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kolibrios/contrib/sdk/sources/libstdc++-v3/include/tr1/random.tcc

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sdk: build libsupc++ from libstdc++ source git-svn-id: svn://kolibrios.org@5134 a494cfbc-eb01-0410-851d-a64ba20cac60
2014-09-21 12:51:57 +02:00
// random number generation (out of line) -*- C++ -*-
// Copyright (C) 2009-2013 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/random.tcc
* This is an internal header file, included by other library headers.
* Do not attempt to use it directly. @headername{tr1/random}
*/
#ifndef _GLIBCXX_TR1_RANDOM_TCC
#define _GLIBCXX_TR1_RANDOM_TCC 1
namespace std _GLIBCXX_VISIBILITY(default)
{
namespace tr1
{
/*
* (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.
//
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; }
};
_GLIBCXX_END_NAMESPACE_VERSION
} // namespace __detail
_GLIBCXX_BEGIN_NAMESPACE_VERSION
template<class _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
const _UIntType
linear_congruential<_UIntType, __a, __c, __m>::multiplier;
template<class _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
const _UIntType
linear_congruential<_UIntType, __a, __c, __m>::increment;
template<class _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
const _UIntType
linear_congruential<_UIntType, __a, __c, __m>::modulus;
/**
* 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>
const int
mersenne_twister<_UIntType, __w, __n, __m, __r, __a, __u, __s,
__b, __t, __c, __l>::word_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>
const int
mersenne_twister<_UIntType, __w, __n, __m, __r, __a, __u, __s,
__b, __t, __c, __l>::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>
const int
mersenne_twister<_UIntType, __w, __n, __m, __r, __a, __u, __s,
__b, __t, __c, __l>::shift_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>
const int
mersenne_twister<_UIntType, __w, __n, __m, __r, __a, __u, __s,
__b, __t, __c, __l>::mask_bits;
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>
const _UIntType
mersenne_twister<_UIntType, __w, __n, __m, __r, __a, __u, __s,
__b, __t, __c, __l>::parameter_a;
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>
const int
mersenne_twister<_UIntType, __w, __n, __m, __r, __a, __u, __s,
__b, __t, __c, __l>::output_u;
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>
const int
mersenne_twister<_UIntType, __w, __n, __m, __r, __a, __u, __s,
__b, __t, __c, __l>::output_s;
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>
const _UIntType
mersenne_twister<_UIntType, __w, __n, __m, __r, __a, __u, __s,
__b, __t, __c, __l>::output_b;
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>
const int
mersenne_twister<_UIntType, __w, __n, __m, __r, __a, __u, __s,
__b, __t, __c, __l>::output_t;
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>
const _UIntType
mersenne_twister<_UIntType, __w, __n, __m, __r, __a, __u, __s,
__b, __t, __c, __l>::output_c;
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>
const int
mersenne_twister<_UIntType, __w, __n, __m, __r, __a, __u, __s,
__b, __t, __c, __l>::output_l;
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>
const _IntType
subtract_with_carry<_IntType, __m, __s, __r>::modulus;
template<typename _IntType, _IntType __m, int __s, int __r>
const int
subtract_with_carry<_IntType, __m, __s, __r>::long_lag;
template<typename _IntType, _IntType __m, int __s, int __r>
const int
subtract_with_carry<_IntType, __m, __s, __r>::short_lag;
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::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>
const int
subtract_with_carry_01<_RealType, __w, __s, __r>::word_size;
template<typename _RealType, int __w, int __s, int __r>
const int
subtract_with_carry_01<_RealType, __w, __s, __r>::long_lag;
template<typename _RealType, int __w, int __s, int __r>
const int
subtract_with_carry_01<_RealType, __w, __s, __r>::short_lag;
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::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::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>
const int
discard_block<_UniformRandomNumberGenerator, __p, __r>::block_size;
template<class _UniformRandomNumberGenerator, int __p, int __r>
const int
discard_block<_UniformRandomNumberGenerator, __p, __r>::used_block;
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>
const int
xor_combine<_UniformRandomNumberGenerator1, __s1,
_UniformRandomNumberGenerator2, __s2>::shift1;
template<class _UniformRandomNumberGenerator1, int __s1,
class _UniformRandomNumberGenerator2, int __s2>
const int
xor_combine<_UniformRandomNumberGenerator1, __s1,
_UniformRandomNumberGenerator2, __s2>::shift2;
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::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::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::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::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::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::tr1::lgamma(__np + 1)
+ std::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::tr1::lgamma(__np + __x + 1)
+ std::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_VERSION
}
}
#endif
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