 *
405 * The formula for the beta probability density function is:
406 * @f[
407 * p(x|\alpha,\beta) = \frac{1}{B(\alpha,\beta)}
408 * x^{\alpha - 1} (1 - x)^{\beta - 1}
409 * @f]
410 */
411 template<typename _RealType = double>
412 class beta_distribution
413 {
414 static_assert(std::is_floating_point<_RealType>::value,
415 "template argument not a floating point type");
416
417 public:
418 /** The type of the range of the distribution. */
419 typedef _RealType result_type;
420
421 /** Parameter type. */
422 struct param_type
423 {
424 typedef beta_distribution<_RealType> distribution_type;
425 friend class beta_distribution<_RealType>;
426
427 param_type() : param_type(1) { }
428
429 explicit
430 param_type(_RealType __alpha_val, _RealType __beta_val = _RealType(1))
431 : _M_alpha(__alpha_val), _M_beta(__beta_val)
432 {
433 __glibcxx_assert(_M_alpha > _RealType(0));
434 __glibcxx_assert(_M_beta > _RealType(0));
435 }
436
437 _RealType
438 alpha() const
439 { return _M_alpha; }
440
441 _RealType
442 beta() const
443 { return _M_beta; }
444
445 friend bool
446 operator==(const param_type& __p1, const param_type& __p2)
447 { return (__p1._M_alpha == __p2._M_alpha
448 && __p1._M_beta == __p2._M_beta); }
449
450 friend bool
451 operator!=(const param_type& __p1, const param_type& __p2)
452 { return !(__p1 == __p2); }
453
454 private:
455 void
456 _M_initialize();
457
458 _RealType _M_alpha;
459 _RealType _M_beta;
460 };
461
462 public:
463 beta_distribution() : beta_distribution(1.0) { }
464
465 /**
466 * @brief Constructs a beta distribution with parameters
467 * @f$\alpha@f$ and @f$\beta@f$.
468 */
469 explicit
470 beta_distribution(_RealType __alpha_val,
471 _RealType __beta_val = _RealType(1))
472 : _M_param(__alpha_val, __beta_val)
473 { }
474
475 explicit
476 beta_distribution(const param_type& __p)
477 : _M_param(__p)
478 { }
479
480 /**
481 * @brief Resets the distribution state.
482 */
483 void
484 reset()
485 { }
486
487 /**
488 * @brief Returns the @f$\alpha@f$ of the distribution.
489 */
490 _RealType
491 alpha() const
492 { return _M_param.alpha(); }
493
494 /**
495 * @brief Returns the @f$\beta@f$ of the distribution.
496 */
497 _RealType
498 beta() const
499 { return _M_param.beta(); }
500
501 /**
502 * @brief Returns the parameter set of the distribution.
503 */
504 param_type
505 param() const
506 { return _M_param; }
507
508 /**
509 * @brief Sets the parameter set of the distribution.
510 * @param __param The new parameter set of the distribution.
511 */
512 void
513 param(const param_type& __param)
514 { _M_param = __param; }
515
516 /**
517 * @brief Returns the greatest lower bound value of the distribution.
518 */
519 result_type
520 min() const
521 { return result_type(0); }
522
523 /**
524 * @brief Returns the least upper bound value of the distribution.
525 */
526 result_type
527 max() const
528 { return result_type(1); }
529
530 /**
531 * @brief Generating functions.
532 */
533 template<typename _UniformRandomNumberGenerator>
534 result_type
535 operator()(_UniformRandomNumberGenerator& __urng)
536 { return this->operator()(__urng, _M_param); }
537
538 template<typename _UniformRandomNumberGenerator>
539 result_type
540 operator()(_UniformRandomNumberGenerator& __urng,
541 const param_type& __p);
542
543 template<typename _ForwardIterator,
544 typename _UniformRandomNumberGenerator>
545 void
546 __generate(_ForwardIterator __f, _ForwardIterator __t,
547 _UniformRandomNumberGenerator& __urng)
548 { this->__generate(__f, __t, __urng, _M_param); }
549
550 template<typename _ForwardIterator,
551 typename _UniformRandomNumberGenerator>
552 void
553 __generate(_ForwardIterator __f, _ForwardIterator __t,
554 _UniformRandomNumberGenerator& __urng,
555 const param_type& __p)
556 { this->__generate_impl(__f, __t, __urng, __p); }
557
558 template<typename _UniformRandomNumberGenerator>
559 void
560 __generate(result_type* __f, result_type* __t,
561 _UniformRandomNumberGenerator& __urng,
562 const param_type& __p)
563 { this->__generate_impl(__f, __t, __urng, __p); }
564
565 /**
566 * @brief Return true if two beta distributions have the same
567 * parameters and the sequences that would be generated
568 * are equal.
569 */
570 friend bool
571 operator==(const beta_distribution& __d1,
572 const beta_distribution& __d2)
573 { return __d1._M_param == __d2._M_param; }
574
575 /**
576 * @brief Inserts a %beta_distribution random number distribution
577 * @p __x into the output stream @p __os.
578 *
579 * @param __os An output stream.
580 * @param __x A %beta_distribution random number distribution.
581 *
582 * @returns The output stream with the state of @p __x inserted or in
583 * an error state.
584 */
585 template<typename _RealType1, typename _CharT, typename _Traits>
586 friend std::basic_ostream<_CharT, _Traits>&
587 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
588 const __gnu_cxx::beta_distribution<_RealType1>& __x);
589
590 /**
591 * @brief Extracts a %beta_distribution random number distribution
592 * @p __x from the input stream @p __is.
593 *
594 * @param __is An input stream.
595 * @param __x A %beta_distribution random number generator engine.
596 *
597 * @returns The input stream with @p __x extracted or in an error state.
598 */
599 template<typename _RealType1, typename _CharT, typename _Traits>
600 friend std::basic_istream<_CharT, _Traits>&
601 operator>>(std::basic_istream<_CharT, _Traits>& __is,
602 __gnu_cxx::beta_distribution<_RealType1>& __x);
603
604 private:
605 template<typename _ForwardIterator,
606 typename _UniformRandomNumberGenerator>
607 void
608 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
609 _UniformRandomNumberGenerator& __urng,
610 const param_type& __p);
611
612 param_type _M_param;
613 };
614
615 /**
616 * @brief Return true if two beta distributions are different.
617 */
618 template<typename _RealType>
619 inline bool
620 operator!=(const __gnu_cxx::beta_distribution<_RealType>& __d1,
621 const __gnu_cxx::beta_distribution<_RealType>& __d2)
622 { return !(__d1 == __d2); }
623
624
625 /**
626 * @brief A multi-variate normal continuous distribution for random numbers.
627 *
628 * The formula for the normal probability density function is
629 * @f[
630 * p(\overrightarrow{x}|\overrightarrow{\mu },\Sigma) =
631 * \frac{1}{\sqrt{(2\pi )^k\det(\Sigma))}}
632 * e^{-\frac{1}{2}(\overrightarrow{x}-\overrightarrow{\mu})^\text{T}
633 * \Sigma ^{-1}(\overrightarrow{x}-\overrightarrow{\mu})}
634 * @f]
635 *
636 * where @f$\overrightarrow{x}@f$ and @f$\overrightarrow{\mu}@f$ are
637 * vectors of dimension @f$k@f$ and @f$\Sigma@f$ is the covariance
638 * matrix (which must be positive-definite).
639 */
640 template<std::size_t _Dimen, typename _RealType = double>
641 class normal_mv_distribution
642 {
643 static_assert(std::is_floating_point<_RealType>::value,
644 "template argument not a floating point type");
645 static_assert(_Dimen != 0, "dimension is zero");
646
647 public:
648 /** The type of the range of the distribution. */
649 typedef std::array<_RealType, _Dimen> result_type;
650 /** Parameter type. */
651 class param_type
652 {
653 static constexpr size_t _M_t_size = _Dimen * (_Dimen + 1) / 2;
654
655 public:
656 typedef normal_mv_distribution<_Dimen, _RealType> distribution_type;
657 friend class normal_mv_distribution<_Dimen, _RealType>;
658
659 param_type()
660 {
661 std::fill(_M_mean.begin(), _M_mean.end(), _RealType(0));
662 auto __it = _M_t.begin();
663 for (size_t __i = 0; __i < _Dimen; ++__i)
664 {
665 std::fill_n(__it, __i, _RealType(0));
666 __it += __i;
667 *__it++ = _RealType(1);
668 }
669 }
670
671 template<typename _ForwardIterator1, typename _ForwardIterator2>
672 param_type(_ForwardIterator1 __meanbegin,
673 _ForwardIterator1 __meanend,
674 _ForwardIterator2 __varcovbegin,
675 _ForwardIterator2 __varcovend)
676 {
677 __glibcxx_function_requires(_ForwardIteratorConcept<
678 _ForwardIterator1>)
679 __glibcxx_function_requires(_ForwardIteratorConcept<
680 _ForwardIterator2>)
681 _GLIBCXX_DEBUG_ASSERT(std::distance(__meanbegin, __meanend)
682 <= _Dimen);
683 const auto __dist = std::distance(__varcovbegin, __varcovend);
684 _GLIBCXX_DEBUG_ASSERT(__dist == _Dimen * _Dimen
685 || __dist == _Dimen * (_Dimen + 1) / 2
686 || __dist == _Dimen);
687
688 if (__dist == _Dimen * _Dimen)
689 _M_init_full(__meanbegin, __meanend, __varcovbegin, __varcovend);
690 else if (__dist == _Dimen * (_Dimen + 1) / 2)
691 _M_init_lower(__meanbegin, __meanend, __varcovbegin, __varcovend);
692 else
693 {
694 __glibcxx_assert(__dist == _Dimen);
695 _M_init_diagonal(__meanbegin, __meanend,
696 __varcovbegin, __varcovend);
697 }
698 }
699
700 param_type(std::initializer_list<_RealType> __mean,
701 std::initializer_list<_RealType> __varcov)
702 {
703 _GLIBCXX_DEBUG_ASSERT(__mean.size() <= _Dimen);
704 _GLIBCXX_DEBUG_ASSERT(__varcov.size() == _Dimen * _Dimen
705 || __varcov.size() == _Dimen * (_Dimen + 1) / 2
706 || __varcov.size() == _Dimen);
707
708 if (__varcov.size() == _Dimen * _Dimen)
709 _M_init_full(__mean.begin(), __mean.end(),
710 __varcov.begin(), __varcov.end());
711 else if (__varcov.size() == _Dimen * (_Dimen + 1) / 2)
712 _M_init_lower(__mean.begin(), __mean.end(),
713 __varcov.begin(), __varcov.end());
714 else
715 {
716 __glibcxx_assert(__varcov.size() == _Dimen);
717 _M_init_diagonal(__mean.begin(), __mean.end(),
718 __varcov.begin(), __varcov.end());
719 }
720 }
721
722 std::array<_RealType, _Dimen>
723 mean() const
724 { return _M_mean; }
725
726 std::array<_RealType, _M_t_size>
727 varcov() const
728 { return _M_t; }
729
730 friend bool
731 operator==(const param_type& __p1, const param_type& __p2)
732 { return __p1._M_mean == __p2._M_mean && __p1._M_t == __p2._M_t; }
733
734 friend bool
735 operator!=(const param_type& __p1, const param_type& __p2)
736 { return !(__p1 == __p2); }
737
738 private:
739 template <typename _InputIterator1, typename _InputIterator2>
740 void _M_init_full(_InputIterator1 __meanbegin,
741 _InputIterator1 __meanend,
742 _InputIterator2 __varcovbegin,
743 _InputIterator2 __varcovend);
744 template <typename _InputIterator1, typename _InputIterator2>
745 void _M_init_lower(_InputIterator1 __meanbegin,
746 _InputIterator1 __meanend,
747 _InputIterator2 __varcovbegin,
748 _InputIterator2 __varcovend);
749 template <typename _InputIterator1, typename _InputIterator2>
750 void _M_init_diagonal(_InputIterator1 __meanbegin,
751 _InputIterator1 __meanend,
752 _InputIterator2 __varbegin,
753 _InputIterator2 __varend);
754
755 // param_type constructors apply Cholesky decomposition to the
756 // varcov matrix in _M_init_full and _M_init_lower, but the
757 // varcov matrix output ot a stream is already decomposed, so
758 // we need means to restore it as-is when reading it back in.
759 template<size_t _Dimen1, typename _RealType1,
760 typename _CharT, typename _Traits>
761 friend std::basic_istream<_CharT, _Traits>&
762 operator>>(std::basic_istream<_CharT, _Traits>& __is,
763 __gnu_cxx::normal_mv_distribution<_Dimen1, _RealType1>&
764 __x);
765 param_type(std::array<_RealType, _Dimen> const &__mean,
766 std::array<_RealType, _M_t_size> const &__varcov)
767 : _M_mean (__mean), _M_t (__varcov)
768 {}
769
770 std::array<_RealType, _Dimen> _M_mean;
771 std::array<_RealType, _M_t_size> _M_t;
772 };
773
774 public:
775 normal_mv_distribution()
776 : _M_param(), _M_nd()
777 { }
778
779 template<typename _ForwardIterator1, typename _ForwardIterator2>
780 normal_mv_distribution(_ForwardIterator1 __meanbegin,
781 _ForwardIterator1 __meanend,
782 _ForwardIterator2 __varcovbegin,
783 _ForwardIterator2 __varcovend)
784 : _M_param(__meanbegin, __meanend, __varcovbegin, __varcovend),
785 _M_nd()
786 { }
787
788 normal_mv_distribution(std::initializer_list<_RealType> __mean,
789 std::initializer_list<_RealType> __varcov)
790 : _M_param(__mean, __varcov), _M_nd()
791 { }
792
793 explicit
794 normal_mv_distribution(const param_type& __p)
795 : _M_param(__p), _M_nd()
796 { }
797
798 /**
799 * @brief Resets the distribution state.
800 */
801 void
802 reset()
803 { _M_nd.reset(); }
804
805 /**
806 * @brief Returns the mean of the distribution.
807 */
808 result_type
809 mean() const
810 { return _M_param.mean(); }
811
812 /**
813 * @brief Returns the compact form of the variance/covariance
814 * matrix of the distribution.
815 */
816 std::array<_RealType, _Dimen * (_Dimen + 1) / 2>
817 varcov() const
818 { return _M_param.varcov(); }
819
820 /**
821 * @brief Returns the parameter set of the distribution.
822 */
823 param_type
824 param() const
825 { return _M_param; }
826
827 /**
828 * @brief Sets the parameter set of the distribution.
