| // random number generation (out of line) -*- C++ -*- |
| |
| // Copyright (C) 2009, 2010, 2011, 2012 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) |
| { |
| /* |
| * (Further) implementation-space details. |
| */ |
| namespace __detail |
| { |
| _GLIBCXX_BEGIN_NAMESPACE_VERSION |
| |
| // General case for x = (ax + c) mod m -- use Schrage's algorithm to |
| // avoid integer overflow. |
| // |
| // Because a and c are compile-time integral constants the compiler |
| // kindly elides any unreachable paths. |
| // |
| // Preconditions: a > 0, m > 0. |
| // |
| // XXX FIXME: as-is, only works correctly for __m % __a < __m / __a. |
| // |
| template<typename _Tp, _Tp __m, _Tp __a, _Tp __c, bool> |
| struct _Mod |
| { |
| static _Tp |
| __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; |
| } |
| }; |
| |
| // Special case for m == 0 -- use unsigned integer overflow as modulo |
| // operator. |
| template<typename _Tp, _Tp __m, _Tp __a, _Tp __c> |
| struct _Mod<_Tp, __m, __a, __c, true> |
| { |
| static _Tp |
| __calc(_Tp __x) |
| { return __a * __x + __c; } |
| }; |
| |
| template<typename _InputIterator, typename _OutputIterator, |
| typename _UnaryOperation> |
| _OutputIterator |
| __transform(_InputIterator __first, _InputIterator __last, |
| _OutputIterator __result, _UnaryOperation __unary_op) |
| { |
| for (; __first != __last; ++__first, ++__result) |
| *__result = __unary_op(*__first); |
| return __result; |
| } |
| |
| _GLIBCXX_END_NAMESPACE_VERSION |
| } // namespace __detail |
| |
| _GLIBCXX_BEGIN_NAMESPACE_VERSION |
| |
| 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> |
| typename std::enable_if<std::is_class<_Sseq>::value>::type |
| linear_congruential_engine<_UIntType, __a, __c, __m>:: |
| seed(_Sseq& __q) |
| { |
| 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) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| __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) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::dec); |
| |
| __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> |
| typename std::enable_if<std::is_class<_Sseq>::value>::type |
| mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, |
| __s, __b, __t, __c, __l, __f>:: |
| seed(_Sseq& __q) |
| { |
| 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; |
| } |
| |
| 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) |
| { |
| 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; |
| } |
| |
| // 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) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const _CharT __space = __os.widen(' '); |
| __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); |
| __os.fill(__space); |
| |
| for (size_t __i = 0; __i < __n - 1; ++__i) |
| __os << __x._M_x[__i] << __space; |
| __os << __x._M_x[__n - 1]; |
| |
| __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) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::dec | __ios_base::skipws); |
| |
| for (size_t __i = 0; __i < __n; ++__i) |
| __is >> __x._M_x[__i]; |
| |
| __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> |
| typename std::enable_if<std::is_class<_Sseq>::value>::type |
| subtract_with_carry_engine<_UIntType, __w, __s, __r>:: |
| seed(_Sseq& __q) |
| { |
| 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) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const _CharT __space = __os.widen(' '); |
| __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); |
| __os.fill(__space); |
| |
| for (size_t __i = 0; __i < __r; ++__i) |
| __os << __x._M_x[__i] << __space; |
| __os << __x._M_carry; |
| |
| __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) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::dec | __ios_base::skipws); |
| |
| for (size_t __i = 0; __i < __r; ++__i) |
| __is >> __x._M_x[__i]; |
| __is >> __x._M_carry; |
| |
| __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) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const _CharT __space = __os.widen(' '); |
| __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); |
| __os.fill(__space); |
| |
| __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) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::dec | __ios_base::skipws); |
| |
| __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()() |
