head 1.1; branch 1.1.1; access; symbols netbsd-11-0-RC4:1.1.1.10 netbsd-11-0-RC3:1.1.1.10 netbsd-11-0-RC2:1.1.1.10 netbsd-11-0-RC1:1.1.1.10 gcc-14-3-0:1.1.1.11 perseant-exfatfs-base-20250801:1.1.1.10 netbsd-11:1.1.1.10.0.4 netbsd-11-base:1.1.1.10 gcc-12-5-0:1.1.1.10 netbsd-10-1-RELEASE:1.1.1.9 perseant-exfatfs-base-20240630:1.1.1.10 gcc-12-4-0:1.1.1.10 perseant-exfatfs:1.1.1.10.0.2 perseant-exfatfs-base:1.1.1.10 netbsd-8-3-RELEASE:1.1.1.2 netbsd-9-4-RELEASE:1.1.1.4 netbsd-10-0-RELEASE:1.1.1.9 netbsd-10-0-RC6:1.1.1.9 netbsd-10-0-RC5:1.1.1.9 netbsd-10-0-RC4:1.1.1.9 netbsd-10-0-RC3:1.1.1.9 netbsd-10-0-RC2:1.1.1.9 netbsd-10-0-RC1:1.1.1.9 gcc-12-3-0:1.1.1.10 gcc-10-5-0:1.1.1.9 netbsd-10:1.1.1.9.0.6 netbsd-10-base:1.1.1.9 netbsd-9-3-RELEASE:1.1.1.4 gcc-10-4-0:1.1.1.9 cjep_sun2x-base1:1.1.1.9 cjep_sun2x:1.1.1.9.0.4 cjep_sun2x-base:1.1.1.9 cjep_staticlib_x-base1:1.1.1.9 netbsd-9-2-RELEASE:1.1.1.4 cjep_staticlib_x:1.1.1.9.0.2 cjep_staticlib_x-base:1.1.1.9 gcc-10-3-0:1.1.1.9 netbsd-9-1-RELEASE:1.1.1.4 gcc-9-3-0:1.1.1.8 gcc-7-5-0:1.1.1.6 phil-wifi-20200421:1.1.1.5 phil-wifi-20200411:1.1.1.5 is-mlppp:1.1.1.5.0.2 is-mlppp-base:1.1.1.5 phil-wifi-20200406:1.1.1.5 netbsd-8-2-RELEASE:1.1.1.2 gcc-8-4-0:1.1.1.7 netbsd-9-0-RELEASE:1.1.1.4 netbsd-9-0-RC2:1.1.1.4 netbsd-9-0-RC1:1.1.1.4 phil-wifi-20191119:1.1.1.5 gcc-8-3-0:1.1.1.5 netbsd-9:1.1.1.4.0.2 netbsd-9-base:1.1.1.4 phil-wifi-20190609:1.1.1.4 netbsd-8-1-RELEASE:1.1.1.2 netbsd-8-1-RC1:1.1.1.2 pgoyette-compat-merge-20190127:1.1.1.3.2.1 pgoyette-compat-20190127:1.1.1.4 gcc-7-4-0:1.1.1.4 pgoyette-compat-20190118:1.1.1.3 pgoyette-compat-1226:1.1.1.3 pgoyette-compat-1126:1.1.1.3 gcc-6-5-0:1.1.1.3 pgoyette-compat-1020:1.1.1.3 pgoyette-compat-0930:1.1.1.3 pgoyette-compat-0906:1.1.1.3 netbsd-7-2-RELEASE:1.1.1.1 pgoyette-compat-0728:1.1.1.3 netbsd-8-0-RELEASE:1.1.1.2 phil-wifi:1.1.1.3.0.4 phil-wifi-base:1.1.1.3 pgoyette-compat-0625:1.1.1.3 netbsd-8-0-RC2:1.1.1.2 pgoyette-compat-0521:1.1.1.3 pgoyette-compat-0502:1.1.1.3 pgoyette-compat-0422:1.1.1.3 netbsd-8-0-RC1:1.1.1.2 pgoyette-compat-0415:1.1.1.3 pgoyette-compat-0407:1.1.1.3 pgoyette-compat-0330:1.1.1.3 pgoyette-compat-0322:1.1.1.3 pgoyette-compat-0315:1.1.1.3 netbsd-7-1-2-RELEASE:1.1.1.1 pgoyette-compat:1.1.1.3.0.2 pgoyette-compat-base:1.1.1.3 gcc-6-4-0:1.1.1.3 netbsd-7-1-1-RELEASE:1.1.1.1 gcc-5-5-0:1.1.1.2 matt-nb8-mediatek:1.1.1.2.0.12 matt-nb8-mediatek-base:1.1.1.2 perseant-stdc-iso10646:1.1.1.2.0.10 perseant-stdc-iso10646-base:1.1.1.2 netbsd-8:1.1.1.2.0.8 netbsd-8-base:1.1.1.2 prg-localcount2-base3:1.1.1.2 prg-localcount2-base2:1.1.1.2 prg-localcount2-base1:1.1.1.2 prg-localcount2:1.1.1.2.0.6 prg-localcount2-base:1.1.1.2 pgoyette-localcount-20170426:1.1.1.2 bouyer-socketcan-base1:1.1.1.2 pgoyette-localcount-20170320:1.1.1.2 netbsd-7-1:1.1.1.1.0.14 netbsd-7-1-RELEASE:1.1.1.1 netbsd-7-1-RC2:1.1.1.1 netbsd-7-nhusb-base-20170116:1.1.1.1 bouyer-socketcan:1.1.1.2.0.4 bouyer-socketcan-base:1.1.1.2 pgoyette-localcount-20170107:1.1.1.2 netbsd-7-1-RC1:1.1.1.1 pgoyette-localcount-20161104:1.1.1.2 netbsd-7-0-2-RELEASE:1.1.1.1 localcount-20160914:1.1.1.2 netbsd-7-nhusb:1.1.1.1.0.12 netbsd-7-nhusb-base:1.1.1.1 pgoyette-localcount-20160806:1.1.1.2 pgoyette-localcount-20160726:1.1.1.2 pgoyette-localcount:1.1.1.2.0.2 pgoyette-localcount-base:1.1.1.2 gcc-5-4-0:1.1.1.2 netbsd-7-0-1-RELEASE:1.1.1.1 gcc-5-3-0:1.1.1.2 netbsd-7-0:1.1.1.1.0.10 netbsd-7-0-RELEASE:1.1.1.1 gcc-4-8-5-pre-gcc-old-import:1.1.1.1 netbsd-7-0-RC3:1.1.1.1 netbsd-7-0-RC2:1.1.1.1 post-gcc-4-8-5-merge:1.1.1.1 gcc-4-8-5:1.1.1.1 netbsd-7-0-RC1:1.1.1.1 gcc-4-8-4:1.1.1.1 gcc-4-8-20141009:1.1.1.1 tls-maxphys-base:1.1.1.1 tls-maxphys:1.1.1.1.0.8 netbsd-7:1.1.1.1.0.6 netbsd-7-base:1.1.1.1 gcc-4-8-3:1.1.1.1 yamt-pagecache:1.1.1.1.0.4 yamt-pagecache-base9:1.1.1.1 tls-earlyentropy:1.1.1.1.0.2 tls-earlyentropy-base:1.1.1.1 riastradh-xf86-video-intel-2-7-1-pre-2-21-15:1.1.1.1 riastradh-drm2-base3:1.1.1.1 gcc-4-8-3-pre-r208254:1.1.1.1 gcc-4-8-3-pre-r206687:1.1.1.1 FSF:1.1.1; 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commitid jtc8rnCzWiEEHGqB; 1.1.1.3.4.2 date 2020.04.13.07.58.38; author martin; state Exp; branches; next ; commitid X01YhRUPVUDaec4C; desc @@ 1.1 log @Initial revision @ text @// Random number extensions -*- C++ -*- // Copyright (C) 2012-2013 Free Software Foundation, Inc. // // This file is part of the GNU ISO C++ Library. This library is free // software; you can redistribute it and/or modify it under the // terms of the GNU General Public License as published by the // Free Software Foundation; either version 3, or (at your option) // any later version. // This library is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // Under Section 7 of GPL version 3, you are granted additional // permissions described in the GCC Runtime Library Exception, version // 3.1, as published by the Free Software Foundation. // You should have received a copy of the GNU General Public License and // a copy of the GCC Runtime Library Exception along with this program; // see the files COPYING3 and COPYING.RUNTIME respectively. If not, see // . /** @@file ext/random.tcc * This is an internal header file, included by other library headers. * Do not attempt to use it directly. @@headername{ext/random} */ #ifndef _EXT_RANDOM_TCC #define _EXT_RANDOM_TCC 1 #pragma GCC system_header namespace __gnu_cxx _GLIBCXX_VISIBILITY(default) { _GLIBCXX_BEGIN_NAMESPACE_VERSION #if __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__ template void simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>:: seed(_UIntType __seed) { _M_state32[0] = static_cast(__seed); for (size_t __i = 1; __i < _M_nstate32; ++__i) _M_state32[__i] = (1812433253UL * (_M_state32[__i - 1] ^ (_M_state32[__i - 1] >> 30)) + __i); _M_pos = state_size; _M_period_certification(); } namespace { inline uint32_t _Func1(uint32_t __x) { return (__x ^ (__x >> 27)) * UINT32_C(1664525); } inline uint32_t _Func2(uint32_t __x) { return (__x ^ (__x >> 27)) * UINT32_C(1566083941); } } template template typename std::enable_if::value>::type simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>:: seed(_Sseq& __q) { size_t __lag; if (_M_nstate32 >= 623) __lag = 11; else if (_M_nstate32 >= 68) __lag = 7; else if (_M_nstate32 >= 39) __lag = 5; else __lag = 3; const size_t __mid = (_M_nstate32 - __lag) / 2; std::fill(_M_state32, _M_state32 + _M_nstate32, UINT32_C(0x8b8b8b8b)); uint32_t __arr[_M_nstate32]; __q.generate(__arr + 0, __arr + _M_nstate32); uint32_t __r = _Func1(_M_state32[0] ^ _M_state32[__mid] ^ _M_state32[_M_nstate32 - 1]); _M_state32[__mid] += __r; __r += _M_nstate32; _M_state32[__mid + __lag] += __r; _M_state32[0] = __r; for (size_t __i = 1, __j = 0; __j < _M_nstate32; ++__j) { __r = _Func1(_M_state32[__i] ^ _M_state32[(__i + __mid) % _M_nstate32] ^ _M_state32[(__i + _M_nstate32 - 1) % _M_nstate32]); _M_state32[(__i + __mid) % _M_nstate32] += __r; __r += __arr[__j] + __i; _M_state32[(__i + __mid + __lag) % _M_nstate32] += __r; _M_state32[__i] = __r; __i = (__i + 1) % _M_nstate32; } for (size_t __j = 0; __j < _M_nstate32; ++__j) { const size_t __i = (__j + 1) % _M_nstate32; __r = _Func2(_M_state32[__i] + _M_state32[(__i + __mid) % _M_nstate32] + _M_state32[(__i + _M_nstate32 - 1) % _M_nstate32]); _M_state32[(__i + __mid) % _M_nstate32] ^= __r; __r -= __i; _M_state32[(__i + __mid + __lag) % _M_nstate32] ^= __r; _M_state32[__i] = __r; } _M_pos = state_size; _M_period_certification(); } template void simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>:: _M_period_certification(void) { static const uint32_t __parity[4] = { __parity1, __parity2, __parity3, __parity4 }; uint32_t __inner = 0; for (size_t __i = 0; __i < 4; ++__i) if (__parity[__i] != 0) __inner ^= _M_state32[__i] & __parity[__i]; if (__builtin_parity(__inner) & 1) return; for (size_t __i = 0; __i < 4; ++__i) if (__parity[__i] != 0) { _M_state32[__i] ^= 1 << (__builtin_ffs(__parity[__i]) - 1); return; } __builtin_unreachable(); } template void simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>:: discard(unsigned long long __z) { while (__z > state_size - _M_pos) { __z -= state_size - _M_pos; _M_gen_rand(); } _M_pos += __z; } #ifndef _GLIBCXX_OPT_HAVE_RANDOM_SFMT_GEN_READ namespace { template inline void __rshift(uint32_t *__out, const uint32_t *__in) { uint64_t __th = ((static_cast(__in[3]) << 32) | static_cast(__in[2])); uint64_t __tl = ((static_cast(__in[1]) << 32) | static_cast(__in[0])); uint64_t __oh = __th >> (__shift * 8); uint64_t __ol = __tl >> (__shift * 8); __ol |= __th << (64 - __shift * 8); __out[1] = static_cast(__ol >> 32); __out[0] = static_cast(__ol); __out[3] = static_cast(__oh >> 32); __out[2] = static_cast(__oh); } template inline void __lshift(uint32_t *__out, const uint32_t *__in) { uint64_t __th = ((static_cast(__in[3]) << 32) | static_cast(__in[2])); uint64_t __tl = ((static_cast(__in[1]) << 32) | static_cast(__in[0])); uint64_t __oh = __th << (__shift * 8); uint64_t __ol = __tl << (__shift * 8); __oh |= __tl >> (64 - __shift * 8); __out[1] = static_cast(__ol >> 32); __out[0] = static_cast(__ol); __out[3] = static_cast(__oh >> 32); __out[2] = static_cast(__oh); } template inline void __recursion(uint32_t *__r, const uint32_t *__a, const uint32_t *__b, const uint32_t *__c, const uint32_t *__d) { uint32_t __x[4]; uint32_t __y[4]; __lshift<__sl2>(__x, __a); __rshift<__sr2>(__y, __c); __r[0] = (__a[0] ^ __x[0] ^ ((__b[0] >> __sr1) & __msk1) ^ __y[0] ^ (__d[0] << __sl1)); __r[1] = (__a[1] ^ __x[1] ^ ((__b[1] >> __sr1) & __msk2) ^ __y[1] ^ (__d[1] << __sl1)); __r[2] = (__a[2] ^ __x[2] ^ ((__b[2] >> __sr1) & __msk3) ^ __y[2] ^ (__d[2] << __sl1)); __r[3] = (__a[3] ^ __x[3] ^ ((__b[3] >> __sr1) & __msk4) ^ __y[3] ^ (__d[3] << __sl1)); } } template void simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>:: _M_gen_rand(void) { const uint32_t *__r1 = &_M_state32[_M_nstate32 - 8]; const uint32_t *__r2 = &_M_state32[_M_nstate32 - 4]; static constexpr size_t __pos1_32 = __pos1 * 4; size_t __i; for (__i = 0; __i < _M_nstate32 - __pos1_32; __i += 4) { __recursion<__sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4> (&_M_state32[__i], &_M_state32[__i], &_M_state32[__i + __pos1_32], __r1, __r2); __r1 = __r2; __r2 = &_M_state32[__i]; } for (; __i < _M_nstate32; __i += 4) { __recursion<__sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4> (&_M_state32[__i], &_M_state32[__i], &_M_state32[__i + __pos1_32 - _M_nstate32], __r1, __r2); __r1 = __r2; __r2 = &_M_state32[__i]; } _M_pos = 0; } #endif #ifndef _GLIBCXX_OPT_HAVE_RANDOM_SFMT_OPERATOREQUAL template bool operator==(const __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>& __lhs, const __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>& __rhs) { typedef __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4> __engine; return (std::equal(__lhs._M_stateT, __lhs._M_stateT + __engine::state_size, __rhs._M_stateT) && __lhs._M_pos == __rhs._M_pos); } #endif template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); __os.fill(__space); for (size_t __i = 0; __i < __x._M_nstate32; ++__i) __os << __x._M_state32[__i] << __space; __os << __x._M_pos; __os.flags(__flags); __os.fill(__fill); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); for (size_t __i = 0; __i < __x._M_nstate32; ++__i) __is >> __x._M_state32[__i]; __is >> __x._M_pos; __is.flags(__flags); return __is; } #endif // __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__ /** * Iteration method due to M.D. Jhnk. * * M.D. Jhnk, Erzeugung von betaverteilten und gammaverteilten * Zufallszahlen, Metrika, Volume 8, 1964 */ template template typename beta_distribution<_RealType>::result_type beta_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __param) { std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); result_type __x, __y; do { __x = std::exp(std::log(__aurng()) / __param.alpha()); __y = std::exp(std::log(__aurng()) / __param.beta()); } while (__x + __y > result_type(1)); return __x / (__x + __y); } template template void beta_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); while (__f != __t) { result_type __x, __y; do { __x = std::exp(std::log(__aurng()) / __param.alpha()); __y = std::exp(std::log(__aurng()) / __param.beta()); } while (__x + __y > result_type(1)); *__f++ = __x / (__x + __y); } } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::beta_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.alpha() << __space << __x.beta(); __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::beta_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __alpha_val, __beta_val; __is >> __alpha_val >> __beta_val; __x.param(typename __gnu_cxx::beta_distribution<_RealType>:: param_type(__alpha_val, __beta_val)); __is.flags(__flags); return __is; } template template void normal_mv_distribution<_Dimen, _RealType>::param_type:: _M_init_full(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varcovbegin, _InputIterator2 __varcovend) { __glibcxx_function_requires(_InputIteratorConcept<_InputIterator1>) __glibcxx_function_requires(_InputIteratorConcept<_InputIterator2>) std::fill(std::copy(__meanbegin, __meanend, _M_mean.begin()), _M_mean.end(), _RealType(0)); // Perform the Cholesky decomposition auto __w = _M_t.begin(); for (size_t __j = 0; __j < _Dimen; ++__j) { _RealType __sum = _RealType(0); auto __slitbegin = __w; auto __cit = _M_t.begin(); for (size_t __i = 0; __i < __j; ++__i) { auto __slit = __slitbegin; _RealType __s = *__varcovbegin++; for (size_t __k = 0; __k < __i; ++__k) __s -= *__slit++ * *__cit++; *__w++ = __s /= *__cit++; __sum += __s * __s; } __sum = *__varcovbegin - __sum; if (__builtin_expect(__sum <= _RealType(0), 0)) std::__throw_runtime_error(__N("normal_mv_distribution::" "param_type::_M_init_full")); *__w++ = std::sqrt(__sum); std::advance(__varcovbegin, _Dimen - __j); } } template template void normal_mv_distribution<_Dimen, _RealType>::param_type:: _M_init_lower(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varcovbegin, _InputIterator2 __varcovend) { __glibcxx_function_requires(_InputIteratorConcept<_InputIterator1>) __glibcxx_function_requires(_InputIteratorConcept<_InputIterator2>) std::fill(std::copy(__meanbegin, __meanend, _M_mean.begin()), _M_mean.end(), _RealType(0)); // Perform the Cholesky decomposition auto __w = _M_t.begin(); for (size_t __j = 0; __j < _Dimen; ++__j) { _RealType __sum = _RealType(0); auto __slitbegin = __w; auto __cit = _M_t.begin(); for (size_t __i = 0; __i < __j; ++__i) { auto __slit = __slitbegin; _RealType __s = *__varcovbegin++; for (size_t __k = 0; __k < __i; ++__k) __s -= *__slit++ * *__cit++; *__w++ = __s /= *__cit++; __sum += __s * __s; } __sum = *__varcovbegin++ - __sum; if (__builtin_expect(__sum <= _RealType(0), 0)) std::__throw_runtime_error(__N("normal_mv_distribution::" "param_type::_M_init_full")); *__w++ = std::sqrt(__sum); } } template template void normal_mv_distribution<_Dimen, _RealType>::param_type:: _M_init_diagonal(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varbegin, _InputIterator2 __varend) { __glibcxx_function_requires(_InputIteratorConcept<_InputIterator1>) __glibcxx_function_requires(_InputIteratorConcept<_InputIterator2>) std::fill(std::copy(__meanbegin, __meanend, _M_mean.begin()), _M_mean.end(), _RealType(0)); auto __w = _M_t.begin(); size_t __step = 0; while (__varbegin != __varend) { std::fill_n(__w, __step, _RealType(0)); __w += __step++; if (__builtin_expect(*__varbegin < _RealType(0), 0)) std::__throw_runtime_error(__N("normal_mv_distribution::" "param_type::_M_init_diagonal")); *__w++ = std::sqrt(*__varbegin++); } } template template typename normal_mv_distribution<_Dimen, _RealType>::result_type normal_mv_distribution<_Dimen, _RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __param) { result_type __ret; _M_nd.__generate(__ret.begin(), __ret.end(), __urng); auto __t_it = __param._M_t.crbegin(); for (size_t __i = _Dimen; __i > 0; --__i) { _RealType __sum = _RealType(0); for (size_t __j = __i; __j > 0; --__j) __sum += __ret[__j - 1] * *__t_it++; __ret[__i - 1] = __sum; } return __ret; } template template void normal_mv_distribution<_Dimen, _RealType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_Mutable_ForwardIteratorConcept< _ForwardIterator>) while (__f != __t) *__f++ = this->operator()(__urng, __param); } template bool operator==(const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __d1, const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __d2) { return __d1._M_param == __d2._M_param && __d1._M_nd == __d2._M_nd; } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); auto __mean = __x._M_param.mean(); for (auto __it : __mean) __os << __it << __space; auto __t = __x._M_param.varcov(); for (auto __it : __t) __os << __it << __space; __os << __x._M_nd; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); std::array<_RealType, _Dimen> __mean; for (auto& __it : __mean) __is >> __it; std::array<_RealType, _Dimen * (_Dimen + 1) / 2> __varcov; for (auto& __it : __varcov) __is >> __it; __is >> __x._M_nd; __x.param(typename normal_mv_distribution<_Dimen, _RealType>:: param_type(__mean.begin(), __mean.end(), __varcov.begin(), __varcov.end())); __is.flags(__flags); return __is; } template template void rice_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) while (__f != __t) { typename std::normal_distribution::param_type __px(__p.nu(), __p.sigma()), __py(result_type(0), __p.sigma()); result_type __x = this->_M_ndx(__px, __urng); result_type __y = this->_M_ndy(__py, __urng); #if _GLIBCXX_USE_C99_MATH_TR1 *__f++ = std::hypot(__x, __y); #else *__f++ = std::sqrt(__x * __x + __y * __y); #endif } } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const rice_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.nu() << __space << __x.sigma(); __os << __space << __x._M_ndx; __os << __space << __x._M_ndy; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, rice_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __nu_val, __sigma_val; __is >> __nu_val >> __sigma_val; __is >> __x._M_ndx; __is >> __x._M_ndy; __x.param(typename rice_distribution<_RealType>:: param_type(__nu_val, __sigma_val)); __is.flags(__flags); return __is; } template template void nakagami_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) typename std::gamma_distribution::param_type __pg(__p.mu(), __p.omega() / __p.mu()); while (__f != __t) *__f++ = std::sqrt(this->_M_gd(__pg, __urng)); } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const nakagami_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.mu() << __space << __x.omega(); __os << __space << __x._M_gd; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, nakagami_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __mu_val, __omega_val; __is >> __mu_val >> __omega_val; __is >> __x._M_gd; __x.param(typename nakagami_distribution<_RealType>:: param_type(__mu_val, __omega_val)); __is.flags(__flags); return __is; } template template void pareto_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) result_type __mu_val = __p.mu(); result_type __malphinv = -result_type(1) / __p.alpha(); while (__f != __t) *__f++ = __mu_val * std::pow(this->_M_ud(__urng), __malphinv); } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const pareto_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.alpha() << __space << __x.mu(); __os << __space << __x._M_ud; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, pareto_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __alpha_val, __mu_val; __is >> __alpha_val >> __mu_val; __is >> __x._M_ud; __x.param(typename pareto_distribution<_RealType>:: param_type(__alpha_val, __mu_val)); __is.flags(__flags); return __is; } template template typename k_distribution<_RealType>::result_type k_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng) { result_type __x = this->_M_gd1(__urng); result_type __y = this->_M_gd2(__urng); return std::sqrt(__x * __y); } template template typename k_distribution<_RealType>::result_type k_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { typename std::gamma_distribution::param_type __p1(__p.lambda(), result_type(1) / __p.lambda()), __p2(__p.nu(), __p.mu() / __p.nu()); result_type __x = this->_M_gd1(__p1, __urng); result_type __y = this->_M_gd2(__p2, __urng); return std::sqrt(__x * __y); } template template void k_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) typename std::gamma_distribution::param_type __p1(__p.lambda(), result_type(1) / __p.lambda()), __p2(__p.nu(), __p.mu() / __p.nu()); while (__f != __t) { result_type __x = this->_M_gd1(__p1, __urng); result_type __y = this->_M_gd2(__p2, __urng); *__f++ = std::sqrt(__x * __y); } } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const k_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.lambda() << __space << __x.mu() << __space << __x.nu(); __os << __space << __x._M_gd1; __os << __space << __x._M_gd2; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, k_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __lambda_val, __mu_val, __nu_val; __is >> __lambda_val >> __mu_val >> __nu_val; __is >> __x._M_gd1; __is >> __x._M_gd2; __x.param(typename k_distribution<_RealType>:: param_type(__lambda_val, __mu_val, __nu_val)); __is.flags(__flags); return __is; } template template void arcsine_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) result_type __dif = __p.b() - __p.a(); result_type __sum = __p.a() + __p.b(); while (__f != __t) { result_type __x = std::sin(this->_M_ud(__urng)); *__f++ = (__x * __dif + __sum) / result_type(2); } } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const arcsine_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.a() << __space << __x.b(); __os << __space << __x._M_ud; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, arcsine_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __a, __b; __is >> __a >> __b; __is >> __x._M_ud; __x.param(typename arcsine_distribution<_RealType>:: param_type(__a, __b)); __is.flags(__flags); return __is; } template template typename hoyt_distribution<_RealType>::result_type hoyt_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng) { result_type __x = this->_M_ad(__urng); result_type __y = this->_M_ed(__urng); return (result_type(2) * this->q() / (result_type(1) + this->q() * this->q())) * std::sqrt(this->omega() * __x * __y); } template template typename hoyt_distribution<_RealType>::result_type hoyt_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { result_type __q2 = __p.q() * __p.q(); result_type __num = result_type(0.5L) * (result_type(1) + __q2); typename __gnu_cxx::arcsine_distribution::param_type __pa(__num, __num / __q2); result_type __x = this->_M_ad(__pa, __urng); result_type __y = this->_M_ed(__urng); return (result_type(2) * __p.q() / (result_type(1) + __q2)) * std::sqrt(__p.omega() * __x * __y); } template template void hoyt_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) result_type __2q = result_type(2) * __p.q(); result_type __q2 = __p.q() * __p.q(); result_type __q2p1 = result_type(1) + __q2; result_type __num = result_type(0.5L) * __q2p1; result_type __omega = __p.omega(); typename __gnu_cxx::arcsine_distribution::param_type __pa(__num, __num / __q2); while (__f != __t) { result_type __x = this->_M_ad(__pa, __urng); result_type __y = this->_M_ed(__urng); *__f++ = (__2q / __q2p1) * std::sqrt(__omega * __x * __y); } } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const hoyt_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.q() << __space << __x.omega(); __os << __space << __x._M_ad; __os << __space << __x._M_ed; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, hoyt_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __q, __omega; __is >> __q >> __omega; __is >> __x._M_ad; __is >> __x._M_ed; __x.param(typename hoyt_distribution<_RealType>:: param_type(__q, __omega)); __is.flags(__flags); return __is; } template template void triangular_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) while (__f != __t) *__f++ = this->operator()(__urng, __param); } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::triangular_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.a() << __space << __x.b() << __space << __x.c(); __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::triangular_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __a, __b, __c; __is >> __a >> __b >> __c; __x.param(typename __gnu_cxx::triangular_distribution<_RealType>:: param_type(__a, __b, __c)); __is.flags(__flags); return __is; } template template void von_mises_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) while (__f != __t) *__f++ = this->operator()(__urng, __param); } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::von_mises_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.mu() << __space << __x.kappa(); __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::von_mises_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __mu, __kappa; __is >> __mu >> __kappa; __x.param(typename __gnu_cxx::von_mises_distribution<_RealType>:: param_type(__mu, __kappa)); __is.flags(__flags); return __is; } _GLIBCXX_END_NAMESPACE_VERSION } // namespace #endif // _EXT_RANDOM_TCC @ 1.1.1.1 log @import GCC 4.8 branch at r206687. highlights from: http://gcc.gnu.org/gcc-4.6/changes.html GCC now has stricter checks for invalid command-line options New -Wunused-but-set-variable and -Wunused-but-set-parameter warnings Many platforms have been obsoleted Link-time optimization