]>
Commit | Line | Data |
---|---|---|
8e79468d BK |
1 | // random number generation (out of line) -*- C++ -*- |
2 | ||
15ecdcc6 | 3 | // Copyright (C) 2009, 2010 Free Software Foundation, Inc. |
8e79468d BK |
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 | |
748086b7 | 8 | // Free Software Foundation; either version 3, or (at your option) |
8e79468d BK |
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 | ||
748086b7 JJ |
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. | |
8e79468d | 19 | |
748086b7 JJ |
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/>. | |
8e79468d BK |
24 | |
25 | /** @file bits/random.tcc | |
26 | * This is an internal header file, included by other library headers. | |
27 | * You should not attempt to use it directly. | |
28 | */ | |
29 | ||
06f29237 PC |
30 | #ifndef _RANDOM_TCC |
31 | #define _RANDOM_TCC 1 | |
32 | ||
247d8075 | 33 | #include <numeric> // std::accumulate and std::partial_sum |
8e79468d BK |
34 | |
35 | namespace std | |
36 | { | |
8e79468d BK |
37 | /* |
38 | * (Further) implementation-space details. | |
39 | */ | |
40 | namespace __detail | |
41 | { | |
42 | // General case for x = (ax + c) mod m -- use Schrage's algorithm to | |
43 | // avoid integer overflow. | |
44 | // | |
45 | // Because a and c are compile-time integral constants the compiler | |
46 | // kindly elides any unreachable paths. | |
47 | // | |
48 | // Preconditions: a > 0, m > 0. | |
49 | // | |
b94f4bef | 50 | template<typename _Tp, _Tp __m, _Tp __a, _Tp __c, bool> |
8e79468d BK |
51 | struct _Mod |
52 | { | |
53 | static _Tp | |
54 | __calc(_Tp __x) | |
55 | { | |
56 | if (__a == 1) | |
57 | __x %= __m; | |
58 | else | |
59 | { | |
60 | static const _Tp __q = __m / __a; | |
61 | static const _Tp __r = __m % __a; | |
62 | ||
63 | _Tp __t1 = __a * (__x % __q); | |
64 | _Tp __t2 = __r * (__x / __q); | |
65 | if (__t1 >= __t2) | |
66 | __x = __t1 - __t2; | |
67 | else | |
68 | __x = __m - __t2 + __t1; | |
69 | } | |
70 | ||
71 | if (__c != 0) | |
72 | { | |
73 | const _Tp __d = __m - __x; | |
74 | if (__d > __c) | |
75 | __x += __c; | |
76 | else | |
77 | __x = __c - __d; | |
78 | } | |
79 | return __x; | |
80 | } | |
81 | }; | |
82 | ||
83 | // Special case for m == 0 -- use unsigned integer overflow as modulo | |
84 | // operator. | |
b94f4bef PC |
85 | template<typename _Tp, _Tp __m, _Tp __a, _Tp __c> |
86 | struct _Mod<_Tp, __m, __a, __c, true> | |
8e79468d BK |
87 | { |
88 | static _Tp | |
89 | __calc(_Tp __x) | |
90 | { return __a * __x + __c; } | |
91 | }; | |
247d8075 | 92 | |
afcd05a7 PC |
93 | template<typename _InputIterator, typename _OutputIterator, |
94 | typename _UnaryOperation> | |
95 | _OutputIterator | |
96 | __transform(_InputIterator __first, _InputIterator __last, | |
97 | _OutputIterator __result, _UnaryOperation __unary_op) | |
98 | { | |
99 | for (; __first != __last; ++__first, ++__result) | |
100 | *__result = __unary_op(*__first); | |
101 | return __result; | |
102 | } | |
8e79468d BK |
103 | } // namespace __detail |
104 | ||
300ea283 PC |
105 | |
106 | template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> | |
107 | const _UIntType | |
108 | linear_congruential_engine<_UIntType, __a, __c, __m>::multiplier; | |
109 | ||
110 | template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> | |
111 | const _UIntType | |
112 | linear_congruential_engine<_UIntType, __a, __c, __m>::increment; | |
113 | ||
114 | template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> | |
115 | const _UIntType | |
116 | linear_congruential_engine<_UIntType, __a, __c, __m>::modulus; | |
117 | ||
118 | template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> | |
119 | const _UIntType | |
120 | linear_congruential_engine<_UIntType, __a, __c, __m>::default_seed; | |
121 | ||
8e79468d | 122 | /** |
96a9203b | 123 | * Seeds the LCR with integral value @p __s, adjusted so that the |
8e79468d BK |
124 | * ring identity is never a member of the convergence set. |
125 | */ | |
126 | template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> | |
127 | void | |
128 | linear_congruential_engine<_UIntType, __a, __c, __m>:: | |
96a9203b | 129 | seed(result_type __s) |
8e79468d | 130 | { |
b94f4bef PC |
131 | if ((__detail::__mod<_UIntType, __m>(__c) == 0) |
132 | && (__detail::__mod<_UIntType, __m>(__s) == 0)) | |
133 | _M_x = 1; | |
8e79468d | 134 | else |
b94f4bef | 135 | _M_x = __detail::__mod<_UIntType, __m>(__s); |
8e79468d BK |
136 | } |
137 | ||
138 | /** | |
96a9203b | 139 | * Seeds the LCR engine with a value generated by @p __q. |
8e79468d BK |
140 | */ |
141 | template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> | |
05eeebfe PC |
142 | template<typename _Sseq> |
143 | typename std::enable_if<std::is_class<_Sseq>::value>::type | |
15ecdcc6 PC |
144 | linear_congruential_engine<_UIntType, __a, __c, __m>:: |
145 | seed(_Sseq& __q) | |
146 | { | |
147 | const _UIntType __k0 = __m == 0 ? std::numeric_limits<_UIntType>::digits | |
148 | : std::__lg(__m); | |
149 | const _UIntType __k = (__k0 + 31) / 32; | |
150 | uint_least32_t __arr[__k + 3]; | |
151 | __q.generate(__arr + 0, __arr + __k + 3); | |
152 | _UIntType __factor = 1u; | |
153 | _UIntType __sum = 0u; | |
154 | for (size_t __j = 0; __j < __k; ++__j) | |
155 | { | |
156 | __sum += __arr[__j + 3] * __factor; | |
157 | __factor *= __detail::_Shift<_UIntType, 32>::__value; | |
158 | } | |
159 | seed(__sum); | |
160 | } | |
8e79468d | 161 | |
8e79468d BK |
162 | template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m, |
163 | typename _CharT, typename _Traits> | |
164 | std::basic_ostream<_CharT, _Traits>& | |
165 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
166 | const linear_congruential_engine<_UIntType, | |
167 | __a, __c, __m>& __lcr) | |
168 | { | |
169 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
170 | typedef typename __ostream_type::ios_base __ios_base; | |
171 | ||
172 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
173 | const _CharT __fill = __os.fill(); | |
95fe602e | 174 | __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); |
8e79468d BK |
175 | __os.fill(__os.widen(' ')); |
176 | ||
177 | __os << __lcr._M_x; | |
178 | ||
179 | __os.flags(__flags); | |
180 | __os.fill(__fill); | |
181 | return __os; | |
182 | } | |
183 | ||
184 | template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m, | |
185 | typename _CharT, typename _Traits> | |
186 | std::basic_istream<_CharT, _Traits>& | |
187 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
188 | linear_congruential_engine<_UIntType, __a, __c, __m>& __lcr) | |
189 | { | |
190 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
191 | typedef typename __istream_type::ios_base __ios_base; | |
192 | ||
193 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
194 | __is.flags(__ios_base::dec); | |
195 | ||
196 | __is >> __lcr._M_x; | |
197 | ||
198 | __is.flags(__flags); | |
199 | return __is; | |
200 | } | |
201 | ||
202 | ||
300ea283 PC |
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 | const size_t | |
209 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, | |
210 | __s, __b, __t, __c, __l, __f>::word_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 | const size_t | |
218 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, | |
219 | __s, __b, __t, __c, __l, __f>::state_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 | const size_t | |
227 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, | |
228 | __s, __b, __t, __c, __l, __f>::shift_size; | |
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 | const size_t | |
236 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, | |
237 | __s, __b, __t, __c, __l, __f>::mask_bits; | |
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 | const _UIntType | |
245 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, | |
246 | __s, __b, __t, __c, __l, __f>::xor_mask; | |
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 | const size_t | |
254 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, | |
255 | __s, __b, __t, __c, __l, __f>::tempering_u; | |
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 | const _UIntType | |
263 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, | |
264 | __s, __b, __t, __c, __l, __f>::tempering_d; | |
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 | const size_t | |
272 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, | |
273 | __s, __b, __t, __c, __l, __f>::tempering_s; | |
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 | const _UIntType | |
281 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, | |
282 | __s, __b, __t, __c, __l, __f>::tempering_b; | |
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 | const size_t | |
290 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, | |
291 | __s, __b, __t, __c, __l, __f>::tempering_t; | |
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 | const _UIntType | |
299 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, | |
300 | __s, __b, __t, __c, __l, __f>::tempering_c; | |
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 | const size_t | |
308 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, | |
309 | __s, __b, __t, __c, __l, __f>::tempering_l; | |
310 | ||
311 | template<typename _UIntType, | |
312 | size_t __w, size_t __n, size_t __m, size_t __r, | |
313 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, | |
314 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, | |
315 | _UIntType __f> | |
316 | const _UIntType | |
317 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, | |
318 | __s, __b, __t, __c, __l, __f>:: | |
319 | initialization_multiplier; | |
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 | const _UIntType | |
327 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, | |
328 | __s, __b, __t, __c, __l, __f>::default_seed; | |
329 | ||
8e79468d BK |
330 | template<typename _UIntType, |
331 | size_t __w, size_t __n, size_t __m, size_t __r, | |
332 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, | |
333 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, | |
334 | _UIntType __f> | |
335 | void | |
336 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, | |
337 | __s, __b, __t, __c, __l, __f>:: | |
338 | seed(result_type __sd) | |
339 | { | |
b94f4bef | 340 | _M_x[0] = __detail::__mod<_UIntType, |
8e79468d BK |
341 | __detail::_Shift<_UIntType, __w>::__value>(__sd); |
342 | ||
343 | for (size_t __i = 1; __i < state_size; ++__i) | |
344 | { | |
345 | _UIntType __x = _M_x[__i - 1]; | |
346 | __x ^= __x >> (__w - 2); | |
347 | __x *= __f; | |
b94f4bef PC |
348 | __x += __detail::__mod<_UIntType, __n>(__i); |
349 | _M_x[__i] = __detail::__mod<_UIntType, | |
8e79468d BK |
350 | __detail::_Shift<_UIntType, __w>::__value>(__x); |
351 | } | |
352 | _M_p = state_size; | |
353 | } | |
354 | ||
355 | template<typename _UIntType, | |
356 | size_t __w, size_t __n, size_t __m, size_t __r, | |
357 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, | |
358 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, | |
359 | _UIntType __f> | |
05eeebfe PC |
360 | template<typename _Sseq> |
361 | typename std::enable_if<std::is_class<_Sseq>::value>::type | |
15ecdcc6 | 362 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
8e79468d | 363 | __s, __b, __t, __c, __l, __f>:: |
15ecdcc6 PC |
364 | seed(_Sseq& __q) |
365 | { | |
366 | const _UIntType __upper_mask = (~_UIntType()) << __r; | |
367 | const size_t __k = (__w + 31) / 32; | |
368 | uint_least32_t __arr[__n * __k]; | |
369 | __q.generate(__arr + 0, __arr + __n * __k); | |
370 | ||
371 | bool __zero = true; | |
372 | for (size_t __i = 0; __i < state_size; ++__i) | |
373 | { | |
374 | _UIntType __factor = 1u; | |
375 | _UIntType __sum = 0u; | |
376 | for (size_t __j = 0; __j < __k; ++__j) | |
377 | { | |
378 | __sum += __arr[__k * __i + __j] * __factor; | |
379 | __factor *= __detail::_Shift<_UIntType, 32>::__value; | |
380 | } | |
381 | _M_x[__i] = __detail::__mod<_UIntType, | |
382 | __detail::_Shift<_UIntType, __w>::__value>(__sum); | |
383 | ||
384 | if (__zero) | |
385 | { | |
386 | if (__i == 0) | |
387 | { | |
388 | if ((_M_x[0] & __upper_mask) != 0u) | |
389 | __zero = false; | |
390 | } | |
391 | else if (_M_x[__i] != 0u) | |
392 | __zero = false; | |
393 | } | |
394 | } | |
8e79468d | 395 | if (__zero) |
b94f4bef | 396 | _M_x[0] = __detail::_Shift<_UIntType, __w - 1>::__value; |
15ecdcc6 | 397 | } |
8e79468d BK |
398 | |
399 | template<typename _UIntType, size_t __w, | |
400 | size_t __n, size_t __m, size_t __r, | |
401 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, | |
402 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, | |
403 | _UIntType __f> | |
404 | typename | |
405 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, | |
406 | __s, __b, __t, __c, __l, __f>::result_type | |
407 | mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, | |
408 | __s, __b, __t, __c, __l, __f>:: | |
409 | operator()() | |
410 | { | |
411 | // Reload the vector - cost is O(n) amortized over n calls. | |
412 | if (_M_p >= state_size) | |
413 | { | |
414 | const _UIntType __upper_mask = (~_UIntType()) << __r; | |
