1 // Random number extensions -*- C++ -*- 2 3 // Copyright (C) 2012-2019 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 ext/random.tcc 26 * This is an internal header file, included by other library headers. 27 * Do not attempt to use it directly. @headername{ext/random} 28 */ 29 30 #ifndef _EXT_RANDOM_TCC 31 #define _EXT_RANDOM_TCC 1 32 33 #pragma GCC system_header 34 35 namespace __gnu_cxx _GLIBCXX_VISIBILITY(default) 36 { 37 _GLIBCXX_BEGIN_NAMESPACE_VERSION 38 39 #if __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__ 40 41 template<typename _UIntType, size_t __m, 42 size_t __pos1, size_t __sl1, size_t __sl2, 43 size_t __sr1, size_t __sr2, 44 uint32_t __msk1, uint32_t __msk2, 45 uint32_t __msk3, uint32_t __msk4, 46 uint32_t __parity1, uint32_t __parity2, 47 uint32_t __parity3, uint32_t __parity4> 48 void simd_fast_mersenne_twister_engine<_UIntType, __m, 49 __pos1, __sl1, __sl2, __sr1, __sr2, 50 __msk1, __msk2, __msk3, __msk4, 51 __parity1, __parity2, __parity3, 52 __parity4>:: 53 seed(_UIntType __seed) 54 { 55 _M_state32[0] = static_cast<uint32_t>(__seed); 56 for (size_t __i = 1; __i < _M_nstate32; ++__i) 57 _M_state32[__i] = (1812433253UL 58 * (_M_state32[__i - 1] ^ (_M_state32[__i - 1] >> 30)) 59 + __i); 60 _M_pos = state_size; 61 _M_period_certification(); 62 } 63 64 65 namespace { 66 67 inline uint32_t _Func1(uint32_t __x) 68 { 69 return (__x ^ (__x >> 27)) * UINT32_C(1664525); 70 } 71 72 inline uint32_t _Func2(uint32_t __x) 73 { 74 return (__x ^ (__x >> 27)) * UINT32_C(1566083941); 75 } 76 77 } 78 79 80 template<typename _UIntType, size_t __m, 81 size_t __pos1, size_t __sl1, size_t __sl2, 82 size_t __sr1, size_t __sr2, 83 uint32_t __msk1, uint32_t __msk2, 84 uint32_t __msk3, uint32_t __msk4, 85 uint32_t __parity1, uint32_t __parity2, 86 uint32_t __parity3, uint32_t __parity4> 87 template<typename _Sseq> 88 auto 89 simd_fast_mersenne_twister_engine<_UIntType, __m, 90 __pos1, __sl1, __sl2, __sr1, __sr2, 91 __msk1, __msk2, __msk3, __msk4, 92 __parity1, __parity2, __parity3, 93 __parity4>:: 94 seed(_Sseq& __q) 95 -> _If_seed_seq<_Sseq> 96 { 97 size_t __lag; 98 99 if (_M_nstate32 >= 623) 100 __lag = 11; 101 else if (_M_nstate32 >= 68) 102 __lag = 7; 103 else if (_M_nstate32 >= 39) 104 __lag = 5; 105 else 106 __lag = 3; 107 const size_t __mid = (_M_nstate32 - __lag) / 2; 108 109 std::fill(_M_state32, _M_state32 + _M_nstate32, UINT32_C(0x8b8b8b8b)); 110 uint32_t __arr[_M_nstate32]; 111 __q.generate(__arr + 0, __arr + _M_nstate32); 112 113 uint32_t __r = _Func1(_M_state32[0] ^ _M_state32[__mid] 114 ^ _M_state32[_M_nstate32 - 1]); 115 _M_state32[__mid] += __r; 116 __r += _M_nstate32; 117 _M_state32[__mid + __lag] += __r; 118 _M_state32[0] = __r; 119 120 for (size_t __i = 1, __j = 0; __j < _M_nstate32; ++__j) 121 { 122 __r = _Func1(_M_state32[__i] 123 ^ _M_state32[(__i + __mid) % _M_nstate32] 124 ^ _M_state32[(__i + _M_nstate32 - 1) % _M_nstate32]); 125 _M_state32[(__i + __mid) % _M_nstate32] += __r; 126 __r += __arr[__j] + __i; 127 _M_state32[(__i + __mid + __lag) % _M_nstate32] += __r; 128 _M_state32[__i] = __r; 129 __i = (__i + 1) % _M_nstate32; 130 } 131 for (size_t __j = 0; __j < _M_nstate32; ++__j) 132 { 133 const size_t __i = (__j + 1) % _M_nstate32; 134 __r = _Func2(_M_state32[__i] 135 + _M_state32[(__i + __mid) % _M_nstate32] 136 + _M_state32[(__i + _M_nstate32 - 1) % _M_nstate32]); 137 _M_state32[(__i + __mid) % _M_nstate32] ^= __r; 138 __r -= __i; 139 _M_state32[(__i + __mid + __lag) % _M_nstate32] ^= __r; 140 _M_state32[__i] = __r; 141 } 142 143 _M_pos = state_size; 144 _M_period_certification(); 145 } 146 147 148 template<typename _UIntType, size_t __m, 149 size_t __pos1, size_t __sl1, size_t __sl2, 150 size_t __sr1, size_t __sr2, 151 uint32_t __msk1, uint32_t __msk2, 152 uint32_t __msk3, uint32_t __msk4, 153 uint32_t __parity1, uint32_t __parity2, 154 uint32_t __parity3, uint32_t __parity4> 155 void simd_fast_mersenne_twister_engine<_UIntType, __m, 156 __pos1, __sl1, __sl2, __sr1, __sr2, 157 __msk1, __msk2, __msk3, __msk4, 158 __parity1, __parity2, __parity3, 159 __parity4>:: 160 _M_period_certification(void) 161 { 162 static const uint32_t __parity[4] = { __parity1, __parity2, 163 __parity3, __parity4 }; 164 uint32_t __inner = 0; 165 for (size_t __i = 0; __i < 4; ++__i) 166 if (__parity[__i] != 0) 167 __inner ^= _M_state32[__i] & __parity[__i]; 168 169 if (__builtin_parity(__inner) & 1) 170 return; 171 for (size_t __i = 0; __i < 4; ++__i) 172 if (__parity[__i] != 0) 173 { 174 _M_state32[__i] ^= 1 << (__builtin_ffs(__parity[__i]) - 1); 175 return; 176 } 177 __builtin_unreachable(); 178 } 179 180 181 template<typename _UIntType, size_t __m, 182 size_t __pos1, size_t __sl1, size_t __sl2, 183 size_t __sr1, size_t __sr2, 184 uint32_t __msk1, uint32_t __msk2, 185 uint32_t __msk3, uint32_t __msk4, 186 uint32_t __parity1, uint32_t __parity2, 187 uint32_t __parity3, uint32_t __parity4> 188 void simd_fast_mersenne_twister_engine<_UIntType, __m, 189 __pos1, __sl1, __sl2, __sr1, __sr2, 190 __msk1, __msk2, __msk3, __msk4, 191 __parity1, __parity2, __parity3, 192 __parity4>:: 193 discard(unsigned long long __z) 194 { 195 while (__z > state_size - _M_pos) 196 { 197 __z -= state_size - _M_pos; 198 199 _M_gen_rand(); 200 } 201 202 _M_pos += __z; 203 } 204 205 206 #ifndef _GLIBCXX_OPT_HAVE_RANDOM_SFMT_GEN_READ 207 208 namespace { 209 210 template<size_t __shift> 211 inline void __rshift(uint32_t *__out, const uint32_t *__in) 212 { 213 uint64_t __th = ((static_cast<uint64_t>(__in[3]) << 32) 214 | static_cast<uint64_t>(__in[2])); 215 uint64_t __tl = ((static_cast<uint64_t>(__in[1]) << 32) 216 | static_cast<uint64_t>(__in[0])); 217 218 uint64_t __oh = __th >> (__shift * 8); 219 uint64_t __ol = __tl >> (__shift * 8); 220 __ol |= __th << (64 - __shift * 8); 221 __out[1] = static_cast<uint32_t>(__ol >> 32); 222 __out[0] = static_cast<uint32_t>(__ol); 223 __out[3] = static_cast<uint32_t>(__oh >> 32); 224 __out[2] = static_cast<uint32_t>(__oh); 225 } 226 227 228 template<size_t __shift> 229 inline void __lshift(uint32_t *__out, const uint32_t *__in) 230 { 231 uint64_t __th = ((static_cast<uint64_t>(__in[3]) << 32) 232 | static_cast<uint64_t>(__in[2])); 233 uint64_t __tl = ((static_cast<uint64_t>(__in[1]) << 32) 234 | static_cast<uint64_t>(__in[0])); 235 236 uint64_t __oh = __th << (__shift * 8); 237 uint64_t __ol = __tl << (__shift * 8); 238 __oh |= __tl >> (64 - __shift * 8); 239 __out[1] = static_cast<uint32_t>(__ol >> 32); 240 __out[0] = static_cast<uint32_t>(__ol); 241 __out[3] = static_cast<uint32_t>(__oh >> 32); 242 __out[2] = static_cast<uint32_t>(__oh); 243 } 244 245 246 template<size_t __sl1, size_t __sl2, size_t __sr1, size_t __sr2, 247 uint32_t __msk1, uint32_t __msk2, uint32_t __msk3, uint32_t __msk4> 248 inline void __recursion(uint32_t *__r, 249 const uint32_t *__a, const uint32_t *__b, 250 const uint32_t *__c, const uint32_t *__d) 251 { 252 uint32_t __x[4]; 253 uint32_t __y[4]; 254 255 __lshift<__sl2>(__x, __a); 256 __rshift<__sr2>(__y, __c); 257 __r[0] = (__a[0] ^ __x[0] ^ ((__b[0] >> __sr1) & __msk1) 258 ^ __y[0] ^ (__d[0] << __sl1)); 259 __r[1] = (__a[1] ^ __x[1] ^ ((__b[1] >> __sr1) & __msk2) 260 ^ __y[1] ^ (__d[1] << __sl1)); 261 __r[2] = (__a[2] ^ __x[2] ^ ((__b[2] >> __sr1) & __msk3) 262 ^ __y[2] ^ (__d[2] << __sl1)); 263 __r[3] = (__a[3] ^ __x[3] ^ ((__b[3] >> __sr1) & __msk4) 264 ^ __y[3] ^ (__d[3] << __sl1)); 265 } 266 267 } 268 269 270 template<typename _UIntType, size_t __m, 271 size_t __pos1, size_t __sl1, size_t __sl2, 272 size_t __sr1, size_t __sr2, 273 uint32_t __msk1, uint32_t __msk2, 274 uint32_t __msk3, uint32_t __msk4, 275 uint32_t __parity1, uint32_t __parity2, 276 uint32_t __parity3, uint32_t __parity4> 277 void simd_fast_mersenne_twister_engine<_UIntType, __m, 278 __pos1, __sl1, __sl2, __sr1, __sr2, 279 __msk1, __msk2, __msk3, __msk4, 280 __parity1, __parity2, __parity3, 281 __parity4>:: 282 _M_gen_rand(void) 283 { 284 const uint32_t *__r1 = &_M_state32[_M_nstate32 - 8]; 285 const uint32_t *__r2 = &_M_state32[_M_nstate32 - 4]; 286 static constexpr size_t __pos1_32 = __pos1 * 4; 287 288 size_t __i; 289 for (__i = 0; __i < _M_nstate32 - __pos1_32; __i += 4) 290 { 291 __recursion<__sl1, __sl2, __sr1, __sr2, 292 __msk1, __msk2, __msk3, __msk4> 293 (&_M_state32[__i], &_M_state32[__i], 294 &_M_state32[__i + __pos1_32], __r1, __r2); 295 __r1 = __r2; 296 __r2 = &_M_state32[__i]; 297 } 298 299 for (; __i < _M_nstate32; __i += 4) 300 { 301 __recursion<__sl1, __sl2, __sr1, __sr2, 302 __msk1, __msk2, __msk3, __msk4> 303 (&_M_state32[__i], &_M_state32[__i], 304 &_M_state32[__i + __pos1_32 - _M_nstate32], __r1, __r2); 305 __r1 = __r2; 306 __r2 = &_M_state32[__i]; 307 } 308 309 _M_pos = 0; 310 } 311 312 #endif 313 314 #ifndef _GLIBCXX_OPT_HAVE_RANDOM_SFMT_OPERATOREQUAL 315 template<typename _UIntType, size_t __m, 316 size_t __pos1, size_t __sl1, size_t __sl2, 317 size_t __sr1, size_t __sr2, 318 uint32_t __msk1, uint32_t __msk2, 319 uint32_t __msk3, uint32_t __msk4, 320 uint32_t __parity1, uint32_t __parity2, 321 uint32_t __parity3, uint32_t __parity4> 322 bool 323 operator==(const __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, 324 __m, __pos1, __sl1, __sl2, __sr1, __sr2, 325 __msk1, __msk2, __msk3, __msk4, 326 __parity1, __parity2, __parity3, __parity4>& __lhs, 327 const __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, 328 __m, __pos1, __sl1, __sl2, __sr1, __sr2, 329 __msk1, __msk2, __msk3, __msk4, 330 __parity1, __parity2, __parity3, __parity4>& __rhs) 331 { 332 typedef __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, 333 __m, __pos1, __sl1, __sl2, __sr1, __sr2, 334 __msk1, __msk2, __msk3, __msk4, 335 __parity1, __parity2, __parity3, __parity4> __engine; 336 return (std::equal(__lhs._M_stateT, 337 __lhs._M_stateT + __engine::state_size, 338 __rhs._M_stateT) 339 && __lhs._M_pos == __rhs._M_pos); 340 } 341 #endif 342 343 template<typename _UIntType, size_t __m, 344 size_t __pos1, size_t __sl1, size_t __sl2, 345 size_t __sr1, size_t __sr2, 346 uint32_t __msk1, uint32_t __msk2, 347 uint32_t __msk3, uint32_t __msk4, 348 uint32_t __parity1, uint32_t __parity2, 349 uint32_t __parity3, uint32_t __parity4, 350 typename _CharT, typename _Traits> 351 std::basic_ostream<_CharT, _Traits>& 352 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 353 const __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, 354 __m, __pos1, __sl1, __sl2, __sr1, __sr2, 355 __msk1, __msk2, __msk3, __msk4, 356 __parity1, __parity2, __parity3, __parity4>& __x) 357 { 358 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 359 typedef typename __ostream_type::ios_base __ios_base; 360 361 const typename __ios_base::fmtflags __flags = __os.flags(); 362 const _CharT __fill = __os.fill(); 363 const _CharT __space = __os.widen(' '); 364 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 365 __os.fill(__space); 366 367 for (size_t __i = 0; __i < __x._M_nstate32; ++__i) 368 __os << __x._M_state32[__i] << __space; 369 __os << __x._M_pos; 370 371 __os.flags(__flags); 372 __os.fill(__fill); 373 return __os; 374 } 375 376 377 template<typename _UIntType, size_t __m, 378 size_t __pos1, size_t __sl1, size_t __sl2, 379 size_t __sr1, size_t __sr2, 380 uint32_t __msk1, uint32_t __msk2, 381 uint32_t __msk3, uint32_t __msk4, 382 uint32_t __parity1, uint32_t __parity2, 383 uint32_t __parity3, uint32_t __parity4, 384 typename _CharT, typename _Traits> 385 std::basic_istream<_CharT, _Traits>& 386 operator>>(std::basic_istream<_CharT, _Traits>& __is, 387 __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, 388 __m, __pos1, __sl1, __sl2, __sr1, __sr2, 389 __msk1, __msk2, __msk3, __msk4, 390 __parity1, __parity2, __parity3, __parity4>& __x) 391 { 392 typedef std::basic_istream<_CharT, _Traits> __istream_type; 393 typedef typename __istream_type::ios_base __ios_base; 394 395 const typename __ios_base::fmtflags __flags = __is.flags(); 396 __is.flags(__ios_base::dec | __ios_base::skipws); 397 398 for (size_t __i = 0; __i < __x._M_nstate32; ++__i) 399 __is >> __x._M_state32[__i]; 400 __is >> __x._M_pos; 401 402 __is.flags(__flags); 403 return __is; 404 } 405 406 #endif // __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__ 407 408 /** 409 * Iteration method due to M.D. J<o:>hnk. 410 * 411 * M.D. J<o:>hnk, Erzeugung von betaverteilten und gammaverteilten 412 * Zufallszahlen, Metrika, Volume 8, 1964 413 */ 414 template<typename _RealType> 415 template<typename _UniformRandomNumberGenerator> 416 typename beta_distribution<_RealType>::result_type 417 beta_distribution<_RealType>:: 418 operator()(_UniformRandomNumberGenerator& __urng, 419 const param_type& __param) 420 { 421 std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 422 __aurng(__urng); 423 424 result_type __x, __y; 425 do 426 { 427 __x = std::exp(std::log(__aurng()) / __param.alpha()); 428 __y = std::exp(std::log(__aurng()) / __param.beta()); 429 } 430 while (__x + __y > result_type(1)); 431 432 return __x / (__x + __y); 433 } 434 435 template<typename _RealType> 436 template<typename _OutputIterator, 437 typename _UniformRandomNumberGenerator> 438 void 439 beta_distribution<_RealType>:: 440 __generate_impl(_OutputIterator __f, _OutputIterator __t, 441 _UniformRandomNumberGenerator& __urng, 442 const param_type& __param) 443 { 444 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 445 result_type>) 446 447 std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 448 __aurng(__urng); 449 450 while (__f != __t) 451 { 452 result_type __x, __y; 453 do 454 { 455 __x = std::exp(std::log(__aurng()) / __param.alpha()); 456 __y = std::exp(std::log(__aurng()) / __param.beta()); 457 } 458 while (__x + __y > result_type(1)); 459 460 *__f++ = __x / (__x + __y); 461 } 462 } 463 464 template<typename _RealType, typename _CharT, typename _Traits> 465 std::basic_ostream<_CharT, _Traits>& 466 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 467 const __gnu_cxx::beta_distribution<_RealType>& __x) 468 { 469 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 470 typedef typename __ostream_type::ios_base __ios_base; 471 472 const typename __ios_base::fmtflags __flags = __os.flags(); 473 const _CharT __fill = __os.fill(); 474 const std::streamsize __precision = __os.precision(); 475 const _CharT __space = __os.widen(' '); 476 __os.flags(__ios_base::scientific | __ios_base::left); 477 __os.fill(__space); 478 __os.precision(std::numeric_limits<_RealType>::max_digits10); 479 480 __os << __x.alpha() << __space << __x.beta(); 481 482 __os.flags(__flags); 483 __os.fill(__fill); 484 __os.precision(__precision); 485 return __os; 486 } 487 488 template<typename _RealType, typename _CharT, typename _Traits> 489 std::basic_istream<_CharT, _Traits>& 490 operator>>(std::basic_istream<_CharT, _Traits>& __is, 491 __gnu_cxx::beta_distribution<_RealType>& __x) 492 { 493 typedef std::basic_istream<_CharT, _Traits> __istream_type; 