1 | // random number generation (out of line) -*- C++ -*- |
2 | |
3 | // Copyright (C) 2009-2021 Free Software Foundation, Inc. |
4 | // |
5 | // This file is part of the GNU ISO C++ Library. This library is free |
6 | // software; you can redistribute it and/or modify it under the |
7 | // terms of the GNU General Public License as published by the |
8 | // Free Software Foundation; either version 3, or (at your option) |
9 | // any later version. |
10 | |
11 | // This library is distributed in the hope that it will be useful, |
12 | // but WITHOUT ANY WARRANTY; without even the implied warranty of |
13 | // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
14 | // GNU General Public License for more details. |
15 | |
16 | // Under Section 7 of GPL version 3, you are granted additional |
17 | // permissions described in the GCC Runtime Library Exception, version |
18 | // 3.1, as published by the Free Software Foundation. |
19 | |
20 | // You should have received a copy of the GNU General Public License and |
21 | // a copy of the GCC Runtime Library Exception along with this program; |
22 | // see the files COPYING3 and COPYING.RUNTIME respectively. If not, see |
23 | // <http://www.gnu.org/licenses/>. |
24 | |
25 | /** @file bits/random.tcc |
26 | * This is an internal header file, included by other library headers. |
27 | * Do not attempt to use it directly. @headername{random} |
28 | */ |
29 | |
30 | #ifndef _RANDOM_TCC |
31 | #define _RANDOM_TCC 1 |
32 | |
33 | #include <numeric> // std::accumulate and std::partial_sum |
34 | |
35 | namespace std _GLIBCXX_VISIBILITY(default) |
36 | { |
37 | _GLIBCXX_BEGIN_NAMESPACE_VERSION |
38 | |
39 | /// @cond undocumented |
40 | // (Further) implementation-space details. |
41 | namespace __detail |
42 | { |
43 | // General case for x = (ax + c) mod m -- use Schrage's algorithm |
44 | // to avoid integer overflow. |
45 | // |
46 | // Preconditions: a > 0, m > 0. |
47 | // |
48 | // Note: only works correctly for __m % __a < __m / __a. |
49 | template<typename _Tp, _Tp __m, _Tp __a, _Tp __c> |
50 | _Tp |
51 | _Mod<_Tp, __m, __a, __c, false, true>:: |
52 | __calc(_Tp __x) |
53 | { |
54 | if (__a == 1) |
55 | __x %= __m; |
56 | else |
57 | { |
58 | static const _Tp __q = __m / __a; |
59 | static const _Tp __r = __m % __a; |
60 | |
61 | _Tp __t1 = __a * (__x % __q); |
62 | _Tp __t2 = __r * (__x / __q); |
63 | if (__t1 >= __t2) |
64 | __x = __t1 - __t2; |
65 | else |
66 | __x = __m - __t2 + __t1; |
67 | } |
68 | |
69 | if (__c != 0) |
70 | { |
71 | const _Tp __d = __m - __x; |
72 | if (__d > __c) |
73 | __x += __c; |
74 | else |
75 | __x = __c - __d; |
76 | } |
77 | return __x; |
78 | } |
79 | |
80 | template<typename _InputIterator, typename _OutputIterator, |
81 | typename _Tp> |
82 | _OutputIterator |
83 | __normalize(_InputIterator __first, _InputIterator __last, |
84 | _OutputIterator __result, const _Tp& __factor) |
85 | { |
86 | for (; __first != __last; ++__first, ++__result) |
87 | *__result = *__first / __factor; |
88 | return __result; |
89 | } |
90 | |
91 | } // namespace __detail |
92 | /// @endcond |
93 | |
94 | template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> |
95 | constexpr _UIntType |
96 | linear_congruential_engine<_UIntType, __a, __c, __m>::multiplier; |
97 | |
98 | template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> |
99 | constexpr _UIntType |
100 | linear_congruential_engine<_UIntType, __a, __c, __m>::increment; |
101 | |
102 | template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> |
103 | constexpr _UIntType |
104 | linear_congruential_engine<_UIntType, __a, __c, __m>::modulus; |
105 | |
106 | template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> |
107 | constexpr _UIntType |
108 | linear_congruential_engine<_UIntType, __a, __c, __m>::default_seed; |
109 | |
110 | /** |
111 | * Seeds the LCR with integral value @p __s, adjusted so that the |
112 | * ring identity is never a member of the convergence set. |
113 | */ |
114 | template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> |
115 | void |
116 | linear_congruential_engine<_UIntType, __a, __c, __m>:: |
117 | seed(result_type __s) |
118 | { |
119 | if ((__detail::__mod<_UIntType, __m>(__c) == 0) |
120 | && (__detail::__mod<_UIntType, __m>(__s) == 0)) |
121 | _M_x = 1; |
122 | else |
123 | _M_x = __detail::__mod<_UIntType, __m>(__s); |
124 | } |
125 | |
126 | /** |
127 | * Seeds the LCR engine with a value generated by @p __q. |
128 | */ |
129 | template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> |
130 | template<typename _Sseq> |
131 | auto |
132 | linear_congruential_engine<_UIntType, __a, __c, __m>:: |
133 | seed(_Sseq& __q) |
134 | -> _If_seed_seq<_Sseq> |
135 | { |
136 | const _UIntType __k0 = __m == 0 ? std::numeric_limits<_UIntType>::digits |
137 | : std::__lg(__m); |
138 | const _UIntType __k = (__k0 + 31) / 32; |
139 | uint_least32_t __arr[__k + 3]; |
140 | __q.generate(__arr + 0, __arr + __k + 3); |
141 | _UIntType __factor = 1u; |
142 | _UIntType __sum = 0u; |
143 | for (size_t __j = 0; __j < __k; ++__j) |
144 | { |
145 | __sum += __arr[__j + 3] * __factor; |
146 | __factor *= __detail::_Shift<_UIntType, 32>::__value; |
147 | } |
148 | seed(__sum); |
149 | } |
150 | |
151 | template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m, |
152 | typename _CharT, typename _Traits> |
153 | std::basic_ostream<_CharT, _Traits>& |
154 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
155 | const linear_congruential_engine<_UIntType, |
156 | __a, __c, __m>& __lcr) |
157 | { |
158 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
159 | |
160 | const typename __ios_base::fmtflags __flags = __os.flags(); |
161 | const _CharT __fill = __os.fill(); |
162 | __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); |
163 | __os.fill(__os.widen(' ')); |
164 | |
165 | __os << __lcr._M_x; |
166 | |
167 | __os.flags(__flags); |
168 | __os.fill(__fill); |
169 | return __os; |
170 | } |
171 | |
172 | template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m, |
173 | typename _CharT, typename _Traits> |
174 | std::basic_istream<_CharT, _Traits>& |
175 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
176 | linear_congruential_engine<_UIntType, __a, __c, __m>& __lcr) |
177 | { |
178 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
179 | |
180 | const typename __ios_base::fmtflags __flags = __is.flags(); |
181 | __is.flags(__ios_base::dec); |
182 | |
183 | __is >> __lcr._M_x; |
184 | |
185 | __is.flags(__flags); |
186 | return __is; |
187 | } |
188 | |
189 | |
190 | template<typename _UIntType, |
191 | size_t __w, size_t __n, size_t __m, size_t __r, |
192 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
193 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
194 | _UIntType __f> |
195 | constexpr size_t |
196 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
197 | __s, __b, __t, __c, __l, __f>::word_size; |
198 | |
199 | template<typename _UIntType, |
200 | size_t __w, size_t __n, size_t __m, size_t __r, |
201 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
202 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
203 | _UIntType __f> |
204 | constexpr size_t |
205 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
206 | __s, __b, __t, __c, __l, __f>::state_size; |
207 | |
208 | template<typename _UIntType, |
209 | size_t __w, size_t __n, size_t __m, size_t __r, |
210 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
211 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
212 | _UIntType __f> |
213 | constexpr size_t |
214 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
215 | __s, __b, __t, __c, __l, __f>::shift_size; |
216 | |
217 | template<typename _UIntType, |
218 | size_t __w, size_t __n, size_t __m, size_t __r, |
219 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
220 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
221 | _UIntType __f> |
222 | constexpr size_t |
223 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
224 | __s, __b, __t, __c, __l, __f>::mask_bits; |
225 | |
226 | template<typename _UIntType, |
227 | size_t __w, size_t __n, size_t __m, size_t __r, |
228 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
229 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
230 | _UIntType __f> |
231 | constexpr _UIntType |
232 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
233 | __s, __b, __t, __c, __l, __f>::xor_mask; |
234 | |
235 | template<typename _UIntType, |
236 | size_t __w, size_t __n, size_t __m, size_t __r, |
237 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
238 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
239 | _UIntType __f> |
240 | constexpr size_t |
241 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
242 | __s, __b, __t, __c, __l, __f>::tempering_u; |
243 | |
244 | template<typename _UIntType, |
245 | size_t __w, size_t __n, size_t __m, size_t __r, |
246 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
247 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
248 | _UIntType __f> |
249 | constexpr _UIntType |
250 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
251 | __s, __b, __t, __c, __l, __f>::tempering_d; |
252 | |
253 | template<typename _UIntType, |
254 | size_t __w, size_t __n, size_t __m, size_t __r, |
255 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
256 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
257 | _UIntType __f> |
258 | constexpr size_t |
259 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
260 | __s, __b, __t, __c, __l, __f>::tempering_s; |
261 | |
262 | template<typename _UIntType, |
263 | size_t __w, size_t __n, size_t __m, size_t __r, |
264 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
265 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
266 | _UIntType __f> |
267 | constexpr _UIntType |
268 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
269 | __s, __b, __t, __c, __l, __f>::tempering_b; |
270 | |
271 | template<typename _UIntType, |
272 | size_t __w, size_t __n, size_t __m, size_t __r, |
273 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
274 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
275 | _UIntType __f> |
276 | constexpr size_t |
277 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
278 | __s, __b, __t, __c, __l, __f>::tempering_t; |
279 | |
280 | template<typename _UIntType, |
281 | size_t __w, size_t __n, size_t __m, size_t __r, |
282 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
283 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
284 | _UIntType __f> |
285 | constexpr _UIntType |
286 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
287 | __s, __b, __t, __c, __l, __f>::tempering_c; |
288 | |
289 | template<typename _UIntType, |
290 | size_t __w, size_t __n, size_t __m, size_t __r, |
291 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
292 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
293 | _UIntType __f> |
294 | constexpr size_t |
295 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
296 | __s, __b, __t, __c, __l, __f>::tempering_l; |
297 | |
298 | template<typename _UIntType, |
299 | size_t __w, size_t __n, size_t __m, size_t __r, |
300 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
301 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
302 | _UIntType __f> |
303 | constexpr _UIntType |
304 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
305 | __s, __b, __t, __c, __l, __f>:: |
306 | initialization_multiplier; |
307 | |
308 | template<typename _UIntType, |
309 | size_t __w, size_t __n, size_t __m, size_t __r, |
310 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
311 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
312 | _UIntType __f> |
313 | constexpr _UIntType |
314 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
315 | __s, __b, __t, __c, __l, __f>::default_seed; |
316 | |
317 | template<typename _UIntType, |
318 | size_t __w, size_t __n, size_t __m, size_t __r, |
319 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
320 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
321 | _UIntType __f> |
322 | void |
323 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
324 | __s, __b, __t, __c, __l, __f>:: |
325 | seed(result_type __sd) |
326 | { |
327 | _M_x[0] = __detail::__mod<_UIntType, |
328 | __detail::_Shift<_UIntType, __w>::__value>(__sd); |
329 | |
330 | for (size_t __i = 1; __i < state_size; ++__i) |
331 | { |
332 | _UIntType __x = _M_x[__i - 1]; |
333 | __x ^= __x >> (__w - 2); |
334 | __x *= __f; |
335 | __x += __detail::__mod<_UIntType, __n>(__i); |
336 | _M_x[__i] = __detail::__mod<_UIntType, |
337 | __detail::_Shift<_UIntType, __w>::__value>(__x); |
338 | } |
339 | _M_p = state_size; |
340 | } |
341 | |
342 | template<typename _UIntType, |
343 | size_t __w, size_t __n, size_t __m, size_t __r, |
344 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
345 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
346 | _UIntType __f> |
347 | template<typename _Sseq> |
348 | auto |
349 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
350 | __s, __b, __t, __c, __l, __f>:: |
351 | seed(_Sseq& __q) |
352 | -> _If_seed_seq<_Sseq> |
353 | { |
354 | const _UIntType __upper_mask = (~_UIntType()) << __r; |
355 | const size_t __k = (__w + 31) / 32; |
356 | uint_least32_t __arr[__n * __k]; |
357 | __q.generate(__arr + 0, __arr + __n * __k); |
358 | |
359 | bool __zero = true; |
360 | for (size_t __i = 0; __i < state_size; ++__i) |
361 | { |
362 | _UIntType __factor = 1u; |
363 | _UIntType __sum = 0u; |
364 | for (size_t __j = 0; __j < __k; ++__j) |
365 | { |
366 | __sum += __arr[__k * __i + __j] * __factor; |
367 | __factor *= __detail::_Shift<_UIntType, 32>::__value; |
368 | } |
369 | _M_x[__i] = __detail::__mod<_UIntType, |
370 | __detail::_Shift<_UIntType, __w>::__value>(__sum); |
371 | |
372 | if (__zero) |
373 | { |
374 | if (__i == 0) |
375 | { |
376 | if ((_M_x[0] & __upper_mask) != 0u) |
377 | __zero = false; |
378 | } |
379 | else if (_M_x[__i] != 0u) |
380 | __zero = false; |
381 | } |
382 | } |
383 | if (__zero) |
384 | _M_x[0] = __detail::_Shift<_UIntType, __w - 1>::__value; |
385 | _M_p = state_size; |
386 | } |
387 | |
388 | template<typename _UIntType, size_t __w, |
389 | size_t __n, size_t __m, size_t __r, |
390 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
391 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
392 | _UIntType __f> |
393 | void |
394 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
395 | __s, __b, __t, __c, __l, __f>:: |
396 | _M_gen_rand(void) |
397 | { |
398 | const _UIntType __upper_mask = (~_UIntType()) << __r; |
399 | const _UIntType __lower_mask = ~__upper_mask; |
400 | |
401 | for (size_t __k = 0; __k < (__n - __m); ++__k) |
402 | { |
403 | _UIntType __y = ((_M_x[__k] & __upper_mask) |
404 | | (_M_x[__k + 1] & __lower_mask)); |
405 | _M_x[__k] = (_M_x[__k + __m] ^ (__y >> 1) |
406 | ^ ((__y & 0x01) ? __a : 0)); |