415 | const _UIntType __lower_mask = ~__upper_mask; | |
416 | ||
417 | for (size_t __k = 0; __k < (__n - __m); ++__k) | |
418 | { | |
419 | _UIntType __y = ((_M_x[__k] & __upper_mask) | |
420 | | (_M_x[__k + 1] & __lower_mask)); | |
421 | _M_x[__k] = (_M_x[__k + __m] ^ (__y >> 1) | |
422 | ^ ((__y & 0x01) ? __a : 0)); | |
423 | } | |
424 | ||
425 | for (size_t __k = (__n - __m); __k < (__n - 1); ++__k) | |
426 | { | |
427 | _UIntType __y = ((_M_x[__k] & __upper_mask) | |
428 | | (_M_x[__k + 1] & __lower_mask)); | |
429 | _M_x[__k] = (_M_x[__k + (__m - __n)] ^ (__y >> 1) | |
430 | ^ ((__y & 0x01) ? __a : 0)); | |
431 | } | |
432 | ||
433 | _UIntType __y = ((_M_x[__n - 1] & __upper_mask) | |
434 | | (_M_x[0] & __lower_mask)); | |
435 | _M_x[__n - 1] = (_M_x[__m - 1] ^ (__y >> 1) | |
436 | ^ ((__y & 0x01) ? __a : 0)); | |
437 | _M_p = 0; | |
438 | } | |
439 | ||
440 | // Calculate o(x(i)). | |
441 | result_type __z = _M_x[_M_p++]; | |
442 | __z ^= (__z >> __u) & __d; | |
443 | __z ^= (__z << __s) & __b; | |
444 | __z ^= (__z << __t) & __c; | |
445 | __z ^= (__z >> __l); | |
446 | ||
447 | return __z; | |
448 | } | |
449 | ||
450 | template<typename _UIntType, size_t __w, | |
451 | size_t __n, size_t __m, size_t __r, | |
452 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, | |
453 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, | |
454 | _UIntType __f, typename _CharT, typename _Traits> | |
455 | std::basic_ostream<_CharT, _Traits>& | |
456 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
457 | const mersenne_twister_engine<_UIntType, __w, __n, __m, | |
458 | __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x) | |
459 | { | |
460 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
461 | typedef typename __ostream_type::ios_base __ios_base; | |
462 | ||
463 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
464 | const _CharT __fill = __os.fill(); | |
465 | const _CharT __space = __os.widen(' '); | |
95fe602e | 466 | __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); |
8e79468d BK |
467 | __os.fill(__space); |
468 | ||
469 | for (size_t __i = 0; __i < __n - 1; ++__i) | |
470 | __os << __x._M_x[__i] << __space; | |
471 | __os << __x._M_x[__n - 1]; | |
472 | ||
473 | __os.flags(__flags); | |
474 | __os.fill(__fill); | |
475 | return __os; | |
476 | } | |
477 | ||
478 | template<typename _UIntType, size_t __w, | |
479 | size_t __n, size_t __m, size_t __r, | |
480 | _UIntType __a, size_t __u, _UIntType __d, size_t __s, | |
481 | _UIntType __b, size_t __t, _UIntType __c, size_t __l, | |
482 | _UIntType __f, typename _CharT, typename _Traits> | |
483 | std::basic_istream<_CharT, _Traits>& | |
484 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
485 | mersenne_twister_engine<_UIntType, __w, __n, __m, | |
486 | __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x) | |
487 | { | |
488 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
489 | typedef typename __istream_type::ios_base __ios_base; | |
490 | ||
491 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
492 | __is.flags(__ios_base::dec | __ios_base::skipws); | |
493 | ||
494 | for (size_t __i = 0; __i < __n; ++__i) | |
495 | __is >> __x._M_x[__i]; | |
496 | ||
497 | __is.flags(__flags); | |
498 | return __is; | |
499 | } | |
500 | ||
501 | ||
300ea283 PC |
502 | template<typename _UIntType, size_t __w, size_t __s, size_t __r> |
503 | const size_t | |
504 | subtract_with_carry_engine<_UIntType, __w, __s, __r>::word_size; | |
505 | ||
506 | template<typename _UIntType, size_t __w, size_t __s, size_t __r> | |
507 | const size_t | |
508 | subtract_with_carry_engine<_UIntType, __w, __s, __r>::short_lag; | |
509 | ||
510 | template<typename _UIntType, size_t __w, size_t __s, size_t __r> | |
511 | const size_t | |
512 | subtract_with_carry_engine<_UIntType, __w, __s, __r>::long_lag; | |
513 | ||
514 | template<typename _UIntType, size_t __w, size_t __s, size_t __r> | |
515 | const _UIntType | |
516 | subtract_with_carry_engine<_UIntType, __w, __s, __r>::default_seed; | |
517 | ||
8e79468d BK |
518 | template<typename _UIntType, size_t __w, size_t __s, size_t __r> |
519 | void | |
520 | subtract_with_carry_engine<_UIntType, __w, __s, __r>:: | |
521 | seed(result_type __value) | |
522 | { | |
b94f4bef PC |
523 | std::linear_congruential_engine<result_type, 40014u, 0u, 2147483563u> |
524 | __lcg(__value == 0u ? default_seed : __value); | |
8e79468d | 525 | |
b94f4bef | 526 | const size_t __n = (__w + 31) / 32; |
8e79468d BK |
527 | |
528 | for (size_t __i = 0; __i < long_lag; ++__i) | |
529 | { | |
b94f4bef PC |
530 | _UIntType __sum = 0u; |
531 | _UIntType __factor = 1u; | |
8e79468d BK |
532 | for (size_t __j = 0; __j < __n; ++__j) |
533 | { | |
b94f4bef PC |
534 | __sum += __detail::__mod<uint_least32_t, |
535 | __detail::_Shift<uint_least32_t, 32>::__value> | |
8e79468d BK |
536 | (__lcg()) * __factor; |
537 | __factor *= __detail::_Shift<_UIntType, 32>::__value; | |
538 | } | |
b94f4bef | 539 | _M_x[__i] = __detail::__mod<_UIntType, |
96a9203b | 540 | __detail::_Shift<_UIntType, __w>::__value>(__sum); |
8e79468d BK |
541 | } |
542 | _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0; | |
543 | _M_p = 0; | |
544 | } | |
545 | ||
546 | template<typename _UIntType, size_t __w, size_t __s, size_t __r> | |
05eeebfe PC |
547 | template<typename _Sseq> |
548 | typename std::enable_if<std::is_class<_Sseq>::value>::type | |
15ecdcc6 PC |
549 | subtract_with_carry_engine<_UIntType, __w, __s, __r>:: |
550 | seed(_Sseq& __q) | |
551 | { | |
552 | const size_t __k = (__w + 31) / 32; | |
553 | uint_least32_t __arr[__r * __k]; | |
554 | __q.generate(__arr + 0, __arr + __r * __k); | |
555 | ||
556 | for (size_t __i = 0; __i < long_lag; ++__i) | |
557 | { | |
558 | _UIntType __sum = 0u; | |
559 | _UIntType __factor = 1u; | |
560 | for (size_t __j = 0; __j < __k; ++__j) | |
561 | { | |
562 | __sum += __arr[__k * __i + __j] * __factor; | |
563 | __factor *= __detail::_Shift<_UIntType, 32>::__value; | |
564 | } | |
565 | _M_x[__i] = __detail::__mod<_UIntType, | |
566 | __detail::_Shift<_UIntType, __w>::__value>(__sum); | |
567 | } | |
568 | _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0; | |
569 | _M_p = 0; | |
570 | } | |
8e79468d BK |
571 | |
572 | template<typename _UIntType, size_t __w, size_t __s, size_t __r> | |
573 | typename subtract_with_carry_engine<_UIntType, __w, __s, __r>:: | |
574 | result_type | |
575 | subtract_with_carry_engine<_UIntType, __w, __s, __r>:: | |
576 | operator()() | |
577 | { | |
578 | // Derive short lag index from current index. | |
579 | long __ps = _M_p - short_lag; | |
580 | if (__ps < 0) | |
581 | __ps += long_lag; | |
582 | ||
583 | // Calculate new x(i) without overflow or division. | |
584 | // NB: Thanks to the requirements for _UIntType, _M_x[_M_p] + _M_carry | |
585 | // cannot overflow. | |
586 | _UIntType __xi; | |
587 | if (_M_x[__ps] >= _M_x[_M_p] + _M_carry) | |
588 | { | |
589 | __xi = _M_x[__ps] - _M_x[_M_p] - _M_carry; | |
590 | _M_carry = 0; | |
591 | } | |
592 | else | |
593 | { | |
96a9203b PC |
594 | __xi = (__detail::_Shift<_UIntType, __w>::__value |
595 | - _M_x[_M_p] - _M_carry + _M_x[__ps]); | |
8e79468d BK |
596 | _M_carry = 1; |
597 | } | |
598 | _M_x[_M_p] = __xi; | |
599 | ||
600 | // Adjust current index to loop around in ring buffer. | |
601 | if (++_M_p >= long_lag) | |
602 | _M_p = 0; | |
603 | ||
604 | return __xi; | |
605 | } | |
606 | ||
607 | template<typename _UIntType, size_t __w, size_t __s, size_t __r, | |
608 | typename _CharT, typename _Traits> | |
609 | std::basic_ostream<_CharT, _Traits>& | |
610 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
611 | const subtract_with_carry_engine<_UIntType, | |
612 | __w, __s, __r>& __x) | |
613 | { | |
614 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
615 | typedef typename __ostream_type::ios_base __ios_base; | |
616 | ||
617 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
618 | const _CharT __fill = __os.fill(); | |
619 | const _CharT __space = __os.widen(' '); | |
95fe602e | 620 | __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); |
8e79468d BK |
621 | __os.fill(__space); |
622 | ||
623 | for (size_t __i = 0; __i < __r; ++__i) | |
624 | __os << __x._M_x[__i] << __space; | |
625 | __os << __x._M_carry; | |
626 | ||
627 | __os.flags(__flags); | |
628 | __os.fill(__fill); | |
629 | return __os; | |
630 | } | |
631 | ||
632 | template<typename _UIntType, size_t __w, size_t __s, size_t __r, | |
633 | typename _CharT, typename _Traits> | |
634 | std::basic_istream<_CharT, _Traits>& | |
635 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
636 | subtract_with_carry_engine<_UIntType, __w, __s, __r>& __x) | |
637 | { | |
638 | typedef std::basic_ostream<_CharT, _Traits> __istream_type; | |
639 | typedef typename __istream_type::ios_base __ios_base; | |
640 | ||
641 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
642 | __is.flags(__ios_base::dec | __ios_base::skipws); | |
643 | ||
644 | for (size_t __i = 0; __i < __r; ++__i) | |
645 | __is >> __x._M_x[__i]; | |
646 | __is >> __x._M_carry; | |
647 | ||
648 | __is.flags(__flags); | |
649 | return __is; | |
650 | } | |
651 | ||
652 | ||
300ea283 PC |
653 | template<typename _RandomNumberEngine, size_t __p, size_t __r> |
654 | const size_t | |
655 | discard_block_engine<_RandomNumberEngine, __p, __r>::block_size; | |
656 | ||
657 | template<typename _RandomNumberEngine, size_t __p, size_t __r> | |
658 | const size_t | |
659 | discard_block_engine<_RandomNumberEngine, __p, __r>::used_block; | |
660 | ||
8e79468d BK |
661 | template<typename _RandomNumberEngine, size_t __p, size_t __r> |
662 | typename discard_block_engine<_RandomNumberEngine, | |
663 | __p, __r>::result_type | |
664 | discard_block_engine<_RandomNumberEngine, __p, __r>:: | |
665 | operator()() | |
666 | { | |
667 | if (_M_n >= used_block) | |
668 | { | |
669 | _M_b.discard(block_size - _M_n); | |
670 | _M_n = 0; | |
671 | } | |
672 | ++_M_n; | |
673 | return _M_b(); | |
674 | } | |
675 | ||
676 | template<typename _RandomNumberEngine, size_t __p, size_t __r, | |
677 | typename _CharT, typename _Traits> | |
678 | std::basic_ostream<_CharT, _Traits>& | |
679 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
680 | const discard_block_engine<_RandomNumberEngine, | |
681 | __p, __r>& __x) | |
682 | { | |
683 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
684 | typedef typename __ostream_type::ios_base __ios_base; | |
685 | ||
686 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
687 | const _CharT __fill = __os.fill(); | |
688 | const _CharT __space = __os.widen(' '); | |
95fe602e | 689 | __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); |
8e79468d BK |
690 | __os.fill(__space); |
691 | ||
692 | __os << __x.base() << __space << __x._M_n; | |
693 | ||
694 | __os.flags(__flags); | |
695 | __os.fill(__fill); | |
696 | return __os; | |
697 | } | |
698 | ||
699 | template<typename _RandomNumberEngine, size_t __p, size_t __r, | |
700 | typename _CharT, typename _Traits> | |
701 | std::basic_istream<_CharT, _Traits>& | |
702 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
703 | discard_block_engine<_RandomNumberEngine, __p, __r>& __x) | |
704 | { | |
705 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
706 | typedef typename __istream_type::ios_base __ios_base; | |
707 | ||
708 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
709 | __is.flags(__ios_base::dec | __ios_base::skipws); | |
710 | ||
711 | __is >> __x._M_b >> __x._M_n; | |
712 | ||
713 | __is.flags(__flags); | |
714 | return __is; | |
715 | } | |
716 | ||
717 | ||
718 | template<typename _RandomNumberEngine, size_t __w, typename _UIntType> | |
719 | typename independent_bits_engine<_RandomNumberEngine, __w, _UIntType>:: | |
720 | result_type | |
721 | independent_bits_engine<_RandomNumberEngine, __w, _UIntType>:: | |
722 | operator()() | |
723 | { | |
6855fe45 PC |
724 | const long double __r = static_cast<long double>(_M_b.max()) |
725 | - static_cast<long double>(_M_b.min()) + 1.0L; | |
b01630bb | 726 | const result_type __m = std::log(__r) / std::log(2.0L); |
8e79468d BK |
727 | result_type __n, __n0, __y0, __y1, __s0, __s1; |
728 | for (size_t __i = 0; __i < 2; ++__i) | |
729 | { | |
730 | __n = (__w + __m - 1) / __m + __i; | |
731 | __n0 = __n - __w % __n; | |
732 | const result_type __w0 = __w / __n; | |
733 | const result_type __w1 = __w0 + 1; | |
6855fe45 PC |
734 | __s0 = result_type(1) << __w0; |
735 | __s1 = result_type(1) << __w1; | |
8e79468d BK |
736 | __y0 = __s0 * (__r / __s0); |
737 | __y1 = __s1 * (__r / __s1); | |
738 | if (__r - __y0 <= __y0 / __n) | |
739 | break; | |
740 | } | |
741 | ||
742 | result_type __sum = 0; | |
743 | for (size_t __k = 0; __k < __n0; ++__k) | |
744 | { | |
745 | result_type __u; | |
746 | do | |
6855fe45 | 747 | __u = _M_b() - _M_b.min(); |
8e79468d | 748 | while (__u >= __y0); |
6855fe45 | 749 | __sum = __s0 * __sum + __u % __s0; |
8e79468d BK |
750 | } |
751 | for (size_t __k = __n0; __k < __n; ++__k) | |
752 | { | |
753 | result_type __u; | |
754 | do | |
6855fe45 | 755 | __u = _M_b() - _M_b.min(); |
8e79468d | 756 | while (__u >= __y1); |
6855fe45 | 757 | __sum = __s1 * __sum + __u % __s1; |
