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// 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_sumnamespace 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_VERSIONtemplate<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>constexpr _UIntTypelinear_congruential_engine<_UIntType, __a, __c, __m>::multiplier;template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>constexpr _UIntTypelinear_congruential_engine<_UIntType, __a, __c, __m>::increment;template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>constexpr _UIntTypelinear_congruential_engine<_UIntType, __a, __c, __m>::modulus;template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>constexpr _UIntTypelinear_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>voidlinear_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>::typelinear_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_tmersenne_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_tmersenne_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_tmersenne_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_tmersenne_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 _UIntTypemersenne_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_tmersenne_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 _UIntTypemersenne_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_tmersenne_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 _UIntTypemersenne_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_tmersenne_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 _UIntTypemersenne_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_tmersenne_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 _UIntTypemersenne_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 _UIntTypemersenne_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>voidmersenne_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>::typemersenne_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>typenamemersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,__s, __b, __t, __c, __l, __f>::result_typemersenne_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; ++__i)__os << __x._M_x[__i] << __space;__os << __x._M_p;__os.flags(__flags);__os.fill(__fill);return __os;}template<typename _UIntType, size_t __w,size_t __n, size_t __m, size_t __r,_UIntType __a, size_t __u, _UIntType __d, size_t __s,_UIntType __b, size_t __t, _UIntType __c, size_t __l,_UIntType __f, typename _CharT, typename _Traits>std::basic_istream<_CharT, _Traits>&operator>>(std::basic_istream<_CharT, _Traits>& __is,mersenne_twister_engine<_UIntType, __w, __n, __m,__r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x){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<typename _UIntType, size_t __w, size_t __s, size_t __r>constexpr size_tsubtract_with_carry_engine<_UIntType, __w, __s, __r>::word_size;template<typename _UIntType, size_t __w, size_t __s, size_t __r>constexpr size_tsubtract_with_carry_engine<_UIntType, __w, __s, __r>::short_lag;template<typename _UIntType, size_t __w, size_t __s, size_t __r>constexpr size_tsubtract_with_carry_engine<_UIntType, __w, __s, __r>::long_lag;template<typename _UIntType, size_t __w, size_t __s, size_t __r>constexpr _UIntTypesubtract_with_carry_engine<_UIntType, __w, __s, __r>::default_seed;template<typename _UIntType, size_t __w, size_t __s, size_t __r>voidsubtract_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>::typesubtract_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_typesubtract_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 << __space << __x._M_p;__os.flags(__flags);__os.fill(__fill);return __os;}template<typename _UIntType, size_t __w, size_t __s, size_t __r,typename _CharT, typename _Traits>std::basic_istream<_CharT, _Traits>&operator>>(std::basic_istream<_CharT, _Traits>& __is,subtract_with_carry_engine<_UIntType, __w, __s, __r>& __x){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<typename _RandomNumberEngine, size_t __p, size_t __r>constexpr size_tdiscard_block_engine<_RandomNumberEngine, __p, __r>::block_size;template<typename _RandomNumberEngine, size_t __p, size_t __r>constexpr size_tdiscard_block_engine<_RandomNumberEngine, __p, __r>::used_block;template<typename _RandomNumberEngine, size_t __p, size_t __r>typename discard_block_engine<_RandomNumberEngine,__p, __r>::result_typediscard_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_typeindependent_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_tshuffle_order_engine<_RandomNumberEngine, __k>::table_size;template<typename _RandomNumberEngine, size_t __k>typename shuffle_order_engine<_RandomNumberEngine, __k>::result_typeshuffle_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_typeuniform_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){// downscalingconst __uctype __uerange = __urange + 1; // __urange can be zeroconst __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 + lowwherehigh in [0, urange / (urngrange + 1)]andlow in [0, urngrange].