improvements A new switch -fstack-usage has been added A new function attribute leaf was introduced A new warning, enabled by -Wdouble-promotion Support for selectively enabling and disabling warnings via #pragma GCC diagnostic has been added There is now experimental support for some features from the upcoming C1X revision of the ISO C standard Improved experimental support for the upcoming C++0x ISO C++ standard G++ now issues clearer diagnostics in several cases Updates for ARM, x86, MIPS, PPC/PPC64, SPARC Darwin, FreeBSD, Solaris 2, MinGW and Cygwin now all support __float128 on 32-bit and 64-bit x86 targets. [*1] highlights from: http://gcc.gnu.org/gcc-4.7/changes.html The -fconserve-space flag has been deprecated Support for a new parameter --param case-values-threshold=n was added Interprocedural and Link-time optimization improvements A new built-in, __builtin_assume_aligned, has been added A new warning option -Wunused-local-typedefs was added A new experimental command-line option -ftrack-macro-expansion was added Support for atomic operations specifying the C++11/C11 memory model has been added There is support for some more features from the C11 revision of the ISO C standard Improved experimental support for the new ISO C++ standard, C++11 Updates for ARM, x86, MIPS, PPC/PPC64, SH, SPARC, TILE* A new option (-grecord-gcc-switches) was added highlights from: http://gcc.gnu.org/gcc-4.8/changes.html GCC now uses C++ as its implementation language. This means that to build GCC from sources, you will need a C++ compiler that understands C++ 2003 DWARF4 is now the default when generating DWARF debug information A new general optimization level, -Og, has been introduced A new option -ftree-partial-pre was added The option -fconserve-space has been removed The command-line options -fipa-struct-reorg and -fipa-matrix-reorg have been removed Interprocedural and Link-time optimization improvements AddressSanitizer, a fast memory error detector, has been added [*2] A new -Wsizeof-pointer-memaccess warning has been added G++ now supports a -std=c++1y option for experimentation with features proposed for the next revision of the standard, expected around 2014 Improved experimental support for the new ISO C++ standard, C++11 A new port has been added to support AArch64 Updates for ARM, x86, MIPS, PPC/PPC64, SH, SPARC, TILE* [*1] we should support this too! [*2] we should look into this. https://code.google.com/p/address-sanitizer/ @ text @@ 1.1.1.2 log @import GCC 5.3.0. see these urls for details which are too large to include here: http://gcc.gnu.org/gcc-4.9/changes.html http://gcc.gnu.org/gcc-5/changes.html (note that GCC 5.x is a release stream like GCC 4.9.x, 4.8.x, etc.) the main issues we will have are: The default mode for C is now -std=gnu11 instead of -std=gnu89. ARM: The deprecated option -mwords-little-endian has been removed. The options -mapcs, -mapcs-frame, -mtpcs-frame and -mtpcs-leaf-frame which are only applicable to the old ABI have been deprecated. MIPS: The o32 ABI has been modified and extended. The o32 64-bit floating-point register support is now obsolete and has been removed. It has been replaced by three ABI extensions FPXX, FP64A, and FP64. The meaning of the -mfp64 command-line option has changed. It is now used to enable the FP64A and FP64 ABI extensions. @ text @d3 1 a3 1 // Copyright (C) 2012-2015 Free Software Foundation, Inc. d35 1 a1251 41 template typename von_mises_distribution<_RealType>::result_type von_mises_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { const result_type __pi = __gnu_cxx::__math_constants::__pi; std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); result_type __f; while (1) { result_type __rnd = std::cos(__pi * __aurng()); __f = (result_type(1) + __p._M_r * __rnd) / (__p._M_r + __rnd); result_type __c = __p._M_kappa * (__p._M_r - __f); result_type __rnd2 = __aurng(); if (__c * (result_type(2) - __c) > __rnd2) break; if (std::log(__c / __rnd2) >= __c - result_type(1)) break; } result_type __res = std::acos(__f); #if _GLIBCXX_USE_C99_MATH_TR1 __res = std::copysign(__res, __aurng() - result_type(0.5)); #else if (__aurng() < result_type(0.5)) __res = -__res; #endif __res += __p._M_mu; if (__res > __pi) __res -= result_type(2) * __pi; else if (__res < -__pi) __res += result_type(2) * __pi; return __res; } template a1309 322 template template typename hypergeometric_distribution<_UIntType>::result_type hypergeometric_distribution<_UIntType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __param) { std::__detail::_Adaptor<_UniformRandomNumberGenerator, double> __aurng(__urng); result_type __a = __param.successful_size(); result_type __b = __param.total_size(); result_type __k = 0; if (__param.total_draws() < __param.total_size() / 2) { for (result_type __i = 0; __i < __param.total_draws(); ++__i) { if (__b * __aurng() < __a) { ++__k; if (__k == __param.successful_size()) return __k; --__a; } --__b; } return __k; } else { for (result_type __i = 0; __i < __param.unsuccessful_size(); ++__i) { if (__b * __aurng() < __a) { ++__k; if (__k == __param.successful_size()) return __param.successful_size() - __k; --__a; } --__b; } return __param.successful_size() - __k; } } template template void hypergeometric_distribution<_UIntType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) while (__f != __t) *__f++ = this->operator()(__urng); } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::hypergeometric_distribution<_UIntType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_UIntType>::max_digits10); __os << __x.total_size() << __space << __x.successful_size() << __space << __x.total_draws(); __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::hypergeometric_distribution<_UIntType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _UIntType __total_size, __successful_size, __total_draws; __is >> __total_size >> __successful_size >> __total_draws; __x.param(typename __gnu_cxx::hypergeometric_distribution<_UIntType>:: param_type(__total_size, __successful_size, __total_draws)); __is.flags(__flags); return __is; } template template typename logistic_distribution<_RealType>::result_type logistic_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); result_type __arg = result_type(1); while (__arg == result_type(1) || __arg == result_type(0)) __arg = __aurng(); return __p.a() + __p.b() * std::log(__arg / (result_type(1) - __arg)); } template template void logistic_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); while (__f != __t) { result_type __arg = result_type(1); while (__arg == result_type(1) || __arg == result_type(0)) __arg = __aurng(); *__f++ = __p.a() + __p.b() * std::log(__arg / (result_type(1) - __arg)); } } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const logistic_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.a() << __space << __x.b(); __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, logistic_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __a, __b; __is >> __a >> __b; __x.param(typename logistic_distribution<_RealType>:: param_type(__a, __b)); __is.flags(__flags); return __is; } namespace { // Helper class for the uniform_on_sphere_distribution generation // function. template class uniform_on_sphere_helper { typedef typename uniform_on_sphere_distribution<_Dimen, _RealType>:: result_type result_type; public: template result_type operator()(_NormalDistribution& __nd, _UniformRandomNumberGenerator& __urng) { result_type __ret; typename result_type::value_type __norm; do { auto __sum = _RealType(0); std::generate(__ret.begin(), __ret.end(), [&__nd, &__urng, &__sum](){ _RealType __t = __nd(__urng); __sum += __t * __t; return __t; }); __norm = std::sqrt(__sum); } while (__norm == _RealType(0) || ! std::isfinite(__norm)); std::transform(__ret.begin(), __ret.end(), __ret.begin(), [__norm](_RealType __val){ return __val / __norm; }); return __ret; } }; template class uniform_on_sphere_helper<2, _RealType> { typedef typename uniform_on_sphere_distribution<2, _RealType>:: result_type result_type; public: template result_type operator()(_NormalDistribution&, _UniformRandomNumberGenerator& __urng) { result_type __ret; _RealType __sq; std::__detail::_Adaptor<_UniformRandomNumberGenerator, _RealType> __aurng(__urng); do { __ret[0] = _RealType(2) * __aurng() - _RealType(1); __ret[1] = _RealType(2) * __aurng() - _RealType(1); __sq = __ret[0] * __ret[0] + __ret[1] * __ret[1]; } while (__sq == _RealType(0) || __sq > _RealType(1)); #if _GLIBCXX_USE_C99_MATH_TR1 // Yes, we do not just use sqrt(__sq) because hypot() is more // accurate. auto __norm = std::hypot(__ret[0], __ret[1]); #else auto __norm = std::sqrt(__sq); #endif __ret[0] /= __norm; __ret[1] /= __norm; return __ret; } }; } template template typename uniform_on_sphere_distribution<_Dimen, _RealType>::result_type uniform_on_sphere_distribution<_Dimen, _RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { uniform_on_sphere_helper<_Dimen, _RealType> __helper; return __helper(_M_nd, __urng); } template template void uniform_on_sphere_distribution<_Dimen, _RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) while (__f != __t) *__f++ = this->operator()(__urng, __param); } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::uniform_on_sphere_distribution<_Dimen, _RealType>& __x) { return __os << __x._M_nd; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::uniform_on_sphere_distribution<_Dimen, _RealType>& __x) { return __is >> __x._M_nd; } @ 1.1.1.3 log @import GCC 6.4.0. see this url for details which are too large to include here: http://gcc.gnu.org/gcc-6/changes.html the main visible changes appear to be: - The default mode for C++ is now -std=gnu++14 instead of -std=gnu++98. - The C and C++ compilers now support attributes on enumerators. - Diagnostics can now contain "fix-it hints" - more warnings (some added to -Wall) @ text @d3 1 a3 1 // Copyright (C) 2012-2016 Free Software Foundation, Inc. d1573 1 a1573 1 while (__norm == _RealType(0) || ! __builtin_isfinite(__norm)); @ 1.1.1.3.4.1 log @Sync with HEAD @ text @d3 1 a3 1 // Copyright (C) 2012-2017 Free Software Foundation, Inc. d443 1 a443 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d728 1 a728 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d802 1 a802 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d866 1 a866 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d956 1 a956 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1027 1 a1027 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1124 1 a1124 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1199 1 a1199 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1300 1 a1300 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1406 1 a1406 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1484 1 a1484 3 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1646 1 a1646 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) a1671 161 namespace { // Helper class for the uniform_inside_sphere_distribution generation // function. template class uniform_inside_sphere_helper; template class uniform_inside_sphere_helper<_Dimen, false, _RealType> { using result_type = typename uniform_inside_sphere_distribution<_Dimen, _RealType>:: result_type; public: template result_type operator()(_UniformOnSphereDistribution& __uosd, _UniformRandomNumberGenerator& __urng, _RealType __radius) { std::__detail::_Adaptor<_UniformRandomNumberGenerator, _RealType> __aurng(__urng); _RealType __pow = 1 / _RealType(_Dimen); _RealType __urt = __radius * std::pow(__aurng(), __pow); result_type __ret = __uosd(__aurng); std::transform(__ret.begin(), __ret.end(), __ret.begin(), [__urt](_RealType __val) { return __val * __urt; }); return __ret; } }; // Helper class for the uniform_inside_sphere_distribution generation // function specialized for small dimensions. template class uniform_inside_sphere_helper<_Dimen, true, _RealType> { using result_type = typename uniform_inside_sphere_distribution<_Dimen, _RealType>:: result_type; public: template result_type operator()(_UniformOnSphereDistribution&, _UniformRandomNumberGenerator& __urng, _RealType __radius) { result_type __ret; _RealType __sq; _RealType __radsq = __radius * __radius; std::__detail::_Adaptor<_UniformRandomNumberGenerator, _RealType> __aurng(__urng); do { __sq = _RealType(0); for (int i = 0; i < _Dimen; ++i) { __ret[i] = _RealType(2) * __aurng() - _RealType(1); __sq += __ret[i] * __ret[i]; } } while (__sq > _RealType(1)); for (int i = 0; i < _Dimen; ++i) __ret[i] *= __radius; return __ret; } }; } // namespace // // Experiments have shown that rejection is more efficient than transform // for dimensions less than 8. // template