mirror/gcc
2
0
Fork 0
mirror of git://gcc.gnu.org/git/gcc.git synced 2025-03-18 08:50:28 +08:00
Code Issues Packages Projects Releases Wiki Activity

random.tcc (negative_binomial_distribution<>::operator() (_UniformRandomNumberGenerator&, const param_type&): Tweak to use a class member gamma_distribution.

Browse Source 
2009-06-11  Paolo Carlini  <paolo.carlini@oracle.com>

	* include/bits/random.tcc
	(negative_binomial_distribution<>::operator()
	(_UniformRandomNumberGenerator&, const param_type&): Tweak to use a
	class member gamma_distribution.
	(negative_binomial_distribution<>::operator()
	(_UniformRandomNumberGenerator&)): Implement out of line here.
	(operator<<(basic_ostream<>&, negative_binomial_distribution<>),
	operator>>(basic_ostream<>&, negative_binomial_distribution<>): Adjust.
	(student_t_distribution<>::operator()
	(_UniformRandomNumberGenerator&, const param_type&): Move inline,
	simplify.
	(operator<<(basic_ostream<>&, student_t_distribution<>),
	operator>>(basic_ostream<>&, student_t_distribution<>): Adjust.
	(chi_squared_distribution<>::operator()
	(_UniformRandomNumberGenerator&, const param_type&): Move inline,
	tweak to use a class member gamma_distribution.
	(operator<<(basic_ostream<>&, chi_squared_distribution<>),
	operator>>(basic_ostream<>&, chi_squared_distribution<>): Adjust.
	(fisher_f_distribution<>::operator() (_UniformRandomNumberGenerator&,
	const param_type&): Move inline, tweak to use class member
	gamma_distributions.
	(operator<<(basic_ostream<>&, fisher_f_distribution<>),
	operator>>(basic_ostream<>&, fisher_f_distribution<>): Adjust.
	* include/bits/random.h: Adjust, minor tweaks.

From-SVN: r148393
This commit is contained in:
Paolo Carlini 2009-06-11 18:32:30 +00:00
parent 2995ebee5c
commit f9b09dec19
3 changed files with 286 additions and 277 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

30
libstdc++-v3/ChangeLog
View File

@ -1,7 +1,33 @@
2009-06-11 Paolo Carlini <paolo.carlini@oracle.com>
* include/bits/random.tcc
(negative_binomial_distribution<>::operator()
(_UniformRandomNumberGenerator&, const param_type&): Tweak to use a
class member gamma_distribution.
(negative_binomial_distribution<>::operator()
(_UniformRandomNumberGenerator&)): Implement out of line here.
(operator<<(basic_ostream<>&, negative_binomial_distribution<>),
operator>>(basic_ostream<>&, negative_binomial_distribution<>): Adjust.
(student_t_distribution<>::operator()
(_UniformRandomNumberGenerator&, const param_type&): Move inline,
simplify.
(operator<<(basic_ostream<>&, student_t_distribution<>),
operator>>(basic_ostream<>&, student_t_distribution<>): Adjust.
(chi_squared_distribution<>::operator()
(_UniformRandomNumberGenerator&, const param_type&): Move inline,
tweak to use a class member gamma_distribution.
(operator<<(basic_ostream<>&, chi_squared_distribution<>),
operator>>(basic_ostream<>&, chi_squared_distribution<>): Adjust.
(fisher_f_distribution<>::operator() (_UniformRandomNumberGenerator&,
const param_type&): Move inline, tweak to use class member
gamma_distributions.
(operator<<(basic_ostream<>&, fisher_f_distribution<>),
operator>>(basic_ostream<>&, fisher_f_distribution<>): Adjust.
* include/bits/random.h: Adjust, minor tweaks.
2009-06-10 Tom Tromey <tromey@redhat.com>
* python/libstdcxx/v6/printers.py (lookup_function): Remove extra
';'.
* python/libstdcxx/v6/printers.py (lookup_function): Remove extra ';'.
(build_libstdcxx_dictionary): Accept shortened form of
basic_string names.
(StdStringPrinter.to_string): Remove reference to WideEncoding.