407 | } |
408 | |
409 | for (size_t __k = (__n - __m); __k < (__n - 1); ++__k) |
410 | { |
411 | _UIntType __y = ((_M_x[__k] & __upper_mask) |
412 | | (_M_x[__k + 1] & __lower_mask)); |
413 | _M_x[__k] = (_M_x[__k + (__m - __n)] ^ (__y >> 1) |
414 | ^ ((__y & 0x01) ? __a : 0)); |
415 | } |
416 | |
417 | _UIntType __y = ((_M_x[__n - 1] & __upper_mask) |
418 | | (_M_x[0] & __lower_mask)); |
419 | _M_x[__n - 1] = (_M_x[__m - 1] ^ (__y >> 1) |
420 | ^ ((__y & 0x01) ? __a : 0)); |
421 | _M_p = 0; |
422 | } |
423 | |
424 | template<typename _UIntType, size_t __w, |
425 | size_t __n, size_t __m, size_t __r, |
426 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
427 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
428 | _UIntType __f> |
429 | void |
430 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
431 | __s, __b, __t, __c, __l, __f>:: |
432 | discard(unsigned long long __z) |
433 | { |
434 | while (__z > state_size - _M_p) |
435 | { |
436 | __z -= state_size - _M_p; |
437 | _M_gen_rand(); |
438 | } |
439 | _M_p += __z; |
440 | } |
441 | |
442 | template<typename _UIntType, size_t __w, |
443 | size_t __n, size_t __m, size_t __r, |
444 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
445 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
446 | _UIntType __f> |
447 | typename |
448 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
449 | __s, __b, __t, __c, __l, __f>::result_type |
450 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
451 | __s, __b, __t, __c, __l, __f>:: |
452 | operator()() |
453 | { |
454 | // Reload the vector - cost is O(n) amortized over n calls. |
455 | if (_M_p >= state_size) |
456 | _M_gen_rand(); |
457 | |
458 | // Calculate o(x(i)). |
459 | result_type __z = _M_x[_M_p++]; |
460 | __z ^= (__z >> __u) & __d; |
461 | __z ^= (__z << __s) & __b; |
462 | __z ^= (__z << __t) & __c; |
463 | __z ^= (__z >> __l); |
464 | |
465 | return __z; |
466 | } |
467 | |
468 | template<typename _UIntType, size_t __w, |
469 | size_t __n, size_t __m, size_t __r, |
470 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
471 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
472 | _UIntType __f, typename _CharT, typename _Traits> |
473 | std::basic_ostream<_CharT, _Traits>& |
474 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
475 | const mersenne_twister_engine<_UIntType, __w, __n, __m, |
476 | __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x) |
477 | { |
478 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
479 | |
480 | const typename __ios_base::fmtflags __flags = __os.flags(); |
481 | const _CharT __fill = __os.fill(); |
482 | const _CharT __space = __os.widen(' '); |
483 | __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); |
484 | __os.fill(__space); |
485 | |
486 | for (size_t __i = 0; __i < __n; ++__i) |
487 | __os << __x._M_x[__i] << __space; |
488 | __os << __x._M_p; |
489 | |
490 | __os.flags(__flags); |
491 | __os.fill(__fill); |
492 | return __os; |
493 | } |
494 | |
495 | template<typename _UIntType, size_t __w, |
496 | size_t __n, size_t __m, size_t __r, |
497 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, |
498 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, |
499 | _UIntType __f, typename _CharT, typename _Traits> |
500 | std::basic_istream<_CharT, _Traits>& |
501 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
502 | mersenne_twister_engine<_UIntType, __w, __n, __m, |
503 | __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x) |
504 | { |
505 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
506 | |
507 | const typename __ios_base::fmtflags __flags = __is.flags(); |
508 | __is.flags(__ios_base::dec | __ios_base::skipws); |
509 | |
510 | for (size_t __i = 0; __i < __n; ++__i) |
511 | __is >> __x._M_x[__i]; |
512 | __is >> __x._M_p; |
513 | |
514 | __is.flags(__flags); |
515 | return __is; |
516 | } |
517 | |
518 | |
519 | template<typename _UIntType, size_t __w, size_t __s, size_t __r> |
520 | constexpr size_t |
521 | subtract_with_carry_engine<_UIntType, __w, __s, __r>::word_size; |
522 | |
523 | template<typename _UIntType, size_t __w, size_t __s, size_t __r> |
524 | constexpr size_t |
525 | subtract_with_carry_engine<_UIntType, __w, __s, __r>::short_lag; |
526 | |
527 | template<typename _UIntType, size_t __w, size_t __s, size_t __r> |
528 | constexpr size_t |
529 | subtract_with_carry_engine<_UIntType, __w, __s, __r>::long_lag; |
530 | |
531 | template<typename _UIntType, size_t __w, size_t __s, size_t __r> |
532 | constexpr _UIntType |
533 | subtract_with_carry_engine<_UIntType, __w, __s, __r>::default_seed; |
534 | |
535 | template<typename _UIntType, size_t __w, size_t __s, size_t __r> |
536 | void |
537 | subtract_with_carry_engine<_UIntType, __w, __s, __r>:: |
538 | seed(result_type __value) |
539 | { |
540 | std::linear_congruential_engine<result_type, 40014u, 0u, 2147483563u> |
541 | __lcg(__value == 0u ? default_seed : __value); |
542 | |
543 | const size_t __n = (__w + 31) / 32; |
544 | |
545 | for (size_t __i = 0; __i < long_lag; ++__i) |
546 | { |
547 | _UIntType __sum = 0u; |
548 | _UIntType __factor = 1u; |
549 | for (size_t __j = 0; __j < __n; ++__j) |
550 | { |
551 | __sum += __detail::__mod<uint_least32_t, |
552 | __detail::_Shift<uint_least32_t, 32>::__value> |
553 | (__lcg()) * __factor; |
554 | __factor *= __detail::_Shift<_UIntType, 32>::__value; |
555 | } |
556 | _M_x[__i] = __detail::__mod<_UIntType, |
557 | __detail::_Shift<_UIntType, __w>::__value>(__sum); |
558 | } |
559 | _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0; |
560 | _M_p = 0; |
561 | } |
562 | |
563 | template<typename _UIntType, size_t __w, size_t __s, size_t __r> |
564 | template<typename _Sseq> |
565 | auto |
566 | subtract_with_carry_engine<_UIntType, __w, __s, __r>:: |
567 | seed(_Sseq& __q) |
568 | -> _If_seed_seq<_Sseq> |
569 | { |
570 | const size_t __k = (__w + 31) / 32; |
571 | uint_least32_t __arr[__r * __k]; |
572 | __q.generate(__arr + 0, __arr + __r * __k); |
573 | |
574 | for (size_t __i = 0; __i < long_lag; ++__i) |
575 | { |
576 | _UIntType __sum = 0u; |
577 | _UIntType __factor = 1u; |
578 | for (size_t __j = 0; __j < __k; ++__j) |
579 | { |
580 | __sum += __arr[__k * __i + __j] * __factor; |
581 | __factor *= __detail::_Shift<_UIntType, 32>::__value; |
582 | } |
583 | _M_x[__i] = __detail::__mod<_UIntType, |
584 | __detail::_Shift<_UIntType, __w>::__value>(__sum); |
585 | } |
586 | _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0; |
587 | _M_p = 0; |
588 | } |
589 | |
590 | template<typename _UIntType, size_t __w, size_t __s, size_t __r> |
591 | typename subtract_with_carry_engine<_UIntType, __w, __s, __r>:: |
592 | result_type |
593 | subtract_with_carry_engine<_UIntType, __w, __s, __r>:: |
594 | operator()() |
595 | { |
596 | // Derive short lag index from current index. |
597 | long __ps = _M_p - short_lag; |
598 | if (__ps < 0) |
599 | __ps += long_lag; |
600 | |
601 | // Calculate new x(i) without overflow or division. |
602 | // NB: Thanks to the requirements for _UIntType, _M_x[_M_p] + _M_carry |
603 | // cannot overflow. |
604 | _UIntType __xi; |
605 | if (_M_x[__ps] >= _M_x[_M_p] + _M_carry) |
606 | { |
607 | __xi = _M_x[__ps] - _M_x[_M_p] - _M_carry; |
608 | _M_carry = 0; |
609 | } |
610 | else |
611 | { |
612 | __xi = (__detail::_Shift<_UIntType, __w>::__value |
613 | - _M_x[_M_p] - _M_carry + _M_x[__ps]); |
614 | _M_carry = 1; |
615 | } |
616 | _M_x[_M_p] = __xi; |
617 | |
618 | // Adjust current index to loop around in ring buffer. |
619 | if (++_M_p >= long_lag) |
620 | _M_p = 0; |
621 | |
622 | return __xi; |
623 | } |
624 | |
625 | template<typename _UIntType, size_t __w, size_t __s, size_t __r, |
626 | typename _CharT, typename _Traits> |
627 | std::basic_ostream<_CharT, _Traits>& |
628 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
629 | const subtract_with_carry_engine<_UIntType, |
630 | __w, __s, __r>& __x) |
631 | { |
632 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
633 | |
634 | const typename __ios_base::fmtflags __flags = __os.flags(); |
635 | const _CharT __fill = __os.fill(); |
636 | const _CharT __space = __os.widen(' '); |
637 | __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); |
638 | __os.fill(__space); |
639 | |
640 | for (size_t __i = 0; __i < __r; ++__i) |
641 | __os << __x._M_x[__i] << __space; |
642 | __os << __x._M_carry << __space << __x._M_p; |
643 | |
644 | __os.flags(__flags); |
645 | __os.fill(__fill); |
646 | return __os; |
647 | } |
648 | |
649 | template<typename _UIntType, size_t __w, size_t __s, size_t __r, |
650 | typename _CharT, typename _Traits> |
651 | std::basic_istream<_CharT, _Traits>& |
652 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
653 | subtract_with_carry_engine<_UIntType, __w, __s, __r>& __x) |
654 | { |
655 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
656 | |
657 | const typename __ios_base::fmtflags __flags = __is.flags(); |
658 | __is.flags(__ios_base::dec | __ios_base::skipws); |
659 | |
660 | for (size_t __i = 0; __i < __r; ++__i) |
661 | __is >> __x._M_x[__i]; |
662 | __is >> __x._M_carry; |
663 | __is >> __x._M_p; |
664 | |
665 | __is.flags(__flags); |
666 | return __is; |
667 | } |
668 | |
669 | |
670 | template<typename _RandomNumberEngine, size_t __p, size_t __r> |
671 | constexpr size_t |
672 | discard_block_engine<_RandomNumberEngine, __p, __r>::block_size; |
673 | |
674 | template<typename _RandomNumberEngine, size_t __p, size_t __r> |
675 | constexpr size_t |
676 | discard_block_engine<_RandomNumberEngine, __p, __r>::used_block; |
677 | |
678 | template<typename _RandomNumberEngine, size_t __p, size_t __r> |
679 | typename discard_block_engine<_RandomNumberEngine, |
680 | __p, __r>::result_type |
681 | discard_block_engine<_RandomNumberEngine, __p, __r>:: |
682 | operator()() |
683 | { |
684 | if (_M_n >= used_block) |
685 | { |
686 | _M_b.discard(block_size - _M_n); |
687 | _M_n = 0; |
688 | } |
689 | ++_M_n; |
690 | return _M_b(); |
691 | } |
692 | |
693 | template<typename _RandomNumberEngine, size_t __p, size_t __r, |
694 | typename _CharT, typename _Traits> |
695 | std::basic_ostream<_CharT, _Traits>& |
696 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
697 | const discard_block_engine<_RandomNumberEngine, |
698 | __p, __r>& __x) |
699 | { |
700 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
701 | |
702 | const typename __ios_base::fmtflags __flags = __os.flags(); |
703 | const _CharT __fill = __os.fill(); |
704 | const _CharT __space = __os.widen(' '); |
705 | __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); |
706 | __os.fill(__space); |
707 | |
708 | __os << __x.base() << __space << __x._M_n; |
709 | |
710 | __os.flags(__flags); |
711 | __os.fill(__fill); |
712 | return __os; |
713 | } |
714 | |
715 | template<typename _RandomNumberEngine, size_t __p, size_t __r, |
716 | typename _CharT, typename _Traits> |
717 | std::basic_istream<_CharT, _Traits>& |
718 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
719 | discard_block_engine<_RandomNumberEngine, __p, __r>& __x) |
720 | { |
721 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
722 | |
723 | const typename __ios_base::fmtflags __flags = __is.flags(); |
724 | __is.flags(__ios_base::dec | __ios_base::skipws); |
725 | |
726 | __is >> __x._M_b >> __x._M_n; |
727 | |
728 | __is.flags(__flags); |
729 | return __is; |
730 | } |
731 | |
732 | |
733 | template<typename _RandomNumberEngine, size_t __w, typename _UIntType> |
734 | typename independent_bits_engine<_RandomNumberEngine, __w, _UIntType>:: |
735 | result_type |
736 | independent_bits_engine<_RandomNumberEngine, __w, _UIntType>:: |
737 | operator()() |
738 | { |
739 | typedef typename _RandomNumberEngine::result_type _Eresult_type; |
740 | const _Eresult_type __r |
741 | = (_M_b.max() - _M_b.min() < std::numeric_limits<_Eresult_type>::max() |
742 | ? _M_b.max() - _M_b.min() + 1 : 0); |
743 | const unsigned __edig = std::numeric_limits<_Eresult_type>::digits; |
744 | const unsigned __m = __r ? std::__lg(__r) : __edig; |
745 | |
746 | typedef typename std::common_type<_Eresult_type, result_type>::type |
747 | __ctype; |
748 | const unsigned __cdig = std::numeric_limits<__ctype>::digits; |
749 | |
750 | unsigned __n, __n0; |
751 | __ctype __s0, __s1, __y0, __y1; |
752 | |
753 | for (size_t __i = 0; __i < 2; ++__i) |
754 | { |
755 | __n = (__w + __m - 1) / __m + __i; |
756 | __n0 = __n - __w % __n; |
757 | const unsigned __w0 = __w / __n; // __w0 <= __m |
758 | |
759 | __s0 = 0; |
760 | __s1 = 0; |
761 | if (__w0 < __cdig) |
762 | { |
763 | __s0 = __ctype(1) << __w0; |
764 | __s1 = __s0 << 1; |
765 | } |
766 | |
767 | __y0 = 0; |
768 | __y1 = 0; |
769 | if (__r) |
770 | { |
771 | __y0 = __s0 * (__r / __s0); |
772 | if (__s1) |
773 | __y1 = __s1 * (__r / __s1); |
774 | |
775 | if (__r - __y0 <= __y0 / __n) |
776 | break; |
777 | } |
778 | else |
779 | break; |
780 | } |
781 | |
782 | result_type __sum = 0; |
783 | for (size_t __k = 0; __k < __n0; ++__k) |
784 | { |
785 | __ctype __u; |
786 | do |
787 | __u = _M_b() - _M_b.min(); |
788 | while (__y0 && __u >= __y0); |
789 | __sum = __s0 * __sum + (__s0 ? __u % __s0 : __u); |
790 | } |
791 | for (size_t __k = __n0; __k < __n; ++__k) |
792 | { |
793 | __ctype __u; |
794 | do |
795 | __u = _M_b() - _M_b.min(); |
796 | while (__y1 && __u >= __y1); |
797 | __sum = __s1 * __sum + (__s1 ? __u % __s1 : __u); |
798 | } |
799 | return __sum; |
800 | } |
801 | |
802 | |
803 | template<typename _RandomNumberEngine, size_t __k> |
804 | constexpr size_t |
805 | shuffle_order_engine<_RandomNumberEngine, __k>::table_size; |
806 | |
807 | namespace __detail |
808 | { |
809 | // Determine whether an integer is representable as double. |
810 | template<typename _Tp> |
811 | constexpr bool |
812 | __representable_as_double(_Tp __x) noexcept |
813 | { |
814 | static_assert(numeric_limits<_Tp>::is_integer, "" ); |
815 | static_assert(!numeric_limits<_Tp>::is_signed, "" ); |
816 | // All integers <= 2^53 are representable. |
817 | return (__x <= (1ull << __DBL_MANT_DIG__)) |
818 | // Between 2^53 and 2^54 only even numbers are representable. |
819 | || (!(__x & 1) && __detail::__representable_as_double(__x >> 1)); |
820 | } |
821 | |
822 | // Determine whether x+1 is representable as double. |
823 | template<typename _Tp> |
824 | constexpr bool |
825 | __p1_representable_as_double(_Tp __x) noexcept |
826 | { |
827 | static_assert(numeric_limits<_Tp>::is_integer, "" ); |
828 | static_assert(!numeric_limits<_Tp>::is_signed, "" ); |
829 | return numeric_limits<_Tp>::digits < __DBL_MANT_DIG__ |
830 | || (bool(__x + 1u) // return false if x+1 wraps around to zero |
831 | && __detail::__representable_as_double(__x + 1u)); |
832 | } |
833 | } |
834 | |
835 | template<typename _RandomNumberEngine, size_t __k> |
836 | typename shuffle_order_engine<_RandomNumberEngine, __k>::result_type |
