| blob | 01ee5824cd9f7df64c564e0910d285f749c53f73 |
1 // random number generation (out of line) -*- C++ -*-
3 // Copyright (C) 2009, 2010, 2011 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.
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.
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.
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/>.
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 */
30 #ifndef _RANDOM_TCC
31 #define _RANDOM_TCC 1
33 #include <numeric> // std::accumulate and std::partial_sum
35 namespace std _GLIBCXX_VISIBILITY(default)
36 {
37 /*
38 * (Further) implementation-space details.
39 */
40 namespace __detail
41 {
42 _GLIBCXX_BEGIN_NAMESPACE_VERSION
44 // General case for x = (ax + c) mod m -- use Schrage's algorithm to
45 // avoid integer overflow.
46 //
47 // Because a and c are compile-time integral constants the compiler
48 // kindly elides any unreachable paths.
49 //
50 // Preconditions: a > 0, m > 0.
51 //
52 template<typename _Tp, _Tp __m, _Tp __a, _Tp __c, bool>
53 struct _Mod
54 {
55 static _Tp
56 __calc(_Tp __x)
57 {
58 if (__a == 1)
59 __x %= __m;
60 else
61 {
62 static const _Tp __q = __m / __a;
63 static const _Tp __r = __m % __a;
65 _Tp __t1 = __a * (__x % __q);
66 _Tp __t2 = __r * (__x / __q);
67 if (__t1 >= __t2)
68 __x = __t1 - __t2;
69 else
70 __x = __m - __t2 + __t1;
71 }
73 if (__c != 0)
74 {
75 const _Tp __d = __m - __x;
76 if (__d > __c)
77 __x += __c;
78 else
79 __x = __c - __d;
80 }
81 return __x;
82 }
83 };
85 // Special case for m == 0 -- use unsigned integer overflow as modulo
86 // operator.
87 template<typename _Tp, _Tp __m, _Tp __a, _Tp __c>
88 struct _Mod<_Tp, __m, __a, __c, true>
89 {
90 static _Tp
91 __calc(_Tp __x)
92 { return __a * __x + __c; }
93 };
95 template<typename _InputIterator, typename _OutputIterator,
96 typename _UnaryOperation>
97 _OutputIterator
98 __transform(_InputIterator __first, _InputIterator __last,
99 _OutputIterator __result, _UnaryOperation __unary_op)
100 {
101 for (; __first != __last; ++__first, ++__result)
102 *__result = __unary_op(*__first);
103 return __result;
104 }
106 _GLIBCXX_END_NAMESPACE_VERSION
107 } // namespace __detail
109 _GLIBCXX_BEGIN_NAMESPACE_VERSION
111 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
112 constexpr _UIntType
113 linear_congruential_engine<_UIntType, __a, __c, __m>::multiplier;
115 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
116 constexpr _UIntType
117 linear_congruential_engine<_UIntType, __a, __c, __m>::increment;
119 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
120 constexpr _UIntType
121 linear_congruential_engine<_UIntType, __a, __c, __m>::modulus;
123 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
124 constexpr _UIntType
125 linear_congruential_engine<_UIntType, __a, __c, __m>::default_seed;
127 /**
128 * Seeds the LCR with integral value @p __s, adjusted so that the
129 * ring identity is never a member of the convergence set.
130 */
131 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
132 void
133 linear_congruential_engine<_UIntType, __a, __c, __m>::
134 seed(result_type __s)
135 {
136 if ((__detail::__mod<_UIntType, __m>(__c) == 0)
137 && (__detail::__mod<_UIntType, __m>(__s) == 0))
138 _M_x = 1;
139 else
140 _M_x = __detail::__mod<_UIntType, __m>(__s);
141 }
143 /**
144 * Seeds the LCR engine with a value generated by @p __q.
145 */
146 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
147 template<typename _Sseq>
148 typename std::enable_if<std::is_class<_Sseq>::value>::type
149 linear_congruential_engine<_UIntType, __a, __c, __m>::
150 seed(_Sseq& __q)
151 {
152 const _UIntType __k0 = __m == 0 ? std::numeric_limits<_UIntType>::digits
153 : std::__lg(__m);
154 const _UIntType __k = (__k0 + 31) / 32;
155 uint_least32_t __arr[__k + 3];
156 __q.generate(__arr + 0, __arr + __k + 3);
157 _UIntType __factor = 1u;
158 _UIntType __sum = 0u;
159 for (size_t __j = 0; __j < __k; ++__j)
160 {
161 __sum += __arr[__j + 3] * __factor;
162 __factor *= __detail::_Shift<_UIntType, 32>::__value;
163 }
164 seed(__sum);
165 }
167 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m,
168 typename _CharT, typename _Traits>
169 std::basic_ostream<_CharT, _Traits>&
170 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
171 const linear_congruential_engine<_UIntType,
172 __a, __c, __m>& __lcr)
173 {
174 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
175 typedef typename __ostream_type::ios_base __ios_base;
177 const typename __ios_base::fmtflags __flags = __os.flags();
178 const _CharT __fill = __os.fill();
179 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
180 __os.fill(__os.widen(' '));
182 __os << __lcr._M_x;
184 __os.flags(__flags);
185 __os.fill(__fill);
186 return __os;
187 }
189 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m,
190 typename _CharT, typename _Traits>
191 std::basic_istream<_CharT, _Traits>&
192 operator>>(std::basic_istream<_CharT, _Traits>& __is,
193 linear_congruential_engine<_UIntType, __a, __c, __m>& __lcr)
194 {
195 typedef std::basic_istream<_CharT, _Traits> __istream_type;
196 typedef typename __istream_type::ios_base __ios_base;
198 const typename __ios_base::fmtflags __flags = __is.flags();
199 __is.flags(__ios_base::dec);
201 __is >> __lcr._M_x;
203 __is.flags(__flags);
204 return __is;
205 }
208 template<typename _UIntType,
209 size_t __w, size_t __n, size_t __m, size_t __r,
210 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
211 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
212 _UIntType __f>
213 constexpr size_t
214 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
215 __s, __b, __t, __c, __l, __f>::word_size;
217 template<typename _UIntType,
218 size_t __w, size_t __n, size_t __m, size_t __r,
219 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
220 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
221 _UIntType __f>
222 constexpr size_t
223 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
224 __s, __b, __t, __c, __l, __f>::state_size;
226 template<typename _UIntType,
227 size_t __w, size_t __n, size_t __m, size_t __r,
228 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
229 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
230 _UIntType __f>
231 constexpr size_t
232 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
233 __s, __b, __t, __c, __l, __f>::shift_size;
235 template<typename _UIntType,
236 size_t __w, size_t __n, size_t __m, size_t __r,
237 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
238 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
239 _UIntType __f>
240 constexpr size_t
241 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
242 __s, __b, __t, __c, __l, __f>::mask_bits;
244 template<typename _UIntType,
245 size_t __w, size_t __n, size_t __m, size_t __r,
246 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
247 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
248 _UIntType __f>
249 constexpr _UIntType
250 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
251 __s, __b, __t, __c, __l, __f>::xor_mask;
253 template<typename _UIntType,
254 size_t __w, size_t __n, size_t __m, size_t __r,
255 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
256 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
257 _UIntType __f>
258 constexpr size_t
259 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
260 __s, __b, __t, __c, __l, __f>::tempering_u;
262 template<typename _UIntType,
263 size_t __w, size_t __n, size_t __m, size_t __r,
264 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
265 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
266 _UIntType __f>
267 constexpr _UIntType
268 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
269 __s, __b, __t, __c, __l, __f>::tempering_d;
271 template<typename _UIntType,
272 size_t __w, size_t __n, size_t __m, size_t __r,
273 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
274 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
275 _UIntType __f>
276 constexpr size_t
277 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
278 __s, __b, __t, __c, __l, __f>::tempering_s;
280 template<typename _UIntType,
281 size_t __w, size_t __n, size_t __m, size_t __r,
282 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
283 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
284 _UIntType __f>
285 constexpr _UIntType
286 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
287 __s, __b, __t, __c, __l, __f>::tempering_b;
289 template<typename _UIntType,
290 size_t __w, size_t __n, size_t __m, size_t __r,
291 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
292 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
293 _UIntType __f>
294 constexpr size_t
295 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
296 __s, __b, __t, __c, __l, __f>::tempering_t;
298 template<typename _UIntType,
299 size_t __w, size_t __n, size_t __m, size_t __r,
300 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
301 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
302 _UIntType __f>
303 constexpr _UIntType
304 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
305 __s, __b, __t, __c, __l, __f>::tempering_c;
307 template<typename _UIntType,
308 size_t __w, size_t __n, size_t __m, size_t __r,
309 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
310 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
311 _UIntType __f>
312 constexpr size_t
313 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
314 __s, __b, __t, __c, __l, __f>::tempering_l;
316 template<typename _UIntType,
317 size_t __w, size_t __n, size_t __m, size_t __r,
318 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
319 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
320 _UIntType __f>
321 constexpr _UIntType
322 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
323 __s, __b, __t, __c, __l, __f>::
324 initialization_multiplier;
326 template<typename _UIntType,
327 size_t __w, size_t __n, size_t __m, size_t __r,
328 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
329 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
330 _UIntType __f>
331 constexpr _UIntType
332 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
333 __s, __b, __t, __c, __l, __f>::default_seed;
335 template<typename _UIntType,
336 size_t __w, size_t __n, size_t __m, size_t __r,
337 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
338 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
339 _UIntType __f>
340 void
341 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
342 __s, __b, __t, __c, __l, __f>::
343 seed(result_type __sd)
344 {
345 _M_x[0] = __detail::__mod<_UIntType,
346 __detail::_Shift<_UIntType, __w>::__value>(__sd);
348 for (size_t __i = 1; __i < state_size; ++__i)
349 {
350 _UIntType __x = _M_x[__i - 1];
351 __x ^= __x >> (__w - 2);
352 __x *= __f;
353 __x += __detail::__mod<_UIntType, __n>(__i);
354 _M_x[__i] = __detail::__mod<_UIntType,
355 __detail::_Shift<_UIntType, __w>::__value>(__x);
356 }
357 _M_p = state_size;
358 }
360 template<typename _UIntType,
361 size_t __w, size_t __n, size_t __m, size_t __r,
362 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
363 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
364 _UIntType __f>
365 template<typename _Sseq>
366 typename std::enable_if<std::is_class<_Sseq>::value>::type
367 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
368 __s, __b, __t, __c, __l, __f>::
369 seed(_Sseq& __q)
370 {
371 const _UIntType __upper_mask = (~_UIntType()) << __r;
372 const size_t __k = (__w + 31) / 32;
373 uint_least32_t __arr[__n * __k];
374 __q.generate(__arr + 0, __arr + __n * __k);
376 bool __zero = true;
377 for (size_t __i = 0; __i < state_size; ++__i)
378 {
379 _UIntType __factor = 1u;
380 _UIntType __sum = 0u;
381 for (size_t __j = 0; __j < __k; ++__j)
382 {
383 __sum += __arr[__k * __i + __j] * __factor;
384 __factor *= __detail::_Shift<_UIntType, 32>::__value;
385 }
386 _M_x[__i] = __detail::__mod<_UIntType,
387 __detail::_Shift<_UIntType, __w>::__value>(__sum);
389 if (__zero)
390 {
391 if (__i == 0)
392 {
393 if ((_M_x[0] & __upper_mask) != 0u)
394 __zero = false;
395 }
396 else if (_M_x[__i] != 0u)
397 __zero = false;
398 }
399 }
400 if (__zero)
401 _M_x[0] = __detail::_Shift<_UIntType, __w - 1>::__value;
402 }
404 template<typename _UIntType, size_t __w,
405 size_t __n, size_t __m, size_t __r,
406 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
407 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
408 _UIntType __f>
409 typename
410 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
411 __s, __b, __t, __c, __l, __f>::result_type
412 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
413 __s, __b, __t, __c, __l, __f>::
414 operator()()
415 {
416 // Reload the vector - cost is O(n) amortized over n calls.
