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The C and C++ Include Header Files
/usr/include/c++/13/ext/random.tcc
$ cat -n /usr/include/c++/13/ext/random.tcc 1 // Random number extensions -*- C++ -*- 2 3 // Copyright (C) 2012-2023 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 #include <bits/requires_hosted.h> // GNU extensions are currently omitted 36 37 namespace __gnu_cxx _GLIBCXX_VISIBILITY(default) 38 { 39 _GLIBCXX_BEGIN_NAMESPACE_VERSION 40 41 #if __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__ 42 43 template<typename _UIntType, size_t __m, 44 size_t __pos1, size_t __sl1, size_t __sl2, 45 size_t __sr1, size_t __sr2, 46 uint32_t __msk1, uint32_t __msk2, 47 uint32_t __msk3, uint32_t __msk4, 48 uint32_t __parity1, uint32_t __parity2, 49 uint32_t __parity3, uint32_t __parity4> 50 void simd_fast_mersenne_twister_engine<_UIntType, __m, 51 __pos1, __sl1, __sl2, __sr1, __sr2, 52 __msk1, __msk2, __msk3, __msk4, 53 __parity1, __parity2, __parity3, 54 __parity4>:: 55 seed(_UIntType __seed) 56 { 57 _M_state32[0] = static_cast<uint32_t>(__seed); 58 for (size_t __i = 1; __i < _M_nstate32; ++__i) 59 _M_state32[__i] = (1812433253UL 60 * (_M_state32[__i - 1] ^ (_M_state32[__i - 1] >> 30)) 61 + __i); 62 _M_pos = state_size; 63 _M_period_certification(); 64 } 65 66 67 namespace { 68 69 inline uint32_t _Func1(uint32_t __x) 70 { 71 return (__x ^ (__x >> 27)) * UINT32_C(1664525); 72 } 73 74 inline uint32_t _Func2(uint32_t __x) 75 { 76 return (__x ^ (__x >> 27)) * UINT32_C(1566083941); 77 } 78 79 } 80 81 82 template<typename _UIntType, size_t __m, 83 size_t __pos1, size_t __sl1, size_t __sl2, 84 size_t __sr1, size_t __sr2, 85 uint32_t __msk1, uint32_t __msk2, 86 uint32_t __msk3, uint32_t __msk4, 87 uint32_t __parity1, uint32_t __parity2, 88 uint32_t __parity3, uint32_t __parity4> 89 template<typename _Sseq> 90 auto 91 simd_fast_mersenne_twister_engine<_UIntType, __m, 92 __pos1, __sl1, __sl2, __sr1, __sr2, 93 __msk1, __msk2, __msk3, __msk4, 94 __parity1, __parity2, __parity3, 95 __parity4>:: 96 seed(_Sseq& __q) 97 -> _If_seed_seq<_Sseq> 98 { 99 size_t __lag; 100 101 if (_M_nstate32 >= 623) 102 __lag = 11; 103 else if (_M_nstate32 >= 68) 104 __lag = 7; 105 else if (_M_nstate32 >= 39) 106 __lag = 5; 107 else 108 __lag = 3; 109 const size_t __mid = (_M_nstate32 - __lag) / 2; 110 111 std::fill(_M_state32, _M_state32 + _M_nstate32, UINT32_C(0x8b8b8b8b)); 112 uint32_t __arr[_M_nstate32]; 113 __q.generate(__arr + 0, __arr + _M_nstate32); 114 115 uint32_t __r = _Func1(_M_state32[0] ^ _M_state32[__mid] 116 ^ _M_state32[_M_nstate32 - 1]); 117 _M_state32[__mid] += __r; 118 __r += _M_nstate32; 119 _M_state32[__mid + __lag] += __r; 120 _M_state32[0] = __r; 121 122 for (size_t __i = 1, __j = 0; __j < _M_nstate32; ++__j) 123 { 124 __r = _Func1(_M_state32[__i] 125 ^ _M_state32[(__i + __mid) % _M_nstate32] 126 ^ _M_state32[(__i + _M_nstate32 - 1) % _M_nstate32]); 127 _M_state32[(__i + __mid) % _M_nstate32] += __r; 128 __r += __arr[__j] + __i; 129 _M_state32[(__i + __mid + __lag) % _M_nstate32] += __r; 130 _M_state32[__i] = __r; 131 __i = (__i + 1) % _M_nstate32; 132 } 133 for (size_t __j = 0; __j < _M_nstate32; ++__j) 134 { 135 const size_t __i = (__j + 1) % _M_nstate32; 136 __r = _Func2(_M_state32[__i] 137 + _M_state32[(__i + __mid) % _M_nstate32] 138 + _M_state32[(__i + _M_nstate32 - 1) % _M_nstate32]); 139 _M_state32[(__i + __mid) % _M_nstate32] ^= __r; 140 __r -= __i; 141 _M_state32[(__i + __mid + __lag) % _M_nstate32] ^= __r; 142 _M_state32[__i] = __r; 143 } 144 145 _M_pos = state_size; 146 _M_period_certification(); 147 } 148 149 150 template<typename _UIntType, size_t __m, 151 size_t __pos1, size_t __sl1, size_t __sl2, 152 size_t __sr1, size_t __sr2, 153 uint32_t __msk1, uint32_t __msk2, 154 uint32_t __msk3, uint32_t __msk4, 155 uint32_t __parity1, uint32_t __parity2, 156 uint32_t __parity3, uint32_t __parity4> 157 void simd_fast_mersenne_twister_engine<_UIntType, __m, 158 __pos1, __sl1, __sl2, __sr1, __sr2, 159 __msk1, __msk2, __msk3, __msk4, 160 __parity1, __parity2, __parity3, 161 __parity4>:: 162 _M_period_certification(void) 163 { 164 static const uint32_t __parity[4] = { __parity1, __parity2, 165 __parity3, __parity4 }; 166 uint32_t __inner = 0; 167 for (size_t __i = 0; __i < 4; ++__i) 168 if (__parity[__i] != 0) 169 __inner ^= _M_state32[__i] & __parity[__i]; 170 171 if (__builtin_parity(__inner) & 1) 172 return; 173 for (size_t __i = 0; __i < 4; ++__i) 174 if (__parity[__i] != 0) 175 { 176 _M_state32[__i] ^= 1 << (__builtin_ffs(__parity[__i]) - 1); 177 return; 178 } 179 __builtin_unreachable(); 180 } 181 182 183 template<typename _UIntType, size_t __m, 184 size_t __pos1, size_t __sl1, size_t __sl2, 185 size_t __sr1, size_t __sr2, 186 uint32_t __msk1, uint32_t __msk2, 187 uint32_t __msk3, uint32_t __msk4, 188 uint32_t __parity1, uint32_t __parity2, 189 uint32_t __parity3, uint32_t __parity4> 190 void simd_fast_mersenne_twister_engine<_UIntType, __m, 191 __pos1, __sl1, __sl2, __sr1, __sr2, 192 __msk1, __msk2, __msk3, __msk4, 193 __parity1, __parity2, __parity3, 194 __parity4>:: 195 discard(unsigned long long __z) 196 { 197 while (__z > state_size - _M_pos) 198 { 199 __z -= state_size - _M_pos; 200 201 _M_gen_rand(); 202 } 203 204 _M_pos += __z; 205 } 206 207 208 #ifndef _GLIBCXX_OPT_HAVE_RANDOM_SFMT_GEN_READ 209 210 namespace { 211 212 template<size_t __shift> 213 inline void __rshift(uint32_t *__out, const uint32_t *__in) 214 { 215 uint64_t __th = ((static_cast<uint64_t>(__in[3]) << 32) 216 | static_cast<uint64_t>(__in[2])); 217 uint64_t __tl = ((static_cast<uint64_t>(__in[1]) << 32) 218 | static_cast<uint64_t>(__in[0])); 219 220 uint64_t __oh = __th >> (__shift * 8); 221 uint64_t __ol = __tl >> (__shift * 8); 222 __ol |= __th << (64 - __shift * 8); 223 __out[1] = static_cast<uint32_t>(__ol >> 32); 224 __out[0] = static_cast<uint32_t>(__ol); 225 __out[3] = static_cast<uint32_t>(__oh >> 32); 226 __out[2] = static_cast<uint32_t>(__oh); 227 } 228 229 230 template<size_t __shift> 231 inline void __lshift(uint32_t *__out, const uint32_t *__in) 232 { 233 uint64_t __th = ((static_cast<uint64_t>(__in[3]) << 32) 234 | static_cast<uint64_t>(__in[2])); 235 uint64_t __tl = ((static_cast<uint64_t>(__in[1]) << 32) 236 | static_cast<uint64_t>(__in[0])); 237 238 uint64_t __oh = __th << (__shift * 8); 239 uint64_t __ol = __tl << (__shift * 8); 240 __oh |= __tl >> (64 - __shift * 8); 241 __out[1] = static_cast<uint32_t>(__ol >> 32); 242 __out[0] = static_cast<uint32_t>(__ol); 243 __out[3] = static_cast<uint32_t>(__oh >> 32); 244 __out[2] = static_cast<uint32_t>(__oh); 245 } 246 247 248 template<size_t __sl1, size_t __sl2, size_t __sr1, size_t __sr2, 249 uint32_t __msk1, uint32_t __msk2, uint32_t __msk3, uint32_t __msk4> 250 inline void __recursion(uint32_t *__r, 251 const uint32_t *__a, const uint32_t *__b, 252 const uint32_t *__c, const uint32_t *__d) 253 { 254 uint32_t __x[4]; 255 uint32_t __y[4]; 256 257 __lshift<__sl2>(__x, __a); 258 __rshift<__sr2>(__y, __c); 259 __r[0] = (__a[0] ^ __x[0] ^ ((__b[0] >> __sr1) & __msk1) 260 ^ __y[0] ^ (__d[0] << __sl1)); 261 __r[1] = (__a[1] ^ __x[1] ^ ((__b[1] >> __sr1) & __msk2) 262 ^ __y[1] ^ (__d[1] << __sl1)); 263 __r[2] = (__a[2] ^ __x[2] ^ ((__b[2] >> __sr1) & __msk3) 264 ^ __y[2] ^ (__d[2] << __sl1)); 265 __r[3] = (__a[3] ^ __x[3] ^ ((__b[3] >> __sr1) & __msk4) 266 ^ __y[3] ^ (__d[3] << __sl1)); 267 } 268 269 } 270 271 272 template<typename _UIntType, size_t __m, 273 size_t __pos1, size_t __sl1, size_t __sl2, 274 size_t __sr1, size_t __sr2, 275 uint32_t __msk1, uint32_t __msk2, 276 uint32_t __msk3, uint32_t __msk4, 277 uint32_t __parity1, uint32_t __parity2, 278 uint32_t __parity3, uint32_t __parity4> 279 void simd_fast_mersenne_twister_engine<_UIntType, __m, 280 __pos1, __sl1, __sl2, __sr1, __sr2, 281 __msk1, __msk2, __msk3, __msk4, 282 __parity1, __parity2, __parity3, 283 __parity4>:: 284 _M_gen_rand(void) 285 { 286 const uint32_t *__r1 = &_M_state32[_M_nstate32 - 8]; 287 const uint32_t *__r2 = &_M_state32[_M_nstate32 - 4]; 288 static constexpr size_t __pos1_32 = __pos1 * 4; 289 290 size_t __i; 291 for (__i = 0; __i < _M_nstate32 - __pos1_32; __i += 4) 292 { 293 __recursion<__sl1, __sl2, __sr1, __sr2, 294 __msk1, __msk2, __msk3, __msk4> 295 (&_M_state32[__i], &_M_state32[__i], 296 &_M_state32[__i + __pos1_32], __r1, __r2); 297 __r1 = __r2; 298 __r2 = &_M_state32[__i]; 299 } 300 301 for (; __i < _M_nstate32; __i += 4) 302 { 303 __recursion<__sl1, __sl2, __sr1, __sr2, 304 __msk1, __msk2, __msk3, __msk4> 305 (&_M_state32[__i], &_M_state32[__i], 306 &_M_state32[__i + __pos1_32 - _M_nstate32], __r1, __r2); 307 __r1 = __r2; 308 __r2 = &_M_state32[__i]; 309 } 310 311 _M_pos = 0; 312 } 313 314 #endif 315 316 #ifndef _GLIBCXX_OPT_HAVE_RANDOM_SFMT_OPERATOREQUAL 317 template<typename _UIntType, size_t __m, 318 size_t __pos1, size_t __sl1, size_t __sl2, 319 size_t __sr1, size_t __sr2, 320 uint32_t __msk1, uint32_t __msk2, 321 uint32_t __msk3, uint32_t __msk4, 322 uint32_t __parity1, uint32_t __parity2, 323 uint32_t __parity3, uint32_t __parity4> 324 bool 325 operator==(const __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, 326 __m, __pos1, __sl1, __sl2, __sr1, __sr2, 327 __msk1, __msk2, __msk3, __msk4, 328 __parity1, __parity2, __parity3, __parity4>& __lhs, 329 const __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, 330 __m, __pos1, __sl1, __sl2, __sr1, __sr2, 331 __msk1, __msk2, __msk3, __msk4, 332 __parity1, __parity2, __parity3, __parity4>& __rhs) 333 { 334 typedef __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, 335 __m, __pos1, __sl1, __sl2, __sr1, __sr2, 336 __msk1, __msk2, __msk3, __msk4, 337 __parity1, __parity2, __parity3, __parity4> __engine; 338 return (std::equal(__lhs._M_stateT, 339 __lhs._M_stateT + __engine::state_size, 340 __rhs._M_stateT) 341 && __lhs._M_pos == __rhs._M_pos); 342 } 343 #endif 344 345 template<typename _UIntType, size_t __m, 346 size_t __pos1, size_t __sl1, size_t __sl2, 347 size_t __sr1, size_t __sr2, 348 uint32_t __msk1, uint32_t __msk2, 349 uint32_t __msk3, uint32_t __msk4, 350 uint32_t __parity1, uint32_t __parity2, 351 uint32_t __parity3, uint32_t __parity4, 352 typename _CharT, typename _Traits> 353 std::basic_ostream<_CharT, _Traits>& 354 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 355 const __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, 356 __m, __pos1, __sl1, __sl2, __sr1, __sr2, 357 __msk1, __msk2, __msk3, __msk4, 358 __parity1, __parity2, __parity3, __parity4>& __x) 359 { 360 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 361 typedef typename __ostream_type::ios_base __ios_base; 362 363 const typename __ios_base::fmtflags __flags = __os.flags(); 364 const _CharT __fill = __os.fill(); 365 const _CharT __space = __os.widen(' '); 366 __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); 367 __os.fill(__space); 368 369 for (size_t __i = 0; __i < __x._M_nstate32; ++__i) 370 __os << __x._M_state32[__i] << __space; 371 __os << __x._M_pos; 372 373 __os.flags(__flags); 374 __os.fill(__fill); 375 return __os; 376 } 377 378 379 template<typename _UIntType, size_t __m, 380 size_t __pos1, size_t __sl1, size_t __sl2, 381 size_t __sr1, size_t __sr2, 382 uint32_t __msk1, uint32_t __msk2, 383 uint32_t __msk3, uint32_t __msk4, 384 uint32_t __parity1, uint32_t __parity2, 385 uint32_t __parity3, uint32_t __parity4, 386 typename _CharT, typename _Traits> 387 std::basic_istream<_CharT, _Traits>& 388 operator>>(std::basic_istream<_CharT, _Traits>& __is, 389 __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, 390 __m, __pos1, __sl1, __sl2, __sr1, __sr2, 391 __msk1, __msk2, __msk3, __msk4, 392 __parity1, __parity2, __parity3, __parity4>& __x) 393 { 394 typedef std::basic_istream<_CharT, _Traits> __istream_type; 395 typedef typename __istream_type::ios_base __ios_base; 396 397 const typename __ios_base::fmtflags __flags = __is.flags(); 398 __is.flags(__ios_base::dec | __ios_base::skipws); 399 400 for (size_t __i = 0; __i < __x._M_nstate32; ++__i) 401 __is >> __x._M_state32[__i]; 402 __is >> __x._M_pos; 403 404 __is.flags(__flags); 405 return __is; 406 } 407 408 #endif // __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__ 409 410 /** 411 * Iteration method due to M.D. J<o:>hnk. 412 * 413 * M.D. J<o:>hnk, Erzeugung von betaverteilten und gammaverteilten 414 * Zufallszahlen, Metrika, Volume 8, 1964 415 */ 416 template<typename _RealType> 417 template<typename _UniformRandomNumberGenerator> 418 typename beta_distribution<_RealType>::result_type 419 beta_distribution<_RealType>:: 420 operator()(_UniformRandomNumberGenerator& __urng, 421 const param_type& __param) 422 { 423 std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 424 __aurng(__urng); 425 426 result_type __x, __y; 427 do 428 { 429 __x = std::exp(std::log(__aurng()) / __param.alpha()); 430 __y = std::exp(std::log(__aurng()) / __param.beta()); 431 } 432 while (__x + __y > result_type(1)); 433 434 return __x / (__x + __y); 435 } 436 437 template<typename _RealType> 438 template<typename _OutputIterator, 439 typename _UniformRandomNumberGenerator> 440 void 441 beta_distribution<_RealType>:: 442 __generate_impl(_OutputIterator __f, _OutputIterator __t, 443 _UniformRandomNumberGenerator& __urng, 444 const param_type& __param) 445 { 446 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 447 result_type>) 448 449 std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 450 __aurng(__urng); 451 452 while (__f != __t) 453 { 454 result_type __x, __y; 455 do 456 { 457 __x = std::exp(std::log(__aurng()) / __param.alpha()); 458 __y = std::exp(std::log(__aurng()) / __param.beta()); 459 } 460 while (__x + __y > result_type(1)); 461 462 *__f++ = __x / (__x + __y); 463 } 464 } 465 466 template<typename _RealType, typename _CharT, typename _Traits> 467 std::basic_ostream<_CharT, _Traits>& 468 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 469 const __gnu_cxx::beta_distribution<_RealType>& __x) 470 { 471 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 472 typedef typename __ostream_type::ios_base __ios_base; 473 474 const typename __ios_base::fmtflags __flags = __os.flags(); 475 const _CharT __fill = __os.fill(); 476 const std::streamsize __precision = __os.precision(); 477 const _CharT __space = __os.widen(' '); 478 __os.flags(__ios_base::scientific | __ios_base::left); 479 __os.fill(__space); 480 __os.precision(std::numeric_limits<_RealType>::max_digits10); 481 482 __os << __x.alpha() << __space << __x.beta(); 483 484 __os.flags(__flags); 485 __os.fill(__fill); 486 __os.precision(__precision); 487 return __os; 488 } 489 490 template<typename _RealType, typename _CharT, typename _Traits> 491 std::basic_istream<_CharT, _Traits>& 492 operator>>(std::basic_istream<_CharT, _Traits>& __is, 493 __gnu_cxx::beta_distribution<_RealType>& __x) 494 { 495 typedef std::basic_istream<_CharT, _Traits> __istream_type; 496 typedef typename __istream_type::ios_base __ios_base; 497 498 const typename __ios_base::fmtflags __flags = __is.flags(); 499 __is.flags(__ios_base::dec | __ios_base::skipws); 500 501 _RealType __alpha_val, __beta_val; 502 __is >> __alpha_val >> __beta_val; 503 __x.param(typename __gnu_cxx::beta_distribution<_RealType>:: 504 param_type(__alpha_val, __beta_val)); 505 506 __is.flags(__flags); 507 return __is; 508 } 509 510 511 template<std::size_t _Dimen, typename _RealType> 512 template<typename _InputIterator1, typename _InputIterator2> 513 void 514 normal_mv_distribution<_Dimen, _RealType>::param_type:: 515 _M_init_full(_InputIterator1 __meanbegin, _InputIterator1 __meanend, 516 _InputIterator2 __varcovbegin, _InputIterator2 __varcovend) 517 { 518 __glibcxx_function_requires(_InputIteratorConcept<_InputIterator1>) 519 __glibcxx_function_requires(_InputIteratorConcept<_InputIterator2>) 520 std::fill(std::copy(__meanbegin, __meanend, _M_mean.begin()), 521 _M_mean.end(), _RealType(0)); 522 523 // Perform the Cholesky decomposition 524 auto __w = _M_t.begin(); 525 for (size_t __j = 0; __j < _Dimen; ++__j) 526 { 527 _RealType __sum = _RealType(0); 528 529 auto __slitbegin = __w; 530 auto __cit = _M_t.begin(); 531 for (size_t __i = 0; __i < __j; ++__i) 532 { 533 auto __slit = __slitbegin; 534 _RealType __s = *__varcovbegin++; 535 for (size_t __k = 0; __k < __i; ++__k) 536 __s -= *__slit++ * *__cit++; 537 538 *__w++ = __s /= *__cit++; 539 __sum += __s * __s; 540 } 541 542 __sum = *__varcovbegin - __sum; 543 if (__builtin_expect(__sum <= _RealType(0), 0)) 544 std::__throw_runtime_error(__N("normal_mv_distribution::" 545 "param_type::_M_init_full")); 546 *__w++ = std::sqrt(__sum); 547 548 std::advance(__varcovbegin, _Dimen - __j); 549 } 550 } 551 552 template<std::size_t _Dimen, typename _RealType> 553 template<typename _InputIterator1, typename _InputIterator2> 554 void 555 normal_mv_distribution<_Dimen, _RealType>::param_type:: 556 _M_init_lower(_InputIterator1 __meanbegin, _InputIterator1 __meanend, 557 _InputIterator2 __varcovbegin, _InputIterator2 __varcovend) 558 { 559 __glibcxx_function_requires(_InputIteratorConcept<_InputIterator1>) 560 __glibcxx_function_requires(_InputIteratorConcept<_InputIterator2>) 561 std::fill(std::copy(__meanbegin, __meanend, _M_mean.begin()), 562 _M_mean.end(), _RealType(0)); 563 564 // Perform the Cholesky decomposition 565 auto __w = _M_t.begin(); 566 for (size_t __j = 0; __j < _Dimen; ++__j) 567 { 568 _RealType __sum = _RealType(0); 569 570 auto __slitbegin = __w; 571 auto __cit = _M_t.begin(); 572 for (size_t __i = 0; __i < __j; ++__i) 573 { 574 auto __slit = __slitbegin; 575 _RealType __s = *__varcovbegin++; 576 for (size_t __k = 0; __k < __i; ++__k) 577 __s -= *__slit++ * *__cit++; 578 579 *__w++ = __s /= *__cit++; 580 __sum += __s * __s; 581 } 582 583 __sum = *__varcovbegin++ - __sum; 584 if (__builtin_expect(__sum <= _RealType(0), 0)) 585 std::__throw_runtime_error(__N("normal_mv_distribution::" 586 "param_type::_M_init_lower")); 587 *__w++ = std::sqrt(__sum); 588 } 589 } 590 591 template<std::size_t _Dimen, typename _RealType> 592 template<typename _InputIterator1, typename _InputIterator2> 593 void 594 normal_mv_distribution<_Dimen, _RealType>::param_type:: 595 _M_init_diagonal(_InputIterator1 __meanbegin, _InputIterator1 __meanend, 596 _InputIterator2 __varbegin, _InputIterator2 __varend) 597 { 598 __glibcxx_function_requires(_InputIteratorConcept<_InputIterator1>) 599 __glibcxx_function_requires(_InputIteratorConcept<_InputIterator2>) 600 std::fill(std::copy(__meanbegin, __meanend, _M_mean.begin()), 601 _M_mean.end(), _RealType(0)); 602 603 auto __w = _M_t.begin(); 604 size_t __step = 0; 605 while (__varbegin != __varend) 606 { 607 std::fill_n(__w, __step, _RealType(0)); 608 __w += __step++; 609 if (__builtin_expect(*__varbegin < _RealType(0), 0)) 610 std::__throw_runtime_error(__N("normal_mv_distribution::" 611 "param_type::_M_init_diagonal")); 612 *__w++ = std::sqrt(*__varbegin++); 613 } 614 } 615 616 template<std::size_t _Dimen, typename _RealType> 617 template<typename _UniformRandomNumberGenerator> 618 typename normal_mv_distribution<_Dimen, _RealType>::result_type 619 normal_mv_distribution<_Dimen, _RealType>:: 620 operator()(_UniformRandomNumberGenerator& __urng, 621 const param_type& __param) 622 { 623 result_type __ret; 624 625 _M_nd.__generate(__ret.begin(), __ret.end(), __urng); 626 627 auto __t_it = __param._M_t.crbegin(); 628 for (size_t __i = _Dimen; __i > 0; --__i) 629 { 630 _RealType __sum = _RealType(0); 631 for (size_t __j = __i; __j > 0; --__j) 632 __sum += __ret[__j - 1] * *__t_it++; 633 __ret[__i - 1] = __sum; 634 } 635 636 return __ret; 637 } 638 639 template<std::size_t _Dimen, typename _RealType> 640 template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> 641 void 642 normal_mv_distribution<_Dimen, _RealType>:: 643 __generate_impl(_ForwardIterator __f, _ForwardIterator __t, 644 _UniformRandomNumberGenerator& __urng, 645 const param_type& __param) 646 { 647 __glibcxx_function_requires(_Mutable_ForwardIteratorConcept< 648 _ForwardIterator>) 649 while (__f != __t) 650 *__f++ = this->operator()(__urng, __param); 651 } 652 653 template<size_t _Dimen, typename _RealType> 654 bool 655 operator==(const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& 656 __d1, 657 const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& 658 __d2) 659 { 660 return __d1._M_param == __d2._M_param && __d1._M_nd == __d2._M_nd; 661 } 662 663 template<size_t _Dimen, typename _RealType, typename _CharT, typename _Traits> 664 std::basic_ostream<_CharT, _Traits>& 665 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 666 const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __x) 667 { 668 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 669 typedef typename __ostream_type::ios_base __ios_base; 670 671 const typename __ios_base::fmtflags __flags = __os.flags(); 672 const _CharT __fill = __os.fill(); 673 const std::streamsize __precision = __os.precision(); 674 const _CharT __space = __os.widen(' '); 675 __os.flags(__ios_base::scientific | __ios_base::left); 676 __os.fill(__space); 677 __os.precision(std::numeric_limits<_RealType>::max_digits10); 678 679 auto __mean = __x._M_param.mean(); 680 for (auto __it : __mean) 681 __os << __it << __space; 682 auto __t = __x._M_param.varcov(); 683 for (auto __it : __t) 684 __os << __it << __space; 685 686 __os << __x._M_nd; 687 688 __os.flags(__flags); 689 __os.fill(__fill); 690 __os.precision(__precision); 691 return __os; 692 } 693 694 template<size_t _Dimen, typename _RealType, typename _CharT, typename _Traits> 695 std::basic_istream<_CharT, _Traits>& 696 operator>>(std::basic_istream<_CharT, _Traits>& __is, 697 __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __x) 698 { 699 typedef std::basic_istream<_CharT, _Traits> __istream_type; 700 typedef typename __istream_type::ios_base __ios_base; 701 702 const typename __ios_base::fmtflags __flags = __is.flags(); 703 __is.flags(__ios_base::dec | __ios_base::skipws); 704 705 std::array<_RealType, _Dimen> __mean; 706 for (auto& __it : __mean) 707 __is >> __it; 708 std::array<_RealType, _Dimen * (_Dimen + 1) / 2> __varcov; 709 for (auto& __it : __varcov) 710 __is >> __it; 711 712 __is >> __x._M_nd; 713 714 // The param_type temporary is built with a private constructor, 715 // to skip the Cholesky decomposition that would be performed 716 // otherwise. 