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/usr/include/c++/11/bits/random.tcc
$ cat -n /usr/include/c++/11/bits/random.tcc 1 // random number generation (out of line) -*- C++ -*- 2 3 // Copyright (C) 2009-2021 Free Software Foundation, Inc. 4 // 5 // This file is part of the GNU ISO C++ Library. This library is free 6 // software; you can redistribute it and/or modify it under the 7 // terms of the GNU General Public License as published by the 8 // Free Software Foundation; either version 3, or (at your option) 9 // any later version. 10 11 // This library is distributed in the hope that it will be useful, 12 // but WITHOUT ANY WARRANTY; without even the implied warranty of 13 // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 14 // GNU General Public License for more details. 15 16 // Under Section 7 of GPL version 3, you are granted additional 17 // permissions described in the GCC Runtime Library Exception, version 18 // 3.1, as published by the Free Software Foundation. 19 20 // You should have received a copy of the GNU General Public License and 21 // a copy of the GCC Runtime Library Exception along with this program; 22 // see the files COPYING3 and COPYING.RUNTIME respectively. If not, see 23 // <http://www.gnu.org/licenses/>. 24 25 /** @file bits/random.tcc 26 * This is an internal header file, included by other library headers. 27 * Do not attempt to use it directly. @headername{random} 28 */ 29 30 #ifndef _RANDOM_TCC 31 #define _RANDOM_TCC 1 32 33 #include <numeric> // std::accumulate and std::partial_sum 34 35 namespace std _GLIBCXX_VISIBILITY(default) 36 { 37 _GLIBCXX_BEGIN_NAMESPACE_VERSION 38 39 /// @cond undocumented 40 // (Further) implementation-space details. 41 namespace __detail 42 { 43 // General case for x = (ax + c) mod m -- use Schrage's algorithm 44 // to avoid integer overflow. 45 // 46 // Preconditions: a > 0, m > 0. 47 // 48 // Note: only works correctly for __m % __a < __m / __a. 49 template<typename _Tp, _Tp __m, _Tp __a, _Tp __c> 50 _Tp 51 _Mod<_Tp, __m, __a, __c, false, true>:: 52 __calc(_Tp __x) 53 { 54 if (__a == 1) 55 __x %= __m; 56 else 57 { 58 static const _Tp __q = __m / __a; 59 static const _Tp __r = __m % __a; 60 61 _Tp __t1 = __a * (__x % __q); 62 _Tp __t2 = __r * (__x / __q); 63 if (__t1 >= __t2) 64 __x = __t1 - __t2; 65 else 66 __x = __m - __t2 + __t1; 67 } 68 69 if (__c != 0) 70 { 71 const _Tp __d = __m - __x; 72 if (__d > __c) 73 __x += __c; 74 else 75 __x = __c - __d; 76 } 77 return __x; 78 } 79 80 template<typename _InputIterator, typename _OutputIterator, 81 typename _Tp> 82 _OutputIterator 83 __normalize(_InputIterator __first, _InputIterator __last, 84 _OutputIterator __result, const _Tp& __factor) 85 { 86 for (; __first != __last; ++__first, ++__result) 87 *__result = *__first / __factor; 88 return __result; 89 } 90 91 } // namespace __detail 92 /// @endcond 93 94 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> 95 constexpr _UIntType 96 linear_congruential_engine<_UIntType, __a, __c, __m>::multiplier; 97 98 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> 99 constexpr _UIntType 100 linear_congruential_engine<_UIntType, __a, __c, __m>::increment; 101 102 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> 103 constexpr _UIntType 104 linear_congruential_engine<_UIntType, __a, __c, __m>::modulus; 105 106 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> 107 constexpr _UIntType 108 linear_congruential_engine<_UIntType, __a, __c, __m>::default_seed; 109 110 /** 111 * Seeds the LCR with integral value @p __s, adjusted so that the 112 * ring identity is never a member of the convergence set. 113 */ 114 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> 115 void 116 linear_congruential_engine<_UIntType, __a, __c, __m>:: 117 seed(result_type __s) 118 { 119 if ((__detail::__mod<_UIntType, __m>(__c) == 0) 120 && (__detail::__mod<_UIntType, __m>(__s) == 0)) 121 _M_x = 1; 122 else 123 _M_x = __detail::__mod<_UIntType, __m>(__s); 124 } 125 126 /** 127 * Seeds the LCR engine with a value generated by @p __q. 128 */ 129 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> 130 template<typename _Sseq> 131 auto 132 linear_congruential_engine<_UIntType, __a, __c, __m>:: 133 seed(_Sseq& __q) 134 -> _If_seed_seq<_Sseq> 135 { 136 const _UIntType __k0 = __m == 0 ? std::numeric_limits<_UIntType>::digits 137 : std::__lg(__m); 138 const _UIntType __k = (__k0 + 31) / 32; 139 uint_least32_t __arr[__k + 3]; 140 __q.generate(__arr + 0, __arr + __k + 3); 141 _UIntType __factor = 1u; 142 _UIntType __sum = 0u; 143 for (size_t __j = 0; __j < __k; ++__j) 144 { 145 __sum += __arr[__j + 3] * __factor; 146 __factor *= __detail::_Shift<_UIntType, 32>::__value; 147 } 148 seed(__sum); 149 } 150 151 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m, 152 typename _CharT, typename _Traits> 153 std::basic_ostream<_CharT, _Traits>& 154 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 155 const linear_congruential_engine<_UIntType, 156 __a, __c, __m>& __lcr) 157 { 158 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 159 160 const typename __ios_base::fmtflags __flags = __os.flags(); 161 const _CharT __fill = __os.fill(); 162 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 163 __os.fill(__os.widen(' ')); 164 165 __os << __lcr._M_x; 166 167 __os.flags(__flags); 168 __os.fill(__fill); 169 return __os; 170 } 171 172 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m, 173 typename _CharT, typename _Traits> 174 std::basic_istream<_CharT, _Traits>& 175 operator>>(std::basic_istream<_CharT, _Traits>& __is, 176 linear_congruential_engine<_UIntType, __a, __c, __m>& __lcr) 177 { 178 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 179 180 const typename __ios_base::fmtflags __flags = __is.flags(); 181 __is.flags(__ios_base::dec); 182 183 __is >> __lcr._M_x; 184 185 __is.flags(__flags); 186 return __is; 187 } 188 189 190 template<typename _UIntType, 191 size_t __w, size_t __n, size_t __m, size_t __r, 192 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 193 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 194 _UIntType __f> 195 constexpr size_t 196 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 197 __s, __b, __t, __c, __l, __f>::word_size; 198 199 template<typename _UIntType, 200 size_t __w, size_t __n, size_t __m, size_t __r, 201 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 202 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 203 _UIntType __f> 204 constexpr size_t 205 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 206 __s, __b, __t, __c, __l, __f>::state_size; 207 208 template<typename _UIntType, 209 size_t __w, size_t __n, size_t __m, size_t __r, 210 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 211 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 212 _UIntType __f> 213 constexpr size_t 214 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 215 __s, __b, __t, __c, __l, __f>::shift_size; 216 217 template<typename _UIntType, 218 size_t __w, size_t __n, size_t __m, size_t __r, 219 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 220 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 221 _UIntType __f> 222 constexpr size_t 223 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 224 __s, __b, __t, __c, __l, __f>::mask_bits; 225 226 template<typename _UIntType, 227 size_t __w, size_t __n, size_t __m, size_t __r, 228 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 229 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 230 _UIntType __f> 231 constexpr _UIntType 232 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 233 __s, __b, __t, __c, __l, __f>::xor_mask; 234 235 template<typename _UIntType, 236 size_t __w, size_t __n, size_t __m, size_t __r, 237 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 238 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 239 _UIntType __f> 240 constexpr size_t 241 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 242 __s, __b, __t, __c, __l, __f>::tempering_u; 243 244 template<typename _UIntType, 245 size_t __w, size_t __n, size_t __m, size_t __r, 246 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 247 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 248 _UIntType __f> 249 constexpr _UIntType 250 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 251 __s, __b, __t, __c, __l, __f>::tempering_d; 252 253 template<typename _UIntType, 254 size_t __w, size_t __n, size_t __m, size_t __r, 255 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 256 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 257 _UIntType __f> 258 constexpr size_t 259 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 260 __s, __b, __t, __c, __l, __f>::tempering_s; 261 262 template<typename _UIntType, 263 size_t __w, size_t __n, size_t __m, size_t __r, 264 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 265 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 266 _UIntType __f> 267 constexpr _UIntType 268 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 269 __s, __b, __t, __c, __l, __f>::tempering_b; 270 271 template<typename _UIntType, 272 size_t __w, size_t __n, size_t __m, size_t __r, 273 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 274 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 275 _UIntType __f> 276 constexpr size_t 277 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 278 __s, __b, __t, __c, __l, __f>::tempering_t; 279 280 template<typename _UIntType, 281 size_t __w, size_t __n, size_t __m, size_t __r, 282 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 283 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 284 _UIntType __f> 285 constexpr _UIntType 286 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 287 __s, __b, __t, __c, __l, __f>::tempering_c; 288 289 template<typename _UIntType, 290 size_t __w, size_t __n, size_t __m, size_t __r, 291 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 292 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 293 _UIntType __f> 294 constexpr size_t 295 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 296 __s, __b, __t, __c, __l, __f>::tempering_l; 297 298 template<typename _UIntType, 299 size_t __w, size_t __n, size_t __m, size_t __r, 300 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 301 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 302 _UIntType __f> 303 constexpr _UIntType 304 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 305 __s, __b, __t, __c, __l, __f>:: 306 initialization_multiplier; 307 308 template<typename _UIntType, 309 size_t __w, size_t __n, size_t __m, size_t __r, 310 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 311 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 312 _UIntType __f> 313 constexpr _UIntType 314 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 315 __s, __b, __t, __c, __l, __f>::default_seed; 316 317 template<typename _UIntType, 318 size_t __w, size_t __n, size_t __m, size_t __r, 319 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 320 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 321 _UIntType __f> 322 void 323 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 324 __s, __b, __t, __c, __l, __f>:: 325 seed(result_type __sd) 326 { 327 _M_x[0] = __detail::__mod<_UIntType, 328 __detail::_Shift<_UIntType, __w>::__value>(__sd); 329 330 for (size_t __i = 1; __i < state_size; ++__i) 331 { 332 _UIntType __x = _M_x[__i - 1]; 333 __x ^= __x >> (__w - 2); 334 __x *= __f; 335 __x += __detail::__mod<_UIntType, __n>(__i); 336 _M_x[__i] = __detail::__mod<_UIntType, 337 __detail::_Shift<_UIntType, __w>::__value>(__x); 338 } 339 _M_p = state_size; 340 } 341 342 template<typename _UIntType, 343 size_t __w, size_t __n, size_t __m, size_t __r, 344 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 345 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 346 _UIntType __f> 347 template<typename _Sseq> 348 auto 349 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 350 __s, __b, __t, __c, __l, __f>:: 351 seed(_Sseq& __q) 352 -> _If_seed_seq<_Sseq> 353 { 354 const _UIntType __upper_mask = (~_UIntType()) << __r; 355 const size_t __k = (__w + 31) / 32; 356 uint_least32_t __arr[__n * __k]; 357 __q.generate(__arr + 0, __arr + __n * __k); 358 359 bool __zero = true; 360 for (size_t __i = 0; __i < state_size; ++__i) 361 { 362 _UIntType __factor = 1u; 363 _UIntType __sum = 0u; 364 for (size_t __j = 0; __j < __k; ++__j) 365 { 366 __sum += __arr[__k * __i + __j] * __factor; 367 __factor *= __detail::_Shift<_UIntType, 32>::__value; 368 } 369 _M_x[__i] = __detail::__mod<_UIntType, 370 __detail::_Shift<_UIntType, __w>::__value>(__sum); 371 372 if (__zero) 373 { 374 if (__i == 0) 375 { 376 if ((_M_x[0] & __upper_mask) != 0u) 377 __zero = false; 378 } 379 else if (_M_x[__i] != 0u) 380 __zero = false; 381 } 382 } 383 if (__zero) 384 _M_x[0] = __detail::_Shift<_UIntType, __w - 1>::__value; 385 _M_p = state_size; 386 } 387 388 template<typename _UIntType, size_t __w, 389 size_t __n, size_t __m, size_t __r, 390 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 391 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 392 _UIntType __f> 393 void 394 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 395 __s, __b, __t, __c, __l, __f>:: 396 _M_gen_rand(void) 397 { 398 const _UIntType __upper_mask = (~_UIntType()) << __r; 399 const _UIntType __lower_mask = ~__upper_mask; 400 401 for (size_t __k = 0; __k < (__n - __m); ++__k) 402 { 403 _UIntType __y = ((_M_x[__k] & __upper_mask) 404 | (_M_x[__k + 1] & __lower_mask)); 405 _M_x[__k] = (_M_x[__k + __m] ^ (__y >> 1) 406 ^ ((__y & 0x01) ? __a : 0)); 407 } 408 409 for (size_t __k = (__n - __m); __k < (__n - 1); ++__k) 410 { 411 _UIntType __y = ((_M_x[__k] & __upper_mask) 412 | (_M_x[__k + 1] & __lower_mask)); 413 _M_x[__k] = (_M_x[__k + (__m - __n)] ^ (__y >> 1) 414 ^ ((__y & 0x01) ? __a : 0)); 415 } 416 417 _UIntType __y = ((_M_x[__n - 1] & __upper_mask) 418 | (_M_x[0] & __lower_mask)); 419 _M_x[__n - 1] = (_M_x[__m - 1] ^ (__y >> 1) 420 ^ ((__y & 0x01) ? __a : 0)); 421 _M_p = 0; 422 } 423 424 template<typename _UIntType, size_t __w, 425 size_t __n, size_t __m, size_t __r, 426 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 427 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 428 _UIntType __f> 429 void 430 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 431 __s, __b, __t, __c, __l, __f>:: 432 discard(unsigned long long __z) 433 { 434 while (__z > state_size - _M_p) 435 { 436 __z -= state_size - _M_p; 437 _M_gen_rand(); 438 } 439 _M_p += __z; 440 } 441 442 template<typename _UIntType, size_t __w, 443 size_t __n, size_t __m, size_t __r, 444 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 445 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 446 _UIntType __f> 447 typename 448 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 449 __s, __b, __t, __c, __l, __f>::result_type 450 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 451 __s, __b, __t, __c, __l, __f>:: 452 operator()() 453 { 454 // Reload the vector - cost is O(n) amortized over n calls. 