1 // random number generation (out of line) -*- C++ -*-
2
3 // Copyright (C) 2009-2013 Free Software Foundation, Inc.
4 //
5 // This file is part of the GNU ISO C++ Library. This library is free
6 // software; you can redistribute it and/or modify it under the
7 // terms of the GNU General Public License as published by the
8 // Free Software Foundation; either version 3, or (at your option)
9 // any later version.
10
11 // This library is distributed in the hope that it will be useful,
12 // but WITHOUT ANY WARRANTY; without even the implied warranty of
13 // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
14 // GNU General Public License for more details.
15
16 // Under Section 7 of GPL version 3, you are granted additional
17 // permissions described in the GCC Runtime Library Exception, version
18 // 3.1, as published by the Free Software Foundation.
19
20 // You should have received a copy of the GNU General Public License and
21 // a copy of the GCC Runtime Library Exception along with this program;
22 // see the files COPYING3 and COPYING.RUNTIME respectively. If not, see
23 // <http://www.gnu.org/licenses/>.
24
25 /** @file bits/random.tcc
26 * This is an internal header file, included by other library headers.
27 * Do not attempt to use it directly. @headername{random}
28 */
29
30 #ifndef _RANDOM_TCC
31 #define _RANDOM_TCC 1
32
33 #include <numeric> // std::accumulate and std::partial_sum
34
35 namespace std _GLIBCXX_VISIBILITY(default)
36 {
37 /*
38 * (Further) implementation-space details.
39 */
40 namespace __detail
41 {
42 _GLIBCXX_BEGIN_NAMESPACE_VERSION
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 _GLIBCXX_END_NAMESPACE_VERSION
93 } // namespace __detail
94
95 _GLIBCXX_BEGIN_NAMESPACE_VERSION
96
97 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
98 constexpr _UIntType
99 linear_congruential_engine<_UIntType, __a, __c, __m>::multiplier;
100
101 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
102 constexpr _UIntType
103 linear_congruential_engine<_UIntType, __a, __c, __m>::increment;
104
105 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
106 constexpr _UIntType
107 linear_congruential_engine<_UIntType, __a, __c, __m>::modulus;
108
109 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
110 constexpr _UIntType
111 linear_congruential_engine<_UIntType, __a, __c, __m>::default_seed;
112
113 /**
114 * Seeds the LCR with integral value @p __s, adjusted so that the
115 * ring identity is never a member of the convergence set.
116 */
117 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
118 void
119 linear_congruential_engine<_UIntType, __a, __c, __m>::
120 seed(result_type __s)
121 {
122 if ((__detail::__mod<_UIntType, __m>(__c) == 0)
123 && (__detail::__mod<_UIntType, __m>(__s) == 0))
124 _M_x = 1;
125 else
126 _M_x = __detail::__mod<_UIntType, __m>(__s);
127 }
128
129 /**
130 * Seeds the LCR engine with a value generated by @p __q.
131 */
132 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
133 template<typename _Sseq>
134 typename std::enable_if<std::is_class<_Sseq>::value>::type
135 linear_congruential_engine<_UIntType, __a, __c, __m>::
136 seed(_Sseq& __q)
137 {
138 const _UIntType __k0 = __m == 0 ? std::numeric_limits<_UIntType>::digits
139 : std::__lg(__m);
140 const _UIntType __k = (__k0 + 31) / 32;
141 uint_least32_t __arr[__k + 3];
142 __q.generate(__arr + 0, __arr + __k + 3);
143 _UIntType __factor = 1u;
144 _UIntType __sum = 0u;
145 for (size_t __j = 0; __j < __k; ++__j)
146 {
147 __sum += __arr[__j + 3] * __factor;
148 __factor *= __detail::_Shift<_UIntType, 32>::__value;
149 }
150 seed(__sum);
151 }
152
153 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m,
154 typename _CharT, typename _Traits>
155 std::basic_ostream<_CharT, _Traits>&
156 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
157 const linear_congruential_engine<_UIntType,
158 __a, __c, __m>& __lcr)
159 {
160 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
161 typedef typename __ostream_type::ios_base __ios_base;
162
163 const typename __ios_base::fmtflags __flags = __os.flags();
164 const _CharT __fill = __os.fill();
165 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
166 __os.fill(__os.widen(' '));
167
168 __os << __lcr._M_x;
169
170 __os.flags(__flags);
171 __os.fill(__fill);
172 return __os;
173 }
174
175 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m,
176 typename _CharT, typename _Traits>
177 std::basic_istream<_CharT, _Traits>&
178 operator>>(std::basic_istream<_CharT, _Traits>& __is,
179 linear_congruential_engine<_UIntType, __a, __c, __m>& __lcr)
180 {
181 typedef std::basic_istream<_CharT, _Traits> __istream_type;
182 typedef typename __istream_type::ios_base __ios_base;
183
184 const typename __ios_base::fmtflags __flags = __is.flags();
185 __is.flags(__ios_base::dec);
186
187 __is >> __lcr._M_x;
188
189 __is.flags(__flags);
190 return __is;
191 }
192
193
194 template<typename _UIntType,
195 size_t __w, size_t __n, size_t __m, size_t __r,
196 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
197 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
198 _UIntType __f>
199 constexpr size_t
200 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
201 __s, __b, __t, __c, __l, __f>::word_size;
202
203 template<typename _UIntType,
204 size_t __w, size_t __n, size_t __m, size_t __r,
205 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
206 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
207 _UIntType __f>
208 constexpr size_t
209 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
210 __s, __b, __t, __c, __l, __f>::state_size;
211
212 template<typename _UIntType,
213 size_t __w, size_t __n, size_t __m, size_t __r,
214 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
215 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
216 _UIntType __f>
217 constexpr size_t
218 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
219 __s, __b, __t, __c, __l, __f>::shift_size;
220
221 template<typename _UIntType,
222 size_t __w, size_t __n, size_t __m, size_t __r,
223 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
224 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
225 _UIntType __f>
226 constexpr size_t
227 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
228 __s, __b, __t, __c, __l, __f>::mask_bits;
229
230 template<typename _UIntType,
231 size_t __w, size_t __n, size_t __m, size_t __r,
232 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
233 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
234 _UIntType __f>
235 constexpr _UIntType
236 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
237 __s, __b, __t, __c, __l, __f>::xor_mask;
238
239 template<typename _UIntType,
240 size_t __w, size_t __n, size_t __m, size_t __r,
241 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
242 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
243 _UIntType __f>
244 constexpr size_t
245 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
246 __s, __b, __t, __c, __l, __f>::tempering_u;
247
248 template<typename _UIntType,
249 size_t __w, size_t __n, size_t __m, size_t __r,
250 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
251 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
252 _UIntType __f>
253 constexpr _UIntType
254 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
255 __s, __b, __t, __c, __l, __f>::tempering_d;
256
257 template<typename _UIntType,
258 size_t __w, size_t __n, size_t __m, size_t __r,
259 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
260 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
261 _UIntType __f>
262 constexpr size_t
263 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
264 __s, __b, __t, __c, __l, __f>::tempering_s;
265
266 template<typename _UIntType,
267 size_t __w, size_t __n, size_t __m, size_t __r,
268 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
269 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
270 _UIntType __f>
271 constexpr _UIntType
272 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
273 __s, __b, __t, __c, __l, __f>::tempering_b;
274
275 template<typename _UIntType,
276 size_t __w, size_t __n, size_t __m, size_t __r,
277 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
278 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
279 _UIntType __f>
280 constexpr size_t
281 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
282 __s, __b, __t, __c, __l, __f>::tempering_t;
283
284 template<typename _UIntType,
285 size_t __w, size_t __n, size_t __m, size_t __r,
286 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
287 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
288 _UIntType __f>
289 constexpr _UIntType
290 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
291 __s, __b, __t, __c, __l, __f>::tempering_c;
292
293 template<typename _UIntType,
294 size_t __w, size_t __n, size_t __m, size_t __r,
295 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
296 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
297 _UIntType __f>
298 constexpr size_t
299 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
300 __s, __b, __t, __c, __l, __f>::tempering_l;
301
302 template<typename _UIntType,
303 size_t __w, size_t __n, size_t __m, size_t __r,
304 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
305 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
306 _UIntType __f>
307 constexpr _UIntType
308 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
309 __s, __b, __t, __c, __l, __f>::
310 initialization_multiplier;
311
312 template<typename _UIntType,
313 size_t __w, size_t __n, size_t __m, size_t __r,
314 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
315 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
316 _UIntType __f>
317 constexpr _UIntType
318 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
319 __s, __b, __t, __c, __l, __f>::default_seed;
320
321 template<typename _UIntType,
322 size_t __w, size_t __n, size_t __m, size_t __r,
323 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
324 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
325 _UIntType __f>
326 void
327 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
328 __s, __b, __t, __c, __l, __f>::
329 seed(result_type __sd)
330 {
331 _M_x[0] = __detail::__mod<_UIntType,
332 __detail::_Shift<_UIntType, __w>::__value>(__sd);
333
334 for (size_t __i = 1; __i < state_size; ++__i)
335 {
336 _UIntType __x = _M_x[__i - 1];
337 __x ^= __x >> (__w - 2);
338 __x *= __f;
339 __x += __detail::__mod<_UIntType, __n>(__i);
340 _M_x[__i] = __detail::__mod<_UIntType,
341 __detail::_Shift<_UIntType, __w>::__value>(__x);
342 }
343 _M_p = state_size;
344 }
345
346 template<typename _UIntType,
347 size_t __w, size_t __n, size_t __m, size_t __r,
348 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
349 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
350 _UIntType __f>
351 template<typename _Sseq>
352 typename std::enable_if<std::is_class<_Sseq>::value>::type
353 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
354 __s, __b, __t, __c, __l, __f>::
355 seed(_Sseq& __q)
356 {
357 const _UIntType __upper_mask = (~_UIntType()) << __r;
358 const size_t __k = (__w + 31) / 32;
359 uint_least32_t __arr[__n * __k];
360 __q.generate(__arr + 0, __arr + __n * __k);
361
362 bool __zero = true;
363 for (size_t __i = 0; __i < state_size; ++__i)
364 {
365 _UIntType __factor = 1u;
366 _UIntType __sum = 0u;
367 for (size_t __j = 0; __j < __k; ++__j)
368 {
369 __sum += __arr[__k * __i + __j] * __factor;
370 __factor *= __detail::_Shift<_UIntType, 32>::__value;
371 }
372 _M_x[__i] = __detail::__mod<_UIntType,
373 __detail::_Shift<_UIntType, __w>::__value>(__sum);
374
375 if (__zero)
376 {
377 if (__i == 0)
378 {
379 if ((_M_x[0] & __upper_mask) != 0u)
380 __zero = false;
381 }
382 else if (_M_x[__i] != 0u)
383 __zero = false;
384 }
385 }
386 if (__zero)
387 _M_x[0] = __detail::_Shift<_UIntType, __w - 1>::__value;
388 _M_p = state_size;
389 }
390
391 template<typename _UIntType, size_t __w,
392 size_t __n, size_t __m, size_t __r,
393 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
394 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
395 _UIntType __f>
396 void
397 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
398 __s, __b, __t, __c, __l, __f>::
399 _M_gen_rand(void)
400 {
401 const _UIntType __upper_mask = (~_UIntType()) << __r;
402 const _UIntType __lower_mask = ~__upper_mask;
403
404 for (size_t __k = 0; __k < (__n - __m); ++__k)
405 {
406 _UIntType __y = ((_M_x[__k] & __upper_mask)
407 | (_M_x[__k + 1] & __lower_mask));
408 _M_x[__k] = (_M_x[__k + __m] ^ (__y >> 1)
409 ^ ((__y & 0x01) ? __a : 0));
410 }
411
412 for (size_t __k = (__n - __m); __k < (__n - 1); ++__k)
413 {
414 _UIntType __y = ((_M_x[__k] & __upper_mask)
415 | (_M_x[__k + 1] & __lower_mask));
416 _M_x[__k] = (_M_x[__k + (__m - __n)] ^ (__y >> 1)
417 ^ ((__y & 0x01) ? __a : 0));
418 }
419
420 _UIntType __y = ((_M_x[__n - 1] & __upper_mask)
421 | (_M_x[0] & __lower_mask));
422 _M_x[__n - 1] = (_M_x[__m - 1] ^ (__y >> 1)
423 ^ ((__y & 0x01) ? __a : 0));
424 _M_p = 0;
425 }
426
427 template<typename _UIntType, size_t __w,
428 size_t __n, size_t __m, size_t __r,
429 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
430 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
431 _UIntType __f>
432 void
433 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
434 __s, __b, __t, __c, __l, __f>::
435 discard(unsigned long long __z)
436 {
437 while (__z > state_size - _M_p)
438 {
439 __z -= state_size - _M_p;
440 _M_gen_rand();
441 }
442 _M_p += __z;
443 }
444
445 template<typename _UIntType, size_t __w,
446 size_t __n, size_t __m, size_t __r,
447 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
448 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
449 _UIntType __f>
450 typename
451 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
452 __s, __b, __t, __c, __l, __f>::result_type
453 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
454 __s, __b, __t, __c, __l, __f>::
455 operator()()
456 {
457 // Reload the vector - cost is O(n) amortized over n calls.
458 if (_M_p >= state_size)
459 _M_gen_rand();
460
461 // Calculate o(x(i)).