494 typedef typename __istream_type::ios_base __ios_base; 495 496 const typename __ios_base::fmtflags __flags = __is.flags(); 497 __is.flags(__ios_base::dec | __ios_base::skipws); 498 499 _RealType __alpha_val, __beta_val; 500 __is >> __alpha_val >> __beta_val; 501 __x.param(typename __gnu_cxx::beta_distribution<_RealType>:: 502 param_type(__alpha_val, __beta_val)); 503 504 __is.flags(__flags); 505 return __is; 506 } 507 508 509 template<std::size_t _Dimen, typename _RealType> 510 template<typename _InputIterator1, typename _InputIterator2> 511 void 512 normal_mv_distribution<_Dimen, _RealType>::param_type:: 513 _M_init_full(_InputIterator1 __meanbegin, _InputIterator1 __meanend, 514 _InputIterator2 __varcovbegin, _InputIterator2 __varcovend) 515 { 516 __glibcxx_function_requires(_InputIteratorConcept<_InputIterator1>) 517 __glibcxx_function_requires(_InputIteratorConcept<_InputIterator2>) 518 std::fill(std::copy(__meanbegin, __meanend, _M_mean.begin()), 519 _M_mean.end(), _RealType(0)); 520 521 // Perform the Cholesky decomposition 522 auto __w = _M_t.begin(); 523 for (size_t __j = 0; __j < _Dimen; ++__j) 524 { 525 _RealType __sum = _RealType(0); 526 527 auto __slitbegin = __w; 528 auto __cit = _M_t.begin(); 529 for (size_t __i = 0; __i < __j; ++__i) 530 { 531 auto __slit = __slitbegin; 532 _RealType __s = *__varcovbegin++; 533 for (size_t __k = 0; __k < __i; ++__k) 534 __s -= *__slit++ * *__cit++; 535 536 *__w++ = __s /= *__cit++; 537 __sum += __s * __s; 538 } 539 540 __sum = *__varcovbegin - __sum; 541 if (__builtin_expect(__sum <= _RealType(0), 0)) 542 std::__throw_runtime_error(__N("normal_mv_distribution::" 543 "param_type::_M_init_full")); 544 *__w++ = std::sqrt(__sum); 545 546 std::advance(__varcovbegin, _Dimen - __j); 547 } 548 } 549 550 template<std::size_t _Dimen, typename _RealType> 551 template<typename _InputIterator1, typename _InputIterator2> 552 void 553 normal_mv_distribution<_Dimen, _RealType>::param_type:: 554 _M_init_lower(_InputIterator1 __meanbegin, _InputIterator1 __meanend, 555 _InputIterator2 __varcovbegin, _InputIterator2 __varcovend) 556 { 557 __glibcxx_function_requires(_InputIteratorConcept<_InputIterator1>) 558 __glibcxx_function_requires(_InputIteratorConcept<_InputIterator2>) 559 std::fill(std::copy(__meanbegin, __meanend, _M_mean.begin()), 560 _M_mean.end(), _RealType(0)); 561 562 // Perform the Cholesky decomposition 563 auto __w = _M_t.begin(); 564 for (size_t __j = 0; __j < _Dimen; ++__j) 565 { 566 _RealType __sum = _RealType(0); 567 568 auto __slitbegin = __w; 569 auto __cit = _M_t.begin(); 570 for (size_t __i = 0; __i < __j; ++__i) 571 { 572 auto __slit = __slitbegin; 573 _RealType __s = *__varcovbegin++; 574 for (size_t __k = 0; __k < __i; ++__k) 575 __s -= *__slit++ * *__cit++; 576 577 *__w++ = __s /= *__cit++; 578 __sum += __s * __s; 579 } 580 581 __sum = *__varcovbegin++ - __sum; 582 if (__builtin_expect(__sum <= _RealType(0), 0)) 583 std::__throw_runtime_error(__N("normal_mv_distribution::" 584 "param_type::_M_init_full")); 585 *__w++ = std::sqrt(__sum); 586 } 587 } 588 589 template<std::size_t _Dimen, typename _RealType> 590 template<typename _InputIterator1, typename _InputIterator2> 591 void 592 normal_mv_distribution<_Dimen, _RealType>::param_type:: 593 _M_init_diagonal(_InputIterator1 __meanbegin, _InputIterator1 __meanend, 594 _InputIterator2 __varbegin, _InputIterator2 __varend) 595 { 596 __glibcxx_function_requires(_InputIteratorConcept<_InputIterator1>) 597 __glibcxx_function_requires(_InputIteratorConcept<_InputIterator2>) 598 std::fill(std::copy(__meanbegin, __meanend, _M_mean.begin()), 599 _M_mean.end(), _RealType(0)); 600 601 auto __w = _M_t.begin(); 602 size_t __step = 0; 603 while (__varbegin != __varend) 604 { 605 std::fill_n(__w, __step, _RealType(0)); 606 __w += __step++; 607 if (__builtin_expect(*__varbegin < _RealType(0), 0)) 608 std::__throw_runtime_error(__N("normal_mv_distribution::" 609 "param_type::_M_init_diagonal")); 610 *__w++ = std::sqrt(*__varbegin++); 611 } 612 } 613 614 template<std::size_t _Dimen, typename _RealType> 615 template<typename _UniformRandomNumberGenerator> 616 typename normal_mv_distribution<_Dimen, _RealType>::result_type 617 normal_mv_distribution<_Dimen, _RealType>:: 618 operator()(_UniformRandomNumberGenerator& __urng, 619 const param_type& __param) 620 { 621 result_type __ret; 622 623 _M_nd.__generate(__ret.begin(), __ret.end(), __urng); 624 625 auto __t_it = __param._M_t.crbegin(); 626 for (size_t __i = _Dimen; __i > 0; --__i) 627 { 628 _RealType __sum = _RealType(0); 629 for (size_t __j = __i; __j > 0; --__j) 630 __sum += __ret[__j - 1] * *__t_it++; 631 __ret[__i - 1] = __sum; 632 } 633 634 return __ret; 635 } 636 637 template<std::size_t _Dimen, typename _RealType> 638 template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> 639 void 640 normal_mv_distribution<_Dimen, _RealType>:: 641 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 642 _UniformRandomNumberGenerator& __urng, 643 const param_type& __param) 644 { 645 __glibcxx_function_requires(_Mutable_ForwardIteratorConcept< 646 _ForwardIterator>) 647 while (__f != __t) 648 *__f++ = this->operator()(__urng, __param); 649 } 650 651 template<size_t _Dimen, typename _RealType> 652 bool 653 operator==(const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& 654 __d1, 655 const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& 656 __d2) 657 { 658 return __d1._M_param == __d2._M_param && __d1._M_nd == __d2._M_nd; 659 } 660 661 template<size_t _Dimen, typename _RealType, typename _CharT, typename _Traits> 662 std::basic_ostream<_CharT, _Traits>& 663 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 664 const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __x) 665 { 666 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 667 typedef typename __ostream_type::ios_base __ios_base; 668 669 const typename __ios_base::fmtflags __flags = __os.flags(); 670 const _CharT __fill = __os.fill(); 671 const std::streamsize __precision = __os.precision(); 672 const _CharT __space = __os.widen(' '); 673 __os.flags(__ios_base::scientific | __ios_base::left); 674 __os.fill(__space); 675 __os.precision(std::numeric_limits<_RealType>::max_digits10); 676 677 auto __mean = __x._M_param.mean(); 678 for (auto __it : __mean) 679 __os << __it << __space; 680 auto __t = __x._M_param.varcov(); 681 for (auto __it : __t) 682 __os << __it << __space; 683 684 __os << __x._M_nd; 685 686 __os.flags(__flags); 687 __os.fill(__fill); 688 __os.precision(__precision); 689 return __os; 690 } 691 692 template<size_t _Dimen, typename _RealType, typename _CharT, typename _Traits> 693 std::basic_istream<_CharT, _Traits>& 694 operator>>(std::basic_istream<_CharT, _Traits>& __is, 695 __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __x) 696 { 697 typedef std::basic_istream<_CharT, _Traits> __istream_type; 698 typedef typename __istream_type::ios_base __ios_base; 699 700 const typename __ios_base::fmtflags __flags = __is.flags(); 701 __is.flags(__ios_base::dec | __ios_base::skipws); 702 703 std::array<_RealType, _Dimen> __mean; 704 for (auto& __it : __mean) 705 __is >> __it; 706 std::array<_RealType, _Dimen * (_Dimen + 1) / 2> __varcov; 707 for (auto& __it : __varcov) 708 __is >> __it; 709 710 __is >> __x._M_nd; 711 712 __x.param(typename normal_mv_distribution<_Dimen, _RealType>:: 713 param_type(__mean.begin(), __mean.end(), 714 __varcov.begin(), __varcov.end())); 