426
libstdc++-v3/include/bits/random.h
View File

@ -2078,7 +2078,7 @@ namespace std
private:
param_type _M_param;
normal_distribution<result_type> _M_nd;
std::normal_distribution<result_type> _M_nd;
};
/**
@ -2111,15 +2111,16 @@ namespace std
operator>>(std::basic_istream<_CharT, _Traits>&,
std::lognormal_distribution<_RealType>&);
/**
* @brief A chi_squared_distribution random number distribution.
* @brief A gamma continuous distribution for random numbers.
*
* The formula for the normal probability mass function is
* @f$ p(x|n) = \frac{x^{(n/2) - 1}e^{-x/2}}{\Gamma(n/2) 2^{n/2}} @f$
* The formula for the gamma probability density function is
* @f$ p(x|\alpha,\beta) = \frac{1}{\beta\Gamma(\alpha)}
* (x/\beta)^{\alpha - 1} e^{-x/\beta} @f$.
*/
template<typename _RealType = double>
class chi_squared_distribution
class gamma_distribution
{
public:
/** The type of the range of the distribution. */
@ -2127,29 +2128,50 @@ namespace std
/** Parameter type. */
struct param_type
{
typedef chi_squared_distribution<_RealType> distribution_type;
typedef gamma_distribution<_RealType> distribution_type;
friend class gamma_distribution<_RealType>;
explicit
param_type(_RealType __n = _RealType(1))
: _M_n(__n)
{ }
param_type(_RealType __alpha_val = _RealType(1),
_RealType __beta_val = _RealType(1))
: _M_alpha(__alpha_val), _M_beta(__beta_val)
{
_GLIBCXX_DEBUG_ASSERT(_M_alpha > _RealType(0));
_M_initialize();
}
_RealType
n() const
{ return _M_n; }
alpha() const
{ return _M_alpha; }
_RealType
beta() const
{ return _M_beta; }
private:
_RealType _M_n;
void
_M_initialize();
_RealType _M_alpha;
_RealType _M_beta;
_RealType _M_malpha, _M_a2;
};
public:
/**
* @brief Constructs a gamma distribution with parameters
* @f$ \alpha @f$ and @f$ \beta @f$.
*/
explicit
chi_squared_distribution(_RealType __n = _RealType(1))
: _M_param(__n)
gamma_distribution(_RealType __alpha_val = _RealType(1),
_RealType __beta_val = _RealType(1))
: _M_param(__alpha_val, __beta_val), _M_nd()
{ }
explicit
chi_squared_distribution(const param_type& __p)
: _M_param(__p)
gamma_distribution(const param_type& __p)
: _M_param(__p), _M_nd()
{ }
/**
@ -2157,14 +2179,21 @@ namespace std
*/
void
reset()
{ }
{ _M_nd.reset(); }
/**
*
* @brief Returns the @f$ \alpha @f$ of the distribution.
*/
_RealType
n() const
{ return _M_param.n(); }
alpha() const
{ return _M_param.alpha(); }
/**
* @brief Returns the @f$ \beta @f$ of the distribution.
*/
_RealType
beta() const
{ return _M_param.beta(); }
/**
* @brief Returns the parameter set of the distribution.
@ -2207,6 +2236,142 @@ namespace std
private:
param_type _M_param;
std::normal_distribution<result_type> _M_nd;
};
/**
* @brief Inserts a %gamma_distribution random number distribution
* @p __x into the output stream @p __os.
*
* @param __os An output stream.
* @param __x A %gamma_distribution random number distribution.
*
* @returns The output stream with the state of @p __x inserted or in
* an error state.
*/
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>&,
const std::gamma_distribution<_RealType>&);
/**
* @brief Extracts a %gamma_distribution random number distribution
* @p __x from the input stream @p __is.
*
* @param __is An input stream.
* @param __x A %gamma_distribution random number generator engine.
*
* @returns The input stream with @p __x extracted or in an error state.
*/
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_istream<_CharT, _Traits>&
operator>>(std::basic_istream<_CharT, _Traits>&,
std::gamma_distribution<_RealType>&);
/**
* @brief A chi_squared_distribution random number distribution.
