head 1.1; branch 1.1.1; access; symbols netbsd-11-0-RC4:1.1.1.11 netbsd-11-0-RC3:1.1.1.11 netbsd-11-0-RC2:1.1.1.11 netbsd-11-0-RC1:1.1.1.11 gcc-14-3-0:1.1.1.12 perseant-exfatfs-base-20250801:1.1.1.11 netbsd-11:1.1.1.11.0.4 netbsd-11-base:1.1.1.11 gcc-12-5-0:1.1.1.11 netbsd-10-1-RELEASE:1.1.1.10 perseant-exfatfs-base-20240630:1.1.1.11 gcc-12-4-0:1.1.1.11 perseant-exfatfs:1.1.1.11.0.2 perseant-exfatfs-base:1.1.1.11 netbsd-8-3-RELEASE:1.1.1.2 netbsd-9-4-RELEASE:1.1.1.4 netbsd-10-0-RELEASE:1.1.1.10 netbsd-10-0-RC6:1.1.1.10 netbsd-10-0-RC5:1.1.1.10 netbsd-10-0-RC4:1.1.1.10 netbsd-10-0-RC3:1.1.1.10 netbsd-10-0-RC2:1.1.1.10 netbsd-10-0-RC1:1.1.1.10 gcc-12-3-0:1.1.1.11 gcc-10-5-0:1.1.1.10 netbsd-10:1.1.1.10.0.2 netbsd-10-base:1.1.1.10 netbsd-9-3-RELEASE:1.1.1.4 gcc-10-4-0:1.1.1.10 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; locks; strict; comment @# @; 1.1 date 2014.03.01.08.41.32; author mrg; state Exp; branches 1.1.1.1; next ; commitid TtaB91QNTknAoYqx; 1.1.1.1 date 2014.03.01.08.41.32; author mrg; state Exp; branches 1.1.1.1.4.1 1.1.1.1.8.1; next 1.1.1.2; commitid TtaB91QNTknAoYqx; 1.1.1.2 date 2016.01.24.06.05.52; author mrg; state Exp; branches; next 1.1.1.3; commitid uWWfbLp08zOK79Sy; 1.1.1.3 date 2018.02.02.01.59.03; author mrg; state Exp; branches 1.1.1.3.2.1 1.1.1.3.4.1; next 1.1.1.4; commitid XNKaycqpfhzd5epA; 1.1.1.4 date 2019.01.19.10.14.12; author mrg; state Exp; branches; next 1.1.1.5; commitid VQ8OwWIg5RS9kn8B; 1.1.1.5 date 2019.10.01.09.36.07; author mrg; state Exp; branches; next 1.1.1.6; commitid smvgr2IPAQDr89FB; 1.1.1.6 date 2020.08.11.05.10.39; author mrg; state Exp; branches; next 1.1.1.7; commitid 5dBRDT7i6e65xBjC; 1.1.1.7 date 2020.08.11.05.30.09; author mrg; state Exp; branches; next 1.1.1.8; commitid 7AI4OfpLi4eqEBjC; 1.1.1.8 date 2020.09.05.07.52.09; author mrg; state Exp; branches; next 1.1.1.9; commitid ZRYA7IOuwfMjAPmC; 1.1.1.9 date 2021.04.10.22.10.04; author mrg; state Exp; branches; next 1.1.1.10; commitid eC4g0MRpqTvEkNOC; 1.1.1.10 date 2022.07.22.19.52.36; author mrg; state Exp; branches; next 1.1.1.11; commitid fUYPgdKzIHqhwVMD; 1.1.1.11 date 2023.07.30.05.21.20; author mrg; state Exp; branches; next 1.1.1.12; commitid tk6nV4mbc9nVEMyE; 1.1.1.12 date 2025.09.13.23.45.48; author mrg; state Exp; branches; next ; commitid KwhwN4krNWa6XBaG; 1.1.1.1.4.1 date 2014.03.01.08.41.32; author yamt; state dead; branches; next 1.1.1.1.4.2; commitid DX8bafDLmqEbpyBx; 1.1.1.1.4.2 date 2014.05.22.16.37.49; author yamt; state Exp; branches; next ; commitid DX8bafDLmqEbpyBx; 1.1.1.1.8.1 date 2014.03.01.08.41.32; author tls; state dead; branches; next 1.1.1.1.8.2; commitid jTnpym9Qu0o4R1Nx; 1.1.1.1.8.2 date 2014.08.19.23.54.50; author tls; state Exp; branches; next ; commitid jTnpym9Qu0o4R1Nx; 1.1.1.3.2.1 date 2019.01.26.21.59.35; author pgoyette; state Exp; branches; next ; commitid JKpcmvSjdT25dl9B; 1.1.1.3.4.1 date 2019.06.10.21.54.52; author christos; state Exp; branches; next 1.1.1.3.4.2; 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 * This file is a GNU extension to the Standard C++ Library. */ #ifndef _EXT_RANDOM #define _EXT_RANDOM 1 #pragma GCC system_header #if __cplusplus < 201103L # include #else #include #include #include #ifdef __SSE2__ # include #endif #ifdef _GLIBCXX_USE_C99_STDINT_TR1 namespace __gnu_cxx _GLIBCXX_VISIBILITY(default) { _GLIBCXX_BEGIN_NAMESPACE_VERSION #if __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__ /* Mersenne twister implementation optimized for vector operations. * * Reference: http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/ */ template class simd_fast_mersenne_twister_engine { static_assert(std::is_unsigned<_UIntType>::value, "template argument " "substituting _UIntType not an unsigned integral type"); static_assert(__sr1 < 32, "first right shift too large"); static_assert(__sr2 < 16, "second right shift too large"); static_assert(__sl1 < 32, "first left shift too large"); static_assert(__sl2 < 16, "second left shift too large"); public: typedef _UIntType result_type; private: static constexpr size_t m_w = sizeof(result_type) * 8; static constexpr size_t _M_nstate = __m / 128 + 1; static constexpr size_t _M_nstate32 = _M_nstate * 4; static_assert(std::is_unsigned<_UIntType>::value, "template argument " "substituting _UIntType not an unsigned integral type"); static_assert(__pos1 < _M_nstate, "POS1 not smaller than state size"); static_assert(16 % sizeof(_UIntType) == 0, "UIntType size must divide 16"); public: static constexpr size_t state_size = _M_nstate * (16 / sizeof(result_type)); static constexpr result_type default_seed = 5489u; // constructors and member function explicit simd_fast_mersenne_twister_engine(result_type __sd = default_seed) { seed(__sd); } template::value> ::type> explicit simd_fast_mersenne_twister_engine(_Sseq& __q) { seed(__q); } void seed(result_type __sd = default_seed); template typename std::enable_if::value>::type seed(_Sseq& __q); static constexpr result_type min() { return 0; }; static constexpr result_type max() { return std::numeric_limits::max(); } void discard(unsigned long long __z); result_type operator()() { if (__builtin_expect(_M_pos >= state_size, 0)) _M_gen_rand(); return _M_stateT[_M_pos++]; } template friend bool operator==(const simd_fast_mersenne_twister_engine<_UIntType_2, __m_2, __pos1_2, __sl1_2, __sl2_2, __sr1_2, __sr2_2, __msk1_2, __msk2_2, __msk3_2, __msk4_2, __parity1_2, __parity2_2, __parity3_2, __parity4_2>& __lhs, const simd_fast_mersenne_twister_engine<_UIntType_2, __m_2, __pos1_2, __sl1_2, __sl2_2, __sr1_2, __sr2_2, __msk1_2, __msk2_2, __msk3_2, __msk4_2, __parity1_2, __parity2_2, __parity3_2, __parity4_2>& __rhs); template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::simd_fast_mersenne_twister_engine <_UIntType_2, __m_2, __pos1_2, __sl1_2, __sl2_2, __sr1_2, __sr2_2, __msk1_2, __msk2_2, __msk3_2, __msk4_2, __parity1_2, __parity2_2, __parity3_2, __parity4_2>& __x); template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType_2, __m_2, __pos1_2, __sl1_2, __sl2_2, __sr1_2, __sr2_2, __msk1_2, __msk2_2, __msk3_2, __msk4_2, __parity1_2, __parity2_2, __parity3_2, __parity4_2>& __x); private: union { #ifdef __SSE2__ __m128i _M_state[_M_nstate]; #endif uint32_t _M_state32[_M_nstate32]; result_type _M_stateT[state_size]; } __attribute__ ((__aligned__ (16))); size_t _M_pos; void _M_gen_rand(void); void _M_period_certification(); }; template inline 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) { return !(__lhs == __rhs); } /* Definitions for the SIMD-oriented Fast Mersenne Twister as defined * in the C implementation by Daito and Matsumoto, as both a 32-bit * and 64-bit version. */ typedef simd_fast_mersenne_twister_engine sfmt607; typedef simd_fast_mersenne_twister_engine sfmt607_64; typedef simd_fast_mersenne_twister_engine sfmt1279; typedef simd_fast_mersenne_twister_engine sfmt1279_64; typedef simd_fast_mersenne_twister_engine sfmt2281; typedef simd_fast_mersenne_twister_engine sfmt2281_64; typedef simd_fast_mersenne_twister_engine sfmt4253; typedef simd_fast_mersenne_twister_engine sfmt4253_64; typedef simd_fast_mersenne_twister_engine sfmt11213; typedef simd_fast_mersenne_twister_engine sfmt11213_64; typedef simd_fast_mersenne_twister_engine sfmt19937; typedef simd_fast_mersenne_twister_engine sfmt19937_64; typedef simd_fast_mersenne_twister_engine sfmt44497; typedef simd_fast_mersenne_twister_engine sfmt44497_64; typedef simd_fast_mersenne_twister_engine sfmt86243; typedef simd_fast_mersenne_twister_engine sfmt86243_64; typedef simd_fast_mersenne_twister_engine sfmt132049; typedef simd_fast_mersenne_twister_engine sfmt132049_64; typedef simd_fast_mersenne_twister_engine sfmt216091; typedef simd_fast_mersenne_twister_engine sfmt216091_64; #endif // __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__ /** * @@brief A beta continuous distribution for random numbers. * * The formula for the beta probability density function is: * @@f[ * p(x|\alpha,\beta) = \frac{1}{B(\alpha,\beta)} * x^{\alpha - 1} (1 - x)^{\beta - 1} * @@f] */ template class beta_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef beta_distribution<_RealType> distribution_type; friend class beta_distribution<_RealType>; explicit param_type(_RealType __alpha_val = _RealType(1), _RealType __beta_val = _RealType(1)) : _M_alpha(__alpha_val), _M_beta(__beta_val) { _GLIBCXX_DEBUG_ASSERT(_M_alpha > _RealType(0)); _GLIBCXX_DEBUG_ASSERT(_M_beta > _RealType(0)); } _RealType alpha() const { return _M_alpha; } _RealType beta() const { return _M_beta; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return (__p1._M_alpha == __p2._M_alpha && __p1._M_beta == __p2._M_beta); } private: void _M_initialize(); _RealType _M_alpha; _RealType _M_beta; }; public: /** * @@brief Constructs a beta distribution with parameters * @@f$\alpha@@f$ and @@f$\beta@@f$. */ explicit beta_distribution(_RealType __alpha_val = _RealType(1), _RealType __beta_val = _RealType(1)) : _M_param(__alpha_val, __beta_val) { } explicit beta_distribution(const param_type& __p) : _M_param(__p) { } /** * @@brief Resets the distribution state. */ void reset() { } /** * @@brief Returns the @@f$\alpha@@f$ of the distribution. */ _RealType alpha() const { return _M_param.alpha(); } /** * @@brief Returns the @@f$\beta@@f$ of the distribution. */ _RealType beta() const { return _M_param.beta(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return result_type(0); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return result_type(1); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two beta distributions have the same * parameters and the sequences that would be generated * are equal. */ friend bool operator==(const beta_distribution& __d1, const beta_distribution& __d2) { return __d1._M_param == __d2._M_param; } /** * @@brief Inserts a %beta_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %beta_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::beta_distribution<_RealType1>& __x); /** * @@brief Extracts a %beta_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %beta_distribution random number generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::beta_distribution<_RealType1>& __x); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; }; /** * @@brief Return true if two beta distributions are different. */ template inline bool operator!=(const __gnu_cxx::beta_distribution<_RealType>& __d1, const __gnu_cxx::beta_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A multi-variate normal continuous distribution for random numbers. * * The formula for the normal probability density function is * @@f[ * p(\overrightarrow{x}|\overrightarrow{\mu },\Sigma) = * \frac{1}{\sqrt{(2\pi )^k\det(\Sigma))}} * e^{-\frac{1}{2}(\overrightarrow{x}-\overrightarrow{\mu})^\text{T} * \Sigma ^{-1}(\overrightarrow{x}-\overrightarrow{\mu})} * @@f] * * where @@f$\overrightarrow{x}@@f$ and @@f$\overrightarrow{\mu}@@f$ are * vectors of dimension @@f$k@@f$ and @@f$\Sigma@@f$ is the covariance * matrix (which must be positive-definite). */ template class normal_mv_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); static_assert(_Dimen != 0, "dimension is zero"); public: /** The type of the range of the distribution. */ typedef std::array<_RealType, _Dimen> result_type; /** Parameter type. */ class param_type { static constexpr size_t _M_t_size = _Dimen * (_Dimen + 1) / 2; public: typedef normal_mv_distribution<_Dimen, _RealType> distribution_type; friend class normal_mv_distribution<_Dimen, _RealType>; param_type() { std::fill(_M_mean.begin(), _M_mean.end(), _RealType(0)); auto __it = _M_t.begin(); for (size_t __i = 0; __i < _Dimen; ++__i) { std::fill_n(__it, __i, _RealType(0)); __it += __i; *__it++ = _RealType(1); } } template param_type(_ForwardIterator1 __meanbegin, _ForwardIterator1 __meanend, _ForwardIterator2 __varcovbegin, _ForwardIterator2 __varcovend) { __glibcxx_function_requires(_ForwardIteratorConcept< _ForwardIterator1>) __glibcxx_function_requires(_ForwardIteratorConcept< _ForwardIterator2>) _GLIBCXX_DEBUG_ASSERT(std::distance(__meanbegin, __meanend) <= _Dimen); const auto __dist = std::distance(__varcovbegin, __varcovend); _GLIBCXX_DEBUG_ASSERT(__dist == _Dimen * _Dimen || __dist == _Dimen * (_Dimen + 1) / 2 || __dist == _Dimen); if (__dist == _Dimen * _Dimen) _M_init_full(__meanbegin, __meanend, __varcovbegin, __varcovend); else if (__dist == _Dimen * (_Dimen + 1) / 2) _M_init_lower(__meanbegin, __meanend, __varcovbegin, __varcovend); else _M_init_diagonal(__meanbegin, __meanend, __varcovbegin, __varcovend); } param_type(std::initializer_list<_RealType> __mean, std::initializer_list<_RealType> __varcov) { _GLIBCXX_DEBUG_ASSERT(__mean.size() <= _Dimen); _GLIBCXX_DEBUG_ASSERT(__varcov.size() == _Dimen * _Dimen || __varcov.size() == _Dimen * (_Dimen + 1) / 2 || __varcov.size() == _Dimen); if (__varcov.size() == _Dimen * _Dimen) _M_init_full(__mean.begin(), __mean.end(), __varcov.begin(), __varcov.end()); else if (__varcov.size() == _Dimen * (_Dimen + 1) / 2) _M_init_lower(__mean.begin(), __mean.end(), __varcov.begin(), __varcov.end()); else _M_init_diagonal(__mean.begin(), __mean.end(), __varcov.begin(), __varcov.end()); } std::array<_RealType, _Dimen> mean() const { return _M_mean; } std::array<_RealType, _M_t_size> varcov() const { return _M_t; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_mean == __p2._M_mean && __p1._M_t == __p2._M_t; } private: template void _M_init_full(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varcovbegin, _InputIterator2 __varcovend); template void _M_init_lower(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varcovbegin, _InputIterator2 __varcovend); template void _M_init_diagonal(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varbegin, _InputIterator2 __varend); std::array<_RealType, _Dimen> _M_mean; std::array<_RealType, _M_t_size> _M_t; }; public: normal_mv_distribution() : _M_param(), _M_nd() { } template normal_mv_distribution(_ForwardIterator1 __meanbegin, _ForwardIterator1 __meanend, _ForwardIterator2 __varcovbegin, _ForwardIterator2 __varcovend) : _M_param(__meanbegin, __meanend, __varcovbegin, __varcovend), _M_nd() { } normal_mv_distribution(std::initializer_list<_RealType> __mean, std::initializer_list<_RealType> __varcov) : _M_param(__mean, __varcov), _M_nd() { } explicit normal_mv_distribution(const param_type& __p) : _M_param(__p), _M_nd() { } /** * @@brief Resets the distribution state. */ void reset() { _M_nd.reset(); } /** * @@brief Returns the mean of the distribution. */ result_type mean() const { return _M_param.mean(); } /** * @@brief Returns the compact form of the variance/covariance * matrix of the distribution. */ std::array<_RealType, _Dimen * (_Dimen + 1) / 2> varcov() const { return _M_param.varcov(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { result_type __res; __res.fill(std::numeric_limits<_RealType>::lowest()); return __res; } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { result_type __res; __res.fill(std::numeric_limits<_RealType>::max()); return __res; } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { return this->__generate_impl(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { return this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two multi-variant normal distributions have * the same parameters and the sequences that would * be generated are equal. */ template friend bool operator==(const __gnu_cxx::normal_mv_distribution<_Dimen1, _RealType1>& __d1, const __gnu_cxx::normal_mv_distribution<_Dimen1, _RealType1>& __d2); /** * @@brief Inserts a %normal_mv_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %normal_mv_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::normal_mv_distribution<_Dimen1, _RealType1>& __x); /** * @@brief Extracts a %normal_mv_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %normal_mv_distribution random number generator engine. * * @@returns The input stream with @@p __x extracted or in an error * state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::normal_mv_distribution<_Dimen1, _RealType1>& __x); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::normal_distribution<_RealType> _M_nd; }; /** * @@brief Return true if two multi-variate normal distributions are * different. */ template inline bool operator!=(const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __d1, const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A Rice continuous distribution for random numbers. * * The formula for the Rice probability density function is * @@f[ * p(x|\nu,\sigma) = \frac{x}{\sigma^2} * \exp\left(-\frac{x^2+\nu^2}{2\sigma^2}\right) * I_0\left(\frac{x \nu}{\sigma^2}\right) * @@f] * where @@f$I_0(z)@@f$ is the modified Bessel function of the first kind * of order 0 and @@f$\nu >= 0@@f$ and @@f$\sigma > 0@@f$. * * * * * * *
Distribution Statistics
Mean@@f$\sqrt{\pi/2}L_{1/2}(-\nu^2/2\sigma^2)@@f$
Variance@@f$2\sigma^2 + \nu^2 * + (\pi\sigma^2/2)L^2_{1/2}(-\nu^2/2\sigma^2)@@f$
Range@@f$[0, \infty)@@f$
