1 // random number generation (out of line) -*- C++ -*- 2 3 // Copyright (C) 2009-2014 Free Software Foundation, Inc. 4 // 5 // This file is part of the GNU ISO C++ Library. This library is free 6 // software; you can redistribute it and/or modify it under the 7 // terms of the GNU General Public License as published by the 8 // Free Software Foundation; either version 3, or (at your option) 9 // any later version. 10 11 // This library is distributed in the hope that it will be useful, 12 // but WITHOUT ANY WARRANTY; without even the implied warranty of 13 // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 14 // GNU General Public License for more details. 15 16 // Under Section 7 of GPL version 3, you are granted additional 17 // permissions described in the GCC Runtime Library Exception, version 18 // 3.1, as published by the Free Software Foundation. 19 20 // You should have received a copy of the GNU General Public License and 21 // a copy of the GCC Runtime Library Exception along with this program; 22 // see the files COPYING3 and COPYING.RUNTIME respectively. If not, see 23 // <http://www.gnu.org/licenses/>. 24 25 /** @file bits/random.tcc 26 * This is an internal header file, included by other library headers. 27 * Do not attempt to use it directly. @headername{random} 28 */ 29 30 #ifndef _RANDOM_TCC 31 #define _RANDOM_TCC 1 32 33 #include <numeric> // std::accumulate and std::partial_sum 34 35 namespace std _GLIBCXX_VISIBILITY(default) 36 { 37 /* 38 * (Further) implementation-space details. 39 */ 40 namespace __detail 41 { 42 _GLIBCXX_BEGIN_NAMESPACE_VERSION 43 44 // General case for x = (ax + c) mod m -- use Schrage's algorithm 45 // to avoid integer overflow. 46 // 47 // Preconditions: a > 0, m > 0. 48 // 49 // Note: only works correctly for __m % __a < __m / __a. 50 template<typename _Tp, _Tp __m, _Tp __a, _Tp __c> 51 _Tp 52 _Mod<_Tp, __m, __a, __c, false, true>:: 53 __calc(_Tp __x) 54 { 55 if (__a == 1) 56 __x %= __m; 57 else 58 { 59 static const _Tp __q = __m / __a; 60 static const _Tp __r = __m % __a; 61 62 _Tp __t1 = __a * (__x % __q); 63 _Tp __t2 = __r * (__x / __q); 64 if (__t1 >= __t2) 65 __x = __t1 - __t2; 66 else 67 __x = __m - __t2 + __t1; 68 } 69 70 if (__c != 0) 71 { 72 const _Tp __d = __m - __x; 73 if (__d > __c) 74 __x += __c; 75 else 76 __x = __c - __d; 77 } 78 return __x; 79 } 80 81 template<typename _InputIterator, typename _OutputIterator, 82 typename _Tp> 83 _OutputIterator 84 __normalize(_InputIterator __first, _InputIterator __last, 85 _OutputIterator __result, const _Tp& __factor) 86 { 87 for (; __first != __last; ++__first, ++__result) 88 *__result = *__first / __factor; 89 return __result; 90 } 91 92 _GLIBCXX_END_NAMESPACE_VERSION 93 } // namespace __detail 94 95 _GLIBCXX_BEGIN_NAMESPACE_VERSION 96 97 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> 98 constexpr _UIntType 99 linear_congruential_engine<_UIntType, __a, __c, __m>::multiplier; 100 101 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> 102 constexpr _UIntType 103 linear_congruential_engine<_UIntType, __a, __c, __m>::increment; 104 105 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> 106 constexpr _UIntType 107 linear_congruential_engine<_UIntType, __a, __c, __m>::modulus; 108 109 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> 110 constexpr _UIntType 111 linear_congruential_engine<_UIntType, __a, __c, __m>::default_seed; 112 113 /** 114 * Seeds the LCR with integral value @p __s, adjusted so that the 115 * ring identity is never a member of the convergence set. 116 */ 117 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> 118 void 119 linear_congruential_engine<_UIntType, __a, __c, __m>:: 120 seed(result_type __s) 121 { 122 if ((__detail::__mod<_UIntType, __m>(__c) == 0) 123 && (__detail::__mod<_UIntType, __m>(__s) == 0)) 124 _M_x = 1; 125 else 126 _M_x = __detail::__mod<_UIntType, __m>(__s); 127 } 128 129 /** 130 * Seeds the LCR engine with a value generated by @p __q. 131 */ 132 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> 133 template<typename _Sseq> 134 typename std::enable_if<std::is_class<_Sseq>::value>::type 135 linear_congruential_engine<_UIntType, __a, __c, __m>:: 136 seed(_Sseq& __q) 137 { 138 const _UIntType __k0 = __m == 0 ? std::numeric_limits<_UIntType>::digits 139 : std::__lg(__m); 140 const _UIntType __k = (__k0 + 31) / 32; 141 uint_least32_t __arr[__k + 3]; 142 __q.generate(__arr + 0, __arr + __k + 3); 143 _UIntType __factor = 1u; 144 _UIntType __sum = 0u; 145 for (size_t __j = 0; __j < __k; ++__j) 146 { 147 __sum += __arr[__j + 3] * __factor; 148 __factor *= __detail::_Shift<_UIntType, 32>::__value; 149 } 150 seed(__sum); 151 } 152 153 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m, 154 typename _CharT, typename _Traits> 155 std::basic_ostream<_CharT, _Traits>& 156 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 157 const linear_congruential_engine<_UIntType, 158 __a, __c, __m>& __lcr) 159 { 160 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 161 typedef typename __ostream_type::ios_base __ios_base; 162 163 const typename __ios_base::fmtflags __flags = __os.flags(); 164 const _CharT __fill = __os.fill(); 165 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 166 __os.fill(__os.widen(' ')); 167 168 __os << __lcr._M_x; 169 170 __os.flags(__flags); 171 __os.fill(__fill); 172 return __os; 173 } 174 175 template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m, 176 typename _CharT, typename _Traits> 177 std::basic_istream<_CharT, _Traits>& 178 operator>>(std::basic_istream<_CharT, _Traits>& __is, 179 linear_congruential_engine<_UIntType, __a, __c, __m>& __lcr) 180 { 181 typedef std::basic_istream<_CharT, _Traits> __istream_type; 182 typedef typename __istream_type::ios_base __ios_base; 183 184 const typename __ios_base::fmtflags __flags = __is.flags(); 185 __is.flags(__ios_base::dec); 186 187 __is >> __lcr._M_x; 188 189 __is.flags(__flags); 190 return __is; 191 } 192 193 194 template<typename _UIntType, 195 size_t __w, size_t __n, size_t __m, size_t __r, 196 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 197 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 198 _UIntType __f> 199 constexpr size_t 200 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 201 __s, __b, __t, __c, __l, __f>::word_size; 202 203 template<typename _UIntType, 204 size_t __w, size_t __n, size_t __m, size_t __r, 205 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 206 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 207 _UIntType __f> 208 constexpr size_t 209 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 210 __s, __b, __t, __c, __l, __f>::state_size; 211 212 template<typename _UIntType, 213 size_t __w, size_t __n, size_t __m, size_t __r, 214 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 215 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 216 _UIntType __f> 217 constexpr size_t 218 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 219 __s, __b, __t, __c, __l, __f>::shift_size; 220 221 template<typename _UIntType, 222 size_t __w, size_t __n, size_t __m, size_t __r, 223 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 224 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 225 _UIntType __f> 226 constexpr size_t 227 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 228 __s, __b, __t, __c, __l, __f>::mask_bits; 229 230 template<typename _UIntType, 231 size_t __w, size_t __n, size_t __m, size_t __r, 232 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 233 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 234 _UIntType __f> 235 constexpr _UIntType 236 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 237 __s, __b, __t, __c, __l, __f>::xor_mask; 238 239 template<typename _UIntType, 240 size_t __w, size_t __n, size_t __m, size_t __r, 241 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 242 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 243 _UIntType __f> 244 constexpr size_t 245 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 246 __s, __b, __t, __c, __l, __f>::tempering_u; 247 248 template<typename _UIntType, 249 size_t __w, size_t __n, size_t __m, size_t __r, 250 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 251 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 252 _UIntType __f> 253 constexpr _UIntType 254 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 255 __s, __b, __t, __c, __l, __f>::tempering_d; 256 257 template<typename _UIntType, 258 size_t __w, size_t __n, size_t __m, size_t __r, 259 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 260 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 261 _UIntType __f> 262 constexpr size_t 263 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 264 __s, __b, __t, __c, __l, __f>::tempering_s; 265 266 template<typename _UIntType, 267 size_t __w, size_t __n, size_t __m, size_t __r, 268 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 269 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 270 _UIntType __f> 271 constexpr _UIntType 272 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 273 __s, __b, __t, __c, __l, __f>::tempering_b; 274 275 template<typename _UIntType, 276 size_t __w, size_t __n, size_t __m, size_t __r, 277 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 278 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 279 _UIntType __f> 280 constexpr size_t 281 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 282 __s, __b, __t, __c, __l, __f>::tempering_t; 283 284 template<typename _UIntType, 285 size_t __w, size_t __n, size_t __m, size_t __r, 286 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 287 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 288 _UIntType __f> 289 constexpr _UIntType 290 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 291 __s, __b, __t, __c, __l, __f>::tempering_c; 292 293 template<typename _UIntType, 294 size_t __w, size_t __n, size_t __m, size_t __r, 295 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 296 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 297 _UIntType __f> 298 constexpr size_t 299 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 300 __s, __b, __t, __c, __l, __f>::tempering_l; 301 302 template<typename _UIntType, 303 size_t __w, size_t __n, size_t __m, size_t __r, 304 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 305 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 306 _UIntType __f> 307 constexpr _UIntType 308 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 309 __s, __b, __t, __c, __l, __f>:: 310 initialization_multiplier; 311 312 template<typename _UIntType, 313 size_t __w, size_t __n, size_t __m, size_t __r, 314 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 315 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 316 _UIntType __f> 317 constexpr _UIntType 318 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 319 __s, __b, __t, __c, __l, __f>::default_seed; 320 321 template<typename _UIntType, 322 size_t __w, size_t __n, size_t __m, size_t __r, 323 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 324 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 325 _UIntType __f> 326 void 327 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 328 __s, __b, __t, __c, __l, __f>:: 329 seed(result_type __sd) 330 { 331 _M_x[0] = __detail::__mod<_UIntType, 332 __detail::_Shift<_UIntType, __w>::__value>(__sd); 333 334 for (size_t __i = 1; __i < state_size; ++__i) 335 { 336 _UIntType __x = _M_x[__i - 1]; 337 __x ^= __x >> (__w - 2); 338 __x *= __f; 339 __x += __detail::__mod<_UIntType, __n>(__i); 340 _M_x[__i] = __detail::__mod<_UIntType, 341 __detail::_Shift<_UIntType, __w>::__value>(__x); 342 } 343 _M_p = state_size; 344 } 345 346 template<typename _UIntType, 347 size_t __w, size_t __n, size_t __m, size_t __r, 348 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 349 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 350 _UIntType __f> 351 template<typename _Sseq> 352 typename std::enable_if<std::is_class<_Sseq>::value>::type 353 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 354 __s, __b, __t, __c, __l, __f>:: 355 seed(_Sseq& __q) 356 { 357 const _UIntType __upper_mask = (~_UIntType()) << __r; 358 const size_t __k = (__w + 31) / 32; 359 uint_least32_t __arr[__n * __k]; 360 __q.generate(__arr + 0, __arr + __n * __k); 361 362 bool __zero = true; 