// random number generation (out of line) -*- C++ -*- // Copyright (C) 2009-2020 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 bits/random.tcc * This is an internal header file, included by other library headers. * Do not attempt to use it directly. @headername{random} */ #ifndef _RANDOM_TCC #define _RANDOM_TCC 1 #include <numeric> // std::accumulate and std::partial_sum namespace std _GLIBCXX_VISIBILITY(default) { _GLIBCXX_BEGIN_NAMESPACE_VERSION /* * (Further) implementation-space details. */ namespace __detail { // General case for x = (ax + c) mod m -- use Schrage's algorithm // to avoid integer overflow. // // Preconditions: a > 0, m > 0. // // Note: only works correctly for __m % __a < __m / __a. template<typename _Tp, _Tp __m, _Tp __a, _Tp __c> _Tp _Mod<_Tp, __m, __a, __c, false, true>:: __calc(_Tp __x) { if (__a == 1) __x %= __m; else { static const _Tp __q = __m / __a; static const _Tp __r = __m % __a; _Tp __t1 = __a * (__x % __q); _Tp __t2 = __r * (__x / __q); if (__t1 >= __t2) __x = __t1 - __t2; else __x = __m - __t2 + __t1; } if (__c != 0) { const _Tp __d = __m - __x; if (__d > __c) __x += __c; else __x = __c - __d; } return __x; } template<typename _InputIterator, typename _OutputIterator, typename _Tp> _OutputIterator __normalize(_InputIterator __first, _InputIterator __last, _OutputIterator __result, const _Tp& __factor) { for (; __first != __last; ++__first, ++__result) *__result = *__first / __factor; return __result; } } // namespace __detail template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> constexpr _UIntType linear_congruential_engine<_UIntType, __a, __c, __m>::multiplier; template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> constexpr _UIntType linear_congruential_engine<_UIntType, __a, __c, __m>::increment; template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> constexpr _UIntType linear_congruential_engine<_UIntType, __a, __c, __m>::modulus; template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> constexpr _UIntType linear_congruential_engine<_UIntType, __a, __c, __m>::default_seed; /** * Seeds the LCR with integral value @p __s, adjusted so that the * ring identity is never a member of the convergence set. */ template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> void linear_congruential_engine<_UIntType, __a, __c, __m>:: seed(result_type __s) { if ((__detail::__mod<_UIntType, __m>(__c) == 0) && (__detail::__mod<_UIntType, __m>(__s) == 0)) _M_x = 1; else _M_x = __detail::__mod<_UIntType, __m>(__s); } /** * Seeds the LCR engine with a value generated by @p __q. */ template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m> template<typename _Sseq> auto linear_congruential_engine<_UIntType, __a, __c, __m>:: seed(_Sseq& __q) -> _If_seed_seq<_Sseq> { const _UIntType __k0 = __m == 0 ? std::numeric_limits<_UIntType>::digits : std::__lg(__m); const _UIntType __k = (__k0 + 31) / 32; uint_least32_t __arr[__k + 3]; __q.generate(__arr + 0, __arr + __k + 3); _UIntType __factor = 1u; _UIntType __sum = 0u; for (size_t __j = 0; __j < __k; ++__j) { __sum += __arr[__j + 3] * __factor; __factor *= __detail::_Shift<_UIntType, 32>::__value; } seed(__sum); } template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const linear_congruential_engine<_UIntType, __a, __c, __m>& __lcr) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); __os.fill(__os.widen(' ')); __os << __lcr._M_x; __os.flags(__flags); __os.fill(__fill); return __os; } template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, linear_congruential_engine<_UIntType, __a, __c, __m>& __lcr) { using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec); __is >> __lcr._M_x; __is.flags(__flags); return __is; } template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f> constexpr size_t mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>::word_size; template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f> constexpr size_t mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>::state_size; template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f> constexpr size_t mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>::shift_size; template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f> constexpr size_t mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>::mask_bits; template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f> constexpr _UIntType mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>::xor_mask; template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f> constexpr size_t mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>::tempering_u; template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f> constexpr _UIntType mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>::tempering_d; template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f> constexpr size_t mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>::tempering_s; template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f> constexpr _UIntType mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>::tempering_b; template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f> constexpr size_t mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>::tempering_t; template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f> constexpr _UIntType mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>::tempering_c; template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f> constexpr size_t mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>::tempering_l; template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f> constexpr _UIntType mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>:: initialization_multiplier; template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f> constexpr _UIntType mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>::default_seed; template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f> void mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>:: seed(result_type __sd) { _M_x[0] = __detail::__mod<_UIntType, __detail::_Shift<_UIntType, __w>::__value>(__sd); for (size_t __i = 1; __i < state_size; ++__i) { _UIntType __x = _M_x[__i - 1]; __x ^= __x >> (__w - 2); __x *= __f; __x += __detail::__mod<_UIntType, __n>(__i); _M_x[__i] = __detail::__mod<_UIntType, __detail::_Shift<_UIntType, __w>::__value>(__x); } _M_p = state_size; } template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f> template<typename _Sseq> auto mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>:: seed(_Sseq& __q) -> _If_seed_seq<_Sseq> { const _UIntType __upper_mask = (~_UIntType()) << __r; const size_t __k = (__w + 31) / 32; uint_least32_t __arr[__n * __k]; __q.generate(__arr + 0, __arr + __n * __k); bool __zero = true; for (size_t __i = 0; __i < state_size; ++__i) { _UIntType __factor = 1u; _UIntType __sum = 0u; for (size_t __j = 0; __j < __k; ++__j) { __sum += __arr[__k * __i + __j] * __factor; __factor *= __detail::_Shift<_UIntType, 32>::__value; } _M_x[__i] = __detail::__mod<_UIntType, __detail::_Shift<_UIntType, __w>::__value>(__sum); if (__zero) { if (__i == 0) { if ((_M_x[0] & __upper_mask) != 0u) __zero = false; } else if (_M_x[__i] != 0u) __zero = false; } } if (__zero) _M_x[0] = __detail::_Shift<_UIntType, __w - 1>::__value; _M_p = state_size; } template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f> void mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>:: _M_gen_rand(void) { const _UIntType __upper_mask = (~_UIntType()) << __r; const _UIntType __lower_mask = ~__upper_mask; for (size_t __k = 0; __k < (__n - __m); ++__k) { _UIntType __y = ((_M_x[__k] & __upper_mask) | (_M_x[__k + 1] & __lower_mask)); _M_x[__k] = (_M_x[__k + __m] ^ (__y >> 1) ^ ((__y & 0x01) ? __a : 0)); } for (size_t __k = (__n - __m); __k < (__n - 1); ++__k) { _UIntType __y = ((_M_x[__k] & __upper_mask) | (_M_x[__k + 1] & __lower_mask)); _M_x[__k] = (_M_x[__k + (__m - __n)] ^ (__y >> 1) ^ ((__y & 0x01) ? __a : 0)); } _UIntType __y = ((_M_x[__n - 1] & __upper_mask) | (_M_x[0] & __lower_mask)); _M_x[__n - 1] = (_M_x[__m - 1] ^ (__y >> 1) ^ ((__y & 0x01) ? __a : 0)); _M_p = 0; } template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f> void mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>:: discard(unsigned long long __z) { while (__z > state_size - _M_p) { __z -= state_size - _M_p; _M_gen_rand(); } _M_p += __z; } template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f> typename mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>::result_type mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>:: operator()() { // Reload the vector - cost is O(n) amortized over n calls. if (_M_p >= state_size) _M_gen_rand(); // Calculate o(x(i)). result_type __z = _M_x[_M_p++]; __z ^= (__z >> __u) & __d; __z ^= (__z << __s) & __b; __z ^= (__z << __t) & __c; __z ^= (__z >> __l); return __z; } template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); __os.fill(__space); for (size_t __i = 0; __i < __n; ++__i) __os << __x._M_x[__i] << __space; __os << __x._M_p; __os.flags(__flags); __os.fill(__fill); return __os; } template<typename _UIntType, size_t __w, size_t __n, size_t __m, size_t __r, _UIntType __a, size_t __u, _UIntType __d, size_t __s, _UIntType __b, size_t __t, _UIntType __c, size_t __l, _UIntType __f, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x) { using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); for (size_t __i = 0; __i < __n; ++__i) __is >> __x._M_x[__i]; __is >> __x._M_p; __is.flags(__flags); return __is; } template<typename _UIntType, size_t __w, size_t __s, size_t __r> constexpr size_t subtract_with_carry_engine<_UIntType, __w, __s, __r>::word_size; template<typename _UIntType, size_t __w, size_t __s, size_t __r> constexpr size_t subtract_with_carry_engine<_UIntType, __w, __s, __r>::short_lag; template<typename _UIntType, size_t __w, size_t __s, size_t __r> constexpr size_t subtract_with_carry_engine<_UIntType, __w, __s, __r>::long_lag; template<typename _UIntType, size_t __w, size_t __s, size_t __r> constexpr _UIntType subtract_with_carry_engine<_UIntType, __w, __s, __r>::default_seed; template<typename _UIntType, size_t __w, size_t __s, size_t __r> void subtract_with_carry_engine<_UIntType, __w, __s, __r>:: seed(result_type __value) { std::linear_congruential_engine<result_type, 40014u, 0u, 2147483563u> __lcg(__value == 0u ? default_seed : __value); const size_t __n = (__w + 31) / 32; for (size_t __i = 0; __i < long_lag; ++__i) { _UIntType __sum = 0u; _UIntType __factor = 1u; for (size_t __j = 0; __j < __n; ++__j) { __sum += __detail::__mod<uint_least32_t, __detail::_Shift<uint_least32_t, 32>::__value> (__lcg()) * __factor; __factor *= __detail::_Shift<_UIntType, 32>::__value; } _M_x[__i] = __detail::__mod<_UIntType, __detail::_Shift<_UIntType, __w>::__value>(__sum); } _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0; _M_p = 0; } template<typename _UIntType, size_t __w, size_t __s, size_t __r> template<typename _Sseq> auto subtract_with_carry_engine<_UIntType, __w, __s, __r>:: seed(_Sseq& __q) -> _If_seed_seq<_Sseq> { const size_t __k = (__w + 31) / 32; uint_least32_t __arr[__r * __k]; __q.generate(__arr + 0, __arr + __r * __k); for (size_t __i = 0; __i < long_lag; ++__i) { _UIntType __sum = 0u; _UIntType __factor = 1u; for (size_t __j = 0; __j < __k; ++__j) { __sum += __arr[__k * __i + __j] * __factor; __factor *= __detail::_Shift<_UIntType, 32>::__value; } _M_x[__i] = __detail::__mod<_UIntType, __detail::_Shift<_UIntType, __w>::__value>(__sum); } _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0; _M_p = 0; } template<typename _UIntType, size_t __w, size_t __s, size_t __r> typename subtract_with_carry_engine<_UIntType, __w, __s, __r>:: result_type subtract_with_carry_engine<_UIntType, __w, __s, __r>:: operator()() { // Derive short lag index from current index. long __ps = _M_p - short_lag; if (__ps < 0) __ps += long_lag; // Calculate new x(i) without overflow or division. // NB: Thanks to the requirements for _UIntType, _M_x[_M_p] + _M_carry // cannot overflow. _UIntType __xi; if (_M_x[__ps] >= _M_x[_M_p] + _M_carry) { __xi = _M_x[__ps] - _M_x[_M_p] - _M_carry; _M_carry = 0; } else { __xi = (__detail::_Shift<_UIntType, __w>::__value - _M_x[_M_p] - _M_carry + _M_x[__ps]); _M_carry = 1; } _M_x[_M_p] = __xi; // Adjust current index to loop around in ring buffer. if (++_M_p >= long_lag) _M_p = 0; return __xi; } template<typename _UIntType, size_t __w, size_t __s, size_t __r, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const subtract_with_carry_engine<_UIntType, __w, __s, __r>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); __os.fill(__space); for (size_t __i = 0; __i < __r; ++__i) __os << __x._M_x[__i] << __space; __os << __x._M_carry << __space << __x._M_p; __os.flags(__flags); __os.fill(__fill); return __os; } template<typename _UIntType, size_t __w, size_t __s, size_t __r, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, subtract_with_carry_engine<_UIntType, __w, __s, __r>& __x) { using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); for (size_t __i = 0; __i < __r; ++__i) __is >> __x._M_x[__i]; __is >> __x._M_carry; __is >> __x._M_p; __is.flags(__flags); return __is; } template<typename _RandomNumberEngine, size_t __p, size_t __r> constexpr size_t discard_block_engine<_RandomNumberEngine, __p, __r>::block_size; template<typename _RandomNumberEngine, size_t __p, size_t __r> constexpr size_t discard_block_engine<_RandomNumberEngine, __p, __r>::used_block; template<typename _RandomNumberEngine, size_t __p, size_t __r> typename discard_block_engine<_RandomNumberEngine, __p, __r>::result_type discard_block_engine<_RandomNumberEngine, __p, __r>:: operator()() { if (_M_n >= used_block) { _M_b.discard(block_size - _M_n); _M_n = 0; } ++_M_n; return _M_b(); } template<typename _RandomNumberEngine, size_t __p, size_t __r, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const discard_block_engine<_RandomNumberEngine, __p, __r>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); __os.fill(__space); __os << __x.base() << __space << __x._M_n; __os.flags(__flags); __os.fill(__fill); return __os; } template<typename _RandomNumberEngine, size_t __p, size_t __r, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, discard_block_engine<_RandomNumberEngine, __p, __r>& __x) { using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); __is >> __x._M_b >> __x._M_n; __is.flags(__flags); return __is; } template<typename _RandomNumberEngine, size_t __w, typename _UIntType> typename independent_bits_engine<_RandomNumberEngine, __w, _UIntType>:: result_type independent_bits_engine<_RandomNumberEngine, __w, _UIntType>:: operator()() { typedef typename _RandomNumberEngine::result_type _Eresult_type; const _Eresult_type __r = (_M_b.max() - _M_b.min() < std::numeric_limits<_Eresult_type>::max() ? _M_b.max() - _M_b.min() + 1 : 0); const unsigned __edig = std::numeric_limits<_Eresult_type>::digits; const unsigned __m = __r ? std::__lg(__r) : __edig; typedef typename std::common_type<_Eresult_type, result_type>::type __ctype; const unsigned __cdig = std::numeric_limits<__ctype>::digits; unsigned __n, __n0; __ctype __s0, __s1, __y0, __y1; for (size_t __i = 0; __i < 2; ++__i) { __n = (__w + __m - 1) / __m + __i; __n0 = __n - __w % __n; const unsigned __w0 = __w / __n; // __w0 <= __m __s0 = 0; __s1 = 0; if (__w0 < __cdig) { __s0 = __ctype(1) << __w0; __s1 = __s0 << 1; } __y0 = 0; __y1 = 0; if (__r) { __y0 = __s0 * (__r / __s0); if (__s1) __y1 = __s1 * (__r / __s1); if (__r - __y0 <= __y0 / __n) break; } else break; } result_type __sum = 0; for (size_t __k = 0; __k < __n0; ++__k) { __ctype __u; do __u = _M_b() - _M_b.min(); while (__y0 && __u >= __y0); __sum = __s0 * __sum + (__s0 ? __u % __s0 : __u); } for (size_t __k = __n0; __k < __n; ++__k) { __ctype __u; do __u = _M_b() - _M_b.min(); while (__y1 && __u >= __y1); __sum = __s1 * __sum + (__s1 ? __u % __s1 : __u); } return __sum; } template<typename _RandomNumberEngine, size_t __k> constexpr size_t shuffle_order_engine<_RandomNumberEngine, __k>::table_size; template<typename _RandomNumberEngine, size_t __k> typename shuffle_order_engine<_RandomNumberEngine, __k>::result_type shuffle_order_engine<_RandomNumberEngine, __k>:: operator()() { size_t __j = __k * ((_M_y - _M_b.min()) / (_M_b.max() - _M_b.min() + 1.0L)); _M_y = _M_v[__j]; _M_v[__j] = _M_b(); return _M_y; } template<typename _RandomNumberEngine, size_t __k, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const shuffle_order_engine<_RandomNumberEngine, __k>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); __os.fill(__space); __os << __x.base(); for (size_t __i = 