--- /dev/null
+// random number generation (out of line) -*- C++ -*-
+
+// Copyright (C) 2007, 2009 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 tr1_impl/random.tcc
+ * This is an internal header file, included by other library headers.
+ * You should not attempt to use it directly.
+ */
+
+namespace std
+{
+_GLIBCXX_BEGIN_NAMESPACE_TR1
+
+ /*
+ * (Further) implementation-space details.
+ */
+ namespace __detail
+ {
+ // 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.
+ //
+ template<typename _Tp, _Tp __a, _Tp __c, _Tp __m, 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 __a, _Tp __c, _Tp __m>
+ struct _Mod<_Tp, __a, __c, __m, true>
+ {
+ static _Tp
+ __calc(_Tp __x)
+ { return __a * __x + __c; }
+ };
+ } // namespace __detail
+
+ /**
+ * Seeds the LCR with integral value @p __x0, adjusted so that the
+ * ring identity is never a member of the convergence set.
+ */
+ template<class _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
+ void
+ linear_congruential<_UIntType, __a, __c, __m>::
+ seed(unsigned long __x0)
+ {
+ if ((__detail::__mod<_UIntType, 1, 0, __m>(__c) == 0)
+ && (__detail::__mod<_UIntType, 1, 0, __m>(__x0) == 0))
+ _M_x = __detail::__mod<_UIntType, 1, 0, __m>(1);
+ else
+ _M_x = __detail::__mod<_UIntType, 1, 0, __m>(__x0);
+ }
+
+ /**
+ * Seeds the LCR engine with a value generated by @p __g.
+ */
+ template<class _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
+ template<class _Gen>
+ void
+ linear_congruential<_UIntType, __a, __c, __m>::
+ seed(_Gen& __g, false_type)
+ {
+ _UIntType __x0 = __g();
+ if ((__detail::__mod<_UIntType, 1, 0, __m>(__c) == 0)
+ && (__detail::__mod<_UIntType, 1, 0, __m>(__x0) == 0))
+ _M_x = __detail::__mod<_UIntType, 1, 0, __m>(1);
+ else
+ _M_x = __detail::__mod<_UIntType, 1, 0, __m>(__x0);
+ }
+
+ /**
+ * Gets the next generated value in sequence.
+ */
+ template<class _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
+ typename linear_congruential<_UIntType, __a, __c, __m>::result_type
+ linear_congruential<_UIntType, __a, __c, __m>::
+ operator()()
+ {
+ _M_x = __detail::__mod<_UIntType, __a, __c, __m>(_M_x);
+ return _M_x;
+ }
+
+ template<class _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<_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<class _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<_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<class _UIntType, int __w, int __n, int __m, int __r,
+ _UIntType __a, int __u, int __s,
+ _UIntType __b, int __t, _UIntType __c, int __l>
+ void
+ mersenne_twister<_UIntType, __w, __n, __m, __r, __a, __u, __s,
+ __b, __t, __c, __l>::
+ seed(unsigned long __value)
+ {
+ _M_x[0] = __detail::__mod<_UIntType, 1, 0,
+ __detail::_Shift<_UIntType, __w>::__value>(__value);
+
+ for (int __i = 1; __i < state_size; ++__i)
+ {
+ _UIntType __x = _M_x[__i - 1];
+ __x ^= __x >> (__w - 2);
+ __x *= 1812433253ul;
+ __x += __i;
+ _M_x[__i] = __detail::__mod<_UIntType, 1, 0,
+ __detail::_Shift<_UIntType, __w>::__value>(__x);
+ }
+ _M_p = state_size;
+ }
+
+ template<class _UIntType, int __w, int __n, int __m, int __r,
+ _UIntType __a, int __u, int __s,
+ _UIntType __b, int __t, _UIntType __c, int __l>
+ template<class _Gen>
+ void
+ mersenne_twister<_UIntType, __w, __n, __m, __r, __a, __u, __s,
+ __b, __t, __c, __l>::
+ seed(_Gen& __gen, false_type)
+ {
+ for (int __i = 0; __i < state_size; ++__i)
+ _M_x[__i] = __detail::__mod<_UIntType, 1, 0,
+ __detail::_Shift<_UIntType, __w>::__value>(__gen());
+ _M_p = state_size;
+ }
+
+ template<class _UIntType, int __w, int __n, int __m, int __r,
+ _UIntType __a, int __u, int __s,
+ _UIntType __b, int __t, _UIntType __c, int __l>
+ typename
+ mersenne_twister<_UIntType, __w, __n, __m, __r, __a, __u, __s,
+ __b, __t, __c, __l>::result_type
+ mersenne_twister<_UIntType, __w, __n, __m, __r, __a, __u, __s,
+ __b, __t, __c, __l>::
+ 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 (int __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 (int __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);