829 * @param __param The new parameter set of the distribution.
830 */
831 void
832 param(const param_type& __param)
833 { _M_param = __param; }
834
835 /**
836 * @brief Returns the greatest lower bound value of the distribution.
837 */
838 result_type
839 min() const
840 { result_type __res;
841 __res.fill(std::numeric_limits<_RealType>::lowest());
842 return __res; }
843
844 /**
845 * @brief Returns the least upper bound value of the distribution.
846 */
847 result_type
848 max() const
849 { result_type __res;
850 __res.fill(std::numeric_limits<_RealType>::max());
851 return __res; }
852
853 /**
854 * @brief Generating functions.
855 */
856 template<typename _UniformRandomNumberGenerator>
857 result_type
858 operator()(_UniformRandomNumberGenerator& __urng)
859 { return this->operator()(__urng, _M_param); }
860
861 template<typename _UniformRandomNumberGenerator>
862 result_type
863 operator()(_UniformRandomNumberGenerator& __urng,
864 const param_type& __p);
865
866 template<typename _ForwardIterator,
867 typename _UniformRandomNumberGenerator>
868 void
869 __generate(_ForwardIterator __f, _ForwardIterator __t,
870 _UniformRandomNumberGenerator& __urng)
871 { return this->__generate_impl(__f, __t, __urng, _M_param); }
872
873 template<typename _ForwardIterator,
874 typename _UniformRandomNumberGenerator>
875 void
876 __generate(_ForwardIterator __f, _ForwardIterator __t,
877 _UniformRandomNumberGenerator& __urng,
878 const param_type& __p)
879 { return this->__generate_impl(__f, __t, __urng, __p); }
880
881 /**
882 * @brief Return true if two multi-variant normal distributions have
883 * the same parameters and the sequences that would
884 * be generated are equal.
885 */
886 template<size_t _Dimen1, typename _RealType1>
887 friend bool
888 operator==(const
889 __gnu_cxx::normal_mv_distribution<_Dimen1, _RealType1>&
890 __d1,
891 const
892 __gnu_cxx::normal_mv_distribution<_Dimen1, _RealType1>&
893 __d2);
894
895 /**
896 * @brief Inserts a %normal_mv_distribution random number distribution
897 * @p __x into the output stream @p __os.
898 *
899 * @param __os An output stream.
900 * @param __x A %normal_mv_distribution random number distribution.
901 *
902 * @returns The output stream with the state of @p __x inserted or in
903 * an error state.
904 */
905 template<size_t _Dimen1, typename _RealType1,
906 typename _CharT, typename _Traits>
907 friend std::basic_ostream<_CharT, _Traits>&
908 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
909 const
910 __gnu_cxx::normal_mv_distribution<_Dimen1, _RealType1>&
911 __x);
912
913 /**
914 * @brief Extracts a %normal_mv_distribution random number distribution
915 * @p __x from the input stream @p __is.
916 *
917 * @param __is An input stream.
918 * @param __x A %normal_mv_distribution random number generator engine.
919 *
920 * @returns The input stream with @p __x extracted or in an error
921 * state.
922 */
923 template<size_t _Dimen1, typename _RealType1,
924 typename _CharT, typename _Traits>
925 friend std::basic_istream<_CharT, _Traits>&
926 operator>>(std::basic_istream<_CharT, _Traits>& __is,
927 __gnu_cxx::normal_mv_distribution<_Dimen1, _RealType1>&
928 __x);
929
930 private:
931 template<typename _ForwardIterator,
932 typename _UniformRandomNumberGenerator>
933 void
934 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
935 _UniformRandomNumberGenerator& __urng,
936 const param_type& __p);
937
938 param_type _M_param;
939 std::normal_distribution<_RealType> _M_nd;
940 };
941
942 /**
943 * @brief Return true if two multi-variate normal distributions are
944 * different.
945 */
946 template<size_t _Dimen, typename _RealType>
947 inline bool
948 operator!=(const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>&
949 __d1,
950 const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>&
951 __d2)
952 { return !(__d1 == __d2); }
953
954
955 /**
956 * @brief A Rice continuous distribution for random numbers.
957 *
958 * The formula for the Rice probability density function is
959 * @f[
960 * p(x|\nu,\sigma) = \frac{x}{\sigma^2}
961 * \exp\left(-\frac{x^2+\nu^2}{2\sigma^2}\right)
962 * I_0\left(\frac{x \nu}{\sigma^2}\right)
963 * @f]
964 * where @f$I_0(z)@f$ is the modified Bessel function of the first kind
965 * of order 0 and @f$\nu >= 0@f$ and @f$\sigma > 0@f$.
966 *
967 * <table border=1 cellpadding=10 cellspacing=0>
968 * <caption align=top>Distribution Statistics</caption>
969 * <tr><td>Mean</td><td>@f$\sqrt{\pi/2}L_{1/2}(-\nu^2/2\sigma^2)@f$</td></tr>
970 * <tr><td>Variance</td><td>@f$2\sigma^2 + \nu^2
971 * + (\pi\sigma^2/2)L^2_{1/2}(-\nu^2/2\sigma^2)@f$</td></tr>
972 * <tr><td>Range</td><td>@f$[0, \infty)@f$</td></tr>
973 * </table>
974 * where @f$L_{1/2}(x)@f$ is the Laguerre polynomial of order 1/2.
975 */
976 template<typename _RealType = double>
977 class
978 rice_distribution
979 {
980 static_assert(std::is_floating_point<_RealType>::value,
981 "template argument not a floating point type");
982 public:
983 /** The type of the range of the distribution. */
984 typedef _RealType result_type;
985
986 /** Parameter type. */
987 struct param_type
988 {
989 typedef rice_distribution<result_type> distribution_type;
990
991 param_type() : param_type(0) { }
992
993 param_type(result_type __nu_val,
994 result_type __sigma_val = result_type(1))
995 : _M_nu(__nu_val), _M_sigma(__sigma_val)
996 {
997 __glibcxx_assert(_M_nu >= result_type(0));
998 __glibcxx_assert(_M_sigma > result_type(0));
999 }
1000
1001 result_type
1002 nu() const
1003 { return _M_nu; }
1004
1005 result_type
1006 sigma() const
1007 { return _M_sigma; }
1008
1009 friend bool
1010 operator==(const param_type& __p1, const param_type& __p2)
1011 { return __p1._M_nu == __p2._M_nu && __p1._M_sigma == __p2._M_sigma; }
1012
1013 friend bool
1014 operator!=(const param_type& __p1, const param_type& __p2)
1015 { return !(__p1 == __p2); }
1016
1017 private:
1018 void _M_initialize();
1019
1020 result_type _M_nu;
1021 result_type _M_sigma;
1022 };
1023
1024 /**
1025 * @brief Constructors.
1026 * @{
1027 */
1028
1029 rice_distribution() : rice_distribution(0) { }
1030
1031 explicit
1032 rice_distribution(result_type __nu_val,
1033 result_type __sigma_val = result_type(1))
1034 : _M_param(__nu_val, __sigma_val),
1035 _M_ndx(__nu_val, __sigma_val),
1036 _M_ndy(result_type(0), __sigma_val)
1037 { }
1038
1039 explicit
1040 rice_distribution(const param_type& __p)
1041 : _M_param(__p),
1042 _M_ndx(__p.nu(), __p.sigma()),
1043 _M_ndy(result_type(0), __p.sigma())
1044 { }
1045
1046 /// @}
1047
1048 /**
1049 * @brief Resets the distribution state.
1050 */
1051 void
1052 reset()
1053 {
1054 _M_ndx.reset();
1055 _M_ndy.reset();
1056 }
1057
1058 /**
1059 * @brief Return the parameters of the distribution.
1060 */
1061 result_type
1062 nu() const
1063 { return _M_param.nu(); }
1064
1065 result_type
1066 sigma() const
1067 { return _M_param.sigma(); }
1068
1069 /**
1070 * @brief Returns the parameter set of the distribution.
1071 */
1072 param_type
1073 param() const
1074 { return _M_param; }
1075
1076 /**
1077 * @brief Sets the parameter set of the distribution.
1078 * @param __param The new parameter set of the distribution.
1079 */
1080 void
1081 param(const param_type& __param)
1082 { _M_param = __param; }
1083
1084 /**
1085 * @brief Returns the greatest lower bound value of the distribution.
1086 */
1087 result_type
1088 min() const
1089 { return result_type(0); }
1090
1091 /**
1092 * @brief Returns the least upper bound value of the distribution.
1093 */
1094 result_type
1095 max() const
1096 { return std::numeric_limits<result_type>::max(); }
1097
1098 /**
1099 * @brief Generating functions.
1100 */
1101 template<typename _UniformRandomNumberGenerator>
1102 result_type
1103 operator()(_UniformRandomNumberGenerator& __urng)
1104 {
1105 result_type __x = this->_M_ndx(__urng);
1106 result_type __y = this->_M_ndy(__urng);
1107#if _GLIBCXX_USE_C99_MATH_TR1
1108 return std::hypot(__x, __y);
1109#else
1110 return std::sqrt(__x * __x + __y * __y);
1111#endif
1112 }
1113
1114 template<typename _UniformRandomNumberGenerator>
1115 result_type
1116 operator()(_UniformRandomNumberGenerator& __urng,
1117 const param_type& __p)
1118 {
1119 typename std::normal_distribution<result_type>::param_type
1120 __px(__p.nu(), __p.sigma()), __py(result_type(0), __p.sigma());
1121 result_type __x = this->_M_ndx(__px, __urng);
1122 result_type __y = this->_M_ndy(__py, __urng);
1123#if _GLIBCXX_USE_C99_MATH_TR1
1124 return std::hypot(__x, __y);
1125#else
1126 return std::sqrt(__x * __x + __y * __y);
1127#endif
1128 }
1129
1130 template<typename _ForwardIterator,
1131 typename _UniformRandomNumberGenerator>
1132 void
1133 __generate(_ForwardIterator __f, _ForwardIterator __t,
1134 _UniformRandomNumberGenerator& __urng)
1135 { this->__generate(__f, __t, __urng, _M_param); }
1136
1137 template<typename _ForwardIterator,
1138 typename _UniformRandomNumberGenerator>
1139 void
1140 __generate(_ForwardIterator __f, _ForwardIterator __t,
1141 _UniformRandomNumberGenerator& __urng,
1142 const param_type& __p)
1143 { this->__generate_impl(__f, __t, __urng, __p); }
1144
1145 template<typename _UniformRandomNumberGenerator>
1146 void
1147 __generate(result_type* __f, result_type* __t,
1148 _UniformRandomNumberGenerator& __urng,
1149 const param_type& __p)
1150 { this->__generate_impl(__f, __t, __urng, __p); }
1151
1152 /**
1153 * @brief Return true if two Rice distributions have
1154 * the same parameters and the sequences that would
1155 * be generated are equal.
1156 */
1157 friend bool
1158 operator==(const rice_distribution& __d1,
1159 const rice_distribution& __d2)
1160 { return (__d1._M_param == __d2._M_param
1161 && __d1._M_ndx == __d2._M_ndx
1162 && __d1._M_ndy == __d2._M_ndy); }
1163
1164 /**
1165 * @brief Inserts a %rice_distribution random number distribution
1166 * @p __x into the output stream @p __os.
1167 *
1168 * @param __os An output stream.
1169 * @param __x A %rice_distribution random number distribution.
1170 *
1171 * @returns The output stream with the state of @p __x inserted or in
1172 * an error state.
1173 */
1174 template<typename _RealType1, typename _CharT, typename _Traits>
1175 friend std::basic_ostream<_CharT, _Traits>&
1176 operator<<(std::basic_ostream<_CharT, _Traits>&,
1177 const rice_distribution<_RealType1>&);
1178
1179 /**
1180 * @brief Extracts a %rice_distribution random number distribution
1181 * @p __x from the input stream @p __is.
1182 *
1183 * @param __is An input stream.
1184 * @param __x A %rice_distribution random number
1185 * generator engine.
1186 *
1187 * @returns The input stream with @p __x extracted or in an error state.
1188 */
1189 template<typename _RealType1, typename _CharT, typename _Traits>
1190 friend std::basic_istream<_CharT, _Traits>&
1191 operator>>(std::basic_istream<_CharT, _Traits>&,
1192 rice_distribution<_RealType1>&);
1193
1194 private:
1195 template<typename _ForwardIterator,
1196 typename _UniformRandomNumberGenerator>
1197 void
1198 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1199 _UniformRandomNumberGenerator& __urng,
1200 const param_type& __p);
1201
1202 param_type _M_param;
1203
1204 std::normal_distribution<result_type> _M_ndx;
1205 std::normal_distribution<result_type> _M_ndy;
1206 };
1207
1208 /**
1209 * @brief Return true if two Rice distributions are not equal.
1210 */
1211 template<typename _RealType1>
1212 inline bool
1213 operator!=(const rice_distribution<_RealType1>& __d1,
1214 const rice_distribution<_RealType1>& __d2)
1215 { return !(__d1 == __d2); }
1216
1217
1218 /**
1219 * @brief A Nakagami continuous distribution for random numbers.
1220 *
1221 * The formula for the Nakagami probability density function is
1222 * @f[
1223 * p(x|\mu,\omega) = \frac{2\mu^\mu}{\Gamma(\mu)\omega^\mu}
1224 * x^{2\mu-1}e^{-\mu x / \omega}
1225 * @f]
1226 * where @f$\Gamma(z)@f$ is the gamma function and @f$\mu >= 0.5@f$
1227 * and @f$\omega > 0@f$.
1228 */
1229 template<typename _RealType = double>
1230 class
1231 nakagami_distribution
1232 {
1233 static_assert(std::is_floating_point<_RealType>::value,
1234 "template argument not a floating point type");
1235
1236 public:
1237 /** The type of the range of the distribution. */
1238 typedef _RealType result_type;
1239
1240 /** Parameter type. */
1241 struct param_type
1242 {
1243 typedef nakagami_distribution<result_type> distribution_type;
1244
1245 param_type() : param_type(1) { }
1246
1247 param_type(result_type __mu_val,
1248 result_type __omega_val = result_type(1))
1249 : _M_mu(__mu_val), _M_omega(__omega_val)
1250 {
1251 __glibcxx_assert(_M_mu >= result_type(0.5L));
1252 __glibcxx_assert(_M_omega > result_type(0));
1253 }
1254
1255 result_type
1256 mu() const
1257 { return _M_mu; }
1258
1259 result_type
1260 omega() const
1261 { return _M_omega; }
1262
1263 friend bool
1264 operator==(const param_type& __p1, const param_type& __p2)
1265 { return __p1._M_mu == __p2._M_mu && __p1._M_omega == __p2._M_omega; }
1266
1267 friend bool
1268 operator!=(const param_type& __p1, const param_type& __p2)
1269 { return !(__p1 == __p2); }
1270
1271 private:
1272 void _M_initialize();
1273
1274 result_type _M_mu;
1275 result_type _M_omega;
1276 };
1277
1278 /**
1279 * @brief Constructors.
