1 // random number generation (out of line) -*- C++ -*- 2 3 // Copyright (C) 2009-2018 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 /* 40 * (Further) implementation-space details. 41 */ 42 namespace __detail 43 { 44 // General case for x = (ax + c) mod m -- use Schrage's algorithm 45 // to avoid integer overflow. 46 // 47 // Preconditions: a > 0, m > 0. 48 // 49 // Note: only works correctly for __m % __a < __m / __a. 50 template<typename _Tp, _Tp __m, _Tp __a, _Tp __c> 51 _Tp 52 _Mod<_Tp, __m, __a, __c, false, true>:: 53 __calc(_Tp __x) 54 { 55 if (__a == 1) 56 __x %= __m; 57 else 58 { 59 static const _Tp __q = __m / __a; 60 static const _Tp __r = __m % __a; 61 62 _Tp __t1 = __a * (__x % __q); 63 _Tp __t2 = __r * (__x / __q); 64 if (__t1 >= __t2) 65 __x = __t1 - __t2; 66 else 67 __x = __m - __t2 + __t1; 68 } 69 70 if (__c != 0) 71 { 72 const _Tp __d = __m - __x; 73 if (__d > __c) 74 __x += __c; 75 else 76 __x = __c - __d; 77 } 78 return __x; 79 } 80 81 template<typename _InputIterator, typename _OutputIterator, 82 typename _Tp> 83 _OutputIterator 84 __normalize(_InputIterator __first, _InputIterator __last, 85 _OutputIterator __result, const _Tp& __factor) 86 { 87 for (; __first != __last; ++__first, ++__result) 88 *__result = *__first / __factor; 89 return __result; 90 } 91 92 } // namespace __detail 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 typename std::enable_if<std::is_class<_Sseq>::value>::type 132 linear_congruential_engine<_UIntType, __a, __c, __m>:: 133 seed(_Sseq& __q) 134 { 135 const _UIntType __k0 = __m == 0 ? std::numeric_limits<_UIntType>::digits 136 : std::__lg(__m); 137 const _UIntType __k = (__k0 + 31) / 32; 138 uint_least32_t __arr[__k + 3]; 139 __q.generate(__arr + 0, __arr + __k + 3); 140 _UIntType __factor = 1u; 141 _UIntType __sum = 0u; 142 for (size_t __j = 0; __j < __k; ++__j) 143 { 144 __sum += __arr[__j + 3] * __factor; 145 __factor *= __detail::_Shift<_UIntType, 32>::__value; 146 } 147 seed(__sum); 148 } 149 150 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m, 151 typename _CharT, typename _Traits> 152 std::basic_ostream<_CharT, _Traits>& 153 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 154 const linear_congruential_engine<_UIntType, 155 __a, __c, __m>& __lcr) 156 { 157 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 158 typedef typename __ostream_type::ios_base __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 typedef std::basic_istream<_CharT, _Traits> __istream_type; 179 typedef typename __istream_type::ios_base __ios_base; 180 181 const typename __ios_base::fmtflags __flags = __is.flags(); 182 __is.flags(__ios_base::dec); 183 184 __is >> __lcr._M_x; 185 186 __is.flags(__flags); 187 return __is; 188 } 189 190 191 template<typename _UIntType, 192 size_t __w, size_t __n, size_t __m, size_t __r, 193 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 194 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 195 _UIntType __f> 196 constexpr size_t 197 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 198 __s, __b, __t, __c, __l, __f>::word_size; 199 200 template<typename _UIntType, 201 size_t __w, size_t __n, size_t __m, size_t __r, 202 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 203 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 204 _UIntType __f> 205 constexpr size_t 206 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 207 __s, __b, __t, __c, __l, __f>::state_size; 208 209 template<typename _UIntType, 210 size_t __w, size_t __n, size_t __m, size_t __r, 211 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 212 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 213 _UIntType __f> 214 constexpr size_t 215 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 216 __s, __b, __t, __c, __l, __f>::shift_size; 217 218 template<typename _UIntType, 219 size_t __w, size_t __n, size_t __m, size_t __r, 220 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 221 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 222 _UIntType __f> 223 constexpr size_t 224 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 225 __s, __b, __t, __c, __l, __f>::mask_bits; 226 227 template<typename _UIntType, 228 size_t __w, size_t __n, size_t __m, size_t __r, 229 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 230 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 231 _UIntType __f> 232 constexpr _UIntType 233 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 234 __s, __b, __t, __c, __l, __f>::xor_mask; 235 236 template<typename _UIntType, 237 size_t __w, size_t __n, size_t __m, size_t __r, 238 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 239 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 240 _UIntType __f> 241 constexpr size_t 242 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 243 __s, __b, __t, __c, __l, __f>::tempering_u; 244 245 template<typename _UIntType, 246 size_t __w, size_t __n, size_t __m, size_t __r, 247 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 248 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 249 _UIntType __f> 250 constexpr _UIntType 251 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 252 __s, __b, __t, __c, __l, __f>::tempering_d; 253 254 template<typename _UIntType, 255 size_t __w, size_t __n, size_t __m, size_t __r, 256 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 257 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 258 _UIntType __f> 259 constexpr size_t 260 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 261 __s, __b, __t, __c, __l, __f>::tempering_s; 262 263 template<typename _UIntType, 264 size_t __w, size_t __n, size_t __m, size_t __r, 265 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 266 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 267 _UIntType __f> 268 constexpr _UIntType 269 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 270 __s, __b, __t, __c, __l, __f>::tempering_b; 271 272 template<typename _UIntType, 273 size_t __w, size_t __n, size_t __m, size_t __r, 274 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 275 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 276 _UIntType __f> 277 constexpr size_t 278 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 279 __s, __b, __t, __c, __l, __f>::tempering_t; 280 281 template<typename _UIntType, 282 size_t __w, size_t __n, size_t __m, size_t __r, 283 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 284 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 285 _UIntType __f> 286 constexpr _UIntType 287 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 288 __s, __b, __t, __c, __l, __f>::tempering_c; 289 290 template<typename _UIntType, 291 size_t __w, size_t __n, size_t __m, size_t __r, 292 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 293 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 294 _UIntType __f> 295 constexpr size_t 296 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 297 __s, __b, __t, __c, __l, __f>::tempering_l; 298 299 template<typename _UIntType, 300 size_t __w, size_t __n, size_t __m, size_t __r, 301 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 302 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 303 _UIntType __f> 304 constexpr _UIntType 305 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 306 __s, __b, __t, __c, __l, __f>:: 307 initialization_multiplier; 308 309 template<typename _UIntType, 310 size_t __w, size_t __n, size_t __m, size_t __r, 311 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 312 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 313 _UIntType __f> 314 constexpr _UIntType 315 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 316 __s, __b, __t, __c, __l, __f>::default_seed; 317 318 template<typename _UIntType, 319 size_t __w, size_t __n, size_t __m, size_t __r, 320 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 321 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 322 _UIntType __f> 323 void 324 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 325 __s, __b, __t, __c, __l, __f>:: 326 seed(result_type __sd) 327 { 328 _M_x[0] = __detail::__mod<_UIntType, 329 __detail::_Shift<_UIntType, __w>::__value>(__sd); 330 331 for (size_t __i = 1; __i < state_size; ++__i) 332 { 333 _UIntType __x = _M_x[__i - 1]; 334 __x ^= __x >> (__w - 2); 335 __x *= __f; 336 __x += __detail::__mod<_UIntType, __n>(__i); 337 _M_x[__i] = __detail::__mod<_UIntType, 338 __detail::_Shift<_UIntType, __w>::__value>(__x); 339 } 340 _M_p = state_size; 341 } 342 343 template<typename _UIntType, 344 size_t __w, size_t __n, size_t __m, size_t __r, 345 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 346 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 347 _UIntType __f> 348 template<typename _Sseq> 349 typename std::enable_if<std::is_class<_Sseq>::value>::type 350 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 351 __s, __b, __t, __c, __l, __f>:: 352 seed(_Sseq& __q) 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 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 479 typedef typename __ostream_type::ios_base __ios_base; 480 481 const typename __ios_base::fmtflags __flags = __os.flags(); 482 const _CharT __fill = __os.fill(); 483 const _CharT __space = __os.widen(' '); 484 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 485 __os.fill(__space); 486 487 for (size_t __i = 0; __i < __n; ++__i) 488 __os << __x._M_x[__i] << __space; 489 __os << __x._M_p; 490 491 __os.flags(__flags); 492 __os.fill(__fill); 493 return __os; 494 } 495 496 template<typename _UIntType, size_t __w, 497 size_t __n, size_t __m, size_t __r, 498 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 499 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 500 _UIntType __f, typename _CharT, typename _Traits> 501 std::basic_istream<_CharT, _Traits>& 502 operator>>(std::basic_istream<_CharT, _Traits>& __is, 503 mersenne_twister_engine<_UIntType, __w, __n, __m, 504 __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x) 505 { 506 typedef std::basic_istream<_CharT, _Traits> __istream_type; 507 typedef typename __istream_type::ios_base __ios_base; 508 509 const typename __ios_base::fmtflags __flags = __is.flags(); 510 __is.flags(__ios_base::dec | __ios_base::skipws); 511 512 for (size_t __i = 0; __i < __n; ++__i) 513 __is >> __x._M_x[__i]; 514 __is >> __x._M_p; 515 516 __is.flags(__flags); 517 return __is; 518 } 519 520 521 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 522 constexpr size_t 523 subtract_with_carry_engine<_UIntType, __w, __s, __r>::word_size; 524 525 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 526 constexpr size_t 527 subtract_with_carry_engine<_UIntType, __w, __s, __r>::short_lag; 528 529 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 530 constexpr size_t 531 subtract_with_carry_engine<_UIntType, __w, __s, __r>::long_lag; 532 533 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 534 constexpr _UIntType 535 subtract_with_carry_engine<_UIntType, __w, __s, __r>::default_seed; 536 537 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 538 void 539 subtract_with_carry_engine<_UIntType, __w, __s, __r>:: 540 seed(result_type __value) 541 { 542 std::linear_congruential_engine<result_type, 40014u, 0u, 2147483563u> 543 __lcg(__value == 0u ? default_seed : __value); 544 545 const size_t __n = (__w + 31) / 32; 546 547 for (size_t __i = 0; __i < long_lag; ++__i) 548 { 549 _UIntType __sum = 0u; 550 _UIntType __factor = 1u; 551 for (size_t __j = 0; __j < __n; ++__j) 552 { 553 __sum += __detail::__mod<uint_least32_t, 554 __detail::_Shift<uint_least32_t, 32>::__value> 555 (__lcg()) * __factor; 556 __factor *= __detail::_Shift<_UIntType, 32>::__value; 557 } 558 _M_x[__i] = __detail::__mod<_UIntType, 559 __detail::_Shift<_UIntType, __w>::__value>(__sum); 560 } 561 _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0; 562 _M_p = 0; 563 } 564 565 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 566 template<typename _Sseq> 567 typename std::enable_if<std::is_class<_Sseq>::value>::type 568 subtract_with_carry_engine<_UIntType, __w, __s, __r>:: 569 seed(_Sseq& __q) 570 { 571 const size_t __k = (__w + 31) / 32; 572 uint_least32_t __arr[__r * __k]; 573 __q.generate(__arr + 0, __arr + __r * __k); 574 575 for (size_t __i = 0; __i < long_lag; ++__i) 576 { 577 _UIntType __sum = 0u; 578 _UIntType __factor = 1u; 579 for (size_t __j = 0; __j < __k; ++__j) 580 { 581 __sum += __arr[__k * __i + __j] * __factor; 582 __factor *= __detail::_Shift<_UIntType, 32>::__value; 583 } 584 _M_x[__i] = __detail::__mod<_UIntType, 585 __detail::_Shift<_UIntType, __w>::__value>(__sum); 586 } 587 _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0; 588 _M_p = 0; 589 } 590 591 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 592 typename subtract_with_carry_engine<_UIntType, __w, __s, __r>:: 593 result_type 594 subtract_with_carry_engine<_UIntType, __w, __s, __r>:: 595 operator()() 596 { 597 // Derive short lag index from current index. 