1// random number generation (out of line) -*- C++ -*- 2 3// Copyright (C) 2009-2013 Free Software Foundation, Inc. 4// 5// This file is part of the GNU ISO C++ Library. This library is free 6// software; you can redistribute it and/or modify it under the 7// terms of the GNU General Public License as published by the 8// Free Software Foundation; either version 3, or (at your option) 9// any later version. 10 11// This library is distributed in the hope that it will be useful, 12// but WITHOUT ANY WARRANTY; without even the implied warranty of 13// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 14// GNU General Public License for more details. 15 16// Under Section 7 of GPL version 3, you are granted additional 17// permissions described in the GCC Runtime Library Exception, version 18// 3.1, as published by the Free Software Foundation. 19 20// You should have received a copy of the GNU General Public License and 21// a copy of the GCC Runtime Library Exception along with this program; 22// see the files COPYING3 and COPYING.RUNTIME respectively. If not, see 23// <http://www.gnu.org/licenses/>. 24 25/** @file bits/random.tcc 26 * This is an internal header file, included by other library headers. 27 * Do not attempt to use it directly. @headername{random} 28 */ 29 30#ifndef _RANDOM_TCC 31#define _RANDOM_TCC 1 32 33#include <numeric> // std::accumulate and std::partial_sum 34 35namespace std _GLIBCXX_VISIBILITY(default) 36{ 37 /* 38 * (Further) implementation-space details. 39 */ 40 namespace __detail 41 { 42 _GLIBCXX_BEGIN_NAMESPACE_VERSION 43 44 // General case for x = (ax + c) mod m -- use Schrage's algorithm 45 // to avoid integer overflow. 46 // 47 // Preconditions: a > 0, m > 0. 48 // 49 // Note: only works correctly for __m % __a < __m / __a. 50 template<typename _Tp, _Tp __m, _Tp __a, _Tp __c> 51 _Tp 52 _Mod<_Tp, __m, __a, __c, false, true>:: 53 __calc(_Tp __x) 54 { 55 if (__a == 1) 56 __x %= __m; 57 else 58 { 59 static const _Tp __q = __m / __a; 60 static const _Tp __r = __m % __a; 61 62 _Tp __t1 = __a * (__x % __q); 63 _Tp __t2 = __r * (__x / __q); 64 if (__t1 >= __t2) 65 __x = __t1 - __t2; 66 else 67 __x = __m - __t2 + __t1; 68 } 69 70 if (__c != 0) 71 { 72 const _Tp __d = __m - __x; 73 if (__d > __c) 74 __x += __c; 75 else 76 __x = __c - __d; 77 } 78 return __x; 79 } 80 81 template<typename _InputIterator, typename _OutputIterator, 82 typename _Tp> 83 _OutputIterator 84 __normalize(_InputIterator __first, _InputIterator __last, 85 _OutputIterator __result, const _Tp& __factor) 86 { 87 for (; __first != __last; ++__first, ++__result) 88 *__result = *__first / __factor; 89 return __result; 90 } 91 92 _GLIBCXX_END_NAMESPACE_VERSION 93 } // namespace __detail 94 95_GLIBCXX_BEGIN_NAMESPACE_VERSION 96 97 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> 98 constexpr _UIntType 99 linear_congruential_engine<_UIntType, __a, __c, __m>::multiplier; 100 101 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> 102 constexpr _UIntType 103 linear_congruential_engine<_UIntType, __a, __c, __m>::increment; 104 105 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> 106 constexpr _UIntType 107 linear_congruential_engine<_UIntType, __a, __c, __m>::modulus; 108 109 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> 110 constexpr _UIntType 111 linear_congruential_engine<_UIntType, __a, __c, __m>::default_seed; 112 113 /** 114 * Seeds the LCR with integral value @p __s, adjusted so that the 115 * ring identity is never a member of the convergence set. 116 */ 117 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> 118 void 119 linear_congruential_engine<_UIntType, __a, __c, __m>:: 120 seed(result_type __s) 121 { 122 if ((__detail::__mod<_UIntType, __m>(__c) == 0) 123 && (__detail::__mod<_UIntType, __m>(__s) == 0)) 124 _M_x = 1; 125 else 126 _M_x = __detail::__mod<_UIntType, __m>(__s); 127 } 128 129 /** 130 * Seeds the LCR engine with a value generated by @p __q. 131 */ 132 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> 133 template<typename _Sseq> 134 typename std::enable_if<std::is_class<_Sseq>::value>::type 135 linear_congruential_engine<_UIntType, __a, __c, __m>:: 136 seed(_Sseq& __q) 137 { 138 const _UIntType __k0 = __m == 0 ? std::numeric_limits<_UIntType>::digits 139 : std::__lg(__m); 140 const _UIntType __k = (__k0 + 31) / 32; 141 uint_least32_t __arr[__k + 3]; 142 __q.generate(__arr + 0, __arr + __k + 3); 143 _UIntType __factor = 1u; 144 _UIntType __sum = 0u; 145 for (size_t __j = 0; __j < __k; ++__j) 146 { 147 __sum += __arr[__j + 3] * __factor; 148 __factor *= __detail::_Shift<_UIntType, 32>::__value; 149 } 150 seed(__sum); 151 } 152 153 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m, 154 typename _CharT, typename _Traits> 155 std::basic_ostream<_CharT, _Traits>& 156 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 157 const linear_congruential_engine<_UIntType, 158 __a, __c, __m>& __lcr) 159 { 160 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 161 typedef typename __ostream_type::ios_base __ios_base; 162 163 const typename __ios_base::fmtflags __flags = __os.flags(); 164 const _CharT __fill = __os.fill(); 165 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 166 __os.fill(__os.widen(' ')); 167 168 __os << __lcr._M_x; 169 170 __os.flags(__flags); 171 __os.fill(__fill); 172 return __os; 173 } 174 175 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m, 176 typename _CharT, typename _Traits> 177 std::basic_istream<_CharT, _Traits>& 178 operator>>(std::basic_istream<_CharT, _Traits>& __is, 179 linear_congruential_engine<_UIntType, __a, __c, __m>& __lcr) 180 { 181 typedef std::basic_istream<_CharT, _Traits> __istream_type; 182 typedef typename __istream_type::ios_base __ios_base; 183 184 const typename __ios_base::fmtflags __flags = __is.flags(); 185 __is.flags(__ios_base::dec); 186 187 __is >> __lcr._M_x; 188 189 __is.flags(__flags); 190 return __is; 191 } 192 193 194 template<typename _UIntType, 195 size_t __w, size_t __n, size_t __m, size_t __r, 196 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 197 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 198 _UIntType __f> 199 constexpr size_t 200 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 201 __s, __b, __t, __c, __l, __f>::word_size; 202 203 template<typename _UIntType, 204 size_t __w, size_t __n, size_t __m, size_t __r, 205 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 206 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 207 _UIntType __f> 208 constexpr size_t 209 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 210 __s, __b, __t, __c, __l, __f>::state_size; 211 212 template<typename _UIntType, 213 size_t __w, size_t __n, size_t __m, size_t __r, 214 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 215 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 216 _UIntType __f> 217 constexpr size_t 218 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 219 __s, __b, __t, __c, __l, __f>::shift_size; 220 221 template<typename _UIntType, 222 size_t __w, size_t __n, size_t __m, size_t __r, 223 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 224 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 225 _UIntType __f> 226 constexpr size_t 227 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 228 __s, __b, __t, __c, __l, __f>::mask_bits; 229 230 template<typename _UIntType, 231 size_t __w, size_t __n, size_t __m, size_t __r, 232 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 233 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 234 _UIntType __f> 235 constexpr _UIntType 236 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 237 __s, __b, __t, __c, __l, __f>::xor_mask; 238 239 template<typename _UIntType, 240 size_t __w, size_t __n, size_t __m, size_t __r, 241 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 242 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 243 _UIntType __f> 244 constexpr size_t 245 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 246 __s, __b, __t, __c, __l, __f>::tempering_u; 247 248 template<typename _UIntType, 249 size_t __w, size_t __n, size_t __m, size_t __r, 250 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 251 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 252 _UIntType __f> 253 constexpr _UIntType 254 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 255 __s, __b, __t, __c, __l, __f>::tempering_d; 256 257 template<typename _UIntType, 258 size_t __w, size_t __n, size_t __m, size_t __r, 259 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 260 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 261 _UIntType __f> 262 constexpr size_t 263 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 264 __s, __b, __t, __c, __l, __f>::tempering_s; 265 266 template<typename _UIntType, 267 size_t __w, size_t __n, size_t __m, size_t __r, 268 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 269 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 270 _UIntType __f> 271 constexpr _UIntType 272 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 273 __s, __b, __t, __c, __l, __f>::tempering_b; 274 275 template<typename _UIntType, 276 size_t __w, size_t __n, size_t __m, size_t __r, 277 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 278 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 279 _UIntType __f> 280 constexpr size_t 281 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 282 __s, __b, __t, __c, __l, __f>::tempering_t; 283 284 template<typename _UIntType, 285 size_t __w, size_t __n, size_t __m, size_t __r, 286 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 287 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 288 _UIntType __f> 289 constexpr _UIntType 290 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 291 __s, __b, __t, __c, __l, __f>::tempering_c; 292 293 template<typename _UIntType, 294 size_t __w, size_t __n, size_t __m, size_t __r, 295 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 296 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 297 _UIntType __f> 298 constexpr size_t 299 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 300 __s, __b, __t, __c, __l, __f>::tempering_l; 301 302 template<typename _UIntType, 303 size_t __w, size_t __n, size_t __m, size_t __r, 304 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 305 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 306 _UIntType __f> 307 constexpr _UIntType 308 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 309 __s, __b, __t, __c, __l, __f>:: 310 initialization_multiplier; 311 312 template<typename _UIntType, 313 size_t __w, size_t __n, size_t __m, size_t __r, 314 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 315 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 316 _UIntType __f> 317 constexpr _UIntType 318 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 319 __s, __b, __t, __c, __l, __f>::default_seed; 320 321 template<typename _UIntType, 322 size_t __w, size_t __n, size_t __m, size_t __r, 323 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 324 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 325 _UIntType __f> 326 void 327 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 328 __s, __b, __t, __c, __l, __f>:: 329 seed(result_type __sd) 330 { 331 _M_x[0] = __detail::__mod<_UIntType, 332 __detail::_Shift<_UIntType, __w>::__value>(__sd); 333 334 for (size_t __i = 1; __i < state_size; ++__i) 335 { 336 _UIntType __x = _M_x[__i - 1]; 337 __x ^= __x >> (__w - 2); 338 __x *= __f; 339 __x += __detail::__mod<_UIntType, __n>(__i); 340 _M_x[__i] = __detail::__mod<_UIntType, 341 __detail::_Shift<_UIntType, __w>::__value>(__x); 342 } 343 _M_p = state_size; 344 } 345 346 template<typename _UIntType, 347 size_t __w, size_t __n, size_t __m, size_t __r, 348 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 349 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 350 _UIntType __f> 351 template<typename _Sseq> 352 typename std::enable_if<std::is_class<_Sseq>::value>::type 353 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 354 __s, __b, __t, __c, __l, __f>:: 355 seed(_Sseq& __q) 356 { 357 const _UIntType __upper_mask = (~_UIntType()) << __r; 358 const size_t __k = (__w + 31) / 32; 359 uint_least32_t __arr[__n * __k]; 360 __q.generate(__arr + 0, __arr + __n * __k); 361 362 bool __zero = true; 363 for (size_t __i = 0; __i < state_size; ++__i) 364 { 365 _UIntType __factor = 1u; 366 _UIntType __sum = 0u; 367 for (size_t __j = 0; __j < __k; ++__j) 368 { 369 __sum += __arr[__k * __i + __j] * __factor; 370 __factor *= __detail::_Shift<_UIntType, 32>::__value; 371 } 372 _M_x[__i] = __detail::__mod<_UIntType, 373 __detail::_Shift<_UIntType, __w>::__value>(__sum); 374 375 if (__zero) 376 { 377 if (__i == 0) 378 { 379 if ((_M_x[0] & __upper_mask) != 0u) 380 __zero = false; 381 } 382 else if (_M_x[__i] != 0u) 383 __zero = false; 384 } 385 } 386 if (__zero) 387 _M_x[0] = __detail::_Shift<_UIntType, __w - 1>::__value; 388 _M_p = state_size; 389 } 390 391 template<typename _UIntType, size_t __w, 392 size_t __n, size_t __m, size_t __r, 393 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 394 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 395 _UIntType __f> 396 void 397 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 398 __s, __b, __t, __c, __l, __f>:: 399 _M_gen_rand(void) 400 { 401 const _UIntType __upper_mask = (~_UIntType()) << __r; 402 const _UIntType __lower_mask = ~__upper_mask; 403 404 for (size_t __k = 0; __k < (__n - __m); ++__k) 405 { 406 _UIntType __y = ((_M_x[__k] & __upper_mask) 407 | (_M_x[__k + 1] & __lower_mask)); 408 _M_x[__k] = (_M_x[__k + __m] ^ (__y >> 1) 409 ^ ((__y & 0x01) ? __a : 0)); 410 } 411 412 for (size_t __k = (__n - __m); __k < (__n - 1); ++__k) 413 { 414 _UIntType __y = ((_M_x[__k] & __upper_mask) 415 | (_M_x[__k + 1] & __lower_mask)); 416 _M_x[__k] = (_M_x[__k + (__m - __n)] ^ (__y >> 1) 417 ^ ((__y & 0x01) ? __a : 0)); 418 } 419 420 _UIntType __y = ((_M_x[__n - 1] & __upper_mask) 421 | (_M_x[0] & __lower_mask)); 422 _M_x[__n - 1] = (_M_x[__m - 1] ^ (__y >> 1) 423 ^ ((__y & 0x01) ? __a : 0)); 424 _M_p = 0; 425 } 426 427 template<typename _UIntType, size_t __w, 428 size_t __n, size_t __m, size_t __r, 429 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 430 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 431 _UIntType __f> 432 void 433 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 434 __s, __b, __t, __c, __l, __f>:: 435 discard(unsigned long long __z) 436 { 437 while (__z > state_size - _M_p) 438 { 439 __z -= state_size - _M_p; 440 _M_gen_rand(); 441 } 442 _M_p += __z; 443 } 444 445 template<typename _UIntType, size_t __w, 446 size_t __n, size_t __m, size_t __r, 447 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 448 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 449 _UIntType __f> 450 typename 451 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 452 __s, __b, __t, __c, __l, __f>::result_type 453 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 454 __s, __b, __t, __c, __l, __f>:: 455 operator()() 456 { 457 // Reload the vector - cost is O(n) amortized over n calls. 