455 if (_M_p >= state_size) 456 _M_gen_rand(); 457 458 // Calculate o(x(i)). 459 result_type __z = _M_x[_M_p++]; 460 __z ^= (__z >> __u) & __d; 461 __z ^= (__z << __s) & __b; 462 __z ^= (__z << __t) & __c; 463 __z ^= (__z >> __l); 464 465 return __z; 466 } 467 468 template<typename _UIntType, size_t __w, 469 size_t __n, size_t __m, size_t __r, 470 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 471 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 472 _UIntType __f, typename _CharT, typename _Traits> 473 std::basic_ostream<_CharT, _Traits>& 474 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 475 const mersenne_twister_engine<_UIntType, __w, __n, __m, 476 __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x) 477 { 478 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 479 480 const typename __ios_base::fmtflags __flags = __os.flags(); 481 const _CharT __fill = __os.fill(); 482 const _CharT __space = __os.widen(' '); 483 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 484 __os.fill(__space); 485 486 for (size_t __i = 0; __i < __n; ++__i) 487 __os << __x._M_x[__i] << __space; 488 __os << __x._M_p; 489 490 __os.flags(__flags); 491 __os.fill(__fill); 492 return __os; 493 } 494 495 template<typename _UIntType, size_t __w, 496 size_t __n, size_t __m, size_t __r, 497 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 498 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 499 _UIntType __f, typename _CharT, typename _Traits> 500 std::basic_istream<_CharT, _Traits>& 501 operator>>(std::basic_istream<_CharT, _Traits>& __is, 502 mersenne_twister_engine<_UIntType, __w, __n, __m, 503 __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x) 504 { 505 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 506 507 const typename __ios_base::fmtflags __flags = __is.flags(); 508 __is.flags(__ios_base::dec | __ios_base::skipws); 509 510 for (size_t __i = 0; __i < __n; ++__i) 511 __is >> __x._M_x[__i]; 512 __is >> __x._M_p; 513 514 __is.flags(__flags); 515 return __is; 516 } 517 518 519 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 520 constexpr size_t 521 subtract_with_carry_engine<_UIntType, __w, __s, __r>::word_size; 522 523 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 524 constexpr size_t 525 subtract_with_carry_engine<_UIntType, __w, __s, __r>::short_lag; 526 527 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 528 constexpr size_t 529 subtract_with_carry_engine<_UIntType, __w, __s, __r>::long_lag; 530 531 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 532 constexpr _UIntType 533 subtract_with_carry_engine<_UIntType, __w, __s, __r>::default_seed; 534 535 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 536 void 537 subtract_with_carry_engine<_UIntType, __w, __s, __r>:: 538 seed(result_type __value) 539 { 540 std::linear_congruential_engine<result_type, 40014u, 0u, 2147483563u> 541 __lcg(__value == 0u ? default_seed : __value); 542 543 const size_t __n = (__w + 31) / 32; 544 545 for (size_t __i = 0; __i < long_lag; ++__i) 546 { 547 _UIntType __sum = 0u; 548 _UIntType __factor = 1u; 549 for (size_t __j = 0; __j < __n; ++__j) 550 { 551 __sum += __detail::__mod<uint_least32_t, 552 __detail::_Shift<uint_least32_t, 32>::__value> 553 (__lcg()) * __factor; 554 __factor *= __detail::_Shift<_UIntType, 32>::__value; 555 } 556 _M_x[__i] = __detail::__mod<_UIntType, 557 __detail::_Shift<_UIntType, __w>::__value>(__sum); 558 } 559 _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0; 560 _M_p = 0; 561 } 562 563 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 564 template<typename _Sseq> 565 auto 566 subtract_with_carry_engine<_UIntType, __w, __s, __r>:: 567 seed(_Sseq& __q) 568 -> _If_seed_seq<_Sseq> 569 { 570 const size_t __k = (__w + 31) / 32; 571 uint_least32_t __arr[__r * __k]; 572 __q.generate(__arr + 0, __arr + __r * __k); 573 574 for (size_t __i = 0; __i < long_lag; ++__i) 575 { 576 _UIntType __sum = 0u; 577 _UIntType __factor = 1u; 578 for (size_t __j = 0; __j < __k; ++__j) 579 { 580 __sum += __arr[__k * __i + __j] * __factor; 581 __factor *= __detail::_Shift<_UIntType, 32>::__value; 582 } 583 _M_x[__i] = __detail::__mod<_UIntType, 584 __detail::_Shift<_UIntType, __w>::__value>(__sum); 585 } 586 _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0; 587 _M_p = 0; 588 } 589 590 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 591 typename subtract_with_carry_engine<_UIntType, __w, __s, __r>:: 592 result_type 593 subtract_with_carry_engine<_UIntType, __w, __s, __r>:: 594 operator()() 595 { 596 // Derive short lag index from current index. 597 long __ps = _M_p - short_lag; 598 if (__ps < 0) 599 __ps += long_lag; 600 601 // Calculate new x(i) without overflow or division. 602 // NB: Thanks to the requirements for _UIntType, _M_x[_M_p] + _M_carry 603 // cannot overflow. 604 _UIntType __xi; 605 if (_M_x[__ps] >= _M_x[_M_p] + _M_carry) 606 { 607 __xi = _M_x[__ps] - _M_x[_M_p] - _M_carry; 608 _M_carry = 0; 609 } 610 else 611 { 612 __xi = (__detail::_Shift<_UIntType, __w>::__value 613 - _M_x[_M_p] - _M_carry + _M_x[__ps]); 614 _M_carry = 1; 615 } 616 _M_x[_M_p] = __xi; 617 618 // Adjust current index to loop around in ring buffer. 619 if (++_M_p >= long_lag) 620 _M_p = 0; 621 622 return __xi; 623 } 624 625 template<typename _UIntType, size_t __w, size_t __s, size_t __r, 626 typename _CharT, typename _Traits> 627 std::basic_ostream<_CharT, _Traits>& 628 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 629 const subtract_with_carry_engine<_UIntType, 630 __w, __s, __r>& __x) 631 { 632 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 633 634 const typename __ios_base::fmtflags __flags = __os.flags(); 635 const _CharT __fill = __os.fill(); 636 const _CharT __space = __os.widen(' '); 637 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 638 __os.fill(__space); 639 640 for (size_t __i = 0; __i < __r; ++__i) 641 __os << __x._M_x[__i] << __space; 642 __os << __x._M_carry << __space << __x._M_p; 643 644 __os.flags(__flags); 645 __os.fill(__fill); 646 return __os; 647 } 648 649 template<typename _UIntType, size_t __w, size_t __s, size_t __r, 650 typename _CharT, typename _Traits> 651 std::basic_istream<_CharT, _Traits>& 652 operator>>(std::basic_istream<_CharT, _Traits>& __is, 653 subtract_with_carry_engine<_UIntType, __w, __s, __r>& __x) 654 { 655 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 656 657 const typename __ios_base::fmtflags __flags = __is.flags(); 658 __is.flags(__ios_base::dec | __ios_base::skipws); 659 660 for (size_t __i = 0; __i < __r; ++__i) 661 __is >> __x._M_x[__i]; 662 __is >> __x._M_carry; 663 __is >> __x._M_p; 664 665 __is.flags(__flags); 666 return __is; 667 } 668 669 670 template<typename _RandomNumberEngine, size_t __p, size_t __r> 671 constexpr size_t 672 discard_block_engine<_RandomNumberEngine, __p, __r>::block_size; 673 674 template<typename _RandomNumberEngine, size_t __p, size_t __r> 675 constexpr size_t 676 discard_block_engine<_RandomNumberEngine, __p, __r>::used_block; 677 678 template<typename _RandomNumberEngine, size_t __p, size_t __r> 679 typename discard_block_engine<_RandomNumberEngine, 680 __p, __r>::result_type 681 discard_block_engine<_RandomNumberEngine, __p, __r>:: 682 operator()() 683 { 684 if (_M_n >= used_block) 685 { 686 _M_b.discard(block_size - _M_n); 687 _M_n = 0; 688 } 689 ++_M_n; 690 return _M_b(); 691 } 692 693 template<typename _RandomNumberEngine, size_t __p, size_t __r, 694 typename _CharT, typename _Traits> 695 std::basic_ostream<_CharT, _Traits>& 696 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 697 const discard_block_engine<_RandomNumberEngine, 698 __p, __r>& __x) 699 { 700 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 701 702 const typename __ios_base::fmtflags __flags = __os.flags(); 703 const _CharT __fill = __os.fill(); 704 const _CharT __space = __os.widen(' '); 705 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 706 __os.fill(__space); 707 708 __os << __x.base() << __space << __x._M_n; 709 710 __os.flags(__flags); 711 __os.fill(__fill); 712 return __os; 713 } 714 715 template<typename _RandomNumberEngine, size_t __p, size_t __r, 716 typename _CharT, typename _Traits> 717 std::basic_istream<_CharT, _Traits>& 718 operator>>(std::basic_istream<_CharT, _Traits>& __is, 719 discard_block_engine<_RandomNumberEngine, __p, __r>& __x) 720 { 721 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 722 723 const typename __ios_base::fmtflags __flags = __is.flags(); 724 __is.flags(__ios_base::dec | __ios_base::skipws); 725 726 __is >> __x._M_b >> __x._M_n; 727 728 __is.flags(__flags); 729 return __is; 730 } 731 732 733 template<typename _RandomNumberEngine, size_t __w, typename _UIntType> 734 typename independent_bits_engine<_RandomNumberEngine, __w, _UIntType>:: 735 result_type 736 independent_bits_engine<_RandomNumberEngine, __w, _UIntType>:: 737 operator()() 738 { 739 typedef typename _RandomNumberEngine::result_type _Eresult_type; 740 const _Eresult_type __r 741 = (_M_b.max() - _M_b.min() < std::numeric_limits<_Eresult_type>::max() 742 ? _M_b.max() - _M_b.min() + 1 : 0); 743 const unsigned __edig = std::numeric_limits<_Eresult_type>::digits; 744 const unsigned __m = __r ? std::__lg(__r) : __edig; 745 746 typedef typename std::common_type<_Eresult_type, result_type>::type 747 __ctype; 748 const unsigned __cdig = std::numeric_limits<__ctype>::digits; 749 750 unsigned __n, __n0; 751 __ctype __s0, __s1, __y0, __y1; 752 753 for (size_t __i = 0; __i < 2; ++__i) 754 { 755 __n = (__w + __m - 1) / __m + __i; 756 __n0 = __n - __w % __n; 757 const unsigned __w0 = __w / __n; // __w0 <= __m 758 759 __s0 = 0; 760 __s1 = 0; 761 if (__w0 < __cdig) 762 { 763 __s0 = __ctype(1) << __w0; 764 __s1 = __s0 << 1; 765 } 766 767 __y0 = 0; 768 __y1 = 0; 769 if (__r) 770 { 771 __y0 = __s0 * (__r / __s0); 772 if (__s1) 773 __y1 = __s1 * (__r / __s1); 774 775 if (__r - __y0 <= __y0 / __n) 776 break; 777 } 778 else 779 break; 780 } 781 782 result_type __sum = 0; 783 for (size_t __k = 0; __k < __n0; ++__k) 784 { 785 __ctype __u; 786 do 787 __u = _M_b() - _M_b.min(); 788 while (__y0 && __u >= __y0); 789 __sum = __s0 * __sum + (__s0 ? __u % __s0 : __u); 790 } 791 for (size_t __k = __n0; __k < __n; ++__k) 792 { 793 __ctype __u; 794 do 795 __u = _M_b() - _M_b.min(); 796 while (__y1 && __u >= __y1); 797 __sum = __s1 * __sum + (__s1 ? __u % __s1 : __u); 798 } 799 return __sum; 800 } 801 802 803 template<typename _RandomNumberEngine, size_t __k> 804 constexpr size_t 805 shuffle_order_engine<_RandomNumberEngine, __k>::table_size; 806 807 namespace __detail 808 { 809 // Determine whether an integer is representable as double. 810 template<typename _Tp> 811 constexpr bool 812 __representable_as_double(_Tp __x) noexcept 813 { 814 static_assert(numeric_limits<_Tp>::is_integer, ""); 815 static_assert(!numeric_limits<_Tp>::is_signed, ""); 816 // All integers <= 2^53 are representable. 817 return (__x <= (1ull << __DBL_MANT_DIG__)) 818 // Between 2^53 and 2^54 only even numbers are representable. 819 || (!(__x & 1) && __detail::__representable_as_double(__x >> 1)); 820 } 821 822 // Determine whether x+1 is representable as double. 