363 for (size_t __i = 0; __i < state_size; ++__i) 364 { 365 _UIntType __factor = 1u; 366 _UIntType __sum = 0u; 367 for (size_t __j = 0; __j < __k; ++__j) 368 { 369 __sum += __arr[__k * __i + __j] * __factor; 370 __factor *= __detail::_Shift<_UIntType, 32>::__value; 371 } 372 _M_x[__i] = __detail::__mod<_UIntType, 373 __detail::_Shift<_UIntType, __w>::__value>(__sum); 374 375 if (__zero) 376 { 377 if (__i == 0) 378 { 379 if ((_M_x[0] & __upper_mask) != 0u) 380 __zero = false; 381 } 382 else if (_M_x[__i] != 0u) 383 __zero = false; 384 } 385 } 386 if (__zero) 387 _M_x[0] = __detail::_Shift<_UIntType, __w - 1>::__value; 388 _M_p = state_size; 389 } 390 391 template<typename _UIntType, size_t __w, 392 size_t __n, size_t __m, size_t __r, 393 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 394 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 395 _UIntType __f> 396 void 397 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 398 __s, __b, __t, __c, __l, __f>:: 399 _M_gen_rand(void) 400 { 401 const _UIntType __upper_mask = (~_UIntType()) << __r; 402 const _UIntType __lower_mask = ~__upper_mask; 403 404 for (size_t __k = 0; __k < (__n - __m); ++__k) 405 { 406 _UIntType __y = ((_M_x[__k] & __upper_mask) 407 | (_M_x[__k + 1] & __lower_mask)); 408 _M_x[__k] = (_M_x[__k + __m] ^ (__y >> 1) 409 ^ ((__y & 0x01) ? __a : 0)); 410 } 411 412 for (size_t __k = (__n - __m); __k < (__n - 1); ++__k) 413 { 414 _UIntType __y = ((_M_x[__k] & __upper_mask) 415 | (_M_x[__k + 1] & __lower_mask)); 416 _M_x[__k] = (_M_x[__k + (__m - __n)] ^ (__y >> 1) 417 ^ ((__y & 0x01) ? __a : 0)); 418 } 419 420 _UIntType __y = ((_M_x[__n - 1] & __upper_mask) 421 | (_M_x[0] & __lower_mask)); 422 _M_x[__n - 1] = (_M_x[__m - 1] ^ (__y >> 1) 423 ^ ((__y & 0x01) ? __a : 0)); 424 _M_p = 0; 425 } 426 427 template<typename _UIntType, size_t __w, 428 size_t __n, size_t __m, size_t __r, 429 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 430 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 431 _UIntType __f> 432 void 433 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 434 __s, __b, __t, __c, __l, __f>:: 435 discard(unsigned long long __z) 436 { 437 while (__z > state_size - _M_p) 438 { 439 __z -= state_size - _M_p; 440 _M_gen_rand(); 441 } 442 _M_p += __z; 443 } 444 445 template<typename _UIntType, size_t __w, 446 size_t __n, size_t __m, size_t __r, 447 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 448 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 449 _UIntType __f> 450 typename 451 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 452 __s, __b, __t, __c, __l, __f>::result_type 453 mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, 454 __s, __b, __t, __c, __l, __f>:: 455 operator()() 456 { 457 // Reload the vector - cost is O(n) amortized over n calls. 458 if (_M_p >= state_size) 459 _M_gen_rand(); 460 461 // Calculate o(x(i)). 462 result_type __z = _M_x[_M_p++]; 463 __z ^= (__z >> __u) & __d; 464 __z ^= (__z << __s) & __b; 465 __z ^= (__z << __t) & __c; 466 __z ^= (__z >> __l); 467 468 return __z; 469 } 470 471 template<typename _UIntType, size_t __w, 472 size_t __n, size_t __m, size_t __r, 473 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 474 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 475 _UIntType __f, typename _CharT, typename _Traits> 476 std::basic_ostream<_CharT, _Traits>& 477 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 478 const mersenne_twister_engine<_UIntType, __w, __n, __m, 479 __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x) 480 { 481 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 482 typedef typename __ostream_type::ios_base __ios_base; 483 484 const typename __ios_base::fmtflags __flags = __os.flags(); 485 const _CharT __fill = __os.fill(); 486 const _CharT __space = __os.widen(' '); 487 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 488 __os.fill(__space); 489 490 for (size_t __i = 0; __i < __n; ++__i) 491 __os << __x._M_x[__i] << __space; 492 __os << __x._M_p; 493 494 __os.flags(__flags); 495 __os.fill(__fill); 496 return __os; 497 } 498 499 template<typename _UIntType, size_t __w, 500 size_t __n, size_t __m, size_t __r, 501 _UIntType __a, size_t __u, _UIntType __d, size_t __s, 502 _UIntType __b, size_t __t, _UIntType __c, size_t __l, 503 _UIntType __f, typename _CharT, typename _Traits> 504 std::basic_istream<_CharT, _Traits>& 505 operator>>(std::basic_istream<_CharT, _Traits>& __is, 506 mersenne_twister_engine<_UIntType, __w, __n, __m, 507 __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x) 508 { 509 typedef std::basic_istream<_CharT, _Traits> __istream_type; 510 typedef typename __istream_type::ios_base __ios_base; 511 512 const typename __ios_base::fmtflags __flags = __is.flags(); 513 __is.flags(__ios_base::dec | __ios_base::skipws); 514 515 for (size_t __i = 0; __i < __n; ++__i) 516 __is >> __x._M_x[__i]; 517 __is >> __x._M_p; 518 519 __is.flags(__flags); 520 return __is; 521 } 522 523 524 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 525 constexpr size_t 526 subtract_with_carry_engine<_UIntType, __w, __s, __r>::word_size; 527 528 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 529 constexpr size_t 530 subtract_with_carry_engine<_UIntType, __w, __s, __r>::short_lag; 531 532 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 533 constexpr size_t 534 subtract_with_carry_engine<_UIntType, __w, __s, __r>::long_lag; 535 536 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 537 constexpr _UIntType 538 subtract_with_carry_engine<_UIntType, __w, __s, __r>::default_seed; 539 540 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 541 void 542 subtract_with_carry_engine<_UIntType, __w, __s, __r>:: 543 seed(result_type __value) 544 { 545 std::linear_congruential_engine<result_type, 40014u, 0u, 2147483563u> 546 __lcg(__value == 0u ? default_seed : __value); 547 548 const size_t __n = (__w + 31) / 32; 549 550 for (size_t __i = 0; __i < long_lag; ++__i) 551 { 552 _UIntType __sum = 0u; 553 _UIntType __factor = 1u; 554 for (size_t __j = 0; __j < __n; ++__j) 555 { 556 __sum += __detail::__mod<uint_least32_t, 557 __detail::_Shift<uint_least32_t, 32>::__value> 558 (__lcg()) * __factor; 559 __factor *= __detail::_Shift<_UIntType, 32>::__value; 560 } 561 _M_x[__i] = __detail::__mod<_UIntType, 562 __detail::_Shift<_UIntType, __w>::__value>(__sum); 563 } 564 _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0; 565 _M_p = 0; 566 } 567 568 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 569 template<typename _Sseq> 570 typename std::enable_if<std::is_class<_Sseq>::value>::type 571 subtract_with_carry_engine<_UIntType, __w, __s, __r>:: 572 seed(_Sseq& __q) 573 { 574 const size_t __k = (__w + 31) / 32; 575 uint_least32_t __arr[__r * __k]; 576 __q.generate(__arr + 0, __arr + __r * __k); 577 578 for (size_t __i = 0; __i < long_lag; ++__i) 579 { 580 _UIntType __sum = 0u; 581 _UIntType __factor = 1u; 582 for (size_t __j = 0; __j < __k; ++__j) 583 { 584 __sum += __arr[__k * __i + __j] * __factor; 585 __factor *= __detail::_Shift<_UIntType, 32>::__value; 586 } 587 _M_x[__i] = __detail::__mod<_UIntType, 588 __detail::_Shift<_UIntType, __w>::__value>(__sum); 589 } 590 _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0; 591 _M_p = 0; 592 } 593 594 template<typename _UIntType, size_t __w, size_t __s, size_t __r> 595 typename subtract_with_carry_engine<_UIntType, __w, __s, __r>:: 596 result_type 597 subtract_with_carry_engine<_UIntType, __w, __s, __r>:: 598 operator()() 599 { 600 // Derive short lag index from current index. 601 long __ps = _M_p - short_lag; 602 if (__ps < 0) 603 __ps += long_lag; 604 605 // Calculate new x(i) without overflow or division. 606 // NB: Thanks to the requirements for _UIntType, _M_x[_M_p] + _M_carry 607 // cannot overflow. 608 _UIntType __xi; 609 if (_M_x[__ps] >= _M_x[_M_p] + _M_carry) 610 { 611 __xi = _M_x[__ps] - _M_x[_M_p] - _M_carry; 612 _M_carry = 0; 613 } 614 else 615 { 616 __xi = (__detail::_Shift<_UIntType, __w>::__value 617 - _M_x[_M_p] - _M_carry + _M_x[__ps]); 618 _M_carry = 1; 619 } 620 _M_x[_M_p] = __xi; 621 622 // Adjust current index to loop around in ring buffer. 623 if (++_M_p >= long_lag) 624 _M_p = 0; 625 626 return __xi; 627 } 628 629 template<typename _UIntType, size_t __w, size_t __s, size_t __r, 630 typename _CharT, typename _Traits> 631 std::basic_ostream<_CharT, _Traits>& 632 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 633 const subtract_with_carry_engine<_UIntType, 634 __w, __s, __r>& __x) 635 { 636 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 637 typedef typename __ostream_type::ios_base __ios_base; 638 639 const typename __ios_base::fmtflags __flags = __os.flags(); 640 const _CharT __fill = __os.fill(); 641 const _CharT __space = __os.widen(' '); 642 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 643 __os.fill(__space); 644 645 for (size_t __i = 0; __i < __r; ++__i) 646 __os << __x._M_x[__i] << __space; 647 __os << __x._M_carry << __space << __x._M_p; 648 649 __os.flags(__flags); 650 __os.fill(__fill); 651 return __os; 652 } 653 654 template<typename _UIntType, size_t __w, size_t __s, size_t __r, 655 typename _CharT, typename _Traits> 656 std::basic_istream<_CharT, _Traits>& 657 operator>>(std::basic_istream<_CharT, _Traits>& __is, 658 subtract_with_carry_engine<_UIntType, __w, __s, __r>& __x) 659 { 660 typedef std::basic_ostream<_CharT, _Traits> __istream_type; 661 typedef typename __istream_type::ios_base __ios_base; 662 663 const typename __ios_base::fmtflags __flags = __is.flags(); 664 __is.flags(__ios_base::dec | __ios_base::skipws); 665 666 for (size_t __i = 0; __i < __r; ++__i) 667 __is >> __x._M_x[__i]; 668 __is >> __x._M_carry; 669 __is >> __x._M_p; 670 671 __is.flags(__flags); 672 return __is; 673 } 674 675 676 template<typename _RandomNumberEngine, size_t __p, size_t __r> 677 constexpr size_t 678 discard_block_engine<_RandomNumberEngine, __p, __r>::block_size; 679 680 template<typename _RandomNumberEngine, size_t __p, size_t __r> 681 constexpr size_t 682 discard_block_engine<_RandomNumberEngine, __p, __r>::used_block; 683 684 template<typename _RandomNumberEngine, size_t __p, size_t __r> 685 typename discard_block_engine<_RandomNumberEngine, 686 __p, __r>::result_type 687 discard_block_engine<_RandomNumberEngine, __p, __r>:: 688 operator()() 689 { 690 if (_M_n >= used_block) 691 { 692 _M_b.discard(block_size - _M_n); 693 _M_n = 0; 694 } 695 ++_M_n; 696 return _M_b(); 697 } 698 699 template<typename _RandomNumberEngine, size_t __p, size_t __r, 700 typename _CharT, typename _Traits> 701 std::basic_ostream<_CharT, _Traits>& 702 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 703 const discard_block_engine<_RandomNumberEngine, 704 __p, __r>& __x) 705 { 706 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 707 typedef typename __ostream_type::ios_base __ios_base; 708 709 const typename __ios_base::fmtflags __flags = __os.flags(); 710 const _CharT __fill = __os.fill(); 711 const _CharT __space = __os.widen(' '); 712 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 713 __os.fill(__space); 714 715 __os << __x.base() << __space << __x._M_n; 716 717 __os.flags(__flags); 718 __os.fill(__fill); 719 return __os; 720 } 721 722 template<typename _RandomNumberEngine, size_t __p, size_t __r, 723 typename _CharT, typename _Traits> 724 std::basic_istream<_CharT, _Traits>& 725 operator>>(std::basic_istream<_CharT, _Traits>& __is, 726 discard_block_engine<_RandomNumberEngine, __p, __r>& __x) 727 { 728 typedef std::basic_istream<_CharT, _Traits> __istream_type; 729 typedef typename __istream_type::ios_base __ios_base; 730 731 const typename __ios_base::fmtflags __flags = __is.flags(); 732 __is.flags(__ios_base::dec | __ios_base::skipws); 733 734 __is >> __x._M_b >> __x._M_n; 735 736 __is.flags(__flags); 737 return __is; 738 } 739 740 741 template<typename _RandomNumberEngine, size_t __w, typename _UIntType> 742 typename independent_bits_engine<_RandomNumberEngine, __w, _UIntType>:: 743 result_type 744 independent_bits_engine<_RandomNumberEngine, __w, _UIntType>:: 745 operator()() 746 { 747 typedef typename _RandomNumberEngine::result_type _Eresult_type; 748 const _Eresult_type __r 749 = (_M_b.max() - _M_b.min() < std::numeric_limits<_Eresult_type>::max() 750 ? _M_b.max() - _M_b.min() + 1 : 0); 751 const unsigned __edig = std::numeric_limits<_Eresult_type>::digits; 752 const unsigned __m = __r ? std::__lg(__r) : __edig; 753 754 typedef typename std::common_type<_Eresult_type, result_type>::type 755 __ctype; 756 const unsigned __cdig = std::numeric_limits<__ctype>::digits; 757 758 unsigned __n, __n0; 759 __ctype __s0, __s1, __y0, __y1; 760 761 for (size_t __i = 0; __i < 2; ++__i) 762 { 763 __n = (__w + __m - 1) / __m + __i; 764 __n0 = __n - __w % __n; 765 const unsigned __w0 = __w / __n; // __w0 <= __m 766 767 __s0 = 0; 768 __s1 = 0; 769 if (__w0 < __cdig) 770 { 771 __s0 = __ctype(1) << __w0; 772 __s1 = __s0 << 1; 773 } 774 775 __y0 = 0; 776 __y1 = 0; 777 if (__r) 778 { 779 __y0 = __s0 * (__r / __s0); 780 if (__s1) 781 __y1 = __s1 * (__r / __s1); 782 783 if (__r - __y0 <= __y0 / __n) 784 break; 785 } 786 else 787 break; 788 } 789 790 result_type __sum = 0; 791 for (size_t __k = 0; __k < __n0; ++__k) 792 { 793 __ctype __u; 794 do 795 __u = _M_b() - _M_b.min(); 796 while (__y0 && __u >= __y0); 797 __sum = __s0 * __sum + (__s0 ? __u % __s0 : __u); 798 } 799 for (size_t __k = __n0; __k < __n; ++__k) 800 { 801 __ctype __u; 802 do 803 __u = _M_b() - _M_b.min(); 804 while (__y1 && __u >= __y1); 805 __sum = __s1 * __sum + (__s1 ? __u % __s1 : __u); 806 } 807 return __sum; 808 } 809 810 811 template<typename _RandomNumberEngine, size_t __k> 812 constexpr size_t 813 shuffle_order_engine<_RandomNumberEngine, __k>::table_size; 814 815 template<typename _RandomNumberEngine, size_t __k> 816 typename shuffle_order_engine<_RandomNumberEngine, __k>::result_type 817 shuffle_order_engine<_RandomNumberEngine, __k>:: 818 operator()() 819 { 820 size_t __j = __k * ((_M_y - _M_b.min()) 821 / (_M_b.max() - _M_b.min() + 1.0L)); 822 _M_y = _M_v[__j]; 823 _M_v[__j] = _M_b(); 824 825 return _M_y; 826 } 827 828 template<typename _RandomNumberEngine, size_t __k, 829 typename _CharT, typename _Traits> 830 std::basic_ostream<_CharT, _Traits>& 831 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 832 const shuffle_order_engine<_RandomNumberEngine, __k>& __x) 833 { 834 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 835 typedef typename __ostream_type::ios_base __ios_base; 836 837 const typename __ios_base::fmtflags __flags = __os.flags(); 838 const _CharT __fill = __os.fill(); 839 const _CharT __space = __os.widen(' '); 840 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 841 __os.fill(__space); 842 843 __os << __x.base(); 844 for (size_t __i = 0; __i < __k; ++__i) 845 __os << __space << __x._M_v[__i]; 846 __os << __space << __x._M_y; 847 848 __os.flags(__flags); 849 __os.fill(__fill); 850 return __os; 851 } 852 853 template<typename _RandomNumberEngine, size_t __k, 854 typename _CharT, typename _Traits> 855 std::basic_istream<_CharT, _Traits>& 856 operator>>(std::basic_istream<_CharT, _Traits>& __is, 857 shuffle_order_engine<_RandomNumberEngine, __k>& __x) 858 { 859 typedef std::basic_istream<_CharT, _Traits> __istream_type; 860 typedef typename __istream_type::ios_base __ios_base; 861 862 const typename __ios_base::fmtflags __flags = __is.flags(); 863 __is.flags(__ios_base::dec | __ios_base::skipws); 864 865 __is >> __x._M_b; 866 for (size_t __i = 0; __i < __k; ++__i) 867 __is >> __x._M_v[__i]; 868 __is >> __x._M_y; 869 870 __is.flags(__flags); 871 return __is; 872 } 873 874 875 template<typename _IntType> 876 template<typename _UniformRandomNumberGenerator> 877 typename uniform_int_distribution<_IntType>::result_type 878 uniform_int_distribution<_IntType>:: 879 operator()(_UniformRandomNumberGenerator& __urng, 880 const param_type& __param) 881 { 882 typedef typename _UniformRandomNumberGenerator::result_type 883 _Gresult_type; 884 typedef typename std::make_unsigned<result_type>::type __utype; 885 typedef typename std::common_type<_Gresult_type, __utype>::type 886 __uctype; 887 888 const __uctype __urngmin = __urng.min(); 889 const __uctype __urngmax = __urng.max(); 890 const __uctype __urngrange = __urngmax - __urngmin; 891 const __uctype __urange 892 = __uctype(__param.b()) - __uctype(__param.a()); 893 894 __uctype __ret; 895 896 if (__urngrange > __urange) 897 { 898 // downscaling 899 const __uctype __uerange = __urange + 1; // __urange can be zero 900 const __uctype __scaling = __urngrange / __uerange; 901 const __uctype __past = __uerange * __scaling; 902 do 903 __ret = __uctype(__urng()) - __urngmin; 904 while (__ret >= __past); 905 __ret /= __scaling; 906 } 907 else if (__urngrange < __urange) 908 { 909 // upscaling 910 /* 911 Note that every value in [0, urange] 912 can be written uniquely as 913 914 (urngrange + 1) * high + low 915 916 where 917 918 high in [0, urange / (urngrange + 1)] 919 920 and 921 922 low in [0, urngrange]. 923 */ 924 __uctype __tmp; // wraparound control 925 do 926 { 927 const __uctype __uerngrange = __urngrange + 1; 928 __tmp = (__uerngrange * operator() 929 (__urng, param_type(0, __urange / __uerngrange))); 930 __ret = __tmp + (__uctype(__urng()) - __urngmin); 931 } 932 while (__ret > __urange || __ret < __tmp); 933 } 934 else 935 __ret = __uctype(__urng()) - __urngmin; 936 937 return __ret + __param.a(); 938 } 939 940 941 template<typename _IntType> 942 template<typename _ForwardIterator, 943 typename _UniformRandomNumberGenerator> 944 void 945 uniform_int_distribution<_IntType>:: 946 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 947 _UniformRandomNumberGenerator& __urng, 948 const param_type& __param) 949 { 950 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 951 typedef typename _UniformRandomNumberGenerator::result_type 952 _Gresult_type; 953 typedef typename std::make_unsigned<result_type>::type __utype; 954 typedef typename std::common_type<_Gresult_type, __utype>::type 955 __uctype; 956 957 const __uctype __urngmin = __urng.min(); 958 const __uctype __urngmax = __urng.max(); 959 const __uctype __urngrange = __urngmax - __urngmin; 960 const __uctype __urange 961 = __uctype(__param.b()) - __uctype(__param.a()); 962 963 __uctype __ret; 964 965 if (__urngrange > __urange) 966 { 967 if (__detail::_Power_of_2(__urngrange + 1) 968 && __detail::_Power_of_2(__urange + 1)) 969 { 970 while (__f != __t) 971 { 972 __ret = __uctype(__urng()) - __urngmin; 973 *__f++ = (__ret & __urange) + __param.a(); 974 } 975 } 976 else 977 { 978 // downscaling 979 const __uctype __uerange = __urange + 1; // __urange can be zero 980 const __uctype __scaling = __urngrange / __uerange; 981 const __uctype __past = __uerange * __scaling; 982 while (__f != __t) 983 { 984 do 985 __ret = __uctype(__urng()) - __urngmin; 986 while (__ret >= __past); 987 *__f++ = __ret / __scaling + __param.a(); 988 } 989 } 990 } 991 else if (__urngrange < __urange) 992 { 993 // upscaling 994 /* 995 Note that every value in [0, urange] 996 can be written uniquely as 997 998 (urngrange + 1) * high + low 999 1000 where 1001 1002 high in [0, urange / (urngrange + 1)] 1003 1004 and 1005 1006 low in [0, urngrange]. 1007 */ 1008 __uctype __tmp; // wraparound control 1009 while (__f != __t) 1010 { 1011 do 1012 { 1013 const __uctype __uerngrange = __urngrange + 1; 1014 __tmp = (__uerngrange * operator() 1015 (__urng, param_type(0, __urange / __uerngrange))); 1016 __ret = __tmp + (__uctype(__urng()) - __urngmin); 1017 } 1018 while (__ret > __urange || __ret < __tmp); 1019 *__f++ = __ret; 1020 } 1021 } 1022 else 1023 while (__f != __t) 1024 *__f++ = __uctype(__urng()) - __urngmin + __param.a(); 1025 } 1026 1027 template<typename _IntType, typename _CharT, typename _Traits> 1028 std::basic_ostream<_CharT, _Traits>& 1029 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1030 const uniform_int_distribution<_IntType>& __x) 1031 { 1032 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1033 typedef typename __ostream_type::ios_base __ios_base; 1034 1035 const typename __ios_base::fmtflags __flags = __os.flags(); 1036 const _CharT __fill = __os.fill(); 1037 const _CharT __space = __os.widen(' '); 1038 __os.flags(__ios_base::scientific | __ios_base::left); 1039 __os.fill(__space); 1040 1041 __os << __x.a() << __space << __x.b(); 1042 1043 __os.flags(__flags); 1044 __os.fill(__fill); 1045 return __os; 1046 } 1047 1048 template<typename _IntType, typename _CharT, typename _Traits> 1049 std::basic_istream<_CharT, _Traits>& 1050 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1051 uniform_int_distribution<_IntType>& __x) 1052 { 1053 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1054 typedef typename __istream_type::ios_base __ios_base; 1055 1056 const typename __ios_base::fmtflags __flags = __is.flags(); 1057 __is.flags(__ios_base::dec | __ios_base::skipws); 1058 1059 _IntType __a, __b; 1060 __is >> __a >> __b; 1061 __x.param(typename uniform_int_distribution<_IntType>:: 1062 param_type(__a, __b)); 1063 1064 __is.flags(__flags); 1065 return __is; 1066 } 1067 1068 1069 template<typename _RealType> 1070 template<typename _ForwardIterator, 1071 typename _UniformRandomNumberGenerator> 1072 void 1073 uniform_real_distribution<_RealType>:: 1074 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1075 _UniformRandomNumberGenerator& __urng, 1076 const param_type& __p) 1077 { 1078 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1079 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 1080 __aurng(__urng); 1081 auto __range = __p.b() - __p.a(); 1082 while (__f != __t) 1083 *__f++ = __aurng() * __range + __p.a(); 1084 } 1085 1086 template<typename _RealType, typename _CharT, typename _Traits> 1087 std::basic_ostream<_CharT, _Traits>& 1088 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1089 const uniform_real_distribution<_RealType>& __x) 1090 { 1091 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1092 typedef typename __ostream_type::ios_base __ios_base; 1093 1094 const typename __ios_base::fmtflags __flags = __os.flags(); 1095 const _CharT __fill = __os.fill(); 1096 const std::streamsize __precision = __os.precision(); 1097 const _CharT __space = __os.widen(' '); 1098 __os.flags(__ios_base::scientific | __ios_base::left); 1099 __os.fill(__space); 1100 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1101 1102 __os << __x.a() << __space << __x.b(); 1103 1104 __os.flags(__flags); 1105 __os.fill(__fill); 1106 __os.precision(__precision); 1107 return __os; 1108 } 1109 1110 template<typename _RealType, typename _CharT, typename _Traits> 1111 std::basic_istream<_CharT, _Traits>& 1112 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1113 uniform_real_distribution<_RealType>& __x) 1114 { 1115 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1116 typedef typename __istream_type::ios_base __ios_base; 1117 1118 const typename __ios_base::fmtflags __flags = __is.flags(); 1119 __is.flags(__ios_base::skipws); 1120 1121 _RealType __a, __b; 1122 __is >> __a >> __b; 1123 __x.param(typename uniform_real_distribution<_RealType>:: 1124 param_type(__a, __b)); 1125 1126 __is.flags(__flags); 1127 return __is; 1128 } 1129 1130 1131 template<typename _ForwardIterator, 1132 typename _UniformRandomNumberGenerator> 1133 void 1134 std::bernoulli_distribution:: 1135 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1136 _UniformRandomNumberGenerator& __urng, 1137 const param_type& __p) 1138 { 1139 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1140 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1141 __aurng(__urng); 1142 auto __limit = __p.p() * (__aurng.max() - __aurng.min()); 1143 1144 while (__f != __t) 1145 *__f++ = (__aurng() - __aurng.min()) < __limit; 1146 } 1147 1148 template<typename _CharT, typename _Traits> 1149 std::basic_ostream<_CharT, _Traits>& 1150 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1151 const bernoulli_distribution& __x) 1152 { 1153 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1154 typedef typename __ostream_type::ios_base __ios_base; 1155 1156 const typename __ios_base::fmtflags __flags = __os.flags(); 1157 const _CharT __fill = __os.fill(); 1158 const std::streamsize __precision = __os.precision(); 1159 __os.flags(__ios_base::scientific | __ios_base::left); 1160 __os.fill(__os.widen(' ')); 1161 __os.precision(std::numeric_limits<double>::max_digits10); 1162 1163 __os << __x.p(); 1164 1165 __os.flags(__flags); 1166 __os.fill(__fill); 1167 __os.precision(__precision); 1168 return __os; 1169 } 1170 1171 1172 template<typename _IntType> 1173 template<typename _UniformRandomNumberGenerator> 1174 typename geometric_distribution<_IntType>::result_type 1175 geometric_distribution<_IntType>:: 1176 operator()(_UniformRandomNumberGenerator& __urng, 1177 const param_type& __param) 1178 { 1179 // About the epsilon thing see this thread: 1180 // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html 1181 const double __naf = 1182 (1 - std::numeric_limits<double>::epsilon()) / 2; 1183 // The largest _RealType convertible to _IntType. 