0; __i < __k; ++__i) __os << __space << __x._M_v[__i]; __os << __space << __x._M_y; __os.flags(__flags); __os.fill(__fill); return __os; } template<typename _RandomNumberEngine, size_t __k, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, shuffle_order_engine<_RandomNumberEngine, __k>& __x) { using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); __is >> __x._M_b; for (size_t __i = 0; __i < __k; ++__i) __is >> __x._M_v[__i]; __is >> __x._M_y; __is.flags(__flags); return __is; } template<typename _IntType, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const uniform_int_distribution<_IntType>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os << __x.a() << __space << __x.b(); __os.flags(__flags); __os.fill(__fill); return __os; } template<typename _IntType, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, uniform_int_distribution<_IntType>& __x) { using param_type = typename uniform_int_distribution<_IntType>::param_type; using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _IntType __a, __b; if (__is >> __a >> __b) __x.param(param_type(__a, __b)); __is.flags(__flags); return __is; } template<typename _RealType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void uniform_real_distribution<_RealType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); auto __range = __p.b() - __p.a(); while (__f != __t) *__f++ = __aurng() * __range + __p.a(); } template<typename _RealType, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const uniform_real_distribution<_RealType>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.a() << __space << __x.b(); __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template<typename _RealType, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, uniform_real_distribution<_RealType>& __x) { using param_type = typename uniform_real_distribution<_RealType>::param_type; using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::skipws); _RealType __a, __b; if (__is >> __a >> __b) __x.param(param_type(__a, __b)); __is.flags(__flags); return __is; } template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void std::bernoulli_distribution:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) __detail::_Adaptor<_UniformRandomNumberGenerator, double> __aurng(__urng); auto __limit = __p.p() * (__aurng.max() - __aurng.min()); while (__f != __t) *__f++ = (__aurng() - __aurng.min()) < __limit; } template<typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const bernoulli_distribution& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__os.widen(' ')); __os.precision(std::numeric_limits<double>::max_digits10); __os << __x.p(); __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template<typename _IntType> template<typename _UniformRandomNumberGenerator> typename geometric_distribution<_IntType>::result_type geometric_distribution<_IntType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __param) { // About the epsilon thing see this thread: // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html const double __naf = (1 - std::numeric_limits<double>::epsilon()) / 2; // The largest _RealType convertible to _IntType. const double __thr = std::numeric_limits<_IntType>::max() + __naf; __detail::_Adaptor<_UniformRandomNumberGenerator, double> __aurng(__urng); double __cand; do __cand = std::floor(std::log(1.0 - __aurng()) / __param._M_log_1_p); while (__cand >= __thr); return result_type(__cand + __naf); } template<typename _IntType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void geometric_distribution<_IntType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) // About the epsilon thing see this thread: // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html const double __naf = (1 - std::numeric_limits<double>::epsilon()) / 2; // The largest _RealType convertible to _IntType. const double __thr = std::numeric_limits<_IntType>::max() + __naf; __detail::_Adaptor<_UniformRandomNumberGenerator, double> __aurng(__urng); while (__f != __t) { double __cand; do __cand = std::floor(std::log(1.0 - __aurng()) / __param._M_log_1_p); while (__cand >= __thr); *__f++ = __cand + __naf; } } template<typename _IntType, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const geometric_distribution<_IntType>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__os.widen(' ')); __os.precision(std::numeric_limits<double>::max_digits10); __os << __x.p(); __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template<typename _IntType, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, geometric_distribution<_IntType>& __x) { using param_type = typename geometric_distribution<_IntType>::param_type; using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::skipws); double __p; if (__is >> __p) __x.param(param_type(__p)); __is.flags(__flags); return __is; } // This is Leger's algorithm, also in Devroye, Ch. X, Example 1.5. template<typename _IntType> template<typename _UniformRandomNumberGenerator> typename negative_binomial_distribution<_IntType>::result_type negative_binomial_distribution<_IntType>:: operator()(_UniformRandomNumberGenerator& __urng) { const double __y = _M_gd(__urng); // XXX Is the constructor too slow? std::poisson_distribution<result_type> __poisson(__y); return __poisson(__urng); } template<typename _IntType> template<typename _UniformRandomNumberGenerator> typename negative_binomial_distribution<_IntType>::result_type negative_binomial_distribution<_IntType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { typedef typename std::gamma_distribution<double>::param_type param_type; const double __y = _M_gd(__urng, param_type(__p.k(), (1.0 - __p.p()) / __p.p())); std::poisson_distribution<result_type> __poisson(__y); return __poisson(__urng); } template<typename _IntType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void negative_binomial_distribution<_IntType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) while (__f != __t) { const double __y = _M_gd(__urng); // XXX Is the constructor too slow? std::poisson_distribution<result_type> __poisson(__y); *__f++ = __poisson(__urng); } } template<typename _IntType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void negative_binomial_distribution<_IntType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) typename std::gamma_distribution<result_type>::param_type __p2(__p.k(), (1.0 - __p.p()) / __p.p()); while (__f != __t) { const double __y = _M_gd(__urng, __p2); std::poisson_distribution<result_type> __poisson(__y); *__f++ = __poisson(__urng); } } template<typename _IntType, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const negative_binomial_distribution<_IntType>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__os.widen(' ')); __os.precision(std::numeric_limits<double>::max_digits10); __os << __x.k() << __space << __x.p() << __space << __x._M_gd; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template<typename _IntType, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, negative_binomial_distribution<_IntType>& __x) { using param_type = typename negative_binomial_distribution<_IntType>::param_type; using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::skipws); _IntType __k; double __p; if (__is >> __k >> __p >> __x._M_gd) __x.param(param_type(__k, __p)); __is.flags(__flags); return __is; } template<typename _IntType> void poisson_distribution<_IntType>::param_type:: _M_initialize() { #if _GLIBCXX_USE_C99_MATH_TR1 if (_M_mean >= 12) { const double __m = std::floor(_M_mean); _M_lm_thr = std::log(_M_mean); _M_lfm = std::lgamma(__m + 1); _M_sm = std::sqrt(__m); const double __pi_4 = 0.7853981633974483096156608458198757L; const double __dx = std::sqrt(2 * __m * std::log(32 * __m / __pi_4)); _M_d = std::round(std::max<double>(6.0, std::min(__m, __dx))); const double __cx = 2 * __m + _M_d; _M_scx = std::sqrt(__cx / 2); _M_1cx = 1 / __cx; _M_c2b = std::sqrt(__pi_4 * __cx) * std::exp(_M_1cx); _M_cb = 2 * __cx * std::exp(-_M_d * _M_1cx * (1 + _M_d / 2)) / _M_d; } else #endif _M_lm_thr = std::exp(-_M_mean); } /** * A rejection algorithm when mean >= 12 and a simple method based * upon the multiplication of uniform random variates otherwise. * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1 * is defined. * * Reference: * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, * New York, 1986, Ch. X, Sects. 