8e79468d BK |
758 | } |
759 | return __sum; | |
760 | } | |
761 | ||
762 | ||
300ea283 PC |
763 | template<typename _RandomNumberEngine, size_t __k> |
764 | const size_t | |
765 | shuffle_order_engine<_RandomNumberEngine, __k>::table_size; | |
766 | ||
8e79468d BK |
767 | template<typename _RandomNumberEngine, size_t __k> |
768 | typename shuffle_order_engine<_RandomNumberEngine, __k>::result_type | |
769 | shuffle_order_engine<_RandomNumberEngine, __k>:: | |
770 | operator()() | |
771 | { | |
96a9203b PC |
772 | size_t __j = __k * ((_M_y - _M_b.min()) |
773 | / (_M_b.max() - _M_b.min() + 1.0L)); | |
8e79468d BK |
774 | _M_y = _M_v[__j]; |
775 | _M_v[__j] = _M_b(); | |
776 | ||
777 | return _M_y; | |
778 | } | |
779 | ||
780 | template<typename _RandomNumberEngine, size_t __k, | |
781 | typename _CharT, typename _Traits> | |
782 | std::basic_ostream<_CharT, _Traits>& | |
783 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
784 | const shuffle_order_engine<_RandomNumberEngine, __k>& __x) | |
785 | { | |
786 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
787 | typedef typename __ostream_type::ios_base __ios_base; | |
788 | ||
789 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
790 | const _CharT __fill = __os.fill(); | |
791 | const _CharT __space = __os.widen(' '); | |
95fe602e | 792 | __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); |
8e79468d BK |
793 | __os.fill(__space); |
794 | ||
795 | __os << __x.base(); | |
796 | for (size_t __i = 0; __i < __k; ++__i) | |
797 | __os << __space << __x._M_v[__i]; | |
798 | __os << __space << __x._M_y; | |
799 | ||
800 | __os.flags(__flags); | |
801 | __os.fill(__fill); | |
802 | return __os; | |
803 | } | |
804 | ||
805 | template<typename _RandomNumberEngine, size_t __k, | |
806 | typename _CharT, typename _Traits> | |
807 | std::basic_istream<_CharT, _Traits>& | |
808 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
809 | shuffle_order_engine<_RandomNumberEngine, __k>& __x) | |
810 | { | |
811 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
812 | typedef typename __istream_type::ios_base __ios_base; | |
813 | ||
814 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
815 | __is.flags(__ios_base::dec | __ios_base::skipws); | |
816 | ||
817 | __is >> __x._M_b; | |
818 | for (size_t __i = 0; __i < __k; ++__i) | |
819 | __is >> __x._M_v[__i]; | |
820 | __is >> __x._M_y; | |
821 | ||
822 | __is.flags(__flags); | |
823 | return __is; | |
824 | } | |
825 | ||
826 | ||
827 | template<typename _IntType> | |
828 | template<typename _UniformRandomNumberGenerator> | |
829 | typename uniform_int_distribution<_IntType>::result_type | |
830 | uniform_int_distribution<_IntType>:: | |
9b88236b PC |
831 | operator()(_UniformRandomNumberGenerator& __urng, |
832 | const param_type& __param) | |
8e79468d | 833 | { |
83c290e2 | 834 | typedef typename std::make_unsigned<typename |
b7d3d70f | 835 | _UniformRandomNumberGenerator::result_type>::type __urngtype; |
83c290e2 | 836 | typedef typename std::make_unsigned<result_type>::type __utype; |
b7d3d70f PC |
837 | typedef typename std::conditional<(sizeof(__urngtype) |
838 | > sizeof(__utype)), | |
839 | __urngtype, __utype>::type __uctype; | |
840 | ||
841 | const __uctype __urngmin = __urng.min(); | |
842 | const __uctype __urngmax = __urng.max(); | |
843 | const __uctype __urngrange = __urngmax - __urngmin; | |
844 | const __uctype __urange | |
845 | = __uctype(__param.b()) - __uctype(__param.a()); | |
846 | ||
847 | __uctype __ret; | |
848 | ||
849 | if (__urngrange > __urange) | |
850 | { | |
851 | // downscaling | |
852 | const __uctype __uerange = __urange + 1; // __urange can be zero | |
853 | const __uctype __scaling = __urngrange / __uerange; | |
854 | const __uctype __past = __uerange * __scaling; | |
855 | do | |
856 | __ret = __uctype(__urng()) - __urngmin; | |
857 | while (__ret >= __past); | |
858 | __ret /= __scaling; | |
859 | } | |
860 | else if (__urngrange < __urange) | |
861 | { | |
862 | // upscaling | |
863 | /* | |
864 | Note that every value in [0, urange] | |
865 | can be written uniquely as | |
866 | ||
867 | (urngrange + 1) * high + low | |
868 | ||
869 | where | |
870 | ||
871 | high in [0, urange / (urngrange + 1)] | |
872 | ||
873 | and | |
874 | ||
875 | low in [0, urngrange]. | |
876 | */ | |
877 | __uctype __tmp; // wraparound control | |
878 | do | |
879 | { | |
880 | const __uctype __uerngrange = __urngrange + 1; | |
881 | __tmp = (__uerngrange * operator() | |
882 | (__urng, param_type(0, __urange / __uerngrange))); | |
883 | __ret = __tmp + (__uctype(__urng()) - __urngmin); | |
884 | } | |
885 | while (__ret > __urange || __ret < __tmp); | |
886 | } | |
887 | else | |
888 | __ret = __uctype(__urng()) - __urngmin; | |
8e79468d | 889 | |
9b88236b | 890 | return __ret + __param.a(); |
8e79468d BK |
891 | } |
892 | ||
893 | template<typename _IntType, typename _CharT, typename _Traits> | |
894 | std::basic_ostream<_CharT, _Traits>& | |
895 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
896 | const uniform_int_distribution<_IntType>& __x) | |
897 | { | |
898 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
899 | typedef typename __ostream_type::ios_base __ios_base; | |
900 | ||
901 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
902 | const _CharT __fill = __os.fill(); | |
903 | const _CharT __space = __os.widen(' '); | |
904 | __os.flags(__ios_base::scientific | __ios_base::left); | |
905 | __os.fill(__space); | |
906 | ||
907 | __os << __x.a() << __space << __x.b(); | |
908 | ||
909 | __os.flags(__flags); | |
910 | __os.fill(__fill); | |
911 | return __os; | |
912 | } | |
913 | ||
914 | template<typename _IntType, typename _CharT, typename _Traits> | |
915 | std::basic_istream<_CharT, _Traits>& | |
916 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
917 | uniform_int_distribution<_IntType>& __x) | |
918 | { | |
919 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
920 | typedef typename __istream_type::ios_base __ios_base; | |
921 | ||
922 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
923 | __is.flags(__ios_base::dec | __ios_base::skipws); | |
924 | ||
925 | _IntType __a, __b; | |
926 | __is >> __a >> __b; | |
927 | __x.param(typename uniform_int_distribution<_IntType>:: | |
928 | param_type(__a, __b)); | |
929 | ||
930 | __is.flags(__flags); | |
931 | return __is; | |
932 | } | |
933 | ||
934 | ||
935 | template<typename _RealType, typename _CharT, typename _Traits> | |
936 | std::basic_ostream<_CharT, _Traits>& | |
937 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
938 | const uniform_real_distribution<_RealType>& __x) | |
939 | { | |
940 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
941 | typedef typename __ostream_type::ios_base __ios_base; | |
942 | ||
943 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
944 | const _CharT __fill = __os.fill(); | |
945 | const std::streamsize __precision = __os.precision(); | |
946 | const _CharT __space = __os.widen(' '); | |
947 | __os.flags(__ios_base::scientific | __ios_base::left); | |
948 | __os.fill(__space); | |
7703dc47 | 949 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
8e79468d BK |
950 | |
951 | __os << __x.a() << __space << __x.b(); | |
952 | ||
953 | __os.flags(__flags); | |
954 | __os.fill(__fill); | |
955 | __os.precision(__precision); | |
956 | return __os; | |
957 | } | |
958 | ||
959 | template<typename _RealType, typename _CharT, typename _Traits> | |
960 | std::basic_istream<_CharT, _Traits>& | |
961 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
962 | uniform_real_distribution<_RealType>& __x) | |
963 | { | |
964 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
965 | typedef typename __istream_type::ios_base __ios_base; | |
966 | ||
967 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
968 | __is.flags(__ios_base::skipws); | |
969 | ||
970 | _RealType __a, __b; | |
971 | __is >> __a >> __b; | |
972 | __x.param(typename uniform_real_distribution<_RealType>:: | |
973 | param_type(__a, __b)); | |
974 | ||
975 | __is.flags(__flags); | |
976 | return __is; | |
977 | } | |
978 | ||
979 | ||
980 | template<typename _CharT, typename _Traits> | |
981 | std::basic_ostream<_CharT, _Traits>& | |
982 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
983 | const bernoulli_distribution& __x) | |
984 | { | |
985 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
986 | typedef typename __ostream_type::ios_base __ios_base; | |
987 | ||
988 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
989 | const _CharT __fill = __os.fill(); | |
990 | const std::streamsize __precision = __os.precision(); | |
991 | __os.flags(__ios_base::scientific | __ios_base::left); | |
992 | __os.fill(__os.widen(' ')); | |
7703dc47 | 993 | __os.precision(std::numeric_limits<double>::max_digits10); |
8e79468d BK |
994 | |
995 | __os << __x.p(); | |
996 | ||
997 | __os.flags(__flags); | |
998 | __os.fill(__fill); | |
999 | __os.precision(__precision); | |
1000 | return __os; | |
1001 | } | |
1002 | ||
1003 | ||
1004 | template<typename _IntType> | |
1005 | template<typename _UniformRandomNumberGenerator> | |
1006 | typename geometric_distribution<_IntType>::result_type | |
1007 | geometric_distribution<_IntType>:: | |
1008 | operator()(_UniformRandomNumberGenerator& __urng, | |
1009 | const param_type& __param) | |
1010 | { | |
1011 | // About the epsilon thing see this thread: | |
1012 | // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html | |
1013 | const double __naf = | |
1014 | (1 - std::numeric_limits<double>::epsilon()) / 2; | |
1015 | // The largest _RealType convertible to _IntType. | |
1016 | const double __thr = | |
1017 | std::numeric_limits<_IntType>::max() + __naf; | |
9b88236b | 1018 | __detail::_Adaptor<_UniformRandomNumberGenerator, double> |
8e79468d BK |
1019 | __aurng(__urng); |
1020 | ||
1021 | double __cand; | |
1022 | do | |
1023 | __cand = std::ceil(std::log(__aurng()) / __param._M_log_p); | |
1024 | while (__cand >= __thr); | |
1025 | ||
1026 | return result_type(__cand + __naf); | |
1027 | } | |
1028 | ||
1029 | template<typename _IntType, | |
1030 | typename _CharT, typename _Traits> | |
1031 | std::basic_ostream<_CharT, _Traits>& | |
1032 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
1033 | const geometric_distribution<_IntType>& __x) | |
1034 | { | |
1035 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
1036 | typedef typename __ostream_type::ios_base __ios_base; | |
1037 | ||
1038 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
1039 | const _CharT __fill = __os.fill(); | |
1040 | const std::streamsize __precision = __os.precision(); | |
1041 | __os.flags(__ios_base::scientific | __ios_base::left); | |
1042 | __os.fill(__os.widen(' ')); | |
7703dc47 | 1043 | __os.precision(std::numeric_limits<double>::max_digits10); |
8e79468d BK |
1044 | |
1045 | __os << __x.p(); | |
1046 | ||
1047 | __os.flags(__flags); | |
1048 | __os.fill(__fill); | |
1049 | __os.precision(__precision); | |
1050 | return __os; | |
1051 | } | |
1052 | ||
1053 | template<typename _IntType, | |
1054 | typename _CharT, typename _Traits> | |
1055 | std::basic_istream<_CharT, _Traits>& | |
1056 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
1057 | geometric_distribution<_IntType>& __x) | |
1058 | { | |
1059 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
1060 | typedef typename __istream_type::ios_base __ios_base; | |
1061 | ||
1062 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
1063 | __is.flags(__ios_base::skipws); | |
1064 | ||
1065 | double __p; | |
1066 | __is >> __p; | |
1067 | __x.param(typename geometric_distribution<_IntType>::param_type(__p)); | |
1068 | ||
1069 | __is.flags(__flags); | |
1070 | return __is; | |
1071 | } | |
1072 | ||
f9b09dec PC |
1073 | |
1074 | template<typename _IntType> | |
1075 | template<typename _UniformRandomNumberGenerator> | |
1076 | typename negative_binomial_distribution<_IntType>::result_type | |
1077 | negative_binomial_distribution<_IntType>:: | |
1078 | operator()(_UniformRandomNumberGenerator& __urng) | |
1079 | { | |
1080 | const double __y = _M_gd(__urng); | |
1081 | ||
1082 | // XXX Is the constructor too slow? | |
1083 | std::poisson_distribution<result_type> __poisson(__y); | |
1084 | return __poisson(__urng); | |
1085 | } | |
1086 | ||
8e79468d BK |
1087 | template<typename _IntType> |
1088 | template<typename _UniformRandomNumberGenerator> | |
1089 | typename negative_binomial_distribution<_IntType>::result_type | |
1090 | negative_binomial_distribution<_IntType>:: | |
1091 | operator()(_UniformRandomNumberGenerator& __urng, | |
1092 | const param_type& __p) | |
1093 | { | |
f9b09dec PC |
1094 | typedef typename std::gamma_distribution<result_type>::param_type |
1095 | param_type; | |
1096 | ||
1097 | const double __y = | |
1098 | _M_gd(__urng, param_type(__p.k(), __p.p() / (1.0 - __p.p()))); | |
8e79468d | 1099 | |
f9b09dec | 1100 | std::poisson_distribution<result_type> __poisson(__y); |
b01630bb | 1101 | return __poisson(__urng); |
8e79468d BK |
1102 | } |
1103 | ||
1104 | template<typename _IntType, typename _CharT, typename _Traits> | |
1105 | std::basic_ostream<_CharT, _Traits>& | |