* where @@f$L_{1/2}(x)@@f$ is the Laguerre polynomial of order 1/2. */ template class rice_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef rice_distribution distribution_type; param_type(result_type __nu_val = result_type(0), result_type __sigma_val = result_type(1)) : _M_nu(__nu_val), _M_sigma(__sigma_val) { _GLIBCXX_DEBUG_ASSERT(_M_nu >= result_type(0)); _GLIBCXX_DEBUG_ASSERT(_M_sigma > result_type(0)); } result_type nu() const { return _M_nu; } result_type sigma() const { return _M_sigma; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_nu == __p2._M_nu && __p1._M_sigma == __p2._M_sigma; } private: void _M_initialize(); result_type _M_nu; result_type _M_sigma; }; /** * @@brief Constructors. */ explicit rice_distribution(result_type __nu_val = result_type(0), result_type __sigma_val = result_type(1)) : _M_param(__nu_val, __sigma_val), _M_ndx(__nu_val, __sigma_val), _M_ndy(result_type(0), __sigma_val) { } explicit rice_distribution(const param_type& __p) : _M_param(__p), _M_ndx(__p.nu(), __p.sigma()), _M_ndy(result_type(0), __p.sigma()) { } /** * @@brief Resets the distribution state. */ void reset() { _M_ndx.reset(); _M_ndy.reset(); } /** * @@brief Return the parameters of the distribution. */ result_type nu() const { return _M_param.nu(); } result_type sigma() const { return _M_param.sigma(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return result_type(0); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return std::numeric_limits::max(); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { result_type __x = this->_M_ndx(__urng); result_type __y = this->_M_ndy(__urng); #if _GLIBCXX_USE_C99_MATH_TR1 return std::hypot(__x, __y); #else return std::sqrt(__x * __x + __y * __y); #endif } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { 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 return std::hypot(__x, __y); #else return std::sqrt(__x * __x + __y * __y); #endif } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two Rice distributions have * the same parameters and the sequences that would * be generated are equal. */ friend bool operator==(const rice_distribution& __d1, const rice_distribution& __d2) { return (__d1._M_param == __d2._M_param && __d1._M_ndx == __d2._M_ndx && __d1._M_ndy == __d2._M_ndy); } /** * @@brief Inserts a %rice_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %rice_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>&, const rice_distribution<_RealType1>&); /** * @@brief Extracts a %rice_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %rice_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, rice_distribution<_RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::normal_distribution _M_ndx; std::normal_distribution _M_ndy; }; /** * @@brief Return true if two Rice distributions are not equal. */ template inline bool operator!=(const rice_distribution<_RealType1>& __d1, const rice_distribution<_RealType1>& __d2) { return !(__d1 == __d2); } /** * @@brief A Nakagami continuous distribution for random numbers. * * The formula for the Nakagami probability density function is * @@f[ * p(x|\mu,\omega) = \frac{2\mu^\mu}{\Gamma(\mu)\omega^\mu} * x^{2\mu-1}e^{-\mu x / \omega} * @@f] * where @@f$\Gamma(z)@@f$ is the gamma function and @@f$\mu >= 0.5@@f$ * and @@f$\omega > 0@@f$. */ template class nakagami_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef nakagami_distribution distribution_type; param_type(result_type __mu_val = result_type(1), result_type __omega_val = result_type(1)) : _M_mu(__mu_val), _M_omega(__omega_val) { _GLIBCXX_DEBUG_ASSERT(_M_mu >= result_type(0.5L)); _GLIBCXX_DEBUG_ASSERT(_M_omega > result_type(0)); } result_type mu() const { return _M_mu; } result_type omega() const { return _M_omega; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_mu == __p2._M_mu && __p1._M_omega == __p2._M_omega; } private: void _M_initialize(); result_type _M_mu; result_type _M_omega; }; /** * @@brief Constructors. */ explicit nakagami_distribution(result_type __mu_val = result_type(1), result_type __omega_val = result_type(1)) : _M_param(__mu_val, __omega_val), _M_gd(__mu_val, __omega_val / __mu_val) { } explicit nakagami_distribution(const param_type& __p) : _M_param(__p), _M_gd(__p.mu(), __p.omega() / __p.mu()) { } /** * @@brief Resets the distribution state. */ void reset() { _M_gd.reset(); } /** * @@brief Return the parameters of the distribution. */ result_type mu() const { return _M_param.mu(); } result_type omega() const { return _M_param.omega(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return result_type(0); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return std::numeric_limits::max(); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return std::sqrt(this->_M_gd(__urng)); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { typename std::gamma_distribution::param_type __pg(__p.mu(), __p.omega() / __p.mu()); return std::sqrt(this->_M_gd(__pg, __urng)); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two Nakagami distributions have * the same parameters and the sequences that would * be generated are equal. */ friend bool operator==(const nakagami_distribution& __d1, const nakagami_distribution& __d2) { return (__d1._M_param == __d2._M_param && __d1._M_gd == __d2._M_gd); } /** * @@brief Inserts a %nakagami_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %nakagami_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>&, const nakagami_distribution<_RealType1>&); /** * @@brief Extracts a %nakagami_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %nakagami_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, nakagami_distribution<_RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::gamma_distribution _M_gd; }; /** * @@brief Return true if two Nakagami distributions are not equal. */ template inline bool operator!=(const nakagami_distribution<_RealType>& __d1, const nakagami_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A Pareto continuous distribution for random numbers. * * The formula for the Pareto cumulative probability function is * @@f[ * P(x|\alpha,\mu) = 1 - \left(\frac{\mu}{x}\right)^\alpha * @@f] * The formula for the Pareto probability density function is * @@f[ * p(x|\alpha,\mu) = \frac{\alpha + 1}{\mu} * \left(\frac{\mu}{x}\right)^{\alpha + 1} * @@f] * where @@f$x >= \mu@@f$ and @@f$\mu > 0@@f$, @@f$\alpha > 0@@f$. * * * * * * *
Distribution Statistics
Mean@@f$\alpha \mu / (\alpha - 1)@@f$ * for @@f$\alpha > 1@@f$
Variance@@f$\alpha \mu^2 / [(\alpha - 1)^2(\alpha - 2)]@@f$ * for @@f$\alpha > 2@@f$
Range@@f$[\mu, \infty)@@f$
*/ template class pareto_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef pareto_distribution distribution_type; param_type(result_type __alpha_val = result_type(1), result_type __mu_val = result_type(1)) : _M_alpha(__alpha_val), _M_mu(__mu_val) { _GLIBCXX_DEBUG_ASSERT(_M_alpha > result_type(0)); _GLIBCXX_DEBUG_ASSERT(_M_mu > result_type(0)); } result_type alpha() const { return _M_alpha; } result_type mu() const { return _M_mu; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_alpha == __p2._M_alpha && __p1._M_mu == __p2._M_mu; } private: void _M_initialize(); result_type _M_alpha; result_type _M_mu; }; /** * @@brief Constructors. */ explicit pareto_distribution(result_type __alpha_val = result_type(1), result_type __mu_val = result_type(1)) : _M_param(__alpha_val, __mu_val), _M_ud() { } explicit pareto_distribution(const param_type& __p) : _M_param(__p), _M_ud() { } /** * @@brief Resets the distribution state. */ void reset() { _M_ud.reset(); } /** * @@brief Return the parameters of the distribution. */ result_type alpha() const { return _M_param.alpha(); } result_type mu() const { return _M_param.mu(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return this->mu(); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return std::numeric_limits::max(); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->mu() * std::pow(this->_M_ud(__urng), -result_type(1) / this->alpha()); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { return __p.mu() * std::pow(this->_M_ud(__urng), -result_type(1) / __p.alpha()); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two Pareto distributions have * the same parameters and the sequences that would * be generated are equal. */ friend bool operator==(const pareto_distribution& __d1, const pareto_distribution& __d2) { return (__d1._M_param == __d2._M_param && __d1._M_ud == __d2._M_ud); } /** * @@brief Inserts a %pareto_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %pareto_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>&, const pareto_distribution<_RealType1>&); /** * @@brief Extracts a %pareto_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %pareto_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, pareto_distribution<_RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::uniform_real_distribution _M_ud; }; /** * @@brief Return true if two Pareto distributions are not equal. */ template inline bool operator!=(const pareto_distribution<_RealType>& __d1, const pareto_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A K continuous distribution for random numbers. * * The formula for the K probability density function is * @@f[ * p(x|\lambda, \mu, \nu) = \frac{2}{x} * \left(\frac{\lambda\nu x}{\mu}\right)^{\frac{\lambda + \nu}{2}} * \frac{1}{\Gamma(\lambda)\Gamma(\nu)} * K_{\nu - \lambda}\left(2\sqrt{\frac{\lambda\nu x}{\mu}}\right) * @@f] * where @@f$I_0(z)@@f$ is the modified Bessel function of the second kind * of order @@f$\nu - \lambda@@f$ and @@f$\lambda > 0@@f$, @@f$\mu > 0@@f$ * and @@f$\nu > 0@@f$. * * * * * * *
Distribution Statistics
Mean@@f$\mu@@f$
Variance@@f$\mu^2\frac{\lambda + \nu + 1}{\lambda\nu}@@f$
Range@@f$[0, \infty)@@f$
*/ template class k_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef k_distribution distribution_type; param_type(result_type __lambda_val = result_type(1), result_type __mu_val = result_type(1), result_type __nu_val = result_type(1)) : _M_lambda(__lambda_val), _M_mu(__mu_val), _M_nu(__nu_val) { _GLIBCXX_DEBUG_ASSERT(_M_lambda > result_type(0)); _GLIBCXX_DEBUG_ASSERT(_M_mu > result_type(0)); _GLIBCXX_DEBUG_ASSERT(_M_nu > result_type(0)); } result_type lambda() const { return _M_lambda; } result_type mu() const { return _M_mu; } result_type nu() const { return _M_nu; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_lambda == __p2._M_lambda && __p1._M_mu == __p2._M_mu && __p1._M_nu == __p2._M_nu; } private: void _M_initialize(); result_type _M_lambda; result_type _M_mu; result_type _M_nu; }; /** * @@brief Constructors. */ explicit k_distribution(result_type __lambda_val = result_type(1), result_type __mu_val = result_type(1), result_type __nu_val = result_type(1)) : _M_param(__lambda_val, __mu_val, __nu_val), _M_gd1(__lambda_val, result_type(1) / __lambda_val), _M_gd2(__nu_val, __mu_val / __nu_val) { } explicit k_distribution(const param_type& __p) : _M_param(__p), _M_gd1(__p.lambda(), result_type(1) / __p.lambda()), _M_gd2(__p.nu(), __p.mu() / __p.nu()) { } /** * @@brief Resets the distribution state. */ void reset() { _M_gd1.reset(); _M_gd2.reset(); } /** * @@brief Return the parameters of the distribution. */ result_type lambda() const { return _M_param.lambda(); } result_type mu() const { return _M_param.mu(); } result_type nu() const { return _M_param.nu(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return result_type(0); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return std::numeric_limits::max(); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator&); template result_type operator()(_UniformRandomNumberGenerator&, const param_type&); template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two K distributions have * the same parameters and the sequences that would * be generated are equal. */ friend bool operator==(const k_distribution& __d1, const k_distribution& __d2) { return (__d1._M_param == __d2._M_param && __d1._M_gd1 == __d2._M_gd1 && __d1._M_gd2 == __d2._M_gd2); } /** * @@brief Inserts a %k_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %k_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>&, const k_distribution<_RealType1>&); /** * @@brief Extracts a %k_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %k_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, k_distribution<_RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::gamma_distribution _M_gd1; std::gamma_distribution _M_gd2; }; /** * @@brief Return true if two K distributions are not equal. */ template inline bool operator!=(const k_distribution<_RealType>& __d1, const k_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief An arcsine continuous distribution for random numbers. * * The formula for the arcsine probability density function is * @@f[ * p(x|a,b) = \frac{1}{\pi \sqrt{(x - a)(b - x)}} * @@f] * where @@f$x >= a@@f$ and @@f$x <= b@@f$. * * * * * * *
Distribution Statistics
Mean@@f$ (a + b) / 2 @@f$
Variance@@f$ (b - a)^2 / 8 @@f$
Range@@f$[a, b]@@f$
*/ template class arcsine_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef arcsine_distribution distribution_type; param_type(result_type __a = result_type(0), result_type __b = result_type(1)) : _M_a(__a), _M_b(__b) { _GLIBCXX_DEBUG_ASSERT(_M_a <= _M_b); } result_type a() const { return _M_a; } result_type b() const { return _M_b; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b; } private: void _M_initialize(); result_type _M_a; result_type _M_b; }; /** * @@brief Constructors. */ explicit arcsine_distribution(result_type __a = result_type(0), result_type __b = result_type(1)) : _M_param(__a, __b), _M_ud(-1.5707963267948966192313216916397514L, +1.5707963267948966192313216916397514L) { } explicit arcsine_distribution(const param_type& __p) : _M_param(__p), _M_ud(-1.5707963267948966192313216916397514L, +1.5707963267948966192313216916397514L) { } /** * @@brief Resets the distribution state. */ void reset() { _M_ud.reset(); } /** * @@brief Return the parameters of the distribution. */ result_type a() const { return _M_param.a(); } result_type b() const { return _M_param.b(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return this->a(); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return this->b(); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { result_type __x = std::sin(this->_M_ud(__urng)); return (__x * (this->b() - this->a()) + this->a() + this->b()) / result_type(2); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { result_type __x = std::sin(this->_M_ud(__urng)); return (__x * (__p.b() - __p.a()) + __p.a() + __p.b()) / result_type(2); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two arcsine distributions have * the same parameters and the sequences that would * be generated are equal. */ friend bool operator==(const arcsine_distribution& __d1, const arcsine_distribution& __d2) { return (__d1._M_param == __d2._M_param && __d1._M_ud == __d2._M_ud); } /** * @@brief Inserts a %arcsine_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %arcsine_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>&, const arcsine_distribution<_RealType1>&); /** * @@brief Extracts a %arcsine_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %arcsine_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, arcsine_distribution<_RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::uniform_real_distribution _M_ud; }; /** * @@brief Return true if two arcsine distributions are not equal. */ template inline bool operator!=(const arcsine_distribution<_RealType>& __d1, const arcsine_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A Hoyt continuous distribution for random numbers. * * The formula for the Hoyt probability density function is * @@f[ * p(x|q,\omega) = \frac{(1 + q^2)x}{q\omega} * \exp\left(-\frac{(1 + q^2)^2 x^2}{4 q^2 \omega}\right) * I_0\left(\frac{(1 - q^4) x^2}{4 q^2 \omega}\right) * @@f] * where @@f$I_0(z)@@f$ is the modified Bessel function of the first kind * of order 0 and @@f$0 < q < 1@@f$. * * * * * * *
Distribution Statistics
Mean@@f$ \sqrt{\frac{2}{\pi}} \sqrt{\frac{\omega}{1 + q^2}} * E(1 - q^2) @@f$
Variance@@f$ \omega \left(1 - \frac{2E^2(1 - q^2)} * {\pi (1 + q^2)}\right) @@f$
Range@@f$[0, \infty)@@f$
* where @@f$E(x)@@f$ is the elliptic function of the second kind. */ template class hoyt_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef hoyt_distribution distribution_type; param_type(result_type __q = result_type(0.5L), result_type __omega = result_type(1)) : _M_q(__q), _M_omega(__omega) { _GLIBCXX_DEBUG_ASSERT(_M_q > result_type(0)); _GLIBCXX_DEBUG_ASSERT(_M_q < result_type(1)); } result_type q() const { return _M_q; } result_type omega() const { return _M_omega; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_q == __p2._M_q && __p1._M_omega == __p2._M_omega; } private: void _M_initialize(); result_type _M_q; result_type _M_omega; }; /** * @@brief Constructors. */ explicit hoyt_distribution(result_type __q = result_type(0.5L), result_type __omega = result_type(1)) : _M_param(__q, __omega), _M_ad(result_type(0.5L) * (result_type(1) + __q * __q), result_type(0.5L) * (result_type(1) + __q * __q) / (__q * __q)), _M_ed(result_type(1)) { } explicit hoyt_distribution(const param_type& __p) : _M_param(__p), _M_ad(result_type(0.5L) * (result_type(1) + __p.q() * __p.q()), result_type(0.5L) * (result_type(1) + __p.q() * __p.q()) / (__p.q() * __p.q())), _M_ed(result_type(1)) { } /** * @@brief Resets the distribution state. */ void reset() { _M_ad.reset(); _M_ed.reset(); } /** * @@brief Return the parameters of the distribution. */ result_type q() const { return _M_param.q(); } result_type omega() const { return _M_param.omega(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return result_type(0); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return std::numeric_limits::max(); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng); template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two Hoyt distributions have * the same parameters and the sequences that would * be generated are equal. */ friend bool operator==(const hoyt_distribution& __d1, const hoyt_distribution& __d2) { return (__d1._M_param == __d2._M_param && __d1._M_ad == __d2._M_ad && __d1._M_ed == __d2._M_ed); } /** * @@brief Inserts a %hoyt_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %hoyt_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>&, const hoyt_distribution<_RealType1>&); /** * @@brief Extracts a %hoyt_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %hoyt_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, hoyt_distribution<_RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; __gnu_cxx::arcsine_distribution _M_ad; std::exponential_distribution _M_ed; }; /** * @@brief Return true if two Hoyt distributions are not equal. */ template inline bool operator!=(const hoyt_distribution<_RealType>& __d1, const hoyt_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A triangular distribution for random numbers. * * The formula for the triangular probability density function is * @@f[ * / 0 for x < a * p(x|a,b,c) = | \frac{2(x-a)}{(c-a)(b-a)} for a <= x <= b * | \frac{2(c-x)}{(c-a)(c-b)} for b < x <= c * \ 0 for c < x * @@f] * * * * * * *
Distribution Statistics
Mean@@f$ \frac{a+b+c}{2} @@f$
Variance@@f$ \frac{a^2+b^2+c^2-ab-ac-bc} * {18}@@f$
Range@@f$[a, c]@@f$
*/ template class triangular_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { friend class triangular_distribution<_RealType>; explicit param_type(_RealType __a = _RealType(0), _RealType __b = _RealType(0.5), _RealType __c = _RealType(1)) : _M_a(__a), _M_b(__b), _M_c(__c) { _GLIBCXX_DEBUG_ASSERT(_M_a <= _M_b); _GLIBCXX_DEBUG_ASSERT(_M_b <= _M_c); _GLIBCXX_DEBUG_ASSERT(_M_a < _M_c); _M_r_ab = (_M_b - _M_a) / (_M_c - _M_a); _M_f_ab_ac = (_M_b - _M_a) * (_M_c - _M_a); _M_f_bc_ac = (_M_c - _M_b) * (_M_c - _M_a); } _RealType a() const { return _M_a; } _RealType b() const { return _M_b; } _RealType c() const { return _M_c; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return (__p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b && __p1._M_c == __p2._M_c); } private: _RealType _M_a; _RealType _M_b; _RealType _M_c; _RealType _M_r_ab; _RealType _M_f_ab_ac; _RealType _M_f_bc_ac; }; /** * @@brief Constructs a triangle distribution with parameters * @@f$ a @@f$, @@f$ b @@f$ and @@f$ c @@f$. */ explicit triangular_distribution(result_type __a = result_type(0), result_type __b = result_type(0.5), result_type __c = result_type(1)) : _M_param(__a, __b, __c) { } explicit triangular_distribution(const param_type& __p) : _M_param(__p) { } /** * @@brief Resets the distribution state. */ void reset() { } /** * @@brief Returns the @@f$ a @@f$ of the distribution. */ result_type a() const { return _M_param.a(); } /** * @@brief Returns the @@f$ b @@f$ of the distribution. */ result_type b() const { return _M_param.b(); } /** * @@brief Returns the @@f$ c @@f$ of the distribution. */ result_type c() const { return _M_param.c(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return _M_param._M_a; } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return _M_param._M_c; } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); result_type __rnd = __aurng(); if (__rnd <= __p._M_r_ab) return __p.a() + std::sqrt(__rnd * __p._M_f_ab_ac); else return __p.c() - std::sqrt((result_type(1) - __rnd) * __p._M_f_bc_ac); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two triangle distributions have the same * parameters and the sequences that would be generated * are equal. */ friend bool operator==(const triangular_distribution& __d1, const triangular_distribution& __d2) { return __d1._M_param == __d2._M_param; } /** * @@brief Inserts a %triangular_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %triangular_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::triangular_distribution<_RealType1>& __x); /** * @@brief Extracts a %triangular_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %triangular_distribution random number generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::triangular_distribution<_RealType1>& __x); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; }; /** * @@brief Return true if two triangle distributions are different. */ template inline bool operator!=(const __gnu_cxx::triangular_distribution<_RealType>& __d1, const __gnu_cxx::triangular_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A von Mises distribution for random numbers. * * The formula for the von Mises probability density function is * @@f[ * p(x|\mu,\kappa) = \frac{e^{\kappa \cos(x-\mu)}} * {2\pi I_0(\kappa)} * @@f] * * The generating functions use the method according to: * * D. J. Best and N. I. Fisher, 1979. "Efficient Simulation of the * von Mises Distribution", Journal of the Royal Statistical Society. * Series C (Applied Statistics), Vol. 28, No. 2, pp. 152-157. * * * * * * *
