libstdc++

bits/random.tcc
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1 // random number generation (out of line) -*- C++ -*-
2
3 // Copyright (C) 2009-2013 Free Software Foundation, Inc.
4 //
5 // This file is part of the GNU ISO C++ Library. This library is free
6 // software; you can redistribute it and/or modify it under the
7 // terms of the GNU General Public License as published by the
8 // Free Software Foundation; either version 3, or (at your option)
9 // any later version.
10
11 // This library is distributed in the hope that it will be useful,
12 // but WITHOUT ANY WARRANTY; without even the implied warranty of
13 // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
14 // GNU General Public License for more details.
15
16 // Under Section 7 of GPL version 3, you are granted additional
17 // permissions described in the GCC Runtime Library Exception, version
18 // 3.1, as published by the Free Software Foundation.
19
20 // You should have received a copy of the GNU General Public License and
21 // a copy of the GCC Runtime Library Exception along with this program;
22 // see the files COPYING3 and COPYING.RUNTIME respectively. If not, see
23 // <http://www.gnu.org/licenses/>.
24
25 /** @file bits/random.tcc
26 * This is an internal header file, included by other library headers.
27 * Do not attempt to use it directly. @headername{random}
28 */
29
30 #ifndef _RANDOM_TCC
31 #define _RANDOM_TCC 1
32
33 #include <numeric> // std::accumulate and std::partial_sum
34
35 namespace std _GLIBCXX_VISIBILITY(default)
36 {
37 /*
38 * (Further) implementation-space details.
39 */
40 namespace __detail
41 {
42 _GLIBCXX_BEGIN_NAMESPACE_VERSION
43
44 // General case for x = (ax + c) mod m -- use Schrage's algorithm
45 // to avoid integer overflow.
46 //
47 // Preconditions: a > 0, m > 0.
48 //
49 // Note: only works correctly for __m % __a < __m / __a.
50 template<typename _Tp, _Tp __m, _Tp __a, _Tp __c>
51 _Tp
52 _Mod<_Tp, __m, __a, __c, false, true>::
53 __calc(_Tp __x)
54 {
55 if (__a == 1)
56 __x %= __m;
57 else
58 {
59 static const _Tp __q = __m / __a;
60 static const _Tp __r = __m % __a;
61
62 _Tp __t1 = __a * (__x % __q);
63 _Tp __t2 = __r * (__x / __q);
64 if (__t1 >= __t2)
65 __x = __t1 - __t2;
66 else
67 __x = __m - __t2 + __t1;
68 }
69
70 if (__c != 0)
71 {
72 const _Tp __d = __m - __x;
73 if (__d > __c)
74 __x += __c;
75 else
76 __x = __c - __d;
77 }
78 return __x;
79 }
80
81 template<typename _InputIterator, typename _OutputIterator,
82 typename _Tp>
83 _OutputIterator
84 __normalize(_InputIterator __first, _InputIterator __last,
85 _OutputIterator __result, const _Tp& __factor)
86 {
87 for (; __first != __last; ++__first, ++__result)
88 *__result = *__first / __factor;
89 return __result;
90 }
91
92 _GLIBCXX_END_NAMESPACE_VERSION
93 } // namespace __detail
94
95 _GLIBCXX_BEGIN_NAMESPACE_VERSION
96
97 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
98 constexpr _UIntType
99 linear_congruential_engine<_UIntType, __a, __c, __m>::multiplier;
100
101 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
102 constexpr _UIntType
103 linear_congruential_engine<_UIntType, __a, __c, __m>::increment;
104
105 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
106 constexpr _UIntType
107 linear_congruential_engine<_UIntType, __a, __c, __m>::modulus;
108
109 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
110 constexpr _UIntType
111 linear_congruential_engine<_UIntType, __a, __c, __m>::default_seed;
112
113 /**
114 * Seeds the LCR with integral value @p __s, adjusted so that the
115 * ring identity is never a member of the convergence set.
116 */
117 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
118 void
119 linear_congruential_engine<_UIntType, __a, __c, __m>::
120 seed(result_type __s)
121 {
122 if ((__detail::__mod<_UIntType, __m>(__c) == 0)
123 && (__detail::__mod<_UIntType, __m>(__s) == 0))
124 _M_x = 1;
125 else
126 _M_x = __detail::__mod<_UIntType, __m>(__s);
127 }
128
129 /**
130 * Seeds the LCR engine with a value generated by @p __q.
131 */
132 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
133 template<typename _Sseq>
134 typename std::enable_if<std::is_class<_Sseq>::value>::type
135 linear_congruential_engine<_UIntType, __a, __c, __m>::
136 seed(_Sseq& __q)
137 {
138 const _UIntType __k0 = __m == 0 ? std::numeric_limits<_UIntType>::digits
139 : std::__lg(__m);
140 const _UIntType __k = (__k0 + 31) / 32;
141 uint_least32_t __arr[__k + 3];
142 __q.generate(__arr + 0, __arr + __k + 3);
143 _UIntType __factor = 1u;
144 _UIntType __sum = 0u;
145 for (size_t __j = 0; __j < __k; ++__j)
146 {
147 __sum += __arr[__j + 3] * __factor;
148 __factor *= __detail::_Shift<_UIntType, 32>::__value;
149 }
150 seed(__sum);
151 }
152
153 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m,
154 typename _CharT, typename _Traits>
155 std::basic_ostream<_CharT, _Traits>&
156 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
157 const linear_congruential_engine<_UIntType,
158 __a, __c, __m>& __lcr)
159 {
160 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
161 typedef typename __ostream_type::ios_base __ios_base;
162
163 const typename __ios_base::fmtflags __flags = __os.flags();
164 const _CharT __fill = __os.fill();
165 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
166 __os.fill(__os.widen(' '));
167
168 __os << __lcr._M_x;
169
170 __os.flags(__flags);
171 __os.fill(__fill);
172 return __os;
173 }
174
175 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m,
176 typename _CharT, typename _Traits>
177 std::basic_istream<_CharT, _Traits>&
178 operator>>(std::basic_istream<_CharT, _Traits>& __is,
179 linear_congruential_engine<_UIntType, __a, __c, __m>& __lcr)
180 {
181 typedef std::basic_istream<_CharT, _Traits> __istream_type;
182 typedef typename __istream_type::ios_base __ios_base;
183
184 const typename __ios_base::fmtflags __flags = __is.flags();
185 __is.flags(__ios_base::dec);
186
187 __is >> __lcr._M_x;
188
189 __is.flags(__flags);
190 return __is;
191 }
192
193
194 template<typename _UIntType,
195 size_t __w, size_t __n, size_t __m, size_t __r,
196 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
197 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
198 _UIntType __f>
199 constexpr size_t
200 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
201 __s, __b, __t, __c, __l, __f>::word_size;
202
203 template<typename _UIntType,
204 size_t __w, size_t __n, size_t __m, size_t __r,
205 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
206 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
207 _UIntType __f>
208 constexpr size_t
209 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
210 __s, __b, __t, __c, __l, __f>::state_size;
211
212 template<typename _UIntType,
213 size_t __w, size_t __n, size_t __m, size_t __r,
214 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
215 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
216 _UIntType __f>
217 constexpr size_t
218 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
219 __s, __b, __t, __c, __l, __f>::shift_size;
220
221 template<typename _UIntType,
222 size_t __w, size_t __n, size_t __m, size_t __r,
223 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
224 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
225 _UIntType __f>
226 constexpr size_t
227 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
228 __s, __b, __t, __c, __l, __f>::mask_bits;
229
230 template<typename _UIntType,
231 size_t __w, size_t __n, size_t __m, size_t __r,
232 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
233 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
234 _UIntType __f>
235 constexpr _UIntType
236 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
237 __s, __b, __t, __c, __l, __f>::xor_mask;
238
239 template<typename _UIntType,
240 size_t __w, size_t __n, size_t __m, size_t __r,
241 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
242 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
243 _UIntType __f>
244 constexpr size_t
245 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
246 __s, __b, __t, __c, __l, __f>::tempering_u;
247
248 template<typename _UIntType,
249 size_t __w, size_t __n, size_t __m, size_t __r,
250 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
251 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
252 _UIntType __f>
253 constexpr _UIntType
254 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
255 __s, __b, __t, __c, __l, __f>::tempering_d;
256
257 template<typename _UIntType,
258 size_t __w, size_t __n, size_t __m, size_t __r,
259 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
260 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
261 _UIntType __f>
262 constexpr size_t
263 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
264 __s, __b, __t, __c, __l, __f>::tempering_s;
265
266 template<typename _UIntType,
267 size_t __w, size_t __n, size_t __m, size_t __r,
268 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
269 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
270 _UIntType __f>
271 constexpr _UIntType
272 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
273 __s, __b, __t, __c, __l, __f>::tempering_b;
274
275 template<typename _UIntType,
276 size_t __w, size_t __n, size_t __m, size_t __r,
277 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
278 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
279 _UIntType __f>
280 constexpr size_t
281 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
282 __s, __b, __t, __c, __l, __f>::tempering_t;
283
284 template<typename _UIntType,
285 size_t __w, size_t __n, size_t __m, size_t __r,
286 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
287 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
288 _UIntType __f>
289 constexpr _UIntType
290 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
291 __s, __b, __t, __c, __l, __f>::tempering_c;
292
293 template<typename _UIntType,
294 size_t __w, size_t __n, size_t __m, size_t __r,
295 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
296 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
297 _UIntType __f>
298 constexpr size_t
299 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
300 __s, __b, __t, __c, __l, __f>::tempering_l;
301
302 template<typename _UIntType,
303 size_t __w, size_t __n, size_t __m, size_t __r,
304 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
305 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
306 _UIntType __f>
307 constexpr _UIntType
308 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
309 __s, __b, __t, __c, __l, __f>::
310 initialization_multiplier;
311
312 template<typename _UIntType,
313 size_t __w, size_t __n, size_t __m, size_t __r,
314 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
315 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
316 _UIntType __f>
317 constexpr _UIntType
318 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
319 __s, __b, __t, __c, __l, __f>::default_seed;
320
321 template<typename _UIntType,
322 size_t __w, size_t __n, size_t __m, size_t __r,
323 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
324 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
325 _UIntType __f>
326 void
327 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
328 __s, __b, __t, __c, __l, __f>::
329 seed(result_type __sd)
330 {
331 _M_x[0] = __detail::__mod<_UIntType,
332 __detail::_Shift<_UIntType, __w>::__value>(__sd);
333
334 for (size_t __i = 1; __i < state_size; ++__i)
335 {
336 _UIntType __x = _M_x[__i - 1];
337 __x ^= __x >> (__w - 2);
338 __x *= __f;
339 __x += __detail::__mod<_UIntType, __n>(__i);
340 _M_x[__i] = __detail::__mod<_UIntType,
341 __detail::_Shift<_UIntType, __w>::__value>(__x);
342 }
343 _M_p = state_size;
344 }
345
346 template<typename _UIntType,
347 size_t __w, size_t __n, size_t __m, size_t __r,
348 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
349 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
350 _UIntType __f>
351 template<typename _Sseq>
352 typename std::enable_if<std::is_class<_Sseq>::value>::type
353 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
354 __s, __b, __t, __c, __l, __f>::
355 seed(_Sseq& __q)
356 {
357 const _UIntType __upper_mask = (~_UIntType()) << __r;
358 const size_t __k = (__w + 31) / 32;
359 uint_least32_t __arr[__n * __k];
360 __q.generate(__arr + 0, __arr + __n * __k);
361
362 bool __zero = true;
363 for (size_t __i = 0; __i < state_size; ++__i)
364 {
365 _UIntType __factor = 1u;
366 _UIntType __sum = 0u;
367 for (size_t __j = 0; __j < __k; ++__j)
368 {
369 __sum += __arr[__k * __i + __j] * __factor;
370 __factor *= __detail::_Shift<_UIntType, 32>::__value;
371 }
372 _M_x[__i] = __detail::__mod<_UIntType,
373 __detail::_Shift<_UIntType, __w>::__value>(__sum);
374
375 if (__zero)
376 {
377 if (__i == 0)
378 {
379 if ((_M_x[0] & __upper_mask) != 0u)
380 __zero = false;
381 }
382 else if (_M_x[__i] != 0u)
383 __zero = false;
384 }
385 }
386 if (__zero)
387 _M_x[0] = __detail::_Shift<_UIntType, __w - 1>::__value;
388 _M_p = state_size;
389 }
390
391 template<typename _UIntType, size_t __w,
392 size_t __n, size_t __m, size_t __r,
393 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
394 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
395 _UIntType __f>
396 void
397 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
398 __s, __b, __t, __c, __l, __f>::
399 _M_gen_rand(void)
400 {
401 const _UIntType __upper_mask = (~_UIntType()) << __r;
402 const _UIntType __lower_mask = ~__upper_mask;
403
404 for (size_t __k = 0; __k < (__n - __m); ++__k)
405 {
406 _UIntType __y = ((_M_x[__k] & __upper_mask)
407 | (_M_x[__k + 1] & __lower_mask));
408 _M_x[__k] = (_M_x[__k + __m] ^ (__y >> 1)
409 ^ ((__y & 0x01) ? __a : 0));
410 }
411
412 for (size_t __k = (__n - __m); __k < (__n - 1); ++__k)
413 {
414 _UIntType __y = ((_M_x[__k] & __upper_mask)
415 | (_M_x[__k + 1] & __lower_mask));
416 _M_x[__k] = (_M_x[__k + (__m - __n)] ^ (__y >> 1)
417 ^ ((__y & 0x01) ? __a : 0));
418 }
419
420 _UIntType __y = ((_M_x[__n - 1] & __upper_mask)
421 | (_M_x[0] & __lower_mask));
422 _M_x[__n - 1] = (_M_x[__m - 1] ^ (__y >> 1)
423 ^ ((__y & 0x01) ? __a : 0));
424 _M_p = 0;
425 }
426
427 template<typename _UIntType, size_t __w,
428 size_t __n, size_t __m, size_t __r,
429 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
430 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
431 _UIntType __f>
432 void
433 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
434 __s, __b, __t, __c, __l, __f>::
435 discard(unsigned long long __z)
436 {
437 while (__z > state_size - _M_p)
438 {
439 __z -= state_size - _M_p;
440 _M_gen_rand();
441 }
442 _M_p += __z;
443 }
444
445 template<typename _UIntType, size_t __w,
446 size_t __n, size_t __m, size_t __r,
447 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
448 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
449 _UIntType __f>
450 typename
451 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
452 __s, __b, __t, __c, __l, __f>::result_type
453 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
454 __s, __b, __t, __c, __l, __f>::
455 operator()()
456 {
457 // Reload the vector - cost is O(n) amortized over n calls.