template typename uniform_inside_sphere_distribution<_Dimen, _RealType>::result_type uniform_inside_sphere_distribution<_Dimen, _RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { uniform_inside_sphere_helper<_Dimen, _Dimen < 8, _RealType> __helper; return __helper(_M_uosd, __urng, __p.radius()); } template template void uniform_inside_sphere_distribution<_Dimen, _RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) while (__f != __t) *__f++ = this->operator()(__urng, __param); } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::uniform_inside_sphere_distribution<_Dimen, _RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.radius() << __space << __x._M_uosd; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::uniform_inside_sphere_distribution<_Dimen, _RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __radius_val; __is >> __radius_val >> __x._M_uosd; __x.param(typename uniform_inside_sphere_distribution<_Dimen, _RealType>:: param_type(__radius_val)); __is.flags(__flags); return __is; } d1673 1 a1673 1 } // namespace __gnu_cxx @ 1.1.1.3.4.2 log @Mostly merge changes from HEAD upto 20200411 @ text @d3 1 a3 1 // Copyright (C) 2012-2018 Free Software Foundation, Inc. @ 1.1.1.3.2.1 log @Sync with HEAD @ text @d3 1 a3 1 // Copyright (C) 2012-2017 Free Software Foundation, Inc. d443 1 a443 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d728 1 a728 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d802 1 a802 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d866 1 a866 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d956 1 a956 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1027 1 a1027 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1124 1 a1124 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1199 1 a1199 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1300 1 a1300 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1406 1 a1406 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1484 1 a1484 3 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1646 1 a1646 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) a1671 161 namespace { // Helper class for the uniform_inside_sphere_distribution generation // function. template class uniform_inside_sphere_helper; template class uniform_inside_sphere_helper<_Dimen, false, _RealType> { using result_type = typename uniform_inside_sphere_distribution<_Dimen, _RealType>:: result_type; public: template result_type operator()(_UniformOnSphereDistribution& __uosd, _UniformRandomNumberGenerator& __urng, _RealType __radius) { std::__detail::_Adaptor<_UniformRandomNumberGenerator, _RealType> __aurng(__urng); _RealType __pow = 1 / _RealType(_Dimen); _RealType __urt = __radius * std::pow(__aurng(), __pow); result_type __ret = __uosd(__aurng); std::transform(__ret.begin(), __ret.end(), __ret.begin(), [__urt](_RealType __val) { return __val * __urt; }); return __ret; } }; // Helper class for the uniform_inside_sphere_distribution generation // function specialized for small dimensions. template class uniform_inside_sphere_helper<_Dimen, true, _RealType> { using result_type = typename uniform_inside_sphere_distribution<_Dimen, _RealType>:: result_type; public: template result_type operator()(_UniformOnSphereDistribution&, _UniformRandomNumberGenerator& __urng, _RealType __radius) { result_type __ret; _RealType __sq; _RealType __radsq = __radius * __radius; std::__detail::_Adaptor<_UniformRandomNumberGenerator, _RealType> __aurng(__urng); do { __sq = _RealType(0); for (int i = 0; i < _Dimen; ++i) { __ret[i] = _RealType(2) * __aurng() - _RealType(1); __sq += __ret[i] * __ret[i]; } } while (__sq > _RealType(1)); for (int i = 0; i < _Dimen; ++i) __ret[i] *= __radius; return __ret; } }; } // namespace // // Experiments have shown that rejection is more efficient than transform // for dimensions less than 8. // template template typename uniform_inside_sphere_distribution<_Dimen, _RealType>::result_type uniform_inside_sphere_distribution<_Dimen, _RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { uniform_inside_sphere_helper<_Dimen, _Dimen < 8, _RealType> __helper; return __helper(_M_uosd, __urng, __p.radius()); } template template void uniform_inside_sphere_distribution<_Dimen, _RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) while (__f != __t) *__f++ = this->operator()(__urng, __param); } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::uniform_inside_sphere_distribution<_Dimen, _RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.radius() << __space << __x._M_uosd; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::uniform_inside_sphere_distribution<_Dimen, _RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __radius_val; __is >> __radius_val >> __x._M_uosd; __x.param(typename uniform_inside_sphere_distribution<_Dimen, _RealType>:: param_type(__radius_val)); __is.flags(__flags); return __is; } d1673 1 a1673 1 } // namespace __gnu_cxx @ 1.1.1.4 log @import GCC 7.4.0. main changes include: The non-standard C++0x type traits has_trivial_default_constructor, has_trivial_copy_constructor and has_trivial_copy_assign have been removed. On ARM targets (arm*-*-*), a bug introduced in GCC 5 that affects conformance to the procedure call standard (AAPCS) has been fixed. Many optimiser improvements DWARF-5 support. Many new and enhanced warnings. Warnings about format strings now underline the pertinent part of the string, and can offer suggested fixes. Several new warnings related to buffer overflows and buffer truncation. New __builtin_add_overflow_p, __builtin_sub_overflow_p, __builtin_mul_overflow_p built-ins added that test for overflow. The C++ front end has experimental support for all of the current C++17 draft. The -fverbose-asm option has been expanded to prints comments showing the source lines that correspond to the assembly. The gcc and g++ driver programs will now provide suggestions for misspelled arguments to command-line options. AArch64 specific: GCC has been updated to the latest revision of the procedure call standard (AAPCS64) to provide support for parameter passing when data types have been over-aligned. The ARMv8.2-A and ARMv8.3-A architecture are now supported. ARM specific: Support for the ARMv5 and ARMv5E architectures has been deprecated (which have no known implementations). A new command-line option -mpure-code has been added. It does not allow constant data to be placed in code sections. x86 specific: Support for the AVX-512 4FMAPS, 4VNNIW, VPOPCNTDQ and Software Guard Extensions (SGX) ISA extensions has been added. PPC specific: GCC now diagnoses inline assembly that clobbers register r2. RISC-V specific: Support for the RISC-V instruction set has been added. SH specific: Support for SH5/SH64 has been removed. Support for SH2A has been enhanced. @ text @d3 1 a3 1 // Copyright (C) 2012-2017 Free Software Foundation, Inc. d443 1 a443 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d728 1 a728 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d802 1 a802 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d866 1 a866 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d956 1 a956 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1027 1 a1027 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1124 1 a1124 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1199 1 a1199 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1300 1 a1300 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1406 1 a1406 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1484 1 a1484 3 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) d1646 1 a1646 2 __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) a1671 161 namespace { // Helper class for the uniform_inside_sphere_distribution generation // function. template class uniform_inside_sphere_helper; template class uniform_inside_sphere_helper<_Dimen, false, _RealType> { using result_type = typename uniform_inside_sphere_distribution<_Dimen, _RealType>:: result_type; public: template result_type operator()(_UniformOnSphereDistribution& __uosd, _UniformRandomNumberGenerator& __urng, _RealType __radius) { std::__detail::_Adaptor<_UniformRandomNumberGenerator, _RealType> __aurng(__urng); _RealType __pow = 1 / _RealType(_Dimen); _RealType __urt = __radius * std::pow(__aurng(), __pow); result_type __ret = __uosd(__aurng); std::transform(__ret.begin(), __ret.end(), __ret.begin(), [__urt](_RealType __val) { return __val * __urt; }); return __ret; } }; // Helper class for the uniform_inside_sphere_distribution generation // function specialized for small dimensions. template class uniform_inside_sphere_helper<_Dimen, true, _RealType> { using result_type = typename uniform_inside_sphere_distribution<_Dimen, _RealType>:: result_type; public: template result_type operator()(_UniformOnSphereDistribution&, _UniformRandomNumberGenerator& __urng, _RealType __radius) { result_type __ret; _RealType __sq; _RealType __radsq = __radius * __radius; std::__detail::_Adaptor<_UniformRandomNumberGenerator, _RealType> __aurng(__urng); do { __sq = _RealType(0); for (int i = 0; i < _Dimen; ++i) { __ret[i] = _RealType(2) * __aurng() - _RealType(1); __sq += __ret[i] * __ret[i]; } } while (__sq > _RealType(1)); for (int i = 0; i < _Dimen; ++i) __ret[i] *= __radius; return __ret; } }; } // namespace // // Experiments have shown that rejection is more efficient than transform // for dimensions less than 8. // template template typename uniform_inside_sphere_distribution<_Dimen, _RealType>::result_type uniform_inside_sphere_distribution<_Dimen, _RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { uniform_inside_sphere_helper<_Dimen, _Dimen < 8, _RealType> __helper; return __helper(_M_uosd, __urng, __p.radius()); } template template void uniform_inside_sphere_distribution<_Dimen, _RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) while (__f != __t) *__f++ = this->operator()(__urng, __param); } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::uniform_inside_sphere_distribution<_Dimen, _RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.radius() << __space << __x._M_uosd; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::uniform_inside_sphere_distribution<_Dimen, _RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __radius_val; __is >> __radius_val >> __x._M_uosd; __x.param(typename uniform_inside_sphere_distribution<_Dimen, _RealType>:: param_type(__radius_val)); __is.flags(__flags); return __is; } d1673 1 a1673 1 } // namespace __gnu_cxx @ 1.1.1.5 log @import GCC 8.3. it includes these new features: - many optimisations improved: inter-procedural, profile-directed, LTO, loops including user-controllable unroll support, and more. - columns numbers added to line numbers in dwarf - gcov extended significantly - many sanitizer updates - many new warning messages - many better hints and more useful error messages - minor ABI changes on x86-64 libstdc++, and some c++17 modes - draft c++2a features - better c++17 experimental support - Armv8.4-A supported, better 8.2-A and 8.3-A support, including 32 bit arm port. cortex a-55, a-75 and a-55.a-75 combo support. - in the GCC bugzilla, 8.1 shows 1149 bugs fixed, 8.2 shows 100, and 8.3 shows 158. @ text @d3 1 a3 1 // Copyright (C) 2012-2018 Free Software Foundation, Inc. @ 1.1.1.6 log @import GCC 7.5.0. doing this here so that the vendor branch has the code we'll merge into gcc.old and the netbsd-9 tree gcc tree. GCC 8.4.0 will be imported immediately on top of this again, restoring the current status. these PRs in the GCC bugzilla are fixed with this update: 89869 80693 89795 84272 85593 86669 87148 87647 87895 88103 88107 88563 88870 88976 89002 89187 89195 89234 89303 89314 89354 89361 89403 89412 89512 89520 89590 89621 89663 89679 89704 89734 89872 89933 90090 90208 87075 85870 89009 89242 88167 80864 81933 85890 86608 87145 88857 89024 89119 89214 89511 89612 89705 89400 81740 82186 84552 86554 87609 88105 88149 88415 88739 88903 89135 89223 89296 89505 89572 89677 89698 89710 90006 90020 90071 90328 90474 91126 91162 91812 91887 90075 88998 89945 87047 87506 88074 88656 88740 91137 89008 84010 89349 91136 91347 91995 89397 87030 60702 78884 85594 87649 87725 88181 88470 88553 88568 88588 88620 88644 88906 88949 89246 89587 89726 89768 89796 89998 90108 90756 90950 91704 88825 88983 86538 51333 89446 90220 91308 92143 89392 90213 90278 91131 91200 91510 89037 91481 87673 88418 88938 88948 90547 27221 58321 61250 67183 67958 77583 83531 86215 88648 88720 88726 89091 89466 89629 90105 90329 90585 90760 90924 91087 89222 81956 71861 35031 69455 81849 82993 85798 88138 88155 88169 88205 88206 88228 88249 88269 88376 77703 