Distribution Statistics
Mean@@f$ \mu @@f$
Variance@@f$ 1-I_1(\kappa)/I_0(\kappa) @@f$
Range@@f$[-\pi, \pi]@@f$
*/ template class von_mises_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { friend class von_mises_distribution<_RealType>; explicit param_type(_RealType __mu = _RealType(0), _RealType __kappa = _RealType(1)) : _M_mu(__mu), _M_kappa(__kappa) { const _RealType __pi = __gnu_cxx::__math_constants<_RealType>::__pi; _GLIBCXX_DEBUG_ASSERT(_M_mu >= -__pi && _M_mu <= __pi); _GLIBCXX_DEBUG_ASSERT(_M_kappa >= _RealType(0)); auto __tau = std::sqrt(_RealType(4) * _M_kappa * _M_kappa + _RealType(1)) + _RealType(1); auto __rho = ((__tau - std::sqrt(_RealType(2) * __tau)) / (_RealType(2) * _M_kappa)); _M_r = (_RealType(1) + __rho * __rho) / (_RealType(2) * __rho); } _RealType mu() const { return _M_mu; } _RealType kappa() const { return _M_kappa; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return (__p1._M_mu == __p2._M_mu && __p1._M_kappa == __p2._M_kappa); } private: _RealType _M_mu; _RealType _M_kappa; _RealType _M_r; }; /** * @@brief Constructs a von Mises distribution with parameters * @@f$\mu@@f$ and @@f$\kappa@@f$. */ explicit von_mises_distribution(result_type __mu = result_type(0), result_type __kappa = result_type(1)) : _M_param(__mu, __kappa) { } explicit von_mises_distribution(const param_type& __p) : _M_param(__p) { } /** * @@brief Resets the distribution state. */ void reset() { } /** * @@brief Returns the @@f$ \mu @@f$ of the distribution. */ result_type mu() const { return _M_param.mu(); } /** * @@brief Returns the @@f$ \kappa @@f$ of the distribution. */ result_type kappa() const { return _M_param.kappa(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return -__gnu_cxx::__math_constants::__pi; } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return __gnu_cxx::__math_constants::__pi; } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template result_type 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 void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two von Mises distributions have the same * parameters and the sequences that would be generated * are equal. */ friend bool operator==(const von_mises_distribution& __d1, const von_mises_distribution& __d2) { return __d1._M_param == __d2._M_param; } /** * @@brief Inserts a %von_mises_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %von_mises_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::von_mises_distribution<_RealType1>& __x); /** * @@brief Extracts a %von_mises_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %von_mises_distribution random number generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::von_mises_distribution<_RealType1>& __x); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; }; /** * @@brief Return true if two von Mises distributions are different. */ template inline bool operator!=(const __gnu_cxx::von_mises_distribution<_RealType>& __d1, const __gnu_cxx::von_mises_distribution<_RealType>& __d2) { return !(__d1 == __d2); } _GLIBCXX_END_NAMESPACE_VERSION } // namespace __gnu_cxx #include "ext/opt_random.h" #include "random.tcc" #endif // _GLIBCXX_USE_C99_STDINT_TR1 #endif // C++11 #endif // _EXT_RANDOM @ 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. a38 1 #include d600 1 a600 1 { return !(__d1 == __d2); } d2577 1 a2577 1 { return !(__d1 == __d2); } d2731 35 a2765 1 const param_type& __p); d2846 1 a2846 676 { return !(__d1 == __d2); } /** * @@brief A discrete hypergeometric random number distribution. * * The hypergeometric distribution is a discrete probability distribution * that describes the probability of @@p k successes in @@p n draws @@a without * replacement from a finite population of size @@p N containing exactly @@p K * successes. * * The formula for the hypergeometric probability density function is * @@f[ * p(k|N,K,n) = \frac{\binom{K}{k} \binom{N-K}{n-k}}{\binom{N}{n}} * @@f] * where @@f$N@@f$ is the total population of the distribution, * @@f$K@@f$ is the total population of the distribution. * * * * * * *
Distribution Statistics
Mean@@f$ n\frac{K}{N} @@f$
Variance@@f$ n\frac{K}{N}\frac{N-K}{N}\frac{N-n}{N-1} * @@f$
Range@@f$[max(0, n+K-N), min(K, n)]@@f$
*/ template class hypergeometric_distribution { static_assert(std::is_unsigned<_UIntType>::value, "template argument " "substituting _UIntType not an unsigned integral type"); public: /** The type of the range of the distribution. */ typedef _UIntType result_type; /** Parameter type. */ struct param_type { typedef hypergeometric_distribution<_UIntType> distribution_type; friend class hypergeometric_distribution<_UIntType>; explicit param_type(result_type __N = 10, result_type __K = 5, result_type __n = 1) : _M_N{__N}, _M_K{__K}, _M_n{__n} { _GLIBCXX_DEBUG_ASSERT(_M_N >= _M_K); _GLIBCXX_DEBUG_ASSERT(_M_N >= _M_n); } result_type total_size() const { return _M_N; } result_type successful_size() const { return _M_K; } result_type unsuccessful_size() const { return _M_N - _M_K; } result_type total_draws() const { return _M_n; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return (__p1._M_N == __p2._M_N) && (__p1._M_K == __p2._M_K) && (__p1._M_n == __p2._M_n); } private: result_type _M_N; result_type _M_K; result_type _M_n; }; // constructors and member function explicit hypergeometric_distribution(result_type __N = 10, result_type __K = 5, result_type __n = 1) : _M_param{__N, __K, __n} { } explicit hypergeometric_distribution(const param_type& __p) : _M_param{__p} { } /** * @@brief Resets the distribution state. */ void reset() { } /** * @@brief Returns the distribution parameter @@p N, * the total number of items. */ result_type total_size() const { return this->_M_param.total_size(); } /** * @@brief Returns the distribution parameter @@p K, * the total number of successful items. */ result_type successful_size() const { return this->_M_param.successful_size(); } /** * @@brief Returns the total number of unsuccessful items @@f$ N - K @@f$. */ result_type unsuccessful_size() const { return this->_M_param.unsuccessful_size(); } /** * @@brief Returns the distribution parameter @@p n, * the total number of draws. */ result_type total_draws() const { return this->_M_param.total_draws(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return this->_M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { this->_M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { using _IntType = typename std::make_signed::type; return static_cast(std::max(static_cast<_IntType>(0), static_cast<_IntType>(this->total_draws() - this->unsuccessful_size()))); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return std::min(this->successful_size(), this->total_draws()); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, this->_M_param); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, this->_M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two hypergeometric distributions have the same * parameters and the sequences that would be generated * are equal. */ friend bool operator==(const hypergeometric_distribution& __d1, const hypergeometric_distribution& __d2) { return __d1._M_param == __d2._M_param; } /** * @@brief Inserts a %hypergeometric_distribution random number * distribution @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %hypergeometric_distribution random number * distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::hypergeometric_distribution<_UIntType1>& __x); /** * @@brief Extracts a %hypergeometric_distribution random number * distribution @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %hypergeometric_distribution random number generator * distribution. * * @@returns The input stream with @@p __x extracted or in an error * state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::hypergeometric_distribution<_UIntType1>& __x); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; }; /** * @@brief Return true if two hypergeometric distributions are different. */ template inline bool operator!=(const __gnu_cxx::hypergeometric_distribution<_UIntType>& __d1, const __gnu_cxx::hypergeometric_distribution<_UIntType>& __d2) { return !(__d1 == __d2); } /** * @@brief A logistic continuous distribution for random numbers. * * The formula for the logistic probability density function is * @@f[ * p(x|\a,\b) = \frac{e^{(x - a)/b}}{b[1 + e^{(x - a)/b}]^2} * @@f] * where @@f$b > 0@@f$. * * The formula for the logistic probability function is * @@f[ * cdf(x|\a,\b) = \frac{e^{(x - a)/b}}{1 + e^{(x - a)/b}} * @@f] * where @@f$b > 0@@f$. * * * * * * *
Distribution Statistics
Mean@@f$a@@f$
Variance@@f$b^2\pi^2/3@@f$
Range@@f$[0, \infty)@@f$
*/ template class logistic_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef logistic_distribution distribution_type; param_type(result_type __a = result_type(0), result_type __b = result_type(1)) : _M_a(__a), _M_b(__b) { _GLIBCXX_DEBUG_ASSERT(_M_b > result_type(0)); } result_type a() const { return _M_a; } result_type b() const { return _M_b; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b; } private: void _M_initialize(); result_type _M_a; result_type _M_b; }; /** * @@brief Constructors. */ explicit logistic_distribution(result_type __a = result_type(0), result_type __b = result_type(1)) : _M_param(__a, __b) { } explicit logistic_distribution(const param_type& __p) : _M_param(__p) { } /** * @@brief Resets the distribution state. */ void reset() { } /** * @@brief Return the parameters of the distribution. */ result_type a() const { return _M_param.a(); } result_type b() const { return _M_param.b(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return -std::numeric_limits::max(); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return std::numeric_limits::max(); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, this->_M_param); } template result_type operator()(_UniformRandomNumberGenerator&, const param_type&); template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, this->param()); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two logistic distributions have * the same parameters and the sequences that would * be generated are equal. */ template friend bool operator==(const logistic_distribution<_RealType1>& __d1, const logistic_distribution<_RealType1>& __d2) { return __d1.param() == __d2.param(); } /** * @@brief Inserts a %logistic_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %logistic_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>&, const logistic_distribution<_RealType1>&); /** * @@brief Extracts a %logistic_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %logistic_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, logistic_distribution<_RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; }; /** * @@brief Return true if two logistic distributions are not equal. */ template inline bool operator!=(const logistic_distribution<_RealType1>& __d1, const logistic_distribution<_RealType1>& __d2) { return !(__d1 == __d2); } /** * @@brief A distribution for random coordinates on a unit sphere. * * The method used in the generation function is attributed by Donald Knuth * to G. W. Brown, Modern Mathematics for the Engineer (1956). */ template class uniform_on_sphere_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); static_assert(_Dimen != 0, "dimension is zero"); public: /** The type of the range of the distribution. */ typedef std::array<_RealType, _Dimen> result_type; /** Parameter type. */ struct param_type { explicit param_type() { } friend bool operator==(const param_type& __p1, const param_type& __p2) { return true; } }; /** * @@brief Constructs a uniform on sphere distribution. */ explicit uniform_on_sphere_distribution() : _M_param(), _M_nd() { } explicit uniform_on_sphere_distribution(const param_type& __p) : _M_param(__p), _M_nd() { } /** * @@brief Resets the distribution state. */ void reset() { _M_nd.reset(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. * This function makes no sense for this distribution. */ result_type min() const { result_type __res; __res.fill(0); return __res; } /** * @@brief Returns the least upper bound value of the distribution. * This function makes no sense for this distribution. */ result_type max() const { result_type __res; __res.fill(0); return __res; } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, this->param()); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two uniform on sphere distributions have * the same parameters and the sequences that would be * generated are equal. */ friend bool operator==(const uniform_on_sphere_distribution& __d1, const uniform_on_sphere_distribution& __d2) { return __d1._M_nd == __d2._M_nd; } /** * @@brief Inserts a %uniform_on_sphere_distribution random number * distribution @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %uniform_on_sphere_distribution random number * distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::uniform_on_sphere_distribution<_Dimen1, _RealType1>& __x); /** * @@brief Extracts a %uniform_on_sphere_distribution random number * distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %uniform_on_sphere_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::uniform_on_sphere_distribution<_Dimen1, _RealType1>& __x); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::normal_distribution<_RealType> _M_nd; }; /** * @@brief Return true if two uniform on sphere distributions are different. */ template inline bool operator!=(const __gnu_cxx::uniform_on_sphere_distribution<_Dimen, _RealType>& __d1, const __gnu_cxx::uniform_on_sphere_distribution<_Dimen, _RealType>& __d2) { return !(__d1 == __d2); } @ 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. d43 1 a43 1 # include d46 1 a46 1 #if defined(_GLIBCXX_USE_C99_STDINT_TR1) && defined(UINT32_C) d418 2 a419 2 __glibcxx_assert(_M_alpha > _RealType(0)); __glibcxx_assert(_M_beta > _RealType(0)); d672 2 a673 5 { __glibcxx_assert(__dist == _Dimen); _M_init_diagonal(__meanbegin, __meanend, __varcovbegin, __varcovend); } d691 2 a692 5 { __glibcxx_assert(__varcov.size() == _Dimen); _M_init_diagonal(__mean.begin(), __mean.end(), __varcov.begin(), __varcov.end()); } d948 2 a949 2 __glibcxx_assert(_M_nu >= result_type(0)); __glibcxx_assert(_M_sigma > result_type(0)); d1190 2 a1191 2 __glibcxx_assert(_M_mu >= result_type(0.5L)); __glibcxx_assert(_M_omega > result_type(0)); d1423 2 a1424 2 __glibcxx_assert(_M_alpha > result_type(0)); __glibcxx_assert(_M_mu > result_type(0)); d1658 3 a1660 3 __glibcxx_assert(_M_lambda > result_type(0)); __glibcxx_assert(_M_mu > result_type(0)); __glibcxx_assert(_M_nu > result_type(0)); d1896 1 a1896 1 __glibcxx_assert(_M_a <= _M_b); d2132 2 a2133 2 __glibcxx_assert(_M_q > result_type(0)); __glibcxx_assert(_M_q < result_type(1)); d2367 3 a2369 3 __glibcxx_assert(_M_a <= _M_b); __glibcxx_assert(_M_b <= _M_c); __glibcxx_assert(_M_a < _M_c); d2623 2 a2624 2 __glibcxx_assert(_M_mu >= -__pi && _M_mu <= __pi); __glibcxx_assert(_M_kappa >= _RealType(0)); d2860 2 a2861 2 __glibcxx_assert(_M_N >= _M_K); __glibcxx_assert(_M_N >= _M_n); d3117 1 a3117 1 __glibcxx_assert(_M_b > result_type(0)); d3496 1 a3496 1 #endif // _GLIBCXX_USE_C99_STDINT_TR1 && UINT32_C @ 1.1.1.3.4.1 log @Sync with HEAD @ text @d3 1 a3 1 // Copyright (C) 2012-2017 Free Software Foundation, Inc. a406 1 a434 4 friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a712 4 friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a944 1 d968 2 a969 5 { return __p1._M_nu == __p2._M_nu && __p1._M_sigma == __p2._M_sigma; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a1186 1 d1210 2 a1211 5 { return __p1._M_mu == __p2._M_mu && __p1._M_omega == __p2._M_omega; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a1419 1 a1444 4 friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a1653 1 d1683 1 a1683 2 { return __p1._M_lambda == __p2._M_lambda d1685 1 a1685 6 && __p1._M_nu == __p2._M_nu; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a1892 1 a1916 4 friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a2128 1 d2152 2 a2153 5 { return __p1._M_q == __p2._M_q && __p1._M_omega == __p2._M_omega; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a2361 1 d2396 2 a2397 8 { return (__p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b && __p1._M_c == __p2._M_c); } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } d2649 2 a2650 5 { return __p1._M_mu == __p2._M_mu && __p1._M_kappa == __p2._M_kappa; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a2891 4 friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a3113 1 d3136 2 a3137 5 { return __p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a3319 1 d3328 1 a3328 1 operator==(const param_type&, const param_type&) a3329 4 friend bool operator!=(const param_type&, const param_type&) { return false; } a3495 216 /** * @@brief A distribution for random coordinates inside a unit sphere. */ template class uniform_inside_sphere_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); static_assert(_Dimen != 0, "dimension is zero"); public: /** The type of the range of the distribution. */ using result_type = std::array<_RealType, _Dimen>; /** Parameter type. */ struct param_type { using distribution_type = uniform_inside_sphere_distribution<_Dimen, _RealType>; friend class uniform_inside_sphere_distribution<_Dimen, _RealType>; explicit param_type(_RealType __radius = _RealType(1)) : _M_radius(__radius) { __glibcxx_assert(_M_radius > _RealType(0)); } _RealType radius() const { return _M_radius; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_radius == __p2._M_radius; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } private: _RealType _M_radius; }; /** * @@brief Constructors. */ explicit uniform_inside_sphere_distribution(_RealType __radius = _RealType(1)) : _M_param(__radius), _M_uosd() { } explicit uniform_inside_sphere_distribution(const param_type& __p) : _M_param(__p), _M_uosd() { } /** * @@brief Resets the distribution state. */ void reset() { _M_uosd.reset(); } /** * @@brief Returns the @@f$radius@@f$ of the distribution. */ _RealType radius() const { return _M_param.radius(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. * This function makes no sense for this distribution. */ result_type min() const { result_type __res; __res.fill(0); return __res; } /** * @@brief Returns the least upper bound value of the distribution. * This function makes no sense for this distribution. */ result_type max() const { result_type __res; __res.fill(0); return __res; } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, this->param()); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two uniform on sphere distributions have * the same parameters and the sequences that would be * generated are equal. */ friend bool operator==(const uniform_inside_sphere_distribution& __d1, const uniform_inside_sphere_distribution& __d2) { return __d1._M_param == __d2._M_param && __d1._M_uosd == __d2._M_uosd; } /** * @@brief Inserts a %uniform_inside_sphere_distribution random number * distribution @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %uniform_inside_sphere_distribution random number * distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::uniform_inside_sphere_distribution<_Dimen1, _RealType1>& ); /** * @@brief Extracts a %uniform_inside_sphere_distribution random number * distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %uniform_inside_sphere_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::uniform_inside_sphere_distribution<_Dimen1, _RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; uniform_on_sphere_distribution<_Dimen, _RealType> _M_uosd; }; /** * @@brief Return true if two uniform on sphere distributions are different. */ template inline bool operator!