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re PR libstdc++/55215 (Constructor seeding is broken for Mersenne twister)
[thirdparty/gcc.git] / libstdc++-v3 / include / bits / random.tcc
1 // random number generation (out of line) -*- C++ -*-
2
3 // Copyright (C) 2009-2012 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 _UnaryOperation>
83 _OutputIterator
84 __transform(_InputIterator __first, _InputIterator __last,
85 _OutputIterator __result, _UnaryOperation __unary_op)
86 {
87 for (; __first != __last; ++__first, ++__result)
88 *__result = __unary_op(*__first);
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<result_type>::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, _IntType __t)
1652 {
1653 _IntType __x = 0;
1654 double __sum = 0.0;
1655 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
1656 __aurng(__urng);
1657
1658 do
1659 {
1660 const double __e = -std::log(1.0 - __aurng());
1661 __sum += __e / (__t - __x);
1662 __x += 1;
1663 }
1664 while (__sum <= _M_param._M_q);
1665
1666 return __x - 1;
1667 }
1668
1669 /**
1670 * A rejection algorithm when t * p >= 8 and a simple waiting time
1671 * method - the second in the referenced book - otherwise.
1672 * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1
1673 * is defined.
1674 *
1675 * Reference:
1676 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
1677 * New York, 1986, Ch. X, Sect. 4 (+ Errata!).
1678 */
1679 template<typename _IntType>
1680 template<typename _UniformRandomNumberGenerator>
1681 typename binomial_distribution<_IntType>::result_type
1682 binomial_distribution<_IntType>::
1683 operator()(_UniformRandomNumberGenerator& __urng,
1684 const param_type& __param)
1685 {
1686 result_type __ret;
1687 const _IntType __t = __param.t();
1688 const double __p = __param.p();
1689 const double __p12 = __p <= 0.5 ? __p : 1.0 - __p;
1690 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
1691 __aurng(__urng);
1692
1693 #if _GLIBCXX_USE_C99_MATH_TR1
1694 if (!__param._M_easy)
1695 {
1696 double __x;
1697
1698 // See comments above...
1699 const double __naf =
1700 (1 - std::numeric_limits<double>::epsilon()) / 2;
1701 const double __thr =
1702 std::numeric_limits<_IntType>::max() + __naf;
1703
1704 const double __np = std::floor(__t * __p12);
1705
1706 // sqrt(pi / 2)
1707 const double __spi_2 = 1.2533141373155002512078826424055226L;
1708 const double __a1 = __param._M_a1;
1709 const double __a12 = __a1 + __param._M_s2 * __spi_2;
1710 const double __a123 = __param._M_a123;
1711 const double __s1s = __param._M_s1 * __param._M_s1;
1712 const double __s2s = __param._M_s2 * __param._M_s2;
1713
1714 bool __reject;
1715 do
1716 {
1717 const double __u = __param._M_s * __aurng();
1718
1719 double __v;
1720
1721 if (__u <= __a1)
1722 {
1723 const double __n = _M_nd(__urng);
1724 const double __y = __param._M_s1 * std::abs(__n);
1725 __reject = __y >= __param._M_d1;
1726 if (!__reject)
1727 {
1728 const double __e = -std::log(1.0 - __aurng());
1729 __x = std::floor(__y);
1730 __v = -__e - __n * __n / 2 + __param._M_c;
1731 }
1732 }
1733 else if (__u <= __a12)
1734 {
1735 const double __n = _M_nd(__urng);
1736 const double __y = __param._M_s2 * std::abs(__n);
1737 __reject = __y >= __param._M_d2;
1738 if (!__reject)
1739 {
1740 const double __e = -std::log(1.0 - __aurng());
1741 __x = std::floor(-__y);
1742 __v = -__e - __n * __n / 2;
1743 }
1744 }
1745 else if (__u <= __a123)
1746 {
1747 const double __e1 = -std::log(1.0 - __aurng());
1748 const double __e2 = -std::log(1.0 - __aurng());
1749
1750 const double __y = __param._M_d1
1751 + 2 * __s1s * __e1 / __param._M_d1;
1752 __x = std::floor(__y);
1753 __v = (-__e2 + __param._M_d1 * (1 / (__t - __np)
1754 -__y / (2 * __s1s)));
1755 __reject = false;
1756 }
1757 else
1758 {
1759 const double __e1 = -std::log(1.0 - __aurng());
1760 const double __e2 = -std::log(1.0 - __aurng());
1761
1762 const double __y = __param._M_d2
1763 + 2 * __s2s * __e1 / __param._M_d2;
1764 __x = std::floor(-__y);
1765 __v = -__e2 - __param._M_d2 * __y / (2 * __s2s);
1766 __reject = false;
1767 }
1768
1769 __reject = __reject || __x < -__np || __x > __t - __np;
1770 if (!__reject)
1771 {
1772 const double __lfx =
1773 std::lgamma(__np + __x + 1)
1774 + std::lgamma(__t - (__np + __x) + 1);
1775 __reject = __v > __param._M_lf - __lfx
1776 + __x * __param._M_lp1p;
1777 }
1778
1779 __reject |= __x + __np >= __thr;
1780 }
1781 while (__reject);
1782
1783 __x += __np + __naf;
1784
1785 const _IntType __z = _M_waiting(__urng, __t - _IntType(__x));
1786 __ret = _IntType(__x) + __z;
1787 }
1788 else
1789 #endif
1790 __ret = _M_waiting(__urng, __t);
1791
1792 if (__p12 != __p)
1793 __ret = __t - __ret;
1794 return __ret;
1795 }
1796
1797 template<typename _IntType>
1798 template<typename _ForwardIterator,
1799 typename _UniformRandomNumberGenerator>
1800 void
1801 binomial_distribution<_IntType>::
1802 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1803 _UniformRandomNumberGenerator& __urng,
1804 const param_type& __param)
1805 {
1806 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
1807 // We could duplicate everything from operator()...
1808 while (__f != __t)
1809 *__f++ = this->operator()(__urng, __param);
1810 }
1811
1812 template<typename _IntType,
1813 typename _CharT, typename _Traits>
1814 std::basic_ostream<_CharT, _Traits>&
1815 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1816 const binomial_distribution<_IntType>& __x)
1817 {
1818 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1819 typedef typename __ostream_type::ios_base __ios_base;
1820
1821 const typename __ios_base::fmtflags __flags = __os.flags();
1822 const _CharT __fill = __os.fill();
1823 const std::streamsize __precision = __os.precision();
1824 const _CharT __space = __os.widen(' ');
1825 __os.flags(__ios_base::scientific | __ios_base::left);
1826 __os.fill(__space);
1827 __os.precision(std::numeric_limits<double>::max_digits10);
1828
1829 __os << __x.t() << __space << __x.p()
1830 << __space << __x._M_nd;
1831
1832 __os.flags(__flags);
1833 __os.fill(__fill);
1834 __os.precision(__precision);
1835 return __os;
1836 }
1837
1838 template<typename _IntType,
1839 typename _CharT, typename _Traits>
1840 std::basic_istream<_CharT, _Traits>&
1841 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1842 binomial_distribution<_IntType>& __x)
1843 {
1844 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1845 typedef typename __istream_type::ios_base __ios_base;
1846
1847 const typename __ios_base::fmtflags __flags = __is.flags();
1848 __is.flags(__ios_base::dec | __ios_base::skipws);
1849
1850 _IntType __t;
1851 double __p;
1852 __is >> __t >> __p >> __x._M_nd;
1853 __x.param(typename binomial_distribution<_IntType>::
1854 param_type(__t, __p));
1855
1856 __is.flags(__flags);
1857 return __is;
1858 }
1859
1860
1861 template<typename _RealType>
1862 template<typename _ForwardIterator,
1863 typename _UniformRandomNumberGenerator>
1864 void
1865 std::exponential_distribution<_RealType>::
1866 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1867 _UniformRandomNumberGenerator& __urng,
1868 const param_type& __p)
1869 {
1870 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
1871 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
1872 __aurng(__urng);
1873 while (__f != __t)
1874 *__f++ = -std::log(result_type(1) - __aurng()) / __p.lambda();
1875 }
1876
1877 template<typename _RealType, typename _CharT, typename _Traits>
1878 std::basic_ostream<_CharT, _Traits>&
1879 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1880 const exponential_distribution<_RealType>& __x)
1881 {
1882 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1883 typedef typename __ostream_type::ios_base __ios_base;
1884
1885 const typename __ios_base::fmtflags __flags = __os.flags();
1886 const _CharT __fill = __os.fill();
1887 const std::streamsize __precision = __os.precision();
1888 __os.flags(__ios_base::scientific | __ios_base::left);
1889 __os.fill(__os.widen(' '));
1890 __os.precision(std::numeric_limits<_RealType>::max_digits10);
1891
1892 __os << __x.lambda();
1893
1894 __os.flags(__flags);
1895 __os.fill(__fill);
1896 __os.precision(__precision);
1897 return __os;
1898 }
1899
1900 template<typename _RealType, typename _CharT, typename _Traits>
1901 std::basic_istream<_CharT, _Traits>&
1902 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1903 exponential_distribution<_RealType>& __x)
1904 {
1905 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1906 typedef typename __istream_type::ios_base __ios_base;
1907
1908 const typename __ios_base::fmtflags __flags = __is.flags();
1909 __is.flags(__ios_base::dec | __ios_base::skipws);
1910
1911 _RealType __lambda;
1912 __is >> __lambda;
1913 __x.param(typename exponential_distribution<_RealType>::
1914 param_type(__lambda));
1915
1916 __is.flags(__flags);
1917 return __is;
1918 }
1919
1920
1921 /**
1922 * Polar method due to Marsaglia.