| { |
| const long double __r = static_cast<long double>(_M_b.max()) |
| - static_cast<long double>(_M_b.min()) + 1.0L; |
| const result_type __m = std::log(__r) / std::log(2.0L); |
| result_type __n, __n0, __y0, __y1, __s0, __s1; |
| for (size_t __i = 0; __i < 2; ++__i) |
| { |
| __n = (__w + __m - 1) / __m + __i; |
| __n0 = __n - __w % __n; |
| const result_type __w0 = __w / __n; |
| const result_type __w1 = __w0 + 1; |
| __s0 = result_type(1) << __w0; |
| __s1 = result_type(1) << __w1; |
| __y0 = __s0 * (__r / __s0); |
| __y1 = __s1 * (__r / __s1); |
| if (__r - __y0 <= __y0 / __n) |
| break; |
| } |
| |
| result_type __sum = 0; |
| for (size_t __k = 0; __k < __n0; ++__k) |
| { |
| result_type __u; |
| do |
| __u = _M_b() - _M_b.min(); |
| while (__u >= __y0); |
| __sum = __s0 * __sum + __u % __s0; |
| } |
| for (size_t __k = __n0; __k < __n; ++__k) |
| { |
| result_type __u; |
| do |
| __u = _M_b() - _M_b.min(); |
| while (__u >= __y1); |
| __sum = __s1 * __sum + __u % __s1; |
| } |
| 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) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const _CharT __space = __os.widen(' '); |
| __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left); |
| __os.fill(__space); |
| |
| __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) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::dec | __ios_base::skipws); |
| |
| __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> |
| template<typename _UniformRandomNumberGenerator> |
| typename uniform_int_distribution<_IntType>::result_type |
| uniform_int_distribution<_IntType>:: |
| operator()(_UniformRandomNumberGenerator& __urng, |
| const param_type& __param) |
| { |
| typedef typename std::make_unsigned<typename |
| _UniformRandomNumberGenerator::result_type>::type __urngtype; |
| typedef typename std::make_unsigned<result_type>::type __utype; |
| typedef typename std::conditional<(sizeof(__urngtype) |
| > sizeof(__utype)), |
| __urngtype, __utype>::type __uctype; |
| |
| const __uctype __urngmin = __urng.min(); |
| const __uctype __urngmax = __urng.max(); |
| const __uctype __urngrange = __urngmax - __urngmin; |
| const __uctype __urange |
| = __uctype(__param.b()) - __uctype(__param.a()); |
| |
| __uctype __ret; |
| |
| if (__urngrange > __urange) |
| { |
| // downscaling |
| const __uctype __uerange = __urange + 1; // __urange can be zero |
| const __uctype __scaling = __urngrange / __uerange; |
| const __uctype __past = __uerange * __scaling; |
| do |
| __ret = __uctype(__urng()) - __urngmin; |
| while (__ret >= __past); |
| __ret /= __scaling; |
| } |
| else if (__urngrange < __urange) |
| { |
| // upscaling |
| /* |
| Note that every value in [0, urange] |
| can be written uniquely as |
| |
| (urngrange + 1) * high + low |
| |
| where |
| |
| high in [0, urange / (urngrange + 1)] |
| |
| and |
| |
| low in [0, urngrange]. |
| */ |
| __uctype __tmp; // wraparound control |
| do |
| { |
| const __uctype __uerngrange = __urngrange + 1; |
| __tmp = (__uerngrange * operator() |
| (__urng, param_type(0, __urange / __uerngrange))); |
| __ret = __tmp + (__uctype(__urng()) - __urngmin); |
| } |
| while (__ret > __urange || __ret < __tmp); |
| } |
| else |
| __ret = __uctype(__urng()) - __urngmin; |
| |
| return __ret + __param.a(); |
| } |
| |
| 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) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const _CharT __space = __os.widen(' '); |
| __os.flags(__ios_base::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) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::dec | __ios_base::skipws); |
| |
| _IntType __a, __b; |
| __is >> __a >> __b; |
| __x.param(typename uniform_int_distribution<_IntType>:: |
| param_type(__a, __b)); |
| |
| __is.flags(__flags); |
| return __is; |
| } |
| |
| |
| 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) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const std::streamsize __precision = __os.precision(); |
| const _CharT __space = __os.widen(' '); |
| __os.flags(__ios_base::scientific | __ios_base::left); |
| __os.fill(__space); |
| __os.precision(std::numeric_limits<_RealType>::max_digits10); |
| |
| __os << __x.a() << __space << __x.b(); |
| |
| __os.flags(__flags); |
| __os.fill(__fill); |