717 __x.param(typename normal_mv_distribution<_Dimen, _RealType>:: 718 param_type(__mean, __varcov)); 719 720 __is.flags(__flags); 721 return __is; 722 } 723 724 725 template<typename _RealType> 726 template<typename _OutputIterator, 727 typename _UniformRandomNumberGenerator> 728 void 729 rice_distribution<_RealType>:: 730 __generate_impl(_OutputIterator __f, _OutputIterator __t, 731 _UniformRandomNumberGenerator& __urng, 732 const param_type& __p) 733 { 734 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 735 result_type>) 736 737 while (__f != __t) 738 { 739 typename std::normal_distribution<result_type>::param_type 740 __px(__p.nu(), __p.sigma()), __py(result_type(0), __p.sigma()); 741 result_type __x = this->_M_ndx(__px, __urng); 742 result_type __y = this->_M_ndy(__py, __urng); 743 #if _GLIBCXX_USE_C99_MATH_TR1 744 *__f++ = std::hypot(__x, __y); 745 #else 746 *__f++ = std::sqrt(__x * __x + __y * __y); 747 #endif 748 } 749 } 750 751 template<typename _RealType, typename _CharT, typename _Traits> 752 std::basic_ostream<_CharT, _Traits>& 753 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 754 const rice_distribution<_RealType>& __x) 755 { 756 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 757 typedef typename __ostream_type::ios_base __ios_base; 758 759 const typename __ios_base::fmtflags __flags = __os.flags(); 760 const _CharT __fill = __os.fill(); 761 const std::streamsize __precision = __os.precision(); 762 const _CharT __space = __os.widen(' '); 763 __os.flags(__ios_base::scientific | __ios_base::left); 764 __os.fill(__space); 765 __os.precision(std::numeric_limits<_RealType>::max_digits10); 766 767 __os << __x.nu() << __space << __x.sigma(); 768 __os << __space << __x._M_ndx; 769 __os << __space << __x._M_ndy; 770 771 __os.flags(__flags); 772 __os.fill(__fill); 773 __os.precision(__precision); 774 return __os; 775 } 776 777 template<typename _RealType, typename _CharT, typename _Traits> 778 std::basic_istream<_CharT, _Traits>& 779 operator>>(std::basic_istream<_CharT, _Traits>& __is, 780 rice_distribution<_RealType>& __x) 781 { 782 typedef std::basic_istream<_CharT, _Traits> __istream_type; 783 typedef typename __istream_type::ios_base __ios_base; 784 785 const typename __ios_base::fmtflags __flags = __is.flags(); 786 __is.flags(__ios_base::dec | __ios_base::skipws); 787 788 _RealType __nu_val, __sigma_val; 789 __is >> __nu_val >> __sigma_val; 790 __is >> __x._M_ndx; 791 __is >> __x._M_ndy; 792 __x.param(typename rice_distribution<_RealType>:: 793 param_type(__nu_val, __sigma_val)); 794 795 __is.flags(__flags); 796 return __is; 797 } 798 799 800 template<typename _RealType> 801 template<typename _OutputIterator, 802 typename _UniformRandomNumberGenerator> 803 void 804 nakagami_distribution<_RealType>:: 805 __generate_impl(_OutputIterator __f, _OutputIterator __t, 806 _UniformRandomNumberGenerator& __urng, 807 const param_type& __p) 808 { 809 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 810 result_type>) 811 812 typename std::gamma_distribution<result_type>::param_type 813 __pg(__p.mu(), __p.omega() / __p.mu()); 814 while (__f != __t) 815 *__f++ = std::sqrt(this->_M_gd(__pg, __urng)); 816 } 817 818 template<typename _RealType, typename _CharT, typename _Traits> 819 std::basic_ostream<_CharT, _Traits>& 820 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 821 const nakagami_distribution<_RealType>& __x) 822 { 823 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 824 typedef typename __ostream_type::ios_base __ios_base; 825 826 const typename __ios_base::fmtflags __flags = __os.flags(); 827 const _CharT __fill = __os.fill(); 828 const std::streamsize __precision = __os.precision(); 829 const _CharT __space = __os.widen(' '); 830 __os.flags(__ios_base::scientific | __ios_base::left); 831 __os.fill(__space); 832 __os.precision(std::numeric_limits<_RealType>::max_digits10); 833 834 __os << __x.mu() << __space << __x.omega(); 835 __os << __space << __x._M_gd; 836 837 __os.flags(__flags); 838 __os.fill(__fill); 839 __os.precision(__precision); 840 return __os; 841 } 842 843 template<typename _RealType, typename _CharT, typename _Traits> 844 std::basic_istream<_CharT, _Traits>& 845 operator>>(std::basic_istream<_CharT, _Traits>& __is, 846 nakagami_distribution<_RealType>& __x) 847 { 848 typedef std::basic_istream<_CharT, _Traits> __istream_type; 849 typedef typename __istream_type::ios_base __ios_base; 850 851 const typename __ios_base::fmtflags __flags = __is.flags(); 852 __is.flags(__ios_base::dec | __ios_base::skipws); 853 854 _RealType __mu_val, __omega_val; 855 __is >> __mu_val >> __omega_val; 856 __is >> __x._M_gd; 857 __x.param(typename nakagami_distribution<_RealType>:: 858 param_type(__mu_val, __omega_val)); 859 860 __is.flags(__flags); 861 return __is; 862 } 863 864 865 template<typename _RealType> 866 template<typename _OutputIterator, 867 typename _UniformRandomNumberGenerator> 868 void 869 pareto_distribution<_RealType>:: 870 __generate_impl(_OutputIterator __f, _OutputIterator __t, 871 _UniformRandomNumberGenerator& __urng, 872 const param_type& __p) 873 { 874 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 875 result_type>) 876 877 result_type __mu_val = __p.mu(); 878 result_type __malphinv = -result_type(1) / __p.alpha(); 879 while (__f != __t) 880 *__f++ = __mu_val * std::pow(this->_M_ud(__urng), __malphinv); 881 } 882 883 template<typename _RealType, typename _CharT, typename _Traits> 884 std::basic_ostream<_CharT, _Traits>& 885 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 886 const pareto_distribution<_RealType>& __x) 887 { 888 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 889 typedef typename __ostream_type::ios_base __ios_base; 890 891 const typename __ios_base::fmtflags __flags = __os.flags(); 892 const _CharT __fill = __os.fill(); 893 const std::streamsize __precision = __os.precision(); 894 const _CharT __space = __os.widen(' '); 895 __os.flags(__ios_base::scientific | __ios_base::left); 896 __os.fill(__space); 897 __os.precision(std::numeric_limits<_RealType>::max_digits10); 898 899 __os << __x.alpha() << __space << __x.mu(); 900 __os << __space << __x._M_ud; 901 902 __os.flags(__flags); 903 __os.fill(__fill); 904 __os.precision(__precision); 905 return __os; 906 } 907 908 template<typename _RealType, typename _CharT, typename _Traits> 909 std::basic_istream<_CharT, _Traits>& 910 operator>>(std::basic_istream<_CharT, _Traits>& __is, 911 pareto_distribution<_RealType>& __x) 912 { 913 typedef std::basic_istream<_CharT, _Traits> __istream_type; 914 typedef typename __istream_type::ios_base __ios_base; 915 916 const typename __ios_base::fmtflags __flags = __is.flags(); 917 __is.flags(__ios_base::dec | __ios_base::skipws); 918 919 _RealType __alpha_val, __mu_val; 920 __is >> __alpha_val >> __mu_val; 921 __is >> __x._M_ud; 922 __x.param(typename pareto_distribution<_RealType>:: 923 param_type(__alpha_val, __mu_val)); 924 925 __is.flags(__flags); 926 return __is; 927 } 928 929 930 template<typename _RealType> 931 template<typename _UniformRandomNumberGenerator> 932 typename k_distribution<_RealType>::result_type 933 k_distribution<_RealType>:: 934 operator()(_UniformRandomNumberGenerator& __urng) 935 { 936 result_type __x = this->_M_gd1(__urng); 937 result_type __y = this->_M_gd2(__urng); 938 return std::sqrt(__x * __y); 939 } 940 941 template<typename _RealType> 942 template<typename _UniformRandomNumberGenerator> 943 typename k_distribution<_RealType>::result_type 944 k_distribution<_RealType>:: 