458 if (_M_p >= state_size)
459 _M_gen_rand();
460
461 // Calculate o(x(i)).
462 result_type __z = _M_x[_M_p++];
463 __z ^= (__z >> __u) & __d;
464 __z ^= (__z << __s) & __b;
465 __z ^= (__z << __t) & __c;
466 __z ^= (__z >> __l);
467
468 return __z;
469 }
470
471 template<typename _UIntType, size_t __w,
472 size_t __n, size_t __m, size_t __r,
473 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
474 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
475 _UIntType __f, typename _CharT, typename _Traits>
476 std::basic_ostream<_CharT, _Traits>&
477 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
478 const mersenne_twister_engine<_UIntType, __w, __n, __m,
479 __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x)
480 {
481 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
482 typedef typename __ostream_type::ios_base __ios_base;
483
484 const typename __ios_base::fmtflags __flags = __os.flags();
485 const _CharT __fill = __os.fill();
486 const _CharT __space = __os.widen(' ');
487 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
488 __os.fill(__space);
489
490 for (size_t __i = 0; __i < __n; ++__i)
491 __os << __x._M_x[__i] << __space;
492 __os << __x._M_p;
493
494 __os.flags(__flags);
495 __os.fill(__fill);
496 return __os;
497 }
498
499 template<typename _UIntType, size_t __w,
500 size_t __n, size_t __m, size_t __r,
501 _UIntType __a, size_t __u, _UIntType __d, size_t __s,
502 _UIntType __b, size_t __t, _UIntType __c, size_t __l,
503 _UIntType __f, typename _CharT, typename _Traits>
504 std::basic_istream<_CharT, _Traits>&
505 operator>>(std::basic_istream<_CharT, _Traits>& __is,
506 mersenne_twister_engine<_UIntType, __w, __n, __m,
507 __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x)
508 {
509 typedef std::basic_istream<_CharT, _Traits> __istream_type;
510 typedef typename __istream_type::ios_base __ios_base;
511
512 const typename __ios_base::fmtflags __flags = __is.flags();
513 __is.flags(__ios_base::dec | __ios_base::skipws);
514
515 for (size_t __i = 0; __i < __n; ++__i)
516 __is >> __x._M_x[__i];
517 __is >> __x._M_p;
518
519 __is.flags(__flags);
520 return __is;
521 }
522
523
524 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
525 constexpr size_t
526 subtract_with_carry_engine<_UIntType, __w, __s, __r>::word_size;
527
528 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
529 constexpr size_t
530 subtract_with_carry_engine<_UIntType, __w, __s, __r>::short_lag;
531
532 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
533 constexpr size_t
534 subtract_with_carry_engine<_UIntType, __w, __s, __r>::long_lag;
535
536 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
537 constexpr _UIntType
538 subtract_with_carry_engine<_UIntType, __w, __s, __r>::default_seed;
539
540 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
541 void
542 subtract_with_carry_engine<_UIntType, __w, __s, __r>::
543 seed(result_type __value)
544 {
545 std::linear_congruential_engine<result_type, 40014u, 0u, 2147483563u>
546 __lcg(__value == 0u ? default_seed : __value);
547
548 const size_t __n = (__w + 31) / 32;
549
550 for (size_t __i = 0; __i < long_lag; ++__i)
551 {
552 _UIntType __sum = 0u;
553 _UIntType __factor = 1u;
554 for (size_t __j = 0; __j < __n; ++__j)
555 {
556 __sum += __detail::__mod<uint_least32_t,
557 __detail::_Shift<uint_least32_t, 32>::__value>
558 (__lcg()) * __factor;
559 __factor *= __detail::_Shift<_UIntType, 32>::__value;
560 }
561 _M_x[__i] = __detail::__mod<_UIntType,
562 __detail::_Shift<_UIntType, __w>::__value>(__sum);
563 }
564 _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0;
565 _M_p = 0;
566 }
567
568 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
569 template<typename _Sseq>
570 typename std::enable_if<std::is_class<_Sseq>::value>::type
571 subtract_with_carry_engine<_UIntType, __w, __s, __r>::
572 seed(_Sseq& __q)
573 {
574 const size_t __k = (__w + 31) / 32;
575 uint_least32_t __arr[__r * __k];
576 __q.generate(__arr + 0, __arr + __r * __k);
577
578 for (size_t __i = 0; __i < long_lag; ++__i)
579 {
580 _UIntType __sum = 0u;
581 _UIntType __factor = 1u;
582 for (size_t __j = 0; __j < __k; ++__j)
583 {
584 __sum += __arr[__k * __i + __j] * __factor;
585 __factor *= __detail::_Shift<_UIntType, 32>::__value;
586 }
587 _M_x[__i] = __detail::__mod<_UIntType,
588 __detail::_Shift<_UIntType, __w>::__value>(__sum);
589 }
590 _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0;
591 _M_p = 0;
592 }
593
594 template<typename _UIntType, size_t __w, size_t __s, size_t __r>
595 typename subtract_with_carry_engine<_UIntType, __w, __s, __r>::
596 result_type
597 subtract_with_carry_engine<_UIntType, __w, __s, __r>::
598 operator()()
599 {
600 // Derive short lag index from current index.
601 long __ps = _M_p - short_lag;
602 if (__ps < 0)
603 __ps += long_lag;
604
605 // Calculate new x(i) without overflow or division.
606 // NB: Thanks to the requirements for _UIntType, _M_x[_M_p] + _M_carry
607 // cannot overflow.
608 _UIntType __xi;
609 if (_M_x[__ps] >= _M_x[_M_p] + _M_carry)
610 {
611 __xi = _M_x[__ps] - _M_x[_M_p] - _M_carry;
612 _M_carry = 0;
613 }
614 else
615 {
616 __xi = (__detail::_Shift<_UIntType, __w>::__value
617 - _M_x[_M_p] - _M_carry + _M_x[__ps]);
618 _M_carry = 1;
619 }
620 _M_x[_M_p] = __xi;
621
622 // Adjust current index to loop around in ring buffer.
623 if (++_M_p >= long_lag)
624 _M_p = 0;
625
626 return __xi;
627 }
628
629 template<typename _UIntType, size_t __w, size_t __s, size_t __r,
630 typename _CharT, typename _Traits>
631 std::basic_ostream<_CharT, _Traits>&
632 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
633 const subtract_with_carry_engine<_UIntType,
634 __w, __s, __r>& __x)
635 {
636 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
637 typedef typename __ostream_type::ios_base __ios_base;
638
639 const typename __ios_base::fmtflags __flags = __os.flags();
640 const _CharT __fill = __os.fill();
641 const _CharT __space = __os.widen(' ');
642 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
643 __os.fill(__space);
644
645 for (size_t __i = 0; __i < __r; ++__i)
646 __os << __x._M_x[__i] << __space;
647 __os << __x._M_carry << __space << __x._M_p;
648
649 __os.flags(__flags);
650 __os.fill(__fill);
651 return __os;
652 }
653
654 template<typename _UIntType, size_t __w, size_t __s, size_t __r,
655 typename _CharT, typename _Traits>
656 std::basic_istream<_CharT, _Traits>&
657 operator>>(std::basic_istream<_CharT, _Traits>& __is,
658 subtract_with_carry_engine<_UIntType, __w, __s, __r>& __x)
659 {
660 typedef std::basic_ostream<_CharT, _Traits> __istream_type;
661 typedef typename __istream_type::ios_base __ios_base;
662
663 const typename __ios_base::fmtflags __flags = __is.flags();
664 __is.flags(__ios_base::dec | __ios_base::skipws);
665
666 for (size_t __i = 0; __i < __r; ++__i)
667 __is >> __x._M_x[__i];
668 __is >> __x._M_carry;
669 __is >> __x._M_p;
670
671 __is.flags(__flags);
672 return __is;
673 }
674
675
676 template<typename _RandomNumberEngine, size_t __p, size_t __r>
677 constexpr size_t
678 discard_block_engine<_RandomNumberEngine, __p, __r>::block_size;
679
680 template<typename _RandomNumberEngine, size_t __p, size_t __r>
681 constexpr size_t
682 discard_block_engine<_RandomNumberEngine, __p, __r>::used_block;
683
684 template<typename _RandomNumberEngine, size_t __p, size_t __r>
685 typename discard_block_engine<_RandomNumberEngine,
686 __p, __r>::result_type
687 discard_block_engine<_RandomNumberEngine, __p, __r>::
688 operator()()
689 {
690 if (_M_n >= used_block)
691 {
692 _M_b.discard(block_size - _M_n);
693 _M_n = 0;
694 }
695 ++_M_n;
696 return _M_b();
697 }
698
699 template<typename _RandomNumberEngine, size_t __p, size_t __r,
700 typename _CharT, typename _Traits>
701 std::basic_ostream<_CharT, _Traits>&
702 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
703 const discard_block_engine<_RandomNumberEngine,
704 __p, __r>& __x)
705 {
706 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
707 typedef typename __ostream_type::ios_base __ios_base;
708
709 const typename __ios_base::fmtflags __flags = __os.flags();
710 const _CharT __fill = __os.fill();
711 const _CharT __space = __os.widen(' ');
712 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
713 __os.fill(__space);
714
715 __os << __x.base() << __space << __x._M_n;
716
717 __os.flags(__flags);
718 __os.fill(__fill);
719 return __os;
720 }
721
722 template<typename _RandomNumberEngine, size_t __p, size_t __r,
723 typename _CharT, typename _Traits>
724 std::basic_istream<_CharT, _Traits>&
725 operator>>(std::basic_istream<_CharT, _Traits>& __is,
726 discard_block_engine<_RandomNumberEngine, __p, __r>& __x)
727 {
728 typedef std::basic_istream<_CharT, _Traits> __istream_type;
729 typedef typename __istream_type::ios_base __ios_base;
730
731 const typename __ios_base::fmtflags __flags = __is.flags();
732 __is.flags(__ios_base::dec | __ios_base::skipws);
733
734 __is >> __x._M_b >> __x._M_n;
735
736 __is.flags(__flags);
737 return __is;
738 }
739
740
741 template<typename _RandomNumberEngine, size_t __w, typename _UIntType>
742 typename independent_bits_engine<_RandomNumberEngine, __w, _UIntType>::
743 result_type
744 independent_bits_engine<_RandomNumberEngine, __w, _UIntType>::
745 operator()()
746 {
747 typedef typename _RandomNumberEngine::result_type _Eresult_type;
748 const _Eresult_type __r
749 = (_M_b.max() - _M_b.min() < std::numeric_limits<_Eresult_type>::max()
750 ? _M_b.max() - _M_b.min() + 1 : 0);
751 const unsigned __edig = std::numeric_limits<_Eresult_type>::digits;
752 const unsigned __m = __r ? std::__lg(__r) : __edig;
753
754 typedef typename std::common_type<_Eresult_type, result_type>::type
755 __ctype;
756 const unsigned __cdig = std::numeric_limits<__ctype>::digits;
757
758 unsigned __n, __n0;
759 __ctype __s0, __s1, __y0, __y1;
760
761 for (size_t __i = 0; __i < 2; ++__i)
762 {
763 __n = (__w + __m - 1) / __m + __i;
764 __n0 = __n - __w % __n;
765 const unsigned __w0 = __w / __n; // __w0 <= __m
766
767 __s0 = 0;
768 __s1 = 0;
769 if (__w0 < __cdig)
770 {
771 __s0 = __ctype(1) << __w0;
772 __s1 = __s0 << 1;
773 }
774
775 __y0 = 0;
776 __y1 = 0;
777 if (__r)
778 {
779 __y0 = __s0 * (__r / __s0);
780 if (__s1)
781 __y1 = __s1 * (__r / __s1);
782
783 if (__r - __y0 <= __y0 / __n)
784 break;
785 }
786 else
787 break;
788 }
789
790 result_type __sum = 0;
791 for (size_t __k = 0; __k < __n0; ++__k)
792 {
793 __ctype __u;
794 do
795 __u = _M_b() - _M_b.min();
796 while (__y0 && __u >= __y0);
797 __sum = __s0 * __sum + (__s0 ? __u % __s0 : __u);
798 }
799 for (size_t __k = __n0; __k < __n; ++__k)
800 {
801 __ctype __u;
802 do
803 __u = _M_b() - _M_b.min();
804 while (__y1 && __u >= __y1);
805 __sum = __s1 * __sum + (__s1 ? __u % __s1 : __u);
806 }
807 return __sum;
808 }
809
810
811 template<typename _RandomNumberEngine, size_t __k>
812 constexpr size_t
813 shuffle_order_engine<_RandomNumberEngine, __k>::table_size;
814
815 template<typename _RandomNumberEngine, size_t __k>
816 typename shuffle_order_engine<_RandomNumberEngine, __k>::result_type
817 shuffle_order_engine<_RandomNumberEngine, __k>::
818 operator()()
819 {