458 if (_M_p >= state_size) 459 _M_gen_rand(); 460 461 // Calculate o(x(i)). 462 result_type __z = _M_x[_M_p++]; 463 __z ^= (__z >> __u) & __d; 464 __z ^= (__z << __s) & __b; 465 __z ^= (__z << __t) & __c; 466 __z ^= (__z >> __l); 467 468 return __z; 469 } 470 471 template<typename _UIntType, size_t __w, 472 size_t __n, size_t __m, size_t __r, 473 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 474 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 475 _UIntType __f, typename _CharT, typename _Traits> 476 std::basic_ostream<_CharT, _Traits>& 477 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 478 const mersenne_twister_engine<_UIntType, __w, __n, __m, 479 __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x) 480 { 481 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 482 typedef typename __ostream_type::ios_base __ios_base; 483 484 const typename __ios_base::fmtflags __flags = __os.flags(); 485 const _CharT __fill = __os.fill(); 486 const _CharT __space = __os.widen(' '); 487 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 488 __os.fill(__space); 489 490 for (size_t __i = 0; __i < __n; ++__i) 491 __os << __x._M_x[__i] << __space; 492 __os << __x._M_p; 493 494 __os.flags(__flags); 495 __os.fill(__fill); 496 return __os; 497 } 498 499 template<typename _UIntType, size_t __w, 500 size_t __n, size_t __m, size_t __r, 501 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 502 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 503 _UIntType __f, typename _CharT, typename _Traits> 504 std::basic_istream<_CharT, _Traits>& 505 operator>>(std::basic_istream<_CharT, _Traits>& __is, 506 mersenne_twister_engine<_UIntType, __w, __n, __m, 507 __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x) 508 { 509 typedef std::basic_istream<_CharT, _Traits> __istream_type; 510 typedef typename __istream_type::ios_base __ios_base; 511 512 const typename __ios_base::fmtflags __flags = __is.flags(); 513 __is.flags(__ios_base::dec | __ios_base::skipws); 514 515 for (size_t __i = 0; __i < __n; ++__i) 516 __is >> __x._M_x[__i]; 517 __is >> __x._M_p; 518 519 __is.flags(__flags); 520 return __is; 521 } 522 523 524 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 525 constexpr size_t 526 subtract_with_carry_engine<_UIntType, __w, __s, __r>::word_size; 527 528 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 529 constexpr size_t 530 subtract_with_carry_engine<_UIntType, __w, __s, __r>::short_lag; 531 532 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 533 constexpr size_t 534 subtract_with_carry_engine<_UIntType, __w, __s, __r>::long_lag; 535 536 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 537 constexpr _UIntType 538 subtract_with_carry_engine<_UIntType, __w, __s, __r>::default_seed; 539 540 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 541 void 542 subtract_with_carry_engine<_UIntType, __w, __s, __r>:: 543 seed(result_type __value) 544 { 545 std::linear_congruential_engine<result_type, 40014u, 0u, 2147483563u> 546 __lcg(__value == 0u ? default_seed : __value); 547 548 const size_t __n = (__w + 31) / 32; 549 550 for (size_t __i = 0; __i < long_lag; ++__i) 551 { 552 _UIntType __sum = 0u; 553 _UIntType __factor = 1u; 554 for (size_t __j = 0; __j < __n; ++__j) 555 { 556 __sum += __detail::__mod<uint_least32_t, 557 __detail::_Shift<uint_least32_t, 32>::__value> 558 (__lcg()) * __factor; 559 __factor *= __detail::_Shift<_UIntType, 32>::__value; 560 } 561 _M_x[__i] = __detail::__mod<_UIntType, 562 __detail::_Shift<_UIntType, __w>::__value>(__sum); 563 } 564 _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0; 565 _M_p = 0; 566 } 567 568 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 569 template<typename _Sseq> 570 typename std::enable_if<std::is_class<_Sseq>::value>::type 571 subtract_with_carry_engine<_UIntType, __w, __s, __r>:: 572 seed(_Sseq& __q) 573 { 574 const size_t __k = (__w + 31) / 32; 575 uint_least32_t __arr[__r * __k]; 576 __q.generate(__arr + 0, __arr + __r * __k); 577 578 for (size_t __i = 0; __i < long_lag; ++__i) 579 { 580 _UIntType __sum = 0u; 581 _UIntType __factor = 1u; 582 for (size_t __j = 0; __j < __k; ++__j) 583 { 584 __sum += __arr[__k * __i + __j] * __factor; 585 __factor *= __detail::_Shift<_UIntType, 32>::__value; 586 } 587 _M_x[__i] = __detail::__mod<_UIntType, 588 __detail::_Shift<_UIntType, __w>::__value>(__sum); 589 } 590 _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0; 591 _M_p = 0; 592 } 593 594 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 595 typename subtract_with_carry_engine<_UIntType, __w, __s, __r>:: 596 result_type 597 subtract_with_carry_engine<_UIntType, __w, __s, __r>:: 598 operator()() 599 { 600 // Derive short lag index from current index. 601 long __ps = _M_p - short_lag; 602 if (__ps < 0) 603 __ps += long_lag; 604 605 // Calculate new x(i) without overflow or division. 606 // NB: Thanks to the requirements for _UIntType, _M_x[_M_p] + _M_carry 607 // cannot overflow. 608 _UIntType __xi; 609 if (_M_x[__ps] >= _M_x[_M_p] + _M_carry) 610 { 611 __xi = _M_x[__ps] - _M_x[_M_p] - _M_carry; 612 _M_carry = 0; 613 } 614 else 615 { 616 __xi = (__detail::_Shift<_UIntType, __w>::__value 617 - _M_x[_M_p] - _M_carry + _M_x[__ps]); 618 _M_carry = 1; 619 } 620 _M_x[_M_p] = __xi; 621 622 // Adjust current index to loop around in ring buffer. 623 if (++_M_p >= long_lag) 624 _M_p = 0; 625 626 return __xi; 627 } 628 629 template<typename _UIntType, size_t __w, size_t __s, size_t __r, 630 typename _CharT, typename _Traits> 631 std::basic_ostream<_CharT, _Traits>& 632 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 633 const subtract_with_carry_engine<_UIntType, 634 __w, __s, __r>& __x) 635 { 636 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 637 typedef typename __ostream_type::ios_base __ios_base; 638 639 const typename __ios_base::fmtflags __flags = __os.flags(); 640 const _CharT __fill = __os.fill(); 641 const _CharT __space = __os.widen(' '); 642 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 643 __os.fill(__space); 644 645 for (size_t __i = 0; __i < __r; ++__i) 646 __os << __x._M_x[__i] << __space; 647 __os << __x._M_carry << __space << __x._M_p; 648 649 __os.flags(__flags); 650 __os.fill(__fill); 651 return __os; 652 } 653 654 template<typename _UIntType, size_t __w, size_t __s, size_t __r, 655 typename _CharT, typename _Traits> 656 std::basic_istream<_CharT, _Traits>& 657 operator>>(std::basic_istream<_CharT, _Traits>& __is, 658 subtract_with_carry_engine<_UIntType, __w, __s, __r>& __x) 659 { 660 typedef std::basic_ostream<_CharT, _Traits> __istream_type; 661 typedef typename __istream_type::ios_base __ios_base; 662 663 const typename __ios_base::fmtflags __flags = __is.flags(); 664 __is.flags(__ios_base::dec | __ios_base::skipws); 665 666 for (size_t __i = 0; __i < __r; ++__i) 667 __is >> __x._M_x[__i]; 668 __is >> __x._M_carry; 669 __is >> __x._M_p; 670 671 __is.flags(__flags); 672 return __is; 673 } 674 675 676 template<typename _RandomNumberEngine, size_t __p, size_t __r> 677 constexpr size_t 678 discard_block_engine<_RandomNumberEngine, __p, __r>::block_size; 679 680 template<typename _RandomNumberEngine, size_t __p, size_t __r> 681 constexpr size_t 682 discard_block_engine<_RandomNumberEngine, __p, __r>::used_block; 683 684 template<typename _RandomNumberEngine, size_t __p, size_t __r> 685 typename discard_block_engine<_RandomNumberEngine, 686 __p, __r>::result_type 687 discard_block_engine<_RandomNumberEngine, __p, __r>:: 688 operator()() 689 { 690 if (_M_n >= used_block) 691 { 692 _M_b.discard(block_size - _M_n); 693 _M_n = 0; 694 } 695 ++_M_n; 696 return _M_b(); 697 } 698 699 template<typename _RandomNumberEngine, size_t __p, size_t __r, 700 typename _CharT, typename _Traits> 701 std::basic_ostream<_CharT, _Traits>& 702 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 703 const discard_block_engine<_RandomNumberEngine, 704 __p, __r>& __x) 705 { 706 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 707 typedef typename __ostream_type::ios_base __ios_base; 708 709 const typename __ios_base::fmtflags __flags = __os.flags(); 710 const _CharT __fill = __os.fill(); 711 const _CharT __space = __os.widen(' '); 712 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 713 __os.fill(__space); 714 715 __os << __x.base() << __space << __x._M_n; 716 717 __os.flags(__flags); 718 __os.fill(__fill); 719 return __os; 720 } 721 722 template<typename _RandomNumberEngine, size_t __p, size_t __r, 723 typename _CharT, typename _Traits> 724 std::basic_istream<_CharT, _Traits>& 725 operator>>(std::basic_istream<_CharT, _Traits>& __is, 726 discard_block_engine<_RandomNumberEngine, __p, __r>& __x) 727 { 728 typedef std::basic_istream<_CharT, _Traits> __istream_type; 729 typedef typename __istream_type::ios_base __ios_base; 730 731 const typename __ios_base::fmtflags __flags = __is.flags(); 732 __is.flags(__ios_base::dec | __ios_base::skipws); 733 734 __is >> __x._M_b >> __x._M_n; 735 736 __is.flags(__flags); 737 return __is; 738 } 739 740 741 template<typename _RandomNumberEngine, size_t __w, typename _UIntType> 742 typename independent_bits_engine<_RandomNumberEngine, __w, _UIntType>:: 743 result_type 744 independent_bits_engine<_RandomNumberEngine, __w, _UIntType>:: 745 operator()() 746 { 747 typedef typename _RandomNumberEngine::result_type _Eresult_type; 748 const _Eresult_type __r 749 = (_M_b.max() - _M_b.min() < std::numeric_limits<_Eresult_type>::max() 750 ? _M_b.max() - _M_b.min() + 1 : 0); 751 const unsigned __edig = std::numeric_limits<_Eresult_type>::digits; 752 const unsigned __m = __r ? std::__lg(__r) : __edig; 753 754 typedef typename std::common_type<_Eresult_type, result_type>::type 755 __ctype; 756 const unsigned __cdig = std::numeric_limits<__ctype>::digits; 757 758 unsigned __n, __n0; 759 __ctype __s0, __s1, __y0, __y1; 760 761 for (size_t __i = 0; __i < 2; ++__i) 762 { 763 __n = (__w + __m - 1) / __m + __i; 764 __n0 = __n - __w % __n; 765 const unsigned __w0 = __w / __n; // __w0 <= __m 766 767 __s0 = 0; 768 __s1 = 0; 769 if (__w0 < __cdig) 770 { 771 __s0 = __ctype(1) << __w0; 772 __s1 = __s0 << 1; 773 } 774 775 __y0 = 0; 776 __y1 = 0; 777 if (__r) 778 { 779 __y0 = __s0 * (__r / __s0); 780 if (__s1) 781 __y1 = __s1 * (__r / __s1); 782 783 if (__r - __y0 <= __y0 / __n) 784 break; 785 } 786 else 787 break; 788 } 789 790 result_type __sum = 0; 791 for (size_t __k = 0; __k < __n0; ++__k) 792 { 793 __ctype __u; 794 do 795 __u = _M_b() - _M_b.min(); 796 while (__y0 && __u >= __y0); 797 __sum = __s0 * __sum + (__s0 ? __u % __s0 : __u); 798 } 799 for (size_t __k = __n0; __k < __n; ++__k) 800 { 801 __ctype __u; 802 do 803 __u = _M_b() - _M_b.min(); 804 while (__y1 && __u >= __y1); 805 __sum = __s1 * __sum + (__s1 ? __u % __s1 : __u); 806 } 807 return __sum; 808 } 809 810 811 template<typename _RandomNumberEngine, size_t __k> 812 constexpr size_t 813 shuffle_order_engine<_RandomNumberEngine, __k>::table_size; 814 815 template<typename _RandomNumberEngine, size_t __k> 816 typename shuffle_order_engine<_RandomNumberEngine, __k>::result_type 817 shuffle_order_engine<_RandomNumberEngine, __k>:: 818 operator()() 819 { 820 size_t __j = __k * ((_M_y - _M_b.min()) 821 / (_M_b.max() - _M_b.min() + 1.0L)); 822 _M_y = _M_v[__j]; 823 _M_v[__j] = _M_b(); 824 825 return _M_y; 826 } 827 828 template<typename _RandomNumberEngine, size_t __k, 829 typename _CharT, typename _Traits> 830 std::basic_ostream<_CharT, _Traits>& 831 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 832 const shuffle_order_engine<_RandomNumberEngine, __k>& __x) 833 { 834 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 835 typedef typename __ostream_type::ios_base __ios_base; 836 837 const typename __ios_base::fmtflags __flags = __os.flags(); 838 const _CharT __fill = __os.fill(); 839 const _CharT __space = __os.widen(' '); 840 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 841 __os.fill(__space); 842 843 __os << __x.base(); 844 for (size_t __i = 0; __i < __k; ++__i) 845 __os << __space << __x._M_v[__i]; 846 __os << __space << __x._M_y; 847 848 __os.flags(__flags); 849 __os.fill(__fill); 850 return __os; 851 } 852 853 template<typename _RandomNumberEngine, size_t __k, 854 typename _CharT, typename _Traits> 855 std::basic_istream<_CharT, _Traits>& 856 operator>>(std::basic_istream<_CharT, _Traits>& __is, 857 shuffle_order_engine<_RandomNumberEngine, __k>& __x) 858 { 859 typedef std::basic_istream<_CharT, _Traits> __istream_type; 860 typedef typename __istream_type::ios_base __ios_base; 861 862 const typename __ios_base::fmtflags __flags = __is.flags(); 863 __is.flags(__ios_base::dec | __ios_base::skipws); 864 865 __is >> __x._M_b; 866 for (size_t __i = 0; __i < __k; ++__i) 867 __is >> __x._M_v[__i]; 868 __is >> __x._M_y; 869 870 __is.flags(__flags); 871 return __is; 872 } 873 874 875 template<typename _IntType> 876 template<typename _UniformRandomNumberGenerator> 877 typename uniform_int_distribution<_IntType>::result_type 878 uniform_int_distribution<_IntType>:: 879 operator()(_UniformRandomNumberGenerator& __urng, 880 const param_type& __param) 881 { 882 typedef typename _UniformRandomNumberGenerator::result_type 883 _Gresult_type; 884 typedef typename std::make_unsigned<result_type>::type __utype; 885 typedef typename std::common_type<_Gresult_type, __utype>::type 886 __uctype; 887 888 const __uctype __urngmin = __urng.min(); 889 const __uctype __urngmax = __urng.max(); 890 const __uctype __urngrange = __urngmax - __urngmin; 891 const __uctype __urange 892 = __uctype(__param.b()) - __uctype(__param.a()); 893 894 __uctype __ret; 895 896 if (__urngrange > __urange) 897 { 898 // downscaling 899 const __uctype __uerange = __urange + 1; // __urange can be zero 900 const __uctype __scaling = __urngrange / __uerange; 901 const __uctype __past = __uerange * __scaling; 902 do 903 __ret = __uctype(__urng()) - __urngmin; 904 while (__ret >= __past); 905 __ret /= __scaling; 906 } 907 else if (__urngrange < __urange) 908 { 909 // upscaling 910 /* 911 Note that every value in [0, urange] 912 can be written uniquely as 913 914 (urngrange + 1) * high + low 915 916 where 917 918 high in [0, urange / (urngrange + 1)] 919 920 and 921 922 low in [0, urngrange]. 