=(const __gnu_cxx::uniform_inside_sphere_distribution<_Dimen, _RealType>& __d1, const __gnu_cxx::uniform_inside_sphere_distribution<_Dimen, _RealType>& __d2) { return !(__d1 == __d2); } @ 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. d115 1 a115 1 { return 0; } a186 5 #ifdef __ARM_NEON #ifdef __aarch64__ __Uint32x4_t _M_state[_M_nstate]; #endif #endif d3776 2 a3777 2 #include #include @ 1.1.1.3.2.1 log @Sync with HEAD @ text @d3 1 a3 1 // Copyright (C) 2012-2017 Free Software Foundation, Inc. a406 1 a434 4 friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a712 4 friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a944 1 d968 2 a969 5 { return __p1._M_nu == __p2._M_nu && __p1._M_sigma == __p2._M_sigma; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a1186 1 d1210 2 a1211 5 { return __p1._M_mu == __p2._M_mu && __p1._M_omega == __p2._M_omega; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a1419 1 a1444 4 friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a1653 1 d1683 1 a1683 2 { return __p1._M_lambda == __p2._M_lambda d1685 1 a1685 6 && __p1._M_nu == __p2._M_nu; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a1892 1 a1916 4 friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a2128 1 d2152 2 a2153 5 { return __p1._M_q == __p2._M_q && __p1._M_omega == __p2._M_omega; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a2361 1 d2396 2 a2397 8 { return (__p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b && __p1._M_c == __p2._M_c); } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } d2649 2 a2650 5 { return __p1._M_mu == __p2._M_mu && __p1._M_kappa == __p2._M_kappa; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a2891 4 friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a3113 1 d3136 2 a3137 5 { return __p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a3319 1 d3328 1 a3328 1 operator==(const param_type&, const param_type&) a3329 4 friend bool operator!=(const param_type&, const param_type&) { return false; } a3495 216 /** * @@brief A distribution for random coordinates inside a unit sphere. */ template class uniform_inside_sphere_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); static_assert(_Dimen != 0, "dimension is zero"); public: /** The type of the range of the distribution. */ using result_type = std::array<_RealType, _Dimen>; /** Parameter type. */ struct param_type { using distribution_type = uniform_inside_sphere_distribution<_Dimen, _RealType>; friend class uniform_inside_sphere_distribution<_Dimen, _RealType>; explicit param_type(_RealType __radius = _RealType(1)) : _M_radius(__radius) { __glibcxx_assert(_M_radius > _RealType(0)); } _RealType radius() const { return _M_radius; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_radius == __p2._M_radius; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } private: _RealType _M_radius; }; /** * @@brief Constructors. */ explicit uniform_inside_sphere_distribution(_RealType __radius = _RealType(1)) : _M_param(__radius), _M_uosd() { } explicit uniform_inside_sphere_distribution(const param_type& __p) : _M_param(__p), _M_uosd() { } /** * @@brief Resets the distribution state. */ void reset() { _M_uosd.reset(); } /** * @@brief Returns the @@f$radius@@f$ of the distribution. */ _RealType radius() const { return _M_param.radius(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. * This function makes no sense for this distribution. */ result_type min() const { result_type __res; __res.fill(0); return __res; } /** * @@brief Returns the least upper bound value of the distribution. * This function makes no sense for this distribution. */ result_type max() const { result_type __res; __res.fill(0); return __res; } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, this->param()); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two uniform on sphere distributions have * the same parameters and the sequences that would be * generated are equal. */ friend bool operator==(const uniform_inside_sphere_distribution& __d1, const uniform_inside_sphere_distribution& __d2) { return __d1._M_param == __d2._M_param && __d1._M_uosd == __d2._M_uosd; } /** * @@brief Inserts a %uniform_inside_sphere_distribution random number * distribution @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %uniform_inside_sphere_distribution random number * distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::uniform_inside_sphere_distribution<_Dimen1, _RealType1>& ); /** * @@brief Extracts a %uniform_inside_sphere_distribution random number * distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %uniform_inside_sphere_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::uniform_inside_sphere_distribution<_Dimen1, _RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; uniform_on_sphere_distribution<_Dimen, _RealType> _M_uosd; }; /** * @@brief Return true if two uniform on sphere distributions are different. */ template inline bool operator!=(const __gnu_cxx::uniform_inside_sphere_distribution<_Dimen, _RealType>& __d1, const __gnu_cxx::uniform_inside_sphere_distribution<_Dimen, _RealType>& __d2) { return !(__d1 == __d2); } @ 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. a406 1 a434 4 friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a712 4 friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a944 1 d968 2 a969 5 { return __p1._M_nu == __p2._M_nu && __p1._M_sigma == __p2._M_sigma; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a1186 1 d1210 2 a1211 5 { return __p1._M_mu == __p2._M_mu && __p1._M_omega == __p2._M_omega; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a1419 1 a1444 4 friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a1653 1 d1683 1 a1683 2 { return __p1._M_lambda == __p2._M_lambda d1685 1 a1685 6 && __p1._M_nu == __p2._M_nu; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a1892 1 a1916 4 friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a2128 1 d2152 2 a2153 5 { return __p1._M_q == __p2._M_q && __p1._M_omega == __p2._M_omega; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a2361 1 d2396 2 a2397 8 { return (__p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b && __p1._M_c == __p2._M_c); } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } d2649 2 a2650 5 { return __p1._M_mu == __p2._M_mu && __p1._M_kappa == __p2._M_kappa; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a2891 4 friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a3113 1 d3136 2 a3137 5 { return __p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } a3319 1 d3328 1 a3328 1 operator==(const param_type&, const param_type&) a3329 4 friend bool operator!=(const param_type&, const param_type&) { return false; } a3495 216 /** * @@brief A distribution for random coordinates inside a unit sphere. */ template class uniform_inside_sphere_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); static_assert(_Dimen != 0, "dimension is zero"); public: /** The type of the range of the distribution. */ using result_type = std::array<_RealType, _Dimen>; /** Parameter type. */ struct param_type { using distribution_type = uniform_inside_sphere_distribution<_Dimen, _RealType>; friend class uniform_inside_sphere_distribution<_Dimen, _RealType>; explicit param_type(_RealType __radius = _RealType(1)) : _M_radius(__radius) { __glibcxx_assert(_M_radius > _RealType(0)); } _RealType radius() const { return _M_radius; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_radius == __p2._M_radius; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } private: _RealType _M_radius; }; /** * @@brief Constructors. */ explicit uniform_inside_sphere_distribution(_RealType __radius = _RealType(1)) : _M_param(__radius), _M_uosd() { } explicit uniform_inside_sphere_distribution(const param_type& __p) : _M_param(__p), _M_uosd() { } /** * @@brief Resets the distribution state. */ void reset() { _M_uosd.reset(); } /** * @@brief Returns the @@f$radius@@f$ of the distribution. */ _RealType radius() const { return _M_param.radius(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. * This function makes no sense for this distribution. */ result_type min() const { result_type __res; __res.fill(0); return __res; } /** * @@brief Returns the least upper bound value of the distribution. * This function makes no sense for this distribution. */ result_type max() const { result_type __res; __res.fill(0); return __res; } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, this->param()); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two uniform on sphere distributions have * the same parameters and the sequences that would be * generated are equal. */ friend bool operator==(const uniform_inside_sphere_distribution& __d1, const uniform_inside_sphere_distribution& __d2) { return __d1._M_param == __d2._M_param && __d1._M_uosd == __d2._M_uosd; } /** * @@brief Inserts a %uniform_inside_sphere_distribution random number * distribution @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %uniform_inside_sphere_distribution random number * distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::uniform_inside_sphere_distribution<_Dimen1, _RealType1>& ); /** * @@brief Extracts a %uniform_inside_sphere_distribution random number * distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %uniform_inside_sphere_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::uniform_inside_sphere_distribution<_Dimen1, _RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; uniform_on_sphere_distribution<_Dimen, _RealType> _M_uosd; }; /** * @@brief Return true if two uniform on sphere distributions are different. */ template inline bool operator!=(const __gnu_cxx::uniform_inside_sphere_distribution<_Dimen, _RealType>& __d1, const __gnu_cxx::uniform_inside_sphere_distribution<_Dimen, _RealType>& __d2) { return !(__d1 == __d2); } @ 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. d115 1 a115 1 { return 0; } a186 5 #ifdef __ARM_NEON #ifdef __aarch64__ __Uint32x4_t _M_state[_M_nstate]; #endif #endif d3776 2 a3777 2 #include #include @ 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. d115 1 a115 1 { return 0; }; d187 5 d3781 2 a3782 2 #include "ext/opt_random.h" #include "random.tcc" @ 1.1.1.7 log @re-import GCC 8.4.0. @ text @d3 1 a3 1 // Copyright (C) 2012-2018 Free Software Foundation, Inc. d115 1 a115 1 { return 0; } a186 5 #ifdef __ARM_NEON #ifdef __aarch64__ __Uint32x4_t _M_state[_M_nstate]; #endif #endif d3776 2 a3777 2 #include #include @ 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. a87 6 template using _If_seed_seq = typename std::enable_if::value >::type; d93 1 a93 6 // constructors and member functions simd_fast_mersenne_twister_engine() : simd_fast_mersenne_twister_engine(default_seed) { } d95 1 a95 1 simd_fast_mersenne_twister_engine(result_type __sd) d98 4 a101 1 template> d110 1 a110 1 _If_seed_seq<_Sseq> a418 2 param_type() : param_type(1) { } d420 2 a421 1 param_type(_RealType __alpha_val, _RealType __beta_val = _RealType(1)) a453 2 beta_distribution() : beta_distribution(1.0) { } d459 1 a459 1 beta_distribution(_RealType __alpha_val, d965 1 a965 3 param_type() : param_type(0) { } param_type(result_type __nu_val, a997 1 * @@{ a998 3 rice_distribution() : rice_distribution(0) { } d1000 1 a1000 1 rice_distribution(result_type __nu_val, a1013 2 // @@} d1211 1 a1211 3 param_type() : param_type(1) { } param_type(result_type __mu_val, a1243 1 * @@{ a1244 3 nakagami_distribution() : nakagami_distribution(1) { } d1246 1 a1246 1 nakagami_distribution(result_type __mu_val, a1257 2 // @@} d1448 1 a1448 3 param_type() : param_type(1) { } param_type(result_type __alpha_val, a1480 1 * @@{ a1481 3 pareto_distribution() : pareto_distribution(1) { } d1483 1 a1483 1 pareto_distribution(result_type __alpha_val, a1494 2 // @@} d1687 1 a1687 3 param_type() : param_type(1) { } param_type(result_type __lambda_val, a1730 1 * @@{ a1731 3 k_distribution() : k_distribution(1) { } d1733 1 a1733 1 k_distribution(result_type __lambda_val, a1747 2 // @@} d1933 2 a1934 3 param_type() : param_type(0) { } param_type(result_type __a, result_type __b = result_type(1)) a1964 1 * :{ a1965 3 arcsine_distribution() : arcsine_distribution(0) { } d1967 2 a1968 1 arcsine_distribution(result_type __a, result_type __b = result_type(1)) a1980 2 // @@} d2174 2 a2175 3 param_type() : param_type(0.5) { } param_type(result_type __q, result_type __omega = result_type(1)) a2206 1 * @@{ a2207 3 hoyt_distribution() : hoyt_distribution(0.5) { } d2209 2 a2210 1 hoyt_distribution(result_type __q, result_type __omega = result_type(1)) a2410 2 param_type() : param_type(0) { } d2412 1 a2412 1 param_type(_RealType __a, a2458 2 triangular_distribution() : triangular_distribution(0.0) { } d2464 1 a2464 1 triangular_distribution(result_type __a, a2667 1 a2672 2 param_type() : param_type(0) { } d2674 2 a2675 1 param_type(_RealType __mu, _RealType __kappa = _RealType(1)) a2710 2 von_mises_distribution() : von_mises_distribution(0.0) { } d2716 1 a2716 1 von_mises_distribution(result_type __mu, d2718 1 a2718 1 : _M_param(__mu, __kappa) a2913 2 param_type() : param_type(10) { } d2915 1 a2915 1 param_type(result_type __N, result_type __K = 5, d2956 1 a2956 4 // constructors and member functions hypergeometric_distribution() : hypergeometric_distribution(10) { } d2958 1 a2958 1 hypergeometric_distribution(result_type __N, result_type __K = 5, d3177 2 a3178 4 param_type() : param_type(0) { } explicit param_type(result_type __a, result_type __b = result_type(1)) a3208 1 * @@{ a3209 2 logistic_distribution() : logistic_distribution(0.0) { } d3211 2 a3212 1 logistic_distribution(result_type __a, result_type __b = result_type(1)) a3220 2 // @@} d3385 3 a3387 1 param_type() { } d3401 1 a3583 2 param_type() : param_type(1.0) { } d3585 1 a3585 1 param_type(_RealType __radius) a3608 1 * @@{ a3609 5 uniform_inside_sphere_distribution() : uniform_inside_sphere_distribution(1.0) { } d3611 1 a3611 1 uniform_inside_sphere_distribution(_RealType __radius) a3619 2 // @@} @ 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. a754 15 // param_type constructors apply Cholesky decomposition to the // varcov matrix in _M_init_full and _M_init_lower, but the // varcov matrix output ot a stream is already decomposed, so // we need means to restore it as-is when reading it back in. template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::normal_mv_distribution<_Dimen1, _RealType1>& __x); param_type(std::array<_RealType, _Dimen> const &__mean, std::array<_RealType, _M_t_size> const &__varcov) : _M_mean (__mean), _M_t (__varcov) {} @ 1.1.1.10 log @initial import of GCC 10.4.0 sources. mostly a large list of PRs fixed (210 total), plus one x86-64 specific change related to MMX and 64 bit integer return. https://gcc.gnu.org/gcc-10/changes.html links to the full list of PRs fixed. @ text @d1046 1 a1046 1 /// @@} d1298 1 a1298 1 /// @@} d1543 1 a1543 1 /// @@} d1804 1 a1804 1 /// @@} d2043 1 a2043 1 /// @@} d3306 1 a3306 1 /// @@} d3712 1 a3712 1 /// @@} @ 1.1.1.11 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. a348 1 #if __SIZE_WIDTH__ >= 32 a398 1 #endif // __SIZE_WIDTH__ >= 32 @ 1.1.1.12 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. a33 2 #include // GNU extensions are currently omitted d46 1 a46 1 #ifdef UINT32_C d90 3 a92 3 = std::__detail::_If_seed_seq_for<_Sseq, simd_fast_mersenne_twister_engine, result_type>; d209 1 a209 1 #if __cpp_impl_three_way_comparison < 201907L d225 1 a225 1 #endif a451 1 #if __cpp_impl_three_way_comparison < 201907L a454 1 #endif a616 1 #if __cpp_impl_three_way_comparison < 201907L d625 1 a625 1 #endif a735 1 #if __cpp_impl_three_way_comparison < 201907L a738 1 #endif a943 1 #if __cpp_impl_three_way_comparison < 201907L d955 1 a955 1 #endif a1014 1 #if __cpp_impl_three_way_comparison < 201907L a1017 1 #endif d1109 1 a1109 1 #if _GLIBCXX_USE_C99_MATH_FUNCS d1125 1 a1125 1 #if _GLIBCXX_USE_C99_MATH_FUNCS a1209 1 #if __cpp_impl_three_way_comparison < 201907L d1218 1 a1218 1 #endif a1268 1 #if __cpp_impl_three_way_comparison < 201907L a1271 1 #endif a1442 1 #if __cpp_impl_three_way_comparison < 201907L d1451 1 a1451 1 #endif a1513 1 #if __cpp_impl_three_way_comparison < 201907L a1516 1 #endif a1691 1 #if __cpp_impl_three_way_comparison < 201907L d1700 1 a1700 1 #endif a1770 1 #if __cpp_impl_three_way_comparison < 201907L a1773 1 #endif a1950 1 #if __cpp_impl_three_way_comparison < 201907L d1959 1 a1959 1 #endif a2012 1 #if __cpp_impl_three_way_comparison < 201907L a2015 1 #endif a2191 1 #if __cpp_impl_three_way_comparison < 201907L d2200 1 a2200 1 #endif a2260 1 #if __cpp_impl_three_way_comparison < 201907L a2263 1 #endif a2436 1 #if __cpp_impl_three_way_comparison < 201907L d2445 1 a2445 1 #endif a2516 1 #if __cpp_impl_three_way_comparison < 201907L a2519 1 #endif a2700 1 #if __cpp_impl_three_way_comparison < 201907L d2709 1 a2709 1 #endif a2776 1 #if __cpp_impl_three_way_comparison < 201907L a2779 1 #endif a2942 1 #if __cpp_impl_three_way_comparison < 201907L d2951 1 a2951 1 #endif a3024 1 #if __cpp_impl_three_way_comparison < 201907L a3027 1 #endif a3212 1 #if __cpp_impl_three_way_comparison < 201907L a3220 1 #endif a3280 1 #if __cpp_impl_three_way_comparison < 201907L a3283 1 #endif a3443 1 #if __cpp_impl_three_way_comparison < 201907L d3452 1 a3452 1 #endif a3479 1 #if __cpp_impl_three_way_comparison < 201907L a3482 1 #endif a3636 1 #if __cpp_impl_three_way_comparison < 201907L d3647 1 a3647 1 #endif a3686 1 #if __cpp_impl_three_way_comparison < 201907L a3689 1 #endif a3862 1 #if __cpp_impl_three_way_comparison < 201907L a3872 1 #endif d3880 1 a3880 1 #endif // UINT32_C @ 1.1.1.1.8.1 log @file random was added on branch tls-maxphys on 2014-08-19 23:54:50 +0000 @ text @d1 2858 @ 1.1.1.1.8.2 log @Rebase to HEAD as of a few days ago. @ text @a0 2858 // 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 * This file is a GNU extension to the Standard C++ Library. */ #ifndef _EXT_RANDOM #define _EXT_RANDOM 1 #pragma GCC system_header #if __cplusplus < 201103L # include #else #include #include #include #ifdef __SSE2__ # include #endif #ifdef _GLIBCXX_USE_C99_STDINT_TR1 namespace __gnu_cxx _GLIBCXX_VISIBILITY(default) { _GLIBCXX_BEGIN_NAMESPACE_VERSION #if __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__ /* Mersenne twister implementation optimized for vector operations. * * Reference: http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/ */ template class simd_fast_mersenne_twister_engine { static_assert(std::is_unsigned<_UIntType>::value, "template argument " "substituting _UIntType not an unsigned integral type"); static_assert(__sr1 < 32, "first right shift too large"); static_assert(__sr2 < 16, "second right shift too large"); static_assert(__sl1 < 32, "first left shift too large"); static_assert(__sl2 < 16, "second left shift too large"); public: typedef _UIntType result_type; private: static constexpr size_t m_w = sizeof(result_type) * 8; static constexpr size_t _M_nstate = __m / 128 + 1; static constexpr size_t _M_nstate32 = _M_nstate * 4; static_assert(std::is_unsigned<_UIntType>::value, "template argument " "substituting _UIntType not an unsigned integral type"); static_assert(__pos1 < _M_nstate, "POS1 not smaller than state size"); static_assert(16 % sizeof(_UIntType) == 0, "UIntType size must divide 16"); public: static constexpr size_t state_size = _M_nstate * (16 / sizeof(result_type)); static constexpr result_type default_seed = 5489u; // constructors and member function explicit simd_fast_mersenne_twister_engine(result_type __sd = default_seed) { seed(__sd); } template::value> ::type> explicit simd_fast_mersenne_twister_engine(_Sseq& __q) { seed(__q); } void seed(result_type __sd = default_seed); template typename std::enable_if::value>::type seed(_Sseq& __q); static constexpr result_type min() { return 0; }; static constexpr result_type max() { return std::numeric_limits::max(); } void discard(unsigned long long __z); result_type operator()() { if (__builtin_expect(_M_pos >= state_size, 0)) _M_gen_rand(); return _M_stateT[_M_pos++]; } template friend bool operator==(const simd_fast_mersenne_twister_engine<_UIntType_2, __m_2, __pos1_2, __sl1_2, __sl2_2, __sr1_2, __sr2_2, __msk1_2, __msk2_2, __msk3_2, __msk4_2, __parity1_2, __parity2_2, __parity3_2, __parity4_2>& __lhs, const simd_fast_mersenne_twister_engine<_UIntType_2, __m_2, __pos1_2, __sl1_2, __sl2_2, __sr1_2, __sr2_2, __msk1_2, __msk2_2, __msk3_2, __msk4_2, __parity1_2, __parity2_2, __parity3_2, __parity4_2>& __rhs); template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::simd_fast_mersenne_twister_engine <_UIntType_2, __m_2, __pos1_2, __sl1_2, __sl2_2, __sr1_2, __sr2_2, __msk1_2, __msk2_2, __msk3_2, __msk4_2, __parity1_2, __parity2_2, __parity3_2, __parity4_2>& __x); template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType_2, __m_2, __pos1_2, __sl1_2, __sl2_2, __sr1_2, __sr2_2, __msk1_2, __msk2_2, __msk3_2, __msk4_2, __parity1_2, __parity2_2, __parity3_2, __parity4_2>& __x); private: union { #ifdef __SSE2__ __m128i _M_state[_M_nstate]; #endif uint32_t _M_state32[_M_nstate32]; result_type _M_stateT[state_size]; } __attribute__ ((__aligned__ (16))); size_t _M_pos; void _M_gen_rand(void); void _M_period_certification(); }; template inline 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) { return !