| __os.precision(__precision); |
| return __os; |
| } |
| |
| template<typename _RealType, typename _CharT, typename _Traits> |
| std::basic_istream<_CharT, _Traits>& |
| operator>>(std::basic_istream<_CharT, _Traits>& __is, |
| uniform_real_distribution<_RealType>& __x) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::skipws); |
| |
| _RealType __a, __b; |
| __is >> __a >> __b; |
| __x.param(typename uniform_real_distribution<_RealType>:: |
| param_type(__a, __b)); |
| |
| __is.flags(__flags); |
| return __is; |
| } |
| |
| |
| template<typename _CharT, typename _Traits> |
| std::basic_ostream<_CharT, _Traits>& |
| operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
| const bernoulli_distribution& __x) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const std::streamsize __precision = __os.precision(); |
| __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(__aurng()) / __param._M_log_1_p); |
| while (__cand >= __thr); |
| |
| return result_type(__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) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const std::streamsize __precision = __os.precision(); |
| __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) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::skipws); |
| |
| double __p; |
| __is >> __p; |
| __x.param(typename geometric_distribution<_IntType>::param_type(__p)); |
| |
| __is.flags(__flags); |
| return __is; |
| } |
| |
| |
| 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<result_type>::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, typename _CharT, typename _Traits> |
| std::basic_ostream<_CharT, _Traits>& |
| operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
| const negative_binomial_distribution<_IntType>& __x) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const std::streamsize __precision = __os.precision(); |
| const _CharT __space = __os.widen(' '); |
| __os.flags(__ios_base::scientific | __ios_base::left); |
| __os.fill(__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) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::skipws); |
| |
| _IntType __k; |
| double __p; |
| __is >> __k >> __p >> __x._M_gd; |
| __x.param(typename negative_binomial_distribution<_IntType>:: |
| 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(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; |
| // 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(__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; |
| else |
| { |
| const double __v = -std::log(__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, |
| typename _CharT, typename _Traits> |
| std::basic_ostream<_CharT, _Traits>& |
| operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
| const poisson_distribution<_IntType>& __x) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const std::streamsize __precision = __os.precision(); |
| const _CharT __space = __os.widen(' '); |
| __os.flags(__ios_base::scientific | __ios_base::left); |
| __os.fill(__space); |
| __os.precision(std::numeric_limits<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) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::skipws); |
| |
| double __mean; |
| __is >> __mean >> __x._M_nd; |
| __x.param(typename poisson_distribution<_IntType>::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(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(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) |
| { |
| _IntType __x = 0; |
| double __sum = 0.0; |
| __detail::_Adaptor<_UniformRandomNumberGenerator, double> |
| __aurng(__urng); |
| |
| do |
| { |
| const double __e = -std::log(__aurng()); |
| __sum += __e / (__t - __x); |
| __x += 1; |
| } |
| while (__sum <= _M_param._M_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(__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(__aurng()); |
| __x = std::floor(-__y); |
| __v = -__e - __n * __n / 2; |
| } |
| } |
| else if (__u <= __a123) |
| { |
| const double __e1 = -std::log(__aurng()); |
| const double __e2 = -std::log(__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(__aurng()); |
| const double __e2 = -std::log(__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)); |
| __ret = _IntType(__x) + __z; |
| } |
| else |
| #endif |
| __ret = _M_waiting(__urng, __t); |
| |
| if (__p12 != __p) |
