source: Daodan/MSYS2/mingw32/include/c++/11.2.0/bits/random.tcc@ 1181

Last change on this file since 1181 was 1166, checked in by rossy, 3 years ago

Daodan: Replace MinGW build env with an up-to-date MSYS2 env
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[1166]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
35namespace std _GLIBCXX_VISIBILITY(default)
36{
37_GLIBCXX_BEGIN_NAMESPACE_VERSION
38
39 /*
40 * (Further) implementation-space details.
41 */
42 namespace __detail
43 {
44 // General case for x = (ax + c) mod m -- use Schrage's algorithm
45 // to avoid integer overflow.
46 //
47 // Preconditions: a > 0, m > 0.
48 //
49 // Note: only works correctly for __m % __a < __m / __a.
50 template<typename _Tp, _Tp __m, _Tp __a, _Tp __c>
51 _Tp
52 _Mod<_Tp, __m, __a, __c, false, true>::
53 __calc(_Tp __x)
54 {
55 if (__a == 1)
56 __x %= __m;
57 else
58 {
59 static const _Tp __q = __m / __a;
60 static const _Tp __r = __m % __a;
61
62 _Tp __t1 = __a * (__x % __q);
63 _Tp __t2 = __r * (__x / __q);
64 if (__t1 >= __t2)
65 __x = __t1 - __t2;
66 else
67 __x = __m - __t2 + __t1;
68 }
69
70 if (__c != 0)
71 {
72 const _Tp __d = __m - __x;
73 if (__d > __c)
74 __x += __c;
75 else
76 __x = __c - __d;
77 }
78 return __x;
79 }
80
81 template<typename _InputIterator, typename _OutputIterator,
82 typename _Tp>
83 _OutputIterator
84 __normalize(_InputIterator __first, _InputIterator __last,
85 _OutputIterator __result, const _Tp& __factor)
86 {
87 for (; __first != __last; ++__first, ++__result)
88 *__result = *__first / __factor;
89 return __result;
90 }
91
92 } // namespace __detail
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(_M_mean);
1268 _M_lm_thr = std::log(_M_mean);
1269 _M_lfm = std::lgamma(__m + 1);
1270 _M_sm = std::sqrt(__m);
1271
1272 const double __pi_4 = 0.7853981633974483096156608458198757L;
1273 const double __dx = std::sqrt(2 * __m * std::log(32 * __m
1274 / __pi_4));
1275 _M_d = std::round(std::max<double>(6.0, std::min(__m, __dx)));
1276 const double __cx = 2 * __m + _M_d;
1277 _M_scx = std::sqrt(__cx / 2);
1278 _M_1cx = 1 / __cx;
1279
1280 _M_c2b = std::sqrt(__pi_4 * __cx) * std::exp(_M_1cx);
1281 _M_cb = 2 * __cx * std::exp(-_M_d * _M_1cx * (1 + _M_d / 2))
1282 / _M_d;
1283 }
1284 else
1285#endif
1286 _M_lm_thr = std::exp(-_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(__n) * __param._M_sm - 1;
1346 __x = std::floor(__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(__n) * __param._M_scx;
1355 __x = std::ceil(__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(__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(__np * __1p * std::log(32 * __np
1487 / (81 * __pi_4 * __1p)));
1488 _M_d1 = std::round(std::max<double>(1.0, __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(std::max<double>(1.0, __d2x));
1493
1494 // sqrt(pi / 2)
1495 const double __spi_2 = 1.2533141373155002512078826424055226L;
1496 _M_s1 = std::sqrt(__np * __1p) * (1 + _M_d1 / (4 * __np));
1497 _M_s2 = std::sqrt(__np * __1p) * (1 + _M_d2 / (4 * _M_t * __1p));
1498 _M_c = 2 * _M_d1 / __np;
1499 _M_a1 = std::exp(_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(-_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(-_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(__pa / __1p);
1511
1512 _M_q = -std::log(1 - (__p12 - __pa) / __1p);
1513 }
1514 else
1515#endif
1516 _M_q = -std::log(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(__n);
1600 __reject = __y >= __param._M_d1;
1601 if (!__reject)
1602 {
1603 const double __e = -std::log(1.0 - __aurng());
1604 __x = std::floor(__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(__n);
1612 __reject = __y >= __param._M_d2;
1613 if (!__reject)
1614 {
1615 const double __e = -std::log(1.0 - __aurng());
1616 __x = std::floor(-__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(__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(-__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(_M_prob.begin(),
2666 _M_prob.end(), 0.0);
2667 __glibcxx_assert(__sum > 0);
2668 // Now normalize the probabilites.