1106 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
1107 | const negative_binomial_distribution<_IntType>& __x) | |
1108 | { | |
1109 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
1110 | typedef typename __ostream_type::ios_base __ios_base; | |
1111 | ||
1112 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
1113 | const _CharT __fill = __os.fill(); | |
1114 | const std::streamsize __precision = __os.precision(); | |
1115 | const _CharT __space = __os.widen(' '); | |
1116 | __os.flags(__ios_base::scientific | __ios_base::left); | |
1117 | __os.fill(__os.widen(' ')); | |
7703dc47 | 1118 | __os.precision(std::numeric_limits<double>::max_digits10); |
8e79468d | 1119 | |
f9b09dec PC |
1120 | __os << __x.k() << __space << __x.p() |
1121 | << __space << __x._M_gd; | |
8e79468d BK |
1122 | |
1123 | __os.flags(__flags); | |
1124 | __os.fill(__fill); | |
1125 | __os.precision(__precision); | |
1126 | return __os; | |
1127 | } | |
1128 | ||
1129 | template<typename _IntType, typename _CharT, typename _Traits> | |
1130 | std::basic_istream<_CharT, _Traits>& | |
1131 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
1132 | negative_binomial_distribution<_IntType>& __x) | |
1133 | { | |
1134 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
1135 | typedef typename __istream_type::ios_base __ios_base; | |
1136 | ||
1137 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
1138 | __is.flags(__ios_base::skipws); | |
1139 | ||
1140 | _IntType __k; | |
1141 | double __p; | |
f9b09dec | 1142 | __is >> __k >> __p >> __x._M_gd; |
8e79468d BK |
1143 | __x.param(typename negative_binomial_distribution<_IntType>:: |
1144 | param_type(__k, __p)); | |
1145 | ||
1146 | __is.flags(__flags); | |
1147 | return __is; | |
1148 | } | |
1149 | ||
1150 | ||
1151 | template<typename _IntType> | |
1152 | void | |
1153 | poisson_distribution<_IntType>::param_type:: | |
1154 | _M_initialize() | |
1155 | { | |
1156 | #if _GLIBCXX_USE_C99_MATH_TR1 | |
1157 | if (_M_mean >= 12) | |
1158 | { | |
1159 | const double __m = std::floor(_M_mean); | |
1160 | _M_lm_thr = std::log(_M_mean); | |
1161 | _M_lfm = std::lgamma(__m + 1); | |
1162 | _M_sm = std::sqrt(__m); | |
1163 | ||
1164 | const double __pi_4 = 0.7853981633974483096156608458198757L; | |
1165 | const double __dx = std::sqrt(2 * __m * std::log(32 * __m | |
1166 | / __pi_4)); | |
1167 | _M_d = std::round(std::max(6.0, std::min(__m, __dx))); | |
1168 | const double __cx = 2 * __m + _M_d; | |
1169 | _M_scx = std::sqrt(__cx / 2); | |
1170 | _M_1cx = 1 / __cx; | |
1171 | ||
1172 | _M_c2b = std::sqrt(__pi_4 * __cx) * std::exp(_M_1cx); | |
1173 | _M_cb = 2 * __cx * std::exp(-_M_d * _M_1cx * (1 + _M_d / 2)) | |
1174 | / _M_d; | |
1175 | } | |
1176 | else | |
1177 | #endif | |
1178 | _M_lm_thr = std::exp(-_M_mean); | |
1179 | } | |
1180 | ||
1181 | /** | |
1182 | * A rejection algorithm when mean >= 12 and a simple method based | |
1183 | * upon the multiplication of uniform random variates otherwise. | |
1184 | * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1 | |
1185 | * is defined. | |
1186 | * | |
1187 | * Reference: | |
2a60a9f6 | 1188 | * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, |
8e79468d BK |
1189 | * New York, 1986, Ch. X, Sects. 3.3 & 3.4 (+ Errata!). |
1190 | */ | |
1191 | template<typename _IntType> | |
1192 | template<typename _UniformRandomNumberGenerator> | |
1193 | typename poisson_distribution<_IntType>::result_type | |
1194 | poisson_distribution<_IntType>:: | |
1195 | operator()(_UniformRandomNumberGenerator& __urng, | |
1196 | const param_type& __param) | |
1197 | { | |
1198 | __detail::_Adaptor<_UniformRandomNumberGenerator, double> | |
1199 | __aurng(__urng); | |
1200 | #if _GLIBCXX_USE_C99_MATH_TR1 | |
1201 | if (__param.mean() >= 12) | |
1202 | { | |
1203 | double __x; | |
1204 | ||
1205 | // See comments above... | |
1206 | const double __naf = | |
1207 | (1 - std::numeric_limits<double>::epsilon()) / 2; | |
1208 | const double __thr = | |
1209 | std::numeric_limits<_IntType>::max() + __naf; | |
1210 | ||
1211 | const double __m = std::floor(__param.mean()); | |
1212 | // sqrt(pi / 2) | |
1213 | const double __spi_2 = 1.2533141373155002512078826424055226L; | |
1214 | const double __c1 = __param._M_sm * __spi_2; | |
1215 | const double __c2 = __param._M_c2b + __c1; | |
1216 | const double __c3 = __c2 + 1; | |
1217 | const double __c4 = __c3 + 1; | |
1218 | // e^(1 / 78) | |
1219 | const double __e178 = 1.0129030479320018583185514777512983L; | |
1220 | const double __c5 = __c4 + __e178; | |
1221 | const double __c = __param._M_cb + __c5; | |
1222 | const double __2cx = 2 * (2 * __m + __param._M_d); | |
1223 | ||
1224 | bool __reject = true; | |
1225 | do | |
1226 | { | |
1227 | const double __u = __c * __aurng(); | |
1228 | const double __e = -std::log(__aurng()); | |
1229 | ||
1230 | double __w = 0.0; | |
1231 | ||
1232 | if (__u <= __c1) | |
1233 | { | |
1234 | const double __n = _M_nd(__urng); | |
1235 | const double __y = -std::abs(__n) * __param._M_sm - 1; | |
1236 | __x = std::floor(__y); | |
1237 | __w = -__n * __n / 2; | |
1238 | if (__x < -__m) | |
1239 | continue; | |
1240 | } | |
1241 | else if (__u <= __c2) | |
1242 | { | |
1243 | const double __n = _M_nd(__urng); | |
1244 | const double __y = 1 + std::abs(__n) * __param._M_scx; | |
1245 | __x = std::ceil(__y); | |
1246 | __w = __y * (2 - __y) * __param._M_1cx; | |
1247 | if (__x > __param._M_d) | |
1248 | continue; | |
1249 | } | |
1250 | else if (__u <= __c3) | |
1251 | // NB: This case not in the book, nor in the Errata, | |
1252 | // but should be ok... | |
1253 | __x = -1; | |
1254 | else if (__u <= __c4) | |
1255 | __x = 0; | |
1256 | else if (__u <= __c5) | |
1257 | __x = 1; | |
1258 | else | |
1259 | { | |
1260 | const double __v = -std::log(__aurng()); | |
1261 | const double __y = __param._M_d | |
1262 | + __v * __2cx / __param._M_d; | |
1263 | __x = std::ceil(__y); | |
1264 | __w = -__param._M_d * __param._M_1cx * (1 + __y / 2); | |
1265 | } | |
1266 | ||
1267 | __reject = (__w - __e - __x * __param._M_lm_thr | |
1268 | > __param._M_lfm - std::lgamma(__x + __m + 1)); | |
1269 | ||
1270 | __reject |= __x + __m >= __thr; | |
1271 | ||
1272 | } while (__reject); | |
1273 | ||
1274 | return result_type(__x + __m + __naf); | |
1275 | } | |
1276 | else | |
1277 | #endif | |
1278 | { | |
1279 | _IntType __x = 0; | |
1280 | double __prod = 1.0; | |
1281 | ||
1282 | do | |
1283 | { | |
1284 | __prod *= __aurng(); | |
1285 | __x += 1; | |
1286 | } | |
1287 | while (__prod > __param._M_lm_thr); | |
1288 | ||
1289 | return __x - 1; | |
1290 | } | |
1291 | } | |
1292 | ||
1293 | template<typename _IntType, | |
1294 | typename _CharT, typename _Traits> | |
1295 | std::basic_ostream<_CharT, _Traits>& | |
1296 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
1297 | const poisson_distribution<_IntType>& __x) | |
1298 | { | |
1299 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
1300 | typedef typename __ostream_type::ios_base __ios_base; | |
1301 | ||
1302 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
1303 | const _CharT __fill = __os.fill(); | |
1304 | const std::streamsize __precision = __os.precision(); | |
1305 | const _CharT __space = __os.widen(' '); | |
1306 | __os.flags(__ios_base::scientific | __ios_base::left); | |
1307 | __os.fill(__space); | |
7703dc47 | 1308 | __os.precision(std::numeric_limits<double>::max_digits10); |
8e79468d BK |
1309 | |
1310 | __os << __x.mean() << __space << __x._M_nd; | |
1311 | ||
1312 | __os.flags(__flags); | |
1313 | __os.fill(__fill); | |
1314 | __os.precision(__precision); | |
1315 | return __os; | |
1316 | } | |
1317 | ||
1318 | template<typename _IntType, | |
1319 | typename _CharT, typename _Traits> | |
1320 | std::basic_istream<_CharT, _Traits>& | |
1321 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
1322 | poisson_distribution<_IntType>& __x) | |
1323 | { | |
1324 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
1325 | typedef typename __istream_type::ios_base __ios_base; | |
1326 | ||
1327 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
1328 | __is.flags(__ios_base::skipws); | |
1329 | ||
1330 | double __mean; | |
1331 | __is >> __mean >> __x._M_nd; | |
1332 | __x.param(typename poisson_distribution<_IntType>::param_type(__mean)); | |
1333 | ||
1334 | __is.flags(__flags); | |
1335 | return __is; | |
1336 | } | |
1337 | ||
1338 | ||
1339 | template<typename _IntType> | |
1340 | void | |
1341 | binomial_distribution<_IntType>::param_type:: | |
1342 | _M_initialize() | |
1343 | { | |
1344 | const double __p12 = _M_p <= 0.5 ? _M_p : 1.0 - _M_p; | |
1345 | ||
1346 | _M_easy = true; | |
1347 | ||
1348 | #if _GLIBCXX_USE_C99_MATH_TR1 | |
1349 | if (_M_t * __p12 >= 8) | |
1350 | { | |
1351 | _M_easy = false; | |
1352 | const double __np = std::floor(_M_t * __p12); | |
1353 | const double __pa = __np / _M_t; | |
1354 | const double __1p = 1 - __pa; | |
1355 | ||
1356 | const double __pi_4 = 0.7853981633974483096156608458198757L; | |
1357 | const double __d1x = | |
1358 | std::sqrt(__np * __1p * std::log(32 * __np | |
1359 | / (81 * __pi_4 * __1p))); | |
1360 | _M_d1 = std::round(std::max(1.0, __d1x)); | |
1361 | const double __d2x = | |
1362 | std::sqrt(__np * __1p * std::log(32 * _M_t * __1p | |
1363 | / (__pi_4 * __pa))); | |
1364 | _M_d2 = std::round(std::max(1.0, __d2x)); | |
1365 | ||
1366 | // sqrt(pi / 2) | |
1367 | const double __spi_2 = 1.2533141373155002512078826424055226L; | |
1368 | _M_s1 = std::sqrt(__np * __1p) * (1 + _M_d1 / (4 * __np)); | |
1369 | _M_s2 = std::sqrt(__np * __1p) * (1 + _M_d2 / (4 * _M_t * __1p)); | |
1370 | _M_c = 2 * _M_d1 / __np; | |
1371 | _M_a1 = std::exp(_M_c) * _M_s1 * __spi_2; | |
1372 | const double __a12 = _M_a1 + _M_s2 * __spi_2; | |
1373 | const double __s1s = _M_s1 * _M_s1; | |
1374 | _M_a123 = __a12 + (std::exp(_M_d1 / (_M_t * __1p)) | |
1375 | * 2 * __s1s / _M_d1 | |
1376 | * std::exp(-_M_d1 * _M_d1 / (2 * __s1s))); | |
1377 | const double __s2s = _M_s2 * _M_s2; | |
1378 | _M_s = (_M_a123 + 2 * __s2s / _M_d2 | |
1379 | * std::exp(-_M_d2 * _M_d2 / (2 * __s2s))); | |
1380 | _M_lf = (std::lgamma(__np + 1) | |
1381 | + std::lgamma(_M_t - __np + 1)); | |
1382 | _M_lp1p = std::log(__pa / __1p); | |
1383 | ||
1384 | _M_q = -std::log(1 - (__p12 - __pa) / __1p); | |
1385 | } | |
1386 | else | |
1387 | #endif | |
1388 | _M_q = -std::log(1 - __p12); | |
1389 | } | |
1390 | ||
1391 | template<typename _IntType> | |
1392 | template<typename _UniformRandomNumberGenerator> | |
1393 | typename binomial_distribution<_IntType>::result_type | |
1394 | binomial_distribution<_IntType>:: | |
1395 | _M_waiting(_UniformRandomNumberGenerator& __urng, _IntType __t) | |
1396 | { | |
1397 | _IntType __x = 0; | |
1398 | double __sum = 0.0; | |
1399 | __detail::_Adaptor<_UniformRandomNumberGenerator, double> | |
1400 | __aurng(__urng); | |
1401 | ||
1402 | do | |
1403 | { | |
1404 | const double __e = -std::log(__aurng()); | |
1405 | __sum += __e / (__t - __x); | |
1406 | __x += 1; | |
1407 | } | |
1408 | while (__sum <= _M_param._M_q); | |
1409 | ||
1410 | return __x - 1; | |
1411 | } | |
1412 | ||
1413 | /** | |
1414 | * A rejection algorithm when t * p >= 8 and a simple waiting time | |
1415 | * method - the second in the referenced book - otherwise. | |
1416 | * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1 | |
1417 | * is defined. | |
1418 | * | |
1419 | * Reference: | |
2a60a9f6 | 1420 | * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, |
8e79468d BK |
1421 | * New York, 1986, Ch. X, Sect. 4 (+ Errata!). |
1422 | */ | |
1423 | template<typename _IntType> | |
1424 | template<typename _UniformRandomNumberGenerator> | |
1425 | typename binomial_distribution<_IntType>::result_type | |
1426 | binomial_distribution<_IntType>:: | |
1427 | operator()(_UniformRandomNumberGenerator& __urng, | |
1428 | const param_type& __param) | |
1429 | { | |
1430 | result_type __ret; | |
1431 | const _IntType __t = __param.t(); | |
1432 | const _IntType __p = __param.p(); | |
1433 | const double __p12 = __p <= 0.5 ? __p : 1.0 - __p; | |
1434 | __detail::_Adaptor<_UniformRandomNumberGenerator, double> | |
1435 | __aurng(__urng); | |
1436 | ||
1437 | #if _GLIBCXX_USE_C99_MATH_TR1 | |
1438 | if (!__param._M_easy) | |
1439 | { | |
1440 | double __x; | |
1441 | ||
1442 | // See comments above... | |
1443 | const double __naf = | |
1444 | (1 - std::numeric_limits<double>::epsilon()) / 2; | |
1445 | const double __thr = | |
1446 | std::numeric_limits<_IntType>::max() + __naf; | |
1447 | ||
1448 | const double __np = std::floor(__t * __p12); | |
1449 | ||
1450 | // sqrt(pi / 2) | |
1451 | const double __spi_2 = 1.2533141373155002512078826424055226L; | |
1452 | const double __a1 = __param._M_a1; | |
1453 | const double __a12 = __a1 + __param._M_s2 * __spi_2; | |
1454 | const double __a123 = __param._M_a123; | |
1455 | const double __s1s = __param._M_s1 * __param._M_s1; | |
1456 | const double __s2s = __param._M_s2 * __param._M_s2; | |
1457 | ||
1458 | bool __reject; | |
1459 | do | |
1460 | { | |
1461 | const double __u = __param._M_s * __aurng(); | |
1462 | ||
1463 | double __v; | |
1464 | ||
1465 | if (__u <= __a1) | |
1466 | { | |
1467 | const double __n = _M_nd(__urng); | |