715 716 __is.flags(__flags); 717 return __is; 718 } 719 720 721 template<typename _RealType> 722 template<typename _OutputIterator, 723 typename _UniformRandomNumberGenerator> 724 void 725 rice_distribution<_RealType>:: 726 __generate_impl(_OutputIterator __f, _OutputIterator __t, 727 _UniformRandomNumberGenerator& __urng, 728 const param_type& __p) 729 { 730 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 731 result_type>) 732 733 while (__f != __t) 734 { 735 typename std::normal_distribution<result_type>::param_type 736 __px(__p.nu(), __p.sigma()), __py(result_type(0), __p.sigma()); 737 result_type __x = this->_M_ndx(__px, __urng); 738 result_type __y = this->_M_ndy(__py, __urng); 739 #if _GLIBCXX_USE_C99_MATH_TR1 740 *__f++ = std::hypot(__x, __y); 741 #else 742 *__f++ = std::sqrt(__x * __x + __y * __y); 743 #endif 744 } 745 } 746 747 template<typename _RealType, typename _CharT, typename _Traits> 748 std::basic_ostream<_CharT, _Traits>& 749 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 750 const rice_distribution<_RealType>& __x) 751 { 752 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 753 typedef typename __ostream_type::ios_base __ios_base; 754 755 const typename __ios_base::fmtflags __flags = __os.flags(); 756 const _CharT __fill = __os.fill(); 757 const std::streamsize __precision = __os.precision(); 758 const _CharT __space = __os.widen(' '); 759 __os.flags(__ios_base::scientific | __ios_base::left); 760 __os.fill(__space); 761 __os.precision(std::numeric_limits<_RealType>::max_digits10); 762 763 __os << __x.nu() << __space << __x.sigma(); 764 __os << __space << __x._M_ndx; 765 __os << __space << __x._M_ndy; 766 767 __os.flags(__flags); 768 __os.fill(__fill); 769 __os.precision(__precision); 770 return __os; 771 } 772 773 template<typename _RealType, typename _CharT, typename _Traits> 774 std::basic_istream<_CharT, _Traits>& 775 operator>>(std::basic_istream<_CharT, _Traits>& __is, 776 rice_distribution<_RealType>& __x) 777 { 778 typedef std::basic_istream<_CharT, _Traits> __istream_type; 779 typedef typename __istream_type::ios_base __ios_base; 780 781 const typename __ios_base::fmtflags __flags = __is.flags(); 782 __is.flags(__ios_base::dec | __ios_base::skipws); 783 784 _RealType __nu_val, __sigma_val; 785 __is >> __nu_val >> __sigma_val; 786 __is >> __x._M_ndx; 787 __is >> __x._M_ndy; 788 __x.param(typename rice_distribution<_RealType>:: 789 param_type(__nu_val, __sigma_val)); 790 791 __is.flags(__flags); 792 return __is; 793 } 794 795 796 template<typename _RealType> 797 template<typename _OutputIterator, 798 typename _UniformRandomNumberGenerator> 799 void 800 nakagami_distribution<_RealType>:: 801 __generate_impl(_OutputIterator __f, _OutputIterator __t, 802 _UniformRandomNumberGenerator& __urng, 803 const param_type& __p) 804 { 805 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 806 result_type>) 807 808 typename std::gamma_distribution<result_type>::param_type 809 __pg(__p.mu(), __p.omega() / __p.mu()); 810 while (__f != __t) 811 *__f++ = std::sqrt(this->_M_gd(__pg, __urng)); 812 } 813 814 template<typename _RealType, typename _CharT, typename _Traits> 815 std::basic_ostream<_CharT, _Traits>& 816 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 817 const nakagami_distribution<_RealType>& __x) 818 { 819 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 820 typedef typename __ostream_type::ios_base __ios_base; 821 822 const typename __ios_base::fmtflags __flags = __os.flags(); 823 const _CharT __fill = __os.fill(); 824 const std::streamsize __precision = __os.precision(); 825 const _CharT __space = __os.widen(' '); 826 __os.flags(__ios_base::scientific | __ios_base::left); 827 __os.fill(__space); 828 __os.precision(std::numeric_limits<_RealType>::max_digits10); 829 830 __os << __x.mu() << __space << __x.omega(); 831 __os << __space << __x._M_gd; 832 833 __os.flags(__flags); 834 __os.fill(__fill); 835 __os.precision(__precision); 836 return __os; 837 } 838 839 template<typename _RealType, typename _CharT, typename _Traits> 840 std::basic_istream<_CharT, _Traits>& 841 operator>>(std::basic_istream<_CharT, _Traits>& __is, 842 nakagami_distribution<_RealType>& __x) 843 { 844 typedef std::basic_istream<_CharT, _Traits> __istream_type; 845 typedef typename __istream_type::ios_base __ios_base; 846 847 const typename __ios_base::fmtflags __flags = __is.flags(); 848 __is.flags(__ios_base::dec | __ios_base::skipws); 849 850 _RealType __mu_val, __omega_val; 851 __is >> __mu_val >> __omega_val; 852 __is >> __x._M_gd; 853 __x.param(typename nakagami_distribution<_RealType>:: 854 param_type(__mu_val, __omega_val)); 855 856 __is.flags(__flags); 857 return __is; 858 } 859 860 861 template<typename _RealType> 862 template<typename _OutputIterator, 863 typename _UniformRandomNumberGenerator> 864 void 865 pareto_distribution<_RealType>:: 866 __generate_impl(_OutputIterator __f, _OutputIterator __t, 867 _UniformRandomNumberGenerator& __urng, 868 const param_type& __p) 869 { 870 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 871 result_type>) 872 873 result_type __mu_val = __p.mu(); 874 result_type __malphinv = -result_type(1) / __p.alpha(); 875 while (__f != __t) 876 *__f++ = __mu_val * std::pow(this->_M_ud(__urng), __malphinv); 877 } 878 879 template<typename _RealType, typename _CharT, typename _Traits> 880 std::basic_ostream<_CharT, _Traits>& 881 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 882 const pareto_distribution<_RealType>& __x) 883 { 884 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 885 typedef typename __ostream_type::ios_base __ios_base; 886 887 const typename __ios_base::fmtflags __flags = __os.flags(); 888 const _CharT __fill = __os.fill(); 889 const std::streamsize __precision = __os.precision(); 890 const _CharT __space = __os.widen(' '); 891 __os.flags(__ios_base::scientific | __ios_base::left); 892 __os.fill(__space); 893 __os.precision(std::numeric_limits<_RealType>::max_digits10); 894 895 __os << __x.alpha() << __space << __x.mu(); 896 __os << __space << __x._M_ud; 897 898 __os.flags(__flags); 899 __os.fill(__fill); 900 __os.precision(__precision); 901 return __os; 902 } 903 904 template<typename _RealType, typename _CharT, typename _Traits> 905 std::basic_istream<_CharT, _Traits>& 906 operator>>(std::basic_istream<_CharT, _Traits>& __is, 907 pareto_distribution<_RealType>& __x) 908 { 909 typedef std::basic_istream<_CharT, _Traits> __istream_type; 910 typedef typename __istream_type::ios_base __ios_base; 911 912 const typename __ios_base::fmtflags __flags = __is.flags(); 913 __is.flags(__ios_base::dec | __ios_base::skipws); 914 915 _RealType __alpha_val, __mu_val; 916 __is >> __alpha_val >> __mu_val; 917 __is >> __x._M_ud; 918 __x.param(typename pareto_distribution<_RealType>:: 919 param_type(__alpha_val, __mu_val)); 920 921 __is.flags(__flags); 922 return __is; 923 } 924 925 926 template<typename _RealType> 927 template<typename _UniformRandomNumberGenerator> 928 typename k_distribution<_RealType>::result_type 929 k_distribution<_RealType>:: 930 operator()(_UniformRandomNumberGenerator& __urng) 931 { 932 result_type __x = this->_M_gd1(__urng); 933 result_type __y = this->_M_gd2(__urng); 934 return std::sqrt(__x * __y); 935 } 936 937 template<typename _RealType> 938 template<typename _UniformRandomNumberGenerator> 939 typename k_distribution<_RealType>::result_type 940 k_distribution<_RealType>:: 941 