2669 __detail::__normalize(_M_prob.begin(), _M_prob.end(), _M_prob.begin(),
2670 __sum);
2671 // Accumulate partial sums.
2672 _M_cp.reserve(_M_prob.size());
2673 std::partial_sum(_M_prob.begin(), _M_prob.end(),
2674 std::back_inserter(_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
2772namespace __detail
2773{
2774 template<typename _ValT, typename _CharT, typename _Traits>
2775 basic_istream<_CharT, _Traits>&
2776 __extract_params(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(_M_den.begin(),
2831 _M_den.end(), 0.0);
2832 __glibcxx_assert(__sum > 0);
2833
2834 __detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(),
2835 __sum);
2836
2837 _M_cp.reserve(_M_den.size());
2838 std::partial_sum(_M_den.begin(), _M_den.end(),
2839 std::back_inserter(_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(_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(_M_int.size() - 1);
3047 _M_m.reserve(_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(__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(_M_den.begin(), _M_den.end(), _M_den.begin(),
3059 __sum);
3060 // ... and partial sums...
3061 __detail::__normalize(_M_cp.begin(), _M_cp.end(), _M_cp.begin(), __sum);
3062 // ... and slopes.
3063 __detail::__normalize(_M_m.begin(), _M_m.end(), _M_m.begin(), __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(__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 + 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(__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>
3235 seed_seq::seed_seq(std::initializer_list<_IntType> __il)
3236 {
3237 for (auto __iter = __il.begin(); __iter != __il.end(); ++__iter)
3238 _M_v.push_back(__detail::__mod<result_type,
3239 __detail::_Shift<result_type, 32>::__value>(*__iter));
3240 }
3241
3242 template<typename _InputIterator>
3243 seed_seq::seed_seq(_InputIterator __begin, _InputIterator __end)
3244 {
3245 for (_InputIterator __iter = __begin; __iter != __end; ++__iter)
3246 _M_v.push_back(__detail::__mod<result_type,
3247 __detail::_Shift<result_type, 32>::__value>(*__iter));
3248 }
3249
3250 template<typename _RandomAccessIterator>
3251 void
3252 seed_seq::generate(_RandomAccessIterator __begin,
3253 _RandomAccessIterator __end)
3254 {
3255 typedef typename iterator_traits<_RandomAccessIterator>::value_type
3256 _Type;
3257
3258 if (__begin == __end)
3259 return;
3260
3261 std::fill(__begin, __end, _Type(0x8b8b8b8bu));
3262
3263 const size_t __n = __end - __begin;
3264 const size_t __s = _M_v.size();
3265 const size_t __t = (__n >= 623) ? 11
3266 : (__n >= 68) ? 7
3267 : (__n >= 39) ? 5
3268 : (__n >= 7) ? 3
3269 : (__n - 1) / 2;
3270 const size_t __p = (__n - __t) / 2;
3271 const size_t __q = __p + __t;
3272 const size_t __m = std::max(size_t(__s + 1), __n);
3273
3274#ifndef __UINT32_TYPE__
3275 struct _Up
3276 {
3277 _Up(uint_least32_t v) : _M_v(v & 0xffffffffu) { }
3278
3279 operator uint_least32_t() const { return _M_v; }
3280
3281 uint_least32_t _M_v;
3282 };
3283 using uint32_t = _Up;
3284#endif
3285
3286 // k == 0, every element in [begin,end) equals 0x8b8b8b8bu
