Filename: //usr/include/c++///10/ext//////random

// Random number extensions -*- C++ -*- // Copyright (C) 2012-2020 Free Software Foundation, Inc. // // This file is part of the GNU ISO C++ Library. This library is free // software; you can redistribute it and/or modify it under the // terms of the GNU General Public License as published by the // Free Software Foundation; either version 3, or (at your option) // any later version. // This library is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // Under Section 7 of GPL version 3, you are granted additional // permissions described in the GCC Runtime Library Exception, version // 3.1, as published by the Free Software Foundation. // You should have received a copy of the GNU General Public License and // a copy of the GCC Runtime Library Exception along with this program; // see the files COPYING3 and COPYING.RUNTIME respectively. If not, see // <http://www.gnu.org/licenses/>. /** @file ext/random * 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 <bits/c++0x_warning.h> #else #include <random> #include <algorithm> #include <array> #include <ext/cmath> #ifdef __SSE2__ # include <emmintrin.h> #endif #if defined(_GLIBCXX_USE_C99_STDINT_TR1) && defined(UINT32_C) 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<typename _UIntType, size_t __m, size_t __pos1, size_t __sl1, size_t __sl2, size_t __sr1, size_t __sr2, uint32_t __msk1, uint32_t __msk2, uint32_t __msk3, uint32_t __msk4, uint32_t __parity1, uint32_t __parity2, uint32_t __parity3, uint32_t __parity4> 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"); template<typename _Sseq> using _If_seed_seq = typename std::enable_if<std::__detail::__is_seed_seq< _Sseq, simd_fast_mersenne_twister_engine, result_type>::value >::type; public: static constexpr size_t state_size = _M_nstate * (16 / sizeof(result_type)); static constexpr result_type default_seed = 5489u; // constructors and member functions simd_fast_mersenne_twister_engine() : simd_fast_mersenne_twister_engine(default_seed) { } explicit simd_fast_mersenne_twister_engine(result_type __sd) { seed(__sd); } template<typename _Sseq, typename = _If_seed_seq<_Sseq>> explicit simd_fast_mersenne_twister_engine(_Sseq& __q) { seed(__q); } void seed(result_type __sd = default_seed); template<typename _Sseq> _If_seed_seq<_Sseq> seed(_Sseq& __q); static constexpr result_type min() { return 0; } static constexpr result_type max() { return std::numeric_limits<result_type>::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<typename _UIntType_2, size_t __m_2, size_t __pos1_2, size_t __sl1_2, size_t __sl2_2, size_t __sr1_2, size_t __sr2_2, uint32_t __msk1_2, uint32_t __msk2_2, uint32_t __msk3_2, uint32_t __msk4_2, uint32_t __parity1_2, uint32_t __parity2_2, uint32_t __parity3_2, uint32_t __parity4_2> 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<typename _UIntType_2, size_t __m_2, size_t __pos1_2, size_t __sl1_2, size_t __sl2_2, size_t __sr1_2, size_t __sr2_2, uint32_t __msk1_2, uint32_t __msk2_2, uint32_t __msk3_2, uint32_t __msk4_2, uint32_t __parity1_2, uint32_t __parity2_2, uint32_t __parity3_2, uint32_t __parity4_2, typename _CharT, typename _Traits> 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<typename _UIntType_2, size_t __m_2, size_t __pos1_2, size_t __sl1_2, size_t __sl2_2, size_t __sr1_2, size_t __sr2_2, uint32_t __msk1_2, uint32_t __msk2_2, uint32_t __msk3_2, uint32_t __msk4_2, uint32_t __parity1_2, uint32_t __parity2_2, uint32_t __parity3_2, uint32_t __parity4_2, typename _CharT, typename _Traits> 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 #ifdef __ARM_NEON #ifdef __aarch64__ __Uint32x4_t _M_state[_M_nstate]; #endif #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<typename _UIntType, size_t __m, size_t __pos1, size_t __sl1, size_t __sl2, size_t __sr1, size_t __sr2, uint32_t __msk1, uint32_t __msk2, uint32_t __msk3, uint32_t __msk4, uint32_t __parity1, uint32_t __parity2, uint32_t __parity3, uint32_t __parity4> 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<uint32_t, 607, 2, 15, 3, 13, 3, 0xfdff37ffU, 0xef7f3f7dU, 0xff777b7dU, 0x7ff7fb2fU, 0x00000001U, 0x00000000U, 0x00000000U, 0x5986f054U> sfmt607; typedef simd_fast_mersenne_twister_engine<uint64_t, 607, 2, 15, 3, 13, 3, 0xfdff37ffU, 0xef7f3f7dU, 0xff777b7dU, 0x7ff7fb2fU, 0x00000001U, 0x00000000U, 0x00000000U, 0x5986f054U> sfmt607_64; typedef simd_fast_mersenne_twister_engine<uint32_t, 1279, 7, 14, 3, 5, 1, 0xf7fefffdU, 0x7fefcfffU, 0xaff3ef3fU, 0xb5ffff7fU, 0x00000001U, 0x00000000U, 0x00000000U, 