820 size_t __j = __k * ((_M_y - _M_b.min())
821 / (_M_b.max() - _M_b.min() + 1.0L));
822 _M_y = _M_v[__j];
823 _M_v[__j] = _M_b();
824
825 return _M_y;
826 }
827
828 template<typename _RandomNumberEngine, size_t __k,
829 typename _CharT, typename _Traits>
830 std::basic_ostream<_CharT, _Traits>&
831 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
832 const shuffle_order_engine<_RandomNumberEngine, __k>& __x)
833 {
834 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
835 typedef typename __ostream_type::ios_base __ios_base;
836
837 const typename __ios_base::fmtflags __flags = __os.flags();
838 const _CharT __fill = __os.fill();
839 const _CharT __space = __os.widen(' ');
840 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
841 __os.fill(__space);
842
843 __os << __x.base();
844 for (size_t __i = 0; __i < __k; ++__i)
845 __os << __space << __x._M_v[__i];
846 __os << __space << __x._M_y;
847
848 __os.flags(__flags);
849 __os.fill(__fill);
850 return __os;
851 }
852
853 template<typename _RandomNumberEngine, size_t __k,
854 typename _CharT, typename _Traits>
855 std::basic_istream<_CharT, _Traits>&
856 operator>>(std::basic_istream<_CharT, _Traits>& __is,
857 shuffle_order_engine<_RandomNumberEngine, __k>& __x)
858 {
859 typedef std::basic_istream<_CharT, _Traits> __istream_type;
860 typedef typename __istream_type::ios_base __ios_base;
861
862 const typename __ios_base::fmtflags __flags = __is.flags();
863 __is.flags(__ios_base::dec | __ios_base::skipws);
864
865 __is >> __x._M_b;
866 for (size_t __i = 0; __i < __k; ++__i)
867 __is >> __x._M_v[__i];
868 __is >> __x._M_y;
869
870 __is.flags(__flags);
871 return __is;
872 }
873
874
875 template<typename _IntType>
876 template<typename _UniformRandomNumberGenerator>
877 typename uniform_int_distribution<_IntType>::result_type
878 uniform_int_distribution<_IntType>::
879 operator()(_UniformRandomNumberGenerator& __urng,
880 const param_type& __param)
881 {
882 typedef typename _UniformRandomNumberGenerator::result_type
883 _Gresult_type;
884 typedef typename std::make_unsigned<result_type>::type __utype;
885 typedef typename std::common_type<_Gresult_type, __utype>::type
886 __uctype;
887
888 const __uctype __urngmin = __urng.min();
889 const __uctype __urngmax = __urng.max();
890 const __uctype __urngrange = __urngmax - __urngmin;
891 const __uctype __urange
892 = __uctype(__param.b()) - __uctype(__param.a());
893
894 __uctype __ret;
895
896 if (__urngrange > __urange)
897 {
898 // downscaling
899 const __uctype __uerange = __urange + 1; // __urange can be zero
900 const __uctype __scaling = __urngrange / __uerange;
901 const __uctype __past = __uerange * __scaling;
902 do
903 __ret = __uctype(__urng()) - __urngmin;
904 while (__ret >= __past);
905 __ret /= __scaling;
906 }
907 else if (__urngrange < __urange)
908 {
909 // upscaling
910 /*
911 Note that every value in [0, urange]
912 can be written uniquely as
913
914 (urngrange + 1) * high + low
915
916 where
917
918 high in [0, urange / (urngrange + 1)]
919
920 and
921
922 low in [0, urngrange].
923 */
924 __uctype __tmp; // wraparound control
925 do
926 {
927 const __uctype __uerngrange = __urngrange + 1;
928 __tmp = (__uerngrange * operator()
929 (__urng, param_type(0, __urange / __uerngrange)));
930 __ret = __tmp + (__uctype(__urng()) - __urngmin);
931 }
932 while (__ret > __urange || __ret < __tmp);
933 }
934 else
935 __ret = __uctype(__urng()) - __urngmin;
936
937 return __ret + __param.a();
938 }
939
940
941 template<typename _IntType>
942 template<typename _ForwardIterator,
943 typename _UniformRandomNumberGenerator>
944 void
945 uniform_int_distribution<_IntType>::
946 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
947 _UniformRandomNumberGenerator& __urng,
948 const param_type& __param)
949 {
950 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
951 typedef typename _UniformRandomNumberGenerator::result_type
952 _Gresult_type;
953 typedef typename std::make_unsigned<result_type>::type __utype;
954 typedef typename std::common_type<_Gresult_type, __utype>::type
955 __uctype;
956
957 const __uctype __urngmin = __urng.min();
958 const __uctype __urngmax = __urng.max();
959 const __uctype __urngrange = __urngmax - __urngmin;
960 const __uctype __urange
961 = __uctype(__param.b()) - __uctype(__param.a());
962
963 __uctype __ret;
964
965 if (__urngrange > __urange)
966 {
967 if (__detail::_Power_of_2(__urngrange + 1)
968 && __detail::_Power_of_2(__urange + 1))
969 {
970 while (__f != __t)
971 {
972 __ret = __uctype(__urng()) - __urngmin;
973 *__f++ = (__ret & __urange) + __param.a();
974 }
975 }
976 else
977 {
978 // downscaling
979 const __uctype __uerange = __urange + 1; // __urange can be zero
980 const __uctype __scaling = __urngrange / __uerange;
981 const __uctype __past = __uerange * __scaling;
982 while (__f != __t)
983 {
984 do
985 __ret = __uctype(__urng()) - __urngmin;
986 while (__ret >= __past);
987 *__f++ = __ret / __scaling + __param.a();
988 }
989 }
990 }
991 else if (__urngrange < __urange)
992 {
993 // upscaling
994 /*
995 Note that every value in [0, urange]
996 can be written uniquely as
997
998 (urngrange + 1) * high + low
999
1000 where
1001
1002 high in [0, urange / (urngrange + 1)]
1003
1004 and
1005
1006 low in [0, urngrange].
1007 */
1008 __uctype __tmp; // wraparound control
1009 while (__f != __t)
1010 {
1011 do
1012 {
1013 const __uctype __uerngrange = __urngrange + 1;
1014 __tmp = (__uerngrange * operator()
1015 (__urng, param_type(0, __urange / __uerngrange)));
1016 __ret = __tmp + (__uctype(__urng()) - __urngmin);
1017 }
1018 while (__ret > __urange || __ret < __tmp);
1019 *__f++ = __ret;
1020 }
1021 }
1022 else
1023 while (__f != __t)
1024 *__f++ = __uctype(__urng()) - __urngmin + __param.a();
1025 }
1026
1027 template<typename _IntType, typename _CharT, typename _Traits>
1028 std::basic_ostream<_CharT, _Traits>&
1029 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1030 const uniform_int_distribution<_IntType>& __x)
1031 {
1032 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1033 typedef typename __ostream_type::ios_base __ios_base;
1034
1035 const typename __ios_base::fmtflags __flags = __os.flags();
1036 const _CharT __fill = __os.fill();
1037 const _CharT __space = __os.widen(' ');
1038 __os.flags(__ios_base::scientific | __ios_base::left);
1039 __os.fill(__space);
1040
1041 __os << __x.a() << __space << __x.b();
1042
1043 __os.flags(__flags);
1044 __os.fill(__fill);
1045 return __os;
1046 }
1047
1048 template<typename _IntType, typename _CharT, typename _Traits>
1049 std::basic_istream<_CharT, _Traits>&
1050 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1051 uniform_int_distribution<_IntType>& __x)
1052 {
1053 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1054 typedef typename __istream_type::ios_base __ios_base;
1055
1056 const typename __ios_base::fmtflags __flags = __is.flags();
1057 __is.flags(__ios_base::dec | __ios_base::skipws);
1058
1059 _IntType __a, __b;
1060 __is >> __a >> __b;
1061 __x.param(typename uniform_int_distribution<_IntType>::
1062 param_type(__a, __b));
1063
1064 __is.flags(__flags);
1065 return __is;
1066 }
1067
1068
1069 template<typename _RealType>
1070 template<typename _ForwardIterator,
1071 typename _UniformRandomNumberGenerator>
1072 void
1073 uniform_real_distribution<_RealType>::
1074 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1075 _UniformRandomNumberGenerator& __urng,
1076 const param_type& __p)
1077 {
1078 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
1079 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
1080 __aurng(__urng);
1081 auto __range = __p.b() - __p.a();
1082 while (__f != __t)
1083 *__f++ = __aurng() * __range + __p.a();
1084 }
1085
1086 template<typename _RealType, typename _CharT, typename _Traits>
1087 std::basic_ostream<_CharT, _Traits>&
1088 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1089 const uniform_real_distribution<_RealType>& __x)
1090 {
1091 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1092 typedef typename __ostream_type::ios_base __ios_base;
1093
1094 const typename __ios_base::fmtflags __flags = __os.flags();
1095 const _CharT __fill = __os.fill();
1096 const std::streamsize __precision = __os.precision();
1097 const _CharT __space = __os.widen(' ');
1098 __os.flags(__ios_base::scientific | __ios_base::left);
1099 __os.fill(__space);
1100 __os.precision(std::numeric_limits<_RealType>::max_digits10);
1101
1102 __os << __x.a() << __space << __x.b();
1103
1104 __os.flags(__flags);
1105 __os.fill(__fill);
1106 __os.precision(__precision);
1107 return __os;
1108 }
1109
1110 template<typename _RealType, typename _CharT, typename _Traits>
1111 std::basic_istream<_CharT, _Traits>&
1112 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1113 uniform_real_distribution<_RealType>& __x)
1114 {
1115 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1116 typedef typename __istream_type::ios_base __ios_base;
1117
1118 const typename __ios_base::fmtflags __flags = __is.flags();
1119 __is.flags(__ios_base::skipws);
1120
1121 _RealType __a, __b;
1122 __is >> __a >> __b;
1123 __x.param(typename uniform_real_distribution<_RealType>::
1124 param_type(__a, __b));
1125
1126 __is.flags(__flags);
1127 return __is;
1128 }
1129
1130
1131 template<typename _ForwardIterator,
1132 typename _UniformRandomNumberGenerator>
1133 void
1134 std::bernoulli_distribution::
1135 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1136 _UniformRandomNumberGenerator& __urng,
1137 const param_type& __p)
1138 {
1139 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
1140 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
1141 __aurng(__urng);
1142 auto __limit = __p.p() * (__aurng.max() - __aurng.min());
1143
1144 while (__f != __t)
1145 *__f++ = (__aurng() - __aurng.min()) < __limit;
1146 }
1147
1148 template<typename _CharT, typename _Traits>
1149 std::basic_ostream<_CharT, _Traits>&
1150 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1151 const bernoulli_distribution& __x)
1152 {
1153 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1154 typedef typename __ostream_type::ios_base __ios_base;
1155
1156 const typename __ios_base::fmtflags __flags = __os.flags();
1157 const _CharT __fill = __os.fill();
1158 const std::streamsize __precision = __os.precision();
1159 __os.flags(__ios_base::scientific | __ios_base::left);
1160 __os.fill(__os.widen(' '));
1161 __os.precision(std::numeric_limits<double>::max_digits10);
1162
1163 __os << __x.p();
1164
1165 __os.flags(__flags);
1166 __os.fill(__fill);
1167 __os.precision(__precision);
1168 return __os;
1169 }
1170
1171
1172 template<typename _IntType>
1173 template<typename _UniformRandomNumberGenerator>
1174 typename geometric_distribution<_IntType>::result_type
1175 geometric_distribution<_IntType>::
1176 operator()(_UniformRandomNumberGenerator& __urng,
1177 const param_type& __param)
1178 {
1179 // About the epsilon thing see this thread:
1180 // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html
1181 const double __naf =
1182 (1 - std::numeric_limits<double>::epsilon()) / 2;
1183 // The largest _RealType convertible to _IntType.