1184 const double __thr = 1185 std::numeric_limits<_IntType>::max() + __naf; 1186 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1187 __aurng(__urng); 1188 1189 double __cand; 1190 do 1191 __cand = std::floor(std::log(1.0 - __aurng()) / __param._M_log_1_p); 1192 while (__cand >= __thr); 1193 1194 return result_type(__cand + __naf); 1195 } 1196 1197 template<typename _IntType> 1198 template<typename _ForwardIterator, 1199 typename _UniformRandomNumberGenerator> 1200 void 1201 geometric_distribution<_IntType>:: 1202 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1203 _UniformRandomNumberGenerator& __urng, 1204 const param_type& __param) 1205 { 1206 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1207 // About the epsilon thing see this thread: 1208 // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html 1209 const double __naf = 1210 (1 - std::numeric_limits<double>::epsilon()) / 2; 1211 // The largest _RealType convertible to _IntType. 1212 const double __thr = 1213 std::numeric_limits<_IntType>::max() + __naf; 1214 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1215 __aurng(__urng); 1216 1217 while (__f != __t) 1218 { 1219 double __cand; 1220 do 1221 __cand = std::floor(std::log(1.0 - __aurng()) 1222 / __param._M_log_1_p); 1223 while (__cand >= __thr); 1224 1225 *__f++ = __cand + __naf; 1226 } 1227 } 1228 1229 template<typename _IntType, 1230 typename _CharT, typename _Traits> 1231 std::basic_ostream<_CharT, _Traits>& 1232 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1233 const geometric_distribution<_IntType>& __x) 1234 { 1235 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1236 typedef typename __ostream_type::ios_base __ios_base; 1237 1238 const typename __ios_base::fmtflags __flags = __os.flags(); 1239 const _CharT __fill = __os.fill(); 1240 const std::streamsize __precision = __os.precision(); 1241 __os.flags(__ios_base::scientific | __ios_base::left); 1242 __os.fill(__os.widen(' ')); 1243 __os.precision(std::numeric_limits<double>::max_digits10); 1244 1245 __os << __x.p(); 1246 1247 __os.flags(__flags); 1248 __os.fill(__fill); 1249 __os.precision(__precision); 1250 return __os; 1251 } 1252 1253 template<typename _IntType, 1254 typename _CharT, typename _Traits> 1255 std::basic_istream<_CharT, _Traits>& 1256 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1257 geometric_distribution<_IntType>& __x) 1258 { 1259 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1260 typedef typename __istream_type::ios_base __ios_base; 1261 1262 const typename __ios_base::fmtflags __flags = __is.flags(); 1263 __is.flags(__ios_base::skipws); 1264 1265 double __p; 1266 __is >> __p; 1267 __x.param(typename geometric_distribution<_IntType>::param_type(__p)); 1268 1269 __is.flags(__flags); 1270 return __is; 1271 } 1272 1273 // This is Leger's algorithm, also in Devroye, Ch. X, Example 1.5. 1274 template<typename _IntType> 1275 template<typename _UniformRandomNumberGenerator> 1276 typename negative_binomial_distribution<_IntType>::result_type 1277 negative_binomial_distribution<_IntType>:: 1278 operator()(_UniformRandomNumberGenerator& __urng) 1279 { 1280 const double __y = _M_gd(__urng); 1281 1282 // XXX Is the constructor too slow? 1283 std::poisson_distribution<result_type> __poisson(__y); 1284 return __poisson(__urng); 1285 } 1286 1287 template<typename _IntType> 1288 template<typename _UniformRandomNumberGenerator> 1289 typename negative_binomial_distribution<_IntType>::result_type 1290 negative_binomial_distribution<_IntType>:: 1291 operator()(_UniformRandomNumberGenerator& __urng, 1292 const param_type& __p) 1293 { 1294 typedef typename std::gamma_distribution<double>::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, 1652 _IntType __t, double __q) 1653 { 1654 _IntType __x = 0; 1655 double __sum = 0.0; 1656 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1657 __aurng(__urng); 1658 1659 do 1660 { 1661 if (__t == __x) 1662 return __x; 1663 const double __e = -std::log(1.0 - __aurng()); 1664 __sum += __e / (__t - __x); 1665 __x += 1; 1666 } 1667 while (__sum <= __q); 1668 1669 return __x - 1; 1670 } 1671 1672 /** 1673 * A rejection algorithm when t * p >= 8 and a simple waiting time 1674 * method - the second in the referenced book - otherwise. 1675 * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1 1676 * is defined. 1677 * 1678 * Reference: 1679 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, 1680 * New York, 1986, Ch. X, Sect. 4 (+ Errata!). 1681 */ 1682 template<typename _IntType> 1683 template<typename _UniformRandomNumberGenerator> 1684 typename binomial_distribution<_IntType>::result_type 1685 binomial_distribution<_IntType>:: 1686 operator()(_UniformRandomNumberGenerator& __urng, 1687 const param_type& __param) 1688 { 1689 result_type __ret; 1690 const _IntType __t = __param.t(); 1691 const double __p = __param.p(); 1692 const double __p12 = __p <= 0.5 ? __p : 1.0 - __p; 1693 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 1694 __aurng(__urng); 1695 1696 #if _GLIBCXX_USE_C99_MATH_TR1 1697 if (!__param._M_easy) 1698 { 1699 double __x; 1700 1701 // See comments above... 1702 const double __naf = 1703 (1 - std::numeric_limits<double>::epsilon()) / 2; 1704 const double __thr = 1705 std::numeric_limits<_IntType>::max() + __naf; 1706 1707 const double __np = std::floor(__t * __p12); 1708 1709 // sqrt(pi / 2) 1710 const double __spi_2 = 1.2533141373155002512078826424055226L; 1711 const double __a1 = __param._M_a1; 1712 const double __a12 = __a1 + __param._M_s2 * __spi_2; 1713 const double __a123 = __param._M_a123; 1714 const double __s1s = __param._M_s1 * __param._M_s1; 1715 const double __s2s = __param._M_s2 * __param._M_s2; 1716 1717 bool __reject; 1718 do 1719 { 1720 const double __u = __param._M_s * __aurng(); 1721 1722 double __v; 1723 1724 if (__u <= __a1) 1725 { 1726 const double __n = _M_nd(__urng); 1727 const double __y = __param._M_s1 * std::abs(__n); 1728 __reject = __y >= __param._M_d1; 1729 if (!__reject) 1730 { 1731 const double __e = -std::log(1.0 - __aurng()); 1732 __x = std::floor(__y); 1733 __v = -__e - __n * __n / 2 + __param._M_c; 1734 } 1735 } 1736 else if (__u <= __a12) 1737 { 1738 const double __n = _M_nd(__urng); 1739 const double __y = __param._M_s2 * std::abs(__n); 1740 __reject = __y >= __param._M_d2; 1741 if (!__reject) 1742 { 1743 const double __e = -std::log(1.0 - __aurng()); 1744 __x = std::floor(-__y); 1745 __v = -__e - __n * __n / 2; 1746 } 1747 } 1748 else if (__u <= __a123) 1749 { 1750 const double __e1 = -std::log(1.0 - __aurng()); 1751 const double __e2 = -std::log(1.0 - __aurng()); 1752 1753 const double __y = __param._M_d1 1754 + 2 * __s1s * __e1 / __param._M_d1; 1755 __x = std::floor(__y); 1756 __v = (-__e2 + __param._M_d1 * (1 / (__t - __np) 1757 -__y / (2 * __s1s))); 1758 __reject = false; 1759 } 1760 else 1761 { 1762 const double __e1 = -std::log(1.0 - __aurng()); 1763 const double __e2 = -std::log(1.0 - __aurng()); 1764 1765 const double __y = __param._M_d2 1766 + 2 * __s2s * __e1 / __param._M_d2; 1767 __x = std::floor(-__y); 1768 __v = -__e2 - __param._M_d2 * __y / (2 * __s2s); 1769 __reject = false; 1770 } 1771 1772 __reject = __reject || __x < -__np || __x > __t - __np; 1773 if (!__reject) 1774 { 1775 const double __lfx = 1776 std::lgamma(__np + __x + 1) 1777 + std::lgamma(__t - (__np + __x) + 1); 1778 __reject = __v > __param._M_lf - __lfx 1779 + __x * __param._M_lp1p; 1780 } 1781 1782 __reject |= __x + __np >= __thr; 1783 } 1784 while (__reject); 1785 1786 __x += __np + __naf; 1787 1788 const _IntType __z = _M_waiting(__urng, __t - _IntType(__x), 1789 __param._M_q); 1790 __ret = _IntType(__x) + __z; 1791 } 1792 else 1793 #endif 1794 __ret = _M_waiting(__urng, __t, __param._M_q); 1795 1796 if (__p12 != __p) 1797 __ret = __t - __ret; 1798 return __ret; 1799 } 1800 1801 template<typename _IntType> 1802 template<typename _ForwardIterator, 1803 typename _UniformRandomNumberGenerator> 1804 void 1805 binomial_distribution<_IntType>:: 1806 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1807 _UniformRandomNumberGenerator& __urng, 1808 const param_type& __param) 1809 { 1810 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1811 // We could duplicate everything from operator()... 1812 while (__f != __t) 1813 *__f++ = this->operator()(__urng, __param); 1814 } 1815 1816 template<typename _IntType, 1817 typename _CharT, typename _Traits> 1818 std::basic_ostream<_CharT, _Traits>& 1819 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1820 const binomial_distribution<_IntType>& __x) 1821 { 1822 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1823 typedef typename __ostream_type::ios_base __ios_base; 1824 1825 const typename __ios_base::fmtflags __flags = __os.flags(); 1826 const _CharT __fill = __os.fill(); 1827 const std::streamsize __precision = __os.precision(); 1828 const _CharT __space = __os.widen(' '); 1829 __os.flags(__ios_base::scientific | __ios_base::left); 1830 __os.fill(__space); 1831 __os.precision(std::numeric_limits<double>::max_digits10); 1832 1833 __os << __x.t() << __space << __x.p() 1834 << __space << __x._M_nd; 1835 1836 __os.flags(__flags); 1837 __os.fill(__fill); 1838 __os.precision(__precision); 1839 return __os; 1840 } 1841 1842 template<typename _IntType, 1843 typename _CharT, typename _Traits> 1844 std::basic_istream<_CharT, _Traits>& 1845 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1846 binomial_distribution<_IntType>& __x) 1847 { 1848 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1849 typedef typename __istream_type::ios_base __ios_base; 1850 1851 const typename __ios_base::fmtflags __flags = __is.flags(); 1852 __is.flags(__ios_base::dec | __ios_base::skipws); 1853 1854 _IntType __t; 1855 double __p; 1856 __is >> __t >> __p >> __x._M_nd; 1857 __x.param(typename binomial_distribution<_IntType>:: 1858 param_type(__t, __p)); 1859 1860 __is.flags(__flags); 1861 return __is; 1862 } 1863 1864 1865 template<typename _RealType> 1866 template<typename _ForwardIterator, 1867 typename _UniformRandomNumberGenerator> 1868 void 1869 std::exponential_distribution<_RealType>:: 1870 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1871 _UniformRandomNumberGenerator& __urng, 1872 const param_type& __p) 1873 { 1874 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1875 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 1876 __aurng(__urng); 1877 while (__f != __t) 1878 *__f++ = -std::log(result_type(1) - __aurng()) / __p.lambda(); 1879 } 1880 1881 template<typename _RealType, typename _CharT, typename _Traits> 1882 std::basic_ostream<_CharT, _Traits>& 1883 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1884 const exponential_distribution<_RealType>& __x) 1885 { 1886 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1887 typedef typename __ostream_type::ios_base __ios_base; 1888 1889 const typename __ios_base::fmtflags __flags = __os.flags(); 1890 const _CharT __fill = __os.fill(); 1891 const std::streamsize __precision = __os.precision(); 1892 __os.flags(__ios_base::scientific | __ios_base::left); 1893 __os.fill(__os.widen(' ')); 1894 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1895 1896 __os << __x.lambda(); 1897 1898 __os.flags(__flags); 1899 __os.fill(__fill); 1900 __os.precision(__precision); 1901 return __os; 1902 } 1903 1904 template<typename _RealType, typename _CharT, typename _Traits> 1905 std::basic_istream<_CharT, _Traits>& 1906 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1907 exponential_distribution<_RealType>& __x) 1908 { 1909 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1910 typedef typename __istream_type::ios_base __ios_base; 1911 1912 const typename __ios_base::fmtflags __flags = __is.flags(); 1913 __is.flags(__ios_base::dec | __ios_base::skipws); 1914 1915 _RealType __lambda; 1916 __is >> __lambda; 1917 __x.param(typename exponential_distribution<_RealType>:: 1918 param_type(__lambda)); 1919 1920 __is.flags(__flags); 1921 return __is; 1922 } 1923 1924 1925 /** 1926 * Polar method due to Marsaglia. 