462 result_type __z = _M_x[_M_p++];
463 __z ^= (__z >> __u) & __d;
464 __z ^= (__z << __s) & __b;
465 __z ^= (__z << __t) & __c;
466 __z ^= (__z >> __l);
467
468 return __z;
469 }
470
471 template<typename _UIntType, size_t __w,
472 size_t __n, size_t __m, size_t __r,
473 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
474 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
475 _UIntType __f, typename _CharT, typename _Traits>
476 std::basic_ostream<_CharT, _Traits>&
477 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
478 const mersenne_twister_engine<_UIntType, __w, __n, __m,
479 __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x)
480 {
481 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
482 typedef typename __ostream_type::ios_base __ios_base;
483
484 const typename __ios_base::fmtflags __flags = __os.flags();
485 const _CharT __fill = __os.fill();
486 const _CharT __space = __os.widen(' ');
487 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
488 __os.fill(__space);
489
490 for (size_t __i = 0; __i < __n; ++__i)
491 __os << __x._M_x[__i] << __space;
492 __os << __x._M_p;
493
494 __os.flags(__flags);
495 __os.fill(__fill);
496 return __os;
497 }
498
499 template<typename _UIntType, size_t __w,
500 size_t __n, size_t __m, size_t __r,
501 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
502 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
503 _UIntType __f, typename _CharT, typename _Traits>
504 std::basic_istream<_CharT, _Traits>&
505 operator>>(std::basic_istream<_CharT, _Traits>& __is,
506 mersenne_twister_engine<_UIntType, __w, __n, __m,
507 __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x)
508 {
509 typedef std::basic_istream<_CharT, _Traits> __istream_type;
510 typedef typename __istream_type::ios_base __ios_base;
511
512 const typename __ios_base::fmtflags __flags = __is.flags();
513 __is.flags(__ios_base::dec | __ios_base::skipws);
514
515 for (size_t __i = 0; __i < __n; ++__i)
516 __is >> __x._M_x[__i];
517 __is >> __x._M_p;
518
519 __is.flags(__flags);
520 return __is;
521 }
522
523
524 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
525 constexpr size_t
526 subtract_with_carry_engine<_UIntType, __w, __s, __r>::word_size;
527
528 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
529 constexpr size_t
530 subtract_with_carry_engine<_UIntType, __w, __s, __r>::short_lag;
531
532 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
533 constexpr size_t
534 subtract_with_carry_engine<_UIntType, __w, __s, __r>::long_lag;
535
536 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
537 constexpr _UIntType
538 subtract_with_carry_engine<_UIntType, __w, __s, __r>::default_seed;
539
540 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
541 void
542 subtract_with_carry_engine<_UIntType, __w, __s, __r>::
543 seed(result_type __value)
544 {
545 std::linear_congruential_engine<result_type, 40014u, 0u, 2147483563u>
546 __lcg(__value == 0u ? default_seed : __value);
547
548 const size_t __n = (__w + 31) / 32;
549
550 for (size_t __i = 0; __i < long_lag; ++__i)
551 {
552 _UIntType __sum = 0u;
553 _UIntType __factor = 1u;
554 for (size_t __j = 0; __j < __n; ++__j)
555 {
556 __sum += __detail::__mod<uint_least32_t,
557 __detail::_Shift<uint_least32_t, 32>::__value>
558 (__lcg()) * __factor;
559 __factor *= __detail::_Shift<_UIntType, 32>::__value;
560 }
561 _M_x[__i] = __detail::__mod<_UIntType,
562 __detail::_Shift<_UIntType, __w>::__value>(__sum);
563 }
564 _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0;
565 _M_p = 0;
566 }
567
568 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
569 template<typename _Sseq>
570 typename std::enable_if<std::is_class<_Sseq>::value>::type
571 subtract_with_carry_engine<_UIntType, __w, __s, __r>::
572 seed(_Sseq& __q)
573 {
574 const size_t __k = (__w + 31) / 32;
575 uint_least32_t __arr[__r * __k];
576 __q.generate(__arr + 0, __arr + __r * __k);
577
578 for (size_t __i = 0; __i < long_lag; ++__i)
579 {
580 _UIntType __sum = 0u;
581 _UIntType __factor = 1u;
582 for (size_t __j = 0; __j < __k; ++__j)
583 {
584 __sum += __arr[__k * __i + __j] * __factor;
585 __factor *= __detail::_Shift<_UIntType, 32>::__value;
586 }
587 _M_x[__i] = __detail::__mod<_UIntType,
588 __detail::_Shift<_UIntType, __w>::__value>(__sum);
589 }
590 _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0;
591 _M_p = 0;
592 }
593
594 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
595 typename subtract_with_carry_engine<_UIntType, __w, __s, __r>::
596 result_type
597 subtract_with_carry_engine<_UIntType, __w, __s, __r>::
598 operator()()
599 {
600 // Derive short lag index from current index.
601 long __ps = _M_p - short_lag;
602 if (__ps < 0)
603 __ps += long_lag;
604
605 // Calculate new x(i) without overflow or division.
606 // NB: Thanks to the requirements for _UIntType, _M_x[_M_p] + _M_carry
607 // cannot overflow.
608 _UIntType __xi;
609 if (_M_x[__ps] >= _M_x[_M_p] + _M_carry)
610 {
611 __xi = _M_x[__ps] - _M_x[_M_p] - _M_carry;
612 _M_carry = 0;
613 }
614 else
615 {
616 __xi = (__detail::_Shift<_UIntType, __w>::__value
617 - _M_x[_M_p] - _M_carry + _M_x[__ps]);
618 _M_carry = 1;
619 }
620 _M_x[_M_p] = __xi;
621
622 // Adjust current index to loop around in ring buffer.
623 if (++_M_p >= long_lag)
624 _M_p = 0;
625
626 return __xi;
627 }
628
629 template<typename _UIntType, size_t __w, size_t __s, size_t __r,
630 typename _CharT, typename _Traits>
631 std::basic_ostream<_CharT, _Traits>&
632 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
633 const subtract_with_carry_engine<_UIntType,
634 __w, __s, __r>& __x)
635 {
636 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
637 typedef typename __ostream_type::ios_base __ios_base;
638
639 const typename __ios_base::fmtflags __flags = __os.flags();
640 const _CharT __fill = __os.fill();
641 const _CharT __space = __os.widen(' ');
642 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
643 __os.fill(__space);
644
645 for (size_t __i = 0; __i < __r; ++__i)
646 __os << __x._M_x[__i] << __space;
647 __os << __x._M_carry << __space << __x._M_p;
648
649 __os.flags(__flags);
650 __os.fill(__fill);
651 return __os;
652 }
653
654 template<typename _UIntType, size_t __w, size_t __s, size_t __r,
655 typename _CharT, typename _Traits>
656 std::basic_istream<_CharT, _Traits>&
657 operator>>(std::basic_istream<_CharT, _Traits>& __is,
658 subtract_with_carry_engine<_UIntType, __w, __s, __r>& __x)
659 {
660 typedef std::basic_ostream<_CharT, _Traits> __istream_type;
661 typedef typename __istream_type::ios_base __ios_base;
662
663 const typename __ios_base::fmtflags __flags = __is.flags();
664 __is.flags(__ios_base::dec | __ios_base::skipws);
665
666 for (size_t __i = 0; __i < __r; ++__i)
667 __is >> __x._M_x[__i];
668 __is >> __x._M_carry;
669 __is >> __x._M_p;
670
671 __is.flags(__flags);
672 return __is;
673 }
674
675
676 template<typename _RandomNumberEngine, size_t __p, size_t __r>
677 constexpr size_t
678 discard_block_engine<_RandomNumberEngine, __p, __r>::block_size;
679
680 template<typename _RandomNumberEngine, size_t __p, size_t __r>
681 constexpr size_t
682 discard_block_engine<_RandomNumberEngine, __p, __r>::used_block;
683
684 template<typename _RandomNumberEngine, size_t __p, size_t __r>
685 typename discard_block_engine<_RandomNumberEngine,
686 __p, __r>::result_type
687 discard_block_engine<_RandomNumberEngine, __p, __r>::
688 operator()()
689 {
690 if (_M_n >= used_block)
691 {
692 _M_b.discard(block_size - _M_n);
693 _M_n = 0;
694 }
695 ++_M_n;
696 return _M_b();
697 }
698
699 template<typename _RandomNumberEngine, size_t __p, size_t __r,
700 typename _CharT, typename _Traits>
701 std::basic_ostream<_CharT, _Traits>&
702 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
703 const discard_block_engine<_RandomNumberEngine,
704 __p, __r>& __x)
705 {
706 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
707 typedef typename __ostream_type::ios_base __ios_base;
708
709 const typename __ios_base::fmtflags __flags = __os.flags();
710 const _CharT __fill = __os.fill();
711 const _CharT __space = __os.widen(' ');
712 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
713 __os.fill(__space);
714
715 __os << __x.base() << __space << __x._M_n;
716
717 __os.flags(__flags);
718 __os.fill(__fill);
719 return __os;
720 }
721
722 template<typename _RandomNumberEngine, size_t __p, size_t __r,
723 typename _CharT, typename _Traits>
724 std::basic_istream<_CharT, _Traits>&
725 operator>>(std::basic_istream<_CharT, _Traits>& __is,
726 discard_block_engine<_RandomNumberEngine, __p, __r>& __x)
727 {
728 typedef std::basic_istream<_CharT, _Traits> __istream_type;
729 typedef typename __istream_type::ios_base __ios_base;
730
731 const typename __ios_base::fmtflags __flags = __is.flags();
732 __is.flags(__ios_base::dec | __ios_base::skipws);
733
734 __is >> __x._M_b >> __x._M_n;
735
736 __is.flags(__flags);
737 return __is;
738 }
739
740
741 template<typename _RandomNumberEngine, size_t __w, typename _UIntType>
742 typename independent_bits_engine<_RandomNumberEngine, __w, _UIntType>::
743 result_type
744 independent_bits_engine<_RandomNumberEngine, __w, _UIntType>::
745 operator()()
746 {
747 typedef typename _RandomNumberEngine::result_type _Eresult_type;
748 const _Eresult_type __r
749 = (_M_b.max() - _M_b.min() < std::numeric_limits<_Eresult_type>::max()
750 ? _M_b.max() - _M_b.min() + 1 : 0);
751 const unsigned __edig = std::numeric_limits<_Eresult_type>::digits;
752 const unsigned __m = __r ? std::__lg(__r) : __edig;
753
754 typedef typename std::common_type<_Eresult_type, result_type>::type
755 __ctype;
756 const unsigned __cdig = std::numeric_limits<__ctype>::digits;
757
758 unsigned __n, __n0;
759 __ctype __s0, __s1, __y0, __y1;
760
761 for (size_t __i = 0; __i < 2; ++__i)
762 {
763 __n = (__w + __m - 1) / __m + __i;
764 __n0 = __n - __w % __n;
765 const unsigned __w0 = __w / __n; // __w0 <= __m
766
767 __s0 = 0;
768 __s1 = 0;
769 if (__w0 < __cdig)
770 {
771 __s0 = __ctype(1) << __w0;
772 __s1 = __s0 << 1;
773 }
774
775 __y0 = 0;
776 __y1 = 0;
777 if (__r)
778 {
779 __y0 = __s0 * (__r / __s0);
780 if (__s1)
781 __y1 = __s1 * (__r / __s1);
782
783 if (__r - __y0 <= __y0 / __n)
784 break;
785 }
786 else
787 break;
788 }
789
790 result_type __sum = 0;
791 for (size_t __k = 0; __k < __n0; ++__k)
792 {
793 __ctype __u;
794 do
795 __u = _M_b() - _M_b.min();
796 while (__y0 && __u >= __y0);
797 __sum = __s0 * __sum + (__s0 ? __u % __s0 : __u);
798 }
799 for (size_t __k = __n0; __k < __n; ++__k)
800 {
801 __ctype __u;
802 do
803 __u = _M_b() - _M_b.min();
804 while (__y1 && __u >= __y1);
805 __sum = __s1 * __sum + (__s1 ? __u % __s1 : __u);
806 }
807 return __sum;
808 }
809
810
811 template<typename _RandomNumberEngine, size_t __k>
812 constexpr size_t
813 shuffle_order_engine<_RandomNumberEngine, __k>::table_size;
814
815 template<typename _RandomNumberEngine, size_t __k>
816 typename shuffle_order_engine<_RandomNumberEngine, __k>::result_type
817 shuffle_order_engine<_RandomNumberEngine, __k>::
818 operator()()
819 {
820 size_t __j = __k * ((_M_y - _M_b.min())
821 / (_M_b.max() - _M_b.min() + 1.0L));
822 _M_y = _M_v[__j];
823 _M_v[__j] = _M_b();
824
825 return _M_y;
826 }
827
828 template<typename _RandomNumberEngine, size_t __k,
829 typename _CharT, typename _Traits>
830 std::basic_ostream<_CharT, _Traits>&
831 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
832 const shuffle_order_engine<_RandomNumberEngine, __k>& __x)
833 {
834 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
835 typedef typename __ostream_type::ios_base __ios_base;
836
837 const typename __ios_base::fmtflags __flags = __os.flags();
838 const _CharT __fill = __os.fill();
839 const _CharT __space = __os.widen(' ');
840 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
841 __os.fill(__space);
842
843 __os << __x.base();
844 for (size_t __i = 0; __i < __k; ++__i)
845 __os << __space << __x._M_v[__i];
846 __os << __space << __x._M_y;
847
848 __os.flags(__flags);
849 __os.fill(__fill);
850 return __os;
851 }
852
853 template<typename _RandomNumberEngine, size_t __k,
854 typename _CharT, typename _Traits>
855 std::basic_istream<_CharT, _Traits>&
856 operator>>(std::basic_istream<_CharT, _Traits>& __is,
857 shuffle_order_engine<_RandomNumberEngine, __k>& __x)
858 {
859 typedef std::basic_istream<_CharT, _Traits> __istream_type;
860 typedef typename __istream_type::ios_base __ios_base;
861
862 const typename __ios_base::fmtflags __flags = __is.flags();
863 __is.flags(__ios_base::dec | __ios_base::skipws);
864
865 __is >> __x._M_b;
866 for (size_t __i = 0; __i < __k; ++__i)
867 __is >> __x._M_v[__i];
868 __is >> __x._M_y;
869
870 __is.flags(__flags);
871 return __is;
872 }
873
874
875 template<typename _IntType>
876 template<typename _UniformRandomNumberGenerator>
877 typename uniform_int_distribution<_IntType>::result_type
878 uniform_int_distribution<_IntType>::
879 operator()(_UniformRandomNumberGenerator& __urng,
880 const param_type& __param)
881 {
882 typedef typename _UniformRandomNumberGenerator::result_type
883 _Gresult_type;
884 typedef typename std::make_unsigned<result_type>::type __utype;
885 typedef typename std::common_type<_Gresult_type, __utype>::type
886 __uctype;
887
888 const __uctype __urngmin = __urng.min();
889 const __uctype __urngmax = __urng.max();
890 const __uctype __urngrange = __urngmax - __urngmin;
891 const __uctype __urange
892 = __uctype(__param.b()) - __uctype(__param.a());
893
894 __uctype __ret;
895
896 if (__urngrange > __urange)
897 {
898 // downscaling
899 const __uctype __uerange = __urange + 1; // __urange can be zero
900 const __uctype __scaling = __urngrange / __uerange;
901 const __uctype __past = __uerange * __scaling;
902 do
903 __ret = __uctype(__urng()) - __urngmin;
904 while (__ret >= __past);
905 __ret /= __scaling;
906 }
907 else if (__urngrange < __urange)
908 {
909 // upscaling
910 /*
911 Note that every value in [0, urange]
912 can be written uniquely as
913
914 (urngrange + 1) * high + low
915
916 where
917
918 high in [0, urange / (urngrange + 1)]
919
920 and
921
922 low in [0, urngrange].