1184 const double __thr =
1185 std::numeric_limits<_IntType>::max() + __naf;
1186 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
1187 __aurng(__urng);
1188
1189 double __cand;
1190 do
1191 __cand = std::floor(std::log(1.0 - __aurng()) / __param._M_log_1_p);
1192 while (__cand >= __thr);
1193
1194 return result_type(__cand + __naf);
1195 }
1196
1197 template<typename _IntType>
1198 template<typename _ForwardIterator,
1199 typename _UniformRandomNumberGenerator>
1200 void
1201 geometric_distribution<_IntType>::
1202 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1203 _UniformRandomNumberGenerator& __urng,
1204 const param_type& __param)
1205 {
1206 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
1207 // About the epsilon thing see this thread:
1208 // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html
1209 const double __naf =
1210 (1 - std::numeric_limits<double>::epsilon()) / 2;
1211 // The largest _RealType convertible to _IntType.
1212 const double __thr =
1213 std::numeric_limits<_IntType>::max() + __naf;
1214 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
1215 __aurng(__urng);
1216
1217 while (__f != __t)
1218 {
1219 double __cand;
1220 do
1221 __cand = std::floor(std::log(1.0 - __aurng())
1222 / __param._M_log_1_p);
1223 while (__cand >= __thr);
1224
1225 *__f++ = __cand + __naf;
1226 }
1227 }
1228
1229 template<typename _IntType,
1230 typename _CharT, typename _Traits>
1231 std::basic_ostream<_CharT, _Traits>&
1232 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1233 const geometric_distribution<_IntType>& __x)
1234 {
1235 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1236 typedef typename __ostream_type::ios_base __ios_base;
1237
1238 const typename __ios_base::fmtflags __flags = __os.flags();
1239 const _CharT __fill = __os.fill();
1240 const std::streamsize __precision = __os.precision();
1241 __os.flags(__ios_base::scientific | __ios_base::left);
1242 __os.fill(__os.widen(' '));
1243 __os.precision(std::numeric_limits<double>::max_digits10);
1244
1245 __os << __x.p();
1246
1247 __os.flags(__flags);
1248 __os.fill(__fill);
1249 __os.precision(__precision);
1250 return __os;
1251 }
1252
1253 template<typename _IntType,
1254 typename _CharT, typename _Traits>
1255 std::basic_istream<_CharT, _Traits>&
1256 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1257 geometric_distribution<_IntType>& __x)
1258 {
1259 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1260 typedef typename __istream_type::ios_base __ios_base;
1261
1262 const typename __ios_base::fmtflags __flags = __is.flags();
1263 __is.flags(__ios_base::skipws);
1264
1265 double __p;
1266 __is >> __p;
1267 __x.param(typename geometric_distribution<_IntType>::param_type(__p));
1268
1269 __is.flags(__flags);
1270 return __is;
1271 }
1272
1273 // This is Leger's algorithm, also in Devroye, Ch. X, Example 1.5.
1274 template<typename _IntType>
1275 template<typename _UniformRandomNumberGenerator>
1276 typename negative_binomial_distribution<_IntType>::result_type
1277 negative_binomial_distribution<_IntType>::
1278 operator()(_UniformRandomNumberGenerator& __urng)
1279 {
1280 const double __y = _M_gd(__urng);
1281
1282 // XXX Is the constructor too slow?
1283 std::poisson_distribution<result_type> __poisson(__y);
1284 return __poisson(__urng);
1285 }
1286
1287 template<typename _IntType>
1288 template<typename _UniformRandomNumberGenerator>
1289 typename negative_binomial_distribution<_IntType>::result_type
1290 negative_binomial_distribution<_IntType>::
1291 operator()(_UniformRandomNumberGenerator& __urng,
1292 const param_type& __p)
1293 {
1294 typedef typename std::gamma_distribution<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 if (__t == __x)
1661 return __x;
1662 const double __e = -std::log(1.0 - __aurng());
1663 __sum += __e / (__t - __x);
1664 __x += 1;
1665 }
1666 while (__sum <= _M_param._M_q);
1667
1668 return __x - 1;
1669 }
1670
1671 /**
1672 * A rejection algorithm when t * p >= 8 and a simple waiting time
1673 * method - the second in the referenced book - otherwise.
1674 * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1
1675 * is defined.
1676 *
1677 * Reference:
1678 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
1679 * New York, 1986, Ch. X, Sect. 4 (+ Errata!).
1680 */
1681 template<typename _IntType>
1682 template<typename _UniformRandomNumberGenerator>
1683 typename binomial_distribution<_IntType>::result_type
1684 binomial_distribution<_IntType>::
1685 operator()(_UniformRandomNumberGenerator& __urng,
1686 const param_type& __param)
1687 {
1688 result_type __ret;
1689 const _IntType __t = __param.t();
1690 const double __p = __param.p();
1691 const double __p12 = __p <= 0.5 ? __p : 1.0 - __p;
1692 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
1693 __aurng(__urng);
1694
1695 #if _GLIBCXX_USE_C99_MATH_TR1
1696 if (!__param._M_easy)
1697 {
1698 double __x;
1699
1700 // See comments above...
1701 const double __naf =
1702 (1 - std::numeric_limits<double>::epsilon()) / 2;
1703 const double __thr =
1704 std::numeric_limits<_IntType>::max() + __naf;
1705
1706 const double __np = std::floor(__t * __p12);
1707
1708 // sqrt(pi / 2)
1709 const double __spi_2 = 1.2533141373155002512078826424055226L;
1710 const double __a1 = __param._M_a1;
1711 const double __a12 = __a1 + __param._M_s2 * __spi_2;
1712 const double __a123 = __param._M_a123;
1713 const double __s1s = __param._M_s1 * __param._M_s1;
1714 const double __s2s = __param._M_s2 * __param._M_s2;
1715
1716 bool __reject;
1717 do
1718 {
1719 const double __u = __param._M_s * __aurng();
1720
1721 double __v;
1722
1723 if (__u <= __a1)
1724 {
1725 const double __n = _M_nd(__urng);
1726 const double __y = __param._M_s1 * std::abs(__n);
1727 __reject = __y >= __param._M_d1;
1728 if (!__reject)
1729 {
1730 const double __e = -std::log(1.0 - __aurng());
1731 __x = std::floor(__y);
1732 __v = -__e - __n * __n / 2 + __param._M_c;
1733 }
1734 }
1735 else if (__u <= __a12)
1736 {
1737 const double __n = _M_nd(__urng);
1738 const double __y = __param._M_s2 * std::abs(__n);
1739 __reject = __y >= __param._M_d2;
1740 if (!__reject)
1741 {
1742 const double __e = -std::log(1.0 - __aurng());
1743 __x = std::floor(-__y);
1744 __v = -__e - __n * __n / 2;
1745 }
1746 }
1747 else if (__u <= __a123)
1748 {
1749 const double __e1 = -std::log(1.0 - __aurng());
1750 const double __e2 = -std::log(1.0 - __aurng());
1751
1752 const double __y = __param._M_d1
1753 + 2 * __s1s * __e1 / __param._M_d1;
1754 __x = std::floor(__y);
1755 __v = (-__e2 + __param._M_d1 * (1 / (__t - __np)
1756 -__y / (2 * __s1s)));
1757 __reject = false;
1758 }
1759 else
1760 {
1761 const double __e1 = -std::log(1.0 - __aurng());
1762 const double __e2 = -std::log(1.0 - __aurng());
1763
1764 const double __y = __param._M_d2
1765 + 2 * __s2s * __e1 / __param._M_d2;
1766 __x = std::floor(-__y);
1767 __v = -__e2 - __param._M_d2 * __y / (2 * __s2s);
1768 __reject = false;
1769 }
1770
1771 __reject = __reject || __x < -__np || __x > __t - __np;
1772 if (!__reject)
1773 {
1774 const double __lfx =
1775 std::lgamma(__np + __x + 1)
1776 + std::lgamma(__t - (__np + __x) + 1);
1777 __reject = __v > __param._M_lf - __lfx
1778 + __x * __param._M_lp1p;
1779 }
1780
1781 __reject |= __x + __np >= __thr;
1782 }
1783 while (__reject);
1784
1785 __x += __np + __naf;
1786
1787 const _IntType __z = _M_waiting(__urng, __t - _IntType(__x));
1788 __ret = _IntType(__x) + __z;
1789 }
1790 else
1791 #endif
1792 __ret = _M_waiting(__urng, __t);
1793
1794 if (__p12 != __p)
1795 __ret = __t - __ret;
1796 return __ret;
1797 }
1798
1799 template<typename _IntType>
1800 template<typename _ForwardIterator,
1801 typename _UniformRandomNumberGenerator>
1802 void
1803 binomial_distribution<_IntType>::
1804 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1805 _UniformRandomNumberGenerator& __urng,
1806 const param_type& __param)
1807 {
1808 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
1809 // We could duplicate everything from operator()...
1810 while (__f != __t)
1811 *__f++ = this->operator()(__urng, __param);
1812 }
1813
1814 template<typename _IntType,
1815 typename _CharT, typename _Traits>
1816 std::basic_ostream<_CharT, _Traits>&
1817 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1818 const binomial_distribution<_IntType>& __x)
1819 {
1820 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1821 typedef typename __ostream_type::ios_base __ios_base;
1822
1823 const typename __ios_base::fmtflags __flags = __os.flags();
1824 const _CharT __fill = __os.fill();
1825 const std::streamsize __precision = __os.precision();
1826 const _CharT __space = __os.widen(' ');
1827 __os.flags(__ios_base::scientific | __ios_base::left);
1828 __os.fill(__space);
1829 __os.precision(std::numeric_limits<double>::max_digits10);
1830
1831 __os << __x.t() << __space << __x.p()
1832 << __space << __x._M_nd;
1833
1834 __os.flags(__flags);
1835 __os.fill(__fill);
1836 __os.precision(__precision);
1837 return __os;
1838 }
1839
1840 template<typename _IntType,
1841 typename _CharT, typename _Traits>
1842 std::basic_istream<_CharT, _Traits>&
1843 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1844 binomial_distribution<_IntType>& __x)
1845 {
1846 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1847 typedef typename __istream_type::ios_base __ios_base;
1848
1849 const typename __ios_base::fmtflags __flags = __is.flags();
1850 __is.flags(__ios_base::dec | __ios_base::skipws);
1851
1852 _IntType __t;
1853 double __p;
1854 __is >> __t >> __p >> __x._M_nd;
1855 __x.param(typename binomial_distribution<_IntType>::
1856 param_type(__t, __p));
1857
1858 __is.flags(__flags);
1859 return __is;
1860 }
1861
1862
1863 template<typename _RealType>
1864 template<typename _ForwardIterator,
1865 typename _UniformRandomNumberGenerator>
1866 void
1867 std::exponential_distribution<_RealType>::
1868 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1869 _UniformRandomNumberGenerator& __urng,
1870 const param_type& __p)
1871 {
1872 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
1873 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
1874 __aurng(__urng);
1875 while (__f != __t)
1876 *__f++ = -std::log(result_type(1) - __aurng()) / __p.lambda();
1877 }
1878
1879 template<typename _RealType, typename _CharT, typename _Traits>
1880 std::basic_ostream<_CharT, _Traits>&
1881 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
1882 const exponential_distribution<_RealType>& __x)
1883 {
1884 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
1885 typedef typename __ostream_type::ios_base __ios_base;
1886
1887 const typename __ios_base::fmtflags __flags = __os.flags();
1888 const _CharT __fill = __os.fill();
1889 const std::streamsize __precision = __os.precision();
1890 __os.flags(__ios_base::scientific | __ios_base::left);
1891 __os.fill(__os.widen(' '));
1892 __os.precision(std::numeric_limits<_RealType>::max_digits10);
1893
1894 __os << __x.lambda();
1895
1896 __os.flags(__flags);
1897 __os.fill(__fill);
1898 __os.precision(__precision);
1899 return __os;
1900 }
1901
1902 template<typename _RealType, typename _CharT, typename _Traits>
1903 std::basic_istream<_CharT, _Traits>&
1904 operator>>(std::basic_istream<_CharT, _Traits>& __is,
1905 exponential_distribution<_RealType>& __x)
1906 {
1907 typedef std::basic_istream<_CharT, _Traits> __istream_type;
1908 typedef typename __istream_type::ios_base __ios_base;
1909
1910 const typename __ios_base::fmtflags __flags = __is.flags();
1911 __is.flags(__ios_base::dec | __ios_base::skipws);
1912
1913 _RealType __lambda;
1914 __is >> __lambda;
1915 __x.param(typename exponential_distribution<_RealType>::
1916 param_type(__lambda));
1917
1918 __is.flags(__flags);
1919 return __is;
1920 }
1921
1922
1923 /**
1924 * Polar method due to Marsaglia.
1925 *
1926 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
1927 * New York, 1986, Ch. V, Sect. 4.4.