80260 82077 86248 88393 90786 57048 66089 66695 67679 68009 71723 72714 84394 85544 87734 88298 90937 91557 63891 64132 65342 68649 68717 71066 71860 71935 77746 78421 78645 78865 78983 79485 79540 85953 88326 89651 90744 @ text @d3 1 a3 1 // Copyright (C) 2012-2017 Free Software Foundation, Inc. @ 1.1.1.7 log @re-import GCC 8.4.0. @ text @d3 1 a3 1 // Copyright (C) 2012-2018 Free Software Foundation, Inc. @ 1.1.1.8 log @initial import of GCC 9.3.0. changes include: - live patching support - shell completion help - generally better diagnostic output (less verbose/more useful) - diagnostics and optimisation choices can be emitted in json - asan memory usage reduction - many general, and specific to switch, inter-procedure, profile and link-time optimisations. from the release notes: "Overall compile time of Firefox 66 and LibreOffice 6.2.3 on an 8-core machine was reduced by about 5% compared to GCC 8.3" - OpenMP 5.0 support - better spell-guesser - partial experimental support for c2x and c++2a - c++17 is no longer experimental - arm AAPCS GCC 6-8 structure passing bug fixed, may cause incompatibility (restored compat with GCC 5 and earlier.) - openrisc support @ text @d3 1 a3 1 // Copyright (C) 2012-2019 Free Software Foundation, Inc. d88 1 a88 1 auto a94 1 -> _If_seed_seq<_Sseq> @ 1.1.1.9 log @initial import of GCC 10.3.0. main changes include: caveats: - ABI issue between c++14 and c++17 fixed - profile mode is removed from libstdc++ - -fno-common is now the default new features: - new flags -fallocation-dce, -fprofile-partial-training, -fprofile-reproducible, -fprofile-prefix-path, and -fanalyzer - many new compile and link time optimisations - enhanced drive optimisations - openacc 2.6 support - openmp 5.0 features - new warnings: -Wstring-compare and -Wzero-length-bounds - extended warnings: -Warray-bounds, -Wformat-overflow, -Wrestrict, -Wreturn-local-addr, -Wstringop-overflow, -Warith-conversion, -Wmismatched-tags, and -Wredundant-tags - some likely C2X features implemented - more C++20 implemented - many new arm & intel CPUs known hundreds of reported bugs are fixed. full list of changes can be found at: https://gcc.gnu.org/gcc-10/changes.html @ text @d3 1 a3 1 // Copyright (C) 2012-2020 Free Software Foundation, Inc. d584 1 a584 1 "param_type::_M_init_lower")); a711 3 // The param_type temporary is built with a private constructor, // to skip the Cholesky decomposition that would be performed // otherwise. d713 2 a714 1 param_type(__mean, __varcov)); @ 1.1.1.10 log @initial import of GCC 12.3.0. major changes in GCC 11 included: - The default mode for C++ is now -std=gnu++17 instead of -std=gnu++14. - When building GCC itself, the host compiler must now support C++11, rather than C++98. - Some short options of the gcov tool have been renamed: -i to -j and -j to -H. - ThreadSanitizer improvements. - Introduce Hardware-assisted AddressSanitizer support. - For targets that produce DWARF debugging information GCC now defaults to DWARF version 5. This can produce up to 25% more compact debug information compared to earlier versions. - Many optimisations. - The existing malloc attribute has been extended so that it can be used to identify allocator/deallocator API pairs. A pair of new -Wmismatched-dealloc and -Wmismatched-new-delete warnings are added. - Other new warnings: -Wsizeof-array-div, enabled by -Wall, warns about divisions of two sizeof operators when the first one is applied to an array and the divisor does not equal the size of the array element. -Wstringop-overread, enabled by default, warns about calls to string functions reading past the end of the arrays passed to them as arguments. -Wtsan, enabled by default, warns about unsupported features in ThreadSanitizer (currently std::atomic_thread_fence). - Enchanced warnings: -Wfree-nonheap-object detects many more instances of calls to deallocation functions with pointers not returned from a dynamic memory allocation function. -Wmaybe-uninitialized diagnoses passing pointers or references to uninitialized memory to functions taking const-qualified arguments. -Wuninitialized detects reads from uninitialized dynamically allocated memory. -Warray-parameter warns about functions with inconsistent array forms. -Wvla-parameter warns about functions with inconsistent VLA forms. - Several new features from the upcoming C2X revision of the ISO C standard are supported with -std=c2x and -std=gnu2x. - Several C++20 features have been implemented. - The C++ front end has experimental support for some of the upcoming C++23 draft. - Several new C++ warnings. - Enhanced Arm, AArch64, x86, and RISC-V CPU support. - The implementation of how program state is tracked within -fanalyzer has been completely rewritten with many enhancements. see https://gcc.gnu.org/gcc-11/changes.html for a full list. major changes in GCC 12 include: - An ABI incompatibility between C and C++ when passing or returning by value certain aggregates containing zero width bit-fields has been discovered on various targets. x86-64, ARM and AArch64 will always ignore them (so there is a C ABI incompatibility between GCC 11 and earlier with GCC 12 or later), PowerPC64 ELFv2 always take them into account (so there is a C++ ABI incompatibility, GCC 4.4 and earlier compatible with GCC 12 or later, incompatible with GCC 4.5 through GCC 11). RISC-V has changed the handling of these already starting with GCC 10. As the ABI requires, MIPS takes them into account handling function return values so there is a C++ ABI incompatibility with GCC 4.5 through 11. - STABS: Support for emitting the STABS debugging format is deprecated and will be removed in the next release. All ports now default to emit DWARF (version 2 or later) debugging info or are obsoleted. - Vectorization is enabled at -O2 which is now equivalent to the original -O2 -ftree-vectorize -fvect-cost-model=very-cheap. - GCC now supports the ShadowCallStack sanitizer. - Support for __builtin_shufflevector compatible with the clang language extension was added. - Support for attribute unavailable was added. - Support for __builtin_dynamic_object_size compatible with the clang language extension was added. - New warnings: -Wbidi-chars warns about potentially misleading UTF-8 bidirectional control characters. -Warray-compare warns about comparisons between two operands of array type. - Some new features from the upcoming C2X revision of the ISO C standard are supported with -std=c2x and -std=gnu2x. - Several C++23 features have been implemented. - Many C++ enhancements across warnings and -f options. see https://gcc.gnu.org/gcc-12/changes.html for a full list. @ text @d3 1 a3 1 // Copyright (C) 2012-2022 Free Software Foundation, Inc. @ 1.1.1.11 log @initial import of GCC 14.3.0. major changes in GCC 13: - improved sanitizer - zstd debug info compression - LTO improvements - SARIF based diagnostic support - new warnings: -Wxor-used-as-pow, -Wenum-int-mismatch, -Wself-move, -Wdangling-reference - many new -Wanalyzer* specific warnings - enhanced warnings: -Wpessimizing-move, -Wredundant-move - new attributes to mark file descriptors, c++23 "assume" - several C23 features added - several C++23 features added - many new features for Arm, x86, RISC-V major changes in GCC 14: - more strict C99 or newer support - ia64* marked deprecated (but seemingly still in GCC 15.) - several new hardening features - support for "hardbool", which can have user supplied values of true/false - explicit support for stack scrubbing upon function exit - better auto-vectorisation support - added clang-compatible __has_feature and __has_extension - more C23, including -std=c23 - several C++26 features added - better diagnostics in C++ templates - new warnings: -Wnrvo, Welaborated-enum-base - many new features for Arm, x86, RISC-V - possible ABI breaking change for SPARC64 and small structures with arrays of floats. @ text @d3 1 a3 1 // Copyright (C) 2012-2024 Free Software Foundation, Inc. a34 2 #include // GNU extensions are currently omitted d741 1 a741 1 #if _GLIBCXX_USE_C99_MATH_FUNCS d1288 1 a1288 1 #if _GLIBCXX_USE_C99_MATH_FUNCS d1624 1 a1624 1 #if _GLIBCXX_USE_C99_MATH_FUNCS @ 1.1.1.1.8.1 log @file random.tcc was added on branch tls-maxphys on 2014-08-19 23:54:50 +0000 @ text @d1 1314 @ 1.1.1.1.8.2 log @Rebase to HEAD as of a few days ago. @ text @a0 1314 // Random number extensions -*- C++ -*- // Copyright (C) 2012-2013 Free Software Foundation, Inc. // // This file is part of the GNU ISO C++ Library. This library is free // software; you can redistribute it and/or modify it under the // terms of the GNU General Public License as published by the // Free Software Foundation; either version 3, or (at your option) // any later version. // This library is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // Under Section 7 of GPL version 3, you are granted additional // permissions described in the GCC Runtime Library Exception, version // 3.1, as published by the Free Software Foundation. // You should have received a copy of the GNU General Public License and // a copy of the GCC Runtime Library Exception along with this program; // see the files COPYING3 and COPYING.RUNTIME respectively. If not, see // . /** @@file ext/random.tcc * This is an internal header file, included by other library headers. * Do not attempt to use it directly. @@headername{ext/random} */ #ifndef _EXT_RANDOM_TCC #define _EXT_RANDOM_TCC 1 #pragma GCC system_header namespace __gnu_cxx _GLIBCXX_VISIBILITY(default) { _GLIBCXX_BEGIN_NAMESPACE_VERSION #if __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__ template void simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>:: seed(_UIntType __seed) { _M_state32[0] = static_cast(__seed); for (size_t __i = 1; __i < _M_nstate32; ++__i) _M_state32[__i] = (1812433253UL * (_M_state32[__i - 1] ^ (_M_state32[__i - 1] >> 30)) + __i); _M_pos = state_size; _M_period_certification(); } namespace { inline uint32_t _Func1(uint32_t __x) { return (__x ^ (__x >> 27)) * UINT32_C(1664525); } inline uint32_t _Func2(uint32_t __x) { return (__x ^ (__x >> 27)) * UINT32_C(1566083941); } } template template typename std::enable_if::value>::type simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>:: seed(_Sseq& __q) { size_t __lag; if (_M_nstate32 >= 623) __lag = 11; else if (_M_nstate32 >= 68) __lag = 7; else if (_M_nstate32 >= 39) __lag = 5; else __lag = 3; const size_t __mid = (_M_nstate32 - __lag) / 2; std::fill(_M_state32, _M_state32 + _M_nstate32, UINT32_C(0x8b8b8b8b)); uint32_t __arr[_M_nstate32]; __q.generate(__arr + 0, __arr + _M_nstate32); uint32_t __r = _Func1(_M_state32[0] ^ _M_state32[__mid] ^ _M_state32[_M_nstate32 - 1]); _M_state32[__mid] += __r; __r += _M_nstate32; _M_state32[__mid + __lag] += __r; _M_state32[0] = __r; for (size_t __i = 1, __j = 0; __j < _M_nstate32; ++__j) { __r = _Func1(_M_state32[__i] ^ _M_state32[(__i + __mid) % _M_nstate32] ^ _M_state32[(__i + _M_nstate32 - 1) % _M_nstate32]); _M_state32[(__i + __mid) % _M_nstate32] += __r; __r += __arr[__j] + __i; _M_state32[(__i + __mid + __lag) % _M_nstate32] += __r; _M_state32[__i] = __r; __i = (__i + 1) % _M_nstate32; } for (size_t __j = 0; __j < _M_nstate32; ++__j) { const size_t __i = (__j + 1) % _M_nstate32; __r = _Func2(_M_state32[__i] + _M_state32[(__i + __mid) % _M_nstate32] + _M_state32[(__i + _M_nstate32 - 1) % _M_nstate32]); _M_state32[(__i + __mid) % _M_nstate32] ^= __r; __r -= __i; _M_state32[(__i + __mid + __lag) % _M_nstate32] ^= __r; _M_state32[__i] = __r; } _M_pos = state_size; _M_period_certification(); } template void simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>:: _M_period_certification(void) { static const uint32_t __parity[4] = { __parity1, __parity2, __parity3, __parity4 }; uint32_t __inner = 0; for (size_t __i = 0; __i < 4; ++__i) if (__parity[__i] != 0) __inner ^= _M_state32[__i] & __parity[__i]; if (__builtin_parity(__inner) & 1) return; for (size_t __i = 0; __i < 4; ++__i) if (__parity[__i] != 0) { _M_state32[__i] ^= 1 << (__builtin_ffs(__parity[__i]) - 1); return; } __builtin_unreachable(); } template void simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>:: discard(unsigned long long __z) { while (__z > state_size - _M_pos) { __z -= state_size - _M_pos; _M_gen_rand(); } _M_pos += __z; } #ifndef _GLIBCXX_OPT_HAVE_RANDOM_SFMT_GEN_READ namespace { template inline void __rshift(uint32_t *__out, const uint32_t *__in) { uint64_t __th = ((static_cast(__in[3]) << 32) | static_cast(__in[2])); uint64_t __tl = ((static_cast(__in[1]) << 32) | static_cast(__in[0])); uint64_t __oh = __th >> (__shift * 8); uint64_t __ol = __tl >> (__shift * 8); __ol |= __th << (64 - __shift * 