598 long __ps = _M_p - short_lag; 599 if (__ps < 0) 600 __ps += long_lag; 601 602 // Calculate new x(i) without overflow or division. 603 // NB: Thanks to the requirements for _UIntType, _M_x[_M_p] + _M_carry 604 // cannot overflow. 605 _UIntType __xi; 606 if (_M_x[__ps] >= _M_x[_M_p] + _M_carry) 607 { 608 __xi = _M_x[__ps] - _M_x[_M_p] - _M_carry; 609 _M_carry = 0; 610 } 611 else 612 { 613 __xi = (__detail::_Shift<_UIntType, __w>::__value 614 - _M_x[_M_p] - _M_carry + _M_x[__ps]); 615 _M_carry = 1; 616 } 617 _M_x[_M_p] = __xi; 618 619 // Adjust current index to loop around in ring buffer. 620 if (++_M_p >= long_lag) 621 _M_p = 0; 622 623 return __xi; 624 } 625 626 template<typename _UIntType, size_t __w, size_t __s, size_t __r, 627 typename _CharT, typename _Traits> 628 std::basic_ostream<_CharT, _Traits>& 629 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 630 const subtract_with_carry_engine<_UIntType, 631 __w, __s, __r>& __x) 632 { 633 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 634 typedef typename __ostream_type::ios_base __ios_base; 635 636 const typename __ios_base::fmtflags __flags = __os.flags(); 637 const _CharT __fill = __os.fill(); 638 const _CharT __space = __os.widen(' '); 639 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 640 __os.fill(__space); 641 642 for (size_t __i = 0; __i < __r; ++__i) 643 __os << __x._M_x[__i] << __space; 644 __os << __x._M_carry << __space << __x._M_p; 645 646 __os.flags(__flags); 647 __os.fill(__fill); 648 return __os; 649 } 650 651 template<typename _UIntType, size_t __w, size_t __s, size_t __r, 652 typename _CharT, typename _Traits> 653 std::basic_istream<_CharT, _Traits>& 654 operator>>(std::basic_istream<_CharT, _Traits>& __is, 655 subtract_with_carry_engine<_UIntType, __w, __s, __r>& __x) 656 { 657 typedef std::basic_ostream<_CharT, _Traits> __istream_type; 658 typedef typename __istream_type::ios_base __ios_base; 659 660 const typename __ios_base::fmtflags __flags = __is.flags(); 661 __is.flags(__ios_base::dec | __ios_base::skipws); 662 663 for (size_t __i = 0; __i < __r; ++__i) 664 __is >> __x._M_x[__i]; 665 __is >> __x._M_carry; 666 __is >> __x._M_p; 667 668 __is.flags(__flags); 669 return __is; 670 } 671 672 673 template<typename _RandomNumberEngine, size_t __p, size_t __r> 674 constexpr size_t 675 discard_block_engine<_RandomNumberEngine, __p, __r>::block_size; 676 677 template<typename _RandomNumberEngine, size_t __p, size_t __r> 678 constexpr size_t 679 discard_block_engine<_RandomNumberEngine, __p, __r>::used_block; 680 681 template<typename _RandomNumberEngine, size_t __p, size_t __r> 682 typename discard_block_engine<_RandomNumberEngine, 683 __p, __r>::result_type 684 discard_block_engine<_RandomNumberEngine, __p, __r>:: 685 operator()() 686 { 687 if (_M_n >= used_block) 688 { 689 _M_b.discard(block_size - _M_n); 690 _M_n = 0; 691 } 692 ++_M_n; 693 return _M_b(); 694 } 695 696 template<typename _RandomNumberEngine, size_t __p, size_t __r, 697 typename _CharT, typename _Traits> 698 std::basic_ostream<_CharT, _Traits>& 699 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 700 const discard_block_engine<_RandomNumberEngine, 701 __p, __r>& __x) 702 { 703 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 704 typedef typename __ostream_type::ios_base __ios_base; 705 706 const typename __ios_base::fmtflags __flags = __os.flags(); 707 const _CharT __fill = __os.fill(); 708 const _CharT __space = __os.widen(' '); 709 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 710 __os.fill(__space); 711 712 __os << __x.base() << __space << __x._M_n; 713 714 __os.flags(__flags); 715 __os.fill(__fill); 716 return __os; 717 } 718 719 template<typename _RandomNumberEngine, size_t __p, size_t __r, 720 typename _CharT, typename _Traits> 721 std::basic_istream<_CharT, _Traits>& 722 operator>>(std::basic_istream<_CharT, _Traits>& __is, 723 discard_block_engine<_RandomNumberEngine, __p, __r>& __x) 724 { 725 typedef std::basic_istream<_CharT, _Traits> __istream_type; 726 typedef typename __istream_type::ios_base __ios_base; 727 728 const typename __ios_base::fmtflags __flags = __is.flags(); 729 __is.flags(__ios_base::dec | __ios_base::skipws); 730 731 __is >> __x._M_b >> __x._M_n; 732 733 __is.flags(__flags); 734 return __is; 735 } 736 737 738 template<typename _RandomNumberEngine, size_t __w, typename _UIntType> 739 typename independent_bits_engine<_RandomNumberEngine, __w, _UIntType>:: 740 result_type 741 independent_bits_engine<_RandomNumberEngine, __w, _UIntType>:: 742 operator()() 743 { 744 typedef typename _RandomNumberEngine::result_type _Eresult_type; 745 const _Eresult_type __r 746 = (_M_b.max() - _M_b.min() < std::numeric_limits<_Eresult_type>::max() 747 ? _M_b.max() - _M_b.min() + 1 : 0); 748 const unsigned __edig = std::numeric_limits<_Eresult_type>::digits; 749 const unsigned __m = __r ? std::__lg(__r) : __edig; 750 751 typedef typename std::common_type<_Eresult_type, result_type>::type 752 __ctype; 753 const unsigned __cdig = std::numeric_limits<__ctype>::digits; 754 755 unsigned __n, __n0; 756 __ctype __s0, __s1, __y0, __y1; 757 758 for (size_t __i = 0; __i < 2; ++__i) 759 { 760 __n = (__w + __m - 1) / __m + __i; 761 __n0 = __n - __w % __n; 762 const unsigned __w0 = __w / __n; // __w0 <= __m 763 764 __s0 = 0; 765 __s1 = 0; 766 if (__w0 < __cdig) 767 { 768 __s0 = __ctype(1) << __w0; 769 __s1 = __s0 << 1; 770 } 771 772 __y0 = 0; 773 __y1 = 0; 774 if (__r) 775 { 776 __y0 = __s0 * (__r / __s0); 777 if (__s1) 778 __y1 = __s1 * (__r / __s1); 779 780 if (__r - __y0 <= __y0 / __n) 781 break; 782 } 783 else 784 break; 785 } 786 787 result_type __sum = 0; 788 for (size_t __k = 0; __k < __n0; ++__k) 789 { 790 __ctype __u; 791 do 792 __u = _M_b() - _M_b.min(); 793 while (__y0 && __u >= __y0); 794 __sum = __s0 * __sum + (__s0 ? __u % __s0 : __u); 795 } 796 for (size_t __k = __n0; __k < __n; ++__k) 797 { 798 __ctype __u; 799 do 800 __u = _M_b() - _M_b.min(); 801 while (__y1 && __u >= __y1); 802 __sum = __s1 * __sum + (__s1 ? __u % __s1 : __u); 803 } 804 return __sum; 805 } 806 807 808 template<typename _RandomNumberEngine, size_t __k> 809 constexpr size_t 810 shuffle_order_engine<_RandomNumberEngine, __k>::table_size; 811 812 template<typename _RandomNumberEngine, size_t __k> 813 typename shuffle_order_engine<_RandomNumberEngine, __k>::result_type 814 shuffle_order_engine<_RandomNumberEngine, __k>:: 815 operator()() 816 { 817 size_t __j = __k * ((_M_y - _M_b.min()) 818 / (_M_b.max() - _M_b.min() + 1.0L)); 819 _M_y = _M_v[__j]; 820 _M_v[__j] = _M_b(); 821 822 return _M_y; 823 } 824 825 template<typename _RandomNumberEngine, size_t __k, 826 typename _CharT, typename _Traits> 827 std::basic_ostream<_CharT, _Traits>& 828 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 829 const shuffle_order_engine<_RandomNumberEngine, __k>& __x) 830 { 831 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 832 typedef typename __ostream_type::ios_base __ios_base; 833 834 const typename __ios_base::fmtflags __flags = __os.flags(); 835 const _CharT __fill = __os.fill(); 836 const _CharT __space = __os.widen(' '); 837 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 838 __os.fill(__space); 839 840 __os << __x.base(); 841 for (size_t __i = 0; __i < __k; ++__i) 842 __os << __space << __x._M_v[__i]; 843 __os << __space << __x._M_y; 844 845 __os.flags(__flags); 846 __os.fill(__fill); 847 return __os; 848 } 849 850 template<typename _RandomNumberEngine, size_t __k, 851 typename _CharT, typename _Traits> 852 std::basic_istream<_CharT, _Traits>& 853 operator>>(std::basic_istream<_CharT, _Traits>& __is, 854 shuffle_order_engine<_RandomNumberEngine, __k>& __x) 855 { 856 typedef std::basic_istream<_CharT, _Traits> __istream_type; 857 typedef typename __istream_type::ios_base __ios_base; 858 859 const typename __ios_base::fmtflags __flags = __is.flags(); 860 __is.flags(__ios_base::dec | __ios_base::skipws); 861 862 __is >> __x._M_b; 863 for (size_t __i = 0; __i < __k; ++__i) 864 __is >> __x._M_v[__i]; 865 __is >> __x._M_y; 866 867 __is.flags(__flags); 868 return __is; 869 } 870 871 872 template<typename _IntType, typename _CharT, typename _Traits> 873 std::basic_ostream<_CharT, _Traits>& 874 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 875 const uniform_int_distribution<_IntType>& __x) 876 { 877 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 878 typedef typename __ostream_type::ios_base __ios_base; 879 880 const typename __ios_base::fmtflags __flags = __os.flags(); 881 const _CharT __fill = __os.fill(); 882 const _CharT __space = __os.widen(' '); 883 __os.flags(__ios_base::scientific | __ios_base::left); 884 __os.fill(__space); 885 886 __os << __x.a() << __space << __x.b(); 887 888 __os.flags(__flags); 889 __os.fill(__fill); 890 return __os; 891 } 892 893 template<typename _IntType, typename _CharT, typename _Traits> 894 std::basic_istream<_CharT, _Traits>& 895 operator>>(std::basic_istream<_CharT, _Traits>& __is, 896 uniform_int_distribution<_IntType>& __x) 897 { 898 typedef std::basic_istream<_CharT, _Traits> __istream_type; 899 typedef typename __istream_type::ios_base __ios_base; 900 901 const typename __ios_base::fmtflags __flags = __is.flags(); 902 __is.flags(__ios_base::dec | __ios_base::skipws); 903 904 _IntType __a, __b; 905 __is >> __a >> __b; 906 __x.param(typename uniform_int_distribution<_IntType>:: 907 param_type(__a, __b)); 908 909 __is.flags(__flags); 910 return __is; 911 } 912 913 914 template<typename _RealType> 915 template<typename _ForwardIterator, 916 typename _UniformRandomNumberGenerator> 917 void 918 uniform_real_distribution<_RealType>:: 919 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 920 _UniformRandomNumberGenerator& __urng, 921 const param_type& __p) 922 { 923 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 924 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 925 __aurng(__urng); 926 auto __range = __p.b() - __p.a(); 927 while (__f != __t) 928 *__f++ = __aurng() * __range + __p.a(); 929 } 930 931 template<typename _RealType, typename _CharT, typename _Traits> 932 std::basic_ostream<_CharT, _Traits>& 933 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 934 const uniform_real_distribution<_RealType>& __x) 935 { 936 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 937 typedef typename __ostream_type::ios_base __ios_base; 938 939 const typename __ios_base::fmtflags __flags = __os.flags(); 940 const _CharT __fill = __os.fill(); 941 const std::streamsize __precision = __os.precision(); 942 const _CharT __space = __os.widen(' '); 943 __os.flags(__ios_base::scientific | __ios_base::left); 944 __os.fill(__space); 945 __os.precision(std::numeric_limits<_RealType>::max_digits10); 946 947 __os << __x.a() << __space << __x.b(); 948 949 __os.flags(__flags); 950 __os.fill(__fill); 951 __os.precision(__precision); 952 return __os; 953 } 954 955 template<typename _RealType, typename _CharT, typename _Traits> 956 std::basic_istream<_CharT, _Traits>& 957 operator>>(std::basic_istream<_CharT, _Traits>& __is, 958 uniform_real_distribution<_RealType>& __x) 959 { 960 typedef std::basic_istream<_CharT, _Traits> __istream_type; 961 typedef typename __istream_type::ios_base __ios_base; 962 963 const typename __ios_base::fmtflags __flags = __is.flags(); 964 __is.flags(__ios_base::skipws); 965 966 _RealType __a, __b; 967 __is >> __a >> __b; 968 __x.param(typename uniform_real_distribution<_RealType>:: 969 param_type(__a, __b)); 970 971 __is.flags(__flags); 972 return __is; 973 } 974 975 976 template<typename _ForwardIterator, 977 typename _UniformRandomNumberGenerator> 978 void 979 std::bernoulli_distribution:: 980 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 981 _UniformRandomNumberGenerator& __urng, 982 const param_type& __p) 983 { 984 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 985 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 986 __aurng(__urng); 987 auto __limit = __p.p() * (__aurng.max() - __aurng.min()); 988 989 while (__f != __t) 990 *__f++ = (__aurng() - __aurng.min()) < __limit; 991 } 992 993 template<typename _CharT, typename _Traits> 994 std::basic_ostream<_CharT, _Traits>& 995 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 996 const bernoulli_distribution& __x) 997 { 998 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 999 typedef typename __ostream_type::ios_base __ios_base; 1000 1001 const typename __ios_base::fmtflags __flags = __os.flags(); 1002 const _CharT __fill = __os.fill(); 1003 const std::streamsize __precision = __os.precision(); 1004 __os.flags(__ios_base::scientific | __ios_base::left); 1005 __os.fill(__os.widen(' ')); 1006 __os.precision(std::numeric_limits<double>::max_digits10); 1007 1008 __os << __x.p(); 1009 1010 __os.flags(__flags); 1011 __os.fill(__fill); 1012 __os.precision(__precision); 1013 return __os; 1014 } 1015 1016 1017 template<typename _IntType> 1018 template<typename _UniformRandomNumberGenerator> 1019 typename geometric_distribution<_IntType>::result_type 1020 geometric_distribution<_IntType>:: 1021 operator()(_UniformRandomNumberGenerator& __urng, 1022 const param_type& __param) 1023 { 1024 // About the epsilon thing see this thread: 1025 // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html 1026 const double __naf = 1027 (1 - std::numeric_limits<double>::epsilon()) / 2; 1028 // The largest _RealType convertible to _IntType. 