1280 * @{
1281 */
1282
1283 nakagami_distribution() : nakagami_distribution(1) { }
1284
1285 explicit
1286 nakagami_distribution(result_type __mu_val,
1287 result_type __omega_val = result_type(1))
1288 : _M_param(__mu_val, __omega_val),
1289 _M_gd(__mu_val, __omega_val / __mu_val)
1290 { }
1291
1292 explicit
1293 nakagami_distribution(const param_type& __p)
1294 : _M_param(__p),
1295 _M_gd(__p.mu(), __p.omega() / __p.mu())
1296 { }
1297
1298 /// @}
1299
1300 /**
1301 * @brief Resets the distribution state.
1302 */
1303 void
1304 reset()
1305 { _M_gd.reset(); }
1306
1307 /**
1308 * @brief Return the parameters of the distribution.
1309 */
1310 result_type
1311 mu() const
1312 { return _M_param.mu(); }
1313
1314 result_type
1315 omega() const
1316 { return _M_param.omega(); }
1317
1318 /**
1319 * @brief Returns the parameter set of the distribution.
1320 */
1321 param_type
1322 param() const
1323 { return _M_param; }
1324
1325 /**
1326 * @brief Sets the parameter set of the distribution.
1327 * @param __param The new parameter set of the distribution.
1328 */
1329 void
1330 param(const param_type& __param)
1331 { _M_param = __param; }
1332
1333 /**
1334 * @brief Returns the greatest lower bound value of the distribution.
1335 */
1336 result_type
1337 min() const
1338 { return result_type(0); }
1339
1340 /**
1341 * @brief Returns the least upper bound value of the distribution.
1342 */
1343 result_type
1344 max() const
1345 { return std::numeric_limits<result_type>::max(); }
1346
1347 /**
1348 * @brief Generating functions.
1349 */
1350 template<typename _UniformRandomNumberGenerator>
1351 result_type
1352 operator()(_UniformRandomNumberGenerator& __urng)
1353 { return std::sqrt(this->_M_gd(__urng)); }
1354
1355 template<typename _UniformRandomNumberGenerator>
1356 result_type
1357 operator()(_UniformRandomNumberGenerator& __urng,
1358 const param_type& __p)
1359 {
1360 typename std::gamma_distribution<result_type>::param_type
1361 __pg(__p.mu(), __p.omega() / __p.mu());
1362 return std::sqrt(this->_M_gd(__pg, __urng));
1363 }
1364
1365 template<typename _ForwardIterator,
1366 typename _UniformRandomNumberGenerator>
1367 void
1368 __generate(_ForwardIterator __f, _ForwardIterator __t,
1369 _UniformRandomNumberGenerator& __urng)
1370 { this->__generate(__f, __t, __urng, _M_param); }
1371
1372 template<typename _ForwardIterator,
1373 typename _UniformRandomNumberGenerator>
1374 void
1375 __generate(_ForwardIterator __f, _ForwardIterator __t,
1376 _UniformRandomNumberGenerator& __urng,
1377 const param_type& __p)
1378 { this->__generate_impl(__f, __t, __urng, __p); }
1379
1380 template<typename _UniformRandomNumberGenerator>
1381 void
1382 __generate(result_type* __f, result_type* __t,
1383 _UniformRandomNumberGenerator& __urng,
1384 const param_type& __p)
1385 { this->__generate_impl(__f, __t, __urng, __p); }
1386
1387 /**
1388 * @brief Return true if two Nakagami distributions have
1389 * the same parameters and the sequences that would
1390 * be generated are equal.
1391 */
1392 friend bool
1393 operator==(const nakagami_distribution& __d1,
1394 const nakagami_distribution& __d2)
1395 { return (__d1._M_param == __d2._M_param
1396 && __d1._M_gd == __d2._M_gd); }
1397
1398 /**
1399 * @brief Inserts a %nakagami_distribution random number distribution
1400 * @p __x into the output stream @p __os.
1401 *
1402 * @param __os An output stream.
1403 * @param __x A %nakagami_distribution random number distribution.
1404 *
1405 * @returns The output stream with the state of @p __x inserted or in
1406 * an error state.
1407 */
1408 template<typename _RealType1, typename _CharT, typename _Traits>
1409 friend std::basic_ostream<_CharT, _Traits>&
1410 operator<<(std::basic_ostream<_CharT, _Traits>&,
1411 const nakagami_distribution<_RealType1>&);
1412
1413 /**
1414 * @brief Extracts a %nakagami_distribution random number distribution
1415 * @p __x from the input stream @p __is.
1416 *
1417 * @param __is An input stream.
1418 * @param __x A %nakagami_distribution random number
1419 * generator engine.
1420 *
1421 * @returns The input stream with @p __x extracted or in an error state.
1422 */
1423 template<typename _RealType1, typename _CharT, typename _Traits>
1424 friend std::basic_istream<_CharT, _Traits>&
1425 operator>>(std::basic_istream<_CharT, _Traits>&,
1426 nakagami_distribution<_RealType1>&);
1427
1428 private:
1429 template<typename _ForwardIterator,
1430 typename _UniformRandomNumberGenerator>
1431 void
1432 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1433 _UniformRandomNumberGenerator& __urng,
1434 const param_type& __p);
1435
1436 param_type _M_param;
1437
1438 std::gamma_distribution<result_type> _M_gd;
1439 };
1440
1441 /**
1442 * @brief Return true if two Nakagami distributions are not equal.
1443 */
1444 template<typename _RealType>
1445 inline bool
1446 operator!=(const nakagami_distribution<_RealType>& __d1,
1447 const nakagami_distribution<_RealType>& __d2)
1448 { return !(__d1 == __d2); }
1449
1450
1451 /**
1452 * @brief A Pareto continuous distribution for random numbers.
1453 *
1454 * The formula for the Pareto cumulative probability function is
1455 * @f[
1456 * P(x|\alpha,\mu) = 1 - \left(\frac{\mu}{x}\right)^\alpha
1457 * @f]
1458 * The formula for the Pareto probability density function is
1459 * @f[
1460 * p(x|\alpha,\mu) = \frac{\alpha + 1}{\mu}
1461 * \left(\frac{\mu}{x}\right)^{\alpha + 1}
1462 * @f]
1463 * where @f$x >= \mu@f$ and @f$\mu > 0@f$, @f$\alpha > 0@f$.
1464 *
1465 * <table border=1 cellpadding=10 cellspacing=0>
1466 * <caption align=top>Distribution Statistics</caption>
1467 * <tr><td>Mean</td><td>@f$\alpha \mu / (\alpha - 1)@f$
1468 * for @f$\alpha > 1@f$</td></tr>
1469 * <tr><td>Variance</td><td>@f$\alpha \mu^2 / [(\alpha - 1)^2(\alpha - 2)]@f$
1470 * for @f$\alpha > 2@f$</td></tr>
1471 * <tr><td>Range</td><td>@f$[\mu, \infty)@f$</td></tr>
1472 * </table>
1473 */
1474 template<typename _RealType = double>
1475 class
1476 pareto_distribution
1477 {
1478 static_assert(std::is_floating_point<_RealType>::value,
1479 "template argument not a floating point type");
1480
1481 public:
1482 /** The type of the range of the distribution. */
1483 typedef _RealType result_type;
1484
1485 /** Parameter type. */
1486 struct param_type
1487 {
1488 typedef pareto_distribution<result_type> distribution_type;
1489
1490 param_type() : param_type(1) { }
1491
1492 param_type(result_type __alpha_val,
1493 result_type __mu_val = result_type(1))
1494 : _M_alpha(__alpha_val), _M_mu(__mu_val)
1495 {
1496 __glibcxx_assert(_M_alpha > result_type(0));
1497 __glibcxx_assert(_M_mu > result_type(0));
1498 }
1499
1500 result_type
1501 alpha() const
1502 { return _M_alpha; }
1503
1504 result_type
1505 mu() const
1506 { return _M_mu; }
1507
1508 friend bool
1509 operator==(const param_type& __p1, const param_type& __p2)
1510 { return __p1._M_alpha == __p2._M_alpha && __p1._M_mu == __p2._M_mu; }
1511
1512 friend bool
1513 operator!=(const param_type& __p1, const param_type& __p2)
1514 { return !(__p1 == __p2); }
1515
1516 private:
1517 void _M_initialize();
1518
1519 result_type _M_alpha;
1520 result_type _M_mu;
1521 };
1522
1523 /**
1524 * @brief Constructors.
1525 * @{
1526 */
1527
1528 pareto_distribution() : pareto_distribution(1) { }
1529
1530 explicit
1531 pareto_distribution(result_type __alpha_val,
1532 result_type __mu_val = result_type(1))
1533 : _M_param(__alpha_val, __mu_val),
1534 _M_ud()
1535 { }
1536
1537 explicit
1538 pareto_distribution(const param_type& __p)
1539 : _M_param(__p),
1540 _M_ud()
1541 { }
1542
1543 /// @}
1544
1545 /**
1546 * @brief Resets the distribution state.
1547 */
1548 void
1549 reset()
1550 {
1551 _M_ud.reset();
1552 }
1553
1554 /**
1555 * @brief Return the parameters of the distribution.
1556 */
1557 result_type
1558 alpha() const
1559 { return _M_param.alpha(); }
1560
1561 result_type
1562 mu() const
1563 { return _M_param.mu(); }
1564
1565 /**
1566 * @brief Returns the parameter set of the distribution.
1567 */
1568 param_type
1569 param() const
1570 { return _M_param; }
1571
1572 /**
1573 * @brief Sets the parameter set of the distribution.
1574 * @param __param The new parameter set of the distribution.
1575 */
1576 void
1577 param(const param_type& __param)
1578 { _M_param = __param; }
1579
1580 /**
1581 * @brief Returns the greatest lower bound value of the distribution.
1582 */
1583 result_type
1584 min() const
1585 { return this->mu(); }
1586
1587 /**
1588 * @brief Returns the least upper bound value of the distribution.
1589 */
1590 result_type
1591 max() const
1592 { return std::numeric_limits<result_type>::max(); }
1593
1594 /**
1595 * @brief Generating functions.
1596 */
1597 template<typename _UniformRandomNumberGenerator>
1598 result_type
1599 operator()(_UniformRandomNumberGenerator& __urng)
1600 {
1601 return this->mu() * std::pow(this->_M_ud(__urng),
1602 -result_type(1) / this->alpha());
1603 }
1604
1605 template<typename _UniformRandomNumberGenerator>
1606 result_type
1607 operator()(_UniformRandomNumberGenerator& __urng,
1608 const param_type& __p)
1609 {
1610 return __p.mu() * std::pow(this->_M_ud(__urng),
1611 -result_type(1) / __p.alpha());
1612 }
1613
1614 template<typename _ForwardIterator,
1615 typename _UniformRandomNumberGenerator>
1616 void
1617 __generate(_ForwardIterator __f, _ForwardIterator __t,
1618 _UniformRandomNumberGenerator& __urng)
1619 { this->__generate(__f, __t, __urng, _M_param); }
1620
1621 template<typename _ForwardIterator,
1622 typename _UniformRandomNumberGenerator>
1623 void
1624 __generate(_ForwardIterator __f, _ForwardIterator __t,
1625 _UniformRandomNumberGenerator& __urng,
1626 const param_type& __p)
1627 { this->__generate_impl(__f, __t, __urng, __p); }
1628
1629 template<typename _UniformRandomNumberGenerator>
1630 void
1631 __generate(result_type* __f, result_type* __t,
1632 _UniformRandomNumberGenerator& __urng,
1633 const param_type& __p)
1634 { this->__generate_impl(__f, __t, __urng, __p); }
1635
1636 /**
1637 * @brief Return true if two Pareto distributions have
1638 * the same parameters and the sequences that would
1639 * be generated are equal.
1640 */
1641 friend bool
1642 operator==(const pareto_distribution& __d1,
1643 const pareto_distribution& __d2)
1644 { return (__d1._M_param == __d2._M_param
1645 && __d1._M_ud == __d2._M_ud); }
1646
1647 /**
1648 * @brief Inserts a %pareto_distribution random number distribution
1649 * @p __x into the output stream @p __os.
1650 *
1651 * @param __os An output stream.
1652 * @param __x A %pareto_distribution random number distribution.
1653 *
1654 * @returns The output stream with the state of @p __x inserted or in
1655 * an error state.
1656 */
1657 template<typename _RealType1, typename _CharT, typename _Traits>
1658 friend std::basic_ostream<_CharT, _Traits>&
1659 operator<<(std::basic_ostream<_CharT, _Traits>&,
1660 const pareto_distribution<_RealType1>&);
1661
1662 /**
1663 * @brief Extracts a %pareto_distribution random number distribution
1664 * @p __x from the input stream @p __is.
1665 *
1666 * @param __is An input stream.
1667 * @param __x A %pareto_distribution random number
1668 * generator engine.
1669 *
1670 * @returns The input stream with @p __x extracted or in an error state.
1671 */
1672 template<typename _RealType1, typename _CharT, typename _Traits>
1673 friend std::basic_istream<_CharT, _Traits>&
1674 operator>>(std::basic_istream<_CharT, _Traits>&,
1675 pareto_distribution<_RealType1>&);
1676
1677 private:
1678 template<typename _ForwardIterator,
1679 typename _UniformRandomNumberGenerator>
1680 void
1681 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1682 _UniformRandomNumberGenerator& __urng,
1683 const param_type& __p);
1684
1685 param_type _M_param;
1686
1687 std::uniform_real_distribution<result_type> _M_ud;
1688 };
1689
1690 /**
1691 * @brief Return true if two Pareto distributions are not equal.
1692 */
1693 template<typename _RealType>
1694 inline bool
1695 operator!=(const pareto_distribution<_RealType>& __d1,
1696 const pareto_distribution<_RealType>& __d2)
1697 { return !(__d1 == __d2); }
1698
1699
1700 /**
1701 * @brief A K continuous distribution for random numbers.