1923 *
1924 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
1925 * New York, 1986, Ch. V, Sect. 4.4.
1926 */
1927 template<typename _RealType>
1928 template<typename _UniformRandomNumberGenerator>
1929 typename normal_distribution<_RealType>::result_type
1930 normal_distribution<_RealType>::
1931 operator()(_UniformRandomNumberGenerator& __urng,
1932 const param_type& __param)
1933 {
1934 result_type __ret;
1935 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
1936 __aurng(__urng);
1937
1938 if (_M_saved_available)
1939 {
1940 _M_saved_available = false;
1941 __ret = _M_saved;
1942 }
1943 else
1944 {
1945 result_type __x, __y, __r2;
1946 do
1947 {
1948 __x = result_type(2.0) * __aurng() - 1.0;
1949 __y = result_type(2.0) * __aurng() - 1.0;
1950 __r2 = __x * __x + __y * __y;
1951 }
1952 while (__r2 > 1.0 || __r2 == 0.0);
1953
1954 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2);
1955 _M_saved = __x * __mult;
1956 _M_saved_available = true;
1957 __ret = __y * __mult;
1958 }
1959
1960 __ret = __ret * __param.stddev() + __param.mean();
1961 return __ret;
1962 }
1963
1964 template<typename _RealType>
1965 template<typename _ForwardIterator,
1966 typename _UniformRandomNumberGenerator>
1967 void
1968 normal_distribution<_RealType>::
1969 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1970 _UniformRandomNumberGenerator& __urng,
1971 const param_type& __param)
1972 {
1973 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
1974
1975 if (__f == __t)
1976 return;
1977
1978 if (_M_saved_available)
1979 {
1980 _M_saved_available = false;
1981 *__f++ = _M_saved * __param.stddev() + __param.mean();
1982
1983 if (__f == __t)
1984 return;
1985 }
1986
1987 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
1988 __aurng(__urng);
1989
1990 while (__f + 1 < __t)
1991 {
1992 result_type __x, __y, __r2;
1993 do
1994 {
1995 __x = result_type(2.0) * __aurng() - 1.0;
1996 __y = result_type(2.0) * __aurng() - 1.0;
1997 __r2 = __x * __x + __y * __y;
1998 }
1999 while (__r2 > 1.0 || __r2 == 0.0);
2000
2001 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2);
2002 *__f++ = __y * __mult * __param.stddev() + __param.mean();
2003 *__f++ = __x * __mult * __param.stddev() + __param.mean();
2004 }
2005
2006 if (__f != __t)
2007 {
2008 result_type __x, __y, __r2;
2009 do
2010 {
2011 __x = result_type(2.0) * __aurng() - 1.0;
2012 __y = result_type(2.0) * __aurng() - 1.0;
2013 __r2 = __x * __x + __y * __y;
2014 }
2015 while (__r2 > 1.0 || __r2 == 0.0);
2016
2017 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2);
2018 _M_saved = __x * __mult;
2019 _M_saved_available = true;
2020 *__f = __y * __mult * __param.stddev() + __param.mean();
2021 }
2022 }
2023
2024 template<typename _RealType>
2025 bool
2026 operator==(const std::normal_distribution<_RealType>& __d1,
2027 const std::normal_distribution<_RealType>& __d2)
2028 {
2029 if (__d1._M_param == __d2._M_param
2030 && __d1._M_saved_available == __d2._M_saved_available)
2031 {
2032 if (__d1._M_saved_available
2033 && __d1._M_saved == __d2._M_saved)
2034 return true;
2035 else if(!__d1._M_saved_available)
2036 return true;
2037 else
2038 return false;
2039 }
2040 else
2041 return false;
2042 }
2043
2044 template<typename _RealType, typename _CharT, typename _Traits>
2045 std::basic_ostream<_CharT, _Traits>&
2046 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2047 const normal_distribution<_RealType>& __x)
2048 {
2049 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2050 typedef typename __ostream_type::ios_base __ios_base;
2051
2052 const typename __ios_base::fmtflags __flags = __os.flags();
2053 const _CharT __fill = __os.fill();
2054 const std::streamsize __precision = __os.precision();
2055 const _CharT __space = __os.widen(' ');
2056 __os.flags(__ios_base::scientific | __ios_base::left);
2057 __os.fill(__space);
2058 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2059
2060 __os << __x.mean() << __space << __x.stddev()
2061 << __space << __x._M_saved_available;
2062 if (__x._M_saved_available)
2063 __os << __space << __x._M_saved;
2064
2065 __os.flags(__flags);
2066 __os.fill(__fill);
2067 __os.precision(__precision);
2068 return __os;
2069 }
2070
2071 template<typename _RealType, typename _CharT, typename _Traits>
2072 std::basic_istream<_CharT, _Traits>&
2073 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2074 normal_distribution<_RealType>& __x)
2075 {
2076 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2077 typedef typename __istream_type::ios_base __ios_base;
2078
2079 const typename __ios_base::fmtflags __flags = __is.flags();
2080 __is.flags(__ios_base::dec | __ios_base::skipws);
2081
2082 double __mean, __stddev;
2083 __is >> __mean >> __stddev
2084 >> __x._M_saved_available;
2085 if (__x._M_saved_available)
2086 __is >> __x._M_saved;
2087 __x.param(typename normal_distribution<_RealType>::
2088 param_type(__mean, __stddev));
2089
2090 __is.flags(__flags);
2091 return __is;
2092 }
2093
2094
2095 template<typename _RealType>
2096 template<typename _ForwardIterator,
2097 typename _UniformRandomNumberGenerator>
2098 void
2099 lognormal_distribution<_RealType>::
2100 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2101 _UniformRandomNumberGenerator& __urng,
2102 const param_type& __p)
2103 {
2104 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2105 while (__f != __t)
2106 *__f++ = std::exp(__p.s() * _M_nd(__urng) + __p.m());
2107 }
2108
2109 template<typename _RealType, typename _CharT, typename _Traits>
2110 std::basic_ostream<_CharT, _Traits>&
2111 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2112 const lognormal_distribution<_RealType>& __x)
2113 {
2114 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2115 typedef typename __ostream_type::ios_base __ios_base;
2116
2117 const typename __ios_base::fmtflags __flags = __os.flags();
2118 const _CharT __fill = __os.fill();
2119 const std::streamsize __precision = __os.precision();
2120 const _CharT __space = __os.widen(' ');
2121 __os.flags(__ios_base::scientific | __ios_base::left);
2122 __os.fill(__space);
2123 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2124
2125 __os << __x.m() << __space << __x.s()
2126 << __space << __x._M_nd;
2127
2128 __os.flags(__flags);
2129 __os.fill(__fill);
2130 __os.precision(__precision);
2131 return __os;
2132 }
2133
2134 template<typename _RealType, typename _CharT, typename _Traits>
2135 std::basic_istream<_CharT, _Traits>&
2136 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2137 lognormal_distribution<_RealType>& __x)
2138 {
2139 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2140 typedef typename __istream_type::ios_base __ios_base;
2141
2142 const typename __ios_base::fmtflags __flags = __is.flags();
2143 __is.flags(__ios_base::dec | __ios_base::skipws);
2144
2145 _RealType __m, __s;
2146 __is >> __m >> __s >> __x._M_nd;
2147 __x.param(typename lognormal_distribution<_RealType>::
2148 param_type(__m, __s));
2149
2150 __is.flags(__flags);
2151 return __is;
2152 }
2153
2154 template<typename _RealType>
2155 template<typename _ForwardIterator,