417 if (_M_p >= state_size)
418 {
419 const _UIntType __upper_mask = (~_UIntType()) << __r;
420 const _UIntType __lower_mask = ~__upper_mask;
422 for (size_t __k = 0; __k < (__n - __m); ++__k)
423 {
424 _UIntType __y = ((_M_x[__k] & __upper_mask)
425 | (_M_x[__k + 1] & __lower_mask));
426 _M_x[__k] = (_M_x[__k + __m] ^ (__y >> 1)
427 ^ ((__y & 0x01) ? __a : 0));
428 }
430 for (size_t __k = (__n - __m); __k < (__n - 1); ++__k)
431 {
432 _UIntType __y = ((_M_x[__k] & __upper_mask)
433 | (_M_x[__k + 1] & __lower_mask));
434 _M_x[__k] = (_M_x[__k + (__m - __n)] ^ (__y >> 1)
435 ^ ((__y & 0x01) ? __a : 0));
436 }
438 _UIntType __y = ((_M_x[__n - 1] & __upper_mask)
439 | (_M_x[0] & __lower_mask));
440 _M_x[__n - 1] = (_M_x[__m - 1] ^ (__y >> 1)
441 ^ ((__y & 0x01) ? __a : 0));
442 _M_p = 0;
443 }
445 // Calculate o(x(i)).
446 result_type __z = _M_x[_M_p++];
447 __z ^= (__z >> __u) & __d;
448 __z ^= (__z << __s) & __b;
449 __z ^= (__z << __t) & __c;
450 __z ^= (__z >> __l);
452 return __z;
453 }
455 template<typename _UIntType, size_t __w,
456 size_t __n, size_t __m, size_t __r,
457 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
458 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
459 _UIntType __f, typename _CharT, typename _Traits>
460 std::basic_ostream<_CharT, _Traits>&
461 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
462 const mersenne_twister_engine<_UIntType, __w, __n, __m,
463 __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x)
464 {
465 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
466 typedef typename __ostream_type::ios_base __ios_base;
468 const typename __ios_base::fmtflags __flags = __os.flags();
469 const _CharT __fill = __os.fill();
470 const _CharT __space = __os.widen(' ');
471 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
472 __os.fill(__space);
474 for (size_t __i = 0; __i < __n - 1; ++__i)
475 __os << __x._M_x[__i] << __space;
476 __os << __x._M_x[__n - 1];
478 __os.flags(__flags);
479 __os.fill(__fill);
480 return __os;
481 }
483 template<typename _UIntType, size_t __w,
484 size_t __n, size_t __m, size_t __r,
485 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
486 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
487 _UIntType __f, typename _CharT, typename _Traits>
488 std::basic_istream<_CharT, _Traits>&
489 operator>>(std::basic_istream<_CharT, _Traits>& __is,
490 mersenne_twister_engine<_UIntType, __w, __n, __m,
491 __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x)
492 {
493 typedef std::basic_istream<_CharT, _Traits> __istream_type;
494 typedef typename __istream_type::ios_base __ios_base;
496 const typename __ios_base::fmtflags __flags = __is.flags();
497 __is.flags(__ios_base::dec | __ios_base::skipws);
499 for (size_t __i = 0; __i < __n; ++__i)
500 __is >> __x._M_x[__i];
502 __is.flags(__flags);
503 return __is;
504 }
507 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
508 constexpr size_t
509 subtract_with_carry_engine<_UIntType, __w, __s, __r>::word_size;
511 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
512 constexpr size_t
513 subtract_with_carry_engine<_UIntType, __w, __s, __r>::short_lag;
515 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
516 constexpr size_t
517 subtract_with_carry_engine<_UIntType, __w, __s, __r>::long_lag;
519 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
520 constexpr _UIntType
521 subtract_with_carry_engine<_UIntType, __w, __s, __r>::default_seed;
523 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
524 void
525 subtract_with_carry_engine<_UIntType, __w, __s, __r>::
526 seed(result_type __value)
527 {
528 std::linear_congruential_engine<result_type, 40014u, 0u, 2147483563u>
529 __lcg(__value == 0u ? default_seed : __value);
531 const size_t __n = (__w + 31) / 32;
533 for (size_t __i = 0; __i < long_lag; ++__i)
534 {
535 _UIntType __sum = 0u;
536 _UIntType __factor = 1u;
537 for (size_t __j = 0; __j < __n; ++__j)
538 {
539 __sum += __detail::__mod<uint_least32_t,
540 __detail::_Shift<uint_least32_t, 32>::__value>
541 (__lcg()) * __factor;
542 __factor *= __detail::_Shift<_UIntType, 32>::__value;
543 }
544 _M_x[__i] = __detail::__mod<_UIntType,
545 __detail::_Shift<_UIntType, __w>::__value>(__sum);
546 }
547 _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0;
548 _M_p = 0;
549 }
551 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
552 template<typename _Sseq>
553 typename std::enable_if<std::is_class<_Sseq>::value>::type
554 subtract_with_carry_engine<_UIntType, __w, __s, __r>::
555 seed(_Sseq& __q)
556 {
557 const size_t __k = (__w + 31) / 32;
558 uint_least32_t __arr[__r * __k];
559 __q.generate(__arr + 0, __arr + __r * __k);
561 for (size_t __i = 0; __i < long_lag; ++__i)
562 {
563 _UIntType __sum = 0u;
564 _UIntType __factor = 1u;
565 for (size_t __j = 0; __j < __k; ++__j)
566 {
567 __sum += __arr[__k * __i + __j] * __factor;
568 __factor *= __detail::_Shift<_UIntType, 32>::__value;
569 }
570 _M_x[__i] = __detail::__mod<_UIntType,
571 __detail::_Shift<_UIntType, __w>::__value>(__sum);
572 }
573 _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0;
574 _M_p = 0;
575 }
577 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
578 typename subtract_with_carry_engine<_UIntType, __w, __s, __r>::
579 result_type
580 subtract_with_carry_engine<_UIntType, __w, __s, __r>::
581 operator()()
582 {
583 // Derive short lag index from current index.
584 long __ps = _M_p - short_lag;
585 if (__ps < 0)
586 __ps += long_lag;
588 // Calculate new x(i) without overflow or division.
589 // NB: Thanks to the requirements for _UIntType, _M_x[_M_p] + _M_carry
590 // cannot overflow.
591 _UIntType __xi;
592 if (_M_x[__ps] >= _M_x[_M_p] + _M_carry)
593 {
594 __xi = _M_x[__ps] - _M_x[_M_p] - _M_carry;
595 _M_carry = 0;
596 }
597 else
598 {
599 __xi = (__detail::_Shift<_UIntType, __w>::__value
600 - _M_x[_M_p] - _M_carry + _M_x[__ps]);
601 _M_carry = 1;
602 }
603 _M_x[_M_p] = __xi;
605 // Adjust current index to loop around in ring buffer.
606 if (++_M_p >= long_lag)
607 _M_p = 0;
609 return __xi;
610 }
612 template<typename _UIntType, size_t __w, size_t __s, size_t __r,
613 typename _CharT, typename _Traits>
614 std::basic_ostream<_CharT, _Traits>&
615 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
616 const subtract_with_carry_engine<_UIntType,
617 __w, __s, __r>& __x)
618 {
619 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
620 typedef typename __ostream_type::ios_base __ios_base;
622 const typename __ios_base::fmtflags __flags = __os.flags();
623 const _CharT __fill = __os.fill();
624 const _CharT __space = __os.widen(' ');
625 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
626 __os.fill(__space);
628 for (size_t __i = 0; __i < __r; ++__i)
629 __os << __x._M_x[__i] << __space;
630 __os << __x._M_carry;
632 __os.flags(__flags);
633 __os.fill(__fill);
634 return __os;
635 }
637 template<typename _UIntType, size_t __w, size_t __s, size_t __r,
638 typename _CharT, typename _Traits>
639 std::basic_istream<_CharT, _Traits>&
640 operator>>(std::basic_istream<_CharT, _Traits>& __is,
641 subtract_with_carry_engine<_UIntType, __w, __s, __r>& __x)
642 {
643 typedef std::basic_ostream<_CharT, _Traits> __istream_type;
644 typedef typename __istream_type::ios_base __ios_base;
646 const typename __ios_base::fmtflags __flags = __is.flags();
647 __is.flags(__ios_base::dec | __ios_base::skipws);
649 for (size_t __i = 0; __i < __r; ++__i)
650 __is >> __x._M_x[__i];
651 __is >> __x._M_carry;
653 __is.flags(__flags);
654 return __is;
655 }
658 template<typename _RandomNumberEngine, size_t __p, size_t __r>
659 constexpr size_t
660 discard_block_engine<_RandomNumberEngine, __p, __r>::block_size;
662 template<typename _RandomNumberEngine, size_t __p, size_t __r>
663 constexpr size_t
664 discard_block_engine<_RandomNumberEngine, __p, __r>::used_block;
666 template<typename _RandomNumberEngine, size_t __p, size_t __r>
667 typename discard_block_engine<_RandomNumberEngine,
668 __p, __r>::result_type
669 discard_block_engine<_RandomNumberEngine, __p, __r>::
670 operator()()
671 {
672 if (_M_n >= used_block)
673 {
674 _M_b.discard(block_size - _M_n);
675 _M_n = 0;
676 }
677 ++_M_n;
678 return _M_b();
679 }
681 template<typename _RandomNumberEngine, size_t __p, size_t __r,
682 typename _CharT, typename _Traits>
683 std::basic_ostream<_CharT, _Traits>&
684 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
685 const discard_block_engine<_RandomNumberEngine,
686 __p, __r>& __x)
687 {
688 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
689 typedef typename __ostream_type::ios_base __ios_base;
691 const typename __ios_base::fmtflags __flags = __os.flags();
692 const _CharT __fill = __os.fill();
693 const _CharT __space = __os.widen(' ');
694 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
695 __os.fill(__space);
697 __os << __x.base() << __space << __x._M_n;
699 __os.flags(__flags);
700 __os.fill(__fill);
701 return __os;
702 }
704 template<typename _RandomNumberEngine, size_t __p, size_t __r,
705 typename _CharT, typename _Traits>
706 std::basic_istream<_CharT, _Traits>&
707 operator>>(std::basic_istream<_CharT, _Traits>& __is,
708 discard_block_engine<_RandomNumberEngine, __p, __r>& __x)
709 {
710 typedef std::basic_istream<_CharT, _Traits> __istream_type;
711 typedef typename __istream_type::ios_base __ios_base;
713 const typename __ios_base::fmtflags __flags = __is.flags();
714 __is.flags(__ios_base::dec | __ios_base::skipws);
716 __is >> __x._M_b >> __x._M_n;
718 __is.flags(__flags);
719 return __is;
720 }
723 template<typename _RandomNumberEngine, size_t __w, typename _UIntType>
724 typename independent_bits_engine<_RandomNumberEngine, __w, _UIntType>::
725 result_type
726 independent_bits_engine<_RandomNumberEngine, __w, _UIntType>::
727 operator()()
728 {
729 const long double __r = static_cast<long double>(_M_b.max())
730 - static_cast<long double>(_M_b.min()) + 1.0L;
731 const result_type __m = std::log(__r) / std::log(2.0L);
732 result_type __n, __n0, __y0, __y1, __s0, __s1;
733 for (size_t __i = 0; __i < 2; ++__i)
734 {
735 __n = (__w + __m - 1) / __m + __i;
736 __n0 = __n - __w % __n;
737 const result_type __w0 = __w / __n;
738 const result_type __w1 = __w0 + 1;
739 __s0 = result_type(1) << __w0;
740 __s1 = result_type(1) << __w1;
741 __y0 = __s0 * (__r / __s0);
742 __y1 = __s1 * (__r / __s1);
743 if (__r - __y0 <= __y0 / __n)
744 break;
745 }
747 result_type __sum = 0;