1702 *
1703 * The formula for the K probability density function is
1704 * @f[
1705 * p(x|\lambda, \mu, \nu) = \frac{2}{x}
1706 * \left(\frac{\lambda\nu x}{\mu}\right)^{\frac{\lambda + \nu}{2}}
1707 * \frac{1}{\Gamma(\lambda)\Gamma(\nu)}
1708 * K_{\nu - \lambda}\left(2\sqrt{\frac{\lambda\nu x}{\mu}}\right)
1709 * @f]
1710 * where @f$I_0(z)@f$ is the modified Bessel function of the second kind
1711 * of order @f$\nu - \lambda@f$ and @f$\lambda > 0@f$, @f$\mu > 0@f$
1712 * and @f$\nu > 0@f$.
1713 *
1714 * <table border=1 cellpadding=10 cellspacing=0>
1715 * <caption align=top>Distribution Statistics</caption>
1716 * <tr><td>Mean</td><td>@f$\mu@f$</td></tr>
1717 * <tr><td>Variance</td><td>@f$\mu^2\frac{\lambda + \nu + 1}{\lambda\nu}@f$</td></tr>
1718 * <tr><td>Range</td><td>@f$[0, \infty)@f$</td></tr>
1719 * </table>
1720 */
1721 template<typename _RealType = double>
1722 class
1723 k_distribution
1724 {
1725 static_assert(std::is_floating_point<_RealType>::value,
1726 "template argument not a floating point type");
1727
1728 public:
1729 /** The type of the range of the distribution. */
1730 typedef _RealType result_type;
1731
1732 /** Parameter type. */
1733 struct param_type
1734 {
1735 typedef k_distribution<result_type> distribution_type;
1736
1737 param_type() : param_type(1) { }
1738
1739 param_type(result_type __lambda_val,
1740 result_type __mu_val = result_type(1),
1741 result_type __nu_val = result_type(1))
1742 : _M_lambda(__lambda_val), _M_mu(__mu_val), _M_nu(__nu_val)
1743 {
1744 __glibcxx_assert(_M_lambda > result_type(0));
1745 __glibcxx_assert(_M_mu > result_type(0));
1746 __glibcxx_assert(_M_nu > result_type(0));
1747 }
1748
1749 result_type
1750 lambda() const
1751 { return _M_lambda; }
1752
1753 result_type
1754 mu() const
1755 { return _M_mu; }
1756
1757 result_type
1758 nu() const
1759 { return _M_nu; }
1760
1761 friend bool
1762 operator==(const param_type& __p1, const param_type& __p2)
1763 {
1764 return __p1._M_lambda == __p2._M_lambda
1765 && __p1._M_mu == __p2._M_mu
1766 && __p1._M_nu == __p2._M_nu;
1767 }
1768
1769 friend bool
1770 operator!=(const param_type& __p1, const param_type& __p2)
1771 { return !(__p1 == __p2); }
1772
1773 private:
1774 void _M_initialize();
1775
1776 result_type _M_lambda;
1777 result_type _M_mu;
1778 result_type _M_nu;
1779 };
1780
1781 /**
1782 * @brief Constructors.
1783 * @{
1784 */
1785
1786 k_distribution() : k_distribution(1) { }
1787
1788 explicit
1789 k_distribution(result_type __lambda_val,
1790 result_type __mu_val = result_type(1),
1791 result_type __nu_val = result_type(1))
1792 : _M_param(__lambda_val, __mu_val, __nu_val),
1793 _M_gd1(__lambda_val, result_type(1) / __lambda_val),
1794 _M_gd2(__nu_val, __mu_val / __nu_val)
1795 { }
1796
1797 explicit
1798 k_distribution(const param_type& __p)
1799 : _M_param(__p),
1800 _M_gd1(__p.lambda(), result_type(1) / __p.lambda()),
1801 _M_gd2(__p.nu(), __p.mu() / __p.nu())
1802 { }
1803
1804 /// @}
1805
1806 /**
1807 * @brief Resets the distribution state.
1808 */
1809 void
1810 reset()
1811 {
1812 _M_gd1.reset();
1813 _M_gd2.reset();
1814 }
1815
1816 /**
1817 * @brief Return the parameters of the distribution.
1818 */
1819 result_type
1820 lambda() const
1821 { return _M_param.lambda(); }
1822
1823 result_type
1824 mu() const
1825 { return _M_param.mu(); }
1826
1827 result_type
1828 nu() const
1829 { return _M_param.nu(); }
1830
1831 /**
1832 * @brief Returns the parameter set of the distribution.
1833 */
1834 param_type
1835 param() const
1836 { return _M_param; }
1837
1838 /**
1839 * @brief Sets the parameter set of the distribution.
1840 * @param __param The new parameter set of the distribution.
1841 */
1842 void
1843 param(const param_type& __param)
1844 { _M_param = __param; }
1845
1846 /**
1847 * @brief Returns the greatest lower bound value of the distribution.
1848 */
1849 result_type
1850 min() const
1851 { return result_type(0); }
1852
1853 /**
1854 * @brief Returns the least upper bound value of the distribution.
1855 */
1856 result_type
1857 max() const
1858 { return std::numeric_limits<result_type>::max(); }
1859
1860 /**
1861 * @brief Generating functions.
1862 */
1863 template<typename _UniformRandomNumberGenerator>
1864 result_type
1865 operator()(_UniformRandomNumberGenerator&);
1866
1867 template<typename _UniformRandomNumberGenerator>
1868 result_type
1869 operator()(_UniformRandomNumberGenerator&, const param_type&);
1870
1871 template<typename _ForwardIterator,
1872 typename _UniformRandomNumberGenerator>
1873 void
1874 __generate(_ForwardIterator __f, _ForwardIterator __t,
1875 _UniformRandomNumberGenerator& __urng)
1876 { this->__generate(__f, __t, __urng, _M_param); }
1877
1878 template<typename _ForwardIterator,
1879 typename _UniformRandomNumberGenerator>
1880 void
1881 __generate(_ForwardIterator __f, _ForwardIterator __t,
1882 _UniformRandomNumberGenerator& __urng,
1883 const param_type& __p)
1884 { this->__generate_impl(__f, __t, __urng, __p); }
1885
1886 template<typename _UniformRandomNumberGenerator>
1887 void
1888 __generate(result_type* __f, result_type* __t,
1889 _UniformRandomNumberGenerator& __urng,
1890 const param_type& __p)
1891 { this->__generate_impl(__f, __t, __urng, __p); }
1892
1893 /**
1894 * @brief Return true if two K distributions have
1895 * the same parameters and the sequences that would
1896 * be generated are equal.
1897 */
1898 friend bool
1899 operator==(const k_distribution& __d1,
1900 const k_distribution& __d2)
1901 { return (__d1._M_param == __d2._M_param
1902 && __d1._M_gd1 == __d2._M_gd1
1903 && __d1._M_gd2 == __d2._M_gd2); }
1904
1905 /**
1906 * @brief Inserts a %k_distribution random number distribution
1907 * @p __x into the output stream @p __os.
1908 *
1909 * @param __os An output stream.
1910 * @param __x A %k_distribution random number distribution.
1911 *
1912 * @returns The output stream with the state of @p __x inserted or in
1913 * an error state.
1914 */
1915 template<typename _RealType1, typename _CharT, typename _Traits>
1916 friend std::basic_ostream<_CharT, _Traits>&
1917 operator<<(std::basic_ostream<_CharT, _Traits>&,
1918 const k_distribution<_RealType1>&);
1919
1920 /**
1921 * @brief Extracts a %k_distribution random number distribution
1922 * @p __x from the input stream @p __is.
1923 *
1924 * @param __is An input stream.
1925 * @param __x A %k_distribution random number
1926 * generator engine.
1927 *
1928 * @returns The input stream with @p __x extracted or in an error state.
1929 */
1930 template<typename _RealType1, typename _CharT, typename _Traits>
1931 friend std::basic_istream<_CharT, _Traits>&
1932 operator>>(std::basic_istream<_CharT, _Traits>&,
1933 k_distribution<_RealType1>&);
1934
1935 private:
1936 template<typename _ForwardIterator,
1937 typename _UniformRandomNumberGenerator>
1938 void
1939 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1940 _UniformRandomNumberGenerator& __urng,
1941 const param_type& __p);
1942
1943 param_type _M_param;
1944
1945 std::gamma_distribution<result_type> _M_gd1;
1946 std::gamma_distribution<result_type> _M_gd2;
1947 };
1948
1949 /**
1950 * @brief Return true if two K distributions are not equal.
1951 */
1952 template<typename _RealType>
1953 inline bool
1954 operator!=(const k_distribution<_RealType>& __d1,
1955 const k_distribution<_RealType>& __d2)
1956 { return !(__d1 == __d2); }
1957
1958
1959 /**
1960 * @brief An arcsine continuous distribution for random numbers.
1961 *
1962 * The formula for the arcsine probability density function is
1963 * @f[
1964 * p(x|a,b) = \frac{1}{\pi \sqrt{(x - a)(b - x)}}
1965 * @f]
1966 * where @f$x >= a@f$ and @f$x <= b@f$.
1967 *
1968 * <table border=1 cellpadding=10 cellspacing=0>
1969 * <caption align=top>Distribution Statistics</caption>
1970 * <tr><td>Mean</td><td>@f$ (a + b) / 2 @f$</td></tr>
1971 * <tr><td>Variance</td><td>@f$ (b - a)^2 / 8 @f$</td></tr>
1972 * <tr><td>Range</td><td>@f$[a, b]@f$</td></tr>
1973 * </table>
1974 */
1975 template<typename _RealType = double>
1976 class
1977 arcsine_distribution
1978 {
1979 static_assert(std::is_floating_point<_RealType>::value,
1980 "template argument not a floating point type");
1981
1982 public:
1983 /** The type of the range of the distribution. */
1984 typedef _RealType result_type;
1985
1986 /** Parameter type. */
1987 struct param_type
1988 {
1989 typedef arcsine_distribution<result_type> distribution_type;
1990
1991 param_type() : param_type(0) { }
1992
1993 param_type(result_type __a, result_type __b = result_type(1))
1994 : _M_a(__a), _M_b(__b)
1995 {
1996 __glibcxx_assert(_M_a <= _M_b);
1997 }
1998
1999 result_type
2000 a() const
2001 { return _M_a; }
2002
2003 result_type
2004 b() const
2005 { return _M_b; }
2006
2007 friend bool
2008 operator==(const param_type& __p1, const param_type& __p2)
2009 { return __p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b; }
2010
2011 friend bool
2012 operator!=(const param_type& __p1, const param_type& __p2)
2013 { return !(__p1 == __p2); }
2014
2015 private:
2016 void _M_initialize();
2017
2018 result_type _M_a;
2019 result_type _M_b;
2020 };
2021
2022 /**
2023 * @brief Constructors.
2024 * :{
2025 */
2026
2027 arcsine_distribution() : arcsine_distribution(0) { }
2028
2029 explicit
2030 arcsine_distribution(result_type __a, result_type __b = result_type(1))
2031 : _M_param(__a, __b),
2032 _M_ud(-1.5707963267948966192313216916397514L,
2033 +1.5707963267948966192313216916397514L)
2034 { }
2035
2036 explicit
2037 arcsine_distribution(const param_type& __p)
2038 : _M_param(__p),
2039 _M_ud(-1.5707963267948966192313216916397514L,
2040 +1.5707963267948966192313216916397514L)
2041 { }
2042
2043 /// @}
2044
2045 /**
2046 * @brief Resets the distribution state.
2047 */
2048 void
2049 reset()
2050 { _M_ud.reset(); }
2051
2052 /**
2053 * @brief Return the parameters of the distribution.
2054 */
2055 result_type
2056 a() const
2057 { return _M_param.a(); }
2058
2059 result_type
2060 b() const
2061 { return _M_param.b(); }
2062
2063 /**
2064 * @brief Returns the parameter set of the distribution.
2065 */
2066 param_type
2067 param() const
2068 { return _M_param; }
2069
2070 /**
2071 * @brief Sets the parameter set of the distribution.
2072 * @param __param The new parameter set of the distribution.
2073 */
2074 void
2075 param(const param_type& __param)
2076 { _M_param = __param; }
2077
2078 /**
2079 * @brief Returns the greatest lower bound value of the distribution.
2080 */
2081 result_type
2082 min() const
2083 { return this->a(); }
2084
2085 /**
2086 * @brief Returns the least upper bound value of the distribution.
2087 */
2088 result_type
2089 max() const
2090 { return this->b(); }
2091
2092 /**
2093 * @brief Generating functions.
2094 */
2095 template<typename _UniformRandomNumberGenerator>
2096 result_type
2097 operator()(_UniformRandomNumberGenerator& __urng)
2098 {
2099 result_type __x = std::sin(this->_M_ud(__urng));
2100 return (__x * (this->b() - this->a())
2101 + this->a() + this->b()) / result_type(2);
2102 }
2103
2104 template<typename _UniformRandomNumberGenerator>
2105 result_type
2106 operator()(_UniformRandomNumberGenerator& __urng,
2107 const param_type& __p)
2108 {
2109 result_type __x = std::sin(this->_M_ud(__urng));
2110 return (__x * (__p.b() - __p.a())
2111 + __p.a() + __p.b()) / result_type(2);
2112 }
2113
2114 template<typename _ForwardIterator,
2115 typename _UniformRandomNumberGenerator>
2116 void
2117 __generate(_ForwardIterator __f, _ForwardIterator __t,
2118 _UniformRandomNumberGenerator& __urng)
2119 { this->__generate(__f, __t, __urng, _M_param); }
2120
2121 template<typename _ForwardIterator,
2122 typename _UniformRandomNumberGenerator>
2123 void
2124 __generate(_ForwardIterator __f, _ForwardIterator __t,
2125 _UniformRandomNumberGenerator& __urng,
2126 const param_type& __p)
2127 { this->__generate_impl(__f, __t, __urng, __p); }
2128
2129 template<typename _UniformRandomNumberGenerator>
2130 void
2131 __generate(result_type* __f, result_type* __t,
2132 _UniformRandomNumberGenerator& __urng,
2133 const param_type& __p)
2134 { this->__generate_impl(__f, __t, __urng, __p); }
2135
2136 /**
2137 * @brief Return true if two arcsine distributions have
2138 * the same parameters and the sequences that would
2139 * be generated are equal.
2140 */
2141 friend bool
2142 operator==(const arcsine_distribution& __d1,
2143 const arcsine_distribution& __d2)
2144 { return (__d1._M_param == __d2._M_param
2145 && __d1._M_ud == __d2._M_ud); }
2146
2147 /**
2148 * @brief Inserts a %arcsine_distribution random number distribution
2149 * @p __x into the output stream @p __os.