(__lhs == __rhs); } /* Definitions for the SIMD-oriented Fast Mersenne Twister as defined * in the C implementation by Daito and Matsumoto, as both a 32-bit * and 64-bit version. */ typedef simd_fast_mersenne_twister_engine sfmt607; typedef simd_fast_mersenne_twister_engine sfmt607_64; typedef simd_fast_mersenne_twister_engine sfmt1279; typedef simd_fast_mersenne_twister_engine sfmt1279_64; typedef simd_fast_mersenne_twister_engine sfmt2281; typedef simd_fast_mersenne_twister_engine sfmt2281_64; typedef simd_fast_mersenne_twister_engine sfmt4253; typedef simd_fast_mersenne_twister_engine sfmt4253_64; typedef simd_fast_mersenne_twister_engine sfmt11213; typedef simd_fast_mersenne_twister_engine sfmt11213_64; typedef simd_fast_mersenne_twister_engine sfmt19937; typedef simd_fast_mersenne_twister_engine sfmt19937_64; typedef simd_fast_mersenne_twister_engine sfmt44497; typedef simd_fast_mersenne_twister_engine sfmt44497_64; typedef simd_fast_mersenne_twister_engine sfmt86243; typedef simd_fast_mersenne_twister_engine sfmt86243_64; typedef simd_fast_mersenne_twister_engine sfmt132049; typedef simd_fast_mersenne_twister_engine sfmt132049_64; typedef simd_fast_mersenne_twister_engine sfmt216091; typedef simd_fast_mersenne_twister_engine sfmt216091_64; #endif // __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__ /** * @@brief A beta continuous distribution for random numbers. * * The formula for the beta probability density function is: * @@f[ * p(x|\alpha,\beta) = \frac{1}{B(\alpha,\beta)} * x^{\alpha - 1} (1 - x)^{\beta - 1} * @@f] */ template class beta_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef beta_distribution<_RealType> distribution_type; friend class beta_distribution<_RealType>; explicit param_type(_RealType __alpha_val = _RealType(1), _RealType __beta_val = _RealType(1)) : _M_alpha(__alpha_val), _M_beta(__beta_val) { _GLIBCXX_DEBUG_ASSERT(_M_alpha > _RealType(0)); _GLIBCXX_DEBUG_ASSERT(_M_beta > _RealType(0)); } _RealType alpha() const { return _M_alpha; } _RealType beta() const { return _M_beta; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return (__p1._M_alpha == __p2._M_alpha && __p1._M_beta == __p2._M_beta); } private: void _M_initialize(); _RealType _M_alpha; _RealType _M_beta; }; public: /** * @@brief Constructs a beta distribution with parameters * @@f$\alpha@@f$ and @@f$\beta@@f$. */ explicit beta_distribution(_RealType __alpha_val = _RealType(1), _RealType __beta_val = _RealType(1)) : _M_param(__alpha_val, __beta_val) { } explicit beta_distribution(const param_type& __p) : _M_param(__p) { } /** * @@brief Resets the distribution state. */ void reset() { } /** * @@brief Returns the @@f$\alpha@@f$ of the distribution. */ _RealType alpha() const { return _M_param.alpha(); } /** * @@brief Returns the @@f$\beta@@f$ of the distribution. */ _RealType beta() const { return _M_param.beta(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return result_type(0); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return result_type(1); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two beta distributions have the same * parameters and the sequences that would be generated * are equal. */ friend bool operator==(const beta_distribution& __d1, const beta_distribution& __d2) { return __d1._M_param == __d2._M_param; } /** * @@brief Inserts a %beta_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %beta_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::beta_distribution<_RealType1>& __x); /** * @@brief Extracts a %beta_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %beta_distribution random number generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::beta_distribution<_RealType1>& __x); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; }; /** * @@brief Return true if two beta distributions are different. */ template inline bool operator!=(const __gnu_cxx::beta_distribution<_RealType>& __d1, const __gnu_cxx::beta_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A multi-variate normal continuous distribution for random numbers. * * The formula for the normal probability density function is * @@f[ * p(\overrightarrow{x}|\overrightarrow{\mu },\Sigma) = * \frac{1}{\sqrt{(2\pi )^k\det(\Sigma))}} * e^{-\frac{1}{2}(\overrightarrow{x}-\overrightarrow{\mu})^\text{T} * \Sigma ^{-1}(\overrightarrow{x}-\overrightarrow{\mu})} * @@f] * * where @@f$\overrightarrow{x}@@f$ and @@f$\overrightarrow{\mu}@@f$ are * vectors of dimension @@f$k@@f$ and @@f$\Sigma@@f$ is the covariance * matrix (which must be positive-definite). */ template class normal_mv_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); static_assert(_Dimen != 0, "dimension is zero"); public: /** The type of the range of the distribution. */ typedef std::array<_RealType, _Dimen> result_type; /** Parameter type. */ class param_type { static constexpr size_t _M_t_size = _Dimen * (_Dimen + 1) / 2; public: typedef normal_mv_distribution<_Dimen, _RealType> distribution_type; friend class normal_mv_distribution<_Dimen, _RealType>; param_type() { std::fill(_M_mean.begin(), _M_mean.end(), _RealType(0)); auto __it = _M_t.begin(); for (size_t __i = 0; __i < _Dimen; ++__i) { std::fill_n(__it, __i, _RealType(0)); __it += __i; *__it++ = _RealType(1); } } template param_type(_ForwardIterator1 __meanbegin, _ForwardIterator1 __meanend, _ForwardIterator2 __varcovbegin, _ForwardIterator2 __varcovend) { __glibcxx_function_requires(_ForwardIteratorConcept< _ForwardIterator1>) __glibcxx_function_requires(_ForwardIteratorConcept< _ForwardIterator2>) _GLIBCXX_DEBUG_ASSERT(std::distance(__meanbegin, __meanend) <= _Dimen); const auto __dist = std::distance(__varcovbegin, __varcovend); _GLIBCXX_DEBUG_ASSERT(__dist == _Dimen * _Dimen || __dist == _Dimen * (_Dimen + 1) / 2 || __dist == _Dimen); if (__dist == _Dimen * _Dimen) _M_init_full(__meanbegin, __meanend, __varcovbegin, __varcovend); else if (__dist == _Dimen * (_Dimen + 1) / 2) _M_init_lower(__meanbegin, __meanend, __varcovbegin, __varcovend); else _M_init_diagonal(__meanbegin, __meanend, __varcovbegin, __varcovend); } param_type(std::initializer_list<_RealType> __mean, std::initializer_list<_RealType> __varcov) { _GLIBCXX_DEBUG_ASSERT(__mean.size() <= _Dimen); _GLIBCXX_DEBUG_ASSERT(__varcov.size() == _Dimen * _Dimen || __varcov.size() == _Dimen * (_Dimen + 1) / 2 || __varcov.size() == _Dimen); if (__varcov.size() == _Dimen * _Dimen) _M_init_full(__mean.begin(), __mean.end(), __varcov.begin(), __varcov.end()); else if (__varcov.size() == _Dimen * (_Dimen + 1) / 2) _M_init_lower(__mean.begin(), __mean.end(), __varcov.begin(), __varcov.end()); else _M_init_diagonal(__mean.begin(), __mean.end(), __varcov.begin(), __varcov.end()); } std::array<_RealType, _Dimen> mean() const { return _M_mean; } std::array<_RealType, _M_t_size> varcov() const { return _M_t; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_mean == __p2._M_mean && __p1._M_t == __p2._M_t; } private: template void _M_init_full(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varcovbegin, _InputIterator2 __varcovend); template void _M_init_lower(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varcovbegin, _InputIterator2 __varcovend); template void _M_init_diagonal(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varbegin, _InputIterator2 __varend); std::array<_RealType, _Dimen> _M_mean; std::array<_RealType, _M_t_size> _M_t; }; public: normal_mv_distribution() : _M_param(), _M_nd() { } template normal_mv_distribution(_ForwardIterator1 __meanbegin, _ForwardIterator1 __meanend, _ForwardIterator2 __varcovbegin, _ForwardIterator2 __varcovend) : _M_param(__meanbegin, __meanend, __varcovbegin, __varcovend), _M_nd() { } normal_mv_distribution(std::initializer_list<_RealType> __mean, std::initializer_list<_RealType> __varcov) : _M_param(__mean, __varcov), _M_nd() { } explicit normal_mv_distribution(const param_type& __p) : _M_param(__p), _M_nd() { } /** * @@brief Resets the distribution state. */ void reset() { _M_nd.reset(); } /** * @@brief Returns the mean of the distribution. */ result_type mean() const { return _M_param.mean(); } /** * @@brief Returns the compact form of the variance/covariance * matrix of the distribution. */ std::array<_RealType, _Dimen * (_Dimen + 1) / 2> varcov() const { return _M_param.varcov(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { result_type __res; __res.fill(std::numeric_limits<_RealType>::lowest()); return __res; } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { result_type __res; __res.fill(std::numeric_limits<_RealType>::max()); return __res; } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { return this->__generate_impl(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { return this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two multi-variant normal distributions have * the same parameters and the sequences that would * be generated are equal. */ template friend bool operator==(const __gnu_cxx::normal_mv_distribution<_Dimen1, _RealType1>& __d1, const __gnu_cxx::normal_mv_distribution<_Dimen1, _RealType1>& __d2); /** * @@brief Inserts a %normal_mv_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %normal_mv_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::normal_mv_distribution<_Dimen1, _RealType1>& __x); /** * @@brief Extracts a %normal_mv_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %normal_mv_distribution random number generator engine. * * @@returns The input stream with @@p __x extracted or in an error * state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::normal_mv_distribution<_Dimen1, _RealType1>& __x); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::normal_distribution<_RealType> _M_nd; }; /** * @@brief Return true if two multi-variate normal distributions are * different. */ template inline bool operator!=(const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __d1, const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A Rice continuous distribution for random numbers. * * The formula for the Rice probability density function is * @@f[ * p(x|\nu,\sigma) = \frac{x}{\sigma^2} * \exp\left(-\frac{x^2+\nu^2}{2\sigma^2}\right) * I_0\left(\frac{x \nu}{\sigma^2}\right) * @@f] * where @@f$I_0(z)@@f$ is the modified Bessel function of the first kind * of order 0 and @@f$\nu >= 0@@f$ and @@f$\sigma > 0@@f$. * * * * * * *
Distribution Statistics
Mean@@f$\sqrt{\pi/2}L_{1/2}(-\nu^2/2\sigma^2)@@f$
Variance@@f$2\sigma^2 + \nu^2 * + (\pi\sigma^2/2)L^2_{1/2}(-\nu^2/2\sigma^2)@@f$
Range@@f$[0, \infty)@@f$
* where @@f$L_{1/2}(x)@@f$ is the Laguerre polynomial of order 1/2. */ template class rice_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef rice_distribution distribution_type; param_type(result_type __nu_val = result_type(0), result_type __sigma_val = result_type(1)) : _M_nu(__nu_val), _M_sigma(__sigma_val) { _GLIBCXX_DEBUG_ASSERT(_M_nu >= result_type(0)); _GLIBCXX_DEBUG_ASSERT(_M_sigma > result_type(0)); } result_type nu() const { return _M_nu; } result_type sigma() const { return _M_sigma; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_nu == __p2._M_nu && __p1._M_sigma == __p2._M_sigma; } private: void _M_initialize(); result_type _M_nu; result_type _M_sigma; }; /** * @@brief Constructors. */ explicit rice_distribution(result_type __nu_val = result_type(0), result_type __sigma_val = result_type(1)) : _M_param(__nu_val, __sigma_val), _M_ndx(__nu_val, __sigma_val), _M_ndy(result_type(0), __sigma_val) { } explicit rice_distribution(const param_type& __p) : _M_param(__p), _M_ndx(__p.nu(), __p.sigma()), _M_ndy(result_type(0), __p.sigma()) { } /** * @@brief Resets the distribution state. */ void reset() { _M_ndx.reset(); _M_ndy.reset(); } /** * @@brief Return the parameters of the distribution. */ result_type nu() const { return _M_param.nu(); } result_type sigma() const { return _M_param.sigma(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return result_type(0); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return std::numeric_limits::max(); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { result_type __x = this->_M_ndx(__urng); result_type __y = this->_M_ndy(__urng); #if _GLIBCXX_USE_C99_MATH_TR1 return std::hypot(__x, __y); #else return std::sqrt(__x * __x + __y * __y); #endif } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { 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 return std::hypot(__x, __y); #else return std::sqrt(__x * __x + __y * __y); #endif } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two Rice distributions have * the same parameters and the sequences that would * be generated are equal. */ friend bool operator==(const rice_distribution& __d1, const rice_distribution& __d2) { return (__d1._M_param == __d2._M_param && __d1._M_ndx == __d2._M_ndx && __d1._M_ndy == __d2._M_ndy); } /** * @@brief Inserts a %rice_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %rice_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>&, const rice_distribution<_RealType1>&); /** * @@brief Extracts a %rice_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %rice_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, rice_distribution<_RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::normal_distribution _M_ndx; std::normal_distribution _M_ndy; }; /** * @@brief Return true if two Rice distributions are not equal. */ template inline bool operator!=(const rice_distribution<_RealType1>& __d1, const rice_distribution<_RealType1>& __d2) { return !(__d1 == __d2); } /** * @@brief A Nakagami continuous distribution for random numbers. * * The formula for the Nakagami probability density function is * @@f[ * p(x|\mu,\omega) = \frac{2\mu^\mu}{\Gamma(\mu)\omega^\mu} * x^{2\mu-1}e^{-\mu x / \omega} * @@f] * where @@f$\Gamma(z)@@f$ is the gamma function and @@f$\mu >= 0.5@@f$ * and @@f$\omega > 0@@f$. */ template class nakagami_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef nakagami_distribution distribution_type; param_type(result_type __mu_val = result_type(1), result_type __omega_val = result_type(1)) : _M_mu(__mu_val), _M_omega(__omega_val) { _GLIBCXX_DEBUG_ASSERT(_M_mu >= result_type(0.5L)); _GLIBCXX_DEBUG_ASSERT(_M_omega > result_type(0)); } result_type mu() const { return _M_mu; } result_type omega() const { return _M_omega; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_mu == __p2._M_mu && __p1._M_omega == __p2._M_omega; } private: void _M_initialize(); result_type _M_mu; result_type _M_omega; }; /** * @@brief Constructors. */ explicit nakagami_distribution(result_type __mu_val = result_type(1), result_type __omega_val = result_type(1)) : _M_param(__mu_val, __omega_val), _M_gd(__mu_val, __omega_val / __mu_val) { } explicit nakagami_distribution(const param_type& __p) : _M_param(__p), _M_gd(__p.mu(), __p.omega() / __p.mu()) { } /** * @@brief Resets the distribution state. */ void reset() { _M_gd.reset(); } /** * @@brief Return the parameters of the distribution. */ result_type mu() const { return _M_param.mu(); } result_type omega() const { return _M_param.omega(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return result_type(0); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return std::numeric_limits::max(); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return std::sqrt(this->_M_gd(__urng)); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { typename std::gamma_distribution::param_type __pg(__p.mu(), __p.omega() / __p.mu()); return std::sqrt(this->_M_gd(__pg, __urng)); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two Nakagami distributions have * the same parameters and the sequences that would * be generated are equal. */ friend bool operator==(const nakagami_distribution& __d1, const nakagami_distribution& __d2) { return (__d1._M_param == __d2._M_param && __d1._M_gd == __d2._M_gd); } /** * @@brief Inserts a %nakagami_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %nakagami_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>&, const nakagami_distribution<_RealType1>&); /** * @@brief Extracts a %nakagami_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %nakagami_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, nakagami_distribution<_RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::gamma_distribution _M_gd; }; /** * @@brief Return true if two Nakagami distributions are not equal. */ template inline bool operator!=(const nakagami_distribution<_RealType>& __d1, const nakagami_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A Pareto continuous distribution for random numbers. * * The formula for the Pareto cumulative probability function is * @@f[ * P(x|\alpha,\mu) = 1 - \left(\frac{\mu}{x}\right)^\alpha * @@f] * The formula for the Pareto probability density function is * @@f[ * p(x|\alpha,\mu) = \frac{\alpha + 1}{\mu} * \left(\frac{\mu}{x}\right)^{\alpha + 1} * @@f] * where @@f$x >= \mu@@f$ and @@f$\mu > 0@@f$, @@f$\alpha > 0@@f$. * * * * * * *
Distribution Statistics
Mean@@f$\alpha \mu / (\alpha - 1)@@f$ * for @@f$\alpha > 1@@f$
Variance@@f$\alpha \mu^2 / [(\alpha - 1)^2(\alpha - 2)]@@f$ * for @@f$\alpha > 2@@f$
Range@@f$[\mu, \infty)@@f$