8); __out[1] = static_cast(__ol >> 32); __out[0] = static_cast(__ol); __out[3] = static_cast(__oh >> 32); __out[2] = static_cast(__oh); } template inline void __lshift(uint32_t *__out, const uint32_t *__in) { uint64_t __th = ((static_cast(__in[3]) << 32) | static_cast(__in[2])); uint64_t __tl = ((static_cast(__in[1]) << 32) | static_cast(__in[0])); uint64_t __oh = __th << (__shift * 8); uint64_t __ol = __tl << (__shift * 8); __oh |= __tl >> (64 - __shift * 8); __out[1] = static_cast(__ol >> 32); __out[0] = static_cast(__ol); __out[3] = static_cast(__oh >> 32); __out[2] = static_cast(__oh); } template inline void __recursion(uint32_t *__r, const uint32_t *__a, const uint32_t *__b, const uint32_t *__c, const uint32_t *__d) { uint32_t __x[4]; uint32_t __y[4]; __lshift<__sl2>(__x, __a); __rshift<__sr2>(__y, __c); __r[0] = (__a[0] ^ __x[0] ^ ((__b[0] >> __sr1) & __msk1) ^ __y[0] ^ (__d[0] << __sl1)); __r[1] = (__a[1] ^ __x[1] ^ ((__b[1] >> __sr1) & __msk2) ^ __y[1] ^ (__d[1] << __sl1)); __r[2] = (__a[2] ^ __x[2] ^ ((__b[2] >> __sr1) & __msk3) ^ __y[2] ^ (__d[2] << __sl1)); __r[3] = (__a[3] ^ __x[3] ^ ((__b[3] >> __sr1) & __msk4) ^ __y[3] ^ (__d[3] << __sl1)); } } template void simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>:: _M_gen_rand(void) { const uint32_t *__r1 = &_M_state32[_M_nstate32 - 8]; const uint32_t *__r2 = &_M_state32[_M_nstate32 - 4]; static constexpr size_t __pos1_32 = __pos1 * 4; size_t __i; for (__i = 0; __i < _M_nstate32 - __pos1_32; __i += 4) { __recursion<__sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4> (&_M_state32[__i], &_M_state32[__i], &_M_state32[__i + __pos1_32], __r1, __r2); __r1 = __r2; __r2 = &_M_state32[__i]; } for (; __i < _M_nstate32; __i += 4) { __recursion<__sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4> (&_M_state32[__i], &_M_state32[__i], &_M_state32[__i + __pos1_32 - _M_nstate32], __r1, __r2); __r1 = __r2; __r2 = &_M_state32[__i]; } _M_pos = 0; } #endif #ifndef _GLIBCXX_OPT_HAVE_RANDOM_SFMT_OPERATOREQUAL template bool operator==(const __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>& __lhs, const __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>& __rhs) { typedef __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4> __engine; return (std::equal(__lhs._M_stateT, __lhs._M_stateT + __engine::state_size, __rhs._M_stateT) && __lhs._M_pos == __rhs._M_pos); } #endif template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); __os.fill(__space); for (size_t __i = 0; __i < __x._M_nstate32; ++__i) __os << __x._M_state32[__i] << __space; __os << __x._M_pos; __os.flags(__flags); __os.fill(__fill); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); for (size_t __i = 0; __i < __x._M_nstate32; ++__i) __is >> __x._M_state32[__i]; __is >> __x._M_pos; __is.flags(__flags); return __is; } #endif // __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__ /** * Iteration method due to M.D. Jhnk. * * M.D. Jhnk, Erzeugung von betaverteilten und gammaverteilten * Zufallszahlen, Metrika, Volume 8, 1964 */ template template typename beta_distribution<_RealType>::result_type beta_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __param) { std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); result_type __x, __y; do { __x = std::exp(std::log(__aurng()) / __param.alpha()); __y = std::exp(std::log(__aurng()) / __param.beta()); } while (__x + __y > result_type(1)); return __x / (__x + __y); } template template void beta_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); while (__f != __t) { result_type __x, __y; do { __x = std::exp(std::log(__aurng()) / __param.alpha()); __y = std::exp(std::log(__aurng()) / __param.beta()); } while (__x + __y > result_type(1)); *__f++ = __x / (__x + __y); } } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::beta_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.alpha() << __space << __x.beta(); __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::beta_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __alpha_val, __beta_val; __is >> __alpha_val >> __beta_val; __x.param(typename __gnu_cxx::beta_distribution<_RealType>:: param_type(__alpha_val, __beta_val)); __is.flags(__flags); return __is; } template template void normal_mv_distribution<_Dimen, _RealType>::param_type:: _M_init_full(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varcovbegin, _InputIterator2 __varcovend) { __glibcxx_function_requires(_InputIteratorConcept<_InputIterator1>) __glibcxx_function_requires(_InputIteratorConcept<_InputIterator2>) std::fill(std::copy(__meanbegin, __meanend, _M_mean.begin()), _M_mean.end(), _RealType(0)); // Perform the Cholesky decomposition auto __w = _M_t.begin(); for (size_t __j = 0; __j < _Dimen; ++__j) { _RealType __sum = _RealType(0); auto __slitbegin = __w; auto __cit = _M_t.begin(); for (size_t __i = 0; __i < __j; ++__i) { auto __slit = __slitbegin; _RealType __s = *__varcovbegin++; for (size_t __k = 0; __k < __i; ++__k) __s -= *__slit++ * *__cit++; *__w++ = __s /= *__cit++; __sum += __s * __s; } __sum = *__varcovbegin - __sum; if (__builtin_expect(__sum <= _RealType(0), 0)) std::__throw_runtime_error(__N("normal_mv_distribution::" "param_type::_M_init_full")); *__w++ = std::sqrt(__sum); std::advance(__varcovbegin, _Dimen - __j); } } template template void normal_mv_distribution<_Dimen, _RealType>::param_type:: _M_init_lower(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varcovbegin, _InputIterator2 __varcovend) { __glibcxx_function_requires(_InputIteratorConcept<_InputIterator1>) __glibcxx_function_requires(_InputIteratorConcept<_InputIterator2>) std::fill(std::copy(__meanbegin, __meanend, _M_mean.begin()), _M_mean.end(), _RealType(0)); // Perform the Cholesky decomposition auto __w = _M_t.begin(); for (size_t __j = 0; __j < _Dimen; ++__j) { _RealType __sum = _RealType(0); auto __slitbegin = __w; auto __cit = _M_t.begin(); for (size_t __i = 0; __i < __j; ++__i) { auto __slit = __slitbegin; _RealType __s = *__varcovbegin++; for (size_t __k = 0; __k < __i; ++__k) __s -= *__slit++ * *__cit++; *__w++ = __s /= *__cit++; __sum += __s * __s; } __sum = *__varcovbegin++ - __sum; if (__builtin_expect(__sum <= _RealType(0), 0)) std::__throw_runtime_error(__N("normal_mv_distribution::" "param_type::_M_init_full")); *__w++ = std::sqrt(__sum); } } template template void normal_mv_distribution<_Dimen, _RealType>::param_type:: _M_init_diagonal(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varbegin, _InputIterator2 __varend) { __glibcxx_function_requires(_InputIteratorConcept<_InputIterator1>) __glibcxx_function_requires(_InputIteratorConcept<_InputIterator2>) std::fill(std::copy(__meanbegin, __meanend, _M_mean.begin()), _M_mean.end(), _RealType(0)); auto __w = _M_t.begin(); size_t __step = 0; while (__varbegin != __varend) { std::fill_n(__w, __step, _RealType(0)); __w += __step++; if (__builtin_expect(*__varbegin < _RealType(0), 0)) std::__throw_runtime_error(__N("normal_mv_distribution::" "param_type::_M_init_diagonal")); *__w++ = std::sqrt(*__varbegin++); } } template template typename normal_mv_distribution<_Dimen, _RealType>::result_type normal_mv_distribution<_Dimen, _RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __param) { result_type __ret; _M_nd.__generate(__ret.begin(), __ret.end(), __urng); auto __t_it = __param._M_t.crbegin(); for (size_t __i = _Dimen; __i > 0; --__i) { _RealType __sum = _RealType(0); for (size_t __j = __i; __j > 0; --__j) __sum += __ret[__j - 1] * *__t_it++; __ret[__i - 1] = __sum; } return __ret; } template template void normal_mv_distribution<_Dimen, _RealType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_Mutable_ForwardIteratorConcept< _ForwardIterator>) while (__f != __t) *__f++ = this->operator()(__urng, __param); } template bool operator==(const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __d1, const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __d2) { return __d1._M_param == __d2._M_param && __d1._M_nd == __d2._M_nd; } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); auto __mean = __x._M_param.mean(); for (auto __it : __mean) __os << __it << __space; auto __t = __x._M_param.varcov(); for (auto __it : __t) __os << __it << __space; __os << __x._M_nd; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); std::array<_RealType, _Dimen> __mean; for (auto& __it : __mean) __is >> __it; std::array<_RealType, _Dimen * (_Dimen + 1) / 2> __varcov; for (auto& __it : __varcov) __is >> __it; __is >> __x._M_nd; __x.param(typename normal_mv_distribution<_Dimen, _RealType>:: param_type(__mean.begin(), __mean.end(), __varcov.begin(), __varcov.end())); __is.flags(__flags); return __is; } template template void rice_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) while (__f != __t) { typename std::normal_distribution::param_type __px(__p.nu(), __p.sigma()), __py(result_type(0), __p.sigma()); result_type __x = this->_M_ndx(__px, __urng); result_type __y = this->_M_ndy(__py, __urng); #if _GLIBCXX_USE_C99_MATH_TR1 *__f++ = std::hypot(__x, __y); #else *__f++ = std::sqrt(__x * __x + __y * __y); #endif } } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const rice_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.nu() << __space << __x.sigma(); __os << __space << __x._M_ndx; __os << __space << __x._M_ndy; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, rice_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __nu_val, __sigma_val; __is >> __nu_val >> __sigma_val; __is >> __x._M_ndx; __is >> __x._M_ndy; __x.param(typename rice_distribution<_RealType>:: param_type(__nu_val, __sigma_val)); __is.flags(__flags); return __is; } template template void nakagami_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) typename std::gamma_distribution::param_type __pg(__p.mu(), __p.omega() / __p.mu()); while (__f != __t) *__f++ = std::sqrt(this->_M_gd(__pg, __urng)); } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const nakagami_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.mu() << __space << __x.omega(); __os << __space << __x._M_gd; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, nakagami_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __mu_val, __omega_val; __is >> __mu_val >> __omega_val; __is >> __x._M_gd; __x.param(typename nakagami_distribution<_RealType>:: param_type(__mu_val, __omega_val)); __is.flags(__flags); return __is; } template template void pareto_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) result_type __mu_val = __p.mu(); result_type __malphinv = -result_type(1) / __p.alpha(); while (__f != __t) *__f++ = __mu_val * std::pow(this->_M_ud(__urng), __malphinv); } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const pareto_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.alpha() << __space << __x.mu(); __os << __space << __x._M_ud; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, pareto_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __alpha_val, __mu_val; __is >> __alpha_val >> __mu_val; __is >> __x._M_ud; __x.param(typename pareto_distribution<_RealType>:: param_type(__alpha_val, __mu_val)); __is.flags(__flags); return __is; } template template typename k_distribution<_RealType>::result_type k_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng) { result_type __x = this->_M_gd1(__urng); result_type __y = this->_M_gd2(__urng); return std::sqrt(__x * __y); } template template typename k_distribution<_RealType>::result_type k_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { typename std::gamma_distribution::param_type __p1(__p.lambda(), result_type(1) / __p.lambda()), __p2(__p.nu(), __p.mu() / __p.nu()); result_type __x = this->_M_gd1(__p1, __urng); result_type __y = this->_M_gd2(__p2, __urng); return std::sqrt(__x * __y); } template template void k_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) typename std::gamma_distribution::param_type __p1(__p.lambda(), result_type(1) / __p.lambda()), __p2(__p.nu(), __p.mu() / __p.nu()); while (__f != __t) { result_type __x = this->_M_gd1(__p1, __urng); result_type __y = this->_M_gd2(__p2, __urng); *__f++ = std::sqrt(__x * __y); } } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const k_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.lambda() << __space << __x.mu() << __space << __x.nu(); __os << __space << __x._M_gd1; __os << __space << __x._M_gd2; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, k_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __lambda_val, __mu_val, __nu_val; __is >> __lambda_val >> __mu_val >> __nu_val; __is >> __x._M_gd1; __is >> __x._M_gd2; __x.param(typename k_distribution<_RealType>:: param_type(__lambda_val, __mu_val, __nu_val)); __is.flags(__flags); return __is; } template template void