operator()(_UniformRandomNumberGenerator& __urng, 942 const param_type& __p) 943 { 944 typename std::gamma_distribution<result_type>::param_type 945 __p1(__p.lambda(), result_type(1) / __p.lambda()), 946 __p2(__p.nu(), __p.mu() / __p.nu()); 947 result_type __x = this->_M_gd1(__p1, __urng); 948 result_type __y = this->_M_gd2(__p2, __urng); 949 return std::sqrt(__x * __y); 950 } 951 952 template<typename _RealType> 953 template<typename _OutputIterator, 954 typename _UniformRandomNumberGenerator> 955 void 956 k_distribution<_RealType>:: 957 __generate_impl(_OutputIterator __f, _OutputIterator __t, 958 _UniformRandomNumberGenerator& __urng, 959 const param_type& __p) 960 { 961 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 962 result_type>) 963 964 typename std::gamma_distribution<result_type>::param_type 965 __p1(__p.lambda(), result_type(1) / __p.lambda()), 966 __p2(__p.nu(), __p.mu() / __p.nu()); 967 while (__f != __t) 968 { 969 result_type __x = this->_M_gd1(__p1, __urng); 970 result_type __y = this->_M_gd2(__p2, __urng); 971 *__f++ = std::sqrt(__x * __y); 972 } 973 } 974 975 template<typename _RealType, typename _CharT, typename _Traits> 976 std::basic_ostream<_CharT, _Traits>& 977 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 978 const k_distribution<_RealType>& __x) 979 { 980 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 981 typedef typename __ostream_type::ios_base __ios_base; 982 983 const typename __ios_base::fmtflags __flags = __os.flags(); 984 const _CharT __fill = __os.fill(); 985 const std::streamsize __precision = __os.precision(); 986 const _CharT __space = __os.widen(' '); 987 __os.flags(__ios_base::scientific | __ios_base::left); 988 __os.fill(__space); 989 __os.precision(std::numeric_limits<_RealType>::max_digits10); 990 991 __os << __x.lambda() << __space << __x.mu() << __space << __x.nu(); 992 __os << __space << __x._M_gd1; 993 __os << __space << __x._M_gd2; 994 995 __os.flags(__flags); 996 __os.fill(__fill); 997 __os.precision(__precision); 998 return __os; 999 } 1000 1001 template<typename _RealType, typename _CharT, typename _Traits> 1002 std::basic_istream<_CharT, _Traits>& 1003 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1004 k_distribution<_RealType>& __x) 1005 { 1006 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1007 typedef typename __istream_type::ios_base __ios_base; 1008 1009 const typename __ios_base::fmtflags __flags = __is.flags(); 1010 __is.flags(__ios_base::dec | __ios_base::skipws); 1011 1012 _RealType __lambda_val, __mu_val, __nu_val; 1013 __is >> __lambda_val >> __mu_val >> __nu_val; 1014 __is >> __x._M_gd1; 1015 __is >> __x._M_gd2; 1016 __x.param(typename k_distribution<_RealType>:: 1017 param_type(__lambda_val, __mu_val, __nu_val)); 1018 1019 __is.flags(__flags); 1020 return __is; 1021 } 1022 1023 1024 template<typename _RealType> 1025 template<typename _OutputIterator, 1026 typename _UniformRandomNumberGenerator> 1027 void 1028 arcsine_distribution<_RealType>:: 1029 __generate_impl(_OutputIterator __f, _OutputIterator __t, 1030 _UniformRandomNumberGenerator& __urng, 1031 const param_type& __p) 1032 { 1033 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 1034 result_type>) 1035 1036 result_type __dif = __p.b() - __p.a(); 1037 result_type __sum = __p.a() + __p.b(); 1038 while (__f != __t) 1039 { 1040 result_type __x = std::sin(this->_M_ud(__urng)); 1041 *__f++ = (__x * __dif + __sum) / result_type(2); 1042 } 1043 } 1044 1045 template<typename _RealType, typename _CharT, typename _Traits> 1046 std::basic_ostream<_CharT, _Traits>& 1047 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1048 const arcsine_distribution<_RealType>& __x) 1049 { 1050 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1051 typedef typename __ostream_type::ios_base __ios_base; 1052 1053 const typename __ios_base::fmtflags __flags = __os.flags(); 1054 const _CharT __fill = __os.fill(); 1055 const std::streamsize __precision = __os.precision(); 1056 const _CharT __space = __os.widen(' '); 1057 __os.flags(__ios_base::scientific | __ios_base::left); 1058 __os.fill(__space); 1059 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1060 1061 __os << __x.a() << __space << __x.b(); 1062 __os << __space << __x._M_ud; 1063 1064 __os.flags(__flags); 1065 __os.fill(__fill); 1066 __os.precision(__precision); 1067 return __os; 1068 } 1069 1070 template<typename _RealType, typename _CharT, typename _Traits> 1071 std::basic_istream<_CharT, _Traits>& 1072 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1073 arcsine_distribution<_RealType>& __x) 1074 { 1075 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1076 typedef typename __istream_type::ios_base __ios_base; 1077 1078 const typename __ios_base::fmtflags __flags = __is.flags(); 1079 __is.flags(__ios_base::dec | __ios_base::skipws); 1080 1081 _RealType __a, __b; 1082 __is >> __a >> __b; 1083 __is >> __x._M_ud; 1084 __x.param(typename arcsine_distribution<_RealType>:: 1085 param_type(__a, __b)); 1086 1087 __is.flags(__flags); 1088 return __is; 1089 } 1090 1091 1092 template<typename _RealType> 1093 template<typename _UniformRandomNumberGenerator> 1094 typename hoyt_distribution<_RealType>::result_type 1095 hoyt_distribution<_RealType>:: 1096 operator()(_UniformRandomNumberGenerator& __urng) 1097 { 1098 result_type __x = this->_M_ad(__urng); 1099 result_type __y = this->_M_ed(__urng); 1100 return (result_type(2) * this->q() 1101 / (result_type(1) + this->q() * this->q())) 1102 * std::sqrt(this->omega() * __x * __y); 1103 } 1104 1105 template<typename _RealType> 1106 template<typename _UniformRandomNumberGenerator> 1107 typename hoyt_distribution<_RealType>::result_type 1108 hoyt_distribution<_RealType>:: 1109 operator()(_UniformRandomNumberGenerator& __urng, 1110 const param_type& __p) 1111 { 1112 result_type __q2 = __p.q() * __p.q(); 1113 result_type __num = result_type(0.5L) * (result_type(1) + __q2); 1114 typename __gnu_cxx::arcsine_distribution<result_type>::param_type 1115 __pa(__num, __num / __q2); 1116 result_type __x = this->_M_ad(__pa, __urng); 1117 result_type __y = this->_M_ed(__urng); 1118 return (result_type(2) * __p.q() / (result_type(1) + __q2)) 1119 * std::sqrt(__p.omega() * __x * __y); 1120 } 1121 1122 template<typename _RealType> 1123 template<typename _OutputIterator, 1124 typename _UniformRandomNumberGenerator> 1125 void 1126 hoyt_distribution<_RealType>:: 1127 __generate_impl(_OutputIterator __f, _OutputIterator __t, 1128 _UniformRandomNumberGenerator& __urng, 1129 const param_type& __p) 1130 { 1131 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 1132 result_type>) 1133 1134 result_type __2q = result_type(2) * __p.q(); 1135 result_type __q2 = __p.q() * __p.q(); 1136 result_type __q2p1 = result_type(1) + __q2; 1137 result_type __num = result_type(0.5L) * __q2p1; 1138 result_type __omega = __p.omega(); 1139 typename __gnu_cxx::arcsine_distribution<result_type>::param_type 1140 __pa(__num, __num / __q2); 1141 while (__f != __t) 1142 { 1143 result_type __x = this->_M_ad(__pa, __urng); 1144 result_type __y = this->_M_ed(__urng); 1145 *__f++ = (__2q / __q2p1) * std::sqrt(__omega * __x * __y); 1146 } 1147 } 1148 1149 template<typename _RealType, typename _CharT, typename _Traits> 1150 std::basic_ostream<_CharT, _Traits>& 1151 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1152 const hoyt_distribution<_RealType>& __x) 1153 { 1154 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1155 