0x20000000U> sfmt1279; typedef simd_fast_mersenne_twister_engine<uint64_t, 1279, 7, 14, 3, 5, 1, 0xf7fefffdU, 0x7fefcfffU, 0xaff3ef3fU, 0xb5ffff7fU, 0x00000001U, 0x00000000U, 0x00000000U, 0x20000000U> sfmt1279_64; typedef simd_fast_mersenne_twister_engine<uint32_t, 2281, 12, 19, 1, 5, 1, 0xbff7ffbfU, 0xfdfffffeU, 0xf7ffef7fU, 0xf2f7cbbfU, 0x00000001U, 0x00000000U, 0x00000000U, 0x41dfa600U> sfmt2281; typedef simd_fast_mersenne_twister_engine<uint64_t, 2281, 12, 19, 1, 5, 1, 0xbff7ffbfU, 0xfdfffffeU, 0xf7ffef7fU, 0xf2f7cbbfU, 0x00000001U, 0x00000000U, 0x00000000U, 0x41dfa600U> sfmt2281_64; typedef simd_fast_mersenne_twister_engine<uint32_t, 4253, 17, 20, 1, 7, 1, 0x9f7bffffU, 0x9fffff5fU, 0x3efffffbU, 0xfffff7bbU, 0xa8000001U, 0xaf5390a3U, 0xb740b3f8U, 0x6c11486dU> sfmt4253; typedef simd_fast_mersenne_twister_engine<uint64_t, 4253, 17, 20, 1, 7, 1, 0x9f7bffffU, 0x9fffff5fU, 0x3efffffbU, 0xfffff7bbU, 0xa8000001U, 0xaf5390a3U, 0xb740b3f8U, 0x6c11486dU> sfmt4253_64; typedef simd_fast_mersenne_twister_engine<uint32_t, 11213, 68, 14, 3, 7, 3, 0xeffff7fbU, 0xffffffefU, 0xdfdfbfffU, 0x7fffdbfdU, 0x00000001U, 0x00000000U, 0xe8148000U, 0xd0c7afa3U> sfmt11213; typedef simd_fast_mersenne_twister_engine<uint64_t, 11213, 68, 14, 3, 7, 3, 0xeffff7fbU, 0xffffffefU, 0xdfdfbfffU, 0x7fffdbfdU, 0x00000001U, 0x00000000U, 0xe8148000U, 0xd0c7afa3U> sfmt11213_64; typedef simd_fast_mersenne_twister_engine<uint32_t, 19937, 122, 18, 1, 11, 1, 0xdfffffefU, 0xddfecb7fU, 0xbffaffffU, 0xbffffff6U, 0x00000001U, 0x00000000U, 0x00000000U, 0x13c9e684U> sfmt19937; typedef simd_fast_mersenne_twister_engine<uint64_t, 19937, 122, 18, 1, 11, 1, 0xdfffffefU, 0xddfecb7fU, 0xbffaffffU, 0xbffffff6U, 0x00000001U, 0x00000000U, 0x00000000U, 0x13c9e684U> sfmt19937_64; typedef simd_fast_mersenne_twister_engine<uint32_t, 44497, 330, 5, 3, 9, 3, 0xeffffffbU, 0xdfbebfffU, 0xbfbf7befU, 0x9ffd7bffU, 0x00000001U, 0x00000000U, 0xa3ac4000U, 0xecc1327aU> sfmt44497; typedef simd_fast_mersenne_twister_engine<uint64_t, 44497, 330, 5, 3, 9, 3, 0xeffffffbU, 0xdfbebfffU, 0xbfbf7befU, 0x9ffd7bffU, 0x00000001U, 0x00000000U, 0xa3ac4000U, 0xecc1327aU> sfmt44497_64; typedef simd_fast_mersenne_twister_engine<uint32_t, 86243, 366, 6, 7, 19, 1, 0xfdbffbffU, 0xbff7ff3fU, 0xfd77efffU, 0xbf9ff3ffU, 0x00000001U, 0x00000000U, 0x00000000U, 0xe9528d85U> sfmt86243; typedef simd_fast_mersenne_twister_engine<uint64_t, 86243, 366, 6, 7, 19, 1, 0xfdbffbffU, 0xbff7ff3fU, 0xfd77efffU, 0xbf9ff3ffU, 0x00000001U, 0x00000000U, 0x00000000U, 0xe9528d85U> sfmt86243_64; typedef simd_fast_mersenne_twister_engine<uint32_t, 132049, 110, 19, 1, 21, 1, 0xffffbb5fU, 0xfb6ebf95U, 0xfffefffaU, 0xcff77fffU, 0x00000001U, 0x00000000U, 0xcb520000U, 0xc7e91c7dU> sfmt132049; typedef simd_fast_mersenne_twister_engine<uint64_t, 132049, 110, 19, 1, 21, 1, 0xffffbb5fU, 0xfb6ebf95U, 0xfffefffaU, 0xcff77fffU, 0x00000001U, 0x00000000U, 0xcb520000U, 0xc7e91c7dU> sfmt132049_64; typedef simd_fast_mersenne_twister_engine<uint32_t, 216091, 627, 11, 3, 10, 1, 0xbff7bff7U, 0xbfffffffU, 0xbffffa7fU, 0xffddfbfbU, 0xf8000001U, 0x89e80709U, 0x3bd2b64bU, 0x0c64b1e4U> sfmt216091; typedef simd_fast_mersenne_twister_engine<uint64_t, 216091, 627, 11, 3, 10, 1, 0xbff7bff7U, 0xbfffffffU, 0xbffffa7fU, 0xffddfbfbU, 0xf8000001U, 0x89e80709U, 0x3bd2b64bU, 0x0c64b1e4U> 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<typename _RealType = double> 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>; param_type() : param_type(1) { } explicit param_type(_RealType __alpha_val, _RealType __beta_val = _RealType(1)) : _M_alpha(__alpha_val), _M_beta(__beta_val) { __glibcxx_assert(_M_alpha > _RealType(0)); __glibcxx_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); } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } private: void _M_initialize(); _RealType _M_alpha; _RealType _M_beta; }; public: beta_distribution() : beta_distribution(1.0) { } /** * @brief Constructs a beta distribution with parameters * @f$\alpha@f$ and @f$\beta@f$. */ explicit beta_distribution(_RealType __alpha_val, _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<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template<typename _UniformRandomNumberGenerator> 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<typename _RealType1, typename _CharT, typename _Traits> 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<typename _RealType1, typename _CharT, typename _Traits> friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::beta_distribution<_RealType1>& __x); private: template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> 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<typename _RealType> 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<std::size_t _Dimen, typename _RealType = double> 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<typename _ForwardIterator1, typename _ForwardIterator2> 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 { __glibcxx_assert(__dist == _Dimen); _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 { __glibcxx_assert(__varcov.size() == _Dimen); _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; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } private: template <typename _InputIterator1, typename _InputIterator2> void _M_init_full(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varcovbegin, _InputIterator2 __varcovend); template <typename _InputIterator1, typename _InputIterator2> void _M_init_lower(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varcovbegin, _InputIterator2 __varcovend); template <typename _InputIterator1, typename _InputIterator2> void _M_init_diagonal(_InputIterator1 __meanbegin, _InputIterator1 __meanend, _InputIterator2 __varbegin, _InputIterator2 __varend); // 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<size_t _Dimen1, typename _RealType1, typename _CharT, typename _Traits> 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) {} std::array<_RealType, _Dimen> _M_mean; std::array<_RealType, _M_t_size> _M_t; }; public: normal_mv_distribution() : _M_param(), _M_nd() { } template<typename _ForwardIterator1, typename _ForwardIterator2> 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<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { return this->__generate_impl(__f, __t, __urng, _M_param); } template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> 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<size_t _Dimen1, typename _RealType1> 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<size_t _Dimen1, typename _RealType1, typename _CharT, typename _Traits> 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<size_t _Dimen1, typename _RealType1, typename _CharT, typename _Traits> friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::normal_mv_distribution<_Dimen1, _RealType1>& __x); private: template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> 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<size_t _Dimen, typename _RealType> 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$\sigma > 0@f$. * * <table border=1 cellpadding=10 cellspacing=0> * <caption align=top>Distribution Statistics</caption> * <tr><td>Mean</td><td>@f$\sqrt{\pi/2}L_{1/2}(-\nu^2/2\sigma^2)@f$</td></tr> * <tr><td>Variance</td><td>@f$2\sigma^2 + \nu^2 * + (\pi\sigma^2/2)L^2_{1/2}(-\nu^2/2\sigma^2)@f$</td></tr> * <tr><td>Range</td><td>@f$[0, \infty)@f$</td></tr> * </table> * where @f$L_{1/2}(x)@f$ is the Laguerre polynomial of order 1/2. */ template<typename _RealType = double> 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<result_type> distribution_type; param_type() : param_type(0) { } param_type(result_type __nu_val, result_type __sigma_val = result_type(1)) : _M_nu(__nu_val), _M_sigma(__sigma_val) { __glibcxx_assert(_M_nu >= result_type(0)); __glibcxx_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; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } private: void _M_initialize(); result_type _M_nu; result_type _M_sigma; }; /** * @brief Constructors. * @{ */ rice_distribution() : rice_distribution(0) { } explicit rice_distribution(result_type __nu_val, 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<result_type>::max(); } /** * @brief Generating functions. */ template<typename _UniformRandomNumberGenerator> 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<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { typename std::normal_distribution<result_type>::param_type __px(__p.nu(), __p.sigma()), __py(result_type(0), __p.sigma()); result_type __x = this->_M_ndx(__px, __urng); result_type __y = this->_M_ndy(__py, __urng); #if _GLIBCXX_USE_C99_MATH_TR1 return std::hypot(__x, __y); #else return std::sqrt(__x * __x + __y * __y); #endif } template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template<typename _UniformRandomNumberGenerator> 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<typename _RealType1, typename _CharT, typename _Traits> 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<typename _RealType1, typename _CharT, typename _Traits> friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, rice_distribution<_RealType1>&); private: template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::normal_distribution<result_type> _M_ndx; std::normal_distribution<result_type> _M_ndy; }; /** * @brief Return true if two Rice distributions are not equal. */ template<typename _RealType1> 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<typename _RealType = double> 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<result_type> distribution_type; param_type() : param_type(1) { } param_type(result_type __mu_val, result_type __omega_val = result_type(1)) : _M_mu(__mu_val), _M_omega(__omega_val) { __glibcxx_assert(_M_mu >= result_type(0.5L)); __glibcxx_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; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } private: void _M_initialize(); result_type _M_mu; result_type _M_omega; }; /** * @brief Constructors. * @{ */ nakagami_distribution() : nakagami_distribution(1) { } explicit nakagami_distribution(result_type __mu_val, 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<result_type>::max(); } /** * @brief Generating functions. */ template<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng) { return std::sqrt(this->_M_gd(__urng)); } template<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { typename std::gamma_distribution<result_type>::param_type __pg(__p.mu(), __p.omega() / __p.mu()); return std::sqrt(this->_M_gd(__pg, __urng)); } template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template<typename _UniformRandomNumberGenerator> 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<typename _RealType1, typename _CharT, typename _Traits> 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<typename _RealType1, typename _CharT, typename _Traits> friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, nakagami_distribution<_RealType1>&); private: template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::gamma_distribution<result_type> _M_gd; }; /** * @brief Return true if two Nakagami distributions are not equal. */ template<typename _RealType> 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$\alpha > 0@f$. * * <table border=1 cellpadding=10 cellspacing=0> * <caption align=top>Distribution Statistics</caption> * <tr><td>Mean</td><td>@f$\alpha \mu / (\alpha - 1)@f$ * for @f$\alpha > 1@f$</td></tr> * <tr><td>Variance</td><td>@f$\alpha \mu^2 / [(\alpha - 1)^2(\alpha - 2)]@f$ * for @f$\alpha > 2@f$</td></tr> * <tr><td>Range</td><td>@f$[\mu, \infty)@f$</td></tr> * </table> */ template<typename _RealType = double> 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<result_type> distribution_type; param_type() : param_type(1) { } param_type(result_type __alpha_val, result_type __mu_val = result_type(1)) : _M_alpha(__alpha_val), _M_mu(__mu_val) { __glibcxx_assert(_M_alpha > result_type(0)); __glibcxx_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; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } private: void _M_initialize(); result_type _M_alpha; result_type _M_mu; }; /** * @brief Constructors. * @{ */ pareto_distribution() : pareto_distribution(1) { } explicit pareto_distribution(result_type __alpha_val, 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<result_type>::max(); } /** * @brief Generating functions. */ template<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->mu() * std::pow(this->_M_ud(__urng), -result_type(1) / this->alpha()); } template<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { return __p.mu() * std::pow(this->_M_ud(__urng), -result_type(1) / __p.alpha()); } template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template<typename _UniformRandomNumberGenerator> 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<typename _RealType1, typename _CharT, typename _Traits> 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<typename _RealType1, typename _CharT, typename _Traits> friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, pareto_distribution<_RealType1>&); private: template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::uniform_real_distribution<result_type> _M_ud; }; /** * @brief Return true if two Pareto distributions are not equal. */ template<typename _RealType> 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$\nu > 0@f$. * * <table border=1 cellpadding=10 cellspacing=0> * <caption align=top>Distribution Statistics</caption> * <tr><td>Mean</td><td>@f$\mu@f$</td></tr> * <tr><td>Variance</td><td>@f$\mu^2\frac{\lambda + \nu + 