1029 const double __thr = 1030 std::numeric_limits<_IntType>::max() + __naf; 1031 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1032 __aurng(__urng); 1033 1034 double __cand; 1035 do 1036 __cand = std::floor(std::log(1.0 - __aurng()) / __param._M_log_1_p); 1037 while (__cand >= __thr); 1038 1039 return result_type(__cand + __naf); 1040 } 1041 1042 template<typename _IntType> 1043 template<typename _ForwardIterator, 1044 typename _UniformRandomNumberGenerator> 1045 void 1046 geometric_distribution<_IntType>:: 1047 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1048 _UniformRandomNumberGenerator& __urng, 1049 const param_type& __param) 1050 { 1051 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1052 // About the epsilon thing see this thread: 1053 // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html 1054 const double __naf = 1055 (1 - std::numeric_limits<double>::epsilon()) / 2; 1056 // The largest _RealType convertible to _IntType. 1057 const double __thr = 1058 std::numeric_limits<_IntType>::max() + __naf; 1059 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1060 __aurng(__urng); 1061 1062 while (__f != __t) 1063 { 1064 double __cand; 1065 do 1066 __cand = std::floor(std::log(1.0 - __aurng()) 1067 / __param._M_log_1_p); 1068 while (__cand >= __thr); 1069 1070 *__f++ = __cand + __naf; 1071 } 1072 } 1073 1074 template<typename _IntType, 1075 typename _CharT, typename _Traits> 1076 std::basic_ostream<_CharT, _Traits>& 1077 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1078 const geometric_distribution<_IntType>& __x) 1079 { 1080 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1081 typedef typename __ostream_type::ios_base __ios_base; 1082 1083 const typename __ios_base::fmtflags __flags = __os.flags(); 1084 const _CharT __fill = __os.fill(); 1085 const std::streamsize __precision = __os.precision(); 1086 __os.flags(__ios_base::scientific | __ios_base::left); 1087 __os.fill(__os.widen(' ')); 1088 __os.precision(std::numeric_limits<double>::max_digits10); 1089 1090 __os << __x.p(); 1091 1092 __os.flags(__flags); 1093 __os.fill(__fill); 1094 __os.precision(__precision); 1095 return __os; 1096 } 1097 1098 template<typename _IntType, 1099 typename _CharT, typename _Traits> 1100 std::basic_istream<_CharT, _Traits>& 1101 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1102 geometric_distribution<_IntType>& __x) 1103 { 1104 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1105 typedef typename __istream_type::ios_base __ios_base; 1106 1107 const typename __ios_base::fmtflags __flags = __is.flags(); 1108 __is.flags(__ios_base::skipws); 1109 1110 double __p; 1111 __is >> __p; 1112 __x.param(typename geometric_distribution<_IntType>::param_type(__p)); 1113 1114 __is.flags(__flags); 1115 return __is; 1116 } 1117 1118 // This is Leger's algorithm, also in Devroye, Ch. X, Example 1.5. 1119 template<typename _IntType> 1120 template<typename _UniformRandomNumberGenerator> 1121 typename negative_binomial_distribution<_IntType>::result_type 1122 negative_binomial_distribution<_IntType>:: 1123 operator()(_UniformRandomNumberGenerator& __urng) 1124 { 1125 const double __y = _M_gd(__urng); 1126 1127 // XXX Is the constructor too slow? 1128 std::poisson_distribution<result_type> __poisson(__y); 1129 return __poisson(__urng); 1130 } 1131 1132 template<typename _IntType> 1133 template<typename _UniformRandomNumberGenerator> 1134 typename negative_binomial_distribution<_IntType>::result_type 1135 negative_binomial_distribution<_IntType>:: 1136 operator()(_UniformRandomNumberGenerator& __urng, 1137 const param_type& __p) 1138 { 1139 typedef typename std::gamma_distribution<double>::param_type 1140 param_type; 1141 1142 const double __y = 1143 _M_gd(__urng, param_type(__p.k(), (1.0 - __p.p()) / __p.p())); 1144 1145 std::poisson_distribution<result_type> __poisson(__y); 1146 return __poisson(__urng); 1147 } 1148 1149 template<typename _IntType> 1150 template<typename _ForwardIterator, 1151 typename _UniformRandomNumberGenerator> 1152 void 1153 negative_binomial_distribution<_IntType>:: 1154 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1155 _UniformRandomNumberGenerator& __urng) 1156 { 1157 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1158 while (__f != __t) 1159 { 1160 const double __y = _M_gd(__urng); 1161 1162 // XXX Is the constructor too slow? 1163 std::poisson_distribution<result_type> __poisson(__y); 1164 *__f++ = __poisson(__urng); 1165 } 1166 } 1167 1168 template<typename _IntType> 1169 template<typename _ForwardIterator, 1170 typename _UniformRandomNumberGenerator> 1171 void 1172 negative_binomial_distribution<_IntType>:: 1173 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1174 _UniformRandomNumberGenerator& __urng, 1175 const param_type& __p) 1176 { 1177 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1178 typename std::gamma_distribution<result_type>::param_type 1179 __p2(__p.k(), (1.0 - __p.p()) / __p.p()); 1180 1181 while (__f != __t) 1182 { 1183 const double __y = _M_gd(__urng, __p2); 1184 1185 std::poisson_distribution<result_type> __poisson(__y); 1186 *__f++ = __poisson(__urng); 1187 } 1188 } 1189 1190 template<typename _IntType, typename _CharT, typename _Traits> 1191 std::basic_ostream<_CharT, _Traits>& 1192 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1193 const negative_binomial_distribution<_IntType>& __x) 1194 { 1195 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1196 typedef typename __ostream_type::ios_base __ios_base; 1197 1198 const typename __ios_base::fmtflags __flags = __os.flags(); 1199 const _CharT __fill = __os.fill(); 1200 const std::streamsize __precision = __os.precision(); 1201 const _CharT __space = __os.widen(' '); 1202 __os.flags(__ios_base::scientific | __ios_base::left); 1203 __os.fill(__os.widen(' ')); 1204 __os.precision(std::numeric_limits<double>::max_digits10); 1205 1206 __os << __x.k() << __space << __x.p() 1207 << __space << __x._M_gd; 1208 1209 __os.flags(__flags); 1210 __os.fill(__fill); 1211 __os.precision(__precision); 1212 return __os; 1213 } 1214 1215 template<typename _IntType, typename _CharT, typename _Traits> 1216 std::basic_istream<_CharT, _Traits>& 1217 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1218 negative_binomial_distribution<_IntType>& __x) 1219 { 1220 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1221 typedef typename __istream_type::ios_base __ios_base; 1222 1223 const typename __ios_base::fmtflags __flags = __is.flags(); 1224 __is.flags(__ios_base::skipws); 1225 1226 _IntType __k; 1227 double __p; 1228 __is >> __k >> __p >> __x._M_gd; 1229 __x.param(typename negative_binomial_distribution<_IntType>:: 1230 param_type(__k, __p)); 1231 1232 __is.flags(__flags); 1233 return __is; 1234 } 1235 1236 1237 template<typename _IntType> 1238 void 1239 poisson_distribution<_IntType>::param_type:: 1240 _M_initialize() 1241 { 1242 #if _GLIBCXX_USE_C99_MATH_TR1 1243 if (_M_mean >= 12) 1244 { 1245 const double __m = std::floor(_M_mean); 1246 _M_lm_thr = std::log(_M_mean); 1247 _M_lfm = std::lgamma(__m + 1); 1248 _M_sm = std::sqrt(__m); 1249 1250 const double __pi_4 = 0.7853981633974483096156608458198757L; 1251 const double __dx = std::sqrt(2 * __m * std::log(32 * __m 1252 / __pi_4)); 1253 _M_d = std::round(std::max<double>(6.0, std::min(__m, __dx))); 1254 const double __cx = 2 * __m + _M_d; 1255 _M_scx = std::sqrt(__cx / 2); 1256 _M_1cx = 1 / __cx; 1257 1258 _M_c2b = std::sqrt(__pi_4 * __cx) * std::exp(_M_1cx); 1259 _M_cb = 2 * __cx * std::exp(-_M_d * _M_1cx * (1 + _M_d / 2)) 1260 / _M_d; 1261 } 1262 else 1263 #endif 1264 _M_lm_thr = std::exp(-_M_mean); 1265 } 1266 1267 /** 1268 * A rejection algorithm when mean >= 12 and a simple method based 1269 * upon the multiplication of uniform random variates otherwise. 1270 * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1 1271 * is defined. 1272 * 1273 * Reference: 1274 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, 1275 * New York, 1986, Ch. X, Sects. 3.3 & 3.4 (+ Errata!). 1276 */ 1277 template<typename _IntType> 1278 template<typename _UniformRandomNumberGenerator> 1279 typename poisson_distribution<_IntType>::result_type 1280 poisson_distribution<_IntType>:: 1281 operator()(_UniformRandomNumberGenerator& __urng, 1282 const param_type& __param) 1283 { 1284 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1285 __aurng(__urng); 1286 #if _GLIBCXX_USE_C99_MATH_TR1 1287 if (__param.mean() >= 12) 1288 { 1289 double __x; 1290 1291 // See comments above... 1292 const double __naf = 1293 (1 - std::numeric_limits<double>::epsilon()) / 2; 1294 const double __thr = 1295 std::numeric_limits<_IntType>::max() + __naf; 1296 1297 const double __m = std::floor(__param.mean()); 1298 // sqrt(pi / 2) 1299 const double __spi_2 = 1.2533141373155002512078826424055226L; 1300 const double __c1 = __param._M_sm * __spi_2; 1301 const double __c2 = __param._M_c2b + __c1; 1302 const double __c3 = __c2 + 1; 1303 const double __c4 = __c3 + 1; 1304 // 1 / 78 1305 const double __178 = 0.0128205128205128205128205128205128L; 1306 // e^(1 / 78) 1307 const double __e178 = 1.0129030479320018583185514777512983L; 1308 const double __c5 = __c4 + __e178; 1309 const double __c = __param._M_cb + __c5; 1310 const double __2cx = 2 * (2 * __m + __param._M_d); 1311 1312 bool __reject = true; 1313 do 1314 { 1315 const double __u = __c * __aurng(); 1316 const double __e = -std::log(1.0 - __aurng()); 1317 1318 double __w = 0.0; 1319 1320 if (__u <= __c1) 1321 { 1322 const double __n = _M_nd(__urng); 1323 const double __y = -std::abs(__n) * __param._M_sm - 1; 1324 __x = std::floor(__y); 1325 __w = -__n * __n / 2; 1326 if (__x < -__m) 1327 continue; 1328 } 1329 else if (__u <= __c2) 1330 { 1331 const double __n = _M_nd(__urng); 1332 const double __y = 1 + std::abs(__n) * __param._M_scx; 1333 __x = std::ceil(__y); 1334 __w = __y * (2 - __y) * __param._M_1cx; 1335 if (__x > __param._M_d) 1336 continue; 1337 } 1338 else if (__u <= __c3) 1339 // NB: This case not in the book, nor in the Errata, 1340 // but should be ok... 1341 __x = -1; 1342 else if (__u <= __c4) 1343 __x = 0; 1344 else if (__u <= __c5) 1345 { 1346 __x = 1; 1347 // Only in the Errata, see libstdc++/83237. 1348 __w = __178; 1349 } 1350 else 1351 { 1352 const double __v = -std::log(1.0 - __aurng()); 1353 const double __y = __param._M_d 1354 + __v * __2cx / __param._M_d; 1355 __x = std::ceil(__y); 1356 __w = -__param._M_d * __param._M_1cx * (1 + __y / 2); 1357 } 1358 1359 __reject = (__w - __e - __x * __param._M_lm_thr 1360 > __param._M_lfm - std::lgamma(__x + __m + 1)); 1361 1362 __reject |= __x + __m >= __thr; 1363 1364 } while (__reject); 1365 1366 return result_type(__x + __m + __naf); 1367 } 1368 else 1369 #endif 1370 { 1371 _IntType __x = 0; 1372 double __prod = 1.0; 1373 1374 do 1375 { 1376 __prod *= __aurng(); 1377 __x += 1; 1378 } 1379 while (__prod > __param._M_lm_thr); 1380 1381 return __x - 1; 1382 } 1383 } 1384 1385 template<typename _IntType> 1386 template<typename _ForwardIterator, 1387 typename _UniformRandomNumberGenerator> 1388 void 1389 poisson_distribution<_IntType>:: 1390 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1391 _UniformRandomNumberGenerator& __urng, 1392 const param_type& __param) 1393 { 1394 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1395 // We could duplicate everything from operator()... 