945 operator()(_UniformRandomNumberGenerator& __urng, 946 const param_type& __p) 947 { 948 typename std::gamma_distribution<result_type>::param_type 949 __p1(__p.lambda(), result_type(1) / __p.lambda()), 950 __p2(__p.nu(), __p.mu() / __p.nu()); 951 result_type __x = this->_M_gd1(__p1, __urng); 952 result_type __y = this->_M_gd2(__p2, __urng); 953 return std::sqrt(__x * __y); 954 } 955 956 template<typename _RealType> 957 template<typename _OutputIterator, 958 typename _UniformRandomNumberGenerator> 959 void 960 k_distribution<_RealType>:: 961 __generate_impl(_OutputIterator __f, _OutputIterator __t, 962 _UniformRandomNumberGenerator& __urng, 963 const param_type& __p) 964 { 965 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 966 result_type>) 967 968 typename std::gamma_distribution<result_type>::param_type 969 __p1(__p.lambda(), result_type(1) / __p.lambda()), 970 __p2(__p.nu(), __p.mu() / __p.nu()); 971 while (__f != __t) 972 { 973 result_type __x = this->_M_gd1(__p1, __urng); 974 result_type __y = this->_M_gd2(__p2, __urng); 975 *__f++ = std::sqrt(__x * __y); 976 } 977 } 978 979 template<typename _RealType, typename _CharT, typename _Traits> 980 std::basic_ostream<_CharT, _Traits>& 981 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 982 const k_distribution<_RealType>& __x) 983 { 984 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 985 typedef typename __ostream_type::ios_base __ios_base; 986 987 const typename __ios_base::fmtflags __flags = __os.flags(); 988 const _CharT __fill = __os.fill(); 989 const std::streamsize __precision = __os.precision(); 990 const _CharT __space = __os.widen(' '); 991 __os.flags(__ios_base::scientific | __ios_base::left); 992 __os.fill(__space); 993 __os.precision(std::numeric_limits<_RealType>::max_digits10); 994 995 __os << __x.lambda() << __space << __x.mu() << __space << __x.nu(); 996 __os << __space << __x._M_gd1; 997 __os << __space << __x._M_gd2; 998 999 __os.flags(__flags); 1000 __os.fill(__fill); 1001 __os.precision(__precision); 1002 return __os; 1003 } 1004 1005 template<typename _RealType, typename _CharT, typename _Traits> 1006 std::basic_istream<_CharT, _Traits>& 1007 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1008 k_distribution<_RealType>& __x) 1009 { 1010 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1011 typedef typename __istream_type::ios_base __ios_base; 1012 1013 const typename __ios_base::fmtflags __flags = __is.flags(); 1014 __is.flags(__ios_base::dec | __ios_base::skipws); 1015 1016 _RealType __lambda_val, __mu_val, __nu_val; 1017 __is >> __lambda_val >> __mu_val >> __nu_val; 1018 __is >> __x._M_gd1; 1019 __is >> __x._M_gd2; 1020 __x.param(typename k_distribution<_RealType>:: 1021 param_type(__lambda_val, __mu_val, __nu_val)); 1022 1023 __is.flags(__flags); 1024 return __is; 1025 } 1026 1027 1028 template<typename _RealType> 1029 template<typename _OutputIterator, 1030 typename _UniformRandomNumberGenerator> 1031 void 1032 arcsine_distribution<_RealType>:: 1033 __generate_impl(_OutputIterator __f, _OutputIterator __t, 1034 _UniformRandomNumberGenerator& __urng, 1035 const param_type& __p) 1036 { 1037 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 1038 result_type>) 1039 1040 result_type __dif = __p.b() - __p.a(); 1041 result_type __sum = __p.a() + __p.b(); 1042 while (__f != __t) 1043 { 1044 result_type __x = std::sin(this->_M_ud(__urng)); 1045 *__f++ = (__x * __dif + __sum) / result_type(2); 1046 } 1047 } 1048 1049 template<typename _RealType, typename _CharT, typename _Traits> 1050 std::basic_ostream<_CharT, _Traits>& 1051 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1052 const arcsine_distribution<_RealType>& __x) 1053 { 1054 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1055 typedef typename __ostream_type::ios_base __ios_base; 1056 1057 const typename __ios_base::fmtflags __flags = __os.flags(); 1058 const _CharT __fill = __os.fill(); 1059 const std::streamsize __precision = __os.precision(); 1060 const _CharT __space = __os.widen(' '); 1061 __os.flags(__ios_base::scientific | __ios_base::left); 1062 __os.fill(__space); 1063 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1064 1065 __os << __x.a() << __space << __x.b(); 1066 __os << __space << __x._M_ud; 1067 1068 __os.flags(__flags); 1069 __os.fill(__fill); 1070 __os.precision(__precision); 1071 return __os; 1072 } 1073 1074 template<typename _RealType, typename _CharT, typename _Traits> 1075 std::basic_istream<_CharT, _Traits>& 1076 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1077 arcsine_distribution<_RealType>& __x) 1078 { 1079 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1080 typedef typename __istream_type::ios_base __ios_base; 1081 1082 const typename __ios_base::fmtflags __flags = __is.flags(); 1083 __is.flags(__ios_base::dec | __ios_base::skipws); 1084 1085 _RealType __a, __b; 1086 __is >> __a >> __b; 1087 __is >> __x._M_ud; 1088 __x.param(typename arcsine_distribution<_RealType>:: 1089 param_type(__a, __b)); 1090 1091 __is.flags(__flags); 1092 return __is; 1093 } 1094 1095 1096 template<typename _RealType> 1097 template<typename _UniformRandomNumberGenerator> 1098 typename hoyt_distribution<_RealType>::result_type 1099 hoyt_distribution<_RealType>:: 1100 operator()(_UniformRandomNumberGenerator& __urng) 1101 { 1102 result_type __x = this->_M_ad(__urng); 1103 result_type __y = this->_M_ed(__urng); 1104 return (result_type(2) * this->q() 1105 / (result_type(1) + this->q() * this->q())) 1106 * std::sqrt(this->omega() * __x * __y); 1107 } 1108 1109 template<typename _RealType> 1110 template<typename _UniformRandomNumberGenerator> 1111 typename hoyt_distribution<_RealType>::result_type 1112 hoyt_distribution<_RealType>:: 1113 operator()(_UniformRandomNumberGenerator& __urng, 1114 const param_type& __p) 1115 { 1116 result_type __q2 = __p.q() * __p.q(); 1117 result_type __num = result_type(0.5L) * (result_type(1) + __q2); 1118 typename __gnu_cxx::arcsine_distribution<result_type>::param_type 1119 __pa(__num, __num / __q2); 1120 result_type __x = this->_M_ad(__pa, __urng); 1121 result_type __y = this->_M_ed(__urng); 1122 return (result_type(2) * __p.q() / (result_type(1) + __q2)) 1123 * std::sqrt(__p.omega() * __x * __y); 1124 } 1125 1126 template<typename _RealType> 1127 template<typename _OutputIterator, 1128 typename _UniformRandomNumberGenerator> 1129 void 1130 hoyt_distribution<_RealType>:: 1131 __generate_impl(_OutputIterator __f, _OutputIterator __t, 1132 _UniformRandomNumberGenerator& __urng, 1133 const param_type& __p) 1134 { 1135 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 1136 result_type>) 1137 1138 result_type __2q = result_type(2) * __p.q(); 1139 result_type __q2 = __p.q() * __p.q(); 1140 result_type __q2p1 = result_type(1) + __q2; 1141 result_type __num = result_type(0.5L) * __q2p1; 1142 result_type __omega = __p.omega(); 1143 typename __gnu_cxx::arcsine_distribution<result_type>::param_type 1144 __pa(__num, __num / __q2); 1145 while (__f != __t) 1146 { 1147 result_type __x = this->_M_ad(__pa, __urng); 1148 result_type __y = this->_M_ed(__urng); 1149 *__f++ = (__2q / __q2p1) * std::sqrt(__omega * __x * __y); 1150 } 1151 } 1152 1153 template<typename _RealType, typename _CharT, typename _Traits> 1154 std::basic_ostream<_CharT, _Traits>& 1155 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1156 const hoyt_distribution<_RealType>& __x) 1157 { 1158 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1159 