1928 */
1929 template<typename _RealType>
1930 template<typename _UniformRandomNumberGenerator>
1931 typename normal_distribution<_RealType>::result_type
1932 normal_distribution<_RealType>::
1933 operator()(_UniformRandomNumberGenerator& __urng,
1934 const param_type& __param)
1935 {
1936 result_type __ret;
1937 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
1938 __aurng(__urng);
1939
1940 if (_M_saved_available)
1941 {
1942 _M_saved_available = false;
1943 __ret = _M_saved;
1944 }
1945 else
1946 {
1947 result_type __x, __y, __r2;
1948 do
1949 {
1950 __x = result_type(2.0) * __aurng() - 1.0;
1951 __y = result_type(2.0) * __aurng() - 1.0;
1952 __r2 = __x * __x + __y * __y;
1953 }
1954 while (__r2 > 1.0 || __r2 == 0.0);
1955
1956 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2);
1957 _M_saved = __x * __mult;
1958 _M_saved_available = true;
1959 __ret = __y * __mult;
1960 }
1961
1962 __ret = __ret * __param.stddev() + __param.mean();
1963 return __ret;
1964 }
1965
1966 template<typename _RealType>
1967 template<typename _ForwardIterator,
1968 typename _UniformRandomNumberGenerator>
1969 void
1970 normal_distribution<_RealType>::
1971 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
1972 _UniformRandomNumberGenerator& __urng,
1973 const param_type& __param)
1974 {
1975 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
1976
1977 if (__f == __t)
1978 return;
1979
1980 if (_M_saved_available)
1981 {
1982 _M_saved_available = false;
1983 *__f++ = _M_saved * __param.stddev() + __param.mean();
1984
1985 if (__f == __t)
1986 return;
1987 }
1988
1989 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
1990 __aurng(__urng);
1991
1992 while (__f + 1 < __t)
1993 {
1994 result_type __x, __y, __r2;
1995 do
1996 {
1997 __x = result_type(2.0) * __aurng() - 1.0;
1998 __y = result_type(2.0) * __aurng() - 1.0;
1999 __r2 = __x * __x + __y * __y;
2000 }
2001 while (__r2 > 1.0 || __r2 == 0.0);
2002
2003 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2);
2004 *__f++ = __y * __mult * __param.stddev() + __param.mean();
2005 *__f++ = __x * __mult * __param.stddev() + __param.mean();
2006 }
2007
2008 if (__f != __t)
2009 {
2010 result_type __x, __y, __r2;
2011 do
2012 {
2013 __x = result_type(2.0) * __aurng() - 1.0;
2014 __y = result_type(2.0) * __aurng() - 1.0;
2015 __r2 = __x * __x + __y * __y;
2016 }
2017 while (__r2 > 1.0 || __r2 == 0.0);
2018
2019 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2);
2020 _M_saved = __x * __mult;
2021 _M_saved_available = true;
2022 *__f = __y * __mult * __param.stddev() + __param.mean();
2023 }
2024 }
2025
2026 template<typename _RealType>
2027 bool
2028 operator==(const std::normal_distribution<_RealType>& __d1,
2029 const std::normal_distribution<_RealType>& __d2)
2030 {
2031 if (__d1._M_param == __d2._M_param
2032 && __d1._M_saved_available == __d2._M_saved_available)
2033 {
2034 if (__d1._M_saved_available
2035 && __d1._M_saved == __d2._M_saved)
2036 return true;
2037 else if(!__d1._M_saved_available)
2038 return true;
2039 else
2040 return false;
2041 }
2042 else
2043 return false;
2044 }
2045
2046 template<typename _RealType, typename _CharT, typename _Traits>
2047 std::basic_ostream<_CharT, _Traits>&
2048 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2049 const normal_distribution<_RealType>& __x)
2050 {
2051 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2052 typedef typename __ostream_type::ios_base __ios_base;
2053
2054 const typename __ios_base::fmtflags __flags = __os.flags();
2055 const _CharT __fill = __os.fill();
2056 const std::streamsize __precision = __os.precision();
2057 const _CharT __space = __os.widen(' ');
2058 __os.flags(__ios_base::scientific | __ios_base::left);
2059 __os.fill(__space);
2060 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2061
2062 __os << __x.mean() << __space << __x.stddev()
2063 << __space << __x._M_saved_available;
2064 if (__x._M_saved_available)
2065 __os << __space << __x._M_saved;
2066
2067 __os.flags(__flags);
2068 __os.fill(__fill);
2069 __os.precision(__precision);
2070 return __os;
2071 }
2072
2073 template<typename _RealType, typename _CharT, typename _Traits>
2074 std::basic_istream<_CharT, _Traits>&
2075 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2076 normal_distribution<_RealType>& __x)
2077 {
2078 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2079 typedef typename __istream_type::ios_base __ios_base;
2080
2081 const typename __ios_base::fmtflags __flags = __is.flags();
2082 __is.flags(__ios_base::dec | __ios_base::skipws);
2083
2084 double __mean, __stddev;
2085 __is >> __mean >> __stddev
2086 >> __x._M_saved_available;
2087 if (__x._M_saved_available)
2088 __is >> __x._M_saved;
2089 __x.param(typename normal_distribution<_RealType>::
2090 param_type(__mean, __stddev));
2091
2092 __is.flags(__flags);
2093 return __is;
2094 }
2095
2096
2097 template<typename _RealType>
2098 template<typename _ForwardIterator,
2099 typename _UniformRandomNumberGenerator>
2100 void
2101 lognormal_distribution<_RealType>::
2102 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2103 _UniformRandomNumberGenerator& __urng,
2104 const param_type& __p)
2105 {
2106 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2107 while (__f != __t)
2108 *__f++ = std::exp(__p.s() * _M_nd(__urng) + __p.m());
2109 }
2110
2111 template<typename _RealType, typename _CharT, typename _Traits>
2112 std::basic_ostream<_CharT, _Traits>&
2113 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2114 const lognormal_distribution<_RealType>& __x)
2115 {
2116 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2117 typedef typename __ostream_type::ios_base __ios_base;
2118
2119 const typename __ios_base::fmtflags __flags = __os.flags();
2120 const _CharT __fill = __os.fill();
2121 const std::streamsize __precision = __os.precision();
2122 const _CharT __space = __os.widen(' ');
2123 __os.flags(__ios_base::scientific | __ios_base::left);
2124 __os.fill(__space);
2125 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2126
2127 __os << __x.m() << __space << __x.s()
2128 << __space << __x._M_nd;
2129
2130 __os.flags(__flags);
2131 __os.fill(__fill);
2132 __os.precision(__precision);
2133 return __os;
2134 }
2135
2136 template<typename _RealType, typename _CharT, typename _Traits>
2137 std::basic_istream<_CharT, _Traits>&
2138 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2139 lognormal_distribution<_RealType>& __x)
2140 {
2141 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2142 typedef typename __istream_type::ios_base __ios_base;
2143
2144 const typename __ios_base::fmtflags __flags = __is.flags();
2145 __is.flags(__ios_base::dec | __ios_base::skipws);
2146
2147 _RealType __m, __s;
2148 __is >> __m >> __s >> __x._M_nd;
2149 __x.param(typename lognormal_distribution<_RealType>::
2150 param_type(__m, __s));
2151
2152 __is.flags(__flags);
2153 return __is;
2154 }
2155
2156 template<typename _RealType>
2157 template<typename _ForwardIterator,
2158 typename _UniformRandomNumberGenerator>
2159 void
2160 std::chi_squared_distribution<_RealType>::
2161 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2162 _UniformRandomNumberGenerator& __urng)
2163 {
2164 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2165 while (__f != __t)
2166 *__f++ = 2 * _M_gd(__urng);
2167 }
2168
2169 template<typename _RealType>
2170 template<typename _ForwardIterator,
2171 typename _UniformRandomNumberGenerator>
2172 void
2173 std::chi_squared_distribution<_RealType>::
2174 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2175 _UniformRandomNumberGenerator& __urng,
2176 const typename
2177 std::gamma_distribution<result_type>::param_type& __p)
2178 {
2179 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2180 while (__f != __t)
2181 *__f++ = 2 * _M_gd(__urng, __p);
2182 }
2183
2184 template<typename _RealType, typename _CharT, typename _Traits>
2185 std::basic_ostream<_CharT, _Traits>&
2186 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2187 const chi_squared_distribution<_RealType>& __x)
2188 {
2189 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2190 typedef typename __ostream_type::ios_base __ios_base;
2191
2192 const typename __ios_base::fmtflags __flags = __os.flags();
2193 const _CharT __fill = __os.fill();
2194 const std::streamsize __precision = __os.precision();
2195 const _CharT __space = __os.widen(' ');
2196 __os.flags(__ios_base::scientific | __ios_base::left);
2197 __os.fill(__space);
2198 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2199
2200 __os << __x.n() << __space << __x._M_gd;
2201
2202 __os.flags(__flags);
2203 __os.fill(__fill);
2204 __os.precision(__precision);
2205 return __os;
2206 }
2207
2208 template<typename _RealType, typename _CharT, typename _Traits>
2209 std::basic_istream<_CharT, _Traits>&
2210 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2211 chi_squared_distribution<_RealType>& __x)
2212 {
2213 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2214 typedef typename __istream_type::ios_base __ios_base;
2215
2216 const typename __ios_base::fmtflags __flags = __is.flags();
2217 __is.flags(__ios_base::dec | __ios_base::skipws);
2218
2219 _RealType __n;
2220 __is >> __n >> __x._M_gd;
2221 __x.param(typename chi_squared_distribution<_RealType>::
2222 param_type(__n));
2223
2224 __is.flags(__flags);
2225 return __is;
2226 }
2227
2228
2229 template<typename _RealType>
2230 template<typename _UniformRandomNumberGenerator>
2231 typename cauchy_distribution<_RealType>::result_type
2232 cauchy_distribution<_RealType>::
2233 operator()(_UniformRandomNumberGenerator& __urng,
2234 const param_type& __p)
2235 {
2236 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2237 __aurng(__urng);
2238 _RealType __u;
2239 do
2240 __u = __aurng();
2241 while (__u == 0.5);
2242
2243 const _RealType __pi = 3.1415926535897932384626433832795029L;
2244 return __p.a() + __p.b() * std::tan(__pi * __u);
2245 }
2246
2247 template<typename _RealType>
2248 template<typename _ForwardIterator,
2249 typename _UniformRandomNumberGenerator>
2250 void
2251 cauchy_distribution<_RealType>::
2252 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2253 _UniformRandomNumberGenerator& __urng,
2254 const param_type& __p)
2255 {
2256 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2257 const _RealType __pi = 3.1415926535897932384626433832795029L;
2258 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2259 __aurng(__urng);
2260 while (__f != __t)
2261 {
2262 _RealType __u;
2263 do
2264 __u = __aurng();
2265 while (__u == 0.5);
2266
2267 *__f++ = __p.a() + __p.b() * std::tan(__pi * __u);
2268 }
2269 }
2270
2271 template<typename _RealType, typename _CharT, typename _Traits>
2272 std::basic_ostream<_CharT, _Traits>&
2273 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2274 const cauchy_distribution<_RealType>& __x)
2275 {
2276 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2277 typedef typename __ostream_type::ios_base __ios_base;
2278
2279 const typename __ios_base::fmtflags __flags = __os.flags();
2280 const _CharT __fill = __os.fill();
2281 const std::streamsize __precision = __os.precision();
2282 const _CharT __space = __os.widen(' ');
2283 __os.flags(__ios_base::scientific | __ios_base::left);
2284 __os.fill(__space);
2285 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2286
2287 __os << __x.a() << __space << __x.b();
2288
2289 __os.flags(__flags);
2290 __os.fill(__fill);
2291 __os.precision(__precision);
2292 return __os;
2293 }
2294
2295 template<typename _RealType, typename _CharT, typename _Traits>
2296 std::basic_istream<_CharT, _Traits>&
2297 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2298 cauchy_distribution<_RealType>& __x)
2299 {
2300 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2301 typedef typename __istream_type::ios_base __ios_base;
2302
2303 const typename __ios_base::fmtflags __flags = __is.flags();
2304 __is.flags(__ios_base::dec | __ios_base::skipws);
2305
2306 _RealType __a, __b;
2307 __is >> __a >> __b;
2308 __x.param(typename cauchy_distribution<_RealType>::
2309 param_type(__a, __b));
2310
2311 __is.flags(__flags);
2312 return __is;
2313 }
2314
2315
2316 template<typename _RealType>
2317 template<typename _ForwardIterator,
2318 typename _UniformRandomNumberGenerator>
2319 void
2320 std::fisher_f_distribution<_RealType>::
2321 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2322 _UniformRandomNumberGenerator& __urng)
2323 {
2324 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2325 while (__f != __t)
2326 *__f++ = ((_M_gd_x(__urng) * n()) / (_M_gd_y(__urng) * m()));
2327 }
2328
2329 template<typename _RealType>
2330 template<typename _ForwardIterator,
2331 typename _UniformRandomNumberGenerator>
2332 void
2333 std::fisher_f_distribution<_RealType>::
2334 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2335 _UniformRandomNumberGenerator& __urng,
2336 const param_type& __p)
2337 {
2338 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2339 typedef typename std::gamma_distribution<result_type>::param_type
2340 param_type;
2341 param_type __p1(__p.m() / 2);
2342 param_type __p2(__p.n() / 2);
2343 while (__f != __t)
2344 *__f++ = ((_M_gd_x(__urng, __p1) * n())
2345 / (_M_gd_y(__urng, __p2) * m()));
2346 }
2347
2348 template<typename _RealType, typename _CharT, typename _Traits>
2349 std::basic_ostream<_CharT, _Traits>&
2350 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2351 const fisher_f_distribution<_RealType>& __x)
2352 {
2353 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2354 typedef typename __ostream_type::ios_base __ios_base;
2355
2356 const typename __ios_base::fmtflags __flags = __os.flags();
2357 const _CharT __fill = __os.fill();
2358 const std::streamsize __precision = __os.precision();
2359 const _CharT __space = __os.widen(' ');
2360 __os.flags(__ios_base::scientific | __ios_base::left);
2361 __os.fill(__space);
2362 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2363