3.3 & 3.4 (+ Errata!). */ template<typename _IntType> template<typename _UniformRandomNumberGenerator> typename poisson_distribution<_IntType>::result_type poisson_distribution<_IntType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __param) { __detail::_Adaptor<_UniformRandomNumberGenerator, double> __aurng(__urng); #if _GLIBCXX_USE_C99_MATH_TR1 if (__param.mean() >= 12) { double __x; // See comments above... const double __naf = (1 - std::numeric_limits<double>::epsilon()) / 2; const double __thr = std::numeric_limits<_IntType>::max() + __naf; const double __m = std::floor(__param.mean()); // sqrt(pi / 2) const double __spi_2 = 1.2533141373155002512078826424055226L; const double __c1 = __param._M_sm * __spi_2; const double __c2 = __param._M_c2b + __c1; const double __c3 = __c2 + 1; const double __c4 = __c3 + 1; // 1 / 78 const double __178 = 0.0128205128205128205128205128205128L; // e^(1 / 78) const double __e178 = 1.0129030479320018583185514777512983L; const double __c5 = __c4 + __e178; const double __c = __param._M_cb + __c5; const double __2cx = 2 * (2 * __m + __param._M_d); bool __reject = true; do { const double __u = __c * __aurng(); const double __e = -std::log(1.0 - __aurng()); double __w = 0.0; if (__u <= __c1) { const double __n = _M_nd(__urng); const double __y = -std::abs(__n) * __param._M_sm - 1; __x = std::floor(__y); __w = -__n * __n / 2; if (__x < -__m) continue; } else if (__u <= __c2) { const double __n = _M_nd(__urng); const double __y = 1 + std::abs(__n) * __param._M_scx; __x = std::ceil(__y); __w = __y * (2 - __y) * __param._M_1cx; if (__x > __param._M_d) continue; } else if (__u <= __c3) // NB: This case not in the book, nor in the Errata, // but should be ok... __x = -1; else if (__u <= __c4) __x = 0; else if (__u <= __c5) { __x = 1; // Only in the Errata, see libstdc++/83237. __w = __178; } else { const double __v = -std::log(1.0 - __aurng()); const double __y = __param._M_d + __v * __2cx / __param._M_d; __x = std::ceil(__y); __w = -__param._M_d * __param._M_1cx * (1 + __y / 2); } __reject = (__w - __e - __x * __param._M_lm_thr > __param._M_lfm - std::lgamma(__x + __m + 1)); __reject |= __x + __m >= __thr; } while (__reject); return result_type(__x + __m + __naf); } else #endif { _IntType __x = 0; double __prod = 1.0; do { __prod *= __aurng(); __x += 1; } while (__prod > __param._M_lm_thr); return __x - 1; } } template<typename _IntType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void poisson_distribution<_IntType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) // We could duplicate everything from operator()... while (__f != __t) *__f++ = this->operator()(__urng, __param); } template<typename _IntType, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const poisson_distribution<_IntType>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<double>::max_digits10); __os << __x.mean() << __space << __x._M_nd; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template<typename _IntType, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, poisson_distribution<_IntType>& __x) { using param_type = typename poisson_distribution<_IntType>::param_type; using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::skipws); double __mean; if (__is >> __mean >> __x._M_nd) __x.param(param_type(__mean)); __is.flags(__flags); return __is; } template<typename _IntType> void binomial_distribution<_IntType>::param_type:: _M_initialize() { const double __p12 = _M_p <= 0.5 ? _M_p : 1.0 - _M_p; _M_easy = true; #if _GLIBCXX_USE_C99_MATH_TR1 if (_M_t * __p12 >= 8) { _M_easy = false; const double __np = std::floor(_M_t * __p12); const double __pa = __np / _M_t; const double __1p = 1 - __pa; const double __pi_4 = 0.7853981633974483096156608458198757L; const double __d1x = std::sqrt(__np * __1p * std::log(32 * __np / (81 * __pi_4 * __1p))); _M_d1 = std::round(std::max<double>(1.0, __d1x)); const double __d2x = std::sqrt(__np * __1p * std::log(32 * _M_t * __1p / (__pi_4 * __pa))); _M_d2 = std::round(std::max<double>(1.0, __d2x)); // sqrt(pi / 2) const double __spi_2 = 1.2533141373155002512078826424055226L; _M_s1 = std::sqrt(__np * __1p) * (1 + _M_d1 / (4 * __np)); _M_s2 = std::sqrt(__np * __1p) * (1 + _M_d2 / (4 * _M_t * __1p)); _M_c = 2 * _M_d1 / __np; _M_a1 = std::exp(_M_c) * _M_s1 * __spi_2; const double __a12 = _M_a1 + _M_s2 * __spi_2; const double __s1s = _M_s1 * _M_s1; _M_a123 = __a12 + (std::exp(_M_d1 / (_M_t * __1p)) * 2 * __s1s / _M_d1 * std::exp(-_M_d1 * _M_d1 / (2 * __s1s))); const double __s2s = _M_s2 * _M_s2; _M_s = (_M_a123 + 2 * __s2s / _M_d2 * std::exp(-_M_d2 * _M_d2 / (2 * __s2s))); _M_lf = (std::lgamma(__np + 1) + std::lgamma(_M_t - __np + 1)); _M_lp1p = std::log(__pa / __1p); _M_q = -std::log(1 - (__p12 - __pa) / __1p); } else #endif _M_q = -std::log(1 - __p12); } template<typename _IntType> template<typename _UniformRandomNumberGenerator> typename binomial_distribution<_IntType>::result_type binomial_distribution<_IntType>:: _M_waiting(_UniformRandomNumberGenerator& __urng, _IntType __t, double __q) { _IntType __x = 0; double __sum = 0.0; __detail::_Adaptor<_UniformRandomNumberGenerator, double> __aurng(__urng); do { if (__t == __x) return __x; const double __e = -std::log(1.0 - __aurng()); __sum += __e / (__t - __x); __x += 1; } while (__sum <= __q); return __x - 1; } /** * A rejection algorithm when t * p >= 8 and a simple waiting time * method - the second in the referenced book - otherwise. * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1 * is defined. * * Reference: * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, * New York, 1986, Ch. X, Sect. 4 (+ Errata!). */ template<typename _IntType> template<typename _UniformRandomNumberGenerator> typename binomial_distribution<_IntType>::result_type binomial_distribution<_IntType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __param) { result_type __ret; const _IntType __t = __param.t(); const double __p = __param.p(); const double __p12 = __p <= 0.5 ? __p : 1.0 - __p; __detail::_Adaptor<_UniformRandomNumberGenerator, double> __aurng(__urng); #if _GLIBCXX_USE_C99_MATH_TR1 if (!__param._M_easy) { double __x; // See comments above... const double __naf = (1 - std::numeric_limits<double>::epsilon()) / 2; const double __thr = std::numeric_limits<_IntType>::max() + __naf; const double __np = std::floor(__t * __p12); // sqrt(pi / 2) const double __spi_2 = 1.2533141373155002512078826424055226L; const double __a1 = __param._M_a1; const double __a12 = __a1 + __param._M_s2 * __spi_2; const double __a123 = __param._M_a123; const double __s1s = __param._M_s1 * __param._M_s1; const double __s2s = __param._M_s2 * __param._M_s2; bool __reject; do { const double __u = __param._M_s * __aurng(); double __v; if (__u <= __a1) { const double __n = _M_nd(__urng); const double __y = __param._M_s1 * std::abs(__n); __reject = __y >= __param._M_d1; if (!__reject) { const double __e = -std::log(1.0 - __aurng()); __x = std::floor(__y); __v = -__e - __n * __n / 2 + __param._M_c; } } else if (__u <= __a12) { const double __n = _M_nd(__urng); const double __y = __param._M_s2 * std::abs(__n); __reject = __y >= __param._M_d2; if (!__reject) { const double __e = -std::log(1.0 - __aurng()); __x = std::floor(-__y); __v = -__e - __n * __n / 2; } } else if (__u <= __a123) { const double __e1 = -std::log(1.0 - __aurng()); const double __e2 = -std::log(1.0 - __aurng()); const double __y = __param._M_d1 + 2 * __s1s * __e1 / __param._M_d1; __x = std::floor(__y); __v = (-__e2 + __param._M_d1 * (1 / (__t - __np) -__y / (2 * __s1s))); __reject = false; } else { const double __e1 = -std::log(1.0 - __aurng()); const double __e2 = -std::log(1.0 - __aurng()); const double __y = __param._M_d2 + 2 * __s2s * __e1 / __param._M_d2; __x = std::floor(-__y); __v = -__e2 - __param._M_d2 * __y / (2 * __s2s); __reject = false; } __reject = __reject || __x < -__np || __x > __t - __np; if (!