arcsine_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) result_type __dif = __p.b() - __p.a(); result_type __sum = __p.a() + __p.b(); while (__f != __t) { result_type __x = std::sin(this->_M_ud(__urng)); *__f++ = (__x * __dif + __sum) / result_type(2); } } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const arcsine_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.a() << __space << __x.b(); __os << __space << __x._M_ud; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, arcsine_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __a, __b; __is >> __a >> __b; __is >> __x._M_ud; __x.param(typename arcsine_distribution<_RealType>:: param_type(__a, __b)); __is.flags(__flags); return __is; } template template typename hoyt_distribution<_RealType>::result_type hoyt_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng) { result_type __x = this->_M_ad(__urng); result_type __y = this->_M_ed(__urng); return (result_type(2) * this->q() / (result_type(1) + this->q() * this->q())) * std::sqrt(this->omega() * __x * __y); } template template typename hoyt_distribution<_RealType>::result_type hoyt_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { result_type __q2 = __p.q() * __p.q(); result_type __num = result_type(0.5L) * (result_type(1) + __q2); typename __gnu_cxx::arcsine_distribution::param_type __pa(__num, __num / __q2); result_type __x = this->_M_ad(__pa, __urng); result_type __y = this->_M_ed(__urng); return (result_type(2) * __p.q() / (result_type(1) + __q2)) * std::sqrt(__p.omega() * __x * __y); } template template void hoyt_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) result_type __2q = result_type(2) * __p.q(); result_type __q2 = __p.q() * __p.q(); result_type __q2p1 = result_type(1) + __q2; result_type __num = result_type(0.5L) * __q2p1; result_type __omega = __p.omega(); typename __gnu_cxx::arcsine_distribution::param_type __pa(__num, __num / __q2); while (__f != __t) { result_type __x = this->_M_ad(__pa, __urng); result_type __y = this->_M_ed(__urng); *__f++ = (__2q / __q2p1) * std::sqrt(__omega * __x * __y); } } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const hoyt_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.q() << __space << __x.omega(); __os << __space << __x._M_ad; __os << __space << __x._M_ed; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, hoyt_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __q, __omega; __is >> __q >> __omega; __is >> __x._M_ad; __is >> __x._M_ed; __x.param(typename hoyt_distribution<_RealType>:: param_type(__q, __omega)); __is.flags(__flags); return __is; } template template void triangular_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) while (__f != __t) *__f++ = this->operator()(__urng, __param); } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::triangular_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.a() << __space << __x.b() << __space << __x.c(); __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::triangular_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __a, __b, __c; __is >> __a >> __b >> __c; __x.param(typename __gnu_cxx::triangular_distribution<_RealType>:: param_type(__a, __b, __c)); __is.flags(__flags); return __is; } template template void von_mises_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) while (__f != __t) *__f++ = this->operator()(__urng, __param); } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::von_mises_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.mu() << __space << __x.kappa(); __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::von_mises_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __mu, __kappa; __is >> __mu >> __kappa; __x.param(typename __gnu_cxx::von_mises_distribution<_RealType>:: param_type(__mu, __kappa)); __is.flags(__flags); return __is; } _GLIBCXX_END_NAMESPACE_VERSION } // namespace #endif // _EXT_RANDOM_TCC @ 1.1.1.1.4.1 log @file random.tcc was added on branch yamt-pagecache on 2014-05-22 16:37:49 +0000 @ text @d1 1314 @ 1.1.1.1.4.2 log @sync with head. for a reference, the tree before this commit was tagged as yamt-pagecache-tag8. this commit was splitted into small chunks to avoid a limitation of cvs. ("Protocol error: too many arguments") @ text @a0 1314 // Random number extensions -*- C++ -*- // Copyright (C) 2012-2013 Free Software Foundation, Inc. // // This file is part of the GNU ISO C++ Library. This library is free // software; you can redistribute it and/or modify it under the // terms of the GNU General Public License as published by the // Free Software Foundation; either version 3, or (at your option) // any later version. // This library is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // Under Section 7 of GPL version 3, you are granted additional // permissions described in the GCC Runtime Library Exception, version // 3.1, as published by the Free Software Foundation. // You should have received a copy of the GNU General Public License and // a copy of the GCC Runtime Library Exception along with this program; // see the files COPYING3 and COPYING.RUNTIME respectively. If not, see // . /** @@file ext/random.tcc * This is an internal header file, included by other library headers. * Do not attempt to use it directly. @@headername{ext/random} */ #ifndef _EXT_RANDOM_TCC #define _EXT_RANDOM_TCC 1 #pragma GCC system_header namespace __gnu_cxx _GLIBCXX_VISIBILITY(default) { _GLIBCXX_BEGIN_NAMESPACE_VERSION #if __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__ template void simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>:: seed(_UIntType __seed) { _M_state32[0] = static_cast(__seed); for (size_t __i = 1; __i < _M_nstate32; ++__i) _M_state32[__i] = (1812433253UL * (_M_state32[__i - 1] ^ (_M_state32[__i - 1] >> 30)) + __i); _M_pos = state_size; _M_period_certification(); } namespace { inline uint32_t _Func1(uint32_t __x) { return (__x ^ (__x >> 27)) * UINT32_C(1664525); } inline uint32_t _Func2(uint32_t __x) { return (__x ^ (__x >> 27)) * UINT32_C(1566083941); } } template template typename std::enable_if::value>::type simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>:: seed(_Sseq& __q) { size_t __lag; if (_M_nstate32 >= 623) __lag = 11; else if (_M_nstate32 >= 68) __lag = 7; else if (_M_nstate32 >= 39) __lag = 5; else __lag = 3; const size_t __mid = (_M_nstate32 - __lag) / 2; std::fill(_M_state32, _M_state32 + _M_nstate32, UINT32_C(0x8b8b8b8b)); uint32_t __arr[_M_nstate32]; __q.generate(__arr + 0, __arr + _M_nstate32); uint32_t __r = _Func1(_M_state32[0] ^ _M_state32[__mid] ^ _M_state32[_M_nstate32 - 1]); _M_state32[__mid] += __r; __r += _M_nstate32; _M_state32[__mid + __lag] += __r; _M_state32[0] = __r; for (size_t __i = 1, __j = 0; __j < _M_nstate32; ++__j) { __r = _Func1(_M_state32[__i] ^ _M_state32[(__i + __mid) % _M_nstate32] ^ _M_state32[(__i + _M_nstate32 - 1) % _M_nstate32]); _M_state32[(__i + __mid) % _M_nstate32] += __r; __r += __arr[__j] + __i; _M_state32[(__i + __mid + __lag) % _M_nstate32] += __r; _M_state32[__i] = __r; __i = (__i + 1) % _M_nstate32; } for (size_t __j = 0; __j < _M_nstate32; ++__j) { const size_t __i = (__j + 1) % _M_nstate32; __r = _Func2(_M_state32[__i] + _M_state32[(__i + __mid) % _M_nstate32] + _M_state32[(__i + _M_nstate32 - 1) % _M_nstate32]); _M_state32[(__i + __mid) % _M_nstate32] ^= __r; __r -= __i; _M_state32[(__i + __mid + __lag) % _M_nstate32] ^= __r; _M_state32[__i] = __r; } _M_pos = state_size; _M_period_certification(); } template void simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>:: _M_period_certification(void) { static const uint32_t __parity[4] = { __parity1, __parity2, __parity3, __parity4 }; uint32_t __inner = 0; for (size_t __i = 0; __i < 4; ++__i) if (__parity[__i] != 0) __inner ^= _M_state32[__i] & __parity[__i]; if (__builtin_parity(__inner) & 1) return; for (size_t __i = 0; __i < 4; ++__i) if (__parity[__i] != 0) { _M_state32[__i] ^= 1 << (__builtin_ffs(__parity[__i]) - 1); return; } __builtin_unreachable(); } template void simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>:: discard(unsigned long long __z) { while (__z > state_size - _M_pos) { __z -= state_size - _M_pos; _M_gen_rand(); } _M_pos += __z; } #ifndef _GLIBCXX_OPT_HAVE_RANDOM_SFMT_GEN_READ namespace { template inline void __rshift(uint32_t *__out, const uint32_t *__in) { uint64_t __th = ((static_cast(__in[3]) << 32) | static_cast(__in[2])); uint64_t __tl = ((static_cast(__in[1]) << 32) | static_cast(__in[0])); uint64_t __oh = __th >> (__shift * 8); uint64_t __ol = __tl >> (__shift * 8); __ol |= __th << (64 - __shift * 8); __out[1] = static_cast(__ol >> 32); __out[0] = static_cast(__ol); __out[3] = static_cast(__oh >> 32); __out[2] = static_cast(__oh); } template inline void __lshift(uint32_t *__out, const uint32_t *__in) { uint64_t __th = ((static_cast(__in[3]) << 32) | static_cast(__in[2])); uint64_t __tl = ((static_cast(__in[1]) << 32) | static_cast(__in[0])); uint64_t __oh = __th << (__shift * 8); uint64_t __ol = __tl << (__shift * 8); __oh |= __tl >> (64 - __shift * 8); __out[1] = static_cast(__ol >> 32); __out[0] = static_cast(__ol); __out[3] = static_cast(__oh >> 32); __out[2] = static_cast(__oh); } template inline void __recursion(uint32_t *__r, const uint32_t *__a, const uint32_t *__b, const uint32_t *__c, const uint32_t *__d) { uint32_t __x[4]; uint32_t __y[4]; __lshift<__sl2>(__x, __a); __rshift<__sr2>(__y, __c); __r[0] = (__a[0] ^ __x[0] ^ ((__b[0] >> __sr1) & __msk1) ^ __y[0] ^ (__d[0] << __sl1)); __r[1] = (__a[1] ^ __x[1] ^ ((__b[1] >> __sr1) & __msk2) ^ __y[1] ^ (__d[1] << __sl1)); __r[2] = (__a[2] ^ __x[2] ^ ((__b[2] >> __sr1) & __msk3) ^ __y[2] ^ (__d[2] << __sl1)); __r[3] = (__a[3] ^ __x[3] ^ ((__b[3] >> __sr1) & __msk4) ^ __y[3] ^ (__d[3] << __sl1)); } } template void simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>:: _M_gen_rand(void) { const uint32_t *__r1 = &_M_state32[_M_nstate32 - 8]; const uint32_t *__r2 = &_M_state32[_M_nstate32 - 4]; static constexpr size_t __pos1_32 = __pos1 * 4; size_t __i; for (__i = 0; __i < _M_nstate32 - __pos1_32; __i += 4) { __recursion<__sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4> (&_M_state32[__i], &_M_state32[__i], &_M_state32[__i + __pos1_32], __r1, __r2); __r1 = __r2; __r2 = &_M_state32[__i]; } for (; __i < _M_nstate32; __i += 4) { __recursion<__sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4> (&_M_state32[__i], &_M_state32[__i], &_M_state32[__i + __pos1_32 - _M_nstate32], __r1, __r2); __r1 = __r2; __r2 = &_M_state32[__i]; } _M_pos = 0; } #endif #ifndef _GLIBCXX_OPT_HAVE_RANDOM_SFMT_OPERATOREQUAL template bool operator==(const __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>& __lhs, const __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>& __rhs) { typedef __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4> __engine; return (std::equal(__lhs._M_stateT, __lhs._M_stateT + __engine::state_size, __rhs._M_stateT) && __lhs._M_pos == __rhs._M_pos); } #endif template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); __os.fill(__space); for (size_t __i = 0; __i < __x._M_nstate32; ++__i) __os << __x._M_state32[__i] << __space; __os << __x._M_pos; __os.flags(__flags); __os.fill(__fill); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); for (size_t __i = 0; __i < __x._M_nstate32; ++__i) __is >> __x._M_state32[__i]; __is >> __x._M_pos; __is.flags(__flags); return __is; } #endif // __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__ /** * Iteration method due to M.D. Jhnk. * * M.D. Jhnk, Erzeugung von betaverteilten und gammaverteilten * Zufallszahlen, Metrika, Volume 8, 1964 */ template template typename beta_distribution<_RealType>::result_type beta_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __param) { std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); result_type __x, __y; do { __x = std::exp(std::log(__aurng()) / __param.alpha()); __y = std::exp(std::log(__aurng()) / __param.beta()); } while (__x + __y > result_type(1)); return __x / (__x + __y); } template template void beta_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); while (__f != __t) { result_type __x, __y; do { __x = std::exp(std::log(__aurng()) / __param.alpha()); __y = std::exp(std::log(__aurng()) / __param.beta()); } while (__x + __y > result_type(1)); *__f++ = __x / (__x + __y); } } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::beta_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.alpha() << __space << __x.beta(); __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::beta_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __alpha_val, __beta_val; __is >> __alpha_val >> __beta_val; __x.param(typename __gnu_cxx::beta_distribution<_RealType>:: param_type(__alpha_val, __beta_val)); __is.flags(__flags); return __is; } template template void normal_mv_distribution<_Dimen, _RealType>::param_type:: _M_init_full(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varcovbegin, _InputIterator2 __varcovend) { __glibcxx_function_requires(_InputIteratorConcept<_InputIterator1>) __glibcxx_function_requires(_InputIteratorConcept<_InputIterator2>) std::fill(std::copy(__meanbegin, __meanend, _M_mean.begin()), _M_mean.end(), _RealType(0)); // Perform the Cholesky decomposition auto __w = _M_t.begin(); for (size_t __j = 0; __j < _Dimen; ++__j) { _RealType __sum = _RealType(0); auto __slitbegin = __w; auto __cit = _M_t.begin(); for (size_t __i = 0; __i < __j; ++__i) { auto __slit = __slitbegin; _RealType __s = *__varcovbegin++; for (size_t __k = 0; __k < __i; ++__k) __s -= *__slit++ * *__cit++; *__w++ = __s /= *__cit++; __sum += __s * __s; } __sum = *__varcovbegin - __sum; if (__builtin_expect(__sum <= _RealType(0), 0)) std::__throw_runtime_error(__N("normal_mv_distribution::" "param_type::_M_init_full")); *__w++ = std::sqrt(__sum); std::advance(__varcovbegin, _Dimen - __j); } } template template void normal_mv_distribution<_Dimen, _RealType>::param_type:: _M_init_lower(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varcovbegin, _InputIterator2 __varcovend) { __glibcxx_function_requires(_InputIteratorConcept<_InputIterator1>) __glibcxx_function_requires(_InputIteratorConcept<_InputIterator2>) std::fill(std::copy(__meanbegin, __meanend, _M_mean.begin()), _M_mean.end(), _RealType(0)); // Perform the Cholesky decomposition auto __w = _M_t.begin(); for (size_t __j = 0; __j < _Dimen; ++__j) { _RealType __sum = _RealType(0); auto __slitbegin = __w; auto __cit = _M_t.begin(); for (size_t __i = 0; __i < __j; ++__i) { auto __slit = __slitbegin; _RealType __s = *__varcovbegin++; for (size_t __k = 0; __k < __i; ++__k) __s -= *__slit++ * *__cit++; *__w++ = __s /= *__cit++; __sum += __s * __s; } __sum = *__varcovbegin++ - __sum; if (__builtin_expect(__sum <= _RealType(0), 0)) std::__throw_runtime_error(__N("normal_mv_distribution::" "param_type::_M_init_full")); *__w++ = std::sqrt(__sum); } } template template void normal_mv_distribution<_Dimen, _RealType>::param_type:: _M_init_diagonal(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varbegin, _InputIterator2 __varend) { __glibcxx_function_requires(_InputIteratorConcept<_InputIterator1>) __glibcxx_function_requires(_InputIteratorConcept<_InputIterator2>) std::fill(std::copy(__meanbegin, __meanend, _M_mean.begin()), _M_mean.end(), _RealType(0)); auto __w = _M_t.begin(); size_t __step = 0; while (__varbegin != __varend) { std::fill_n(__w, __step, _RealType(0)); __w += __step++; if (__builtin_expect(*__varbegin < _RealType(0), 0)) std::__throw_runtime_error(__N("normal_mv_distribution::" "param_type::_M_init_diagonal")); *__w++ = std::sqrt(*__varbegin++); } } template template typename normal_mv_distribution<_Dimen, _RealType>::result_type normal_mv_distribution<_Dimen, _RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __param) { result_type __ret; _M_nd.__generate(__ret.begin(), __ret.end(), __urng); auto __t_it = __param._M_t.crbegin(); for (size_t __i = _Dimen; __i > 0; --__i) { _RealType __sum = _RealType(0); for (size_t __j = __i; __j > 0; --__j) __sum += __ret[__j - 1] * *__t_it++; __ret[__i - 1] = __sum; } return __ret; } template template void normal_mv_distribution<_Dimen, _RealType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_Mutable_ForwardIteratorConcept< _ForwardIterator>) while (__f != __t) *__f++ = this->operator()(__urng, __param); } template bool operator==(const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __d1, const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __d2) { return __d1._M_param == __d2._M_param && __d1._M_nd == __d2._M_nd; } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); auto __mean = __x._M_param.mean(); for (auto __it : __mean) __os << __it << __space; auto __t = __x._M_param.varcov(); for (auto __it : __t) __os << __it << __space; __os << __x._M_nd; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); std::array<_RealType, _Dimen> __mean; for (auto& __it : __mean) __is >> __it; std::array<_RealType, _Dimen * (_Dimen + 1) / 2> __varcov; for (auto& __it : __varcov) __is >> __it; __is >> __x._M_nd; __x.param(typename normal_mv_distribution<_Dimen, _RealType>:: param_type(__mean.begin(), __mean.end(), __varcov.begin(), __varcov.end())); __is.flags(__flags); return __is; } template template void rice_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) while (__f != __t) { typename std::normal_distribution::param_type __px(__p.nu(), __p.sigma()), __py(result_type(0), __p.sigma()); result_type __x = this->_M_ndx(__px, __urng); result_type __y = this->_M_ndy(__py, __urng); #if _GLIBCXX_USE_C99_MATH_TR1 *__f++ = std::hypot(__x, __y); #else *__f++ = std::sqrt(__x * __x + __y * __y); #endif } } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const rice_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.nu() << __space << __x.sigma(); __os << __space << __x._M_ndx; __os << __space << __x._M_ndy; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, rice_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __nu_val, __sigma_val; __is >> __nu_val >> __sigma_val; __is >> __x._M_ndx; __is >> __x._M_ndy; __x.param(typename rice_distribution<_RealType>:: param_type(__nu_val, __sigma_val)); __is.flags(__flags); return __is; } template template void nakagami_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) typename std::gamma_distribution::param_type __pg(__p.mu(), __p.omega() / __p.mu()); while (__f != __t) *__f++ = std::sqrt(this->_M_gd(__pg, __urng)); } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const nakagami_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.mu() << __space << __x.omega(); __os << __space << __x._M_gd; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, nakagami_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __mu_val, __omega_val; __is >> __mu_val >> __omega_val; __is >> __x._M_gd; __x.param(typename nakagami_distribution<_RealType>:: param_type(__mu_val, __omega_val)); __is.flags(__flags); return __is; } template template void pareto_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) result_type __mu_val = __p.mu(); result_type __malphinv = -result_type(1) / __p.alpha(); while (__f != __t) *__f++ = __mu_val * std::pow(this->_M_ud(__urng), __malphinv); } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const pareto_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.alpha() << __space << __x.mu(); __os << __space << __x._M_ud; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, pareto_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __alpha_val, __mu_val; __is >> __alpha_val >> __mu_val; __is >> __x._M_ud; __x.param(typename pareto_distribution<_RealType>:: param_type(__alpha_val, __mu_val)); __is.flags(__flags); return __is; } template template typename k_distribution<_RealType>::result_type k_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng) { result_type __x = this->_M_gd1(__urng); result_type __y = this->_M_gd2(__urng); return std::sqrt(__x * __y); } template template typename k_distribution<_RealType>::result_type k_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { typename std::gamma_distribution::param_type __p1(__p.lambda(), result_type(1) / __p.lambda()), __p2(__p.nu(), __p.mu() / __p.nu()); result_type __x = this->_M_gd1(__p1, __urng); result_type __y = this->_M_gd2(__p2, __urng); return std::sqrt(__x * __y); } template template void k_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) typename std::gamma_distribution::param_type __p1(__p.lambda(), result_type(1) / __p.lambda()), __p2(__p.nu(), __p.mu() / __p.nu()); while (__f != __t) { result_type __x = this->_M_gd1(__p1, __urng); result_type __y = this->_M_gd2(__p2, __urng); *__f++ = std::sqrt(__x * __y); } } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const k_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.lambda() << __space << __x.mu() << __space << __x.nu(); __os << __space << __x._M_gd1; __os << __space << __x._M_gd2; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, k_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __lambda_val, __mu_val, __nu_val; __is >> __lambda_val >> __mu_val >> __nu_val; __is >> __x._M_gd1; __is >> __x._M_gd2; __x.param(typename k_distribution<_RealType>:: param_type(__lambda_val, __mu_val, __nu_val)); __is.flags(__flags); return __is; } template template void arcsine_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) result_type __dif = __p.b() - __p.a(); result_type __sum = __p.a() + __p.b(); while (__f != __t) { result_type __x = std::sin(this->_M_ud(__urng)); *__f++ = (__x * __dif + __sum) / result_type(2); } } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const arcsine_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.a() << __space << __x.b(); __os << __space << __x._M_ud; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, arcsine_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __a, __b; __is >> __a >> __b; __is >> __x._M_ud; __x.param(typename arcsine_distribution<_RealType>:: param_type(__a, __b)); __is.flags(__flags); return __is; } template template typename hoyt_distribution<_RealType>::result_type hoyt_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng) { result_type __x = this->_M_ad(__urng); result_type __y = this->_M_ed(__urng); return (result_type(2) * this->q() / (result_type(1) + this->q() * this->q())) * std::sqrt(this->omega() * __x * __y); } template template typename hoyt_distribution<_RealType>::result_type hoyt_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { result_type __q2 = __p.q() * __p.q(); result_type __num = result_type(0.5L) * (result_type(1) + __q2); typename __gnu_cxx::arcsine_distribution::param_type __pa(__num, __num / __q2); result_type __x = this->_M_ad(__pa, __urng); result_type __y = this->_M_ed(__urng); return (result_type(2) * __p.q() / (result_type(1) + __q2)) * std::sqrt(__p.omega() * __x * __y); } template template void hoyt_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) result_type __2q = result_type(2) * __p.q(); result_type __q2 = __p.q() * __p.q(); result_type __q2p1 = result_type(1) + __q2; result_type __num = result_type(0.5L) * __q2p1; result_type __omega = __p.omega(); typename __gnu_cxx::arcsine_distribution::param_type __pa(__num, __num / __q2); while (__f != __t) { result_type __x = this->_M_ad(__pa, __urng); result_type __y = this->_M_ed(__urng); *__f++ = (__2q / __q2p1) * std::sqrt(__omega * __x * __y); } } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const hoyt_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.q() << __space << __x.omega(); __os << __space << __x._M_ad; __os << __space << __x._M_ed; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, hoyt_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __q, __omega; __is >> __q >> __omega; __is >> __x._M_ad; __is >> __x._M_ed; __x.param(typename hoyt_distribution<_RealType>:: param_type(__q, __omega)); __is.flags(__flags); return __is; } template template void triangular_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) while (__f != __t) *__f++ = this->operator()(__urng, __param); } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::triangular_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.a() << __space << __x.b() << __space << __x.c(); __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::triangular_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __a, __b, __c; __is >> __a >> __b >> __c; __x.param(typename __gnu_cxx::triangular_distribution<_RealType>:: param_type(__a, __b, __c)); __is.flags(__flags); return __is; } template template void von_mises_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator>) while (__f != __t) *__f++ = this->operator()(__urng, __param); } template std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::von_mises_distribution<_RealType>& __x) { typedef std::basic_ostream<_CharT, _Traits> __ostream_type; typedef typename __ostream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.mu() << __space << __x.kappa(); __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::von_mises_distribution<_RealType>& __x) { typedef std::basic_istream<_CharT, _Traits> __istream_type; typedef typename __istream_type::ios_base __ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __mu, __kappa; __is >> __mu >> __kappa; __x.param(typename __gnu_cxx::von_mises_distribution<_RealType>:: param_type(__mu, __kappa)); __is.flags(__flags); return __is; } _GLIBCXX_END_NAMESPACE_VERSION } // namespace #endif // _EXT_RANDOM_TCC @