837 | shuffle_order_engine<_RandomNumberEngine, __k>:: |
838 | operator()() |
839 | { |
840 | constexpr result_type __range = max() - min(); |
841 | size_t __j = __k; |
842 | const result_type __y = _M_y - min(); |
843 | // Avoid using slower long double arithmetic if possible. |
844 | if _GLIBCXX17_CONSTEXPR (__detail::__p1_representable_as_double(__range)) |
845 | __j *= __y / (__range + 1.0); |
846 | else |
847 | __j *= __y / (__range + 1.0L); |
848 | _M_y = _M_v[__j]; |
849 | _M_v[__j] = _M_b(); |
850 | |
851 | return _M_y; |
852 | } |
853 | |
854 | template<typename _RandomNumberEngine, size_t __k, |
855 | typename _CharT, typename _Traits> |
856 | std::basic_ostream<_CharT, _Traits>& |
857 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
858 | const shuffle_order_engine<_RandomNumberEngine, __k>& __x) |
859 | { |
860 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
861 | |
862 | const typename __ios_base::fmtflags __flags = __os.flags(); |
863 | const _CharT __fill = __os.fill(); |
864 | const _CharT __space = __os.widen(' '); |
865 | __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); |
866 | __os.fill(__space); |
867 | |
868 | __os << __x.base(); |
869 | for (size_t __i = 0; __i < __k; ++__i) |
870 | __os << __space << __x._M_v[__i]; |
871 | __os << __space << __x._M_y; |
872 | |
873 | __os.flags(__flags); |
874 | __os.fill(__fill); |
875 | return __os; |
876 | } |
877 | |
878 | template<typename _RandomNumberEngine, size_t __k, |
879 | typename _CharT, typename _Traits> |
880 | std::basic_istream<_CharT, _Traits>& |
881 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
882 | shuffle_order_engine<_RandomNumberEngine, __k>& __x) |
883 | { |
884 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
885 | |
886 | const typename __ios_base::fmtflags __flags = __is.flags(); |
887 | __is.flags(__ios_base::dec | __ios_base::skipws); |
888 | |
889 | __is >> __x._M_b; |
890 | for (size_t __i = 0; __i < __k; ++__i) |
891 | __is >> __x._M_v[__i]; |
892 | __is >> __x._M_y; |
893 | |
894 | __is.flags(__flags); |
895 | return __is; |
896 | } |
897 | |
898 | |
899 | template<typename _IntType, typename _CharT, typename _Traits> |
900 | std::basic_ostream<_CharT, _Traits>& |
901 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
902 | const uniform_int_distribution<_IntType>& __x) |
903 | { |
904 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
905 | |
906 | const typename __ios_base::fmtflags __flags = __os.flags(); |
907 | const _CharT __fill = __os.fill(); |
908 | const _CharT __space = __os.widen(' '); |
909 | __os.flags(__ios_base::scientific | __ios_base::left); |
910 | __os.fill(__space); |
911 | |
912 | __os << __x.a() << __space << __x.b(); |
913 | |
914 | __os.flags(__flags); |
915 | __os.fill(__fill); |
916 | return __os; |
917 | } |
918 | |
919 | template<typename _IntType, typename _CharT, typename _Traits> |
920 | std::basic_istream<_CharT, _Traits>& |
921 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
922 | uniform_int_distribution<_IntType>& __x) |
923 | { |
924 | using param_type |
925 | = typename uniform_int_distribution<_IntType>::param_type; |
926 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
927 | |
928 | const typename __ios_base::fmtflags __flags = __is.flags(); |
929 | __is.flags(__ios_base::dec | __ios_base::skipws); |
930 | |
931 | _IntType __a, __b; |
932 | if (__is >> __a >> __b) |
933 | __x.param(param_type(__a, __b)); |
934 | |
935 | __is.flags(__flags); |
936 | return __is; |
937 | } |
938 | |
939 | |
940 | template<typename _RealType> |
941 | template<typename _ForwardIterator, |
942 | typename _UniformRandomNumberGenerator> |
943 | void |
944 | uniform_real_distribution<_RealType>:: |
945 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
946 | _UniformRandomNumberGenerator& __urng, |
947 | const param_type& __p) |
948 | { |
949 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
950 | __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> |
951 | __aurng(__urng); |
952 | auto __range = __p.b() - __p.a(); |
953 | while (__f != __t) |
954 | *__f++ = __aurng() * __range + __p.a(); |
955 | } |
956 | |
957 | template<typename _RealType, typename _CharT, typename _Traits> |
958 | std::basic_ostream<_CharT, _Traits>& |
959 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
960 | const uniform_real_distribution<_RealType>& __x) |
961 | { |
962 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
963 | |
964 | const typename __ios_base::fmtflags __flags = __os.flags(); |
965 | const _CharT __fill = __os.fill(); |
966 | const std::streamsize __precision = __os.precision(); |
967 | const _CharT __space = __os.widen(' '); |
968 | __os.flags(__ios_base::scientific | __ios_base::left); |
969 | __os.fill(__space); |
970 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
971 | |
972 | __os << __x.a() << __space << __x.b(); |
973 | |
974 | __os.flags(__flags); |
975 | __os.fill(__fill); |
976 | __os.precision(__precision); |
977 | return __os; |
978 | } |
979 | |
980 | template<typename _RealType, typename _CharT, typename _Traits> |
981 | std::basic_istream<_CharT, _Traits>& |
982 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
983 | uniform_real_distribution<_RealType>& __x) |
984 | { |
985 | using param_type |
986 | = typename uniform_real_distribution<_RealType>::param_type; |
987 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
988 | |
989 | const typename __ios_base::fmtflags __flags = __is.flags(); |
990 | __is.flags(__ios_base::skipws); |
991 | |
992 | _RealType __a, __b; |
993 | if (__is >> __a >> __b) |
994 | __x.param(param_type(__a, __b)); |
995 | |
996 | __is.flags(__flags); |
997 | return __is; |
998 | } |
999 | |
1000 | |
1001 | template<typename _ForwardIterator, |
1002 | typename _UniformRandomNumberGenerator> |
1003 | void |
1004 | std::bernoulli_distribution:: |
1005 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
1006 | _UniformRandomNumberGenerator& __urng, |
1007 | const param_type& __p) |
1008 | { |
1009 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
1010 | __detail::_Adaptor<_UniformRandomNumberGenerator, double> |
1011 | __aurng(__urng); |
1012 | auto __limit = __p.p() * (__aurng.max() - __aurng.min()); |
1013 | |
1014 | while (__f != __t) |
1015 | *__f++ = (__aurng() - __aurng.min()) < __limit; |
1016 | } |
1017 | |
1018 | template<typename _CharT, typename _Traits> |
1019 | std::basic_ostream<_CharT, _Traits>& |
1020 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
1021 | const bernoulli_distribution& __x) |
1022 | { |
1023 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
1024 | |
1025 | const typename __ios_base::fmtflags __flags = __os.flags(); |
1026 | const _CharT __fill = __os.fill(); |
1027 | const std::streamsize __precision = __os.precision(); |
1028 | __os.flags(__ios_base::scientific | __ios_base::left); |
1029 | __os.fill(__os.widen(' ')); |
1030 | __os.precision(std::numeric_limits<double>::max_digits10); |
1031 | |
1032 | __os << __x.p(); |
1033 | |
1034 | __os.flags(__flags); |
1035 | __os.fill(__fill); |
1036 | __os.precision(__precision); |
1037 | return __os; |
1038 | } |
1039 | |
1040 | |
1041 | template<typename _IntType> |
1042 | template<typename _UniformRandomNumberGenerator> |
1043 | typename geometric_distribution<_IntType>::result_type |
1044 | geometric_distribution<_IntType>:: |
1045 | operator()(_UniformRandomNumberGenerator& __urng, |
1046 | const param_type& __param) |
1047 | { |
1048 | // About the epsilon thing see this thread: |
1049 | // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html |
1050 | const double __naf = |
1051 | (1 - std::numeric_limits<double>::epsilon()) / 2; |
1052 | // The largest _RealType convertible to _IntType. |
1053 | const double __thr = |
1054 | std::numeric_limits<_IntType>::max() + __naf; |
1055 | __detail::_Adaptor<_UniformRandomNumberGenerator, double> |
1056 | __aurng(__urng); |
1057 | |
1058 | double __cand; |
1059 | do |
1060 | __cand = std::floor(std::log(1.0 - __aurng()) / __param._M_log_1_p); |
1061 | while (__cand >= __thr); |
1062 | |
1063 | return result_type(__cand + __naf); |
1064 | } |
1065 | |
1066 | template<typename _IntType> |
1067 | template<typename _ForwardIterator, |
1068 | typename _UniformRandomNumberGenerator> |
1069 | void |
1070 | geometric_distribution<_IntType>:: |
1071 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
1072 | _UniformRandomNumberGenerator& __urng, |
1073 | const param_type& __param) |
1074 | { |
1075 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
1076 | // About the epsilon thing see this thread: |
1077 | // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html |
1078 | const double __naf = |
1079 | (1 - std::numeric_limits<double>::epsilon()) / 2; |
1080 | // The largest _RealType convertible to _IntType. |
1081 | const double __thr = |
1082 | std::numeric_limits<_IntType>::max() + __naf; |
1083 | __detail::_Adaptor<_UniformRandomNumberGenerator, double> |
1084 | __aurng(__urng); |
1085 | |
1086 | while (__f != __t) |
1087 | { |
1088 | double __cand; |
1089 | do |
1090 | __cand = std::floor(std::log(1.0 - __aurng()) |
1091 | / __param._M_log_1_p); |
1092 | while (__cand >= __thr); |
1093 | |
1094 | *__f++ = __cand + __naf; |
1095 | } |
1096 | } |
1097 | |
1098 | template<typename _IntType, |
1099 | typename _CharT, typename _Traits> |
1100 | std::basic_ostream<_CharT, _Traits>& |
1101 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
1102 | const geometric_distribution<_IntType>& __x) |
1103 | { |
1104 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
1105 | |
1106 | const typename __ios_base::fmtflags __flags = __os.flags(); |
1107 | const _CharT __fill = __os.fill(); |
1108 | const std::streamsize __precision = __os.precision(); |
1109 | __os.flags(__ios_base::scientific | __ios_base::left); |
1110 | __os.fill(__os.widen(' ')); |
1111 | __os.precision(std::numeric_limits<double>::max_digits10); |
1112 | |
1113 | __os << __x.p(); |
1114 | |
1115 | __os.flags(__flags); |
1116 | __os.fill(__fill); |
1117 | __os.precision(__precision); |
1118 | return __os; |
1119 | } |
1120 | |
1121 | template<typename _IntType, |
1122 | typename _CharT, typename _Traits> |
1123 | std::basic_istream<_CharT, _Traits>& |
1124 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
1125 | geometric_distribution<_IntType>& __x) |
1126 | { |
1127 | using param_type = typename geometric_distribution<_IntType>::param_type; |
1128 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
1129 | |
1130 | const typename __ios_base::fmtflags __flags = __is.flags(); |
1131 | __is.flags(__ios_base::skipws); |
1132 | |
1133 | double __p; |
1134 | if (__is >> __p) |
1135 | __x.param(param_type(__p)); |
1136 | |
1137 | __is.flags(__flags); |
1138 | return __is; |
1139 | } |
1140 | |
1141 | // This is Leger's algorithm, also in Devroye, Ch. X, Example 1.5. |
1142 | template<typename _IntType> |
1143 | template<typename _UniformRandomNumberGenerator> |
1144 | typename negative_binomial_distribution<_IntType>::result_type |
1145 | negative_binomial_distribution<_IntType>:: |
1146 | operator()(_UniformRandomNumberGenerator& __urng) |
1147 | { |
1148 | const double __y = _M_gd(__urng); |
1149 | |
1150 | // XXX Is the constructor too slow? |
1151 | std::poisson_distribution<result_type> __poisson(__y); |
1152 | return __poisson(__urng); |
1153 | } |
1154 | |
1155 | template<typename _IntType> |
1156 | template<typename _UniformRandomNumberGenerator> |
1157 | typename negative_binomial_distribution<_IntType>::result_type |
1158 | negative_binomial_distribution<_IntType>:: |
1159 | operator()(_UniformRandomNumberGenerator& __urng, |
1160 | const param_type& __p) |
1161 | { |
1162 | typedef typename std::gamma_distribution<double>::param_type |
1163 | param_type; |
1164 | |
1165 | const double __y = |
1166 | _M_gd(__urng, param_type(__p.k(), (1.0 - __p.p()) / __p.p())); |
1167 | |
1168 | std::poisson_distribution<result_type> __poisson(__y); |
1169 | return __poisson(__urng); |
1170 | } |
1171 | |
1172 | template<typename _IntType> |
1173 | template<typename _ForwardIterator, |
1174 | typename _UniformRandomNumberGenerator> |
1175 | void |
1176 | negative_binomial_distribution<_IntType>:: |
1177 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
1178 | _UniformRandomNumberGenerator& __urng) |
1179 | { |
1180 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
1181 | while (__f != __t) |
1182 | { |
1183 | const double __y = _M_gd(__urng); |
1184 | |
1185 | // XXX Is the constructor too slow? |
1186 | std::poisson_distribution<result_type> __poisson(__y); |
1187 | *__f++ = __poisson(__urng); |
1188 | } |
1189 | } |
1190 | |
1191 | template<typename _IntType> |
1192 | template<typename _ForwardIterator, |
1193 | typename _UniformRandomNumberGenerator> |
1194 | void |
1195 | negative_binomial_distribution<_IntType>:: |
1196 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
1197 | _UniformRandomNumberGenerator& __urng, |
1198 | const param_type& __p) |
1199 | { |
1200 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
1201 | typename std::gamma_distribution<result_type>::param_type |
1202 | __p2(__p.k(), (1.0 - __p.p()) / __p.p()); |
1203 | |
1204 | while (__f != __t) |
1205 | { |
1206 | const double __y = _M_gd(__urng, __p2); |
1207 | |
1208 | std::poisson_distribution<result_type> __poisson(__y); |
1209 | *__f++ = __poisson(__urng); |
1210 | } |
1211 | } |
1212 | |
1213 | template<typename _IntType, typename _CharT, typename _Traits> |
1214 | std::basic_ostream<_CharT, _Traits>& |
1215 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
1216 | const negative_binomial_distribution<_IntType>& __x) |
1217 | { |
1218 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
1219 | |
1220 | const typename __ios_base::fmtflags __flags = __os.flags(); |
1221 | const _CharT __fill = __os.fill(); |
1222 | const std::streamsize __precision = __os.precision(); |
1223 | const _CharT __space = __os.widen(' '); |
1224 | __os.flags(__ios_base::scientific | __ios_base::left); |
1225 | __os.fill(__os.widen(' ')); |
1226 | __os.precision(std::numeric_limits<double>::max_digits10); |
1227 | |
1228 | __os << __x.k() << __space << __x.p() |
1229 | << __space << __x._M_gd; |
1230 | |
1231 | __os.flags(__flags); |
1232 | __os.fill(__fill); |
1233 | __os.precision(__precision); |
1234 | return __os; |
1235 | } |
1236 | |
1237 | template<typename _IntType, typename _CharT, typename _Traits> |
1238 | std::basic_istream<_CharT, _Traits>& |
1239 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
1240 | negative_binomial_distribution<_IntType>& __x) |
1241 | { |
1242 | using param_type |
1243 | = typename negative_binomial_distribution<_IntType>::param_type; |
1244 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
1245 | |
1246 | const typename __ios_base::fmtflags __flags = __is.flags(); |
1247 | __is.flags(__ios_base::skipws); |
1248 | |