*/ template class pareto_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef pareto_distribution distribution_type; param_type(result_type __alpha_val = result_type(1), result_type __mu_val = result_type(1)) : _M_alpha(__alpha_val), _M_mu(__mu_val) { _GLIBCXX_DEBUG_ASSERT(_M_alpha > result_type(0)); _GLIBCXX_DEBUG_ASSERT(_M_mu > result_type(0)); } result_type alpha() const { return _M_alpha; } result_type mu() const { return _M_mu; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_alpha == __p2._M_alpha && __p1._M_mu == __p2._M_mu; } private: void _M_initialize(); result_type _M_alpha; result_type _M_mu; }; /** * @@brief Constructors. */ explicit pareto_distribution(result_type __alpha_val = result_type(1), result_type __mu_val = result_type(1)) : _M_param(__alpha_val, __mu_val), _M_ud() { } explicit pareto_distribution(const param_type& __p) : _M_param(__p), _M_ud() { } /** * @@brief Resets the distribution state. */ void reset() { _M_ud.reset(); } /** * @@brief Return the parameters of the distribution. */ result_type alpha() const { return _M_param.alpha(); } result_type mu() const { return _M_param.mu(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return this->mu(); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return std::numeric_limits::max(); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->mu() * std::pow(this->_M_ud(__urng), -result_type(1) / this->alpha()); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { return __p.mu() * std::pow(this->_M_ud(__urng), -result_type(1) / __p.alpha()); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two Pareto distributions have * the same parameters and the sequences that would * be generated are equal. */ friend bool operator==(const pareto_distribution& __d1, const pareto_distribution& __d2) { return (__d1._M_param == __d2._M_param && __d1._M_ud == __d2._M_ud); } /** * @@brief Inserts a %pareto_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %pareto_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>&, const pareto_distribution<_RealType1>&); /** * @@brief Extracts a %pareto_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %pareto_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, pareto_distribution<_RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::uniform_real_distribution _M_ud; }; /** * @@brief Return true if two Pareto distributions are not equal. */ template inline bool operator!=(const pareto_distribution<_RealType>& __d1, const pareto_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A K continuous distribution for random numbers. * * The formula for the K probability density function is * @@f[ * p(x|\lambda, \mu, \nu) = \frac{2}{x} * \left(\frac{\lambda\nu x}{\mu}\right)^{\frac{\lambda + \nu}{2}} * \frac{1}{\Gamma(\lambda)\Gamma(\nu)} * K_{\nu - \lambda}\left(2\sqrt{\frac{\lambda\nu x}{\mu}}\right) * @@f] * where @@f$I_0(z)@@f$ is the modified Bessel function of the second kind * of order @@f$\nu - \lambda@@f$ and @@f$\lambda > 0@@f$, @@f$\mu > 0@@f$ * and @@f$\nu > 0@@f$. * * * * * * *
Distribution Statistics
Mean@@f$\mu@@f$
Variance@@f$\mu^2\frac{\lambda + \nu + 1}{\lambda\nu}@@f$
Range@@f$[0, \infty)@@f$
*/ template class k_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef k_distribution distribution_type; param_type(result_type __lambda_val = result_type(1), result_type __mu_val = result_type(1), result_type __nu_val = result_type(1)) : _M_lambda(__lambda_val), _M_mu(__mu_val), _M_nu(__nu_val) { _GLIBCXX_DEBUG_ASSERT(_M_lambda > result_type(0)); _GLIBCXX_DEBUG_ASSERT(_M_mu > result_type(0)); _GLIBCXX_DEBUG_ASSERT(_M_nu > result_type(0)); } result_type lambda() const { return _M_lambda; } result_type mu() const { return _M_mu; } result_type nu() const { return _M_nu; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_lambda == __p2._M_lambda && __p1._M_mu == __p2._M_mu && __p1._M_nu == __p2._M_nu; } private: void _M_initialize(); result_type _M_lambda; result_type _M_mu; result_type _M_nu; }; /** * @@brief Constructors. */ explicit k_distribution(result_type __lambda_val = result_type(1), result_type __mu_val = result_type(1), result_type __nu_val = result_type(1)) : _M_param(__lambda_val, __mu_val, __nu_val), _M_gd1(__lambda_val, result_type(1) / __lambda_val), _M_gd2(__nu_val, __mu_val / __nu_val) { } explicit k_distribution(const param_type& __p) : _M_param(__p), _M_gd1(__p.lambda(), result_type(1) / __p.lambda()), _M_gd2(__p.nu(), __p.mu() / __p.nu()) { } /** * @@brief Resets the distribution state. */ void reset() { _M_gd1.reset(); _M_gd2.reset(); } /** * @@brief Return the parameters of the distribution. */ result_type lambda() const { return _M_param.lambda(); } result_type mu() const { return _M_param.mu(); } result_type nu() const { return _M_param.nu(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return result_type(0); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return std::numeric_limits::max(); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator&); template result_type operator()(_UniformRandomNumberGenerator&, const param_type&); template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two K distributions have * the same parameters and the sequences that would * be generated are equal. */ friend bool operator==(const k_distribution& __d1, const k_distribution& __d2) { return (__d1._M_param == __d2._M_param && __d1._M_gd1 == __d2._M_gd1 && __d1._M_gd2 == __d2._M_gd2); } /** * @@brief Inserts a %k_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %k_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>&, const k_distribution<_RealType1>&); /** * @@brief Extracts a %k_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %k_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, k_distribution<_RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::gamma_distribution _M_gd1; std::gamma_distribution _M_gd2; }; /** * @@brief Return true if two K distributions are not equal. */ template inline bool operator!=(const k_distribution<_RealType>& __d1, const k_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief An arcsine continuous distribution for random numbers. * * The formula for the arcsine probability density function is * @@f[ * p(x|a,b) = \frac{1}{\pi \sqrt{(x - a)(b - x)}} * @@f] * where @@f$x >= a@@f$ and @@f$x <= b@@f$. * * * * * * *
Distribution Statistics
Mean@@f$ (a + b) / 2 @@f$
Variance@@f$ (b - a)^2 / 8 @@f$
Range@@f$[a, b]@@f$
*/ template class arcsine_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef arcsine_distribution distribution_type; param_type(result_type __a = result_type(0), result_type __b = result_type(1)) : _M_a(__a), _M_b(__b) { _GLIBCXX_DEBUG_ASSERT(_M_a <= _M_b); } result_type a() const { return _M_a; } result_type b() const { return _M_b; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b; } private: void _M_initialize(); result_type _M_a; result_type _M_b; }; /** * @@brief Constructors. */ explicit arcsine_distribution(result_type __a = result_type(0), result_type __b = result_type(1)) : _M_param(__a, __b), _M_ud(-1.5707963267948966192313216916397514L, +1.5707963267948966192313216916397514L) { } explicit arcsine_distribution(const param_type& __p) : _M_param(__p), _M_ud(-1.5707963267948966192313216916397514L, +1.5707963267948966192313216916397514L) { } /** * @@brief Resets the distribution state. */ void reset() { _M_ud.reset(); } /** * @@brief Return the parameters of the distribution. */ result_type a() const { return _M_param.a(); } result_type b() const { return _M_param.b(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return this->a(); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return this->b(); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { result_type __x = std::sin(this->_M_ud(__urng)); return (__x * (this->b() - this->a()) + this->a() + this->b()) / result_type(2); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { result_type __x = std::sin(this->_M_ud(__urng)); return (__x * (__p.b() - __p.a()) + __p.a() + __p.b()) / result_type(2); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two arcsine distributions have * the same parameters and the sequences that would * be generated are equal. */ friend bool operator==(const arcsine_distribution& __d1, const arcsine_distribution& __d2) { return (__d1._M_param == __d2._M_param && __d1._M_ud == __d2._M_ud); } /** * @@brief Inserts a %arcsine_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %arcsine_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>&, const arcsine_distribution<_RealType1>&); /** * @@brief Extracts a %arcsine_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %arcsine_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, arcsine_distribution<_RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::uniform_real_distribution _M_ud; }; /** * @@brief Return true if two arcsine distributions are not equal. */ template inline bool operator!=(const arcsine_distribution<_RealType>& __d1, const arcsine_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A Hoyt continuous distribution for random numbers. * * The formula for the Hoyt probability density function is * @@f[ * p(x|q,\omega) = \frac{(1 + q^2)x}{q\omega} * \exp\left(-\frac{(1 + q^2)^2 x^2}{4 q^2 \omega}\right) * I_0\left(\frac{(1 - q^4) x^2}{4 q^2 \omega}\right) * @@f] * where @@f$I_0(z)@@f$ is the modified Bessel function of the first kind * of order 0 and @@f$0 < q < 1@@f$. * * * * * * *
Distribution Statistics
Mean@@f$ \sqrt{\frac{2}{\pi}} \sqrt{\frac{\omega}{1 + q^2}} * E(1 - q^2) @@f$
Variance@@f$ \omega \left(1 - \frac{2E^2(1 - q^2)} * {\pi (1 + q^2)}\right) @@f$
Range@@f$[0, \infty)@@f$
* where @@f$E(x)@@f$ is the elliptic function of the second kind. */ template class hoyt_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef hoyt_distribution distribution_type; param_type(result_type __q = result_type(0.5L), result_type __omega = result_type(1)) : _M_q(__q), _M_omega(__omega) { _GLIBCXX_DEBUG_ASSERT(_M_q > result_type(0)); _GLIBCXX_DEBUG_ASSERT(_M_q < result_type(1)); } result_type q() const { return _M_q; } result_type omega() const { return _M_omega; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_q == __p2._M_q && __p1._M_omega == __p2._M_omega; } private: void _M_initialize(); result_type _M_q; result_type _M_omega; }; /** * @@brief Constructors. */ explicit hoyt_distribution(result_type __q = result_type(0.5L), result_type __omega = result_type(1)) : _M_param(__q, __omega), _M_ad(result_type(0.5L) * (result_type(1) + __q * __q), result_type(0.5L) * (result_type(1) + __q * __q) / (__q * __q)), _M_ed(result_type(1)) { } explicit hoyt_distribution(const param_type& __p) : _M_param(__p), _M_ad(result_type(0.5L) * (result_type(1) + __p.q() * __p.q()), result_type(0.5L) * (result_type(1) + __p.q() * __p.q()) / (__p.q() * __p.q())), _M_ed(result_type(1)) { } /** * @@brief Resets the distribution state. */ void reset() { _M_ad.reset(); _M_ed.reset(); } /** * @@brief Return the parameters of the distribution. */ result_type q() const { return _M_param.q(); } result_type omega() const { return _M_param.omega(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return result_type(0); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return std::numeric_limits::max(); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng); template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two Hoyt distributions have * the same parameters and the sequences that would * be generated are equal. */ friend bool operator==(const hoyt_distribution& __d1, const hoyt_distribution& __d2) { return (__d1._M_param == __d2._M_param && __d1._M_ad == __d2._M_ad && __d1._M_ed == __d2._M_ed); } /** * @@brief Inserts a %hoyt_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %hoyt_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>&, const hoyt_distribution<_RealType1>&); /** * @@brief Extracts a %hoyt_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %hoyt_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, hoyt_distribution<_RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; __gnu_cxx::arcsine_distribution _M_ad; std::exponential_distribution _M_ed; }; /** * @@brief Return true if two Hoyt distributions are not equal. */ template inline bool operator!=(const hoyt_distribution<_RealType>& __d1, const hoyt_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A triangular distribution for random numbers. * * The formula for the triangular probability density function is * @@f[ * / 0 for x < a * p(x|a,b,c) = | \frac{2(x-a)}{(c-a)(b-a)} for a <= x <= b * | \frac{2(c-x)}{(c-a)(c-b)} for b < x <= c * \ 0 for c < x * @@f] * * * * * * *
Distribution Statistics
Mean@@f$ \frac{a+b+c}{2} @@f$
Variance@@f$ \frac{a^2+b^2+c^2-ab-ac-bc} * {18}@@f$
Range@@f$[a, c]@@f$
*/ template class triangular_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { friend class triangular_distribution<_RealType>; explicit param_type(_RealType __a = _RealType(0), _RealType __b = _RealType(0.5), _RealType __c = _RealType(1)) : _M_a(__a), _M_b(__b), _M_c(__c) { _GLIBCXX_DEBUG_ASSERT(_M_a <= _M_b); _GLIBCXX_DEBUG_ASSERT(_M_b <= _M_c); _GLIBCXX_DEBUG_ASSERT(_M_a < _M_c); _M_r_ab = (_M_b - _M_a) / (_M_c - _M_a); _M_f_ab_ac = (_M_b - _M_a) * (_M_c - _M_a); _M_f_bc_ac = (_M_c - _M_b) * (_M_c - _M_a); } _RealType a() const { return _M_a; } _RealType b() const { return _M_b; } _RealType c() const { return _M_c; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return (__p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b && __p1._M_c == __p2._M_c); } private: _RealType _M_a; _RealType _M_b; _RealType _M_c; _RealType _M_r_ab; _RealType _M_f_ab_ac; _RealType _M_f_bc_ac; }; /** * @@brief Constructs a triangle distribution with parameters * @@f$ a @@f$, @@f$ b @@f$ and @@f$ c @@f$. */ explicit triangular_distribution(result_type __a = result_type(0), result_type __b = result_type(0.5), result_type __c = result_type(1)) : _M_param(__a, __b, __c) { } explicit triangular_distribution(const param_type& __p) : _M_param(__p) { } /** * @@brief Resets the distribution state. */ void reset() { } /** * @@brief Returns the @@f$ a @@f$ of the distribution. */ result_type a() const { return _M_param.a(); } /** * @@brief Returns the @@f$ b @@f$ of the distribution. */ result_type b() const { return _M_param.b(); } /** * @@brief Returns the @@f$ c @@f$ of the distribution. */ result_type c() const { return _M_param.c(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return _M_param._M_a; } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return _M_param._M_c; } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); result_type __rnd = __aurng(); if (__rnd <= __p._M_r_ab) return __p.a() + std::sqrt(__rnd * __p._M_f_ab_ac); else return __p.c() - std::sqrt((result_type(1) - __rnd) * __p._M_f_bc_ac); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two triangle distributions have the same * parameters and the sequences that would be generated * are equal. */ friend bool operator==(const triangular_distribution& __d1, const triangular_distribution& __d2) { return __d1._M_param == __d2._M_param; } /** * @@brief Inserts a %triangular_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %triangular_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::triangular_distribution<_RealType1>& __x); /** * @@brief Extracts a %triangular_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %triangular_distribution random number generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::triangular_distribution<_RealType1>& __x); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; }; /** * @@brief Return true if two triangle distributions are different. */ template inline bool operator!=(const __gnu_cxx::triangular_distribution<_RealType>& __d1, const __gnu_cxx::triangular_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A von Mises distribution for random numbers. * * The formula for the von Mises probability density function is * @@f[ * p(x|\mu,\kappa) = \frac{e^{\kappa \cos(x-\mu)}} * {2\pi I_0(\kappa)} * @@f] * * The generating functions use the method according to: * * D. J. Best and N. I. Fisher, 1979. "Efficient Simulation of the * von Mises Distribution", Journal of the Royal Statistical Society. * Series C (Applied Statistics), Vol. 28, No. 2, pp. 152-157. * * * * * * *
Distribution Statistics
Mean@@f$ \mu @@f$
Variance@@f$ 1-I_1(\kappa)/I_0(\kappa) @@f$
Range@@f$[-\pi, \pi]@@f$
*/ template class von_mises_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { friend class von_mises_distribution<_RealType>; explicit param_type(_RealType __mu = _RealType(0), _RealType __kappa = _RealType(1)) : _M_mu(__mu), _M_kappa(__kappa) { const _RealType __pi = __gnu_cxx::__math_constants<_RealType>::__pi; _GLIBCXX_DEBUG_ASSERT(_M_mu >= -__pi && _M_mu <= __pi); _GLIBCXX_DEBUG_ASSERT(_M_kappa >= _RealType(0)); auto __tau = std::sqrt(_RealType(4) * _M_kappa * _M_kappa + _RealType(1)) + _RealType(1); auto __rho = ((__tau - std::sqrt(_RealType(2) * __tau)) / (_RealType(2) * _M_kappa)); _M_r = (_RealType(1) + __rho * __rho) / (_RealType(2) * __rho); } _RealType mu() const { return _M_mu; } _RealType kappa() const { return _M_kappa; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return (__p1._M_mu == __p2._M_mu && __p1._M_kappa == __p2._M_kappa); } private: _RealType _M_mu; _RealType _M_kappa; _RealType _M_r; }; /** * @@brief Constructs a von Mises distribution with parameters * @@f$\mu@@f$ and @@f$\kappa@@f$. */ explicit von_mises_distribution(result_type __mu = result_type(0), result_type __kappa = result_type(1)) : _M_param(__mu, __kappa) { } explicit von_mises_distribution(const param_type& __p) : _M_param(__p) { } /** * @@brief Resets the distribution state. */ void reset() { } /** * @@brief Returns the @@f$ \mu @@f$ of the distribution. */ result_type mu() const { return _M_param.mu(); } /** * @@brief Returns the @@f$ \kappa @@f$ of the distribution. */ result_type kappa() const { return _M_param.kappa(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return -__gnu_cxx::__math_constants::__pi; } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return __gnu_cxx::__math_constants::__pi; } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template result_type 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 void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two von Mises distributions have the same * parameters and the sequences that would be generated * are equal. */ friend bool operator==(const von_mises_distribution& __d1, const von_mises_distribution& __d2) { return __d1._M_param == __d2._M_param; } /** * @@brief Inserts a %von_mises_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %von_mises_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::von_mises_distribution<_RealType1>& __x); /** * @@brief Extracts a %von_mises_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %von_mises_distribution random number generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::von_mises_distribution<_RealType1>& __x); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; }; /** * @@brief Return true if two von Mises distributions are different. */ template inline bool operator!=(const __gnu_cxx::von_mises_distribution<_RealType>& __d1, const __gnu_cxx::von_mises_distribution<_RealType>& __d2) { return !