1468 | const double __y = __param._M_s1 * std::abs(__n); | |
1469 | __reject = __y >= __param._M_d1; | |
1470 | if (!__reject) | |
1471 | { | |
1472 | const double __e = -std::log(__aurng()); | |
1473 | __x = std::floor(__y); | |
1474 | __v = -__e - __n * __n / 2 + __param._M_c; | |
1475 | } | |
1476 | } | |
1477 | else if (__u <= __a12) | |
1478 | { | |
1479 | const double __n = _M_nd(__urng); | |
1480 | const double __y = __param._M_s2 * std::abs(__n); | |
1481 | __reject = __y >= __param._M_d2; | |
1482 | if (!__reject) | |
1483 | { | |
1484 | const double __e = -std::log(__aurng()); | |
1485 | __x = std::floor(-__y); | |
1486 | __v = -__e - __n * __n / 2; | |
1487 | } | |
1488 | } | |
1489 | else if (__u <= __a123) | |
1490 | { | |
1491 | const double __e1 = -std::log(__aurng()); | |
1492 | const double __e2 = -std::log(__aurng()); | |
1493 | ||
1494 | const double __y = __param._M_d1 | |
1495 | + 2 * __s1s * __e1 / __param._M_d1; | |
1496 | __x = std::floor(__y); | |
1497 | __v = (-__e2 + __param._M_d1 * (1 / (__t - __np) | |
1498 | -__y / (2 * __s1s))); | |
1499 | __reject = false; | |
1500 | } | |
1501 | else | |
1502 | { | |
1503 | const double __e1 = -std::log(__aurng()); | |
1504 | const double __e2 = -std::log(__aurng()); | |
1505 | ||
1506 | const double __y = __param._M_d2 | |
1507 | + 2 * __s2s * __e1 / __param._M_d2; | |
1508 | __x = std::floor(-__y); | |
1509 | __v = -__e2 - __param._M_d2 * __y / (2 * __s2s); | |
1510 | __reject = false; | |
1511 | } | |
1512 | ||
1513 | __reject = __reject || __x < -__np || __x > __t - __np; | |
1514 | if (!__reject) | |
1515 | { | |
1516 | const double __lfx = | |
1517 | std::lgamma(__np + __x + 1) | |
1518 | + std::lgamma(__t - (__np + __x) + 1); | |
1519 | __reject = __v > __param._M_lf - __lfx | |
1520 | + __x * __param._M_lp1p; | |
1521 | } | |
1522 | ||
1523 | __reject |= __x + __np >= __thr; | |
1524 | } | |
1525 | while (__reject); | |
1526 | ||
1527 | __x += __np + __naf; | |
1528 | ||
1529 | const _IntType __z = _M_waiting(__urng, __t - _IntType(__x)); | |
1530 | __ret = _IntType(__x) + __z; | |
1531 | } | |
1532 | else | |
1533 | #endif | |
1534 | __ret = _M_waiting(__urng, __t); | |
1535 | ||
1536 | if (__p12 != __p) | |
1537 | __ret = __t - __ret; | |
1538 | return __ret; | |
1539 | } | |
1540 | ||
1541 | template<typename _IntType, | |
1542 | typename _CharT, typename _Traits> | |
1543 | std::basic_ostream<_CharT, _Traits>& | |
1544 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
1545 | const binomial_distribution<_IntType>& __x) | |
1546 | { | |
1547 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
1548 | typedef typename __ostream_type::ios_base __ios_base; | |
1549 | ||
1550 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
1551 | const _CharT __fill = __os.fill(); | |
1552 | const std::streamsize __precision = __os.precision(); | |
1553 | const _CharT __space = __os.widen(' '); | |
1554 | __os.flags(__ios_base::scientific | __ios_base::left); | |
1555 | __os.fill(__space); | |
7703dc47 | 1556 | __os.precision(std::numeric_limits<double>::max_digits10); |
8e79468d BK |
1557 | |
1558 | __os << __x.t() << __space << __x.p() | |
1559 | << __space << __x._M_nd; | |
1560 | ||
1561 | __os.flags(__flags); | |
1562 | __os.fill(__fill); | |
1563 | __os.precision(__precision); | |
1564 | return __os; | |
1565 | } | |
1566 | ||
1567 | template<typename _IntType, | |
1568 | typename _CharT, typename _Traits> | |
1569 | std::basic_istream<_CharT, _Traits>& | |
1570 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
1571 | binomial_distribution<_IntType>& __x) | |
1572 | { | |
1573 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
1574 | typedef typename __istream_type::ios_base __ios_base; | |
1575 | ||
1576 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
1577 | __is.flags(__ios_base::dec | __ios_base::skipws); | |
1578 | ||
1579 | _IntType __t; | |
1580 | double __p; | |
1581 | __is >> __t >> __p >> __x._M_nd; | |
1582 | __x.param(typename binomial_distribution<_IntType>:: | |
1583 | param_type(__t, __p)); | |
1584 | ||
1585 | __is.flags(__flags); | |
1586 | return __is; | |
1587 | } | |
1588 | ||
1589 | ||
1590 | template<typename _RealType, typename _CharT, typename _Traits> | |
1591 | std::basic_ostream<_CharT, _Traits>& | |
1592 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
1593 | const exponential_distribution<_RealType>& __x) | |
1594 | { | |
1595 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
1596 | typedef typename __ostream_type::ios_base __ios_base; | |
1597 | ||
1598 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
1599 | const _CharT __fill = __os.fill(); | |
1600 | const std::streamsize __precision = __os.precision(); | |
1601 | __os.flags(__ios_base::scientific | __ios_base::left); | |
1602 | __os.fill(__os.widen(' ')); | |
7703dc47 | 1603 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
8e79468d BK |
1604 | |
1605 | __os << __x.lambda(); | |
1606 | ||
1607 | __os.flags(__flags); | |
1608 | __os.fill(__fill); | |
1609 | __os.precision(__precision); | |
1610 | return __os; | |
1611 | } | |
1612 | ||
1613 | template<typename _RealType, typename _CharT, typename _Traits> | |
1614 | std::basic_istream<_CharT, _Traits>& | |
1615 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
1616 | exponential_distribution<_RealType>& __x) | |
1617 | { | |
1618 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
1619 | typedef typename __istream_type::ios_base __ios_base; | |
1620 | ||
1621 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
1622 | __is.flags(__ios_base::dec | __ios_base::skipws); | |
1623 | ||
1624 | _RealType __lambda; | |
1625 | __is >> __lambda; | |
1626 | __x.param(typename exponential_distribution<_RealType>:: | |
1627 | param_type(__lambda)); | |
1628 | ||
1629 | __is.flags(__flags); | |
1630 | return __is; | |
1631 | } | |
1632 | ||
1633 | ||
8e79468d BK |
1634 | /** |
1635 | * Polar method due to Marsaglia. | |
1636 | * | |
2a60a9f6 | 1637 | * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, |
8e79468d BK |
1638 | * New York, 1986, Ch. V, Sect. 4.4. |
1639 | */ | |
1640 | template<typename _RealType> | |
1641 | template<typename _UniformRandomNumberGenerator> | |
1642 | typename normal_distribution<_RealType>::result_type | |
1643 | normal_distribution<_RealType>:: | |
1644 | operator()(_UniformRandomNumberGenerator& __urng, | |
1645 | const param_type& __param) | |
1646 | { | |
1647 | result_type __ret; | |
1648 | __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> | |
1649 | __aurng(__urng); | |
1650 | ||
1651 | if (_M_saved_available) | |
1652 | { | |
1653 | _M_saved_available = false; | |
1654 | __ret = _M_saved; | |
1655 | } | |
1656 | else | |
1657 | { | |
1658 | result_type __x, __y, __r2; | |
1659 | do | |
1660 | { | |
1661 | __x = result_type(2.0) * __aurng() - 1.0; | |
1662 | __y = result_type(2.0) * __aurng() - 1.0; | |
1663 | __r2 = __x * __x + __y * __y; | |
1664 | } | |
1665 | while (__r2 > 1.0 || __r2 == 0.0); | |
1666 | ||
1667 | const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2); | |
1668 | _M_saved = __x * __mult; | |
1669 | _M_saved_available = true; | |
1670 | __ret = __y * __mult; | |
1671 | } | |
1672 | ||
1673 | __ret = __ret * __param.stddev() + __param.mean(); | |
1674 | return __ret; | |
1675 | } | |
1676 | ||
fc05e1ba PC |
1677 | template<typename _RealType> |
1678 | bool | |
1679 | operator==(const std::normal_distribution<_RealType>& __d1, | |
1680 | const std::normal_distribution<_RealType>& __d2) | |
1681 | { | |
1682 | if (__d1._M_param == __d2._M_param | |
1683 | && __d1._M_saved_available == __d2._M_saved_available) | |
1684 | { | |
1685 | if (__d1._M_saved_available | |
1686 | && __d1._M_saved == __d2._M_saved) | |
1687 | return true; | |
1688 | else if(!__d1._M_saved_available) | |
1689 | return true; | |
1690 | else | |
1691 | return false; | |
1692 | } | |
1693 | else | |
1694 | return false; | |
1695 | } | |
1696 | ||
8e79468d BK |
1697 | template<typename _RealType, typename _CharT, typename _Traits> |
1698 | std::basic_ostream<_CharT, _Traits>& | |
1699 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
1700 | const normal_distribution<_RealType>& __x) | |
1701 | { | |
1702 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
1703 | typedef typename __ostream_type::ios_base __ios_base; | |
1704 | ||
1705 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
1706 | const _CharT __fill = __os.fill(); | |
1707 | const std::streamsize __precision = __os.precision(); | |
1708 | const _CharT __space = __os.widen(' '); | |
1709 | __os.flags(__ios_base::scientific | __ios_base::left); | |
1710 | __os.fill(__space); | |
7703dc47 | 1711 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
8e79468d BK |
1712 | |
1713 | __os << __x.mean() << __space << __x.stddev() | |
1714 | << __space << __x._M_saved_available; | |
1715 | if (__x._M_saved_available) | |
1716 | __os << __space << __x._M_saved; | |
1717 | ||
1718 | __os.flags(__flags); | |
1719 | __os.fill(__fill); | |
1720 | __os.precision(__precision); | |
1721 | return __os; | |
1722 | } | |
1723 | ||
1724 | template<typename _RealType, typename _CharT, typename _Traits> | |
1725 | std::basic_istream<_CharT, _Traits>& | |
1726 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
1727 | normal_distribution<_RealType>& __x) | |
1728 | { | |
1729 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
1730 | typedef typename __istream_type::ios_base __ios_base; | |
1731 | ||
1732 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
1733 | __is.flags(__ios_base::dec | __ios_base::skipws); | |
1734 | ||
1735 | double __mean, __stddev; | |
1736 | __is >> __mean >> __stddev | |
1737 | >> __x._M_saved_available; | |
1738 | if (__x._M_saved_available) | |
1739 | __is >> __x._M_saved; | |
1740 | __x.param(typename normal_distribution<_RealType>:: | |
1741 | param_type(__mean, __stddev)); | |
1742 | ||
1743 | __is.flags(__flags); | |
1744 | return __is; | |
1745 | } | |
1746 | ||
1747 | ||
8e79468d BK |
1748 | template<typename _RealType, typename _CharT, typename _Traits> |
1749 | std::basic_ostream<_CharT, _Traits>& | |
1750 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
1751 | const lognormal_distribution<_RealType>& __x) | |
1752 | { | |
1753 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
1754 | typedef typename __ostream_type::ios_base __ios_base; | |
1755 | ||
1756 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
1757 | const _CharT __fill = __os.fill(); | |
1758 | const std::streamsize __precision = __os.precision(); | |
1759 | const _CharT __space = __os.widen(' '); | |
1760 | __os.flags(__ios_base::scientific | __ios_base::left); | |
1761 | __os.fill(__space); | |
7703dc47 | 1762 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
8e79468d | 1763 | |
b01630bb PC |
1764 | __os << __x.m() << __space << __x.s() |
1765 | << __space << __x._M_nd; | |
8e79468d BK |
1766 | |
1767 | __os.flags(__flags); | |
1768 | __os.fill(__fill); | |
1769 | __os.precision(__precision); | |
1770 | return __os; | |
1771 | } | |
1772 | ||
1773 | template<typename _RealType, typename _CharT, typename _Traits> | |
1774 | std::basic_istream<_CharT, _Traits>& | |
1775 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
1776 | lognormal_distribution<_RealType>& __x) | |
1777 | { | |
1778 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
1779 | typedef typename __istream_type::ios_base __ios_base; | |
1780 | ||
1781 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
1782 | __is.flags(__ios_base::dec | __ios_base::skipws); | |
1783 | ||
1784 | _RealType __m, __s; | |
b01630bb | 1785 | __is >> __m >> __s >> __x._M_nd; |
8e79468d BK |
1786 | __x.param(typename lognormal_distribution<_RealType>:: |
1787 | param_type(__m, __s)); | |
1788 | ||
1789 | __is.flags(__flags); | |
1790 | return __is; | |
1791 | } | |
1792 | ||
1793 | ||
8e79468d BK |
1794 | template<typename _RealType, typename _CharT, typename _Traits> |
1795 | std::basic_ostream<_CharT, _Traits>& | |
1796 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
1797 | const chi_squared_distribution<_RealType>& __x) | |
1798 | { | |
1799 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
1800 | typedef typename __ostream_type::ios_base __ios_base; | |
1801 | ||
1802 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
1803 | const _CharT __fill = __os.fill(); | |
1804 | const std::streamsize __precision = __os.precision(); | |
1805 | const _CharT __space = __os.widen(' '); | |
1806 | __os.flags(__ios_base::scientific | __ios_base::left); | |
1807 | __os.fill(__space); | |
7703dc47 | 1808 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
8e79468d | 1809 | |
f9b09dec | 1810 | __os << __x.n() << __space << __x._M_gd; |
8e79468d BK |
1811 | |
1812 | __os.flags(__flags); | |
1813 | __os.fill(__fill); | |
1814 | __os.precision(__precision); | |
1815 | return __os; | |
1816 | } | |
1817 | ||
1818 | template<typename _RealType, typename _CharT, typename _Traits> | |
1819 | std::basic_istream<_CharT, _Traits>& | |
1820 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
1821 | chi_squared_distribution<_RealType>& __x) | |
1822 | { | |