748 for (size_t __k = 0; __k < __n0; ++__k)
749 {
750 result_type __u;
751 do
752 __u = _M_b() - _M_b.min();
753 while (__u >= __y0);
754 __sum = __s0 * __sum + __u % __s0;
755 }
756 for (size_t __k = __n0; __k < __n; ++__k)
757 {
758 result_type __u;
759 do
760 __u = _M_b() - _M_b.min();
761 while (__u >= __y1);
762 __sum = __s1 * __sum + __u % __s1;
763 }
764 return __sum;
765 }
768 template<typename _RandomNumberEngine, size_t __k>
769 constexpr size_t
770 shuffle_order_engine<_RandomNumberEngine, __k>::table_size;
772 template<typename _RandomNumberEngine, size_t __k>
773 typename shuffle_order_engine<_RandomNumberEngine, __k>::result_type
774 shuffle_order_engine<_RandomNumberEngine, __k>::
775 operator()()
776 {
777 size_t __j = __k * ((_M_y - _M_b.min())
778 / (_M_b.max() - _M_b.min() + 1.0L));
779 _M_y = _M_v[__j];
780 _M_v[__j] = _M_b();
782 return _M_y;
783 }
785 template<typename _RandomNumberEngine, size_t __k,
786 typename _CharT, typename _Traits>
787 std::basic_ostream<_CharT, _Traits>&
788 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
789 const shuffle_order_engine<_RandomNumberEngine, __k>& __x)
790 {
791 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
792 typedef typename __ostream_type::ios_base __ios_base;
794 const typename __ios_base::fmtflags __flags = __os.flags();
795 const _CharT __fill = __os.fill();
796 const _CharT __space = __os.widen(' ');
797 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
798 __os.fill(__space);
800 __os << __x.base();
801 for (size_t __i = 0; __i < __k; ++__i)
802 __os << __space << __x._M_v[__i];
803 __os << __space << __x._M_y;
805 __os.flags(__flags);
806 __os.fill(__fill);
807 return __os;
808 }
810 template<typename _RandomNumberEngine, size_t __k,
811 typename _CharT, typename _Traits>
812 std::basic_istream<_CharT, _Traits>&
813 operator>>(std::basic_istream<_CharT, _Traits>& __is,
814 shuffle_order_engine<_RandomNumberEngine, __k>& __x)
815 {
816 typedef std::basic_istream<_CharT, _Traits> __istream_type;
817 typedef typename __istream_type::ios_base __ios_base;
819 const typename __ios_base::fmtflags __flags = __is.flags();
820 __is.flags(__ios_base::dec | __ios_base::skipws);
822 __is >> __x._M_b;
823 for (size_t __i = 0; __i < __k; ++__i)
824 __is >> __x._M_v[__i];
825 __is >> __x._M_y;
827 __is.flags(__flags);
828 return __is;
829 }
832 template<typename _IntType>
833 template<typename _UniformRandomNumberGenerator>
834 typename uniform_int_distribution<_IntType>::result_type
835 uniform_int_distribution<_IntType>::
836 operator()(_UniformRandomNumberGenerator& __urng,
837 const param_type& __param)
838 {
839 typedef typename std::make_unsigned<typename
840 _UniformRandomNumberGenerator::result_type>::type __urngtype;
841 typedef typename std::make_unsigned<result_type>::type __utype;
842 typedef typename std::conditional<(sizeof(__urngtype)
843 > sizeof(__utype)),
844 __urngtype, __utype>::type __uctype;
846 const __uctype __urngmin = __urng.min();
847 const __uctype __urngmax = __urng.max();
848 const __uctype __urngrange = __urngmax - __urngmin;
849 const __uctype __urange
850 = __uctype(__param.b()) - __uctype(__param.a());
852 __uctype __ret;
854 if (__urngrange > __urange)
855 {
856 // downscaling
857 const __uctype __uerange = __urange + 1; // __urange can be zero
858 const __uctype __scaling = __urngrange / __uerange;
859 const __uctype __past = __uerange * __scaling;
860 do
861 __ret = __uctype(__urng()) - __urngmin;
862 while (__ret >= __past);
863 __ret /= __scaling;
864 }
865 else if (__urngrange < __urange)
866 {
867 // upscaling
868 /*
869 Note that every value in [0, urange]
870 can be written uniquely as
872 (urngrange + 1) * high + low
874 where
876 high in [0, urange / (urngrange + 1)]
878 and
880 low in [0, urngrange].
881 */
882 __uctype __tmp; // wraparound control
883 do
884 {
885 const __uctype __uerngrange = __urngrange + 1;
886 __tmp = (__uerngrange * operator()
887 (__urng, param_type(0, __urange / __uerngrange)));
888 __ret = __tmp + (__uctype(__urng()) - __urngmin);
889 }
890 while (__ret > __urange || __ret < __tmp);
891 }
892 else
893 __ret = __uctype(__urng()) - __urngmin;
895 return __ret + __param.a();
896 }
898 template<typename _IntType, typename _CharT, typename _Traits>
899 std::basic_ostream<_CharT, _Traits>&
900 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
901 const uniform_int_distribution<_IntType>& __x)
902 {
903 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
904 typedef typename __ostream_type::ios_base __ios_base;
906 const typename __ios_base::fmtflags __flags = __os.flags();
907 const _CharT __fill = __os.fill();
908 const _CharT __space = __os.widen(' ');
909 __os.flags(__ios_base::scientific | __ios_base::left);
910 __os.fill(__space);
912 __os << __x.a() << __space << __x.b();
914 __os.flags(__flags);
915 __os.fill(__fill);
916 return __os;
917 }
919 template<typename _IntType, typename _CharT, typename _Traits>
920 std::basic_istream<_CharT, _Traits>&
921 operator>>(std::basic_istream<_CharT, _Traits>& __is,
922 uniform_int_distribution<_IntType>& __x)
923 {
924 typedef std::basic_istream<_CharT, _Traits> __istream_type;
925 typedef typename __istream_type::ios_base __ios_base;
927 const typename __ios_base::fmtflags __flags = __is.flags();
928 __is.flags(__ios_base::dec | __ios_base::skipws);
930 _IntType __a, __b;
931 __is >> __a >> __b;
932 __x.param(typename uniform_int_distribution<_IntType>::
933 param_type(__a, __b));
935 __is.flags(__flags);
936 return __is;
937 }
940 template<typename _RealType, typename _CharT, typename _Traits>
941 std::basic_ostream<_CharT, _Traits>&
942 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
943 const uniform_real_distribution<_RealType>& __x)
944 {
945 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
946 typedef typename __ostream_type::ios_base __ios_base;
948 const typename __ios_base::fmtflags __flags = __os.flags();
949 const _CharT __fill = __os.fill();
950 const std::streamsize __precision = __os.precision();
951 const _CharT __space = __os.widen(' ');
952 __os.flags(__ios_base::scientific | __ios_base::left);
953 __os.fill(__space);
954 __os.precision(std::numeric_limits<_RealType>::max_digits10);
956 __os << __x.a() << __space << __x.b();
958 __os.flags(__flags);
959 __os.fill(__fill);
960 __os.precision(__precision);
961 return __os;
962 }
964 template<typename _RealType, typename _CharT, typename _Traits>
965 std::basic_istream<_CharT, _Traits>&
966 operator>>(std::basic_istream<_CharT, _Traits>& __is,
967 uniform_real_distribution<_RealType>& __x)
968 {
969 typedef std::basic_istream<_CharT, _Traits> __istream_type;
970 typedef typename __istream_type::ios_base __ios_base;
972 const typename __ios_base::fmtflags __flags = __is.flags();
973 __is.flags(__ios_base::skipws);
975 _RealType __a, __b;
976 __is >> __a >> __b;
977 __x.param(typename uniform_real_distribution<_RealType>::
978 param_type(__a, __b));
980 __is.flags(__flags);
981 return __is;
982 }
985 template<typename _CharT, typename _Traits>
986 std::basic_ostream<_CharT, _Traits>&
987 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
988 const bernoulli_distribution& __x)
989 {
990 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
991 typedef typename __ostream_type::ios_base __ios_base;
993 const typename __ios_base::fmtflags __flags = __os.flags();
994 const _CharT __fill = __os.fill();
995 const std::streamsize __precision = __os.precision();
996 __os.flags(__ios_base::scientific | __ios_base::left);
997 __os.fill(__os.widen(' '));
998 __os.precision(std::numeric_limits<double>::max_digits10);
1000 __os << __x.p();
1002 __os.flags(__flags);
1003 __os.fill(__fill);
1004 __os.precision(__precision);
1005 return __os;
1006 }
1009 template<typename _IntType>
1010 template<typename _UniformRandomNumberGenerator>
1011 typename geometric_distribution<_IntType>::result_type
1012 geometric_distribution<_IntType>::
1013 operator()(_UniformRandomNumberGenerator& __urng,
1014 const param_type& __param)
1015 {
1016 // About the epsilon thing see this thread:
1017 // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html
1018 const double __naf =
1019 (1 - std::numeric_limits<double>::epsilon()) / 2;
1020 // The largest _RealType convertible to _IntType.
1021 const double __thr =
1022 std::numeric_limits<_IntType>::max() + __naf;
1023 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
1024 __aurng(__urng);
1026 double __cand;
1027 do
1028 __cand = std::floor(std::log(__aurng()) / __param._M_log_1_p);
1029 while (__cand >= __thr);
1031 return result_type(__cand + __naf);
1032 }
1034 template<typename _IntType,
1035 typename _CharT, typename _Traits>
1036 std::basic_ostream<_CharT, _Traits>&
1037 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1038 const geometric_distribution<_IntType>& __x)
1039 {
1040 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1041 typedef typename __ostream_type::ios_base __ios_base;
1043 const typename __ios_base::fmtflags __flags = __os.flags();
1044 const _CharT __fill = __os.fill();
1045 const std::streamsize __precision = __os.precision();
1046 __os.flags(__ios_base::scientific | __ios_base::left);
1047 __os.fill(__os.widen(' '));
1048 __os.precision(std::numeric_limits<double>::max_digits10);
1050 __os << __x.p();
1052 __os.flags(__flags);
1053 __os.fill(__fill);
1054 __os.precision(__precision);
1055 return __os;
1056 }
1058 template<typename _IntType,
1059 typename _CharT, typename _Traits>
1060 std::basic_istream<_CharT, _Traits>&
1061 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1062 geometric_distribution<_IntType>& __x)
1063 {
1064 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1065 typedef typename __istream_type::ios_base __ios_base;
1067 const typename __ios_base::fmtflags __flags = __is.flags();
1068 __is.flags(__ios_base::skipws);
1070 double __p;
1071 __is >> __p;
1072 __x.param(typename geometric_distribution<_IntType>::param_type(__p));
1074 __is.flags(__flags);
1075 return __is;
1076 }
1078 // This is Leger's algorithm, also in Devroye, Ch. X, Example 1.5.
1079 template<typename _IntType>
1080 template<typename _UniformRandomNumberGenerator>
1081 typename negative_binomial_distribution<_IntType>::result_type
1082 negative_binomial_distribution<_IntType>::
1083 operator()(_UniformRandomNumberGenerator& __urng)
1084 {
1085 const double __y = _M_gd(__urng);
1087 // XXX Is the constructor too slow?