(__d1 == __d2); } _GLIBCXX_END_NAMESPACE_VERSION } // namespace __gnu_cxx #include "ext/opt_random.h" #include "random.tcc" #endif // _GLIBCXX_USE_C99_STDINT_TR1 #endif // C++11 #endif // _EXT_RANDOM @ 1.1.1.1.4.1 log @file random was added on branch yamt-pagecache on 2014-05-22 16:37:49 +0000 @ text @d1 2858 @ 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 2858 // 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 * This file is a GNU extension to the Standard C++ Library. */ #ifndef _EXT_RANDOM #define _EXT_RANDOM 1 #pragma GCC system_header #if __cplusplus < 201103L # include #else #include #include #include #ifdef __SSE2__ # include #endif #ifdef _GLIBCXX_USE_C99_STDINT_TR1 namespace __gnu_cxx _GLIBCXX_VISIBILITY(default) { _GLIBCXX_BEGIN_NAMESPACE_VERSION #if __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__ /* Mersenne twister implementation optimized for vector operations. * * Reference: http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/ */ template class simd_fast_mersenne_twister_engine { static_assert(std::is_unsigned<_UIntType>::value, "template argument " "substituting _UIntType not an unsigned integral type"); static_assert(__sr1 < 32, "first right shift too large"); static_assert(__sr2 < 16, "second right shift too large"); static_assert(__sl1 < 32, "first left shift too large"); static_assert(__sl2 < 16, "second left shift too large"); public: typedef _UIntType result_type; private: static constexpr size_t m_w = sizeof(result_type) * 8; static constexpr size_t _M_nstate = __m / 128 + 1; static constexpr size_t _M_nstate32 = _M_nstate * 4; static_assert(std::is_unsigned<_UIntType>::value, "template argument " "substituting _UIntType not an unsigned integral type"); static_assert(__pos1 < _M_nstate, "POS1 not smaller than state size"); static_assert(16 % sizeof(_UIntType) == 0, "UIntType size must divide 16"); public: static constexpr size_t state_size = _M_nstate * (16 / sizeof(result_type)); static constexpr result_type default_seed = 5489u; // constructors and member function explicit simd_fast_mersenne_twister_engine(result_type __sd = default_seed) { seed(__sd); } template::value> ::type> explicit simd_fast_mersenne_twister_engine(_Sseq& __q) { seed(__q); } void seed(result_type __sd = default_seed); template typename std::enable_if::value>::type seed(_Sseq& __q); static constexpr result_type min() { return 0; }; static constexpr result_type max() { return std::numeric_limits::max(); } void discard(unsigned long long __z); result_type operator()() { if (__builtin_expect(_M_pos >= state_size, 0)) _M_gen_rand(); return _M_stateT[_M_pos++]; } template friend bool operator==(const simd_fast_mersenne_twister_engine<_UIntType_2, __m_2, __pos1_2, __sl1_2, __sl2_2, __sr1_2, __sr2_2, __msk1_2, __msk2_2, __msk3_2, __msk4_2, __parity1_2, __parity2_2, __parity3_2, __parity4_2>& __lhs, const simd_fast_mersenne_twister_engine<_UIntType_2, __m_2, __pos1_2, __sl1_2, __sl2_2, __sr1_2, __sr2_2, __msk1_2, __msk2_2, __msk3_2, __msk4_2, __parity1_2, __parity2_2, __parity3_2, __parity4_2>& __rhs); template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::simd_fast_mersenne_twister_engine <_UIntType_2, __m_2, __pos1_2, __sl1_2, __sl2_2, __sr1_2, __sr2_2, __msk1_2, __msk2_2, __msk3_2, __msk4_2, __parity1_2, __parity2_2, __parity3_2, __parity4_2>& __x); template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType_2, __m_2, __pos1_2, __sl1_2, __sl2_2, __sr1_2, __sr2_2, __msk1_2, __msk2_2, __msk3_2, __msk4_2, __parity1_2, __parity2_2, __parity3_2, __parity4_2>& __x); private: union { #ifdef __SSE2__ __m128i _M_state[_M_nstate]; #endif uint32_t _M_state32[_M_nstate32]; result_type _M_stateT[state_size]; } __attribute__ ((__aligned__ (16))); size_t _M_pos; void _M_gen_rand(void); void _M_period_certification(); }; template inline 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) { return !(__lhs == __rhs); } /* Definitions for the SIMD-oriented Fast Mersenne Twister as defined * in the C implementation by Daito and Matsumoto, as both a 32-bit * and 64-bit version. */ typedef simd_fast_mersenne_twister_engine sfmt607; typedef simd_fast_mersenne_twister_engine sfmt607_64; typedef simd_fast_mersenne_twister_engine sfmt1279; typedef simd_fast_mersenne_twister_engine sfmt1279_64; typedef simd_fast_mersenne_twister_engine sfmt2281; typedef simd_fast_mersenne_twister_engine sfmt2281_64; typedef simd_fast_mersenne_twister_engine sfmt4253; typedef simd_fast_mersenne_twister_engine sfmt4253_64; typedef simd_fast_mersenne_twister_engine sfmt11213; typedef simd_fast_mersenne_twister_engine sfmt11213_64; typedef simd_fast_mersenne_twister_engine sfmt19937; typedef simd_fast_mersenne_twister_engine sfmt19937_64; typedef simd_fast_mersenne_twister_engine sfmt44497; typedef simd_fast_mersenne_twister_engine sfmt44497_64; typedef simd_fast_mersenne_twister_engine sfmt86243; typedef simd_fast_mersenne_twister_engine sfmt86243_64; typedef simd_fast_mersenne_twister_engine sfmt132049; typedef simd_fast_mersenne_twister_engine sfmt132049_64; typedef simd_fast_mersenne_twister_engine sfmt216091; typedef simd_fast_mersenne_twister_engine sfmt216091_64; #endif // __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__ /** * @@brief A beta continuous distribution for random numbers. * * The formula for the beta probability density function is: * @@f[ * p(x|\alpha,\beta) = \frac{1}{B(\alpha,\beta)} * x^{\alpha - 1} (1 - x)^{\beta - 1} * @@f] */ template class beta_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef beta_distribution<_RealType> distribution_type; friend class beta_distribution<_RealType>; explicit param_type(_RealType __alpha_val = _RealType(1), _RealType __beta_val = _RealType(1)) : _M_alpha(__alpha_val), _M_beta(__beta_val) { _GLIBCXX_DEBUG_ASSERT(_M_alpha > _RealType(0)); _GLIBCXX_DEBUG_ASSERT(_M_beta > _RealType(0)); } _RealType alpha() const { return _M_alpha; } _RealType beta() const { return _M_beta; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return (__p1._M_alpha == __p2._M_alpha && __p1._M_beta == __p2._M_beta); } private: void _M_initialize(); _RealType _M_alpha; _RealType _M_beta; }; public: /** * @@brief Constructs a beta distribution with parameters * @@f$\alpha@@f$ and @@f$\beta@@f$. */ explicit beta_distribution(_RealType __alpha_val = _RealType(1), _RealType __beta_val = _RealType(1)) : _M_param(__alpha_val, __beta_val) { } explicit beta_distribution(const param_type& __p) : _M_param(__p) { } /** * @@brief Resets the distribution state. */ void reset() { } /** * @@brief Returns the @@f$\alpha@@f$ of the distribution. */ _RealType alpha() const { return _M_param.alpha(); } /** * @@brief Returns the @@f$\beta@@f$ of the distribution. */ _RealType beta() const { return _M_param.beta(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return result_type(0); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return result_type(1); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two beta distributions have the same * parameters and the sequences that would be generated * are equal. */ friend bool operator==(const beta_distribution& __d1, const beta_distribution& __d2) { return __d1._M_param == __d2._M_param; } /** * @@brief Inserts a %beta_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %beta_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::beta_distribution<_RealType1>& __x); /** * @@brief Extracts a %beta_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %beta_distribution random number generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::beta_distribution<_RealType1>& __x); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; }; /** * @@brief Return true if two beta distributions are different. */ template inline bool operator!=(const __gnu_cxx::beta_distribution<_RealType>& __d1, const __gnu_cxx::beta_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A multi-variate normal continuous distribution for random numbers. * * The formula for the normal probability density function is * @@f[ * p(\overrightarrow{x}|\overrightarrow{\mu },\Sigma) = * \frac{1}{\sqrt{(2\pi )^k\det(\Sigma))}} * e^{-\frac{1}{2}(\overrightarrow{x}-\overrightarrow{\mu})^\text{T} * \Sigma ^{-1}(\overrightarrow{x}-\overrightarrow{\mu})} * @@f] * * where @@f$\overrightarrow{x}@@f$ and @@f$\overrightarrow{\mu}@@f$ are * vectors of dimension @@f$k@@f$ and @@f$\Sigma@@f$ is the covariance * matrix (which must be positive-definite). */ template class normal_mv_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); static_assert(_Dimen != 0, "dimension is zero"); public: /** The type of the range of the distribution. */ typedef std::array<_RealType, _Dimen> result_type; /** Parameter type. */ class param_type { static constexpr size_t _M_t_size = _Dimen * (_Dimen + 1) / 2; public: typedef normal_mv_distribution<_Dimen, _RealType> distribution_type; friend class normal_mv_distribution<_Dimen, _RealType>; param_type() { std::fill(_M_mean.begin(), _M_mean.end(), _RealType(0)); auto __it = _M_t.begin(); for (size_t __i = 0; __i < _Dimen; ++__i) { std::fill_n(__it, __i, _RealType(0)); __it += __i; *__it++ = _RealType(1); } } template param_type(_ForwardIterator1 __meanbegin, _ForwardIterator1 __meanend, _ForwardIterator2 __varcovbegin, _ForwardIterator2 __varcovend) { __glibcxx_function_requires(_ForwardIteratorConcept< _ForwardIterator1>) __glibcxx_function_requires(_ForwardIteratorConcept< _ForwardIterator2>) _GLIBCXX_DEBUG_ASSERT(std::distance(__meanbegin, __meanend) <= _Dimen); const auto __dist = std::distance(__varcovbegin, __varcovend); _GLIBCXX_DEBUG_ASSERT(__dist == _Dimen * _Dimen || __dist == _Dimen * (_Dimen + 1) / 2 || __dist == _Dimen); if (__dist == _Dimen * _Dimen) _M_init_full(__meanbegin, __meanend, __varcovbegin, __varcovend); else if (__dist == _Dimen * (_Dimen + 1) / 2) _M_init_lower(__meanbegin, __meanend, __varcovbegin, __varcovend); else _M_init_diagonal(__meanbegin, __meanend, __varcovbegin, __varcovend); } param_type(std::initializer_list<_RealType> __mean, std::initializer_list<_RealType> __varcov) { _GLIBCXX_DEBUG_ASSERT(__mean.size() <= _Dimen); _GLIBCXX_DEBUG_ASSERT(__varcov.size() == _Dimen * _Dimen || __varcov.size() == _Dimen * (_Dimen + 1) / 2 || __varcov.size() == _Dimen); if (__varcov.size() == _Dimen * _Dimen) _M_init_full(__mean.begin(), __mean.end(), __varcov.begin(), __varcov.end()); else if (__varcov.size() == _Dimen * (_Dimen + 1) / 2) _M_init_lower(__mean.begin(), __mean.end(), __varcov.begin(), __varcov.end()); else _M_init_diagonal(__mean.begin(), __mean.end(), __varcov.begin(), __varcov.end()); } std::array<_RealType, _Dimen> mean() const { return _M_mean; } std::array<_RealType, _M_t_size> varcov() const { return _M_t; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_mean == __p2._M_mean && __p1._M_t == __p2._M_t; } private: template void _M_init_full(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varcovbegin, _InputIterator2 __varcovend); template void _M_init_lower(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varcovbegin, _InputIterator2 __varcovend); template void _M_init_diagonal(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varbegin, _InputIterator2 __varend); std::array<_RealType, _Dimen> _M_mean; std::array<_RealType, _M_t_size> _M_t; }; public: normal_mv_distribution() : _M_param(), _M_nd() { } template normal_mv_distribution(_ForwardIterator1 __meanbegin, _ForwardIterator1 __meanend, _ForwardIterator2 __varcovbegin, _ForwardIterator2 __varcovend) : _M_param(__meanbegin, __meanend, __varcovbegin, __varcovend), _M_nd() { } normal_mv_distribution(std::initializer_list<_RealType> __mean, std::initializer_list<_RealType> __varcov) : _M_param(__mean, __varcov), _M_nd() { } explicit normal_mv_distribution(const param_type& __p) : _M_param(__p), _M_nd() { } /** * @@brief Resets the distribution state. */ void reset() { _M_nd.reset(); } /** * @@brief Returns the mean of the distribution. */ result_type mean() const { return _M_param.mean(); } /** * @@brief Returns the compact form of the variance/covariance * matrix of the distribution. */ std::array<_RealType, _Dimen * (_Dimen + 1) / 2> varcov() const { return _M_param.varcov(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { result_type __res; __res.fill(std::numeric_limits<_RealType>::lowest()); return __res; } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { result_type __res; __res.fill(std::numeric_limits<_RealType>::max()); return __res; } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { return this->__generate_impl(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { return this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two multi-variant normal distributions have * the same parameters and the sequences that would * be generated are equal. */ template friend bool operator==(const __gnu_cxx::normal_mv_distribution<_Dimen1, _RealType1>& __d1, const __gnu_cxx::normal_mv_distribution<_Dimen1, _RealType1>& __d2); /** * @@brief Inserts a %normal_mv_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %normal_mv_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::normal_mv_distribution<_Dimen1, _RealType1>& __x); /** * @@brief Extracts a %normal_mv_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %normal_mv_distribution random number generator engine. * * @@returns The input stream with @@p __x extracted or in an error * state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::normal_mv_distribution<_Dimen1, _RealType1>& __x); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::normal_distribution<_RealType> _M_nd; }; /** * @@brief Return true if two multi-variate normal distributions are * different. */ template inline bool operator!=(const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __d1, const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A Rice continuous distribution for random numbers. * * The formula for the Rice probability density function is * @@f[ * p(x|\nu,\sigma) = \frac{x}{\sigma^2} * \exp\left(-\frac{x^2+\nu^2}{2\sigma^2}\right) * I_0\left(\frac{x \nu}{\sigma^2}\right) * @@f] * where @@f$I_0(z)@@f$ is the modified Bessel function of the first kind * of order 0 and @@f$\nu >= 0@@f$ and @@f$\sigma > 0@@f$. * * * * * * *
Distribution Statistics
Mean@@f$\sqrt{\pi/2}L_{1/2}(-\nu^2/2\sigma^2)@@f$
Variance@@f$2\sigma^2 + \nu^2 * + (\pi\sigma^2/2)L^2_{1/2}(-\nu^2/2\sigma^2)@@f$
Range@@f$[0, \infty)@@f$
* where @@f$L_{1/2}(x)@@f$ is the Laguerre polynomial of order 1/2. */ template class rice_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef rice_distribution distribution_type; param_type(result_type __nu_val = result_type(0), result_type __sigma_val = result_type(1)) : _M_nu(__nu_val), _M_sigma(__sigma_val) { _GLIBCXX_DEBUG_ASSERT(_M_nu >= result_type(0)); _GLIBCXX_DEBUG_ASSERT(_M_sigma > result_type(0)); } result_type nu() const { return _M_nu; } result_type sigma() const { return _M_sigma; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_nu == __p2._M_nu && __p1._M_sigma == __p2._M_sigma; } private: void _M_initialize(); result_type _M_nu; result_type _M_sigma; }; /** * @@brief Constructors. */ explicit rice_distribution(result_type __nu_val = result_type(0), result_type __sigma_val = result_type(1)) : _M_param(__nu_val, __sigma_val), _M_ndx(__nu_val, __sigma_val), _M_ndy(result_type(0), __sigma_val) { } explicit rice_distribution(const param_type& __p) : _M_param(__p), _M_ndx(__p.nu(), __p.sigma()), _M_ndy(result_type(0), __p.sigma()) { } /** * @@brief Resets the distribution state. */ void reset() { _M_ndx.reset(); _M_ndy.reset(); } /** * @@brief Return the parameters of the distribution. */ result_type nu() const { return _M_param.nu(); } result_type sigma() const { return _M_param.sigma(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return result_type(0); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return std::numeric_limits::max(); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { result_type __x = this->_M_ndx(__urng); result_type __y = this->_M_ndy(__urng); #if _GLIBCXX_USE_C99_MATH_TR1 return std::hypot(__x, __y); #else return std::sqrt(__x * __x + __y * __y); #endif } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { 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 return std::hypot(__x, __y); #else return std::sqrt(__x * __x + __y * __y); #endif } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two Rice distributions have * the same parameters and the sequences that would * be generated are equal. */ friend bool operator==(const rice_distribution& __d1, const rice_distribution& __d2) { return (__d1._M_param == __d2._M_param && __d1._M_ndx == __d2._M_ndx && __d1._M_ndy == __d2._M_ndy); } /** * @@brief Inserts a %rice_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %rice_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>&, const rice_distribution<_RealType1>&); /** * @@brief Extracts a %rice_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %rice_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, rice_distribution<_RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::normal_distribution _M_ndx; std::normal_distribution _M_ndy; }; /** * @@brief Return true if two Rice distributions are not equal. */ template inline bool operator!=(const rice_distribution<_RealType1>& __d1, const rice_distribution<_RealType1>& __d2) { return !(__d1 == __d2); } /** * @@brief A Nakagami continuous distribution for random numbers. * * The formula for the Nakagami probability density function is * @@f[ * p(x|\mu,\omega) = \frac{2\mu^\mu}{\Gamma(\mu)\omega^\mu} * x^{2\mu-1}e^{-\mu x / \omega} * @@f] * where @@f$\Gamma(z)@@f$ is the gamma function and @@f$\mu >= 0.5@@f$ * and @@f$\omega > 0@@f$. */ template class nakagami_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef nakagami_distribution distribution_type; param_type(result_type __mu_val = result_type(1), result_type __omega_val = result_type(1)) : _M_mu(__mu_val), _M_omega(__omega_val) { _GLIBCXX_DEBUG_ASSERT(_M_mu >= result_type(0.5L)); _GLIBCXX_DEBUG_ASSERT(_M_omega > result_type(0)); } result_type mu() const { return _M_mu; } result_type omega() const { return _M_omega; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_mu == __p2._M_mu && __p1._M_omega == __p2._M_omega; } private: void _M_initialize(); result_type _M_mu; result_type _M_omega; }; /** * @@brief Constructors. */ explicit nakagami_distribution(result_type __mu_val = result_type(1), result_type __omega_val = result_type(1)) : _M_param(__mu_val, __omega_val), _M_gd(__mu_val, __omega_val / __mu_val) { } explicit nakagami_distribution(const param_type& __p) : _M_param(__p), _M_gd(__p.mu(), __p.omega() / __p.mu()) { } /** * @@brief Resets the distribution state. */ void reset() { _M_gd.reset(); } /** * @@brief Return the parameters of the distribution. */ result_type mu() const { return _M_param.mu(); } result_type omega() const { return _M_param.omega(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return result_type(0); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return std::numeric_limits::max(); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return std::sqrt(this->_M_gd(__urng)); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { typename std::gamma_distribution::param_type __pg(__p.mu(), __p.omega() / __p.mu()); return std::sqrt(this->_M_gd(__pg, __urng)); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two Nakagami distributions have * the same parameters and the sequences that would * be generated are equal. */ friend bool operator==(const nakagami_distribution& __d1, const nakagami_distribution& __d2) { return (__d1._M_param == __d2._M_param && __d1._M_gd == __d2._M_gd); } /** * @@brief Inserts a %nakagami_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %nakagami_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>&, const nakagami_distribution<_RealType1>&); /** * @@brief Extracts a %nakagami_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %nakagami_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, nakagami_distribution<_RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::gamma_distribution _M_gd; }; /** * @@brief Return true if two Nakagami distributions are not equal. */ template inline bool operator!=(const nakagami_distribution<_RealType>& __d1, const nakagami_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A Pareto continuous distribution for random numbers. * * The formula for the Pareto cumulative probability function is * @@f[ * P(x|\alpha,\mu) = 1 - \left(\frac{\mu}{x}\right)^\alpha * @@f] * The formula for the Pareto probability density function is * @@f[ * p(x|\alpha,\mu) = \frac{\alpha + 1}{\mu} * \left(\frac{\mu}{x}\right)^{\alpha + 1} * @@f] * where @@f$x >= \mu@@f$ and @@f$\mu > 0@@f$, @@f$\alpha > 0@@f$. * * * * * * *
Distribution Statistics
Mean@@f$\alpha \mu / (\alpha - 1)@@f$ * for @@f$\alpha > 1@@f$
Variance@@f$\alpha \mu^2 / [(\alpha - 1)^2(\alpha - 2)]@@f$ * for @@f$\alpha > 2@@f$
Range@@f$[\mu, \infty)@@f$