1823 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
1824 | typedef typename __istream_type::ios_base __ios_base; | |
1825 | ||
1826 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
1827 | __is.flags(__ios_base::dec | __ios_base::skipws); | |
1828 | ||
1829 | _RealType __n; | |
f9b09dec | 1830 | __is >> __n >> __x._M_gd; |
8e79468d BK |
1831 | __x.param(typename chi_squared_distribution<_RealType>:: |
1832 | param_type(__n)); | |
1833 | ||
1834 | __is.flags(__flags); | |
1835 | return __is; | |
1836 | } | |
1837 | ||
1838 | ||
1839 | template<typename _RealType> | |
1840 | template<typename _UniformRandomNumberGenerator> | |
1841 | typename cauchy_distribution<_RealType>::result_type | |
1842 | cauchy_distribution<_RealType>:: | |
1843 | operator()(_UniformRandomNumberGenerator& __urng, | |
1844 | const param_type& __p) | |
1845 | { | |
1846 | __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> | |
1847 | __aurng(__urng); | |
1848 | _RealType __u; | |
1849 | do | |
e1a02963 | 1850 | __u = __aurng(); |
8e79468d BK |
1851 | while (__u == 0.5); |
1852 | ||
e1a02963 PC |
1853 | const _RealType __pi = 3.1415926535897932384626433832795029L; |
1854 | return __p.a() + __p.b() * std::tan(__pi * __u); | |
8e79468d BK |
1855 | } |
1856 | ||
1857 | template<typename _RealType, typename _CharT, typename _Traits> | |
1858 | std::basic_ostream<_CharT, _Traits>& | |
1859 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
1860 | const cauchy_distribution<_RealType>& __x) | |
1861 | { | |
1862 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
1863 | typedef typename __ostream_type::ios_base __ios_base; | |
1864 | ||
1865 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
1866 | const _CharT __fill = __os.fill(); | |
1867 | const std::streamsize __precision = __os.precision(); | |
1868 | const _CharT __space = __os.widen(' '); | |
1869 | __os.flags(__ios_base::scientific | __ios_base::left); | |
1870 | __os.fill(__space); | |
7703dc47 | 1871 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
8e79468d BK |
1872 | |
1873 | __os << __x.a() << __space << __x.b(); | |
1874 | ||
1875 | __os.flags(__flags); | |
1876 | __os.fill(__fill); | |
1877 | __os.precision(__precision); | |
1878 | return __os; | |
1879 | } | |
1880 | ||
1881 | template<typename _RealType, typename _CharT, typename _Traits> | |
1882 | std::basic_istream<_CharT, _Traits>& | |
1883 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
1884 | cauchy_distribution<_RealType>& __x) | |
1885 | { | |
1886 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
1887 | typedef typename __istream_type::ios_base __ios_base; | |
1888 | ||
1889 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
1890 | __is.flags(__ios_base::dec | __ios_base::skipws); | |
1891 | ||
1892 | _RealType __a, __b; | |
1893 | __is >> __a >> __b; | |
1894 | __x.param(typename cauchy_distribution<_RealType>:: | |
1895 | param_type(__a, __b)); | |
1896 | ||
1897 | __is.flags(__flags); | |
1898 | return __is; | |
1899 | } | |
1900 | ||
1901 | ||
8e79468d BK |
1902 | template<typename _RealType, typename _CharT, typename _Traits> |
1903 | std::basic_ostream<_CharT, _Traits>& | |
1904 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
1905 | const fisher_f_distribution<_RealType>& __x) | |
1906 | { | |
1907 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
1908 | typedef typename __ostream_type::ios_base __ios_base; | |
1909 | ||
1910 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
1911 | const _CharT __fill = __os.fill(); | |
1912 | const std::streamsize __precision = __os.precision(); | |
1913 | const _CharT __space = __os.widen(' '); | |
1914 | __os.flags(__ios_base::scientific | __ios_base::left); | |
1915 | __os.fill(__space); | |
7703dc47 | 1916 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
8e79468d | 1917 | |
f9b09dec PC |
1918 | __os << __x.m() << __space << __x.n() |
1919 | << __space << __x._M_gd_x << __space << __x._M_gd_y; | |
8e79468d BK |
1920 | |
1921 | __os.flags(__flags); | |
1922 | __os.fill(__fill); | |
1923 | __os.precision(__precision); | |
1924 | return __os; | |
1925 | } | |
1926 | ||
1927 | template<typename _RealType, typename _CharT, typename _Traits> | |
1928 | std::basic_istream<_CharT, _Traits>& | |
1929 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
1930 | fisher_f_distribution<_RealType>& __x) | |
1931 | { | |
1932 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
1933 | typedef typename __istream_type::ios_base __ios_base; | |
1934 | ||
1935 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
1936 | __is.flags(__ios_base::dec | __ios_base::skipws); | |
1937 | ||
1938 | _RealType __m, __n; | |
f9b09dec | 1939 | __is >> __m >> __n >> __x._M_gd_x >> __x._M_gd_y; |
8e79468d BK |
1940 | __x.param(typename fisher_f_distribution<_RealType>:: |
1941 | param_type(__m, __n)); | |
1942 | ||
1943 | __is.flags(__flags); | |
1944 | return __is; | |
1945 | } | |
1946 | ||
1947 | ||
8e79468d BK |
1948 | template<typename _RealType, typename _CharT, typename _Traits> |
1949 | std::basic_ostream<_CharT, _Traits>& | |
1950 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
1951 | const student_t_distribution<_RealType>& __x) | |
1952 | { | |
1953 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
1954 | typedef typename __ostream_type::ios_base __ios_base; | |
1955 | ||
1956 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
1957 | const _CharT __fill = __os.fill(); | |
1958 | const std::streamsize __precision = __os.precision(); | |
1959 | const _CharT __space = __os.widen(' '); | |
1960 | __os.flags(__ios_base::scientific | __ios_base::left); | |
1961 | __os.fill(__space); | |
7703dc47 | 1962 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
8e79468d | 1963 | |
f9b09dec | 1964 | __os << __x.n() << __space << __x._M_nd << __space << __x._M_gd; |
8e79468d BK |
1965 | |
1966 | __os.flags(__flags); | |
1967 | __os.fill(__fill); | |
1968 | __os.precision(__precision); | |
1969 | return __os; | |
1970 | } | |
1971 | ||
1972 | template<typename _RealType, typename _CharT, typename _Traits> | |
1973 | std::basic_istream<_CharT, _Traits>& | |
1974 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
1975 | student_t_distribution<_RealType>& __x) | |
1976 | { | |
1977 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
1978 | typedef typename __istream_type::ios_base __ios_base; | |
1979 | ||
1980 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
1981 | __is.flags(__ios_base::dec | __ios_base::skipws); | |
1982 | ||
1983 | _RealType __n; | |
f9b09dec | 1984 | __is >> __n >> __x._M_nd >> __x._M_gd; |
8e79468d BK |
1985 | __x.param(typename student_t_distribution<_RealType>::param_type(__n)); |
1986 | ||
1987 | __is.flags(__flags); | |
1988 | return __is; | |
1989 | } | |
1990 | ||
1991 | ||
1992 | template<typename _RealType> | |
1993 | void | |
1994 | gamma_distribution<_RealType>::param_type:: | |
1995 | _M_initialize() | |
1996 | { | |
b01630bb PC |
1997 | _M_malpha = _M_alpha < 1.0 ? _M_alpha + _RealType(1.0) : _M_alpha; |
1998 | ||
1999 | const _RealType __a1 = _M_malpha - _RealType(1.0) / _RealType(3.0); | |
2000 | _M_a2 = _RealType(1.0) / std::sqrt(_RealType(9.0) * __a1); | |
8e79468d BK |
2001 | } |
2002 | ||
2003 | /** | |
b01630bb PC |
2004 | * Marsaglia, G. and Tsang, W. W. |
2005 | * "A Simple Method for Generating Gamma Variables" | |
2006 | * ACM Transactions on Mathematical Software, 26, 3, 363-372, 2000. | |
8e79468d BK |
2007 | */ |
2008 | template<typename _RealType> | |
2009 | template<typename _UniformRandomNumberGenerator> | |
2010 | typename gamma_distribution<_RealType>::result_type | |
2011 | gamma_distribution<_RealType>:: | |
2012 | operator()(_UniformRandomNumberGenerator& __urng, | |
2013 | const param_type& __param) | |
2014 | { | |
8e79468d BK |
2015 | __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> |
2016 | __aurng(__urng); | |
2017 | ||
b01630bb PC |
2018 | result_type __u, __v, __n; |
2019 | const result_type __a1 = (__param._M_malpha | |
2020 | - _RealType(1.0) / _RealType(3.0)); | |
8e79468d | 2021 | |
b01630bb PC |
2022 | do |
2023 | { | |
8e79468d BK |
2024 | do |
2025 | { | |
b01630bb PC |
2026 | __n = _M_nd(__urng); |
2027 | __v = result_type(1.0) + __param._M_a2 * __n; | |
8e79468d | 2028 | } |
b01630bb PC |
2029 | while (__v <= 0.0); |
2030 | ||
2031 | __v = __v * __v * __v; | |
2032 | __u = __aurng(); | |
8e79468d | 2033 | } |
b01630bb PC |
2034 | while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n |
2035 | && (std::log(__u) > (0.5 * __n * __n + __a1 | |
2036 | * (1.0 - __v + std::log(__v))))); | |
2037 | ||
2038 | if (__param.alpha() == __param._M_malpha) | |
2039 | return __a1 * __v * __param.beta(); | |
8e79468d BK |
2040 | else |
2041 | { | |
8e79468d | 2042 | do |
b01630bb PC |
2043 | __u = __aurng(); |
2044 | while (__u == 0.0); | |
2045 | ||
2046 | return (std::pow(__u, result_type(1.0) / __param.alpha()) | |
2047 | * __a1 * __v * __param.beta()); | |
8e79468d | 2048 | } |
8e79468d BK |
2049 | } |
2050 | ||
2051 | template<typename _RealType, typename _CharT, typename _Traits> | |
2052 | std::basic_ostream<_CharT, _Traits>& | |
2053 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
2054 | const gamma_distribution<_RealType>& __x) | |
2055 | { | |
2056 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
2057 | typedef typename __ostream_type::ios_base __ios_base; | |
2058 | ||
2059 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
2060 | const _CharT __fill = __os.fill(); | |
2061 | const std::streamsize __precision = __os.precision(); | |
2062 | const _CharT __space = __os.widen(' '); | |
2063 | __os.flags(__ios_base::scientific | __ios_base::left); | |
2064 | __os.fill(__space); | |
7703dc47 | 2065 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
8e79468d | 2066 | |
b01630bb PC |
2067 | __os << __x.alpha() << __space << __x.beta() |
2068 | << __space << __x._M_nd; | |
8e79468d BK |
2069 | |
2070 | __os.flags(__flags); | |
2071 | __os.fill(__fill); | |
2072 | __os.precision(__precision); | |
2073 | return __os; | |
2074 | } | |
2075 | ||
2076 | template<typename _RealType, typename _CharT, typename _Traits> | |
2077 | std::basic_istream<_CharT, _Traits>& | |
2078 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
2079 | gamma_distribution<_RealType>& __x) | |
2080 | { | |
2081 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
2082 | typedef typename __istream_type::ios_base __ios_base; | |
2083 | ||
2084 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
2085 | __is.flags(__ios_base::dec | __ios_base::skipws); | |
2086 | ||
ed2807f4 | 2087 | _RealType __alpha_val, __beta_val; |
b01630bb | 2088 | __is >> __alpha_val >> __beta_val >> __x._M_nd; |
8e79468d | 2089 | __x.param(typename gamma_distribution<_RealType>:: |
ed2807f4 | 2090 | param_type(__alpha_val, __beta_val)); |
8e79468d BK |
2091 | |
2092 | __is.flags(__flags); | |
2093 | return __is; | |
2094 | } | |
2095 | ||
2096 | ||
b01630bb PC |
2097 | template<typename _RealType> |
2098 | template<typename _UniformRandomNumberGenerator> | |
2099 | typename weibull_distribution<_RealType>::result_type | |
2100 | weibull_distribution<_RealType>:: | |
2101 | operator()(_UniformRandomNumberGenerator& __urng, | |
2102 | const param_type& __p) | |
2103 | { | |
2104 | __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> | |
2105 | __aurng(__urng); | |
2106 | return __p.b() * std::pow(-std::log(__aurng()), | |
2107 | result_type(1) / __p.a()); | |
2108 | } | |
2109 | ||
8e79468d BK |
2110 | template<typename _RealType, typename _CharT, typename _Traits> |
2111 | std::basic_ostream<_CharT, _Traits>& | |
2112 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
2113 | const weibull_distribution<_RealType>& __x) | |
2114 | { | |
2115 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
2116 | typedef typename __ostream_type::ios_base __ios_base; | |
2117 | ||
2118 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
2119 | const _CharT __fill = __os.fill(); | |
2120 | const std::streamsize __precision = __os.precision(); | |
2121 | const _CharT __space = __os.widen(' '); | |
2122 | __os.flags(__ios_base::scientific | __ios_base::left); | |
2123 | __os.fill(__space); | |
7703dc47 | 2124 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
8e79468d BK |
2125 | |
2126 | __os << __x.a() << __space << __x.b(); | |
2127 | ||
2128 | __os.flags(__flags); | |
2129 | __os.fill(__fill); | |
2130 | __os.precision(__precision); | |
2131 | return __os; | |
2132 | } | |
2133 | ||
2134 | template<typename _RealType, typename _CharT, typename _Traits> | |
2135 | std::basic_istream<_CharT, _Traits>& | |
2136 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
2137 | weibull_distribution<_RealType>& __x) | |
2138 | { | |
2139 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
2140 | typedef typename __istream_type::ios_base __ios_base; | |
2141 | ||
2142 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
2143 | __is.flags(__ios_base::dec | __ios_base::skipws); | |
2144 | ||
2145 | _RealType __a, __b; | |
2146 | __is >> __a >> __b; | |
2147 | __x.param(typename weibull_distribution<_RealType>:: | |
2148 | param_type(__a, __b)); | |