+ __z ^= (__z << __s) & __b;
+ __z ^= (__z << __t) & __c;
+ __z ^= (__z >> __l);
+
+ return __z;
+ }
+
+ template<class _UIntType, int __w, int __n, int __m, int __r,
+ _UIntType __a, int __u, int __s, _UIntType __b, int __t,
+ _UIntType __c, int __l,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const mersenne_twister<_UIntType, __w, __n, __m,
+ __r, __a, __u, __s, __b, __t, __c, __l>& __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 (int __i = 0; __i < __n - 1; ++__i)
+ __os << __x._M_x[__i] << __space;
+ __os << __x._M_x[__n - 1];
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ return __os;
+ }
+
+ template<class _UIntType, int __w, int __n, int __m, int __r,
+ _UIntType __a, int __u, int __s, _UIntType __b, int __t,
+ _UIntType __c, int __l,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ mersenne_twister<_UIntType, __w, __n, __m,
+ __r, __a, __u, __s, __b, __t, __c, __l>& __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 (int __i = 0; __i < __n; ++__i)
+ __is >> __x._M_x[__i];
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _IntType, _IntType __m, int __s, int __r>
+ void
+ subtract_with_carry<_IntType, __m, __s, __r>::
+ seed(unsigned long __value)
+ {
+ if (__value == 0)
+ __value = 19780503;
+
+ std::_GLIBCXX_TR1 linear_congruential<unsigned long, 40014, 0, 2147483563>
+ __lcg(__value);
+
+ for (int __i = 0; __i < long_lag; ++__i)
+ _M_x[__i] = __detail::__mod<_UIntType, 1, 0, modulus>(__lcg());
+
+ _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0;
+ _M_p = 0;
+ }
+
+ template<typename _IntType, _IntType __m, int __s, int __r>
+ template<class _Gen>
+ void
+ subtract_with_carry<_IntType, __m, __s, __r>::
+ seed(_Gen& __gen, false_type)
+ {
+ const int __n = (std::numeric_limits<_UIntType>::digits + 31) / 32;
+
+ for (int __i = 0; __i < long_lag; ++__i)
+ {
+ _UIntType __tmp = 0;
+ _UIntType __factor = 1;
+ for (int __j = 0; __j < __n; ++__j)
+ {
+ __tmp += __detail::__mod<__detail::_UInt32Type, 1, 0, 0>
+ (__gen()) * __factor;
+ __factor *= __detail::_Shift<_UIntType, 32>::__value;
+ }
+ _M_x[__i] = __detail::__mod<_UIntType, 1, 0, modulus>(__tmp);
+ }
+ _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0;
+ _M_p = 0;
+ }
+
+ template<typename _IntType, _IntType __m, int __s, int __r>
+ typename subtract_with_carry<_IntType, __m, __s, __r>::result_type
+ subtract_with_carry<_IntType, __m, __s, __r>::
+ operator()()
+ {
+ // Derive short lag index from current index.
+ int __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 _IntType, _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 = modulus - _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 _IntType, _IntType __m, int __s, int __r,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const subtract_with_carry<_IntType, __m, __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 (int __i = 0; __i < __r; ++__i)
+ __os << __x._M_x[__i] << __space;
+ __os << __x._M_carry;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ return __os;
+ }
+
+ template<typename _IntType, _IntType __m, int __s, int __r,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ subtract_with_carry<_IntType, __m, __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 (int __i = 0; __i < __r; ++__i)
+ __is >> __x._M_x[__i];
+ __is >> __x._M_carry;
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType, int __w, int __s, int __r>
+ void
+ subtract_with_carry_01<_RealType, __w, __s, __r>::
+ _M_initialize_npows()
+ {
+ for (int __j = 0; __j < __n; ++__j)
+#if _GLIBCXX_USE_C99_MATH_TR1
+ _M_npows[__j] = std::_GLIBCXX_TR1 ldexp(_RealType(1), -__w + __j * 32);
+#else
+ _M_npows[__j] = std::pow(_RealType(2), -__w + __j * 32);
+#endif
+ }
+
+ template<typename _RealType, int __w, int __s, int __r>
+ void
+ subtract_with_carry_01<_RealType, __w, __s, __r>::
+ seed(unsigned long __value)
+ {
+ if (__value == 0)
+ __value = 19780503;
+
+ // _GLIBCXX_RESOLVE_LIB_DEFECTS
+ // 512. Seeding subtract_with_carry_01 from a single unsigned long.