1088 std::poisson_distribution<result_type> __poisson(__y);
1089 return __poisson(__urng);
1090 }
1092 template<typename _IntType>
1093 template<typename _UniformRandomNumberGenerator>
1094 typename negative_binomial_distribution<_IntType>::result_type
1095 negative_binomial_distribution<_IntType>::
1096 operator()(_UniformRandomNumberGenerator& __urng,
1097 const param_type& __p)
1098 {
1099 typedef typename std::gamma_distribution<result_type>::param_type
1100 param_type;
1102 const double __y =
1103 _M_gd(__urng, param_type(__p.k(), (1.0 - __p.p()) / __p.p()));
1105 std::poisson_distribution<result_type> __poisson(__y);
1106 return __poisson(__urng);
1107 }
1109 template<typename _IntType, typename _CharT, typename _Traits>
1110 std::basic_ostream<_CharT, _Traits>&
1111 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1112 const negative_binomial_distribution<_IntType>& __x)
1113 {
1114 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1115 typedef typename __ostream_type::ios_base __ios_base;
1117 const typename __ios_base::fmtflags __flags = __os.flags();
1118 const _CharT __fill = __os.fill();
1119 const std::streamsize __precision = __os.precision();
1120 const _CharT __space = __os.widen(' ');
1121 __os.flags(__ios_base::scientific | __ios_base::left);
1122 __os.fill(__os.widen(' '));
1123 __os.precision(std::numeric_limits<double>::max_digits10);
1125 __os << __x.k() << __space << __x.p()
1126 << __space << __x._M_gd;
1128 __os.flags(__flags);
1129 __os.fill(__fill);
1130 __os.precision(__precision);
1131 return __os;
1132 }
1134 template<typename _IntType, typename _CharT, typename _Traits>
1135 std::basic_istream<_CharT, _Traits>&
1136 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1137 negative_binomial_distribution<_IntType>& __x)
1138 {
1139 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1140 typedef typename __istream_type::ios_base __ios_base;
1142 const typename __ios_base::fmtflags __flags = __is.flags();
1143 __is.flags(__ios_base::skipws);
1145 _IntType __k;
1146 double __p;
1147 __is >> __k >> __p >> __x._M_gd;
1148 __x.param(typename negative_binomial_distribution<_IntType>::
1149 param_type(__k, __p));
1151 __is.flags(__flags);
1152 return __is;
1153 }
1156 template<typename _IntType>
1157 void
1158 poisson_distribution<_IntType>::param_type::
1159 _M_initialize()
1160 {
1161 #if _GLIBCXX_USE_C99_MATH_TR1
1162 if (_M_mean >= 12)
1163 {
1164 const double __m = std::floor(_M_mean);
1165 _M_lm_thr = std::log(_M_mean);
1166 _M_lfm = std::lgamma(__m + 1);
1167 _M_sm = std::sqrt(__m);
1169 const double __pi_4 = 0.7853981633974483096156608458198757L;
1170 const double __dx = std::sqrt(2 * __m * std::log(32 * __m
1171 / __pi_4));
1172 _M_d = std::round(std::max(6.0, std::min(__m, __dx)));
1173 const double __cx = 2 * __m + _M_d;
1174 _M_scx = std::sqrt(__cx / 2);
1175 _M_1cx = 1 / __cx;
1177 _M_c2b = std::sqrt(__pi_4 * __cx) * std::exp(_M_1cx);
1178 _M_cb = 2 * __cx * std::exp(-_M_d * _M_1cx * (1 + _M_d / 2))
1179 / _M_d;
1180 }
1181 else
1182 #endif
1183 _M_lm_thr = std::exp(-_M_mean);
1184 }
1186 /**
1187 * A rejection algorithm when mean >= 12 and a simple method based
1188 * upon the multiplication of uniform random variates otherwise.
1189 * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1
1190 * is defined.
1191 *
1192 * Reference:
1193 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
1194 * New York, 1986, Ch. X, Sects. 3.3 & 3.4 (+ Errata!).
1195 */
1196 template<typename _IntType>
1197 template<typename _UniformRandomNumberGenerator>
1198 typename poisson_distribution<_IntType>::result_type
1199 poisson_distribution<_IntType>::
1200 operator()(_UniformRandomNumberGenerator& __urng,
1201 const param_type& __param)
1202 {
1203 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
1204 __aurng(__urng);
1205 #if _GLIBCXX_USE_C99_MATH_TR1
1206 if (__param.mean() >= 12)
1207 {
1208 double __x;
1210 // See comments above...
1211 const double __naf =
1212 (1 - std::numeric_limits<double>::epsilon()) / 2;
1213 const double __thr =
1214 std::numeric_limits<_IntType>::max() + __naf;
1216 const double __m = std::floor(__param.mean());
1217 // sqrt(pi / 2)
1218 const double __spi_2 = 1.2533141373155002512078826424055226L;
1219 const double __c1 = __param._M_sm * __spi_2;
1220 const double __c2 = __param._M_c2b + __c1;
1221 const double __c3 = __c2 + 1;
1222 const double __c4 = __c3 + 1;
1223 // e^(1 / 78)
1224 const double __e178 = 1.0129030479320018583185514777512983L;
1225 const double __c5 = __c4 + __e178;
1226 const double __c = __param._M_cb + __c5;
1227 const double __2cx = 2 * (2 * __m + __param._M_d);
1229 bool __reject = true;
1230 do
1231 {
1232 const double __u = __c * __aurng();
1233 const double __e = -std::log(__aurng());
1235 double __w = 0.0;
1237 if (__u <= __c1)
1238 {
1239 const double __n = _M_nd(__urng);
1240 const double __y = -std::abs(__n) * __param._M_sm - 1;
1241 __x = std::floor(__y);
1242 __w = -__n * __n / 2;
1243 if (__x < -__m)
1244 continue;
1245 }
1246 else if (__u <= __c2)
1247 {
1248 const double __n = _M_nd(__urng);
1249 const double __y = 1 + std::abs(__n) * __param._M_scx;
1250 __x = std::ceil(__y);
1251 __w = __y * (2 - __y) * __param._M_1cx;
1252 if (__x > __param._M_d)
1253 continue;
1254 }
1255 else if (__u <= __c3)
1256 // NB: This case not in the book, nor in the Errata,
1257 // but should be ok...
1258 __x = -1;
1259 else if (__u <= __c4)
1260 __x = 0;
1261 else if (__u <= __c5)
1262 __x = 1;
1263 else
1264 {
1265 const double __v = -std::log(__aurng());
1266 const double __y = __param._M_d
1267 + __v * __2cx / __param._M_d;
1268 __x = std::ceil(__y);
1269 __w = -__param._M_d * __param._M_1cx * (1 + __y / 2);
1270 }
1272 __reject = (__w - __e - __x * __param._M_lm_thr
1273 > __param._M_lfm - std::lgamma(__x + __m + 1));
1275 __reject |= __x + __m >= __thr;
1277 } while (__reject);
1279 return result_type(__x + __m + __naf);
1280 }
1281 else
1282 #endif
1283 {
1284 _IntType __x = 0;
1285 double __prod = 1.0;
1287 do
1288 {
1289 __prod *= __aurng();
1290 __x += 1;
1291 }
1292 while (__prod > __param._M_lm_thr);
1294 return __x - 1;
1295 }
1296 }
1298 template<typename _IntType,
1299 typename _CharT, typename _Traits>
1300 std::basic_ostream<_CharT, _Traits>&
1301 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1302 const poisson_distribution<_IntType>& __x)
1303 {
1304 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1305 typedef typename __ostream_type::ios_base __ios_base;
1307 const typename __ios_base::fmtflags __flags = __os.flags();
1308 const _CharT __fill = __os.fill();
1309 const std::streamsize __precision = __os.precision();
1310 const _CharT __space = __os.widen(' ');
1311 __os.flags(__ios_base::scientific | __ios_base::left);
1312 __os.fill(__space);
1313 __os.precision(std::numeric_limits<double>::max_digits10);
1315 __os << __x.mean() << __space << __x._M_nd;
1317 __os.flags(__flags);
1318 __os.fill(__fill);
1319 __os.precision(__precision);
1320 return __os;
1321 }
1323 template<typename _IntType,
1324 typename _CharT, typename _Traits>
1325 std::basic_istream<_CharT, _Traits>&
1326 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1327 poisson_distribution<_IntType>& __x)
1328 {
1329 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1330 typedef typename __istream_type::ios_base __ios_base;
1332 const typename __ios_base::fmtflags __flags = __is.flags();
1333 __is.flags(__ios_base::skipws);
1335 double __mean;
1336 __is >> __mean >> __x._M_nd;
1337 __x.param(typename poisson_distribution<_IntType>::param_type(__mean));
1339 __is.flags(__flags);
1340 return __is;
1341 }
1344 template<typename _IntType>
1345 void
1346 binomial_distribution<_IntType>::param_type::
1347 _M_initialize()
1348 {
1349 const double __p12 = _M_p <= 0.5 ? _M_p : 1.0 - _M_p;
1351 _M_easy = true;
1353 #if _GLIBCXX_USE_C99_MATH_TR1
1354 if (_M_t * __p12 >= 8)
1355 {
1356 _M_easy = false;
1357 const double __np = std::floor(_M_t * __p12);
1358 const double __pa = __np / _M_t;
1359 const double __1p = 1 - __pa;
1361 const double __pi_4 = 0.7853981633974483096156608458198757L;
1362 const double __d1x =
1363 std::sqrt(__np * __1p * std::log(32 * __np
1364 / (81 * __pi_4 * __1p)));
1365 _M_d1 = std::round(std::max(1.0, __d1x));
1366 const double __d2x =
1367 std::sqrt(__np * __1p * std::log(32 * _M_t * __1p
1368 / (__pi_4 * __pa)));
1369 _M_d2 = std::round(std::max(1.0, __d2x));
1371 // sqrt(pi / 2)
1372 const double __spi_2 = 1.2533141373155002512078826424055226L;
1373 _M_s1 = std::sqrt(__np * __1p) * (1 + _M_d1 / (4 * __np));
1374 _M_s2 = std::sqrt(__np * __1p) * (1 + _M_d2 / (4 * _M_t * __1p));
1375 _M_c = 2 * _M_d1 / __np;
1376 _M_a1 = std::exp(_M_c) * _M_s1 * __spi_2;
1377 const double __a12 = _M_a1 + _M_s2 * __spi_2;
1378 const double __s1s = _M_s1 * _M_s1;
1379 _M_a123 = __a12 + (std::exp(_M_d1 / (_M_t * __1p))
1380 * 2 * __s1s / _M_d1
1381 * std::exp(-_M_d1 * _M_d1 / (2 * __s1s)));
1382 const double __s2s = _M_s2 * _M_s2;
1383 _M_s = (_M_a123 + 2 * __s2s / _M_d2
1384 * std::exp(-_M_d2 * _M_d2 / (2 * __s2s)));
1385 _M_lf = (std::lgamma(__np + 1)
1386 + std::lgamma(_M_t - __np + 1));
1387 _M_lp1p = std::log(__pa / __1p);
1389 _M_q = -std::log(1 - (__p12 - __pa) / __1p);
1390 }
1391 else
1392 #endif
1393 _M_q = -std::log(1 - __p12);
1394 }
1396 template<typename _IntType>
1397 template<typename _UniformRandomNumberGenerator>
1398 typename binomial_distribution<_IntType>::result_type
1399 binomial_distribution<_IntType>::
1400 _M_waiting(_UniformRandomNumberGenerator& __urng, _IntType __t)
1401 {
1402 _IntType __x = 0;
1403 double __sum = 0.0;
1404 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
1405 __aurng(__urng);
1407 do
1408 {
1409 const double __e = -std::log(__aurng());
1410 __sum += __e / (__t - __x);
1411 __x += 1;
1412 }
1413 while (__sum <= _M_param._M_q);
1415 return __x - 1;
1416 }
1418 /**
1419 * A rejection algorithm when t * p >= 8 and a simple waiting time
1420 * method - the second in the referenced book - otherwise.
1421 * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1
1422 * is defined.
1423 *
1424 * Reference:
1425 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
1426 * New York, 1986, Ch. X, Sect. 4 (+ Errata!).
1427 */
1428 template<typename _IntType>
1429 template<typename _UniformRandomNumberGenerator>
1430 typename binomial_distribution<_IntType>::result_type
1431 binomial_distribution<_IntType>::
1432 operator()(_UniformRandomNumberGenerator& __urng,
1433 const param_type& __param)
1434 {
1435 result_type __ret;
1436 const _IntType __t = __param.t();
1437 const double __p = __param.p();
1438 const double __p12 = __p <= 0.5 ? __p : 1.0 - __p;
1439 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
1440 __aurng(__urng);
1442 #if _GLIBCXX_USE_C99_MATH_TR1
1443 if (!__param._M_easy)
1444 {
1445 double __x;
1447 // See comments above...