2364 __os << __x.m() << __space << __x.n()
2365 << __space << __x._M_gd_x << __space << __x._M_gd_y;
2366
2367 __os.flags(__flags);
2368 __os.fill(__fill);
2369 __os.precision(__precision);
2370 return __os;
2371 }
2372
2373 template<typename _RealType, typename _CharT, typename _Traits>
2374 std::basic_istream<_CharT, _Traits>&
2375 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2376 fisher_f_distribution<_RealType>& __x)
2377 {
2378 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2379 typedef typename __istream_type::ios_base __ios_base;
2380
2381 const typename __ios_base::fmtflags __flags = __is.flags();
2382 __is.flags(__ios_base::dec | __ios_base::skipws);
2383
2384 _RealType __m, __n;
2385 __is >> __m >> __n >> __x._M_gd_x >> __x._M_gd_y;
2386 __x.param(typename fisher_f_distribution<_RealType>::
2387 param_type(__m, __n));
2388
2389 __is.flags(__flags);
2390 return __is;
2391 }
2392
2393
2394 template<typename _RealType>
2395 template<typename _ForwardIterator,
2396 typename _UniformRandomNumberGenerator>
2397 void
2398 std::student_t_distribution<_RealType>::
2399 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2400 _UniformRandomNumberGenerator& __urng)
2401 {
2402 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2403 while (__f != __t)
2404 *__f++ = _M_nd(__urng) * std::sqrt(n() / _M_gd(__urng));
2405 }
2406
2407 template<typename _RealType>
2408 template<typename _ForwardIterator,
2409 typename _UniformRandomNumberGenerator>
2410 void
2411 std::student_t_distribution<_RealType>::
2412 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2413 _UniformRandomNumberGenerator& __urng,
2414 const param_type& __p)
2415 {
2416 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2417 typename std::gamma_distribution<result_type>::param_type
2418 __p2(__p.n() / 2, 2);
2419 while (__f != __t)
2420 *__f++ = _M_nd(__urng) * std::sqrt(__p.n() / _M_gd(__urng, __p2));
2421 }
2422
2423 template<typename _RealType, typename _CharT, typename _Traits>
2424 std::basic_ostream<_CharT, _Traits>&
2425 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2426 const student_t_distribution<_RealType>& __x)
2427 {
2428 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2429 typedef typename __ostream_type::ios_base __ios_base;
2430
2431 const typename __ios_base::fmtflags __flags = __os.flags();
2432 const _CharT __fill = __os.fill();
2433 const std::streamsize __precision = __os.precision();
2434 const _CharT __space = __os.widen(' ');
2435 __os.flags(__ios_base::scientific | __ios_base::left);
2436 __os.fill(__space);
2437 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2438
2439 __os << __x.n() << __space << __x._M_nd << __space << __x._M_gd;
2440
2441 __os.flags(__flags);
2442 __os.fill(__fill);
2443 __os.precision(__precision);
2444 return __os;
2445 }
2446
2447 template<typename _RealType, typename _CharT, typename _Traits>
2448 std::basic_istream<_CharT, _Traits>&
2449 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2450 student_t_distribution<_RealType>& __x)
2451 {
2452 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2453 typedef typename __istream_type::ios_base __ios_base;
2454
2455 const typename __ios_base::fmtflags __flags = __is.flags();
2456 __is.flags(__ios_base::dec | __ios_base::skipws);
2457
2458 _RealType __n;
2459 __is >> __n >> __x._M_nd >> __x._M_gd;
2460 __x.param(typename student_t_distribution<_RealType>::param_type(__n));
2461
2462 __is.flags(__flags);
2463 return __is;
2464 }
2465
2466
2467 template<typename _RealType>
2468 void
2469 gamma_distribution<_RealType>::param_type::
2470 _M_initialize()
2471 {
2472 _M_malpha = _M_alpha < 1.0 ? _M_alpha + _RealType(1.0) : _M_alpha;
2473
2474 const _RealType __a1 = _M_malpha - _RealType(1.0) / _RealType(3.0);
2475 _M_a2 = _RealType(1.0) / std::sqrt(_RealType(9.0) * __a1);
2476 }
2477
2478 /**
2479 * Marsaglia, G. and Tsang, W. W.
2480 * "A Simple Method for Generating Gamma Variables"
2481 * ACM Transactions on Mathematical Software, 26, 3, 363-372, 2000.
2482 */
2483 template<typename _RealType>
2484 template<typename _UniformRandomNumberGenerator>
2485 typename gamma_distribution<_RealType>::result_type
2486 gamma_distribution<_RealType>::
2487 operator()(_UniformRandomNumberGenerator& __urng,
2488 const param_type& __param)
2489 {
2490 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2491 __aurng(__urng);
2492
2493 result_type __u, __v, __n;
2494 const result_type __a1 = (__param._M_malpha
2495 - _RealType(1.0) / _RealType(3.0));
2496
2497 do
2498 {
2499 do
2500 {
2501 __n = _M_nd(__urng);
2502 __v = result_type(1.0) + __param._M_a2 * __n;
2503 }
2504 while (__v <= 0.0);
2505
2506 __v = __v * __v * __v;
2507 __u = __aurng();
2508 }
2509 while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n
2510 && (std::log(__u) > (0.5 * __n * __n + __a1
2511 * (1.0 - __v + std::log(__v)))));
2512
2513 if (__param.alpha() == __param._M_malpha)
2514 return __a1 * __v * __param.beta();
2515 else
2516 {
2517 do
2518 __u = __aurng();
2519 while (__u == 0.0);
2520
2521 return (std::pow(__u, result_type(1.0) / __param.alpha())
2522 * __a1 * __v * __param.beta());
2523 }
2524 }
2525
2526 template<typename _RealType>
2527 template<typename _ForwardIterator,
2528 typename _UniformRandomNumberGenerator>
2529 void
2530 gamma_distribution<_RealType>::
2531 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2532 _UniformRandomNumberGenerator& __urng,
2533 const param_type& __param)
2534 {
2535 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2536 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2537 __aurng(__urng);
2538
2539 result_type __u, __v, __n;
2540 const result_type __a1 = (__param._M_malpha
2541 - _RealType(1.0) / _RealType(3.0));
2542
2543 if (__param.alpha() == __param._M_malpha)
2544 while (__f != __t)
2545 {
2546 do
2547 {
2548 do
2549 {
2550 __n = _M_nd(__urng);
2551 __v = result_type(1.0) + __param._M_a2 * __n;
2552 }
2553 while (__v <= 0.0);
2554
2555 __v = __v * __v * __v;
2556 __u = __aurng();
2557 }
2558 while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n
2559 && (std::log(__u) > (0.5 * __n * __n + __a1
2560 * (1.0 - __v + std::log(__v)))));
2561
2562 *__f++ = __a1 * __v * __param.beta();
2563 }
2564 else
2565 while (__f != __t)
2566 {
2567 do
2568 {
2569 do
2570 {
2571 __n = _M_nd(__urng);
2572 __v = result_type(1.0) + __param._M_a2 * __n;
2573 }
2574 while (__v <= 0.0);
2575
2576 __v = __v * __v * __v;
2577 __u = __aurng();
2578 }
2579 while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n
2580 && (std::log(__u) > (0.5 * __n * __n + __a1
2581 * (1.0 - __v + std::log(__v)))));
2582
2583 do
2584 __u = __aurng();
2585 while (__u == 0.0);
2586
2587 *__f++ = (std::pow(__u, result_type(1.0) / __param.alpha())
2588 * __a1 * __v * __param.beta());
2589 }
2590 }
2591
2592 template<typename _RealType, typename _CharT, typename _Traits>
2593 std::basic_ostream<_CharT, _Traits>&
2594 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2595 const gamma_distribution<_RealType>& __x)
2596 {
2597 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2598 typedef typename __ostream_type::ios_base __ios_base;
2599
2600 const typename __ios_base::fmtflags __flags = __os.flags();
2601 const _CharT __fill = __os.fill();
2602 const std::streamsize __precision = __os.precision();
2603 const _CharT __space = __os.widen(' ');
2604 __os.flags(__ios_base::scientific | __ios_base::left);
2605 __os.fill(__space);
2606 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2607
2608 __os << __x.alpha() << __space << __x.beta()
2609 << __space << __x._M_nd;
2610
2611 __os.flags(__flags);
2612 __os.fill(__fill);
2613 __os.precision(__precision);
2614 return __os;
2615 }
2616
2617 template<typename _RealType, typename _CharT, typename _Traits>
2618 std::basic_istream<_CharT, _Traits>&
2619 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2620 gamma_distribution<_RealType>& __x)
2621 {
2622 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2623 typedef typename __istream_type::ios_base __ios_base;
2624
2625 const typename __ios_base::fmtflags __flags = __is.flags();
2626 __is.flags(__ios_base::dec | __ios_base::skipws);
2627
2628 _RealType __alpha_val, __beta_val;
2629 __is >> __alpha_val >> __beta_val >> __x._M_nd;
2630 __x.param(typename gamma_distribution<_RealType>::
2631 param_type(__alpha_val, __beta_val));
2632
2633 __is.flags(__flags);
2634 return __is;
2635 }
2636
2637
2638 template<typename _RealType>
2639 template<typename _UniformRandomNumberGenerator>
2640 typename weibull_distribution<_RealType>::result_type
2641 weibull_distribution<_RealType>::
2642 operator()(_UniformRandomNumberGenerator& __urng,
2643 const param_type& __p)
2644 {
2645 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2646 __aurng(__urng);
2647 return __p.b() * std::pow(-std::log(result_type(1) - __aurng()),
2648 result_type(1) / __p.a());
2649 }
2650
2651 template<typename _RealType>
2652 template<typename _ForwardIterator,
2653 typename _UniformRandomNumberGenerator>
2654 void
2655 weibull_distribution<_RealType>::
2656 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2657 _UniformRandomNumberGenerator& __urng,
2658 const param_type& __p)
2659 {
2660 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2661 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2662 __aurng(__urng);
2663 auto __inv_a = result_type(1) / __p.a();
2664
2665 while (__f != __t)
2666 *__f++ = __p.b() * std::pow(-std::log(result_type(1) - __aurng()),
2667 __inv_a);
2668 }
2669
2670 template<typename _RealType, typename _CharT, typename _Traits>
2671 std::basic_ostream<_CharT, _Traits>&
2672 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2673 const weibull_distribution<_RealType>& __x)
2674 {
2675 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2676 typedef typename __ostream_type::ios_base __ios_base;
2677
2678 const typename __ios_base::fmtflags __flags = __os.flags();
2679 const _CharT __fill = __os.fill();
2680 const std::streamsize __precision = __os.precision();
2681 const _CharT __space = __os.widen(' ');
2682 __os.flags(__ios_base::scientific | __ios_base::left);
2683 __os.fill(__space);
2684 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2685
2686 __os << __x.a() << __space << __x.b();
2687
2688 __os.flags(__flags);
2689 __os.fill(__fill);
2690 __os.precision(__precision);
2691 return __os;
2692 }
2693
2694 template<typename _RealType, typename _CharT, typename _Traits>
2695 std::basic_istream<_CharT, _Traits>&
2696 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2697 weibull_distribution<_RealType>& __x)
2698 {
2699 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2700 typedef typename __istream_type::ios_base __ios_base;
2701
2702 const typename __ios_base::fmtflags __flags = __is.flags();
2703 __is.flags(__ios_base::dec | __ios_base::skipws);
2704
2705 _RealType __a, __b;
2706 __is >> __a >> __b;
2707 __x.param(typename weibull_distribution<_RealType>::
2708 param_type(__a, __b));
2709
2710 __is.flags(__flags);
2711 return __is;
2712 }
2713
2714
2715 template<typename _RealType>
2716 template<typename _UniformRandomNumberGenerator>
2717 typename extreme_value_distribution<_RealType>::result_type
2718 extreme_value_distribution<_RealType>::
2719 operator()(_UniformRandomNumberGenerator& __urng,
2720 const param_type& __p)
2721 {
2722 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2723 __aurng(__urng);
2724 return __p.a() - __p.b() * std::log(-std::log(result_type(1)
2725 - __aurng()));
2726 }
2727
2728 template<typename _RealType>
2729 template<typename _ForwardIterator,
2730 typename _UniformRandomNumberGenerator>
2731 void
2732 extreme_value_distribution<_RealType>::
2733 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2734 _UniformRandomNumberGenerator& __urng,
2735 const param_type& __p)
2736 {
2737 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2738 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
2739 __aurng(__urng);
2740
2741 while (__f != __t)
2742 *__f++ = __p.a() - __p.b() * std::log(-std::log(result_type(1)
2743 - __aurng()));
2744 }
2745
2746 template<typename _RealType, typename _CharT, typename _Traits>
2747 std::basic_ostream<_CharT, _Traits>&
2748 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2749 const extreme_value_distribution<_RealType>& __x)
2750 {
2751 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2752 typedef typename __ostream_type::ios_base __ios_base;
2753
2754 const typename __ios_base::fmtflags __flags = __os.flags();
2755 const _CharT __fill = __os.fill();
2756 const std::streamsize __precision = __os.precision();
2757 const _CharT __space = __os.widen(' ');
2758 __os.flags(__ios_base::scientific | __ios_base::left);
2759 __os.fill(__space);
2760 __os.precision(std::numeric_limits<_RealType>::max_digits10);
2761
2762 __os << __x.a() << __space << __x.b();
2763
2764 __os.flags(__flags);
2765 __os.fill(__fill);
2766 __os.precision(__precision);
2767 return __os;
2768 }
2769
2770 template<typename _RealType, typename _CharT, typename _Traits>
2771 std::basic_istream<_CharT, _Traits>&
2772 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2773 extreme_value_distribution<_RealType>& __x)
2774 {
2775 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2776 typedef typename __istream_type::ios_base __ios_base;
2777
2778 const typename __ios_base::fmtflags __flags = __is.flags();
2779 __is.flags(__ios_base::dec | __ios_base::skipws);
2780
2781 _RealType __a, __b;
2782 __is >> __a >> __b;
2783 __x.param(typename extreme_value_distribution<_RealType>::
2784 param_type(__a, __b));
2785
2786 __is.flags(__flags);
2787 return __is;
2788 }
2789
2790
2791 template<typename _IntType>
2792 void
2793 discrete_distribution<_IntType>::param_type::
2794 _M_initialize()
2795 {
2796 if (_M_prob.size() < 2)
2797 {
2798 _M_prob.clear();
2799 return;
2800 }
2801
2802 const double __sum = std::accumulate(_M_prob.begin(),
2803 _M_prob.end(), 0.0);
2804 // Now normalize the probabilites.