+ std::_GLIBCXX_TR1 linear_congruential<unsigned long, 40014, 0, 2147483563>
+ __lcg(__value);
+
+ this->seed(__lcg);
+ }
+
+ template<typename _RealType, int __w, int __s, int __r>
+ template<class _Gen>
+ void
+ subtract_with_carry_01<_RealType, __w, __s, __r>::
+ seed(_Gen& __gen, false_type)
+ {
+ for (int __i = 0; __i < long_lag; ++__i)
+ {
+ for (int __j = 0; __j < __n - 1; ++__j)
+ _M_x[__i][__j] = __detail::__mod<_UInt32Type, 1, 0, 0>(__gen());
+ _M_x[__i][__n - 1] = __detail::__mod<_UInt32Type, 1, 0,
+ __detail::_Shift<_UInt32Type, __w % 32>::__value>(__gen());
+ }
+
+ _M_carry = 1;
+ for (int __j = 0; __j < __n; ++__j)
+ if (_M_x[long_lag - 1][__j] != 0)
+ {
+ _M_carry = 0;
+ break;
+ }
+
+ _M_p = 0;
+ }
+
+ template<typename _RealType, int __w, int __s, int __r>
+ typename subtract_with_carry_01<_RealType, __w, __s, __r>::result_type
+ subtract_with_carry_01<_RealType, __w, __s, __r>::
+ operator()()
+ {
+ // Derive short lag index from current index.
+ int __ps = _M_p - short_lag;
+ if (__ps < 0)
+ __ps += long_lag;
+
+ _UInt32Type __new_carry;
+ for (int __j = 0; __j < __n - 1; ++__j)
+ {
+ if (_M_x[__ps][__j] > _M_x[_M_p][__j]
+ || (_M_x[__ps][__j] == _M_x[_M_p][__j] && _M_carry == 0))
+ __new_carry = 0;
+ else
+ __new_carry = 1;
+
+ _M_x[_M_p][__j] = _M_x[__ps][__j] - _M_x[_M_p][__j] - _M_carry;
+ _M_carry = __new_carry;
+ }
+
+ if (_M_x[__ps][__n - 1] > _M_x[_M_p][__n - 1]
+ || (_M_x[__ps][__n - 1] == _M_x[_M_p][__n - 1] && _M_carry == 0))
+ __new_carry = 0;
+ else
+ __new_carry = 1;
+
+ _M_x[_M_p][__n - 1] = __detail::__mod<_UInt32Type, 1, 0,
+ __detail::_Shift<_UInt32Type, __w % 32>::__value>
+ (_M_x[__ps][__n - 1] - _M_x[_M_p][__n - 1] - _M_carry);
+ _M_carry = __new_carry;
+
+ result_type __ret = 0.0;
+ for (int __j = 0; __j < __n; ++__j)
+ __ret += _M_x[_M_p][__j] * _M_npows[__j];
+
+ // Adjust current index to loop around in ring buffer.