823 template<typename _Tp> 824 constexpr bool 825 __p1_representable_as_double(_Tp __x) noexcept 826 { 827 static_assert(numeric_limits<_Tp>::is_integer, ""); 828 static_assert(!numeric_limits<_Tp>::is_signed, ""); 829 return numeric_limits<_Tp>::digits < __DBL_MANT_DIG__ 830 || (bool(__x + 1u) // return false if x+1 wraps around to zero 831 && __detail::__representable_as_double(__x + 1u)); 832 } 833 } 834 835 template<typename _RandomNumberEngine, size_t __k> 836 typename shuffle_order_engine<_RandomNumberEngine, __k>::result_type 837 shuffle_order_engine<_RandomNumberEngine, __k>:: 838 operator()() 839 { 840 constexpr result_type __range = max() - min(); 841 size_t __j = __k; 842 const result_type __y = _M_y - min(); 843 // Avoid using slower long double arithmetic if possible. 844 if _GLIBCXX17_CONSTEXPR (__detail::__p1_representable_as_double(__range)) 845 __j *= __y / (__range + 1.0); 846 else 847 __j *= __y / (__range + 1.0L); 848 _M_y = _M_v[__j]; 849 _M_v[__j] = _M_b(); 850 851 return _M_y; 852 } 853 854 template<typename _RandomNumberEngine, size_t __k, 855 typename _CharT, typename _Traits> 856 std::basic_ostream<_CharT, _Traits>& 857 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 858 const shuffle_order_engine<_RandomNumberEngine, __k>& __x) 859 { 860 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 861 862 const typename __ios_base::fmtflags __flags = __os.flags(); 863 const _CharT __fill = __os.fill(); 864 const _CharT __space = __os.widen(' '); 865 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 866 __os.fill(__space); 867 868 __os << __x.base(); 869 for (size_t __i = 0; __i < __k; ++__i) 870 __os << __space << __x._M_v[__i]; 871 __os << __space << __x._M_y; 872 873 __os.flags(__flags); 874 __os.fill(__fill); 875 return __os; 876 } 877 878 template<typename _RandomNumberEngine, size_t __k, 879 typename _CharT, typename _Traits> 880 std::basic_istream<_CharT, _Traits>& 881 operator>>(std::basic_istream<_CharT, _Traits>& __is, 882 shuffle_order_engine<_RandomNumberEngine, __k>& __x) 883 { 884 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 885 886 const typename __ios_base::fmtflags __flags = __is.flags(); 887 __is.flags(__ios_base::dec | __ios_base::skipws); 888 889 __is >> __x._M_b; 890 for (size_t __i = 0; __i < __k; ++__i) 891 __is >> __x._M_v[__i]; 892 __is >> __x._M_y; 893 894 __is.flags(__flags); 895 return __is; 896 } 897 898 899 template<typename _IntType, typename _CharT, typename _Traits> 900 std::basic_ostream<_CharT, _Traits>& 901 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 902 const uniform_int_distribution<_IntType>& __x) 903 { 904 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 905 906 const typename __ios_base::fmtflags __flags = __os.flags(); 907 const _CharT __fill = __os.fill(); 908 const _CharT __space = __os.widen(' '); 909 __os.flags(__ios_base::scientific | __ios_base::left); 910 __os.fill(__space); 911 912 __os << __x.a() << __space << __x.b(); 913 914 __os.flags(__flags); 915 __os.fill(__fill); 916 return __os; 917 } 918 919 template<typename _IntType, typename _CharT, typename _Traits> 920 std::basic_istream<_CharT, _Traits>& 921 operator>>(std::basic_istream<_CharT, _Traits>& __is, 922 uniform_int_distribution<_IntType>& __x) 923 { 924 using param_type 925 = typename uniform_int_distribution<_IntType>::param_type; 926 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 927 928 const typename __ios_base::fmtflags __flags = __is.flags(); 929 __is.flags(__ios_base::dec | __ios_base::skipws); 930 931 _IntType __a, __b; 932 if (__is >> __a >> __b) 933 __x.param(param_type(__a, __b)); 934 935 __is.flags(__flags); 936 return __is; 937 } 938 939 940 template<typename _RealType> 941 template<typename _ForwardIterator, 942 typename _UniformRandomNumberGenerator> 943 void 944 uniform_real_distribution<_RealType>:: 945 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 946 _UniformRandomNumberGenerator& __urng, 947 const param_type& __p) 948 { 949 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 950 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 951 __aurng(__urng); 952 auto __range = __p.b() - __p.a(); 953 while (__f != __t) 954 *__f++ = __aurng() * __range + __p.a(); 955 } 956 957 template<typename _RealType, typename _CharT, typename _Traits> 958 std::basic_ostream<_CharT, _Traits>& 959 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 960 const uniform_real_distribution<_RealType>& __x) 961 { 962 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 963 964 const typename __ios_base::fmtflags __flags = __os.flags(); 965 const _CharT __fill = __os.fill(); 966 const std::streamsize __precision = __os.precision(); 967 const _CharT __space = __os.widen(' '); 968 __os.flags(__ios_base::scientific | __ios_base::left); 969 __os.fill(__space); 970 __os.precision(std::numeric_limits<_RealType>::max_digits10); 971 972 __os << __x.a() << __space << __x.b(); 973 974 __os.flags(__flags); 975 __os.fill(__fill); 976 __os.precision(__precision); 977 return __os; 978 } 979 980 template<typename _RealType, typename _CharT, typename _Traits> 981 std::basic_istream<_CharT, _Traits>& 982 operator>>(std::basic_istream<_CharT, _Traits>& __is, 983 uniform_real_distribution<_RealType>& __x) 984 { 985 using param_type 986 = typename uniform_real_distribution<_RealType>::param_type; 987 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 988 989 const typename __ios_base::fmtflags __flags = __is.flags(); 990 __is.flags(__ios_base::skipws); 991 992 _RealType __a, __b; 993 if (__is >> __a >> __b) 994 __x.param(param_type(__a, __b)); 995 996 __is.flags(__flags); 997 return __is; 998 } 999 1000 1001 template<typename _ForwardIterator, 1002 typename _UniformRandomNumberGenerator> 1003 void 1004 std::bernoulli_distribution:: 1005 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1006 _UniformRandomNumberGenerator& __urng, 1007 const param_type& __p) 1008 { 1009 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1010 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1011 __aurng(__urng); 1012 auto __limit = __p.p() * (__aurng.max() - __aurng.min()); 1013 1014 while (__f != __t) 1015 *__f++ = (__aurng() - __aurng.min()) < __limit; 1016 } 1017 1018 template<typename _CharT, typename _Traits> 1019 std::basic_ostream<_CharT, _Traits>& 1020 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1021 const bernoulli_distribution& __x) 1022 { 1023 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 1024 1025 const typename __ios_base::fmtflags __flags = __os.flags(); 1026 const _CharT __fill = __os.fill(); 1027 const std::streamsize __precision = __os.precision(); 1028 __os.flags(__ios_base::scientific | __ios_base::left); 1029 __os.fill(__os.widen(' ')); 1030 __os.precision(std::numeric_limits<double>::max_digits10); 1031 1032 __os << __x.p(); 1033 1034 __os.flags(__flags); 1035 __os.fill(__fill); 1036 __os.precision(__precision); 1037 return __os; 1038 } 1039 1040 1041 template<typename _IntType> 1042 template<typename _UniformRandomNumberGenerator> 1043 typename geometric_distribution<_IntType>::result_type 1044 geometric_distribution<_IntType>:: 1045 operator()(_UniformRandomNumberGenerator& __urng, 1046 const param_type& __param) 1047 { 1048 // About the epsilon thing see this thread: 1049 // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html 1050 const double __naf = 1051 (1 - std::numeric_limits<double>::epsilon()) / 2; 1052 // The largest _RealType convertible to _IntType. 1053 const double __thr = 1054 std::numeric_limits<_IntType>::max() + __naf; 1055 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1056 __aurng(__urng); 1057 1058 double __cand; 1059 do 1060 __cand = std::floor(std::log(1.0 - __aurng()) / __param._M_log_1_p); 1061 while (__cand >= __thr); 1062 1063 return result_type(__cand + __naf); 1064 } 1065 1066 template<typename _IntType> 1067 template<typename _ForwardIterator, 1068 typename _UniformRandomNumberGenerator> 1069 void 1070 geometric_distribution<_IntType>:: 1071 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1072 _UniformRandomNumberGenerator& __urng, 1073 const param_type& __param) 1074 { 1075 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1076 // About the epsilon thing see this thread: 1077 // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html 1078 const double __naf = 1079 (1 - std::numeric_limits<double>::epsilon()) / 2; 1080 // The largest _RealType convertible to _IntType. 1081 const double __thr = 1082 std::numeric_limits<_IntType>::max() + __naf; 1083 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1084 __aurng(__urng); 1085 1086 while (__f != __t) 1087 { 1088 double __cand; 1089 do 1090 __cand = std::floor(std::log(1.0 - __aurng()) 1091 / __param._M_log_1_p); 1092 while (__cand >= __thr); 1093 1094 *__f++ = __cand + __naf; 1095 } 1096 } 1097 1098 template<typename _IntType, 1099 typename _CharT, typename _Traits> 1100 std::basic_ostream<_CharT, _Traits>& 1101 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1102 const geometric_distribution<_IntType>& __x) 1103 { 1104 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 1105 1106 const typename __ios_base::fmtflags __flags = __os.flags(); 1107 const _CharT __fill = __os.fill(); 1108 const std::streamsize __precision = __os.precision(); 1109 __os.flags(__ios_base::scientific | __ios_base::left); 1110 __os.fill(__os.widen(' ')); 1111 __os.precision(std::numeric_limits<double>::max_digits10); 1112 1113 __os << __x.p(); 1114 1115 __os.flags(__flags); 1116 __os.fill(__fill); 1117 __os.precision(__precision); 1118 return __os; 1119 } 1120 1121 template<typename _IntType, 1122 typename _CharT, typename _Traits> 1123 std::basic_istream<_CharT, _Traits>& 1124 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1125 geometric_distribution<_IntType>& __x) 1126 { 1127 using param_type = typename geometric_distribution<_IntType>::param_type; 1128 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 1129 1130 const typename __ios_base::fmtflags __flags = __is.flags(); 1131 __is.flags(__ios_base::skipws); 1132 1133 double __p; 1134 if (__is >> __p) 1135 __x.param(param_type(__p)); 1136 1137 __is.flags(__flags); 1138 return __is; 1139 } 1140 1141 // This is Leger's algorithm, also in Devroye, Ch. X, Example 1.5. 1142 template<typename _IntType> 1143 template<typename _UniformRandomNumberGenerator> 1144 typename negative_binomial_distribution<_IntType>::result_type 1145 negative_binomial_distribution<_IntType>:: 1146 operator()(_UniformRandomNumberGenerator& __urng) 1147 { 1148 const double __y = _M_gd(__urng); 1149 1150 // XXX Is the constructor too slow? 1151 std::poisson_distribution<result_type> __poisson(__y); 1152 return __poisson(__urng); 1153 } 1154 1155 template<typename _IntType> 1156 template<typename _UniformRandomNumberGenerator> 1157 typename negative_binomial_distribution<_IntType>::result_type 1158 negative_binomial_distribution<_IntType>:: 1159 operator()(_UniformRandomNumberGenerator& __urng, 1160 const param_type& __p) 1161 { 1162 typedef typename std::gamma_distribution<double>::param_type 1163 param_type; 1164 1165 const double __y = 1166 _M_gd(__urng, param_type(__p.k(), (1.0 - __p.p()) / __p.p())); 1167 1168 std::poisson_distribution<result_type> __poisson(__y); 1169 return __poisson(__urng); 1170 } 1171 1172 template<typename _IntType> 1173 template<typename _ForwardIterator, 1174 typename _UniformRandomNumberGenerator> 1175 void 1176 negative_binomial_distribution<_IntType>:: 1177 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1178 _UniformRandomNumberGenerator& __urng) 1179 { 1180 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1181 while (__f != __t) 1182 { 1183 const double __y = _M_gd(__urng); 1184 1185 // XXX Is the constructor too slow? 1186 std::poisson_distribution<result_type> __poisson(__y); 1187 *__f++ = __poisson(__urng); 1188 } 1189 } 1190 1191 template<typename _IntType> 1192 template<typename _ForwardIterator, 1193 typename _UniformRandomNumberGenerator> 1194 void 1195 negative_binomial_distribution<_IntType>:: 1196 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1197 _UniformRandomNumberGenerator& __urng, 1198 const param_type& __p) 1199 { 1200 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1201 typename std::gamma_distribution<result_type>::param_type 1202 __p2(__p.k(), (1.0 - __p.p()) / __p.p()); 1203 1204 while (__f != __t) 1205 { 1206 const double __y = _M_gd(__urng, __p2); 1207 1208 std::poisson_distribution<result_type> __poisson(__y); 1209 *__f++ = __poisson(__urng); 1210 } 1211 } 1212 1213 template<typename _IntType, typename _CharT, typename _Traits> 1214 std::basic_ostream<_CharT, _Traits>& 1215 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1216 const negative_binomial_distribution<_IntType>& __x) 1217 { 1218 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 1219 1220 const typename __ios_base::fmtflags __flags = __os.flags(); 1221 const _CharT __fill = __os.fill(); 1222 const std::streamsize __precision = __os.precision(); 1223 const _CharT __space = __os.widen(' '); 1224 __os.flags(__ios_base::scientific | __ios_base::left); 1225 __os.fill(__os.widen(' ')); 1226 __os.precision(std::numeric_limits<double>::max_digits10); 1227 1228 __os << __x.k() << __space << __x.p() 1229 << __space << __x._M_gd; 1230 1231 __os.flags(__flags); 1232 __os.fill(__fill); 1233 __os.precision(__precision); 1234 return __os; 1235 } 1236 1237 template<typename _IntType, typename _CharT, typename _Traits> 1238 std::basic_istream<_CharT, _Traits>& 1239 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1240 negative_binomial_distribution<_IntType>& __x) 1241 { 1242 using param_type 1243 = typename negative_binomial_distribution<_IntType>::param_type; 1244 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 1245 1246 const typename __ios_base::fmtflags __flags = __is.flags(); 1247 __is.flags(__ios_base::skipws); 1248 1249 _IntType __k; 1250 double __p; 1251 if (__is >> __k >> __p >> __x._M_gd) 1252 __x.param(param_type(__k, __p)); 1253 1254 __is.flags(__flags); 1255 return __is; 1256 } 1257 1258 1259 template<typename _IntType> 1260 void 1261 poisson_distribution<_IntType>::param_type:: 1262 _M_initialize() 1263 { 1264 #if _GLIBCXX_USE_C99_MATH_TR1 1265 if (_M_mean >= 12) 1266 { 1267 const double __m = std::floor(_M_mean); 1268 _M_lm_thr = std::log(_M_mean); 1269 _M_lfm = std::lgamma(__m + 1); 1270 _M_sm = std::sqrt(__m); 1271 1272 const double __pi_4 = 0.7853981633974483096156608458198757L; 1273 const double __dx = std::sqrt(2 * __m * std::log(32 * __m 1274 / __pi_4)); 1275 _M_d = std::round(std::max<double>(6.0, std::min(__m, __dx))); 1276 const double __cx = 2 * __m + _M_d; 1277 _M_scx = std::sqrt(__cx / 2); 1278 _M_1cx = 1 / __cx; 1279 1280 _M_c2b = std::sqrt(__pi_4 * __cx) * std::exp(_M_1cx); 1281 _M_cb = 2 * __cx * std::exp(-_M_d * _M_1cx * (1 + _M_d / 2)) 1282 / _M_d; 1283 } 1284 else 1285 #endif 1286 _M_lm_thr = std::exp(-_M_mean); 1287 } 1288 1289 /** 1290 * A rejection algorithm when mean >= 12 and a simple method based 1291 * upon the multiplication of uniform random variates otherwise. 