2805 __detail::__normalize(_M_prob.begin(), _M_prob.end(), _M_prob.begin(),
2806 __sum);
2807 // Accumulate partial sums.
2808 _M_cp.reserve(_M_prob.size());
2809 std::partial_sum(_M_prob.begin(), _M_prob.end(),
2810 std::back_inserter(_M_cp));
2811 // Make sure the last cumulative probability is one.
2812 _M_cp[_M_cp.size() - 1] = 1.0;
2813 }
2814
2815 template<typename _IntType>
2816 template<typename _Func>
2817 discrete_distribution<_IntType>::param_type::
2818 param_type(size_t __nw, double __xmin, double __xmax, _Func __fw)
2819 : _M_prob(), _M_cp()
2820 {
2821 const size_t __n = __nw == 0 ? 1 : __nw;
2822 const double __delta = (__xmax - __xmin) / __n;
2823
2824 _M_prob.reserve(__n);
2825 for (size_t __k = 0; __k < __nw; ++__k)
2826 _M_prob.push_back(__fw(__xmin + __k * __delta + 0.5 * __delta));
2827
2828 _M_initialize();
2829 }
2830
2831 template<typename _IntType>
2832 template<typename _UniformRandomNumberGenerator>
2833 typename discrete_distribution<_IntType>::result_type
2834 discrete_distribution<_IntType>::
2835 operator()(_UniformRandomNumberGenerator& __urng,
2836 const param_type& __param)
2837 {
2838 if (__param._M_cp.empty())
2839 return result_type(0);
2840
2841 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
2842 __aurng(__urng);
2843
2844 const double __p = __aurng();
2845 auto __pos = std::lower_bound(__param._M_cp.begin(),
2846 __param._M_cp.end(), __p);
2847
2848 return __pos - __param._M_cp.begin();
2849 }
2850
2851 template<typename _IntType>
2852 template<typename _ForwardIterator,
2853 typename _UniformRandomNumberGenerator>
2854 void
2855 discrete_distribution<_IntType>::
2856 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
2857 _UniformRandomNumberGenerator& __urng,
2858 const param_type& __param)
2859 {
2860 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
2861
2862 if (__param._M_cp.empty())
2863 {
2864 while (__f != __t)
2865 *__f++ = result_type(0);
2866 return;
2867 }
2868
2869 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
2870 __aurng(__urng);
2871
2872 while (__f != __t)
2873 {
2874 const double __p = __aurng();
2875 auto __pos = std::lower_bound(__param._M_cp.begin(),
2876 __param._M_cp.end(), __p);
2877
2878 *__f++ = __pos - __param._M_cp.begin();
2879 }
2880 }
2881
2882 template<typename _IntType, typename _CharT, typename _Traits>
2883 std::basic_ostream<_CharT, _Traits>&
2884 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
2885 const discrete_distribution<_IntType>& __x)
2886 {
2887 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
2888 typedef typename __ostream_type::ios_base __ios_base;
2889
2890 const typename __ios_base::fmtflags __flags = __os.flags();
2891 const _CharT __fill = __os.fill();
2892 const std::streamsize __precision = __os.precision();
2893 const _CharT __space = __os.widen(' ');
2894 __os.flags(__ios_base::scientific | __ios_base::left);
2895 __os.fill(__space);
2896 __os.precision(std::numeric_limits<double>::max_digits10);
2897
2898 std::vector<double> __prob = __x.probabilities();
2899 __os << __prob.size();
2900 for (auto __dit = __prob.begin(); __dit != __prob.end(); ++__dit)
2901 __os << __space << *__dit;
2902
2903 __os.flags(__flags);
2904 __os.fill(__fill);
2905 __os.precision(__precision);
2906 return __os;
2907 }
2908
2909 template<typename _IntType, typename _CharT, typename _Traits>
2910 std::basic_istream<_CharT, _Traits>&
2911 operator>>(std::basic_istream<_CharT, _Traits>& __is,
2912 discrete_distribution<_IntType>& __x)
2913 {
2914 typedef std::basic_istream<_CharT, _Traits> __istream_type;
2915 typedef typename __istream_type::ios_base __ios_base;
2916
2917 const typename __ios_base::fmtflags __flags = __is.flags();
2918 __is.flags(__ios_base::dec | __ios_base::skipws);
2919
2920 size_t __n;
2921 __is >> __n;
2922
2923 std::vector<double> __prob_vec;
2924 __prob_vec.reserve(__n);
2925 for (; __n != 0; --__n)
2926 {
2927 double __prob;
2928 __is >> __prob;
2929 __prob_vec.push_back(__prob);
2930 }
2931
2932 __x.param(typename discrete_distribution<_IntType>::
2933 param_type(__prob_vec.begin(), __prob_vec.end()));
2934
2935 __is.flags(__flags);
2936 return __is;
2937 }
2938
2939
2940 template<typename _RealType>
2941 void
2942 piecewise_constant_distribution<_RealType>::param_type::
2943 _M_initialize()
2944 {
2945 if (_M_int.size() < 2
2946 || (_M_int.size() == 2
2947 && _M_int[0] == _RealType(0)
2948 && _M_int[1] == _RealType(1)))
2949 {
2950 _M_int.clear();
2951 _M_den.clear();
2952 return;
2953 }
2954
2955 const double __sum = std::accumulate(_M_den.begin(),
2956 _M_den.end(), 0.0);
2957
2958 __detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(),
2959 __sum);
2960
2961 _M_cp.reserve(_M_den.size());
2962 std::partial_sum(_M_den.begin(), _M_den.end(),
2963 std::back_inserter(_M_cp));
2964
2965 // Make sure the last cumulative probability is one.
2966 _M_cp[_M_cp.size() - 1] = 1.0;
2967
2968 for (size_t __k = 0; __k < _M_den.size(); ++__k)
2969 _M_den[__k] /= _M_int[__k + 1] - _M_int[__k];
2970 }
2971
2972 template<typename _RealType>
2973 template<typename _InputIteratorB, typename _InputIteratorW>
2974 piecewise_constant_distribution<_RealType>::param_type::
2975 param_type(_InputIteratorB __bbegin,
2976 _InputIteratorB __bend,
2977 _InputIteratorW __wbegin)
2978 : _M_int(), _M_den(), _M_cp()
2979 {
2980 if (__bbegin != __bend)
2981 {
2982 for (;;)
2983 {
2984 _M_int.push_back(*__bbegin);
2985 ++__bbegin;
2986 if (__bbegin == __bend)
2987 break;
2988
2989 _M_den.push_back(*__wbegin);
2990 ++__wbegin;
2991 }
2992 }
2993
2994 _M_initialize();
2995 }
2996
2997 template<typename _RealType>
2998 template<typename _Func>
2999 piecewise_constant_distribution<_RealType>::param_type::
3000 param_type(initializer_list<_RealType> __bl, _Func __fw)
3001 : _M_int(), _M_den(), _M_cp()
3002 {
3003 _M_int.reserve(__bl.size());
3004 for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter)
3005 _M_int.push_back(*__biter);
3006
3007 _M_den.reserve(_M_int.size() - 1);
3008 for (size_t __k = 0; __k < _M_int.size() - 1; ++__k)
3009 _M_den.push_back(__fw(0.5 * (_M_int[__k + 1] + _M_int[__k])));
3010
3011 _M_initialize();
3012 }
3013
3014 template<typename _RealType>
3015 template<typename _Func>
3016 piecewise_constant_distribution<_RealType>::param_type::
3017 param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw)
3018 : _M_int(), _M_den(), _M_cp()
3019 {
3020 const size_t __n = __nw == 0 ? 1 : __nw;
3021 const _RealType __delta = (__xmax - __xmin) / __n;
3022
3023 _M_int.reserve(__n + 1);
3024 for (size_t __k = 0; __k <= __nw; ++__k)
3025 _M_int.push_back(__xmin + __k * __delta);
3026
3027 _M_den.reserve(__n);
3028 for (size_t __k = 0; __k < __nw; ++__k)
3029 _M_den.push_back(__fw(_M_int[__k] + 0.5 * __delta));
3030
3031 _M_initialize();
3032 }
3033
3034 template<typename _RealType>
3035 template<typename _UniformRandomNumberGenerator>
3036 typename piecewise_constant_distribution<_RealType>::result_type
3037 piecewise_constant_distribution<_RealType>::
3038 operator()(_UniformRandomNumberGenerator& __urng,
3039 const param_type& __param)
3040 {
3041 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
3042 __aurng(__urng);
3043
3044 const double __p = __aurng();
3045 if (__param._M_cp.empty())
3046 return __p;
3047
3048 auto __pos = std::lower_bound(__param._M_cp.begin(),
3049 __param._M_cp.end(), __p);
3050 const size_t __i = __pos - __param._M_cp.begin();
3051
3052 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0;
3053
3054 return __param._M_int[__i] + (__p - __pref) / __param._M_den[__i];
3055 }
3056
3057 template<typename _RealType>
3058 template<typename _ForwardIterator,
3059 typename _UniformRandomNumberGenerator>
3060 void
3061 piecewise_constant_distribution<_RealType>::
3062 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
3063 _UniformRandomNumberGenerator& __urng,
3064 const param_type& __param)
3065 {
3066 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
3067 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
3068 __aurng(__urng);
3069
3070 if (__param._M_cp.empty())
3071 {
3072 while (__f != __t)
3073 *__f++ = __aurng();
3074 return;
3075 }
3076
3077 while (__f != __t)
3078 {
3079 const double __p = __aurng();
3080
3081 auto __pos = std::lower_bound(__param._M_cp.begin(),
3082 __param._M_cp.end(), __p);
3083 const size_t __i = __pos - __param._M_cp.begin();
3084
3085 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0;
3086
3087 *__f++ = (__param._M_int[__i]
3088 + (__p - __pref) / __param._M_den[__i]);
3089 }
3090 }
3091
3092 template<typename _RealType, typename _CharT, typename _Traits>
3093 std::basic_ostream<_CharT, _Traits>&
3094 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
3095 const piecewise_constant_distribution<_RealType>& __x)
3096 {
3097 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
3098 typedef typename __ostream_type::ios_base __ios_base;
3099
3100 const typename __ios_base::fmtflags __flags = __os.flags();
3101 const _CharT __fill = __os.fill();
3102 const std::streamsize __precision = __os.precision();
3103 const _CharT __space = __os.widen(' ');
3104 __os.flags(__ios_base::scientific | __ios_base::left);
3105 __os.fill(__space);
3106 __os.precision(std::numeric_limits<_RealType>::max_digits10);
3107
3108 std::vector<_RealType> __int = __x.intervals();
3109 __os << __int.size() - 1;
3110
3111 for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit)
3112 __os << __space << *__xit;
3113
3114 std::vector<double> __den = __x.densities();
3115 for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit)
3116 __os << __space << *__dit;
3117
3118 __os.flags(__flags);
3119 __os.fill(__fill);
3120 __os.precision(__precision);
3121 return __os;
3122 }
3123
3124 template<typename _RealType, typename _CharT, typename _Traits>
3125 std::basic_istream<_CharT, _Traits>&
3126 operator>>(std::basic_istream<_CharT, _Traits>& __is,
3127 piecewise_constant_distribution<_RealType>& __x)
3128 {
3129 typedef std::basic_istream<_CharT, _Traits> __istream_type;
3130 typedef typename __istream_type::ios_base __ios_base;
3131
3132 const typename __ios_base::fmtflags __flags = __is.flags();
3133 __is.flags(__ios_base::dec | __ios_base::skipws);
3134
3135 size_t __n;
3136 __is >> __n;
3137
3138 std::vector<_RealType> __int_vec;
3139 __int_vec.reserve(__n + 1);
3140 for (size_t __i = 0; __i <= __n; ++__i)
3141 {
3142 _RealType __int;
3143 __is >> __int;
3144 __int_vec.push_back(__int);
3145 }
3146
3147 std::vector<double> __den_vec;
3148 __den_vec.reserve(__n);
3149 for (size_t __i = 0; __i < __n; ++__i)
3150 {
3151 double __den;
3152 __is >> __den;
3153 __den_vec.push_back(__den);
3154 }
3155
3156 __x.param(typename piecewise_constant_distribution<_RealType>::
3157 param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin()));
3158
3159 __is.flags(__flags);
3160 return __is;
3161 }
3162
3163
3164 template<typename _RealType>
3165 void
3166 piecewise_linear_distribution<_RealType>::param_type::
3167 _M_initialize()
3168 {
3169 if (_M_int.size() < 2
3170 || (_M_int.size() == 2
3171 && _M_int[0] == _RealType(0)
3172 && _M_int[1] == _RealType(1)
3173 && _M_den[0] == _M_den[1]))
3174 {
3175 _M_int.clear();
3176 _M_den.clear();
3177 return;
3178 }
3179
3180 double __sum = 0.0;
3181 _M_cp.reserve(_M_int.size() - 1);
3182 _M_m.reserve(_M_int.size() - 1);
3183 for (size_t __k = 0; __k < _M_int.size() - 1; ++__k)
3184 {
3185 const _RealType __delta = _M_int[__k + 1] - _M_int[__k];
3186 __sum += 0.5 * (_M_den[__k + 1] + _M_den[__k]) * __delta;
3187 _M_cp.push_back(__sum);
3188 _M_m.push_back((_M_den[__k + 1] - _M_den[__k]) / __delta);
3189 }
3190
3191 // Now normalize the densities...