+ if (++_M_p >= long_lag)
+ _M_p = 0;
+
+ return __ret;
+ }
+
+ template<typename _RealType, int __w, int __s, int __r,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const subtract_with_carry_01<_RealType, __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 (int __i = 0; __i < __r; ++__i)
+ for (int __j = 0; __j < __x.__n; ++__j)
+ __os << __x._M_x[__i][__j] << __space;
+ __os << __x._M_carry;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ return __os;
+ }
+
+ template<typename _RealType, int __w, int __s, int __r,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ subtract_with_carry_01<_RealType, __w, __s, __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);
+
+ for (int __i = 0; __i < __r; ++__i)
+ for (int __j = 0; __j < __x.__n; ++__j)
+ __is >> __x._M_x[__i][__j];
+ __is >> __x._M_carry;
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<class _UniformRandomNumberGenerator, int __p, int __r>
+ typename discard_block<_UniformRandomNumberGenerator,
+ __p, __r>::result_type
+ discard_block<_UniformRandomNumberGenerator, __p, __r>::
+ operator()()
+ {
+ if (_M_n >= used_block)
+ {
+ while (_M_n < block_size)
+ {
+ _M_b();
+ ++_M_n;
+ }
+ _M_n = 0;
+ }
+ ++_M_n;
+ return _M_b();
+ }
+
+ template<class _UniformRandomNumberGenerator, int __p, int __r,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const discard_block<_UniformRandomNumberGenerator,
+ __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._M_b << __space << __x._M_n;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ return __os;
+ }
+
+ template<class _UniformRandomNumberGenerator, int __p, int __r,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ discard_block<_UniformRandomNumberGenerator, __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<class _UniformRandomNumberGenerator1, int __s1,
+ class _UniformRandomNumberGenerator2, int __s2>
+ void
+ xor_combine<_UniformRandomNumberGenerator1, __s1,
+ _UniformRandomNumberGenerator2, __s2>::
+ _M_initialize_max()
+ {
+ const int __w = std::numeric_limits<result_type>::digits;
+
+ const result_type __m1 =
+ std::min(result_type(_M_b1.max() - _M_b1.min()),
+ __detail::_Shift<result_type, __w - __s1>::__value - 1);
+
+ const result_type __m2 =
+ std::min(result_type(_M_b2.max() - _M_b2.min()),
+ __detail::_Shift<result_type, __w - __s2>::__value - 1);
+
+ // NB: In TR1 s1 is not required to be >= s2.
+ if (__s1 < __s2)
+ _M_max = _M_initialize_max_aux(__m2, __m1, __s2 - __s1) << __s1;
+ else
+ _M_max = _M_initialize_max_aux(__m1, __m2, __s1 - __s2) << __s2;
+ }
+
+ template<class _UniformRandomNumberGenerator1, int __s1,
+ class _UniformRandomNumberGenerator2, int __s2>
+ typename xor_combine<_UniformRandomNumberGenerator1, __s1,
+ _UniformRandomNumberGenerator2, __s2>::result_type
+ xor_combine<_UniformRandomNumberGenerator1, __s1,
+ _UniformRandomNumberGenerator2, __s2>::
+ _M_initialize_max_aux(result_type __a, result_type __b, int __d)
+ {
+ const result_type __two2d = result_type(1) << __d;
+ const result_type __c = __a * __two2d;
+
+ if (__a == 0 || __b < __two2d)
+ return __c + __b;
+
+ const result_type __t = std::max(__c, __b);
+ const result_type __u = std::min(__c, __b);
+
+ result_type __ub = __u;
+ result_type __p;
+ for (__p = 0; __ub != 1; __ub >>= 1)
+ ++__p;
+
+ const result_type __two2p = result_type(1) << __p;
+ const result_type __k = __t / __two2p;
+
+ if (__k & 1)
+ return (__k + 1) * __two2p - 1;
+
+ if (__c >= __b)
+ return (__k + 1) * __two2p + _M_initialize_max_aux((__t % __two2p)
+ / __two2d,
+ __u % __two2p, __d);
+ else
+ return (__k + 1) * __two2p + _M_initialize_max_aux((__u % __two2p)
+ / __two2d,
+ __t % __two2p, __d);
+ }
+
+ template<class _UniformRandomNumberGenerator1, int __s1,
+ class _UniformRandomNumberGenerator2, int __s2,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const xor_combine<_UniformRandomNumberGenerator1, __s1,
+ _UniformRandomNumberGenerator2, __s2>& __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.base1() << __space << __x.base2();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ return __os;
+ }
+
+ template<class _UniformRandomNumberGenerator1, int __s1,
+ class _UniformRandomNumberGenerator2, int __s2,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ xor_combine<_UniformRandomNumberGenerator1, __s1,
+ _UniformRandomNumberGenerator2, __s2>& __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);
+
+ __is >> __x._M_b1 >> __x._M_b2;
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _IntType>
+ template<typename _UniformRandomNumberGenerator>
+ typename uniform_int<_IntType>::result_type
+ uniform_int<_IntType>::
+ _M_call(_UniformRandomNumberGenerator& __urng,
+ result_type __min, result_type __max, true_type)
+ {
+ // XXX Must be fixed to work well for *arbitrary* __urng.max(),
+ // __urng.min(), __max, __min. Currently works fine only in the
+ // most common case __urng.max() - __urng.min() >= __max - __min,
+ // with __urng.max() > __urng.min() >= 0.