2150 *
2151 * @param __os An output stream.
2152 * @param __x A %arcsine_distribution random number distribution.
2153 *
2154 * @returns The output stream with the state of @p __x inserted or in
2155 * an error state.
2156 */
2157 template<typename _RealType1, typename _CharT, typename _Traits>
2158 friend std::basic_ostream<_CharT, _Traits>&
2159 operator<<(std::basic_ostream<_CharT, _Traits>&,
2160 const arcsine_distribution<_RealType1>&);
2161
2162 /**
2163 * @brief Extracts a %arcsine_distribution random number distribution
2164 * @p __x from the input stream @p __is.
2165 *
2166 * @param __is An input stream.
2167 * @param __x A %arcsine_distribution random number
2168 * generator engine.
2169 *
2170 * @returns The input stream with @p __x extracted or in an error state.
2171 */
2172 template<typename _RealType1, typename _CharT, typename _Traits>
2173 friend std::basic_istream<_CharT, _Traits>&
2174 operator>>(std::basic_istream<_CharT, _Traits>&,
2175 arcsine_distribution<_RealType1>&);
2176
2177 private:
2178 template<typename _ForwardIterator,
2179 typename _UniformRandomNumberGenerator>
2180 void
2181 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2182 _UniformRandomNumberGenerator& __urng,
2183 const param_type& __p);
2184
2185 param_type _M_param;
2186
2187 std::uniform_real_distribution<result_type> _M_ud;
2188 };
2189
2190 /**
2191 * @brief Return true if two arcsine distributions are not equal.
2192 */
2193 template<typename _RealType>
2194 inline bool
2195 operator!=(const arcsine_distribution<_RealType>& __d1,
2196 const arcsine_distribution<_RealType>& __d2)
2197 { return !(__d1 == __d2); }
2198
2199
2200 /**
2201 * @brief A Hoyt continuous distribution for random numbers.
2202 *
2203 * The formula for the Hoyt probability density function is
2204 * @f[
2205 * p(x|q,\omega) = \frac{(1 + q^2)x}{q\omega}
2206 * \exp\left(-\frac{(1 + q^2)^2 x^2}{4 q^2 \omega}\right)
2207 * I_0\left(\frac{(1 - q^4) x^2}{4 q^2 \omega}\right)
2208 * @f]
2209 * where @f$I_0(z)@f$ is the modified Bessel function of the first kind
2210 * of order 0 and @f$0 < q < 1@f$.
2211 *
2212 * <table border=1 cellpadding=10 cellspacing=0>
2213 * <caption align=top>Distribution Statistics</caption>
2214 * <tr><td>Mean</td><td>@f$ \sqrt{\frac{2}{\pi}} \sqrt{\frac{\omega}{1 + q^2}}
2215 * E(1 - q^2) @f$</td></tr>
2216 * <tr><td>Variance</td><td>@f$ \omega \left(1 - \frac{2E^2(1 - q^2)}
2217 * {\pi (1 + q^2)}\right) @f$</td></tr>
2218 * <tr><td>Range</td><td>@f$[0, \infty)@f$</td></tr>
2219 * </table>
2220 * where @f$E(x)@f$ is the elliptic function of the second kind.
2221 */
2222 template<typename _RealType = double>
2223 class
2224 hoyt_distribution
2225 {
2226 static_assert(std::is_floating_point<_RealType>::value,
2227 "template argument not a floating point type");
2228
2229 public:
2230 /** The type of the range of the distribution. */
2231 typedef _RealType result_type;
2232
2233 /** Parameter type. */
2234 struct param_type
2235 {
2236 typedef hoyt_distribution<result_type> distribution_type;
2237
2238 param_type() : param_type(0.5) { }
2239
2240 param_type(result_type __q, result_type __omega = result_type(1))
2241 : _M_q(__q), _M_omega(__omega)
2242 {
2243 __glibcxx_assert(_M_q > result_type(0));
2244 __glibcxx_assert(_M_q < result_type(1));
2245 }
2246
2247 result_type
2248 q() const
2249 { return _M_q; }
2250
2251 result_type
2252 omega() const
2253 { return _M_omega; }
2254
2255 friend bool
2256 operator==(const param_type& __p1, const param_type& __p2)
2257 { return __p1._M_q == __p2._M_q && __p1._M_omega == __p2._M_omega; }
2258
2259 friend bool
2260 operator!=(const param_type& __p1, const param_type& __p2)
2261 { return !(__p1 == __p2); }
2262
2263 private:
2264 void _M_initialize();
2265
2266 result_type _M_q;
2267 result_type _M_omega;
2268 };
2269
2270 /**
2271 * @brief Constructors.
2272 * @{
2273 */
2274
2275 hoyt_distribution() : hoyt_distribution(0.5) { }
2276
2277 explicit
2278 hoyt_distribution(result_type __q, result_type __omega = result_type(1))
2279 : _M_param(__q, __omega),
2280 _M_ad(result_type(0.5L) * (result_type(1) + __q * __q),
2281 result_type(0.5L) * (result_type(1) + __q * __q)
2282 / (__q * __q)),
2283 _M_ed(result_type(1))
2284 { }
2285
2286 explicit
2287 hoyt_distribution(const param_type& __p)
2288 : _M_param(__p),
2289 _M_ad(result_type(0.5L) * (result_type(1) + __p.q() * __p.q()),
2290 result_type(0.5L) * (result_type(1) + __p.q() * __p.q())
2291 / (__p.q() * __p.q())),
2292 _M_ed(result_type(1))
2293 { }
2294
2295 /**
2296 * @brief Resets the distribution state.
2297 */
2298 void
2299 reset()
2300 {
2301 _M_ad.reset();
2302 _M_ed.reset();
2303 }
2304
2305 /**
2306 * @brief Return the parameters of the distribution.
2307 */
2308 result_type
2309 q() const
2310 { return _M_param.q(); }
2311
2312 result_type
2313 omega() const
2314 { return _M_param.omega(); }
2315
2316 /**
2317 * @brief Returns the parameter set of the distribution.
2318 */
2319 param_type
2320 param() const
2321 { return _M_param; }
2322
2323 /**
2324 * @brief Sets the parameter set of the distribution.
2325 * @param __param The new parameter set of the distribution.
2326 */
2327 void
2328 param(const param_type& __param)
2329 { _M_param = __param; }
2330
2331 /**
2332 * @brief Returns the greatest lower bound value of the distribution.
2333 */
2334 result_type
2335 min() const
2336 { return result_type(0); }
2337
2338 /**
2339 * @brief Returns the least upper bound value of the distribution.
2340 */
2341 result_type
2342 max() const
2343 { return std::numeric_limits<result_type>::max(); }
2344
2345 /**
2346 * @brief Generating functions.
2347 */
2348 template<typename _UniformRandomNumberGenerator>
2349 result_type
2350 operator()(_UniformRandomNumberGenerator& __urng);
2351
2352 template<typename _UniformRandomNumberGenerator>
2353 result_type
2354 operator()(_UniformRandomNumberGenerator& __urng,
2355 const param_type& __p);
2356
2357 template<typename _ForwardIterator,
2358 typename _UniformRandomNumberGenerator>
2359 void
2360 __generate(_ForwardIterator __f, _ForwardIterator __t,
2361 _UniformRandomNumberGenerator& __urng)
2362 { this->__generate(__f, __t, __urng, _M_param); }
2363
2364 template<typename _ForwardIterator,
2365 typename _UniformRandomNumberGenerator>
2366 void
2367 __generate(_ForwardIterator __f, _ForwardIterator __t,
2368 _UniformRandomNumberGenerator& __urng,
2369 const param_type& __p)
2370 { this->__generate_impl(__f, __t, __urng, __p); }
2371
2372 template<typename _UniformRandomNumberGenerator>
2373 void
2374 __generate(result_type* __f, result_type* __t,
2375 _UniformRandomNumberGenerator& __urng,
2376 const param_type& __p)
2377 { this->__generate_impl(__f, __t, __urng, __p); }
2378
2379 /**
2380 * @brief Return true if two Hoyt distributions have
2381 * the same parameters and the sequences that would
2382 * be generated are equal.
2383 */
2384 friend bool
2385 operator==(const hoyt_distribution& __d1,
2386 const hoyt_distribution& __d2)
2387 { return (__d1._M_param == __d2._M_param
2388 && __d1._M_ad == __d2._M_ad
2389 && __d1._M_ed == __d2._M_ed); }
2390
2391 /**
2392 * @brief Inserts a %hoyt_distribution random number distribution
2393 * @p __x into the output stream @p __os.
2394 *
2395 * @param __os An output stream.
2396 * @param __x A %hoyt_distribution random number distribution.
2397 *
2398 * @returns The output stream with the state of @p __x inserted or in
2399 * an error state.
2400 */
2401 template<typename _RealType1, typename _CharT, typename _Traits>
2402 friend std::basic_ostream<_CharT, _Traits>&
2403 operator<<(std::basic_ostream<_CharT, _Traits>&,
2404 const hoyt_distribution<_RealType1>&);
2405
2406 /**
2407 * @brief Extracts a %hoyt_distribution random number distribution
2408 * @p __x from the input stream @p __is.
2409 *
2410 * @param __is An input stream.
2411 * @param __x A %hoyt_distribution random number
2412 * generator engine.
2413 *
2414 * @returns The input stream with @p __x extracted or in an error state.
2415 */
2416 template<typename _RealType1, typename _CharT, typename _Traits>
2417 friend std::basic_istream<_CharT, _Traits>&
2418 operator>>(std::basic_istream<_CharT, _Traits>&,
2419 hoyt_distribution<_RealType1>&);
2420
2421 private:
2422 template<typename _ForwardIterator,
2423 typename _UniformRandomNumberGenerator>
2424 void
2425 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2426 _UniformRandomNumberGenerator& __urng,
2427 const param_type& __p);
2428
2429 param_type _M_param;
2430
2431 __gnu_cxx::arcsine_distribution<result_type> _M_ad;
2432 std::exponential_distribution<result_type> _M_ed;
2433 };
2434
2435 /**
2436 * @brief Return true if two Hoyt distributions are not equal.
2437 */
2438 template<typename _RealType>
2439 inline bool
2440 operator!=(const hoyt_distribution<_RealType>& __d1,
2441 const hoyt_distribution<_RealType>& __d2)
2442 { return !(__d1 == __d2); }
2443
2444
2445 /**
2446 * @brief A triangular distribution for random numbers.
2447 *
2448 * The formula for the triangular probability density function is
2449 * @f[
2450 * / 0 for x < a
2451 * p(x|a,b,c) = | \frac{2(x-a)}{(c-a)(b-a)} for a <= x <= b
2452 * | \frac{2(c-x)}{(c-a)(c-b)} for b < x <= c
2453 * \ 0 for c < x
2454 * @f]
2455 *
2456 * <table border=1 cellpadding=10 cellspacing=0>
2457 * <caption align=top>Distribution Statistics</caption>
2458 * <tr><td>Mean</td><td>@f$ \frac{a+b+c}{2} @f$</td></tr>
2459 * <tr><td>Variance</td><td>@f$ \frac{a^2+b^2+c^2-ab-ac-bc}
2460 * {18}@f$</td></tr>
2461 * <tr><td>Range</td><td>@f$[a, c]@f$</td></tr>
2462 * </table>
2463 */
2464 template<typename _RealType = double>
2465 class triangular_distribution
2466 {
2467 static_assert(std::is_floating_point<_RealType>::value,
2468 "template argument not a floating point type");
2469
2470 public:
2471 /** The type of the range of the distribution. */
2472 typedef _RealType result_type;
2473
2474 /** Parameter type. */
2475 struct param_type
2476 {
2477 friend class triangular_distribution<_RealType>;
2478
2479 param_type() : param_type(0) { }
2480
2481 explicit
2482 param_type(_RealType __a,
2483 _RealType __b = _RealType(0.5),
2484 _RealType __c = _RealType(1))
2485 : _M_a(__a), _M_b(__b), _M_c(__c)
2486 {
2487 __glibcxx_assert(_M_a <= _M_b);
2488 __glibcxx_assert(_M_b <= _M_c);
2489 __glibcxx_assert(_M_a < _M_c);
2490
2491 _M_r_ab = (_M_b - _M_a) / (_M_c - _M_a);
2492 _M_f_ab_ac = (_M_b - _M_a) * (_M_c - _M_a);
2493 _M_f_bc_ac = (_M_c - _M_b) * (_M_c - _M_a);
2494 }
2495
2496 _RealType
2497 a() const
2498 { return _M_a; }
2499
2500 _RealType
2501 b() const
2502 { return _M_b; }
2503
2504 _RealType
2505 c() const
2506 { return _M_c; }
2507
2508 friend bool
2509 operator==(const param_type& __p1, const param_type& __p2)
2510 {
2511 return (__p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b
2512 && __p1._M_c == __p2._M_c);
2513 }
2514
2515 friend bool
2516 operator!=(const param_type& __p1, const param_type& __p2)
2517 { return !(__p1 == __p2); }
2518
2519 private:
2520
2521 _RealType _M_a;
2522 _RealType _M_b;
2523 _RealType _M_c;
2524 _RealType _M_r_ab;
2525 _RealType _M_f_ab_ac;
2526 _RealType _M_f_bc_ac;
2527 };
2528
2529 triangular_distribution() : triangular_distribution(0.0) { }
2530
2531 /**
2532 * @brief Constructs a triangle distribution with parameters
2533 * @f$ a @f$, @f$ b @f$ and @f$ c @f$.
2534 */
2535 explicit
2536 triangular_distribution(result_type __a,
2537 result_type __b = result_type(0.5),
2538 result_type __c = result_type(1))
2539 : _M_param(__a, __b, __c)
2540 { }
2541
2542 explicit
2543 triangular_distribution(const param_type& __p)
2544 : _M_param(__p)
2545 { }
2546
2547 /**
2548 * @brief Resets the distribution state.
2549 */
2550 void
2551 reset()
2552 { }
2553
2554 /**
2555 * @brief Returns the @f$ a @f$ of the distribution.
2556 */
2557 result_type
2558 a() const
2559 { return _M_param.a(); }
2560
2561 /**
2562 * @brief Returns the @f$ b @f$ of the distribution.
2563 */
2564 result_type
2565 b() const
2566 { return _M_param.b(); }
2567
2568 /**
2569 * @brief Returns the @f$ c @f$ of the distribution.
