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