1249 | _IntType __k; |
1250 | double __p; |
1251 | if (__is >> __k >> __p >> __x._M_gd) |
1252 | __x.param(param_type(__k, __p)); |
1253 | |
1254 | __is.flags(__flags); |
1255 | return __is; |
1256 | } |
1257 | |
1258 | |
1259 | template<typename _IntType> |
1260 | void |
1261 | poisson_distribution<_IntType>::param_type:: |
1262 | _M_initialize() |
1263 | { |
1264 | #if _GLIBCXX_USE_C99_MATH_TR1 |
1265 | if (_M_mean >= 12) |
1266 | { |
1267 | const double __m = std::floor(x: _M_mean); |
1268 | _M_lm_thr = std::log(x: _M_mean); |
1269 | _M_lfm = std::lgamma(__m + 1); |
1270 | _M_sm = std::sqrt(x: __m); |
1271 | |
1272 | const double __pi_4 = 0.7853981633974483096156608458198757L; |
1273 | const double __dx = std::sqrt(x: 2 * __m * std::log(x: 32 * __m |
1274 | / __pi_4)); |
1275 | _M_d = std::round(x: std::max<double>(a: 6.0, b: std::min(a: __m, b: __dx))); |
1276 | const double __cx = 2 * __m + _M_d; |
1277 | _M_scx = std::sqrt(x: __cx / 2); |
1278 | _M_1cx = 1 / __cx; |
1279 | |
1280 | _M_c2b = std::sqrt(x: __pi_4 * __cx) * std::exp(x: _M_1cx); |
1281 | _M_cb = 2 * __cx * std::exp(x: -_M_d * _M_1cx * (1 + _M_d / 2)) |
1282 | / _M_d; |
1283 | } |
1284 | else |
1285 | #endif |
1286 | _M_lm_thr = std::exp(x: -_M_mean); |
1287 | } |
1288 | |
1289 | /** |
1290 | * A rejection algorithm when mean >= 12 and a simple method based |
1291 | * upon the multiplication of uniform random variates otherwise. |
1292 | * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1 |
1293 | * is defined. |
1294 | * |
1295 | * Reference: |
1296 | * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, |
1297 | * New York, 1986, Ch. X, Sects. 3.3 & 3.4 (+ Errata!). |
1298 | */ |
1299 | template<typename _IntType> |
1300 | template<typename _UniformRandomNumberGenerator> |
1301 | typename poisson_distribution<_IntType>::result_type |
1302 | poisson_distribution<_IntType>:: |
1303 | operator()(_UniformRandomNumberGenerator& __urng, |
1304 | const param_type& __param) |
1305 | { |
1306 | __detail::_Adaptor<_UniformRandomNumberGenerator, double> |
1307 | __aurng(__urng); |
1308 | #if _GLIBCXX_USE_C99_MATH_TR1 |
1309 | if (__param.mean() >= 12) |
1310 | { |
1311 | double __x; |
1312 | |
1313 | // See comments above... |
1314 | const double __naf = |
1315 | (1 - std::numeric_limits<double>::epsilon()) / 2; |
1316 | const double __thr = |
1317 | std::numeric_limits<_IntType>::max() + __naf; |
1318 | |
1319 | const double __m = std::floor(__param.mean()); |
1320 | // sqrt(pi / 2) |
1321 | const double __spi_2 = 1.2533141373155002512078826424055226L; |
1322 | const double __c1 = __param._M_sm * __spi_2; |
1323 | const double __c2 = __param._M_c2b + __c1; |
1324 | const double __c3 = __c2 + 1; |
1325 | const double __c4 = __c3 + 1; |
1326 | // 1 / 78 |
1327 | const double __178 = 0.0128205128205128205128205128205128L; |
1328 | // e^(1 / 78) |
1329 | const double __e178 = 1.0129030479320018583185514777512983L; |
1330 | const double __c5 = __c4 + __e178; |
1331 | const double __c = __param._M_cb + __c5; |
1332 | const double __2cx = 2 * (2 * __m + __param._M_d); |
1333 | |
1334 | bool __reject = true; |
1335 | do |
1336 | { |
1337 | const double __u = __c * __aurng(); |
1338 | const double __e = -std::log(1.0 - __aurng()); |
1339 | |
1340 | double __w = 0.0; |
1341 | |
1342 | if (__u <= __c1) |
1343 | { |
1344 | const double __n = _M_nd(__urng); |
1345 | const double __y = -std::abs(x: __n) * __param._M_sm - 1; |
1346 | __x = std::floor(x: __y); |
1347 | __w = -__n * __n / 2; |
1348 | if (__x < -__m) |
1349 | continue; |
1350 | } |
1351 | else if (__u <= __c2) |
1352 | { |
1353 | const double __n = _M_nd(__urng); |
1354 | const double __y = 1 + std::abs(x: __n) * __param._M_scx; |
1355 | __x = std::ceil(x: __y); |
1356 | __w = __y * (2 - __y) * __param._M_1cx; |
1357 | if (__x > __param._M_d) |
1358 | continue; |
1359 | } |
1360 | else if (__u <= __c3) |
1361 | // NB: This case not in the book, nor in the Errata, |
1362 | // but should be ok... |
1363 | __x = -1; |
1364 | else if (__u <= __c4) |
1365 | __x = 0; |
1366 | else if (__u <= __c5) |
1367 | { |
1368 | __x = 1; |
1369 | // Only in the Errata, see libstdc++/83237. |
1370 | __w = __178; |
1371 | } |
1372 | else |
1373 | { |
1374 | const double __v = -std::log(1.0 - __aurng()); |
1375 | const double __y = __param._M_d |
1376 | + __v * __2cx / __param._M_d; |
1377 | __x = std::ceil(x: __y); |
1378 | __w = -__param._M_d * __param._M_1cx * (1 + __y / 2); |
1379 | } |
1380 | |
1381 | __reject = (__w - __e - __x * __param._M_lm_thr |
1382 | > __param._M_lfm - std::lgamma(__x + __m + 1)); |
1383 | |
1384 | __reject |= __x + __m >= __thr; |
1385 | |
1386 | } while (__reject); |
1387 | |
1388 | return result_type(__x + __m + __naf); |
1389 | } |
1390 | else |
1391 | #endif |
1392 | { |
1393 | _IntType __x = 0; |
1394 | double __prod = 1.0; |
1395 | |
1396 | do |
1397 | { |
1398 | __prod *= __aurng(); |
1399 | __x += 1; |
1400 | } |
1401 | while (__prod > __param._M_lm_thr); |
1402 | |
1403 | return __x - 1; |
1404 | } |
1405 | } |
1406 | |
1407 | template<typename _IntType> |
1408 | template<typename _ForwardIterator, |
1409 | typename _UniformRandomNumberGenerator> |
1410 | void |
1411 | poisson_distribution<_IntType>:: |
1412 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
1413 | _UniformRandomNumberGenerator& __urng, |
1414 | const param_type& __param) |
1415 | { |
1416 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
1417 | // We could duplicate everything from operator()... |
1418 | while (__f != __t) |
1419 | *__f++ = this->operator()(__urng, __param); |
1420 | } |
1421 | |
1422 | template<typename _IntType, |
1423 | typename _CharT, typename _Traits> |
1424 | std::basic_ostream<_CharT, _Traits>& |
1425 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
1426 | const poisson_distribution<_IntType>& __x) |
1427 | { |
1428 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
1429 | |
1430 | const typename __ios_base::fmtflags __flags = __os.flags(); |
1431 | const _CharT __fill = __os.fill(); |
1432 | const std::streamsize __precision = __os.precision(); |
1433 | const _CharT __space = __os.widen(' '); |
1434 | __os.flags(__ios_base::scientific | __ios_base::left); |
1435 | __os.fill(__space); |
1436 | __os.precision(std::numeric_limits<double>::max_digits10); |
1437 | |
1438 | __os << __x.mean() << __space << __x._M_nd; |
1439 | |
1440 | __os.flags(__flags); |
1441 | __os.fill(__fill); |
1442 | __os.precision(__precision); |
1443 | return __os; |
1444 | } |
1445 | |
1446 | template<typename _IntType, |
1447 | typename _CharT, typename _Traits> |
1448 | std::basic_istream<_CharT, _Traits>& |
1449 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
1450 | poisson_distribution<_IntType>& __x) |
1451 | { |
1452 | using param_type = typename poisson_distribution<_IntType>::param_type; |
1453 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
1454 | |
1455 | const typename __ios_base::fmtflags __flags = __is.flags(); |
1456 | __is.flags(__ios_base::skipws); |
1457 | |
1458 | double __mean; |
1459 | if (__is >> __mean >> __x._M_nd) |
1460 | __x.param(param_type(__mean)); |
1461 | |
1462 | __is.flags(__flags); |
1463 | return __is; |
1464 | } |
1465 | |
1466 | |
1467 | template<typename _IntType> |
1468 | void |
1469 | binomial_distribution<_IntType>::param_type:: |
1470 | _M_initialize() |
1471 | { |
1472 | const double __p12 = _M_p <= 0.5 ? _M_p : 1.0 - _M_p; |
1473 | |
1474 | _M_easy = true; |
1475 | |
1476 | #if _GLIBCXX_USE_C99_MATH_TR1 |
1477 | if (_M_t * __p12 >= 8) |
1478 | { |
1479 | _M_easy = false; |
1480 | const double __np = std::floor(_M_t * __p12); |
1481 | const double __pa = __np / _M_t; |
1482 | const double __1p = 1 - __pa; |
1483 | |
1484 | const double __pi_4 = 0.7853981633974483096156608458198757L; |
1485 | const double __d1x = |
1486 | std::sqrt(x: __np * __1p * std::log(x: 32 * __np |
1487 | / (81 * __pi_4 * __1p))); |
1488 | _M_d1 = std::round(x: std::max<double>(a: 1.0, b: __d1x)); |
1489 | const double __d2x = |
1490 | std::sqrt(__np * __1p * std::log(32 * _M_t * __1p |
1491 | / (__pi_4 * __pa))); |
1492 | _M_d2 = std::round(x: std::max<double>(a: 1.0, b: __d2x)); |
1493 | |
1494 | // sqrt(pi / 2) |
1495 | const double __spi_2 = 1.2533141373155002512078826424055226L; |
1496 | _M_s1 = std::sqrt(x: __np * __1p) * (1 + _M_d1 / (4 * __np)); |
1497 | _M_s2 = std::sqrt(x: __np * __1p) * (1 + _M_d2 / (4 * _M_t * __1p)); |
1498 | _M_c = 2 * _M_d1 / __np; |
1499 | _M_a1 = std::exp(x: _M_c) * _M_s1 * __spi_2; |
1500 | const double __a12 = _M_a1 + _M_s2 * __spi_2; |
1501 | const double __s1s = _M_s1 * _M_s1; |
1502 | _M_a123 = __a12 + (std::exp(_M_d1 / (_M_t * __1p)) |
1503 | * 2 * __s1s / _M_d1 |
1504 | * std::exp(x: -_M_d1 * _M_d1 / (2 * __s1s))); |
1505 | const double __s2s = _M_s2 * _M_s2; |
1506 | _M_s = (_M_a123 + 2 * __s2s / _M_d2 |
1507 | * std::exp(x: -_M_d2 * _M_d2 / (2 * __s2s))); |
1508 | _M_lf = (std::lgamma(__np + 1) |
1509 | + std::lgamma(_M_t - __np + 1)); |
1510 | _M_lp1p = std::log(x: __pa / __1p); |
1511 | |
1512 | _M_q = -std::log(x: 1 - (__p12 - __pa) / __1p); |
1513 | } |
1514 | else |
1515 | #endif |
1516 | _M_q = -std::log(x: 1 - __p12); |
1517 | } |
1518 | |
1519 | template<typename _IntType> |
1520 | template<typename _UniformRandomNumberGenerator> |
1521 | typename binomial_distribution<_IntType>::result_type |
1522 | binomial_distribution<_IntType>:: |
1523 | _M_waiting(_UniformRandomNumberGenerator& __urng, |
1524 | _IntType __t, double __q) |
1525 | { |
1526 | _IntType __x = 0; |
1527 | double __sum = 0.0; |
1528 | __detail::_Adaptor<_UniformRandomNumberGenerator, double> |
1529 | __aurng(__urng); |
1530 | |
1531 | do |
1532 | { |
1533 | if (__t == __x) |
1534 | return __x; |
1535 | const double __e = -std::log(1.0 - __aurng()); |
1536 | __sum += __e / (__t - __x); |
1537 | __x += 1; |
1538 | } |
1539 | while (__sum <= __q); |
1540 | |
1541 | return __x - 1; |
1542 | } |
1543 | |
1544 | /** |
1545 | * A rejection algorithm when t * p >= 8 and a simple waiting time |
1546 | * method - the second in the referenced book - otherwise. |
1547 | * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1 |
1548 | * is defined. |
1549 | * |
1550 | * Reference: |
1551 | * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, |
1552 | * New York, 1986, Ch. X, Sect. 4 (+ Errata!). |
1553 | */ |
1554 | template<typename _IntType> |
1555 | template<typename _UniformRandomNumberGenerator> |
1556 | typename binomial_distribution<_IntType>::result_type |
1557 | binomial_distribution<_IntType>:: |
1558 | operator()(_UniformRandomNumberGenerator& __urng, |
1559 | const param_type& __param) |
1560 | { |
1561 | result_type __ret; |
1562 | const _IntType __t = __param.t(); |
1563 | const double __p = __param.p(); |
1564 | const double __p12 = __p <= 0.5 ? __p : 1.0 - __p; |
1565 | __detail::_Adaptor<_UniformRandomNumberGenerator, double> |
1566 | __aurng(__urng); |
1567 | |
1568 | #if _GLIBCXX_USE_C99_MATH_TR1 |
1569 | if (!__param._M_easy) |
1570 | { |
1571 | double __x; |
1572 | |
1573 | // See comments above... |
1574 | const double __naf = |
1575 | (1 - std::numeric_limits<double>::epsilon()) / 2; |
1576 | const double __thr = |
1577 | std::numeric_limits<_IntType>::max() + __naf; |
1578 | |
1579 | const double __np = std::floor(__t * __p12); |
1580 | |
1581 | // sqrt(pi / 2) |
1582 | const double __spi_2 = 1.2533141373155002512078826424055226L; |
1583 | const double __a1 = __param._M_a1; |
1584 | const double __a12 = __a1 + __param._M_s2 * __spi_2; |
1585 | const double __a123 = __param._M_a123; |
1586 | const double __s1s = __param._M_s1 * __param._M_s1; |
1587 | const double __s2s = __param._M_s2 * __param._M_s2; |
1588 | |
1589 | bool __reject; |
1590 | do |
1591 | { |
1592 | const double __u = __param._M_s * __aurng(); |
1593 | |
1594 | double __v; |
1595 | |
1596 | if (__u <= __a1) |
1597 | { |
1598 | const double __n = _M_nd(__urng); |
1599 | const double __y = __param._M_s1 * std::abs(x: __n); |
1600 | __reject = __y >= __param._M_d1; |
1601 | if (!__reject) |
1602 | { |
1603 | const double __e = -std::log(1.0 - __aurng()); |
1604 | __x = std::floor(x: __y); |
1605 | __v = -__e - __n * __n / 2 + __param._M_c; |
1606 | } |
1607 | } |
1608 | else if (__u <= __a12) |
1609 | { |
1610 | const double __n = _M_nd(__urng); |
1611 | const double __y = __param._M_s2 * std::abs(x: __n); |
1612 | __reject = __y >= __param._M_d2; |
1613 | if (!__reject) |
1614 | { |
1615 | const double __e = -std::log(1.0 - __aurng()); |
1616 | __x = std::floor(x: -__y); |
1617 | __v = -__e - __n * __n / 2; |
1618 | } |
1619 | } |
1620 | else if (__u <= __a123) |
1621 | { |
1622 | const double __e1 = -std::log(1.0 - __aurng()); |
1623 | const double __e2 = -std::log(1.0 - __aurng()); |
1624 | |
1625 | const double __y = __param._M_d1 |
1626 | + 2 * __s1s * __e1 / __param._M_d1; |
1627 | __x = std::floor(x: __y); |
1628 | __v = (-__e2 + __param._M_d1 * (1 / (__t - __np) |
1629 | -__y / (2 * __s1s))); |
1630 | __reject = false; |
1631 | } |
1632 | else |
1633 | { |
1634 | const double __e1 = -std::log(1.0 - __aurng()); |
1635 | const double __e2 = -std::log(1.0 - __aurng()); |
1636 | |
1637 | const double __y = __param._M_d2 |
1638 | + 2 * __s2s * __e1 / __param._M_d2; |
1639 | __x = std::floor(x: -__y); |
1640 | __v = -__e2 - __param._M_d2 * __y / (2 * __s2s); |
1641 | __reject = false; |
1642 | } |
1643 | |
1644 | __reject = __reject || __x < -__np || __x > __t - __np; |
1645 | if (!__reject) |
1646 | { |
1647 | const double __lfx = |
1648 | std::lgamma(__np + __x + 1) |
1649 | + std::lgamma(__t - (__np + __x) + 1); |
1650 | __reject = __v > __param._M_lf - __lfx |
1651 | + __x * __param._M_lp1p; |
1652 | } |
1653 | |
1654 | __reject |= __x + __np >= __thr; |
1655 | } |
1656 | while (__reject); |
1657 | |
1658 | __x += __np + __naf; |
1659 | |
1660 | const _IntType __z = _M_waiting(__urng, __t - _IntType(__x), |
1661 | __param._M_q); |
1662 | __ret = _IntType(__x) + __z; |
1663 | } |
1664 | else |
1665 | #endif |
1666 | __ret = _M_waiting(__urng, __t, __param._M_q); |
1667 | |
1668 | if (__p12 != __p) |
1669 | __ret = __t - __ret; |
1670 | return __ret; |
1671 | } |
1672 | |
1673 | template<typename _IntType> |
1674 | template<typename _ForwardIterator, |
1675 | typename _UniformRandomNumberGenerator> |
1676 | void |
1677 | binomial_distribution<_IntType>:: |
1678 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
1679 | _UniformRandomNumberGenerator& __urng, |
1680 | const param_type& __param) |
1681 | { |
1682 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
1683 | // We could duplicate everything from operator()... |
1684 | while (__f != __t) |
1685 | *__f++ = this->operator()(__urng, __param); |
1686 | } |
1687 | |
1688 | template<typename _IntType, |
1689 | typename _CharT, typename _Traits> |
1690 | std::basic_ostream<_CharT, _Traits>& |
1691 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