923 */
924 __uctype __tmp; // wraparound control
925 do
926 {
927 const __uctype __uerngrange = __urngrange + 1;
928 __tmp = (__uerngrange * operator()
929 (__urng, param_type(0, __urange / __uerngrange)));
930 __ret = __tmp + (__uctype(__urng()) - __urngmin);
931 }
932 while (__ret > __urange || __ret < __tmp);
933 }
934 else
935 __ret = __uctype(__urng()) - __urngmin;
936
937 return __ret + __param.a();
938 }
939
940
941 template<typename _IntType>
942 template<typename _ForwardIterator,
943 typename _UniformRandomNumberGenerator>
944 void
945 uniform_int_distribution<_IntType>::
946 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
947 _UniformRandomNumberGenerator& __urng,
948 const param_type& __param)
949 {
950 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
951 typedef typename _UniformRandomNumberGenerator::result_type
952 _Gresult_type;
953 typedef typename std::make_unsigned<result_type>::type __utype;
954 typedef typename std::common_type<_Gresult_type, __utype>::type
955 __uctype;
956
957 const __uctype __urngmin = __urng.min();
958 const __uctype __urngmax = __urng.max();
959 const __uctype __urngrange = __urngmax - __urngmin;
960 const __uctype __urange
961 = __uctype(__param.b()) - __uctype(__param.a());
962
963 __uctype __ret;
964
965 if (__urngrange > __urange)
966 {
967 if (__detail::_Power_of_2(__urngrange + 1)
968 && __detail::_Power_of_2(__urange + 1))
969 {
970 while (__f != __t)
971 {
972 __ret = __uctype(__urng()) - __urngmin;
973 *__f++ = (__ret & __urange) + __param.a();
974 }
975 }
976 else
977 {
978 // downscaling
979 const __uctype __uerange = __urange + 1; // __urange can be zero
980 const __uctype __scaling = __urngrange / __uerange;
981 const __uctype __past = __uerange * __scaling;
982 while (__f != __t)
983 {
984 do
985 __ret = __uctype(__urng()) - __urngmin;
986 while (__ret >= __past);
987 *__f++ = __ret / __scaling + __param.a();
988 }
989 }
990 }
991 else if (__urngrange < __urange)
992 {
993 // upscaling
994 /*
995 Note that every value in [0, urange]
996 can be written uniquely as
997
998 (urngrange + 1) * high + low
999
1000 where
1001
1002 high in [0, urange / (urngrange + 1)]
1003
1004 and
1005
1006 low in [0, urngrange].
1007 */
1008 __uctype __tmp; // wraparound control
1009 while (__f != __t)
1010 {
1011 do
1012 {
1013 const __uctype __uerngrange = __urngrange + 1;
1014 __tmp = (__uerngrange * operator()
1015 (__urng, param_type(0, __urange / __uerngrange)));
1016 __ret = __tmp + (__uctype(__urng()) - __urngmin);
1017 }
1018 while (__ret > __urange || __ret < __tmp);
1019 *__f++ = __ret;
1020 }
1021 }
1022 else
1023 while (__f != __t)
1024 *__f++ = __uctype(__urng()) - __urngmin + __param.a();
1025 }
1026
1027 template<typename _IntType, typename _CharT, typename _Traits>
1028 std::basic_ostream<_CharT, _Traits>&
1029 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1030 const uniform_int_distribution<_IntType>& __x)
1031 {
1032 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1033 typedef typename __ostream_type::ios_base __ios_base;
1034
1035 const typename __ios_base::fmtflags __flags = __os.flags();
1036 const _CharT __fill = __os.fill();
1037 const _CharT __space = __os.widen(' ');
1038 __os.flags(__ios_base::scientific | __ios_base::left);
1039 __os.fill(__space);
1040
1041 __os << __x.a() << __space << __x.b();
1042
1043 __os.flags(__flags);
1044 __os.fill(__fill);
1045 return __os;
1046 }
1047
1048 template<typename _IntType, typename _CharT, typename _Traits>
1049 std::basic_istream<_CharT, _Traits>&
1050 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1051 uniform_int_distribution<_IntType>& __x)
1052 {
1053 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1054 typedef typename __istream_type::ios_base __ios_base;
1055
1056 const typename __ios_base::fmtflags __flags = __is.flags();
1057 __is.flags(__ios_base::dec | __ios_base::skipws);
1058
1059 _IntType __a, __b;
1060 __is >> __a >> __b;
1061 __x.param(typename uniform_int_distribution<_IntType>::
1062 param_type(__a, __b));
1063
1064 __is.flags(__flags);
1065 return __is;
1066 }
1067
1068
1069 template<typename _RealType>
1070 template<typename _ForwardIterator,
1071 typename _UniformRandomNumberGenerator>
1072 void
1073 uniform_real_distribution<_RealType>::
1074 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1075 _UniformRandomNumberGenerator& __urng,
1076 const param_type& __p)
1077 {
1078 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
1079 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
1080 __aurng(__urng);
1081 auto __range = __p.b() - __p.a();
1082 while (__f != __t)
1083 *__f++ = __aurng() * __range + __p.a();
1084 }
1085
1086 template<typename _RealType, typename _CharT, typename _Traits>
1087 std::basic_ostream<_CharT, _Traits>&
1088 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1089 const uniform_real_distribution<_RealType>& __x)
1090 {
1091 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1092 typedef typename __ostream_type::ios_base __ios_base;
1093
1094 const typename __ios_base::fmtflags __flags = __os.flags();
1095 const _CharT __fill = __os.fill();
1096 const std::streamsize __precision = __os.precision();
1097 const _CharT __space = __os.widen(' ');
1098 __os.flags(__ios_base::scientific | __ios_base::left);
1099 __os.fill(__space);
1100 __os.precision(std::numeric_limits<_RealType>::max_digits10);
1101
1102 __os << __x.a() << __space << __x.b();
1103
1104 __os.flags(__flags);
1105 __os.fill(__fill);
1106 __os.precision(__precision);
1107 return __os;
1108 }
1109
1110 template<typename _RealType, typename _CharT, typename _Traits>
1111 std::basic_istream<_CharT, _Traits>&
1112 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1113 uniform_real_distribution<_RealType>& __x)
1114 {
1115 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1116 typedef typename __istream_type::ios_base __ios_base;
1117
1118 const typename __ios_base::fmtflags __flags = __is.flags();
1119 __is.flags(__ios_base::skipws);
1120
1121 _RealType __a, __b;
1122 __is >> __a >> __b;
1123 __x.param(typename uniform_real_distribution<_RealType>::
1124 param_type(__a, __b));
1125
1126 __is.flags(__flags);
1127 return __is;
1128 }
1129
1130
1131 template<typename _ForwardIterator,
1132 typename _UniformRandomNumberGenerator>
1133 void
1134 std::bernoulli_distribution::
1135 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1136 _UniformRandomNumberGenerator& __urng,
1137 const param_type& __p)
1138 {
1139 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
1140 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
1141 __aurng(__urng);
1142 auto __limit = __p.p() * (__aurng.max() - __aurng.min());
1143
1144 while (__f != __t)
1145 *__f++ = (__aurng() - __aurng.min()) < __limit;
1146 }
1147
1148 template<typename _CharT, typename _Traits>
1149 std::basic_ostream<_CharT, _Traits>&
1150 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1151 const bernoulli_distribution& __x)
1152 {
1153 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1154 typedef typename __ostream_type::ios_base __ios_base;
1155
1156 const typename __ios_base::fmtflags __flags = __os.flags();
1157 const _CharT __fill = __os.fill();
1158 const std::streamsize __precision = __os.precision();
1159 __os.flags(__ios_base::scientific | __ios_base::left);
1160 __os.fill(__os.widen(' '));
1161 __os.precision(std::numeric_limits<double>::max_digits10);
1162
1163 __os << __x.p();
1164
1165 __os.flags(__flags);
1166 __os.fill(__fill);
1167 __os.precision(__precision);
1168 return __os;
1169 }
1170
1171
1172 template<typename _IntType>
1173 template<typename _UniformRandomNumberGenerator>
1174 typename geometric_distribution<_IntType>::result_type
1175 geometric_distribution<_IntType>::
1176 operator()(_UniformRandomNumberGenerator& __urng,
1177 const param_type& __param)
1178 {
1179 // About the epsilon thing see this thread:
1180 // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html
1181 const double __naf =
1182 (1 - std::numeric_limits<double>::epsilon()) / 2;
1183 // The largest _RealType convertible to _IntType.
1184 const double __thr =
1185 std::numeric_limits<_IntType>::max() + __naf;
1186 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
1187 __aurng(__urng);
1188
1189 double __cand;
1190 do
1191 __cand = std::floor(std::log(1.0 - __aurng()) / __param._M_log_1_p);
1192 while (__cand >= __thr);
1193
1194 return result_type(__cand + __naf);
1195 }
1196
1197 template<typename _IntType>
1198 template<typename _ForwardIterator,
1199 typename _UniformRandomNumberGenerator>
1200 void
1201 geometric_distribution<_IntType>::
1202 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1203 _UniformRandomNumberGenerator& __urng,
1204 const param_type& __param)
1205 {
1206 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
1207 // About the epsilon thing see this thread:
1208 // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html
1209 const double __naf =
1210 (1 - std::numeric_limits<double>::epsilon()) / 2;
1211 // The largest _RealType convertible to _IntType.
1212 const double __thr =
1213 std::numeric_limits<_IntType>::max() + __naf;
1214 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
1215 __aurng(__urng);
1216
1217 while (__f != __t)
1218 {
1219 double __cand;
1220 do
1221 __cand = std::floor(std::log(1.0 - __aurng())
1222 / __param._M_log_1_p);
1223 while (__cand >= __thr);
1224
1225 *__f++ = __cand + __naf;
1226 }
1227 }
1228
1229 template<typename _IntType,
1230 typename _CharT, typename _Traits>
1231 std::basic_ostream<_CharT, _Traits>&
1232 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1233 const geometric_distribution<_IntType>& __x)
1234 {
1235 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1236 typedef typename __ostream_type::ios_base __ios_base;
1237
1238 const typename __ios_base::fmtflags __flags = __os.flags();
1239 const _CharT __fill = __os.fill();
1240 const std::streamsize __precision = __os.precision();
1241 __os.flags(__ios_base::scientific | __ios_base::left);
1242 __os.fill(__os.widen(' '));
1243 __os.precision(std::numeric_limits<double>::max_digits10);
1244
1245 __os << __x.p();
1246
1247 __os.flags(__flags);
1248 __os.fill(__fill);
1249 __os.precision(__precision);
1250 return __os;
1251 }
1252
1253 template<typename _IntType,
1254 typename _CharT, typename _Traits>
1255 std::basic_istream<_CharT, _Traits>&
1256 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1257 geometric_distribution<_IntType>& __x)
1258 {
1259 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1260 typedef typename __istream_type::ios_base __ios_base;
1261
1262 const typename __ios_base::fmtflags __flags = __is.flags();
1263 __is.flags(__ios_base::skipws);
1264
1265 double __p;
1266 __is >> __p;
1267 __x.param(typename geometric_distribution<_IntType>::param_type(__p));
1268
1269 __is.flags(__flags);
1270 return __is;
1271 }
1272
1273 // This is Leger's algorithm, also in Devroye, Ch. X, Example 1.5.
1274 template<typename _IntType>
1275 template<typename _UniformRandomNumberGenerator>
1276 typename negative_binomial_distribution<_IntType>::result_type
1277 negative_binomial_distribution<_IntType>::
1278 operator()(_UniformRandomNumberGenerator& __urng)
1279 {
1280 const double __y = _M_gd(__urng);
1281
1282 // XXX Is the constructor too slow?
1283 std::poisson_distribution<result_type> __poisson(__y);
1284 return __poisson(__urng);
1285 }
1286
1287 template<typename _IntType>
1288 template<typename _UniformRandomNumberGenerator>
1289 typename negative_binomial_distribution<_IntType>::result_type
1290 negative_binomial_distribution<_IntType>::
1291 operator()(_UniformRandomNumberGenerator& __urng,
1292 const param_type& __p)
1293 {
1294 typedef typename std::gamma_distribution<double>::param_type
1295 param_type;
1296
1297 const double __y =
1298 _M_gd(__urng, param_type(__p.k(), (1.0 - __p.p()) / __p.p()));
1299
1300 std::poisson_distribution<result_type> __poisson(__y);
1301 return __poisson(__urng);
1302 }
1303
1304 template<typename _IntType>
1305 template<typename _ForwardIterator,
1306 typename _UniformRandomNumberGenerator>
1307 void
1308 negative_binomial_distribution<_IntType>::
1309 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1310 _UniformRandomNumberGenerator& __urng)
1311 {
1312 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
1313 while (__f != __t)
1314 {
1315 const double __y = _M_gd(__urng);
1316
1317 // XXX Is the constructor too slow?