*/__uctype __tmp; // wraparound controldo{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_typegeometric_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.htmlconst 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;}// This is Leger's algorithm, also in Devroye, Ch. X, Example 1.5.template<typename _IntType>template<typename _UniformRandomNumberGenerator>typename negative_binomial_distribution<_IntType>::result_typenegative_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_typenegative_binomial_distribution<_IntType>::operator()(_UniformRandomNumberGenerator& __urng,const param_type& __p){typedef typename std::gamma_distribution<result_type>::param_typeparam_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>voidpoisson_distribution<_IntType>::param_type::_M_initialize(){#if _GLIBCXX_USE_C99_MATH_TR1if (_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_typepoisson_distribution<_IntType>::operator()(_UniformRandomNumberGenerator& __urng,const param_type& __param){__detail::_Adaptor<_UniformRandomNumberGenerator, double>__aurng(__urng);#if _GLIBCXX_USE_C99_MATH_TR1if (__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>voidbinomial_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_TR1if (_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_typebinomial_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_typebinomial_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_TR1if (!__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_typenormal_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>booloperator==(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;elsereturn false;}elsereturn 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_typecauchy_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>voidgamma_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_typegamma_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_typeweibull_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_typeextreme_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>voiddiscrete_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_typediscrete_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>voidpiecewise_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_typepiecewise_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>voidpiecewise_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_typepiecewise_linear_distribution<_RealType>::operator()(_UniformRandomNumberGenerator& __urng,const param_type& __param){__detail::_Adaptor<_UniformRandomNumberGenerator, double>__aurng(__urng);const double __p = __aurng();if (__param._M_cp.empty())return __p;auto __pos = std::lower_bound(__param._M_cp.begin(),__param._M_cp.end(), __p);const size_t __i = __pos - __param._M_cp.begin();const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0;const double __a = 0.5 * __param._M_m[__i];const double __b = __param._M_den[__i];const double __cm = __p - __pref;_RealType __x = __param._M_int[__i];if (__a == 0)__x += __cm / __b;else{const double __d = __b * __b + 4.0 * __a * __cm;__x += 0.5 * (std::sqrt(__d) - __b) / __a;}return __x;}template<typename _RealType, typename _CharT, typename _Traits>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<double> __den = __x.densities();for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit)__os << __space << *__dit;__os.flags(__flags);__os.fill(__fill);__os.precision(__precision);return __os;}template<typename _RealType, typename _CharT, typename _Traits>std::basic_istream<_CharT, _Traits>&operator>>(std::basic_istream<_CharT, _Traits>& __is,piecewise_linear_distribution<_RealType>& __x){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 + 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<typename _IntType>seed_seq::seed_seq(std::initializer_list<_IntType> __il){for (auto __iter = __il.begin(); __iter != __il.end(); ++__iter)_M_v.push_back(__detail::__mod<result_type,__detail::_Shift<result_type, 32>::__value>(*__iter));}template<typename _InputIterator>seed_seq::seed_seq(_InputIterator __begin, _InputIterator __end){for (_InputIterator __iter = __begin; __iter != __end; ++__iter)_M_v.push_back(__detail::__mod<result_type,__detail::_Shift<result_type, 32>::__value>(*__iter));}template<typename _RandomAccessIterator>voidseed_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(__s + 1, __n);for (size_t __k = 0; __k < __m; ++__k){_Type __arg = (__begin[__k % __n]^ __begin[(__k + __p) % __n]^ __begin[(__k - 1) % __n]);_Type __r1 = __arg ^ (__arg >> 27);__r1 = __detail::__mod<_Type,__detail::_Shift<_Type, 32>::__value>(1664525u * __r1);_Type __r2 = __r1;if (__k == 0)__r2 += __s;else if (__k <= __s)__r2 += __k % __n + _M_v[__k - 1];else__r2 += __k % __n;__r2 = __detail::__mod<_Type,__detail::_Shift<_Type, 32>::__value>(__r2);__begin[(__k + __p) % __n] += __r1;__begin[(__k + __q) % __n] += __r2;__begin[__k % __n] = __r2;}for (size_t __k = __m; __k < __m + __n; ++__k){_Type __arg = (__begin[__k % __n]+ __begin[(__k + __p) % __n]+ __begin[(__k - 1) % __n]);_Type __r3 = __arg ^ (__arg >> 27);__r3 = __detail::__mod<_Type,__detail::_Shift<_Type, 32>::__value>(1566083941u * __r3);_Type __r4 = __r3 - __k % __n;__r4 = __detail::__mod<_Type,__detail::_Shift<_Type, 32>::__value>(__r4);__begin[(__k + __p) % __n] ^= __r3;__begin[(__k + __q) % __n] ^= __r4;__begin[__k % __n] = __r4;}}template<typename _RealType, size_t __bits,typename _UniformRandomNumberGenerator>_RealTypegenerate_canonical(_UniformRandomNumberGenerator& __urng){const size_t __b= std::min(static_cast<size_t>(std::numeric_limits<_RealType>::digits),__bits);const long double __r = static_cast<long double>(__urng.max())- static_cast<long double>(__urng.min()) + 1.0L;const size_t __log2r = std::log(__r) / std::log(2.0L);size_t __k = std::max<size_t>(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