1692 | const binomial_distribution<_IntType>& __x) |
1693 | { |
1694 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
1695 | |
1696 | const typename __ios_base::fmtflags __flags = __os.flags(); |
1697 | const _CharT __fill = __os.fill(); |
1698 | const std::streamsize __precision = __os.precision(); |
1699 | const _CharT __space = __os.widen(' '); |
1700 | __os.flags(__ios_base::scientific | __ios_base::left); |
1701 | __os.fill(__space); |
1702 | __os.precision(std::numeric_limits<double>::max_digits10); |
1703 | |
1704 | __os << __x.t() << __space << __x.p() |
1705 | << __space << __x._M_nd; |
1706 | |
1707 | __os.flags(__flags); |
1708 | __os.fill(__fill); |
1709 | __os.precision(__precision); |
1710 | return __os; |
1711 | } |
1712 | |
1713 | template<typename _IntType, |
1714 | typename _CharT, typename _Traits> |
1715 | std::basic_istream<_CharT, _Traits>& |
1716 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
1717 | binomial_distribution<_IntType>& __x) |
1718 | { |
1719 | using param_type = typename binomial_distribution<_IntType>::param_type; |
1720 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
1721 | |
1722 | const typename __ios_base::fmtflags __flags = __is.flags(); |
1723 | __is.flags(__ios_base::dec | __ios_base::skipws); |
1724 | |
1725 | _IntType __t; |
1726 | double __p; |
1727 | if (__is >> __t >> __p >> __x._M_nd) |
1728 | __x.param(param_type(__t, __p)); |
1729 | |
1730 | __is.flags(__flags); |
1731 | return __is; |
1732 | } |
1733 | |
1734 | |
1735 | template<typename _RealType> |
1736 | template<typename _ForwardIterator, |
1737 | typename _UniformRandomNumberGenerator> |
1738 | void |
1739 | std::exponential_distribution<_RealType>:: |
1740 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
1741 | _UniformRandomNumberGenerator& __urng, |
1742 | const param_type& __p) |
1743 | { |
1744 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
1745 | __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> |
1746 | __aurng(__urng); |
1747 | while (__f != __t) |
1748 | *__f++ = -std::log(result_type(1) - __aurng()) / __p.lambda(); |
1749 | } |
1750 | |
1751 | template<typename _RealType, typename _CharT, typename _Traits> |
1752 | std::basic_ostream<_CharT, _Traits>& |
1753 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
1754 | const exponential_distribution<_RealType>& __x) |
1755 | { |
1756 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
1757 | |
1758 | const typename __ios_base::fmtflags __flags = __os.flags(); |
1759 | const _CharT __fill = __os.fill(); |
1760 | const std::streamsize __precision = __os.precision(); |
1761 | __os.flags(__ios_base::scientific | __ios_base::left); |
1762 | __os.fill(__os.widen(' ')); |
1763 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
1764 | |
1765 | __os << __x.lambda(); |
1766 | |
1767 | __os.flags(__flags); |
1768 | __os.fill(__fill); |
1769 | __os.precision(__precision); |
1770 | return __os; |
1771 | } |
1772 | |
1773 | template<typename _RealType, typename _CharT, typename _Traits> |
1774 | std::basic_istream<_CharT, _Traits>& |
1775 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
1776 | exponential_distribution<_RealType>& __x) |
1777 | { |
1778 | using param_type |
1779 | = typename exponential_distribution<_RealType>::param_type; |
1780 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
1781 | |
1782 | const typename __ios_base::fmtflags __flags = __is.flags(); |
1783 | __is.flags(__ios_base::dec | __ios_base::skipws); |
1784 | |
1785 | _RealType __lambda; |
1786 | if (__is >> __lambda) |
1787 | __x.param(param_type(__lambda)); |
1788 | |
1789 | __is.flags(__flags); |
1790 | return __is; |
1791 | } |
1792 | |
1793 | |
1794 | /** |
1795 | * Polar method due to Marsaglia. |
1796 | * |
1797 | * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, |
1798 | * New York, 1986, Ch. V, Sect. 4.4. |
1799 | */ |
1800 | template<typename _RealType> |
1801 | template<typename _UniformRandomNumberGenerator> |
1802 | typename normal_distribution<_RealType>::result_type |
1803 | normal_distribution<_RealType>:: |
1804 | operator()(_UniformRandomNumberGenerator& __urng, |
1805 | const param_type& __param) |
1806 | { |
1807 | result_type __ret; |
1808 | __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> |
1809 | __aurng(__urng); |
1810 | |
1811 | if (_M_saved_available) |
1812 | { |
1813 | _M_saved_available = false; |
1814 | __ret = _M_saved; |
1815 | } |
1816 | else |
1817 | { |
1818 | result_type __x, __y, __r2; |
1819 | do |
1820 | { |
1821 | __x = result_type(2.0) * __aurng() - 1.0; |
1822 | __y = result_type(2.0) * __aurng() - 1.0; |
1823 | __r2 = __x * __x + __y * __y; |
1824 | } |
1825 | while (__r2 > 1.0 || __r2 == 0.0); |
1826 | |
1827 | const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2); |
1828 | _M_saved = __x * __mult; |
1829 | _M_saved_available = true; |
1830 | __ret = __y * __mult; |
1831 | } |
1832 | |
1833 | __ret = __ret * __param.stddev() + __param.mean(); |
1834 | return __ret; |
1835 | } |
1836 | |
1837 | template<typename _RealType> |
1838 | template<typename _ForwardIterator, |
1839 | typename _UniformRandomNumberGenerator> |
1840 | void |
1841 | normal_distribution<_RealType>:: |
1842 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
1843 | _UniformRandomNumberGenerator& __urng, |
1844 | const param_type& __param) |
1845 | { |
1846 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
1847 | |
1848 | if (__f == __t) |
1849 | return; |
1850 | |
1851 | if (_M_saved_available) |
1852 | { |
1853 | _M_saved_available = false; |
1854 | *__f++ = _M_saved * __param.stddev() + __param.mean(); |
1855 | |
1856 | if (__f == __t) |
1857 | return; |
1858 | } |
1859 | |
1860 | __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> |
1861 | __aurng(__urng); |
1862 | |
1863 | while (__f + 1 < __t) |
1864 | { |
1865 | result_type __x, __y, __r2; |
1866 | do |
1867 | { |
1868 | __x = result_type(2.0) * __aurng() - 1.0; |
1869 | __y = result_type(2.0) * __aurng() - 1.0; |
1870 | __r2 = __x * __x + __y * __y; |
1871 | } |
1872 | while (__r2 > 1.0 || __r2 == 0.0); |
1873 | |
1874 | const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2); |
1875 | *__f++ = __y * __mult * __param.stddev() + __param.mean(); |
1876 | *__f++ = __x * __mult * __param.stddev() + __param.mean(); |
1877 | } |
1878 | |
1879 | if (__f != __t) |
1880 | { |
1881 | result_type __x, __y, __r2; |
1882 | do |
1883 | { |
1884 | __x = result_type(2.0) * __aurng() - 1.0; |
1885 | __y = result_type(2.0) * __aurng() - 1.0; |
1886 | __r2 = __x * __x + __y * __y; |
1887 | } |
1888 | while (__r2 > 1.0 || __r2 == 0.0); |
1889 | |
1890 | const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2); |
1891 | _M_saved = __x * __mult; |
1892 | _M_saved_available = true; |
1893 | *__f = __y * __mult * __param.stddev() + __param.mean(); |
1894 | } |
1895 | } |
1896 | |
1897 | template<typename _RealType> |
1898 | bool |
1899 | operator==(const std::normal_distribution<_RealType>& __d1, |
1900 | const std::normal_distribution<_RealType>& __d2) |
1901 | { |
1902 | if (__d1._M_param == __d2._M_param |
1903 | && __d1._M_saved_available == __d2._M_saved_available) |
1904 | { |
1905 | if (__d1._M_saved_available |
1906 | && __d1._M_saved == __d2._M_saved) |
1907 | return true; |
1908 | else if(!__d1._M_saved_available) |
1909 | return true; |
1910 | else |
1911 | return false; |
1912 | } |
1913 | else |
1914 | return false; |
1915 | } |
1916 | |
1917 | template<typename _RealType, typename _CharT, typename _Traits> |
1918 | std::basic_ostream<_CharT, _Traits>& |
1919 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
1920 | const normal_distribution<_RealType>& __x) |
1921 | { |
1922 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
1923 | |
1924 | const typename __ios_base::fmtflags __flags = __os.flags(); |
1925 | const _CharT __fill = __os.fill(); |
1926 | const std::streamsize __precision = __os.precision(); |
1927 | const _CharT __space = __os.widen(' '); |
1928 | __os.flags(__ios_base::scientific | __ios_base::left); |
1929 | __os.fill(__space); |
1930 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
1931 | |
1932 | __os << __x.mean() << __space << __x.stddev() |
1933 | << __space << __x._M_saved_available; |
1934 | if (__x._M_saved_available) |
1935 | __os << __space << __x._M_saved; |
1936 | |
1937 | __os.flags(__flags); |
1938 | __os.fill(__fill); |
1939 | __os.precision(__precision); |
1940 | return __os; |
1941 | } |
1942 | |
1943 | template<typename _RealType, typename _CharT, typename _Traits> |
1944 | std::basic_istream<_CharT, _Traits>& |
1945 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
1946 | normal_distribution<_RealType>& __x) |
1947 | { |
1948 | using param_type = typename normal_distribution<_RealType>::param_type; |
1949 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
1950 | |
1951 | const typename __ios_base::fmtflags __flags = __is.flags(); |
1952 | __is.flags(__ios_base::dec | __ios_base::skipws); |
1953 | |
1954 | double __mean, __stddev; |
1955 | bool __saved_avail; |
1956 | if (__is >> __mean >> __stddev >> __saved_avail) |
1957 | { |
1958 | if (!__saved_avail || (__is >> __x._M_saved)) |
1959 | { |
1960 | __x._M_saved_available = __saved_avail; |
1961 | __x.param(param_type(__mean, __stddev)); |
1962 | } |
1963 | } |
1964 | |
1965 | __is.flags(__flags); |
1966 | return __is; |
1967 | } |
1968 | |
1969 | |
1970 | template<typename _RealType> |
1971 | template<typename _ForwardIterator, |
1972 | typename _UniformRandomNumberGenerator> |
1973 | void |
1974 | lognormal_distribution<_RealType>:: |
1975 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
1976 | _UniformRandomNumberGenerator& __urng, |
1977 | const param_type& __p) |
1978 | { |
1979 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
1980 | while (__f != __t) |
1981 | *__f++ = std::exp(__p.s() * _M_nd(__urng) + __p.m()); |
1982 | } |
1983 | |
1984 | template<typename _RealType, typename _CharT, typename _Traits> |
1985 | std::basic_ostream<_CharT, _Traits>& |
1986 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
1987 | const lognormal_distribution<_RealType>& __x) |
1988 | { |
1989 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
1990 | |
1991 | const typename __ios_base::fmtflags __flags = __os.flags(); |
1992 | const _CharT __fill = __os.fill(); |
1993 | const std::streamsize __precision = __os.precision(); |
1994 | const _CharT __space = __os.widen(' '); |
1995 | __os.flags(__ios_base::scientific | __ios_base::left); |
1996 | __os.fill(__space); |
1997 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
1998 | |
1999 | __os << __x.m() << __space << __x.s() |
2000 | << __space << __x._M_nd; |
2001 | |
2002 | __os.flags(__flags); |
2003 | __os.fill(__fill); |
2004 | __os.precision(__precision); |
2005 | return __os; |
2006 | } |
2007 | |
2008 | template<typename _RealType, typename _CharT, typename _Traits> |
2009 | std::basic_istream<_CharT, _Traits>& |
2010 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
2011 | lognormal_distribution<_RealType>& __x) |
2012 | { |
2013 | using param_type |
2014 | = typename lognormal_distribution<_RealType>::param_type; |
2015 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
2016 | |
2017 | const typename __ios_base::fmtflags __flags = __is.flags(); |
2018 | __is.flags(__ios_base::dec | __ios_base::skipws); |
2019 | |
2020 | _RealType __m, __s; |
2021 | if (__is >> __m >> __s >> __x._M_nd) |
2022 | __x.param(param_type(__m, __s)); |
2023 | |
2024 | __is.flags(__flags); |
2025 | return __is; |
2026 | } |
2027 | |
2028 | template<typename _RealType> |
2029 | template<typename _ForwardIterator, |
2030 | typename _UniformRandomNumberGenerator> |
2031 | void |
2032 | std::chi_squared_distribution<_RealType>:: |
2033 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
2034 | _UniformRandomNumberGenerator& __urng) |
2035 | { |
2036 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
2037 | while (__f != __t) |
2038 | *__f++ = 2 * _M_gd(__urng); |
2039 | } |
2040 | |
2041 | template<typename _RealType> |
2042 | template<typename _ForwardIterator, |
2043 | typename _UniformRandomNumberGenerator> |
2044 | void |
2045 | std::chi_squared_distribution<_RealType>:: |
2046 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
2047 | _UniformRandomNumberGenerator& __urng, |
2048 | const typename |
2049 | std::gamma_distribution<result_type>::param_type& __p) |
2050 | { |
2051 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
2052 | while (__f != __t) |
2053 | *__f++ = 2 * _M_gd(__urng, __p); |
2054 | } |
2055 | |
2056 | template<typename _RealType, typename _CharT, typename _Traits> |
2057 | std::basic_ostream<_CharT, _Traits>& |
2058 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
2059 | const chi_squared_distribution<_RealType>& __x) |
2060 | { |
2061 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
2062 | |
2063 | const typename __ios_base::fmtflags __flags = __os.flags(); |
2064 | const _CharT __fill = __os.fill(); |
2065 | const std::streamsize __precision = __os.precision(); |
2066 | const _CharT __space = __os.widen(' '); |
2067 | __os.flags(__ios_base::scientific | __ios_base::left); |
2068 | __os.fill(__space); |
2069 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
2070 | |
2071 | __os << __x.n() << __space << __x._M_gd; |
2072 | |
2073 | __os.flags(__flags); |
2074 | __os.fill(__fill); |
2075 | __os.precision(__precision); |
2076 | return __os; |
2077 | } |
2078 | |
2079 | template<typename _RealType, typename _CharT, typename _Traits> |
2080 | std::basic_istream<_CharT, _Traits>& |
2081 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
2082 | chi_squared_distribution<_RealType>& __x) |
2083 | { |
2084 | using param_type |
2085 | = typename chi_squared_distribution<_RealType>::param_type; |
2086 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
2087 | |
2088 | const typename __ios_base::fmtflags __flags = __is.flags(); |
2089 | __is.flags(__ios_base::dec | __ios_base::skipws); |
2090 | |
2091 | _RealType __n; |
2092 | if (__is >> __n >> __x._M_gd) |
2093 | __x.param(param_type(__n)); |
2094 | |
2095 | __is.flags(__flags); |
2096 | return __is; |
2097 | } |
2098 | |
2099 | |
2100 | template<typename _RealType> |
2101 | template<typename _UniformRandomNumberGenerator> |
2102 | typename cauchy_distribution<_RealType>::result_type |
2103 | cauchy_distribution<_RealType>:: |
2104 | operator()(_UniformRandomNumberGenerator& __urng, |
2105 | const param_type& __p) |
2106 | { |
2107 | __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> |
2108 | __aurng(__urng); |
2109 | _RealType __u; |
2110 | do |
2111 | __u = __aurng(); |
2112 | while (__u == 0.5); |
2113 | |
2114 | const _RealType __pi = 3.1415926535897932384626433832795029L; |
2115 | return __p.a() + __p.b() * std::tan(__pi * __u); |
2116 | } |
2117 | |