typedef typename __ostream_type::ios_base __ios_base; 1156 1157 const typename __ios_base::fmtflags __flags = __os.flags(); 1158 const _CharT __fill = __os.fill(); 1159 const std::streamsize __precision = __os.precision(); 1160 const _CharT __space = __os.widen(' '); 1161 __os.flags(__ios_base::scientific | __ios_base::left); 1162 __os.fill(__space); 1163 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1164 1165 __os << __x.q() << __space << __x.omega(); 1166 __os << __space << __x._M_ad; 1167 __os << __space << __x._M_ed; 1168 1169 __os.flags(__flags); 1170 __os.fill(__fill); 1171 __os.precision(__precision); 1172 return __os; 1173 } 1174 1175 template<typename _RealType, typename _CharT, typename _Traits> 1176 std::basic_istream<_CharT, _Traits>& 1177 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1178 hoyt_distribution<_RealType>& __x) 1179 { 1180 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1181 typedef typename __istream_type::ios_base __ios_base; 1182 1183 const typename __ios_base::fmtflags __flags = __is.flags(); 1184 __is.flags(__ios_base::dec | __ios_base::skipws); 1185 1186 _RealType __q, __omega; 1187 __is >> __q >> __omega; 1188 __is >> __x._M_ad; 1189 __is >> __x._M_ed; 1190 __x.param(typename hoyt_distribution<_RealType>:: 1191 param_type(__q, __omega)); 1192 1193 __is.flags(__flags); 1194 return __is; 1195 } 1196 1197 1198 template<typename _RealType> 1199 template<typename _OutputIterator, 1200 typename _UniformRandomNumberGenerator> 1201 void 1202 triangular_distribution<_RealType>:: 1203 __generate_impl(_OutputIterator __f, _OutputIterator __t, 1204 _UniformRandomNumberGenerator& __urng, 1205 const param_type& __param) 1206 { 1207 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 1208 result_type>) 1209 1210 while (__f != __t) 1211 *__f++ = this->operator()(__urng, __param); 1212 } 1213 1214 template<typename _RealType, typename _CharT, typename _Traits> 1215 std::basic_ostream<_CharT, _Traits>& 1216 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1217 const __gnu_cxx::triangular_distribution<_RealType>& __x) 1218 { 1219 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1220 typedef typename __ostream_type::ios_base __ios_base; 1221 1222 const typename __ios_base::fmtflags __flags = __os.flags(); 1223 const _CharT __fill = __os.fill(); 1224 const std::streamsize __precision = __os.precision(); 1225 const _CharT __space = __os.widen(' '); 1226 __os.flags(__ios_base::scientific | __ios_base::left); 1227 __os.fill(__space); 1228 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1229 1230 __os << __x.a() << __space << __x.b() << __space << __x.c(); 1231 1232 __os.flags(__flags); 1233 __os.fill(__fill); 1234 __os.precision(__precision); 1235 return __os; 1236 } 1237 1238 template<typename _RealType, typename _CharT, typename _Traits> 1239 std::basic_istream<_CharT, _Traits>& 1240 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1241 __gnu_cxx::triangular_distribution<_RealType>& __x) 1242 { 1243 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1244 typedef typename __istream_type::ios_base __ios_base; 1245 1246 const typename __ios_base::fmtflags __flags = __is.flags(); 1247 __is.flags(__ios_base::dec | __ios_base::skipws); 1248 1249 _RealType __a, __b, __c; 1250 __is >> __a >> __b >> __c; 1251 __x.param(typename __gnu_cxx::triangular_distribution<_RealType>:: 1252 param_type(__a, __b, __c)); 1253 1254 __is.flags(__flags); 1255 return __is; 1256 } 1257 1258 1259 template<typename _RealType> 1260 template<typename _UniformRandomNumberGenerator> 1261 typename von_mises_distribution<_RealType>::result_type 1262 von_mises_distribution<_RealType>:: 1263 operator()(_UniformRandomNumberGenerator& __urng, 1264 const param_type& __p) 1265 { 1266 const result_type __pi 1267 = __gnu_cxx::__math_constants<result_type>::__pi; 1268 std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 1269 __aurng(__urng); 1270 1271 result_type __f; 1272 while (1) 1273 { 1274 result_type __rnd = std::cos(__pi * __aurng()); 1275 __f = (result_type(1) + __p._M_r * __rnd) / (__p._M_r + __rnd); 1276 result_type __c = __p._M_kappa * (__p._M_r - __f); 1277 1278 result_type __rnd2 = __aurng(); 1279 if (__c * (result_type(2) - __c) > __rnd2) 1280 break; 1281 if (std::log(__c / __rnd2) >= __c - result_type(1)) 1282 break; 1283 } 1284 1285 result_type __res = std::acos(__f); 1286 #if _GLIBCXX_USE_C99_MATH_TR1 1287 __res = std::copysign(__res, __aurng() - result_type(0.5)); 1288 #else 1289 if (__aurng() < result_type(0.5)) 1290 __res = -__res; 1291 #endif 1292 __res += __p._M_mu; 1293 if (__res > __pi) 1294 __res -= result_type(2) * __pi; 1295 else if (__res < -__pi) 1296 __res += result_type(2) * __pi; 1297 return __res; 1298 } 1299 1300 template<typename _RealType> 1301 template<typename _OutputIterator, 1302 typename _UniformRandomNumberGenerator> 1303 void 1304 von_mises_distribution<_RealType>:: 1305 __generate_impl(_OutputIterator __f, _OutputIterator __t, 1306 _UniformRandomNumberGenerator& __urng, 1307 const param_type& __param) 1308 { 1309 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 1310 result_type>) 1311 1312 while (__f != __t) 1313 *__f++ = this->operator()(__urng, __param); 1314 } 1315 1316 template<typename _RealType, typename _CharT, typename _Traits> 1317 std::basic_ostream<_CharT, _Traits>& 1318 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1319 const __gnu_cxx::von_mises_distribution<_RealType>& __x) 1320 { 1321 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1322 typedef typename __ostream_type::ios_base __ios_base; 1323 1324 const typename __ios_base::fmtflags __flags = __os.flags(); 1325 const _CharT __fill = __os.fill(); 1326 const std::streamsize __precision = __os.precision(); 1327 const _CharT __space = __os.widen(' '); 1328 __os.flags(__ios_base::scientific | __ios_base::left); 1329 __os.fill(__space); 1330 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1331 1332 __os << __x.mu() << __space << __x.kappa(); 1333 1334 __os.flags(__flags); 1335 __os.fill(__fill); 1336 __os.precision(__precision); 1337 return __os; 1338 } 1339 1340 template<typename _RealType, typename _CharT, typename _Traits> 1341 std::basic_istream<_CharT, _Traits>& 1342 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1343 __gnu_cxx::von_mises_distribution<_RealType>& __x) 1344 { 1345 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1346 typedef typename __istream_type::ios_base __ios_base; 1347 1348 const typename __ios_base::fmtflags __flags = __is.flags(); 1349 __is.flags(__ios_base::dec | __ios_base::skipws); 1350 1351 _RealType __mu, __kappa; 1352 __is >> __mu >> __kappa; 1353 __x.param(typename __gnu_cxx::von_mises_distribution<_RealType>:: 1354 param_type(__mu, __kappa)); 1355 1356 __is.flags(__flags); 1357 return __is; 1358 } 1359 1360 1361 template<typename _UIntType> 1362 template<typename _UniformRandomNumberGenerator> 1363 typename hypergeometric_distribution<_UIntType>::result_type 1364 hypergeometric_distribution<_UIntType>:: 1365 operator()(_UniformRandomNumberGenerator& __urng, 1366 const param_type& __param) 1367 { 1368 std::__detail::_Adaptor<_UniformRandomNumberGenerator, double> 1369 __aurng(__urng); 1370 1371 result_type __a = __param.successful_size(); 1372 result_type __b = __param.total_size(); 1373 result_type __k = 0; 1374 1375 if (__param.total_draws() < __param.total_size() / 2) 1376 { 1377 