1318 std::poisson_distribution<result_type> __poisson(__y);
1319 *__f++ = __poisson(__urng);
1320 }
1321 }
1322
1323 template<typename _IntType>
1324 template<typename _ForwardIterator,
1325 typename _UniformRandomNumberGenerator>
1326 void
1327 negative_binomial_distribution<_IntType>::
1328 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1329 _UniformRandomNumberGenerator& __urng,
1330 const param_type& __p)
1331 {
1332 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
1333 typename std::gamma_distribution<result_type>::param_type
1334 __p2(__p.k(), (1.0 - __p.p()) / __p.p());
1335
1336 while (__f != __t)
1337 {
1338 const double __y = _M_gd(__urng, __p2);
1339
1340 std::poisson_distribution<result_type> __poisson(__y);
1341 *__f++ = __poisson(__urng);
1342 }
1343 }
1344
1345 template<typename _IntType, typename _CharT, typename _Traits>
1346 std::basic_ostream<_CharT, _Traits>&
1347 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1348 const negative_binomial_distribution<_IntType>& __x)
1349 {
1350 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1351 typedef typename __ostream_type::ios_base __ios_base;
1352
1353 const typename __ios_base::fmtflags __flags = __os.flags();
1354 const _CharT __fill = __os.fill();
1355 const std::streamsize __precision = __os.precision();
1356 const _CharT __space = __os.widen(' ');
1357 __os.flags(__ios_base::scientific | __ios_base::left);
1358 __os.fill(__os.widen(' '));
1359 __os.precision(std::numeric_limits<double>::max_digits10);
1360
1361 __os << __x.k() << __space << __x.p()
1362 << __space << __x._M_gd;
1363
1364 __os.flags(__flags);
1365 __os.fill(__fill);
1366 __os.precision(__precision);
1367 return __os;
1368 }
1369
1370 template<typename _IntType, typename _CharT, typename _Traits>
1371 std::basic_istream<_CharT, _Traits>&
1372 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1373 negative_binomial_distribution<_IntType>& __x)
1374 {
1375 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1376 typedef typename __istream_type::ios_base __ios_base;
1377
1378 const typename __ios_base::fmtflags __flags = __is.flags();
1379 __is.flags(__ios_base::skipws);
1380
1381 _IntType __k;
1382 double __p;
1383 __is >> __k >> __p >> __x._M_gd;
1384 __x.param(typename negative_binomial_distribution<_IntType>::
1385 param_type(__k, __p));
1386
1387 __is.flags(__flags);
1388 return __is;
1389 }
1390
1391
1392 template<typename _IntType>
1393 void
1394 poisson_distribution<_IntType>::param_type::
1395 _M_initialize()
1396 {
1397 #if _GLIBCXX_USE_C99_MATH_TR1
1398 if (_M_mean >= 12)
1399 {
1400 const double __m = std::floor(_M_mean);
1401 _M_lm_thr = std::log(_M_mean);
1402 _M_lfm = std::lgamma(__m + 1);
1403 _M_sm = std::sqrt(__m);
1404
1405 const double __pi_4 = 0.7853981633974483096156608458198757L;
1406 const double __dx = std::sqrt(2 * __m * std::log(32 * __m
1407 / __pi_4));
1408 _M_d = std::round(std::max(6.0, std::min(__m, __dx)));
1409 const double __cx = 2 * __m + _M_d;
1410 _M_scx = std::sqrt(__cx / 2);
1411 _M_1cx = 1 / __cx;
1412
1413 _M_c2b = std::sqrt(__pi_4 * __cx) * std::exp(_M_1cx);
1414 _M_cb = 2 * __cx * std::exp(-_M_d * _M_1cx * (1 + _M_d / 2))
1415 / _M_d;
1416 }
1417 else
1418 #endif
1419 _M_lm_thr = std::exp(-_M_mean);
1420 }
1421
1422 /**
1423 * A rejection algorithm when mean >= 12 and a simple method based
1424 * upon the multiplication of uniform random variates otherwise.
1425 * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1
1426 * is defined.
1427 *
1428 * Reference:
1429 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
1430 * New York, 1986, Ch. X, Sects. 3.3 & 3.4 (+ Errata!).
1431 */
1432 template<typename _IntType>
1433 template<typename _UniformRandomNumberGenerator>
1434 typename poisson_distribution<_IntType>::result_type
1435 poisson_distribution<_IntType>::
1436 operator()(_UniformRandomNumberGenerator& __urng,
1437 const param_type& __param)
1438 {
1439 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
1440 __aurng(__urng);
1441 #if _GLIBCXX_USE_C99_MATH_TR1
1442 if (__param.mean() >= 12)
1443 {
1444 double __x;
1445
1446 // See comments above...
1447 const double __naf =
1448 (1 - std::numeric_limits<double>::epsilon()) / 2;
1449 const double __thr =
1450 std::numeric_limits<_IntType>::max() + __naf;
1451
1452 const double __m = std::floor(__param.mean());
1453 // sqrt(pi / 2)
1454 const double __spi_2 = 1.2533141373155002512078826424055226L;
1455 const double __c1 = __param._M_sm * __spi_2;
1456 const double __c2 = __param._M_c2b + __c1;
1457 const double __c3 = __c2 + 1;
1458 const double __c4 = __c3 + 1;
1459 // e^(1 / 78)
1460 const double __e178 = 1.0129030479320018583185514777512983L;
1461 const double __c5 = __c4 + __e178;
1462 const double __c = __param._M_cb + __c5;
1463 const double __2cx = 2 * (2 * __m + __param._M_d);
1464
1465 bool __reject = true;
1466 do
1467 {
1468 const double __u = __c * __aurng();
1469 const double __e = -std::log(1.0 - __aurng());
1470
1471 double __w = 0.0;
1472
1473 if (__u <= __c1)
1474 {
1475 const double __n = _M_nd(__urng);
1476 const double __y = -std::abs(__n) * __param._M_sm - 1;
1477 __x = std::floor(__y);
1478 __w = -__n * __n / 2;
1479 if (__x < -__m)
1480 continue;
1481 }
1482 else if (__u <= __c2)
1483 {
1484 const double __n = _M_nd(__urng);
1485 const double __y = 1 + std::abs(__n) * __param._M_scx;
1486 __x = std::ceil(__y);
1487 __w = __y * (2 - __y) * __param._M_1cx;
1488 if (__x > __param._M_d)
1489 continue;
1490 }
1491 else if (__u <= __c3)
1492 // NB: This case not in the book, nor in the Errata,
1493 // but should be ok...
1494 __x = -1;
1495 else if (__u <= __c4)
1496 __x = 0;
1497 else if (__u <= __c5)
1498 __x = 1;
1499 else
1500 {
1501 const double __v = -std::log(1.0 - __aurng());
1502 const double __y = __param._M_d
1503 + __v * __2cx / __param._M_d;
1504 __x = std::ceil(__y);
1505 __w = -__param._M_d * __param._M_1cx * (1 + __y / 2);
1506 }
1507
1508 __reject = (__w - __e - __x * __param._M_lm_thr
1509 > __param._M_lfm - std::lgamma(__x + __m + 1));
1510
1511 __reject |= __x + __m >= __thr;
1512
1513 } while (__reject);
1514
1515 return result_type(__x + __m + __naf);
1516 }
1517 else
1518 #endif
1519 {
1520 _IntType __x = 0;
1521 double __prod = 1.0;
1522
1523 do
1524 {
1525 __prod *= __aurng();
1526 __x += 1;
1527 }
1528 while (__prod > __param._M_lm_thr);
1529
1530 return __x - 1;
1531 }
1532 }
1533
1534 template<typename _IntType>
1535 template<typename _ForwardIterator,
1536 typename _UniformRandomNumberGenerator>
1537 void
1538 poisson_distribution<_IntType>::
1539 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1540 _UniformRandomNumberGenerator& __urng,
1541 const param_type& __param)
1542 {
1543 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
1544 // We could duplicate everything from operator()...
1545 while (__f != __t)
1546 *__f++ = this->operator()(__urng, __param);
1547 }
1548
1549 template<typename _IntType,
1550 typename _CharT, typename _Traits>
1551 std::basic_ostream<_CharT, _Traits>&
1552 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1553 const poisson_distribution<_IntType>& __x)
1554 {
1555 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1556 typedef typename __ostream_type::ios_base __ios_base;
1557
1558 const typename __ios_base::fmtflags __flags = __os.flags();
1559 const _CharT __fill = __os.fill();
1560 const std::streamsize __precision = __os.precision();
1561 const _CharT __space = __os.widen(' ');
1562 __os.flags(__ios_base::scientific | __ios_base::left);
1563 __os.fill(__space);
1564 __os.precision(std::numeric_limits<double>::max_digits10);
1565
1566 __os << __x.mean() << __space << __x._M_nd;
1567
1568 __os.flags(__flags);
1569 __os.fill(__fill);
1570 __os.precision(__precision);
1571 return __os;
1572 }
1573
1574 template<typename _IntType,
1575 typename _CharT, typename _Traits>
1576 std::basic_istream<_CharT, _Traits>&
1577 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1578 poisson_distribution<_IntType>& __x)
1579 {
1580 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1581 typedef typename __istream_type::ios_base __ios_base;
1582
1583 const typename __ios_base::fmtflags __flags = __is.flags();
1584 __is.flags(__ios_base::skipws);
1585
1586 double __mean;
1587 __is >> __mean >> __x._M_nd;
1588 __x.param(typename poisson_distribution<_IntType>::param_type(__mean));
1589
1590 __is.flags(__flags);
1591 return __is;
1592 }
1593
1594
1595 template<typename _IntType>
1596 void
1597 binomial_distribution<_IntType>::param_type::
1598 _M_initialize()
1599 {
1600 const double __p12 = _M_p <= 0.5 ? _M_p : 1.0 - _M_p;
1601
1602 _M_easy = true;
1603
1604 #if _GLIBCXX_USE_C99_MATH_TR1
1605 if (_M_t * __p12 >= 8)
1606 {
1607 _M_easy = false;
1608 const double __np = std::floor(_M_t * __p12);
1609 const double __pa = __np / _M_t;
1610 const double __1p = 1 - __pa;
1611
1612 const double __pi_4 = 0.7853981633974483096156608458198757L;
1613 const double __d1x =
1614 std::sqrt(__np * __1p * std::log(32 * __np
1615 / (81 * __pi_4 * __1p)));
1616 _M_d1 = std::round(std::max(1.0, __d1x));
1617 const double __d2x =
1618 std::sqrt(__np * __1p * std::log(32 * _M_t * __1p
1619 / (__pi_4 * __pa)));
1620 _M_d2 = std::round(std::max(1.0, __d2x));
1621
1622 // sqrt(pi / 2)
1623 const double __spi_2 = 1.2533141373155002512078826424055226L;
1624 _M_s1 = std::sqrt(__np * __1p) * (1 + _M_d1 / (4 * __np));
1625 _M_s2 = std::sqrt(__np * __1p) * (1 + _M_d2 / (4 * _M_t * __1p));
1626 _M_c = 2 * _M_d1 / __np;
1627 _M_a1 = std::exp(_M_c) * _M_s1 * __spi_2;
1628 const double __a12 = _M_a1 + _M_s2 * __spi_2;
1629 const double __s1s = _M_s1 * _M_s1;
1630 _M_a123 = __a12 + (std::exp(_M_d1 / (_M_t * __1p))
1631 * 2 * __s1s / _M_d1
1632 * std::exp(-_M_d1 * _M_d1 / (2 * __s1s)));
1633 const double __s2s = _M_s2 * _M_s2;
1634 _M_s = (_M_a123 + 2 * __s2s / _M_d2
1635 * std::exp(-_M_d2 * _M_d2 / (2 * __s2s)));
1636 _M_lf = (std::lgamma(__np + 1)
1637 + std::lgamma(_M_t - __np + 1));
1638 _M_lp1p = std::log(__pa / __1p);
1639
1640 _M_q = -std::log(1 - (__p12 - __pa) / __1p);
1641 }
1642 else
1643 #endif
1644 _M_q = -std::log(1 - __p12);
1645 }
1646
1647 template<typename _IntType>
1648 template<typename _UniformRandomNumberGenerator>
1649 typename binomial_distribution<_IntType>::result_type
1650 binomial_distribution<_IntType>::
1651 _M_waiting(_UniformRandomNumberGenerator& __urng,
1652 _IntType __t, double __q)
1653 {
1654 _IntType __x = 0;
1655 double __sum = 0.0;
1656 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
1657 __aurng(__urng);
1658
1659 do
1660 {
1661 if (__t == __x)
1662 return __x;
1663 const double __e = -std::log(1.0 - __aurng());
1664 __sum += __e / (__t - __x);
1665 __x += 1;
1666 }
1667 while (__sum <= __q);
1668
1669 return __x - 1;
1670 }
1671
1672 /**
1673 * A rejection algorithm when t * p >= 8 and a simple waiting time
1674 * method - the second in the referenced book - otherwise.
1675 * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1
1676 * is defined.
1677 *
1678 * Reference:
1679 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
1680 * New York, 1986, Ch. X, Sect. 4 (+ Errata!).
1681 */
1682 template<typename _IntType>
1683 template<typename _UniformRandomNumberGenerator>
1684 typename binomial_distribution<_IntType>::result_type
1685 binomial_distribution<_IntType>::
1686 operator()(_UniformRandomNumberGenerator& __urng,
1687 const param_type& __param)
1688 {
1689 result_type __ret;
1690 const _IntType __t = __param.t();
1691 const double __p = __param.p();
1692 const double __p12 = __p <= 0.5 ? __p : 1.0 - __p;
1693 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
1694 __aurng(__urng);
1695
1696 #if _GLIBCXX_USE_C99_MATH_TR1
1697 if (!__param._M_easy)
1698 {
1699 double __x;
1700
1701 // See comments above...
1702 const double __naf =
1703 (1 - std::numeric_limits<double>::epsilon()) / 2;
1704 const double __thr =
1705 std::numeric_limits<_IntType>::max() + __naf;
1706
1707 const double __np = std::floor(__t * __p12);
1708
1709 // sqrt(pi / 2)
1710 const double __spi_2 = 1.2533141373155002512078826424055226L;
1711 const double __a1 = __param._M_a1;
1712 const double __a12 = __a1 + __param._M_s2 * __spi_2;
1713 const double __a123 = __param._M_a123;
1714 const double __s1s = __param._M_s1 * __param._M_s1;
1715 const double __s2s = __param._M_s2 * __param._M_s2;
1716
1717 bool __reject;
1718 do
1719 {
1720 const double __u = __param._M_s * __aurng();
1721
1722 double __v;
1723
1724 if (__u <= __a1)
1725 {
1726 const double __n = _M_nd(__urng);
1727 const double __y = __param._M_s1 * std::abs(__n);
1728 __reject = __y >= __param._M_d1;
1729 if (!__reject)
1730 {
1731 const double __e = -std::log(1.0 - __aurng());
1732 __x = std::floor(__y);
1733 __v = -__e - __n * __n / 2 + __param._M_c;
1734 }
1735 }
1736 else if (__u <= __a12)
1737 {
1738 const double __n = _M_nd(__urng);
1739 const double __y = __param._M_s2 * std::abs(__n);
1740 __reject = __y >= __param._M_d2;
1741 if (!__reject)
1742 {
1743 const double __e = -std::log(1.0 - __aurng());
1744 __x = std::floor(-__y);
1745 __v = -__e - __n * __n / 2;
1746 }
1747 }
1748 else if (__u <= __a123)
1749 {
1750 const double __e1 = -std::log(1.0 - __aurng());
1751 const double __e2 = -std::log(1.0 - __aurng());
1752
1753 const double __y = __param._M_d1
1754 + 2 * __s1s * __e1 / __param._M_d1;
1755 __x = std::floor(__y);
1756 __v = (-__e2 + __param._M_d1 * (1 / (__t - __np)
1757 -__y / (2 * __s1s)));
1758 __reject = false;
1759 }
1760 else
1761 {
1762 const double __e1 = -std::log(1.0 - __aurng());
1763 const double __e2 = -std::log(1.0 - __aurng());
1764
1765 const double __y = __param._M_d2
1766 + 2 * __s2s * __e1 / __param._M_d2;
1767 __x = std::floor(-__y);
1768 __v = -__e2 - __param._M_d2 * __y / (2 * __s2s);
1769 __reject = false;
1770 }
1771
1772 __reject = __reject || __x < -__np || __x > __t - __np;
1773 if (!__reject)
1774 {
1775 const double __lfx =
1776 std::lgamma(__np + __x + 1)
1777 + std::lgamma(__t - (__np + __x) + 1);
1778 __reject = __v > __param._M_lf - __lfx
1779 + __x * __param._M_lp1p;
1780 }
1781
1782 __reject |= __x + __np >= __thr;
1783 }
1784 while (__reject);
1785
1786 __x += __np + __naf;
1787
1788 const _IntType __z = _M_waiting(__urng, __t - _IntType(__x),
1789 __param._M_q);
1790 __ret = _IntType(__x) + __z;
1791 }
1792 else
1793 #endif
1794 __ret = _M_waiting(__urng, __t, __param._M_q);
1795
1796 if (__p12 != __p)
1797 __ret = __t - __ret;
1798 return __ret;
1799 }
1800
1801 template<typename _IntType>
1802 template<typename _ForwardIterator,
1803 typename _UniformRandomNumberGenerator>
1804 void
1805 binomial_distribution<_IntType>::
1806 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1807 _UniformRandomNumberGenerator& __urng,
1808 const param_type& __param)
1809 {
1810 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
1811 // We could duplicate everything from operator()...