1927 * 1928 * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, 1929 * New York, 1986, Ch. V, Sect. 4.4. 1930 */ 1931 template<typename _RealType> 1932 template<typename _UniformRandomNumberGenerator> 1933 typename normal_distribution<_RealType>::result_type 1934 normal_distribution<_RealType>:: 1935 operator()(_UniformRandomNumberGenerator& __urng, 1936 const param_type& __param) 1937 { 1938 result_type __ret; 1939 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 1940 __aurng(__urng); 1941 1942 if (_M_saved_available) 1943 { 1944 _M_saved_available = false; 1945 __ret = _M_saved; 1946 } 1947 else 1948 { 1949 result_type __x, __y, __r2; 1950 do 1951 { 1952 __x = result_type(2.0) * __aurng() - 1.0; 1953 __y = result_type(2.0) * __aurng() - 1.0; 1954 __r2 = __x * __x + __y * __y; 1955 } 1956 while (__r2 > 1.0 || __r2 == 0.0); 1957 1958 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2); 1959 _M_saved = __x * __mult; 1960 _M_saved_available = true; 1961 __ret = __y * __mult; 1962 } 1963 1964 __ret = __ret * __param.stddev() + __param.mean(); 1965 return __ret; 1966 } 1967 1968 template<typename _RealType> 1969 template<typename _ForwardIterator, 1970 typename _UniformRandomNumberGenerator> 1971 void 1972 normal_distribution<_RealType>:: 1973 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 1974 _UniformRandomNumberGenerator& __urng, 1975 const param_type& __param) 1976 { 1977 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 1978 1979 if (__f == __t) 1980 return; 1981 1982 if (_M_saved_available) 1983 { 1984 _M_saved_available = false; 1985 *__f++ = _M_saved * __param.stddev() + __param.mean(); 1986 1987 if (__f == __t) 1988 return; 1989 } 1990 1991 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 1992 __aurng(__urng); 1993 1994 while (__f + 1 < __t) 1995 { 1996 result_type __x, __y, __r2; 1997 do 1998 { 1999 __x = result_type(2.0) * __aurng() - 1.0; 2000 __y = result_type(2.0) * __aurng() - 1.0; 2001 __r2 = __x * __x + __y * __y; 2002 } 2003 while (__r2 > 1.0 || __r2 == 0.0); 2004 2005 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2); 2006 *__f++ = __y * __mult * __param.stddev() + __param.mean(); 2007 *__f++ = __x * __mult * __param.stddev() + __param.mean(); 2008 } 2009 2010 if (__f != __t) 2011 { 2012 result_type __x, __y, __r2; 2013 do 2014 { 2015 __x = result_type(2.0) * __aurng() - 1.0; 2016 __y = result_type(2.0) * __aurng() - 1.0; 2017 __r2 = __x * __x + __y * __y; 2018 } 2019 while (__r2 > 1.0 || __r2 == 0.0); 2020 2021 const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2); 2022 _M_saved = __x * __mult; 2023 _M_saved_available = true; 2024 *__f = __y * __mult * __param.stddev() + __param.mean(); 2025 } 2026 } 2027 2028 template<typename _RealType> 2029 bool 2030 operator==(const std::normal_distribution<_RealType>& __d1, 2031 const std::normal_distribution<_RealType>& __d2) 2032 { 2033 if (__d1._M_param == __d2._M_param 2034 && __d1._M_saved_available == __d2._M_saved_available) 2035 { 2036 if (__d1._M_saved_available 2037 && __d1._M_saved == __d2._M_saved) 2038 return true; 2039 else if(!__d1._M_saved_available) 2040 return true; 2041 else 2042 return false; 2043 } 2044 else 2045 return false; 2046 } 2047 2048 template<typename _RealType, typename _CharT, typename _Traits> 2049 std::basic_ostream<_CharT, _Traits>& 2050 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2051 const normal_distribution<_RealType>& __x) 2052 { 2053 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2054 typedef typename __ostream_type::ios_base __ios_base; 2055 2056 const typename __ios_base::fmtflags __flags = __os.flags(); 2057 const _CharT __fill = __os.fill(); 2058 const std::streamsize __precision = __os.precision(); 2059 const _CharT __space = __os.widen(' '); 2060 __os.flags(__ios_base::scientific | __ios_base::left); 2061 __os.fill(__space); 2062 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2063 2064 __os << __x.mean() << __space << __x.stddev() 2065 << __space << __x._M_saved_available; 2066 if (__x._M_saved_available) 2067 __os << __space << __x._M_saved; 2068 2069 __os.flags(__flags); 2070 __os.fill(__fill); 2071 __os.precision(__precision); 2072 return __os; 2073 } 2074 2075 template<typename _RealType, typename _CharT, typename _Traits> 2076 std::basic_istream<_CharT, _Traits>& 2077 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2078 normal_distribution<_RealType>& __x) 2079 { 2080 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2081 typedef typename __istream_type::ios_base __ios_base; 2082 2083 const typename __ios_base::fmtflags __flags = __is.flags(); 2084 __is.flags(__ios_base::dec | __ios_base::skipws); 2085 2086 double __mean, __stddev; 2087 __is >> __mean >> __stddev 2088 >> __x._M_saved_available; 2089 if (__x._M_saved_available) 2090 __is >> __x._M_saved; 2091 __x.param(typename normal_distribution<_RealType>:: 2092 param_type(__mean, __stddev)); 2093 2094 __is.flags(__flags); 2095 return __is; 2096 } 2097 2098 2099 template<typename _RealType> 2100 template<typename _ForwardIterator, 2101 typename _UniformRandomNumberGenerator> 2102 void 2103 lognormal_distribution<_RealType>:: 2104 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2105 _UniformRandomNumberGenerator& __urng, 2106 const param_type& __p) 2107 { 2108 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2109 while (__f != __t) 2110 *__f++ = std::exp(__p.s() * _M_nd(__urng) + __p.m()); 2111 } 2112 2113 template<typename _RealType, typename _CharT, typename _Traits> 2114 std::basic_ostream<_CharT, _Traits>& 2115 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2116 const lognormal_distribution<_RealType>& __x) 2117 { 2118 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2119 typedef typename __ostream_type::ios_base __ios_base; 2120 2121 const typename __ios_base::fmtflags __flags = __os.flags(); 2122 const _CharT __fill = __os.fill(); 2123 const std::streamsize __precision = __os.precision(); 2124 const _CharT __space = __os.widen(' '); 2125 __os.flags(__ios_base::scientific | __ios_base::left); 2126 __os.fill(__space); 2127 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2128 2129 __os << __x.m() << __space << __x.s() 2130 << __space << __x._M_nd; 2131 2132 __os.flags(__flags); 2133 __os.fill(__fill); 2134 __os.precision(__precision); 2135 return __os; 2136 } 2137 2138 template<typename _RealType, typename _CharT, typename _Traits> 2139 std::basic_istream<_CharT, _Traits>& 2140 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2141 lognormal_distribution<_RealType>& __x) 2142 { 2143 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2144 typedef typename __istream_type::ios_base __ios_base; 2145 2146 const typename __ios_base::fmtflags __flags = __is.flags(); 2147 __is.flags(__ios_base::dec | __ios_base::skipws); 2148 2149 _RealType __m, __s; 2150 __is >> __m >> __s >> __x._M_nd; 2151 __x.param(typename lognormal_distribution<_RealType>:: 2152 param_type(__m, __s)); 2153 2154 __is.flags(__flags); 2155 return __is; 2156 } 2157 2158 template<typename _RealType> 2159 template<typename _ForwardIterator, 2160 typename _UniformRandomNumberGenerator> 2161 void 2162 std::chi_squared_distribution<_RealType>:: 2163 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2164 _UniformRandomNumberGenerator& __urng) 2165 { 2166 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2167 while (__f != __t) 2168 *__f++ = 2 * _M_gd(__urng); 2169 } 2170 2171 template<typename _RealType> 2172 template<typename _ForwardIterator, 2173 typename _UniformRandomNumberGenerator> 2174 void 2175 std::chi_squared_distribution<_RealType>:: 2176 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2177 _UniformRandomNumberGenerator& __urng, 2178 const typename 2179 std::gamma_distribution<result_type>::param_type& __p) 2180 { 2181 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2182 while (__f != __t) 2183 *__f++ = 2 * _M_gd(__urng, __p); 2184 } 2185 2186 template<typename _RealType, typename _CharT, typename _Traits> 2187 std::basic_ostream<_CharT, _Traits>& 2188 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2189 const chi_squared_distribution<_RealType>& __x) 2190 { 2191 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2192 typedef typename __ostream_type::ios_base __ios_base; 2193 2194 const typename __ios_base::fmtflags __flags = __os.flags(); 2195 const _CharT __fill = __os.fill(); 2196 const std::streamsize __precision = __os.precision(); 2197 const _CharT __space = __os.widen(' '); 2198 __os.flags(__ios_base::scientific | __ios_base::left); 2199 __os.fill(__space); 2200 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2201 2202 __os << __x.n() << __space << __x._M_gd; 2203 2204 __os.flags(__flags); 2205 __os.fill(__fill); 2206 __os.precision(__precision); 2207 return __os; 2208 } 2209 2210 template<typename _RealType, typename _CharT, typename _Traits> 2211 std::basic_istream<_CharT, _Traits>& 2212 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2213 chi_squared_distribution<_RealType>& __x) 2214 { 2215 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2216 typedef typename __istream_type::ios_base __ios_base; 2217 2218 const typename __ios_base::fmtflags __flags = __is.flags(); 2219 __is.flags(__ios_base::dec | __ios_base::skipws); 2220 2221 _RealType __n; 2222 __is >> __n >> __x._M_gd; 2223 __x.param(typename chi_squared_distribution<_RealType>:: 2224 param_type(__n)); 2225 2226 __is.flags(__flags); 2227 return __is; 2228 } 2229 2230 2231 template<typename _RealType> 2232 template<typename _UniformRandomNumberGenerator> 2233 typename cauchy_distribution<_RealType>::result_type 2234 cauchy_distribution<_RealType>:: 2235 operator()(_UniformRandomNumberGenerator& __urng, 2236 const param_type& __p) 2237 { 2238 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2239 __aurng(__urng); 2240 _RealType __u; 2241 do 2242 __u = __aurng(); 2243 while (__u == 0.5); 2244 2245 const _RealType __pi = 3.1415926535897932384626433832795029L; 2246 return __p.a() + __p.b() * std::tan(__pi * __u); 2247 } 2248 2249 template<typename _RealType> 2250 template<typename _ForwardIterator, 2251 typename _UniformRandomNumberGenerator> 2252 void 2253 cauchy_distribution<_RealType>:: 2254 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2255 _UniformRandomNumberGenerator& __urng, 2256 const param_type& __p) 2257 { 2258 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2259 const _RealType __pi = 3.1415926535897932384626433832795029L; 2260 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2261 __aurng(__urng); 2262 while (__f != __t) 2263 { 2264 _RealType __u; 2265 do 2266 __u = __aurng(); 2267 while (__u == 0.5); 2268 2269 *__f++ = __p.a() + __p.b() * std::tan(__pi * __u); 2270 } 2271 } 2272 2273 template<typename _RealType, typename _CharT, typename _Traits> 2274 std::basic_ostream<_CharT, _Traits>& 2275 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2276 const cauchy_distribution<_RealType>& __x) 2277 { 2278 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2279 typedef typename __ostream_type::ios_base __ios_base; 2280 2281 const typename __ios_base::fmtflags __flags = __os.flags(); 2282 const _CharT __fill = __os.fill(); 2283 const std::streamsize __precision = __os.precision(); 2284 const _CharT __space = __os.widen(' '); 2285 __os.flags(__ios_base::scientific | __ios_base::left); 2286 __os.fill(__space); 2287 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2288 2289 __os << __x.a() << __space << __x.b(); 2290 2291 __os.flags(__flags); 2292 __os.fill(__fill); 2293 __os.precision(__precision); 2294 return __os; 2295 } 2296 2297 template<typename _RealType, typename _CharT, typename _Traits> 2298 std::basic_istream<_CharT, _Traits>& 2299 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2300 cauchy_distribution<_RealType>& __x) 2301 { 2302 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2303 typedef typename __istream_type::ios_base __ios_base; 2304 2305 const typename __ios_base::fmtflags __flags = __is.flags(); 2306 __is.flags(__ios_base::dec | __ios_base::skipws); 2307 2308 _RealType __a, __b; 2309 __is >> __a >> __b; 2310 __x.param(typename cauchy_distribution<_RealType>:: 