1396 while (__f != __t) 1397 *__f++ = this->operator()(__urng, __param); 1398 } 1399 1400 template<typename _IntType, 1401 typename _CharT, typename _Traits> 1402 std::basic_ostream<_CharT, _Traits>& 1403 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1404 const poisson_distribution<_IntType>& __x) 1405 { 1406 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1407 typedef typename __ostream_type::ios_base __ios_base; 1408 1409 const typename __ios_base::fmtflags __flags = __os.flags(); 1410 const _CharT __fill = __os.fill(); 1411 const std::streamsize __precision = __os.precision(); 1412 const _CharT __space = __os.widen(' '); 1413 __os.flags(__ios_base::scientific | __ios_base::left); 1414 __os.fill(__space); 1415 __os.precision(std::numeric_limits<double>::max_digits10); 1416 1417 __os << __x.mean() << __space << __x._M_nd; 1418 1419 __os.flags(__flags); 1420 __os.fill(__fill); 1421 __os.precision(__precision); 1422 return __os; 1423 } 1424 1425 template<typename _IntType, 1426 typename _CharT, typename _Traits> 1427 std::basic_istream<_CharT, _Traits>& 1428 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1429 poisson_distribution<_IntType>& __x) 1430 { 1431 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1432 typedef typename __istream_type::ios_base __ios_base; 1433 1434 const typename __ios_base::fmtflags __flags = __is.flags(); 1435 __is.flags(__ios_base::skipws); 1436 1437 double __mean; 1438 __is >> __mean >> __x._M_nd; 1439 __x.param(typename poisson_distribution<_IntType>::param_type(__mean)); 1440 1441 __is.flags(__flags); 1442 return __is; 1443 } 1444 1445 1446 template<typename _IntType> 1447 void 1448 binomial_distribution<_IntType>::param_type:: 1449 _M_initialize() 1450 { 1451 const double __p12 = _M_p <= 0.5 ? _M_p : 1.0 - _M_p; 1452 1453 _M_easy = true; 1454 1455 #if _GLIBCXX_USE_C99_MATH_TR1 1456 if (_M_t * __p12 >= 8) 1457 { 1458 _M_easy = false; 1459 const double __np = std::floor(_M_t * __p12); 1460 const double __pa = __np / _M_t; 1461 const double __1p = 1 - __pa; 1462 1463 const double __pi_4 = 0.7853981633974483096156608458198757L; 1464 const double __d1x = 1465 std::sqrt(__np * __1p * std::log(32 * __np 1466 / (81 * __pi_4 * __1p))); 1467 _M_d1 = std::round(std::max<double>(1.0, __d1x)); 1468 const double __d2x = 1469 std::sqrt(__np * __1p * std::log(32 * _M_t * __1p 1470 / (__pi_4 * __pa))); 1471 _M_d2 = std::round(std::max<double>(1.0, __d2x)); 1472 1473 // sqrt(pi / 2) 1474 const double __spi_2 = 1.2533141373155002512078826424055226L; 1475 _M_s1 = std::sqrt(__np * __1p) * (1 + _M_d1 / (4 * __np)); 1476 _M_s2 = std::sqrt(__np * __1p) * (1 + _M_d2 / (4 * _M_t * __1p)); 1477 _M_c = 2 * _M_d1 / __np; 1478 _M_a1 = std::exp(_M_c) * _M_s1 * __spi_2; 1479 const double __a12 = _M_a1 + _M_s2 * __spi_2; 1480 const double __s1s = _M_s1 * _M_s1; 1481 _M_a123 = __a12 + (std::exp(_M_d1 / (_M_t * __1p)) 1482 * 2 * __s1s / _M_d1 1483 * std::exp(-_M_d1 * _M_d1 / (2 * __s1s))); 1484 const double __s2s = _M_s2 * _M_s2; 1485 _M_s = (_M_a123 + 2 * __s2s / _M_d2 1486 * std::exp(-_M_d2 * _M_d2 / (2 * __s2s))); 1487 _M_lf = (std::lgamma(__np + 1) 1488 + std::lgamma(_M_t - __np + 1)); 1489 _M_lp1p = std::log(__pa / __1p); 1490 1491 _M_q = -std::log(1 - (__p12 - __pa) / __1p); 1492 } 1493 else 1494 #endif 1495 _M_q = -std::log(1 - __p12); 1496 } 1497 1498 template<typename _IntType> 1499 template<typename _UniformRandomNumberGenerator> 1500 typename binomial_distribution<_IntType>::result_type 1501 binomial_distribution<_IntType>:: 1502 _M_waiting(_UniformRandomNumberGenerator& __urng, 1503 _IntType __t, double __q) 1504 { 1505 _IntType __x = 0; 1506 double __sum = 0.0; 1507 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1508 __aurng(__urng); 1509 1510 do 1511 { 1512 if (__t == __x) 1513 return __x; 1514 const double __e = -std::log(1.0 - __aurng()); 1515 __sum += __e / (__t - __x); 1516 __x += 1; 1517 } 1518 while (__sum <= __q); 1519 1520 return __x - 1; 1521 } 1522 1523 /** 1524 * A rejection algorithm when t * p >= 8 and a simple waiting time 1525 * method - the second in the referenced book - otherwise. 1526 * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1 1527 * is defined. 1528 * 1529 * Reference: 1530 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, 1531 * New York, 1986, Ch. X, Sect. 4 (+ Errata!). 1532 */ 1533 template<typename _IntType> 1534 template<typename _UniformRandomNumberGenerator> 1535 typename binomial_distribution<_IntType>::result_type 1536 binomial_distribution<_IntType>:: 1537 operator()(_UniformRandomNumberGenerator& __urng, 1538 const param_type& __param) 1539 { 1540 result_type __ret; 1541 const _IntType __t = __param.t(); 1542 const double __p = __param.p(); 1543 const double __p12 = __p <= 0.5 ? __p : 1.0 - __p; 1544 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1545 __aurng(__urng); 1546 1547 #if _GLIBCXX_USE_C99_MATH_TR1 1548 if (!__param._M_easy) 1549 { 1550 double __x; 1551 1552 // See comments above... 1553 const double __naf = 1554 (1 - std::numeric_limits<double>::epsilon()) / 2; 1555 const double __thr = 1556 std::numeric_limits<_IntType>::max() + __naf; 1557 1558 const double __np = std::floor(__t * __p12); 1559 1560 // sqrt(pi / 2) 1561 const double __spi_2 = 1.2533141373155002512078826424055226L; 1562 const double __a1 = __param._M_a1; 1563 const double __a12 = __a1 + __param._M_s2 * __spi_2; 1564 const double __a123 = __param._M_a123; 1565 const double __s1s = __param._M_s1 * __param._M_s1; 1566 const double __s2s = __param._M_s2 * __param._M_s2; 1567 1568 bool __reject; 1569 do 1570 { 1571 const double __u = __param._M_s * __aurng(); 1572 1573 double __v; 1574 1575 if (__u <= __a1) 1576 { 1577 const double __n = _M_nd(__urng); 1578 const double __y = __param._M_s1 * std::abs(__n); 1579 __reject = __y >= __param._M_d1; 1580 if (!__reject) 1581 { 1582 const double __e = -std::log(1.0 - __aurng()); 1583 __x = std::floor(__y); 1584 __v = -__e - __n * __n / 2 + __param._M_c; 1585 } 1586 } 1587 else if (__u <= __a12) 1588 { 1589 const double __n = _M_nd(__urng); 1590 const double __y = __param._M_s2 * std::abs(__n); 1591 __reject = __y >= __param._M_d2; 1592 if (!__reject) 1593 { 1594 const double __e = -std::log(1.0 - __aurng()); 1595 __x = std::floor(-__y); 1596 __v = -__e - __n * __n / 2; 1597 } 1598 } 1599 else if (__u <= __a123) 1600 { 1601 const double __e1 = -std::log(1.0 - __aurng()); 1602 const double __e2 = -std::log(1.0 - __aurng()); 1603 1604 const double __y = __param._M_d1 1605 + 2 * __s1s * __e1 / __param._M_d1; 1606 __x = std::floor(__y); 1607 __v = (-__e2 + __param._M_d1 * (1 / (__t - __np) 1608 -__y / (2 * __s1s))); 1609 __reject = false; 1610 } 1611 else 1612 { 1613 const double __e1 = -std::log(1.0 - __aurng()); 1614 const double __e2 = -std::log(1.0 - __aurng()); 1615 1616 const double __y = __param._M_d2 1617 + 2 * __s2s * __e1 / __param._M_d2; 1618 __x = std::floor(-__y); 1619 __v = -__e2 - __param._M_d2 * __y / (2 * __s2s); 1620 __reject = false; 1621 } 1622 1623 __reject = __reject || __x < -__np || __x > __t - __np; 1624 if (!__reject) 1625 { 1626 const double __lfx = 1627 std::lgamma(__np + __x + 1) 1628 + std::lgamma(__t - (__np + __x) + 1); 1629 __reject = __v > __param._M_lf - __lfx 1630 + __x * __param._M_lp1p; 1631 } 1632 1633 __reject |= __x + __np >= __thr; 1634 } 1635 while (__reject); 1636 1637 __x += __np + __naf; 1638 1639 const _IntType __z = _M_waiting(__urng, __t - _IntType(__x), 1640 __param._M_q); 1641 __ret = _IntType(__x) + __z; 1642 } 1643 else 1644 #endif 1645 __ret = _M_waiting(__urng, __t, __param._M_q); 1646 1647 if (__p12 != __p) 1648 __ret = __t - __ret; 1649 return __ret; 1650 } 1651 1652 template<typename _IntType> 1653 template<typename _ForwardIterator, 1654 typename _UniformRandomNumberGenerator> 1655 void 1656 binomial_distribution<_IntType>:: 1657 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1658 _UniformRandomNumberGenerator& __urng, 1659 const param_type& __param) 1660 { 1661 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1662 // We could duplicate everything from operator()... 1663 while (__f != __t) 1664 *__f++ = this->operator()(__urng, __param); 1665 } 1666 1667 template<typename _IntType, 1668 typename _CharT, typename _Traits> 1669 std::basic_ostream<_CharT, _Traits>& 1670 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1671 const binomial_distribution<_IntType>& __x) 1672 { 1673 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1674 typedef typename __ostream_type::ios_base __ios_base; 1675 1676 const typename __ios_base::fmtflags __flags = __os.flags(); 1677 const _CharT __fill = __os.fill(); 1678 const std::streamsize __precision = __os.precision(); 1679 const _CharT __space = __os.widen(' '); 1680 __os.flags(__ios_base::scientific | __ios_base::left); 1681 __os.fill(__space); 1682 __os.precision(std::numeric_limits<double>::max_digits10); 1683 1684 __os << __x.t() << __space << __x.p() 1685 << __space << __x._M_nd; 1686 1687 __os.flags(__flags); 1688 __os.fill(__fill); 1689 __os.precision(__precision); 1690 return __os; 1691 } 1692 1693 template<typename _IntType, 1694 typename _CharT, typename _Traits> 1695 std::basic_istream<_CharT, _Traits>& 1696 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1697 binomial_distribution<_IntType>& __x) 1698 { 1699 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1700 typedef typename __istream_type::ios_base __ios_base; 1701 1702 const typename __ios_base::fmtflags __flags = __is.flags(); 1703 __is.flags(__ios_base::dec | __ios_base::skipws); 1704 1705 _IntType __t; 1706 double __p; 1707 __is >> __t >> __p >> __x._M_nd; 1708 __x.param(typename binomial_distribution<_IntType>:: 1709 param_type(__t, __p)); 1710 1711 __is.flags(__flags); 1712 return __is; 1713 } 1714 1715 1716 template<typename _RealType> 1717 template<typename _ForwardIterator, 1718 typename _UniformRandomNumberGenerator> 1719 void 1720 std::exponential_distribution<_RealType>:: 1721 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1722 _UniformRandomNumberGenerator& __urng, 1723 const param_type& __p) 1724 { 1725 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1726 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 1727 __aurng(__urng); 1728 while (__f != __t) 1729 *__f++ = -std::log(result_type(1) - __aurng()) / __p.lambda(); 1730 } 1731 1732 template<typename _RealType, typename _CharT, typename _Traits> 1733 std::basic_ostream<_CharT, _Traits>& 1734 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1735 const exponential_distribution<_RealType>& __x) 1736 { 1737 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1738 typedef typename __ostream_type::ios_base __ios_base; 1739 1740 const typename __ios_base::fmtflags __flags = __os.flags(); 1741 const _CharT __fill = __os.fill(); 1742 const std::streamsize __precision = __os.precision(); 1743 __os.flags(__ios_base::scientific | __ios_base::left); 1744 __os.fill(__os.widen(' ')); 1745 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1746 1747 __os << __x.lambda(); 1748 1749 __os.flags(__flags); 1750 __os.fill(__fill); 1751 __os.precision(__precision); 1752 return __os; 1753 } 1754 1755 template<typename _RealType, typename _CharT, typename _Traits> 1756 std::basic_istream<_CharT, _Traits>& 1757 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1758 exponential_distribution<_RealType>& __x) 1759 { 1760 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1761 typedef typename __istream_type::ios_base __ios_base; 1762 1763 const typename __ios_base::fmtflags __flags = __is.flags(); 1764 __is.flags(__ios_base::dec | __ios_base::skipws); 1765 1766 _RealType __lambda; 1767 __is >> __lambda; 1768 __x.param(typename exponential_distribution<_RealType>:: 1769 param_type(__lambda)); 1770 1771 __is.flags(__flags); 1772 return __is; 1773 } 1774 1775 1776 /** 1777 * Polar method due to Marsaglia. 