2156 typename _UniformRandomNumberGenerator>
2157 void
2158 std::chi_squared_distribution<_RealType>::
2159 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2160 _UniformRandomNumberGenerator& __urng)
2161 {
2162 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2163 while (__f != __t)
2164 *__f++ = 2 * _M_gd(__urng);
2165 }
2166
2167 template<typename _RealType>
2168 template<typename _ForwardIterator,
2169 typename _UniformRandomNumberGenerator>
2170 void
2171 std::chi_squared_distribution<_RealType>::
2172 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2173 _UniformRandomNumberGenerator& __urng,
2174 const typename
2175 std::gamma_distribution<result_type>::param_type& __p)
2176 {
2177 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2178 while (__f != __t)
2179 *__f++ = 2 * _M_gd(__urng, __p);
2180 }
2181
2182 template<typename _RealType, typename _CharT, typename _Traits>
2183 std::basic_ostream<_CharT, _Traits>&
2184 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2185 const chi_squared_distribution<_RealType>& __x)
2186 {
2187 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2188 typedef typename __ostream_type::ios_base __ios_base;
2189
2190 const typename __ios_base::fmtflags __flags = __os.flags();
2191 const _CharT __fill = __os.fill();
2192 const std::streamsize __precision = __os.precision();
2193 const _CharT __space = __os.widen(' ');
2194 __os.flags(__ios_base::scientific | __ios_base::left);
2195 __os.fill(__space);
2196 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2197
2198 __os << __x.n() << __space << __x._M_gd;
2199
2200 __os.flags(__flags);
2201 __os.fill(__fill);
2202 __os.precision(__precision);
2203 return __os;
2204 }
2205
2206 template<typename _RealType, typename _CharT, typename _Traits>
2207 std::basic_istream<_CharT, _Traits>&
2208 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2209 chi_squared_distribution<_RealType>& __x)
2210 {
2211 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2212 typedef typename __istream_type::ios_base __ios_base;
2213
2214 const typename __ios_base::fmtflags __flags = __is.flags();
2215 __is.flags(__ios_base::dec | __ios_base::skipws);
2216
2217 _RealType __n;
2218 __is >> __n >> __x._M_gd;
2219 __x.param(typename chi_squared_distribution<_RealType>::
2220 param_type(__n));
2221
2222 __is.flags(__flags);
2223 return __is;
2224 }
2225
2226
2227 template<typename _RealType>
2228 template<typename _UniformRandomNumberGenerator>
2229 typename cauchy_distribution<_RealType>::result_type
2230 cauchy_distribution<_RealType>::
2231 operator()(_UniformRandomNumberGenerator& __urng,
2232 const param_type& __p)
2233 {
2234 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2235 __aurng(__urng);
2236 _RealType __u;
2237 do
2238 __u = __aurng();
2239 while (__u == 0.5);
2240
2241 const _RealType __pi = 3.1415926535897932384626433832795029L;
2242 return __p.a() + __p.b() * std::tan(__pi * __u);
2243 }
2244
2245 template<typename _RealType>
2246 template<typename _ForwardIterator,
2247 typename _UniformRandomNumberGenerator>
2248 void
2249 cauchy_distribution<_RealType>::
2250 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2251 _UniformRandomNumberGenerator& __urng,
2252 const param_type& __p)
2253 {
2254 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2255 const _RealType __pi = 3.1415926535897932384626433832795029L;
2256 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2257 __aurng(__urng);
2258 while (__f != __t)
2259 {
2260 _RealType __u;
2261 do
2262 __u = __aurng();
2263 while (__u == 0.5);
2264
2265 *__f++ = __p.a() + __p.b() * std::tan(__pi * __u);
2266 }
2267 }
2268
2269 template<typename _RealType, typename _CharT, typename _Traits>
2270 std::basic_ostream<_CharT, _Traits>&
2271 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2272 const cauchy_distribution<_RealType>& __x)
2273 {
2274 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2275 typedef typename __ostream_type::ios_base __ios_base;
2276
2277 const typename __ios_base::fmtflags __flags = __os.flags();
2278 const _CharT __fill = __os.fill();
2279 const std::streamsize __precision = __os.precision();
2280 const _CharT __space = __os.widen(' ');
2281 __os.flags(__ios_base::scientific | __ios_base::left);
2282 __os.fill(__space);
2283 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2284
2285 __os << __x.a() << __space << __x.b();
2286
2287 __os.flags(__flags);
2288 __os.fill(__fill);
2289 __os.precision(__precision);
2290 return __os;
2291 }
2292
2293 template<typename _RealType, typename _CharT, typename _Traits>
2294 std::basic_istream<_CharT, _Traits>&
2295 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2296 cauchy_distribution<_RealType>& __x)
2297 {
2298 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2299 typedef typename __istream_type::ios_base __ios_base;
2300
2301 const typename __ios_base::fmtflags __flags = __is.flags();
2302 __is.flags(__ios_base::dec | __ios_base::skipws);
2303
2304 _RealType __a, __b;
2305 __is >> __a >> __b;
2306 __x.param(typename cauchy_distribution<_RealType>::
2307 param_type(__a, __b));
2308
2309 __is.flags(__flags);
2310 return __is;
2311 }
2312
2313
2314 template<typename _RealType>
2315 template<typename _ForwardIterator,
2316 typename _UniformRandomNumberGenerator>
2317 void
2318 std::fisher_f_distribution<_RealType>::
2319 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2320 _UniformRandomNumberGenerator& __urng)
2321 {
2322 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2323 while (__f != __t)
2324 *__f++ = ((_M_gd_x(__urng) * n()) / (_M_gd_y(__urng) * m()));
2325 }
2326
2327 template<typename _RealType>
2328 template<typename _ForwardIterator,
2329 typename _UniformRandomNumberGenerator>
2330 void
2331 std::fisher_f_distribution<_RealType>::
2332 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2333 _UniformRandomNumberGenerator& __urng,
2334 const param_type& __p)
2335 {
2336 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2337 typedef typename std::gamma_distribution<result_type>::param_type
2338 param_type;
2339 param_type __p1(__p.m() / 2);
2340 param_type __p2(__p.n() / 2);
2341 while (__f != __t)
2342 *__f++ = ((_M_gd_x(__urng, __p1) * n())
2343 / (_M_gd_y(__urng, __p2) * m()));
2344 }
2345
2346 template<typename _RealType, typename _CharT, typename _Traits>
2347 std::basic_ostream<_CharT, _Traits>&
2348 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2349 const fisher_f_distribution<_RealType>& __x)
2350 {
2351 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2352 typedef typename __ostream_type::ios_base __ios_base;
2353
2354 const typename __ios_base::fmtflags __flags = __os.flags();
2355 const _CharT __fill = __os.fill();
2356 const std::streamsize __precision = __os.precision();
2357 const _CharT __space = __os.widen(' ');
2358 __os.flags(__ios_base::scientific | __ios_base::left);
2359 __os.fill(__space);
2360 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2361
2362 __os << __x.m() << __space << __x.n()
2363 << __space << __x._M_gd_x << __space << __x._M_gd_y;
2364
2365 __os.flags(__flags);