1812 while (__f != __t)
1813 *__f++ = this->operator()(__urng, __param);
1814 }
1815
1816 template<typename _IntType,
1817 typename _CharT, typename _Traits>
1818 std::basic_ostream<_CharT, _Traits>&
1819 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1820 const binomial_distribution<_IntType>& __x)
1821 {
1822 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1823 typedef typename __ostream_type::ios_base __ios_base;
1824
1825 const typename __ios_base::fmtflags __flags = __os.flags();
1826 const _CharT __fill = __os.fill();
1827 const std::streamsize __precision = __os.precision();
1828 const _CharT __space = __os.widen(' ');
1829 __os.flags(__ios_base::scientific | __ios_base::left);
1830 __os.fill(__space);
1831 __os.precision(std::numeric_limits<double>::max_digits10);
1832
1833 __os << __x.t() << __space << __x.p()
1834 << __space << __x._M_nd;
1835
1836 __os.flags(__flags);
1837 __os.fill(__fill);
1838 __os.precision(__precision);
1839 return __os;
1840 }
1841
1842 template<typename _IntType,
1843 typename _CharT, typename _Traits>
1844 std::basic_istream<_CharT, _Traits>&
1845 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1846 binomial_distribution<_IntType>& __x)
1847 {
1848 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1849 typedef typename __istream_type::ios_base __ios_base;
1850
1851 const typename __ios_base::fmtflags __flags = __is.flags();
1852 __is.flags(__ios_base::dec | __ios_base::skipws);
1853
1854 _IntType __t;
1855 double __p;
1856 __is >> __t >> __p >> __x._M_nd;
1857 __x.param(typename binomial_distribution<_IntType>::
1858 param_type(__t, __p));
1859
1860 __is.flags(__flags);
1861 return __is;
1862 }
1863
1864
1865 template<typename _RealType>
1866 template<typename _ForwardIterator,
1867 typename _UniformRandomNumberGenerator>
1868 void
1869 std::exponential_distribution<_RealType>::
1870 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1871 _UniformRandomNumberGenerator& __urng,
1872 const param_type& __p)
1873 {
1874 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
1875 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
1876 __aurng(__urng);
1877 while (__f != __t)
1878 *__f++ = -std::log(result_type(1) - __aurng()) / __p.lambda();
1879 }
1880
1881 template<typename _RealType, typename _CharT, typename _Traits>
1882 std::basic_ostream<_CharT, _Traits>&
1883 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1884 const exponential_distribution<_RealType>& __x)
1885 {
1886 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1887 typedef typename __ostream_type::ios_base __ios_base;
1888
1889 const typename __ios_base::fmtflags __flags = __os.flags();
1890 const _CharT __fill = __os.fill();
1891 const std::streamsize __precision = __os.precision();
1892 __os.flags(__ios_base::scientific | __ios_base::left);
1893 __os.fill(__os.widen(' '));
1894 __os.precision(std::numeric_limits<_RealType>::max_digits10);
1895
1896 __os << __x.lambda();
1897
1898 __os.flags(__flags);
1899 __os.fill(__fill);
1900 __os.precision(__precision);
1901 return __os;
1902 }
1903
1904 template<typename _RealType, typename _CharT, typename _Traits>
1905 std::basic_istream<_CharT, _Traits>&
1906 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1907 exponential_distribution<_RealType>& __x)
1908 {
1909 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1910 typedef typename __istream_type::ios_base __ios_base;
1911
1912 const typename __ios_base::fmtflags __flags = __is.flags();
1913 __is.flags(__ios_base::dec | __ios_base::skipws);
1914
1915 _RealType __lambda;
1916 __is >> __lambda;
1917 __x.param(typename exponential_distribution<_RealType>::
1918 param_type(__lambda));
1919
1920 __is.flags(__flags);
1921 return __is;
1922 }
1923
1924
1925 /**
1926 * Polar method due to Marsaglia.
1927 *
1928 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
1929 * New York, 1986, Ch. V, Sect. 4.4.
1930 */
1931 template<typename _RealType>
1932 template<typename _UniformRandomNumberGenerator>
1933 typename normal_distribution<_RealType>::result_type
1934 normal_distribution<_RealType>::
1935 operator()(_UniformRandomNumberGenerator& __urng,
1936 const param_type& __param)
1937 {
1938 result_type __ret;
1939 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
1940 __aurng(__urng);
1941
1942 if (_M_saved_available)
1943 {
1944 _M_saved_available = false;
1945 __ret = _M_saved;
1946 }
1947 else
1948 {
1949 result_type __x, __y, __r2;
1950 do
1951 {
1952 __x = result_type(2.0) * __aurng() - 1.0;
1953 __y = result_type(2.0) * __aurng() - 1.0;
1954 __r2 = __x * __x + __y * __y;
1955 }
1956 while (__r2 > 1.0 || __r2 == 0.0);
1957
1958 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2);
1959 _M_saved = __x * __mult;
1960 _M_saved_available = true;
1961 __ret = __y * __mult;
1962 }
1963
1964 __ret = __ret * __param.stddev() + __param.mean();
1965 return __ret;
1966 }
1967
1968 template<typename _RealType>
1969 template<typename _ForwardIterator,
1970 typename _UniformRandomNumberGenerator>
1971 void
1972 normal_distribution<_RealType>::
1973 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1974 _UniformRandomNumberGenerator& __urng,
1975 const param_type& __param)
1976 {
1977 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
1978
1979 if (__f == __t)
1980 return;
1981
1982 if (_M_saved_available)
1983 {
1984 _M_saved_available = false;
1985 *__f++ = _M_saved * __param.stddev() + __param.mean();
1986
1987 if (__f == __t)
1988 return;
1989 }
1990
1991 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
1992 __aurng(__urng);
1993
1994 while (__f + 1 < __t)
1995 {
1996 result_type __x, __y, __r2;
1997 do
1998 {
1999 __x = result_type(2.0) * __aurng() - 1.0;
2000 __y = result_type(2.0) * __aurng() - 1.0;
2001 __r2 = __x * __x + __y * __y;
2002 }
2003 while (__r2 > 1.0 || __r2 == 0.0);
2004
2005 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2);
2006 *__f++ = __y * __mult * __param.stddev() + __param.mean();
2007 *__f++ = __x * __mult * __param.stddev() + __param.mean();
2008 }
2009
2010 if (__f != __t)
2011 {
2012 result_type __x, __y, __r2;
2013 do
2014 {
2015 __x = result_type(2.0) * __aurng() - 1.0;
2016 __y = result_type(2.0) * __aurng() - 1.0;
2017 __r2 = __x * __x + __y * __y;
2018 }
2019 while (__r2 > 1.0 || __r2 == 0.0);
2020
2021 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2);
2022 _M_saved = __x * __mult;
2023 _M_saved_available = true;
2024 *__f = __y * __mult * __param.stddev() + __param.mean();
2025 }
2026 }
2027
2028 template<typename _RealType>
2029 bool
2030 operator==(const std::normal_distribution<_RealType>& __d1,
2031 const std::normal_distribution<_RealType>& __d2)
2032 {
2033 if (__d1._M_param == __d2._M_param
2034 && __d1._M_saved_available == __d2._M_saved_available)
2035 {
2036 if (__d1._M_saved_available
2037 && __d1._M_saved == __d2._M_saved)
2038 return true;
2039 else if(!__d1._M_saved_available)
2040 return true;
2041 else
2042 return false;
2043 }
2044 else
2045 return false;
2046 }
2047
2048 template<typename _RealType, typename _CharT, typename _Traits>
2049 std::basic_ostream<_CharT, _Traits>&
2050 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2051 const normal_distribution<_RealType>& __x)
2052 {
2053 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2054 typedef typename __ostream_type::ios_base __ios_base;
2055
2056 const typename __ios_base::fmtflags __flags = __os.flags();
2057 const _CharT __fill = __os.fill();
2058 const std::streamsize __precision = __os.precision();
2059 const _CharT __space = __os.widen(' ');
2060 __os.flags(__ios_base::scientific | __ios_base::left);
2061 __os.fill(__space);
2062 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2063
2064 __os << __x.mean() << __space << __x.stddev()
2065 << __space << __x._M_saved_available;
2066 if (__x._M_saved_available)
2067 __os << __space << __x._M_saved;
2068
2069 __os.flags(__flags);
2070 __os.fill(__fill);
2071 __os.precision(__precision);
2072 return __os;
2073 }
2074
2075 template<typename _RealType, typename _CharT, typename _Traits>
2076 std::basic_istream<_CharT, _Traits>&
2077 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2078 normal_distribution<_RealType>& __x)
2079 {
2080 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2081 typedef typename __istream_type::ios_base __ios_base;
2082
2083 const typename __ios_base::fmtflags __flags = __is.flags();
2084 __is.flags(__ios_base::dec | __ios_base::skipws);
2085
2086 double __mean, __stddev;
2087 __is >> __mean >> __stddev
2088 >> __x._M_saved_available;
2089 if (__x._M_saved_available)
2090 __is >> __x._M_saved;
2091 __x.param(typename normal_distribution<_RealType>::
2092 param_type(__mean, __stddev));
2093
2094 __is.flags(__flags);
2095 return __is;
2096 }
2097
2098
2099 template<typename _RealType>
2100 template<typename _ForwardIterator,
2101 typename _UniformRandomNumberGenerator>
2102 void
2103 lognormal_distribution<_RealType>::
2104 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2105 _UniformRandomNumberGenerator& __urng,
2106 const param_type& __p)
2107 {
2108 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2109 while (__f != __t)
2110 *__f++ = std::exp(__p.s() * _M_nd(__urng) + __p.m());
2111 }
2112
2113 template<typename _RealType, typename _CharT, typename _Traits>
2114 std::basic_ostream<_CharT, _Traits>&
2115 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2116 const lognormal_distribution<_RealType>& __x)
2117 {
2118 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2119 typedef typename __ostream_type::ios_base __ios_base;
2120
2121 const typename __ios_base::fmtflags __flags = __os.flags();
2122 const _CharT __fill = __os.fill();
2123 const std::streamsize __precision = __os.precision();
2124 const _CharT __space = __os.widen(' ');
2125 __os.flags(__ios_base::scientific | __ios_base::left);
2126 __os.fill(__space);
2127 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2128
2129 __os << __x.m() << __space << __x.s()
2130 << __space << __x._M_nd;
2131
2132 __os.flags(__flags);
2133 __os.fill(__fill);
2134 __os.precision(__precision);
2135 return __os;
2136 }
2137
2138 template<typename _RealType, typename _CharT, typename _Traits>
2139 std::basic_istream<_CharT, _Traits>&
2140 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2141 lognormal_distribution<_RealType>& __x)
2142 {
2143 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2144 typedef typename __istream_type::ios_base __ios_base;
2145
2146 const typename __ios_base::fmtflags __flags = __is.flags();
2147 __is.flags(__ios_base::dec | __ios_base::skipws);
2148
2149 _RealType __m, __s;
2150 __is >> __m >> __s >> __x._M_nd;
2151 __x.param(typename lognormal_distribution<_RealType>::
2152 param_type(__m, __s));
2153
2154 __is.flags(__flags);
2155 return __is;
2156 }
2157
2158 template<typename _RealType>
2159 template<typename _ForwardIterator,
2160 typename _UniformRandomNumberGenerator>
2161 void
2162 std::chi_squared_distribution<_RealType>::
2163 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2164 _UniformRandomNumberGenerator& __urng)
2165 {
2166 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2167 while (__f != __t)
2168 *__f++ = 2 * _M_gd(__urng);
2169 }
2170
2171 template<typename _RealType>
2172 template<typename _ForwardIterator,
2173 typename _UniformRandomNumberGenerator>
2174 void
2175 std::chi_squared_distribution<_RealType>::
2176 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2177 _UniformRandomNumberGenerator& __urng,
2178 const typename
2179 std::gamma_distribution<result_type>::param_type& __p)
2180 {
2181 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2182 while (__f != __t)
2183 *__f++ = 2 * _M_gd(__urng, __p);
2184 }
2185
2186 template<typename _RealType, typename _CharT, typename _Traits>
2187 std::basic_ostream<_CharT, _Traits>&
2188 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2189 const chi_squared_distribution<_RealType>& __x)
2190 {
2191 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2192 typedef typename __ostream_type::ios_base __ios_base;
2193
2194 const typename __ios_base::fmtflags __flags = __os.flags();
2195 const _CharT __fill = __os.fill();
2196 const std::streamsize __precision = __os.precision();
2197 const _CharT __space = __os.widen(' ');
2198 __os.flags(__ios_base::scientific | __ios_base::left);
2199 __os.fill(__space);
2200 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2201
2202 __os << __x.n() << __space << __x._M_gd;
2203
2204 __os.flags(__flags);
2205 __os.fill(__fill);
2206 __os.precision(__precision);
2207 return __os;
2208 }
2209
2210 template<typename _RealType, typename _CharT, typename _Traits>
2211 std::basic_istream<_CharT, _Traits>&
2212 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2213 chi_squared_distribution<_RealType>& __x)
2214 {
2215 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2216 typedef typename __istream_type::ios_base __ios_base;
2217
2218 const typename __ios_base::fmtflags __flags = __is.flags();
2219 __is.flags(__ios_base::dec | __ios_base::skipws);
2220
2221 _RealType __n;
2222 __is >> __n >> __x._M_gd;
2223 __x.param(typename chi_squared_distribution<_RealType>::
2224 param_type(__n));
2225
2226 __is.flags(__flags);
2227 return __is;
2228 }
2229
2230
2231 template<typename _RealType>
2232 template<typename _UniformRandomNumberGenerator>
2233 typename cauchy_distribution<_RealType>::result_type
2234 cauchy_distribution<_RealType>::
2235 operator()(_UniformRandomNumberGenerator& __urng,
2236 const param_type& __p)
2237 {
2238 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2239 __aurng(__urng);
2240 _RealType __u;
2241 do
2242 __u = __aurng();
2243 while (__u == 0.5);
2244
2245 const _RealType __pi = 3.1415926535897932384626433832795029L;
2246 return __p.a() + __p.b() * std::tan(__pi * __u);
2247 }
2248
2249 template<typename _RealType>
2250 template<typename _ForwardIterator,
2251 typename _UniformRandomNumberGenerator>
2252 void
2253 cauchy_distribution<_RealType>::
2254 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2255 _UniformRandomNumberGenerator& __urng,
2256 const param_type& __p)
2257 {
2258 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2259 const _RealType __pi = 3.1415926535897932384626433832795029L;
2260 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2261 __aurng(__urng);
2262 while (__f != __t)
2263 {
2264 _RealType __u;
2265 do
2266 __u = __aurng();
2267 while (__u == 0.5);
2268
2269 *__f++ = __p.a() + __p.b() * std::tan(__pi * __u);