typedef typename __ostream_type::ios_base __ios_base; 1160 1161 const typename __ios_base::fmtflags __flags = __os.flags(); 1162 const _CharT __fill = __os.fill(); 1163 const std::streamsize __precision = __os.precision(); 1164 const _CharT __space = __os.widen(' '); 1165 __os.flags(__ios_base::scientific | __ios_base::left); 1166 __os.fill(__space); 1167 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1168 1169 __os << __x.q() << __space << __x.omega(); 1170 __os << __space << __x._M_ad; 1171 __os << __space << __x._M_ed; 1172 1173 __os.flags(__flags); 1174 __os.fill(__fill); 1175 __os.precision(__precision); 1176 return __os; 1177 } 1178 1179 template<typename _RealType, typename _CharT, typename _Traits> 1180 std::basic_istream<_CharT, _Traits>& 1181 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1182 hoyt_distribution<_RealType>& __x) 1183 { 1184 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1185 typedef typename __istream_type::ios_base __ios_base; 1186 1187 const typename __ios_base::fmtflags __flags = __is.flags(); 1188 __is.flags(__ios_base::dec | __ios_base::skipws); 1189 1190 _RealType __q, __omega; 1191 __is >> __q >> __omega; 1192 __is >> __x._M_ad; 1193 __is >> __x._M_ed; 1194 __x.param(typename hoyt_distribution<_RealType>:: 1195 param_type(__q, __omega)); 1196 1197 __is.flags(__flags); 1198 return __is; 1199 } 1200 1201 1202 template<typename _RealType> 1203 template<typename _OutputIterator, 1204 typename _UniformRandomNumberGenerator> 1205 void 1206 triangular_distribution<_RealType>:: 1207 __generate_impl(_OutputIterator __f, _OutputIterator __t, 1208 _UniformRandomNumberGenerator& __urng, 1209 const param_type& __param) 1210 { 1211 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 1212 result_type>) 1213 1214 while (__f != __t) 1215 *__f++ = this->operator()(__urng, __param); 1216 } 1217 1218 template<typename _RealType, typename _CharT, typename _Traits> 1219 std::basic_ostream<_CharT, _Traits>& 1220 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1221 const __gnu_cxx::triangular_distribution<_RealType>& __x) 1222 { 1223 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1224 typedef typename __ostream_type::ios_base __ios_base; 1225 1226 const typename __ios_base::fmtflags __flags = __os.flags(); 1227 const _CharT __fill = __os.fill(); 1228 const std::streamsize __precision = __os.precision(); 1229 const _CharT __space = __os.widen(' '); 1230 __os.flags(__ios_base::scientific | __ios_base::left); 1231 __os.fill(__space); 1232 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1233 1234 __os << __x.a() << __space << __x.b() << __space << __x.c(); 1235 1236 __os.flags(__flags); 1237 __os.fill(__fill); 1238 __os.precision(__precision); 1239 return __os; 1240 } 1241 1242 template<typename _RealType, typename _CharT, typename _Traits> 1243 std::basic_istream<_CharT, _Traits>& 1244 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1245 __gnu_cxx::triangular_distribution<_RealType>& __x) 1246 { 1247 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1248 typedef typename __istream_type::ios_base __ios_base; 1249 1250 const typename __ios_base::fmtflags __flags = __is.flags(); 1251 __is.flags(__ios_base::dec | __ios_base::skipws); 1252 1253 _RealType __a, __b, __c; 1254 __is >> __a >> __b >> __c; 1255 __x.param(typename __gnu_cxx::triangular_distribution<_RealType>:: 1256 param_type(__a, __b, __c)); 1257 1258 __is.flags(__flags); 1259 return __is; 1260 } 1261 1262 1263 template<typename _RealType> 1264 template<typename _UniformRandomNumberGenerator> 1265 typename von_mises_distribution<_RealType>::result_type 1266 von_mises_distribution<_RealType>:: 1267 operator()(_UniformRandomNumberGenerator& __urng, 1268 const param_type& __p) 1269 { 1270 const result_type __pi 1271 = __gnu_cxx::__math_constants<result_type>::__pi; 1272 std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 1273 __aurng(__urng); 1274 1275 result_type __f; 1276 while (1) 1277 { 1278 result_type __rnd = std::cos(__pi * __aurng()); 1279 __f = (result_type(1) + __p._M_r * __rnd) / (__p._M_r + __rnd); 1280 result_type __c = __p._M_kappa * (__p._M_r - __f); 1281 1282 result_type __rnd2 = __aurng(); 1283 if (__c * (result_type(2) - __c) > __rnd2) 1284 break; 1285 if (std::log(__c / __rnd2) >= __c - result_type(1)) 1286 break; 1287 } 1288 1289 result_type __res = std::acos(__f); 1290 #if _GLIBCXX_USE_C99_MATH_TR1 1291 __res = std::copysign(__res, __aurng() - result_type(0.5)); 1292 #else 1293 if (__aurng() < result_type(0.5)) 1294 __res = -__res; 1295 #endif 1296 __res += __p._M_mu; 1297 if (__res > __pi) 1298 __res -= result_type(2) * __pi; 1299 else if (__res < -__pi) 1300 __res += result_type(2) * __pi; 1301 return __res; 1302 } 1303 1304 template<typename _RealType> 1305 template<typename _OutputIterator, 1306 typename _UniformRandomNumberGenerator> 1307 void 1308 von_mises_distribution<_RealType>:: 1309 __generate_impl(_OutputIterator __f, _OutputIterator __t, 1310 _UniformRandomNumberGenerator& __urng, 1311 const param_type& __param) 1312 { 1313 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 1314 result_type>) 1315 1316 while (__f != __t) 1317 *__f++ = this->operator()(__urng, __param); 1318 } 1319 1320 template<typename _RealType, typename _CharT, typename _Traits> 1321 std::basic_ostream<_CharT, _Traits>& 1322 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1323 const __gnu_cxx::von_mises_distribution<_RealType>& __x) 1324 { 1325 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1326 typedef typename __ostream_type::ios_base __ios_base; 1327 1328 const typename __ios_base::fmtflags __flags = __os.flags(); 1329 const _CharT __fill = __os.fill(); 1330 const std::streamsize __precision = __os.precision(); 1331 const _CharT __space = __os.widen(' '); 1332 __os.flags(__ios_base::scientific | __ios_base::left); 1333 __os.fill(__space); 1334 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1335 1336 __os << __x.mu() << __space << __x.kappa(); 1337 1338 __os.flags(__flags); 1339 __os.fill(__fill); 1340 __os.precision(__precision); 1341 return __os; 1342 } 1343 1344 template<typename _RealType, typename _CharT, typename _Traits> 1345 std::basic_istream<_CharT, _Traits>& 1346 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1347 __gnu_cxx::von_mises_distribution<_RealType>& __x) 1348 { 1349 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1350 typedef typename __istream_type::ios_base __ios_base; 1351 1352 const typename __ios_base::fmtflags __flags = __is.flags(); 1353 __is.flags(__ios_base::dec | __ios_base::skipws); 1354 1355 _RealType __mu, __kappa; 1356 __is >> __mu >> __kappa; 1357 __x.param(typename __gnu_cxx::von_mises_distribution<_RealType>:: 1358 param_type(__mu, __kappa)); 1359 1360 __is.flags(__flags); 1361 return __is; 1362 } 1363 1364 1365 template<typename _UIntType> 1366 template<typename _UniformRandomNumberGenerator> 1367 typename hypergeometric_distribution<_UIntType>::result_type 1368 hypergeometric_distribution<_UIntType>:: 1369 operator()(_UniformRandomNumberGenerator& __urng, 1370 const param_type& __param) 1371 { 1372 std::__detail::_Adaptor<_UniformRandomNumberGenerator, double> 1373 __aurng(__urng); 1374 1375 result_type __a = __param.successful_size(); 1376 result_type __b = __param.total_size(); 1377 result_type __k = 0; 1378 1379 if (__param.total_draws() < __param.total_size() / 2) 1380 { 1381 for (result_type __i = 0; __i < __param.total_draws(); ++__i) 1382 { 1383 if (__b * __aurng() < __a) 1384 { 1385 ++__k; 1386 if (__k == __param.successful_size()) 1387 return __k; 1388 --__a; 1389 } 1390 --__b; 1391 } 1392 return __k; 1393 } 1394 else 1395 { 1396 for (result_type __i = 0; __i < __param.unsuccessful_size(); ++__i) 1397 { 1398 if (__b * __aurng() < __a) 1399 { 1400 ++__k; 1401 if (__k == __param.successful_size()) 1402 return __param.successful_size() - __k; 1403 --__a; 1404 } 1405 --__b; 1406 } 1407 return __param.successful_size() - __k; 1408 } 1409 } 1410 1411 template<typename _UIntType> 1412 template<typename _OutputIterator, 1413 typename _UniformRandomNumberGenerator> 1414 void 1415 hypergeometric_distribution<_UIntType>:: 1416 __generate_impl(_OutputIterator __f, _OutputIterator __t, 1417 _UniformRandomNumberGenerator& __urng, 1418 const param_type& __param) 1419 { 1420 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 1421 result_type>) 1422 1423 while (__f != __t) 1424 *__f++ = this->operator()(__urng); 1425 } 1426 1427 template<typename _UIntType, typename _CharT, typename _Traits> 1428 std::basic_ostream<_CharT, _Traits>& 1429 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1430 const __gnu_cxx::hypergeometric_distribution<_UIntType>& __x) 1431 { 1432 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1433 typedef typename __ostream_type::ios_base __ios_base; 1434 1435 const typename __ios_base::fmtflags __flags = __os.flags(); 1436 const _CharT __fill = __os.fill(); 1437 const std::streamsize __precision = __os.precision(); 1438 const _CharT __space = __os.widen(' '); 1439 __os.flags(__ios_base::scientific | __ios_base::left); 1440 __os.fill(__space); 1441 __os.precision(std::numeric_limits<_UIntType>::max_digits10); 1442 1443 __os << __x.total_size() << __space << __x.successful_size() << __space 1444 << __x.total_draws(); 1445 1446 __os.flags(__flags); 1447 __os.fill(__fill); 1448 __os.precision(__precision); 1449 return __os; 1450 } 1451 1452 template<typename _UIntType, typename _CharT, typename _Traits> 1453 std::basic_istream<_CharT, _Traits>& 1454 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1455 __gnu_cxx::hypergeometric_distribution<_UIntType>& __x) 1456 { 1457 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1458 typedef typename __istream_type::ios_base __ios_base; 1459 1460 const typename __ios_base::fmtflags __flags = __is.flags(); 1461 __is.flags(__ios_base::dec | __ios_base::skipws); 1462 1463 _UIntType __total_size, __successful_size, __total_draws; 1464 __is >> __total_size >> __successful_size >> __total_draws; 1465 __x.param(typename __gnu_cxx::hypergeometric_distribution<_UIntType>:: 1466 param_type(__total_size, __successful_size, __total_draws)); 1467 1468 __is.flags(__flags); 1469 return __is; 1470 } 1471 1472 1473 template<typename _RealType> 1474 template<typename _UniformRandomNumberGenerator> 1475 typename logistic_distribution<_RealType>::result_type 1476 logistic_distribution<_RealType>:: 1477 operator()(_UniformRandomNumberGenerator& __urng, 1478 const param_type& __p) 1479 { 1480 std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 1481 __aurng(__urng); 1482 1483 result_type __arg = result_type(1); 1484 while (__arg == result_type(1) || __arg == result_type(0)) 1485 __arg = __aurng(); 1486 return __p.a() 1487 + __p.b() * std::log(__arg / (result_type(1) - __arg)); 1488 } 1489 1490 template<typename _RealType> 1491 template<typename _OutputIterator, 1492 typename _UniformRandomNumberGenerator> 1493 void 1494 logistic_distribution<_RealType>:: 1495 __generate_impl(_OutputIterator __f, _OutputIterator __t, 1496 _UniformRandomNumberGenerator& __urng, 1497 const param_type& __p) 1498 { 1499 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 1500 result_type>) 1501 1502 std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> 1503 __aurng(__urng); 1504 1505 while (__f != __t) 1506 { 1507 result_type __arg = result_type(1); 1508 while (__arg == result_type(1) || __arg == result_type(0)) 1509 __arg = __aurng(); 1510 *__f++ = __p.a() 1511 + __p.b() * std::log(__arg / (result_type(1) - __arg)); 1512 } 1513 } 1514 1515 template<typename _RealType, typename _CharT, typename _Traits> 1516 std::basic_ostream<_CharT, _Traits>& 1517 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1518 const logistic_distribution<_RealType>& __x) 1519 { 1520 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1521 typedef typename __ostream_type::ios_base __ios_base; 1522 1523 const typename __ios_base::fmtflags __flags = __os.flags(); 1524 const _CharT __fill = __os.fill(); 1525 const std::streamsize __precision = __os.precision(); 1526 const _CharT __space = __os.widen(' '); 1527 __os.flags(__ios_base::scientific | __ios_base::left); 1528 __os.fill(__space); 1529 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1530 1531 __os << __x.a() << __space << __x.b(); 1532 1533 __os.flags(__flags); 1534 __os.fill(__fill); 1535 __os.precision(__precision); 1536 return __os; 1537 } 1538 1539 template<typename _RealType, typename _CharT, typename _Traits> 1540 std::basic_istream<_CharT, _Traits>& 1541 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1542 logistic_distribution<_RealType>& __x) 1543 { 1544 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1545 typedef typename __istream_type::ios_base __ios_base; 1546 1547 const typename __ios_base::fmtflags __flags = __is.flags(); 1548 __is.flags(__ios_base::dec | __ios_base::skipws); 1549 1550 _RealType __a, __b; 1551 __is >> __a >> __b; 1552 __x.param(typename logistic_distribution<_RealType>:: 1553 param_type(__a, __b)); 1554 1555 __is.flags(__flags); 1556 return __is; 1557 } 1558 1559 1560 namespace { 1561 1562 // Helper class for the uniform_on_sphere_distribution generation 1563 // function. 1564 template<std::size_t _Dimen, typename _RealType> 1565 class uniform_on_sphere_helper 1566 { 1567 typedef typename uniform_on_sphere_distribution<_Dimen, _RealType>:: 1568 result_type result_type; 1569 1570 public: 1571 template<typename _NormalDistribution, 1572 typename _UniformRandomNumberGenerator> 1573 result_type operator()(_NormalDistribution& __nd, 1574 _UniformRandomNumberGenerator& __urng) 1575 { 1576 result_type __ret; 1577 typename result_type::value_type __norm; 1578 1579 do 1580 { 1581 auto __sum = _RealType(0); 1582 1583 std::generate(__ret.begin(), __ret.end(), 1584 [&__nd, &__urng, &__sum](){ 1585 _RealType __t = __nd(__urng); 1586 __sum += __t * __t; 1587 return __t; }); 1588 __norm = std::sqrt(__sum); 1589 } 1590 while (__norm == _RealType(0) || ! __builtin_isfinite(__norm)); 1591 1592 std::transform(__ret.begin(), __ret.end(), __ret.begin(), 1593 [__norm](_RealType __val){ return __val / __norm; }); 1594 1595 return __ret; 1596 } 1597 }; 1598 1599 1600 template<typename _RealType> 1601 class uniform_on_sphere_helper<2, _RealType> 1602 { 1603 typedef typename uniform_on_sphere_distribution<2, _RealType>:: 1604 result_type result_type; 1605 1606 public: 1607 template<typename _NormalDistribution, 1608 typename _UniformRandomNumberGenerator> 1609 result_type operator()(_NormalDistribution&, 1610 _UniformRandomNumberGenerator& __urng) 1611 { 1612 result_type __ret; 1613 _RealType __sq; 1614 std::__detail::_Adaptor<_UniformRandomNumberGenerator, 1615 _RealType> __aurng(__urng); 1616 1617 do 1618 { 1619 __ret[0] = _RealType(2) * __aurng() - _RealType(1); 1620 __ret[1] = _RealType(2) * __aurng() - _RealType(1); 1621 1622 __sq = __ret[0] * __ret[0] + __ret[1] * __ret[1]; 1623 } 1624 while (__sq == _RealType(0) || __sq > _RealType(1)); 1625 1626 #if _GLIBCXX_USE_C99_MATH_TR1 1627 // Yes, we do not just use sqrt(__sq) because hypot() is more 1628 // accurate. 