2118 | template<typename _RealType> |
2119 | template<typename _ForwardIterator, |
2120 | typename _UniformRandomNumberGenerator> |
2121 | void |
2122 | cauchy_distribution<_RealType>:: |
2123 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
2124 | _UniformRandomNumberGenerator& __urng, |
2125 | const param_type& __p) |
2126 | { |
2127 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
2128 | const _RealType __pi = 3.1415926535897932384626433832795029L; |
2129 | __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> |
2130 | __aurng(__urng); |
2131 | while (__f != __t) |
2132 | { |
2133 | _RealType __u; |
2134 | do |
2135 | __u = __aurng(); |
2136 | while (__u == 0.5); |
2137 | |
2138 | *__f++ = __p.a() + __p.b() * std::tan(__pi * __u); |
2139 | } |
2140 | } |
2141 | |
2142 | template<typename _RealType, typename _CharT, typename _Traits> |
2143 | std::basic_ostream<_CharT, _Traits>& |
2144 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
2145 | const cauchy_distribution<_RealType>& __x) |
2146 | { |
2147 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
2148 | |
2149 | const typename __ios_base::fmtflags __flags = __os.flags(); |
2150 | const _CharT __fill = __os.fill(); |
2151 | const std::streamsize __precision = __os.precision(); |
2152 | const _CharT __space = __os.widen(' '); |
2153 | __os.flags(__ios_base::scientific | __ios_base::left); |
2154 | __os.fill(__space); |
2155 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
2156 | |
2157 | __os << __x.a() << __space << __x.b(); |
2158 | |
2159 | __os.flags(__flags); |
2160 | __os.fill(__fill); |
2161 | __os.precision(__precision); |
2162 | return __os; |
2163 | } |
2164 | |
2165 | template<typename _RealType, typename _CharT, typename _Traits> |
2166 | std::basic_istream<_CharT, _Traits>& |
2167 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
2168 | cauchy_distribution<_RealType>& __x) |
2169 | { |
2170 | using param_type = typename cauchy_distribution<_RealType>::param_type; |
2171 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
2172 | |
2173 | const typename __ios_base::fmtflags __flags = __is.flags(); |
2174 | __is.flags(__ios_base::dec | __ios_base::skipws); |
2175 | |
2176 | _RealType __a, __b; |
2177 | if (__is >> __a >> __b) |
2178 | __x.param(param_type(__a, __b)); |
2179 | |
2180 | __is.flags(__flags); |
2181 | return __is; |
2182 | } |
2183 | |
2184 | |
2185 | template<typename _RealType> |
2186 | template<typename _ForwardIterator, |
2187 | typename _UniformRandomNumberGenerator> |
2188 | void |
2189 | std::fisher_f_distribution<_RealType>:: |
2190 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
2191 | _UniformRandomNumberGenerator& __urng) |
2192 | { |
2193 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
2194 | while (__f != __t) |
2195 | *__f++ = ((_M_gd_x(__urng) * n()) / (_M_gd_y(__urng) * m())); |
2196 | } |
2197 | |
2198 | template<typename _RealType> |
2199 | template<typename _ForwardIterator, |
2200 | typename _UniformRandomNumberGenerator> |
2201 | void |
2202 | std::fisher_f_distribution<_RealType>:: |
2203 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
2204 | _UniformRandomNumberGenerator& __urng, |
2205 | const param_type& __p) |
2206 | { |
2207 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
2208 | typedef typename std::gamma_distribution<result_type>::param_type |
2209 | param_type; |
2210 | param_type __p1(__p.m() / 2); |
2211 | param_type __p2(__p.n() / 2); |
2212 | while (__f != __t) |
2213 | *__f++ = ((_M_gd_x(__urng, __p1) * n()) |
2214 | / (_M_gd_y(__urng, __p2) * m())); |
2215 | } |
2216 | |
2217 | template<typename _RealType, typename _CharT, typename _Traits> |
2218 | std::basic_ostream<_CharT, _Traits>& |
2219 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
2220 | const fisher_f_distribution<_RealType>& __x) |
2221 | { |
2222 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
2223 | |
2224 | const typename __ios_base::fmtflags __flags = __os.flags(); |
2225 | const _CharT __fill = __os.fill(); |
2226 | const std::streamsize __precision = __os.precision(); |
2227 | const _CharT __space = __os.widen(' '); |
2228 | __os.flags(__ios_base::scientific | __ios_base::left); |
2229 | __os.fill(__space); |
2230 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
2231 | |
2232 | __os << __x.m() << __space << __x.n() |
2233 | << __space << __x._M_gd_x << __space << __x._M_gd_y; |
2234 | |
2235 | __os.flags(__flags); |
2236 | __os.fill(__fill); |
2237 | __os.precision(__precision); |
2238 | return __os; |
2239 | } |
2240 | |
2241 | template<typename _RealType, typename _CharT, typename _Traits> |
2242 | std::basic_istream<_CharT, _Traits>& |
2243 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
2244 | fisher_f_distribution<_RealType>& __x) |
2245 | { |
2246 | using param_type |
2247 | = typename fisher_f_distribution<_RealType>::param_type; |
2248 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
2249 | |
2250 | const typename __ios_base::fmtflags __flags = __is.flags(); |
2251 | __is.flags(__ios_base::dec | __ios_base::skipws); |
2252 | |
2253 | _RealType __m, __n; |
2254 | if (__is >> __m >> __n >> __x._M_gd_x >> __x._M_gd_y) |
2255 | __x.param(param_type(__m, __n)); |
2256 | |
2257 | __is.flags(__flags); |
2258 | return __is; |
2259 | } |
2260 | |
2261 | |
2262 | template<typename _RealType> |
2263 | template<typename _ForwardIterator, |
2264 | typename _UniformRandomNumberGenerator> |
2265 | void |
2266 | std::student_t_distribution<_RealType>:: |
2267 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
2268 | _UniformRandomNumberGenerator& __urng) |
2269 | { |
2270 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
2271 | while (__f != __t) |
2272 | *__f++ = _M_nd(__urng) * std::sqrt(n() / _M_gd(__urng)); |
2273 | } |
2274 | |
2275 | template<typename _RealType> |
2276 | template<typename _ForwardIterator, |
2277 | typename _UniformRandomNumberGenerator> |
2278 | void |
2279 | std::student_t_distribution<_RealType>:: |
2280 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
2281 | _UniformRandomNumberGenerator& __urng, |
2282 | const param_type& __p) |
2283 | { |
2284 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
2285 | typename std::gamma_distribution<result_type>::param_type |
2286 | __p2(__p.n() / 2, 2); |
2287 | while (__f != __t) |
2288 | *__f++ = _M_nd(__urng) * std::sqrt(__p.n() / _M_gd(__urng, __p2)); |
2289 | } |
2290 | |
2291 | template<typename _RealType, typename _CharT, typename _Traits> |
2292 | std::basic_ostream<_CharT, _Traits>& |
2293 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
2294 | const student_t_distribution<_RealType>& __x) |
2295 | { |
2296 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
2297 | |
2298 | const typename __ios_base::fmtflags __flags = __os.flags(); |
2299 | const _CharT __fill = __os.fill(); |
2300 | const std::streamsize __precision = __os.precision(); |
2301 | const _CharT __space = __os.widen(' '); |
2302 | __os.flags(__ios_base::scientific | __ios_base::left); |
2303 | __os.fill(__space); |
2304 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
2305 | |
2306 | __os << __x.n() << __space << __x._M_nd << __space << __x._M_gd; |
2307 | |
2308 | __os.flags(__flags); |
2309 | __os.fill(__fill); |
2310 | __os.precision(__precision); |
2311 | return __os; |
2312 | } |
2313 | |
2314 | template<typename _RealType, typename _CharT, typename _Traits> |
2315 | std::basic_istream<_CharT, _Traits>& |
2316 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
2317 | student_t_distribution<_RealType>& __x) |
2318 | { |
2319 | using param_type |
2320 | = typename student_t_distribution<_RealType>::param_type; |
2321 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
2322 | |
2323 | const typename __ios_base::fmtflags __flags = __is.flags(); |
2324 | __is.flags(__ios_base::dec | __ios_base::skipws); |
2325 | |
2326 | _RealType __n; |
2327 | if (__is >> __n >> __x._M_nd >> __x._M_gd) |
2328 | __x.param(param_type(__n)); |
2329 | |
2330 | __is.flags(__flags); |
2331 | return __is; |
2332 | } |
2333 | |
2334 | |
2335 | template<typename _RealType> |
2336 | void |
2337 | gamma_distribution<_RealType>::param_type:: |
2338 | _M_initialize() |
2339 | { |
2340 | _M_malpha = _M_alpha < 1.0 ? _M_alpha + _RealType(1.0) : _M_alpha; |
2341 | |
2342 | const _RealType __a1 = _M_malpha - _RealType(1.0) / _RealType(3.0); |
2343 | _M_a2 = _RealType(1.0) / std::sqrt(_RealType(9.0) * __a1); |
2344 | } |
2345 | |
2346 | /** |
2347 | * Marsaglia, G. and Tsang, W. W. |
2348 | * "A Simple Method for Generating Gamma Variables" |
2349 | * ACM Transactions on Mathematical Software, 26, 3, 363-372, 2000. |
2350 | */ |
2351 | template<typename _RealType> |
2352 | template<typename _UniformRandomNumberGenerator> |
2353 | typename gamma_distribution<_RealType>::result_type |
2354 | gamma_distribution<_RealType>:: |
2355 | operator()(_UniformRandomNumberGenerator& __urng, |
2356 | const param_type& __param) |
2357 | { |
2358 | __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> |
2359 | __aurng(__urng); |
2360 | |
2361 | result_type __u, __v, __n; |
2362 | const result_type __a1 = (__param._M_malpha |
2363 | - _RealType(1.0) / _RealType(3.0)); |
2364 | |
2365 | do |
2366 | { |
2367 | do |
2368 | { |
2369 | __n = _M_nd(__urng); |
2370 | __v = result_type(1.0) + __param._M_a2 * __n; |
2371 | } |
2372 | while (__v <= 0.0); |
2373 | |
2374 | __v = __v * __v * __v; |
2375 | __u = __aurng(); |
2376 | } |
2377 | while (__u > result_type(1.0) - 0.0331 * __n * __n * __n * __n |
2378 | && (std::log(__u) > (0.5 * __n * __n + __a1 |
2379 | * (1.0 - __v + std::log(__v))))); |
2380 | |
2381 | if (__param.alpha() == __param._M_malpha) |
2382 | return __a1 * __v * __param.beta(); |
2383 | else |
2384 | { |
2385 | do |
2386 | __u = __aurng(); |
2387 | while (__u == 0.0); |
2388 | |
2389 | return (std::pow(__u, result_type(1.0) / __param.alpha()) |
2390 | * __a1 * __v * __param.beta()); |
2391 | } |
2392 | } |
2393 | |
2394 | template<typename _RealType> |
2395 | template<typename _ForwardIterator, |
2396 | typename _UniformRandomNumberGenerator> |
2397 | void |
2398 | gamma_distribution<_RealType>:: |
2399 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
2400 | _UniformRandomNumberGenerator& __urng, |
2401 | const param_type& __param) |
2402 | { |
2403 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
2404 | __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> |
2405 | __aurng(__urng); |
2406 | |
2407 | result_type __u, __v, __n; |
2408 | const result_type __a1 = (__param._M_malpha |
2409 | - _RealType(1.0) / _RealType(3.0)); |
2410 | |
2411 | if (__param.alpha() == __param._M_malpha) |
2412 | while (__f != __t) |
2413 | { |
2414 | do |
2415 | { |
2416 | do |
2417 | { |
2418 | __n = _M_nd(__urng); |
2419 | __v = result_type(1.0) + __param._M_a2 * __n; |
2420 | } |
2421 | while (__v <= 0.0); |
2422 | |
2423 | __v = __v * __v * __v; |
2424 | __u = __aurng(); |
2425 | } |
2426 | while (__u > result_type(1.0) - 0.0331 * __n * __n * __n * __n |
2427 | && (std::log(__u) > (0.5 * __n * __n + __a1 |
2428 | * (1.0 - __v + std::log(__v))))); |
2429 | |
2430 | *__f++ = __a1 * __v * __param.beta(); |
2431 | } |
2432 | else |
2433 | while (__f != __t) |
2434 | { |
2435 | do |
2436 | { |
2437 | do |
2438 | { |
2439 | __n = _M_nd(__urng); |
2440 | __v = result_type(1.0) + __param._M_a2 * __n; |
2441 | } |
2442 | while (__v <= 0.0); |
2443 | |
2444 | __v = __v * __v * __v; |
2445 | __u = __aurng(); |
2446 | } |
2447 | while (__u > result_type(1.0) - 0.0331 * __n * __n * __n * __n |
2448 | && (std::log(__u) > (0.5 * __n * __n + __a1 |
2449 | * (1.0 - __v + std::log(__v))))); |
2450 | |
2451 | do |
2452 | __u = __aurng(); |
2453 | while (__u == 0.0); |
2454 | |
2455 | *__f++ = (std::pow(__u, result_type(1.0) / __param.alpha()) |
2456 | * __a1 * __v * __param.beta()); |
2457 | } |
2458 | } |
2459 | |
2460 | template<typename _RealType, typename _CharT, typename _Traits> |
2461 | std::basic_ostream<_CharT, _Traits>& |
2462 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
2463 | const gamma_distribution<_RealType>& __x) |
2464 | { |
2465 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
2466 | |
2467 | const typename __ios_base::fmtflags __flags = __os.flags(); |
2468 | const _CharT __fill = __os.fill(); |
2469 | const std::streamsize __precision = __os.precision(); |
2470 | const _CharT __space = __os.widen(' '); |
2471 | __os.flags(__ios_base::scientific | __ios_base::left); |
2472 | __os.fill(__space); |
2473 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
2474 | |
2475 | __os << __x.alpha() << __space << __x.beta() |
2476 | << __space << __x._M_nd; |
2477 | |
2478 | __os.flags(__flags); |
2479 | __os.fill(__fill); |
2480 | __os.precision(__precision); |
2481 | return __os; |
2482 | } |
2483 | |
2484 | template<typename _RealType, typename _CharT, typename _Traits> |
2485 | std::basic_istream<_CharT, _Traits>& |
2486 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
2487 | gamma_distribution<_RealType>& __x) |
2488 | { |
2489 | using param_type = typename gamma_distribution<_RealType>::param_type; |
2490 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
2491 | |
2492 | const typename __ios_base::fmtflags __flags = __is.flags(); |
2493 | __is.flags(__ios_base::dec | __ios_base::skipws); |
2494 | |
2495 | _RealType __alpha_val, __beta_val; |
2496 | if (__is >> __alpha_val >> __beta_val >> __x._M_nd) |
2497 | __x.param(param_type(__alpha_val, __beta_val)); |
2498 | |
2499 | __is.flags(__flags); |
2500 | return __is; |
2501 | } |
2502 | |
2503 | |
2504 | template<typename _RealType> |
2505 | template<typename _UniformRandomNumberGenerator> |
2506 | typename weibull_distribution<_RealType>::result_type |
2507 | weibull_distribution<_RealType>:: |
2508 | operator()(_UniformRandomNumberGenerator& __urng, |
2509 | const param_type& __p) |
2510 | { |
2511 | __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> |
2512 | __aurng(__urng); |
2513 | return __p.b() * std::pow(-std::log(result_type(1) - __aurng()), |
2514 | result_type(1) / __p.a()); |
2515 | } |
2516 | |
2517 | template<typename _RealType> |
2518 | template<typename _ForwardIterator, |
2519 | typename _UniformRandomNumberGenerator> |
2520 | void |
2521 | weibull_distribution<_RealType>:: |
2522 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
2523 | _UniformRandomNumberGenerator& __urng, |
2524 | const param_type& __p) |
2525 | { |
2526 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
2527 | __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> |
2528 | __aurng(__urng); |
2529 | auto __inv_a = result_type(1) / __p.a(); |
2530 | |
2531 | while (__f != __t) |
2532 | *__f++ = __p.b() * std::pow(-std::log(result_type(1) - __aurng()), |
2533 | __inv_a); |
2534 | } |
2535 | |
2536 | template<typename _RealType, typename _CharT, typename _Traits> |
2537 | std::basic_ostream<_CharT, _Traits>& |