1292 * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1 1293 * is defined. 1294 * 1295 * Reference: 1296 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, 1297 * New York, 1986, Ch. X, Sects. 3.3 & 3.4 (+ Errata!). 1298 */ 1299 template<typename _IntType> 1300 template<typename _UniformRandomNumberGenerator> 1301 typename poisson_distribution<_IntType>::result_type 1302 poisson_distribution<_IntType>:: 1303 operator()(_UniformRandomNumberGenerator& __urng, 1304 const param_type& __param) 1305 { 1306 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1307 __aurng(__urng); 1308 #if _GLIBCXX_USE_C99_MATH_TR1 1309 if (__param.mean() >= 12) 1310 { 1311 double __x; 1312 1313 // See comments above... 1314 const double __naf = 1315 (1 - std::numeric_limits<double>::epsilon()) / 2; 1316 const double __thr = 1317 std::numeric_limits<_IntType>::max() + __naf; 1318 1319 const double __m = std::floor(__param.mean()); 1320 // sqrt(pi / 2) 1321 const double __spi_2 = 1.2533141373155002512078826424055226L; 1322 const double __c1 = __param._M_sm * __spi_2; 1323 const double __c2 = __param._M_c2b + __c1; 1324 const double __c3 = __c2 + 1; 1325 const double __c4 = __c3 + 1; 1326 // 1 / 78 1327 const double __178 = 0.0128205128205128205128205128205128L; 1328 // e^(1 / 78) 1329 const double __e178 = 1.0129030479320018583185514777512983L; 1330 const double __c5 = __c4 + __e178; 1331 const double __c = __param._M_cb + __c5; 1332 const double __2cx = 2 * (2 * __m + __param._M_d); 1333 1334 bool __reject = true; 1335 do 1336 { 1337 const double __u = __c * __aurng(); 1338 const double __e = -std::log(1.0 - __aurng()); 1339 1340 double __w = 0.0; 1341 1342 if (__u <= __c1) 1343 { 1344 const double __n = _M_nd(__urng); 1345 const double __y = -std::abs(__n) * __param._M_sm - 1; 1346 __x = std::floor(__y); 1347 __w = -__n * __n / 2; 1348 if (__x < -__m) 1349 continue; 1350 } 1351 else if (__u <= __c2) 1352 { 1353 const double __n = _M_nd(__urng); 1354 const double __y = 1 + std::abs(__n) * __param._M_scx; 1355 __x = std::ceil(__y); 1356 __w = __y * (2 - __y) * __param._M_1cx; 1357 if (__x > __param._M_d) 1358 continue; 1359 } 1360 else if (__u <= __c3) 1361 // NB: This case not in the book, nor in the Errata, 1362 // but should be ok... 1363 __x = -1; 1364 else if (__u <= __c4) 1365 __x = 0; 1366 else if (__u <= __c5) 1367 { 1368 __x = 1; 1369 // Only in the Errata, see libstdc++/83237. 1370 __w = __178; 1371 } 1372 else 1373 { 1374 const double __v = -std::log(1.0 - __aurng()); 1375 const double __y = __param._M_d 1376 + __v * __2cx / __param._M_d; 1377 __x = std::ceil(__y); 1378 __w = -__param._M_d * __param._M_1cx * (1 + __y / 2); 1379 } 1380 1381 __reject = (__w - __e - __x * __param._M_lm_thr 1382 > __param._M_lfm - std::lgamma(__x + __m + 1)); 1383 1384 __reject |= __x + __m >= __thr; 1385 1386 } while (__reject); 1387 1388 return result_type(__x + __m + __naf); 1389 } 1390 else 1391 #endif 1392 { 1393 _IntType __x = 0; 1394 double __prod = 1.0; 1395 1396 do 1397 { 1398 __prod *= __aurng(); 1399 __x += 1; 1400 } 1401 while (__prod > __param._M_lm_thr); 1402 1403 return __x - 1; 1404 } 1405 } 1406 1407 template<typename _IntType> 1408 template<typename _ForwardIterator, 1409 typename _UniformRandomNumberGenerator> 1410 void 1411 poisson_distribution<_IntType>:: 1412 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1413 _UniformRandomNumberGenerator& __urng, 1414 const param_type& __param) 1415 { 1416 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1417 // We could duplicate everything from operator()... 1418 while (__f != __t) 1419 *__f++ = this->operator()(__urng, __param); 1420 } 1421 1422 template<typename _IntType, 1423 typename _CharT, typename _Traits> 1424 std::basic_ostream<_CharT, _Traits>& 1425 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1426 const poisson_distribution<_IntType>& __x) 1427 { 1428 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 1429 1430 const typename __ios_base::fmtflags __flags = __os.flags(); 1431 const _CharT __fill = __os.fill(); 1432 const std::streamsize __precision = __os.precision(); 1433 const _CharT __space = __os.widen(' '); 1434 __os.flags(__ios_base::scientific | __ios_base::left); 1435 __os.fill(__space); 1436 __os.precision(std::numeric_limits<double>::max_digits10); 1437 1438 __os << __x.mean() << __space << __x._M_nd; 1439 1440 __os.flags(__flags); 1441 __os.fill(__fill); 1442 __os.precision(__precision); 1443 return __os; 1444 } 1445 1446 template<typename _IntType, 1447 typename _CharT, typename _Traits> 1448 std::basic_istream<_CharT, _Traits>& 1449 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1450 poisson_distribution<_IntType>& __x) 1451 { 1452 using param_type = typename poisson_distribution<_IntType>::param_type; 1453 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 1454 1455 const typename __ios_base::fmtflags __flags = __is.flags(); 1456 __is.flags(__ios_base::skipws); 1457 1458 double __mean; 1459 if (__is >> __mean >> __x._M_nd) 1460 __x.param(param_type(__mean)); 1461 1462 __is.flags(__flags); 1463 return __is; 1464 } 1465 1466 1467 template<typename _IntType> 1468 void 1469 binomial_distribution<_IntType>::param_type:: 1470 _M_initialize() 1471 { 1472 const double __p12 = _M_p <= 0.5 ? _M_p : 1.0 - _M_p; 1473 1474 _M_easy = true; 1475 1476 #if _GLIBCXX_USE_C99_MATH_TR1 1477 if (_M_t * __p12 >= 8) 1478 { 1479 _M_easy = false; 1480 const double __np = std::floor(_M_t * __p12); 1481 const double __pa = __np / _M_t; 1482 const double __1p = 1 - __pa; 1483 1484 const double __pi_4 = 0.7853981633974483096156608458198757L; 1485 const double __d1x = 1486 std::sqrt(__np * __1p * std::log(32 * __np 1487 / (81 * __pi_4 * __1p))); 1488 _M_d1 = std::round(std::max<double>(1.0, __d1x)); 1489 const double __d2x = 1490 std::sqrt(__np * __1p * std::log(32 * _M_t * __1p 1491 / (__pi_4 * __pa))); 1492 _M_d2 = std::round(std::max<double>(1.0, __d2x)); 1493 1494 // sqrt(pi / 2) 1495 const double __spi_2 = 1.2533141373155002512078826424055226L; 1496 _M_s1 = std::sqrt(__np * __1p) * (1 + _M_d1 / (4 * __np)); 1497 _M_s2 = std::sqrt(__np * __1p) * (1 + _M_d2 / (4 * _M_t * __1p)); 1498 _M_c = 2 * _M_d1 / __np; 1499 _M_a1 = std::exp(_M_c) * _M_s1 * __spi_2; 1500 const double __a12 = _M_a1 + _M_s2 * __spi_2; 1501 const double __s1s = _M_s1 * _M_s1; 1502 _M_a123 = __a12 + (std::exp(_M_d1 / (_M_t * __1p)) 1503 * 2 * __s1s / _M_d1 1504 * std::exp(-_M_d1 * _M_d1 / (2 * __s1s))); 1505 const double __s2s = _M_s2 * _M_s2; 1506 _M_s = (_M_a123 + 2 * __s2s / _M_d2 1507 * std::exp(-_M_d2 * _M_d2 / (2 * __s2s))); 1508 _M_lf = (std::lgamma(__np + 1) 1509 + std::lgamma(_M_t - __np + 1)); 1510 _M_lp1p = std::log(__pa / __1p); 1511 1512 _M_q = -std::log(1 - (__p12 - __pa) / __1p); 1513 } 1514 else 1515 #endif 1516 _M_q = -std::log(1 - __p12); 1517 } 1518 1519 template<typename _IntType> 1520 template<typename _UniformRandomNumberGenerator> 1521 typename binomial_distribution<_IntType>::result_type 1522 binomial_distribution<_IntType>:: 1523 _M_waiting(_UniformRandomNumberGenerator& __urng, 1524 _IntType __t, double __q) 1525 { 1526 _IntType __x = 0; 1527 double __sum = 0.0; 1528 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1529 __aurng(__urng); 1530 1531 do 1532 { 1533 if (__t == __x) 1534 return __x; 1535 const double __e = -std::log(1.0 - __aurng()); 1536 __sum += __e / (__t - __x); 1537 __x += 1; 1538 } 1539 while (__sum <= __q); 1540 1541 return __x - 1; 1542 } 1543 1544 /** 1545 * A rejection algorithm when t * p >= 8 and a simple waiting time 1546 * method - the second in the referenced book - otherwise. 1547 * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1 1548 * is defined. 1549 * 1550 * Reference: 1551 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, 1552 * New York, 1986, Ch. X, Sect. 4 (+ Errata!). 1553 */ 1554 template<typename _IntType> 1555 template<typename _UniformRandomNumberGenerator> 1556 typename binomial_distribution<_IntType>::result_type 1557 binomial_distribution<_IntType>:: 1558 operator()(_UniformRandomNumberGenerator& __urng, 1559 const param_type& __param) 1560 { 1561 result_type __ret; 1562 const _IntType __t = __param.t(); 1563 const double __p = __param.p(); 1564 const double __p12 = __p <= 0.5 ? __p : 1.0 - __p; 1565 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1566 __aurng(__urng); 1567 1568 #if _GLIBCXX_USE_C99_MATH_TR1 1569 if (!__param._M_easy) 1570 { 1571 double __x; 1572 1573 // See comments above... 1574 const double __naf = 1575 (1 - std::numeric_limits<double>::epsilon()) / 2; 1576 const double __thr = 1577 std::numeric_limits<_IntType>::max() + __naf; 1578 1579 const double __np = std::floor(__t * __p12); 1580 1581 // sqrt(pi / 2) 1582 const double __spi_2 = 1.2533141373155002512078826424055226L; 1583 const double __a1 = __param._M_a1; 1584 const double __a12 = __a1 + __param._M_s2 * __spi_2; 1585 const double __a123 = __param._M_a123; 1586 const double __s1s = __param._M_s1 * __param._M_s1; 1587 const double __s2s = __param._M_s2 * __param._M_s2; 1588 1589 bool __reject; 1590 do 1591 { 1592 const double __u = __param._M_s * __aurng(); 1593 1594 double __v; 1595 1596 if (__u <= __a1) 1597 { 1598 const double __n = _M_nd(__urng); 1599 const double __y = __param._M_s1 * std::abs(__n); 1600 __reject = __y >= __param._M_d1; 1601 if (!__reject) 1602 { 1603 const double __e = -std::log(1.0 - __aurng()); 1604 __x = std::floor(__y); 1605 __v = -__e - __n * __n / 2 + __param._M_c; 1606 } 1607 } 1608 else if (__u <= __a12) 1609 { 1610 const double __n = _M_nd(__urng); 1611 const double __y = __param._M_s2 * std::abs(__n); 1612 __reject = __y >= __param._M_d2; 1613 if (!__reject) 1614 { 1615 const double __e = -std::log(1.0 - __aurng()); 1616 __x = std::floor(-__y); 1617 __v = -__e - __n * __n / 2; 1618 } 1619 } 1620 else if (__u <= __a123) 1621 { 1622 const double __e1 = -std::log(1.0 - __aurng()); 1623 const double __e2 = -std::log(1.0 - __aurng()); 1624 1625 const double __y = __param._M_d1 1626 + 2 * __s1s * __e1 / __param._M_d1; 1627 __x = std::floor(__y); 1628 __v = (-__e2 + __param._M_d1 * (1 / (__t - __np) 1629 -__y / (2 * __s1s))); 1630 __reject = false; 1631 } 1632 else 1633 { 1634 const double __e1 = -std::log(1.0 - __aurng()); 1635 const double __e2 = -std::log(1.0 - __aurng()); 1636 1637 const double __y = __param._M_d2 1638 + 2 * __s2s * __e1 / __param._M_d2; 1639 __x = std::floor(-__y); 1640 __v = -__e2 - __param._M_d2 * __y / (2 * __s2s); 1641 __reject = false; 1642 } 1643 1644 __reject = __reject || __x < -__np || __x > __t - __np; 1645 if (!__reject) 1646 { 1647 const double __lfx = 1648 std::lgamma(__np + __x + 1) 1649 + std::lgamma(__t - (__np + __x) + 1); 1650 __reject = __v > __param._M_lf - __lfx 1651 + __x * __param._M_lp1p; 1652 } 1653 1654 __reject |= __x + __np >= __thr; 1655 } 1656 while (__reject); 1657 1658 __x += __np + __naf; 1659 1660 const _IntType __z = _M_waiting(__urng, __t - _IntType(__x), 1661 __param._M_q); 1662 __ret = _IntType(__x) + __z; 1663 } 1664 else 1665 #endif 1666 __ret = _M_waiting(__urng, __t, __param._M_q); 1667 1668 if (__p12 != __p) 1669 __ret = __t - __ret; 1670 return __ret; 1671 } 1672 1673 template<typename _IntType> 1674 template<typename _ForwardIterator, 1675 typename _UniformRandomNumberGenerator> 1676 void 1677 binomial_distribution<_IntType>:: 1678 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1679 _UniformRandomNumberGenerator& __urng, 1680 const param_type& __param) 1681 { 1682 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1683 // We could duplicate everything from operator()... 