2149 | ||
2150 | __is.flags(__flags); | |
2151 | return __is; | |
2152 | } | |
2153 | ||
2154 | ||
2155 | template<typename _RealType> | |
2156 | template<typename _UniformRandomNumberGenerator> | |
2157 | typename extreme_value_distribution<_RealType>::result_type | |
2158 | extreme_value_distribution<_RealType>:: | |
2159 | operator()(_UniformRandomNumberGenerator& __urng, | |
2160 | const param_type& __p) | |
2161 | { | |
2162 | __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> | |
2163 | __aurng(__urng); | |
2164 | return __p.a() - __p.b() * std::log(-std::log(__aurng())); | |
2165 | } | |
2166 | ||
2167 | template<typename _RealType, typename _CharT, typename _Traits> | |
2168 | std::basic_ostream<_CharT, _Traits>& | |
2169 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
2170 | const extreme_value_distribution<_RealType>& __x) | |
2171 | { | |
2172 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
2173 | typedef typename __ostream_type::ios_base __ios_base; | |
2174 | ||
2175 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
2176 | const _CharT __fill = __os.fill(); | |
2177 | const std::streamsize __precision = __os.precision(); | |
2178 | const _CharT __space = __os.widen(' '); | |
2179 | __os.flags(__ios_base::scientific | __ios_base::left); | |
2180 | __os.fill(__space); | |
7703dc47 | 2181 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
8e79468d BK |
2182 | |
2183 | __os << __x.a() << __space << __x.b(); | |
2184 | ||
2185 | __os.flags(__flags); | |
2186 | __os.fill(__fill); | |
2187 | __os.precision(__precision); | |
2188 | return __os; | |
2189 | } | |
2190 | ||
2191 | template<typename _RealType, typename _CharT, typename _Traits> | |
2192 | std::basic_istream<_CharT, _Traits>& | |
2193 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
2194 | extreme_value_distribution<_RealType>& __x) | |
2195 | { | |
2196 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
2197 | typedef typename __istream_type::ios_base __ios_base; | |
2198 | ||
2199 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
2200 | __is.flags(__ios_base::dec | __ios_base::skipws); | |
2201 | ||
2202 | _RealType __a, __b; | |
2203 | __is >> __a >> __b; | |
2204 | __x.param(typename extreme_value_distribution<_RealType>:: | |
2205 | param_type(__a, __b)); | |
2206 | ||
2207 | __is.flags(__flags); | |
2208 | return __is; | |
2209 | } | |
2210 | ||
2211 | ||
2212 | template<typename _IntType> | |
2213 | void | |
2214 | discrete_distribution<_IntType>::param_type:: | |
2215 | _M_initialize() | |
2216 | { | |
2217 | if (_M_prob.size() < 2) | |
2218 | { | |
2219 | _M_prob.clear(); | |
2220 | _M_prob.push_back(1.0); | |
2221 | return; | |
2222 | } | |
2223 | ||
f8dd9e0d PC |
2224 | const double __sum = std::accumulate(_M_prob.begin(), |
2225 | _M_prob.end(), 0.0); | |
2226 | // Now normalize the probabilites. | |
247d8075 PC |
2227 | __detail::__transform(_M_prob.begin(), _M_prob.end(), _M_prob.begin(), |
2228 | std::bind2nd(std::divides<double>(), __sum)); | |
f8dd9e0d PC |
2229 | // Accumulate partial sums. |
2230 | _M_cp.reserve(_M_prob.size()); | |
8e79468d BK |
2231 | std::partial_sum(_M_prob.begin(), _M_prob.end(), |
2232 | std::back_inserter(_M_cp)); | |
f8dd9e0d | 2233 | // Make sure the last cumulative probability is one. |
8e79468d BK |
2234 | _M_cp[_M_cp.size() - 1] = 1.0; |
2235 | } | |
2236 | ||
2237 | template<typename _IntType> | |
2238 | template<typename _Func> | |
2239 | discrete_distribution<_IntType>::param_type:: | |
f8dd9e0d | 2240 | param_type(size_t __nw, double __xmin, double __xmax, _Func __fw) |
8e79468d BK |
2241 | : _M_prob(), _M_cp() |
2242 | { | |
f8dd9e0d PC |
2243 | const size_t __n = __nw == 0 ? 1 : __nw; |
2244 | const double __delta = (__xmax - __xmin) / __n; | |
2245 | ||
2246 | _M_prob.reserve(__n); | |
2247 | for (size_t __k = 0; __k < __nw; ++__k) | |
2248 | _M_prob.push_back(__fw(__xmin + __k * __delta + 0.5 * __delta)); | |
8e79468d BK |
2249 | |
2250 | _M_initialize(); | |
2251 | } | |
2252 | ||
2253 | template<typename _IntType> | |
2254 | template<typename _UniformRandomNumberGenerator> | |
2255 | typename discrete_distribution<_IntType>::result_type | |
2256 | discrete_distribution<_IntType>:: | |
2257 | operator()(_UniformRandomNumberGenerator& __urng, | |
2258 | const param_type& __param) | |
2259 | { | |
9b88236b | 2260 | __detail::_Adaptor<_UniformRandomNumberGenerator, double> |
8e79468d BK |
2261 | __aurng(__urng); |
2262 | ||
2263 | const double __p = __aurng(); | |
2264 | auto __pos = std::lower_bound(__param._M_cp.begin(), | |
2265 | __param._M_cp.end(), __p); | |
8e79468d | 2266 | |
f8dd9e0d | 2267 | return __pos - __param._M_cp.begin(); |
8e79468d BK |
2268 | } |
2269 | ||
2270 | template<typename _IntType, typename _CharT, typename _Traits> | |
2271 | std::basic_ostream<_CharT, _Traits>& | |
2272 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
2273 | const discrete_distribution<_IntType>& __x) | |
2274 | { | |
2275 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
2276 | typedef typename __ostream_type::ios_base __ios_base; | |
2277 | ||
2278 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
2279 | const _CharT __fill = __os.fill(); | |
2280 | const std::streamsize __precision = __os.precision(); | |
2281 | const _CharT __space = __os.widen(' '); | |
2282 | __os.flags(__ios_base::scientific | __ios_base::left); | |
2283 | __os.fill(__space); | |
7703dc47 | 2284 | __os.precision(std::numeric_limits<double>::max_digits10); |
8e79468d BK |
2285 | |
2286 | std::vector<double> __prob = __x.probabilities(); | |
2287 | __os << __prob.size(); | |
2288 | for (auto __dit = __prob.begin(); __dit != __prob.end(); ++__dit) | |
2289 | __os << __space << *__dit; | |
2290 | ||
2291 | __os.flags(__flags); | |
2292 | __os.fill(__fill); | |
2293 | __os.precision(__precision); | |
2294 | return __os; | |
2295 | } | |
2296 | ||
2297 | template<typename _IntType, typename _CharT, typename _Traits> | |
2298 | std::basic_istream<_CharT, _Traits>& | |
2299 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
2300 | discrete_distribution<_IntType>& __x) | |
2301 | { | |
2302 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
2303 | typedef typename __istream_type::ios_base __ios_base; | |
2304 | ||
2305 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
2306 | __is.flags(__ios_base::dec | __ios_base::skipws); | |
2307 | ||
2308 | size_t __n; | |
2309 | __is >> __n; | |
2310 | ||
2311 | std::vector<double> __prob_vec; | |
f8dd9e0d | 2312 | __prob_vec.reserve(__n); |
8e79468d BK |
2313 | for (; __n != 0; --__n) |
2314 | { | |
2315 | double __prob; | |
2316 | __is >> __prob; | |
2317 | __prob_vec.push_back(__prob); | |
2318 | } | |
2319 | ||
2320 | __x.param(typename discrete_distribution<_IntType>:: | |
2321 | param_type(__prob_vec.begin(), __prob_vec.end())); | |
2322 | ||
2323 | __is.flags(__flags); | |
2324 | return __is; | |
2325 | } | |
2326 | ||
2327 | ||
2328 | template<typename _RealType> | |
2329 | void | |
2330 | piecewise_constant_distribution<_RealType>::param_type:: | |
2331 | _M_initialize() | |
2332 | { | |
2333 | if (_M_int.size() < 2) | |
2334 | { | |
2335 | _M_int.clear(); | |
f8dd9e0d | 2336 | _M_int.reserve(2); |
8e79468d BK |
2337 | _M_int.push_back(_RealType(0)); |
2338 | _M_int.push_back(_RealType(1)); | |
2339 | ||
2340 | _M_den.clear(); | |
2341 | _M_den.push_back(1.0); | |
2342 | ||
2343 | return; | |
2344 | } | |
2345 | ||
f8dd9e0d PC |
2346 | const double __sum = std::accumulate(_M_den.begin(), |
2347 | _M_den.end(), 0.0); | |
8e79468d | 2348 | |
247d8075 PC |
2349 | __detail::__transform(_M_den.begin(), _M_den.end(), _M_den.begin(), |
2350 | std::bind2nd(std::divides<double>(), __sum)); | |
f8dd9e0d PC |
2351 | |
2352 | _M_cp.reserve(_M_den.size()); | |
2353 | std::partial_sum(_M_den.begin(), _M_den.end(), | |
2354 | std::back_inserter(_M_cp)); | |
2355 | ||
2356 | // Make sure the last cumulative probability is one. | |
8e79468d | 2357 | _M_cp[_M_cp.size() - 1] = 1.0; |
f8dd9e0d PC |
2358 | |
2359 | for (size_t __k = 0; __k < _M_den.size(); ++__k) | |
2360 | _M_den[__k] /= _M_int[__k + 1] - _M_int[__k]; | |
8e79468d BK |
2361 | } |
2362 | ||
8e79468d BK |
2363 | template<typename _RealType> |
2364 | template<typename _InputIteratorB, typename _InputIteratorW> | |
2365 | piecewise_constant_distribution<_RealType>::param_type:: | |
2366 | param_type(_InputIteratorB __bbegin, | |
2367 | _InputIteratorB __bend, | |
2368 | _InputIteratorW __wbegin) | |
2369 | : _M_int(), _M_den(), _M_cp() | |
2370 | { | |
f8dd9e0d | 2371 | if (__bbegin != __bend) |
8e79468d | 2372 | { |
f8dd9e0d | 2373 | for (;;) |
8e79468d | 2374 | { |
f8dd9e0d PC |
2375 | _M_int.push_back(*__bbegin); |
2376 | ++__bbegin; | |
2377 | if (__bbegin == __bend) | |
2378 | break; | |
2379 | ||
8e79468d BK |
2380 | _M_den.push_back(*__wbegin); |
2381 | ++__wbegin; | |
2382 | } | |
2383 | } | |
8e79468d BK |
2384 | |
2385 | _M_initialize(); | |
2386 | } | |
2387 | ||
2388 | template<typename _RealType> | |
2389 | template<typename _Func> | |
2390 | piecewise_constant_distribution<_RealType>::param_type:: | |
f8dd9e0d | 2391 | param_type(initializer_list<_RealType> __bl, _Func __fw) |
8e79468d BK |
2392 | : _M_int(), _M_den(), _M_cp() |
2393 | { | |
f8dd9e0d PC |
2394 | _M_int.reserve(__bl.size()); |
2395 | for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter) | |
8e79468d BK |
2396 | _M_int.push_back(*__biter); |
2397 | ||
f8dd9e0d PC |
2398 | _M_den.reserve(_M_int.size() - 1); |
2399 | for (size_t __k = 0; __k < _M_int.size() - 1; ++__k) | |
2400 | _M_den.push_back(__fw(0.5 * (_M_int[__k + 1] + _M_int[__k]))); | |
8e79468d BK |
2401 | |
2402 | _M_initialize(); | |
2403 | } | |
2404 | ||
2405 | template<typename _RealType> | |
2406 | template<typename _Func> | |
2407 | piecewise_constant_distribution<_RealType>::param_type:: | |
b01630bb | 2408 | param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw) |
8e79468d BK |
2409 | : _M_int(), _M_den(), _M_cp() |
2410 | { | |
f8dd9e0d PC |
2411 | const size_t __n = __nw == 0 ? 1 : __nw; |
2412 | const _RealType __delta = (__xmax - __xmin) / __n; | |
2413 | ||
2414 | _M_int.reserve(__n + 1); | |
2415 | for (size_t __k = 0; __k <= __nw; ++__k) | |
2416 | _M_int.push_back(__xmin + __k * __delta); | |
2417 | ||
2418 | _M_den.reserve(__n); | |
2419 | for (size_t __k = 0; __k < __nw; ++__k) | |
2420 | _M_den.push_back(__fw(_M_int[__k] + 0.5 * __delta)); | |
8e79468d BK |
2421 | |
2422 | _M_initialize(); | |
2423 | } | |
2424 | ||
2425 | template<typename _RealType> | |
2426 | template<typename _UniformRandomNumberGenerator> | |
2427 | typename piecewise_constant_distribution<_RealType>::result_type | |
2428 | piecewise_constant_distribution<_RealType>:: | |
2429 | operator()(_UniformRandomNumberGenerator& __urng, | |
2430 | const param_type& __param) | |
2431 | { | |
9b88236b | 2432 | __detail::_Adaptor<_UniformRandomNumberGenerator, double> |
8e79468d BK |
2433 | __aurng(__urng); |
2434 | ||
2435 | const double __p = __aurng(); | |
2436 | auto __pos = std::lower_bound(__param._M_cp.begin(), | |
2437 | __param._M_cp.end(), __p); | |
2438 | const size_t __i = __pos - __param._M_cp.begin(); | |
2439 | ||
f8dd9e0d PC |
2440 | const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0; |
2441 | ||
2442 | return __param._M_int[__i] + (__p - __pref) / __param._M_den[__i]; | |
8e79468d BK |
2443 | } |
2444 | ||
2445 | template<typename _RealType, typename _CharT, typename _Traits> | |
2446 | std::basic_ostream<_CharT, _Traits>& | |
2447 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
2448 | const piecewise_constant_distribution<_RealType>& __x) | |
2449 | { | |
2450 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
2451 | typedef typename __ostream_type::ios_base __ios_base; | |
2452 | ||
2453 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
2454 | const _CharT __fill = __os.fill(); | |
2455 | const std::streamsize __precision = __os.precision(); | |
2456 | const _CharT __space = __os.widen(' '); | |
2457 | __os.flags(__ios_base::scientific | __ios_base::left); | |
2458 | __os.fill(__space); | |
7703dc47 | 2459 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
8e79468d BK |
2460 | |
2461 | std::vector<_RealType> __int = __x.intervals(); | |
2462 | __os << __int.size() - 1; | |
2463 | ||
2464 | for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit) | |
2465 | __os << __space << *__xit; | |
2466 | ||
2467 | std::vector<double> __den = __x.densities(); | |
2468 | for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit) | |
2469 | __os << __space << *__dit; | |
2470 | ||
2471 | __os.flags(__flags); | |
2472 | __os.fill(__fill); | |
2473 | __os.precision(__precision); | |
2474 | return __os; | |
2475 | } | |
2476 | ||
2477 | template<typename _RealType, typename _CharT, typename _Traits> | |
2478 | std::basic_istream<_CharT, _Traits>& | |
2479 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
2480 | piecewise_constant_distribution<_RealType>& __x) | |
2481 | { | |
2482 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
2483 | typedef typename __istream_type::ios_base __ios_base; | |
2484 | ||
2485 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