3192 __detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(),
3193 __sum);
3194 // ... and partial sums...
3195 __detail::__normalize(_M_cp.begin(), _M_cp.end(), _M_cp.begin(), __sum);
3196 // ... and slopes.
3197 __detail::__normalize(_M_m.begin(), _M_m.end(), _M_m.begin(), __sum);
3198
3199 // Make sure the last cumulative probablility is one.
3200 _M_cp[_M_cp.size() - 1] = 1.0;
3201 }
3202
3203 template<typename _RealType>
3204 template<typename _InputIteratorB, typename _InputIteratorW>
3205 piecewise_linear_distribution<_RealType>::param_type::
3206 param_type(_InputIteratorB __bbegin,
3207 _InputIteratorB __bend,
3208 _InputIteratorW __wbegin)
3209 : _M_int(), _M_den(), _M_cp(), _M_m()
3210 {
3211 for (; __bbegin != __bend; ++__bbegin, ++__wbegin)
3212 {
3213 _M_int.push_back(*__bbegin);
3214 _M_den.push_back(*__wbegin);
3215 }
3216
3217 _M_initialize();
3218 }
3219
3220 template<typename _RealType>
3221 template<typename _Func>
3222 piecewise_linear_distribution<_RealType>::param_type::
3223 param_type(initializer_list<_RealType> __bl, _Func __fw)
3224 : _M_int(), _M_den(), _M_cp(), _M_m()
3225 {
3226 _M_int.reserve(__bl.size());
3227 _M_den.reserve(__bl.size());
3228 for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter)
3229 {
3230 _M_int.push_back(*__biter);
3231 _M_den.push_back(__fw(*__biter));
3232 }
3233
3234 _M_initialize();
3235 }
3236
3237 template<typename _RealType>
3238 template<typename _Func>
3239 piecewise_linear_distribution<_RealType>::param_type::
3240 param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw)
3241 : _M_int(), _M_den(), _M_cp(), _M_m()
3242 {
3243 const size_t __n = __nw == 0 ? 1 : __nw;
3244 const _RealType __delta = (__xmax - __xmin) / __n;
3245
3246 _M_int.reserve(__n + 1);
3247 _M_den.reserve(__n + 1);
3248 for (size_t __k = 0; __k <= __nw; ++__k)
3249 {
3250 _M_int.push_back(__xmin + __k * __delta);
3251 _M_den.push_back(__fw(_M_int[__k] + __delta));
3252 }
3253
3254 _M_initialize();
3255 }
3256
3257 template<typename _RealType>
3258 template<typename _UniformRandomNumberGenerator>
3259 typename piecewise_linear_distribution<_RealType>::result_type
3260 piecewise_linear_distribution<_RealType>::
3261 operator()(_UniformRandomNumberGenerator& __urng,
3262 const param_type& __param)
3263 {
3264 __detail::_Adaptor<_UniformRandomNumberGenerator, double>
3265 __aurng(__urng);
3266
3267 const double __p = __aurng();
3268 if (__param._M_cp.empty())
3269 return __p;
3270
3271 auto __pos = std::lower_bound(__param._M_cp.begin(),
3272 __param._M_cp.end(), __p);
3273 const size_t __i = __pos - __param._M_cp.begin();
3274
3275 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0;
3276
3277 const double __a = 0.5 * __param._M_m[__i];
3278 const double __b = __param._M_den[__i];
3279 const double __cm = __p - __pref;
3280
3281 _RealType __x = __param._M_int[__i];
3282 if (__a == 0)
3283 __x += __cm / __b;
3284 else
3285 {
3286 const double __d = __b * __b + 4.0 * __a * __cm;
3287 __x += 0.5 * (std::sqrt(__d) - __b) / __a;
3288 }
3289
3290 return __x;
3291 }
3292
3293 template<typename _RealType>
3294 template<typename _ForwardIterator,
3295 typename _UniformRandomNumberGenerator>
3296 void
3297 piecewise_linear_distribution<_RealType>::
3298 __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
3299 _UniformRandomNumberGenerator& __urng,
3300 const param_type& __param)
3301 {
3302 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
3303 // We could duplicate everything from operator()...
3304 while (__f != __t)
3305 *__f++ = this->operator()(__urng, __param);
3306 }
3307
3308 template<typename _RealType, typename _CharT, typename _Traits>
3309 std::basic_ostream<_CharT, _Traits>&
3310 operator<<(std::basic_ostream<_CharT, _Traits>& __os,
3311 const piecewise_linear_distribution<_RealType>& __x)
3312 {
3313 typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
3314 typedef typename __ostream_type::ios_base __ios_base;
3315
3316 const typename __ios_base::fmtflags __flags = __os.flags();
3317 const _CharT __fill = __os.fill();
3318 const std::streamsize __precision = __os.precision();
3319 const _CharT __space = __os.widen(' ');
3320 __os.flags(__ios_base::scientific | __ios_base::left);
3321 __os.fill(__space);
3322 __os.precision(std::numeric_limits<_RealType>::max_digits10);
3323
3324 std::vector<_RealType> __int = __x.intervals();
3325 __os << __int.size() - 1;
3326
3327 for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit)
3328 __os << __space << *__xit;
3329
3330 std::vector<double> __den = __x.densities();
3331 for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit)
3332 __os << __space << *__dit;
3333
3334 __os.flags(__flags);
3335 __os.fill(__fill);
3336 __os.precision(__precision);
3337 return __os;
3338 }
3339
3340 template<typename _RealType, typename _CharT, typename _Traits>
3341 std::basic_istream<_CharT, _Traits>&
3342 operator>>(std::basic_istream<_CharT, _Traits>& __is,
3343 piecewise_linear_distribution<_RealType>& __x)
3344 {
3345 typedef std::basic_istream<_CharT, _Traits> __istream_type;
3346 typedef typename __istream_type::ios_base __ios_base;
3347
3348 const typename __ios_base::fmtflags __flags = __is.flags();
3349 __is.flags(__ios_base::dec | __ios_base::skipws);
3350
3351 size_t __n;
3352 __is >> __n;
3353
3354 std::vector<_RealType> __int_vec;
3355 __int_vec.reserve(__n + 1);
3356 for (size_t __i = 0; __i <= __n; ++__i)
3357 {
3358 _RealType __int;
3359 __is >> __int;
3360 __int_vec.push_back(__int);
3361 }
3362
3363 std::vector<double> __den_vec;
3364 __den_vec.reserve(__n + 1);
3365 for (size_t __i = 0; __i <= __n; ++__i)
3366 {
3367 double __den;
3368 __is >> __den;
3369 __den_vec.push_back(__den);
3370 }
3371
3372 __x.param(typename piecewise_linear_distribution<_RealType>::
3373 param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin()));
3374
3375 __is.flags(__flags);
3376 return __is;
3377 }
3378
3379
3380 template<typename _IntType>
3381 seed_seq::seed_seq(std::initializer_list<_IntType> __il)
3382 {
3383 for (auto __iter = __il.begin(); __iter != __il.end(); ++__iter)
3384 _M_v.push_back(__detail::__mod<result_type,
3385 __detail::_Shift<result_type, 32>::__value>(*__iter));
3386 }
3387
3388 template<typename _InputIterator>
3389 seed_seq::seed_seq(_InputIterator __begin, _InputIterator __end)
3390 {
3391 for (_InputIterator __iter = __begin; __iter != __end; ++__iter)
3392 _M_v.push_back(__detail::__mod<result_type,
3393 __detail::_Shift<result_type, 32>::__value>(*__iter));
3394 }
3395
3396 template<typename _RandomAccessIterator>
3397 void
3398 seed_seq::generate(_RandomAccessIterator __begin,
3399 _RandomAccessIterator __end)
3400 {
3401 typedef typename iterator_traits<_RandomAccessIterator>::value_type
3402 _Type;
3403
3404 if (__begin == __end)
3405 return;
3406
3407 std::fill(__begin, __end, _Type(0x8b8b8b8bu));
3408
3409 const size_t __n = __end - __begin;
3410 const size_t __s = _M_v.size();
3411 const size_t __t = (__n >= 623) ? 11
3412 : (__n >= 68) ? 7
3413 : (__n >= 39) ? 5
3414 : (__n >= 7) ? 3
3415 : (__n - 1) / 2;
3416 const size_t __p = (__n - __t) / 2;
3417 const size_t __q = __p + __t;
3418 const size_t __m = std::max(size_t(__s + 1), __n);
3419
3420 for (size_t __k = 0; __k < __m; ++__k)
3421 {
3422 _Type __arg = (__begin[__k % __n]
3423 ^ __begin[(__k + __p) % __n]
3424 ^ __begin[(__k - 1) % __n]);
3425 _Type __r1 = __arg ^ (__arg >> 27);
3426 __r1 = __detail::__mod<_Type,
3427 __detail::_Shift<_Type, 32>::__value>(1664525u * __r1);
3428 _Type __r2 = __r1;
3429 if (__k == 0)
3430 __r2 += __s;
3431 else if (__k <= __s)
3432 __r2 += __k % __n + _M_v[__k - 1];
3433 else
3434 __r2 += __k % __n;
3435 __r2 = __detail::__mod<_Type,
3436 __detail::_Shift<_Type, 32>::__value>(__r2);
3437 __begin[(__k + __p) % __n] += __r1;
3438 __begin[(__k + __q) % __n] += __r2;
3439 __begin[__k % __n] = __r2;
3440 }
3441
3442 for (size_t __k = __m; __k < __m + __n; ++__k)
3443 {
3444 _Type __arg = (__begin[__k % __n]
3445 + __begin[(__k + __p) % __n]
3446 + __begin[(__k - 1) % __n]);
3447 _Type __r3 = __arg ^ (__arg >> 27);
3448 __r3 = __detail::__mod<_Type,
3449 __detail::_Shift<_Type, 32>::__value>(1566083941u * __r3);
3450 _Type __r4 = __r3 - __k % __n;
3451 __r4 = __detail::__mod<_Type,
3452 __detail::_Shift<_Type, 32>::__value>(__r4);
3453 __begin[(__k + __p) % __n] ^= __r3;
3454 __begin[(__k + __q) % __n] ^= __r4;
3455 __begin[__k % __n] = __r4;
3456 }
3457 }
3458
3459 template<typename _RealType, size_t __bits,
3460 typename _UniformRandomNumberGenerator>
3461 _RealType
3462 generate_canonical(_UniformRandomNumberGenerator& __urng)
3463 {
3464 const size_t __b
3465 = std::min(static_cast<size_t>(std::numeric_limits<_RealType>::digits),
3466 __bits);
3467 const long double __r = static_cast<long double>(__urng.max())
3468 - static_cast<long double>(__urng.min()) + 1.0L;
3469 const size_t __log2r = std::log(__r) / std::log(2.0L);
3470 size_t __k = std::max<size_t>(1UL, (__b + __log2r - 1UL) / __log2r);
3471 _RealType __sum = _RealType(0);
3472 _RealType __tmp = _RealType(1);
3473 for (; __k != 0; --__k)
3474 {
3475 __sum += _RealType(__urng() - __urng.min()) * __tmp;
3476 __tmp *= __r;
3477 }
3478 return __sum / __tmp;
3479 }
3480
3481 _GLIBCXX_END_NAMESPACE_VERSION
3482 } // namespace
3483
3484 #endif