1}{\lambda\nu}@f$</td></tr> * <tr><td>Range</td><td>@f$[0, \infty)@f$</td></tr> * </table> */ template<typename _RealType = double> 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<result_type> distribution_type; param_type() : param_type(1) { } param_type(result_type __lambda_val, 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_assert(_M_lambda > result_type(0)); __glibcxx_assert(_M_mu > result_type(0)); __glibcxx_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; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } private: void _M_initialize(); result_type _M_lambda; result_type _M_mu; result_type _M_nu; }; /** * @brief Constructors. * @{ */ k_distribution() : k_distribution(1) { } explicit k_distribution(result_type __lambda_val, 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<result_type>::max(); } /** * @brief Generating functions. */ template<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator&); template<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator&, const param_type&); template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template<typename _UniformRandomNumberGenerator> 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<typename _RealType1, typename _CharT, typename _Traits> 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<typename _RealType1, typename _CharT, typename _Traits> friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, k_distribution<_RealType1>&); private: template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::gamma_distribution<result_type> _M_gd1; std::gamma_distribution<result_type> _M_gd2; }; /** * @brief Return true if two K distributions are not equal. */ template<typename _RealType> 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$x <= b@f$. * * <table border=1 cellpadding=10 cellspacing=0> * <caption align=top>Distribution Statistics</caption> * <tr><td>Mean</td><td>@f$ (a + b) / 2 @f$</td></tr> * <tr><td>Variance</td><td>@f$ (b - a)^2 / 8 @f$</td></tr> * <tr><td>Range</td><td>@f$[a, b]@f$</td></tr> * </table> */ template<typename _RealType = double> 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<result_type> distribution_type; param_type() : param_type(0) { } param_type(result_type __a, result_type __b = result_type(1)) : _M_a(__a), _M_b(__b) { __glibcxx_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; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } private: void _M_initialize(); result_type _M_a; result_type _M_b; }; /** * @brief Constructors. * :{ */ arcsine_distribution() : arcsine_distribution(0) { } explicit arcsine_distribution(result_type __a, 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<typename _UniformRandomNumberGenerator> 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<typename _UniformRandomNumberGenerator> 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<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template<typename _UniformRandomNumberGenerator> 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<typename _RealType1, typename _CharT, typename _Traits> 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<typename _RealType1, typename _CharT, typename _Traits> friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, arcsine_distribution<_RealType1>&); private: template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; std::uniform_real_distribution<result_type> _M_ud; }; /** * @brief Return true if two arcsine distributions are not equal. */ template<typename _RealType> 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$0 < q < 1@f$. * * <table border=1 cellpadding=10 cellspacing=0> * <caption align=top>Distribution Statistics</caption> * <tr><td>Mean</td><td>@f$ \sqrt{\frac{2}{\pi}} \sqrt{\frac{\omega}{1 + q^2}} * E(1 - q^2) @f$</td></tr> * <tr><td>Variance</td><td>@f$ \omega \left(1 - \frac{2E^2(1 - q^2)} * {\pi (1 + q^2)}\right) @f$</td></tr> * <tr><td>Range</td><td>@f$[0, \infty)@f$</td></tr> * </table> * where @f$E(x)@f$ is the elliptic function of the second kind. */ template<typename _RealType = double> 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<result_type> distribution_type; param_type() : param_type(0.5) { } param_type(result_type __q, result_type __omega = result_type(1)) : _M_q(__q), _M_omega(__omega) { __glibcxx_assert(_M_q > result_type(0)); __glibcxx_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; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } private: void _M_initialize(); result_type _M_q; result_type _M_omega; }; /** * @brief Constructors. * @{ */ hoyt_distribution() : hoyt_distribution(0.5) { } explicit