2311 param_type(__a, __b)); 2312 2313 __is.flags(__flags); 2314 return __is; 2315 } 2316 2317 2318 template<typename _RealType> 2319 template<typename _ForwardIterator, 2320 typename _UniformRandomNumberGenerator> 2321 void 2322 std::fisher_f_distribution<_RealType>:: 2323 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2324 _UniformRandomNumberGenerator& __urng) 2325 { 2326 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2327 while (__f != __t) 2328 *__f++ = ((_M_gd_x(__urng) * n()) / (_M_gd_y(__urng) * m())); 2329 } 2330 2331 template<typename _RealType> 2332 template<typename _ForwardIterator, 2333 typename _UniformRandomNumberGenerator> 2334 void 2335 std::fisher_f_distribution<_RealType>:: 2336 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2337 _UniformRandomNumberGenerator& __urng, 2338 const param_type& __p) 2339 { 2340 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2341 typedef typename std::gamma_distribution<result_type>::param_type 2342 param_type; 2343 param_type __p1(__p.m() / 2); 2344 param_type __p2(__p.n() / 2); 2345 while (__f != __t) 2346 *__f++ = ((_M_gd_x(__urng, __p1) * n()) 2347 / (_M_gd_y(__urng, __p2) * m())); 2348 } 2349 2350 template<typename _RealType, typename _CharT, typename _Traits> 2351 std::basic_ostream<_CharT, _Traits>& 2352 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2353 const fisher_f_distribution<_RealType>& __x) 2354 { 2355 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2356 typedef typename __ostream_type::ios_base __ios_base; 2357 2358 const typename __ios_base::fmtflags __flags = __os.flags(); 2359 const _CharT __fill = __os.fill(); 2360 const std::streamsize __precision = __os.precision(); 2361 const _CharT __space = __os.widen(' '); 2362 __os.flags(__ios_base::scientific | __ios_base::left); 2363 __os.fill(__space); 2364 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2365 2366 __os << __x.m() << __space << __x.n() 2367 << __space << __x._M_gd_x << __space << __x._M_gd_y; 2368 2369 __os.flags(__flags); 2370 __os.fill(__fill); 2371 __os.precision(__precision); 2372 return __os; 2373 } 2374 2375 template<typename _RealType, typename _CharT, typename _Traits> 2376 std::basic_istream<_CharT, _Traits>& 2377 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2378 fisher_f_distribution<_RealType>& __x) 2379 { 2380 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2381 typedef typename __istream_type::ios_base __ios_base; 2382 2383 const typename __ios_base::fmtflags __flags = __is.flags(); 2384 __is.flags(__ios_base::dec | __ios_base::skipws); 2385 2386 _RealType __m, __n; 2387 __is >> __m >> __n >> __x._M_gd_x >> __x._M_gd_y; 2388 __x.param(typename fisher_f_distribution<_RealType>:: 2389 param_type(__m, __n)); 2390 2391 __is.flags(__flags); 2392 return __is; 2393 } 2394 2395 2396 template<typename _RealType> 2397 template<typename _ForwardIterator, 2398 typename _UniformRandomNumberGenerator> 2399 void 2400 std::student_t_distribution<_RealType>:: 2401 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2402 _UniformRandomNumberGenerator& __urng) 2403 { 2404 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2405 while (__f != __t) 2406 *__f++ = _M_nd(__urng) * std::sqrt(n() / _M_gd(__urng)); 2407 } 2408 2409 template<typename _RealType> 2410 template<typename _ForwardIterator, 2411 typename _UniformRandomNumberGenerator> 2412 void 2413 std::student_t_distribution<_RealType>:: 2414 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2415 _UniformRandomNumberGenerator& __urng, 2416 const param_type& __p) 2417 { 2418 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2419 typename std::gamma_distribution<result_type>::param_type 2420 __p2(__p.n() / 2, 2); 2421 while (__f != __t) 2422 *__f++ = _M_nd(__urng) * std::sqrt(__p.n() / _M_gd(__urng, __p2)); 2423 } 2424 2425 template<typename _RealType, typename _CharT, typename _Traits> 2426 std::basic_ostream<_CharT, _Traits>& 2427 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2428 const student_t_distribution<_RealType>& __x) 2429 { 2430 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2431 typedef typename __ostream_type::ios_base __ios_base; 2432 2433 const typename __ios_base::fmtflags __flags = __os.flags(); 2434 const _CharT __fill = __os.fill(); 2435 const std::streamsize __precision = __os.precision(); 2436 const _CharT __space = __os.widen(' '); 2437 __os.flags(__ios_base::scientific | __ios_base::left); 2438 __os.fill(__space); 2439 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2440 2441 __os << __x.n() << __space << __x._M_nd << __space << __x._M_gd; 2442 2443 __os.flags(__flags); 2444 __os.fill(__fill); 2445 __os.precision(__precision); 2446 return __os; 2447 } 2448 2449 template<typename _RealType, typename _CharT, typename _Traits> 2450 std::basic_istream<_CharT, _Traits>& 2451 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2452 student_t_distribution<_RealType>& __x) 2453 { 2454 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2455 typedef typename __istream_type::ios_base __ios_base; 2456 2457 const typename __ios_base::fmtflags __flags = __is.flags(); 2458 __is.flags(__ios_base::dec | __ios_base::skipws); 2459 2460 _RealType __n; 2461 __is >> __n >> __x._M_nd >> __x._M_gd; 2462 __x.param(typename student_t_distribution<_RealType>::param_type(__n)); 2463 2464 __is.flags(__flags); 2465 return __is; 2466 } 2467 2468 2469 template<typename _RealType> 2470 void 2471 gamma_distribution<_RealType>::param_type:: 2472 _M_initialize() 2473 { 2474 _M_malpha = _M_alpha < 1.0 ? _M_alpha + _RealType(1.0) : _M_alpha; 2475 2476 const _RealType __a1 = _M_malpha - _RealType(1.0) / _RealType(3.0); 2477 _M_a2 = _RealType(1.0) / std::sqrt(_RealType(9.0) * __a1); 2478 } 2479 2480 /** 2481 * Marsaglia, G. and Tsang, W. W. 2482 * "A Simple Method for Generating Gamma Variables" 2483 * ACM Transactions on Mathematical Software, 26, 3, 363-372, 2000. 2484 */ 2485 template<typename _RealType> 2486 template<typename _UniformRandomNumberGenerator> 2487 typename gamma_distribution<_RealType>::result_type 2488 gamma_distribution<_RealType>:: 2489 operator()(_UniformRandomNumberGenerator& __urng, 2490 const param_type& __param) 2491 { 2492 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2493 __aurng(__urng); 2494 2495 result_type __u, __v, __n; 2496 const result_type __a1 = (__param._M_malpha 2497 - _RealType(1.0) / _RealType(3.0)); 2498 2499 do 2500 { 2501 do 2502 { 2503 __n = _M_nd(__urng); 2504 __v = result_type(1.0) + __param._M_a2 * __n; 2505 } 2506 while (__v <= 0.0); 2507 2508 __v = __v * __v * __v; 2509 __u = __aurng(); 2510 } 2511 while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n 2512 && (std::log(__u) > (0.5 * __n * __n + __a1 2513 * (1.0 - __v + std::log(__v))))); 2514 2515 if (__param.alpha() == __param._M_malpha) 2516 return __a1 * __v * __param.beta(); 2517 else 2518 { 2519 do 2520 __u = __aurng(); 2521 while (__u == 0.0); 2522 2523 return (std::pow(__u, result_type(1.0) / __param.alpha()) 2524 * __a1 * __v * __param.beta()); 2525 } 2526 } 2527 2528 template<typename _RealType> 2529 template<typename _ForwardIterator, 2530 typename _UniformRandomNumberGenerator> 2531 void 2532 gamma_distribution<_RealType>:: 2533 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2534 _UniformRandomNumberGenerator& __urng, 2535 const param_type& __param) 2536 { 2537 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2538 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2539 __aurng(__urng); 2540 2541 result_type __u, __v, __n; 2542 const result_type __a1 = (__param._M_malpha 2543 - _RealType(1.0) / _RealType(3.0)); 2544 2545 if (__param.alpha() == __param._M_malpha) 2546 while (__f != __t) 2547 { 2548 do 2549 { 2550 do 2551 { 2552 __n = _M_nd(__urng); 2553 __v = result_type(1.0) + __param._M_a2 * __n; 2554 } 2555 while (__v <= 0.0); 2556 2557 __v = __v * __v * __v; 2558 __u = __aurng(); 2559 } 2560 while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n 2561 && (std::log(__u) > (0.5 * __n * __n + __a1 2562 * (1.0 - __v + std::log(__v))))); 2563 2564 *__f++ = __a1 * __v * __param.beta(); 2565 } 2566 else 2567 while (__f != __t) 2568 { 2569 do 2570 { 2571 do 2572 { 2573 __n = _M_nd(__urng); 2574 __v = result_type(1.0) + __param._M_a2 * __n; 2575 } 2576 while (__v <= 0.0); 2577 2578 __v = __v * __v * __v; 2579 __u = __aurng(); 2580 } 2581 while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n 2582 && (std::log(__u) > (0.5 * __n * __n + __a1 2583 * (1.0 - __v + std::log(__v))))); 2584 2585 do 2586 __u = __aurng(); 2587 while (__u == 0.0); 2588 2589 *__f++ = (std::pow(__u, result_type(1.0) / __param.alpha()) 2590 * __a1 * __v * __param.beta()); 2591 } 2592 } 2593 2594 template<typename _RealType, typename _CharT, typename _Traits> 2595 std::basic_ostream<_CharT, _Traits>& 2596 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2597 const gamma_distribution<_RealType>& __x) 2598 { 2599 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2600 typedef typename __ostream_type::ios_base __ios_base; 2601 2602 const typename __ios_base::fmtflags __flags = __os.flags(); 2603 const _CharT __fill = __os.fill(); 2604 const std::streamsize __precision = __os.precision(); 2605 const _CharT __space = __os.widen(' '); 2606 __os.flags(__ios_base::scientific | __ios_base::left); 2607 __os.fill(__space); 2608 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2609 2610 __os << __x.alpha() << __space << __x.beta() 2611 << __space << __x._M_nd; 2612 2613 __os.flags(__flags); 2614 __os.fill(__fill); 2615 __os.precision(__precision); 2616 return __os; 2617 } 2618 2619 template<typename _RealType, typename _CharT, typename _Traits> 2620 std::basic_istream<_CharT, _Traits>& 2621 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2622 gamma_distribution<_RealType>& __x) 2623 { 2624 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2625 typedef typename __istream_type::ios_base __ios_base; 2626 2627 const typename __ios_base::fmtflags __flags = __is.flags(); 2628 __is.flags(__ios_base::dec | __ios_base::skipws); 2629 2630 _RealType __alpha_val, __beta_val; 2631 __is >> __alpha_val >> __beta_val >> __x._M_nd; 2632 __x.param(typename gamma_distribution<_RealType>:: 2633 param_type(__alpha_val, __beta_val)); 2634 2635 __is.flags(__flags); 2636 return __is; 2637 } 2638 2639 2640 template<typename _RealType> 2641 template<typename _UniformRandomNumberGenerator> 2642 typename weibull_distribution<_RealType>::result_type 2643 weibull_distribution<_RealType>:: 2644 operator()(_UniformRandomNumberGenerator& __urng, 2645 const param_type& __p) 2646 { 2647 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2648 __aurng(__urng); 2649 return __p.b() * std::pow(-std::log(result_type(1) - __aurng()), 2650 result_type(1) / __p.a()); 2651 } 2652 2653 template<typename _RealType> 2654 template<typename _ForwardIterator, 2655 typename _UniformRandomNumberGenerator> 2656 void 2657 weibull_distribution<_RealType>:: 2658 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2659 _UniformRandomNumberGenerator& __urng, 2660 const param_type& __p) 2661 { 2662 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2663 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2664 __aurng(__urng); 2665 auto __inv_a = result_type(1) / __p.a(); 2666 2667 while (__f != __t) 2668 *__f++ = __p.b() * std::pow(-std::log(result_type(1) - __aurng()), 2669 __inv_a); 2670 } 2671 2672 template<typename _RealType, typename _CharT, typename _Traits> 2673 std::basic_ostream<_CharT, _Traits>& 2674 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2675 const weibull_distribution<_RealType>& __x) 2676 { 2677 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2678 typedef typename __ostream_type::ios_base __ios_base; 2679 2680 const typename __ios_base::fmtflags __flags = __os.flags(); 2681 const _CharT __fill = __os.fill(); 2682 const std::streamsize __precision = __os.precision(); 2683 const _CharT __space = __os.widen(' '); 2684 __os.flags(__ios_base::scientific | __ios_base::left); 2685 __os.fill(__space); 2686 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2687 2688 __os << __x.a() << __space << __x.b(); 2689 2690 __os.flags(__flags); 2691 __os.fill(__fill); 2692 __os.precision(__precision); 2693 return __os; 2694 } 2695 2696 template<typename _RealType, typename _CharT, typename _Traits> 2697 std::basic_istream<_CharT, _Traits>& 2698 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2699 weibull_distribution<_RealType>& __x) 2700 { 2701 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2702 typedef typename __istream_type::ios_base __ios_base; 2703 2704 const typename __ios_base::fmtflags __flags = __is.flags(); 2705 __is.flags(__ios_base::dec | __ios_base::skipws); 2706 2707 _RealType __a, __b; 2708 __is >> __a >> __b; 2709 __x.param(typename weibull_distribution<_RealType>:: 2710 param_type(__a, __b)); 2711 2712 __is.flags(__flags); 2713 return __is; 2714 } 2715 2716 2717 template<typename _RealType> 2718 template<typename _UniformRandomNumberGenerator> 2719 typename extreme_value_distribution<_RealType>::result_type 2720 extreme_value_distribution<_RealType>:: 2721 operator()(_UniformRandomNumberGenerator& __urng, 2722 const param_type& __p) 2723 { 2724 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2725 __aurng(__urng); 2726 return __p.a() - __p.b() * std::log(-std::log(result_type(1) 2727 - __aurng())); 2728 } 2729 2730 template<typename _RealType> 2731 template<typename _ForwardIterator, 2732 typename _UniformRandomNumberGenerator> 2733 void 2734 extreme_value_distribution<_RealType>:: 2735 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2736 _UniformRandomNumberGenerator& __urng, 2737 const param_type& __p) 2738 { 2739 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2740 __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 2741 __aurng(__urng); 2742 2743 while (__f != __t) 2744 *__f++ = __p.a() - __p.b() * std::log(-std::log(result_type(1) 2745 - __aurng())); 2746 } 2747 2748 template<typename _RealType, typename _CharT, typename _Traits> 2749 std::basic_ostream<_CharT, _Traits>& 2750 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2751 const extreme_value_distribution<_RealType>& __x) 2752 { 2753 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2754 typedef typename __ostream_type::ios_base __ios_base; 2755 2756 const typename __ios_base::fmtflags __flags = __os.flags(); 2757 const _CharT __fill = __os.fill(); 2758 const std::streamsize __precision = __os.precision(); 2759 const _CharT __space = __os.widen(' '); 2760 __os.flags(__ios_base::scientific | __ios_base::left); 2761 __os.fill(__space); 2762 __os.precision(std::numeric_limits<_RealType>::max_digits10); 2763 2764 __os << __x.a() << __space << __x.b(); 2765 2766 __os.flags(__flags); 2767 __os.fill(__fill); 2768 __os.precision(__precision); 2769 return __os; 2770 } 2771 2772 template<typename _RealType, typename _CharT, typename _Traits> 2773 std::basic_istream<_CharT, _Traits>& 2774 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2775 extreme_value_distribution<_RealType>& __x) 2776 { 2777 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2778 typedef typename __istream_type::ios_base __ios_base; 2779 2780 const typename __ios_base::fmtflags __flags = __is.flags(); 2781 __is.flags(__ios_base::dec | __ios_base::skipws); 2782 2783 _RealType __a, __b; 2784 __is >> __a >> __b; 2785 __x.param(typename extreme_value_distribution<_RealType>:: 2786 param_type(__a, __b)); 2787 2788 __is.flags(__flags); 2789 return __is; 2790 } 2791 2792 2793 template<typename _IntType> 2794 void 2795 discrete_distribution<_IntType>::param_type:: 2796 _M_initialize() 2797 { 2798 if (_M_prob.size() < 2) 2799 { 2800 _M_prob.clear(); 2801 return; 2802 } 2803 2804 const double __sum = std::accumulate(_M_prob.begin(), 2805 _M_prob.end(), 0.0); 2806 // Now normalize the probabilites. 2807 __detail::__normalize(_M_prob.begin(), _M_prob.end(), _M_prob.begin(), 2808 __sum); 2809 // Accumulate partial sums. 2810 _M_cp.reserve(_M_prob.size()); 2811 std::partial_sum(_M_prob.begin(), _M_prob.end(), 2812 std::back_inserter(_M_cp)); 2813 // Make sure the last cumulative probability is one. 2814 _M_cp[_M_cp.size() - 1] = 1.0; 2815 } 2816 2817 template<typename _IntType> 2818 template<typename _Func> 2819 discrete_distribution<_IntType>::param_type:: 2820 param_type(size_t __nw, double __xmin, double __xmax, _Func __fw) 2821 : _M_prob(), _M_cp() 2822 { 2823 const size_t __n = __nw == 0 ? 1 : __nw; 2824 const double __delta = (__xmax - __xmin) / __n; 2825 2826 _M_prob.reserve(__n); 2827 for (size_t __k = 0; __k < __nw; ++__k) 2828 _M_prob.push_back(__fw(__xmin + __k * __delta + 0.5 * __delta)); 2829 2830 _M_initialize(); 2831 } 2832 2833 template<typename _IntType> 2834 template<typename _UniformRandomNumberGenerator> 2835 typename discrete_distribution<_IntType>::result_type 2836 discrete_distribution<_IntType>:: 2837 operator()(_UniformRandomNumberGenerator& __urng, 2838 const param_type& __param) 2839 { 2840 if (__param._M_cp.empty()) 2841 return result_type(0); 2842 2843 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 2844 __aurng(__urng); 2845 2846 const double __p = __aurng(); 2847 auto __pos = std::lower_bound(__param._M_cp.begin(), 2848 __param._M_cp.end(), __p); 2849 2850 return __pos - __param._M_cp.begin(); 2851 } 2852 2853 template<typename _IntType> 2854 template<typename _ForwardIterator, 2855 typename _UniformRandomNumberGenerator> 2856 void 2857 discrete_distribution<_IntType>:: 2858 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 2859 _UniformRandomNumberGenerator& __urng, 2860 const param_type& __param) 2861 { 2862 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 2863 2864 if (__param._M_cp.empty()) 2865 { 2866 while (__f != __t) 2867 *__f++ = result_type(0); 2868 return; 2869 } 2870 2871 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 2872 __aurng(__urng); 2873 2874 while (__f != __t) 2875 { 2876 const double __p = __aurng(); 2877 auto __pos = std::lower_bound(__param._M_cp.begin(), 2878 __param._M_cp.end(), __p); 2879 2880 *__f++ = __pos - __param._M_cp.begin(); 2881 } 2882 } 2883 2884 template<typename _IntType, typename _CharT, typename _Traits> 2885 std::basic_ostream<_CharT, _Traits>& 2886 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 2887 const discrete_distribution<_IntType>& __x) 2888 { 2889 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 2890 typedef typename __ostream_type::ios_base __ios_base; 2891 2892 const typename __ios_base::fmtflags __flags = __os.flags(); 2893 const _CharT __fill = __os.fill(); 2894 const std::streamsize __precision = __os.precision(); 2895 const _CharT __space = __os.widen(' '); 2896 __os.flags(__ios_base::scientific | __ios_base::left); 2897 __os.fill(__space); 2898 __os.precision(std::numeric_limits<double>::max_digits10); 2899 2900 std::vector<double> __prob = __x.probabilities(); 2901 __os << __prob.size(); 2902 for (auto __dit = __prob.begin(); __dit != __prob.end(); ++__dit) 2903 __os << __space << *__dit; 2904 2905 __os.flags(__flags); 2906 __os.fill(__fill); 2907 __os.precision(__precision); 2908 return __os; 2909 } 2910 2911 template<typename _IntType, typename _CharT, typename _Traits> 2912 std::basic_istream<_CharT, _Traits>& 2913 operator>>(std::basic_istream<_CharT, _Traits>& __is, 2914 discrete_distribution<_IntType>& __x) 2915 { 2916 typedef std::basic_istream<_CharT, _Traits> __istream_type; 2917 typedef typename __istream_type::ios_base __ios_base; 2918 2919 const typename __ios_base::fmtflags __flags = __is.flags(); 2920 __is.flags(__ios_base::dec | __ios_base::skipws); 2921 2922 size_t __n; 2923 __is >> __n; 2924 2925 std::vector<double> __prob_vec; 2926 __prob_vec.reserve(__n); 2927 for (; __n != 0; --__n) 2928 { 2929 double __prob; 2930 __is >> __prob; 2931 __prob_vec.push_back(__prob); 2932 } 2933 2934 __x.param(typename discrete_distribution<_IntType>:: 2935 param_type(__prob_vec.begin(), __prob_vec.end())); 2936 2937 __is.flags(__flags); 2938 return __is; 2939 } 2940 2941 2942 template<typename _RealType> 2943 void 2944 piecewise_constant_distribution<_RealType>::param_type:: 2945 _M_initialize() 2946 { 2947 if (_M_int.size() < 2 2948 || (_M_int.size() == 2 2949 && _M_int[0] == _RealType(0) 2950 && _M_int[1] == _RealType(1))) 2951 { 2952 _M_int.clear(); 2953 _M_den.clear(); 2954 return; 2955 } 2956 2957 const double __sum = std::accumulate(_M_den.begin(), 2958 _M_den.end(), 0.0); 2959 2960 __detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(), 2961 __sum); 2962 2963 _M_cp.reserve(_M_den.size()); 2964 std::partial_sum(_M_den.begin(), _M_den.end(), 2965 std::back_inserter(_M_cp)); 2966 2967 // Make sure the last cumulative probability is one. 2968 _M_cp[_M_cp.size() - 1] = 1.0; 2969 2970 for (size_t __k = 0; __k < _M_den.size(); ++__k) 2971 _M_den[__k] /= _M_int[__k + 1] - _M_int[__k]; 2972 } 2973 2974 template<typename _RealType> 2975 template<typename _InputIteratorB, typename _InputIteratorW> 2976 piecewise_constant_distribution<_RealType>::param_type:: 2977 param_type(_InputIteratorB __bbegin, 2978 _InputIteratorB __bend, 2979 _InputIteratorW __wbegin) 2980 : _M_int(), _M_den(), _M_cp() 2981 { 2982 if (__bbegin != __bend) 2983 { 2984 for (;;) 2985 { 2986 _M_int.push_back(*__bbegin); 2987 ++__bbegin; 2988 if (__bbegin == __bend) 2989 break; 2990 2991 _M_den.push_back(*__wbegin); 2992 ++__wbegin; 2993 } 2994 } 2995 2996 _M_initialize(); 2997 } 2998 2999 template<typename _RealType> 3000 template<typename _Func> 3001 piecewise_constant_distribution<_RealType>::param_type:: 3002 param_type(initializer_list<_RealType> __bl, _Func __fw) 3003 : _M_int(), _M_den(), _M_cp() 3004 { 3005 _M_int.reserve(__bl.size()); 3006 for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter) 3007 _M_int.push_back(*__biter); 3008 3009 _M_den.reserve(_M_int.size() - 1); 3010 for (size_t __k = 0; __k < _M_int.size() - 1; ++__k) 3011 _M_den.push_back(__fw(0.5 * (_M_int[__k + 1] + _M_int[__k]))); 3012 3013 _M_initialize(); 3014 } 3015 3016 template<typename _RealType> 3017 template<typename _Func> 3018 piecewise_constant_distribution<_RealType>::param_type:: 3019 param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw) 3020 : _M_int(), _M_den(), _M_cp() 3021 { 3022 const size_t __n = __nw == 0 ? 1 : __nw; 3023 const _RealType __delta = (__xmax - __xmin) / __n; 3024 3025 _M_int.reserve(__n + 1); 3026 for (size_t __k = 0; __k <= __nw; ++__k) 3027 _M_int.push_back(__xmin + __k * __delta); 3028 3029 _M_den.reserve(__n); 3030 for (size_t __k = 0; __k < __nw; ++__k) 3031 _M_den.push_back(__fw(_M_int[__k] + 0.5 * __delta)); 3032 3033 _M_initialize(); 3034 } 3035 3036 template<typename _RealType> 3037 template<typename _UniformRandomNumberGenerator> 3038 typename piecewise_constant_distribution<_RealType>::result_type 3039 piecewise_constant_distribution<_RealType>:: 3040 operator()(_UniformRandomNumberGenerator& __urng, 3041 const param_type& __param) 3042 { 3043 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 3044 __aurng(__urng); 3045 3046 const double __p = __aurng(); 3047 if (__param._M_cp.empty()) 3048 return __p; 3049 3050 auto __pos = std::lower_bound(__param._M_cp.begin(), 3051 __param._M_cp.end(), __p); 3052 const size_t __i = __pos - __param._M_cp.begin(); 3053 3054 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0; 3055 3056 return __param._M_int[__i] + (__p - __pref) / __param._M_den[__i]; 3057 } 3058 3059 template<typename _RealType> 3060 template<typename _ForwardIterator, 3061 typename _UniformRandomNumberGenerator> 3062 void 3063 piecewise_constant_distribution<_RealType>:: 3064 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 3065 _UniformRandomNumberGenerator& __urng, 3066 const param_type& __param) 3067 { 3068 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 3069 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 3070 __aurng(__urng); 3071 3072 if (__param._M_cp.empty()) 3073 { 3074 while (__f != __t) 3075 *__f++ = __aurng(); 3076 return; 3077 } 3078 3079 while (__f != __t) 3080 { 3081 const double __p = __aurng(); 3082 3083 auto __pos = std::lower_bound(__param._M_cp.begin(), 3084 __param._M_cp.end(), __p); 3085 const size_t __i = __pos - __param._M_cp.begin(); 3086 3087 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0; 3088 3089 *__f++ = (__param._M_int[__i] 3090 + (__p - __pref) / __param._M_den[__i]); 3091 } 3092 } 3093 3094 template<typename _RealType, typename _CharT, typename _Traits> 3095 std::basic_ostream<_CharT, _Traits>& 3096 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 3097 const piecewise_constant_distribution<_RealType>& __x) 