*
* The formula for the normal probability mass function is
* @f$ p(x|n) = \frac{x^{(n/2) - 1}e^{-x/2}}{\Gamma(n/2) 2^{n/2}} @f$
*/
template<typename _RealType = double>
class chi_squared_distribution
{
public:
/** The type of the range of the distribution. */
typedef _RealType result_type;
/** Parameter type. */
struct param_type
{
typedef chi_squared_distribution<_RealType> distribution_type;
explicit
param_type(_RealType __n = _RealType(1))
: _M_n(__n)
{ }
_RealType
n() const
{ return _M_n; }
private:
_RealType _M_n;
};
explicit
chi_squared_distribution(_RealType __n = _RealType(1))
: _M_param(__n), _M_gd(__n / 2)
{ }
explicit
chi_squared_distribution(const param_type& __p)
: _M_param(__p), _M_gd(__p.n() / 2)
{ }
/**
* @brief Resets the distribution state.
*/
void
reset()
{ _M_gd.reset(); }
/**
*
*/
_RealType
n() const
{ return _M_param.n(); }
/**
* @brief Returns the parameter set of the distribution.
*/
param_type
param() const
{ return _M_param; }
/**
* @brief Sets the parameter set of the distribution.
* @param __param The new parameter set of the distribution.
*/
void
param(const param_type& __param)
{ _M_param = __param; }
/**
* @brief Returns the greatest lower bound value of the distribution.
*/
result_type
min() const
{ return result_type(0); }
/**
* @brief Returns the least upper bound value of the distribution.
*/
result_type
max() const
{ return std::numeric_limits<result_type>::max(); }
template<typename _UniformRandomNumberGenerator>
result_type
operator()(_UniformRandomNumberGenerator& __urng)
{ return 2 * _M_gd(__urng); }
template<typename _UniformRandomNumberGenerator>
result_type
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __p)
{
typedef typename std::gamma_distribution<result_type>::param_type
param_type;
return 2 * _M_gd(__urng, param_type(__p.n() / 2));
}
private:
param_type _M_param;
std::gamma_distribution<result_type> _M_gd;
};
/**
@ -2420,12 +2585,12 @@ namespace std
explicit
fisher_f_distribution(_RealType __m = _RealType(1),
_RealType __n = _RealType(1))
: _M_param(__m, __n)
: _M_param(__m, __n), _M_gd_x(__m / 2), _M_gd_y(__n / 2)
{ }
explicit
fisher_f_distribution(const param_type& __p)
: _M_param(__p)
: _M_param(__p), _M_gd_x(__p.m() / 2), _M_gd_y(__p.n() / 2)
{ }
/**
@ -2433,7 +2598,10 @@ namespace std
*/
void
reset()
{ }
{
_M_gd_x.reset();
_M_gd_y.reset();
}
/**
*
@ -2478,15 +2646,23 @@ namespace std
template<typename _UniformRandomNumberGenerator>
result_type
operator()(_UniformRandomNumberGenerator& __urng)
{ return this->operator()(__urng, this->param()); }
{ return (_M_gd_x(__urng) * n()) / (_M_gd_y(__urng) * m()); }
template<typename _UniformRandomNumberGenerator>
result_type
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __p);
const param_type& __p)
{
typedef typename std::gamma_distribution<result_type>::param_type
param_type;
return ((_M_gd_x(__urng, param_type(__p.m() / 2)) * n())
/ (_M_gd_y(__urng, param_type(__p.n() / 2)) * m()));
}
private:
param_type _M_param;
std::gamma_distribution<result_type> _M_gd_x, _M_gd_y;
};
/**
@ -2553,12 +2729,12 @@ namespace std
explicit
student_t_distribution(_RealType __n = _RealType(1))
: _M_param(__n), _M_nd()
: _M_param(__n), _M_nd(), _M_gd(__n / 2, 2)
{ }
explicit
student_t_distribution(const param_type& __p)
: _M_param(__p), _M_nd()
: _M_param(__p), _M_nd(), _M_gd(__p.n() / 2, 2)
{ }
/**
@ -2566,7 +2742,10 @@ namespace std
*/
void
reset()
{ _M_nd.reset(); }
{
_M_nd.reset();
_M_gd.reset();
}
/**
*
@ -2606,18 +2785,26 @@ namespace std
template<typename _UniformRandomNumberGenerator>
result_type
operator()(_UniformRandomNumberGenerator& __urng)
{ return this->operator()(__urng, this->param()); }
operator()(_UniformRandomNumberGenerator& __urng)
{ return _M_nd(__urng) * std::sqrt(n() / _M_gd(__urng)); }
template<typename _UniformRandomNumberGenerator>
result_type
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __p);
const param_type& __p)
{
typedef typename std::gamma_distribution<result_type>::param_type
param_type;
const result_type __g = _M_gd(__urng, param_type(__p.n() / 2, 2));
return _M_nd(__urng) * std::sqrt(__p.n() / __g);
}
private:
param_type _M_param;
normal_distribution<result_type> _M_nd;
std::normal_distribution<result_type> _M_nd;
std::gamma_distribution<result_type> _M_gd;
};
/**
@ -2977,7 +3164,7 @@ namespace std
param_type _M_param;
// NB: Unused when _GLIBCXX_USE_C99_MATH_TR1 is undefined.