1684 while (__f != __t) 1685 *__f++ = this->operator()(__urng, __param); 1686 } 1687 1688 template<typename _IntType, 1689 typename _CharT, typename _Traits> 1690 std::basic_ostream<_CharT, _Traits>& 1691 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1692 const binomial_distribution<_IntType>& __x) 1693 { 1694 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 1695 1696 const typename __ios_base::fmtflags __flags = __os.flags(); 1697 const _CharT __fill = __os.fill(); 1698 const std::streamsize __precision = __os.precision(); 1699 const _CharT __space = __os.widen(' '); 1700 __os.flags(__ios_base::scientific | __ios_base::left); 1701 __os.fill(__space); 1702 __os.precision(std::numeric_limits<double>::max_digits10); 1703 1704 __os << __x.t() << __space << __x.p() 1705 << __space << __x._M_nd; 1706 1707 __os.flags(__flags); 1708 __os.fill(__fill); 1709 __os.precision(__precision); 1710 return __os; 1711 } 1712 1713 template<typename _IntType, 1714 typename _CharT, typename _Traits> 1715 std::basic_istream<_CharT, _Traits>& 1716 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1717 binomial_distribution<_IntType>& __x) 1718 { 1719 using param_type = typename binomial_distribution<_IntType>::param_type; 1720 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 1721 1722 const typename __ios_base::fmtflags __flags = __is.flags(); 1723 __is.flags(__ios_base::dec | __ios_base::skipws); 1724 1725 _IntType __t; 1726 double __p; 1727 if (__is >> __t >> __p >> __x._M_nd) 1728 __x.param(param_type(__t, __p)); 1729 1730 __is.flags(__flags); 1731 return __is; 1732 } 1733 1734 1735 template<typename _RealType> 1736 template<typename _ForwardIterator, 1737 typename _UniformRandomNumberGenerator> 1738 void 1739 std::exponential_distribution<_RealType>:: 1740 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1741 _UniformRandomNumberGenerator& __urng, 1742 const param_type& __p) 1743 { 1744 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1745 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 1746 __aurng(__urng); 1747 while (__f != __t) 1748 *__f++ = -std::log(result_type(1) - __aurng()) / __p.lambda(); 1749 } 1750 1751 template<typename _RealType, typename _CharT, typename _Traits> 1752 std::basic_ostream<_CharT, _Traits>& 1753 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1754 const exponential_distribution<_RealType>& __x) 1755 { 1756 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 1757 1758 const typename __ios_base::fmtflags __flags = __os.flags(); 1759 const _CharT __fill = __os.fill(); 1760 const std::streamsize __precision = __os.precision(); 1761 __os.flags(__ios_base::scientific | __ios_base::left); 1762 __os.fill(__os.widen(' ')); 1763 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1764 1765 __os << __x.lambda(); 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 exponential_distribution<_RealType>& __x) 1777 { 1778 using param_type 1779 = typename exponential_distribution<_RealType>::param_type; 1780 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 1781 1782 const typename __ios_base::fmtflags __flags = __is.flags(); 1783 __is.flags(__ios_base::dec | __ios_base::skipws); 1784 1785 _RealType __lambda; 1786 if (__is >> __lambda) 1787 __x.param(param_type(__lambda)); 1788 1789 __is.flags(__flags); 1790 return __is; 1791 } 1792 1793 1794 /** 1795 * Polar method due to Marsaglia. 1796 * 1797 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, 1798 * New York, 1986, Ch. V, Sect. 4.4. 1799 */ 1800 template<typename _RealType> 1801 template<typename _UniformRandomNumberGenerator> 1802 typename normal_distribution<_RealType>::result_type 1803 normal_distribution<_RealType>:: 1804 operator()(_UniformRandomNumberGenerator& __urng, 1805 const param_type& __param) 1806 { 1807 result_type __ret; 1808 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 1809 __aurng(__urng); 1810 1811 if (_M_saved_available) 1812 { 1813 _M_saved_available = false; 1814 __ret = _M_saved; 1815 } 1816 else 1817 { 1818 result_type __x, __y, __r2; 1819 do 1820 { 1821 __x = result_type(2.0) * __aurng() - 1.0; 1822 __y = result_type(2.0) * __aurng() - 1.0; 1823 __r2 = __x * __x + __y * __y; 1824 } 1825 while (__r2 > 1.0 || __r2 == 0.0); 1826 1827 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2); 1828 _M_saved = __x * __mult; 1829 _M_saved_available = true; 1830 __ret = __y * __mult; 1831 } 1832 1833 __ret = __ret * __param.stddev() + __param.mean(); 1834 return __ret; 1835 } 1836 1837 template<typename _RealType> 1838 template<typename _ForwardIterator, 1839 typename _UniformRandomNumberGenerator> 1840 void 1841 normal_distribution<_RealType>:: 1842 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1843 _UniformRandomNumberGenerator& __urng, 1844 const param_type& __param) 1845 { 1846 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1847 1848 if (__f == __t) 1849 return; 1850 1851 if (_M_saved_available) 1852 { 1853 _M_saved_available = false; 1854 *__f++ = _M_saved * __param.stddev() + __param.mean(); 1855 1856 if (__f == __t) 1857 return; 1858 } 1859 1860 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 1861 __aurng(__urng); 1862 1863 while (__f + 1 < __t) 1864 { 1865 result_type __x, __y, __r2; 1866 do 1867 { 1868 __x = result_type(2.0) * __aurng() - 1.0; 1869 __y = result_type(2.0) * __aurng() - 1.0; 1870 __r2 = __x * __x + __y * __y; 1871 } 1872 while (__r2 > 1.0 || __r2 == 0.0); 1873 1874 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2); 1875 *__f++ = __y * __mult * __param.stddev() + __param.mean(); 1876 *__f++ = __x * __mult * __param.stddev() + __param.mean(); 1877 } 1878 1879 if (__f != __t) 1880 { 1881 result_type __x, __y, __r2; 1882 do 1883 { 1884 __x = result_type(2.0) * __aurng() - 1.0; 1885 __y = result_type(2.0) * __aurng() - 1.0; 1886 __r2 = __x * __x + __y * __y; 1887 } 1888 while (__r2 > 1.0 || __r2 == 0.0); 1889 1890 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2); 1891 _M_saved = __x * __mult; 1892 _M_saved_available = true; 1893 *__f = __y * __mult * __param.stddev() + __param.mean(); 1894 } 1895 } 1896 1897 template<typename _RealType> 1898 bool 1899 operator==(const std::normal_distribution<_RealType>& __d1, 1900 const std::normal_distribution<_RealType>& __d2) 1901 { 1902 if (__d1._M_param == __d2._M_param 1903 && __d1._M_saved_available == __d2._M_saved_available) 1904 { 1905 if (__d1._M_saved_available 1906 && __d1._M_saved == __d2._M_saved) 1907 return true; 1908 else if(!__d1._M_saved_available) 1909 return true; 1910 else 1911 return false; 1912 } 1913 else 1914 return false; 1915 } 1916 1917 template<typename _RealType, typename _CharT, typename _Traits> 1918 std::basic_ostream<_CharT, _Traits>& 1919 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1920 const normal_distribution<_RealType>& __x) 1921 { 1922 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 1923 1924 const typename __ios_base::fmtflags __flags = __os.flags(); 1925 const _CharT __fill = __os.fill(); 1926 const std::streamsize __precision = __os.precision(); 1927 const _CharT __space = __os.widen(' '); 1928 __os.flags(__ios_base::scientific | __ios_base::left); 1929 __os.fill(__space); 1930 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1931 1932 __os << __x.mean() << __space << __x.stddev() 1933 << __space << __x._M_saved_available; 1934 if (__x._M_saved_available) 1935 __os << __space << __x._M_saved; 1936 1937 __os.flags(__flags); 1938 __os.fill(__fill); 1939 __os.precision(__precision); 1940 return __os; 1941 } 1942 1943 template<typename _RealType, typename _CharT, typename _Traits> 1944 std::basic_istream<_CharT, _Traits>& 1945 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1946 normal_distribution<_RealType>& __x) 1947 { 1948 using param_type = typename normal_distribution<_RealType>::param_type; 1949 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 1950 1951 const typename __ios_base::fmtflags __flags = __is.flags(); 1952 __is.flags(__ios_base::dec | __ios_base::skipws); 1953 1954 double __mean, __stddev; 1955 bool __saved_avail; 1956 if (__is >> __mean >> __stddev >> __saved_avail) 1957 { 1958 if (!__saved_avail || (__is >> __x._M_saved)) 1959 { 1960 __x._M_saved_available = __saved_avail; 1961 __x.param(param_type(__mean, __stddev)); 1962 } 1963 } 1964 1965 __is.flags(__flags); 1966 return __is; 1967 } 1968 1969 1970 template<typename _RealType> 1971 template<typename _ForwardIterator, 1972 typename _UniformRandomNumberGenerator> 1973 void 1974 lognormal_distribution<_RealType>:: 1975 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1976 _UniformRandomNumberGenerator& __urng, 1977 const param_type& __p) 1978 { 1979 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1980 while (__f != __t) 1981 *__f++ = std::exp(__p.s() * _M_nd(__urng) + __p.m()); 1982 } 1983 1984 template<typename _RealType, typename _CharT, typename _Traits> 1985 std::basic_ostream<_CharT, _Traits>& 1986 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1987 const lognormal_distribution<_RealType>& __x) 1988 { 1989 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 1990 1991 const typename __ios_base::fmtflags __flags = __os.flags(); 1992 const _CharT __fill = __os.fill(); 1993 const std::streamsize __precision = __os.precision(); 1994 const _CharT __space = __os.widen(' '); 1995 __os.flags(__ios_base::scientific | __ios_base::left); 1996 __os.fill(__space); 1997 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1998 1999 __os << __x.m() << __space << __x.s() 2000 << __space << __x._M_nd; 2001 2002 __os.flags(__flags); 2003 __os.fill(__fill); 2004 __os.precision(__precision); 2005 return __os; 2006 } 2007 2008 template<typename _RealType, typename _CharT, typename _Traits> 2009 std::basic_istream<_CharT, _Traits>& 2010 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2011 lognormal_distribution<_RealType>& __x) 2012 { 2013 using param_type 2014 = typename lognormal_distribution<_RealType>::param_type; 2015 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 2016 2017 const typename __ios_base::fmtflags __flags = __is.flags(); 2018 __is.flags(__ios_base::dec | __ios_base::skipws); 2019 2020 _RealType __m, __s; 2021 if (__is >> __m >> __s >> __x._M_nd) 2022 __x.param(param_type(__m, __s)); 2023 2024 __is.flags(__flags); 2025 return __is; 2026 } 2027 2028 template<typename _RealType> 2029 template<typename _ForwardIterator, 2030 typename _UniformRandomNumberGenerator> 2031 void 2032 std::chi_squared_distribution<_RealType>:: 2033 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2034 _UniformRandomNumberGenerator& __urng) 2035 { 2036 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2037 while (__f != __t) 2038 *__f++ = 2 * _M_gd(__urng); 2039 } 2040 2041 template<typename _RealType> 2042 template<typename _ForwardIterator, 2043 typename _UniformRandomNumberGenerator> 2044 void 2045 std::chi_squared_distribution<_RealType>:: 2046 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2047 _UniformRandomNumberGenerator& __urng, 2048 const typename 2049 std::gamma_distribution<result_type>::param_type& __p) 2050 { 2051 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2052 while (__f != __t) 2053 *__f++ = 2 * _M_gd(__urng, __p); 2054 } 2055 2056 template<typename _RealType, typename _CharT, typename _Traits> 2057 std::basic_ostream<_CharT, _Traits>& 2058 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2059 const chi_squared_distribution<_RealType>& __x) 2060 { 2061 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 2062 2063 const typename __ios_base::fmtflags __flags = __os.flags(); 2064 const _CharT __fill = __os.fill(); 2065 const std::streamsize __precision = __os.precision(); 2066 const _CharT __space = __os.widen(' '); 2067 __os.flags(__ios_base::scientific | __ios_base::left); 2068 __os.fill(__space); 2069 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2070 2071 __os << __x.n() << __space << __x._M_gd; 2072 2073 __os.flags(__flags); 2074 __os.fill(__fill); 2075 __os.precision(__precision); 2076 return __os; 2077 } 2078 2079 template<typename _RealType, typename _CharT, typename _Traits> 2080 std::basic_istream<_CharT, _Traits>& 2081 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2082 chi_squared_distribution<_RealType>& __x) 2083 { 2084 using param_type 2085 = typename chi_squared_distribution<_RealType>::param_type; 2086 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 2087 2088 const typename __ios_base::fmtflags __flags = __is.flags(); 2089 __is.flags(__ios_base::dec | __ios_base::skipws); 2090 2091 _RealType __n; 2092 if (__is >> __n >> __x._M_gd) 2093 __x.param(param_type(__n)); 2094 2095 __is.flags(__flags); 2096 return __is; 2097 } 2098 2099 2100 template<typename _RealType> 2101 template<typename _UniformRandomNumberGenerator> 2102 typename cauchy_distribution<_RealType>::result_type 2103 cauchy_distribution<_RealType>:: 2104 operator()(_UniformRandomNumberGenerator& __urng, 2105 const param_type& __p) 2106 { 2107 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2108 __aurng(__urng); 2109 _RealType __u; 2110 do 2111 __u = __aurng(); 2112 while (__u == 0.5); 2113 2114 const _RealType __pi = 3.1415926535897932384626433832795029L; 