2570 */
2571 result_type
2572 c() const
2573 { return _M_param.c(); }
2574
2575 /**
2576 * @brief Returns the parameter set of the distribution.
2577 */
2578 param_type
2579 param() const
2580 { return _M_param; }
2581
2582 /**
2583 * @brief Sets the parameter set of the distribution.
2584 * @param __param The new parameter set of the distribution.
2585 */
2586 void
2587 param(const param_type& __param)
2588 { _M_param = __param; }
2589
2590 /**
2591 * @brief Returns the greatest lower bound value of the distribution.
2592 */
2593 result_type
2594 min() const
2595 { return _M_param._M_a; }
2596
2597 /**
2598 * @brief Returns the least upper bound value of the distribution.
2599 */
2600 result_type
2601 max() const
2602 { return _M_param._M_c; }
2603
2604 /**
2605 * @brief Generating functions.
2606 */
2607 template<typename _UniformRandomNumberGenerator>
2608 result_type
2609 operator()(_UniformRandomNumberGenerator& __urng)
2610 { return this->operator()(__urng, _M_param); }
2611
2612 template<typename _UniformRandomNumberGenerator>
2613 result_type
2614 operator()(_UniformRandomNumberGenerator& __urng,
2615 const param_type& __p)
2616 {
2617 std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2618 __aurng(__urng);
2619 result_type __rnd = __aurng();
2620 if (__rnd <= __p._M_r_ab)
2621 return __p.a() + std::sqrt(__rnd * __p._M_f_ab_ac);
2622 else
2623 return __p.c() - std::sqrt((result_type(1) - __rnd)
2624 * __p._M_f_bc_ac);
2625 }
2626
2627 template<typename _ForwardIterator,
2628 typename _UniformRandomNumberGenerator>
2629 void
2630 __generate(_ForwardIterator __f, _ForwardIterator __t,
2631 _UniformRandomNumberGenerator& __urng)
2632 { this->__generate(__f, __t, __urng, _M_param); }
2633
2634 template<typename _ForwardIterator,
2635 typename _UniformRandomNumberGenerator>
2636 void
2637 __generate(_ForwardIterator __f, _ForwardIterator __t,
2638 _UniformRandomNumberGenerator& __urng,
2639 const param_type& __p)
2640 { this->__generate_impl(__f, __t, __urng, __p); }
2641
2642 template<typename _UniformRandomNumberGenerator>
2643 void
2644 __generate(result_type* __f, result_type* __t,
2645 _UniformRandomNumberGenerator& __urng,
2646 const param_type& __p)
2647 { this->__generate_impl(__f, __t, __urng, __p); }
2648
2649 /**
2650 * @brief Return true if two triangle distributions have the same
2651 * parameters and the sequences that would be generated
2652 * are equal.
2653 */
2654 friend bool
2655 operator==(const triangular_distribution& __d1,
2656 const triangular_distribution& __d2)
2657 { return __d1._M_param == __d2._M_param; }
2658
2659 /**
2660 * @brief Inserts a %triangular_distribution random number distribution
2661 * @p __x into the output stream @p __os.
2662 *
2663 * @param __os An output stream.
2664 * @param __x A %triangular_distribution random number distribution.
2665 *
2666 * @returns The output stream with the state of @p __x inserted or in
2667 * an error state.
2668 */
2669 template<typename _RealType1, typename _CharT, typename _Traits>
2670 friend std::basic_ostream<_CharT, _Traits>&
2671 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2672 const __gnu_cxx::triangular_distribution<_RealType1>& __x);
2673
2674 /**
2675 * @brief Extracts a %triangular_distribution random number distribution
2676 * @p __x from the input stream @p __is.
2677 *
2678 * @param __is An input stream.
2679 * @param __x A %triangular_distribution random number generator engine.
2680 *
2681 * @returns The input stream with @p __x extracted or in an error state.
2682 */
2683 template<typename _RealType1, typename _CharT, typename _Traits>
2684 friend std::basic_istream<_CharT, _Traits>&
2685 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2686 __gnu_cxx::triangular_distribution<_RealType1>& __x);
2687
2688 private:
2689 template<typename _ForwardIterator,
2690 typename _UniformRandomNumberGenerator>
2691 void
2692 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2693 _UniformRandomNumberGenerator& __urng,
2694 const param_type& __p);
2695
2696 param_type _M_param;
2697 };
2698
2699 /**
2700 * @brief Return true if two triangle distributions are different.
2701 */
2702 template<typename _RealType>
2703 inline bool
2704 operator!=(const __gnu_cxx::triangular_distribution<_RealType>& __d1,
2705 const __gnu_cxx::triangular_distribution<_RealType>& __d2)
2706 { return !(__d1 == __d2); }
2707
2708
2709 /**
2710 * @brief A von Mises distribution for random numbers.
2711 *
2712 * The formula for the von Mises probability density function is
2713 * @f[
2714 * p(x|\mu,\kappa) = \frac{e^{\kappa \cos(x-\mu)}}
2715 * {2\pi I_0(\kappa)}
2716 * @f]
2717 *
2718 * The generating functions use the method according to:
2719 *
2720 * D. J. Best and N. I. Fisher, 1979. "Efficient Simulation of the
2721 * von Mises Distribution", Journal of the Royal Statistical Society.
2722 * Series C (Applied Statistics), Vol. 28, No. 2, pp. 152-157.
2723 *
2724 * <table border=1 cellpadding=10 cellspacing=0>
2725 * <caption align=top>Distribution Statistics</caption>
2726 * <tr><td>Mean</td><td>@f$ \mu @f$</td></tr>
2727 * <tr><td>Variance</td><td>@f$ 1-I_1(\kappa)/I_0(\kappa) @f$</td></tr>
2728 * <tr><td>Range</td><td>@f$[-\pi, \pi]@f$</td></tr>
2729 * </table>
2730 */
2731 template<typename _RealType = double>
2732 class von_mises_distribution
2733 {
2734 static_assert(std::is_floating_point<_RealType>::value,
2735 "template argument not a floating point type");
2736
2737 public:
2738 /** The type of the range of the distribution. */
2739 typedef _RealType result_type;
2740
2741 /** Parameter type. */
2742 struct param_type
2743 {
2744 friend class von_mises_distribution<_RealType>;
2745
2746 param_type() : param_type(0) { }
2747
2748 explicit
2749 param_type(_RealType __mu, _RealType __kappa = _RealType(1))
2750 : _M_mu(__mu), _M_kappa(__kappa)
2751 {
2752 const _RealType __pi = __gnu_cxx::__math_constants<_RealType>::__pi;
2753 __glibcxx_assert(_M_mu >= -__pi && _M_mu <= __pi);
2754 __glibcxx_assert(_M_kappa >= _RealType(0));
2755
2756 auto __tau = std::sqrt(_RealType(4) * _M_kappa * _M_kappa
2757 + _RealType(1)) + _RealType(1);
2758 auto __rho = ((__tau - std::sqrt(_RealType(2) * __tau))
2759 / (_RealType(2) * _M_kappa));
2760 _M_r = (_RealType(1) + __rho * __rho) / (_RealType(2) * __rho);
2761 }
2762
2763 _RealType
2764 mu() const
2765 { return _M_mu; }
2766
2767 _RealType
2768 kappa() const
2769 { return _M_kappa; }
2770
2771 friend bool
2772 operator==(const param_type& __p1, const param_type& __p2)
2773 { return __p1._M_mu == __p2._M_mu && __p1._M_kappa == __p2._M_kappa; }
2774
2775 friend bool
2776 operator!=(const param_type& __p1, const param_type& __p2)
2777 { return !(__p1 == __p2); }
2778
2779 private:
2780 _RealType _M_mu;
2781 _RealType _M_kappa;
2782 _RealType _M_r;
2783 };
2784
2785 von_mises_distribution() : von_mises_distribution(0.0) { }
2786
2787 /**
2788 * @brief Constructs a von Mises distribution with parameters
2789 * @f$\mu@f$ and @f$\kappa@f$.
2790 */
2791 explicit
2792 von_mises_distribution(result_type __mu,
2793 result_type __kappa = result_type(1))
2794 : _M_param(__mu, __kappa)
2795 { }
2796
2797 explicit
2798 von_mises_distribution(const param_type& __p)
2799 : _M_param(__p)
2800 { }
2801
2802 /**
2803 * @brief Resets the distribution state.
2804 */
2805 void
2806 reset()
2807 { }
2808
2809 /**
2810 * @brief Returns the @f$ \mu @f$ of the distribution.
2811 */
2812 result_type
2813 mu() const
2814 { return _M_param.mu(); }
2815
2816 /**
2817 * @brief Returns the @f$ \kappa @f$ of the distribution.
2818 */
2819 result_type
2820 kappa() const
2821 { return _M_param.kappa(); }
2822
2823 /**
2824 * @brief Returns the parameter set of the distribution.
2825 */
2826 param_type
2827 param() const
2828 { return _M_param; }
2829
2830 /**
2831 * @brief Sets the parameter set of the distribution.
2832 * @param __param The new parameter set of the distribution.
2833 */
2834 void
2835 param(const param_type& __param)
2836 { _M_param = __param; }
2837
2838 /**
2839 * @brief Returns the greatest lower bound value of the distribution.
2840 */
2841 result_type
2842 min() const
2843 {
2844 return -__gnu_cxx::__math_constants<result_type>::__pi;
2845 }
2846
2847 /**
2848 * @brief Returns the least upper bound value of the distribution.
2849 */
2850 result_type
2851 max() const
2852 {
2853 return __gnu_cxx::__math_constants<result_type>::__pi;
2854 }
2855
2856 /**
2857 * @brief Generating functions.
2858 */
2859 template<typename _UniformRandomNumberGenerator>
2860 result_type
2861 operator()(_UniformRandomNumberGenerator& __urng)
2862 { return this->operator()(__urng, _M_param); }
2863
2864 template<typename _UniformRandomNumberGenerator>
2865 result_type
2866 operator()(_UniformRandomNumberGenerator& __urng,
2867 const param_type& __p);
2868
2869 template<typename _ForwardIterator,
2870 typename _UniformRandomNumberGenerator>
2871 void
2872 __generate(_ForwardIterator __f, _ForwardIterator __t,
2873 _UniformRandomNumberGenerator& __urng)
2874 { this->__generate(__f, __t, __urng, _M_param); }
2875
2876 template<typename _ForwardIterator,
2877 typename _UniformRandomNumberGenerator>
2878 void
2879 __generate(_ForwardIterator __f, _ForwardIterator __t,
2880 _UniformRandomNumberGenerator& __urng,
2881 const param_type& __p)
2882 { this->__generate_impl(__f, __t, __urng, __p); }
2883
2884 template<typename _UniformRandomNumberGenerator>
2885 void
2886 __generate(result_type* __f, result_type* __t,
2887 _UniformRandomNumberGenerator& __urng,
2888 const param_type& __p)
2889 { this->__generate_impl(__f, __t, __urng, __p); }
2890
2891 /**
2892 * @brief Return true if two von Mises distributions have the same
2893 * parameters and the sequences that would be generated
2894 * are equal.
2895 */
2896 friend bool
2897 operator==(const von_mises_distribution& __d1,
2898 const von_mises_distribution& __d2)
2899 { return __d1._M_param == __d2._M_param; }
2900
2901 /**
2902 * @brief Inserts a %von_mises_distribution random number distribution
2903 * @p __x into the output stream @p __os.
2904 *
2905 * @param __os An output stream.
2906 * @param __x A %von_mises_distribution random number distribution.
2907 *
2908 * @returns The output stream with the state of @p __x inserted or in
2909 * an error state.
2910 */
2911 template<typename _RealType1, typename _CharT, typename _Traits>
2912 friend std::basic_ostream<_CharT, _Traits>&
2913 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2914 const __gnu_cxx::von_mises_distribution<_RealType1>& __x);
2915
2916 /**
2917 * @brief Extracts a %von_mises_distribution random number distribution
2918 * @p __x from the input stream @p __is.
2919 *
2920 * @param __is An input stream.
2921 * @param __x A %von_mises_distribution random number generator engine.
2922 *
2923 * @returns The input stream with @p __x extracted or in an error state.
2924 */
2925 template<typename _RealType1, typename _CharT, typename _Traits>
2926 friend std::basic_istream<_CharT, _Traits>&
2927 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2928 __gnu_cxx::von_mises_distribution<_RealType1>& __x);
2929
2930 private:
2931 template<typename _ForwardIterator,
2932 typename _UniformRandomNumberGenerator>
2933 void
2934 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2935 _UniformRandomNumberGenerator& __urng,
2936 const param_type& __p);
2937
2938 param_type _M_param;
2939 };
2940
2941 /**
2942 * @brief Return true if two von Mises distributions are different.
2943 */
2944 template<typename _RealType>
2945 inline bool
2946 operator!=(const __gnu_cxx::von_mises_distribution<_RealType>& __d1,
2947 const __gnu_cxx::von_mises_distribution<_RealType>& __d2)
2948 { return !(__d1 == __d2); }
2949
2950
2951 /**
2952 * @brief A discrete hypergeometric random number distribution.
2953 *
2954 * The hypergeometric distribution is a discrete probability distribution
2955 * that describes the probability of @p k successes in @p n draws @a without
2956 * replacement from a finite population of size @p N containing exactly @p K
2957 * successes.
2958 *
2959 * The formula for the hypergeometric probability density function is
2960 * @f[
2961 * p(k|N,K,n) = \frac{\binom{K}{k} \binom{N-K}{n-k}}{\binom{N}{n}}
2962 * @f]
2963 * where @f$N@f$ is the total population of the distribution,
2964 * @f$K@f$ is the total population of the distribution.