923 */ 924 __uctype __tmp; // wraparound control 925 do 926 { 927 const __uctype __uerngrange = __urngrange + 1; 928 __tmp = (__uerngrange * operator() 929 (__urng, param_type(0, __urange / __uerngrange))); 930 __ret = __tmp + (__uctype(__urng()) - __urngmin); 931 } 932 while (__ret > __urange || __ret < __tmp); 933 } 934 else 935 __ret = __uctype(__urng()) - __urngmin; 936 937 return __ret + __param.a(); 938 } 939 940 941 template<typename _IntType> 942 template<typename _ForwardIterator, 943 typename _UniformRandomNumberGenerator> 944 void 945 uniform_int_distribution<_IntType>:: 946 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 947 _UniformRandomNumberGenerator& __urng, 948 const param_type& __param) 949 { 950 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 951 typedef typename _UniformRandomNumberGenerator::result_type 952 _Gresult_type; 953 typedef typename std::make_unsigned<result_type>::type __utype; 954 typedef typename std::common_type<_Gresult_type, __utype>::type 955 __uctype; 956 957 const __uctype __urngmin = __urng.min(); 958 const __uctype __urngmax = __urng.max(); 959 const __uctype __urngrange = __urngmax - __urngmin; 960 const __uctype __urange 961 = __uctype(__param.b()) - __uctype(__param.a()); 962 963 __uctype __ret; 964 965 if (__urngrange > __urange) 966 { 967 if (__detail::_Power_of_2(__urngrange + 1) 968 && __detail::_Power_of_2(__urange + 1)) 969 { 970 while (__f != __t) 971 { 972 __ret = __uctype(__urng()) - __urngmin; 973 *__f++ = (__ret & __urange) + __param.a(); 974 } 975 } 976 else 977 { 978 // downscaling 979 const __uctype __uerange = __urange + 1; // __urange can be zero 980 const __uctype __scaling = __urngrange / __uerange; 981 const __uctype __past = __uerange * __scaling; 982 while (__f != __t) 983 { 984 do 985 __ret = __uctype(__urng()) - __urngmin; 986 while (__ret >= __past); 987 *__f++ = __ret / __scaling + __param.a(); 988 } 989 } 990 } 991 else if (__urngrange < __urange) 992 { 993 // upscaling 994 /* 995 Note that every value in [0, urange] 996 can be written uniquely as 997 998 (urngrange + 1) * high + low 999 1000 where 1001 1002 high in [0, urange / (urngrange + 1)] 1003 1004 and 1005 1006 low in [0, urngrange]. 1007 */ 1008 __uctype __tmp; // wraparound control 1009 while (__f != __t) 1010 { 1011 do 1012 { 1013 const __uctype __uerngrange = __urngrange + 1; 1014 __tmp = (__uerngrange * operator() 1015 (__urng, param_type(0, __urange / __uerngrange))); 1016 __ret = __tmp + (__uctype(__urng()) - __urngmin); 1017 } 1018 while (__ret > __urange || __ret < __tmp); 1019 *__f++ = __ret; 1020 } 1021 } 1022 else 1023 while (__f != __t) 1024 *__f++ = __uctype(__urng()) - __urngmin + __param.a(); 1025 } 1026 1027 template<typename _IntType, typename _CharT, typename _Traits> 1028 std::basic_ostream<_CharT, _Traits>& 1029 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1030 const uniform_int_distribution<_IntType>& __x) 1031 { 1032 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1033 typedef typename __ostream_type::ios_base __ios_base; 1034 1035 const typename __ios_base::fmtflags __flags = __os.flags(); 1036 const _CharT __fill = __os.fill(); 1037 const _CharT __space = __os.widen(' '); 1038 __os.flags(__ios_base::scientific | __ios_base::left); 1039 __os.fill(__space); 1040 1041 __os << __x.a() << __space << __x.b(); 1042 1043 __os.flags(__flags); 1044 __os.fill(__fill); 1045 return __os; 1046 } 1047 1048 template<typename _IntType, typename _CharT, typename _Traits> 1049 std::basic_istream<_CharT, _Traits>& 1050 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1051 uniform_int_distribution<_IntType>& __x) 1052 { 1053 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1054 typedef typename __istream_type::ios_base __ios_base; 1055 1056 const typename __ios_base::fmtflags __flags = __is.flags(); 1057 __is.flags(__ios_base::dec | __ios_base::skipws); 1058 1059 _IntType __a, __b; 1060 __is >> __a >> __b; 1061 __x.param(typename uniform_int_distribution<_IntType>:: 1062 param_type(__a, __b)); 1063 1064 __is.flags(__flags); 1065 return __is; 1066 } 1067 1068 1069 template<typename _RealType> 1070 template<typename _ForwardIterator, 1071 typename _UniformRandomNumberGenerator> 1072 void 1073 uniform_real_distribution<_RealType>:: 1074 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1075 _UniformRandomNumberGenerator& __urng, 1076 const param_type& __p) 1077 { 1078 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1079 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 1080 __aurng(__urng); 1081 auto __range = __p.b() - __p.a(); 1082 while (__f != __t) 1083 *__f++ = __aurng() * __range + __p.a(); 1084 } 1085 1086 template<typename _RealType, typename _CharT, typename _Traits> 1087 std::basic_ostream<_CharT, _Traits>& 1088 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1089 const uniform_real_distribution<_RealType>& __x) 1090 { 1091 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1092 typedef typename __ostream_type::ios_base __ios_base; 1093 1094 const typename __ios_base::fmtflags __flags = __os.flags(); 1095 const _CharT __fill = __os.fill(); 1096 const std::streamsize __precision = __os.precision(); 1097 const _CharT __space = __os.widen(' '); 1098 __os.flags(__ios_base::scientific | __ios_base::left); 1099 __os.fill(__space); 1100 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1101 1102 __os << __x.a() << __space << __x.b(); 1103 1104 __os.flags(__flags); 1105 __os.fill(__fill); 1106 __os.precision(__precision); 1107 return __os; 1108 } 1109 1110 template<typename _RealType, typename _CharT, typename _Traits> 1111 std::basic_istream<_CharT, _Traits>& 1112 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1113 uniform_real_distribution<_RealType>& __x) 1114 { 1115 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1116 typedef typename __istream_type::ios_base __ios_base; 1117 1118 const typename __ios_base::fmtflags __flags = __is.flags(); 1119 __is.flags(__ios_base::skipws); 1120 1121 _RealType __a, __b; 1122 __is >> __a >> __b; 1123 __x.param(typename uniform_real_distribution<_RealType>:: 1124 param_type(__a, __b)); 1125 1126 __is.flags(__flags); 1127 return __is; 1128 } 1129 1130 1131 template<typename _ForwardIterator, 1132 typename _UniformRandomNumberGenerator> 1133 void 1134 std::bernoulli_distribution:: 1135 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1136 _UniformRandomNumberGenerator& __urng, 1137 const param_type& __p) 1138 { 1139 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1140 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1141 __aurng(__urng); 1142 auto __limit = __p.p() * (__aurng.max() - __aurng.min()); 1143 1144 while (__f != __t) 1145 *__f++ = (__aurng() - __aurng.min()) < __limit; 1146 } 1147 1148 template<typename _CharT, typename _Traits> 1149 std::basic_ostream<_CharT, _Traits>& 1150 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1151 const bernoulli_distribution& __x) 1152 { 1153 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1154 typedef typename __ostream_type::ios_base __ios_base; 1155 1156 const typename __ios_base::fmtflags __flags = __os.flags(); 1157 const _CharT __fill = __os.fill(); 1158 const std::streamsize __precision = __os.precision(); 1159 __os.flags(__ios_base::scientific | __ios_base::left); 1160 __os.fill(__os.widen(' ')); 1161 __os.precision(std::numeric_limits<double>::max_digits10); 1162 1163 __os << __x.p(); 1164 1165 __os.flags(__flags); 1166 __os.fill(__fill); 1167 __os.precision(__precision); 1168 return __os; 1169 } 1170 1171 1172 template<typename _IntType> 1173 template<typename _UniformRandomNumberGenerator> 1174 typename geometric_distribution<_IntType>::result_type 1175 geometric_distribution<_IntType>:: 1176 operator()(_UniformRandomNumberGenerator& __urng, 1177 const param_type& __param) 1178 { 1179 // About the epsilon thing see this thread: 1180 // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html 1181 const double __naf = 1182 (1 - std::numeric_limits<double>::epsilon()) / 2; 1183 // The largest _RealType convertible to _IntType. 1184 const double __thr = 1185 std::numeric_limits<_IntType>::max() + __naf; 1186 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1187 __aurng(__urng); 1188 1189 double __cand; 1190 do 1191 __cand = std::floor(std::log(1.0 - __aurng()) / __param._M_log_1_p); 1192 while (__cand >= __thr); 1193 1194 return result_type(__cand + __naf); 1195 } 1196 1197 template<typename _IntType> 1198 template<typename _ForwardIterator, 1199 typename _UniformRandomNumberGenerator> 1200 void 1201 geometric_distribution<_IntType>:: 1202 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1203 _UniformRandomNumberGenerator& __urng, 1204 const param_type& __param) 1205 { 1206 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1207 // About the epsilon thing see this thread: 1208 // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html 1209 const double __naf = 1210 (1 - std::numeric_limits<double>::epsilon()) / 2; 1211 // The largest _RealType convertible to _IntType. 1212 const double __thr = 1213 std::numeric_limits<_IntType>::max() + __naf; 1214 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1215 __aurng(__urng); 1216 1217 while (__f != __t) 1218 { 1219 double __cand; 1220 do 1221 __cand = std::floor(std::log(1.0 - __aurng()) 1222 / __param._M_log_1_p); 1223 while (__cand >= __thr); 1224 1225 *__f++ = __cand + __naf; 1226 } 1227 } 1228 1229 template<typename _IntType, 1230 typename _CharT, typename _Traits> 1231 std::basic_ostream<_CharT, _Traits>& 1232 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1233 const geometric_distribution<_IntType>& __x) 1234 { 1235 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1236 typedef typename __ostream_type::ios_base __ios_base; 1237 1238 const typename __ios_base::fmtflags __flags = __os.flags(); 1239 const _CharT __fill = __os.fill(); 1240 const std::streamsize __precision = __os.precision(); 1241 __os.flags(__ios_base::scientific | __ios_base::left); 1242 __os.fill(__os.widen(' ')); 1243 __os.precision(std::numeric_limits<double>::max_digits10); 1244 1245 __os << __x.p(); 1246 1247 __os.flags(__flags); 1248 __os.fill(__fill); 1249 __os.precision(__precision); 1250 return __os; 1251 } 1252 1253 template<typename _IntType, 1254 typename _CharT, typename _Traits> 1255 std::basic_istream<_CharT, _Traits>& 1256 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1257 geometric_distribution<_IntType>& __x) 1258 { 1259 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1260 typedef typename __istream_type::ios_base __ios_base; 1261 1262 const typename __ios_base::fmtflags __flags = __is.flags(); 1263 __is.flags(__ios_base::skipws); 1264 1265 double __p; 1266 __is >> __p; 1267 __x.param(typename geometric_distribution<_IntType>::param_type(__p)); 1268 1269 __is.flags(__flags); 1270 return __is; 1271 } 1272 1273 // This is Leger's algorithm, also in Devroye, Ch. X, Example 1.5. 