2538 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
2539 | const weibull_distribution<_RealType>& __x) |
2540 | { |
2541 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
2542 | |
2543 | const typename __ios_base::fmtflags __flags = __os.flags(); |
2544 | const _CharT __fill = __os.fill(); |
2545 | const std::streamsize __precision = __os.precision(); |
2546 | const _CharT __space = __os.widen(' '); |
2547 | __os.flags(__ios_base::scientific | __ios_base::left); |
2548 | __os.fill(__space); |
2549 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
2550 | |
2551 | __os << __x.a() << __space << __x.b(); |
2552 | |
2553 | __os.flags(__flags); |
2554 | __os.fill(__fill); |
2555 | __os.precision(__precision); |
2556 | return __os; |
2557 | } |
2558 | |
2559 | template<typename _RealType, typename _CharT, typename _Traits> |
2560 | std::basic_istream<_CharT, _Traits>& |
2561 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
2562 | weibull_distribution<_RealType>& __x) |
2563 | { |
2564 | using param_type = typename weibull_distribution<_RealType>::param_type; |
2565 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
2566 | |
2567 | const typename __ios_base::fmtflags __flags = __is.flags(); |
2568 | __is.flags(__ios_base::dec | __ios_base::skipws); |
2569 | |
2570 | _RealType __a, __b; |
2571 | if (__is >> __a >> __b) |
2572 | __x.param(param_type(__a, __b)); |
2573 | |
2574 | __is.flags(__flags); |
2575 | return __is; |
2576 | } |
2577 | |
2578 | |
2579 | template<typename _RealType> |
2580 | template<typename _UniformRandomNumberGenerator> |
2581 | typename extreme_value_distribution<_RealType>::result_type |
2582 | extreme_value_distribution<_RealType>:: |
2583 | operator()(_UniformRandomNumberGenerator& __urng, |
2584 | const param_type& __p) |
2585 | { |
2586 | __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> |
2587 | __aurng(__urng); |
2588 | return __p.a() - __p.b() * std::log(-std::log(result_type(1) |
2589 | - __aurng())); |
2590 | } |
2591 | |
2592 | template<typename _RealType> |
2593 | template<typename _ForwardIterator, |
2594 | typename _UniformRandomNumberGenerator> |
2595 | void |
2596 | extreme_value_distribution<_RealType>:: |
2597 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
2598 | _UniformRandomNumberGenerator& __urng, |
2599 | const param_type& __p) |
2600 | { |
2601 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
2602 | __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> |
2603 | __aurng(__urng); |
2604 | |
2605 | while (__f != __t) |
2606 | *__f++ = __p.a() - __p.b() * std::log(-std::log(result_type(1) |
2607 | - __aurng())); |
2608 | } |
2609 | |
2610 | template<typename _RealType, typename _CharT, typename _Traits> |
2611 | std::basic_ostream<_CharT, _Traits>& |
2612 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
2613 | const extreme_value_distribution<_RealType>& __x) |
2614 | { |
2615 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
2616 | |
2617 | const typename __ios_base::fmtflags __flags = __os.flags(); |
2618 | const _CharT __fill = __os.fill(); |
2619 | const std::streamsize __precision = __os.precision(); |
2620 | const _CharT __space = __os.widen(' '); |
2621 | __os.flags(__ios_base::scientific | __ios_base::left); |
2622 | __os.fill(__space); |
2623 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
2624 | |
2625 | __os << __x.a() << __space << __x.b(); |
2626 | |
2627 | __os.flags(__flags); |
2628 | __os.fill(__fill); |
2629 | __os.precision(__precision); |
2630 | return __os; |
2631 | } |
2632 | |
2633 | template<typename _RealType, typename _CharT, typename _Traits> |
2634 | std::basic_istream<_CharT, _Traits>& |
2635 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
2636 | extreme_value_distribution<_RealType>& __x) |
2637 | { |
2638 | using param_type |
2639 | = typename extreme_value_distribution<_RealType>::param_type; |
2640 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
2641 | |
2642 | const typename __ios_base::fmtflags __flags = __is.flags(); |
2643 | __is.flags(__ios_base::dec | __ios_base::skipws); |
2644 | |
2645 | _RealType __a, __b; |
2646 | if (__is >> __a >> __b) |
2647 | __x.param(param_type(__a, __b)); |
2648 | |
2649 | __is.flags(__flags); |
2650 | return __is; |
2651 | } |
2652 | |
2653 | |
2654 | template<typename _IntType> |
2655 | void |
2656 | discrete_distribution<_IntType>::param_type:: |
2657 | _M_initialize() |
2658 | { |
2659 | if (_M_prob.size() < 2) |
2660 | { |
2661 | _M_prob.clear(); |
2662 | return; |
2663 | } |
2664 | |
2665 | const double __sum = std::accumulate(first: _M_prob.begin(), |
2666 | last: _M_prob.end(), init: 0.0); |
2667 | __glibcxx_assert(__sum > 0); |
2668 | // Now normalize the probabilites. |
2669 | __detail::__normalize(first: _M_prob.begin(), last: _M_prob.end(), result: _M_prob.begin(), |
2670 | factor: __sum); |
2671 | // Accumulate partial sums. |
2672 | _M_cp.reserve(n: _M_prob.size()); |
2673 | std::partial_sum(first: _M_prob.begin(), last: _M_prob.end(), |
2674 | result: std::back_inserter(x&: _M_cp)); |
2675 | // Make sure the last cumulative probability is one. |
2676 | _M_cp[_M_cp.size() - 1] = 1.0; |
2677 | } |
2678 | |
2679 | template<typename _IntType> |
2680 | template<typename _Func> |
2681 | discrete_distribution<_IntType>::param_type:: |
2682 | param_type(size_t __nw, double __xmin, double __xmax, _Func __fw) |
2683 | : _M_prob(), _M_cp() |
2684 | { |
2685 | const size_t __n = __nw == 0 ? 1 : __nw; |
2686 | const double __delta = (__xmax - __xmin) / __n; |
2687 | |
2688 | _M_prob.reserve(__n); |
2689 | for (size_t __k = 0; __k < __nw; ++__k) |
2690 | _M_prob.push_back(__fw(__xmin + __k * __delta + 0.5 * __delta)); |
2691 | |
2692 | _M_initialize(); |
2693 | } |
2694 | |
2695 | template<typename _IntType> |
2696 | template<typename _UniformRandomNumberGenerator> |
2697 | typename discrete_distribution<_IntType>::result_type |
2698 | discrete_distribution<_IntType>:: |
2699 | operator()(_UniformRandomNumberGenerator& __urng, |
2700 | const param_type& __param) |
2701 | { |
2702 | if (__param._M_cp.empty()) |
2703 | return result_type(0); |
2704 | |
2705 | __detail::_Adaptor<_UniformRandomNumberGenerator, double> |
2706 | __aurng(__urng); |
2707 | |
2708 | const double __p = __aurng(); |
2709 | auto __pos = std::lower_bound(__param._M_cp.begin(), |
2710 | __param._M_cp.end(), __p); |
2711 | |
2712 | return __pos - __param._M_cp.begin(); |
2713 | } |
2714 | |
2715 | template<typename _IntType> |
2716 | template<typename _ForwardIterator, |
2717 | typename _UniformRandomNumberGenerator> |
2718 | void |
2719 | discrete_distribution<_IntType>:: |
2720 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
2721 | _UniformRandomNumberGenerator& __urng, |
2722 | const param_type& __param) |
2723 | { |
2724 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
2725 | |
2726 | if (__param._M_cp.empty()) |
2727 | { |
2728 | while (__f != __t) |
2729 | *__f++ = result_type(0); |
2730 | return; |
2731 | } |
2732 | |
2733 | __detail::_Adaptor<_UniformRandomNumberGenerator, double> |
2734 | __aurng(__urng); |
2735 | |
2736 | while (__f != __t) |
2737 | { |
2738 | const double __p = __aurng(); |
2739 | auto __pos = std::lower_bound(__param._M_cp.begin(), |
2740 | __param._M_cp.end(), __p); |
2741 | |
2742 | *__f++ = __pos - __param._M_cp.begin(); |
2743 | } |
2744 | } |
2745 | |
2746 | template<typename _IntType, typename _CharT, typename _Traits> |
2747 | std::basic_ostream<_CharT, _Traits>& |
2748 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
2749 | const discrete_distribution<_IntType>& __x) |
2750 | { |
2751 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
2752 | |
2753 | const typename __ios_base::fmtflags __flags = __os.flags(); |
2754 | const _CharT __fill = __os.fill(); |
2755 | const std::streamsize __precision = __os.precision(); |
2756 | const _CharT __space = __os.widen(' '); |
2757 | __os.flags(__ios_base::scientific | __ios_base::left); |
2758 | __os.fill(__space); |
2759 | __os.precision(std::numeric_limits<double>::max_digits10); |
2760 | |
2761 | std::vector<double> __prob = __x.probabilities(); |
2762 | __os << __prob.size(); |
2763 | for (auto __dit = __prob.begin(); __dit != __prob.end(); ++__dit) |
2764 | __os << __space << *__dit; |
2765 | |
2766 | __os.flags(__flags); |
2767 | __os.fill(__fill); |
2768 | __os.precision(__precision); |
2769 | return __os; |
2770 | } |
2771 | |
2772 | namespace __detail |
2773 | { |
2774 | template<typename _ValT, typename _CharT, typename _Traits> |
2775 | basic_istream<_CharT, _Traits>& |
2776 | (basic_istream<_CharT, _Traits>& __is, |
2777 | vector<_ValT>& __vals, size_t __n) |
2778 | { |
2779 | __vals.reserve(__n); |
2780 | while (__n--) |
2781 | { |
2782 | _ValT __val; |
2783 | if (__is >> __val) |
2784 | __vals.push_back(__val); |
2785 | else |
2786 | break; |
2787 | } |
2788 | return __is; |
2789 | } |
2790 | } // namespace __detail |
2791 | |
2792 | template<typename _IntType, typename _CharT, typename _Traits> |
2793 | std::basic_istream<_CharT, _Traits>& |
2794 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
2795 | discrete_distribution<_IntType>& __x) |
2796 | { |
2797 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
2798 | |
2799 | const typename __ios_base::fmtflags __flags = __is.flags(); |
2800 | __is.flags(__ios_base::dec | __ios_base::skipws); |
2801 | |
2802 | size_t __n; |
2803 | if (__is >> __n) |
2804 | { |
2805 | std::vector<double> __prob_vec; |
2806 | if (__detail::__extract_params(__is, __prob_vec, __n)) |
2807 | __x.param({__prob_vec.begin(), __prob_vec.end()}); |
2808 | } |
2809 | |
2810 | __is.flags(__flags); |
2811 | return __is; |
2812 | } |
2813 | |
2814 | |
2815 | template<typename _RealType> |
2816 | void |
2817 | piecewise_constant_distribution<_RealType>::param_type:: |
2818 | _M_initialize() |
2819 | { |
2820 | if (_M_int.size() < 2 |
2821 | || (_M_int.size() == 2 |
2822 | && _M_int[0] == _RealType(0) |
2823 | && _M_int[1] == _RealType(1))) |
2824 | { |
2825 | _M_int.clear(); |
2826 | _M_den.clear(); |
2827 | return; |
2828 | } |
2829 | |
2830 | const double __sum = std::accumulate(first: _M_den.begin(), |
2831 | last: _M_den.end(), init: 0.0); |
2832 | __glibcxx_assert(__sum > 0); |
2833 | |
2834 | __detail::__normalize(first: _M_den.begin(), last: _M_den.end(), result: _M_den.begin(), |
2835 | factor: __sum); |
2836 | |
2837 | _M_cp.reserve(n: _M_den.size()); |
2838 | std::partial_sum(first: _M_den.begin(), last: _M_den.end(), |
2839 | result: std::back_inserter(x&: _M_cp)); |
2840 | |
2841 | // Make sure the last cumulative probability is one. |
2842 | _M_cp[_M_cp.size() - 1] = 1.0; |
2843 | |
2844 | for (size_t __k = 0; __k < _M_den.size(); ++__k) |
2845 | _M_den[__k] /= _M_int[__k + 1] - _M_int[__k]; |
2846 | } |
2847 | |
2848 | template<typename _RealType> |
2849 | template<typename _InputIteratorB, typename _InputIteratorW> |
2850 | piecewise_constant_distribution<_RealType>::param_type:: |
2851 | param_type(_InputIteratorB __bbegin, |
2852 | _InputIteratorB __bend, |
2853 | _InputIteratorW __wbegin) |
2854 | : _M_int(), _M_den(), _M_cp() |
2855 | { |
2856 | if (__bbegin != __bend) |
2857 | { |
2858 | for (;;) |
2859 | { |
2860 | _M_int.push_back(*__bbegin); |
2861 | ++__bbegin; |
2862 | if (__bbegin == __bend) |
2863 | break; |
2864 | |
2865 | _M_den.push_back(*__wbegin); |
2866 | ++__wbegin; |
2867 | } |
2868 | } |
2869 | |
2870 | _M_initialize(); |
2871 | } |
2872 | |
2873 | template<typename _RealType> |
2874 | template<typename _Func> |
2875 | piecewise_constant_distribution<_RealType>::param_type:: |
2876 | param_type(initializer_list<_RealType> __bl, _Func __fw) |
2877 | : _M_int(), _M_den(), _M_cp() |
2878 | { |
2879 | _M_int.reserve(__bl.size()); |
2880 | for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter) |
2881 | _M_int.push_back(*__biter); |
2882 | |
2883 | _M_den.reserve(n: _M_int.size() - 1); |
2884 | for (size_t __k = 0; __k < _M_int.size() - 1; ++__k) |
2885 | _M_den.push_back(__fw(0.5 * (_M_int[__k + 1] + _M_int[__k]))); |
2886 | |
2887 | _M_initialize(); |
2888 | } |
2889 | |
2890 | template<typename _RealType> |
2891 | template<typename _Func> |
2892 | piecewise_constant_distribution<_RealType>::param_type:: |
2893 | param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw) |
2894 | : _M_int(), _M_den(), _M_cp() |
2895 | { |
2896 | const size_t __n = __nw == 0 ? 1 : __nw; |
2897 | const _RealType __delta = (__xmax - __xmin) / __n; |
2898 | |
2899 | _M_int.reserve(__n + 1); |
2900 | for (size_t __k = 0; __k <= __nw; ++__k) |
2901 | _M_int.push_back(__xmin + __k * __delta); |
2902 | |
2903 | _M_den.reserve(__n); |
2904 | for (size_t __k = 0; __k < __nw; ++__k) |
2905 | _M_den.push_back(__fw(_M_int[__k] + 0.5 * __delta)); |
2906 | |
2907 | _M_initialize(); |
2908 | } |
2909 | |
2910 | template<typename _RealType> |
2911 | template<typename _UniformRandomNumberGenerator> |
2912 | typename piecewise_constant_distribution<_RealType>::result_type |
2913 | piecewise_constant_distribution<_RealType>:: |
2914 | operator()(_UniformRandomNumberGenerator& __urng, |
2915 | const param_type& __param) |
2916 | { |
2917 | __detail::_Adaptor<_UniformRandomNumberGenerator, double> |
2918 | __aurng(__urng); |
2919 | |
2920 | const double __p = __aurng(); |
2921 | if (__param._M_cp.empty()) |
2922 | return __p; |
2923 | |
2924 | auto __pos = std::lower_bound(__param._M_cp.begin(), |
2925 | __param._M_cp.end(), __p); |
2926 | const size_t __i = __pos - __param._M_cp.begin(); |
2927 | |
2928 | const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0; |
2929 | |
2930 | return __param._M_int[__i] + (__p - __pref) / __param._M_den[__i]; |
2931 | } |
2932 | |
2933 | template<typename _RealType> |
2934 | template<typename _ForwardIterator, |
2935 | typename _UniformRandomNumberGenerator> |
2936 | void |
2937 | piecewise_constant_distribution<_RealType>:: |
2938 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
2939 | _UniformRandomNumberGenerator& __urng, |
2940 | const param_type& __param) |
2941 | { |
2942 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
2943 | __detail::_Adaptor<_UniformRandomNumberGenerator, double> |
2944 | __aurng(__urng); |
2945 | |
2946 | if (__param._M_cp.empty()) |
2947 | { |
2948 | while (__f != __t) |
2949 | *__f++ = __aurng(); |
2950 | return; |
2951 | } |
2952 | |
2953 | while (__f != __t) |
2954 | { |
2955 | const double __p = __aurng(); |
2956 | |
2957 | auto __pos = std::lower_bound(__param._M_cp.begin(), |
2958 | __param._M_cp.end(), __p); |
2959 | const size_t __i = __pos - __param._M_cp.begin(); |
2960 | |
2961 | const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0; |
2962 | |
2963 | *__f++ = (__param._M_int[__i] |
2964 | + (__p - __pref) / __param._M_den[__i]); |
2965 | } |
2966 | } |
2967 | |
2968 | template<typename _RealType, typename _CharT, typename _Traits> |