+ typedef typename __gnu_cxx::__add_unsigned<typename
+ _UniformRandomNumberGenerator::result_type>::__type __urntype;
+ typedef typename __gnu_cxx::__add_unsigned<result_type>::__type
+ __utype;
+ typedef typename __gnu_cxx::__conditional_type<(sizeof(__urntype)
+ > sizeof(__utype)),
+ __urntype, __utype>::__type __uctype;
+
+ result_type __ret;
+
+ const __urntype __urnmin = __urng.min();
+ const __urntype __urnmax = __urng.max();
+ const __urntype __urnrange = __urnmax - __urnmin;
+ const __uctype __urange = __max - __min;
+ const __uctype __udenom = (__urnrange <= __urange
+ ? 1 : __urnrange / (__urange + 1));
+ do
+ __ret = (__urntype(__urng()) - __urnmin) / __udenom;
+ while (__ret > __max - __min);
+
+ return __ret + __min;
+ }
+
+ template<typename _IntType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const uniform_int<_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.min() << __space << __x.max();
+
+ __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<_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);
+
+ __is >> __x._M_min >> __x._M_max;
+
+ __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(__gnu_cxx::__numeric_traits<double>::__max_digits10);
+
+ __os << __x.p();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+
+ template<typename _IntType, typename _RealType>
+ template<class _UniformRandomNumberGenerator>
+ typename geometric_distribution<_IntType, _RealType>::result_type
+ geometric_distribution<_IntType, _RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng)
+ {
+ // About the epsilon thing see this thread:
+ // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html
+ const _RealType __naf =
+ (1 - std::numeric_limits<_RealType>::epsilon()) / 2;
+ // The largest _RealType convertible to _IntType.
+ const _RealType __thr =
+ std::numeric_limits<_IntType>::max() + __naf;
+
+ _RealType __cand;
+ do
+ __cand = std::ceil(std::log(__urng()) / _M_log_p);
+ while (__cand >= __thr);
+
+ return result_type(__cand + __naf);
+ }
+
+ template<typename _IntType, typename _RealType,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const geometric_distribution<_IntType, _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(__gnu_cxx::__numeric_traits<_RealType>::__max_digits10);
+
+ __os << __x.p();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+
+ template<typename _IntType, typename _RealType>
+ void
+ poisson_distribution<_IntType, _RealType>::
+ _M_initialize()
+ {
+#if _GLIBCXX_USE_C99_MATH_TR1
+ if (_M_mean >= 12)
+ {
+ const _RealType __m = std::floor(_M_mean);
+ _M_lm_thr = std::log(_M_mean);
+ _M_lfm = std::_GLIBCXX_TR1 lgamma(__m + 1);
+ _M_sm = std::sqrt(__m);
+
+ const _RealType __pi_4 = 0.7853981633974483096156608458198757L;
+ const _RealType __dx = std::sqrt(2 * __m * std::log(32 * __m
+ / __pi_4));
+ _M_d = std::_GLIBCXX_TR1 round(std::max(_RealType(6),
+ std::min(__m, __dx)));
+ const _RealType __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, typename _RealType>
+ template<class _UniformRandomNumberGenerator>
+ typename poisson_distribution<_IntType, _RealType>::result_type
+ poisson_distribution<_IntType, _RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng)
+ {
+#if _GLIBCXX_USE_C99_MATH_TR1
+ if (_M_mean >= 12)
+ {
+ _RealType __x;
+
+ // See comments above...