hoyt_distribution(result_type __q, 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<result_type>::max(); } /** * @brief Generating functions. */ template<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng); template<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template<typename _UniformRandomNumberGenerator> 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<typename _RealType1, typename _CharT, typename _Traits> 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<typename _RealType1, typename _CharT, typename _Traits> friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, hoyt_distribution<_RealType1>&); private: template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p); param_type _M_param; __gnu_cxx::arcsine_distribution<result_type> _M_ad; std::exponential_distribution<result_type> _M_ed; }; /** * @brief Return true if two Hoyt distributions are not equal. */ template<typename _RealType> 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] * * <table border=1 cellpadding=10 cellspacing=0> * <caption align=top>Distribution Statistics</caption> * <tr><td>Mean</td><td>@f$ \frac{a+b+c}{2} @f$</td></tr> * <tr><td>Variance</td><td>@f$ \frac{a^2+b^2+c^2-ab-ac-bc} * {18}@f$</td></tr> * <tr><td>Range</td><td>@f$[a, c]@f$</td></tr> * </table> */ template<typename _RealType = double> class triangular_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { friend class triangular_distribution<_RealType>; param_type() : param_type(0) { } explicit param_type(_RealType __a, _RealType __b = _RealType(0.5), _RealType __c = _RealType(1)) : _M_a(__a), _M_b(__b), _M_c(__c) { __glibcxx_assert(_M_a <= _M_b); __glibcxx_assert(_M_b <= _M_c); __glibcxx_assert(_M_a < _M_c); _M_r_ab = (_M_b - _M_a) / (_M_c - _M_a); _M_f_ab_ac = (_M_b - _M_a) * (_M_c - _M_a); _M_f_bc_ac = (_M_c - _M_b) * (_M_c - _M_a); } _RealType a() const { return _M_a; } _RealType b() const { return _M_b; } _RealType c() const { return _M_c; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return (__p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b && __p1._M_c == __p2._M_c); } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } private: _RealType _M_a; _RealType _M_b; _RealType _M_c; _RealType _M_r_ab; _RealType _M_f_ab_ac; _RealType _M_f_bc_ac; }; triangular_distribution() : triangular_distribution(0.0) { } /** * @brief Constructs a triangle distribution with parameters * @f$ a @f$, @f$ b @f$ and @f$ c @f$. */ explicit triangular_distribution(result_type __a, result_type __b = result_type(0.5), result_type __c = result_type(1)) : _M_param(__a, __b, __c) { } explicit triangular_distribution(const param_type& __p) : _M_param(__p) { } /** * @brief Resets the distribution state. */ void reset() { } /** * @brief Returns the @f$ 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<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template<typename _UniformRandomNumberGenerator> 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<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template<typename _UniformRandomNumberGenerator> 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<typename _RealType1, typename _CharT, typename _Traits> 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<typename _RealType1, typename _CharT, typename _Traits> friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::triangular_distribution<_RealType1>& __x); private: template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> 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<typename _RealType> 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. * * <table border=1 cellpadding=10 cellspacing=0> * <caption align=top>Distribution Statistics</caption> * <tr><td>Mean</td><td>@f$ \mu @f$</td></tr> * <tr><td>Variance</td><td>@f$ 1-I_1(\kappa)/I_0(\kappa) @f$</td></tr> * <tr><td>Range</td><td>@f$[-\pi, \pi]@f$</td></tr> * </table> */ template<typename _RealType = double> class von_mises_distribution { static_assert(std::is_floating_point<_RealType>::value, "template argument not a floating point type"); public: /** The type of the range of the distribution. */ typedef _RealType result_type; /** Parameter type. */ struct param_type { friend class von_mises_distribution<_RealType>; param_type() : param_type(0) { } explicit param_type(_RealType __mu, _RealType __kappa = _RealType(1)) : _M_mu(__mu), _M_kappa(__kappa) { const _RealType __pi = __gnu_cxx::__math_constants<_RealType>::__pi; __glibcxx_assert(_M_mu >= -__pi && _M_mu <= __pi); __glibcxx_assert(_M_kappa >= _RealType(0)); auto __tau = std::sqrt(_RealType(4) * _M_kappa * _M_kappa + _RealType(1)) + _RealType(1); auto __rho = ((__tau - std::sqrt(_RealType(2) * __tau)) / (_RealType(2) * _M_kappa)); _M_r = (_RealType(1) + __rho * __rho) / (_RealType(2) * __rho); } _RealType mu() const { return _M_mu; } _RealType kappa() const { return _M_kappa; } friend bool operator==(const param_type& __p1, const param_type& __p2) { return __p1._M_mu == __p2._M_mu && __p1._M_kappa == __p2._M_kappa; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } private: _RealType _M_mu; _RealType _M_kappa; _RealType _M_r; }; von_mises_distribution() : von_mises_distribution(0.0) { } /** * @brief Constructs a von Mises distribution with parameters * @f$\mu@f$ and @f$\kappa@f$. */ explicit von_mises_distribution(result_type __mu, 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<result_type>::__pi; } /** * @brief Returns the least upper bound value of the distribution. */ result_type max() const { return __gnu_cxx::__math_constants<result_type>::__pi; } /** * @brief Generating functions. */ template<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, _M_param); } template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template<typename _UniformRandomNumberGenerator> 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<typename _RealType1, typename _CharT, typename _Traits> 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<typename _RealType1, typename _CharT, typename _Traits> friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::von_mises_distribution<_RealType1>& __x); private: template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> 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<typename _RealType> inline bool operator!=(const __gnu_cxx::von_mises_distribution<_RealType>& __d1, const __gnu_cxx::von_mises_distribution<_RealType>& __d2) { 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. * * <table border=1 cellpadding=10 cellspacing=0> * <caption align=top>Distribution Statistics</caption> * <tr><td>Mean</td><td>@f$ n\frac{K}{N} @f$</td></tr> * <tr><td>Variance</td><td>@f$ n\frac{K}{N}\frac{N-K}{N}\frac{N-n}{N-1} * @f$</td></tr> * <tr><td>Range</td><td>@f$[max(0, n+K-N), min(K, n)]@f$</td></tr> * </table> */ template<typename _UIntType = unsigned int> 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>; param_type() : param_type(10) { } explicit param_type(result_type __N, result_type __K = 5, result_type __n = 1) : _M_N{__N}, _M_K{__K}, _M_n{__n} { __glibcxx_assert(_M_N >= _M_K); __glibcxx_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); } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } private: result_type _M_N; result_type _M_K; result_type _M_n; }; // constructors and member functions hypergeometric_distribution() : hypergeometric_distribution(10) { } explicit hypergeometric_distribution(result_type __N, 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<result_type>::type; return static_cast<result_type>(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<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, this->_M_param); } template<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, this->_M_param); } template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template<typename _UniformRandomNumberGenerator> 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<typename _UIntType1, typename _CharT, typename _Traits> 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<typename _UIntType1, typename _CharT, typename _Traits> friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::hypergeometric_distribution<_UIntType1>& __x); private: template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> 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<typename _UIntType> 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$. * * <table border=1 cellpadding=10 cellspacing=0> * <caption align=top>Distribution Statistics</caption> * <tr><td>Mean</td><td>@f$a@f$</td></tr> * <tr><td>Variance</td><td>@f$b^2\pi^2/3@f$</td></tr> * <tr><td>Range</td><td>@f$[0, \infty)@f$</td></tr> * </table> */ template<typename _RealType = double> 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<result_type> distribution_type; param_type() : param_type(0) { } explicit param_type(result_type __a, result_type __b = result_type(1)) : _M_a(__a), _M_b(__b) { __glibcxx_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; } friend bool operator!=(const param_type& __p1, const param_type& __p2) { return !