| __ret = __t - __ret; |
| return __ret; |
| } |
| |
| template<typename _IntType, |
| typename _CharT, typename _Traits> |
| std::basic_ostream<_CharT, _Traits>& |
| operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
| const binomial_distribution<_IntType>& __x) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const std::streamsize __precision = __os.precision(); |
| const _CharT __space = __os.widen(' '); |
| __os.flags(__ios_base::scientific | __ios_base::left); |
| __os.fill(__space); |
| __os.precision(std::numeric_limits<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) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::dec | __ios_base::skipws); |
| |
| _IntType __t; |
| double __p; |
| __is >> __t >> __p >> __x._M_nd; |
| __x.param(typename binomial_distribution<_IntType>:: |
| param_type(__t, __p)); |
| |
| __is.flags(__flags); |
| return __is; |
| } |
| |
| |
| template<typename _RealType, typename _CharT, typename _Traits> |
| std::basic_ostream<_CharT, _Traits>& |
| operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
| const exponential_distribution<_RealType>& __x) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const std::streamsize __precision = __os.precision(); |
| __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) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::dec | __ios_base::skipws); |
| |
| _RealType __lambda; |
| __is >> __lambda; |
| __x.param(typename exponential_distribution<_RealType>:: |
| 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> |
| 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) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const std::streamsize __precision = __os.precision(); |
| const _CharT __space = __os.widen(' '); |
| __os.flags(__ios_base::scientific | __ios_base::left); |
| __os.fill(__space); |
| __os.precision(std::numeric_limits<_RealType>::max_digits10); |
| |
| __os << __x.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) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::dec | __ios_base::skipws); |
| |
| double __mean, __stddev; |
| __is >> __mean >> __stddev |
| >> __x._M_saved_available; |
| if (__x._M_saved_available) |
| __is >> __x._M_saved; |
| __x.param(typename normal_distribution<_RealType>:: |
| param_type(__mean, __stddev)); |
| |
| __is.flags(__flags); |
| return __is; |
| } |
| |
| |
| template<typename _RealType, typename _CharT, typename _Traits> |
| std::basic_ostream<_CharT, _Traits>& |
| operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
| const lognormal_distribution<_RealType>& __x) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const std::streamsize __precision = __os.precision(); |
| const _CharT __space = __os.widen(' '); |
| __os.flags(__ios_base::scientific | __ios_base::left); |
| __os.fill(__space); |
| __os.precision(std::numeric_limits<_RealType>::max_digits10); |
| |
| __os << __x.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) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::dec | __ios_base::skipws); |
| |
| _RealType __m, __s; |
| __is >> __m >> __s >> __x._M_nd; |
| __x.param(typename lognormal_distribution<_RealType>:: |
| param_type(__m, __s)); |
| |
| __is.flags(__flags); |
| return __is; |
| } |
| |
| |
| 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) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const std::streamsize __precision = __os.precision(); |
| const _CharT __space = __os.widen(' '); |
| __os.flags(__ios_base::scientific | __ios_base::left); |
| __os.fill(__space); |
| __os.precision(std::numeric_limits<_RealType>::max_digits10); |
| |
| __os << __x.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) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::dec | __ios_base::skipws); |
| |
| _RealType __n; |
| __is >> __n >> __x._M_gd; |
| __x.param(typename chi_squared_distribution<_RealType>:: |
| 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, typename _CharT, typename _Traits> |
| std::basic_ostream<_CharT, _Traits>& |
| operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
| const cauchy_distribution<_RealType>& __x) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const std::streamsize __precision = __os.precision(); |
| const _CharT __space = __os.widen(' '); |
| __os.flags(__ios_base::scientific | __ios_base::left); |
