// Random number extensions -*- C++ -*- // Copyright (C) 2012-2020 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 // <http://www.gnu.org/licenses/>. /** @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<typename _UIntType, size_t __m, size_t __pos1, size_t __sl1, size_t __sl2, size_t __sr1, size_t __sr2, uint32_t __msk1, uint32_t __msk2, uint32_t __msk3, uint32_t __msk4, uint32_t __parity1, uint32_t __parity2, uint32_t __parity3, uint32_t __parity4> 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<uint32_t>(__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<typename _UIntType, size_t __m, size_t __pos1, size_t __sl1, size_t __sl2, size_t __sr1, size_t __sr2, uint32_t __msk1, uint32_t __msk2, uint32_t __msk3, uint32_t __msk4, uint32_t __parity1, uint32_t __parity2, uint32_t __parity3, uint32_t __parity4> template<typename _Sseq> auto simd_fast_mersenne_twister_engine<_UIntType, __m, __pos1, __sl1, __sl2, __sr1, __sr2, __msk1, __msk2, __msk3, __msk4, __parity1, __parity2, __parity3, __parity4>:: seed(_Sseq& __q) -> _If_seed_seq<_Sseq> { 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<typename _UIntType, size_t __m, size_t __pos1, size_t __sl1, size_t __sl2, size_t __sr1, size_t __sr2, uint32_t __msk1, uint32_t __msk2, uint32_t __msk3, uint32_t __msk4, uint32_t __parity1, uint32_t __parity2, uint32_t __parity3, uint32_t __parity4> 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<typename _UIntType, size_t __m, size_t __pos1, size_t __sl1, size_t __sl2, size_t __sr1, size_t __sr2, uint32_t __msk1, uint32_t __msk2, uint32_t __msk3, uint32_t __msk4, uint32_t __parity1, uint32_t __parity2, uint32_t __parity3, uint32_t __parity4> 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<size_t __shift> inline void __rshift(uint32_t *__out, const uint32_t *__in) { uint64_t __th = ((static_cast<uint64_t>(__in[3]) << 32) | static_cast<uint64_t>(__in[2])); uint64_t __tl = ((static_cast<uint64_t>(__in[1]) << 32) | static_cast<uint64_t>(__in[0])); uint64_t __oh = __th >> (__shift * 8); uint64_t __ol = __tl >> (__shift * 8); __ol |= __th << (64 - __shift * 8); __out[1] = static_cast<uint32_t>(__ol >> 32); __out[0] = static_cast<uint32_t>(__ol); __out[3] = static_cast<uint32_t>(__oh >> 32); __out[2] = static_cast<uint32_t>(__oh); } template<size_t __shift> inline void __lshift(uint32_t *__out, const uint32_t *__in) { uint64_t __th = ((static_cast<uint64_t>(__in[3]) << 32) | static_cast<uint64_t>(__in[2])); uint64_t __tl = ((static_cast<uint64_t>(__in[1]) << 32) | static_cast<uint64_t>(__in[0])); uint64_t __oh = __th << (__shift * 8); uint64_t __ol = __tl << (__shift * 8); __oh |= __tl >> (64 - __shift * 8); __out[1] = static_cast<uint32_t>(__ol >> 32); __out[0] = static_cast<uint32_t>(__ol); __out[3] = static_cast<uint32_t>(__oh >> 32); __out[2] = static_cast<uint32_t>(__oh); } template<size_t __sl1, size_t __sl2, size_t __sr1, size_t __sr2, uint32_t __msk1, uint32_t __msk2, uint32_t __msk3, uint32_t __msk4> 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<typename _UIntType, size_t __m, size_t __pos1, size_t __sl1, size_t __sl2, size_t __sr1, size_t __sr2, uint32_t __msk1, uint32_t __msk2, uint32_t __msk3, uint32_t __msk4, uint32_t __parity1, uint32_t __parity2, uint32_t __parity3, uint32_t __parity4> 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<typename _UIntType, size_t __m, size_t __pos1, size_t __sl1, size_t __sl2, size_t __sr1, size_t __sr2, uint32_t __msk1, uint32_t __msk2, uint32_t __msk3, uint32_t __msk4, uint32_t __parity1, uint32_t __parity2, uint32_t __parity3, uint32_t __parity4> 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<typename _UIntType, size_t __m, size_t __pos1, size_t __sl1, size_t __sl2, size_t __sr1, size_t __sr2, uint32_t __msk1, uint32_t __msk2, uint32_t __msk3, uint32_t __msk4, uint32_t __parity1, uint32_t __parity2, uint32_t __parity3, uint32_t __parity4, typename _CharT, typename _Traits> 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<typename _UIntType, size_t __m, size_t __pos1, size_t __sl1, size_t __sl2, size_t __sr1, size_t __sr2, uint32_t __msk1, uint32_t __msk2, uint32_t __msk3, uint32_t __msk4, uint32_t __parity1, uint32_t __parity2, uint32_t __parity3, uint32_t __parity4, typename _CharT, typename _Traits> 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. J<o:>hnk. * * M.D. J<o:>hnk, Erzeugung von betaverteilten und gammaverteilten * Zufallszahlen, Metrika, Volume 8, 1964 */ template<typename _RealType> template<typename _UniformRandomNumberGenerator> 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<typename _RealType> template<typename _OutputIterator, typename _UniformRandomNumberGenerator> void beta_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) 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<typename _RealType, typename _CharT, typename _Traits> 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<typename _RealType, typename _CharT, typename _Traits> 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<std::size_t _Dimen, typename _RealType> template<typename _InputIterator1, typename _InputIterator2> 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<std::size_t _Dimen, typename _RealType> template<typename _InputIterator1, typename _InputIterator2> 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_lower")); *__w++ = std::sqrt(__sum); } } template<std::size_t _Dimen, typename _RealType> template<typename _InputIterator1, typename _InputIterator2> 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<std::size_t _Dimen, typename _RealType> template<typename _UniformRandomNumberGenerator> 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<std::size_t _Dimen, typename _RealType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> 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<size_t _Dimen, typename _RealType> 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<size_t _Dimen, typename _RealType, typename _CharT, typename _Traits> 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<size_t _Dimen, typename _RealType, typename _CharT, typename _Traits> 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; // The param_type temporary is built with a private constructor, // to skip the Cholesky decomposition that would be performed // otherwise. __x.param(typename normal_mv_distribution<_Dimen, _RealType>:: param_type(__mean, __varcov)); __is.flags(__flags); return __is; } template<typename _RealType> template<typename _OutputIterator, typename _UniformRandomNumberGenerator> void rice_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) while (__f != __t) { typename std::normal_distribution<result_type>::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<typename _RealType, typename _CharT, typename _Traits> 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<typename _RealType, typename _CharT, typename _Traits> 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<typename _RealType> template<typename _OutputIterator, typename _UniformRandomNumberGenerator> void nakagami_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) typename std::gamma_distribution<result_type>::param_type __pg(__p.mu(), __p.omega() / __p.mu()); while (__f != __t) *__f++ = std::sqrt(this->_M_gd(__pg, __urng)); } template<typename _RealType, typename _CharT, typename _Traits> 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<typename _RealType, typename _CharT, typename _Traits> 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<typename _RealType> template<typename _OutputIterator, typename _UniformRandomNumberGenerator> void pareto_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) 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<typename _RealType, typename _CharT, typename _Traits> 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<typename _RealType, typename _CharT, typename _Traits> 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<typename _RealType> template<typename _UniformRandomNumberGenerator> 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<typename _RealType> template<typename _UniformRandomNumberGenerator> typename k_distribution<_RealType>::result_type k_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { typename std::gamma_distribution<result_type>::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<typename _RealType> template<typename _OutputIterator, typename _UniformRandomNumberGenerator> void k_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) typename std::gamma_distribution<result_type>::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<typename _RealType, typename _CharT, typename _Traits> 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<typename _RealType, typename _CharT, typename _Traits> 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<typename _RealType> template<typename _OutputIterator, typename _UniformRandomNumberGenerator> void arcsine_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) 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<typename _RealType, typename _CharT, typename _Traits> 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<typename _RealType, typename _CharT, typename _Traits> 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<typename _RealType> template<typename _UniformRandomNumberGenerator> 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<typename _RealType> template<typename _UniformRandomNumberGenerator> 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<result_type>::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<typename _RealType> template<typename _OutputIterator, typename _UniformRandomNumberGenerator> void hoyt_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) 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<result_type>::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<typename _RealType, typename _CharT, typename _Traits> 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<typename _RealType, typename _CharT, typename _Traits> 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<typename _RealType> template<typename _OutputIterator, typename _UniformRandomNumberGenerator> void triangular_distribution<_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<typename _RealType, typename _CharT, typename _Traits> 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<typename _RealType, typename _CharT, typename _Traits> 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<typename _RealType> template<typename _UniformRandomNumberGenerator> 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<result_type>::__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<typename _RealType> template<typename _OutputIterator, typename _UniformRandomNumberGenerator> void von_mises_distribution<_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<typename _RealType, typename _CharT, typename _Traits> 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<typename _RealType, typename _CharT, typename _Traits> 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; } template<typename _UIntType> template<typename _UniformRandomNumberGenerator> 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<typename _UIntType> template<typename _OutputIterator, typename _UniformRandomNumberGenerator> void hypergeometric_distribution<_UIntType>:: __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); } template<typename _UIntType, typename _CharT, typename _Traits> 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<typename _UIntType, typename _CharT, typename _Traits> 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<typename _RealType> template<typename _UniformRandomNumberGenerator> 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<typename _RealType> template<typename _OutputIterator, typename _UniformRandomNumberGenerator> void logistic_distribution<_RealType>:: __generate_impl(_OutputIterator __f, _OutputIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator, result_type>) 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<typename _RealType, typename _CharT, typename _Traits> 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<typename _RealType, typename _CharT, typename _Traits> 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<std::size_t _Dimen, typename _RealType> class uniform_on_sphere_helper { typedef typename uniform_on_sphere_distribution<_Dimen, _RealType>:: result_type result_type; public: template<typename _NormalDistribution, typename _UniformRandomNumberGenerator> 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) || ! __builtin_isfinite(__norm)); std::transform(__ret.begin(), __ret.end(), __ret.begin(), [__norm](_RealType __val){ return __val / __norm; }); return __ret; } }; template<typename _RealType> class uniform_on_sphere_helper<2, _RealType> { typedef typename uniform_on_sphere_distribution<2, _RealType>:: result_type result_type; public: template<typename _NormalDistribution, typename _UniformRandomNumberGenerator> 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<std::size_t _Dimen, typename _RealType> template<typename _UniformRandomNumberGenerator> 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<std::size_t _Dimen, typename _RealType> template<typename _OutputIterator, typename _UniformRandomNumberGenerator> void uniform_on_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::size_t _Dimen, typename _RealType, typename _CharT, typename _Traits> 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::size_t _Dimen, typename _RealType, typename _CharT, typename _Traits> 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; } namespace { // Helper class for the uniform_inside_sphere_distribution generation // function. template<std::size_t _Dimen, bool _SmallDimen, typename _RealType> class uniform_inside_sphere_helper; template<std::size_t _Dimen, typename _RealType> class uniform_inside_sphere_helper<_Dimen, false, _RealType> { using result_type = typename uniform_inside_sphere_distribution<_Dimen, _RealType>:: result_type; public: template<typename _UniformOnSphereDistribution, typename _UniformRandomNumberGenerator> 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<std::size_t _Dimen, typename _RealType> class uniform_inside_sphere_helper<_Dimen, true, _RealType> { using result_type = typename uniform_inside_sphere_distribution<_Dimen, _RealType>:: result_type; public: template<typename _UniformOnSphereDistribution, typename _UniformRandomNumberGenerator> 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<std::size_t _Dimen, typename _RealType> template<typename _UniformRandomNumberGenerator> 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<std::size_t _Dimen, typename _RealType> template<typename _OutputIterator, typename _UniformRandomNumberGenerator> 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::size_t _Dimen, typename _RealType, typename _CharT, typename _Traits> 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::size_t _Dimen, typename _RealType, typename _CharT, typename _Traits> 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; } _GLIBCXX_END_NAMESPACE_VERSION } // namespace __gnu_cxx #endif // _EXT_RANDOM_TCC