1778 * 1779 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, 1780 * New York, 1986, Ch. V, Sect. 4.4. 1781 */ 1782 template<typename _RealType> 1783 template<typename _UniformRandomNumberGenerator> 1784 typename normal_distribution<_RealType>::result_type 1785 normal_distribution<_RealType>:: 1786 operator()(_UniformRandomNumberGenerator& __urng, 1787 const param_type& __param) 1788 { 1789 result_type __ret; 1790 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 1791 __aurng(__urng); 1792 1793 if (_M_saved_available) 1794 { 1795 _M_saved_available = false; 1796 __ret = _M_saved; 1797 } 1798 else 1799 { 1800 result_type __x, __y, __r2; 1801 do 1802 { 1803 __x = result_type(2.0) * __aurng() - 1.0; 1804 __y = result_type(2.0) * __aurng() - 1.0; 1805 __r2 = __x * __x + __y * __y; 1806 } 1807 while (__r2 > 1.0 || __r2 == 0.0); 1808 1809 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2); 1810 _M_saved = __x * __mult; 1811 _M_saved_available = true; 1812 __ret = __y * __mult; 1813 } 1814 1815 __ret = __ret * __param.stddev() + __param.mean(); 1816 return __ret; 1817 } 1818 1819 template<typename _RealType> 1820 template<typename _ForwardIterator, 1821 typename _UniformRandomNumberGenerator> 1822 void 1823 normal_distribution<_RealType>:: 1824 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1825 _UniformRandomNumberGenerator& __urng, 1826 const param_type& __param) 1827 { 1828 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1829 1830 if (__f == __t) 1831 return; 1832 1833 if (_M_saved_available) 1834 { 1835 _M_saved_available = false; 1836 *__f++ = _M_saved * __param.stddev() + __param.mean(); 1837 1838 if (__f == __t) 1839 return; 1840 } 1841 1842 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 1843 __aurng(__urng); 1844 1845 while (__f + 1 < __t) 1846 { 1847 result_type __x, __y, __r2; 1848 do 1849 { 1850 __x = result_type(2.0) * __aurng() - 1.0; 1851 __y = result_type(2.0) * __aurng() - 1.0; 1852 __r2 = __x * __x + __y * __y; 1853 } 1854 while (__r2 > 1.0 || __r2 == 0.0); 1855 1856 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2); 1857 *__f++ = __y * __mult * __param.stddev() + __param.mean(); 1858 *__f++ = __x * __mult * __param.stddev() + __param.mean(); 1859 } 1860 1861 if (__f != __t) 1862 { 1863 result_type __x, __y, __r2; 1864 do 1865 { 1866 __x = result_type(2.0) * __aurng() - 1.0; 1867 __y = result_type(2.0) * __aurng() - 1.0; 1868 __r2 = __x * __x + __y * __y; 1869 } 1870 while (__r2 > 1.0 || __r2 == 0.0); 1871 1872 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2); 1873 _M_saved = __x * __mult; 1874 _M_saved_available = true; 1875 *__f = __y * __mult * __param.stddev() + __param.mean(); 1876 } 1877 } 1878 1879 template<typename _RealType> 1880 bool 1881 operator==(const std::normal_distribution<_RealType>& __d1, 1882 const std::normal_distribution<_RealType>& __d2) 1883 { 1884 if (__d1._M_param == __d2._M_param 1885 && __d1._M_saved_available == __d2._M_saved_available) 1886 { 1887 if (__d1._M_saved_available 1888 && __d1._M_saved == __d2._M_saved) 1889 return true; 1890 else if(!__d1._M_saved_available) 1891 return true; 1892 else 1893 return false; 1894 } 1895 else 1896 return false; 1897 } 1898 1899 template<typename _RealType, typename _CharT, typename _Traits> 1900 std::basic_ostream<_CharT, _Traits>& 1901 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1902 const normal_distribution<_RealType>& __x) 1903 { 1904 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1905 typedef typename __ostream_type::ios_base __ios_base; 1906 1907 const typename __ios_base::fmtflags __flags = __os.flags(); 1908 const _CharT __fill = __os.fill(); 1909 const std::streamsize __precision = __os.precision(); 1910 const _CharT __space = __os.widen(' '); 1911 __os.flags(__ios_base::scientific | __ios_base::left); 1912 __os.fill(__space); 1913 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1914 1915 __os << __x.mean() << __space << __x.stddev() 1916 << __space << __x._M_saved_available; 1917 if (__x._M_saved_available) 1918 __os << __space << __x._M_saved; 1919 1920 __os.flags(__flags); 1921 __os.fill(__fill); 1922 __os.precision(__precision); 1923 return __os; 1924 } 1925 1926 template<typename _RealType, typename _CharT, typename _Traits> 1927 std::basic_istream<_CharT, _Traits>& 1928 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1929 normal_distribution<_RealType>& __x) 1930 { 1931 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1932 typedef typename __istream_type::ios_base __ios_base; 1933 1934 const typename __ios_base::fmtflags __flags = __is.flags(); 1935 __is.flags(__ios_base::dec | __ios_base::skipws); 1936 1937 double __mean, __stddev; 1938 __is >> __mean >> __stddev 1939 >> __x._M_saved_available; 1940 if (__x._M_saved_available) 1941 __is >> __x._M_saved; 1942 __x.param(typename normal_distribution<_RealType>:: 1943 param_type(__mean, __stddev)); 1944 1945 __is.flags(__flags); 1946 return __is; 1947 } 1948 1949 1950 template<typename _RealType> 1951 template<typename _ForwardIterator, 1952 typename _UniformRandomNumberGenerator> 1953 void 1954 lognormal_distribution<_RealType>:: 1955 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1956 _UniformRandomNumberGenerator& __urng, 1957 const param_type& __p) 1958 { 1959 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1960 while (__f != __t) 1961 *__f++ = std::exp(__p.s() * _M_nd(__urng) + __p.m()); 1962 } 1963 1964 template<typename _RealType, typename _CharT, typename _Traits> 1965 std::basic_ostream<_CharT, _Traits>& 1966 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1967 const lognormal_distribution<_RealType>& __x) 1968 { 1969 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1970 typedef typename __ostream_type::ios_base __ios_base; 1971 1972 const typename __ios_base::fmtflags __flags = __os.flags(); 1973 const _CharT __fill = __os.fill(); 1974 const std::streamsize __precision = __os.precision(); 1975 const _CharT __space = __os.widen(' '); 1976 __os.flags(__ios_base::scientific | __ios_base::left); 1977 __os.fill(__space); 1978 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1979 1980 __os << __x.m() << __space << __x.s() 1981 << __space << __x._M_nd; 1982 1983 __os.flags(__flags); 1984 __os.fill(__fill); 1985 __os.precision(__precision); 1986 return __os; 1987 } 1988 1989 template<typename _RealType, typename _CharT, typename _Traits> 1990 std::basic_istream<_CharT, _Traits>& 1991 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1992 lognormal_distribution<_RealType>& __x) 1993 { 1994 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1995 typedef typename __istream_type::ios_base __ios_base; 1996 1997 const typename __ios_base::fmtflags __flags = __is.flags(); 1998 __is.flags(__ios_base::dec | __ios_base::skipws); 1999 2000 _RealType __m, __s; 2001 __is >> __m >> __s >> __x._M_nd; 2002 __x.param(typename lognormal_distribution<_RealType>:: 2003 param_type(__m, __s)); 2004 2005 __is.flags(__flags); 2006 return __is; 2007 } 2008 2009 template<typename _RealType> 2010 template<typename _ForwardIterator, 2011 typename _UniformRandomNumberGenerator> 2012 void 2013 std::chi_squared_distribution<_RealType>:: 2014 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2015 _UniformRandomNumberGenerator& __urng) 2016 { 2017 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2018 while (__f != __t) 2019 *__f++ = 2 * _M_gd(__urng); 2020 } 2021 2022 template<typename _RealType> 2023 template<typename _ForwardIterator, 2024 typename _UniformRandomNumberGenerator> 2025 void 2026 std::chi_squared_distribution<_RealType>:: 2027 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2028 _UniformRandomNumberGenerator& __urng, 2029 const typename 2030 std::gamma_distribution<result_type>::param_type& __p) 2031 { 2032 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2033 while (__f != __t) 2034 *__f++ = 2 * _M_gd(__urng, __p); 2035 } 2036 2037 template<typename _RealType, typename _CharT, typename _Traits> 2038 std::basic_ostream<_CharT, _Traits>& 2039 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2040 const chi_squared_distribution<_RealType>& __x) 2041 { 2042 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2043 typedef typename __ostream_type::ios_base __ios_base; 2044 2045 const typename __ios_base::fmtflags __flags = __os.flags(); 2046 const _CharT __fill = __os.fill(); 2047 const std::streamsize __precision = __os.precision(); 2048 const _CharT __space = __os.widen(' '); 2049 __os.flags(__ios_base::scientific | __ios_base::left); 2050 __os.fill(__space); 2051 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2052 2053 __os << __x.n() << __space << __x._M_gd; 2054 2055 __os.flags(__flags); 2056 __os.fill(__fill); 2057 __os.precision(__precision); 2058 return __os; 2059 } 2060 2061 template<typename _RealType, typename _CharT, typename _Traits> 2062 std::basic_istream<_CharT, _Traits>& 2063 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2064 chi_squared_distribution<_RealType>& __x) 2065 { 2066 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2067 typedef typename __istream_type::ios_base __ios_base; 2068 2069 const typename __ios_base::fmtflags __flags = __is.flags(); 2070 __is.flags(__ios_base::dec | __ios_base::skipws); 2071 2072 _RealType __n; 2073 __is >> __n >> __x._M_gd; 2074 __x.param(typename chi_squared_distribution<_RealType>:: 2075 param_type(__n)); 2076 2077 __is.flags(__flags); 2078 return __is; 2079 } 2080 2081 2082 template<typename _RealType> 2083 template<typename _UniformRandomNumberGenerator> 2084 typename cauchy_distribution<_RealType>::result_type 2085 cauchy_distribution<_RealType>:: 2086 operator()(_UniformRandomNumberGenerator& __urng, 2087 const param_type& __p) 2088 { 2089 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2090 __aurng(__urng); 2091 _RealType __u; 2092 do 2093 __u = __aurng(); 2094 while (__u == 0.5); 2095 2096 const _RealType __pi = 3.1415926535897932384626433832795029L; 2097 return __p.a() + __p.b() * std::tan(__pi * __u); 2098 } 2099 2100 template<typename _RealType> 2101 template<typename _ForwardIterator, 2102 typename _UniformRandomNumberGenerator> 2103 void 2104 cauchy_distribution<_RealType>:: 2105 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2106 _UniformRandomNumberGenerator& __urng, 2107 const param_type& __p) 2108 { 2109 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2110 const _RealType __pi = 3.1415926535897932384626433832795029L; 2111 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2112 __aurng(__urng); 2113 while (__f != __t) 2114 { 2115 _RealType __u; 2116 do 2117 __u = __aurng(); 2118 while (__u == 0.5); 2119 2120 *__f++ = __p.a() + __p.b() * std::tan(__pi * __u); 2121 } 2122 } 2123 2124 template<typename _RealType, typename _CharT, typename _Traits> 2125 std::basic_ostream<_CharT, _Traits>& 2126 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2127 const cauchy_distribution<_RealType>& __x) 2128 { 2129 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2130 typedef typename __ostream_type::ios_base __ios_base; 2131 2132 const typename __ios_base::fmtflags __flags = __os.flags(); 2133 const _CharT __fill = __os.fill(); 2134 const std::streamsize __precision = __os.precision(); 2135 const _CharT __space = __os.widen(' '); 2136 __os.flags(__ios_base::scientific | __ios_base::left); 2137 __os.fill(__space); 2138 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2139 2140 __os << __x.a() << __space << __x.b(); 2141 2142 __os.flags(__flags); 2143 __os.fill(__fill); 2144 __os.precision(__precision); 2145 return __os; 2146 } 2147 2148 template<typename _RealType, typename _CharT, typename _Traits> 2149 std::basic_istream<_CharT, _Traits>& 2150 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2151 cauchy_distribution<_RealType>& __x) 2152 { 2153 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2154 typedef typename __istream_type::ios_base __ios_base; 2155 2156 const typename __ios_base::fmtflags __flags = __is.flags(); 2157 __is.flags(__ios_base::dec | __ios_base::skipws); 2158 2159 _RealType __a, __b; 2160 __is >> __a >> __b; 2161 __x.param(typename cauchy_distribution<_RealType>:: 2162 param_type(__a, __b)); 2163 2164 __is.flags(__flags); 2165 return __is; 2166 } 2167 2168 2169 template<typename _RealType> 2170 template<typename _ForwardIterator, 2171 typename _UniformRandomNumberGenerator> 2172 void 2173 std::fisher_f_distribution<_RealType>:: 2174 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2175 _UniformRandomNumberGenerator& __urng) 2176 { 2177 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2178 while (__f != __t) 2179 *__f++ = ((_M_gd_x(__urng) * n()) / (_M_gd_y(__urng) * m())); 2180 } 2181 2182 template<typename _RealType> 2183 template<typename _ForwardIterator, 2184 typename _UniformRandomNumberGenerator> 2185 void 2186 std::fisher_f_distribution<_RealType>:: 2187 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2188 _UniformRandomNumberGenerator& __urng, 2189 const param_type& __p) 2190 { 2191 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2192 typedef typename std::gamma_distribution<result_type>::param_type 2193 param_type; 2194 param_type __p1(__p.m() / 2); 2195 param_type __p2(__p.n() / 2); 2196 while (__f != __t) 2197 *__f++ = ((_M_gd_x(__urng, __p1) * n()) 2198 / (_M_gd_y(__urng, __p2) * m())); 2199 } 2200 2201 template<typename _RealType, typename _CharT, typename _Traits> 2202 std::basic_ostream<_CharT, _Traits>& 2203 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2204 const fisher_f_distribution<_RealType>& __x) 2205 { 2206 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2207 typedef typename __ostream_type::ios_base __ios_base; 2208 2209 const typename __ios_base::fmtflags __flags = __os.flags(); 2210 const _CharT __fill = __os.fill(); 2211 const std::streamsize __precision = __os.precision(); 2212 const _CharT __space = __os.widen(' '); 2213 __os.flags(__ios_base::scientific | __ios_base::left); 2214 __os.fill(__space); 2215 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2216 2217 __os << __x.m() << __space << __x.n() 2218 << __space << __x._M_gd_x << __space << __x._M_gd_y; 2219 2220 __os.flags(__flags); 2221 __os.fill(__fill); 2222 __os.precision(__precision); 2223 return __os; 2224 } 2225 2226 template<typename _RealType, typename _CharT, typename _Traits> 2227 std::basic_istream<_CharT, _Traits>& 2228 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2229 fisher_f_distribution<_RealType>& __x) 2230 { 2231 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2232 typedef typename __istream_type::ios_base __ios_base; 2233 2234 const typename __ios_base::fmtflags __flags = __is.flags(); 2235 __is.flags(__ios_base::dec | __ios_base::skipws); 2236 2237 _RealType __m, __n; 2238 __is >> __m >> __n >> __x._M_gd_x >> __x._M_gd_y; 2239 __x.param(typename fisher_f_distribution<_RealType>:: 2240 param_type(__m, __n)); 2241 2242 __is.flags(__flags); 2243 return __is; 2244 } 2245 2246 2247 template<typename _RealType> 2248 template<typename _ForwardIterator, 2249 typename _UniformRandomNumberGenerator> 2250 void 2251 std::student_t_distribution<_RealType>:: 2252 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2253 _UniformRandomNumberGenerator& __urng) 2254 { 2255 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2256 while (__f != __t) 2257 *__f++ = _M_nd(__urng) * std::sqrt(n() / _M_gd(__urng)); 2258 } 2259 2260 template<typename _RealType> 2261 template<typename _ForwardIterator, 2262 typename _UniformRandomNumberGenerator> 2263 void 2264 std::student_t_distribution<_RealType>:: 2265 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2266 _UniformRandomNumberGenerator& __urng, 2267 const param_type& __p) 2268 { 2269 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2270 typename std::gamma_distribution<result_type>::param_type 2271 __p2(__p.n() / 2, 2); 2272 while (__f != __t) 2273 *__f++ = _M_nd(__urng) * std::sqrt(__p.n() / _M_gd(__urng, __p2)); 2274 } 2275 2276 template<typename _RealType, typename _CharT, typename _Traits> 2277 std::basic_ostream<_CharT, _Traits>& 2278 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2279 const student_t_distribution<_RealType>& __x) 2280 { 2281 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2282 typedef typename __ostream_type::ios_base __ios_base; 2283 2284 const typename __ios_base::fmtflags __flags = __os.flags(); 2285 const _CharT __fill = __os.fill(); 2286 const std::streamsize __precision = __os.precision(); 2287 const _CharT __space = __os.widen(' '); 2288 __os.flags(__ios_base::scientific | __ios_base::left); 2289 __os.fill(__space); 2290 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2291 2292 __os << __x.n() << __space << __x._M_nd << __space << __x._M_gd; 2293 2294 __os.flags(__flags); 2295 __os.fill(__fill); 2296 __os.precision(__precision); 2297 return __os; 2298 } 2299 2300 template<typename _RealType, typename _CharT, typename _Traits> 2301 std::basic_istream<_CharT, _Traits>& 2302 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2303 student_t_distribution<_RealType>& __x) 2304 { 2305 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2306 typedef typename __istream_type::ios_base __ios_base; 2307 2308 const typename __ios_base::fmtflags __flags = __is.flags(); 2309 __is.flags(__ios_base::dec | __ios_base::skipws); 2310 2311 _RealType __n; 2312 __is >> __n >> __x._M_nd >> __x._M_gd; 2313 __x.param(typename student_t_distribution<_RealType>::param_type(__n)); 2314 2315 __is.flags(__flags); 2316 return __is; 2317 } 2318 2319 2320 template<typename _RealType> 2321 void 2322 gamma_distribution<_RealType>::param_type:: 2323 _M_initialize() 2324 { 2325 _M_malpha = _M_alpha < 1.0 ? _M_alpha + _RealType(1.0) : _M_alpha; 2326 2327 const _RealType __a1 = _M_malpha - _RealType(1.0) / _RealType(3.0); 2328 _M_a2 = _RealType(1.0) / std::sqrt(_RealType(9.0) * __a1); 2329 } 2330 2331 /** 2332 * Marsaglia, G. and Tsang, W. W. 2333 * "A Simple Method for Generating Gamma Variables" 2334 * ACM Transactions on Mathematical Software, 26, 3, 363-372, 2000. 2335 */ 2336 template<typename _RealType> 2337 template<typename _UniformRandomNumberGenerator> 2338 typename gamma_distribution<_RealType>::result_type 2339 gamma_distribution<_RealType>:: 2340 operator()(_UniformRandomNumberGenerator& __urng, 2341 const param_type& __param) 2342 { 2343 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2344 __aurng(__urng); 2345 2346 result_type __u, __v, __n; 2347 const result_type __a1 = (__param._M_malpha 2348 - _RealType(1.0) / _RealType(3.0)); 2349 2350 do 2351 { 2352 do 2353 { 2354 __n = _M_nd(__urng); 2355 __v = result_type(1.0) + __param._M_a2 * __n; 2356 } 2357 while (__v <= 0.0); 2358 2359 __v = __v * __v * __v; 2360 __u = __aurng(); 2361 } 2362 while (__u > result_type(1.0) - 0.0331 * __n * __n * __n * __n 2363 && (std::log(__u) > (0.5 * __n * __n + __a1 2364 * (1.0 - __v + std::log(__v))))); 2365 2366 if (__param.alpha() == __param._M_malpha) 2367 return __a1 * __v * __param.beta(); 2368 else 2369 { 2370 do 2371 __u = __aurng(); 2372 while (__u == 0.0); 2373 2374 return (std::pow(__u, result_type(1.0) / __param.alpha()) 2375 * __a1 * __v * __param.beta()); 2376 } 2377 } 2378 2379 template<typename _RealType> 2380 template<typename _ForwardIterator, 2381 typename _UniformRandomNumberGenerator> 2382 void 2383 gamma_distribution<_RealType>:: 2384 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2385 _UniformRandomNumberGenerator& __urng, 2386 const param_type& __param) 2387 { 2388 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2389 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2390 __aurng(__urng); 2391 2392 result_type __u, __v, __n; 2393 const result_type __a1 = (__param._M_malpha 2394 - _RealType(1.0) / _RealType(3.0)); 2395 2396 if (__param.alpha() == __param._M_malpha) 2397 while (__f != __t) 2398 { 2399 do 2400 { 2401 do 2402 { 2403 __n = _M_nd(__urng); 2404 __v = result_type(1.0) + __param._M_a2 * __n; 2405 } 2406 while (__v <= 0.0); 2407 2408 __v = __v * __v * __v; 2409 __u = __aurng(); 2410 } 2411 while (__u > result_type(1.0) - 0.0331 * __n * __n * __n * __n 2412 && (std::log(__u) > (0.5 * __n * __n + __a1 2413 * (1.0 - __v + std::log(__v))))); 2414 2415 *__f++ = __a1 * __v * __param.beta(); 2416 } 2417 else 2418 while (__f != __t) 2419 { 2420 do 2421 { 2422 do 2423 { 2424 __n = _M_nd(__urng); 2425 __v = result_type(1.0) + __param._M_a2 * __n; 2426 } 2427 while (__v <= 0.0); 2428 2429 __v = __v * __v * __v; 2430 __u = __aurng(); 2431 } 2432 while (__u > result_type(1.0) - 0.0331 * __n * __n * __n * __n 2433 && (std::log(__u) > (0.5 * __n * __n + __a1 2434 * (1.0 - __v + std::log(__v))))); 2435 2436 do 2437 __u = __aurng(); 2438 while (__u == 0.0); 2439 2440 *__f++ = (std::pow(__u, result_type(1.0) / __param.alpha()) 2441 * __a1 * __v * __param.beta()); 2442 } 2443 } 2444 2445 template<typename _RealType, typename _CharT, typename _Traits> 2446 std::basic_ostream<_CharT, _Traits>& 2447 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2448 const gamma_distribution<_RealType>& __x) 2449 { 2450 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2451 typedef typename __ostream_type::ios_base __ios_base; 2452 2453 const typename __ios_base::fmtflags __flags = __os.flags(); 2454 const _CharT __fill = __os.fill(); 2455 const std::streamsize __precision = __os.precision(); 2456 const _CharT __space = __os.widen(' '); 2457 __os.flags(__ios_base::scientific | __ios_base::left); 2458 __os.fill(__space); 2459 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2460 2461 __os << __x.alpha() << __space << __x.beta() 2462 << __space << __x._M_nd; 2463 2464 __os.flags(__flags); 2465 __os.fill(__fill); 2466 __os.precision(__precision); 2467 return __os; 2468 } 2469 2470 template<typename _RealType, typename _CharT, typename _Traits> 2471 std::basic_istream<_CharT, _Traits>& 2472 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2473 gamma_distribution<_RealType>& __x) 2474 { 2475 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2476 typedef typename __istream_type::ios_base __ios_base; 2477 2478 const typename __ios_base::fmtflags __flags = __is.flags(); 2479 __is.flags(__ios_base::dec | __ios_base::skipws); 2480 2481 _RealType __alpha_val, __beta_val; 2482 __is >> __alpha_val >> __beta_val >> __x._M_nd; 2483 __x.param(typename gamma_distribution<_RealType>:: 2484 param_type(__alpha_val, __beta_val)); 2485 2486 __is.flags(__flags); 2487 return __is; 2488 } 2489 2490 2491 template<typename _RealType> 2492 template<typename _UniformRandomNumberGenerator> 2493 typename weibull_distribution<_RealType>::result_type 2494 weibull_distribution<_RealType>:: 2495 operator()(_UniformRandomNumberGenerator& __urng, 2496 const param_type& __p) 2497 { 2498 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2499 __aurng(__urng); 2500 return __p.b() * std::pow(-std::log(result_type(1) - __aurng()), 2501 result_type(1) / __p.a()); 2502 } 2503 2504 template<typename _RealType> 2505 template<typename _ForwardIterator, 2506 typename _UniformRandomNumberGenerator> 2507 void 2508 weibull_distribution<_RealType>:: 2509 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2510 _UniformRandomNumberGenerator& __urng, 2511 const param_type& __p) 2512 { 2513 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2514 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2515 __aurng(__urng); 2516 auto __inv_a = result_type(1) / __p.a(); 2517 2518 while (__f != __t) 2519 *__f++ = __p.b() * std::pow(-std::log(result_type(1) - __aurng()), 2520 __inv_a); 2521 } 2522 2523 template<typename _RealType, typename _CharT, typename _Traits> 2524 std::basic_ostream<_CharT, _Traits>& 2525 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2526 const weibull_distribution<_RealType>& __x) 2527 { 2528 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2529 typedef typename __ostream_type::ios_base __ios_base; 2530 2531 const typename __ios_base::fmtflags __flags = __os.flags(); 2532 const _CharT __fill = __os.fill(); 2533 const std::streamsize __precision = __os.precision(); 2534 const _CharT __space = __os.widen(' '); 2535 __os.flags(__ios_base::scientific | __ios_base::left); 2536 __os.fill(__space); 2537 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2538 2539 __os << __x.a() << __space << __x.b(); 2540 2541 __os.flags(__flags); 2542 __os.fill(__fill); 2543 __os.precision(__precision); 2544 return __os; 2545 } 2546 2547 template<typename _RealType, typename _CharT, typename _Traits> 2548 std::basic_istream<_CharT, _Traits>& 2549 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2550 weibull_distribution<_RealType>& __x) 2551 { 2552 