2366 __os.fill(__fill);
2367 __os.precision(__precision);
2368 return __os;
2369 }
2370
2371 template<typename _RealType, typename _CharT, typename _Traits>
2372 std::basic_istream<_CharT, _Traits>&
2373 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2374 fisher_f_distribution<_RealType>& __x)
2375 {
2376 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2377 typedef typename __istream_type::ios_base __ios_base;
2378
2379 const typename __ios_base::fmtflags __flags = __is.flags();
2380 __is.flags(__ios_base::dec | __ios_base::skipws);
2381
2382 _RealType __m, __n;
2383 __is >> __m >> __n >> __x._M_gd_x >> __x._M_gd_y;
2384 __x.param(typename fisher_f_distribution<_RealType>::
2385 param_type(__m, __n));
2386
2387 __is.flags(__flags);
2388 return __is;
2389 }
2390
2391
2392 template<typename _RealType>
2393 template<typename _ForwardIterator,
2394 typename _UniformRandomNumberGenerator>
2395 void
2396 std::student_t_distribution<_RealType>::
2397 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2398 _UniformRandomNumberGenerator& __urng)
2399 {
2400 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2401 while (__f != __t)
2402 *__f++ = _M_nd(__urng) * std::sqrt(n() / _M_gd(__urng));
2403 }
2404
2405 template<typename _RealType>
2406 template<typename _ForwardIterator,
2407 typename _UniformRandomNumberGenerator>
2408 void
2409 std::student_t_distribution<_RealType>::
2410 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2411 _UniformRandomNumberGenerator& __urng,
2412 const param_type& __p)
2413 {
2414 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2415 typename std::gamma_distribution<result_type>::param_type
2416 __p2(__p.n() / 2, 2);
2417 while (__f != __t)
2418 *__f++ = _M_nd(__urng) * std::sqrt(__p.n() / _M_gd(__urng, __p2));
2419 }
2420
2421 template<typename _RealType, typename _CharT, typename _Traits>
2422 std::basic_ostream<_CharT, _Traits>&
2423 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2424 const student_t_distribution<_RealType>& __x)
2425 {
2426 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2427 typedef typename __ostream_type::ios_base __ios_base;
2428
2429 const typename __ios_base::fmtflags __flags = __os.flags();
2430 const _CharT __fill = __os.fill();
2431 const std::streamsize __precision = __os.precision();
2432 const _CharT __space = __os.widen(' ');
2433 __os.flags(__ios_base::scientific | __ios_base::left);
2434 __os.fill(__space);
2435 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2436
2437 __os << __x.n() << __space << __x._M_nd << __space << __x._M_gd;
2438
2439 __os.flags(__flags);
2440 __os.fill(__fill);
2441 __os.precision(__precision);
2442 return __os;
2443 }
2444
2445 template<typename _RealType, typename _CharT, typename _Traits>
2446 std::basic_istream<_CharT, _Traits>&
2447 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2448 student_t_distribution<_RealType>& __x)
2449 {
2450 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2451 typedef typename __istream_type::ios_base __ios_base;
2452
2453 const typename __ios_base::fmtflags __flags = __is.flags();
2454 __is.flags(__ios_base::dec | __ios_base::skipws);
2455
2456 _RealType __n;
2457 __is >> __n >> __x._M_nd >> __x._M_gd;
2458 __x.param(typename student_t_distribution<_RealType>::param_type(__n));
2459
2460 __is.flags(__flags);
2461 return __is;
2462 }
2463
2464
2465 template<typename _RealType>
2466 void
2467 gamma_distribution<_RealType>::param_type::
2468 _M_initialize()
2469 {
2470 _M_malpha = _M_alpha < 1.0 ? _M_alpha + _RealType(1.0) : _M_alpha;
2471
2472 const _RealType __a1 = _M_malpha - _RealType(1.0) / _RealType(3.0);
2473 _M_a2 = _RealType(1.0) / std::sqrt(_RealType(9.0) * __a1);
2474 }
2475
2476 /**
2477 * Marsaglia, G. and Tsang, W. W.
2478 * "A Simple Method for Generating Gamma Variables"
2479 * ACM Transactions on Mathematical Software, 26, 3, 363-372, 2000.
2480 */
2481 template<typename _RealType>
2482 template<typename _UniformRandomNumberGenerator>
2483 typename gamma_distribution<_RealType>::result_type
2484 gamma_distribution<_RealType>::
2485 operator()(_UniformRandomNumberGenerator& __urng,
2486 const param_type& __param)
2487 {
2488 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2489 __aurng(__urng);
2490
2491 result_type __u, __v, __n;
2492 const result_type __a1 = (__param._M_malpha
2493 - _RealType(1.0) / _RealType(3.0));
2494
2495 do
2496 {
2497 do
2498 {
2499 __n = _M_nd(__urng);
2500 __v = result_type(1.0) + __param._M_a2 * __n;
2501 }
2502 while (__v <= 0.0);
2503
2504 __v = __v * __v * __v;
2505 __u = __aurng();
2506 }
2507 while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n
2508 && (std::log(__u) > (0.5 * __n * __n + __a1
2509 * (1.0 - __v + std::log(__v)))));
2510
2511 if (__param.alpha() == __param._M_malpha)
2512 return __a1 * __v * __param.beta();
2513 else
2514 {
2515 do
2516 __u = __aurng();
2517 while (__u == 0.0);
2518
2519 return (std::pow(__u, result_type(1.0) / __param.alpha())
2520 * __a1 * __v * __param.beta());
2521 }
2522 }
2523
2524 template<typename _RealType>
2525 template<typename _ForwardIterator,
2526 typename _UniformRandomNumberGenerator>
2527 void
2528 gamma_distribution<_RealType>::
2529 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2530 _UniformRandomNumberGenerator& __urng,
2531 const param_type& __param)
2532 {
2533 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2534 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2535 __aurng(__urng);
2536
2537 result_type __u, __v, __n;
2538 const result_type __a1 = (__param._M_malpha
2539 - _RealType(1.0) / _RealType(3.0));
2540
2541 if (__param.alpha() == __param._M_malpha)
2542 while (__f != __t)
2543 {
2544 do
2545 {
2546 do
2547 {
2548 __n = _M_nd(__urng);
2549 __v = result_type(1.0) + __param._M_a2 * __n;
2550 }
2551 while (__v <= 0.0);
2552
2553 __v = __v * __v * __v;
2554 __u = __aurng();
2555 }
2556 while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n
2557 && (std::log(__u) > (0.5 * __n * __n + __a1
2558 * (1.0 - __v + std::log(__v)))));
2559
2560 *__f++ = __a1 * __v * __param.beta();
2561 }
2562 else
2563 while (__f != __t)
2564 {
2565 do
2566 {
2567 do
2568 {
2569 __n = _M_nd(__urng);
2570 __v = result_type(1.0) + __param._M_a2 * __n;
2571 }
2572 while (__v <= 0.0);
2573
2574 __v = __v * __v * __v;
2575 __u = __aurng();
2576 }
2577 while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n
2578 && (std::log(__u) > (0.5 * __n * __n + __a1
2579 * (1.0 - __v + std::log(__v)))));
2580
2581 do
2582 __u = __aurng();
2583 while (__u == 0.0);
2584
2585 *__f++ = (std::pow(__u, result_type(1.0) / __param.alpha())
2586 * __a1 * __v * __param.beta());
2587 }
2588 }
2589
2590 template<typename _RealType, typename _CharT, typename _Traits>
2591 std::basic_ostream<_CharT, _Traits>&
2592 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2593 const gamma_distribution<_RealType>& __x)
2594 {
2595 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2596 typedef typename __ostream_type::ios_base __ios_base;
2597
2598 const typename __ios_base::fmtflags __flags = __os.flags();