*/ template class pareto_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef pareto_distribution distribution_type; param_type(result_type __alpha_val = result_type(1), result_type __mu_val = result_type(1)) : _M_alpha(__alpha_val), _M_mu(__mu_val) { _GLIBCXX_DEBUG_ASSERT(_M_alpha > result_type(0)); _GLIBCXX_DEBUG_ASSERT(_M_mu > result_type(0)); } result_type alpha() const { return _M_alpha; } result_type mu() const { return _M_mu; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_alpha == __p2._M_alpha && __p1._M_mu == __p2._M_mu; } private: void _M_initialize(); result_type _M_alpha; result_type _M_mu; }; /** * @@brief Constructors. */ explicit pareto_distribution(result_type __alpha_val = result_type(1), result_type __mu_val = result_type(1)) : _M_param(__alpha_val, __mu_val), _M_ud() { } explicit pareto_distribution(const param_type& __p) : _M_param(__p), _M_ud() { } /** * @@brief Resets the distribution state. */ void reset() { _M_ud.reset(); } /** * @@brief Return the parameters of the distribution. */ result_type alpha() const { return _M_param.alpha(); } result_type mu() const { return _M_param.mu(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return this->mu(); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return std::numeric_limits::max(); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->mu() * std::pow(this->_M_ud(__urng), -result_type(1) / this->alpha()); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { return __p.mu() * std::pow(this->_M_ud(__urng), -result_type(1) / __p.alpha()); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two Pareto distributions have * the same parameters and the sequences that would * be generated are equal. */ friend bool operator==(const pareto_distribution& __d1, const pareto_distribution& __d2) { return (__d1._M_param == __d2._M_param && __d1._M_ud == __d2._M_ud); } /** * @@brief Inserts a %pareto_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %pareto_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>&, const pareto_distribution<_RealType1>&); /** * @@brief Extracts a %pareto_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %pareto_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, pareto_distribution<_RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::uniform_real_distribution _M_ud; }; /** * @@brief Return true if two Pareto distributions are not equal. */ template inline bool operator!=(const pareto_distribution<_RealType>& __d1, const pareto_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A K continuous distribution for random numbers. * * The formula for the K probability density function is * @@f[ * p(x|\lambda, \mu, \nu) = \frac{2}{x} * \left(\frac{\lambda\nu x}{\mu}\right)^{\frac{\lambda + \nu}{2}} * \frac{1}{\Gamma(\lambda)\Gamma(\nu)} * K_{\nu - \lambda}\left(2\sqrt{\frac{\lambda\nu x}{\mu}}\right) * @@f] * where @@f$I_0(z)@@f$ is the modified Bessel function of the second kind * of order @@f$\nu - \lambda@@f$ and @@f$\lambda > 0@@f$, @@f$\mu > 0@@f$ * and @@f$\nu > 0@@f$. * * * * * * *
Distribution Statistics
Mean@@f$\mu@@f$
Variance@@f$\mu^2\frac{\lambda + \nu + 1}{\lambda\nu}@@f$
Range@@f$[0, \infty)@@f$
*/ template class k_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef k_distribution distribution_type; param_type(result_type __lambda_val = result_type(1), result_type __mu_val = result_type(1), result_type __nu_val = result_type(1)) : _M_lambda(__lambda_val), _M_mu(__mu_val), _M_nu(__nu_val) { _GLIBCXX_DEBUG_ASSERT(_M_lambda > result_type(0)); _GLIBCXX_DEBUG_ASSERT(_M_mu > result_type(0)); _GLIBCXX_DEBUG_ASSERT(_M_nu > result_type(0)); } result_type lambda() const { return _M_lambda; } result_type mu() const { return _M_mu; } result_type nu() const { return _M_nu; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_lambda == __p2._M_lambda && __p1._M_mu == __p2._M_mu && __p1._M_nu == __p2._M_nu; } private: void _M_initialize(); result_type _M_lambda; result_type _M_mu; result_type _M_nu; }; /** * @@brief Constructors. */ explicit k_distribution(result_type __lambda_val = result_type(1), result_type __mu_val = result_type(1), result_type __nu_val = result_type(1)) : _M_param(__lambda_val, __mu_val, __nu_val), _M_gd1(__lambda_val, result_type(1) / __lambda_val), _M_gd2(__nu_val, __mu_val / __nu_val) { } explicit k_distribution(const param_type& __p) : _M_param(__p), _M_gd1(__p.lambda(), result_type(1) / __p.lambda()), _M_gd2(__p.nu(), __p.mu() / __p.nu()) { } /** * @@brief Resets the distribution state. */ void reset() { _M_gd1.reset(); _M_gd2.reset(); } /** * @@brief Return the parameters of the distribution. */ result_type lambda() const { return _M_param.lambda(); } result_type mu() const { return _M_param.mu(); } result_type nu() const { return _M_param.nu(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return result_type(0); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return std::numeric_limits::max(); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator&); template result_type operator()(_UniformRandomNumberGenerator&, const param_type&); template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two K distributions have * the same parameters and the sequences that would * be generated are equal. */ friend bool operator==(const k_distribution& __d1, const k_distribution& __d2) { return (__d1._M_param == __d2._M_param && __d1._M_gd1 == __d2._M_gd1 && __d1._M_gd2 == __d2._M_gd2); } /** * @@brief Inserts a %k_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %k_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>&, const k_distribution<_RealType1>&); /** * @@brief Extracts a %k_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %k_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, k_distribution<_RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::gamma_distribution _M_gd1; std::gamma_distribution _M_gd2; }; /** * @@brief Return true if two K distributions are not equal. */ template inline bool operator!=(const k_distribution<_RealType>& __d1, const k_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief An arcsine continuous distribution for random numbers. * * The formula for the arcsine probability density function is * @@f[ * p(x|a,b) = \frac{1}{\pi \sqrt{(x - a)(b - x)}} * @@f] * where @@f$x >= a@@f$ and @@f$x <= b@@f$. * * * * * * *
Distribution Statistics
Mean@@f$ (a + b) / 2 @@f$
Variance@@f$ (b - a)^2 / 8 @@f$
Range@@f$[a, b]@@f$
*/ template class arcsine_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef arcsine_distribution distribution_type; param_type(result_type __a = result_type(0), result_type __b = result_type(1)) : _M_a(__a), _M_b(__b) { _GLIBCXX_DEBUG_ASSERT(_M_a <= _M_b); } result_type a() const { return _M_a; } result_type b() const { return _M_b; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b; } private: void _M_initialize(); result_type _M_a; result_type _M_b; }; /** * @@brief Constructors. */ explicit arcsine_distribution(result_type __a = result_type(0), result_type __b = result_type(1)) : _M_param(__a, __b), _M_ud(-1.5707963267948966192313216916397514L, +1.5707963267948966192313216916397514L) { } explicit arcsine_distribution(const param_type& __p) : _M_param(__p), _M_ud(-1.5707963267948966192313216916397514L, +1.5707963267948966192313216916397514L) { } /** * @@brief Resets the distribution state. */ void reset() { _M_ud.reset(); } /** * @@brief Return the parameters of the distribution. */ result_type a() const { return _M_param.a(); } result_type b() const { return _M_param.b(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return this->a(); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return this->b(); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { result_type __x = std::sin(this->_M_ud(__urng)); return (__x * (this->b() - this->a()) + this->a() + this->b()) / result_type(2); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { result_type __x = std::sin(this->_M_ud(__urng)); return (__x * (__p.b() - __p.a()) + __p.a() + __p.b()) / result_type(2); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two arcsine distributions have * the same parameters and the sequences that would * be generated are equal. */ friend bool operator==(const arcsine_distribution& __d1, const arcsine_distribution& __d2) { return (__d1._M_param == __d2._M_param && __d1._M_ud == __d2._M_ud); } /** * @@brief Inserts a %arcsine_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %arcsine_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>&, const arcsine_distribution<_RealType1>&); /** * @@brief Extracts a %arcsine_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %arcsine_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, arcsine_distribution<_RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::uniform_real_distribution _M_ud; }; /** * @@brief Return true if two arcsine distributions are not equal. */ template inline bool operator!=(const arcsine_distribution<_RealType>& __d1, const arcsine_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A Hoyt continuous distribution for random numbers. * * The formula for the Hoyt probability density function is * @@f[ * p(x|q,\omega) = \frac{(1 + q^2)x}{q\omega} * \exp\left(-\frac{(1 + q^2)^2 x^2}{4 q^2 \omega}\right) * I_0\left(\frac{(1 - q^4) x^2}{4 q^2 \omega}\right) * @@f] * where @@f$I_0(z)@@f$ is the modified Bessel function of the first kind * of order 0 and @@f$0 < q < 1@@f$. * * * * * * *
Distribution Statistics
Mean@@f$ \sqrt{\frac{2}{\pi}} \sqrt{\frac{\omega}{1 + q^2}} * E(1 - q^2) @@f$
Variance@@f$ \omega \left(1 - \frac{2E^2(1 - q^2)} * {\pi (1 + q^2)}\right) @@f$
Range@@f$[0, \infty)@@f$
* where @@f$E(x)@@f$ is the elliptic function of the second kind. */ template class hoyt_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { typedef hoyt_distribution distribution_type; param_type(result_type __q = result_type(0.5L), result_type __omega = result_type(1)) : _M_q(__q), _M_omega(__omega) { _GLIBCXX_DEBUG_ASSERT(_M_q > result_type(0)); _GLIBCXX_DEBUG_ASSERT(_M_q < result_type(1)); } result_type q() const { return _M_q; } result_type omega() const { return _M_omega; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_q == __p2._M_q && __p1._M_omega == __p2._M_omega; } private: void _M_initialize(); result_type _M_q; result_type _M_omega; }; /** * @@brief Constructors. */ explicit hoyt_distribution(result_type __q = result_type(0.5L), result_type __omega = result_type(1)) : _M_param(__q, __omega), _M_ad(result_type(0.5L) * (result_type(1) + __q * __q), result_type(0.5L) * (result_type(1) + __q * __q) / (__q * __q)), _M_ed(result_type(1)) { } explicit hoyt_distribution(const param_type& __p) : _M_param(__p), _M_ad(result_type(0.5L) * (result_type(1) + __p.q() * __p.q()), result_type(0.5L) * (result_type(1) + __p.q() * __p.q()) / (__p.q() * __p.q())), _M_ed(result_type(1)) { } /** * @@brief Resets the distribution state. */ void reset() { _M_ad.reset(); _M_ed.reset(); } /** * @@brief Return the parameters of the distribution. */ result_type q() const { return _M_param.q(); } result_type omega() const { return _M_param.omega(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return result_type(0); } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return std::numeric_limits::max(); } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng); template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two Hoyt distributions have * the same parameters and the sequences that would * be generated are equal. */ friend bool operator==(const hoyt_distribution& __d1, const hoyt_distribution& __d2) { return (__d1._M_param == __d2._M_param && __d1._M_ad == __d2._M_ad && __d1._M_ed == __d2._M_ed); } /** * @@brief Inserts a %hoyt_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %hoyt_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>&, const hoyt_distribution<_RealType1>&); /** * @@brief Extracts a %hoyt_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %hoyt_distribution random number * generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, hoyt_distribution<_RealType1>&); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; __gnu_cxx::arcsine_distribution _M_ad; std::exponential_distribution _M_ed; }; /** * @@brief Return true if two Hoyt distributions are not equal. */ template inline bool operator!=(const hoyt_distribution<_RealType>& __d1, const hoyt_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A triangular distribution for random numbers. * * The formula for the triangular probability density function is * @@f[ * / 0 for x < a * p(x|a,b,c) = | \frac{2(x-a)}{(c-a)(b-a)} for a <= x <= b * | \frac{2(c-x)}{(c-a)(c-b)} for b < x <= c * \ 0 for c < x * @@f] * * * * * * *
Distribution Statistics
Mean@@f$ \frac{a+b+c}{2} @@f$
Variance@@f$ \frac{a^2+b^2+c^2-ab-ac-bc} * {18}@@f$
Range@@f$[a, c]@@f$
*/ template class triangular_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { friend class triangular_distribution<_RealType>; explicit param_type(_RealType __a = _RealType(0), _RealType __b = _RealType(0.5), _RealType __c = _RealType(1)) : _M_a(__a), _M_b(__b), _M_c(__c) { _GLIBCXX_DEBUG_ASSERT(_M_a <= _M_b); _GLIBCXX_DEBUG_ASSERT(_M_b <= _M_c); _GLIBCXX_DEBUG_ASSERT(_M_a < _M_c); _M_r_ab = (_M_b - _M_a) / (_M_c - _M_a); _M_f_ab_ac = (_M_b - _M_a) * (_M_c - _M_a); _M_f_bc_ac = (_M_c - _M_b) * (_M_c - _M_a); } _RealType a() const { return _M_a; } _RealType b() const { return _M_b; } _RealType c() const { return _M_c; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return (__p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b && __p1._M_c == __p2._M_c); } private: _RealType _M_a; _RealType _M_b; _RealType _M_c; _RealType _M_r_ab; _RealType _M_f_ab_ac; _RealType _M_f_bc_ac; }; /** * @@brief Constructs a triangle distribution with parameters * @@f$ a @@f$, @@f$ b @@f$ and @@f$ c @@f$. */ explicit triangular_distribution(result_type __a = result_type(0), result_type __b = result_type(0.5), result_type __c = result_type(1)) : _M_param(__a, __b, __c) { } explicit triangular_distribution(const param_type& __p) : _M_param(__p) { } /** * @@brief Resets the distribution state. */ void reset() { } /** * @@brief Returns the @@f$ a @@f$ of the distribution. */ result_type a() const { return _M_param.a(); } /** * @@brief Returns the @@f$ b @@f$ of the distribution. */ result_type b() const { return _M_param.b(); } /** * @@brief Returns the @@f$ c @@f$ of the distribution. */ result_type c() const { return _M_param.c(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return _M_param._M_a; } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return _M_param._M_c; } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); result_type __rnd = __aurng(); if (__rnd <= __p._M_r_ab) return __p.a() + std::sqrt(__rnd * __p._M_f_ab_ac); else return __p.c() - std::sqrt((result_type(1) - __rnd) * __p._M_f_bc_ac); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two triangle distributions have the same * parameters and the sequences that would be generated * are equal. */ friend bool operator==(const triangular_distribution& __d1, const triangular_distribution& __d2) { return __d1._M_param == __d2._M_param; } /** * @@brief Inserts a %triangular_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %triangular_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::triangular_distribution<_RealType1>& __x); /** * @@brief Extracts a %triangular_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %triangular_distribution random number generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::triangular_distribution<_RealType1>& __x); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; }; /** * @@brief Return true if two triangle distributions are different. */ template inline bool operator!=(const __gnu_cxx::triangular_distribution<_RealType>& __d1, const __gnu_cxx::triangular_distribution<_RealType>& __d2) { return !(__d1 == __d2); } /** * @@brief A von Mises distribution for random numbers. * * The formula for the von Mises probability density function is * @@f[ * p(x|\mu,\kappa) = \frac{e^{\kappa \cos(x-\mu)}} * {2\pi I_0(\kappa)} * @@f] * * The generating functions use the method according to: * * D. J. Best and N. I. Fisher, 1979. "Efficient Simulation of the * von Mises Distribution", Journal of the Royal Statistical Society. * Series C (Applied Statistics), Vol. 28, No. 2, pp. 152-157. * * * * * * *
Distribution Statistics
Mean@@f$ \mu @@f$
Variance@@f$ 1-I_1(\kappa)/I_0(\kappa) @@f$
Range@@f$[-\pi, \pi]@@f$
*/ template class von_mises_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { friend class von_mises_distribution<_RealType>; explicit param_type(_RealType __mu = _RealType(0), _RealType __kappa = _RealType(1)) : _M_mu(__mu), _M_kappa(__kappa) { const _RealType __pi = __gnu_cxx::__math_constants<_RealType>::__pi; _GLIBCXX_DEBUG_ASSERT(_M_mu >= -__pi && _M_mu <= __pi); _GLIBCXX_DEBUG_ASSERT(_M_kappa >= _RealType(0)); auto __tau = std::sqrt(_RealType(4) * _M_kappa * _M_kappa + _RealType(1)) + _RealType(1); auto __rho = ((__tau - std::sqrt(_RealType(2) * __tau)) / (_RealType(2) * _M_kappa)); _M_r = (_RealType(1) + __rho * __rho) / (_RealType(2) * __rho); } _RealType mu() const { return _M_mu; } _RealType kappa() const { return _M_kappa; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return (__p1._M_mu == __p2._M_mu && __p1._M_kappa == __p2._M_kappa); } private: _RealType _M_mu; _RealType _M_kappa; _RealType _M_r; }; /** * @@brief Constructs a von Mises distribution with parameters * @@f$\mu@@f$ and @@f$\kappa@@f$. */ explicit von_mises_distribution(result_type __mu = result_type(0), result_type __kappa = result_type(1)) : _M_param(__mu, __kappa) { } explicit von_mises_distribution(const param_type& __p) : _M_param(__p) { } /** * @@brief Resets the distribution state. */ void reset() { } /** * @@brief Returns the @@f$ \mu @@f$ of the distribution. */ result_type mu() const { return _M_param.mu(); } /** * @@brief Returns the @@f$ \kappa @@f$ of the distribution. */ result_type kappa() const { return _M_param.kappa(); } /** * @@brief Returns the parameter set of the distribution. */ param_type param() const { return _M_param; } /** * @@brief Sets the parameter set of the distribution. * @@param __param The new parameter set of the distribution. */ void param(const param_type& __param) { _M_param = __param; } /** * @@brief Returns the greatest lower bound value of the distribution. */ result_type min() const { return -__gnu_cxx::__math_constants::__pi; } /** * @@brief Returns the least upper bound value of the distribution. */ result_type max() const { return __gnu_cxx::__math_constants::__pi; } /** * @@brief Generating functions. */ template result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template result_type 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 void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template void __generate(result_type* __f, result_type* __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } /** * @@brief Return true if two von Mises distributions have the same * parameters and the sequences that would be generated * are equal. */ friend bool operator==(const von_mises_distribution& __d1, const von_mises_distribution& __d2) { return __d1._M_param == __d2._M_param; } /** * @@brief Inserts a %von_mises_distribution random number distribution * @@p __x into the output stream @@p __os. * * @@param __os An output stream. * @@param __x A %von_mises_distribution random number distribution. * * @@returns The output stream with the state of @@p __x inserted or in * an error state. */ template friend std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const __gnu_cxx::von_mises_distribution<_RealType1>& __x); /** * @@brief Extracts a %von_mises_distribution random number distribution * @@p __x from the input stream @@p __is. * * @@param __is An input stream. * @@param __x A %von_mises_distribution random number generator engine. * * @@returns The input stream with @@p __x extracted or in an error state. */ template friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::von_mises_distribution<_RealType1>& __x); private: template void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; }; /** * @@brief Return true if two von Mises distributions are different. */ template inline bool operator!=(const __gnu_cxx::von_mises_distribution<_RealType>& __d1, const __gnu_cxx::von_mises_distribution<_RealType>& __d2) { return !(__d1 == __d2); } _GLIBCXX_END_NAMESPACE_VERSION } // namespace __gnu_cxx #include "ext/opt_random.h" #include "random.tcc" #endif // _GLIBCXX_USE_C99_STDINT_TR1 #endif // C++11 #endif // _EXT_RANDOM @