X-Git-Url: http://wagnertech.de/git?a=blobdiff_plain;f=i686-linux-gnu-4.7%2Fusr%2Finclude%2Fc%2B%2B%2F4.7%2Fbits%2Frandom.tcc;fp=i686-linux-gnu-4.7%2Fusr%2Finclude%2Fc%2B%2B%2F4.7%2Fbits%2Frandom.tcc;h=db1bd04cede0dcddc01a6c11003dd51e50377e42;hb=94df942c2c7bd3457276fe5b7367623cbb8c1302;hp=0000000000000000000000000000000000000000;hpb=4dd7d9155a920895ff7b1cb6b9c9c676aa62000a;p=cross.git diff --git a/i686-linux-gnu-4.7/usr/include/c++/4.7/bits/random.tcc b/i686-linux-gnu-4.7/usr/include/c++/4.7/bits/random.tcc new file mode 100644 index 0000000..db1bd04 --- /dev/null +++ b/i686-linux-gnu-4.7/usr/include/c++/4.7/bits/random.tcc @@ -0,0 +1,2860 @@ +// random number generation (out of line) -*- C++ -*- + +// Copyright (C) 2009-2012 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 +// . + +/** @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 // 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 + 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 + struct _Mod<_Tp, __m, __a, __c, true> + { + static _Tp + __calc(_Tp __x) + { return __a * __x + __c; } + }; + + template + _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 + constexpr _UIntType + linear_congruential_engine<_UIntType, __a, __c, __m>::multiplier; + + template + constexpr _UIntType + linear_congruential_engine<_UIntType, __a, __c, __m>::increment; + + template + constexpr _UIntType + linear_congruential_engine<_UIntType, __a, __c, __m>::modulus; + + template + 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 + 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 + template + typename std::enable_if::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 + 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 + 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 + constexpr size_t + mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, + __s, __b, __t, __c, __l, __f>::word_size; + + template + constexpr size_t + mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, + __s, __b, __t, __c, __l, __f>::state_size; + + template + constexpr size_t + mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, + __s, __b, __t, __c, __l, __f>::shift_size; + + template + constexpr size_t + mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, + __s, __b, __t, __c, __l, __f>::mask_bits; + + template + constexpr _UIntType + mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, + __s, __b, __t, __c, __l, __f>::xor_mask; + + template + constexpr size_t + mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, + __s, __b, __t, __c, __l, __f>::tempering_u; + + template + constexpr _UIntType + mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, + __s, __b, __t, __c, __l, __f>::tempering_d; + + template + constexpr size_t + mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, + __s, __b, __t, __c, __l, __f>::tempering_s; + + template + constexpr _UIntType + mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, + __s, __b, __t, __c, __l, __f>::tempering_b; + + template + constexpr size_t + mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, + __s, __b, __t, __c, __l, __f>::tempering_t; + + template + constexpr _UIntType + mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, + __s, __b, __t, __c, __l, __f>::tempering_c; + + template + constexpr size_t + mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, + __s, __b, __t, __c, __l, __f>::tempering_l; + + template + constexpr _UIntType + mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, + __s, __b, __t, __c, __l, __f>:: + initialization_multiplier; + + template + constexpr _UIntType + mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, + __s, __b, __t, __c, __l, __f>::default_seed; + + template + 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 + template + typename std::enable_if::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; + _M_p = state_size; + } + + template + 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 + 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; ++__i) + __os << __x._M_x[__i] << __space; + __os << __x._M_p; + + __os.flags(__flags); + __os.fill(__fill); + return __os; + } + + template + 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 >> __x._M_p; + + __is.flags(__flags); + return __is; + } + + + template + constexpr size_t + subtract_with_carry_engine<_UIntType, __w, __s, __r>::word_size; + + template + constexpr size_t + subtract_with_carry_engine<_UIntType, __w, __s, __r>::short_lag; + + template + constexpr size_t + subtract_with_carry_engine<_UIntType, __w, __s, __r>::long_lag; + + template + constexpr _UIntType + subtract_with_carry_engine<_UIntType, __w, __s, __r>::default_seed; + + template + void + subtract_with_carry_engine<_UIntType, __w, __s, __r>:: + seed(result_type __value) + { + std::linear_congruential_engine + __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::__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 + template + typename std::enable_if::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 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 + 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 << __space << __x._M_p; + + __os.flags(__flags); + __os.fill(__fill); + return __os; + } + + template + 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 >> __x._M_p; + + __is.flags(__flags); + return __is; + } + + + template + constexpr size_t + discard_block_engine<_RandomNumberEngine, __p, __r>::block_size; + + template + constexpr size_t + discard_block_engine<_RandomNumberEngine, __p, __r>::used_block; + + template + 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 + 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 + 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 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 + constexpr size_t + shuffle_order_engine<_RandomNumberEngine, __k>::table_size; + + template + 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 + 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 + 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 + template + typename uniform_int_distribution<_IntType>::result_type + uniform_int_distribution<_IntType>:: + operator()(_UniformRandomNumberGenerator& __urng, + const param_type& __param) + { + typedef typename _UniformRandomNumberGenerator::result_type + _Gresult_type; + typedef typename std::make_unsigned::type __utype; + typedef typename std::common_type<_Gresult_type, __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 + 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 + 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 + 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 + 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 + 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::max_digits10); + + __os << __x.p(); + + __os.flags(__flags); + __os.fill(__fill); + __os.precision(__precision); + return __os; + } + + + template + template + 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::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 + 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::max_digits10); + + __os << __x.p(); + + __os.flags(__flags); + __os.fill(__fill); + __os.precision(__precision); + return __os; + } + + template + 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; + } + + // This is Leger's algorithm, also in Devroye, Ch. X, Example 1.5. + template + template + 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 __poisson(__y); + return __poisson(__urng); + } + + template + template + typename negative_binomial_distribution<_IntType>::result_type + negative_binomial_distribution<_IntType>:: + operator()(_UniformRandomNumberGenerator& __urng, + const param_type& __p) + { + typedef typename std::gamma_distribution::param_type + param_type; + + const double __y = + _M_gd(__urng, param_type(__p.k(), (1.0 - __p.p()) / __p.p())); + + std::poisson_distribution __poisson(__y); + return __poisson(__urng); + } + + template + 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::max_digits10); + + __os << __x.k() << __space << __x.p() + << __space << __x._M_gd; + + __os.flags(__flags); + __os.fill(__fill); + __os.precision(__precision); + return __os; + } + + template + 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 + 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 + template + 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::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(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; + 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 + 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::max_digits10); + + __os << __x.mean() << __space << __x._M_nd; + + __os.flags(__flags); + __os.fill(__fill); + __os.precision(__precision); + return __os; + } + + template + 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 + 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 + template + 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(1.0 - __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 + template + 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::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)); + __ret = _IntType(__x) + __z; + } + else +#endif + __ret = _M_waiting(__urng, __t); + + if (__p12 != __p) + __ret = __t - __ret; + return __ret; + } + + template + 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::max_digits10); + + __os << __x.t() << __space << __x.p() + << __space << __x._M_nd; + + __os.flags(__flags); + __os.fill(__fill); + __os.precision(__precision); + return __os; + } + + template + 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 + 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 + 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 + template + 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 + 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 + 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 + 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 + 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 + 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 + 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 + 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 + template + 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 + 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 + 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 + 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 + 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 + 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 + 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 + 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 + template + 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 + 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 + 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 + template + 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 + 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 + 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 + template + 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 + 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 + 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 + 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(), __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 + template + 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 + template + 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 + 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::max_digits10); + + std::vector __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 + 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 __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 + 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(), __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 + template + 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 + template + 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 + template + 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 + template + 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 + 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 __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 + 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 __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 + 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(), __sum)); + // ... and partial sums... + __detail::__transform(_M_cp.begin(), _M_cp.end(), _M_cp.begin(), + std::bind2nd(std::divides(), __sum)); + // ... and slopes. + __detail::__transform(_M_m.begin(), _M_m.end(), _M_m.begin(), + std::bind2nd(std::divides(), __sum)); + // Make sure the last cumulative probablility is one. + _M_cp[_M_cp.size() - 1] = 1.0; + } + + template + template + 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 + template + 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 + template + 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 + template + 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 + std::basic_ostream<_CharT, _Traits>& + operator<<(std::basic_ostream<_CharT, _Traits>& __os, + const piecewise_linear_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 __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 + std::basic_istream<_CharT, _Traits>& + operator>>(std::basic_istream<_CharT, _Traits>& __is, + piecewise_linear_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 __den_vec; + __den_vec.reserve(__n + 1); + for (size_t __i = 0; __i <= __n; ++__i) + { + double __den; + __is >> __den; + __den_vec.push_back(__den); + } + + __x.param(typename piecewise_linear_distribution<_RealType>:: + param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin())); + + __is.flags(__flags); + return __is; + } + + + template + seed_seq::seed_seq(std::initializer_list<_IntType> __il) + { + for (auto __iter = __il.begin(); __iter != __il.end(); ++__iter) + _M_v.push_back(__detail::__mod::__value>(*__iter)); + } + + template + seed_seq::seed_seq(_InputIterator __begin, _InputIterator __end) + { + for (_InputIterator __iter = __begin; __iter != __end; ++__iter) + _M_v.push_back(__detail::__mod::__value>(*__iter)); + } + + template + 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 + _RealType + generate_canonical(_UniformRandomNumberGenerator& __urng) + { + const size_t __b + = std::min(static_cast(std::numeric_limits<_RealType>::digits), + __bits); + const long double __r = static_cast(__urng.max()) + - static_cast(__urng.min()) + 1.0L; + const size_t __log2r = std::log(__r) / std::log(2.0L); + size_t __k = std::max(1UL, (__b + __log2r - 1UL) / __log2r); + _RealType __sum = _RealType(0); + _RealType __tmp = _RealType(1); + for (; __k != 0; --__k) + { + __sum += _RealType(__urng() - __urng.min()) * __tmp; + __tmp *= __r; + } + return __sum / __tmp; + } + +_GLIBCXX_END_NAMESPACE_VERSION +} // namespace + +#endif