2599 const _CharT __fill = __os.fill();
2600 const std::streamsize __precision = __os.precision();
2601 const _CharT __space = __os.widen(' ');
2602 __os.flags(__ios_base::scientific | __ios_base::left);
2603 __os.fill(__space);
2604 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2605
2606 __os << __x.alpha() << __space << __x.beta()
2607 << __space << __x._M_nd;
2608
2609 __os.flags(__flags);
2610 __os.fill(__fill);
2611 __os.precision(__precision);
2612 return __os;
2613 }
2614
2615 template<typename _RealType, typename _CharT, typename _Traits>
2616 std::basic_istream<_CharT, _Traits>&
2617 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2618 gamma_distribution<_RealType>& __x)
2619 {
2620 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2621 typedef typename __istream_type::ios_base __ios_base;
2622
2623 const typename __ios_base::fmtflags __flags = __is.flags();
2624 __is.flags(__ios_base::dec | __ios_base::skipws);
2625
2626 _RealType __alpha_val, __beta_val;
2627 __is >> __alpha_val >> __beta_val >> __x._M_nd;
2628 __x.param(typename gamma_distribution<_RealType>::
2629 param_type(__alpha_val, __beta_val));
2630
2631 __is.flags(__flags);
2632 return __is;
2633 }
2634
2635
2636 template<typename _RealType>
2637 template<typename _UniformRandomNumberGenerator>
2638 typename weibull_distribution<_RealType>::result_type
2639 weibull_distribution<_RealType>::
2640 operator()(_UniformRandomNumberGenerator& __urng,
2641 const param_type& __p)
2642 {
2643 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2644 __aurng(__urng);
2645 return __p.b() * std::pow(-std::log(result_type(1) - __aurng()),
2646 result_type(1) / __p.a());
2647 }
2648
2649 template<typename _RealType>
2650 template<typename _ForwardIterator,
2651 typename _UniformRandomNumberGenerator>
2652 void
2653 weibull_distribution<_RealType>::
2654 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2655 _UniformRandomNumberGenerator& __urng,
2656 const param_type& __p)
2657 {
2658 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2659 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2660 __aurng(__urng);
2661 auto __inv_a = result_type(1) / __p.a();
2662
2663 while (__f != __t)
2664 *__f++ = __p.b() * std::pow(-std::log(result_type(1) - __aurng()),
2665 __inv_a);
2666 }
2667
2668 template<typename _RealType, typename _CharT, typename _Traits>
2669 std::basic_ostream<_CharT, _Traits>&
2670 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2671 const weibull_distribution<_RealType>& __x)
2672 {
2673 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2674 typedef typename __ostream_type::ios_base __ios_base;
2675
2676 const typename __ios_base::fmtflags __flags = __os.flags();
2677 const _CharT __fill = __os.fill();
2678 const std::streamsize __precision = __os.precision();
2679 const _CharT __space = __os.widen(' ');
2680 __os.flags(__ios_base::scientific | __ios_base::left);
2681 __os.fill(__space);
2682 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2683
2684 __os << __x.a() << __space << __x.b();
2685
2686 __os.flags(__flags);
2687 __os.fill(__fill);
2688 __os.precision(__precision);
2689 return __os;
2690 }
2691
2692 template<typename _RealType, typename _CharT, typename _Traits>
2693 std::basic_istream<_CharT, _Traits>&
2694 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2695 weibull_distribution<_RealType>& __x)
2696 {
2697 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2698 typedef typename __istream_type::ios_base __ios_base;
2699
2700 const typename __ios_base::fmtflags __flags = __is.flags();
2701 __is.flags(__ios_base::dec | __ios_base::skipws);
2702
2703 _RealType __a, __b;
2704 __is >> __a >> __b;
2705 __x.param(typename weibull_distribution<_RealType>::
2706 param_type(__a, __b));
2707
2708 __is.flags(__flags);
2709 return __is;
2710 }
2711
2712
2713 template<typename _RealType>
2714 template<typename _UniformRandomNumberGenerator>
2715 typename extreme_value_distribution<_RealType>::result_type
2716 extreme_value_distribution<_RealType>::
2717 operator()(_UniformRandomNumberGenerator& __urng,
2718 const param_type& __p)
2719 {
2720 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2721 __aurng(__urng);
2722 return __p.a() - __p.b() * std::log(-std::log(result_type(1)
2723 - __aurng()));
2724 }
2725
2726 template<typename _RealType>
2727 template<typename _ForwardIterator,
2728 typename _UniformRandomNumberGenerator>
2729 void
2730 extreme_value_distribution<_RealType>::
2731 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2732 _UniformRandomNumberGenerator& __urng,
2733 const param_type& __p)
2734 {
2735 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2736 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2737 __aurng(__urng);
2738
2739 while (__f != __t)
2740 *__f++ = __p.a() - __p.b() * std::log(-std::log(result_type(1)
2741 - __aurng()));
2742 }
2743
2744 template<typename _RealType, typename _CharT, typename _Traits>
2745 std::basic_ostream<_CharT, _Traits>&
2746 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2747 const extreme_value_distribution<_RealType>& __x)
2748 {
2749 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2750 typedef typename __ostream_type::ios_base __ios_base;
2751
2752 const typename __ios_base::fmtflags __flags = __os.flags();
2753 const _CharT __fill = __os.fill();
2754 const std::streamsize __precision = __os.precision();
2755 const _CharT __space = __os.widen(' ');
2756 __os.flags(__ios_base::scientific | __ios_base::left);
2757 __os.fill(__space);
2758 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2759
2760 __os << __x.a() << __space << __x.b();
2761
2762 __os.flags(__flags);
2763 __os.fill(__fill);
2764 __os.precision(__precision);
2765 return __os;
2766 }
2767
2768 template<typename _RealType, typename _CharT, typename _Traits>
2769 std::basic_istream<_CharT, _Traits>&
2770 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2771 extreme_value_distribution<_RealType>& __x)
2772 {
2773 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2774 typedef typename __istream_type::ios_base __ios_base;
2775
2776 const typename __ios_base::fmtflags __flags = __is.flags();
2777 __is.flags(__ios_base::dec | __ios_base::skipws);
2778
2779 _RealType __a, __b;
2780 __is >> __a >> __b;
2781 __x.param(typename extreme_value_distribution<_RealType>::
2782 param_type(__a, __b));
2783
2784 __is.flags(__flags);
2785 return __is;
2786 }
2787
2788
2789 template<typename _IntType>
2790 void
2791 discrete_distribution<_IntType>::param_type::
2792 _M_initialize()
2793 {
2794 if (_M_prob.size() < 2)
2795 {
2796 _M_prob.clear();
2797 return;
2798 }
2799
2800 const double __sum = std::accumulate(_M_prob.begin(),
2801 _M_prob.end(), 0.0);
2802 // Now normalize the probabilites.
2803 __detail::__transform(_M_prob.begin(), _M_prob.end(), _M_prob.begin(),
2804 std::bind2nd(std::divides<double>(), __sum));
2805 // Accumulate partial sums.
2806 _M_cp.reserve(_M_prob.size());
2807 std::partial_sum(_M_prob.begin(), _M_prob.end(),
2808 std::back_inserter(_M_cp));
2809 // Make sure the last cumulative probability is one.