| __os.fill(__space); |
| __os.precision(std::numeric_limits<_RealType>::max_digits10); |
| |
| __os << __x.a() << __space << __x.b(); |
| |
| __os.flags(__flags); |
| __os.fill(__fill); |
| __os.precision(__precision); |
| return __os; |
| } |
| |
| template<typename _RealType, typename _CharT, typename _Traits> |
| std::basic_istream<_CharT, _Traits>& |
| operator>>(std::basic_istream<_CharT, _Traits>& __is, |
| cauchy_distribution<_RealType>& __x) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::dec | __ios_base::skipws); |
| |
| _RealType __a, __b; |
| __is >> __a >> __b; |
| __x.param(typename cauchy_distribution<_RealType>:: |
| param_type(__a, __b)); |
| |
| __is.flags(__flags); |
| return __is; |
| } |
| |
| |
| 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) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const std::streamsize __precision = __os.precision(); |
| const _CharT __space = __os.widen(' '); |
| __os.flags(__ios_base::scientific | __ios_base::left); |
| __os.fill(__space); |
| __os.precision(std::numeric_limits<_RealType>::max_digits10); |
| |
| __os << __x.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) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::dec | __ios_base::skipws); |
| |
| _RealType __m, __n; |
| __is >> __m >> __n >> __x._M_gd_x >> __x._M_gd_y; |
| __x.param(typename fisher_f_distribution<_RealType>:: |
| param_type(__m, __n)); |
| |
| __is.flags(__flags); |
| return __is; |
| } |
| |
| |
| 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) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const std::streamsize __precision = __os.precision(); |
| const _CharT __space = __os.widen(' '); |
| __os.flags(__ios_base::scientific | __ios_base::left); |
| __os.fill(__space); |
| __os.precision(std::numeric_limits<_RealType>::max_digits10); |
| |
| __os << __x.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) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::dec | __ios_base::skipws); |
| |
| _RealType __n; |
| __is >> __n >> __x._M_nd >> __x._M_gd; |
| __x.param(typename student_t_distribution<_RealType>::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) - 0.331 * __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, typename _CharT, typename _Traits> |
| std::basic_ostream<_CharT, _Traits>& |
| operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
| const gamma_distribution<_RealType>& __x) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const std::streamsize __precision = __os.precision(); |
| const _CharT __space = __os.widen(' '); |
| __os.flags(__ios_base::scientific | __ios_base::left); |
| __os.fill(__space); |
| __os.precision(std::numeric_limits<_RealType>::max_digits10); |
| |
| __os << __x.alpha() << __space << __x.beta() |
| << __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) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::dec | __ios_base::skipws); |
| |
| _RealType __alpha_val, __beta_val; |
| __is >> __alpha_val >> __beta_val >> __x._M_nd; |
| __x.param(typename gamma_distribution<_RealType>:: |
| 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(__aurng()), |
| result_type(1) / __p.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) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const std::streamsize __precision = __os.precision(); |
| const _CharT __space = __os.widen(' '); |
| __os.flags(__ios_base::scientific | __ios_base::left); |
| __os.fill(__space); |
| __os.precision(std::numeric_limits<_RealType>::max_digits10); |
| |
| __os << __x.a() << __space << __x.b(); |
| |
| __os.flags(__flags); |
| __os.fill(__fill); |
| __os.precision(__precision); |
| return __os; |
| } |
| |
| template<typename _RealType, typename _CharT, typename _Traits> |
| std::basic_istream<_CharT, _Traits>& |
| operator>>(std::basic_istream<_CharT, _Traits>& __is, |
| weibull_distribution<_RealType>& __x) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::dec | __ios_base::skipws); |
| |
| _RealType __a, __b; |
| __is >> __a >> __b; |
| __x.param(typename weibull_distribution<_RealType>:: |
| 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(__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) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const std::streamsize __precision = __os.precision(); |