2965 *
2966 * <table border=1 cellpadding=10 cellspacing=0>
2967 * <caption align=top>Distribution Statistics</caption>
2968 * <tr><td>Mean</td><td>@f$ n\frac{K}{N} @f$</td></tr>
2969 * <tr><td>Variance</td><td>@f$ n\frac{K}{N}\frac{N-K}{N}\frac{N-n}{N-1}
2970 * @f$</td></tr>
2971 * <tr><td>Range</td><td>@f$[max(0, n+K-N), min(K, n)]@f$</td></tr>
2972 * </table>
2973 */
2974 template<typename _UIntType = unsigned int>
2975 class hypergeometric_distribution
2976 {
2977 static_assert(std::is_unsigned<_UIntType>::value, "template argument "
2978 "substituting _UIntType not an unsigned integral type");
2979
2980 public:
2981 /** The type of the range of the distribution. */
2982 typedef _UIntType result_type;
2983
2984 /** Parameter type. */
2985 struct param_type
2986 {
2987 typedef hypergeometric_distribution<_UIntType> distribution_type;
2988 friend class hypergeometric_distribution<_UIntType>;
2989
2990 param_type() : param_type(10) { }
2991
2992 explicit
2993 param_type(result_type __N, result_type __K = 5,
2994 result_type __n = 1)
2995 : _M_N{__N}, _M_K{__K}, _M_n{__n}
2996 {
2997 __glibcxx_assert(_M_N >= _M_K);
2998 __glibcxx_assert(_M_N >= _M_n);
2999 }
3000
3001 result_type
3002 total_size() const
3003 { return _M_N; }
3004
3005 result_type
3006 successful_size() const
3007 { return _M_K; }
3008
3009 result_type
3010 unsuccessful_size() const
3011 { return _M_N - _M_K; }
3012
3013 result_type
3014 total_draws() const
3015 { return _M_n; }
3016
3017 friend bool
3018 operator==(const param_type& __p1, const param_type& __p2)
3019 { return (__p1._M_N == __p2._M_N)
3020 && (__p1._M_K == __p2._M_K)
3021 && (__p1._M_n == __p2._M_n); }
3022
3023 friend bool
3024 operator!=(const param_type& __p1, const param_type& __p2)
3025 { return !(__p1 == __p2); }
3026
3027 private:
3028
3029 result_type _M_N;
3030 result_type _M_K;
3031 result_type _M_n;
3032 };
3033
3034 // constructors and member functions
3035
3036 hypergeometric_distribution() : hypergeometric_distribution(10) { }
3037
3038 explicit
3039 hypergeometric_distribution(result_type __N, result_type __K = 5,
3040 result_type __n = 1)
3041 : _M_param{__N, __K, __n}
3042 { }
3043
3044 explicit
3045 hypergeometric_distribution(const param_type& __p)
3046 : _M_param{__p}
3047 { }
3048
3049 /**
3050 * @brief Resets the distribution state.
3051 */
3052 void
3053 reset()
3054 { }
3055
3056 /**
3057 * @brief Returns the distribution parameter @p N,
3058 * the total number of items.
3059 */
3060 result_type
3061 total_size() const
3062 { return this->_M_param.total_size(); }
3063
3064 /**
3065 * @brief Returns the distribution parameter @p K,
3066 * the total number of successful items.
3067 */
3068 result_type
3069 successful_size() const
3070 { return this->_M_param.successful_size(); }
3071
3072 /**
3073 * @brief Returns the total number of unsuccessful items @f$ N - K @f$.
3074 */
3075 result_type
3076 unsuccessful_size() const
3077 { return this->_M_param.unsuccessful_size(); }
3078
3079 /**
3080 * @brief Returns the distribution parameter @p n,
3081 * the total number of draws.
3082 */
3083 result_type
3084 total_draws() const
3085 { return this->_M_param.total_draws(); }
3086
3087 /**
3088 * @brief Returns the parameter set of the distribution.
3089 */
3090 param_type
3091 param() const
3092 { return this->_M_param; }
3093
3094 /**
3095 * @brief Sets the parameter set of the distribution.
3096 * @param __param The new parameter set of the distribution.
3097 */
3098 void
3099 param(const param_type& __param)
3100 { this->_M_param = __param; }
3101
3102 /**
3103 * @brief Returns the greatest lower bound value of the distribution.
3104 */
3105 result_type
3106 min() const
3107 {
3108 using _IntType = typename std::make_signed<result_type>::type;
3109 return static_cast<result_type>(std::max(static_cast<_IntType>(0),
3110 static_cast<_IntType>(this->total_draws()
3111 - this->unsuccessful_size())));
3112 }
3113
3114 /**
3115 * @brief Returns the least upper bound value of the distribution.
3116 */
3117 result_type
3118 max() const
3119 { return std::min(this->successful_size(), this->total_draws()); }
3120
3121 /**
3122 * @brief Generating functions.
3123 */
3124 template<typename _UniformRandomNumberGenerator>
3125 result_type
3126 operator()(_UniformRandomNumberGenerator& __urng)
3127 { return this->operator()(__urng, this->_M_param); }
3128
3129 template<typename _UniformRandomNumberGenerator>
3130 result_type
3131 operator()(_UniformRandomNumberGenerator& __urng,
3132 const param_type& __p);
3133
3134 template<typename _ForwardIterator,
3135 typename _UniformRandomNumberGenerator>
3136 void
3137 __generate(_ForwardIterator __f, _ForwardIterator __t,
3138 _UniformRandomNumberGenerator& __urng)
3139 { this->__generate(__f, __t, __urng, this->_M_param); }
3140
3141 template<typename _ForwardIterator,
3142 typename _UniformRandomNumberGenerator>
3143 void
3144 __generate(_ForwardIterator __f, _ForwardIterator __t,
3145 _UniformRandomNumberGenerator& __urng,
3146 const param_type& __p)
3147 { this->__generate_impl(__f, __t, __urng, __p); }
3148
3149 template<typename _UniformRandomNumberGenerator>
3150 void
3151 __generate(result_type* __f, result_type* __t,
3152 _UniformRandomNumberGenerator& __urng,
3153 const param_type& __p)
3154 { this->__generate_impl(__f, __t, __urng, __p); }
3155
3156 /**
3157 * @brief Return true if two hypergeometric distributions have the same
3158 * parameters and the sequences that would be generated
3159 * are equal.
3160 */
3161 friend bool
3162 operator==(const hypergeometric_distribution& __d1,
3163 const hypergeometric_distribution& __d2)
3164 { return __d1._M_param == __d2._M_param; }
3165
3166 /**
3167 * @brief Inserts a %hypergeometric_distribution random number
3168 * distribution @p __x into the output stream @p __os.
3169 *
3170 * @param __os An output stream.
3171 * @param __x A %hypergeometric_distribution random number
3172 * distribution.
3173 *
3174 * @returns The output stream with the state of @p __x inserted or in
3175 * an error state.
3176 */
3177 template<typename _UIntType1, typename _CharT, typename _Traits>
3178 friend std::basic_ostream<_CharT, _Traits>&
3179 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
3180 const __gnu_cxx::hypergeometric_distribution<_UIntType1>&
3181 __x);
3182
3183 /**
3184 * @brief Extracts a %hypergeometric_distribution random number
3185 * distribution @p __x from the input stream @p __is.
3186 *
3187 * @param __is An input stream.
3188 * @param __x A %hypergeometric_distribution random number generator
3189 * distribution.
3190 *
3191 * @returns The input stream with @p __x extracted or in an error
3192 * state.
3193 */
3194 template<typename _UIntType1, typename _CharT, typename _Traits>
3195 friend std::basic_istream<_CharT, _Traits>&
3196 operator>>(std::basic_istream<_CharT, _Traits>& __is,
3197 __gnu_cxx::hypergeometric_distribution<_UIntType1>& __x);
3198
3199 private:
3200
3201 template<typename _ForwardIterator,
3202 typename _UniformRandomNumberGenerator>
3203 void
3204 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
3205 _UniformRandomNumberGenerator& __urng,
3206 const param_type& __p);
3207
3208 param_type _M_param;
3209 };
3210
3211 /**
3212 * @brief Return true if two hypergeometric distributions are different.
3213 */
3214 template<typename _UIntType>
3215 inline bool
3216 operator!=(const __gnu_cxx::hypergeometric_distribution<_UIntType>& __d1,
3217 const __gnu_cxx::hypergeometric_distribution<_UIntType>& __d2)
3218 { return !(__d1 == __d2); }
3219
3220 /**
3221 * @brief A logistic continuous distribution for random numbers.
3222 *
3223 * The formula for the logistic probability density function is
3224 * @f[
3225 * p(x|\a,\b) = \frac{e^{(x - a)/b}}{b[1 + e^{(x - a)/b}]^2}
3226 * @f]
3227 * where @f$b > 0@f$.
3228 *
3229 * The formula for the logistic probability function is
3230 * @f[
3231 * cdf(x|\a,\b) = \frac{e^{(x - a)/b}}{1 + e^{(x - a)/b}}
3232 * @f]
3233 * where @f$b > 0@f$.
3234 *
3235 * <table border=1 cellpadding=10 cellspacing=0>
3236 * <caption align=top>Distribution Statistics</caption>
3237 * <tr><td>Mean</td><td>@f$a@f$</td></tr>
3238 * <tr><td>Variance</td><td>@f$b^2\pi^2/3@f$</td></tr>
3239 * <tr><td>Range</td><td>@f$[0, \infty)@f$</td></tr>
3240 * </table>
3241 */
3242 template<typename _RealType = double>
3243 class
3244 logistic_distribution
3245 {
3246 static_assert(std::is_floating_point<_RealType>::value,
3247 "template argument not a floating point type");
3248
3249 public:
3250 /** The type of the range of the distribution. */
3251 typedef _RealType result_type;
3252
3253 /** Parameter type. */
3254 struct param_type
3255 {
3256 typedef logistic_distribution<result_type> distribution_type;
3257
3258 param_type() : param_type(0) { }
3259
3260 explicit
3261 param_type(result_type __a, result_type __b = result_type(1))
3262 : _M_a(__a), _M_b(__b)
3263 {
3264 __glibcxx_assert(_M_b > result_type(0));
3265 }
3266
3267 result_type
3268 a() const
3269 { return _M_a; }
3270
3271 result_type
3272 b() const
3273 { return _M_b; }
3274
3275 friend bool
3276 operator==(const param_type& __p1, const param_type& __p2)
3277 { return __p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b; }
3278
3279 friend bool
3280 operator!=(const param_type& __p1, const param_type& __p2)
3281 { return !(__p1 == __p2); }
3282
3283 private:
3284 void _M_initialize();
3285
3286 result_type _M_a;
3287 result_type _M_b;
3288 };
3289
3290 /**
3291 * @brief Constructors.
3292 * @{
3293 */
3294 logistic_distribution() : logistic_distribution(0.0) { }
3295
3296 explicit
3297 logistic_distribution(result_type __a, result_type __b = result_type(1))
3298 : _M_param(__a, __b)
3299 { }
3300
3301 explicit
3302 logistic_distribution(const param_type& __p)
3303 : _M_param(__p)
3304 { }
3305
3306 /// @}
3307
3308 /**
3309 * @brief Resets the distribution state.
3310 */
3311 void
3312 reset()
3313 { }
3314
3315 /**
3316 * @brief Return the parameters of the distribution.
3317 */
3318 result_type
3319 a() const
3320 { return _M_param.a(); }
3321
3322 result_type
3323 b() const
3324 { return _M_param.b(); }
3325
3326 /**
3327 * @brief Returns the parameter set of the distribution.
3328 */
3329 param_type
3330 param() const
3331 { return _M_param; }
3332
3333 /**
3334 * @brief Sets the parameter set of the distribution.
3335 * @param __param The new parameter set of the distribution.
3336 */
3337 void
3338 param(const param_type& __param)
3339 { _M_param = __param; }
3340
3341 /**
3342 * @brief Returns the greatest lower bound value of the distribution.
3343 */
3344 result_type
3345 min() const
3346 { return -std::numeric_limits<result_type>::max(); }
3347
3348 /**
3349 * @brief Returns the least upper bound value of the distribution.
3350 */
3351 result_type
3352 max() const
3353 { return std::numeric_limits<result_type>::max(); }
3354
3355 /**
3356 * @brief Generating functions.
3357 */
3358 template<typename _UniformRandomNumberGenerator>
3359 result_type
3360 operator()(_UniformRandomNumberGenerator& __urng)
3361 { return this->operator()(__urng, this->_M_param); }
3362
3363 template<typename _UniformRandomNumberGenerator>
3364 result_type
3365 operator()(_UniformRandomNumberGenerator&,
3366 const param_type&);
3367
3368 template<typename _ForwardIterator,
3369 typename _UniformRandomNumberGenerator>
3370 void
3371 __generate(_ForwardIterator __f, _ForwardIterator __t,
3372 _UniformRandomNumberGenerator& __urng)
3373 { this->__generate(__f, __t, __urng, this->param()); }
3374
3375 template<typename _ForwardIterator,
3376 typename _UniformRandomNumberGenerator>
3377 void
3378 __generate(_ForwardIterator __f, _ForwardIterator __t,
3379 _UniformRandomNumberGenerator& __urng,
3380 const param_type& __p)
3381 { this->__generate_impl(__f, __t, __urng, __p); }
3382
3383 template<typename _UniformRandomNumberGenerator>
3384 void
3385 __generate(result_type* __f, result_type* __t,
3386 _UniformRandomNumberGenerator& __urng,
3387 const param_type& __p)
3388 { this->__generate_impl(__f, __t, __urng, __p); }
3389
3390 /**
3391 * @brief Return true if two logistic distributions have
3392 * the same parameters and the sequences that would
3393 * be generated are equal.
3394 */
3395 template<typename _RealType1>
3396 friend bool
3397 operator==(const logistic_distribution<_RealType1>& __d1,
3398 const logistic_distribution<_RealType1>& __d2)
3399 { return __d1.param() == __d2.param(); }
3400
3401 /**
3402 * @brief Inserts a %logistic_distribution random number distribution
3403 * @p __x into the output stream @p __os.
3404 *
3405 * @param __os An output stream.
3406 * @param __x A %logistic_distribution random number distribution.
3407 *
3408 * @returns The output stream with the state of @p __x inserted or in
3409 * an error state.
3410 */
3411 template<typename _RealType1, typename _CharT, typename _Traits>
3412 friend std::basic_ostream<_CharT, _Traits>&
3413 operator<<(std::basic_ostream<_CharT, _Traits>&,
3414 const logistic_distribution<_RealType1>&);
3415
3416 /**
3417 * @brief Extracts a %logistic_distribution random number distribution
3418 * @p __x from the input stream @p __is.
3419 *
3420 * @param __is An input stream.
3421 * @param __x A %logistic_distribution random number
3422 * generator engine.
3423 *
3424 * @returns The input stream with @p __x extracted or in an error state.
3425 */
3426 template<typename _RealType1, typename _CharT, typename _Traits>
3427 friend std::basic_istream<_CharT, _Traits>&
3428 operator>>(std::basic_istream<_CharT, _Traits>&,
3429 logistic_distribution<_RealType1>&);
3430
3431 private:
3432 template<typename _ForwardIterator,
3433 typename _UniformRandomNumberGenerator>
3434 void
3435 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
3436 _UniformRandomNumberGenerator& __urng,
3437 const param_type& __p);
3438
3439 param_type _M_param;
3440 };
3441
3442 /**
3443 * @brief Return true if two logistic distributions are not equal.