2810 _M_cp[_M_cp.size() - 1] = 1.0;
2811 }
2812
2813 template<typename _IntType>
2814 template<typename _Func>
2815 discrete_distribution<_IntType>::param_type::
2816 param_type(size_t __nw, double __xmin, double __xmax, _Func __fw)
2817 : _M_prob(), _M_cp()
2818 {
2819 const size_t __n = __nw == 0 ? 1 : __nw;
2820 const double __delta = (__xmax - __xmin) / __n;
2821
2822 _M_prob.reserve(__n);
2823 for (size_t __k = 0; __k < __nw; ++__k)
2824 _M_prob.push_back(__fw(__xmin + __k * __delta + 0.5 * __delta));
2825
2826 _M_initialize();
2827 }
2828
2829 template<typename _IntType>
2830 template<typename _UniformRandomNumberGenerator>
2831 typename discrete_distribution<_IntType>::result_type
2832 discrete_distribution<_IntType>::
2833 operator()(_UniformRandomNumberGenerator& __urng,
2834 const param_type& __param)
2835 {
2836 if (__param._M_cp.empty())
2837 return result_type(0);
2838
2839 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
2840 __aurng(__urng);
2841
2842 const double __p = __aurng();
2843 auto __pos = std::lower_bound(__param._M_cp.begin(),
2844 __param._M_cp.end(), __p);
2845
2846 return __pos - __param._M_cp.begin();
2847 }
2848
2849 template<typename _IntType>
2850 template<typename _ForwardIterator,
2851 typename _UniformRandomNumberGenerator>
2852 void
2853 discrete_distribution<_IntType>::
2854 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2855 _UniformRandomNumberGenerator& __urng,
2856 const param_type& __param)
2857 {
2858 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2859
2860 if (__param._M_cp.empty())
2861 {
2862 while (__f != __t)
2863 *__f++ = result_type(0);
2864 return;
2865 }
2866
2867 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
2868 __aurng(__urng);
2869
2870 while (__f != __t)
2871 {
2872 const double __p = __aurng();
2873 auto __pos = std::lower_bound(__param._M_cp.begin(),
2874 __param._M_cp.end(), __p);
2875
2876 *__f++ = __pos - __param._M_cp.begin();
2877 }
2878 }
2879
2880 template<typename _IntType, typename _CharT, typename _Traits>
2881 std::basic_ostream<_CharT, _Traits>&
2882 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2883 const discrete_distribution<_IntType>& __x)
2884 {
2885 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2886 typedef typename __ostream_type::ios_base __ios_base;
2887
2888 const typename __ios_base::fmtflags __flags = __os.flags();
2889 const _CharT __fill = __os.fill();
2890 const std::streamsize __precision = __os.precision();
2891 const _CharT __space = __os.widen(' ');
2892 __os.flags(__ios_base::scientific | __ios_base::left);
2893 __os.fill(__space);
2894 __os.precision(std::numeric_limits<double>::max_digits10);
2895
2896 std::vector<double> __prob = __x.probabilities();
2897 __os << __prob.size();
2898 for (auto __dit = __prob.begin(); __dit != __prob.end(); ++__dit)
2899 __os << __space << *__dit;
2900
2901 __os.flags(__flags);
2902 __os.fill(__fill);
2903 __os.precision(__precision);
2904 return __os;
2905 }
2906
2907 template<typename _IntType, typename _CharT, typename _Traits>
2908 std::basic_istream<_CharT, _Traits>&
2909 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2910 discrete_distribution<_IntType>& __x)
2911 {
2912 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2913 typedef typename __istream_type::ios_base __ios_base;
2914
2915 const typename __ios_base::fmtflags __flags = __is.flags();
2916 __is.flags(__ios_base::dec | __ios_base::skipws);
2917
2918 size_t __n;
2919 __is >> __n;
2920
2921 std::vector<double> __prob_vec;
2922 __prob_vec.reserve(__n);
2923 for (; __n != 0; --__n)
2924 {
2925 double __prob;
2926 __is >> __prob;
2927 __prob_vec.push_back(__prob);
2928 }
2929
2930 __x.param(typename discrete_distribution<_IntType>::
2931 param_type(__prob_vec.begin(), __prob_vec.end()));
2932
2933 __is.flags(__flags);
2934 return __is;
2935 }
2936
2937
2938 template<typename _RealType>
2939 void
2940 piecewise_constant_distribution<_RealType>::param_type::
2941 _M_initialize()
2942 {
2943 if (_M_int.size() < 2
2944 || (_M_int.size() == 2
2945 && _M_int[0] == _RealType(0)
2946 && _M_int[1] == _RealType(1)))
2947 {
2948 _M_int.clear();
2949 _M_den.clear();
2950 return;
2951 }
2952
2953 const double __sum = std::accumulate(_M_den.begin(),
2954 _M_den.end(), 0.0);
2955
2956 __detail::__transform(_M_den.begin(), _M_den.end(), _M_den.begin(),
2957 std::bind2nd(std::divides<double>(), __sum));
2958
2959 _M_cp.reserve(_M_den.size());
2960 std::partial_sum(_M_den.begin(), _M_den.end(),
2961 std::back_inserter(_M_cp));
2962
2963 // Make sure the last cumulative probability is one.
2964 _M_cp[_M_cp.size() - 1] = 1.0;
2965
2966 for (size_t __k = 0; __k < _M_den.size(); ++__k)
2967 _M_den[__k] /= _M_int[__k + 1] - _M_int[__k];
2968 }
2969
2970 template<typename _RealType>
2971 template<typename _InputIteratorB, typename _InputIteratorW>
2972 piecewise_constant_distribution<_RealType>::param_type::
2973 param_type(_InputIteratorB __bbegin,
2974 _InputIteratorB __bend,
2975 _InputIteratorW __wbegin)
2976 : _M_int(), _M_den(), _M_cp()
2977 {
2978 if (__bbegin != __bend)
2979 {
2980 for (;;)
2981 {
2982 _M_int.push_back(*__bbegin);
2983 ++__bbegin;
2984 if (__bbegin == __bend)
2985 break;
2986
2987 _M_den.push_back(*__wbegin);
2988 ++__wbegin;
2989 }
2990 }
2991
2992 _M_initialize();
2993 }
2994
2995 template<typename _RealType>
2996 template<typename _Func>
2997 piecewise_constant_distribution<_RealType>::param_type::
2998 param_type(initializer_list<_RealType> __bl, _Func __fw)
2999 : _M_int(), _M_den(), _M_cp()
3000 {
3001 _M_int.reserve(__bl.size());
3002 for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter)
3003 _M_int.push_back(*__biter);
3004
3005 _M_den.reserve(_M_int.size() - 1);
3006 for (size_t __k = 0; __k < _M_int.size() - 1; ++__k)
3007 _M_den.push_back(__fw(0.5 * (_M_int[__k + 1] + _M_int[__k])));
3008
3009 _M_initialize();
3010 }
3011
3012 template<typename _RealType>
3013 template<typename _Func>
3014 piecewise_constant_distribution<_RealType>::param_type::
3015 param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw)
3016 : _M_int(), _M_den(), _M_cp()
3017 {
3018 const size_t __n = __nw == 0 ? 1 : __nw;
3019 const _RealType __delta = (__xmax - __xmin) / __n;
3020
3021 _M_int.reserve(__n + 1);
3022 for (size_t __k = 0; __k <= __nw; ++__k)
3023 _M_int.push_back(__xmin + __k * __delta);
3024
3025 _M_den.reserve(__n);
3026 for (size_t __k = 0; __k < __nw; ++__k)
3027 _M_den.push_back(__fw(_M_int[__k] + 0.5 * __delta));
3028
3029 _M_initialize();
3030 }
3031
3032 template<typename _RealType>
3033 template<typename _UniformRandomNumberGenerator>
3034 typename piecewise_constant_distribution<_RealType>::result_type
3035 piecewise_constant_distribution<_RealType>::
3036 operator()(_UniformRandomNumberGenerator& __urng,
3037 const param_type& __param)
3038 {
3039 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
3040 __aurng(__urng);
3041
3042 const double __p = __aurng();
3043 if (__param._M_cp.empty())
3044 return __p;
3045
3046 auto __pos = std::lower_bound(__param._M_cp.begin(),
3047 __param._M_cp.end(), __p);
3048 const size_t __i = __pos - __param._M_cp.begin();
3049
3050 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0;
3051
3052 return __param._M_int[__i] + (__p - __pref) / __param._M_den[__i];
3053 }
3054
3055 template<typename _RealType>
3056 template<typename _ForwardIterator,
3057 typename _UniformRandomNumberGenerator>
3058 void
3059 piecewise_constant_distribution<_RealType>::