normal_distribution<double> _M_nd;
std::normal_distribution<double> _M_nd;
};
@ -3166,12 +3353,12 @@ namespace std
explicit
negative_binomial_distribution(_IntType __k = 1, double __p = 0.5)
: _M_param(__k, __p)
: _M_param(__k, __p), _M_gd(__k, __p / (1.0 - __p))
{ }
explicit
negative_binomial_distribution(const param_type& __p)
: _M_param(__p)
: _M_param(__p), _M_gd(__p.k(), __p.p() / (1.0 - __p.p()))
{ }
/**
@ -3179,7 +3366,7 @@ namespace std
*/
void
reset()
{ }
{ _M_gd.reset(); }
/**
* @brief Return the @f$ k @f$ parameter of the distribution.
@ -3226,8 +3413,7 @@ namespace std
template<typename _UniformRandomNumberGenerator>
result_type
operator()(_UniformRandomNumberGenerator& __urng)
{ return this->operator()(__urng, this->param()); }
operator()(_UniformRandomNumberGenerator& __urng);
template<typename _UniformRandomNumberGenerator>
result_type
@ -3236,6 +3422,8 @@ namespace std
private:
param_type _M_param;
std::gamma_distribution<double> _M_gd;
};
/**
@ -3421,7 +3609,7 @@ namespace std
param_type _M_param;
// NB: Unused when _GLIBCXX_USE_C99_MATH_TR1 is undefined.
normal_distribution<double> _M_nd;
std::normal_distribution<double> _M_nd;
};
/**
@ -3574,164 +3762,6 @@ namespace std
std::exponential_distribution<_RealType>&);
/**
* @brief A gamma continuous distribution for random numbers.
*
* The formula for the gamma probability density function is
* @f$ p(x|\alpha,\beta) = \frac{1}{\beta\Gamma(\alpha)}
* (x/\beta)^{\alpha - 1} e^{-x/\beta} @f$.
*/
template<typename _RealType = double>
class gamma_distribution
{
public:
/** The type of the range of the distribution. */
typedef _RealType result_type;
/** Parameter type. */
struct param_type
{
typedef gamma_distribution<_RealType> distribution_type;
friend class gamma_distribution<_RealType>;
explicit
param_type(_RealType __alpha_val = _RealType(1),
_RealType __beta_val = _RealType(1))
: _M_alpha(__alpha_val), _M_beta(__beta_val)
{
_GLIBCXX_DEBUG_ASSERT(_M_alpha > _RealType(0));
_M_initialize();
}
_RealType
alpha() const
{ return _M_alpha; }
_RealType
beta() const
{ return _M_beta; }
private:
void
_M_initialize();
_RealType _M_alpha;
_RealType _M_beta;
_RealType _M_malpha, _M_a2;
};
public:
/**
* @brief Constructs a gamma distribution with parameters
* @f$ \alpha @f$ and @f$ \beta @f$.