1448 const double __naf =
1449 (1 - std::numeric_limits<double>::epsilon()) / 2;
1450 const double __thr =
1451 std::numeric_limits<_IntType>::max() + __naf;
1453 const double __np = std::floor(__t * __p12);
1455 // sqrt(pi / 2)
1456 const double __spi_2 = 1.2533141373155002512078826424055226L;
1457 const double __a1 = __param._M_a1;
1458 const double __a12 = __a1 + __param._M_s2 * __spi_2;
1459 const double __a123 = __param._M_a123;
1460 const double __s1s = __param._M_s1 * __param._M_s1;
1461 const double __s2s = __param._M_s2 * __param._M_s2;
1463 bool __reject;
1464 do
1465 {
1466 const double __u = __param._M_s * __aurng();
1468 double __v;
1470 if (__u <= __a1)
1471 {
1472 const double __n = _M_nd(__urng);
1473 const double __y = __param._M_s1 * std::abs(__n);
1474 __reject = __y >= __param._M_d1;
1475 if (!__reject)
1476 {
1477 const double __e = -std::log(__aurng());
1478 __x = std::floor(__y);
1479 __v = -__e - __n * __n / 2 + __param._M_c;
1480 }
1481 }
1482 else if (__u <= __a12)
1483 {
1484 const double __n = _M_nd(__urng);
1485 const double __y = __param._M_s2 * std::abs(__n);
1486 __reject = __y >= __param._M_d2;
1487 if (!__reject)
1488 {
1489 const double __e = -std::log(__aurng());
1490 __x = std::floor(-__y);
1491 __v = -__e - __n * __n / 2;
1492 }
1493 }
1494 else if (__u <= __a123)
1495 {
1496 const double __e1 = -std::log(__aurng());
1497 const double __e2 = -std::log(__aurng());
1499 const double __y = __param._M_d1
1500 + 2 * __s1s * __e1 / __param._M_d1;
1501 __x = std::floor(__y);
1502 __v = (-__e2 + __param._M_d1 * (1 / (__t - __np)
1503 -__y / (2 * __s1s)));
1504 __reject = false;
1505 }
1506 else
1507 {
1508 const double __e1 = -std::log(__aurng());
1509 const double __e2 = -std::log(__aurng());
1511 const double __y = __param._M_d2
1512 + 2 * __s2s * __e1 / __param._M_d2;
1513 __x = std::floor(-__y);
1514 __v = -__e2 - __param._M_d2 * __y / (2 * __s2s);
1515 __reject = false;
1516 }
1518 __reject = __reject || __x < -__np || __x > __t - __np;
1519 if (!__reject)
1520 {
1521 const double __lfx =
1522 std::lgamma(__np + __x + 1)
1523 + std::lgamma(__t - (__np + __x) + 1);
1524 __reject = __v > __param._M_lf - __lfx
1525 + __x * __param._M_lp1p;
1526 }
1528 __reject |= __x + __np >= __thr;
1529 }
1530 while (__reject);
1532 __x += __np + __naf;
1534 const _IntType __z = _M_waiting(__urng, __t - _IntType(__x));
1535 __ret = _IntType(__x) + __z;
1536 }
1537 else
1538 #endif
1539 __ret = _M_waiting(__urng, __t);
1541 if (__p12 != __p)
1542 __ret = __t - __ret;
1543 return __ret;
1544 }
1546 template<typename _IntType,
1547 typename _CharT, typename _Traits>
1548 std::basic_ostream<_CharT, _Traits>&
1549 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1550 const binomial_distribution<_IntType>& __x)
1551 {
1552 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1553 typedef typename __ostream_type::ios_base __ios_base;
1555 const typename __ios_base::fmtflags __flags = __os.flags();
1556 const _CharT __fill = __os.fill();
1557 const std::streamsize __precision = __os.precision();
1558 const _CharT __space = __os.widen(' ');
1559 __os.flags(__ios_base::scientific | __ios_base::left);
1560 __os.fill(__space);
1561 __os.precision(std::numeric_limits<double>::max_digits10);
1563 __os << __x.t() << __space << __x.p()
1564 << __space << __x._M_nd;
1566 __os.flags(__flags);
1567 __os.fill(__fill);
1568 __os.precision(__precision);
1569 return __os;
1570 }
1572 template<typename _IntType,
1573 typename _CharT, typename _Traits>
1574 std::basic_istream<_CharT, _Traits>&
1575 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1576 binomial_distribution<_IntType>& __x)
1577 {
1578 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1579 typedef typename __istream_type::ios_base __ios_base;
1581 const typename __ios_base::fmtflags __flags = __is.flags();
1582 __is.flags(__ios_base::dec | __ios_base::skipws);
1584 _IntType __t;
1585 double __p;
1586 __is >> __t >> __p >> __x._M_nd;
1587 __x.param(typename binomial_distribution<_IntType>::
1588 param_type(__t, __p));
1590 __is.flags(__flags);
1591 return __is;
1592 }
1595 template<typename _RealType, typename _CharT, typename _Traits>
1596 std::basic_ostream<_CharT, _Traits>&
1597 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1598 const exponential_distribution<_RealType>& __x)
1599 {
1600 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1601 typedef typename __ostream_type::ios_base __ios_base;
1603 const typename __ios_base::fmtflags __flags = __os.flags();
1604 const _CharT __fill = __os.fill();
1605 const std::streamsize __precision = __os.precision();
1606 __os.flags(__ios_base::scientific | __ios_base::left);
1607 __os.fill(__os.widen(' '));
1608 __os.precision(std::numeric_limits<_RealType>::max_digits10);
1610 __os << __x.lambda();
1612 __os.flags(__flags);
1613 __os.fill(__fill);
1614 __os.precision(__precision);
1615 return __os;
1616 }
1618 template<typename _RealType, typename _CharT, typename _Traits>
1619 std::basic_istream<_CharT, _Traits>&
1620 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1621 exponential_distribution<_RealType>& __x)
1622 {
1623 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1624 typedef typename __istream_type::ios_base __ios_base;
1626 const typename __ios_base::fmtflags __flags = __is.flags();
1627 __is.flags(__ios_base::dec | __ios_base::skipws);
1629 _RealType __lambda;
1630 __is >> __lambda;
1631 __x.param(typename exponential_distribution<_RealType>::
1632 param_type(__lambda));
1634 __is.flags(__flags);
1635 return __is;
1636 }
1639 /**
1640 * Polar method due to Marsaglia.
1641 *
1642 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
1643 * New York, 1986, Ch. V, Sect. 4.4.
1644 */
1645 template<typename _RealType>
1646 template<typename _UniformRandomNumberGenerator>
1647 typename normal_distribution<_RealType>::result_type
1648 normal_distribution<_RealType>::
1649 operator()(_UniformRandomNumberGenerator& __urng,
1650 const param_type& __param)
1651 {
1652 result_type __ret;
1653 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
1654 __aurng(__urng);
1656 if (_M_saved_available)
1657 {
1658 _M_saved_available = false;
1659 __ret = _M_saved;
1660 }
1661 else
1662 {
1663 result_type __x, __y, __r2;
1664 do
1665 {
1666 __x = result_type(2.0) * __aurng() - 1.0;
1667 __y = result_type(2.0) * __aurng() - 1.0;
1668 __r2 = __x * __x + __y * __y;
1669 }
1670 while (__r2 > 1.0 || __r2 == 0.0);
1672 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2);
1673 _M_saved = __x * __mult;
1674 _M_saved_available = true;
1675 __ret = __y * __mult;
1676 }
1678 __ret = __ret * __param.stddev() + __param.mean();
1679 return __ret;
1680 }
1682 template<typename _RealType>
1683 bool
1684 operator==(const std::normal_distribution<_RealType>& __d1,
1685 const std::normal_distribution<_RealType>& __d2)
1686 {
1687 if (__d1._M_param == __d2._M_param
1688 && __d1._M_saved_available == __d2._M_saved_available)
1689 {
1690 if (__d1._M_saved_available
1691 && __d1._M_saved == __d2._M_saved)
1692 return true;
1693 else if(!__d1._M_saved_available)
1694 return true;
1695 else
1696 return false;
1697 }
1698 else
1699 return false;
1700 }
1702 template<typename _RealType, typename _CharT, typename _Traits>
1703 std::basic_ostream<_CharT, _Traits>&
1704 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1705 const normal_distribution<_RealType>& __x)
1706 {
1707 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1708 typedef typename __ostream_type::ios_base __ios_base;
1710 const typename __ios_base::fmtflags __flags = __os.flags();
1711 const _CharT __fill = __os.fill();
1712 const std::streamsize __precision = __os.precision();
1713 const _CharT __space = __os.widen(' ');
1714 __os.flags(__ios_base::scientific | __ios_base::left);
1715 __os.fill(__space);
1716 __os.precision(std::numeric_limits<_RealType>::max_digits10);
1718 __os << __x.mean() << __space << __x.stddev()
1719 << __space << __x._M_saved_available;
1720 if (__x._M_saved_available)
1721 __os << __space << __x._M_saved;
1723 __os.flags(__flags);
1724 __os.fill(__fill);
1725 __os.precision(__precision);
1726 return __os;
1727 }
1729 template<typename _RealType, typename _CharT, typename _Traits>
1730 std::basic_istream<_CharT, _Traits>&
1731 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1732 normal_distribution<_RealType>& __x)
1733 {
1734 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1735 typedef typename __istream_type::ios_base __ios_base;
1737 const typename __ios_base::fmtflags __flags = __is.flags();
1738 __is.flags(__ios_base::dec | __ios_base::skipws);
1740 double __mean, __stddev;
1741 __is >> __mean >> __stddev
1742 >> __x._M_saved_available;
1743 if (__x._M_saved_available)
1744 __is >> __x._M_saved;
1745 __x.param(typename normal_distribution<_RealType>::
1746 param_type(__mean, __stddev));
1748 __is.flags(__flags);
1749 return __is;
1750 }
1753 template<typename _RealType, typename _CharT, typename _Traits>
1754 std::basic_ostream<_CharT, _Traits>&
1755 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1756 const lognormal_distribution<_RealType>& __x)
1757 {
1758 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1759 typedef typename __ostream_type::ios_base __ios_base;
1761 const typename __ios_base::fmtflags __flags = __os.flags();
1762 const _CharT __fill = __os.fill();
1763 const std::streamsize __precision = __os.precision();
1764 const _CharT __space = __os.widen(' ');
1765 __os.flags(__ios_base::scientific | __ios_base::left);
1766 __os.fill(__space);
1767 __os.precision(std::numeric_limits<_RealType>::max_digits10);
1769 __os << __x.m() << __space << __x.s()
1770 << __space << __x._M_nd;
1772 __os.flags(__flags);
1773 __os.fill(__fill);
1774 __os.precision(__precision);
1775 return __os;
1776 }
1778 template<typename _RealType, typename _CharT, typename _Traits>
1779 std::basic_istream<_CharT, _Traits>&
1780 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1781 lognormal_distribution<_RealType>& __x)
1782 {
1783 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1784 typedef typename __istream_type::ios_base __ios_base;
1786 const typename __ios_base::fmtflags __flags = __is.flags();
1787 __is.flags(__ios_base::dec | __ios_base::skipws);
1789 _RealType __m, __s;
1790 __is >> __m >> __s >> __x._M_nd;
1791 __x.param(typename lognormal_distribution<_RealType>::
1792 param_type(__m, __s));
1794 __is.flags(__flags);
1795 return __is;
1796 }
1799 template<typename _RealType, typename _CharT, typename _Traits>
1800 std::basic_ostream<_CharT, _Traits>&
1801 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1802 const chi_squared_distribution<_RealType>& __x)
1803 {
1804 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1805 typedef typename __ostream_type::ios_base __ios_base;
1807 const typename __ios_base::fmtflags __flags = __os.flags();
1808 const _CharT __fill = __os.fill();
1809 const std::streamsize __precision = __os.precision();
1810 const _CharT __space = __os.widen(' ');
1811 __os.flags(__ios_base::scientific | __ios_base::left);
1812 __os.fill(__space);
1813 __os.precision(std::numeric_limits<_RealType>::max_digits10);
1815 __os << __x.n() << __space << __x._M_gd;
1817 __os.flags(__flags);
1818 __os.fill(__fill);
1819 __os.precision(__precision);
1820 return __os;
1821 }
1823 template<typename _RealType, typename _CharT, typename _Traits>
1824 std::basic_istream<_CharT, _Traits>&
1825 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1826 chi_squared_distribution<_RealType>& __x)
1827 {
1828 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1829 typedef typename __istream_type::ios_base __ios_base;
1831 const typename __ios_base::fmtflags __flags = __is.flags();
1832 __is.flags(__ios_base::dec | __ios_base::skipws);
1834 _RealType __n;
1835 __is >> __n >> __x._M_gd;
1836 __x.param(typename chi_squared_distribution<_RealType>::
1837 param_type(__n));
1839 __is.flags(__flags);
1840 return __is;
1841 }
1844 template<typename _RealType>
1845 template<typename _UniformRandomNumberGenerator>
1846 typename cauchy_distribution<_RealType>::result_type
1847 cauchy_distribution<_RealType>::
1848 operator()(_UniformRandomNumberGenerator& __urng,
1849 const param_type& __p)
1850 {
1851 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