1274 template<typename _IntType> 1275 template<typename _UniformRandomNumberGenerator> 1276 typename negative_binomial_distribution<_IntType>::result_type 1277 negative_binomial_distribution<_IntType>:: 1278 operator()(_UniformRandomNumberGenerator& __urng) 1279 { 1280 const double __y = _M_gd(__urng); 1281 1282 // XXX Is the constructor too slow? 1283 std::poisson_distribution<result_type> __poisson(__y); 1284 return __poisson(__urng); 1285 } 1286 1287 template<typename _IntType> 1288 template<typename _UniformRandomNumberGenerator> 1289 typename negative_binomial_distribution<_IntType>::result_type 1290 negative_binomial_distribution<_IntType>:: 1291 operator()(_UniformRandomNumberGenerator& __urng, 1292 const param_type& __p) 1293 { 1294 typedef typename std::gamma_distribution<result_type>::param_type 1295 param_type; 1296 1297 const double __y = 1298 _M_gd(__urng, param_type(__p.k(), (1.0 - __p.p()) / __p.p())); 1299 1300 std::poisson_distribution<result_type> __poisson(__y); 1301 return __poisson(__urng); 1302 } 1303 1304 template<typename _IntType> 1305 template<typename _ForwardIterator, 1306 typename _UniformRandomNumberGenerator> 1307 void 1308 negative_binomial_distribution<_IntType>:: 1309 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1310 _UniformRandomNumberGenerator& __urng) 1311 { 1312 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1313 while (__f != __t) 1314 { 1315 const double __y = _M_gd(__urng); 1316 1317 // XXX Is the constructor too slow? 1318 std::poisson_distribution<result_type> __poisson(__y); 1319 *__f++ = __poisson(__urng); 1320 } 1321 } 1322 1323 template<typename _IntType> 1324 template<typename _ForwardIterator, 1325 typename _UniformRandomNumberGenerator> 1326 void 1327 negative_binomial_distribution<_IntType>:: 1328 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1329 _UniformRandomNumberGenerator& __urng, 1330 const param_type& __p) 1331 { 1332 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1333 typename std::gamma_distribution<result_type>::param_type 1334 __p2(__p.k(), (1.0 - __p.p()) / __p.p()); 1335 1336 while (__f != __t) 1337 { 1338 const double __y = _M_gd(__urng, __p2); 1339 1340 std::poisson_distribution<result_type> __poisson(__y); 1341 *__f++ = __poisson(__urng); 1342 } 1343 } 1344 1345 template<typename _IntType, typename _CharT, typename _Traits> 1346 std::basic_ostream<_CharT, _Traits>& 1347 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1348 const negative_binomial_distribution<_IntType>& __x) 1349 { 1350 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1351 typedef typename __ostream_type::ios_base __ios_base; 1352 1353 const typename __ios_base::fmtflags __flags = __os.flags(); 1354 const _CharT __fill = __os.fill(); 1355 const std::streamsize __precision = __os.precision(); 1356 const _CharT __space = __os.widen(' '); 1357 __os.flags(__ios_base::scientific | __ios_base::left); 1358 __os.fill(__os.widen(' ')); 1359 __os.precision(std::numeric_limits<double>::max_digits10); 1360 1361 __os << __x.k() << __space << __x.p() 1362 << __space << __x._M_gd; 1363 1364 __os.flags(__flags); 1365 __os.fill(__fill); 1366 __os.precision(__precision); 1367 return __os; 1368 } 1369 1370 template<typename _IntType, typename _CharT, typename _Traits> 1371 std::basic_istream<_CharT, _Traits>& 1372 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1373 negative_binomial_distribution<_IntType>& __x) 1374 { 1375 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1376 typedef typename __istream_type::ios_base __ios_base; 1377 1378 const typename __ios_base::fmtflags __flags = __is.flags(); 1379 __is.flags(__ios_base::skipws); 1380 1381 _IntType __k; 1382 double __p; 1383 __is >> __k >> __p >> __x._M_gd; 1384 __x.param(typename negative_binomial_distribution<_IntType>:: 1385 param_type(__k, __p)); 1386 1387 __is.flags(__flags); 1388 return __is; 1389 } 1390 1391 1392 template<typename _IntType> 1393 void 1394 poisson_distribution<_IntType>::param_type:: 1395 _M_initialize() 1396 { 1397#if _GLIBCXX_USE_C99_MATH_TR1 1398 if (_M_mean >= 12) 1399 { 1400 const double __m = std::floor(_M_mean); 1401 _M_lm_thr = std::log(_M_mean); 1402 _M_lfm = std::lgamma(__m + 1); 1403 _M_sm = std::sqrt(__m); 1404 1405 const double __pi_4 = 0.7853981633974483096156608458198757L; 1406 const double __dx = std::sqrt(2 * __m * std::log(32 * __m 1407 / __pi_4)); 1408 _M_d = std::round(std::max(6.0, std::min(__m, __dx))); 1409 const double __cx = 2 * __m + _M_d; 1410 _M_scx = std::sqrt(__cx / 2); 1411 _M_1cx = 1 / __cx; 1412 1413 _M_c2b = std::sqrt(__pi_4 * __cx) * std::exp(_M_1cx); 1414 _M_cb = 2 * __cx * std::exp(-_M_d * _M_1cx * (1 + _M_d / 2)) 1415 / _M_d; 1416 } 1417 else 1418#endif 1419 _M_lm_thr = std::exp(-_M_mean); 1420 } 1421 1422 /** 1423 * A rejection algorithm when mean >= 12 and a simple method based 1424 * upon the multiplication of uniform random variates otherwise. 1425 * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1 1426 * is defined. 1427 * 1428 * Reference: 1429 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, 1430 * New York, 1986, Ch. X, Sects. 3.3 & 3.4 (+ Errata!). 1431 */ 1432 template<typename _IntType> 1433 template<typename _UniformRandomNumberGenerator> 1434 typename poisson_distribution<_IntType>::result_type 1435 poisson_distribution<_IntType>:: 1436 operator()(_UniformRandomNumberGenerator& __urng, 1437 const param_type& __param) 1438 { 1439 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1440 __aurng(__urng); 1441#if _GLIBCXX_USE_C99_MATH_TR1 1442 if (__param.mean() >= 12) 1443 { 1444 double __x; 1445 1446 // See comments above... 1447 const double __naf = 1448 (1 - std::numeric_limits<double>::epsilon()) / 2; 1449 const double __thr = 1450 std::numeric_limits<_IntType>::max() + __naf; 1451 1452 const double __m = std::floor(__param.mean()); 1453 // sqrt(pi / 2) 1454 const double __spi_2 = 1.2533141373155002512078826424055226L; 1455 const double __c1 = __param._M_sm * __spi_2; 1456 const double __c2 = __param._M_c2b + __c1; 1457 const double __c3 = __c2 + 1; 1458 const double __c4 = __c3 + 1; 1459 // e^(1 / 78) 1460 const double __e178 = 1.0129030479320018583185514777512983L; 1461 const double __c5 = __c4 + __e178; 1462 const double __c = __param._M_cb + __c5; 1463 const double __2cx = 2 * (2 * __m + __param._M_d); 1464 1465 bool __reject = true; 1466 do 1467 { 1468 const double __u = __c * __aurng(); 1469 const double __e = -std::log(1.0 - __aurng()); 1470 1471 double __w = 0.0; 1472 1473 if (__u <= __c1) 1474 { 1475 const double __n = _M_nd(__urng); 1476 const double __y = -std::abs(__n) * __param._M_sm - 1; 1477 __x = std::floor(__y); 1478 __w = -__n * __n / 2; 1479 if (__x < -__m) 1480 continue; 1481 } 1482 else if (__u <= __c2) 1483 { 1484 const double __n = _M_nd(__urng); 1485 const double __y = 1 + std::abs(__n) * __param._M_scx; 1486 __x = std::ceil(__y); 1487 __w = __y * (2 - __y) * __param._M_1cx; 1488 if (__x > __param._M_d) 1489 continue; 1490 } 1491 else if (__u <= __c3) 1492 // NB: This case not in the book, nor in the Errata, 1493 // but should be ok... 1494 __x = -1; 1495 else if (__u <= __c4) 1496 __x = 0; 1497 else if (__u <= __c5) 1498 __x = 1; 1499 else 1500 { 1501 const double __v = -std::log(1.0 - __aurng()); 1502 const double __y = __param._M_d 1503 + __v * __2cx / __param._M_d; 1504 __x = std::ceil(__y); 1505 __w = -__param._M_d * __param._M_1cx * (1 + __y / 2); 1506 } 1507 1508 __reject = (__w - __e - __x * __param._M_lm_thr 1509 > __param._M_lfm - std::lgamma(__x + __m + 1)); 1510 1511 __reject |= __x + __m >= __thr; 1512 1513 } while (__reject); 1514 1515 return result_type(__x + __m + __naf); 1516 } 1517 else 1518#endif 1519 { 1520 _IntType __x = 0; 1521 double __prod = 1.0; 1522 1523 do 1524 { 1525 __prod *= __aurng(); 1526 __x += 1; 1527 } 1528 while (__prod > __param._M_lm_thr); 1529 1530 return __x - 1; 1531 } 1532 } 1533 1534 template<typename _IntType> 1535 template<typename _ForwardIterator, 1536 typename _UniformRandomNumberGenerator> 1537 void 1538 poisson_distribution<_IntType>:: 1539 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1540 _UniformRandomNumberGenerator& __urng, 1541 const param_type& __param) 1542 { 1543 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1544 // We could duplicate everything from operator()... 1545 while (__f != __t) 1546 *__f++ = this->operator()(__urng, __param); 1547 } 1548 1549 template<typename _IntType, 1550 typename _CharT, typename _Traits> 1551 std::basic_ostream<_CharT, _Traits>& 1552 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1553 const poisson_distribution<_IntType>& __x) 1554 { 1555 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1556 typedef typename __ostream_type::ios_base __ios_base; 1557 1558 const typename __ios_base::fmtflags __flags = __os.flags(); 1559 const _CharT __fill = __os.fill(); 1560 const std::streamsize __precision = __os.precision(); 1561 const _CharT __space = __os.widen(' '); 1562 __os.flags(__ios_base::scientific | __ios_base::left); 1563 __os.fill(__space); 1564 __os.precision(std::numeric_limits<double>::max_digits10); 1565 1566 __os << __x.mean() << __space << __x._M_nd; 1567 1568 __os.flags(__flags); 1569 __os.fill(__fill); 1570 __os.precision(__precision); 1571 return __os; 1572 } 1573 1574 template<typename _IntType, 1575 typename _CharT, typename _Traits> 1576 std::basic_istream<_CharT, _Traits>& 1577 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1578 poisson_distribution<_IntType>& __x) 1579 { 1580 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1581 typedef typename __istream_type::ios_base __ios_base; 1582 1583 const typename __ios_base::fmtflags __flags = __is.flags(); 1584 __is.flags(__ios_base::skipws); 1585 1586 double __mean; 1587 __is >> __mean >> __x._M_nd; 1588 __x.param(typename poisson_distribution<_IntType>::param_type(__mean)); 1589 1590 __is.flags(__flags); 1591 return __is; 1592 } 1593 1594 1595 template<typename _IntType> 1596 void 1597 binomial_distribution<_IntType>::param_type:: 1598 _M_initialize() 1599 { 1600 const double __p12 = _M_p <= 0.5 ? _M_p : 1.0 - _M_p; 1601 1602 _M_easy = true; 1603 1604#if _GLIBCXX_USE_C99_MATH_TR1 1605 if (_M_t * __p12 >= 8) 1606 { 1607 _M_easy = false; 1608 const double __np = std::floor(_M_t * __p12); 1609 const double __pa = __np / _M_t; 1610 const double __1p = 1 - __pa; 1611 1612 const double __pi_4 = 0.7853981633974483096156608458198757L; 1613 const double __d1x = 1614 std::sqrt(__np * __1p * std::log(32 * __np 1615 / (81 * __pi_4 * __1p))); 1616 _M_d1 = std::round(std::max(1.0, __d1x)); 1617 const double __d2x = 1618 std::sqrt(__np * __1p * std::log(32 * _M_t * __1p 1619 / (__pi_4 * __pa))); 1620 _M_d2 = std::round(std::max(1.0, __d2x)); 1621 1622 // sqrt(pi / 2) 1623 const double __spi_2 = 1.2533141373155002512078826424055226L; 1624 _M_s1 = std::sqrt(__np * __1p) * (1 + _M_d1 / (4 * __np)); 1625 _M_s2 = std::sqrt(__np * __1p) * (1 + _M_d2 / (4 * _M_t * __1p)); 1626 _M_c = 2 * _M_d1 / __np; 1627 _M_a1 = std::exp(_M_c) * _M_s1 * __spi_2; 1628 const double __a12 = _M_a1 + _M_s2 * __spi_2; 1629 const double __s1s = _M_s1 * _M_s1; 1630 _M_a123 = __a12 + (std::exp(_M_d1 / (_M_t * __1p)) 1631 * 2 * __s1s / _M_d1 1632 * std::exp(-_M_d1 * _M_d1 / (2 * __s1s))); 1633 const double __s2s = _M_s2 * _M_s2; 1634 _M_s = (_M_a123 + 2 * __s2s / _M_d2 1635 * std::exp(-_M_d2 * _M_d2 / (2 * __s2s))); 1636 _M_lf = (std::lgamma(__np + 1) 1637 + std::lgamma(_M_t - __np + 1)); 1638 _M_lp1p = std::log(__pa / __1p); 1639 1640 _M_q = -std::log(1 - (__p12 - __pa) / __1p); 1641 } 1642 else 1643#endif 1644 _M_q = -std::log(1 - __p12); 1645 } 1646 1647 template<typename _IntType> 1648 template<typename _UniformRandomNumberGenerator> 1649 typename binomial_distribution<_IntType>::result_type 1650 binomial_distribution<_IntType>:: 1651 _M_waiting(_UniformRandomNumberGenerator& __urng, _IntType __t) 1652 { 1653 _IntType __x = 0; 1654 double __sum = 0.0; 1655 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1656 __aurng(__urng); 1657 1658 do 1659 { 1660 if (__t == __x) 1661 return __x; 1662 const double __e = -std::log(1.0 - __aurng()); 1663 __sum += __e / (__t - __x); 1664 __x += 1; 1665 } 1666 while (__sum <= _M_param._M_q); 1667 1668 return __x - 1; 1669 } 1670 1671 /** 1672 * A rejection algorithm when t * p >= 8 and a simple waiting time 1673 * method - the second in the referenced book - otherwise. 1674 * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1 1675 * is defined. 1676 * 1677 * Reference: 1678 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, 1679 * New York, 1986, Ch. X, Sect. 4 (+ Errata!). 1680 */ 1681 template<typename _IntType> 1682 template<typename _UniformRandomNumberGenerator> 1683 typename binomial_distribution<_IntType>::result_type 1684 binomial_distribution<_IntType>:: 1685 operator()(_UniformRandomNumberGenerator& __urng, 1686 const param_type& __param) 1687 { 1688 result_type __ret; 1689 const _IntType __t = __param.t(); 1690 const double __p = __param.p(); 1691 const double __p12 = __p <= 0.5 ? __p : 1.0 - __p; 1692 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1693 __aurng(__urng); 1694 1695#if _GLIBCXX_USE_C99_MATH_TR1 1696 if (!__param._M_easy) 1697 { 1698 double __x; 1699 1700 // See comments above... 1701 const double __naf = 1702 (1 - std::numeric_limits<double>::epsilon()) / 2; 1703 const double __thr = 1704 std::numeric_limits<_IntType>::max() + __naf; 1705 1706 const double __np = std::floor(__t * __p12); 1707 1708 // sqrt(pi / 2) 1709 const double __spi_2 = 1.2533141373155002512078826424055226L; 1710 const double __a1 = __param._M_a1; 1711 const double __a12 = __a1 + __param._M_s2 * __spi_2; 1712 const double __a123 = __param._M_a123; 1713 const double __s1s = __param._M_s1 * __param._M_s1; 1714 const double __s2s = __param._M_s2 * __param._M_s2; 1715 1716 bool __reject; 1717 do 1718 { 1719 const double __u = __param._M_s * __aurng(); 1720 1721 double __v; 1722 1723 if (__u <= __a1) 1724 { 1725 const double __n = _M_nd(__urng); 1726 const double __y = __param._M_s1 * std::abs(__n); 1727 __reject = __y >= __param._M_d1; 1728 if (!__reject) 1729 { 1730 const double __e = -std::log(1.0 - __aurng()); 1731 __x = std::floor(__y); 1732 __v = -__e - __n * __n / 2 + __param._M_c; 1733 } 1734 } 1735 else if (__u <= __a12) 1736 { 1737 const double __n = _M_nd(__urng); 1738 const double __y = __param._M_s2 * std::abs(__n); 1739 __reject = __y >= __param._M_d2; 1740 if (!