| const _CharT __space = __os.widen(' '); |
| __os.flags(__ios_base::scientific | __ios_base::left); |
| __os.fill(__space); |
| __os.precision(std::numeric_limits<_RealType>::max_digits10); |
| |
| __os << __x.a() << __space << __x.b(); |
| |
| __os.flags(__flags); |
| __os.fill(__fill); |
| __os.precision(__precision); |
| return __os; |
| } |
| |
| template<typename _RealType, typename _CharT, typename _Traits> |
| std::basic_istream<_CharT, _Traits>& |
| operator>>(std::basic_istream<_CharT, _Traits>& __is, |
| extreme_value_distribution<_RealType>& __x) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::dec | __ios_base::skipws); |
| |
| _RealType __a, __b; |
| __is >> __a >> __b; |
| __x.param(typename extreme_value_distribution<_RealType>:: |
| 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::__transform(_M_prob.begin(), _M_prob.end(), _M_prob.begin(), |
| std::bind2nd(std::divides<double>(), __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, typename _CharT, typename _Traits> |
| std::basic_ostream<_CharT, _Traits>& |
| operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
| const discrete_distribution<_IntType>& __x) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const std::streamsize __precision = __os.precision(); |
| const _CharT __space = __os.widen(' '); |
| __os.flags(__ios_base::scientific | __ios_base::left); |
| __os.fill(__space); |
| __os.precision(std::numeric_limits<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; |
| } |
| |
| template<typename _IntType, typename _CharT, typename _Traits> |
| std::basic_istream<_CharT, _Traits>& |
| operator>>(std::basic_istream<_CharT, _Traits>& __is, |
| discrete_distribution<_IntType>& __x) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::dec | __ios_base::skipws); |
| |
| size_t __n; |
| __is >> __n; |
| |
| std::vector<double> __prob_vec; |
| __prob_vec.reserve(__n); |
| for (; __n != 0; --__n) |
| { |
| double __prob; |
| __is >> __prob; |
| __prob_vec.push_back(__prob); |
| } |
| |
| __x.param(typename discrete_distribution<_IntType>:: |
| param_type(__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::__transform(_M_den.begin(), _M_den.end(), _M_den.begin(), |
| std::bind2nd(std::divides<double>(), __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, typename _CharT, typename _Traits> |
| std::basic_ostream<_CharT, _Traits>& |
| operator<<(std::basic_ostream<_CharT, _Traits>& __os, |
| const piecewise_constant_distribution<_RealType>& __x) |
| { |
| typedef std::basic_ostream<_CharT, _Traits> __ostream_type; |
| typedef typename __ostream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __os.flags(); |
| const _CharT __fill = __os.fill(); |
| const std::streamsize __precision = __os.precision(); |
| const _CharT __space = __os.widen(' '); |
| __os.flags(__ios_base::scientific | __ios_base::left); |
| __os.fill(__space); |
| __os.precision(std::numeric_limits<_RealType>::max_digits10); |
| |
| 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) |
| { |
| typedef std::basic_istream<_CharT, _Traits> __istream_type; |
| typedef typename __istream_type::ios_base __ios_base; |
| |
| const typename __ios_base::fmtflags __flags = __is.flags(); |
| __is.flags(__ios_base::dec | __ios_base::skipws); |
| |
| size_t __n; |
| __is >> __n; |
| |
| std::vector<_RealType> __int_vec; |
| __int_vec.reserve(__n + 1); |
| for (size_t __i = 0; __i <= __n; ++__i) |
| { |
| _RealType __int; |
| __is >> __int; |
| __int_vec.push_back(__int); |
| } |
| |
| std::vector<double> __den_vec; |
| __den_vec.reserve(__n); |
| for (size_t __i = 0; __i < __n; ++__i) |
| { |
| double __den; |
| __is >> __den; |
| __den_vec.push_back(__den); |
| } |
| |
| __x.param(typename piecewise_constant_distribution<_RealType>:: |
| param_type(__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::__transform(_M_den.begin(), _M_den.end(), _M_den.begin(), |
| std::bind2nd(std::divides<double>(), __sum)); |
| // ... and partial sums... |
| __detail::__transform(_M_cp.begin(), _M_cp.end(), _M_cp.begin(), |
| std::bind2nd(std::divides<double>(), __sum)); |
| // ... and slopes. |
| __detail::__transform(_M_m.begin(), _M_m.end(), _M_m.begin(), |
| std::bind2nd(std::divides<double>(), __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]; |
| |