2270 }
2271 }
2272
2273 template<typename _RealType, typename _CharT, typename _Traits>
2274 std::basic_ostream<_CharT, _Traits>&
2275 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2276 const cauchy_distribution<_RealType>& __x)
2277 {
2278 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2279 typedef typename __ostream_type::ios_base __ios_base;
2280
2281 const typename __ios_base::fmtflags __flags = __os.flags();
2282 const _CharT __fill = __os.fill();
2283 const std::streamsize __precision = __os.precision();
2284 const _CharT __space = __os.widen(' ');
2285 __os.flags(__ios_base::scientific | __ios_base::left);
2286 __os.fill(__space);
2287 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2288
2289 __os << __x.a() << __space << __x.b();
2290
2291 __os.flags(__flags);
2292 __os.fill(__fill);
2293 __os.precision(__precision);
2294 return __os;
2295 }
2296
2297 template<typename _RealType, typename _CharT, typename _Traits>
2298 std::basic_istream<_CharT, _Traits>&
2299 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2300 cauchy_distribution<_RealType>& __x)
2301 {
2302 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2303 typedef typename __istream_type::ios_base __ios_base;
2304
2305 const typename __ios_base::fmtflags __flags = __is.flags();
2306 __is.flags(__ios_base::dec | __ios_base::skipws);
2307
2308 _RealType __a, __b;
2309 __is >> __a >> __b;
2310 __x.param(typename cauchy_distribution<_RealType>::
2311 param_type(__a, __b));
2312
2313 __is.flags(__flags);
2314 return __is;
2315 }
2316
2317
2318 template<typename _RealType>
2319 template<typename _ForwardIterator,
2320 typename _UniformRandomNumberGenerator>
2321 void
2322 std::fisher_f_distribution<_RealType>::
2323 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2324 _UniformRandomNumberGenerator& __urng)
2325 {
2326 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2327 while (__f != __t)
2328 *__f++ = ((_M_gd_x(__urng) * n()) / (_M_gd_y(__urng) * m()));
2329 }
2330
2331 template<typename _RealType>
2332 template<typename _ForwardIterator,
2333 typename _UniformRandomNumberGenerator>
2334 void
2335 std::fisher_f_distribution<_RealType>::
2336 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2337 _UniformRandomNumberGenerator& __urng,
2338 const param_type& __p)
2339 {
2340 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2341 typedef typename std::gamma_distribution<result_type>::param_type
2342 param_type;
2343 param_type __p1(__p.m() / 2);
2344 param_type __p2(__p.n() / 2);
2345 while (__f != __t)
2346 *__f++ = ((_M_gd_x(__urng, __p1) * n())
2347 / (_M_gd_y(__urng, __p2) * m()));
2348 }
2349
2350 template<typename _RealType, typename _CharT, typename _Traits>
2351 std::basic_ostream<_CharT, _Traits>&
2352 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2353 const fisher_f_distribution<_RealType>& __x)
2354 {
2355 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2356 typedef typename __ostream_type::ios_base __ios_base;
2357
2358 const typename __ios_base::fmtflags __flags = __os.flags();
2359 const _CharT __fill = __os.fill();
2360 const std::streamsize __precision = __os.precision();
2361 const _CharT __space = __os.widen(' ');
2362 __os.flags(__ios_base::scientific | __ios_base::left);
2363 __os.fill(__space);
2364 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2365
2366 __os << __x.m() << __space << __x.n()
2367 << __space << __x._M_gd_x << __space << __x._M_gd_y;
2368
2369 __os.flags(__flags);
2370 __os.fill(__fill);
2371 __os.precision(__precision);
2372 return __os;
2373 }
2374
2375 template<typename _RealType, typename _CharT, typename _Traits>
2376 std::basic_istream<_CharT, _Traits>&
2377 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2378 fisher_f_distribution<_RealType>& __x)
2379 {
2380 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2381 typedef typename __istream_type::ios_base __ios_base;
2382
2383 const typename __ios_base::fmtflags __flags = __is.flags();
2384 __is.flags(__ios_base::dec | __ios_base::skipws);
2385
2386 _RealType __m, __n;
2387 __is >> __m >> __n >> __x._M_gd_x >> __x._M_gd_y;
2388 __x.param(typename fisher_f_distribution<_RealType>::
2389 param_type(__m, __n));
2390
2391 __is.flags(__flags);
2392 return __is;
2393 }
2394
2395
2396 template<typename _RealType>
2397 template<typename _ForwardIterator,
2398 typename _UniformRandomNumberGenerator>
2399 void
2400 std::student_t_distribution<_RealType>::
2401 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2402 _UniformRandomNumberGenerator& __urng)
2403 {
2404 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2405 while (__f != __t)
2406 *__f++ = _M_nd(__urng) * std::sqrt(n() / _M_gd(__urng));
2407 }
2408
2409 template<typename _RealType>
2410 template<typename _ForwardIterator,
2411 typename _UniformRandomNumberGenerator>
2412 void
2413 std::student_t_distribution<_RealType>::
2414 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2415 _UniformRandomNumberGenerator& __urng,
2416 const param_type& __p)
2417 {
2418 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2419 typename std::gamma_distribution<result_type>::param_type
2420 __p2(__p.n() / 2, 2);
2421 while (__f != __t)
2422 *__f++ = _M_nd(__urng) * std::sqrt(__p.n() / _M_gd(__urng, __p2));
2423 }
2424
2425 template<typename _RealType, typename _CharT, typename _Traits>
2426 std::basic_ostream<_CharT, _Traits>&
2427 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2428 const student_t_distribution<_RealType>& __x)
2429 {
2430 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2431 typedef typename __ostream_type::ios_base __ios_base;
2432
2433 const typename __ios_base::fmtflags __flags = __os.flags();
2434 const _CharT __fill = __os.fill();
2435 const std::streamsize __precision = __os.precision();
2436 const _CharT __space = __os.widen(' ');
2437 __os.flags(__ios_base::scientific | __ios_base::left);
2438 __os.fill(__space);
2439 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2440
2441 __os << __x.n() << __space << __x._M_nd << __space << __x._M_gd;
2442
2443 __os.flags(__flags);
2444 __os.fill(__fill);
2445 __os.precision(__precision);
2446 return __os;
2447 }
2448
2449 template<typename _RealType, typename _CharT, typename _Traits>
2450 std::basic_istream<_CharT, _Traits>&
2451 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2452 student_t_distribution<_RealType>& __x)
2453 {
2454 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2455 typedef typename __istream_type::ios_base __ios_base;
2456
2457 const typename __ios_base::fmtflags __flags = __is.flags();
2458 __is.flags(__ios_base::dec | __ios_base::skipws);
2459
2460 _RealType __n;
2461 __is >> __n >> __x._M_nd >> __x._M_gd;
2462 __x.param(typename student_t_distribution<_RealType>::param_type(__n));
2463
2464 __is.flags(__flags);
2465 return __is;
2466 }
2467
2468
2469 template<typename _RealType>
2470 void
2471 gamma_distribution<_RealType>::param_type::
2472 _M_initialize()
2473 {
2474 _M_malpha = _M_alpha < 1.0 ? _M_alpha + _RealType(1.0) : _M_alpha;
2475
2476 const _RealType __a1 = _M_malpha - _RealType(1.0) / _RealType(3.0);
2477 _M_a2 = _RealType(1.0) / std::sqrt(_RealType(9.0) * __a1);
2478 }
2479
2480 /**
2481 * Marsaglia, G. and Tsang, W. W.
2482 * "A Simple Method for Generating Gamma Variables"
2483 * ACM Transactions on Mathematical Software, 26, 3, 363-372, 2000.
2484 */
2485 template<typename _RealType>
2486 template<typename _UniformRandomNumberGenerator>
2487 typename gamma_distribution<_RealType>::result_type
2488 gamma_distribution<_RealType>::
2489 operator()(_UniformRandomNumberGenerator& __urng,
2490 const param_type& __param)
2491 {
2492 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2493 __aurng(__urng);
2494
2495 result_type __u, __v, __n;
2496 const result_type __a1 = (__param._M_malpha
2497 - _RealType(1.0) / _RealType(3.0));
2498
2499 do
2500 {
2501 do
2502 {
2503 __n = _M_nd(__urng);
2504 __v = result_type(1.0) + __param._M_a2 * __n;
2505 }
2506 while (__v <= 0.0);
2507
2508 __v = __v * __v * __v;
2509 __u = __aurng();
2510 }
2511 while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n
2512 && (std::log(__u) > (0.5 * __n * __n + __a1
2513 * (1.0 - __v + std::log(__v)))));
2514
2515 if (__param.alpha() == __param._M_malpha)
2516 return __a1 * __v * __param.beta();
2517 else
2518 {
2519 do
2520 __u = __aurng();
2521 while (__u == 0.0);
2522
2523 return (std::pow(__u, result_type(1.0) / __param.alpha())
2524 * __a1 * __v * __param.beta());
2525 }
2526 }
2527
2528 template<typename _RealType>
2529 template<typename _ForwardIterator,
2530 typename _UniformRandomNumberGenerator>
2531 void
2532 gamma_distribution<_RealType>::
2533 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2534 _UniformRandomNumberGenerator& __urng,
2535 const param_type& __param)
2536 {
2537 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2538 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2539 __aurng(__urng);
2540
2541 result_type __u, __v, __n;
2542 const result_type __a1 = (__param._M_malpha
2543 - _RealType(1.0) / _RealType(3.0));
2544
2545 if (__param.alpha() == __param._M_malpha)
2546 while (__f != __t)
2547 {
2548 do
2549 {
2550 do
2551 {
2552 __n = _M_nd(__urng);
2553 __v = result_type(1.0) + __param._M_a2 * __n;
2554 }
2555 while (__v <= 0.0);
2556
2557 __v = __v * __v * __v;
2558 __u = __aurng();
2559 }
2560 while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n
2561 && (std::log(__u) > (0.5 * __n * __n + __a1
2562 * (1.0 - __v + std::log(__v)))));
2563
2564 *__f++ = __a1 * __v * __param.beta();
2565 }
2566 else
2567 while (__f != __t)
2568 {
2569 do
2570 {
2571 do
2572 {
2573 __n = _M_nd(__urng);
2574 __v = result_type(1.0) + __param._M_a2 * __n;
2575 }
2576 while (__v <= 0.0);
2577
2578 __v = __v * __v * __v;
2579 __u = __aurng();
2580 }
2581 while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n
2582 && (std::log(__u) > (0.5 * __n * __n + __a1
2583 * (1.0 - __v + std::log(__v)))));
2584
2585 do
2586 __u = __aurng();
2587 while (__u == 0.0);
2588
2589 *__f++ = (std::pow(__u, result_type(1.0) / __param.alpha())
2590 * __a1 * __v * __param.beta());
2591 }
2592 }
2593
2594 template<typename _RealType, typename _CharT, typename _Traits>
2595 std::basic_ostream<_CharT, _Traits>&
2596 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2597 const gamma_distribution<_RealType>& __x)
2598 {
2599 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2600 typedef typename __ostream_type::ios_base __ios_base;
2601
2602 const typename __ios_base::fmtflags __flags = __os.flags();
2603 const _CharT __fill = __os.fill();
2604 const std::streamsize __precision = __os.precision();
2605 const _CharT __space = __os.widen(' ');
2606 __os.flags(__ios_base::scientific | __ios_base::left);
2607 __os.fill(__space);
2608 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2609
2610 __os << __x.alpha() << __space << __x.beta()
2611 << __space << __x._M_nd;
2612
2613 __os.flags(__flags);
2614 __os.fill(__fill);
2615 __os.precision(__precision);
2616 return __os;
2617 }
2618
2619 template<typename _RealType, typename _CharT, typename _Traits>
2620 std::basic_istream<_CharT, _Traits>&
2621 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2622 gamma_distribution<_RealType>& __x)
2623 {
2624 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2625 typedef typename __istream_type::ios_base __ios_base;
2626
2627 const typename __ios_base::fmtflags __flags = __is.flags();
2628 __is.flags(__ios_base::dec | __ios_base::skipws);
2629
2630 _RealType __alpha_val, __beta_val;
2631 __is >> __alpha_val >> __beta_val >> __x._M_nd;
2632 __x.param(typename gamma_distribution<_RealType>::
2633 param_type(__alpha_val, __beta_val));
2634
2635 __is.flags(__flags);
2636 return __is;
2637 }
2638
2639
2640 template<typename _RealType>
2641 template<typename _UniformRandomNumberGenerator>
2642 typename weibull_distribution<_RealType>::result_type
2643 weibull_distribution<_RealType>::
2644 operator()(_UniformRandomNumberGenerator& __urng,
2645 const param_type& __p)
2646 {
2647 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2648 __aurng(__urng);
2649 return __p.b() * std::pow(-std::log(result_type(1) - __aurng()),
2650 result_type(1) / __p.a());
2651 }
2652
2653 template<typename _RealType>
2654 template<typename _ForwardIterator,
2655 typename _UniformRandomNumberGenerator>
2656 void
2657 weibull_distribution<_RealType>::
2658 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2659 _UniformRandomNumberGenerator& __urng,
2660 const param_type& __p)
2661 {
2662 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2663 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2664 __aurng(__urng);
2665 auto __inv_a = result_type(1) / __p.a();
2666
2667 while (__f != __t)
2668 *__f++ = __p.b() * std::pow(-std::log(result_type(1) - __aurng()),
2669 __inv_a);
2670 }
2671
2672 template<typename _RealType, typename _CharT, typename _Traits>
2673 std::basic_ostream<_CharT, _Traits>&
2674 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2675 const weibull_distribution<_RealType>& __x)
2676 {
2677 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2678 typedef typename __ostream_type::ios_base __ios_base;
2679
2680 const typename __ios_base::fmtflags __flags = __os.flags();
2681 const _CharT __fill = __os.fill();
2682 const std::streamsize __precision = __os.precision();
2683 const _CharT __space = __os.widen(' ');
2684 __os.flags(__ios_base::scientific | __ios_base::left);
2685 __os.fill(__space);
2686 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2687
2688 __os << __x.a() << __space << __x.b();
2689
2690 __os.flags(__flags);
2691 __os.fill(__fill);
2692 __os.precision(__precision);
2693 return __os;
2694 }
2695
2696 template<typename _RealType, typename _CharT, typename _Traits>
2697 std::basic_istream<_CharT, _Traits>&
2698 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2699 weibull_distribution<_RealType>& __x)
2700 {
2701 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2702 typedef typename __istream_type::ios_base __ios_base;
2703
2704 const typename __ios_base::fmtflags __flags = __is.flags();
2705 __is.flags(__ios_base::dec | __ios_base::skipws);
2706
2707 _RealType __a, __b;
2708 __is >> __a >> __b;
2709 __x.param(typename weibull_distribution<_RealType>::
2710 param_type(__a, __b));
2711
2712 __is.flags(__flags);
2713 return __is;
2714 }
2715
2716
2717 template<typename _RealType>
2718 template<typename _UniformRandomNumberGenerator>