(__p1 == __p2); } private: void _M_initialize(); result_type _M_a; result_type _M_b; }; /** * @brief Constructors. * @{ */ logistic_distribution() : logistic_distribution(0.0) { } explicit logistic_distribution(result_type __a, 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<result_type>::max(); } /** * @brief Returns the least upper bound value of the distribution. */ result_type max() const { return std::numeric_limits<result_type>::max(); } /** * @brief Generating functions. */ template<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, this->_M_param); } template<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator&, const param_type&); template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, this->param()); } template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template<typename _UniformRandomNumberGenerator> 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<typename _RealType1> 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<typename _RealType1, typename _CharT, typename _Traits> 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<typename _RealType1, typename _CharT, typename _Traits> friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>&, logistic_distribution<_RealType1>&); private: template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> 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<typename _RealType1> 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<std::size_t _Dimen, typename _RealType = double> 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 { param_type() { } friend bool operator==(const param_type&, const param_type&) { return true; } friend bool operator!=(const param_type&, const param_type&) { return false; } }; /** * @brief Constructs a uniform on sphere distribution. */ 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<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, this->param()); } template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template<typename _UniformRandomNumberGenerator> 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<size_t _Dimen1, typename _RealType1, typename _CharT, typename _Traits> 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<std::size_t _Dimen1, typename _RealType1, typename _CharT, typename _Traits> friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::uniform_on_sphere_distribution<_Dimen1, _RealType1>& __x); private: template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> 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<std::size_t _Dimen, typename _RealType> 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); } /** * @brief A distribution for random coordinates inside a unit sphere. */ template<std::size_t _Dimen, typename _RealType = double> 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>; param_type() : param_type(1.0) { } explicit param_type(_RealType __radius) : _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. * @{ */ uniform_inside_sphere_distribution() : uniform_inside_sphere_distribution(1.0) { } explicit uniform_inside_sphere_distribution(_RealType __radius) : _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<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng) { return this->operator()(__urng, _M_param); } template<typename _UniformRandomNumberGenerator> result_type operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p); template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { this->__generate(__f, __t, __urng, this->param()); } template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void __generate(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { this->__generate_impl(__f, __t, __urng, __p); } template<typename _UniformRandomNumberGenerator> 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<size_t _Dimen1, typename _RealType1, typename _CharT, typename _Traits> 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<std::size_t _Dimen1, typename _RealType1, typename _CharT, typename _Traits> friend std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, __gnu_cxx::uniform_inside_sphere_distribution<_Dimen1, _RealType1>&); private: template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> 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<std::size_t _Dimen, typename _RealType> 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); } _GLIBCXX_END_NAMESPACE_VERSION } // namespace __gnu_cxx #include <ext/opt_random.h> #include <ext/random.tcc> #endif // _GLIBCXX_USE_C99_STDINT_TR1 && UINT32_C #endif // C++11 #endif // _EXT_RANDOM

./Ninja\.