+ const _RealType __naf =
+ (1 - std::numeric_limits<_RealType>::epsilon()) / 2;
+ const _RealType __thr =
+ std::numeric_limits<_IntType>::max() + __naf;
+
+ const _RealType __m = std::floor(_M_mean);
+ // sqrt(pi / 2)
+ const _RealType __spi_2 = 1.2533141373155002512078826424055226L;
+ const _RealType __c1 = _M_sm * __spi_2;
+ const _RealType __c2 = _M_c2b + __c1;
+ const _RealType __c3 = __c2 + 1;
+ const _RealType __c4 = __c3 + 1;
+ // e^(1 / 78)
+ const _RealType __e178 = 1.0129030479320018583185514777512983L;
+ const _RealType __c5 = __c4 + __e178;
+ const _RealType __c = _M_cb + __c5;
+ const _RealType __2cx = 2 * (2 * __m + _M_d);
+
+ bool __reject = true;
+ do
+ {
+ const _RealType __u = __c * __urng();
+ const _RealType __e = -std::log(__urng());
+
+ _RealType __w = 0.0;
+
+ if (__u <= __c1)
+ {
+ const _RealType __n = _M_nd(__urng);
+ const _RealType __y = -std::abs(__n) * _M_sm - 1;
+ __x = std::floor(__y);
+ __w = -__n * __n / 2;
+ if (__x < -__m)
+ continue;
+ }
+ else if (__u <= __c2)
+ {
+ const _RealType __n = _M_nd(__urng);
+ const _RealType __y = 1 + std::abs(__n) * _M_scx;
+ __x = std::ceil(__y);
+ __w = __y * (2 - __y) * _M_1cx;
+ if (__x > _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 _RealType __v = -std::log(__urng());
+ const _RealType __y = _M_d + __v * __2cx / _M_d;
+ __x = std::ceil(__y);
+ __w = -_M_d * _M_1cx * (1 + __y / 2);
+ }
+
+ __reject = (__w - __e - __x * _M_lm_thr
+ > _M_lfm - std::_GLIBCXX_TR1 lgamma(__x + __m + 1));
+
+ __reject |= __x + __m >= __thr;
+
+ } while (__reject);
+
+ return result_type(__x + __m + __naf);
+ }
+ else
+#endif
+ {
+ _IntType __x = 0;
+ _RealType __prod = 1.0;
+
+ do
+ {
+ __prod *= __urng();
+ __x += 1;
+ }
+ while (__prod > _M_lm_thr);
+
+ return __x - 1;
+ }
+ }
+
+ template<typename _IntType, typename _RealType,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const poisson_distribution<_IntType, _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(__gnu_cxx::__numeric_traits<_RealType>::__max_digits10);
+
+ __os << __x.mean() << __space << __x._M_nd;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _IntType, typename _RealType,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ poisson_distribution<_IntType, _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);
+
+ __is >> __x._M_mean >> __x._M_nd;
+ __x._M_initialize();
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _IntType, typename _RealType>
+ void
+ binomial_distribution<_IntType, _RealType>::
+ _M_initialize()
+ {
+ const _RealType __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 _RealType __np = std::floor(_M_t * __p12);
+ const _RealType __pa = __np / _M_t;
+ const _RealType __1p = 1 - __pa;
+
+ const _RealType __pi_4 = 0.7853981633974483096156608458198757L;
+ const _RealType __d1x =
+ std::sqrt(__np * __1p * std::log(32 * __np
+ / (81 * __pi_4 * __1p)));
+ _M_d1 = std::_GLIBCXX_TR1 round(std::max(_RealType(1), __d1x));
+ const _RealType __d2x =
+ std::sqrt(__np * __1p * std::log(32 * _M_t * __1p
+ / (__pi_4 * __pa)));
+ _M_d2 = std::_GLIBCXX_TR1 round(std::max(_RealType(1), __d2x));
+
+ // sqrt(pi / 2)
+ const _RealType __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 _RealType __a12 = _M_a1 + _M_s2 * __spi_2;
+ const _RealType __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 _RealType __s2s = _M_s2 * _M_s2;
+ _M_s = (_M_a123 + 2 * __s2s / _M_d2
+ * std::exp(-_M_d2 * _M_d2 / (2 * __s2s)));
+ _M_lf = (std::_GLIBCXX_TR1 lgamma(__np + 1)
+ + std::_GLIBCXX_TR1 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, typename _RealType>
+ template<class _UniformRandomNumberGenerator>
+ typename binomial_distribution<_IntType, _RealType>::result_type
+ binomial_distribution<_IntType, _RealType>::
+ _M_waiting(_UniformRandomNumberGenerator& __urng, _IntType __t)
+ {
+ _IntType __x = 0;
+ _RealType __sum = 0;
+
+ do
+ {
+ const _RealType __e = -std::log(__urng());
+ __sum += __e / (__t - __x);
+ __x += 1;
+ }
+ while (__sum <= _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, typename _RealType>
+ template<class _UniformRandomNumberGenerator>
+ typename binomial_distribution<_IntType, _RealType>::result_type
+ binomial_distribution<_IntType, _RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng)
+ {
+ result_type __ret;
+ const _RealType __p12 = _M_p <= 0.5 ? _M_p : 1.0 - _M_p;
+
+#if _GLIBCXX_USE_C99_MATH_TR1
+ if (!_M_easy)
+ {
+ _RealType __x;
+
+ // See comments above...