3287 {
3288 uint32_t __r1 = 1371501266u;
3289 uint32_t __r2 = __r1 + __s;
3290 __begin[__p] += __r1;
3291 __begin[__q] = (uint32_t)__begin[__q] + __r2;
3292 __begin[0] = __r2;
3293 }
3294
3295 for (size_t __k = 1; __k <= __s; ++__k)
3296 {
3297 const size_t __kn = __k % __n;
3298 const size_t __kpn = (__k + __p) % __n;
3299 const size_t __kqn = (__k + __q) % __n;
3300 uint32_t __arg = (__begin[__kn]
3301 ^ __begin[__kpn]
3302 ^ __begin[(__k - 1) % __n]);
3303 uint32_t __r1 = 1664525u * (__arg ^ (__arg >> 27));
3304 uint32_t __r2 = __r1 + (uint32_t)__kn + _M_v[__k - 1];
3305 __begin[__kpn] = (uint32_t)__begin[__kpn] + __r1;
3306 __begin[__kqn] = (uint32_t)__begin[__kqn] + __r2;
3307 __begin[__kn] = __r2;
3308 }
3309
3310 for (size_t __k = __s + 1; __k < __m; ++__k)
3311 {
3312 const size_t __kn = __k % __n;
3313 const size_t __kpn = (__k + __p) % __n;
3314 const size_t __kqn = (__k + __q) % __n;
3315 uint32_t __arg = (__begin[__kn]
3316 ^ __begin[__kpn]
3317 ^ __begin[(__k - 1) % __n]);
3318 uint32_t __r1 = 1664525u * (__arg ^ (__arg >> 27));
3319 uint32_t __r2 = __r1 + (uint32_t)__kn;
3320 __begin[__kpn] = (uint32_t)__begin[__kpn] + __r1;
3321 __begin[__kqn] = (uint32_t)__begin[__kqn] + __r2;
3322 __begin[__kn] = __r2;
3323 }
3324
3325 for (size_t __k = __m; __k < __m + __n; ++__k)
3326 {
3327 const size_t __kn = __k % __n;
3328 const size_t __kpn = (__k + __p) % __n;
3329 const size_t __kqn = (__k + __q) % __n;
3330 uint32_t __arg = (__begin[__kn]
3331 + __begin[__kpn]
3332 + __begin[(__k - 1) % __n]);
3333 uint32_t __r3 = 1566083941u * (__arg ^ (__arg >> 27));
3334 uint32_t __r4 = __r3 - __kn;
3335 __begin[__kpn] ^= __r3;
3336 __begin[__kqn] ^= __r4;
3337 __begin[__kn] = __r4;
3338 }
3339 }
3340
3341 template<typename _RealType, size_t __bits,
3342 typename _UniformRandomNumberGenerator>
3343 _RealType
3344 generate_canonical(_UniformRandomNumberGenerator& __urng)
3345 {
3346 static_assert(std::is_floating_point<_RealType>::value,
3347 "template argument must be a floating point type");
3348
3349 const size_t __b
3350 = std::min(static_cast<size_t>(std::numeric_limits<_RealType>::digits),
3351 __bits);
3352 const long double __r = static_cast<long double>(__urng.max())
3353 - static_cast<long double>(__urng.min()) + 1.0L;
3354 const size_t __log2r = std::log(__r) / std::log(2.0L);
3355 const size_t __m = std::max<size_t>(1UL,
3356 (__b + __log2r - 1UL) / __log2r);
3357 _RealType __ret;
3358 _RealType __sum = _RealType(0);
3359 _RealType __tmp = _RealType(1);
3360 for (size_t __k = __m; __k != 0; --__k)
3361 {
3362 __sum += _RealType(__urng() - __urng.min()) * __tmp;
3363 __tmp *= __r;
3364 }
3365 __ret = __sum / __tmp;
3366 if (__builtin_expect(__ret >= _RealType(1), 0))
3367 {
3368#if _GLIBCXX_USE_C99_MATH_TR1
3369 __ret = std::nextafter(_RealType(1), _RealType(0));
3370#else
3371 __ret = _RealType(1)
3372 - std::numeric_limits<_RealType>::epsilon() / _RealType(2);
3373#endif
3374 }
3375 return __ret;
3376 }
3377
3378_GLIBCXX_END_NAMESPACE_VERSION
3379} // namespace
3380
3381#endif
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