*/
explicit
gamma_distribution(_RealType __alpha_val = _RealType(1),
_RealType __beta_val = _RealType(1))
: _M_param(__alpha_val, __beta_val), _M_nd()
{ }
explicit
gamma_distribution(const param_type& __p)
: _M_param(__p), _M_nd()
{ }
/**
* @brief Resets the distribution state.
*/
void
reset()
{ _M_nd.reset(); }
/**
* @brief Returns the @f$ \alpha @f$ of the distribution.
*/
_RealType
alpha() const
{ return _M_param.alpha(); }
/**
* @brief Returns the @f$ \beta @f$ of the distribution.
*/
_RealType
beta() const
{ return _M_param.beta(); }
/**
* @brief Returns the parameter set of the distribution.
*/
param_type
param() const
{ return _M_param; }
/**
* @brief Sets the parameter set of the distribution.
* @param __param The new parameter set of the distribution.
*/
void
param(const param_type& __param)
{ _M_param = __param; }
/**
* @brief Returns the greatest lower bound value of the distribution.
*/
result_type
min() const
{ return result_type(0); }
/**
* @brief Returns the least upper bound value of the distribution.
*/
result_type
max() const
{ return std::numeric_limits<result_type>::max(); }
template<typename _UniformRandomNumberGenerator>
result_type
operator()(_UniformRandomNumberGenerator& __urng)
{ return this->operator()(__urng, this->param()); }
template<typename _UniformRandomNumberGenerator>
result_type
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __p);
private:
param_type _M_param;
normal_distribution<result_type> _M_nd;
};
/**
* @brief Inserts a %gamma_distribution random number distribution
* @p __x into the output stream @p __os.
*
* @param __os An output stream.
* @param __x A %gamma_distribution random number distribution.
*
* @returns The output stream with the state of @p __x inserted or in
* an error state.
*/
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>&,
const std::gamma_distribution<_RealType>&);
/**
* @brief Extracts a %gamma_distribution random number distribution
* @p __x from the input stream @p __is.
*
* @param __is An input stream.
* @param __x A %gamma_distribution random number generator engine.
*
* @returns The input stream with @p __x extracted or in an error state.
*/
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_istream<_CharT, _Traits>&
operator>>(std::basic_istream<_CharT, _Traits>&,
std::gamma_distribution<_RealType>&);
/**
* @brief A weibull_distribution random number distribution.
*

107
libstdc++-v3/include/bits/random.tcc
View File

@ -854,6 +854,20 @@ namespace std
return __is;
}
template<typename _IntType>
template<typename _UniformRandomNumberGenerator>
typename negative_binomial_distribution<_IntType>::result_type
negative_binomial_distribution<_IntType>::
operator()(_UniformRandomNumberGenerator& __urng)
{
const double __y = _M_gd(__urng);
// XXX Is the constructor too slow?
std::poisson_distribution<result_type> __poisson(__y);
return __poisson(__urng);
}
template<typename _IntType>
template<typename _UniformRandomNumberGenerator>
typename negative_binomial_distribution<_IntType>::result_type
@ -861,11 +875,13 @@ namespace std
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __p)
{
gamma_distribution<> __gamma(__p.k(), 1.0);
double __x = __gamma(__urng);
typedef typename std::gamma_distribution<result_type>::param_type
param_type;
const double __y =
_M_gd(__urng, param_type(__p.k(), __p.p() / (1.0 - __p.p())));
poisson_distribution<result_type> __poisson(__x * __p.p()
/ (1.0 - __p.p()));
std::poisson_distribution<result_type> __poisson(__y);
return __poisson(__urng);
}
@ -885,7 +901,8 @@ namespace std
__os.fill(__os.widen(' '));
__os.precision(std::numeric_limits<double>::digits10 + 1);
__os << __x.k() << __space << __x.p();
__os << __x.k() << __space << __x.p()
<< __space << __x._M_gd;