__reject) 1741 { 1742 const double __e = -std::log(1.0 - __aurng()); 1743 __x = std::floor(-__y); 1744 __v = -__e - __n * __n / 2; 1745 } 1746 } 1747 else if (__u <= __a123) 1748 { 1749 const double __e1 = -std::log(1.0 - __aurng()); 1750 const double __e2 = -std::log(1.0 - __aurng()); 1751 1752 const double __y = __param._M_d1 1753 + 2 * __s1s * __e1 / __param._M_d1; 1754 __x = std::floor(__y); 1755 __v = (-__e2 + __param._M_d1 * (1 / (__t - __np) 1756 -__y / (2 * __s1s))); 1757 __reject = false; 1758 } 1759 else 1760 { 1761 const double __e1 = -std::log(1.0 - __aurng()); 1762 const double __e2 = -std::log(1.0 - __aurng()); 1763 1764 const double __y = __param._M_d2 1765 + 2 * __s2s * __e1 / __param._M_d2; 1766 __x = std::floor(-__y); 1767 __v = -__e2 - __param._M_d2 * __y / (2 * __s2s); 1768 __reject = false; 1769 } 1770 1771 __reject = __reject || __x < -__np || __x > __t - __np; 1772 if (!__reject) 1773 { 1774 const double __lfx = 1775 std::lgamma(__np + __x + 1) 1776 + std::lgamma(__t - (__np + __x) + 1); 1777 __reject = __v > __param._M_lf - __lfx 1778 + __x * __param._M_lp1p; 1779 } 1780 1781 __reject |= __x + __np >= __thr; 1782 } 1783 while (__reject); 1784 1785 __x += __np + __naf; 1786 1787 const _IntType __z = _M_waiting(__urng, __t - _IntType(__x)); 1788 __ret = _IntType(__x) + __z; 1789 } 1790 else 1791#endif 1792 __ret = _M_waiting(__urng, __t); 1793 1794 if (__p12 != __p) 1795 __ret = __t - __ret; 1796 return __ret; 1797 } 1798 1799 template<typename _IntType> 1800 template<typename _ForwardIterator, 1801 typename _UniformRandomNumberGenerator> 1802 void 1803 binomial_distribution<_IntType>:: 1804 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1805 _UniformRandomNumberGenerator& __urng, 1806 const param_type& __param) 1807 { 1808 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1809 // We could duplicate everything from operator()... 1810 while (__f != __t) 1811 *__f++ = this->operator()(__urng, __param); 1812 } 1813 1814 template<typename _IntType, 1815 typename _CharT, typename _Traits> 1816 std::basic_ostream<_CharT, _Traits>& 1817 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1818 const binomial_distribution<_IntType>& __x) 1819 { 1820 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1821 typedef typename __ostream_type::ios_base __ios_base; 1822 1823 const typename __ios_base::fmtflags __flags = __os.flags(); 1824 const _CharT __fill = __os.fill(); 1825 const std::streamsize __precision = __os.precision(); 1826 const _CharT __space = __os.widen(' '); 1827 __os.flags(__ios_base::scientific | __ios_base::left); 1828 __os.fill(__space); 1829 __os.precision(std::numeric_limits<double>::max_digits10); 1830 1831 __os << __x.t() << __space << __x.p() 1832 << __space << __x._M_nd; 1833 1834 __os.flags(__flags); 1835 __os.fill(__fill); 1836 __os.precision(__precision); 1837 return __os; 1838 } 1839 1840 template<typename _IntType, 1841 typename _CharT, typename _Traits> 1842 std::basic_istream<_CharT, _Traits>& 1843 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1844 binomial_distribution<_IntType>& __x) 1845 { 1846 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1847 typedef typename __istream_type::ios_base __ios_base; 1848 1849 const typename __ios_base::fmtflags __flags = __is.flags(); 1850 __is.flags(__ios_base::dec | __ios_base::skipws); 1851 1852 _IntType __t; 1853 double __p; 1854 __is >> __t >> __p >> __x._M_nd; 1855 __x.param(typename binomial_distribution<_IntType>:: 1856 param_type(__t, __p)); 1857 1858 __is.flags(__flags); 1859 return __is; 1860 } 1861 1862 1863 template<typename _RealType> 1864 template<typename _ForwardIterator, 1865 typename _UniformRandomNumberGenerator> 1866 void 1867 std::exponential_distribution<_RealType>:: 1868 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1869 _UniformRandomNumberGenerator& __urng, 1870 const param_type& __p) 1871 { 1872 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1873 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 1874 __aurng(__urng); 1875 while (__f != __t) 1876 *__f++ = -std::log(result_type(1) - __aurng()) / __p.lambda(); 1877 } 1878 1879 template<typename _RealType, typename _CharT, typename _Traits> 1880 std::basic_ostream<_CharT, _Traits>& 1881 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1882 const exponential_distribution<_RealType>& __x) 1883 { 1884 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1885 typedef typename __ostream_type::ios_base __ios_base; 1886 1887 const typename __ios_base::fmtflags __flags = __os.flags(); 1888 const _CharT __fill = __os.fill(); 1889 const std::streamsize __precision = __os.precision(); 1890 __os.flags(__ios_base::scientific | __ios_base::left); 1891 __os.fill(__os.widen(' ')); 1892 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1893 1894 __os << __x.lambda(); 1895 1896 __os.flags(__flags); 1897 __os.fill(__fill); 1898 __os.precision(__precision); 1899 return __os; 1900 } 1901 1902 template<typename _RealType, typename _CharT, typename _Traits> 1903 std::basic_istream<_CharT, _Traits>& 1904 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1905 exponential_distribution<_RealType>& __x) 1906 { 1907 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1908 typedef typename __istream_type::ios_base __ios_base; 1909 1910 const typename __ios_base::fmtflags __flags = __is.flags(); 1911 __is.flags(__ios_base::dec | __ios_base::skipws); 1912 1913 _RealType __lambda; 1914 __is >> __lambda; 1915 __x.param(typename exponential_distribution<_RealType>:: 1916 param_type(__lambda)); 1917 1918 __is.flags(__flags); 1919 return __is; 1920 } 1921 1922 1923 /** 1924 * Polar method due to Marsaglia. 1925 * 1926 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, 1927 * New York, 1986, Ch. V, Sect. 4.4. 1928 */ 1929 template<typename _RealType> 1930 template<typename _UniformRandomNumberGenerator> 1931 typename normal_distribution<_RealType>::result_type 1932 normal_distribution<_RealType>:: 1933 operator()(_UniformRandomNumberGenerator& __urng, 1934 const param_type& __param) 1935 { 1936 result_type __ret; 1937 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 1938 __aurng(__urng); 1939 1940 if (_M_saved_available) 1941 { 1942 _M_saved_available = false; 1943 __ret = _M_saved; 1944 } 1945 else 1946 { 1947 result_type __x, __y, __r2; 1948 do 1949 { 1950 __x = result_type(2.0) * __aurng() - 1.0; 1951 __y = result_type(2.0) * __aurng() - 1.0; 1952 __r2 = __x * __x + __y * __y; 1953 } 1954 while (__r2 > 1.0 || __r2 == 0.0); 1955 1956 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2); 1957 _M_saved = __x * __mult; 1958 _M_saved_available = true; 1959 __ret = __y * __mult; 1960 } 1961 1962 __ret = __ret * __param.stddev() + __param.mean(); 1963 return __ret; 1964 } 1965 1966 template<typename _RealType> 1967 template<typename _ForwardIterator, 1968 typename _UniformRandomNumberGenerator> 1969 void 1970 normal_distribution<_RealType>:: 1971 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1972 _UniformRandomNumberGenerator& __urng, 1973 const param_type& __param) 1974 { 1975 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1976 1977 if (__f == __t) 1978 return; 1979 1980 if (_M_saved_available) 1981 { 1982 _M_saved_available = false; 1983 *__f++ = _M_saved * __param.stddev() + __param.mean(); 1984 1985 if (__f == __t) 1986 return; 1987 } 1988 1989 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 1990 __aurng(__urng); 1991 1992 while (__f + 1 < __t) 1993 { 1994 result_type __x, __y, __r2; 1995 do 1996 { 1997 __x = result_type(2.0) * __aurng() - 1.0; 1998 __y = result_type(2.0) * __aurng() - 1.0; 1999 __r2 = __x * __x + __y * __y; 2000 } 2001 while (__r2 > 1.0 || __r2 == 0.0); 2002 2003 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2); 2004 *__f++ = __y * __mult * __param.stddev() + __param.mean(); 2005 *__f++ = __x * __mult * __param.stddev() + __param.mean(); 2006 } 2007 2008 if (__f != __t) 2009 { 2010 result_type __x, __y, __r2; 2011 do 2012 { 2013 __x = result_type(2.0) * __aurng() - 1.0; 2014 __y = result_type(2.0) * __aurng() - 1.0; 2015 __r2 = __x * __x + __y * __y; 2016 } 2017 while (__r2 > 1.0 || __r2 == 0.0); 2018 2019 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2); 2020 _M_saved = __x * __mult; 2021 _M_saved_available = true; 2022 *__f = __y * __mult * __param.stddev() + __param.mean(); 2023 } 2024 } 2025 2026 template<typename _RealType> 2027 bool 2028 operator==(const std::normal_distribution<_RealType>& __d1, 2029 const std::normal_distribution<_RealType>& __d2) 2030 { 2031 if (__d1._M_param == __d2._M_param 2032 && __d1._M_saved_available == __d2._M_saved_available) 2033 { 2034 if (__d1._M_saved_available 2035 && __d1._M_saved == __d2._M_saved) 2036 return true; 2037 else if(!__d1._M_saved_available) 2038 return true; 2039 else 2040 return false; 2041 } 2042 else 2043 return false; 2044 } 2045 2046 template<typename _RealType, typename _CharT, typename _Traits> 2047 std::basic_ostream<_CharT, _Traits>& 2048 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2049 const normal_distribution<_RealType>& __x) 2050 { 2051 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2052 typedef typename __ostream_type::ios_base __ios_base; 2053 2054 const typename __ios_base::fmtflags __flags = __os.flags(); 2055 const _CharT __fill = __os.fill(); 2056 const std::streamsize __precision = __os.precision(); 2057 const _CharT __space = __os.widen(' '); 2058 __os.flags(__ios_base::scientific | __ios_base::left); 2059 __os.fill(__space); 2060 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2061 2062 __os << __x.mean() << __space << __x.stddev() 2063 << __space << __x._M_saved_available; 2064 if (__x._M_saved_available) 2065 __os << __space << __x._M_saved; 2066 2067 __os.flags(__flags); 2068 __os.fill(__fill); 2069 __os.precision(__precision); 2070 return __os; 2071 } 2072 2073 template<typename _RealType, typename _CharT, typename _Traits> 2074 std::basic_istream<_CharT, _Traits>& 2075 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2076 normal_distribution<_RealType>& __x) 2077 { 2078 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2079 typedef typename __istream_type::ios_base __ios_base; 2080 2081 const typename __ios_base::fmtflags __flags = __is.flags(); 2082 __is.flags(__ios_base::dec | __ios_base::skipws); 2083 2084 double __mean, __stddev; 2085 __is >> __mean >> __stddev 2086 >> __x._M_saved_available; 2087 if (__x._M_saved_available) 2088 __is >> __x._M_saved; 2089 __x.param(typename normal_distribution<_RealType>:: 2090 param_type(__mean, __stddev)); 2091 2092 __is.flags(__flags); 2093 return __is; 2094 } 2095 2096 2097 template<typename _RealType> 2098 template<typename _ForwardIterator, 2099 typename _UniformRandomNumberGenerator> 2100 void 2101 lognormal_distribution<_RealType>:: 2102 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2103 _UniformRandomNumberGenerator& __urng, 2104 const param_type& __p) 2105 { 2106 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2107 while (__f != __t) 2108 *__f++ = std::exp(__p.s() * _M_nd(__urng) + __p.m()); 2109 } 2110 2111 template<typename _RealType, typename _CharT, typename _Traits> 2112 std::basic_ostream<_CharT, _Traits>& 2113 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2114 const lognormal_distribution<_RealType>& __x) 2115 { 2116 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2117 typedef typename __ostream_type::ios_base __ios_base; 2118 2119 const typename __ios_base::fmtflags __flags = __os.flags(); 2120 const _CharT __fill = __os.fill(); 2121 const std::streamsize __precision = __os.precision(); 2122 const _CharT __space = __os.widen(' '); 2123 __os.flags(__ios_base::scientific | __ios_base::left); 2124 __os.fill(__space); 2125 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2126 2127 __os << __x.m() << __space << __x.s() 2128 << __space << __x._M_nd; 2129 2130 __os.flags(__flags); 2131 __os.fill(__fill); 2132 __os.precision(__precision); 2133 return __os; 2134 } 2135 2136 template<typename _RealType, typename _CharT, typename _Traits> 2137 std::basic_istream<_CharT, _Traits>& 2138 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2139 lognormal_distribution<_RealType>& __x) 2140 { 2141 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2142 typedef typename __istream_type::ios_base __ios_base; 2143 2144 const typename __ios_base::fmtflags __flags = __is.flags(); 2145 __is.flags(__ios_base::dec | __ios_base::skipws); 2146 2147 _RealType __m, __s; 2148 __is >> __m >> __s >> __x._M_nd; 2149 __x.param(typename lognormal_distribution<_RealType>:: 2150 param_type(__m, __s)); 2151 2152 __is.flags(__flags); 2153 return __is; 2154 } 2155 2156 template<typename _RealType> 2157 template<typename _ForwardIterator, 2158 typename _UniformRandomNumberGenerator> 2159 void 2160 std::chi_squared_distribution<_RealType>:: 2161 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2162 _UniformRandomNumberGenerator& __urng) 2163 { 2164 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2165 while (__f != __t) 2166 *__f++ = 2 * _M_gd(__urng); 2167 } 2168 2169 template<typename _RealType> 2170 template<typename _ForwardIterator, 2171 typename _UniformRandomNumberGenerator> 2172 void 2173 std::chi_squared_distribution<_RealType>:: 2174 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2175 _UniformRandomNumberGenerator& __urng, 2176 const typename 2177 std::gamma_distribution<result_type>::param_type& __p) 2178 { 2179 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2180 while (__f != __t) 2181 *__f++ = 2 * _M_gd(__urng, __p); 2182 } 2183 2184 template<typename _RealType, typename _CharT, typename _Traits> 2185 std::basic_ostream<_CharT, _Traits>& 2186 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2187 const chi_squared_distribution<_RealType>& __x) 2188 { 2189 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2190 typedef typename __ostream_type::ios_base __ios_base; 2191 2192 const typename __ios_base::fmtflags __flags = __os.flags(); 2193 const _CharT __fill = __os.fill(); 2194 const std::streamsize __precision = __os.precision(); 2195 const _CharT __space = __os.widen(' '); 2196 __os.flags(__ios_base::scientific | __ios_base::left); 2197 __os.fill(__space); 2198 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2199 2200 __os << __x.n() << __space << __x._M_gd; 2201 2202 __os.flags(__flags); 2203 __os.fill(__fill); 2204 __os.precision(__precision); 2205 return __os; 2206 } 2207 2208 template<typename _RealType, typename _CharT, typename _Traits> 2209 std::basic_istream<_CharT, _Traits>& 2210 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2211 chi_squared_distribution<_RealType>& __x) 2212 { 2213 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2214 typedef typename __istream_type::ios_base __ios_base; 2215 2216 const typename __ios_base::fmtflags __flags = __is.flags(); 2217 __is.flags(__ios_base::dec | __ios_base::skipws); 2218 2219 _RealType __n; 2220 __is >> __n >> __x._M_gd; 2221 __x.param(typename chi_squared_distribution<_RealType>:: 2222 param_type(__n)); 2223 2224 __is.flags(__flags); 2225 return __is; 2226 } 2227 2228 2229 template<typename _RealType> 2230 template<typename _UniformRandomNumberGenerator> 2231 typename cauchy_distribution<_RealType>::result_type 2232 cauchy_distribution<_RealType>:: 2233 operator()(_UniformRandomNumberGenerator& __urng, 2234 const param_type& __p) 2235 { 2236 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2237 __aurng(__urng); 2238 _RealType __u; 2239 do 2240 __u = __aurng(); 2241 while (__u == 0.5); 2242 2243 const _RealType __pi = 3.1415926535897932384626433832795029L; 2244 return __p.a() + __p.b() * std::tan(__pi * __u); 2245 } 2246 2247 template<typename _RealType> 2248 template<typename _ForwardIterator, 2249 typename _UniformRandomNumberGenerator> 2250 void 2251 cauchy_distribution<_RealType>:: 2252 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2253 _UniformRandomNumberGenerator& __urng, 2254 const param_type& __p) 2255 { 2256 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2257 const _RealType __pi = 3.1415926535897932384626433832795029L; 2258 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2259 __aurng(__urng); 2260 while (__f != __t) 2261 { 2262 _RealType __u; 2263 do 2264 __u = __aurng(); 2265 while (__u == 0.5); 2266 2267 *__f++ = __p.a() + __p.b() * std::tan(__pi * __u); 2268 } 2269 } 2270 2271 template<typename _RealType, typename _CharT, typename _Traits> 2272 std::basic_ostream<_CharT, _Traits>& 2273 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2274 const cauchy_distribution<_RealType>& __x) 2275 { 2276 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2277 typedef typename __ostream_type::ios_base __ios_base; 2278 2279 const typename __ios_base::fmtflags __flags = __os.flags(); 2280 const _CharT __fill = __os.fill(); 2281 const std::streamsize __precision = __os.precision(); 2282 const _CharT __space = __os.widen(' '); 2283 __os.flags(__ios_base::scientific | __ios_base::left); 2284 __os.fill(__space); 2285 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2286 2287 __os << __x.a() << __space << __x.b(); 2288 2289 __os.flags(__flags); 2290 __os.fill(__fill); 2291 __os.precision(__precision); 2292 return __os; 2293 } 2294 2295 template<typename _RealType, typename _CharT, typename _Traits> 2296 std::basic_istream<_CharT, _Traits>& 2297 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2298 cauchy_distribution<_RealType>& __x) 2299 { 2300 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2301 typedef typename __istream_type::ios_base __ios_base; 2302 2303 const typename __ios_base::fmtflags __flags = __is.flags(); 2304 __is.flags(__ios_base::dec | __ios_base::skipws); 2305 2306 _RealType __a, __b; 2307 __is >> __a >> __b; 2308 __x.param(typename cauchy_distribution<_RealType>:: 2309 param_type(__a, __b)); 2310 2311 __is.flags(__flags); 2312 return __is; 2313 } 2314 2315 2316 template<typename _RealType> 2317 template<typename _ForwardIterator, 2318 typename _UniformRandomNumberGenerator> 2319 void 2320 std::fisher_f_distribution<_RealType>:: 2321 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2322 _UniformRandomNumberGenerator& __urng) 2323 { 2324 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2325 while (__f != __t) 2326 *__f++ = ((_M_gd_x(__urng) * n()) / (_M_gd_y(__urng) * m())); 2327 } 2328 2329 template<typename _RealType> 2330 template<typename _ForwardIterator, 2331 typename _UniformRandomNumberGenerator> 2332 void 2333 std::fisher_f_distribution<_RealType>:: 2334 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2335 _UniformRandomNumberGenerator& __urng, 2336 const param_type& __p) 2337 { 2338 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2339 typedef typename std::gamma_distribution<result_type>::param_type 2340 param_type; 2341 param_type __p1(__p.m() / 2); 2342 param_type __p2(__p.n() / 2); 2343 while (__f != __t) 2344 *__f++ = ((_M_gd_x(__urng, __p1) * n()) 2345 / (_M_gd_y(__urng, __p2) * m())); 2346 } 2347 2348 template<typename _RealType, typename _CharT, typename _Traits> 2349 std::basic_ostream<_CharT, _Traits>& 2350 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2351 const fisher_f_distribution<_RealType>& __x) 2352 { 2353 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2354 typedef typename __ostream_type::ios_base __ios_base; 2355 2356 const typename __ios_base::fmtflags __flags = __os.flags(); 2357 const _CharT __fill = __os.fill(); 2358 const std::streamsize __precision = __os.precision(); 2359 const _CharT __space = __os.widen(' '); 2360 __os.flags(__ios_base::scientific | __ios_base::left); 2361 __os.fill(__space); 2362 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2363 2364 __os << __x.m() << __space << __x.n() 2365 << __space << __x._M_gd_x << __space << __x._M_gd_y; 2366 2367 __os.flags(__flags); 2368 __os.fill(__fill); 2369 __os.precision(__precision); 2370 return __os; 2371 } 2372 2373 template<typename _RealType, typename _CharT, typename _Traits> 2374 std::basic_istream<_CharT, _Traits>& 2375 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2376 fisher_f_distribution<_RealType>& __x) 2377 { 2378 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2379 typedef typename __istream_type::ios_base __ios_base; 2380 2381 const typename __ios_base::fmtflags __flags = __is.flags(); 2382 __is.flags(__ios_base::dec | __ios_base::skipws); 2383 2384 _RealType __m, __n; 2385 __is >> __m >> __n >> __x._M_gd_x >> __x._M_gd_y; 2386 __x.param(typename fisher_f_distribution<_RealType>:: 2387 param_type(__m, __n)); 2388 2389 __is.flags(__flags); 2390 return __is; 2391 } 2392 2393 2394 template<typename _RealType> 2395 template<typename _ForwardIterator, 2396 typename _UniformRandomNumberGenerator> 2397 void 2398 std::student_t_distribution<_RealType>:: 2399 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2400 _UniformRandomNumberGenerator& __urng) 2401 { 2402 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2403 while (__f != __t) 2404 *__f++ = _M_nd(__urng) * std::sqrt(n() / _M_gd(__urng)); 2405 } 2406 2407 template<typename _RealType> 2408 template<typename _ForwardIterator, 2409 typename _UniformRandomNumberGenerator> 2410 void 2411 std::student_t_distribution<_RealType>:: 2412 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2413 _UniformRandomNumberGenerator& __urng, 2414 const param_type& __p) 2415 { 2416 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2417 typename std::gamma_distribution<result_type>::param_type 2418 __p2(__p.n() / 2, 2); 2419 while (__f != __t) 2420 *__f++ = _M_nd(__urng) * std::sqrt(__p.n() / _M_gd(__urng, __p2)); 2421 } 2422 2423 template<typename _RealType, typename _CharT, typename _Traits> 2424 std::basic_ostream<_CharT, _Traits>& 2425 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2426 const student_t_distribution<_RealType>& __x) 2427 { 2428 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2429 typedef typename __ostream_type::ios_base __ios_base; 2430 2431 const typename __ios_base::fmtflags __flags = __os.flags(); 2432 const _CharT __fill = __os.fill(); 2433 const std::streamsize __precision = __os.precision(); 2434 const _CharT __space = __os.widen(' '); 2435 __os.flags(__ios_base::scientific | __ios_base::left); 2436 __os.fill(__space); 2437 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2438 2439 __os << __x.n() << __space << __x._M_nd << __space << __x._M_gd; 2440 2441 __os.flags(__flags); 2442 __os.fill(__fill); 2443 __os.precision(__precision); 2444 return __os; 2445 } 2446 2447 template<typename _RealType, typename _CharT, typename _Traits> 2448 std::basic_istream<_CharT, _Traits>& 2449 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2450 student_t_distribution<_RealType>& __x) 2451 { 2452 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2453 typedef typename __istream_type::ios_base __ios_base; 2454 2455 const typename __ios_base::fmtflags __flags = __is.flags(); 2456 __is.flags(__ios_base::dec | __ios_base::skipws); 2457 2458 _RealType __n; 2459 __is >> __n >> __x._M_nd >> __x._M_gd; 2460 __x.param(typename student_t_distribution<_RealType>::param_type(__n)); 2461 2462 __is.flags(__flags); 2463 return __is; 2464 } 2465 2466 2467 template<typename _RealType> 2468 void 2469 gamma_distribution<_RealType>::param_type:: 2470 _M_initialize() 2471 { 2472 _M_malpha = _M_alpha < 1.0 ? _M_alpha + _RealType(1.0) : _M_alpha; 2473 2474 const _RealType __a1 = _M_malpha - _RealType(1.0) / _RealType(3.0); 2475 _M_a2 = _RealType(1.0) / std::sqrt(_RealType(9.0) * __a1); 2476 } 2477 2478 /** 2479 * Marsaglia, G. and Tsang, W. W. 2480 * "A Simple Method for Generating Gamma Variables" 2481 * ACM Transactions on Mathematical Software, 26, 3, 363-372, 2000. 2482 */ 2483 template<typename _RealType> 2484 template<typename _UniformRandomNumberGenerator> 2485 typename gamma_distribution<_RealType>::result_type 2486 gamma_distribution<_RealType>:: 2487 operator()(_UniformRandomNumberGenerator& __urng, 2488 const param_type& __param) 2489 { 2490 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2491 __aurng(__urng); 2492 2493 result_type __u, __v, __n; 2494 const result_type __a1 = (__param._M_malpha 2495 - _RealType(1.0) / _RealType(3.0)); 2496 2497 do 2498 { 2499 do 2500 { 2501 __n = _M_nd(__urng); 2502 __v = result_type(1.0) + __param._M_a2 * __n; 2503 } 2504 while (__v <= 0.0); 2505 2506 __v = __v * __v * __v; 2507 __u = __aurng(); 2508 } 2509 while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n 2510 && (std::log(__u) > (0.5 * __n * __n + __a1 2511 * (1.0 - __v + std::log(__v))))); 2512 2513 if (__param.alpha() == __param._M_malpha) 2514 return __a1 * __v * __param.beta(); 2515 else 2516 { 2517 do 2518 __u = __aurng(); 2519 while (__u == 0.0); 2520 2521 return (std::pow(__u, result_type(1.0) / __param.alpha()) 2522 * __a1 * __v * __param.beta()); 2523 } 2524 } 2525 2526 template<typename _RealType> 2527 template<typename _ForwardIterator, 2528 typename _UniformRandomNumberGenerator> 2529 void 2530 gamma_distribution<_RealType>:: 2531 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2532 _UniformRandomNumberGenerator& __urng, 2533 const param_type& __param) 2534 { 2535 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2536 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2537 __aurng(__urng); 2538 2539 result_type __u, __v, __n; 2540 const result_type __a1 = (__param._M_malpha 2541 - _RealType(1.0) / _RealType(3.0)); 2542 2543 if (__param.alpha() == __param._M_malpha) 2544 while (__f != __t) 2545 { 2546 do 2547 { 2548 do 2549 { 2550 __n = _M_nd(__urng); 2551 __v = result_type(1.0) + __param._M_a2 * __n; 2552 } 2553 while (__v <= 0.0); 2554 2555 __v = __v * __v * __v; 2556 __u = __aurng(); 2557 } 2558 while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n 2559 && (std::log(__u) > (0.5 * __n * __n + __a1 2560 * (1.0 - __v + std::log(__v))))); 2561 2562 *__f++ = __a1 * __v * __param.beta(); 2563 } 2564 else 2565 while (__f != __t) 2566 { 2567 do 2568 { 2569 do 2570 { 2571 __n = _M_nd(__urng); 2572 __v = result_type(1.0) + __param._M_a2 * __n; 2573 } 2574 while (__v <= 0.0); 2575 2576 __v = __v * __v * __v; 2577 __u = __aurng(); 2578 } 2579 while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n 2580 && (std::log(__u) > (0.5 * __n * __n + __a1 2581 * (1.0 - __v + std::log(__v))))); 2582 2583 do 2584 __u = __aurng(); 2585 while (__u == 0.0); 2586 2587 *__f++ = (std::pow(__u, result_type(1.0) / __param.alpha()) 2588 * __a1 * __v * __param.beta()); 2589 } 2590 } 2591 2592 template<typename _RealType, typename _CharT, typename _Traits> 2593 std::basic_ostream<_CharT, _Traits>& 2594 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2595 const gamma_distribution<_RealType>& __x) 2596 { 2597 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2598 typedef typename __ostream_type::ios_base __ios_base; 2599 2600 const typename __ios_base::fmtflags __flags = __os.flags(); 2601 const _CharT __fill = __os.fill(); 2602 const std::streamsize __precision = __os.precision(); 2603 const _CharT __space = __os.widen(' '); 2604 __os.flags(__ios_base::scientific | __ios_base::left); 2605 __os.fill(__space); 2606 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2607 2608 __os << __x.alpha() << __space << __x.beta() 2609 << __space << __x._M_nd; 2610 2611 __os.flags(__flags); 2612 __os.fill(__fill); 2613 __os.precision(__precision); 2614 return __os; 2615 } 2616 2617 template<typename _RealType, typename _CharT, typename _Traits> 2618 std::basic_istream<_CharT, _Traits>& 2619 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2620 gamma_distribution<_RealType>& __x) 2621 { 2622 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2623 typedef typename __istream_type::ios_base __ios_base; 2624 2625 const typename __ios_base::fmtflags __flags = __is.flags(); 2626 __is.flags(__ios_base::dec | __ios_base::skipws); 2627 2628 _RealType __alpha_val, __beta_val; 2629 __is >> __alpha_val >> __beta_val >> __x._M_nd; 2630 __x.param(typename gamma_distribution<_RealType>:: 2631 param_type(__alpha_val, __beta_val)); 2632 2633 __is.flags(__flags); 2634 return __is; 2635 } 2636 2637 2638 template<typename _RealType> 2639 template<typename _UniformRandomNumberGenerator> 2640 typename weibull_distribution<_RealType>::result_type 2641 weibull_distribution<_RealType>:: 2642 operator()(_UniformRandomNumberGenerator& __urng, 2643 const param_type& __p) 2644 { 2645 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2646 __aurng(__urng); 2647 return __p.b() * std::pow(-std::log(result_type(1) - __aurng()), 2648 result_type(1) / __p.a()); 2649 } 2650 2651 template<typename _RealType> 2652 template<typename _ForwardIterator, 2653 typename _UniformRandomNumberGenerator> 2654 void 2655 weibull_distribution<_RealType>:: 2656 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2657 _UniformRandomNumberGenerator& __urng, 2658 const param_type& __p) 2659 { 2660 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2661 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2662 __aurng(__urng); 2663 auto __inv_a = result_type(1) / __p.a(); 2664 2665 while (__f != __t) 2666 *__f++ = __p.b() * std::pow(-std::log(result_type(1) - __aurng()), 2667 __inv_a); 2668 } 2669 2670 template<typename _RealType, typename _CharT, typename _Traits> 2671 std::basic_ostream<_CharT, _Traits>& 2672 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2673 const weibull_distribution<_RealType>& __x) 2674 { 2675 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2676 typedef typename __ostream_type::ios_base __ios_base; 2677 2678 const typename __ios_base::fmtflags __flags = __os.flags(); 2679 const _CharT __fill = __os.fill(); 2680 const std::streamsize __precision = __os.precision(); 2681 const _CharT __space = __os.widen(' '); 2682 __os.flags(__ios_base::scientific | __ios_base::left); 2683 __os.fill(__space); 2684 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2685 2686 __os << __x.a() << __space << __x.b(); 2687 2688 __os.flags(__flags); 2689 __os.fill(__fill); 2690 __os.precision(__precision); 2691 return __os; 2692 } 2693 2694 template<typename _RealType, typename _CharT, typename _Traits> 2695 std::basic_istream<_CharT, _Traits>& 2696 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2697 weibull_distribution<_RealType>& __x) 2698 { 2699 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2700 typedef typename __istream_type::ios_base __ios_base; 2701 2702 const typename __ios_base::fmtflags __flags = __is.flags(); 2703 __is.flags(__ios_base::dec | __ios_base::skipws); 2704 2705 _RealType __a, __b; 2706 __is >> __a >> __b; 2707 __x.param(typename weibull_distribution<_RealType>:: 2708 param_type(__a, __b)); 2709 2710 __is.flags(__flags); 2711 return __is; 2712 } 2713 2714 2715 template<typename _RealType> 2716 template<typename _UniformRandomNumberGenerator> 2717 typename extreme_value_distribution<_RealType>::result_type 2718 extreme_value_distribution<_RealType>:: 2719 operator()(_UniformRandomNumberGenerator& __urng, 2720 const param_type& __p) 2721 { 2722 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2723 __aurng(__urng); 2724 return __p.a() - __p.b() * std::log(-std::log(result_type(1) 2725 - __aurng())); 2726 } 2727 2728 template<typename _RealType> 2729 template<typename _ForwardIterator, 2730 typename _UniformRandomNumberGenerator> 2731 void 2732 extreme_value_distribution<_RealType>:: 2733 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2734 _UniformRandomNumberGenerator& __urng, 2735 const param_type& __p) 2736 { 2737 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2738 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2739 __aurng(__urng); 2740 2741 while (__f != __t) 2742 *__f++ = __p.a() - __p.b() * std::log(-std::log(result_type(1) 2743 - __aurng())); 2744 } 2745 2746 template<typename _RealType, typename _CharT, typename _Traits> 2747 std::basic_ostream<_CharT, _Traits>& 2748 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2749 const extreme_value_distribution<_RealType>& __x) 2750 { 2751 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2752 typedef typename __ostream_type::ios_base __ios_base; 2753 2754 const typename __ios_base::fmtflags __flags = __os.flags(); 2755 const _CharT __fill = __os.fill(); 2756 const std::streamsize __precision = __os.precision(); 2757 const _CharT __space = __os.widen(' '); 2758 __os.flags(__ios_base::scientific | __ios_base::left); 2759 __os.fill(__space); 2760 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2761 2762 __os << __x.a() << __space << __x.b(); 2763 2764 __os.flags(__flags); 2765 __os.fill(__fill); 2766 __os.precision(__precision); 2767 return __os; 2768 } 2769 2770 template<typename _RealType, typename _CharT, typename _Traits> 2771 std::basic_istream<_CharT, _Traits>& 2772 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2773 extreme_value_distribution<_RealType>& __x) 2774 { 2775 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2776 typedef typename __istream_type::ios_base __ios_base; 2777 2778 const typename __ios_base::fmtflags __flags = __is.flags(); 2779 __is.flags(__ios_base::dec | __ios_base::skipws); 2780 2781 _RealType __a, __b; 2782 __is >> __a >> __b; 2783 __x.param(typename extreme_value_distribution<_RealType>:: 2784 param_type(__a, __b)); 2785 2786 __is.flags(__flags); 2787 return __is; 2788 } 2789 2790 2791 template<typename _IntType> 2792 void 2793 discrete_distribution<_IntType>::param_type:: 2794 _M_initialize() 2795 { 2796 if (_M_prob.size() < 2) 2797 { 2798 _M_prob.clear(); 2799 return; 2800 } 2801 2802 const double __sum = std::accumulate(_M_prob.begin(), 2803 _M_prob.end(), 0.0); 2804 // Now normalize the probabilites. 2805 __detail::__normalize(_M_prob.begin(), _M_prob.end(), _M_prob.begin(), 2806 __sum); 2807 // Accumulate partial sums. 2808 _M_cp.reserve(_M_prob.size()); 2809 std::partial_sum(_M_prob.begin(), _M_prob.end(), 2810 std::back_inserter(_M_cp)); 2811 // Make sure the last cumulative probability is one. 2812 _M_cp[_M_cp.size() - 1] = 1.0; 2813 } 2814 2815 template<typename _IntType> 2816 template<typename _Func> 2817 discrete_distribution<_IntType>::param_type:: 2818 param_type(size_t __nw, double __xmin, double __xmax, _Func __fw) 2819 : _M_prob(), _M_cp() 2820 { 2821 const size_t __n = __nw == 0 ? 1 : __nw; 2822 const double __delta = (__xmax - __xmin) / __n; 2823 2824 _M_prob.reserve(__n); 2825 for (size_t __k = 0; __k < __nw; ++__k) 2826 _M_prob.push_back(__fw(__xmin + __k * __delta + 0.5 * __delta)); 2827 2828 _M_initialize(); 2829 } 2830 2831 template<typename _IntType> 2832 template<typename _UniformRandomNumberGenerator> 2833 typename discrete_distribution<_IntType>::result_type 2834 discrete_distribution<_IntType>:: 2835 operator()(_UniformRandomNumberGenerator& __urng, 2836 const param_type& __param) 2837 { 2838 if (__param._M_cp.empty()) 2839 return result_type(0); 2840 2841 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 2842 __aurng(__urng); 2843 2844 const double __p = __aurng(); 2845 auto __pos = std::lower_bound(__param._M_cp.begin(), 2846 __param._M_cp.end(), __p); 2847 2848 return __pos - __param._M_cp.begin(); 2849 } 2850 2851 template<typename _IntType> 2852 template<typename _ForwardIterator, 2853 typename _UniformRandomNumberGenerator> 2854 void 2855 discrete_distribution<_IntType>:: 2856 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2857 _UniformRandomNumberGenerator& __urng, 2858 const param_type& __param) 2859 { 2860 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2861 2862 if (__param._M_cp.empty()) 2863 { 2864 while (__f != __t) 2865 *__f++ = result_type(0); 2866 return; 2867 } 2868 2869 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 2870 __aurng(__urng); 2871 2872 while (__f != __t) 2873 { 2874 const double __p = __aurng(); 2875 auto __pos = std::lower_bound(__param._M_cp.begin(), 2876 __param._M_cp.end(), __p); 2877 2878 *__f++ = __pos - __param._M_cp.begin(); 2879 } 2880 } 2881 2882 template<typename _IntType, typename _CharT, typename _Traits> 2883 std::basic_ostream<_CharT, _Traits>& 2884 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2885 const discrete_distribution<_IntType>& __x) 2886 { 2887 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2888 typedef typename __ostream_type::ios_base __ios_base; 2889 2890 const typename __ios_base::fmtflags __flags = __os.flags(); 2891 const _CharT __fill = __os.fill(); 2892 const std::streamsize __precision = __os.precision(); 2893 const _CharT __space = __os.widen(' '); 2894 __os.flags(__ios_base::scientific | __ios_base::left); 2895 __os.fill(__space); 2896 __os.precision(std::numeric_limits<double>::max_digits10); 2897 2898 std::vector<double> __prob = __x.probabilities(); 2899 __os << __prob.size(); 2900 for (auto __dit = __prob.begin(); __dit != __prob.end(); ++__dit) 2901 __os << __space << *__dit; 2902 2903 __os.flags(__flags); 2904 __os.fill(__fill); 2905 __os.precision(__precision); 2906 return __os; 2907 } 2908 2909 template<typename _IntType, typename _CharT, typename _Traits> 2910 std::basic_istream<_CharT, _Traits>& 2911 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2912 discrete_distribution<_IntType>& __x) 2913 { 2914 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2915 typedef typename __istream_type::ios_base __ios_base; 2916 2917 const typename __ios_base::fmtflags __flags = __is.flags(); 2918 __is.flags(__ios_base::dec | __ios_base::skipws); 2919 2920 size_t __n; 2921 __is >> __n; 2922 2923 std::vector<double> __prob_vec; 2924 __prob_vec.reserve(__n); 2925 for (; __n != 0; --__n) 2926 { 2927 double __prob; 2928 __is >> __prob; 2929 __prob_vec.push_back(__prob); 2930 } 2931 2932 __x.param(typename discrete_distribution<_IntType>:: 2933 param_type(__prob_vec.begin(), __prob_vec.end())); 2934 2935 __is.flags(__flags); 2936 return __is; 2937 } 2938 2939 2940 template<typename _RealType> 2941 void 2942 piecewise_constant_distribution<_RealType>::param_type:: 2943 _M_initialize() 2944 { 2945 if (_M_int.size() < 2 2946 || (_M_int.size() == 2 2947 && _M_int[0] == _RealType(0) 2948 && _M_int[1] == _RealType(1))) 2949 { 2950 _M_int.clear(); 2951 _M_den.clear(); 2952 return; 2953 } 2954 2955 const double __sum = std::accumulate(_M_den.begin(), 2956 _M_den.end(), 0.0); 2957 2958 __detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(), 2959 __sum); 2960 2961 _M_cp.reserve(_M_den.size()); 2962 std::partial_sum(_M_den.begin(), _M_den.end(), 2963 std::back_inserter(_M_cp)); 2964 2965 // Make sure the last cumulative probability is one. 2966 _M_cp[_M_cp.size() - 1] = 1.0; 2967 2968 for (size_t __k = 0; __k < _M_den.size(); ++__k) 2969 _M_den[__k] /= _M_int[__k + 1] - _M_int[__k]; 2970 } 2971 2972 template<typename _RealType> 2973 template<typename _InputIteratorB, typename _InputIteratorW> 2974 piecewise_constant_distribution<_RealType>::param_type:: 2975 param_type(_InputIteratorB __bbegin, 2976 _InputIteratorB __bend, 2977 _InputIteratorW __wbegin) 2978 : _M_int(), _M_den(), _M_cp() 2979 { 2980 if (__bbegin != __bend) 2981 { 2982 for (;;) 2983 { 2984 _M_int.push_back(*__bbegin); 2985 ++__bbegin; 2986 if (__bbegin == __bend) 2987 break; 2988 2989 _M_den.push_back(*__wbegin); 2990 ++__wbegin; 2991 } 2992 } 2993 2994 _M_initialize(); 2995 } 2996 2997 template<typename _RealType> 2998 template<typename _Func> 2999 piecewise_constant_distribution<_RealType>::param_type:: 3000 param_type(initializer_list<_RealType> __bl, _Func __fw) 3001 : _M_int(), _M_den(), _M_cp() 3002 { 3003 _M_int.reserve(__bl.size()); 3004 for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter) 3005 _M_int.push_back(*__biter); 3006 3007 _M_den.reserve(_M_int.size() - 1); 3008 for (size_t __k = 0; __k < _M_int.size() - 1; ++__k) 3009 _M_den.push_back(__fw(0.5 * (_M_int[__k + 1] + _M_int[__k]))); 3010 3011 _M_initialize(); 3012 } 3013 3014 template<typename _RealType> 3015 template<typename _Func> 3016 piecewise_constant_distribution<_RealType>::param_type:: 3017 param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw) 3018 : _M_int(), _M_den(), _M_cp() 3019 { 3020 const size_t __n = __nw == 0 ? 