__reject) { const double __lfx = std::lgamma(__np + __x + 1) + std::lgamma(__t - (__np + __x) + 1); __reject = __v > __param._M_lf - __lfx + __x * __param._M_lp1p; } __reject |= __x + __np >= __thr; } while (__reject); __x += __np + __naf; const _IntType __z = _M_waiting(__urng, __t - _IntType(__x), __param._M_q); __ret = _IntType(__x) + __z; } else #endif __ret = _M_waiting(__urng, __t, __param._M_q); if (__p12 != __p) __ret = __t - __ret; return __ret; } template<typename _IntType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void binomial_distribution<_IntType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) // We could duplicate everything from operator()... while (__f != __t) *__f++ = this->operator()(__urng, __param); } template<typename _IntType, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const binomial_distribution<_IntType>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<double>::max_digits10); __os << __x.t() << __space << __x.p() << __space << __x._M_nd; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template<typename _IntType, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, binomial_distribution<_IntType>& __x) { using param_type = typename binomial_distribution<_IntType>::param_type; using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _IntType __t; double __p; if (__is >> __t >> __p >> __x._M_nd) __x.param(param_type(__t, __p)); __is.flags(__flags); return __is; } template<typename _RealType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void std::exponential_distribution<_RealType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); while (__f != __t) *__f++ = -std::log(result_type(1) - __aurng()) / __p.lambda(); } template<typename _RealType, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const exponential_distribution<_RealType>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__os.widen(' ')); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.lambda(); __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template<typename _RealType, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, exponential_distribution<_RealType>& __x) { using param_type = typename exponential_distribution<_RealType>::param_type; using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __lambda; if (__is >> __lambda) __x.param(param_type(__lambda)); __is.flags(__flags); return __is; } /** * Polar method due to Marsaglia. * * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, * New York, 1986, Ch. V, Sect. 4.4. */ template<typename _RealType> template<typename _UniformRandomNumberGenerator> typename normal_distribution<_RealType>::result_type normal_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __param) { result_type __ret; __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); if (_M_saved_available) { _M_saved_available = false; __ret = _M_saved; } else { result_type __x, __y, __r2; do { __x = result_type(2.0) * __aurng() - 1.0; __y = result_type(2.0) * __aurng() - 1.0; __r2 = __x * __x + __y * __y; } while (__r2 > 1.0 || __r2 == 0.0); const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2); _M_saved = __x * __mult; _M_saved_available = true; __ret = __y * __mult; } __ret = __ret * __param.stddev() + __param.mean(); return __ret; } template<typename _RealType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void normal_distribution<_RealType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) if (__f == __t) return; if (_M_saved_available) { _M_saved_available = false; *__f++ = _M_saved * __param.stddev() + __param.mean(); if (__f == __t) return; } __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); while (__f + 1 < __t) { result_type __x, __y, __r2; do { __x = result_type(2.0) * __aurng() - 1.0; __y = result_type(2.0) * __aurng() - 1.0; __r2 = __x * __x + __y * __y; } while (__r2 > 1.0 || __r2 == 0.0); const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2); *__f++ = __y * __mult * __param.stddev() + __param.mean(); *__f++ = __x * __mult * __param.stddev() + __param.mean(); } if (__f != __t) { result_type __x, __y, __r2; do { __x = result_type(2.0) * __aurng() - 1.0; __y = result_type(2.0) * __aurng() - 1.0; __r2 = __x * __x + __y * __y; } while (__r2 > 1.0 || __r2 == 0.0); const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2); _M_saved = __x * __mult; _M_saved_available = true; *__f = __y * __mult * __param.stddev() + __param.mean(); } } template<typename _RealType> bool operator==(const std::normal_distribution<_RealType>& __d1, const std::normal_distribution<_RealType>& __d2) { if (__d1._M_param == __d2._M_param && __d1._M_saved_available == __d2._M_saved_available) { if (__d1._M_saved_available && __d1._M_saved == __d2._M_saved) return true; else if(!__d1._M_saved_available) return true; else return false; } else return false; } template<typename _RealType, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const normal_distribution<_RealType>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.mean() << __space << __x.stddev() << __space << __x._M_saved_available; if (__x._M_saved_available) __os << __space << __x._M_saved; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template<typename _RealType, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, normal_distribution<_RealType>& __x) { using param_type = typename normal_distribution<_RealType>::param_type; using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); double __mean, __stddev; bool __saved_avail; if (__is >> __mean >> __stddev >> __saved_avail) { if (__saved_avail && (__is >> __x._M_saved)) { __x._M_saved_available = __saved_avail; __x.param(param_type(__mean, __stddev)); } } __is.flags(__flags); return __is; } template<typename _RealType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void lognormal_distribution<_RealType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) while (__f != __t) *__f++ = std::exp(__p.s() * _M_nd(__urng) + __p.m()); } template<typename _RealType, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const lognormal_distribution<_RealType>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.m() << __space << __x.s() << __space << __x._M_nd; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template<typename _RealType, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, lognormal_distribution<_RealType>& __x) { using param_type = typename lognormal_distribution<_RealType>::param_type; using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __m, __s; if (__is >> __m >> __s >> __x._M_nd) __x.param(param_type(__m, __s)); __is.flags(__flags); return __is; } template<typename _RealType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void std::chi_squared_distribution<_RealType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) while (__f != __t) *__f++ = 2 * _M_gd(__urng); } template<typename _RealType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void std::chi_squared_distribution<_RealType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const typename std::gamma_distribution<result_type>::param_type& __p) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) while (__f != __t) *__f++ = 2 * _M_gd(__urng, __p); } template<typename _RealType, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const chi_squared_distribution<_RealType>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.n() << __space << __x._M_gd; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template<typename _RealType, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, chi_squared_distribution<_RealType>& __x) { using param_type = typename chi_squared_distribution<_RealType>::param_type; using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __n; if (__is >> __n >> __x._M_gd) __x.param(param_type(__n)); __is.flags(__flags); return __is; } template<typename _RealType> template<typename _UniformRandomNumberGenerator> typename cauchy_distribution<_RealType>::result_type cauchy_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); _RealType __u; do __u = __aurng(); while (__u == 0.5); const _RealType __pi = 3.1415926535897932384626433832795029L; return __p.a() + __p.b() * std::tan(__pi * __u); } template<typename _RealType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void cauchy_distribution<_RealType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) const _RealType __pi = 3.1415926535897932384626433832795029L; __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); while (__f != __t) { _RealType __u; do __u = __aurng(); while (__u == 0.5); *__f++ = __p.a() + __p.b() * std::tan(__pi * __u); } } template<typename _RealType, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const cauchy_distribution<_RealType>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.a() << __space << __x.b(); __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template<typename _RealType, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, cauchy_distribution<_RealType>& __x) { using param_type = typename cauchy_distribution<_RealType>::param_type; using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __a, __b; if (__is >> __a >> __b) __x.param(param_type(__a, __b)); __is.flags(__flags); return __is; } template<typename _RealType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void std::fisher_f_distribution<_RealType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) while (__f != __t) *__f++ = ((_M_gd_x(__urng) * n()) / (_M_gd_y(__urng) * m())); } template<typename _RealType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void std::fisher_f_distribution<_RealType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) typedef typename std::gamma_distribution<result_type>::param_type param_type; param_type __p1(__p.m() / 2); param_type __p2(__p.n() / 2); while (__f != __t) *__f++ = ((_M_gd_x(__urng, __p1) * n()) / (_M_gd_y(__urng, __p2) * m())); } template<typename _RealType, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const fisher_f_distribution<_RealType>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.m() << __space << __x.n() << __space << __x._M_gd_x << __space << __x._M_gd_y; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template<typename _RealType, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, fisher_f_distribution<_RealType>& __x) { using param_type = typename fisher_f_distribution<_RealType>::param_type; using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __m, __n; if (__is >> __m >> __n >> __x._M_gd_x >> __x._M_gd_y) __x.param(param_type(__m, __n)); __is.flags(__flags); return __is; } template<typename _RealType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void std::student_t_distribution<_RealType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) while (__f != __t) *__f++ = _M_nd(__urng) * std::sqrt(n() / _M_gd(__urng)); } template<typename _RealType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void std::student_t_distribution<_RealType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) typename std::gamma_distribution<result_type>::param_type __p2(__p.n() / 2, 2); while (__f != __t) *__f++ = _M_nd(__urng) * std::sqrt(__p.n() / _M_gd(__urng, __p2)); } template<typename _RealType, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const student_t_distribution<_RealType>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.n() << __space << __x._M_nd << __space << __x._M_gd; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template<typename _RealType, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, student_t_distribution<_RealType>& __x) { using param_type = typename student_t_distribution<_RealType>::param_type; using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __n; if (__is >> __n >> __x._M_nd >> __x._M_gd) __x.param(param_type(__n)); __is.flags(__flags); return __is; } template<typename _RealType> void gamma_distribution<_RealType>::param_type:: _M_initialize() { _M_malpha = _M_alpha < 1.0 ? _M_alpha + _RealType(1.0) : _M_alpha; const _RealType __a1 = _M_malpha - _RealType(1.0) / _RealType(3.0); _M_a2 = _RealType(1.0) / std::sqrt(_RealType(9.0) * __a1); } /** * Marsaglia, G. and Tsang, W. W. * "A Simple Method for Generating Gamma Variables" * ACM Transactions on Mathematical Software, 26, 3, 363-372, 2000. */ template<typename _RealType> template<typename _UniformRandomNumberGenerator> typename gamma_distribution<_RealType>::result_type gamma_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __param) { __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); result_type __u, __v, __n; const result_type __a1 = (__param._M_malpha - _RealType(1.0) / _RealType(3.0)); do { do { __n = _M_nd(__urng); __v = result_type(1.0) + __param._M_a2 * __n; } while (__v <= 0.0); __v = __v * __v * __v; __u = __aurng(); } while (__u > result_type(1.0) - 0.0331 * __n * __n * __n * __n && (std::log(__u) > (0.5 * __n * __n + __a1 * (1.0 - __v + std::log(__v))))); if (__param.alpha() == __param._M_malpha) return __a1 * __v * __param.beta(); else { do __u = __aurng(); while (__u == 0.0); return (std::pow(__u, result_type(1.0) / __param.alpha()) * __a1 * __v * __param.beta()); } } template<typename _RealType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void gamma_distribution<_RealType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); result_type __u, __v, __n; const result_type __a1 = (__param._M_malpha - _RealType(1.0) / _RealType(3.0)); if (__param.alpha() == __param._M_malpha) while (__f != __t) { do { do { __n = _M_nd(__urng); __v = result_type(1.0) + __param._M_a2 * __n; } while (__v <= 0.0); __v = __v * __v * __v; __u = __aurng(); } while (__u > result_type(1.0) - 0.0331 * __n * __n * __n * __n && (std::log(__u) > (0.5 * __n * __n + __a1 * (1.0 - __v + std::log(__v))))); *__f++ = __a1 * __v * __param.beta(); } else while (__f != __t) { do { do { __n = _M_nd(__urng); __v = result_type(1.0) + __param._M_a2 * __n; } while (__v <= 0.0); __v = __v * __v * __v; __u = __aurng(); } while (__u > result_type(1.0) - 0.0331 * __n * __n * __n * __n && (std::log(__u) > (0.5 * __n * __n + __a1 * (1.0 - __v + std::log(__v))))); do __u = __aurng(); while (__u == 0.0); *__f++ = (std::pow(__u, result_type(1.0) / __param.alpha()) * __a1 * __v * __param.beta()); } } template<typename _RealType, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const gamma_distribution<_RealType>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.alpha() << __space << __x.beta() << __space << __x._M_nd; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template<typename _RealType, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, gamma_distribution<_RealType>& __x) { using param_type = typename gamma_distribution<_RealType>::param_type; using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __alpha_val, __beta_val; if (__is >> __alpha_val >> __beta_val >> __x._M_nd) __x.param(param_type(__alpha_val, __beta_val)); __is.flags(__flags); return __is; } template<typename _RealType> template<typename _UniformRandomNumberGenerator> typename weibull_distribution<_RealType>::result_type weibull_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); return __p.b() * std::pow(-std::log(result_type(1) - __aurng()), result_type(1) / __p.a()); } template<typename _RealType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void weibull_distribution<_RealType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); auto __inv_a = result_type(1) / __p.a(); while (__f != __t) *__f++ = __p.b() * std::pow(-std::log(result_type(1) - __aurng()), __inv_a); } template<typename _RealType, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const weibull_distribution<_RealType>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.a() << __space << __x.b(); __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template<typename _RealType, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, weibull_distribution<_RealType>& __x) { using param_type = typename weibull_distribution<_RealType>::param_type; using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __a, __b; if (__is >> __a >> __b) __x.param(param_type(__a, __b)); __is.flags(__flags); return __is; } template<typename _RealType> template<typename _UniformRandomNumberGenerator> typename extreme_value_distribution<_RealType>::result_type extreme_value_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __p) { __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); return __p.a() - __p.b() * std::log(-std::log(result_type(1) - __aurng())); } template<typename _RealType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void extreme_value_distribution<_RealType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __p) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) __detail::_Adaptor<_UniformRandomNumberGenerator, result_type> __aurng(__urng); while (__f != __t) *__f++ = __p.a() - __p.b() * std::log(-std::log(result_type(1) - __aurng())); } template<typename _RealType, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const extreme_value_distribution<_RealType>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); __os << __x.a() << __space << __x.b(); __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template<typename _RealType, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, extreme_value_distribution<_RealType>& __x) { using param_type = typename extreme_value_distribution<_RealType>::param_type; using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); _RealType __a, __b; if (__is >> __a >> __b) __x.param(param_type(__a, __b)); __is.flags(__flags); return __is; } template<typename _IntType> void discrete_distribution<_IntType>::param_type:: _M_initialize() { if (_M_prob.size() < 2) { _M_prob.clear(); return; } const double __sum = std::accumulate(_M_prob.begin(), _M_prob.end(), 0.0); // Now normalize the probabilites. __detail::__normalize(_M_prob.begin(), _M_prob.end(), _M_prob.begin(), __sum); // Accumulate partial sums. _M_cp.reserve(_M_prob.size()); std::partial_sum(_M_prob.begin(), _M_prob.end(), std::back_inserter(_M_cp)); // Make sure the last cumulative probability is one. _M_cp[_M_cp.size() - 1] = 1.0; } template<typename _IntType> template<typename _Func> discrete_distribution<_IntType>::param_type:: param_type(size_t __nw, double __xmin, double __xmax, _Func __fw) : _M_prob(), _M_cp() { const size_t __n = __nw == 0 ? 1 : __nw; const double __delta = (__xmax - __xmin) / __n; _M_prob.reserve(__n); for (size_t __k = 0; __k < __nw; ++__k) _M_prob.push_back(__fw(__xmin + __k * __delta + 0.5 * __delta)); _M_initialize(); } template<typename _IntType> template<typename _UniformRandomNumberGenerator> typename discrete_distribution<_IntType>::result_type discrete_distribution<_IntType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __param) { if (__param._M_cp.empty()) return result_type(0); __detail::_Adaptor<_UniformRandomNumberGenerator, double> __aurng(__urng); const double __p = __aurng(); auto __pos = std::lower_bound(__param._M_cp.begin(), __param._M_cp.end(), __p); return __pos - __param._M_cp.begin(); } template<typename _IntType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void discrete_distribution<_IntType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) if (__param._M_cp.empty()) { while (__f != __t) *__f++ = result_type(0); return; } __detail::_Adaptor<_UniformRandomNumberGenerator, double> __aurng(__urng); while (__f != __t) { const double __p = __aurng(); auto __pos = std::lower_bound(__param._M_cp.begin(), __param._M_cp.end(), __p); *__f++ = __pos - __param._M_cp.begin(); } } template<typename _IntType, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const discrete_distribution<_IntType>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<double>::max_digits10); std::vector<double> __prob = __x.probabilities(); __os << __prob.size(); for (auto __dit = __prob.begin(); __dit != __prob.end(); ++__dit) __os << __space << *__dit; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } namespace __detail { template<typename _ValT, typename _CharT, typename _Traits> basic_istream<_CharT, _Traits>& __extract_params(basic_istream<_CharT, _Traits>& __is, vector<_ValT>& __vals, size_t __n) { __vals.reserve(__n); while (__n--) { _ValT __val; if (__is >> __val) __vals.push_back(__val); else break; } return __is; } } // namespace __detail template<typename _IntType, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, discrete_distribution<_IntType>& __x) { using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); size_t __n; if (__is >> __n) { std::vector<double> __prob_vec; if (__detail::__extract_params(__is, __prob_vec, __n)) __x.param({__prob_vec.begin(), __prob_vec.end()}); } __is.flags(__flags); return __is; } template<typename _RealType> void piecewise_constant_distribution<_RealType>::param_type:: _M_initialize() { if (_M_int.size() < 2 || (_M_int.size() == 2 && _M_int[0] == _RealType(0) && _M_int[1] == _RealType(1))) { _M_int.clear(); _M_den.clear(); return; } const double __sum = std::accumulate(_M_den.begin(), _M_den.end(), 0.0); __detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(), __sum); _M_cp.reserve(_M_den.size()); std::partial_sum(_M_den.begin(), _M_den.end(), std::back_inserter(_M_cp)); // Make sure the last cumulative probability is one. _M_cp[_M_cp.size() - 1] = 1.0; for (size_t __k = 0; __k < _M_den.size(); ++__k) _M_den[__k] /= _M_int[__k + 1] - _M_int[__k]; } template<typename _RealType> template<typename _InputIteratorB, typename _InputIteratorW> piecewise_constant_distribution<_RealType>::param_type:: param_type(_InputIteratorB __bbegin, _InputIteratorB __bend, _InputIteratorW __wbegin) : _M_int(), _M_den(), _M_cp() { if (__bbegin != __bend) { for (;;) { _M_int.push_back(*__bbegin); ++__bbegin; if (__bbegin == __bend) break; _M_den.push_back(*__wbegin); ++__wbegin; } } _M_initialize(); } template<typename _RealType> template<typename _Func> piecewise_constant_distribution<_RealType>::param_type:: param_type(initializer_list<_RealType> __bl, _Func __fw) : _M_int(), _M_den(), _M_cp() { _M_int.reserve(__bl.size()); for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter) _M_int.push_back(*__biter); _M_den.reserve(_M_int.size() - 1); for (size_t __k = 0; __k < _M_int.size() - 1; ++__k) _M_den.push_back(__fw(0.5 * (_M_int[__k + 1] + _M_int[__k]))); _M_initialize(); } template<typename _RealType> template<typename _Func> piecewise_constant_distribution<_RealType>::param_type:: param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw) : _M_int(), _M_den(), _M_cp() { const size_t __n = __nw == 0 ? 1 : __nw; const _RealType __delta = (__xmax - __xmin) / __n; _M_int.reserve(__n + 1); for (size_t __k = 0; __k <= __nw; ++__k) _M_int.push_back(__xmin + __k * __delta); _M_den.reserve(__n); for (size_t __k = 0; __k < __nw; ++__k) _M_den.push_back(__fw(_M_int[__k] + 0.5 * __delta)); _M_initialize(); } template<typename _RealType> template<typename _UniformRandomNumberGenerator> typename piecewise_constant_distribution<_RealType>::result_type piecewise_constant_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __param) { __detail::_Adaptor<_UniformRandomNumberGenerator, double> __aurng(__urng); const double __p = __aurng(); if (__param._M_cp.empty()) return __p; auto __pos = std::lower_bound(__param._M_cp.begin(), __param._M_cp.end(), __p); const size_t __i = __pos - __param._M_cp.begin(); const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0; return __param._M_int[__i] + (__p - __pref) / __param._M_den[__i]; } template<typename _RealType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void piecewise_constant_distribution<_RealType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) __detail::_Adaptor<_UniformRandomNumberGenerator, double> __aurng(__urng); if (__param._M_cp.empty()) { while (__f != __t) *__f++ = __aurng(); return; } while (__f != __t) { const double __p = __aurng(); auto __pos = std::lower_bound(__param._M_cp.begin(), __param._M_cp.end(), __p); const size_t __i = __pos - __param._M_cp.begin(); const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0; *__f++ = (__param._M_int[__i] + (__p - __pref) / __param._M_den[__i]); } } template<typename _RealType, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const piecewise_constant_distribution<_RealType>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); std::vector<_RealType> __int = __x.intervals(); __os << __int.size() - 1; for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit) __os << __space << *__xit; std::vector<double> __den = __x.densities(); for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit) __os << __space << *__dit; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template<typename _RealType, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, piecewise_constant_distribution<_RealType>& __x) { using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); size_t __n; if (__is >> __n) { std::vector<_RealType> __int_vec; if (__detail::__extract_params(__is, __int_vec, __n + 1)) { std::vector<double> __den_vec; if (__detail::__extract_params(__is, __den_vec, __n)) { __x.param({ __int_vec.begin(), __int_vec.end(), __den_vec.begin() }); } } } __is.flags(__flags); return __is; } template<typename _RealType> void piecewise_linear_distribution<_RealType>::param_type:: _M_initialize() { if (_M_int.size() < 2 || (_M_int.size() == 2 && _M_int[0] == _RealType(0) && _M_int[1] == _RealType(1) && _M_den[0] == _M_den[1])) { _M_int.clear(); _M_den.clear(); return; } double __sum = 0.0; _M_cp.reserve(_M_int.size() - 1); _M_m.reserve(_M_int.size() - 1); for (size_t __k = 0; __k < _M_int.size() - 1; ++__k) { const _RealType __delta = _M_int[__k + 1] - _M_int[__k]; __sum += 0.5 * (_M_den[__k + 1] + _M_den[__k]) * __delta; _M_cp.push_back(__sum); _M_m.push_back((_M_den[__k + 1] - _M_den[__k]) / __delta); } // Now normalize the densities... __detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(), __sum); // ... and partial sums... __detail::__normalize(_M_cp.begin(), _M_cp.end(), _M_cp.begin(), __sum); // ... and slopes. __detail::__normalize(_M_m.begin(), _M_m.end(), _M_m.begin(), __sum); // Make sure the last cumulative probablility is one. _M_cp[_M_cp.size() - 1] = 1.0; } template<typename _RealType> template<typename _InputIteratorB, typename _InputIteratorW> piecewise_linear_distribution<_RealType>::param_type:: param_type(_InputIteratorB __bbegin, _InputIteratorB __bend, _InputIteratorW __wbegin) : _M_int(), _M_den(), _M_cp(), _M_m() { for (; __bbegin != __bend; ++__bbegin, ++__wbegin) { _M_int.push_back(*__bbegin); _M_den.push_back(*__wbegin); } _M_initialize(); } template<typename _RealType> template<typename _Func> piecewise_linear_distribution<_RealType>::param_type:: param_type(initializer_list<_RealType> __bl, _Func __fw) : _M_int(), _M_den(), _M_cp(), _M_m() { _M_int.reserve(__bl.size()); _M_den.reserve(__bl.size()); for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter) { _M_int.push_back(*__biter); _M_den.push_back(__fw(*__biter)); } _M_initialize(); } template<typename _RealType> template<typename _Func> piecewise_linear_distribution<_RealType>::param_type:: param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw) : _M_int(), _M_den(), _M_cp(), _M_m() { const size_t __n = __nw == 0 ? 1 : __nw; const _RealType __delta = (__xmax - __xmin) / __n; _M_int.reserve(__n + 1); _M_den.reserve(__n + 1); for (size_t __k = 0; __k <= __nw; ++__k) { _M_int.push_back(__xmin + __k * __delta); _M_den.push_back(__fw(_M_int[__k] + __delta)); } _M_initialize(); } template<typename _RealType> template<typename _UniformRandomNumberGenerator> typename piecewise_linear_distribution<_RealType>::result_type piecewise_linear_distribution<_RealType>:: operator()(_UniformRandomNumberGenerator& __urng, const param_type& __param) { __detail::_Adaptor<_UniformRandomNumberGenerator, double> __aurng(__urng); const double __p = __aurng(); if (__param._M_cp.empty()) return __p; auto __pos = std::lower_bound(__param._M_cp.begin(), __param._M_cp.end(), __p); const size_t __i = __pos - __param._M_cp.begin(); const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0; const double __a = 0.5 * __param._M_m[__i]; const double __b = __param._M_den[__i]; const double __cm = __p - __pref; _RealType __x = __param._M_int[__i]; if (__a == 0) __x += __cm / __b; else { const double __d = __b * __b + 4.0 * __a * __cm; __x += 0.5 * (std::sqrt(__d) - __b) / __a; } return __x; } template<typename _RealType> template<typename _ForwardIterator, typename _UniformRandomNumberGenerator> void piecewise_linear_distribution<_RealType>:: __generate_impl(_ForwardIterator __f, _ForwardIterator __t, _UniformRandomNumberGenerator& __urng, const param_type& __param) { __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>) // We could duplicate everything from operator()... while (__f != __t) *__f++ = this->operator()(__urng, __param); } template<typename _RealType, typename _CharT, typename _Traits> std::basic_ostream<_CharT, _Traits>& operator<<(std::basic_ostream<_CharT, _Traits>& __os, const piecewise_linear_distribution<_RealType>& __x) { using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __os.flags(); const _CharT __fill = __os.fill(); const std::streamsize __precision = __os.precision(); const _CharT __space = __os.widen(' '); __os.flags(__ios_base::scientific | __ios_base::left); __os.fill(__space); __os.precision(std::numeric_limits<_RealType>::max_digits10); std::vector<_RealType> __int = __x.intervals(); __os << __int.size() - 1; for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit) __os << __space << *__xit; std::vector<double> __den = __x.densities(); for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit) __os << __space << *__dit; __os.flags(__flags); __os.fill(__fill); __os.precision(__precision); return __os; } template<typename _RealType, typename _CharT, typename _Traits> std::basic_istream<_CharT, _Traits>& operator>>(std::basic_istream<_CharT, _Traits>& __is, piecewise_linear_distribution<_RealType>& __x) { using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base; const typename __ios_base::fmtflags __flags = __is.flags(); __is.flags(__ios_base::dec | __ios_base::skipws); size_t __n; if (__is >> __n) { vector<_RealType> __int_vec; if (__detail::__extract_params(__is, __int_vec, __n + 1)) { vector<double> __den_vec; if (__detail::__extract_params(__is, __den_vec, __n + 1)) { __x.param({ __int_vec.begin(), __int_vec.end(), __den_vec.begin() }); } } } __is.flags(__flags); return __is; } template<typename _IntType> seed_seq::seed_seq(std::initializer_list<_IntType> __il) { for (auto __iter = __il.begin(); __iter != __il.end(); ++__iter) _M_v.push_back(__detail::__mod<result_type, __detail::_Shift<result_type, 32>::__value>(*__iter)); } template<typename _InputIterator> seed_seq::seed_seq(_InputIterator __begin, _InputIterator __end) { for (_InputIterator __iter = __begin; __iter != __end; ++__iter) _M_v.push_back(__detail::__mod<result_type, __detail::_Shift<result_type, 32>::__value>(*__iter)); } template<typename _RandomAccessIterator> void seed_seq::generate(_RandomAccessIterator __begin, _RandomAccessIterator __end) { typedef typename iterator_traits<_RandomAccessIterator>::value_type _Type; if (__begin == __end) return; std::fill(__begin, __end, _Type(0x8b8b8b8bu)); const size_t __n = __end - __begin; const size_t __s = _M_v.size(); const size_t __t = (__n >= 623) ? 11 : (__n >= 68) ? 7 : (__n >= 39) ? 5 : (__n >= 7) ? 3 : (__n - 1) / 2; const size_t __p = (__n - __t) / 2; const size_t __q = __p + __t; const size_t __m = std::max(size_t(__s + 1), __n); for (size_t __k = 0; __k < __m; ++__k) { _Type __arg = (__begin[__k % __n] ^ __begin[(__k + __p) % __n] ^ __begin[(__k - 1) % __n]); _Type __r1 = __arg ^ (__arg >> 27); __r1 = __detail::__mod<_Type, __detail::_Shift<_Type, 32>::__value>(1664525u * __r1); _Type __r2 = __r1; if (__k == 0) __r2 += __s; else if (__k <= __s) __r2 += __k % __n + _M_v[__k - 1]; else __r2 += __k % __n; __r2 = __detail::__mod<_Type, __detail::_Shift<_Type, 32>::__value>(__r2); __begin[(__k + __p) % __n] += __r1; __begin[(__k + __q) % __n] += __r2; __begin[__k % __n] = __r2; } for (size_t __k = __m; __k < __m + __n; ++__k) { _Type __arg = (__begin[__k % __n] + __begin[(__k + __p) % __n] + __begin[(__k - 1) % __n]); _Type __r3 = __arg ^ (__arg >> 27); __r3 = __detail::__mod<_Type, __detail::_Shift<_Type, 32>::__value>(1566083941u * __r3); _Type __r4 = __r3 - __k % __n; __r4 = __detail::__mod<_Type, __detail::_Shift<_Type, 32>::__value>(__r4); __begin[(__k + __p) % __n] ^= __r3; __begin[(__k + __q) % __n] ^= __r4; __begin[__k % __n] = __r4; } } template<typename _RealType, size_t __bits, typename _UniformRandomNumberGenerator> _RealType generate_canonical(_UniformRandomNumberGenerator& __urng) { static_assert(std::is_floating_point<_RealType>::value, "template argument must be a floating point type"); const size_t __b = std::min(static_cast<size_t>(std::numeric_limits<_RealType>::digits), __bits); const long double __r = static_cast<long double>(__urng.max()) - static_cast<long double>(__urng.min()) + 1.0L; const size_t __log2r = std::log(__r) / std::log(2.0L); const size_t __m = std::max<size_t>(1UL, (__b + __log2r - 1UL) / __log2r); _RealType __ret; _RealType __sum = _RealType(0); _RealType __tmp = _RealType(1); for (size_t __k = __m; __k != 0; --__k) { __sum += _RealType(__urng() - __urng.min()) * __tmp; __tmp *= __r; } __ret = __sum / __tmp; if (__builtin_expect(__ret >= _RealType(1), 0)) { #if _GLIBCXX_USE_C99_MATH_TR1 __ret = std::nextafter(_RealType(1), _RealType(0)); #else __ret = _RealType(1) - std::numeric_limits<_RealType>::epsilon() / _RealType(2); #endif } return __ret; } _GLIBCXX_END_NAMESPACE_VERSION } // namespace #endif