1852 __aurng(__urng);
1853 _RealType __u;
1854 do
1855 __u = __aurng();
1856 while (__u == 0.5);
1858 const _RealType __pi = 3.1415926535897932384626433832795029L;
1859 return __p.a() + __p.b() * std::tan(__pi * __u);
1860 }
1862 template<typename _RealType, typename _CharT, typename _Traits>
1863 std::basic_ostream<_CharT, _Traits>&
1864 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1865 const cauchy_distribution<_RealType>& __x)
1866 {
1867 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1868 typedef typename __ostream_type::ios_base __ios_base;
1870 const typename __ios_base::fmtflags __flags = __os.flags();
1871 const _CharT __fill = __os.fill();
1872 const std::streamsize __precision = __os.precision();
1873 const _CharT __space = __os.widen(' ');
1874 __os.flags(__ios_base::scientific | __ios_base::left);
1875 __os.fill(__space);
1876 __os.precision(std::numeric_limits<_RealType>::max_digits10);
1878 __os << __x.a() << __space << __x.b();
1880 __os.flags(__flags);
1881 __os.fill(__fill);
1882 __os.precision(__precision);
1883 return __os;
1884 }
1886 template<typename _RealType, typename _CharT, typename _Traits>
1887 std::basic_istream<_CharT, _Traits>&
1888 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1889 cauchy_distribution<_RealType>& __x)
1890 {
1891 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1892 typedef typename __istream_type::ios_base __ios_base;
1894 const typename __ios_base::fmtflags __flags = __is.flags();
1895 __is.flags(__ios_base::dec | __ios_base::skipws);
1897 _RealType __a, __b;
1898 __is >> __a >> __b;
1899 __x.param(typename cauchy_distribution<_RealType>::
1900 param_type(__a, __b));
1902 __is.flags(__flags);
1903 return __is;
1904 }
1907 template<typename _RealType, typename _CharT, typename _Traits>
1908 std::basic_ostream<_CharT, _Traits>&
1909 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1910 const fisher_f_distribution<_RealType>& __x)
1911 {
1912 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1913 typedef typename __ostream_type::ios_base __ios_base;
1915 const typename __ios_base::fmtflags __flags = __os.flags();
1916 const _CharT __fill = __os.fill();
1917 const std::streamsize __precision = __os.precision();
1918 const _CharT __space = __os.widen(' ');
1919 __os.flags(__ios_base::scientific | __ios_base::left);
1920 __os.fill(__space);
1921 __os.precision(std::numeric_limits<_RealType>::max_digits10);
1923 __os << __x.m() << __space << __x.n()
1924 << __space << __x._M_gd_x << __space << __x._M_gd_y;
1926 __os.flags(__flags);
1927 __os.fill(__fill);
1928 __os.precision(__precision);
1929 return __os;
1930 }
1932 template<typename _RealType, typename _CharT, typename _Traits>
1933 std::basic_istream<_CharT, _Traits>&
1934 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1935 fisher_f_distribution<_RealType>& __x)
1936 {
1937 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1938 typedef typename __istream_type::ios_base __ios_base;
1940 const typename __ios_base::fmtflags __flags = __is.flags();
1941 __is.flags(__ios_base::dec | __ios_base::skipws);
1943 _RealType __m, __n;
1944 __is >> __m >> __n >> __x._M_gd_x >> __x._M_gd_y;
1945 __x.param(typename fisher_f_distribution<_RealType>::
1946 param_type(__m, __n));
1948 __is.flags(__flags);
1949 return __is;
1950 }
1953 template<typename _RealType, typename _CharT, typename _Traits>
1954 std::basic_ostream<_CharT, _Traits>&
1955 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1956 const student_t_distribution<_RealType>& __x)
1957 {
1958 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1959 typedef typename __ostream_type::ios_base __ios_base;
1961 const typename __ios_base::fmtflags __flags = __os.flags();
1962 const _CharT __fill = __os.fill();
1963 const std::streamsize __precision = __os.precision();
1964 const _CharT __space = __os.widen(' ');
1965 __os.flags(__ios_base::scientific | __ios_base::left);
1966 __os.fill(__space);
1967 __os.precision(std::numeric_limits<_RealType>::max_digits10);
1969 __os << __x.n() << __space << __x._M_nd << __space << __x._M_gd;
1971 __os.flags(__flags);
1972 __os.fill(__fill);
1973 __os.precision(__precision);
1974 return __os;
1975 }
1977 template<typename _RealType, typename _CharT, typename _Traits>
1978 std::basic_istream<_CharT, _Traits>&
1979 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1980 student_t_distribution<_RealType>& __x)
1981 {
1982 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1983 typedef typename __istream_type::ios_base __ios_base;
1985 const typename __ios_base::fmtflags __flags = __is.flags();
1986 __is.flags(__ios_base::dec | __ios_base::skipws);
1988 _RealType __n;
1989 __is >> __n >> __x._M_nd >> __x._M_gd;
1990 __x.param(typename student_t_distribution<_RealType>::param_type(__n));
1992 __is.flags(__flags);
1993 return __is;
1994 }
1997 template<typename _RealType>
1998 void
1999 gamma_distribution<_RealType>::param_type::
2000 _M_initialize()
2001 {
2002 _M_malpha = _M_alpha < 1.0 ? _M_alpha + _RealType(1.0) : _M_alpha;
2004 const _RealType __a1 = _M_malpha - _RealType(1.0) / _RealType(3.0);
2005 _M_a2 = _RealType(1.0) / std::sqrt(_RealType(9.0) * __a1);
2006 }
2008 /**
2009 * Marsaglia, G. and Tsang, W. W.
2010 * "A Simple Method for Generating Gamma Variables"
2011 * ACM Transactions on Mathematical Software, 26, 3, 363-372, 2000.
2012 */
2013 template<typename _RealType>
2014 template<typename _UniformRandomNumberGenerator>
2015 typename gamma_distribution<_RealType>::result_type
2016 gamma_distribution<_RealType>::
2017 operator()(_UniformRandomNumberGenerator& __urng,
2018 const param_type& __param)
2019 {
2020 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2021 __aurng(__urng);
2023 result_type __u, __v, __n;
2024 const result_type __a1 = (__param._M_malpha
2025 - _RealType(1.0) / _RealType(3.0));
2027 do
2028 {
2029 do
2030 {
2031 __n = _M_nd(__urng);
2032 __v = result_type(1.0) + __param._M_a2 * __n;
2033 }
2034 while (__v <= 0.0);
2036 __v = __v * __v * __v;
2037 __u = __aurng();
2038 }
2039 while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n
2040 && (std::log(__u) > (0.5 * __n * __n + __a1
2041 * (1.0 - __v + std::log(__v)))));
2043 if (__param.alpha() == __param._M_malpha)
2044 return __a1 * __v * __param.beta();
2045 else
2046 {
2047 do
2048 __u = __aurng();
2049 while (__u == 0.0);
2051 return (std::pow(__u, result_type(1.0) / __param.alpha())
2052 * __a1 * __v * __param.beta());
2053 }
2054 }
2056 template<typename _RealType, typename _CharT, typename _Traits>
2057 std::basic_ostream<_CharT, _Traits>&
2058 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2059 const gamma_distribution<_RealType>& __x)
2060 {
2061 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2062 typedef typename __ostream_type::ios_base __ios_base;
2064 const typename __ios_base::fmtflags __flags = __os.flags();
2065 const _CharT __fill = __os.fill();
2066 const std::streamsize __precision = __os.precision();
2067 const _CharT __space = __os.widen(' ');
2068 __os.flags(__ios_base::scientific | __ios_base::left);
2069 __os.fill(__space);
2070 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2072 __os << __x.alpha() << __space << __x.beta()
2073 << __space << __x._M_nd;
2075 __os.flags(__flags);
2076 __os.fill(__fill);
2077 __os.precision(__precision);
2078 return __os;
2079 }
2081 template<typename _RealType, typename _CharT, typename _Traits>
2082 std::basic_istream<_CharT, _Traits>&
2083 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2084 gamma_distribution<_RealType>& __x)
2085 {
2086 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2087 typedef typename __istream_type::ios_base __ios_base;
2089 const typename __ios_base::fmtflags __flags = __is.flags();
2090 __is.flags(__ios_base::dec | __ios_base::skipws);
2092 _RealType __alpha_val, __beta_val;
2093 __is >> __alpha_val >> __beta_val >> __x._M_nd;
2094 __x.param(typename gamma_distribution<_RealType>::
2095 param_type(__alpha_val, __beta_val));
2097 __is.flags(__flags);
2098 return __is;
2099 }
2102 template<typename _RealType>
2103 template<typename _UniformRandomNumberGenerator>
2104 typename weibull_distribution<_RealType>::result_type
2105 weibull_distribution<_RealType>::
2106 operator()(_UniformRandomNumberGenerator& __urng,
2107 const param_type& __p)
2108 {
2109 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2110 __aurng(__urng);
2111 return __p.b() * std::pow(-std::log(__aurng()),
2112 result_type(1) / __p.a());
2113 }
2115 template<typename _RealType, typename _CharT, typename _Traits>
2116 std::basic_ostream<_CharT, _Traits>&
2117 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2118 const weibull_distribution<_RealType>& __x)
2119 {
2120 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2121 typedef typename __ostream_type::ios_base __ios_base;
2123 const typename __ios_base::fmtflags __flags = __os.flags();
2124 const _CharT __fill = __os.fill();
2125 const std::streamsize __precision = __os.precision();
2126 const _CharT __space = __os.widen(' ');
2127 __os.flags(__ios_base::scientific | __ios_base::left);
2128 __os.fill(__space);
2129 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2131 __os << __x.a() << __space << __x.b();
2133 __os.flags(__flags);
2134 __os.fill(__fill);
2135 __os.precision(__precision);
2136 return __os;
2137 }
2139 template<typename _RealType, typename _CharT, typename _Traits>
2140 std::basic_istream<_CharT, _Traits>&
2141 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2142 weibull_distribution<_RealType>& __x)
2143 {
2144 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2145 typedef typename __istream_type::ios_base __ios_base;
2147 const typename __ios_base::fmtflags __flags = __is.flags();
2148 __is.flags(__ios_base::dec | __ios_base::skipws);
2150 _RealType __a, __b;
2151 __is >> __a >> __b;
2152 __x.param(typename weibull_distribution<_RealType>::
2153 param_type(__a, __b));
2155 __is.flags(__flags);
2156 return __is;
2157 }
2160 template<typename _RealType>
2161 template<typename _UniformRandomNumberGenerator>
2162 typename extreme_value_distribution<_RealType>::result_type
2163 extreme_value_distribution<_RealType>::
2164 operator()(_UniformRandomNumberGenerator& __urng,
2165 const param_type& __p)
2166 {
2167 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2168 __aurng(__urng);
2169 return __p.a() - __p.b() * std::log(-std::log(__aurng()));
2170 }
2172 template<typename _RealType, typename _CharT, typename _Traits>
2173 std::basic_ostream<_CharT, _Traits>&
2174 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2175 const extreme_value_distribution<_RealType>& __x)
2176 {
2177 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2178 typedef typename __ostream_type::ios_base __ios_base;
2180 const typename __ios_base::fmtflags __flags = __os.flags();
2181 const _CharT __fill = __os.fill();
2182 const std::streamsize __precision = __os.precision();
2183 const _CharT __space = __os.widen(' ');
2184 __os.flags(__ios_base::scientific | __ios_base::left);
2185 __os.fill(__space);
2186 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2188 __os << __x.a() << __space << __x.b();
2190 __os.flags(__flags);
2191 __os.fill(__fill);
2192 __os.precision(__precision);
2193 return __os;
2194 }
2196 template<typename _RealType, typename _CharT, typename _Traits>
2197 std::basic_istream<_CharT, _Traits>&
2198 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2199 extreme_value_distribution<_RealType>& __x)
2200 {
2201 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2202 typedef typename __istream_type::ios_base __ios_base;
2204 const typename __ios_base::fmtflags __flags = __is.flags();
2205 __is.flags(__ios_base::dec | __ios_base::skipws);
2207 _RealType __a, __b;
2208 __is >> __a >> __b;
2209 __x.param(typename extreme_value_distribution<_RealType>::
2210 param_type(__a, __b));
2212 __is.flags(__flags);
2213 return __is;
2214 }
2217 template<typename _IntType>
2218 void
2219 discrete_distribution<_IntType>::param_type::
2220 _M_initialize()
2221 {
2222 if (_M_prob.size() < 2)
2223 {
2224 _M_prob.clear();
2225 return;
2226 }
2228 const double __sum = std::accumulate(_M_prob.begin(),
2229 _M_prob.end(), 0.0);
2230 // Now normalize the probabilites.