1 : __nw; 3021 const _RealType __delta = (__xmax - __xmin) / __n; 3022 3023 _M_int.reserve(__n + 1); 3024 for (size_t __k = 0; __k <= __nw; ++__k) 3025 _M_int.push_back(__xmin + __k * __delta); 3026 3027 _M_den.reserve(__n); 3028 for (size_t __k = 0; __k < __nw; ++__k) 3029 _M_den.push_back(__fw(_M_int[__k] + 0.5 * __delta)); 3030 3031 _M_initialize(); 3032 } 3033 3034 template<typename _RealType> 3035 template<typename _UniformRandomNumberGenerator> 3036 typename piecewise_constant_distribution<_RealType>::result_type 3037 piecewise_constant_distribution<_RealType>:: 3038 operator()(_UniformRandomNumberGenerator& __urng, 3039 const param_type& __param) 3040 { 3041 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 3042 __aurng(__urng); 3043 3044 const double __p = __aurng(); 3045 if (__param._M_cp.empty()) 3046 return __p; 3047 3048 auto __pos = std::lower_bound(__param._M_cp.begin(), 3049 __param._M_cp.end(), __p); 3050 const size_t __i = __pos - __param._M_cp.begin(); 3051 3052 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0; 3053 3054 return __param._M_int[__i] + (__p - __pref) / __param._M_den[__i]; 3055 } 3056 3057 template<typename _RealType> 3058 template<typename _ForwardIterator, 3059 typename _UniformRandomNumberGenerator> 3060 void 3061 piecewise_constant_distribution<_RealType>:: 3062 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 3063 _UniformRandomNumberGenerator& __urng, 3064 const param_type& __param) 3065 { 3066 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 3067 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 3068 __aurng(__urng); 3069 3070 if (__param._M_cp.empty()) 3071 { 3072 while (__f != __t) 3073 *__f++ = __aurng(); 3074 return; 3075 } 3076 3077 while (__f != __t) 3078 { 3079 const double __p = __aurng(); 3080 3081 auto __pos = std::lower_bound(__param._M_cp.begin(), 3082 __param._M_cp.end(), __p); 3083 const size_t __i = __pos - __param._M_cp.begin(); 3084 3085 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0; 3086 3087 *__f++ = (__param._M_int[__i] 3088 + (__p - __pref) / __param._M_den[__i]); 3089 } 3090 } 3091 3092 template<typename _RealType, typename _CharT, typename _Traits> 3093 std::basic_ostream<_CharT, _Traits>& 3094 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 3095 const piecewise_constant_distribution<_RealType>& __x) 3096 { 3097 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 3098 typedef typename __ostream_type::ios_base __ios_base; 3099 3100 const typename __ios_base::fmtflags __flags = __os.flags(); 3101 const _CharT __fill = __os.fill(); 3102 const std::streamsize __precision = __os.precision(); 3103 const _CharT __space = __os.widen(' '); 3104 __os.flags(__ios_base::scientific | __ios_base::left); 3105 __os.fill(__space); 3106 __os.precision(std::numeric_limits<_RealType>::max_digits10); 3107 3108 std::vector<_RealType> __int = __x.intervals(); 3109 __os << __int.size() - 1; 3110 3111 for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit) 3112 __os << __space << *__xit; 3113 3114 std::vector<double> __den = __x.densities(); 3115 for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit) 3116 __os << __space << *__dit; 3117 3118 __os.flags(__flags); 3119 __os.fill(__fill); 3120 __os.precision(__precision); 3121 return __os; 3122 } 3123 3124 template<typename _RealType, typename _CharT, typename _Traits> 3125 std::basic_istream<_CharT, _Traits>& 3126 operator>>(std::basic_istream<_CharT, _Traits>& __is, 3127 piecewise_constant_distribution<_RealType>& __x) 3128 { 3129 typedef std::basic_istream<_CharT, _Traits> __istream_type; 3130 typedef typename __istream_type::ios_base __ios_base; 3131 3132 const typename __ios_base::fmtflags __flags = __is.flags(); 3133 __is.flags(__ios_base::dec | __ios_base::skipws); 3134 3135 size_t __n; 3136 __is >> __n; 3137 3138 std::vector<_RealType> __int_vec; 3139 __int_vec.reserve(__n + 1); 3140 for (size_t __i = 0; __i <= __n; ++__i) 3141 { 3142 _RealType __int; 3143 __is >> __int; 3144 __int_vec.push_back(__int); 3145 } 3146 3147 std::vector<double> __den_vec; 3148 __den_vec.reserve(__n); 3149 for (size_t __i = 0; __i < __n; ++__i) 3150 { 3151 double __den; 3152 __is >> __den; 3153 __den_vec.push_back(__den); 3154 } 3155 3156 __x.param(typename piecewise_constant_distribution<_RealType>:: 3157 param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin())); 3158 3159 __is.flags(__flags); 3160 return __is; 3161 } 3162 3163 3164 template<typename _RealType> 3165 void 3166 piecewise_linear_distribution<_RealType>::param_type:: 3167 _M_initialize() 3168 { 3169 if (_M_int.size() < 2 3170 || (_M_int.size() == 2 3171 && _M_int[0] == _RealType(0) 3172 && _M_int[1] == _RealType(1) 3173 && _M_den[0] == _M_den[1])) 3174 { 3175 _M_int.clear(); 3176 _M_den.clear(); 3177 return; 3178 } 3179 3180 double __sum = 0.0; 3181 _M_cp.reserve(_M_int.size() - 1); 3182 _M_m.reserve(_M_int.size() - 1); 3183 for (size_t __k = 0; __k < _M_int.size() - 1; ++__k) 3184 { 3185 const _RealType __delta = _M_int[__k + 1] - _M_int[__k]; 3186 __sum += 0.5 * (_M_den[__k + 1] + _M_den[__k]) * __delta; 3187 _M_cp.push_back(__sum); 3188 _M_m.push_back((_M_den[__k + 1] - _M_den[__k]) / __delta); 3189 } 3190 3191 // Now normalize the densities... 3192 __detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(), 3193 __sum); 3194 // ... and partial sums... 3195 __detail::__normalize(_M_cp.begin(), _M_cp.end(), _M_cp.begin(), __sum); 3196 // ... and slopes. 3197 __detail::__normalize(_M_m.begin(), _M_m.end(), _M_m.begin(), __sum); 3198 3199 // Make sure the last cumulative probablility is one. 3200 _M_cp[_M_cp.size() - 1] = 1.0; 3201 } 3202 3203 template<typename _RealType> 3204 template<typename _InputIteratorB, typename _InputIteratorW> 3205 piecewise_linear_distribution<_RealType>::param_type:: 3206 param_type(_InputIteratorB __bbegin, 3207 _InputIteratorB __bend, 3208 _InputIteratorW __wbegin) 3209 : _M_int(), _M_den(), _M_cp(), _M_m() 3210 { 3211 for (; __bbegin != __bend; ++__bbegin, ++__wbegin) 3212 { 3213 _M_int.push_back(*__bbegin); 3214 _M_den.push_back(*__wbegin); 3215 } 3216 3217 _M_initialize(); 3218 } 3219 3220 template<typename _RealType> 3221 template<typename _Func> 3222 piecewise_linear_distribution<_RealType>::param_type:: 3223 param_type(initializer_list<_RealType> __bl, _Func __fw) 3224 : _M_int(), _M_den(), _M_cp(), _M_m() 3225 { 3226 _M_int.reserve(__bl.size()); 3227 _M_den.reserve(__bl.size()); 3228 for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter) 3229 { 3230 _M_int.push_back(*__biter); 3231 _M_den.push_back(__fw(*__biter)); 3232 } 3233 3234 _M_initialize(); 3235 } 3236 3237 template<typename _RealType> 3238 template<typename _Func> 3239 piecewise_linear_distribution<_RealType>::param_type:: 3240 param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw) 3241 : _M_int(), _M_den(), _M_cp(), _M_m() 3242 { 3243 const size_t __n = __nw == 0 ? 1 : __nw; 3244 const _RealType __delta = (__xmax - __xmin) / __n; 3245 3246 _M_int.reserve(__n + 1); 3247 _M_den.reserve(__n + 1); 3248 for (size_t __k = 0; __k <= __nw; ++__k) 3249 { 3250 _M_int.push_back(__xmin + __k * __delta); 3251 _M_den.push_back(__fw(_M_int[__k] + __delta)); 3252 } 3253 3254 _M_initialize(); 3255 } 3256 3257 template<typename _RealType> 3258 template<typename _UniformRandomNumberGenerator> 3259 typename piecewise_linear_distribution<_RealType>::result_type 3260 piecewise_linear_distribution<_RealType>:: 3261 operator()(_UniformRandomNumberGenerator& __urng, 3262 const param_type& __param) 3263 { 3264 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 3265 __aurng(__urng); 3266 3267 const double __p = __aurng(); 3268 if (__param._M_cp.empty()) 3269 return __p; 3270 3271 auto __pos = std::lower_bound(__param._M_cp.begin(), 3272 __param._M_cp.end(), __p); 3273 const size_t __i = __pos - __param._M_cp.begin(); 3274 3275 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0; 3276 3277 const double __a = 0.5 * __param._M_m[__i]; 3278 const double __b = __param._M_den[__i]; 3279 const double __cm = __p - __pref; 3280 3281 _RealType __x = __param._M_int[__i]; 3282 if (__a == 0) 3283 __x += __cm / __b; 3284 else 3285 { 3286 const double __d = __b * __b + 4.0 * __a * __cm; 3287 __x += 0.5 * (std::sqrt(__d) - __b) / __a; 3288 } 3289 3290 return __x; 3291 } 3292 3293 template<typename _RealType> 3294 template<typename _ForwardIterator, 3295 typename _UniformRandomNumberGenerator> 3296 void 3297 piecewise_linear_distribution<_RealType>:: 3298 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 3299 _UniformRandomNumberGenerator& __urng, 3300 const param_type& __param) 3301 { 3302 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 3303 // We could duplicate everything from operator()... 3304 while (__f != __t) 3305 *__f++ = this->operator()(__urng, __param); 3306 } 3307 3308 template<typename _RealType, typename _CharT, typename _Traits> 3309 std::basic_ostream<_CharT, _Traits>& 3310 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 3311 const piecewise_linear_distribution<_RealType>& __x) 3312 { 3313 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 3314 typedef typename __ostream_type::ios_base __ios_base; 3315 3316 const typename __ios_base::fmtflags __flags = __os.flags(); 3317 const _CharT __fill = __os.fill(); 3318 const std::streamsize __precision = __os.precision(); 3319 const _CharT __space = __os.widen(' '); 3320 __os.flags(__ios_base::scientific | __ios_base::left); 3321 __os.fill(__space); 3322 __os.precision(std::numeric_limits<_RealType>::max_digits10); 3323 3324 std::vector<_RealType> __int = __x.intervals(); 3325 __os << __int.size() - 1; 3326 3327 for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit) 3328 __os << __space << *__xit; 3329 3330 std::vector<double> __den = __x.densities(); 3331 for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit) 3332 __os << __space << *__dit; 3333 3334 __os.flags(__flags); 3335 __os.fill(__fill); 3336 __os.precision(__precision); 3337 return __os; 3338 } 3339 3340 template<typename _RealType, typename _CharT, typename _Traits> 3341 std::basic_istream<_CharT, _Traits>& 3342 operator>>(std::basic_istream<_CharT, _Traits>& __is, 3343 piecewise_linear_distribution<_RealType>& __x) 3344 { 3345 typedef std::basic_istream<_CharT, _Traits> __istream_type; 3346 typedef typename __istream_type::ios_base __ios_base; 3347 3348 const typename __ios_base::fmtflags __flags = __is.flags(); 3349 __is.flags(__ios_base::dec | __ios_base::skipws); 3350 3351 size_t __n; 3352 __is >> __n; 3353 3354 std::vector<_RealType> __int_vec; 3355 __int_vec.reserve(__n + 1); 3356 for (size_t __i = 0; __i <= __n; ++__i) 3357 { 3358 _RealType __int; 3359 __is >> __int; 3360 __int_vec.push_back(__int); 3361 } 3362 3363 std::vector<double> __den_vec; 3364 __den_vec.reserve(__n + 1); 3365 for (size_t __i = 0; __i <= __n; ++__i) 3366 { 3367 double __den; 3368 __is >> __den; 3369 __den_vec.push_back(__den); 3370 } 3371 3372 __x.param(typename piecewise_linear_distribution<_RealType>:: 3373 param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin())); 3374 3375 __is.flags(__flags); 3376 return __is; 3377 } 3378 3379 3380 template<typename _IntType> 3381 seed_seq::seed_seq(std::initializer_list<_IntType> __il) 3382 { 3383 for (auto __iter = __il.begin(); __iter != __il.end(); ++__iter) 3384 _M_v.push_back(__detail::__mod<result_type, 3385 __detail::_Shift<result_type, 32>::__value>(*__iter)); 3386 } 3387 3388 template<typename _InputIterator> 3389 seed_seq::seed_seq(_InputIterator __begin, _InputIterator __end) 3390 { 3391 for (_InputIterator __iter = __begin; __iter != __end; ++__iter) 3392 _M_v.push_back(__detail::__mod<result_type, 3393 __detail::_Shift<result_type, 32>::__value>(*__iter)); 3394 } 3395 3396 template<typename _RandomAccessIterator> 3397 void 3398 seed_seq::generate(_RandomAccessIterator __begin, 3399 _RandomAccessIterator __end) 3400 { 3401 typedef typename iterator_traits<_RandomAccessIterator>::value_type 3402 _Type; 3403 3404 if (__begin == __end) 3405 return; 3406 3407 std::fill(__begin, __end, _Type(0x8b8b8b8bu)); 3408 3409 const size_t __n = __end - __begin; 3410 const size_t __s = _M_v.size(); 3411 const size_t __t = (__n >= 623) ? 11 3412 : (__n >= 68) ? 7 3413 : (__n >= 39) ? 5 3414 : (__n >= 7) ? 3 3415 : (__n - 1) / 2; 3416 const size_t __p = (__n - __t) / 2; 3417 const size_t __q = __p + __t; 3418 const size_t __m = std::max(size_t(__s + 1), __n); 3419 3420 for (size_t __k = 0; __k < __m; ++__k) 3421 { 3422 _Type __arg = (__begin[__k % __n] 3423 ^ __begin[(__k + __p) % __n] 3424 ^ __begin[(__k - 1) % __n]); 3425 _Type __r1 = __arg ^ (__arg >> 27); 3426 __r1 = __detail::__mod<_Type, 3427 __detail::_Shift<_Type, 32>::__value>(1664525u * __r1); 3428 _Type __r2 = __r1; 3429 if (__k == 0) 3430 __r2 += __s; 3431 else if (__k <= __s) 3432 __r2 += __k % __n + _M_v[__k - 1]; 3433 else 3434 __r2 += __k % __n; 3435 __r2 = __detail::__mod<_Type, 3436 __detail::_Shift<_Type, 32>::__value>(__r2); 3437 __begin[(__k + __p) % __n] += __r1; 3438 __begin[(__k + __q) % __n] += __r2; 3439 __begin[__k % __n] = __r2; 3440 } 3441 3442 for (size_t __k = __m; __k < __m + __n; ++__k) 3443 { 3444 _Type __arg = (__begin[__k % __n] 3445 + __begin[(__k + __p) % __n] 3446 + __begin[(__k - 1) % __n]); 3447 _Type __r3 = __arg ^ (__arg >> 27); 3448 __r3 = __detail::__mod<_Type, 3449 __detail::_Shift<_Type, 32>::__value>(1566083941u * __r3); 3450 _Type __r4 = __r3 - __k % __n; 3451 __r4 = __detail::__mod<_Type, 3452 __detail::_Shift<_Type, 32>::__value>(__r4); 3453 __begin[(__k + __p) % __n] ^= __r3; 3454 __begin[(__k + __q) % __n] ^= __r4; 3455 __begin[__k % __n] = __r4; 3456 } 3457 } 3458 3459 template<typename _RealType, size_t __bits, 3460 typename _UniformRandomNumberGenerator> 3461 _RealType 3462 generate_canonical(_UniformRandomNumberGenerator& __urng) 3463 { 3464 const size_t __b 3465 = std::min(static_cast<size_t>(std::numeric_limits<_RealType>::digits), 3466 __bits); 3467 const long double __r = static_cast<long double>(__urng.max()) 3468 - static_cast<long double>(__urng.min()) + 1.0L; 3469 const size_t __log2r = std::log(__r) / std::log(2.0L); 3470 size_t __k = std::max<size_t>(1UL, (__b + __log2r - 1UL) / __log2r); 3471 _RealType __sum = _RealType(0); 3472 _RealType __tmp = _RealType(1); 3473 for (; __k != 0; --__k) 3474 { 3475 __sum += _RealType(__urng() - __urng.min()) * __tmp; 3476 __tmp *= __r; 3477 } 3478 return __sum / __tmp; 3479 } 3480 3481_GLIBCXX_END_NAMESPACE_VERSION 3482} // namespace 3483 3484#endif 3485