2719 typename extreme_value_distribution<_RealType>::result_type
2720 extreme_value_distribution<_RealType>::
2721 operator()(_UniformRandomNumberGenerator& __urng,
2722 const param_type& __p)
2723 {
2724 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2725 __aurng(__urng);
2726 return __p.a() - __p.b() * std::log(-std::log(result_type(1)
2727 - __aurng()));
2728 }
2729
2730 template<typename _RealType>
2731 template<typename _ForwardIterator,
2732 typename _UniformRandomNumberGenerator>
2733 void
2734 extreme_value_distribution<_RealType>::
2735 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2736 _UniformRandomNumberGenerator& __urng,
2737 const param_type& __p)
2738 {
2739 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2740 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2741 __aurng(__urng);
2742
2743 while (__f != __t)
2744 *__f++ = __p.a() - __p.b() * std::log(-std::log(result_type(1)
2745 - __aurng()));
2746 }
2747
2748 template<typename _RealType, typename _CharT, typename _Traits>
2749 std::basic_ostream<_CharT, _Traits>&
2750 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2751 const extreme_value_distribution<_RealType>& __x)
2752 {
2753 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2754 typedef typename __ostream_type::ios_base __ios_base;
2755
2756 const typename __ios_base::fmtflags __flags = __os.flags();
2757 const _CharT __fill = __os.fill();
2758 const std::streamsize __precision = __os.precision();
2759 const _CharT __space = __os.widen(' ');
2760 __os.flags(__ios_base::scientific | __ios_base::left);
2761 __os.fill(__space);
2762 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2763
2764 __os << __x.a() << __space << __x.b();
2765
2766 __os.flags(__flags);
2767 __os.fill(__fill);
2768 __os.precision(__precision);
2769 return __os;
2770 }
2771
2772 template<typename _RealType, typename _CharT, typename _Traits>
2773 std::basic_istream<_CharT, _Traits>&
2774 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2775 extreme_value_distribution<_RealType>& __x)
2776 {
2777 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2778 typedef typename __istream_type::ios_base __ios_base;
2779
2780 const typename __ios_base::fmtflags __flags = __is.flags();
2781 __is.flags(__ios_base::dec | __ios_base::skipws);
2782
2783 _RealType __a, __b;
2784 __is >> __a >> __b;
2785 __x.param(typename extreme_value_distribution<_RealType>::
2786 param_type(__a, __b));
2787
2788 __is.flags(__flags);
2789 return __is;
2790 }
2791
2792
2793 template<typename _IntType>
2794 void
2795 discrete_distribution<_IntType>::param_type::
2796 _M_initialize()
2797 {
2798 if (_M_prob.size() < 2)
2799 {
2800 _M_prob.clear();
2801 return;
2802 }
2803
2804 const double __sum = std::accumulate(_M_prob.begin(),
2805 _M_prob.end(), 0.0);
2806 // Now normalize the probabilites.
2807 __detail::__normalize(_M_prob.begin(), _M_prob.end(), _M_prob.begin(),
2808 __sum);
2809 // Accumulate partial sums.
2810 _M_cp.reserve(_M_prob.size());
2811 std::partial_sum(_M_prob.begin(), _M_prob.end(),
2812 std::back_inserter(_M_cp));
2813 // Make sure the last cumulative probability is one.
2814 _M_cp[_M_cp.size() - 1] = 1.0;
2815 }
2816
2817 template<typename _IntType>
2818 template<typename _Func>
2819 discrete_distribution<_IntType>::param_type::
2820 param_type(size_t __nw, double __xmin, double __xmax, _Func __fw)
2821 : _M_prob(), _M_cp()
2822 {
2823 const size_t __n = __nw == 0 ? 1 : __nw;
2824 const double __delta = (__xmax - __xmin) / __n;
2825
2826 _M_prob.reserve(__n);
2827 for (size_t __k = 0; __k < __nw; ++__k)
2828 _M_prob.push_back(__fw(__xmin + __k * __delta + 0.5 * __delta));
2829
2830 _M_initialize();
2831 }
2832
2833 template<typename _IntType>
2834 template<typename _UniformRandomNumberGenerator>
2835 typename discrete_distribution<_IntType>::result_type
2836 discrete_distribution<_IntType>::
2837 operator()(_UniformRandomNumberGenerator& __urng,
2838 const param_type& __param)
2839 {
2840 if (__param._M_cp.empty())
2841 return result_type(0);
2842
2843 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
2844 __aurng(__urng);
2845
2846 const double __p = __aurng();
2847 auto __pos = std::lower_bound(__param._M_cp.begin(),
2848 __param._M_cp.end(), __p);
2849
2850 return __pos - __param._M_cp.begin();
2851 }
2852
2853 template<typename _IntType>
2854 template<typename _ForwardIterator,
2855 typename _UniformRandomNumberGenerator>
2856 void
2857 discrete_distribution<_IntType>::
2858 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2859 _UniformRandomNumberGenerator& __urng,
2860 const param_type& __param)
2861 {
2862 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2863
2864 if (__param._M_cp.empty())
2865 {
2866 while (__f != __t)
2867 *__f++ = result_type(0);
2868 return;
2869 }
2870
2871 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
2872 __aurng(__urng);
2873
2874 while (__f != __t)
2875 {
2876 const double __p = __aurng();
2877 auto __pos = std::lower_bound(__param._M_cp.begin(),
2878 __param._M_cp.end(), __p);
2879
2880 *__f++ = __pos - __param._M_cp.begin();
2881 }
2882 }
2883
2884 template<typename _IntType, typename _CharT, typename _Traits>
2885 std::basic_ostream<_CharT, _Traits>&
2886 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2887 const discrete_distribution<_IntType>& __x)
2888 {
2889 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2890 typedef typename __ostream_type::ios_base __ios_base;
2891
2892 const typename __ios_base::fmtflags __flags = __os.flags();
2893 const _CharT __fill = __os.fill();
2894 const std::streamsize __precision = __os.precision();
2895 const _CharT __space = __os.widen(' ');
2896 __os.flags(__ios_base::scientific | __ios_base::left);
2897 __os.fill(__space);
2898 __os.precision(std::numeric_limits<double>::max_digits10);
2899
2900 std::vector<double> __prob = __x.probabilities();
2901 __os << __prob.size();
2902 for (auto __dit = __prob.begin(); __dit != __prob.end(); ++__dit)
2903 __os << __space << *__dit;
2904
2905 __os.flags(__flags);
2906 __os.fill(__fill);
2907 __os.precision(__precision);
2908 return __os;
2909 }
2910
2911 template<typename _IntType, typename _CharT, typename _Traits>
2912 std::basic_istream<_CharT, _Traits>&
2913 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2914 discrete_distribution<_IntType>& __x)
2915 {
2916 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2917 typedef typename __istream_type::ios_base __ios_base;
2918
2919 const typename __ios_base::fmtflags __flags = __is.flags();
2920 __is.flags(__ios_base::dec | __ios_base::skipws);
2921
2922 size_t __n;
2923 __is >> __n;
2924
2925 std::vector<double> __prob_vec;
2926 __prob_vec.reserve(__n);
2927 for (; __n != 0; --__n)
2928 {
2929 double __prob;
2930 __is >> __prob;
2931 __prob_vec.push_back(__prob);
2932 }
2933
2934 __x.param(typename discrete_distribution<_IntType>::
2935 param_type(__prob_vec.begin(), __prob_vec.end()));
2936
2937 __is.flags(__flags);
2938 return __is;
2939 }
2940
2941
2942 template<typename _RealType>
2943 void
2944 piecewise_constant_distribution<_RealType>::param_type::
2945 _M_initialize()
2946 {
2947 if (_M_int.size() < 2
2948 || (_M_int.size() == 2
2949 && _M_int[0] == _RealType(0)
2950 && _M_int[1] == _RealType(1)))
2951 {
2952 _M_int.clear();
2953 _M_den.clear();
2954 return;
2955 }
2956
2957 const double __sum = std::accumulate(_M_den.begin(),
2958 _M_den.end(), 0.0);
2959
2960 __detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(),
2961 __sum);
2962
2963 _M_cp.reserve(_M_den.size());
2964 std::partial_sum(_M_den.begin(), _M_den.end(),
2965 std::back_inserter(_M_cp));
2966
2967 // Make sure the last cumulative probability is one.
2968 _M_cp[_M_cp.size() - 1] = 1.0;
2969
2970 for (size_t __k = 0; __k < _M_den.size(); ++__k)
2971 _M_den[__k] /= _M_int[__k + 1] - _M_int[__k];
2972 }
2973
2974 template<typename _RealType>
2975 template<typename _InputIteratorB, typename _InputIteratorW>
2976 piecewise_constant_distribution<_RealType>::param_type::
2977 param_type(_InputIteratorB __bbegin,
2978 _InputIteratorB __bend,
2979 _InputIteratorW __wbegin)
2980 : _M_int(), _M_den(), _M_cp()
2981 {
2982 if (__bbegin != __bend)
2983 {
2984 for (;;)
2985 {
2986 _M_int.push_back(*__bbegin);
2987 ++__bbegin;
2988 if (__bbegin == __bend)
2989 break;
2990
2991 _M_den.push_back(*__wbegin);
2992 ++__wbegin;
2993 }
2994 }
2995
2996 _M_initialize();
2997 }
2998
2999 template<typename _RealType>
3000 template<typename _Func>
3001 piecewise_constant_distribution<_RealType>::param_type::
3002 param_type(initializer_list<_RealType> __bl, _Func __fw)
3003 : _M_int(), _M_den(), _M_cp()
3004 {
3005 _M_int.reserve(__bl.size());
3006 for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter)
3007 _M_int.push_back(*__biter);
3008
3009 _M_den.reserve(_M_int.size() - 1);
3010 for (size_t __k = 0; __k < _M_int.size() - 1; ++__k)
3011 _M_den.push_back(__fw(0.5 * (_M_int[__k + 1] + _M_int[__k])));
3012
3013 _M_initialize();
3014 }
3015
3016 template<typename _RealType>
3017 template<typename _Func>
3018 piecewise_constant_distribution<_RealType>::param_type::
3019 param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw)
3020 : _M_int(), _M_den(), _M_cp()
3021 {
3022 const size_t __n = __nw == 0 ? 1 : __nw;
3023 const _RealType __delta = (__xmax - __xmin) / __n;
3024
3025 _M_int.reserve(__n + 1);
3026 for (size_t __k = 0; __k <= __nw; ++__k)
3027 _M_int.push_back(__xmin + __k * __delta);
3028
3029 _M_den.reserve(__n);
3030 for (size_t __k = 0; __k < __nw; ++__k)
3031 _M_den.push_back(__fw(_M_int[__k] + 0.5 * __delta));
3032
3033 _M_initialize();
3034 }
3035
3036 template<typename _RealType>
3037 template<typename _UniformRandomNumberGenerator>
3038 typename piecewise_constant_distribution<_RealType>::result_type
3039 piecewise_constant_distribution<_RealType>::
3040 operator()(_UniformRandomNumberGenerator& __urng,
3041 const param_type& __param)
3042 {
3043 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
3044 __aurng(__urng);
3045
3046 const double __p = __aurng();
3047 if (__param._M_cp.empty())
3048 return __p;
3049
3050 auto __pos = std::lower_bound(__param._M_cp.begin(),
3051 __param._M_cp.end(), __p);
3052 const size_t __i = __pos - __param._M_cp.begin();
3053
3054 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0;
3055
3056 return __param._M_int[__i] + (__p - __pref) / __param._M_den[__i];
3057 }
3058
3059 template<typename _RealType>
3060 template<typename _ForwardIterator,
3061 typename _UniformRandomNumberGenerator>
3062 void
3063 piecewise_constant_distribution<_RealType>::
3064 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
3065 _UniformRandomNumberGenerator& __urng,
3066 const param_type& __param)
3067 {
3068 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
3069 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
3070 __aurng(__urng);
3071
3072 if (__param._M_cp.empty())
3073 {
3074 while (__f != __t)
3075 *__f++ = __aurng();
3076 return;
3077 }
3078
3079 while (__f != __t)
3080 {
3081 const double __p = __aurng();
3082
3083 auto __pos = std::lower_bound(__param._M_cp.begin(),
3084 __param._M_cp.end(), __p);
3085 const size_t __i = __pos - __param._M_cp.begin();
3086
3087 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0;
3088
3089 *__f++ = (__param._M_int[__i]
3090 + (__p - __pref) / __param._M_den[__i]);
3091 }
3092 }
3093
3094 template<typename _RealType, typename _CharT, typename _Traits>
3095 std::basic_ostream<_CharT, _Traits>&
3096 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
3097 const piecewise_constant_distribution<_RealType>& __x)
3098 {
3099 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
3100 typedef typename __ostream_type::ios_base __ios_base;
3101
3102 const typename __ios_base::fmtflags __flags = __os.flags();
3103 const _CharT __fill = __os.fill();
3104 const std::streamsize __precision = __os.precision();
3105 const _CharT __space = __os.widen(' ');
3106 __os.flags(__ios_base::scientific | __ios_base::left);
3107 __os.fill(__space);
3108 __os.precision(std::numeric_limits<_RealType>::max_digits10);
3109
3110 std::vector<_RealType> __int = __x.intervals();
3111 __os << __int.size() - 1;
3112
3113 for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit)
3114 __os << __space << *__xit;
3115
3116 std::vector<double> __den = __x.densities();
3117 for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit)
3118 __os << __space << *__dit;
3119
3120 __os.flags(__flags);
3121 __os.fill(__fill);
3122 __os.precision(__precision);
3123 return __os;
3124 }
3125
3126 template<typename _RealType, typename _CharT, typename _Traits>
3127 std::basic_istream<_CharT, _Traits>&
3128 operator>>(std::basic_istream<_CharT, _Traits>& __is,
3129 piecewise_constant_distribution<_RealType>& __x)
3130 {
3131 typedef std::basic_istream<_CharT, _Traits> __istream_type;
3132 typedef typename __istream_type::ios_base __ios_base;
3133
3134 const typename __ios_base::fmtflags __flags = __is.flags();
3135 __is.flags(__ios_base::dec | __ios_base::skipws);
3136
3137 size_t __n;
3138 __is >> __n;
3139
3140 std::vector<_RealType> __int_vec;
3141 __int_vec.reserve(__n + 1);
3142 for (size_t __i = 0; __i <= __n; ++__i)
3143 {
3144 _RealType __int;
3145 __is >> __int;
3146 __int_vec.push_back(__int);
3147 }
3148
3149 std::vector<double> __den_vec;
3150 __den_vec.reserve(__n);
3151 for (size_t __i = 0; __i < __n; ++__i)
3152 {
3153 double __den;
3154 __is >> __den;
3155 __den_vec.push_back(__den);
3156 }
3157
3158 __x.param(typename piecewise_constant_distribution<_RealType>::
3159 param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin()));
3160
3161 __is.flags(__flags);
3162 return __is;
3163 }
3164
3165
3166 template<typename _RealType>
3167 void
3168 piecewise_linear_distribution<_RealType>::param_type::
3169 _M_initialize()
3170 {
3171 if (_M_int.size() < 2
3172 || (_M_int.size() == 2
3173 && _M_int[0] == _RealType(0)
3174 && _M_int[1] == _RealType(1)
3175 && _M_den[0] == _M_den[1]))
3176 {
3177 _M_int.clear();
3178 _M_den.clear();
3179 return;
3180 }
3181
3182 double __sum = 0.0;
3183 _M_cp.reserve(_M_int.size() - 1);
3184 _M_m.reserve(_M_int.size() - 1);
3185 for (size_t __k = 0; __k < _M_int.size() - 1; ++__k)
3186 {
3187 const _RealType __delta = _M_int[__k + 1] - _M_int[__k];
3188 __sum += 0.5 * (_M_den[__k + 1] + _M_den[__k]) * __delta;
3189 _M_cp.push_back(__sum);
3190 _M_m.push_back((_M_den[__k + 1] - _M_den[__k]) / __delta);
3191 }
3192
3193 // Now normalize the densities...