2231 __detail::__transform(_M_prob.begin(), _M_prob.end(), _M_prob.begin(),
2232 std::bind2nd(std::divides<double>(), __sum));
2233 // Accumulate partial sums.
2234 _M_cp.reserve(_M_prob.size());
2235 std::partial_sum(_M_prob.begin(), _M_prob.end(),
2236 std::back_inserter(_M_cp));
2237 // Make sure the last cumulative probability is one.
2238 _M_cp[_M_cp.size() - 1] = 1.0;
2239 }
2241 template<typename _IntType>
2242 template<typename _Func>
2243 discrete_distribution<_IntType>::param_type::
2244 param_type(size_t __nw, double __xmin, double __xmax, _Func __fw)
2245 : _M_prob(), _M_cp()
2246 {
2247 const size_t __n = __nw == 0 ? 1 : __nw;
2248 const double __delta = (__xmax - __xmin) / __n;
2250 _M_prob.reserve(__n);
2251 for (size_t __k = 0; __k < __nw; ++__k)
2252 _M_prob.push_back(__fw(__xmin + __k * __delta + 0.5 * __delta));
2254 _M_initialize();
2255 }
2257 template<typename _IntType>
2258 template<typename _UniformRandomNumberGenerator>
2259 typename discrete_distribution<_IntType>::result_type
2260 discrete_distribution<_IntType>::
2261 operator()(_UniformRandomNumberGenerator& __urng,
2262 const param_type& __param)
2263 {
2264 if (__param._M_cp.empty())
2265 return result_type(0);
2267 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
2268 __aurng(__urng);
2270 const double __p = __aurng();
2271 auto __pos = std::lower_bound(__param._M_cp.begin(),
2272 __param._M_cp.end(), __p);
2274 return __pos - __param._M_cp.begin();
2275 }
2277 template<typename _IntType, typename _CharT, typename _Traits>
2278 std::basic_ostream<_CharT, _Traits>&
2279 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2280 const discrete_distribution<_IntType>& __x)
2281 {
2282 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2283 typedef typename __ostream_type::ios_base __ios_base;
2285 const typename __ios_base::fmtflags __flags = __os.flags();
2286 const _CharT __fill = __os.fill();
2287 const std::streamsize __precision = __os.precision();
2288 const _CharT __space = __os.widen(' ');
2289 __os.flags(__ios_base::scientific | __ios_base::left);
2290 __os.fill(__space);
2291 __os.precision(std::numeric_limits<double>::max_digits10);
2293 std::vector<double> __prob = __x.probabilities();
2294 __os << __prob.size();
2295 for (auto __dit = __prob.begin(); __dit != __prob.end(); ++__dit)
2296 __os << __space << *__dit;
2298 __os.flags(__flags);
2299 __os.fill(__fill);
2300 __os.precision(__precision);
2301 return __os;
2302 }
2304 template<typename _IntType, typename _CharT, typename _Traits>
2305 std::basic_istream<_CharT, _Traits>&
2306 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2307 discrete_distribution<_IntType>& __x)
2308 {
2309 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2310 typedef typename __istream_type::ios_base __ios_base;
2312 const typename __ios_base::fmtflags __flags = __is.flags();
2313 __is.flags(__ios_base::dec | __ios_base::skipws);
2315 size_t __n;
2316 __is >> __n;
2318 std::vector<double> __prob_vec;
2319 __prob_vec.reserve(__n);
2320 for (; __n != 0; --__n)
2321 {
2322 double __prob;
2323 __is >> __prob;
2324 __prob_vec.push_back(__prob);
2325 }
2327 __x.param(typename discrete_distribution<_IntType>::
2328 param_type(__prob_vec.begin(), __prob_vec.end()));
2330 __is.flags(__flags);
2331 return __is;
2332 }
2335 template<typename _RealType>
2336 void
2337 piecewise_constant_distribution<_RealType>::param_type::
2338 _M_initialize()
2339 {
2340 if (_M_int.size() < 2
2341 || (_M_int.size() == 2
2342 && _M_int[0] == _RealType(0)
2343 && _M_int[1] == _RealType(1)))
2344 {
2345 _M_int.clear();
2346 _M_den.clear();
2347 return;
2348 }
2350 const double __sum = std::accumulate(_M_den.begin(),
2351 _M_den.end(), 0.0);
2353 __detail::__transform(_M_den.begin(), _M_den.end(), _M_den.begin(),
2354 std::bind2nd(std::divides<double>(), __sum));
2356 _M_cp.reserve(_M_den.size());
2357 std::partial_sum(_M_den.begin(), _M_den.end(),
2358 std::back_inserter(_M_cp));
2360 // Make sure the last cumulative probability is one.
2361 _M_cp[_M_cp.size() - 1] = 1.0;
2363 for (size_t __k = 0; __k < _M_den.size(); ++__k)
2364 _M_den[__k] /= _M_int[__k + 1] - _M_int[__k];
2365 }
2367 template<typename _RealType>
2368 template<typename _InputIteratorB, typename _InputIteratorW>
2369 piecewise_constant_distribution<_RealType>::param_type::
2370 param_type(_InputIteratorB __bbegin,
2371 _InputIteratorB __bend,
2372 _InputIteratorW __wbegin)
2373 : _M_int(), _M_den(), _M_cp()
2374 {
2375 if (__bbegin != __bend)
2376 {
2377 for (;;)
2378 {
2379 _M_int.push_back(*__bbegin);
2380 ++__bbegin;
2381 if (__bbegin == __bend)
2382 break;
2384 _M_den.push_back(*__wbegin);
2385 ++__wbegin;
2386 }
2387 }
2389 _M_initialize();
2390 }
2392 template<typename _RealType>
2393 template<typename _Func>
2394 piecewise_constant_distribution<_RealType>::param_type::
2395 param_type(initializer_list<_RealType> __bl, _Func __fw)
2396 : _M_int(), _M_den(), _M_cp()
2397 {
2398 _M_int.reserve(__bl.size());
2399 for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter)
2400 _M_int.push_back(*__biter);
2402 _M_den.reserve(_M_int.size() - 1);
2403 for (size_t __k = 0; __k < _M_int.size() - 1; ++__k)
2404 _M_den.push_back(__fw(0.5 * (_M_int[__k + 1] + _M_int[__k])));
2406 _M_initialize();
2407 }
2409 template<typename _RealType>
2410 template<typename _Func>
2411 piecewise_constant_distribution<_RealType>::param_type::
2412 param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw)
2413 : _M_int(), _M_den(), _M_cp()
2414 {
2415 const size_t __n = __nw == 0 ? 1 : __nw;
2416 const _RealType __delta = (__xmax - __xmin) / __n;
2418 _M_int.reserve(__n + 1);
2419 for (size_t __k = 0; __k <= __nw; ++__k)
2420 _M_int.push_back(__xmin + __k * __delta);
2422 _M_den.reserve(__n);
2423 for (size_t __k = 0; __k < __nw; ++__k)
2424 _M_den.push_back(__fw(_M_int[__k] + 0.5 * __delta));
2426 _M_initialize();
2427 }
2429 template<typename _RealType>
2430 template<typename _UniformRandomNumberGenerator>
2431 typename piecewise_constant_distribution<_RealType>::result_type
2432 piecewise_constant_distribution<_RealType>::
2433 operator()(_UniformRandomNumberGenerator& __urng,
2434 const param_type& __param)
2435 {
2436 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
2437 __aurng(__urng);
2439 const double __p = __aurng();
2440 if (__param._M_cp.empty())
2441 return __p;
2443 auto __pos = std::lower_bound(__param._M_cp.begin(),
2444 __param._M_cp.end(), __p);
2445 const size_t __i = __pos - __param._M_cp.begin();
2447 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0;
2449 return __param._M_int[__i] + (__p - __pref) / __param._M_den[__i];
2450 }
2452 template<typename _RealType, typename _CharT, typename _Traits>
2453 std::basic_ostream<_CharT, _Traits>&
2454 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2455 const piecewise_constant_distribution<_RealType>& __x)
2456 {
2457 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2458 typedef typename __ostream_type::ios_base __ios_base;
2460 const typename __ios_base::fmtflags __flags = __os.flags();
2461 const _CharT __fill = __os.fill();
2462 const std::streamsize __precision = __os.precision();
2463 const _CharT __space = __os.widen(' ');
2464 __os.flags(__ios_base::scientific | __ios_base::left);
2465 __os.fill(__space);
2466 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2468 std::vector<_RealType> __int = __x.intervals();
2469 __os << __int.size() - 1;
2471 for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit)
2472 __os << __space << *__xit;
2474 std::vector<double> __den = __x.densities();
2475 for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit)
2476 __os << __space << *__dit;
2478 __os.flags(__flags);
2479 __os.fill(__fill);
2480 __os.precision(__precision);
2481 return __os;
2482 }
2484 template<typename _RealType, typename _CharT, typename _Traits>
2485 std::basic_istream<_CharT, _Traits>&
2486 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2487 piecewise_constant_distribution<_RealType>& __x)
2488 {
2489 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2490 typedef typename __istream_type::ios_base __ios_base;
2492 const typename __ios_base::fmtflags __flags = __is.flags();
2493 __is.flags(__ios_base::dec | __ios_base::skipws);
2495 size_t __n;
2496 __is >> __n;
2498 std::vector<_RealType> __int_vec;
2499 __int_vec.reserve(__n + 1);
2500 for (size_t __i = 0; __i <= __n; ++__i)
2501 {
2502 _RealType __int;
2503 __is >> __int;
2504 __int_vec.push_back(__int);
2505 }
2507 std::vector<double> __den_vec;
2508 __den_vec.reserve(__n);
2509 for (size_t __i = 0; __i < __n; ++__i)
2510 {
2511 double __den;
2512 __is >> __den;
2513 __den_vec.push_back(__den);
2514 }
2516 __x.param(typename piecewise_constant_distribution<_RealType>::
2517 param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin()));
2519 __is.flags(__flags);
2520 return __is;
2521 }
2524 template<typename _RealType>
2525 void
2526 piecewise_linear_distribution<_RealType>::param_type::
2527 _M_initialize()
2528 {
2529 if (_M_int.size() < 2
2530 || (_M_int.size() == 2
2531 && _M_int[0] == _RealType(0)
2532 && _M_int[1] == _RealType(1)
2533 && _M_den[0] == _M_den[1]))
2534 {
2535 _M_int.clear();
2536 _M_den.clear();
2537 return;
2538 }
2540 double __sum = 0.0;
2541 _M_cp.reserve(_M_int.size() - 1);
2542 _M_m.reserve(_M_int.size() - 1);
2543 for (size_t __k = 0; __k < _M_int.size() - 1; ++__k)
2544 {
2545 const _RealType __delta = _M_int[__k + 1] - _M_int[__k];
2546 __sum += 0.5 * (_M_den[__k + 1] + _M_den[__k]) * __delta;
2547 _M_cp.push_back(__sum);
2548 _M_m.push_back((_M_den[__k + 1] - _M_den[__k]) / __delta);
2549 }
2551 // Now normalize the densities...