for (result_type __i = 0; __i < __param.total_draws(); ++__i) 1378 { 1379 if (__b * __aurng() < __a) 1380 { 1381 ++__k; 1382 if (__k == __param.successful_size()) 1383 return __k; 1384 --__a; 1385 } 1386 --__b; 1387 } 1388 return __k; 1389 } 1390 else 1391 { 1392 for (result_type __i = 0; __i < __param.unsuccessful_size(); ++__i) 1393 { 1394 if (__b * __aurng() < __a) 1395 { 1396 ++__k; 1397 if (__k == __param.successful_size()) 1398 return __param.successful_size() - __k; 1399 --__a; 1400 } 1401 --__b; 1402 } 1403 return __param.successful_size() - __k; 1404 } 1405 } 1406 1407 template<typename _UIntType> 1408 template<typename _OutputIterator, 1409 typename _UniformRandomNumberGenerator> 1410 void 1411 hypergeometric_distribution<_UIntType>:: 1412 __generate_impl(_OutputIterator __f, _OutputIterator __t, 1413 _UniformRandomNumberGenerator& __urng, 1414 const param_type& __param) 1415 { 1416 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 1417 result_type>) 1418 1419 while (__f != __t) 1420 *__f++ = this->operator()(__urng); 1421 } 1422 1423 template<typename _UIntType, typename _CharT, typename _Traits> 1424 std::basic_ostream<_CharT, _Traits>& 1425 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1426 const __gnu_cxx::hypergeometric_distribution<_UIntType>& __x) 1427 { 1428 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1429 typedef typename __ostream_type::ios_base __ios_base; 1430 1431 const typename __ios_base::fmtflags __flags = __os.flags(); 1432 const _CharT __fill = __os.fill(); 1433 const std::streamsize __precision = __os.precision(); 1434 const _CharT __space = __os.widen(' '); 1435 __os.flags(__ios_base::scientific | __ios_base::left); 1436 __os.fill(__space); 1437 __os.precision(std::numeric_limits<_UIntType>::max_digits10); 1438 1439 __os << __x.total_size() << __space << __x.successful_size() << __space 1440 << __x.total_draws(); 1441 1442 __os.flags(__flags); 1443 __os.fill(__fill); 1444 __os.precision(__precision); 1445 return __os; 1446 } 1447 1448 template<typename _UIntType, typename _CharT, typename _Traits> 1449 std::basic_istream<_CharT, _Traits>& 1450 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1451 __gnu_cxx::hypergeometric_distribution<_UIntType>& __x) 1452 { 1453 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1454 typedef typename __istream_type::ios_base __ios_base; 1455 1456 const typename __ios_base::fmtflags __flags = __is.flags(); 1457 __is.flags(__ios_base::dec | __ios_base::skipws); 1458 1459 _UIntType __total_size, __successful_size, __total_draws; 1460 __is >> __total_size >> __successful_size >> __total_draws; 1461 __x.param(typename __gnu_cxx::hypergeometric_distribution<_UIntType>:: 1462 param_type(__total_size, __successful_size, __total_draws)); 1463 1464 __is.flags(__flags); 1465 return __is; 1466 } 1467 1468 1469 template<typename _RealType> 1470 template<typename _UniformRandomNumberGenerator> 1471 typename logistic_distribution<_RealType>::result_type 1472 logistic_distribution<_RealType>:: 1473 operator()(_UniformRandomNumberGenerator& __urng, 1474 const param_type& __p) 1475 { 1476 std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 1477 __aurng(__urng); 1478 1479 result_type __arg = result_type(1); 1480 while (__arg == result_type(1) || __arg == result_type(0)) 1481 __arg = __aurng(); 1482 return __p.a() 1483 + __p.b() * std::log(__arg / (result_type(1) - __arg)); 1484 } 1485 1486 template<typename _RealType> 1487 template<typename _OutputIterator, 1488 typename _UniformRandomNumberGenerator> 1489 void 1490 logistic_distribution<_RealType>:: 1491 __generate_impl(_OutputIterator __f, _OutputIterator __t, 1492 _UniformRandomNumberGenerator& __urng, 1493 const param_type& __p) 1494 { 1495 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 1496 result_type>) 1497 1498 std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 1499 __aurng(__urng); 1500 1501 while (__f != __t) 1502 { 1503 result_type __arg = result_type(1); 1504 while (__arg == result_type(1) || __arg == result_type(0)) 1505 __arg = __aurng(); 1506 *__f++ = __p.a() 1507 + __p.b() * std::log(__arg / (result_type(1) - __arg)); 1508 } 1509 } 1510 1511 template<typename _RealType, typename _CharT, typename _Traits> 1512 std::basic_ostream<_CharT, _Traits>& 1513 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1514 const logistic_distribution<_RealType>& __x) 1515 { 1516 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1517 typedef typename __ostream_type::ios_base __ios_base; 1518 1519 const typename __ios_base::fmtflags __flags = __os.flags(); 1520 const _CharT __fill = __os.fill(); 1521 const std::streamsize __precision = __os.precision(); 1522 const _CharT __space = __os.widen(' '); 1523 __os.flags(__ios_base::scientific | __ios_base::left); 1524 __os.fill(__space); 1525 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1526 1527 __os << __x.a() << __space << __x.b(); 1528 1529 __os.flags(__flags); 1530 __os.fill(__fill); 1531 __os.precision(__precision); 1532 return __os; 1533 } 1534 1535 template<typename _RealType, typename _CharT, typename _Traits> 1536 std::basic_istream<_CharT, _Traits>& 1537 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1538 logistic_distribution<_RealType>& __x) 1539 { 1540 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1541 typedef typename __istream_type::ios_base __ios_base; 1542 1543 const typename __ios_base::fmtflags __flags = __is.flags(); 1544 __is.flags(__ios_base::dec | __ios_base::skipws); 1545 1546 _RealType __a, __b; 1547 __is >> __a >> __b; 1548 __x.param(typename logistic_distribution<_RealType>:: 1549 param_type(__a, __b)); 1550 1551 __is.flags(__flags); 1552 return __is; 1553 } 1554 1555 1556 namespace { 1557 1558 // Helper class for the uniform_on_sphere_distribution generation 1559 // function. 1560 template<std::size_t _Dimen, typename _RealType> 1561 class uniform_on_sphere_helper 1562 { 1563 typedef typename uniform_on_sphere_distribution<_Dimen, _RealType>:: 1564 result_type result_type; 1565 1566 public: 1567 template<typename _NormalDistribution, 1568 typename _UniformRandomNumberGenerator> 1569 result_type operator()(_NormalDistribution& __nd, 1570 _UniformRandomNumberGenerator& __urng) 1571 { 1572 result_type __ret; 1573 typename result_type::value_type __norm; 1574 1575 do 1576 { 1577 auto __sum = _RealType(0); 1578 1579 std::generate(__ret.begin(), __ret.end(), 1580 [&__nd, &__urng, &__sum](){ 1581 _RealType __t = __nd(__urng); 1582 __sum += __t * __t; 1583 return __t; }); 1584 __norm = std::sqrt(__sum); 1585 } 1586 while (__norm == _RealType(0) || ! __builtin_isfinite(__norm)); 1587 1588 std::transform(__ret.begin(), __ret.end(), __ret.begin(), 1589 [__norm](_RealType __val){ return __val / __norm; }); 1590 1591 return __ret; 1592 } 1593 }; 1594 1595 1596 template<typename _RealType> 1597 class uniform_on_sphere_helper<2, _RealType> 1598 { 1599 typedef typename uniform_on_sphere_distribution<2, _RealType>:: 1600 result_type result_type; 1601 1602 public: 1603 template<typename _NormalDistribution, 1604 typename _UniformRandomNumberGenerator> 1605 result_type operator()(_NormalDistribution&, 1606 _UniformRandomNumberGenerator& __urng) 1607 { 1608 result_type __ret; 1609 _RealType __sq; 1610 std::__detail::_Adaptor<_UniformRandomNumberGenerator, 1611 _RealType> __aurng(__urng); 1612 1613 do 1614 { 1615 __ret[0] = _RealType(2) * __aurng() - _RealType(1); 1616 __ret[1] = _RealType(2) * __aurng() - _RealType(1); 1617 1618 __sq = __ret[0] * __ret[0] + __ret[1] * __ret[1]; 1619 } 1620 while (__sq == _RealType(0) || __sq > _RealType(1)); 1621 1622 #if _GLIBCXX_USE_C99_MATH_TR1 1623 // Yes, we do not just use sqrt(__sq) because hypot() is more 1624 // accurate. 