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2553 typedef typename __istream_type::ios_base __ios_base; 2554 2555 const typename __ios_base::fmtflags __flags = __is.flags(); 2556 __is.flags(__ios_base::dec | __ios_base::skipws); 2557 2558 _RealType __a, __b; 2559 __is >> __a >> __b; 2560 __x.param(typename weibull_distribution<_RealType>:: 2561 param_type(__a, __b)); 2562 2563 __is.flags(__flags); 2564 return __is; 2565 } 2566 2567 2568 template<typename _RealType> 2569 template<typename _UniformRandomNumberGenerator> 2570 typename extreme_value_distribution<_RealType>::result_type 2571 extreme_value_distribution<_RealType>:: 2572 operator()(_UniformRandomNumberGenerator& __urng, 2573 const param_type& __p) 2574 { 2575 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2576 __aurng(__urng); 2577 return __p.a() - __p.b() * std::log(-std::log(result_type(1) 2578 - __aurng())); 2579 } 2580 2581 template<typename _RealType> 2582 template<typename _ForwardIterator, 2583 typename _UniformRandomNumberGenerator> 2584 void 2585 extreme_value_distribution<_RealType>:: 2586 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2587 _UniformRandomNumberGenerator& __urng, 2588 const param_type& __p) 2589 { 2590 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2591 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2592 __aurng(__urng); 2593 2594 while (__f != __t) 2595 *__f++ = __p.a() - __p.b() * std::log(-std::log(result_type(1) 2596 - __aurng())); 2597 } 2598 2599 template<typename _RealType, typename _CharT, typename _Traits> 2600 std::basic_ostream<_CharT, _Traits>& 2601 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2602 const extreme_value_distribution<_RealType>& __x) 2603 { 2604 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2605 typedef typename __ostream_type::ios_base __ios_base; 2606 2607 const typename __ios_base::fmtflags __flags = __os.flags(); 2608 const _CharT __fill = __os.fill(); 2609 const std::streamsize __precision = __os.precision(); 2610 const _CharT __space = __os.widen(' '); 2611 __os.flags(__ios_base::scientific | __ios_base::left); 2612 __os.fill(__space); 2613 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2614 2615 __os << __x.a() << __space << __x.b(); 2616 2617 __os.flags(__flags); 2618 __os.fill(__fill); 2619 __os.precision(__precision); 2620 return __os; 2621 } 2622 2623 template<typename _RealType, typename _CharT, typename _Traits> 2624 std::basic_istream<_CharT, _Traits>& 2625 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2626 extreme_value_distribution<_RealType>& __x) 2627 { 2628 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2629 typedef typename __istream_type::ios_base __ios_base; 2630 2631 const typename __ios_base::fmtflags __flags = __is.flags(); 2632 __is.flags(__ios_base::dec | __ios_base::skipws); 2633 2634 _RealType __a, __b; 2635 __is >> __a >> __b; 2636 __x.param(typename extreme_value_distribution<_RealType>:: 2637 param_type(__a, __b)); 2638 2639 __is.flags(__flags); 2640 return __is; 2641 } 2642 2643 2644 template<typename _IntType> 2645 void 2646 discrete_distribution<_IntType>::param_type:: 2647 _M_initialize() 2648 { 2649 if (_M_prob.size() < 2) 2650 { 2651 _M_prob.clear(); 2652 return; 2653 } 2654 2655 const double __sum = std::accumulate(_M_prob.begin(), 2656 _M_prob.end(), 0.0); 2657 // Now normalize the probabilites. 2658 __detail::__normalize(_M_prob.begin(), _M_prob.end(), _M_prob.begin(), 2659 __sum); 2660 // Accumulate partial sums. 2661 _M_cp.reserve(_M_prob.size()); 2662 std::partial_sum(_M_prob.begin(), _M_prob.end(), 2663 std::back_inserter(_M_cp)); 2664 // Make sure the last cumulative probability is one. 2665 _M_cp[_M_cp.size() - 1] = 1.0; 2666 } 2667 2668 template<typename _IntType> 2669 template<typename _Func> 2670 discrete_distribution<_IntType>::param_type:: 2671 param_type(size_t __nw, double __xmin, double __xmax, _Func __fw) 2672 : _M_prob(), _M_cp() 2673 { 2674 const size_t __n = __nw == 0 ? 1 : __nw; 2675 const double __delta = (__xmax - __xmin) / __n; 2676 2677 _M_prob.reserve(__n); 2678 for (size_t __k = 0; __k < __nw; ++__k) 2679 _M_prob.push_back(__fw(__xmin + __k * __delta + 0.5 * __delta)); 2680 2681 _M_initialize(); 2682 } 2683 2684 template<typename _IntType> 2685 template<typename _UniformRandomNumberGenerator> 2686 typename discrete_distribution<_IntType>::result_type 2687 discrete_distribution<_IntType>:: 2688 operator()(_UniformRandomNumberGenerator& __urng, 2689 const param_type& __param) 2690 { 2691 if (__param._M_cp.empty()) 2692 return result_type(0); 2693 2694 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 2695 __aurng(__urng); 2696 2697 const double __p = __aurng(); 2698 auto __pos = std::lower_bound(__param._M_cp.begin(), 2699 __param._M_cp.end(), __p); 2700 2701 return __pos - __param._M_cp.begin(); 2702 } 2703 2704 template<typename _IntType> 2705 template<typename _ForwardIterator, 2706 typename _UniformRandomNumberGenerator> 2707 void 2708 discrete_distribution<_IntType>:: 2709 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2710 _UniformRandomNumberGenerator& __urng, 2711 const param_type& __param) 2712 { 2713 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2714 2715 if (__param._M_cp.empty()) 2716 { 2717 while (__f != __t) 2718 *__f++ = result_type(0); 2719 return; 2720 } 2721 2722 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 2723 __aurng(__urng); 2724 2725 while (__f != __t) 2726 { 2727 const double __p = __aurng(); 2728 auto __pos = std::lower_bound(__param._M_cp.begin(), 2729 __param._M_cp.end(), __p); 2730 2731 *__f++ = __pos - __param._M_cp.begin(); 2732 } 2733 } 2734 2735 template<typename _IntType, typename _CharT, typename _Traits> 2736 std::basic_ostream<_CharT, _Traits>& 2737 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2738 const discrete_distribution<_IntType>& __x) 2739 { 2740 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2741 typedef typename __ostream_type::ios_base __ios_base; 2742 2743 const typename __ios_base::fmtflags __flags = __os.flags(); 2744 const _CharT __fill = __os.fill(); 2745 const std::streamsize __precision = __os.precision(); 2746 const _CharT __space = __os.widen(' '); 2747 __os.flags(__ios_base::scientific | __ios_base::left); 2748 __os.fill(__space); 2749 __os.precision(std::numeric_limits<double>::max_digits10); 2750 2751 std::vector<double> __prob = __x.probabilities(); 2752 __os << __prob.size(); 2753 for (auto __dit = __prob.begin(); __dit != __prob.end(); ++__dit) 2754 __os << __space << *__dit; 2755 2756 __os.flags(__flags); 2757 __os.fill(__fill); 2758 __os.precision(__precision); 2759 return __os; 2760 } 2761 2762 template<typename _IntType, typename _CharT, typename _Traits> 2763 std::basic_istream<_CharT, _Traits>& 2764 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2765 discrete_distribution<_IntType>& __x) 2766 { 2767 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2768 typedef typename __istream_type::ios_base __ios_base; 2769 2770 const typename __ios_base::fmtflags __flags = __is.flags(); 2771 __is.flags(__ios_base::dec | __ios_base::skipws); 2772 2773 size_t __n; 2774 __is >> __n; 2775 2776 std::vector<double> __prob_vec; 2777 __prob_vec.reserve(__n); 2778 for (; __n != 0; --__n) 2779 { 2780 double __prob; 2781 __is >> __prob; 2782 __prob_vec.push_back(__prob); 2783 } 2784 2785 __x.param(typename discrete_distribution<_IntType>:: 2786 param_type(__prob_vec.begin(), __prob_vec.end())); 2787 2788 __is.flags(__flags); 2789 return __is; 2790 } 2791 2792 2793 template<typename _RealType> 2794 void 2795 piecewise_constant_distribution<_RealType>::param_type:: 2796 _M_initialize() 2797 { 2798 if (_M_int.size() < 2 2799 || (_M_int.size() == 2 2800 && _M_int[0] == _RealType(0) 2801 && _M_int[1] == _RealType(1))) 2802 { 2803 _M_int.clear(); 2804 _M_den.clear(); 2805 return; 2806 } 2807 2808 const double __sum = std::accumulate(_M_den.begin(), 2809 _M_den.end(), 0.0); 2810 2811 __detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(), 2812 __sum); 2813 2814 _M_cp.reserve(_M_den.size()); 2815 std::partial_sum(_M_den.begin(), _M_den.end(), 2816 std::back_inserter(_M_cp)); 2817 2818 // Make sure the last cumulative probability is one. 2819 _M_cp[_M_cp.size() - 1] = 1.0; 2820 2821 for (size_t __k = 0; __k < _M_den.size(); ++__k) 2822 _M_den[__k] /= _M_int[__k + 1] - _M_int[__k]; 2823 } 2824 2825 template<typename _RealType> 2826 template<typename _InputIteratorB, typename _InputIteratorW> 2827 piecewise_constant_distribution<_RealType>::param_type:: 2828 param_type(_InputIteratorB __bbegin, 2829 _InputIteratorB __bend, 2830 _InputIteratorW __wbegin) 2831 : _M_int(), _M_den(), _M_cp() 2832 { 2833 if (__bbegin != __bend) 2834 { 2835 for (;;) 2836 { 2837 _M_int.push_back(*__bbegin); 2838 ++__bbegin; 2839 if (__bbegin == __bend) 2840 break; 2841 2842 _M_den.push_back(*__wbegin); 2843 ++__wbegin; 2844 } 2845 } 2846 2847 _M_initialize(); 2848 } 2849 2850 template<typename _RealType> 2851 template<typename _Func> 2852 piecewise_constant_distribution<_RealType>::param_type:: 2853 param_type(initializer_list<_RealType> __bl, _Func __fw) 2854 : _M_int(), _M_den(), _M_cp() 2855 { 2856 _M_int.reserve(__bl.size()); 2857 for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter) 2858 _M_int.push_back(*__biter); 2859 2860 _M_den.reserve(_M_int.size() - 1); 2861 for (size_t __k = 0; __k < _M_int.size() - 1; ++__k) 2862 _M_den.push_back(__fw(0.5 * (_M_int[__k + 1] + _M_int[__k]))); 2863 2864 _M_initialize(); 2865 } 2866 2867 template<typename _RealType> 2868 template<typename _Func> 2869 piecewise_constant_distribution<_RealType>::param_type:: 2870 param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw) 2871 : _M_int(), _M_den(), _M_cp() 2872 { 2873 const size_t __n = __nw == 0 ? 1 : __nw; 2874 const _RealType __delta = (__xmax - __xmin) / __n; 2875 2876 _M_int.reserve(__n + 1); 2877 for (size_t __k = 0; __k <= __nw; ++__k) 2878 _M_int.push_back(__xmin + __k * __delta); 2879 2880 _M_den.reserve(__n); 2881 for (size_t __k = 0; __k < __nw; ++__k) 2882 _M_den.push_back(__fw(_M_int[__k] + 0.5 * __delta)); 2883 2884 _M_initialize(); 2885 } 2886 2887 template<typename _RealType> 2888 template<typename _UniformRandomNumberGenerator> 2889 typename piecewise_constant_distribution<_RealType>::result_type 2890 piecewise_constant_distribution<_RealType>:: 2891 operator()(_UniformRandomNumberGenerator& __urng, 2892 const param_type& __param) 2893 { 2894 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 2895 __aurng(__urng); 2896 2897 const double __p = __aurng(); 2898 if (__param._M_cp.empty()) 2899 return __p; 2900 2901 auto __pos = std::lower_bound(__param._M_cp.begin(), 2902 __param._M_cp.end(), __p); 2903 const size_t __i = __pos - __param._M_cp.begin(); 2904 2905 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0; 2906 2907 return __param._M_int[__i] + (__p - __pref) / __param._M_den[__i]; 2908 } 2909 2910 template<typename _RealType> 2911 template<typename _ForwardIterator, 2912 typename _UniformRandomNumberGenerator> 2913 void 2914 piecewise_constant_distribution<_RealType>:: 2915 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2916 _UniformRandomNumberGenerator& __urng, 2917 const param_type& __param) 2918 { 2919 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2920 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 2921 __aurng(__urng); 2922 2923 if (__param._M_cp.empty()) 2924 { 2925 while (__f != __t) 2926 *__f++ = __aurng(); 2927 return; 2928 } 2929 2930 while (__f != __t) 2931 { 2932 const double __p = __aurng(); 2933 2934 auto __pos = std::lower_bound(__param._M_cp.begin(), 2935 __param._M_cp.end(), __p); 2936 const size_t __i = __pos - __param._M_cp.begin(); 2937 2938 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0; 2939 2940 *__f++ = (__param._M_int[__i] 2941 + (__p - __pref) / __param._M_den[__i]); 2942 } 2943 } 2944 2945 template<typename _RealType, typename _CharT, typename _Traits> 2946 std::basic_ostream<_CharT, _Traits>& 2947 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2948 const piecewise_constant_distribution<_RealType>& __x) 2949 { 2950 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2951 typedef typename __ostream_type::ios_base __ios_base; 2952 2953 const typename __ios_base::fmtflags __flags = __os.flags(); 2954 const _CharT __fill = __os.fill(); 2955 const std::streamsize __precision = __os.precision(); 2956 const _CharT __space = __os.widen(' '); 2957 __os.flags(__ios_base::scientific | __ios_base::left); 2958 __os.fill(__space); 2959 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2960 2961 std::vector<_RealType> __int = __x.intervals(); 2962 __os << __int.size() - 1; 2963 2964 for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit) 2965 __os << __space << *__xit; 2966 2967 std::vector<double> __den = __x.densities(); 2968 for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit) 2969 __os << __space << *__dit; 2970 2971 __os.flags(__flags); 2972 __os.fill(__fill); 2973 __os.precision(__precision); 2974 return __os; 2975 } 2976 2977 template<typename _RealType, typename _CharT, typename _Traits> 2978 std::basic_istream<_CharT, _Traits>& 2979 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2980 piecewise_constant_distribution<_RealType>& __x) 2981 { 2982 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2983 typedef typename __istream_type::ios_base __ios_base; 2984 2985 const typename __ios_base::fmtflags __flags = __is.flags(); 2986 __is.flags(__ios_base::dec | __ios_base::skipws); 2987 2988 size_t __n; 2989 __is >> __n; 2990 2991 std::vector<_RealType> __int_vec; 2992 __int_vec.reserve(__n + 1); 2993 for (size_t __i = 0; __i <= __n; ++__i) 2994 { 2995 _RealType __int; 2996 __is >> __int; 2997 __int_vec.push_back(__int); 2998 } 2999 3000 std::vector<double> __den_vec; 3001 __den_vec.reserve(__n); 3002 for (size_t __i = 0; __i < __n; ++__i) 3003 { 3004 double __den; 3005 __is >> __den; 3006 __den_vec.push_back(__den); 3007 } 3008 3009 __x.param(typename piecewise_constant_distribution<_RealType>:: 3010 param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin())); 3011 3012 __is.flags(__flags); 3013 return __is; 3014 } 3015 3016 3017 template<typename _RealType> 3018 void 3019 piecewise_linear_distribution<_RealType>::param_type:: 3020 _M_initialize() 3021 { 3022 if (_M_int.size() < 2 3023 || (_M_int.size() == 2 3024 && _M_int[0] == _RealType(0) 3025 && _M_int[1] == _RealType(1) 3026 && _M_den[0] == _M_den[1])) 3027 { 3028 _M_int.clear(); 3029 _M_den.clear(); 3030 return; 3031 } 3032 3033 double __sum = 0.0; 3034 _M_cp.reserve(_M_int.size() - 1); 3035 _M_m.reserve(_M_int.size() - 1); 3036 for (size_t __k = 0; __k < _M_int.size() - 1; ++__k) 3037 { 3038 const _RealType __delta = _M_int[__k + 1] - _M_int[__k]; 3039 __sum += 0.5 * (_M_den[__k + 1] + _M_den[__k]) * __delta; 3040 _M_cp.push_back(__sum); 3041 _M_m.push_back((_M_den[__k + 1] - _M_den[__k]) / __delta); 3042 } 3043 3044 // Now normalize the densities... 3045 __detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(), 3046 __sum); 3047 // ... and partial sums... 3048 __detail::__normalize(_M_cp.begin(), _M_cp.end(), _M_cp.begin(), __sum); 3049 // ... and slopes. 3050 __detail::__normalize(_M_m.begin(), _M_m.end(), _M_m.begin(), __sum); 3051 3052 // Make sure the last cumulative probablility is one. 3053 _M_cp[_M_cp.size() - 1] = 1.0; 3054 } 3055 3056 template<typename _RealType> 3057 template<typename _InputIteratorB, typename _InputIteratorW> 3058 piecewise_linear_distribution<_RealType>::param_type:: 3059 param_type(_InputIteratorB __bbegin, 3060 _InputIteratorB __bend, 3061 _InputIteratorW __wbegin) 3062 : _M_int(), _M_den(), _M_cp(), _M_m() 3063 { 3064 for (; __bbegin != __bend; ++__bbegin, ++__wbegin) 3065 { 3066 _M_int.push_back(*__bbegin); 3067 _M_den.push_back(*__wbegin); 3068 } 3069 3070 _M_initialize(); 3071 } 3072 3073 template<typename _RealType> 3074 template<typename _Func> 3075 piecewise_linear_distribution<_RealType>::param_type:: 3076 param_type(initializer_list<_RealType> __bl, _Func __fw) 3077 : _M_int(), _M_den(), _M_cp(), _M_m() 3078 { 3079 _M_int.reserve(__bl.size()); 3080 _M_den.reserve(__bl.size()); 3081 for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter) 3082 { 3083 _M_int.push_back(*__biter); 3084 _M_den.push_back(__fw(*__biter)); 3085 } 3086 3087 _M_initialize(); 3088 } 3089 3090 template<typename _RealType> 3091 template<typename _Func> 3092 piecewise_linear_distribution<_RealType>::param_type:: 3093 param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw) 3094 : _M_int(), _M_den(), _M_cp(), _M_m() 3095 { 3096 const size_t __n = __nw == 0 ? 1 : __nw; 3097 const _RealType __delta = (__xmax - __xmin) / __n; 3098 3099 _M_int.reserve(__n + 1); 3100 _M_den.reserve(__n + 1); 3101 for (size_t __k = 0; __k <= __nw; ++__k) 3102 { 3103 _M_int.push_back(__xmin + __k * __delta); 3104 _M_den.push_back(__fw(_M_int[__k] + __delta)); 3105 } 3106 3107 _M_initialize(); 3108 } 3109 3110 template<typename _RealType> 3111 template<typename _UniformRandomNumberGenerator> 3112 typename piecewise_linear_distribution<_RealType>::result_type 3113 piecewise_linear_distribution<_RealType>:: 3114 operator()(_UniformRandomNumberGenerator& __urng, 3115 const param_type& __param) 3116 { 3117 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 3118 __aurng(__urng); 3119 3120 const double __p = __aurng(); 3121 if (__param._M_cp.empty()) 3122 return __p; 3123 3124 auto __pos = std::lower_bound(__param._M_cp.begin(), 3125 __param._M_cp.end(), __p); 3126 const size_t __i = __pos - __param._M_cp.begin(); 3127 3128 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0; 3129 3130 const double __a = 0.5 * __param._M_m[__i]; 3131 const double __b = __param._M_den[__i]; 3132 const double __cm = __p - __pref; 3133 3134 _RealType __x = __param._M_int[__i]; 3135 if (__a == 0) 3136 __x += __cm / __b; 3137 else 3138 { 3139 const double __d = __b * __b + 4.0 * __a * __cm; 3140 __x += 0.5 * (std::sqrt(__d) - __b) / __a; 3141 } 3142 3143 return __x; 3144 } 3145 3146 template<typename _RealType> 3147 template<typename _ForwardIterator, 3148 typename _UniformRandomNumberGenerator> 3149 void 3150 piecewise_linear_distribution<_RealType>:: 3151 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 3152 _UniformRandomNumberGenerator& __urng, 3153 const param_type& __param) 3154 { 3155 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 3156 // We could duplicate everything from operator()... 3157 while (__f != __t) 3158 *__f++ = this->operator()(__urng, __param); 3159 } 3160 3161 template<typename _RealType, typename _CharT, typename _Traits> 3162 std::basic_ostream<_CharT, _Traits>& 3163 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 3164 const piecewise_linear_distribution<_RealType>& __x) 3165 { 3166 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 3167 typedef typename __ostream_type::ios_base __ios_base; 3168 3169 const typename __ios_base::fmtflags __flags = __os.flags(); 3170 const _CharT __fill = __os.fill(); 3171 const std::streamsize __precision = __os.precision(); 3172 const _CharT __space = __os.widen(' '); 3173 __os.flags(__ios_base::scientific | __ios_base::left); 3174 __os.fill(__space); 3175 __os.precision(std::numeric_limits<_RealType>::max_digits10); 3176 3177 std::vector<_RealType> __int = __x.intervals(); 3178 __os << __int.size() - 1; 3179 3180 for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit) 3181 __os << __space << *__xit; 3182 3183 std::vector<double> __den = __x.densities(); 3184 for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit) 3185 __os << __space << *__dit; 3186 3187 __os.flags(__flags); 3188 __os.fill(__fill); 3189 __os.precision(__precision); 3190 return __os; 3191 } 3192 3193 template<typename _RealType, typename _CharT, typename _Traits> 3194 std::basic_istream<_CharT, _Traits>& 3195 operator>>(std::basic_istream<_CharT, _Traits>& __is, 3196 piecewise_linear_distribution<_RealType>& __x) 3197 { 3198 typedef std::basic_istream<_CharT, _Traits> __istream_type; 3199 typedef typename __istream_type::ios_base __ios_base; 3200 3201 const typename __ios_base::fmtflags __flags = __is.flags(); 3202 __is.flags(__ios_base::dec | __ios_base::skipws); 3203 3204 size_t __n; 3205 __is >> __n; 3206 3207 std::vector<_RealType> __int_vec; 3208 __int_vec.reserve(__n + 1); 3209 for (size_t __i = 0; __i <= __n; ++__i) 3210 { 3211 _RealType __int; 3212 __is >> __int; 3213 __int_vec.push_back(__int); 3214 } 3215 3216 std::vector<double> __den_vec; 3217 __den_vec.reserve(__n + 1); 3218 for (size_t __i = 0; __i <= __n; ++__i) 3219 { 3220 double __den; 3221 __is >> __den; 3222 __den_vec.push_back(__den); 3223 } 3224 3225 __x.param(typename piecewise_linear_distribution<_RealType>:: 3226 param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin())); 3227 3228 __is.flags(__flags); 3229 return __is; 3230 } 3231 3232 3233 template<typename _IntType> 3234 seed_seq::seed_seq(std::initializer_list<_IntType> __il) 3235 { 3236 for (auto __iter = __il.begin(); __iter != __il.end(); ++__iter) 3237 _M_v.push_back(__detail::__mod<result_type, 3238 __detail::_Shift<result_type, 32>::__value>(*__iter)); 3239 } 3240 3241 template<typename _InputIterator> 3242 seed_seq::seed_seq(_InputIterator __begin, _InputIterator __end) 3243 { 3244 for (_InputIterator __iter = __begin; __iter != __end; ++__iter) 3245 _M_v.push_back(__detail::__mod<result_type, 3246 __detail::_Shift<result_type, 32>::__value>(*__iter)); 3247 } 3248 3249 template<typename _RandomAccessIterator> 3250 void 3251 seed_seq::generate(_RandomAccessIterator __begin, 3252 _RandomAccessIterator __end) 3253 { 3254 typedef typename iterator_traits<_RandomAccessIterator>::value_type 3255 _Type; 3256 3257 if (__begin == __end) 3258 return; 3259 3260 std::fill(__begin, __end, _Type(0x8b8b8b8bu)); 3261 3262 const size_t __n = __end - __begin; 3263 const size_t __s = _M_v.size(); 3264 const size_t __t = (__n >= 623) ? 11 3265 : (__n >= 68) ? 7 3266 : (__n >= 39) ? 5 3267 : (__n >= 7) ? 3 3268 : (__n - 1) / 2; 3269 const size_t __p = (__n - __t) / 2; 3270 const size_t __q = __p + __t; 3271 const size_t __m = std::max(size_t(__s + 1), __n); 3272 3273 for (size_t __k = 0; __k < __m; ++__k) 3274 { 3275 _Type __arg = (__begin[__k % __n] 3276 ^ __begin[(__k + __p) % __n] 3277 ^ __begin[(__k - 1) % __n]); 3278 _Type __r1 = __arg ^ (__arg >> 27); 3279 __r1 = __detail::__mod<_Type, 3280 __detail::_Shift<_Type, 32>::__value>(1664525u * __r1); 3281 _Type __r2 = __r1; 3282 if (__k == 0) 3283 __r2 += __s; 3284 else if (__k <= __s) 3285 __r2 += __k % __n + _M_v[__k - 1]; 3286 else 3287 __r2 += __k % __n; 3288 __r2 = __detail::__mod<_Type, 3289 __detail::_Shift<_Type, 32>::__value>(__r2); 3290 __begin[(__k + __p) % __n] += __r1; 3291 __begin[(__k + __q) % __n] += __r2; 3292 __begin[__k % __n] = __r2; 3293 } 3294 3295 for (size_t __k = __m; __k < __m + __n; ++__k) 3296 { 3297 _Type __arg = (__begin[__k % __n] 3298 + __begin[(__k + __p) % __n] 3299 + __begin[(__k - 1) % __n]); 3300 _Type __r3 = __arg ^ (__arg >> 27); 3301 __r3 = __detail::__mod<_Type, 3302 __detail::_Shift<_Type, 32>::__value>(1566083941u * __r3); 3303 _Type __r4 = __r3 - __k % __n; 3304 __r4 = __detail::__mod<_Type, 3305 __detail::_Shift<_Type, 32>::__value>(__r4); 3306 __begin[(__k + __p) % __n] ^= __r3; 3307 __begin[(__k + __q) % __n] ^= __r4; 3308 __begin[__k % __n] = __r4; 3309 } 3310 } 3311 3312 template<typename _RealType, size_t __bits, 3313 typename _UniformRandomNumberGenerator> 3314 _RealType 3315 generate_canonical(_UniformRandomNumberGenerator& __urng) 3316 { 3317 static_assert(std::is_floating_point<_RealType>::value, 3318 "template argument must be a floating point type"); 3319 3320 const size_t __b 3321 = std::min(static_cast<size_t>(std::numeric_limits<_RealType>::digits), 3322 __bits); 3323 const long double __r = static_cast<long double>(__urng.max()) 3324 - static_cast<long double>(__urng.min()) + 1.0L; 3325 const size_t __log2r = std::log(__r) / std::log(2.0L); 3326 const size_t __m = std::max<size_t>(1UL, 3327 (__b + __log2r - 1UL) / __log2r); 3328 _RealType __ret; 3329 _RealType __sum = _RealType(0); 3330 _RealType __tmp = _RealType(1); 3331 for (size_t __k = __m; __k != 0; --__k) 3332 { 3333 __sum += _RealType(__urng() - __urng.min()) * __tmp; 3334 __tmp *= __r; 3335 } 3336 __ret = __sum / __tmp; 3337 if (__builtin_expect(__ret >= _RealType(1), 0)) 3338 { 3339 #if _GLIBCXX_USE_C99_MATH_TR1 3340 __ret = std::nextafter(_RealType(1), _RealType(0)); 3341 #else 3342 __ret = _RealType(1) 3343 - std::numeric_limits<_RealType>::epsilon() / _RealType(2); 3344 #endif 3345 } 3346 return __ret; 3347 } 3348 3349 _GLIBCXX_END_NAMESPACE_VERSION 3350 } // namespace 3351 3352 #endif 3353