1629 auto __norm = std::hypot(__ret[0], __ret[1]); 1630 #else 1631 auto __norm = std::sqrt(__sq); 1632 #endif 1633 __ret[0] /= __norm; 1634 __ret[1] /= __norm; 1635 1636 return __ret; 1637 } 1638 }; 1639 1640 } 1641 1642 1643 template<std::size_t _Dimen, typename _RealType> 1644 template<typename _UniformRandomNumberGenerator> 1645 typename uniform_on_sphere_distribution<_Dimen, _RealType>::result_type 1646 uniform_on_sphere_distribution<_Dimen, _RealType>:: 1647 operator()(_UniformRandomNumberGenerator& __urng, 1648 const param_type& __p) 1649 { 1650 uniform_on_sphere_helper<_Dimen, _RealType> __helper; 1651 return __helper(_M_nd, __urng); 1652 } 1653 1654 template<std::size_t _Dimen, typename _RealType> 1655 template<typename _OutputIterator, 1656 typename _UniformRandomNumberGenerator> 1657 void 1658 uniform_on_sphere_distribution<_Dimen, _RealType>:: 1659 __generate_impl(_OutputIterator __f, _OutputIterator __t, 1660 _UniformRandomNumberGenerator& __urng, 1661 const param_type& __param) 1662 { 1663 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 1664 result_type>) 1665 1666 while (__f != __t) 1667 *__f++ = this->operator()(__urng, __param); 1668 } 1669 1670 template<std::size_t _Dimen, typename _RealType, typename _CharT, 1671 typename _Traits> 1672 std::basic_ostream<_CharT, _Traits>& 1673 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1674 const __gnu_cxx::uniform_on_sphere_distribution<_Dimen, 1675 _RealType>& __x) 1676 { 1677 return __os << __x._M_nd; 1678 } 1679 1680 template<std::size_t _Dimen, typename _RealType, typename _CharT, 1681 typename _Traits> 1682 std::basic_istream<_CharT, _Traits>& 1683 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1684 __gnu_cxx::uniform_on_sphere_distribution<_Dimen, 1685 _RealType>& __x) 1686 { 1687 return __is >> __x._M_nd; 1688 } 1689 1690 1691 namespace { 1692 1693 // Helper class for the uniform_inside_sphere_distribution generation 1694 // function. 1695 template<std::size_t _Dimen, bool _SmallDimen, typename _RealType> 1696 class uniform_inside_sphere_helper; 1697 1698 template<std::size_t _Dimen, typename _RealType> 1699 class uniform_inside_sphere_helper<_Dimen, false, _RealType> 1700 { 1701 using result_type 1702 = typename uniform_inside_sphere_distribution<_Dimen, _RealType>:: 1703 result_type; 1704 1705 public: 1706 template<typename _UniformOnSphereDistribution, 1707 typename _UniformRandomNumberGenerator> 1708 result_type 1709 operator()(_UniformOnSphereDistribution& __uosd, 1710 _UniformRandomNumberGenerator& __urng, 1711 _RealType __radius) 1712 { 1713 std::__detail::_Adaptor<_UniformRandomNumberGenerator, 1714 _RealType> __aurng(__urng); 1715 1716 _RealType __pow = 1 / _RealType(_Dimen); 1717 _RealType __urt = __radius * std::pow(__aurng(), __pow); 1718 result_type __ret = __uosd(__aurng); 1719 1720 std::transform(__ret.begin(), __ret.end(), __ret.begin(), 1721 [__urt](_RealType __val) 1722 { return __val * __urt; }); 1723 1724 return __ret; 1725 } 1726 }; 1727 1728 // Helper class for the uniform_inside_sphere_distribution generation 1729 // function specialized for small dimensions. 1730 template<std::size_t _Dimen, typename _RealType> 1731 class uniform_inside_sphere_helper<_Dimen, true, _RealType> 1732 { 1733 using result_type 1734 = typename uniform_inside_sphere_distribution<_Dimen, _RealType>:: 1735 result_type; 1736 1737 public: 1738 template<typename _UniformOnSphereDistribution, 1739 typename _UniformRandomNumberGenerator> 1740 result_type 1741 operator()(_UniformOnSphereDistribution&, 1742 _UniformRandomNumberGenerator& __urng, 1743 _RealType __radius) 1744 { 1745 result_type __ret; 1746 _RealType __sq; 1747 _RealType __radsq = __radius * __radius; 1748 std::__detail::_Adaptor<_UniformRandomNumberGenerator, 1749 _RealType> __aurng(__urng); 1750 1751 do 1752 { 1753 __sq = _RealType(0); 1754 for (int i = 0; i < _Dimen; ++i) 1755 { 1756 __ret[i] = _RealType(2) * __aurng() - _RealType(1); 1757 __sq += __ret[i] * __ret[i]; 1758 } 1759 } 1760 while (__sq > _RealType(1)); 1761 1762 for (int i = 0; i < _Dimen; ++i) 1763 __ret[i] *= __radius; 1764 1765 return __ret; 1766 } 1767 }; 1768 } // namespace 1769 1770 // 1771 // Experiments have shown that rejection is more efficient than transform 1772 // for dimensions less than 8. 1773 // 1774 template<std::size_t _Dimen, typename _RealType> 1775 template<typename _UniformRandomNumberGenerator> 1776 typename uniform_inside_sphere_distribution<_Dimen, _RealType>::result_type 1777 uniform_inside_sphere_distribution<_Dimen, _RealType>:: 1778 operator()(_UniformRandomNumberGenerator& __urng, 1779 const param_type& __p) 1780 { 1781 uniform_inside_sphere_helper<_Dimen, _Dimen < 8, _RealType> __helper; 1782 return __helper(_M_uosd, __urng, __p.radius()); 1783 } 1784 1785 template<std::size_t _Dimen, typename _RealType> 1786 template<typename _OutputIterator, 1787 typename _UniformRandomNumberGenerator> 1788 void 1789 uniform_inside_sphere_distribution<_Dimen, _RealType>:: 1790 __generate_impl(_OutputIterator __f, _OutputIterator __t, 1791 _UniformRandomNumberGenerator& __urng, 1792 const param_type& __param) 1793 { 1794 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, 1795 result_type>) 1796 1797 while (__f != __t) 1798 *__f++ = this->operator()(__urng, __param); 1799 } 1800 1801 template<std::size_t _Dimen, typename _RealType, typename _CharT, 1802 typename _Traits> 1803 std::basic_ostream<_CharT, _Traits>& 1804 operator<<(std::basic_ostream<_CharT, _Traits>& __os, 1805 const __gnu_cxx::uniform_inside_sphere_distribution<_Dimen, 1806 _RealType>& __x) 1807 { 1808 typedef std::basic_ostream<_CharT, _Traits> __ostream_type; 1809 typedef typename __ostream_type::ios_base __ios_base; 1810 1811 const typename __ios_base::fmtflags __flags = __os.flags(); 1812 const _CharT __fill = __os.fill(); 1813 const std::streamsize __precision = __os.precision(); 1814 const _CharT __space = __os.widen(' '); 1815 __os.flags(__ios_base::scientific | __ios_base::left); 1816 __os.fill(__space); 1817 __os.precision(std::numeric_limits<_RealType>::max_digits10); 1818 1819 __os << __x.radius() << __space << __x._M_uosd; 1820 1821 __os.flags(__flags); 1822 __os.fill(__fill); 1823 __os.precision(__precision); 1824 1825 return __os; 1826 } 1827 1828 template<std::size_t _Dimen, typename _RealType, typename _CharT, 1829 typename _Traits> 1830 std::basic_istream<_CharT, _Traits>& 1831 operator>>(std::basic_istream<_CharT, _Traits>& __is, 1832 __gnu_cxx::uniform_inside_sphere_distribution<_Dimen, 1833 _RealType>& __x) 1834 { 1835 typedef std::basic_istream<_CharT, _Traits> __istream_type; 1836 typedef typename __istream_type::ios_base __ios_base; 1837 1838 const typename __ios_base::fmtflags __flags = __is.flags(); 1839 __is.flags(__ios_base::dec | __ios_base::skipws); 1840 1841 _RealType __radius_val; 1842 __is >> __radius_val >> __x._M_uosd; 1843 __x.param(typename uniform_inside_sphere_distribution<_Dimen, _RealType>:: 1844 param_type(__radius_val)); 1845 1846 __is.flags(__flags); 1847 1848 return __is; 1849 } 1850 1851 _GLIBCXX_END_NAMESPACE_VERSION 1852 } // namespace __gnu_cxx 1853 1854 1855 #endif // _EXT_RANDOM_TCC
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