+ const _RealType __naf =
+ (1 - std::numeric_limits<_RealType>::epsilon()) / 2;
+ const _RealType __thr =
+ std::numeric_limits<_IntType>::max() + __naf;
+
+ const _RealType __np = std::floor(_M_t * __p12);
+ const _RealType __pa = __np / _M_t;
+
+ // sqrt(pi / 2)
+ const _RealType __spi_2 = 1.2533141373155002512078826424055226L;
+ const _RealType __a1 = _M_a1;
+ const _RealType __a12 = __a1 + _M_s2 * __spi_2;
+ const _RealType __a123 = _M_a123;
+ const _RealType __s1s = _M_s1 * _M_s1;
+ const _RealType __s2s = _M_s2 * _M_s2;
+
+ bool __reject;
+ do
+ {
+ const _RealType __u = _M_s * __urng();
+
+ _RealType __v;
+
+ if (__u <= __a1)
+ {
+ const _RealType __n = _M_nd(__urng);
+ const _RealType __y = _M_s1 * std::abs(__n);
+ __reject = __y >= _M_d1;
+ if (!__reject)
+ {
+ const _RealType __e = -std::log(__urng());
+ __x = std::floor(__y);
+ __v = -__e - __n * __n / 2 + _M_c;
+ }
+ }
+ else if (__u <= __a12)
+ {
+ const _RealType __n = _M_nd(__urng);
+ const _RealType __y = _M_s2 * std::abs(__n);
+ __reject = __y >= _M_d2;
+ if (!__reject)
+ {
+ const _RealType __e = -std::log(__urng());
+ __x = std::floor(-__y);
+ __v = -__e - __n * __n / 2;
+ }
+ }
+ else if (__u <= __a123)
+ {
+ const _RealType __e1 = -std::log(__urng());
+ const _RealType __e2 = -std::log(__urng());
+
+ const _RealType __y = _M_d1 + 2 * __s1s * __e1 / _M_d1;
+ __x = std::floor(__y);
+ __v = (-__e2 + _M_d1 * (1 / (_M_t - __np)
+ -__y / (2 * __s1s)));
+ __reject = false;
+ }
+ else
+ {
+ const _RealType __e1 = -std::log(__urng());
+ const _RealType __e2 = -std::log(__urng());
+
+ const _RealType __y = _M_d2 + 2 * __s2s * __e1 / _M_d2;
+ __x = std::floor(-__y);
+ __v = -__e2 - _M_d2 * __y / (2 * __s2s);
+ __reject = false;
+ }
+
+ __reject = __reject || __x < -__np || __x > _M_t - __np;
+ if (!__reject)
+ {
+ const _RealType __lfx =
+ std::_GLIBCXX_TR1 lgamma(__np + __x + 1)
+ + std::_GLIBCXX_TR1 lgamma(_M_t - (__np + __x) + 1);
+ __reject = __v > _M_lf - __lfx + __x * _M_lp1p;
+ }
+
+ __reject |= __x + __np >= __thr;
+ }
+ while (__reject);
+
+ __x += __np + __naf;
+
+ const _IntType __z = _M_waiting(__urng, _M_t - _IntType(__x));
+ __ret = _IntType(__x) + __z;
+ }
+ else
+#endif
+ __ret = _M_waiting(__urng, _M_t);
+
+ if (__p12 != _M_p)
+ __ret = _M_t - __ret;
+ return __ret;
+ }
+
+ template<typename _IntType, typename _RealType,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const binomial_distribution<_IntType, _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(__gnu_cxx::__numeric_traits<_RealType>::__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 _RealType,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ binomial_distribution<_IntType, _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);
+
+ __is >> __x._M_t >> __x._M_p >> __x._M_nd;
+ __x._M_initialize();
+
+ __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<_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(__gnu_cxx::__numeric_traits<_RealType>::__max_digits10);
+
+ __os << __x.min() << __space << __x.max();
+
+ __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<_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);
+
+ __is >> __x._M_min >> __x._M_max;
+
+ __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(__gnu_cxx::__numeric_traits<_RealType>::__max_digits10);
+
+ __os << __x.lambda();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+
+ /**