2552 __detail::__transform(_M_den.begin(), _M_den.end(), _M_den.begin(),
2553 std::bind2nd(std::divides<double>(), __sum));
2554 // ... and partial sums...
2555 __detail::__transform(_M_cp.begin(), _M_cp.end(), _M_cp.begin(),
2556 std::bind2nd(std::divides<double>(), __sum));
2557 // ... and slopes.
2558 __detail::__transform(_M_m.begin(), _M_m.end(), _M_m.begin(),
2559 std::bind2nd(std::divides<double>(), __sum));
2560 // Make sure the last cumulative probablility is one.
2561 _M_cp[_M_cp.size() - 1] = 1.0;
2562 }
2564 template<typename _RealType>
2565 template<typename _InputIteratorB, typename _InputIteratorW>
2566 piecewise_linear_distribution<_RealType>::param_type::
2567 param_type(_InputIteratorB __bbegin,
2568 _InputIteratorB __bend,
2569 _InputIteratorW __wbegin)
2570 : _M_int(), _M_den(), _M_cp(), _M_m()
2571 {
2572 for (; __bbegin != __bend; ++__bbegin, ++__wbegin)
2573 {
2574 _M_int.push_back(*__bbegin);
2575 _M_den.push_back(*__wbegin);
2576 }
2578 _M_initialize();
2579 }
2581 template<typename _RealType>
2582 template<typename _Func>
2583 piecewise_linear_distribution<_RealType>::param_type::
2584 param_type(initializer_list<_RealType> __bl, _Func __fw)
2585 : _M_int(), _M_den(), _M_cp(), _M_m()
2586 {
2587 _M_int.reserve(__bl.size());
2588 _M_den.reserve(__bl.size());
2589 for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter)
2590 {
2591 _M_int.push_back(*__biter);
2592 _M_den.push_back(__fw(*__biter));
2593 }
2595 _M_initialize();
2596 }
2598 template<typename _RealType>
2599 template<typename _Func>
2600 piecewise_linear_distribution<_RealType>::param_type::
2601 param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw)
2602 : _M_int(), _M_den(), _M_cp(), _M_m()
2603 {
2604 const size_t __n = __nw == 0 ? 1 : __nw;
2605 const _RealType __delta = (__xmax - __xmin) / __n;
2607 _M_int.reserve(__n + 1);
2608 _M_den.reserve(__n + 1);
2609 for (size_t __k = 0; __k <= __nw; ++__k)
2610 {
2611 _M_int.push_back(__xmin + __k * __delta);
2612 _M_den.push_back(__fw(_M_int[__k] + __delta));
2613 }
2615 _M_initialize();
2616 }
2618 template<typename _RealType>
2619 template<typename _UniformRandomNumberGenerator>
2620 typename piecewise_linear_distribution<_RealType>::result_type
2621 piecewise_linear_distribution<_RealType>::
2622 operator()(_UniformRandomNumberGenerator& __urng,
2623 const param_type& __param)
2624 {
2625 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
2626 __aurng(__urng);
2628 const double __p = __aurng();
2629 if (__param._M_cp.empty())
2630 return __p;
2632 auto __pos = std::lower_bound(__param._M_cp.begin(),
2633 __param._M_cp.end(), __p);
2634 const size_t __i = __pos - __param._M_cp.begin();
2636 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0;
2638 const double __a = 0.5 * __param._M_m[__i];
2639 const double __b = __param._M_den[__i];
2640 const double __cm = __p - __pref;
2642 _RealType __x = __param._M_int[__i];
2643 if (__a == 0)
2644 __x += __cm / __b;
2645 else
2646 {
2647 const double __d = __b * __b + 4.0 * __a * __cm;
2648 __x += 0.5 * (std::sqrt(__d) - __b) / __a;
2649 }
2651 return __x;
2652 }
2654 template<typename _RealType, typename _CharT, typename _Traits>
2655 std::basic_ostream<_CharT, _Traits>&
2656 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2657 const piecewise_linear_distribution<_RealType>& __x)
2658 {
2659 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2660 typedef typename __ostream_type::ios_base __ios_base;
2662 const typename __ios_base::fmtflags __flags = __os.flags();
2663 const _CharT __fill = __os.fill();
2664 const std::streamsize __precision = __os.precision();
2665 const _CharT __space = __os.widen(' ');
2666 __os.flags(__ios_base::scientific | __ios_base::left);
2667 __os.fill(__space);
2668 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2670 std::vector<_RealType> __int = __x.intervals();
2671 __os << __int.size() - 1;
2673 for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit)
2674 __os << __space << *__xit;
2676 std::vector<double> __den = __x.densities();
2677 for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit)
2678 __os << __space << *__dit;
2680 __os.flags(__flags);
2681 __os.fill(__fill);
2682 __os.precision(__precision);
2683 return __os;
2684 }
2686 template<typename _RealType, typename _CharT, typename _Traits>
2687 std::basic_istream<_CharT, _Traits>&
2688 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2689 piecewise_linear_distribution<_RealType>& __x)
2690 {
2691 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2692 typedef typename __istream_type::ios_base __ios_base;
2694 const typename __ios_base::fmtflags __flags = __is.flags();
2695 __is.flags(__ios_base::dec | __ios_base::skipws);
2697 size_t __n;
2698 __is >> __n;
2700 std::vector<_RealType> __int_vec;
2701 __int_vec.reserve(__n + 1);
2702 for (size_t __i = 0; __i <= __n; ++__i)
2703 {
2704 _RealType __int;
2705 __is >> __int;
2706 __int_vec.push_back(__int);
2707 }
2709 std::vector<double> __den_vec;
2710 __den_vec.reserve(__n + 1);
2711 for (size_t __i = 0; __i <= __n; ++__i)
2712 {
2713 double __den;
2714 __is >> __den;
2715 __den_vec.push_back(__den);
2716 }
2718 __x.param(typename piecewise_linear_distribution<_RealType>::
2719 param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin()));
2721 __is.flags(__flags);
2722 return __is;
2723 }
2726 template<typename _IntType>
2727 seed_seq::seed_seq(std::initializer_list<_IntType> __il)
2728 {
2729 for (auto __iter = __il.begin(); __iter != __il.end(); ++__iter)
2730 _M_v.push_back(__detail::__mod<result_type,
2731 __detail::_Shift<result_type, 32>::__value>(*__iter));
2732 }
2734 template<typename _InputIterator>
2735 seed_seq::seed_seq(_InputIterator __begin, _InputIterator __end)
2736 {
2737 for (_InputIterator __iter = __begin; __iter != __end; ++__iter)
2738 _M_v.push_back(__detail::__mod<result_type,
2739 __detail::_Shift<result_type, 32>::__value>(*__iter));
2740 }
2742 template<typename _RandomAccessIterator>
2743 void
2744 seed_seq::generate(_RandomAccessIterator __begin,
2745 _RandomAccessIterator __end)
2746 {
2747 typedef typename iterator_traits<_RandomAccessIterator>::value_type
2748 _Type;
2750 if (__begin == __end)
2751 return;
2753 std::fill(__begin, __end, _Type(0x8b8b8b8bu));
2755 const size_t __n = __end - __begin;
2756 const size_t __s = _M_v.size();
2757 const size_t __t = (__n >= 623) ? 11
2758 : (__n >= 68) ? 7
2759 : (__n >= 39) ? 5
2760 : (__n >= 7) ? 3
2761 : (__n - 1) / 2;
2762 const size_t __p = (__n - __t) / 2;
2763 const size_t __q = __p + __t;
2764 const size_t __m = std::max(__s + 1, __n);
2766 for (size_t __k = 0; __k < __m; ++__k)
2767 {
2768 _Type __arg = (__begin[__k % __n]
2769 ^ __begin[(__k + __p) % __n]
2770 ^ __begin[(__k - 1) % __n]);
2771 _Type __r1 = __arg ^ (__arg << 27);
2772 __r1 = __detail::__mod<_Type, __detail::_Shift<_Type, 32>::__value,
2773 1664525u, 0u>(__r1);
2774 _Type __r2 = __r1;
2775 if (__k == 0)
2776 __r2 += __s;
2777 else if (__k <= __s)
2778 __r2 += __k % __n + _M_v[__k - 1];
2779 else
2780 __r2 += __k % __n;
2781 __r2 = __detail::__mod<_Type,
2782 __detail::_Shift<_Type, 32>::__value>(__r2);
2783 __begin[(__k + __p) % __n] += __r1;
2784 __begin[(__k + __q) % __n] += __r2;
2785 __begin[__k % __n] = __r2;
2786 }
2788 for (size_t __k = __m; __k < __m + __n; ++__k)
2789 {
2790 _Type __arg = (__begin[__k % __n]
2791 + __begin[(__k + __p) % __n]
2792 + __begin[(__k - 1) % __n]);
2793 _Type __r3 = __arg ^ (__arg << 27);
2794 __r3 = __detail::__mod<_Type, __detail::_Shift<_Type, 32>::__value,
2795 1566083941u, 0u>(__r3);
2796 _Type __r4 = __r3 - __k % __n;
2797 __r4 = __detail::__mod<_Type,
2798 __detail::_Shift<_Type, 32>::__value>(__r4);
2799 __begin[(__k + __p) % __n] ^= __r4;
2800 __begin[(__k + __q) % __n] ^= __r3;
2801 __begin[__k % __n] = __r4;
2802 }
2803 }
2805 template<typename _RealType, size_t __bits,
2806 typename _UniformRandomNumberGenerator>
2807 _RealType
2808 generate_canonical(_UniformRandomNumberGenerator& __urng)
2809 {
2810 const size_t __b
2811 = std::min(static_cast<size_t>(std::numeric_limits<_RealType>::digits),
2812 __bits);
2813 const long double __r = static_cast<long double>(__urng.max())
2814 - static_cast<long double>(__urng.min()) + 1.0L;
2815 const size_t __log2r = std::log(__r) / std::log(2.0L);
2816 size_t __k = std::max<size_t>(1UL, (__b + __log2r - 1UL) / __log2r);
2817 _RealType __sum = _RealType(0);
2818 _RealType __tmp = _RealType(1);
2819 for (; __k != 0; --__k)
2820 {
2821 __sum += _RealType(__urng() - __urng.min()) * __tmp;
2822 __tmp *= __r;
2823 }
2824 return __sum / __tmp;
2825 }
2827 _GLIBCXX_END_NAMESPACE_VERSION
2828 } // namespace
2830 #endif