1625 auto __norm = std::hypot(__ret[0], __ret[1]); 1626 #else 1627 auto __norm = std::sqrt(__sq); 1628 #endif 1629 __ret[0] /= __norm; 1630 __ret[1] /= __norm; 1631 1632 return __ret; 1633 } 1634 }; 1635 1636 } 1637 1638 1639 template<std::size_t _Dimen, typename _RealType> 1640 template<typename _UniformRandomNumberGenerator> 1641 typename uniform_on_sphere_distribution<_Dimen, _RealType>::result_type 1642 uniform_on_sphere_distribution<_Dimen, _RealType>:: 1643 operator()(_UniformRandomNumberGenerator& __urng, 1644 const param_type& __p) 1645 { 1646 uniform_on_sphere_helper<_Dimen, _RealType> __helper; 1647 return __helper(_M_nd, __urng); 1648 } 1649 1650 template<std::size_t _Dimen, typename _RealType> 1651 template<typename _OutputIterator, 1652 typename _UniformRandomNumberGenerator> 1653 void 1654 uniform_on_sphere_distribution<_Dimen, _RealType>:: 1655 __generate_impl(_OutputIterator __f, _OutputIterator __t, 1656 _UniformRandomNumberGenerator& __urng, 1657 const param_type& __param) 1658 { 1659 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 1660 result_type>) 1661 1662 while (__f != __t) 1663 *__f++ = this->operator()(__urng, __param); 1664 } 1665 1666 template<std::size_t _Dimen, typename _RealType, typename _CharT, 1667 typename _Traits> 1668 std::basic_ostream<_CharT, _Traits>& 1669 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1670 const __gnu_cxx::uniform_on_sphere_distribution<_Dimen, 1671 _RealType>& __x) 1672 { 1673 return __os << __x._M_nd; 1674 } 1675 1676 template<std::size_t _Dimen, typename _RealType, typename _CharT, 1677 typename _Traits> 1678 std::basic_istream<_CharT, _Traits>& 1679 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1680 __gnu_cxx::uniform_on_sphere_distribution<_Dimen, 1681 _RealType>& __x) 1682 { 1683 return __is >> __x._M_nd; 1684 } 1685 1686 1687 namespace { 1688 1689 // Helper class for the uniform_inside_sphere_distribution generation 1690 // function. 1691 template<std::size_t _Dimen, bool _SmallDimen, typename _RealType> 1692 class uniform_inside_sphere_helper; 1693 1694 template<std::size_t _Dimen, typename _RealType> 1695 class uniform_inside_sphere_helper<_Dimen, false, _RealType> 1696 { 1697 using result_type 1698 = typename uniform_inside_sphere_distribution<_Dimen, _RealType>:: 1699 result_type; 1700 1701 public: 1702 template<typename _UniformOnSphereDistribution, 1703 typename _UniformRandomNumberGenerator> 1704 result_type 1705 operator()(_UniformOnSphereDistribution& __uosd, 1706 _UniformRandomNumberGenerator& __urng, 1707 _RealType __radius) 1708 { 1709 std::__detail::_Adaptor<_UniformRandomNumberGenerator, 1710 _RealType> __aurng(__urng); 1711 1712 _RealType __pow = 1 / _RealType(_Dimen); 1713 _RealType __urt = __radius * std::pow(__aurng(), __pow); 1714 result_type __ret = __uosd(__aurng); 1715 1716 std::transform(__ret.begin(), __ret.end(), __ret.begin(), 1717 [__urt](_RealType __val) 1718 { return __val * __urt; }); 1719 1720 return __ret; 1721 } 1722 }; 1723 1724 // Helper class for the uniform_inside_sphere_distribution generation 1725 // function specialized for small dimensions. 1726 template<std::size_t _Dimen, typename _RealType> 1727 class uniform_inside_sphere_helper<_Dimen, true, _RealType> 1728 { 1729 using result_type 1730 = typename uniform_inside_sphere_distribution<_Dimen, _RealType>:: 1731 result_type; 1732 1733 public: 1734 template<typename _UniformOnSphereDistribution, 1735 typename _UniformRandomNumberGenerator> 1736 result_type 1737 operator()(_UniformOnSphereDistribution&, 1738 _UniformRandomNumberGenerator& __urng, 1739 _RealType __radius) 1740 { 1741 result_type __ret; 1742 _RealType __sq; 1743 _RealType __radsq = __radius * __radius; 1744 std::__detail::_Adaptor<_UniformRandomNumberGenerator, 1745 _RealType> __aurng(__urng); 1746 1747 do 1748 { 1749 __sq = _RealType(0); 1750 for (int i = 0; i < _Dimen; ++i) 1751 { 1752 __ret[i] = _RealType(2) * __aurng() - _RealType(1); 1753 __sq += __ret[i] * __ret[i]; 1754 } 1755 } 1756 while (__sq > _RealType(1)); 1757 1758 for (int i = 0; i < _Dimen; ++i) 1759 __ret[i] *= __radius; 1760 1761 return __ret; 1762 } 1763 }; 1764 } // namespace 1765 1766 // 1767 // Experiments have shown that rejection is more efficient than transform 1768 // for dimensions less than 8. 1769 // 1770 template<std::size_t _Dimen, typename _RealType> 1771 template<typename _UniformRandomNumberGenerator> 1772 typename uniform_inside_sphere_distribution<_Dimen, _RealType>::result_type 1773 uniform_inside_sphere_distribution<_Dimen, _RealType>:: 1774 operator()(_UniformRandomNumberGenerator& __urng, 1775 const param_type& __p) 1776 { 1777 uniform_inside_sphere_helper<_Dimen, _Dimen < 8, _RealType> __helper; 1778 return __helper(_M_uosd, __urng, __p.radius()); 1779 } 1780 1781 template<std::size_t _Dimen, typename _RealType> 1782 template<typename _OutputIterator, 1783 typename _UniformRandomNumberGenerator> 1784 void 1785 uniform_inside_sphere_distribution<_Dimen, _RealType>:: 1786 __generate_impl(_OutputIterator __f, _OutputIterator __t, 1787 _UniformRandomNumberGenerator& __urng, 1788 const param_type& __param) 1789 { 1790 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 1791 result_type>) 1792 1793 while (__f != __t) 1794 *__f++ = this->operator()(__urng, __param); 1795 } 1796 1797 template<std::size_t _Dimen, typename _RealType, typename _CharT, 1798 typename _Traits> 1799 std::basic_ostream<_CharT, _Traits>& 1800 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1801 const __gnu_cxx::uniform_inside_sphere_distribution<_Dimen, 1802 _RealType>& __x) 1803 { 1804 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1805 typedef typename __ostream_type::ios_base __ios_base; 1806 1807 const typename __ios_base::fmtflags __flags = __os.flags(); 1808 const _CharT __fill = __os.fill(); 1809 const std::streamsize __precision = __os.precision(); 1810 const _CharT __space = __os.widen(' '); 1811 __os.flags(__ios_base::scientific | __ios_base::left); 1812 __os.fill(__space); 1813 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1814 1815 __os << __x.radius() << __space << __x._M_uosd; 1816 1817 __os.flags(__flags); 1818 __os.fill(__fill); 1819 __os.precision(__precision); 1820 1821 return __os; 1822 } 1823 1824 template<std::size_t _Dimen, typename _RealType, typename _CharT, 1825 typename _Traits> 1826 std::basic_istream<_CharT, _Traits>& 1827 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1828 __gnu_cxx::uniform_inside_sphere_distribution<_Dimen, 1829 _RealType>& __x) 1830 { 1831 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1832 typedef typename __istream_type::ios_base __ios_base; 1833 1834 const typename __ios_base::fmtflags __flags = __is.flags(); 1835 __is.flags(__ios_base::dec | __ios_base::skipws); 1836 1837 _RealType __radius_val; 1838 __is >> __radius_val >> __x._M_uosd; 1839 __x.param(typename uniform_inside_sphere_distribution<_Dimen, _RealType>:: 1840 param_type(__radius_val)); 1841 1842 __is.flags(__flags); 1843 1844 return __is; 1845 } 1846 1847 _GLIBCXX_END_NAMESPACE_VERSION 1848 } // namespace __gnu_cxx 1849 1850 1851 #endif // _EXT_RANDOM_TCC 1852