+ * 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<class _UniformRandomNumberGenerator>
+ typename normal_distribution<_RealType>::result_type
+ normal_distribution<_RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng)
+ {
+ result_type __ret;
+
+ if (_M_saved_available)
+ {
+ _M_saved_available = false;
+ __ret = _M_saved;
+ }
+ else
+ {
+ result_type __x, __y, __r2;
+ do
+ {
+ __x = result_type(2.0) * __urng() - 1.0;
+ __y = result_type(2.0) * __urng() - 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 * _M_sigma + _M_mean;
+ return __ret;
+ }
+
+ 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(__gnu_cxx::__numeric_traits<_RealType>::__max_digits10);
+
+ __os << __x._M_saved_available << __space
+ << __x.mean() << __space
+ << __x.sigma();
+ 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);
+
+ __is >> __x._M_saved_available >> __x._M_mean
+ >> __x._M_sigma;
+ if (__x._M_saved_available)
+ __is >> __x._M_saved;
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType>
+ void
+ gamma_distribution<_RealType>::
+ _M_initialize()
+ {
+ if (_M_alpha >= 1)
+ _M_l_d = std::sqrt(2 * _M_alpha - 1);
+ else
+ _M_l_d = (std::pow(_M_alpha, _M_alpha / (1 - _M_alpha))
+ * (1 - _M_alpha));
+ }
+
+ /**
+ * Cheng's rejection algorithm GB for alpha >= 1 and a modification
+ * of Vaduva's rejection from Weibull algorithm due to Devroye for
+ * alpha < 1.
+ *
+ * References:
+ * Cheng, R. C. "The Generation of Gamma Random Variables with Non-integral
+ * Shape Parameter." Applied Statistics, 26, 71-75, 1977.
+ *
+ * Vaduva, I. "Computer Generation of Gamma Gandom Variables by Rejection
+ * and Composition Procedures." Math. Operationsforschung and Statistik,
+ * Series in Statistics, 8, 545-576, 1977.
+ *
+ * Devroye, L. "Non-Uniform Random Variates Generation." Springer-Verlag,
+ * New York, 1986, Ch. IX, Sect. 3.4 (+ Errata!).
+ */
+ template<typename _RealType>
+ template<class _UniformRandomNumberGenerator>
+ typename gamma_distribution<_RealType>::result_type
+ gamma_distribution<_RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng)
+ {
+ result_type __x;
+
+ bool __reject;
+ if (_M_alpha >= 1)
+ {
+ // alpha - log(4)
+ const result_type __b = _M_alpha
+ - result_type(1.3862943611198906188344642429163531L);
+ const result_type __c = _M_alpha + _M_l_d;
+ const result_type __1l = 1 / _M_l_d;
+
+ // 1 + log(9 / 2)
+ const result_type __k = 2.5040773967762740733732583523868748L;
+
+ do
+ {
+ const result_type __u = __urng();
+ const result_type __v = __urng();
+
+ const result_type __y = __1l * std::log(__v / (1 - __v));
+ __x = _M_alpha * std::exp(__y);
+
+ const result_type __z = __u * __v * __v;
+ const result_type __r = __b + __c * __y - __x;
+
+ __reject = __r < result_type(4.5) * __z - __k;
+ if (__reject)
+ __reject = __r < std::log(__z);
+ }
+ while (__reject);
+ }
+ else
+ {
+ const result_type __c = 1 / _M_alpha;
+
+ do
+ {
+ const result_type __z = -std::log(__urng());
+ const result_type __e = -std::log(__urng());
+
+ __x = std::pow(__z, __c);
+
+ __reject = __z + __e < _M_l_d + __x;
+ }
+ while (__reject);
+ }
+
+ return __x;
+ }
+
+ 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();
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__os.widen(' '));
+ __os.precision(__gnu_cxx::__numeric_traits<_RealType>::__max_digits10);
+
+ __os << __x.alpha();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+_GLIBCXX_END_NAMESPACE_TR1
+}
projects / msp430-gcc.git / blobdiff