2115 return __p.a() + __p.b() * std::tan(__pi * __u); 2116 } 2117 2118 template<typename _RealType> 2119 template<typename _ForwardIterator, 2120 typename _UniformRandomNumberGenerator> 2121 void 2122 cauchy_distribution<_RealType>:: 2123 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2124 _UniformRandomNumberGenerator& __urng, 2125 const param_type& __p) 2126 { 2127 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2128 const _RealType __pi = 3.1415926535897932384626433832795029L; 2129 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2130 __aurng(__urng); 2131 while (__f != __t) 2132 { 2133 _RealType __u; 2134 do 2135 __u = __aurng(); 2136 while (__u == 0.5); 2137 2138 *__f++ = __p.a() + __p.b() * std::tan(__pi * __u); 2139 } 2140 } 2141 2142 template<typename _RealType, typename _CharT, typename _Traits> 2143 std::basic_ostream<_CharT, _Traits>& 2144 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2145 const cauchy_distribution<_RealType>& __x) 2146 { 2147 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 2148 2149 const typename __ios_base::fmtflags __flags = __os.flags(); 2150 const _CharT __fill = __os.fill(); 2151 const std::streamsize __precision = __os.precision(); 2152 const _CharT __space = __os.widen(' '); 2153 __os.flags(__ios_base::scientific | __ios_base::left); 2154 __os.fill(__space); 2155 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2156 2157 __os << __x.a() << __space << __x.b(); 2158 2159 __os.flags(__flags); 2160 __os.fill(__fill); 2161 __os.precision(__precision); 2162 return __os; 2163 } 2164 2165 template<typename _RealType, typename _CharT, typename _Traits> 2166 std::basic_istream<_CharT, _Traits>& 2167 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2168 cauchy_distribution<_RealType>& __x) 2169 { 2170 using param_type = typename cauchy_distribution<_RealType>::param_type; 2171 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 2172 2173 const typename __ios_base::fmtflags __flags = __is.flags(); 2174 __is.flags(__ios_base::dec | __ios_base::skipws); 2175 2176 _RealType __a, __b; 2177 if (__is >> __a >> __b) 2178 __x.param(param_type(__a, __b)); 2179 2180 __is.flags(__flags); 2181 return __is; 2182 } 2183 2184 2185 template<typename _RealType> 2186 template<typename _ForwardIterator, 2187 typename _UniformRandomNumberGenerator> 2188 void 2189 std::fisher_f_distribution<_RealType>:: 2190 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2191 _UniformRandomNumberGenerator& __urng) 2192 { 2193 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2194 while (__f != __t) 2195 *__f++ = ((_M_gd_x(__urng) * n()) / (_M_gd_y(__urng) * m())); 2196 } 2197 2198 template<typename _RealType> 2199 template<typename _ForwardIterator, 2200 typename _UniformRandomNumberGenerator> 2201 void 2202 std::fisher_f_distribution<_RealType>:: 2203 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2204 _UniformRandomNumberGenerator& __urng, 2205 const param_type& __p) 2206 { 2207 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2208 typedef typename std::gamma_distribution<result_type>::param_type 2209 param_type; 2210 param_type __p1(__p.m() / 2); 2211 param_type __p2(__p.n() / 2); 2212 while (__f != __t) 2213 *__f++ = ((_M_gd_x(__urng, __p1) * n()) 2214 / (_M_gd_y(__urng, __p2) * m())); 2215 } 2216 2217 template<typename _RealType, typename _CharT, typename _Traits> 2218 std::basic_ostream<_CharT, _Traits>& 2219 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2220 const fisher_f_distribution<_RealType>& __x) 2221 { 2222 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 2223 2224 const typename __ios_base::fmtflags __flags = __os.flags(); 2225 const _CharT __fill = __os.fill(); 2226 const std::streamsize __precision = __os.precision(); 2227 const _CharT __space = __os.widen(' '); 2228 __os.flags(__ios_base::scientific | __ios_base::left); 2229 __os.fill(__space); 2230 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2231 2232 __os << __x.m() << __space << __x.n() 2233 << __space << __x._M_gd_x << __space << __x._M_gd_y; 2234 2235 __os.flags(__flags); 2236 __os.fill(__fill); 2237 __os.precision(__precision); 2238 return __os; 2239 } 2240 2241 template<typename _RealType, typename _CharT, typename _Traits> 2242 std::basic_istream<_CharT, _Traits>& 2243 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2244 fisher_f_distribution<_RealType>& __x) 2245 { 2246 using param_type 2247 = typename fisher_f_distribution<_RealType>::param_type; 2248 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 2249 2250 const typename __ios_base::fmtflags __flags = __is.flags(); 2251 __is.flags(__ios_base::dec | __ios_base::skipws); 2252 2253 _RealType __m, __n; 2254 if (__is >> __m >> __n >> __x._M_gd_x >> __x._M_gd_y) 2255 __x.param(param_type(__m, __n)); 2256 2257 __is.flags(__flags); 2258 return __is; 2259 } 2260 2261 2262 template<typename _RealType> 2263 template<typename _ForwardIterator, 2264 typename _UniformRandomNumberGenerator> 2265 void 2266 std::student_t_distribution<_RealType>:: 2267 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2268 _UniformRandomNumberGenerator& __urng) 2269 { 2270 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2271 while (__f != __t) 2272 *__f++ = _M_nd(__urng) * std::sqrt(n() / _M_gd(__urng)); 2273 } 2274 2275 template<typename _RealType> 2276 template<typename _ForwardIterator, 2277 typename _UniformRandomNumberGenerator> 2278 void 2279 std::student_t_distribution<_RealType>:: 2280 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2281 _UniformRandomNumberGenerator& __urng, 2282 const param_type& __p) 2283 { 2284 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2285 typename std::gamma_distribution<result_type>::param_type 2286 __p2(__p.n() / 2, 2); 2287 while (__f != __t) 2288 *__f++ = _M_nd(__urng) * std::sqrt(__p.n() / _M_gd(__urng, __p2)); 2289 } 2290 2291 template<typename _RealType, typename _CharT, typename _Traits> 2292 std::basic_ostream<_CharT, _Traits>& 2293 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2294 const student_t_distribution<_RealType>& __x) 2295 { 2296 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 2297 2298 const typename __ios_base::fmtflags __flags = __os.flags(); 2299 const _CharT __fill = __os.fill(); 2300 const std::streamsize __precision = __os.precision(); 2301 const _CharT __space = __os.widen(' '); 2302 __os.flags(__ios_base::scientific | __ios_base::left); 2303 __os.fill(__space); 2304 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2305 2306 __os << __x.n() << __space << __x._M_nd << __space << __x._M_gd; 2307 2308 __os.flags(__flags); 2309 __os.fill(__fill); 2310 __os.precision(__precision); 2311 return __os; 2312 } 2313 2314 template<typename _RealType, typename _CharT, typename _Traits> 2315 std::basic_istream<_CharT, _Traits>& 2316 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2317 student_t_distribution<_RealType>& __x) 2318 { 2319 using param_type 2320 = typename student_t_distribution<_RealType>::param_type; 2321 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 2322 2323 const typename __ios_base::fmtflags __flags = __is.flags(); 2324 __is.flags(__ios_base::dec | __ios_base::skipws); 2325 2326 _RealType __n; 2327 if (__is >> __n >> __x._M_nd >> __x._M_gd) 2328 __x.param(param_type(__n)); 2329 2330 __is.flags(__flags); 2331 return __is; 2332 } 2333 2334 2335 template<typename _RealType> 2336 void 2337 gamma_distribution<_RealType>::param_type:: 2338 _M_initialize() 2339 { 2340 _M_malpha = _M_alpha < 1.0 ? _M_alpha + _RealType(1.0) : _M_alpha; 2341 2342 const _RealType __a1 = _M_malpha - _RealType(1.0) / _RealType(3.0); 2343 _M_a2 = _RealType(1.0) / std::sqrt(_RealType(9.0) * __a1); 2344 } 2345 2346 /** 2347 * Marsaglia, G. and Tsang, W. W. 2348 * "A Simple Method for Generating Gamma Variables" 2349 * ACM Transactions on Mathematical Software, 26, 3, 363-372, 2000. 2350 */ 2351 template<typename _RealType> 2352 template<typename _UniformRandomNumberGenerator> 2353 typename gamma_distribution<_RealType>::result_type 2354 gamma_distribution<_RealType>:: 2355 operator()(_UniformRandomNumberGenerator& __urng, 2356 const param_type& __param) 2357 { 2358 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2359 __aurng(__urng); 2360 2361 result_type __u, __v, __n; 2362 const result_type __a1 = (__param._M_malpha 2363 - _RealType(1.0) / _RealType(3.0)); 2364 2365 do 2366 { 2367 do 2368 { 2369 __n = _M_nd(__urng); 2370 __v = result_type(1.0) + __param._M_a2 * __n; 2371 } 2372 while (__v <= 0.0); 2373 2374 __v = __v * __v * __v; 2375 __u = __aurng(); 2376 } 2377 while (__u > result_type(1.0) - 0.0331 * __n * __n * __n * __n 2378 && (std::log(__u) > (0.5 * __n * __n + __a1 2379 * (1.0 - __v + std::log(__v))))); 2380 2381 if (__param.alpha() == __param._M_malpha) 2382 return __a1 * __v * __param.beta(); 2383 else 2384 { 2385 do 2386 __u = __aurng(); 2387 while (__u == 0.0); 2388 2389 return (std::pow(__u, result_type(1.0) / __param.alpha()) 2390 * __a1 * __v * __param.beta()); 2391 } 2392 } 2393 2394 template<typename _RealType> 2395 template<typename _ForwardIterator, 2396 typename _UniformRandomNumberGenerator> 2397 void 2398 gamma_distribution<_RealType>:: 2399 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2400 _UniformRandomNumberGenerator& __urng, 2401 const param_type& __param) 2402 { 2403 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2404 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2405 __aurng(__urng); 2406 2407 result_type __u, __v, __n; 2408 const result_type __a1 = (__param._M_malpha 2409 - _RealType(1.0) / _RealType(3.0)); 2410 2411 if (__param.alpha() == __param._M_malpha) 2412 while (__f != __t) 2413 { 2414 do 2415 { 2416 do 2417 { 2418 __n = _M_nd(__urng); 2419 __v = result_type(1.0) + __param._M_a2 * __n; 2420 } 2421 while (__v <= 0.0); 2422 2423 __v = __v * __v * __v; 2424 __u = __aurng(); 2425 } 2426 while (__u > result_type(1.0) - 0.0331 * __n * __n * __n * __n 2427 && (std::log(__u) > (0.5 * __n * __n + __a1 2428 * (1.0 - __v + std::log(__v))))); 2429 2430 *__f++ = __a1 * __v * __param.beta(); 2431 } 2432 else 2433 while (__f != __t) 2434 { 2435 do 2436 { 2437 do 2438 { 2439 __n = _M_nd(__urng); 2440 __v = result_type(1.0) + __param._M_a2 * __n; 2441 } 2442 while (__v <= 0.0); 2443 2444 __v = __v * __v * __v; 2445 __u = __aurng(); 2446 } 2447 while (__u > result_type(1.0) - 0.0331 * __n * __n * __n * __n 2448 && (std::log(__u) > (0.5 * __n * __n + __a1 2449 * (1.0 - __v + std::log(__v))))); 2450 2451 do 2452 __u = __aurng(); 2453 while (__u == 0.0); 2454 2455 *__f++ = (std::pow(__u, result_type(1.0) / __param.alpha()) 2456 * __a1 * __v * __param.beta()); 2457 } 2458 } 2459 2460 template<typename _RealType, typename _CharT, typename _Traits> 2461 std::basic_ostream<_CharT, _Traits>& 2462 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2463 const gamma_distribution<_RealType>& __x) 2464 { 2465 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 2466 2467 const typename __ios_base::fmtflags __flags = __os.flags(); 2468 const _CharT __fill = __os.fill(); 2469 const std::streamsize __precision = __os.precision(); 2470 const _CharT __space = __os.widen(' '); 2471 __os.flags(__ios_base::scientific | __ios_base::left); 2472 __os.fill(__space); 2473 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2474 2475 __os << __x.alpha() << __space << __x.beta() 2476 << __space << __x._M_nd; 2477 2478 __os.flags(__flags); 2479 __os.fill(__fill); 2480 __os.precision(__precision); 2481 return __os; 2482 } 2483 2484 template<typename _RealType, typename _CharT, typename _Traits> 2485 std::basic_istream<_CharT, _Traits>& 2486 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2487 gamma_distribution<_RealType>& __x) 2488 { 2489 using param_type = typename gamma_distribution<_RealType>::param_type; 2490 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 2491 2492 const typename __ios_base::fmtflags __flags = __is.flags(); 2493 __is.flags(__ios_base::dec | __ios_base::skipws); 2494 2495 _RealType __alpha_val, __beta_val; 2496 if (__is >> __alpha_val >> __beta_val >> __x._M_nd) 2497 __x.param(param_type(__alpha_val, __beta_val)); 2498 2499 __is.flags(__flags); 2500 return __is; 2501 } 2502 2503 2504 template<typename _RealType> 2505 template<typename _UniformRandomNumberGenerator> 2506 typename weibull_distribution<_RealType>::result_type 2507 weibull_distribution<_RealType>:: 2508 operator()(_UniformRandomNumberGenerator& __urng, 2509 const param_type& __p) 2510 { 2511 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2512 __aurng(__urng); 2513 return __p.b() * std::pow(-std::log(result_type(1) - __aurng()), 2514 result_type(1) / __p.a()); 2515 } 2516 2517 template<typename _RealType> 2518 template<typename _ForwardIterator, 2519 typename _UniformRandomNumberGenerator> 2520 void 2521 weibull_distribution<_RealType>:: 2522 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2523 _UniformRandomNumberGenerator& __urng, 2524 const param_type& __p) 2525 { 2526 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2527 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2528 __aurng(__urng); 2529 auto __inv_a = result_type(1) / __p.a(); 2530 2531 while (__f != __t) 2532 *__f++ = __p.b() * std::pow(-std::log(result_type(1) - __aurng()), 2533 __inv_a); 2534 } 2535 2536 template<typename _RealType, typename _CharT, typename _Traits> 2537 std::basic_ostream<_CharT, _Traits>& 2538 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2539 const weibull_distribution<_RealType>& __x) 2540 { 2541 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 2542 2543 const typename __ios_base::fmtflags __flags = __os.flags(); 2544 const _CharT __fill = __os.fill(); 2545 const std::streamsize __precision = __os.precision(); 2546 const _CharT __space = __os.widen(' '); 2547 __os.flags(__ios_base::scientific | __ios_base::left); 2548 __os.fill(__space); 2549 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2550 2551 __os << __x.a() << __space << __x.b(); 2552 2553 __os.flags(__flags); 2554 __os.fill(__fill); 2555 __os.precision(__precision); 2556 return __os; 2557 } 2558 2559 template<typename _RealType, typename _CharT, typename _Traits> 2560 std::basic_istream<_CharT, _Traits>& 2561 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2562 weibull_distribution<_RealType>& __x) 2563 { 2564 using param_type = typename weibull_distribution<_RealType>::param_type; 2565 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 2566 2567 const typename __ios_base::fmtflags __flags = __is.flags(); 2568 __is.flags(__ios_base::dec | __ios_base::skipws); 2569 2570 _RealType __a, __b; 2571 if (__is >> __a >> __b) 2572 __x.param(param_type(__a, __b)); 2573 2574 __is.flags(__flags); 2575 return __is; 2576 } 2577 2578 2579 template<typename _RealType> 2580 template<typename _UniformRandomNumberGenerator> 2581 typename extreme_value_distribution<_RealType>::result_type 2582 extreme_value_distribution<_RealType>:: 2583 operator()(_UniformRandomNumberGenerator& __urng, 2584 const param_type& __p) 2585 { 2586 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2587 __aurng(__urng); 2588 return __p.a() - __p.b() * std::log(-std::log(result_type(1) 2589 - __aurng())); 2590 } 2591 2592 template<typename _RealType> 2593 template<typename _ForwardIterator, 2594 typename _UniformRandomNumberGenerator> 2595 void 2596 extreme_value_distribution<_RealType>:: 2597 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2598 _UniformRandomNumberGenerator& __urng, 2599 const param_type& __p) 2600 { 2601 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2602 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2603 __aurng(__urng); 2604 2605 while (__f != __t) 2606 *__f++ = __p.a() - __p.b() * std::log(-std::log(result_type(1) 2607 - __aurng())); 2608 } 2609 2610 template<typename _RealType, typename _CharT, typename _Traits> 2611 std::basic_ostream<_CharT, _Traits>& 2612 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2613 const extreme_value_distribution<_RealType>& __x) 2614 { 2615 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 2616 2617 const typename __ios_base::fmtflags __flags = __os.flags(); 2618 const _CharT __fill = __os.fill(); 2619 const std::streamsize __precision = __os.precision(); 2620 const _CharT __space = __os.widen(' '); 2621 __os.flags(__ios_base::scientific | __ios_base::left); 2622 __os.fill(__space); 2623 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2624 2625 __os << __x.a() << __space << __x.b(); 2626 2627 __os.flags(__flags); 2628 __os.fill(__fill); 2629 __os.precision(__precision); 2630 return __os; 2631 } 2632 2633 template<typename _RealType, typename _CharT, typename _Traits> 2634 std::basic_istream<_CharT, _Traits>& 2635 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2636 extreme_value_distribution<_RealType>& __x) 2637 { 2638 using param_type 2639 = typename extreme_value_distribution<_RealType>::param_type; 2640 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 2641 2642 const typename __ios_base::fmtflags __flags = __is.flags(); 2643 __is.flags(__ios_base::dec | __ios_base::skipws); 2644 2645 _RealType __a, __b; 2646 if (__is >> __a >> __b) 2647 __x.param(param_type(__a, __b)); 2648 2649 __is.flags(__flags); 2650 return __is; 2651 } 2652 2653 2654 template<typename _IntType> 2655 void 2656 discrete_distribution<_IntType>::param_type:: 2657 _M_initialize() 2658 { 2659 if (_M_prob.size() < 2) 2660 { 2661 _M_prob.clear(); 2662 return; 2663 } 2664 2665 const double __sum = std::accumulate(_M_prob.begin(), 2666 _M_prob.end(), 0.0); 2667 __glibcxx_assert(__sum > 0); 2668 // Now normalize the probabilites. 2669 __detail::__normalize(_M_prob.begin(), _M_prob.end(), _M_prob.begin(), 2670 __sum); 2671 // Accumulate partial sums. 2672 _M_cp.reserve(_M_prob.size()); 2673 std::partial_sum(_M_prob.begin(), _M_prob.end(), 2674 std::back_inserter(_M_cp)); 2675 // Make sure the last cumulative probability is one. 2676 _M_cp[_M_cp.size() - 1] = 1.0; 2677 } 2678 2679 template<typename _IntType> 2680 template<typename _Func> 2681 discrete_distribution<_IntType>::param_type:: 2682 param_type(size_t __nw, double __xmin, double __xmax, _Func __fw) 2683 : _M_prob(), _M_cp() 2684 { 2685 const size_t __n = __nw == 0 ? 1 : __nw; 2686 const double __delta = (__xmax - __xmin) / __n; 2687 2688 _M_prob.reserve(__n); 2689 for (size_t __k = 0; __k < __nw; ++__k) 2690 _M_prob.push_back(__fw(__xmin + __k * __delta + 0.5 * __delta)); 2691 2692 _M_initialize(); 2693 } 2694 2695 template<typename _IntType> 2696 template<typename _UniformRandomNumberGenerator> 2697 typename discrete_distribution<_IntType>::result_type 2698 discrete_distribution<_IntType>:: 2699 operator()(_UniformRandomNumberGenerator& __urng, 2700 const param_type& __param) 2701 { 2702 if (__param._M_cp.empty()) 2703 return result_type(0); 2704 2705 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 2706 __aurng(__urng); 2707 2708 const double __p = __aurng(); 2709 auto __pos = std::lower_bound(__param._M_cp.begin(), 2710 __param._M_cp.end(), __p); 2711 2712 return __pos - __param._M_cp.begin(); 2713 } 2714 2715 template<typename _IntType> 2716 template<typename _ForwardIterator, 2717 typename _UniformRandomNumberGenerator> 2718 void 2719 discrete_distribution<_IntType>:: 2720 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2721 _UniformRandomNumberGenerator& __urng, 2722 const param_type& __param) 2723 { 2724 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2725 2726 if (__param._M_cp.empty()) 2727 { 2728 while (__f != __t) 2729 *__f++ = result_type(0); 2730 return; 2731 } 2732 2733 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 2734 __aurng(__urng); 2735 2736 while (__f != __t) 2737 { 2738 const double __p = __aurng(); 2739 auto __pos = std::lower_bound(__param._M_cp.begin(), 2740 __param._M_cp.end(), __p); 2741 2742 *__f++ = __pos - __param._M_cp.begin(); 2743 } 2744 } 2745 2746 template<typename _IntType, typename _CharT, typename _Traits> 2747 std::basic_ostream<_CharT, _Traits>& 2748 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2749 const discrete_distribution<_IntType>& __x) 2750 { 2751 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 2752 2753 const typename __ios_base::fmtflags __flags = __os.flags(); 2754 const _CharT __fill = __os.fill(); 2755 const std::streamsize __precision = __os.precision(); 2756 const _CharT __space = __os.widen(' '); 2757 __os.flags(__ios_base::scientific | __ios_base::left); 2758 __os.fill(__space); 2759 __os.precision(std::numeric_limits<double>::max_digits10); 2760 2761 std::vector<double> __prob = __x.probabilities(); 2762 __os << __prob.size(); 2763 for (auto __dit = __prob.begin(); __dit != __prob.end(); ++__dit) 2764 __os << __space << *__dit; 2765 2766 __os.flags(__flags); 2767 __os.fill(__fill); 2768 __os.precision(__precision); 2769 return __os; 2770 } 2771 2772 namespace __detail 2773 { 2774 template<typename _ValT, typename _CharT, typename _Traits> 2775 basic_istream<_CharT, _Traits>& 2776 __extract_params(basic_istream<_CharT, _Traits>& __is, 2777 vector<_ValT>& __vals, size_t __n) 2778 { 2779 __vals.reserve(__n); 2780 while (__n--) 2781 { 2782 _ValT __val; 2783 if (__is >> __val) 2784 __vals.push_back(__val); 2785 else 2786 break; 2787 } 2788 return __is; 2789 } 2790 } // namespace __detail 2791 2792 template<typename _IntType, typename _CharT, typename _Traits> 2793 std::basic_istream<_CharT, _Traits>& 2794 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2795 discrete_distribution<_IntType>& __x) 2796 { 2797 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 2798 2799 const typename __ios_base::fmtflags __flags = __is.flags(); 2800 __is.flags(__ios_base::dec | __ios_base::skipws); 2801 2802 size_t __n; 2803 if (__is >> __n) 2804 { 2805 std::vector<double> __prob_vec; 2806 if (__detail::__extract_params(__is, __prob_vec, __n)) 2807 __x.param({__prob_vec.begin(), __prob_vec.end()}); 2808 } 2809 2810 __is.flags(__flags); 2811 return __is; 2812 } 2813 2814 2815 template<typename _RealType> 2816 void 2817 piecewise_constant_distribution<_RealType>::param_type:: 2818 _M_initialize() 2819 { 2820 if (_M_int.size() < 2 2821 || (_M_int.size() == 2 2822 && _M_int[0] == _RealType(0) 2823 && _M_int[1] == _RealType(1))) 2824 { 2825 _M_int.clear(); 2826 _M_den.clear(); 2827 return; 2828 } 2829 2830 const double __sum = std::accumulate(_M_den.begin(), 2831 _M_den.end(), 0.0); 2832 __glibcxx_assert(__sum > 0); 2833 2834 __detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(), 2835 __sum); 2836 2837 _M_cp.reserve(_M_den.size()); 2838 std::partial_sum(_M_den.begin(), _M_den.end(), 2839 std::back_inserter(_M_cp)); 2840 2841 // Make sure the last cumulative probability is one. 2842 _M_cp[_M_cp.size() - 1] = 1.0; 2843 2844 for (size_t __k = 0; __k < _M_den.size(); ++__k) 2845 _M_den[__k] /= _M_int[__k + 1] - _M_int[__k]; 2846 } 2847 2848 template<typename _RealType> 2849 template<typename _InputIteratorB, typename _InputIteratorW> 2850 piecewise_constant_distribution<_RealType>::param_type:: 2851 param_type(_InputIteratorB __bbegin, 2852 _InputIteratorB __bend, 2853 _InputIteratorW __wbegin) 2854 : _M_int(), _M_den(), _M_cp() 2855 { 2856 if (__bbegin != __bend) 2857 { 2858 for (;;) 2859 { 2860 _M_int.push_back(*__bbegin); 2861 ++__bbegin; 2862 if (__bbegin == __bend) 2863 break; 2864 2865 _M_den.push_back(*__wbegin); 2866 ++__wbegin; 2867 } 2868 } 2869 2870 _M_initialize(); 2871 } 2872 2873 template<typename _RealType> 2874 template<typename _Func> 2875 piecewise_constant_distribution<_RealType>::param_type:: 2876 param_type(initializer_list<_RealType> __bl, _Func __fw) 2877 : _M_int(), _M_den(), _M_cp() 2878 { 2879 _M_int.reserve(__bl.size()); 2880 for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter) 2881 _M_int.push_back(*__biter); 2882 2883 _M_den.reserve(_M_int.size() - 1); 2884 for (size_t __k = 0; __k < _M_int.size() - 1; ++__k) 2885 _M_den.push_back(__fw(0.5 * (_M_int[__k + 1] + _M_int[__k]))); 2886 2887 _M_initialize(); 2888 } 2889 2890 template<typename _RealType> 2891 template<typename _Func> 2892 piecewise_constant_distribution<_RealType>::param_type:: 2893 param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw) 2894 : _M_int(), _M_den(), _M_cp() 2895 { 2896 const size_t __n = __nw == 0 ? 1 : __nw; 2897 const _RealType __delta = (__xmax - __xmin) / __n; 2898 2899 _M_int.reserve(__n + 1); 2900 for (size_t __k = 0; __k <= __nw; ++__k) 2901 _M_int.push_back(__xmin + __k * __delta); 2902 2903 _M_den.reserve(__n); 2904 for (size_t __k = 0; __k < __nw; ++__k) 2905 _M_den.push_back(__fw(_M_int[__k] + 0.5 * __delta)); 2906 2907 _M_initialize(); 2908 } 2909 2910 template<typename _RealType> 2911 template<typename _UniformRandomNumberGenerator> 2912 typename piecewise_constant_distribution<_RealType>::result_type 2913 piecewise_constant_distribution<_RealType>:: 2914 operator()(_UniformRandomNumberGenerator& __urng, 2915 const param_type& __param) 2916 { 2917 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 2918 __aurng(__urng); 2919 2920 const double __p = __aurng(); 2921 if (__param._M_cp.empty()) 2922 return __p; 2923 2924 auto __pos = std::lower_bound(__param._M_cp.begin(), 2925 __param._M_cp.end(), __p); 2926 const size_t __i = __pos - __param._M_cp.begin(); 2927 2928 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0; 2929 2930 return __param._M_int[__i] + (__p - __pref) / __param._M_den[__i]; 2931 } 2932 2933 template<typename _RealType> 2934 template<typename _ForwardIterator, 2935 typename _UniformRandomNumberGenerator> 2936 void 2937 piecewise_constant_distribution<_RealType>:: 2938 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2939 _UniformRandomNumberGenerator& __urng, 2940 const param_type& __param) 2941 { 2942 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2943 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 2944 __aurng(__urng); 2945 2946 if (__param._M_cp.empty()) 2947 { 2948 while (__f != __t) 2949 *__f++ = __aurng(); 2950 return; 2951 } 2952 2953 while (__f != __t) 2954 { 2955 const double __p = __aurng(); 2956 2957 auto __pos = std::lower_bound(__param._M_cp.begin(), 2958 __param._M_cp.end(), __p); 2959 const size_t __i = __pos - __param._M_cp.begin(); 2960 2961 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0; 2962 2963 *__f++ = (__param._M_int[__i] 2964 + (__p - __pref) / __param._M_den[__i]); 2965 } 2966 } 2967 2968 