3444 */
3445 template<typename _RealType1>
3446 inline bool
3447 operator!=(const logistic_distribution<_RealType1>& __d1,
3448 const logistic_distribution<_RealType1>& __d2)
3449 { return !(__d1 == __d2); }
3450
3451
3452 /**
3453 * @brief A distribution for random coordinates on a unit sphere.
3454 *
3455 * The method used in the generation function is attributed by Donald Knuth
3456 * to G. W. Brown, Modern Mathematics for the Engineer (1956).
3457 */
3458 template<std::size_t _Dimen, typename _RealType = double>
3459 class uniform_on_sphere_distribution
3460 {
3461 static_assert(std::is_floating_point<_RealType>::value,
3462 "template argument not a floating point type");
3463 static_assert(_Dimen != 0, "dimension is zero");
3464
3465 public:
3466 /** The type of the range of the distribution. */
3467 typedef std::array<_RealType, _Dimen> result_type;
3468
3469 /** Parameter type. */
3470 struct param_type
3471 {
3472 param_type() { }
3473
3474 friend bool
3475 operator==(const param_type&, const param_type&)
3476 { return true; }
3477
3478 friend bool
3479 operator!=(const param_type&, const param_type&)
3480 { return false; }
3481 };
3482
3483 /**
3484 * @brief Constructs a uniform on sphere distribution.
3485 */
3486 uniform_on_sphere_distribution()
3487 : _M_param(), _M_nd()
3488 { }
3489
3490 explicit
3491 uniform_on_sphere_distribution(const param_type& __p)
3492 : _M_param(__p), _M_nd()
3493 { }
3494
3495 /**
3496 * @brief Resets the distribution state.
3497 */
3498 void
3499 reset()
3500 { _M_nd.reset(); }
3501
3502 /**
3503 * @brief Returns the parameter set of the distribution.
3504 */
3505 param_type
3506 param() const
3507 { return _M_param; }
3508
3509 /**
3510 * @brief Sets the parameter set of the distribution.
3511 * @param __param The new parameter set of the distribution.
3512 */
3513 void
3514 param(const param_type& __param)
3515 { _M_param = __param; }
3516
3517 /**
3518 * @brief Returns the greatest lower bound value of the distribution.
3519 * This function makes no sense for this distribution.
3520 */
3521 result_type
3522 min() const
3523 {
3524 result_type __res;
3525 __res.fill(0);
3526 return __res;
3527 }
3528
3529 /**
3530 * @brief Returns the least upper bound value of the distribution.
3531 * This function makes no sense for this distribution.
3532 */
3533 result_type
3534 max() const
3535 {
3536 result_type __res;
3537 __res.fill(0);
3538 return __res;
3539 }
3540
3541 /**
3542 * @brief Generating functions.
3543 */
3544 template<typename _UniformRandomNumberGenerator>
3545 result_type
3546 operator()(_UniformRandomNumberGenerator& __urng)
3547 { return this->operator()(__urng, _M_param); }
3548
3549 template<typename _UniformRandomNumberGenerator>
3550 result_type
3551 operator()(_UniformRandomNumberGenerator& __urng,
3552 const param_type& __p);
3553
3554 template<typename _ForwardIterator,
3555 typename _UniformRandomNumberGenerator>
3556 void
3557 __generate(_ForwardIterator __f, _ForwardIterator __t,
3558 _UniformRandomNumberGenerator& __urng)
3559 { this->__generate(__f, __t, __urng, this->param()); }
3560
3561 template<typename _ForwardIterator,
3562 typename _UniformRandomNumberGenerator>
3563 void
3564 __generate(_ForwardIterator __f, _ForwardIterator __t,
3565 _UniformRandomNumberGenerator& __urng,
3566 const param_type& __p)
3567 { this->__generate_impl(__f, __t, __urng, __p); }
3568
3569 template<typename _UniformRandomNumberGenerator>
3570 void
3571 __generate(result_type* __f, result_type* __t,
3572 _UniformRandomNumberGenerator& __urng,
3573 const param_type& __p)
3574 { this->__generate_impl(__f, __t, __urng, __p); }
3575
3576 /**
3577 * @brief Return true if two uniform on sphere distributions have
3578 * the same parameters and the sequences that would be
3579 * generated are equal.
3580 */
3581 friend bool
3582 operator==(const uniform_on_sphere_distribution& __d1,
3583 const uniform_on_sphere_distribution& __d2)
3584 { return __d1._M_nd == __d2._M_nd; }
3585
3586 /**
3587 * @brief Inserts a %uniform_on_sphere_distribution random number
3588 * distribution @p __x into the output stream @p __os.
3589 *
3590 * @param __os An output stream.
3591 * @param __x A %uniform_on_sphere_distribution random number
3592 * distribution.
3593 *
3594 * @returns The output stream with the state of @p __x inserted or in
3595 * an error state.
3596 */
3597 template<size_t _Dimen1, typename _RealType1, typename _CharT,
3598 typename _Traits>
3599 friend std::basic_ostream<_CharT, _Traits>&
3600 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
3601 const __gnu_cxx::uniform_on_sphere_distribution<_Dimen1,
3602 _RealType1>&
3603 __x);
3604
3605 /**
3606 * @brief Extracts a %uniform_on_sphere_distribution random number
3607 * distribution
3608 * @p __x from the input stream @p __is.
3609 *
3610 * @param __is An input stream.
3611 * @param __x A %uniform_on_sphere_distribution random number
3612 * generator engine.
3613 *
3614 * @returns The input stream with @p __x extracted or in an error state.
3615 */
3616 template<std::size_t _Dimen1, typename _RealType1, typename _CharT,
3617 typename _Traits>
3618 friend std::basic_istream<_CharT, _Traits>&
3619 operator>>(std::basic_istream<_CharT, _Traits>& __is,
3620 __gnu_cxx::uniform_on_sphere_distribution<_Dimen1,
3621 _RealType1>& __x);
3622
3623 private:
3624 template<typename _ForwardIterator,
3625 typename _UniformRandomNumberGenerator>
3626 void
3627 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
3628 _UniformRandomNumberGenerator& __urng,
3629 const param_type& __p);
3630
3631 param_type _M_param;
3632 std::normal_distribution<_RealType> _M_nd;
3633 };
3634
3635 /**
3636 * @brief Return true if two uniform on sphere distributions are different.
3637 */
3638 template<std::size_t _Dimen, typename _RealType>
3639 inline bool
3640 operator!=(const __gnu_cxx::uniform_on_sphere_distribution<_Dimen,
3641 _RealType>& __d1,
3642 const __gnu_cxx::uniform_on_sphere_distribution<_Dimen,
3643 _RealType>& __d2)
3644 { return !(__d1 == __d2); }
3645
3646
3647 /**
3648 * @brief A distribution for random coordinates inside a unit sphere.
3649 */
3650 template<std::size_t _Dimen, typename _RealType = double>
3651 class uniform_inside_sphere_distribution
3652 {
3653 static_assert(std::is_floating_point<_RealType>::value,
3654 "template argument not a floating point type");
3655 static_assert(_Dimen != 0, "dimension is zero");
3656
3657 public:
3658 /** The type of the range of the distribution. */
3659 using result_type = std::array<_RealType, _Dimen>;
3660
3661 /** Parameter type. */
3662 struct param_type
3663 {
3664 using distribution_type
3665 = uniform_inside_sphere_distribution<_Dimen, _RealType>;
3666 friend class uniform_inside_sphere_distribution<_Dimen, _RealType>;
3667
3668 param_type() : param_type(1.0) { }
3669
3670 explicit
3671 param_type(_RealType __radius)
3672 : _M_radius(__radius)
3673 {
3674 __glibcxx_assert(_M_radius > _RealType(0));
3675 }
3676
3677 _RealType
3678 radius() const
3679 { return _M_radius; }
3680
3681 friend bool
3682 operator==(const param_type& __p1, const param_type& __p2)
3683 { return __p1._M_radius == __p2._M_radius; }
3684
3685 friend bool
3686 operator!=(const param_type& __p1, const param_type& __p2)
3687 { return !(__p1 == __p2); }
3688
3689 private:
3690 _RealType _M_radius;
3691 };
3692
3693 /**
3694 * @brief Constructors.
3695 * @{
3696 */
3697
3698 uniform_inside_sphere_distribution()
3699 : uniform_inside_sphere_distribution(1.0)
3700 { }
3701
3702 explicit
3703 uniform_inside_sphere_distribution(_RealType __radius)
3704 : _M_param(__radius), _M_uosd()
3705 { }
3706
3707 explicit
3708 uniform_inside_sphere_distribution(const param_type& __p)
3709 : _M_param(__p), _M_uosd()
3710 { }
3711
3712 /// @}
3713
3714 /**
3715 * @brief Resets the distribution state.
3716 */
3717 void
3718 reset()
3719 { _M_uosd.reset(); }
3720
3721 /**
3722 * @brief Returns the @f$radius@f$ of the distribution.
3723 */
3724 _RealType
3725 radius() const
3726 { return _M_param.radius(); }
3727
3728 /**
3729 * @brief Returns the parameter set of the distribution.
3730 */
3731 param_type
3732 param() const
3733 { return _M_param; }
3734
3735 /**
3736 * @brief Sets the parameter set of the distribution.
3737 * @param __param The new parameter set of the distribution.
3738 */
3739 void
3740 param(const param_type& __param)
3741 { _M_param = __param; }
3742
3743 /**
3744 * @brief Returns the greatest lower bound value of the distribution.
3745 * This function makes no sense for this distribution.
3746 */
3747 result_type
3748 min() const
3749 {
3750 result_type __res;
3751 __res.fill(0);
3752 return __res;
3753 }
3754
3755 /**
3756 * @brief Returns the least upper bound value of the distribution.
3757 * This function makes no sense for this distribution.
3758 */
3759 result_type
3760 max() const
3761 {
3762 result_type __res;
3763 __res.fill(0);
3764 return __res;
3765 }
3766
3767 /**
3768 * @brief Generating functions.
3769 */
3770 template<typename _UniformRandomNumberGenerator>
3771 result_type
3772 operator()(_UniformRandomNumberGenerator& __urng)
3773 { return this->operator()(__urng, _M_param); }
3774
3775 template<typename _UniformRandomNumberGenerator>
3776 result_type
3777 operator()(_UniformRandomNumberGenerator& __urng,
3778 const param_type& __p);
3779
3780 template<typename _ForwardIterator,
3781 typename _UniformRandomNumberGenerator>
3782 void
3783 __generate(_ForwardIterator __f, _ForwardIterator __t,
3784 _UniformRandomNumberGenerator& __urng)
3785 { this->__generate(__f, __t, __urng, this->param()); }
3786
3787 template<typename _ForwardIterator,
3788 typename _UniformRandomNumberGenerator>
3789 void
3790 __generate(_ForwardIterator __f, _ForwardIterator __t,
3791 _UniformRandomNumberGenerator& __urng,
3792 const param_type& __p)
3793 { this->__generate_impl(__f, __t, __urng, __p); }
3794
3795 template<typename _UniformRandomNumberGenerator>
3796 void
3797 __generate(result_type* __f, result_type* __t,
3798 _UniformRandomNumberGenerator& __urng,
3799 const param_type& __p)
3800 { this->__generate_impl(__f, __t, __urng, __p); }
3801
3802 /**
3803 * @brief Return true if two uniform on sphere distributions have
3804 * the same parameters and the sequences that would be
3805 * generated are equal.
3806 */
3807 friend bool
3808 operator==(const uniform_inside_sphere_distribution& __d1,
3809 const uniform_inside_sphere_distribution& __d2)
3810 { return __d1._M_param == __d2._M_param && __d1._M_uosd == __d2._M_uosd; }
3811
3812 /**
3813 * @brief Inserts a %uniform_inside_sphere_distribution random number
3814 * distribution @p __x into the output stream @p __os.
3815 *
3816 * @param __os An output stream.
3817 * @param __x A %uniform_inside_sphere_distribution random number
3818 * distribution.
3819 *
3820 * @returns The output stream with the state of @p __x inserted or in
3821 * an error state.
3822 */
3823 template<size_t _Dimen1, typename _RealType1, typename _CharT,
3824 typename _Traits>
3825 friend std::basic_ostream<_CharT, _Traits>&
3826 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
3827 const __gnu_cxx::uniform_inside_sphere_distribution<_Dimen1,
3828 _RealType1>&
3829 );
3830
3831 /**
3832 * @brief Extracts a %uniform_inside_sphere_distribution random number
3833 * distribution
3834 * @p __x from the input stream @p __is.
3835 *
3836 * @param __is An input stream.
3837 * @param __x A %uniform_inside_sphere_distribution random number
3838 * generator engine.
3839 *
3840 * @returns The input stream with @p __x extracted or in an error state.
3841 */
3842 template<std::size_t _Dimen1, typename _RealType1, typename _CharT,
3843 typename _Traits>
3844 friend std::basic_istream<_CharT, _Traits>&
3845 operator>>(std::basic_istream<_CharT, _Traits>& __is,
3846 __gnu_cxx::uniform_inside_sphere_distribution<_Dimen1,
3847 _RealType1>&);
3848
3849 private:
3850 template<typename _ForwardIterator,
3851 typename _UniformRandomNumberGenerator>
3852 void
3853 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
3854 _UniformRandomNumberGenerator& __urng,
3855 const param_type& __p);
3856
3857 param_type _M_param;
3858 uniform_on_sphere_distribution<_Dimen, _RealType> _M_uosd;
3859 };
3860
3861 /**
3862 * @brief Return true if two uniform on sphere distributions are different.
3863 */
3864 template<std::size_t _Dimen, typename _RealType>
3865 inline bool
3866 operator!=(const __gnu_cxx::uniform_inside_sphere_distribution<_Dimen,
3867 _RealType>& __d1,
3868 const __gnu_cxx::uniform_inside_sphere_distribution<_Dimen,
3869 _RealType>& __d2)
3870 { return !(__d1 == __d2); }
3871
3872_GLIBCXX_END_NAMESPACE_VERSION
3873} // namespace __gnu_cxx
3874
3875#include <ext/opt_random.h>
3876#include <ext/random.tcc>
3877
3878#endif // _GLIBCXX_USE_C99_STDINT_TR1 && UINT32_C
3879
3880#endif // C++11
3881
3882#endif // _EXT_RANDOM
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