3060 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
3061 _UniformRandomNumberGenerator& __urng,
3062 const param_type& __param)
3063 {
3064 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
3065 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
3066 __aurng(__urng);
3067
3068 if (__param._M_cp.empty())
3069 {
3070 while (__f != __t)
3071 *__f++ = __aurng();
3072 return;
3073 }
3074
3075 while (__f != __t)
3076 {
3077 const double __p = __aurng();
3078
3079 auto __pos = std::lower_bound(__param._M_cp.begin(),
3080 __param._M_cp.end(), __p);
3081 const size_t __i = __pos - __param._M_cp.begin();
3082
3083 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0;
3084
3085 *__f++ = (__param._M_int[__i]
3086 + (__p - __pref) / __param._M_den[__i]);
3087 }
3088 }
3089
3090 template<typename _RealType, typename _CharT, typename _Traits>
3091 std::basic_ostream<_CharT, _Traits>&
3092 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
3093 const piecewise_constant_distribution<_RealType>& __x)
3094 {
3095 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
3096 typedef typename __ostream_type::ios_base __ios_base;
3097
3098 const typename __ios_base::fmtflags __flags = __os.flags();
3099 const _CharT __fill = __os.fill();
3100 const std::streamsize __precision = __os.precision();
3101 const _CharT __space = __os.widen(' ');
3102 __os.flags(__ios_base::scientific | __ios_base::left);
3103 __os.fill(__space);
3104 __os.precision(std::numeric_limits<_RealType>::max_digits10);
3105
3106 std::vector<_RealType> __int = __x.intervals();
3107 __os << __int.size() - 1;
3108
3109 for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit)
3110 __os << __space << *__xit;
3111
3112 std::vector<double> __den = __x.densities();
3113 for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit)
3114 __os << __space << *__dit;
3115
3116 __os.flags(__flags);
3117 __os.fill(__fill);
3118 __os.precision(__precision);
3119 return __os;
3120 }
3121
3122 template<typename _RealType, typename _CharT, typename _Traits>
3123 std::basic_istream<_CharT, _Traits>&
3124 operator>>(std::basic_istream<_CharT, _Traits>& __is,
3125 piecewise_constant_distribution<_RealType>& __x)
3126 {
3127 typedef std::basic_istream<_CharT, _Traits> __istream_type;
3128 typedef typename __istream_type::ios_base __ios_base;
3129
3130 const typename __ios_base::fmtflags __flags = __is.flags();
3131 __is.flags(__ios_base::dec | __ios_base::skipws);
3132
3133 size_t __n;
3134 __is >> __n;
3135
3136 std::vector<_RealType> __int_vec;
3137 __int_vec.reserve(__n + 1);
3138 for (size_t __i = 0; __i <= __n; ++__i)
3139 {
3140 _RealType __int;
3141 __is >> __int;
3142 __int_vec.push_back(__int);
3143 }
3144
3145 std::vector<double> __den_vec;
3146 __den_vec.reserve(__n);
3147 for (size_t __i = 0; __i < __n; ++__i)
3148 {
3149 double __den;
3150 __is >> __den;
3151 __den_vec.push_back(__den);
3152 }
3153
3154 __x.param(typename piecewise_constant_distribution<_RealType>::
3155 param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin()));
3156
3157 __is.flags(__flags);
3158 return __is;
3159 }
3160
3161
3162 template<typename _RealType>
3163 void
3164 piecewise_linear_distribution<_RealType>::param_type::
3165 _M_initialize()
3166 {
3167 if (_M_int.size() < 2
3168 || (_M_int.size() == 2
3169 && _M_int[0] == _RealType(0)
3170 && _M_int[1] == _RealType(1)
3171 && _M_den[0] == _M_den[1]))
3172 {
3173 _M_int.clear();
3174 _M_den.clear();
3175 return;
3176 }
3177
3178 double __sum = 0.0;
3179 _M_cp.reserve(_M_int.size() - 1);
3180 _M_m.reserve(_M_int.size() - 1);
3181 for (size_t __k = 0; __k < _M_int.size() - 1; ++__k)
3182 {
3183 const _RealType __delta = _M_int[__k + 1] - _M_int[__k];
3184 __sum += 0.5 * (_M_den[__k + 1] + _M_den[__k]) * __delta;
3185 _M_cp.push_back(__sum);
3186 _M_m.push_back((_M_den[__k + 1] - _M_den[__k]) / __delta);
3187 }
3188
3189 // Now normalize the densities...
3190 __detail::__transform(_M_den.begin(), _M_den.end(), _M_den.begin(),
3191 std::bind2nd(std::divides<double>(), __sum));
3192 // ... and partial sums...
3193 __detail::__transform(_M_cp.begin(), _M_cp.end(), _M_cp.begin(),
3194 std::bind2nd(std::divides<double>(), __sum));
3195 // ... and slopes.
3196 __detail::__transform(_M_m.begin(), _M_m.end(), _M_m.begin(),
3197 std::bind2nd(std::divides<double>(), __sum));
3198 // Make sure the last cumulative probablility is one.
3199 _M_cp[_M_cp.size() - 1] = 1.0;
3200 }
3201
3202 template<typename _RealType>
3203 template<typename _InputIteratorB, typename _InputIteratorW>
3204 piecewise_linear_distribution<_RealType>::param_type::
3205 param_type(_InputIteratorB __bbegin,
3206 _InputIteratorB __bend,
3207 _InputIteratorW __wbegin)
3208 : _M_int(), _M_den(), _M_cp(), _M_m()
3209 {
3210 for (; __bbegin != __bend; ++__bbegin, ++__wbegin)
3211 {
3212 _M_int.push_back(*__bbegin);
3213 _M_den.push_back(*__wbegin);
3214 }
3215
3216 _M_initialize();
3217 }
3218
3219 template<typename _RealType>
3220 template<typename _Func>
3221 piecewise_linear_distribution<_RealType>::param_type::
3222 param_type(initializer_list<_RealType> __bl, _Func __fw)
3223 : _M_int(), _M_den(), _M_cp(), _M_m()
3224 {
3225 _M_int.reserve(__bl.size());
3226 _M_den.reserve(__bl.size());
3227 for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter)
3228 {
3229 _M_int.push_back(*__biter);
3230 _M_den.push_back(__fw(*__biter));
3231 }
3232
3233 _M_initialize();
3234 }
3235
3236 template<typename _RealType>
3237 template<typename _Func>
3238 piecewise_linear_distribution<_RealType>::param_type::
3239 param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw)
3240 : _M_int(), _M_den(), _M_cp(), _M_m()
3241 {
3242 const size_t __n = __nw == 0 ? 1 : __nw;
3243 const _RealType __delta = (__xmax - __xmin) / __n;
3244
3245 _M_int.reserve(__n + 1);
3246 _M_den.reserve(__n + 1);
3247 for (size_t __k = 0; __k <= __nw; ++__k)
3248 {
3249 _M_int.push_back(__xmin + __k * __delta);
3250 _M_den.push_back(__fw(_M_int[__k] + __delta));
3251 }
3252
3253 _M_initialize();
3254 }
3255
3256 template<typename _RealType>
3257 template<typename _UniformRandomNumberGenerator>
3258 typename piecewise_linear_distribution<_RealType>::result_type
3259 piecewise_linear_distribution<_RealType>::
3260 operator()(_UniformRandomNumberGenerator& __urng,
3261 const param_type& __param)
3262 {
3263 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
3264 __aurng(__urng);
3265
3266 const double __p = __aurng();
3267 if (__param._M_cp.empty())
3268 return __p;
3269
3270 auto __pos = std::lower_bound(__param._M_cp.begin(),
3271 __param._M_cp.end(), __p);
3272 const size_t __i = __pos - __param._M_cp.begin();
3273
3274 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0;
3275
3276 const double __a = 0.5 * __param._M_m[__i];
3277 const double __b = __param._M_den[__i];
3278 const double __cm = __p - __pref;
3279
3280 _RealType __x = __param._M_int[__i];
3281 if (__a == 0)
3282 __x += __cm / __b;
3283 else
3284 {
3285 const double __d = __b * __b + 4.0 * __a * __cm;
3286 __x += 0.5 * (std::sqrt(__d) - __b) / __a;
3287 }
3288
3289 return __x;
3290 }
3291
3292 template<typename _RealType>
3293 template<typename _ForwardIterator,
3294 typename _UniformRandomNumberGenerator>
3295 void
3296 piecewise_linear_distribution<_RealType>::
3297 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
3298 _UniformRandomNumberGenerator& __urng,
3299 const param_type& __param)
3300 {
3301 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
3302 // We could duplicate everything from operator()...