template<typename _RealType, typename _CharT, typename _Traits> 2969 std::basic_ostream<_CharT, _Traits>& 2970 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2971 const piecewise_constant_distribution<_RealType>& __x) 2972 { 2973 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 2974 2975 const typename __ios_base::fmtflags __flags = __os.flags(); 2976 const _CharT __fill = __os.fill(); 2977 const std::streamsize __precision = __os.precision(); 2978 const _CharT __space = __os.widen(' '); 2979 __os.flags(__ios_base::scientific | __ios_base::left); 2980 __os.fill(__space); 2981 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2982 2983 std::vector<_RealType> __int = __x.intervals(); 2984 __os << __int.size() - 1; 2985 2986 for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit) 2987 __os << __space << *__xit; 2988 2989 std::vector<double> __den = __x.densities(); 2990 for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit) 2991 __os << __space << *__dit; 2992 2993 __os.flags(__flags); 2994 __os.fill(__fill); 2995 __os.precision(__precision); 2996 return __os; 2997 } 2998 2999 template<typename _RealType, typename _CharT, typename _Traits> 3000 std::basic_istream<_CharT, _Traits>& 3001 operator>>(std::basic_istream<_CharT, _Traits>& __is, 3002 piecewise_constant_distribution<_RealType>& __x) 3003 { 3004 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 3005 3006 const typename __ios_base::fmtflags __flags = __is.flags(); 3007 __is.flags(__ios_base::dec | __ios_base::skipws); 3008 3009 size_t __n; 3010 if (__is >> __n) 3011 { 3012 std::vector<_RealType> __int_vec; 3013 if (__detail::__extract_params(__is, __int_vec, __n + 1)) 3014 { 3015 std::vector<double> __den_vec; 3016 if (__detail::__extract_params(__is, __den_vec, __n)) 3017 { 3018 __x.param({ __int_vec.begin(), __int_vec.end(), 3019 __den_vec.begin() }); 3020 } 3021 } 3022 } 3023 3024 __is.flags(__flags); 3025 return __is; 3026 } 3027 3028 3029 template<typename _RealType> 3030 void 3031 piecewise_linear_distribution<_RealType>::param_type:: 3032 _M_initialize() 3033 { 3034 if (_M_int.size() < 2 3035 || (_M_int.size() == 2 3036 && _M_int[0] == _RealType(0) 3037 && _M_int[1] == _RealType(1) 3038 && _M_den[0] == _M_den[1])) 3039 { 3040 _M_int.clear(); 3041 _M_den.clear(); 3042 return; 3043 } 3044 3045 double __sum = 0.0; 3046 _M_cp.reserve(_M_int.size() - 1); 3047 _M_m.reserve(_M_int.size() - 1); 3048 for (size_t __k = 0; __k < _M_int.size() - 1; ++__k) 3049 { 3050 const _RealType __delta = _M_int[__k + 1] - _M_int[__k]; 3051 __sum += 0.5 * (_M_den[__k + 1] + _M_den[__k]) * __delta; 3052 _M_cp.push_back(__sum); 3053 _M_m.push_back((_M_den[__k + 1] - _M_den[__k]) / __delta); 3054 } 3055 __glibcxx_assert(__sum > 0); 3056 3057 // Now normalize the densities... 3058 __detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(), 3059 __sum); 3060 // ... and partial sums... 3061 __detail::__normalize(_M_cp.begin(), _M_cp.end(), _M_cp.begin(), __sum); 3062 // ... and slopes. 3063 __detail::__normalize(_M_m.begin(), _M_m.end(), _M_m.begin(), __sum); 3064 3065 // Make sure the last cumulative probablility is one. 3066 _M_cp[_M_cp.size() - 1] = 1.0; 3067 } 3068 3069 template<typename _RealType> 3070 template<typename _InputIteratorB, typename _InputIteratorW> 3071 piecewise_linear_distribution<_RealType>::param_type:: 3072 param_type(_InputIteratorB __bbegin, 3073 _InputIteratorB __bend, 3074 _InputIteratorW __wbegin) 3075 : _M_int(), _M_den(), _M_cp(), _M_m() 3076 { 3077 for (; __bbegin != __bend; ++__bbegin, ++__wbegin) 3078 { 3079 _M_int.push_back(*__bbegin); 3080 _M_den.push_back(*__wbegin); 3081 } 3082 3083 _M_initialize(); 3084 } 3085 3086 template<typename _RealType> 3087 template<typename _Func> 3088 piecewise_linear_distribution<_RealType>::param_type:: 3089 param_type(initializer_list<_RealType> __bl, _Func __fw) 3090 : _M_int(), _M_den(), _M_cp(), _M_m() 3091 { 3092 _M_int.reserve(__bl.size()); 3093 _M_den.reserve(__bl.size()); 3094 for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter) 3095 { 3096 _M_int.push_back(*__biter); 3097 _M_den.push_back(__fw(*__biter)); 3098 } 3099 3100 _M_initialize(); 3101 } 3102 3103 template<typename _RealType> 3104 template<typename _Func> 3105 piecewise_linear_distribution<_RealType>::param_type:: 3106 param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw) 3107 : _M_int(), _M_den(), _M_cp(), _M_m() 3108 { 3109 const size_t __n = __nw == 0 ? 1 : __nw; 3110 const _RealType __delta = (__xmax - __xmin) / __n; 3111 3112 _M_int.reserve(__n + 1); 3113 _M_den.reserve(__n + 1); 3114 for (size_t __k = 0; __k <= __nw; ++__k) 3115 { 3116 _M_int.push_back(__xmin + __k * __delta); 3117 _M_den.push_back(__fw(_M_int[__k] + __delta)); 3118 } 3119 3120 _M_initialize(); 3121 } 3122 3123 template<typename _RealType> 3124 template<typename _UniformRandomNumberGenerator> 3125 typename piecewise_linear_distribution<_RealType>::result_type 3126 piecewise_linear_distribution<_RealType>:: 3127 operator()(_UniformRandomNumberGenerator& __urng, 3128 const param_type& __param) 3129 { 3130 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 3131 __aurng(__urng); 3132 3133 const double __p = __aurng(); 3134 if (__param._M_cp.empty()) 3135 return __p; 3136 3137 auto __pos = std::lower_bound(__param._M_cp.begin(), 3138 __param._M_cp.end(), __p); 3139 const size_t __i = __pos - __param._M_cp.begin(); 3140 3141 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0; 3142 3143 const double __a = 0.5 * __param._M_m[__i]; 3144 const double __b = __param._M_den[__i]; 3145 const double __cm = __p - __pref; 3146 3147 _RealType __x = __param._M_int[__i]; 3148 if (__a == 0) 3149 __x += __cm / __b; 3150 else 3151 { 3152 const double __d = __b * __b + 4.0 * __a * __cm; 3153 __x += 0.5 * (std::sqrt(__d) - __b) / __a; 3154 } 3155 3156 return __x; 3157 } 3158 3159 template<typename _RealType> 3160 template<typename _ForwardIterator, 3161 typename _UniformRandomNumberGenerator> 3162 void 3163 piecewise_linear_distribution<_RealType>:: 3164 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 3165 _UniformRandomNumberGenerator& __urng, 3166 const param_type& __param) 3167 { 3168 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 3169 // We could duplicate everything from operator()... 3170 while (__f != __t) 3171 *__f++ = this->operator()(__urng, __param); 3172 } 3173 3174 template<typename _RealType, typename _CharT, typename _Traits> 3175 std::basic_ostream<_CharT, _Traits>& 3176 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 3177 const piecewise_linear_distribution<_RealType>& __x) 3178 { 3179 using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; 3180 3181 const typename __ios_base::fmtflags __flags = __os.flags(); 3182 const _CharT __fill = __os.fill(); 3183 const std::streamsize __precision = __os.precision(); 3184 const _CharT __space = __os.widen(' '); 3185 __os.flags(__ios_base::scientific | __ios_base::left); 3186 __os.fill(__space); 3187 __os.precision(std::numeric_limits<_RealType>::max_digits10); 3188 3189 std::vector<_RealType> __int = __x.intervals(); 3190 __os << __int.size() - 1; 3191 3192 for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit) 3193 __os << __space << *__xit; 3194 3195 std::vector<double> __den = __x.densities(); 3196 for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit) 3197 __os << __space << *__dit; 3198 3199 __os.flags(__flags); 3200 __os.fill(__fill); 3201 __os.precision(__precision); 3202 return __os; 3203 } 3204 3205 template<typename _RealType, typename _CharT, typename _Traits> 3206 std::basic_istream<_CharT, _Traits>& 3207 operator>>(std::basic_istream<_CharT, _Traits>& __is, 3208 piecewise_linear_distribution<_RealType>& __x) 3209 { 3210 using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; 3211 3212 const typename __ios_base::fmtflags __flags = __is.flags(); 3213 __is.flags(__ios_base::dec | __ios_base::skipws); 3214 3215 size_t __n; 3216 if (__is >> __n) 3217 { 3218 vector<_RealType> __int_vec; 3219 if (__detail::__extract_params(__is, __int_vec, __n + 1)) 3220 { 3221 vector<double> __den_vec; 3222 if (__detail::__extract_params(__is, __den_vec, __n + 1)) 3223 { 3224 __x.param({ __int_vec.begin(), __int_vec.end(), 3225 __den_vec.begin() }); 3226 } 3227 } 3228 } 3229 __is.flags(__flags); 3230 return __is; 3231 } 3232 3233 3234 template<typename _IntType, typename> 3235 seed_seq::seed_seq(std::initializer_list<_IntType> __il) 3236 { 3237 _M_v.reserve(__il.size()); 3238 for (auto __iter = __il.begin(); __iter != __il.end(); ++__iter) 3239 _M_v.push_back(__detail::__mod<result_type, 3240 __detail::_Shift<result_type, 32>::__value>(*__iter)); 3241 } 3242 3243 template<typename _InputIterator> 3244 seed_seq::seed_seq(_InputIterator __begin, _InputIterator __end) 3245 { 3246 if _GLIBCXX17_CONSTEXPR (__is_random_access_iter<_InputIterator>::value) 3247 _M_v.reserve(std::distance(__begin, __end)); 3248 3249 for (_InputIterator __iter = __begin; __iter != __end; ++__iter) 3250 _M_v.push_back(__detail::__mod<result_type, 3251 __detail::_Shift<result_type, 32>::__value>(*__iter)); 3252 } 3253 3254 template<typename _RandomAccessIterator> 3255 void 3256 seed_seq::generate(_RandomAccessIterator __begin, 3257 _RandomAccessIterator __end) 3258 { 3259 typedef typename iterator_traits<_RandomAccessIterator>::value_type 3260 _Type; 3261 3262 if (__begin == __end) 3263 return; 3264 3265 std::fill(__begin, __end, _Type(0x8b8b8b8bu)); 3266 3267 const size_t __n = __end - __begin; 3268 const size_t __s = _M_v.size(); 3269 const size_t __t = (__n >= 623) ? 11 3270 : (__n >= 68) ? 7 3271 : (__n >= 39) ? 5 3272 : (__n >= 7) ? 3 3273 : (__n - 1) / 2; 3274 const size_t __p = (__n - __t) / 2; 3275 const size_t __q = __p + __t; 3276 const size_t __m = std::max(size_t(__s + 1), __n); 3277 3278 #ifndef __UINT32_TYPE__ 3279 struct _Up 3280 { 3281 _Up(uint_least32_t v) : _M_v(v & 0xffffffffu) { } 3282 3283 operator uint_least32_t() const { return _M_v; } 3284 3285 uint_least32_t _M_v; 3286 }; 3287 using uint32_t = _Up; 3288 #endif 3289 3290 // k == 0, every element in [begin,end) equals 0x8b8b8b8bu 3291 { 3292 uint32_t __r1 = 1371501266u; 3293 uint32_t __r2 = __r1 + __s; 3294 __begin[__p] += __r1; 3295 __begin[__q] = (uint32_t)__begin[__q] + __r2; 3296 __begin[0] = __r2; 3297 } 3298 3299 for (size_t __k = 1; __k <= __s; ++__k) 3300 { 3301 const size_t __kn = __k % __n; 3302 const size_t __kpn = (__k + __p) % __n; 3303 const size_t __kqn = (__k + __q) % __n; 3304 uint32_t __arg = (__begin[__kn] 3305 ^ __begin[__kpn] 3306 ^ __begin[(__k - 1) % __n]); 3307 uint32_t __r1 = 1664525u * (__arg ^ (__arg >> 27)); 3308 uint32_t __r2 = __r1 + (uint32_t)__kn + _M_v[__k - 1]; 3309 __begin[__kpn] = (uint32_t)__begin[__kpn] + __r1; 3310 __begin[__kqn] = (uint32_t)__begin[__kqn] + __r2; 3311 __begin[__kn] = __r2; 3312 } 3313 3314 for (size_t __k = __s + 1; __k < __m; ++__k) 3315 { 3316 const size_t __kn = __k % __n; 3317 const size_t __kpn = (__k + __p) % __n; 3318 const size_t __kqn = (__k + __q) % __n; 3319 uint32_t __arg = (__begin[__kn] 3320 ^ __begin[__kpn] 3321 ^ __begin[(__k - 1) % __n]); 3322 uint32_t __r1 = 1664525u * (__arg ^ (__arg >> 27)); 3323 uint32_t __r2 = __r1 + (uint32_t)__kn; 3324 __begin[__kpn] = (uint32_t)__begin[__kpn] + __r1; 3325 __begin[__kqn] = (uint32_t)__begin[__kqn] + __r2; 3326 __begin[__kn] = __r2; 3327 } 3328 3329 for (size_t __k = __m; __k < __m + __n; ++__k) 3330 { 3331 const size_t __kn = __k % __n; 3332 const size_t __kpn = (__k + __p) % __n; 3333 const size_t __kqn = (__k + __q) % __n; 3334 uint32_t __arg = (__begin[__kn] 3335 + __begin[__kpn] 3336 + __begin[(__k - 1) % __n]); 3337 uint32_t __r3 = 1566083941u * (__arg ^ (__arg >> 27)); 3338 uint32_t __r4 = __r3 - __kn; 3339 __begin[__kpn] ^= __r3; 3340 __begin[__kqn] ^= __r4; 3341 __begin[__kn] = __r4; 3342 } 3343 } 3344 3345 template<typename _RealType, size_t __bits, 3346 typename _UniformRandomNumberGenerator> 3347 _RealType 3348 generate_canonical(_UniformRandomNumberGenerator& __urng) 3349 { 3350 static_assert(std::is_floating_point<_RealType>::value, 3351 "template argument must be a floating point type"); 3352 3353 const size_t __b 3354 = std::min(static_cast<size_t>(std::numeric_limits<_RealType>::digits), 3355 __bits); 3356 const long double __r = static_cast<long double>(__urng.max()) 3357 - static_cast<long double>(__urng.min()) + 1.0L; 3358 const size_t __log2r = std::log(__r) / std::log(2.0L); 3359 const size_t __m = std::max<size_t>(1UL, 3360 (__b + __log2r - 1UL) / __log2r); 3361 _RealType __ret; 3362 _RealType __sum = _RealType(0); 3363 _RealType __tmp = _RealType(1); 3364 for (size_t __k = __m; __k != 0; --__k) 3365 { 3366 __sum += _RealType(__urng() - __urng.min()) * __tmp; 3367 __tmp *= __r; 3368 } 3369 __ret = __sum / __tmp; 3370 if (__builtin_expect(__ret >= _RealType(1), 0)) 3371 { 3372 #if _GLIBCXX_USE_C99_MATH_TR1 3373 __ret = std::nextafter(_RealType(1), _RealType(0)); 3374 #else 3375 __ret = _RealType(1) 3376 - std::numeric_limits<_RealType>::epsilon() / _RealType(2); 3377 #endif 3378 } 3379 return __ret; 3380 } 3381 3382 _GLIBCXX_END_NAMESPACE_VERSION 3383 } // namespace 3384 3385 #endif
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