2486 | __is.flags(__ios_base::dec | __ios_base::skipws); | |
2487 | ||
2488 | size_t __n; | |
2489 | __is >> __n; | |
2490 | ||
2491 | std::vector<_RealType> __int_vec; | |
f8dd9e0d | 2492 | __int_vec.reserve(__n + 1); |
8e79468d BK |
2493 | for (size_t __i = 0; __i <= __n; ++__i) |
2494 | { | |
2495 | _RealType __int; | |
2496 | __is >> __int; | |
2497 | __int_vec.push_back(__int); | |
2498 | } | |
2499 | ||
2500 | std::vector<double> __den_vec; | |
f8dd9e0d | 2501 | __den_vec.reserve(__n); |
8e79468d BK |
2502 | for (size_t __i = 0; __i < __n; ++__i) |
2503 | { | |
2504 | double __den; | |
2505 | __is >> __den; | |
2506 | __den_vec.push_back(__den); | |
2507 | } | |
2508 | ||
2509 | __x.param(typename piecewise_constant_distribution<_RealType>:: | |
2510 | param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin())); | |
2511 | ||
2512 | __is.flags(__flags); | |
2513 | return __is; | |
2514 | } | |
2515 | ||
2516 | ||
2517 | template<typename _RealType> | |
2518 | void | |
2519 | piecewise_linear_distribution<_RealType>::param_type:: | |
2520 | _M_initialize() | |
2521 | { | |
2522 | if (_M_int.size() < 2) | |
2523 | { | |
2524 | _M_int.clear(); | |
f8dd9e0d | 2525 | _M_int.reserve(2); |
8e79468d BK |
2526 | _M_int.push_back(_RealType(0)); |
2527 | _M_int.push_back(_RealType(1)); | |
2528 | ||
2529 | _M_den.clear(); | |
f8dd9e0d | 2530 | _M_den.reserve(2); |
8e79468d BK |
2531 | _M_den.push_back(1.0); |
2532 | _M_den.push_back(1.0); | |
2533 | ||
2534 | return; | |
2535 | } | |
2536 | ||
2537 | double __sum = 0.0; | |
f8dd9e0d PC |
2538 | _M_cp.reserve(_M_int.size() - 1); |
2539 | _M_m.reserve(_M_int.size() - 1); | |
2540 | for (size_t __k = 0; __k < _M_int.size() - 1; ++__k) | |
8e79468d | 2541 | { |
f8dd9e0d PC |
2542 | const _RealType __delta = _M_int[__k + 1] - _M_int[__k]; |
2543 | __sum += 0.5 * (_M_den[__k + 1] + _M_den[__k]) * __delta; | |
8e79468d | 2544 | _M_cp.push_back(__sum); |
f8dd9e0d | 2545 | _M_m.push_back((_M_den[__k + 1] - _M_den[__k]) / __delta); |
8e79468d BK |
2546 | } |
2547 | ||
2548 | // Now normalize the densities... | |
247d8075 PC |
2549 | __detail::__transform(_M_den.begin(), _M_den.end(), _M_den.begin(), |
2550 | std::bind2nd(std::divides<double>(), __sum)); | |
8e79468d | 2551 | // ... and partial sums... |
247d8075 PC |
2552 | __detail::__transform(_M_cp.begin(), _M_cp.end(), _M_cp.begin(), |
2553 | std::bind2nd(std::divides<double>(), __sum)); | |
8e79468d | 2554 | // ... and slopes. |
247d8075 PC |
2555 | __detail::__transform(_M_m.begin(), _M_m.end(), _M_m.begin(), |
2556 | std::bind2nd(std::divides<double>(), __sum)); | |
8e79468d BK |
2557 | // Make sure the last cumulative probablility is one. |
2558 | _M_cp[_M_cp.size() - 1] = 1.0; | |
f8dd9e0d | 2559 | } |
8e79468d BK |
2560 | |
2561 | template<typename _RealType> | |
2562 | template<typename _InputIteratorB, typename _InputIteratorW> | |
2563 | piecewise_linear_distribution<_RealType>::param_type:: | |
2564 | param_type(_InputIteratorB __bbegin, | |
2565 | _InputIteratorB __bend, | |
2566 | _InputIteratorW __wbegin) | |
2567 | : _M_int(), _M_den(), _M_cp(), _M_m() | |
2568 | { | |
2569 | for (; __bbegin != __bend; ++__bbegin, ++__wbegin) | |
2570 | { | |
2571 | _M_int.push_back(*__bbegin); | |
2572 | _M_den.push_back(*__wbegin); | |
2573 | } | |
2574 | ||
2575 | _M_initialize(); | |
2576 | } | |
2577 | ||
2578 | template<typename _RealType> | |
2579 | template<typename _Func> | |
2580 | piecewise_linear_distribution<_RealType>::param_type:: | |
f8dd9e0d | 2581 | param_type(initializer_list<_RealType> __bl, _Func __fw) |
8e79468d BK |
2582 | : _M_int(), _M_den(), _M_cp(), _M_m() |
2583 | { | |
f8dd9e0d PC |
2584 | _M_int.reserve(__bl.size()); |
2585 | _M_den.reserve(__bl.size()); | |
2586 | for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter) | |
8e79468d BK |
2587 | { |
2588 | _M_int.push_back(*__biter); | |
2589 | _M_den.push_back(__fw(*__biter)); | |
2590 | } | |
2591 | ||
2592 | _M_initialize(); | |
2593 | } | |
2594 | ||
2595 | template<typename _RealType> | |
2596 | template<typename _Func> | |
2597 | piecewise_linear_distribution<_RealType>::param_type:: | |
f8dd9e0d | 2598 | param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw) |
8e79468d BK |
2599 | : _M_int(), _M_den(), _M_cp(), _M_m() |
2600 | { | |
f8dd9e0d PC |
2601 | const size_t __n = __nw == 0 ? 1 : __nw; |
2602 | const _RealType __delta = (__xmax - __xmin) / __n; | |
2603 | ||
2604 | _M_int.reserve(__n + 1); | |
2605 | _M_den.reserve(__n + 1); | |
2606 | for (size_t __k = 0; __k <= __nw; ++__k) | |
8e79468d | 2607 | { |
f8dd9e0d PC |
2608 | _M_int.push_back(__xmin + __k * __delta); |
2609 | _M_den.push_back(__fw(_M_int[__k] + __delta)); | |
8e79468d BK |
2610 | } |
2611 | ||
2612 | _M_initialize(); | |
2613 | } | |
2614 | ||
2615 | template<typename _RealType> | |
2616 | template<typename _UniformRandomNumberGenerator> | |
2617 | typename piecewise_linear_distribution<_RealType>::result_type | |
2618 | piecewise_linear_distribution<_RealType>:: | |
2619 | operator()(_UniformRandomNumberGenerator& __urng, | |
2620 | const param_type& __param) | |
2621 | { | |
9b88236b | 2622 | __detail::_Adaptor<_UniformRandomNumberGenerator, double> |
8e79468d BK |
2623 | __aurng(__urng); |
2624 | ||
2625 | const double __p = __aurng(); | |
d3a73504 PC |
2626 | if (__param._M_m.empty()) |
2627 | return __p; | |
2628 | ||
8e79468d BK |
2629 | auto __pos = std::lower_bound(__param._M_cp.begin(), |
2630 | __param._M_cp.end(), __p); | |
2631 | const size_t __i = __pos - __param._M_cp.begin(); | |
f8dd9e0d PC |
2632 | |
2633 | const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0; | |
2634 | ||
8e79468d BK |
2635 | const double __a = 0.5 * __param._M_m[__i]; |
2636 | const double __b = __param._M_den[__i]; | |
f8dd9e0d PC |
2637 | const double __cm = __p - __pref; |
2638 | ||
2639 | _RealType __x = __param._M_int[__i]; | |
2640 | if (__a == 0) | |
2641 | __x += __cm / __b; | |
2642 | else | |
2643 | { | |
2644 | const double __d = __b * __b + 4.0 * __a * __cm; | |
2645 | __x += 0.5 * (std::sqrt(__d) - __b) / __a; | |
2646 | } | |
8e79468d | 2647 | |
f8dd9e0d | 2648 | return __x; |
8e79468d BK |
2649 | } |
2650 | ||
2651 | template<typename _RealType, typename _CharT, typename _Traits> | |
2652 | std::basic_ostream<_CharT, _Traits>& | |
2653 | operator<<(std::basic_ostream<_CharT, _Traits>& __os, | |
2654 | const piecewise_linear_distribution<_RealType>& __x) | |
2655 | { | |
2656 | typedef std::basic_ostream<_CharT, _Traits> __ostream_type; | |
2657 | typedef typename __ostream_type::ios_base __ios_base; | |
2658 | ||
2659 | const typename __ios_base::fmtflags __flags = __os.flags(); | |
2660 | const _CharT __fill = __os.fill(); | |
2661 | const std::streamsize __precision = __os.precision(); | |
2662 | const _CharT __space = __os.widen(' '); | |
2663 | __os.flags(__ios_base::scientific | __ios_base::left); | |
2664 | __os.fill(__space); | |
7703dc47 | 2665 | __os.precision(std::numeric_limits<_RealType>::max_digits10); |
8e79468d BK |
2666 | |
2667 | std::vector<_RealType> __int = __x.intervals(); | |
2668 | __os << __int.size() - 1; | |
2669 | ||
2670 | for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit) | |
2671 | __os << __space << *__xit; | |
2672 | ||
2673 | std::vector<double> __den = __x.densities(); | |
2674 | for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit) | |
2675 | __os << __space << *__dit; | |
2676 | ||
2677 | __os.flags(__flags); | |
2678 | __os.fill(__fill); | |
2679 | __os.precision(__precision); | |
2680 | return __os; | |
2681 | } | |
2682 | ||
2683 | template<typename _RealType, typename _CharT, typename _Traits> | |
2684 | std::basic_istream<_CharT, _Traits>& | |
2685 | operator>>(std::basic_istream<_CharT, _Traits>& __is, | |
2686 | piecewise_linear_distribution<_RealType>& __x) | |
2687 | { | |
2688 | typedef std::basic_istream<_CharT, _Traits> __istream_type; | |
2689 | typedef typename __istream_type::ios_base __ios_base; | |
2690 | ||
2691 | const typename __ios_base::fmtflags __flags = __is.flags(); | |
2692 | __is.flags(__ios_base::dec | __ios_base::skipws); | |
2693 | ||
2694 | size_t __n; | |
2695 | __is >> __n; | |
2696 | ||
2697 | std::vector<_RealType> __int_vec; | |
f8dd9e0d | 2698 | __int_vec.reserve(__n + 1); |
8e79468d BK |
2699 | for (size_t __i = 0; __i <= __n; ++__i) |
2700 | { | |
2701 | _RealType __int; | |
2702 | __is >> __int; | |
2703 | __int_vec.push_back(__int); | |
2704 | } | |
2705 | ||
2706 | std::vector<double> __den_vec; | |
f8dd9e0d | 2707 | __den_vec.reserve(__n + 1); |
8e79468d BK |
2708 | for (size_t __i = 0; __i <= __n; ++__i) |
2709 | { | |
2710 | double __den; | |
2711 | __is >> __den; | |
2712 | __den_vec.push_back(__den); | |
2713 | } | |
2714 | ||
2715 | __x.param(typename piecewise_linear_distribution<_RealType>:: | |
2716 | param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin())); | |
2717 | ||
2718 | __is.flags(__flags); | |
2719 | return __is; | |
2720 | } | |
2721 | ||
2722 | ||
2723 | template<typename _IntType> | |
2724 | seed_seq::seed_seq(std::initializer_list<_IntType> __il) | |
2725 | { | |
2726 | for (auto __iter = __il.begin(); __iter != __il.end(); ++__iter) | |
b94f4bef | 2727 | _M_v.push_back(__detail::__mod<result_type, |
8e79468d BK |
2728 | __detail::_Shift<result_type, 32>::__value>(*__iter)); |
2729 | } | |
2730 | ||
2731 | template<typename _InputIterator> | |
2732 | seed_seq::seed_seq(_InputIterator __begin, _InputIterator __end) | |
2733 | { | |
2734 | for (_InputIterator __iter = __begin; __iter != __end; ++__iter) | |
b94f4bef | 2735 | _M_v.push_back(__detail::__mod<result_type, |
8e79468d BK |
2736 | __detail::_Shift<result_type, 32>::__value>(*__iter)); |
2737 | } | |
2738 | ||
2739 | template<typename _RandomAccessIterator> | |
2740 | void | |
2741 | seed_seq::generate(_RandomAccessIterator __begin, | |
2742 | _RandomAccessIterator __end) | |
2743 | { | |
2744 | typedef typename iterator_traits<_RandomAccessIterator>::value_type | |
6855fe45 | 2745 | _Type; |
8e79468d BK |
2746 | |
2747 | if (__begin == __end) | |
2748 | return; | |
2749 | ||
b94f4bef | 2750 | std::fill(__begin, __end, _Type(0x8b8b8b8bu)); |
8e79468d BK |
2751 | |
2752 | const size_t __n = __end - __begin; | |
2753 | const size_t __s = _M_v.size(); | |
2754 | const size_t __t = (__n >= 623) ? 11 | |
2755 | : (__n >= 68) ? 7 | |
2756 | : (__n >= 39) ? 5 | |
2757 | : (__n >= 7) ? 3 | |
2758 | : (__n - 1) / 2; | |
2759 | const size_t __p = (__n - __t) / 2; | |
2760 | const size_t __q = __p + __t; | |
2761 | const size_t __m = std::max(__s + 1, __n); | |
2762 | ||
2763 | for (size_t __k = 0; __k < __m; ++__k) | |
2764 | { | |
6855fe45 PC |
2765 | _Type __arg = (__begin[__k % __n] |
2766 | ^ __begin[(__k + __p) % __n] | |
2767 | ^ __begin[(__k - 1) % __n]); | |
2768 | _Type __r1 = __arg ^ (__arg << 27); | |
b94f4bef PC |
2769 | __r1 = __detail::__mod<_Type, __detail::_Shift<_Type, 32>::__value, |
2770 | 1664525u, 0u>(__r1); | |
6855fe45 | 2771 | _Type __r2 = __r1; |
8e79468d BK |
2772 | if (__k == 0) |
2773 | __r2 += __s; | |
2774 | else if (__k <= __s) | |
2775 | __r2 += __k % __n + _M_v[__k - 1]; | |
2776 | else | |
2777 | __r2 += __k % __n; | |
b94f4bef PC |
2778 | __r2 = __detail::__mod<_Type, |
2779 | __detail::_Shift<_Type, 32>::__value>(__r2); | |
8e79468d BK |
2780 | __begin[(__k + __p) % __n] += __r1; |
2781 | __begin[(__k + __q) % __n] += __r2; | |
2782 | __begin[__k % __n] = __r2; | |
2783 | } | |
2784 | ||
2785 | for (size_t __k = __m; __k < __m + __n; ++__k) | |
2786 | { | |
6855fe45 PC |
2787 | _Type __arg = (__begin[__k % __n] |
2788 | + __begin[(__k + __p) % __n] | |
2789 | + __begin[(__k - 1) % __n]); | |
2790 | _Type __r3 = __arg ^ (__arg << 27); | |
b94f4bef PC |
2791 | __r3 = __detail::__mod<_Type, __detail::_Shift<_Type, 32>::__value, |
2792 | 1566083941u, 0u>(__r3); | |
6855fe45 | 2793 | _Type __r4 = __r3 - __k % __n; |
b94f4bef PC |
2794 | __r4 = __detail::__mod<_Type, |
2795 | __detail::_Shift<_Type, 32>::__value>(__r4); | |
8e79468d BK |
2796 | __begin[(__k + __p) % __n] ^= __r4; |
2797 | __begin[(__k + __q) % __n] ^= __r3; | |
2798 | __begin[__k % __n] = __r4; | |
2799 | } | |
2800 | } | |
2801 | ||
2802 | template<typename _RealType, size_t __bits, | |
2803 | typename _UniformRandomNumberGenerator> | |
2804 | _RealType | |
2805 | generate_canonical(_UniformRandomNumberGenerator& __urng) | |
2806 | { | |
2807 | const size_t __b | |
2808 | = std::min(static_cast<size_t>(std::numeric_limits<_RealType>::digits), | |
2809 | __bits); | |
2810 | const long double __r = static_cast<long double>(__urng.max()) | |
2811 | - static_cast<long double>(__urng.min()) + 1.0L; | |
b01630bb | 2812 | const size_t __log2r = std::log(__r) / std::log(2.0L); |
8e79468d BK |
2813 | size_t __k = std::max<size_t>(1UL, (__b + __log2r - 1UL) / __log2r); |
2814 | _RealType __sum = _RealType(0); | |
2815 | _RealType __tmp = _RealType(1); | |
2816 | for (; __k != 0; --__k) | |
2817 | { | |
2818 | __sum += _RealType(__urng() - __urng.min()) * __tmp; | |
2819 | __tmp *= __r; | |
2820 | } | |
2821 | return __sum / __tmp; | |
2822 | } | |
8e79468d | 2823 | } |
06f29237 PC |
2824 | |
2825 | #endif |