__os.flags(__flags);
__os.fill(__fill);
@ -906,7 +923,7 @@ namespace std
_IntType __k;
double __p;
__is >> __k >> __p;
__is >> __k >> __p >> __x._M_gd;
__x.param(typename negative_binomial_distribution<_IntType>::
param_type(__k, __p));
@ -1538,17 +1555,6 @@ namespace std
}
template<typename _RealType>
template<typename _UniformRandomNumberGenerator>
typename chi_squared_distribution<_RealType>::result_type
chi_squared_distribution<_RealType>::
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __p)
{
gamma_distribution<_RealType> __gamma(__p.n() / 2, 1.0);
return 2 * __gamma(__urng);
}
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
@ -1565,7 +1571,7 @@ namespace std
__os.fill(__space);
__os.precision(std::numeric_limits<_RealType>::digits10 + 1);
__os << __x.n();
__os << __x.n() << __space << __x._M_gd;
__os.flags(__flags);
__os.fill(__fill);
@ -1585,7 +1591,7 @@ namespace std
__is.flags(__ios_base::dec | __ios_base::skipws);
_RealType __n;
__is >> __n;
__is >> __n >> __x._M_gd;
__x.param(typename chi_squared_distribution<_RealType>::
param_type(__n));
@ -1657,23 +1663,6 @@ namespace std
}
template<typename _RealType>
template<typename _UniformRandomNumberGenerator>
typename fisher_f_distribution<_RealType>::result_type
fisher_f_distribution<_RealType>::
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __p)
{
gamma_distribution<_RealType> __gamma;
_RealType __ym = __gamma(__urng,
typename gamma_distribution<_RealType>::param_type(__p.m() / 2, 2));
_RealType __yn = __gamma(__urng,
typename gamma_distribution<_RealType>::param_type(__p.n() / 2, 2));
return (__ym * __p.n()) / (__yn * __p.m());
}
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
@ -1690,7 +1679,8 @@ namespace std
__os.fill(__space);
__os.precision(std::numeric_limits<_RealType>::digits10 + 1);
__os << __x.m() << __space << __x.n();
__os << __x.m() << __space << __x.n()
<< __space << __x._M_gd_x << __space << __x._M_gd_y;
__os.flags(__flags);
__os.fill(__fill);
@ -1710,7 +1700,7 @@ namespace std
__is.flags(__ios_base::dec | __ios_base::skipws);
_RealType __m, __n;
__is >> __m >> __n;
__is >> __m >> __n >> __x._M_gd_x >> __x._M_gd_y;
__x.param(typename fisher_f_distribution<_RealType>::
param_type(__m, __n));
@ -1719,43 +1709,6 @@ namespace std
}
template<typename _RealType>
template<typename _UniformRandomNumberGenerator>
typename student_t_distribution<_RealType>::result_type
student_t_distribution<_RealType>::
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __param)
{
if (__param.n() <= 2.0)
{
_RealType __y1 = _M_nd(__urng);
chi_squared_distribution<_RealType> __chisq(__param.n());
_RealType __y2 = __chisq(__urng);
return __y1 / std::sqrt(__y2 / __param.n());
}
else
{
_RealType __y1, __y2, __z;
exponential_distribution<_RealType>
__exponential(1.0 / (__param.n() / 2.0 - 1.0));
do
{
__y1 = _M_nd(__urng);
__y2 = __exponential(__urng);
__z = __y1 * __y1 / (__param.n() - 2.0);
}
while (1.0 - __z < 0.0 || std::exp(-__y2 - __z) > (1.0 - __z));
// Note that there is a typo in Knuth's formula, the line below
// is taken from the original paper of Marsaglia, Mathematics of
// Computation, 34 (1980), p 234-256
return __y1 / std::sqrt((1.0 - 2.0 / __param.n()) * (1.0 - __z));
}
}
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
@ -1772,7 +1725,7 @@ namespace std
__os.fill(__space);
__os.precision(std::numeric_limits<_RealType>::digits10 + 1);
__os << __x.n() << __space << __x._M_nd;
__os << __x.n() << __space << __x._M_nd << __space << __x._M_gd;
__os.flags(__flags);
__os.fill(__fill);
@ -1792,7 +1745,7 @@ namespace std
__is.flags(__ios_base::dec | __ios_base::skipws);
_RealType __n;
__is >> __n >> __x._M_nd;
__is >> __n >> __x._M_nd >> __x._M_gd;
__x.param(typename student_t_distribution<_RealType>::param_type(__n));
__is.flags(__flags);