2969 | std::basic_ostream<_CharT, _Traits>& |
2970 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
2971 | const piecewise_constant_distribution<_RealType>& __x) |
2972 | { |
2973 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
2974 | |
2975 | const typename __ios_base::fmtflags __flags = __os.flags(); |
2976 | const _CharT __fill = __os.fill(); |
2977 | const std::streamsize __precision = __os.precision(); |
2978 | const _CharT __space = __os.widen(' '); |
2979 | __os.flags(__ios_base::scientific | __ios_base::left); |
2980 | __os.fill(__space); |
2981 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
2982 | |
2983 | std::vector<_RealType> __int = __x.intervals(); |
2984 | __os << __int.size() - 1; |
2985 | |
2986 | for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit) |
2987 | __os << __space << *__xit; |
2988 | |
2989 | std::vector<double> __den = __x.densities(); |
2990 | for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit) |
2991 | __os << __space << *__dit; |
2992 | |
2993 | __os.flags(__flags); |
2994 | __os.fill(__fill); |
2995 | __os.precision(__precision); |
2996 | return __os; |
2997 | } |
2998 | |
2999 | template<typename _RealType, typename _CharT, typename _Traits> |
3000 | std::basic_istream<_CharT, _Traits>& |
3001 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
3002 | piecewise_constant_distribution<_RealType>& __x) |
3003 | { |
3004 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
3005 | |
3006 | const typename __ios_base::fmtflags __flags = __is.flags(); |
3007 | __is.flags(__ios_base::dec | __ios_base::skipws); |
3008 | |
3009 | size_t __n; |
3010 | if (__is >> __n) |
3011 | { |
3012 | std::vector<_RealType> __int_vec; |
3013 | if (__detail::__extract_params(__is, __int_vec, __n + 1)) |
3014 | { |
3015 | std::vector<double> __den_vec; |
3016 | if (__detail::__extract_params(__is, __den_vec, __n)) |
3017 | { |
3018 | __x.param({ __int_vec.begin(), __int_vec.end(), |
3019 | __den_vec.begin() }); |
3020 | } |
3021 | } |
3022 | } |
3023 | |
3024 | __is.flags(__flags); |
3025 | return __is; |
3026 | } |
3027 | |
3028 | |
3029 | template<typename _RealType> |
3030 | void |
3031 | piecewise_linear_distribution<_RealType>::param_type:: |
3032 | _M_initialize() |
3033 | { |
3034 | if (_M_int.size() < 2 |
3035 | || (_M_int.size() == 2 |
3036 | && _M_int[0] == _RealType(0) |
3037 | && _M_int[1] == _RealType(1) |
3038 | && _M_den[0] == _M_den[1])) |
3039 | { |
3040 | _M_int.clear(); |
3041 | _M_den.clear(); |
3042 | return; |
3043 | } |
3044 | |
3045 | double __sum = 0.0; |
3046 | _M_cp.reserve(n: _M_int.size() - 1); |
3047 | _M_m.reserve(n: _M_int.size() - 1); |
3048 | for (size_t __k = 0; __k < _M_int.size() - 1; ++__k) |
3049 | { |
3050 | const _RealType __delta = _M_int[__k + 1] - _M_int[__k]; |
3051 | __sum += 0.5 * (_M_den[__k + 1] + _M_den[__k]) * __delta; |
3052 | _M_cp.push_back(x: __sum); |
3053 | _M_m.push_back((_M_den[__k + 1] - _M_den[__k]) / __delta); |
3054 | } |
3055 | __glibcxx_assert(__sum > 0); |
3056 | |
3057 | // Now normalize the densities... |
3058 | __detail::__normalize(first: _M_den.begin(), last: _M_den.end(), result: _M_den.begin(), |
3059 | factor: __sum); |
3060 | // ... and partial sums... |
3061 | __detail::__normalize(first: _M_cp.begin(), last: _M_cp.end(), result: _M_cp.begin(), factor: __sum); |
3062 | // ... and slopes. |
3063 | __detail::__normalize(first: _M_m.begin(), last: _M_m.end(), result: _M_m.begin(), factor: __sum); |
3064 | |
3065 | // Make sure the last cumulative probablility is one. |
3066 | _M_cp[_M_cp.size() - 1] = 1.0; |
3067 | } |
3068 | |
3069 | template<typename _RealType> |
3070 | template<typename _InputIteratorB, typename _InputIteratorW> |
3071 | piecewise_linear_distribution<_RealType>::param_type:: |
3072 | param_type(_InputIteratorB __bbegin, |
3073 | _InputIteratorB __bend, |
3074 | _InputIteratorW __wbegin) |
3075 | : _M_int(), _M_den(), _M_cp(), _M_m() |
3076 | { |
3077 | for (; __bbegin != __bend; ++__bbegin, ++__wbegin) |
3078 | { |
3079 | _M_int.push_back(*__bbegin); |
3080 | _M_den.push_back(*__wbegin); |
3081 | } |
3082 | |
3083 | _M_initialize(); |
3084 | } |
3085 | |
3086 | template<typename _RealType> |
3087 | template<typename _Func> |
3088 | piecewise_linear_distribution<_RealType>::param_type:: |
3089 | param_type(initializer_list<_RealType> __bl, _Func __fw) |
3090 | : _M_int(), _M_den(), _M_cp(), _M_m() |
3091 | { |
3092 | _M_int.reserve(__bl.size()); |
3093 | _M_den.reserve(n: __bl.size()); |
3094 | for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter) |
3095 | { |
3096 | _M_int.push_back(*__biter); |
3097 | _M_den.push_back(__fw(*__biter)); |
3098 | } |
3099 | |
3100 | _M_initialize(); |
3101 | } |
3102 | |
3103 | template<typename _RealType> |
3104 | template<typename _Func> |
3105 | piecewise_linear_distribution<_RealType>::param_type:: |
3106 | param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw) |
3107 | : _M_int(), _M_den(), _M_cp(), _M_m() |
3108 | { |
3109 | const size_t __n = __nw == 0 ? 1 : __nw; |
3110 | const _RealType __delta = (__xmax - __xmin) / __n; |
3111 | |
3112 | _M_int.reserve(__n + 1); |
3113 | _M_den.reserve(n: __n + 1); |
3114 | for (size_t __k = 0; __k <= __nw; ++__k) |
3115 | { |
3116 | _M_int.push_back(__xmin + __k * __delta); |
3117 | _M_den.push_back(__fw(_M_int[__k] + __delta)); |
3118 | } |
3119 | |
3120 | _M_initialize(); |
3121 | } |
3122 | |
3123 | template<typename _RealType> |
3124 | template<typename _UniformRandomNumberGenerator> |
3125 | typename piecewise_linear_distribution<_RealType>::result_type |
3126 | piecewise_linear_distribution<_RealType>:: |
3127 | operator()(_UniformRandomNumberGenerator& __urng, |
3128 | const param_type& __param) |
3129 | { |
3130 | __detail::_Adaptor<_UniformRandomNumberGenerator, double> |
3131 | __aurng(__urng); |
3132 | |
3133 | const double __p = __aurng(); |
3134 | if (__param._M_cp.empty()) |
3135 | return __p; |
3136 | |
3137 | auto __pos = std::lower_bound(__param._M_cp.begin(), |
3138 | __param._M_cp.end(), __p); |
3139 | const size_t __i = __pos - __param._M_cp.begin(); |
3140 | |
3141 | const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0; |
3142 | |
3143 | const double __a = 0.5 * __param._M_m[__i]; |
3144 | const double __b = __param._M_den[__i]; |
3145 | const double __cm = __p - __pref; |
3146 | |
3147 | _RealType __x = __param._M_int[__i]; |
3148 | if (__a == 0) |
3149 | __x += __cm / __b; |
3150 | else |
3151 | { |
3152 | const double __d = __b * __b + 4.0 * __a * __cm; |
3153 | __x += 0.5 * (std::sqrt(x: __d) - __b) / __a; |
3154 | } |
3155 | |
3156 | return __x; |
3157 | } |
3158 | |
3159 | template<typename _RealType> |
3160 | template<typename _ForwardIterator, |
3161 | typename _UniformRandomNumberGenerator> |
3162 | void |
3163 | piecewise_linear_distribution<_RealType>:: |
3164 | __generate_impl(_ForwardIterator __f, _ForwardIterator __t, |
3165 | _UniformRandomNumberGenerator& __urng, |
3166 | const param_type& __param) |
3167 | { |
3168 | __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) |
3169 | // We could duplicate everything from operator()... |
3170 | while (__f != __t) |
3171 | *__f++ = this->operator()(__urng, __param); |
3172 | } |
3173 | |
3174 | template<typename _RealType, typename _CharT, typename _Traits> |
3175 | std::basic_ostream<_CharT, _Traits>& |
3176 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
3177 | const piecewise_linear_distribution<_RealType>& __x) |
3178 | { |
3179 | using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; |
3180 | |
3181 | const typename __ios_base::fmtflags __flags = __os.flags(); |
3182 | const _CharT __fill = __os.fill(); |
3183 | const std::streamsize __precision = __os.precision(); |
3184 | const _CharT __space = __os.widen(' '); |
3185 | __os.flags(__ios_base::scientific | __ios_base::left); |
3186 | __os.fill(__space); |
3187 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
3188 | |
3189 | std::vector<_RealType> __int = __x.intervals(); |
3190 | __os << __int.size() - 1; |
3191 | |
3192 | for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit) |
3193 | __os << __space << *__xit; |
3194 | |
3195 | std::vector<double> __den = __x.densities(); |
3196 | for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit) |
3197 | __os << __space << *__dit; |
3198 | |
3199 | __os.flags(__flags); |
3200 | __os.fill(__fill); |
3201 | __os.precision(__precision); |
3202 | return __os; |
3203 | } |
3204 | |
3205 | template<typename _RealType, typename _CharT, typename _Traits> |
3206 | std::basic_istream<_CharT, _Traits>& |
3207 | operator>>(std::basic_istream<_CharT, _Traits>& __is, |
3208 | piecewise_linear_distribution<_RealType>& __x) |
3209 | { |
3210 | using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; |
3211 | |
3212 | const typename __ios_base::fmtflags __flags = __is.flags(); |
3213 | __is.flags(__ios_base::dec | __ios_base::skipws); |
3214 | |
3215 | size_t __n; |
3216 | if (__is >> __n) |
3217 | { |
3218 | vector<_RealType> __int_vec; |
3219 | if (__detail::__extract_params(__is, __int_vec, __n + 1)) |
3220 | { |
3221 | vector<double> __den_vec; |
3222 | if (__detail::__extract_params(__is, __den_vec, __n + 1)) |
3223 | { |
3224 | __x.param({ __int_vec.begin(), __int_vec.end(), |
3225 | __den_vec.begin() }); |
3226 | } |
3227 | } |
3228 | } |
3229 | __is.flags(__flags); |
3230 | return __is; |
3231 | } |
3232 | |
3233 | |
3234 | template<typename _IntType, typename> |
3235 | seed_seq::seed_seq(std::initializer_list<_IntType> __il) |
3236 | { |
3237 | _M_v.reserve(n: __il.size()); |
3238 | for (auto __iter = __il.begin(); __iter != __il.end(); ++__iter) |
3239 | _M_v.push_back(__detail::__mod<result_type, |
3240 | __detail::_Shift<result_type, 32>::__value>(*__iter)); |
3241 | } |
3242 | |
3243 | template<typename _InputIterator> |
3244 | seed_seq::seed_seq(_InputIterator __begin, _InputIterator __end) |
3245 | { |
3246 | if _GLIBCXX17_CONSTEXPR (__is_random_access_iter<_InputIterator>::value) |
3247 | _M_v.reserve(n: std::distance(__begin, __end)); |
3248 | |
3249 | for (_InputIterator __iter = __begin; __iter != __end; ++__iter) |
3250 | _M_v.push_back(__detail::__mod<result_type, |
3251 | __detail::_Shift<result_type, 32>::__value>(*__iter)); |
3252 | } |
3253 | |
3254 | template<typename _RandomAccessIterator> |
3255 | void |
3256 | seed_seq::generate(_RandomAccessIterator __begin, |
3257 | _RandomAccessIterator __end) |
3258 | { |
3259 | typedef typename iterator_traits<_RandomAccessIterator>::value_type |
3260 | _Type; |
3261 | |
3262 | if (__begin == __end) |
3263 | return; |
3264 | |
3265 | std::fill(__begin, __end, _Type(0x8b8b8b8bu)); |
3266 | |
3267 | const size_t __n = __end - __begin; |
3268 | const size_t __s = _M_v.size(); |
3269 | const size_t __t = (__n >= 623) ? 11 |
3270 | : (__n >= 68) ? 7 |
3271 | : (__n >= 39) ? 5 |
3272 | : (__n >= 7) ? 3 |
3273 | : (__n - 1) / 2; |
3274 | const size_t __p = (__n - __t) / 2; |
3275 | const size_t __q = __p + __t; |
3276 | const size_t __m = std::max(a: size_t(__s + 1), b: __n); |
3277 | |
3278 | #ifndef __UINT32_TYPE__ |
3279 | struct _Up |
3280 | { |
3281 | _Up(uint_least32_t v) : _M_v(v & 0xffffffffu) { } |
3282 | |
3283 | operator uint_least32_t() const { return _M_v; } |
3284 | |
3285 | uint_least32_t _M_v; |
3286 | }; |
3287 | using uint32_t = _Up; |
3288 | #endif |
3289 | |
3290 | // k == 0, every element in [begin,end) equals 0x8b8b8b8bu |
3291 | { |
3292 | uint32_t __r1 = 1371501266u; |
3293 | uint32_t __r2 = __r1 + __s; |
3294 | __begin[__p] += __r1; |
3295 | __begin[__q] = (uint32_t)__begin[__q] + __r2; |
3296 | __begin[0] = __r2; |
3297 | } |
3298 | |
3299 | for (size_t __k = 1; __k <= __s; ++__k) |
3300 | { |
3301 | const size_t __kn = __k % __n; |
3302 | const size_t __kpn = (__k + __p) % __n; |
3303 | const size_t __kqn = (__k + __q) % __n; |
3304 | uint32_t __arg = (__begin[__kn] |
3305 | ^ __begin[__kpn] |
3306 | ^ __begin[(__k - 1) % __n]); |
3307 | uint32_t __r1 = 1664525u * (__arg ^ (__arg >> 27)); |
3308 | uint32_t __r2 = __r1 + (uint32_t)__kn + _M_v[__k - 1]; |
3309 | __begin[__kpn] = (uint32_t)__begin[__kpn] + __r1; |
3310 | __begin[__kqn] = (uint32_t)__begin[__kqn] + __r2; |
3311 | __begin[__kn] = __r2; |
3312 | } |
3313 | |
3314 | for (size_t __k = __s + 1; __k < __m; ++__k) |
3315 | { |
3316 | const size_t __kn = __k % __n; |
3317 | const size_t __kpn = (__k + __p) % __n; |
3318 | const size_t __kqn = (__k + __q) % __n; |
3319 | uint32_t __arg = (__begin[__kn] |
3320 | ^ __begin[__kpn] |
3321 | ^ __begin[(__k - 1) % __n]); |
3322 | uint32_t __r1 = 1664525u * (__arg ^ (__arg >> 27)); |
3323 | uint32_t __r2 = __r1 + (uint32_t)__kn; |
3324 | __begin[__kpn] = (uint32_t)__begin[__kpn] + __r1; |
3325 | __begin[__kqn] = (uint32_t)__begin[__kqn] + __r2; |
3326 | __begin[__kn] = __r2; |
3327 | } |
3328 | |
3329 | for (size_t __k = __m; __k < __m + __n; ++__k) |
3330 | { |
3331 | const size_t __kn = __k % __n; |
3332 | const size_t __kpn = (__k + __p) % __n; |
3333 | const size_t __kqn = (__k + __q) % __n; |
3334 | uint32_t __arg = (__begin[__kn] |
3335 | + __begin[__kpn] |
3336 | + __begin[(__k - 1) % __n]); |
3337 | uint32_t __r3 = 1566083941u * (__arg ^ (__arg >> 27)); |
3338 | uint32_t __r4 = __r3 - __kn; |
3339 | __begin[__kpn] ^= __r3; |
3340 | __begin[__kqn] ^= __r4; |
3341 | __begin[__kn] = __r4; |
3342 | } |
3343 | } |
3344 | |
3345 | template<typename _RealType, size_t __bits, |
3346 | typename _UniformRandomNumberGenerator> |
3347 | _RealType |
3348 | generate_canonical(_UniformRandomNumberGenerator& __urng) |
3349 | { |
3350 | static_assert(std::is_floating_point<_RealType>::value, |
3351 | "template argument must be a floating point type" ); |
3352 | |
3353 | const size_t __b |
3354 | = std::min(a: static_cast<size_t>(std::numeric_limits<_RealType>::digits), |
3355 | b: __bits); |
3356 | const long double __r = static_cast<long double>(__urng.max()) |
3357 | - static_cast<long double>(__urng.min()) + 1.0L; |
3358 | const size_t __log2r = std::log(x: __r) / std::log(x: 2.0L); |
3359 | const size_t __m = std::max<size_t>(a: 1UL, |
3360 | b: (__b + __log2r - 1UL) / __log2r); |
3361 | _RealType __ret; |
3362 | _RealType __sum = _RealType(0); |
3363 | _RealType __tmp = _RealType(1); |
3364 | for (size_t __k = __m; __k != 0; --__k) |
3365 | { |
3366 | __sum += _RealType(__urng() - __urng.min()) * __tmp; |
3367 | __tmp *= __r; |
3368 | } |
3369 | __ret = __sum / __tmp; |
3370 | if (__builtin_expect(__ret >= _RealType(1), 0)) |
3371 | { |
3372 | #if _GLIBCXX_USE_C99_MATH_TR1 |
3373 | __ret = std::nextafter(_RealType(1), _RealType(0)); |
3374 | #else |
3375 | __ret = _RealType(1) |
3376 | - std::numeric_limits<_RealType>::epsilon() / _RealType(2); |
3377 | #endif |
3378 | } |
3379 | return __ret; |
3380 | } |
3381 | |
3382 | _GLIBCXX_END_NAMESPACE_VERSION |
3383 | } // namespace |
3384 | |
3385 | #endif |
3386 | |