3303 while (__f != __t)
3304 *__f++ = this->operator()(__urng, __param);
3305 }
3306
3307 template<typename _RealType, typename _CharT, typename _Traits>
3308 std::basic_ostream<_CharT, _Traits>&
3309 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
3310 const piecewise_linear_distribution<_RealType>& __x)
3311 {
3312 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
3313 typedef typename __ostream_type::ios_base __ios_base;
3314
3315 const typename __ios_base::fmtflags __flags = __os.flags();
3316 const _CharT __fill = __os.fill();
3317 const std::streamsize __precision = __os.precision();
3318 const _CharT __space = __os.widen(' ');
3319 __os.flags(__ios_base::scientific | __ios_base::left);
3320 __os.fill(__space);
3321 __os.precision(std::numeric_limits<_RealType>::max_digits10);
3322
3323 std::vector<_RealType> __int = __x.intervals();
3324 __os << __int.size() - 1;
3325
3326 for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit)
3327 __os << __space << *__xit;
3328
3329 std::vector<double> __den = __x.densities();
3330 for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit)
3331 __os << __space << *__dit;
3332
3333 __os.flags(__flags);
3334 __os.fill(__fill);
3335 __os.precision(__precision);
3336 return __os;
3337 }
3338
3339 template<typename _RealType, typename _CharT, typename _Traits>
3340 std::basic_istream<_CharT, _Traits>&
3341 operator>>(std::basic_istream<_CharT, _Traits>& __is,
3342 piecewise_linear_distribution<_RealType>& __x)
3343 {
3344 typedef std::basic_istream<_CharT, _Traits> __istream_type;
3345 typedef typename __istream_type::ios_base __ios_base;
3346
3347 const typename __ios_base::fmtflags __flags = __is.flags();
3348 __is.flags(__ios_base::dec | __ios_base::skipws);
3349
3350 size_t __n;
3351 __is >> __n;
3352
3353 std::vector<_RealType> __int_vec;
3354 __int_vec.reserve(__n + 1);
3355 for (size_t __i = 0; __i <= __n; ++__i)
3356 {
3357 _RealType __int;
3358 __is >> __int;
3359 __int_vec.push_back(__int);
3360 }
3361
3362 std::vector<double> __den_vec;
3363 __den_vec.reserve(__n + 1);
3364 for (size_t __i = 0; __i <= __n; ++__i)
3365 {
3366 double __den;
3367 __is >> __den;
3368 __den_vec.push_back(__den);
3369 }
3370
3371 __x.param(typename piecewise_linear_distribution<_RealType>::
3372 param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin()));
3373
3374 __is.flags(__flags);
3375 return __is;
3376 }
3377
3378
3379 template<typename _IntType>
3380 seed_seq::seed_seq(std::initializer_list<_IntType> __il)
3381 {
3382 for (auto __iter = __il.begin(); __iter != __il.end(); ++__iter)
3383 _M_v.push_back(__detail::__mod<result_type,
3384 __detail::_Shift<result_type, 32>::__value>(*__iter));
3385 }
3386
3387 template<typename _InputIterator>
3388 seed_seq::seed_seq(_InputIterator __begin, _InputIterator __end)
3389 {
3390 for (_InputIterator __iter = __begin; __iter != __end; ++__iter)
3391 _M_v.push_back(__detail::__mod<result_type,
3392 __detail::_Shift<result_type, 32>::__value>(*__iter));
3393 }
3394
3395 template<typename _RandomAccessIterator>
3396 void
3397 seed_seq::generate(_RandomAccessIterator __begin,
3398 _RandomAccessIterator __end)
3399 {
3400 typedef typename iterator_traits<_RandomAccessIterator>::value_type
3401 _Type;
3402
3403 if (__begin == __end)
3404 return;
3405
3406 std::fill(__begin, __end, _Type(0x8b8b8b8bu));
3407
3408 const size_t __n = __end - __begin;
3409 const size_t __s = _M_v.size();
3410 const size_t __t = (__n >= 623) ? 11
3411 : (__n >= 68) ? 7
3412 : (__n >= 39) ? 5
3413 : (__n >= 7) ? 3
3414 : (__n - 1) / 2;
3415 const size_t __p = (__n - __t) / 2;
3416 const size_t __q = __p + __t;
3417 const size_t __m = std::max(size_t(__s + 1), __n);
3418
3419 for (size_t __k = 0; __k < __m; ++__k)
3420 {
3421 _Type __arg = (__begin[__k % __n]
3422 ^ __begin[(__k + __p) % __n]
3423 ^ __begin[(__k - 1) % __n]);
3424 _Type __r1 = __arg ^ (__arg >> 27);
3425 __r1 = __detail::__mod<_Type,
3426 __detail::_Shift<_Type, 32>::__value>(1664525u * __r1);
3427 _Type __r2 = __r1;
3428 if (__k == 0)
3429 __r2 += __s;
3430 else if (__k <= __s)
3431 __r2 += __k % __n + _M_v[__k - 1];
3432 else
3433 __r2 += __k % __n;
3434 __r2 = __detail::__mod<_Type,
3435 __detail::_Shift<_Type, 32>::__value>(__r2);
3436 __begin[(__k + __p) % __n] += __r1;
3437 __begin[(__k + __q) % __n] += __r2;
3438 __begin[__k % __n] = __r2;
3439 }
3440
3441 for (size_t __k = __m; __k < __m + __n; ++__k)
3442 {
3443 _Type __arg = (__begin[__k % __n]
3444 + __begin[(__k + __p) % __n]
3445 + __begin[(__k - 1) % __n]);
3446 _Type __r3 = __arg ^ (__arg >> 27);
3447 __r3 = __detail::__mod<_Type,
3448 __detail::_Shift<_Type, 32>::__value>(1566083941u * __r3);
3449 _Type __r4 = __r3 - __k % __n;
3450 __r4 = __detail::__mod<_Type,
3451 __detail::_Shift<_Type, 32>::__value>(__r4);
3452 __begin[(__k + __p) % __n] ^= __r3;
3453 __begin[(__k + __q) % __n] ^= __r4;
3454 __begin[__k % __n] = __r4;
3455 }
3456 }
3457
3458 template<typename _RealType, size_t __bits,
3459 typename _UniformRandomNumberGenerator>
3460 _RealType
3461 generate_canonical(_UniformRandomNumberGenerator& __urng)
3462 {
3463 const size_t __b
3464 = std::min(static_cast<size_t>(std::numeric_limits<_RealType>::digits),
3465 __bits);
3466 const long double __r = static_cast<long double>(__urng.max())
3467 - static_cast<long double>(__urng.min()) + 1.0L;
3468 const size_t __log2r = std::log(__r) / std::log(2.0L);
3469 size_t __k = std::max<size_t>(1UL, (__b + __log2r - 1UL) / __log2r);
3470 _RealType __sum = _RealType(0);
3471 _RealType __tmp = _RealType(1);
3472 for (; __k != 0; --__k)
3473 {
3474 __sum += _RealType(__urng() - __urng.min()) * __tmp;
3475 __tmp *= __r;
3476 }
3477 return __sum / __tmp;
3478 }
3479
3480 _GLIBCXX_END_NAMESPACE_VERSION
3481 } // namespace
3482
3483 #endif
Mirror of https://gcc.gnu.org/git/gcc.git