3194 __detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(),
3195 __sum);
3196 // ... and partial sums...
3197 __detail::__normalize(_M_cp.begin(), _M_cp.end(), _M_cp.begin(), __sum);
3198 // ... and slopes.
3199 __detail::__normalize(_M_m.begin(), _M_m.end(), _M_m.begin(), __sum);
3200
3201 // Make sure the last cumulative probablility is one.
3202 _M_cp[_M_cp.size() - 1] = 1.0;
3203 }
3204
3205 template<typename _RealType>
3206 template<typename _InputIteratorB, typename _InputIteratorW>
3207 piecewise_linear_distribution<_RealType>::param_type::
3208 param_type(_InputIteratorB __bbegin,
3209 _InputIteratorB __bend,
3210 _InputIteratorW __wbegin)
3211 : _M_int(), _M_den(), _M_cp(), _M_m()
3212 {
3213 for (; __bbegin != __bend; ++__bbegin, ++__wbegin)
3214 {
3215 _M_int.push_back(*__bbegin);
3216 _M_den.push_back(*__wbegin);
3217 }
3218
3219 _M_initialize();
3220 }
3221
3222 template<typename _RealType>
3223 template<typename _Func>
3224 piecewise_linear_distribution<_RealType>::param_type::
3225 param_type(initializer_list<_RealType> __bl, _Func __fw)
3226 : _M_int(), _M_den(), _M_cp(), _M_m()
3227 {
3228 _M_int.reserve(__bl.size());
3229 _M_den.reserve(__bl.size());
3230 for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter)
3231 {
3232 _M_int.push_back(*__biter);
3233 _M_den.push_back(__fw(*__biter));
3234 }
3235
3236 _M_initialize();
3237 }
3238
3239 template<typename _RealType>
3240 template<typename _Func>
3241 piecewise_linear_distribution<_RealType>::param_type::
3242 param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw)
3243 : _M_int(), _M_den(), _M_cp(), _M_m()
3244 {
3245 const size_t __n = __nw == 0 ? 1 : __nw;
3246 const _RealType __delta = (__xmax - __xmin) / __n;
3247
3248 _M_int.reserve(__n + 1);
3249 _M_den.reserve(__n + 1);
3250 for (size_t __k = 0; __k <= __nw; ++__k)
3251 {
3252 _M_int.push_back(__xmin + __k * __delta);
3253 _M_den.push_back(__fw(_M_int[__k] + __delta));
3254 }
3255
3256 _M_initialize();
3257 }
3258
3259 template<typename _RealType>
3260 template<typename _UniformRandomNumberGenerator>
3261 typename piecewise_linear_distribution<_RealType>::result_type
3262 piecewise_linear_distribution<_RealType>::
3263 operator()(_UniformRandomNumberGenerator& __urng,
3264 const param_type& __param)
3265 {
3266 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
3267 __aurng(__urng);
3268
3269 const double __p = __aurng();
3270 if (__param._M_cp.empty())
3271 return __p;
3272
3273 auto __pos = std::lower_bound(__param._M_cp.begin(),
3274 __param._M_cp.end(), __p);
3275 const size_t __i = __pos - __param._M_cp.begin();
3276
3277 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0;
3278
3279 const double __a = 0.5 * __param._M_m[__i];
3280 const double __b = __param._M_den[__i];
3281 const double __cm = __p - __pref;
3282
3283 _RealType __x = __param._M_int[__i];
3284 if (__a == 0)
3285 __x += __cm / __b;
3286 else
3287 {
3288 const double __d = __b * __b + 4.0 * __a * __cm;
3289 __x += 0.5 * (std::sqrt(__d) - __b) / __a;
3290 }
3291
3292 return __x;
3293 }
3294
3295 template<typename _RealType>
3296 template<typename _ForwardIterator,
3297 typename _UniformRandomNumberGenerator>
3298 void
3299 piecewise_linear_distribution<_RealType>::
3300 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
3301 _UniformRandomNumberGenerator& __urng,
3302 const param_type& __param)
3303 {
3304 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
3305 // We could duplicate everything from operator()...
3306 while (__f != __t)
3307 *__f++ = this->operator()(__urng, __param);
3308 }
3309
3310 template<typename _RealType, typename _CharT, typename _Traits>
3311 std::basic_ostream<_CharT, _Traits>&
3312 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
3313 const piecewise_linear_distribution<_RealType>& __x)
3314 {
3315 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
3316 typedef typename __ostream_type::ios_base __ios_base;
3317
3318 const typename __ios_base::fmtflags __flags = __os.flags();
3319 const _CharT __fill = __os.fill();
3320 const std::streamsize __precision = __os.precision();
3321 const _CharT __space = __os.widen(' ');
3322 __os.flags(__ios_base::scientific | __ios_base::left);
3323 __os.fill(__space);
3324 __os.precision(std::numeric_limits<_RealType>::max_digits10);
3325
3326 std::vector<_RealType> __int = __x.intervals();
3327 __os << __int.size() - 1;
3328
3329 for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit)
3330 __os << __space << *__xit;
3331
3332 std::vector<double> __den = __x.densities();
3333 for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit)
3334 __os << __space << *__dit;
3335
3336 __os.flags(__flags);
3337 __os.fill(__fill);
3338 __os.precision(__precision);
3339 return __os;
3340 }
3341
3342 template<typename _RealType, typename _CharT, typename _Traits>
3343 std::basic_istream<_CharT, _Traits>&
3344 operator>>(std::basic_istream<_CharT, _Traits>& __is,
3345 piecewise_linear_distribution<_RealType>& __x)
3346 {
3347 typedef std::basic_istream<_CharT, _Traits> __istream_type;
3348 typedef typename __istream_type::ios_base __ios_base;
3349
3350 const typename __ios_base::fmtflags __flags = __is.flags();
3351 __is.flags(__ios_base::dec | __ios_base::skipws);
3352
3353 size_t __n;
3354 __is >> __n;
3355
3356 std::vector<_RealType> __int_vec;
3357 __int_vec.reserve(__n + 1);
3358 for (size_t __i = 0; __i <= __n; ++__i)
3359 {
3360 _RealType __int;
3361 __is >> __int;
3362 __int_vec.push_back(__int);
3363 }
3364
3365 std::vector<double> __den_vec;
3366 __den_vec.reserve(__n + 1);
3367 for (size_t __i = 0; __i <= __n; ++__i)
3368 {
3369 double __den;
3370 __is >> __den;
3371 __den_vec.push_back(__den);
3372 }
3373
3374 __x.param(typename piecewise_linear_distribution<_RealType>::
3375 param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin()));
3376
3377 __is.flags(__flags);
3378 return __is;
3379 }
3380
3381
3382 template<typename _IntType>
3383 seed_seq::seed_seq(std::initializer_list<_IntType> __il)
3384 {
3385 for (auto __iter = __il.begin(); __iter != __il.end(); ++__iter)
3386 _M_v.push_back(__detail::__mod<result_type,
3387 __detail::_Shift<result_type, 32>::__value>(*__iter));
3388 }
3389
3390 template<typename _InputIterator>
3391 seed_seq::seed_seq(_InputIterator __begin, _InputIterator __end)
3392 {
3393 for (_InputIterator __iter = __begin; __iter != __end; ++__iter)
3394 _M_v.push_back(__detail::__mod<result_type,
3395 __detail::_Shift<result_type, 32>::__value>(*__iter));
3396 }
3397
3398 template<typename _RandomAccessIterator>
3399 void
3400 seed_seq::generate(_RandomAccessIterator __begin,
3401 _RandomAccessIterator __end)
3402 {
3403 typedef typename iterator_traits<_RandomAccessIterator>::value_type
3404 _Type;
3405
3406 if (__begin == __end)
3407 return;
3408
3409 std::fill(__begin, __end, _Type(0x8b8b8b8bu));
3410
3411 const size_t __n = __end - __begin;
3412 const size_t __s = _M_v.size();
3413 const size_t __t = (__n >= 623) ? 11
3414 : (__n >= 68) ? 7
3415 : (__n >= 39) ? 5
3416 : (__n >= 7) ? 3
3417 : (__n - 1) / 2;
3418 const size_t __p = (__n - __t) / 2;
3419 const size_t __q = __p + __t;
3420 const size_t __m = std::max(size_t(__s + 1), __n);
3421
3422 for (size_t __k = 0; __k < __m; ++__k)
3423 {
3424 _Type __arg = (__begin[__k % __n]
3425 ^ __begin[(__k + __p) % __n]
3426 ^ __begin[(__k - 1) % __n]);
3427 _Type __r1 = __arg ^ (__arg >> 27);
3428 __r1 = __detail::__mod<_Type,
3429 __detail::_Shift<_Type, 32>::__value>(1664525u * __r1);
3430 _Type __r2 = __r1;
3431 if (__k == 0)
3432 __r2 += __s;
3433 else if (__k <= __s)
3434 __r2 += __k % __n + _M_v[__k - 1];
3435 else
3436 __r2 += __k % __n;
3437 __r2 = __detail::__mod<_Type,
3438 __detail::_Shift<_Type, 32>::__value>(__r2);
3439 __begin[(__k + __p) % __n] += __r1;
3440 __begin[(__k + __q) % __n] += __r2;
3441 __begin[__k % __n] = __r2;
3442 }
3443
3444 for (size_t __k = __m; __k < __m + __n; ++__k)
3445 {
3446 _Type __arg = (__begin[__k % __n]
3447 + __begin[(__k + __p) % __n]
3448 + __begin[(__k - 1) % __n]);
3449 _Type __r3 = __arg ^ (__arg >> 27);
3450 __r3 = __detail::__mod<_Type,
3451 __detail::_Shift<_Type, 32>::__value>(1566083941u * __r3);
3452 _Type __r4 = __r3 - __k % __n;
3453 __r4 = __detail::__mod<_Type,
3454 __detail::_Shift<_Type, 32>::__value>(__r4);
3455 __begin[(__k + __p) % __n] ^= __r3;
3456 __begin[(__k + __q) % __n] ^= __r4;
3457 __begin[__k % __n] = __r4;
3458 }
3459 }
3460
3461 template<typename _RealType, size_t __bits,
3462 typename _UniformRandomNumberGenerator>
3463 _RealType
3464 generate_canonical(_UniformRandomNumberGenerator& __urng)
3465 {
3466 const size_t __b
3467 = std::min(static_cast<size_t>(std::numeric_limits<_RealType>::digits),
3468 __bits);
3469 const long double __r = static_cast<long double>(__urng.max())
3470 - static_cast<long double>(__urng.min()) + 1.0L;
3471 const size_t __log2r = std::log(__r) / std::log(2.0L);
3472 size_t __k = std::max<size_t>(1UL, (__b + __log2r - 1UL) / __log2r);
3473 _RealType __sum = _RealType(0);
3474 _RealType __tmp = _RealType(1);
3475 for (; __k != 0; --__k)
3476 {
3477 __sum += _RealType(__urng() - __urng.min()) * __tmp;
3478 __tmp *= __r;
3479 }
3480 return __sum / __tmp;
3481 }
3482
3483 _GLIBCXX_END_NAMESPACE_VERSION
3484 } // namespace
3485
3486 #endif
3487