3098 { 3099 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 3100 typedef typename __ostream_type::ios_base __ios_base; 3101 3102 const typename __ios_base::fmtflags __flags = __os.flags(); 3103 const _CharT __fill = __os.fill(); 3104 const std::streamsize __precision = __os.precision(); 3105 const _CharT __space = __os.widen(' '); 3106 __os.flags(__ios_base::scientific | __ios_base::left); 3107 __os.fill(__space); 3108 __os.precision(std::numeric_limits<_RealType>::max_digits10); 3109 3110 std::vector<_RealType> __int = __x.intervals(); 3111 __os << __int.size() - 1; 3112 3113 for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit) 3114 __os << __space << *__xit; 3115 3116 std::vector<double> __den = __x.densities(); 3117 for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit) 3118 __os << __space << *__dit; 3119 3120 __os.flags(__flags); 3121 __os.fill(__fill); 3122 __os.precision(__precision); 3123 return __os; 3124 } 3125 3126 template<typename _RealType, typename _CharT, typename _Traits> 3127 std::basic_istream<_CharT, _Traits>& 3128 operator>>(std::basic_istream<_CharT, _Traits>& __is, 3129 piecewise_constant_distribution<_RealType>& __x) 3130 { 3131 typedef std::basic_istream<_CharT, _Traits> __istream_type; 3132 typedef typename __istream_type::ios_base __ios_base; 3133 3134 const typename __ios_base::fmtflags __flags = __is.flags(); 3135 __is.flags(__ios_base::dec | __ios_base::skipws); 3136 3137 size_t __n; 3138 __is >> __n; 3139 3140 std::vector<_RealType> __int_vec; 3141 __int_vec.reserve(__n + 1); 3142 for (size_t __i = 0; __i <= __n; ++__i) 3143 { 3144 _RealType __int; 3145 __is >> __int; 3146 __int_vec.push_back(__int); 3147 } 3148 3149 std::vector<double> __den_vec; 3150 __den_vec.reserve(__n); 3151 for (size_t __i = 0; __i < __n; ++__i) 3152 { 3153 double __den; 3154 __is >> __den; 3155 __den_vec.push_back(__den); 3156 } 3157 3158 __x.param(typename piecewise_constant_distribution<_RealType>:: 3159 param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin())); 3160 3161 __is.flags(__flags); 3162 return __is; 3163 } 3164 3165 3166 template<typename _RealType> 3167 void 3168 piecewise_linear_distribution<_RealType>::param_type:: 3169 _M_initialize() 3170 { 3171 if (_M_int.size() < 2 3172 || (_M_int.size() == 2 3173 && _M_int[0] == _RealType(0) 3174 && _M_int[1] == _RealType(1) 3175 && _M_den[0] == _M_den[1])) 3176 { 3177 _M_int.clear(); 3178 _M_den.clear(); 3179 return; 3180 } 3181 3182 double __sum = 0.0; 3183 _M_cp.reserve(_M_int.size() - 1); 3184 _M_m.reserve(_M_int.size() - 1); 3185 for (size_t __k = 0; __k < _M_int.size() - 1; ++__k) 3186 { 3187 const _RealType __delta = _M_int[__k + 1] - _M_int[__k]; 3188 __sum += 0.5 * (_M_den[__k + 1] + _M_den[__k]) * __delta; 3189 _M_cp.push_back(__sum); 3190 _M_m.push_back((_M_den[__k + 1] - _M_den[__k]) / __delta); 3191 } 3192 3193 // Now normalize the densities... 3194 __detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(), 3195 __sum); 3196 // ... and partial sums... 3197 __detail::__normalize(_M_cp.begin(), _M_cp.end(), _M_cp.begin(), __sum); 3198 // ... and slopes. 3199 __detail::__normalize(_M_m.begin(), _M_m.end(), _M_m.begin(), __sum); 3200 3201 // Make sure the last cumulative probablility is one. 3202 _M_cp[_M_cp.size() - 1] = 1.0; 3203 } 3204 3205 template<typename _RealType> 3206 template<typename _InputIteratorB, typename _InputIteratorW> 3207 piecewise_linear_distribution<_RealType>::param_type:: 3208 param_type(_InputIteratorB __bbegin, 3209 _InputIteratorB __bend, 3210 _InputIteratorW __wbegin) 3211 : _M_int(), _M_den(), _M_cp(), _M_m() 3212 { 3213 for (; __bbegin != __bend; ++__bbegin, ++__wbegin) 3214 { 3215 _M_int.push_back(*__bbegin); 3216 _M_den.push_back(*__wbegin); 3217 } 3218 3219 _M_initialize(); 3220 } 3221 3222 template<typename _RealType> 3223 template<typename _Func> 3224 piecewise_linear_distribution<_RealType>::param_type:: 3225 param_type(initializer_list<_RealType> __bl, _Func __fw) 3226 : _M_int(), _M_den(), _M_cp(), _M_m() 3227 { 3228 _M_int.reserve(__bl.size()); 3229 _M_den.reserve(__bl.size()); 3230 for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter) 3231 { 3232 _M_int.push_back(*__biter); 3233 _M_den.push_back(__fw(*__biter)); 3234 } 3235 3236 _M_initialize(); 3237 } 3238 3239 template<typename _RealType> 3240 template<typename _Func> 3241 piecewise_linear_distribution<_RealType>::param_type:: 3242 param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw) 3243 : _M_int(), _M_den(), _M_cp(), _M_m() 3244 { 3245 const size_t __n = __nw == 0 ? 1 : __nw; 3246 const _RealType __delta = (__xmax - __xmin) / __n; 3247 3248 _M_int.reserve(__n + 1); 3249 _M_den.reserve(__n + 1); 3250 for (size_t __k = 0; __k <= __nw; ++__k) 3251 { 3252 _M_int.push_back(__xmin + __k * __delta); 3253 _M_den.push_back(__fw(_M_int[__k] + __delta)); 3254 } 3255 3256 _M_initialize(); 3257 } 3258 3259 template<typename _RealType> 3260 template<typename _UniformRandomNumberGenerator> 3261 typename piecewise_linear_distribution<_RealType>::result_type 3262 piecewise_linear_distribution<_RealType>:: 3263 operator()(_UniformRandomNumberGenerator& __urng, 3264 const param_type& __param) 3265 { 3266 __detail::_Adaptor<_UniformRandomNumberGenerator, double> 3267 __aurng(__urng); 3268 3269 const double __p = __aurng(); 3270 if (__param._M_cp.empty()) 3271 return __p; 3272 3273 auto __pos = std::lower_bound(__param._M_cp.begin(), 3274 __param._M_cp.end(), __p); 3275 const size_t __i = __pos - __param._M_cp.begin(); 3276 3277 const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0; 3278 3279 const double __a = 0.5 * __param._M_m[__i]; 3280 const double __b = __param._M_den[__i]; 3281 const double __cm = __p - __pref; 3282 3283 _RealType __x = __param._M_int[__i]; 3284 if (__a == 0) 3285 __x += __cm / __b; 3286 else 3287 { 3288 const double __d = __b * __b + 4.0 * __a * __cm; 3289 __x += 0.5 * (std::sqrt(__d) - __b) / __a; 3290 } 3291 3292 return __x; 3293 } 3294 3295 template<typename _RealType> 3296 template<typename _ForwardIterator, 3297 typename _UniformRandomNumberGenerator> 3298 void 3299 piecewise_linear_distribution<_RealType>:: 3300 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 3301 _UniformRandomNumberGenerator& __urng, 3302 const param_type& __param) 3303 { 3304 __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) 3305 // We could duplicate everything from operator()... 3306 while (__f != __t) 3307 *__f++ = this->operator()(__urng, __param); 3308 } 3309 3310 template<typename _RealType, typename _CharT, typename _Traits> 3311 std::basic_ostream<_CharT, _Traits>& 3312 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 3313 const piecewise_linear_distribution<_RealType>& __x) 3314 { 3315 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 3316 typedef typename __ostream_type::ios_base __ios_base; 3317 3318 const typename __ios_base::fmtflags __flags = __os.flags(); 3319 const _CharT __fill = __os.fill(); 3320 const std::streamsize __precision = __os.precision(); 3321 const _CharT __space = __os.widen(' '); 3322 __os.flags(__ios_base::scientific | __ios_base::left); 3323 __os.fill(__space); 3324 __os.precision(std::numeric_limits<_RealType>::max_digits10); 3325 3326 std::vector<_RealType> __int = __x.intervals(); 3327 __os << __int.size() - 1; 3328 3329 for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit) 3330 __os << __space << *__xit; 3331 3332 std::vector<double> __den = __x.densities(); 3333 for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit) 3334 __os << __space << *__dit; 3335 3336 __os.flags(__flags); 3337 __os.fill(__fill); 3338 __os.precision(__precision); 3339 return __os; 3340 } 3341 3342 template<typename _RealType, typename _CharT, typename _Traits> 3343 std::basic_istream<_CharT, _Traits>& 3344 operator>>(std::basic_istream<_CharT, _Traits>& __is, 3345 piecewise_linear_distribution<_RealType>& __x) 3346 { 3347 typedef std::basic_istream<_CharT, _Traits> __istream_type; 3348 typedef typename __istream_type::ios_base __ios_base; 3349 3350 const typename __ios_base::fmtflags __flags = __is.flags(); 3351 __is.flags(__ios_base::dec | __ios_base::skipws); 3352 3353 size_t __n; 3354 __is >> __n; 3355 3356 std::vector<_RealType> __int_vec; 3357 __int_vec.reserve(__n + 1); 3358 for (size_t __i = 0; __i <= __n; ++__i) 3359 { 3360 _RealType __int; 3361 __is >> __int; 3362 __int_vec.push_back(__int); 3363 } 3364 3365 std::vector<double> __den_vec; 3366 __den_vec.reserve(__n + 1); 3367 for (size_t __i = 0; __i <= __n; ++__i) 3368 { 3369 double __den; 3370 __is >> __den; 3371 __den_vec.push_back(__den); 3372 } 3373 3374 __x.param(typename piecewise_linear_distribution<_RealType>:: 3375 param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin())); 3376 3377 __is.flags(__flags); 3378 return __is; 3379 } 3380 3381 3382 template<typename _IntType> 3383 seed_seq::seed_seq(std::initializer_list<_IntType> __il) 3384 { 3385 for (auto __iter = __il.begin(); __iter != __il.end(); ++__iter) 3386 _M_v.push_back(__detail::__mod<result_type, 3387 __detail::_Shift<result_type, 32>::__value>(*__iter)); 3388 } 3389 3390 template<typename _InputIterator> 3391 seed_seq::seed_seq(_InputIterator __begin, _InputIterator __end) 3392 { 3393 for (_InputIterator __iter = __begin; __iter != __end; ++__iter) 3394 _M_v.push_back(__detail::__mod<result_type, 3395 __detail::_Shift<result_type, 32>::__value>(*__iter)); 3396 } 3397 3398 template<typename _RandomAccessIterator> 3399 void 3400 seed_seq::generate(_RandomAccessIterator __begin, 3401 _RandomAccessIterator __end) 3402 { 3403 typedef typename iterator_traits<_RandomAccessIterator>::value_type 3404 _Type; 3405 3406 if (__begin == __end) 3407 return; 3408 3409 std::fill(__begin, __end, _Type(0x8b8b8b8bu)); 3410 3411 const size_t __n = __end - __begin; 3412 const size_t __s = _M_v.size(); 3413 const size_t __t = (__n >= 623) ? 11 3414 : (__n >= 68) ? 7 3415 : (__n >= 39) ? 5 3416 : (__n >= 7) ? 3 3417 : (__n - 1) / 2; 3418 const size_t __p = (__n - __t) / 2; 3419 const size_t __q = __p + __t; 3420 const size_t __m = std::max(size_t(__s + 1), __n); 3421 3422 for (size_t __k = 0; __k < __m; ++__k) 3423 { 3424 _Type __arg = (__begin[__k % __n] 3425 ^ __begin[(__k + __p) % __n] 3426 ^ __begin[(__k - 1) % __n]); 3427 _Type __r1 = __arg ^ (__arg >> 27); 3428 __r1 = __detail::__mod<_Type, 3429 __detail::_Shift<_Type, 32>::__value>(1664525u * __r1); 3430 _Type __r2 = __r1; 3431 if (__k == 0) 3432 __r2 += __s; 3433 else if (__k <= __s) 3434 __r2 += __k % __n + _M_v[__k - 1]; 3435 else 3436 __r2 += __k % __n; 3437 __r2 = __detail::__mod<_Type, 3438 __detail::_Shift<_Type, 32>::__value>(__r2); 3439 __begin[(__k + __p) % __n] += __r1; 3440 __begin[(__k + __q) % __n] += __r2; 3441 __begin[__k % __n] = __r2; 3442 } 3443 3444 for (size_t __k = __m; __k < __m + __n; ++__k) 3445 { 3446 _Type __arg = (__begin[__k % __n] 3447 + __begin[(__k + __p) % __n] 3448 + __begin[(__k - 1) % __n]); 3449 _Type __r3 = __arg ^ (__arg >> 27); 3450 __r3 = __detail::__mod<_Type, 3451 __detail::_Shift<_Type, 32>::__value>(1566083941u * __r3); 3452 _Type __r4 = __r3 - __k % __n; 3453 __r4 = __detail::__mod<_Type, 3454 __detail::_Shift<_Type, 32>::__value>(__r4); 3455 __begin[(__k + __p) % __n] ^= __r3; 3456 __begin[(__k + __q) % __n] ^= __r4; 3457 __begin[__k % __n] = __r4; 3458 } 3459 } 3460 3461 template<typename _RealType, size_t __bits, 3462 typename _UniformRandomNumberGenerator> 3463 _RealType 3464 generate_canonical(_UniformRandomNumberGenerator& __urng) 3465 { 3466 static_assert(std::is_floating_point<_RealType>::value, 3467 "template argument not a floating point type"); 3468 3469 const size_t __b 3470 = std::min(static_cast<size_t>(std::numeric_limits<_RealType>::digits), 3471 __bits); 3472 const long double __r = static_cast<long double>(__urng.max()) 3473 - static_cast<long double>(__urng.min()) + 1.0L; 3474 const size_t __log2r = std::log(__r) / std::log(2.0L); 3475 size_t __k = std::max<size_t>(1UL, (__b + __log2r - 1UL) / __log2r); 3476 _RealType __sum = _RealType(0); 3477 _RealType __tmp = _RealType(1); 3478 for (; __k != 0; --__k) 3479 { 3480 __sum += _RealType(__urng() - __urng.min()) * __tmp; 3481 __tmp *= __r; 3482 } 3483 return __sum / __tmp; 3484 } 3485 3486 _GLIBCXX_END_NAMESPACE_VERSION 3487 } // namespace 3488 3489 #endif 3490
