[rand.dist.samp.pconst] plus some bug fixes in the tests of the other distributions

git-svn-id: https://llvm.org/svn/llvm-project/libcxx/trunk@104224 91177308-0d34-0410-b5e6-96231b3b80d8
This commit is contained in:
Howard Hinnant
2010-05-20 15:11:46 +00:00
parent 551d8e4ddb
commit d6d1171f2c
56 changed files with 2593 additions and 393 deletions

View File

@@ -371,7 +371,7 @@ typedef subtract_with_carry_engine<uint_fast64_t, 48, 5, 12> ranlux48_base;
typedef discard_block_engine<ranlux24_base, 223, 23> ranlux24;
typedef discard_block_engine<ranlux48_base, 389, 11> ranlux48;
typedef shuffle_order_engine<minstd_rand0, 256> knuth_b;
typedef minstd_rand0 default_random_engine;
typedef minstd_rand default_random_engine;
// Generators
@@ -1477,7 +1477,79 @@ public:
};
template<class RealType = double>
class piecewise_constant_distribution;
class piecewise_constant_distribution
{
// types
typedef RealType result_type;
class param_type
{
public:
typedef piecewise_constant_distribution distribution_type;
param_type();
template<class InputIteratorB, class InputIteratorW>
param_type(InputIteratorB firstB, InputIteratorB lastB,
InputIteratorW firstW);
template<class UnaryOperation>
param_type(initializer_list<result_type> bl, UnaryOperation fw);
template<class UnaryOperation>
param_type(size_t nw, result_type xmin, result_type xmax,
UnaryOperation fw);
vector<result_type> intervals() const;
vector<double> densities() const;
friend bool operator==(const param_type& x, const param_type& y);
friend bool operator!=(const param_type& x, const param_type& y);
};
// constructor and reset functions
piecewise_constant_distribution();
template<class InputIteratorB, class InputIteratorW>
piecewise_constant_distribution(InputIteratorB firstB,
InputIteratorB lastB,
InputIteratorW firstW);
template<class UnaryOperation>
piecewise_constant_distribution(initializer_list<result_type> bl,
UnaryOperation fw);
template<class UnaryOperation>
piecewise_constant_distribution(size_t nw, result_type xmin,
result_type xmax, UnaryOperation fw);
explicit piecewise_constant_distribution(const param_type& parm);
void reset();
// generating functions
template<class URNG> result_type operator()(URNG& g);
template<class URNG> result_type operator()(URNG& g, const param_type& parm);
// property functions
vector<result_type> intervals() const;
vector<double> densities() const;
param_type param() const;
void param(const param_type& parm);
result_type min() const;
result_type max() const;
friend bool operator==(const piecewise_constant_distribution& x,
const piecewise_constant_distribution& y);
friend bool operator!=(const piecewise_constant_distribution& x,
const piecewise_constant_distribution& y);
template <class charT, class traits>
friend
basic_ostream<charT, traits>&
operator<<(basic_ostream<charT, traits>& os,
const piecewise_constant_distribution& x);
template <class charT, class traits>
friend
basic_istream<charT, traits>&
operator>>(basic_istream<charT, traits>& is,
piecewise_constant_distribution& x);
};
template<class RealType = double>
class piecewise_linear_distribution;
@@ -1825,9 +1897,9 @@ operator>>(basic_istream<_CharT, _Traits>& __is,
typedef linear_congruential_engine<uint_fast32_t, 16807, 0, 2147483647>
minstd_rand0;
typedef minstd_rand0 default_random_engine;
typedef linear_congruential_engine<uint_fast32_t, 48271, 0, 2147483647>
minstd_rand;
typedef minstd_rand default_random_engine;
// mersenne_twister_engine
template <class _UIntType, size_t __w, size_t __n, size_t __m, size_t __r,
@@ -3655,7 +3727,8 @@ inline
bernoulli_distribution::result_type
bernoulli_distribution::operator()(_URNG& __g, const param_type& __p)
{
return (__g() - __g.min()) < __p.p() * (__g.max() - __g.min() + 1.);
uniform_real_distribution<double> __gen;
return __gen(__g) < __p.p();
}
template <class _CharT, class _Traits>
@@ -5535,7 +5608,7 @@ operator>>(basic_istream<_CharT, _Traits>& __is,
__is.flags(ios_base::dec | ios_base::skipws);
size_t __n;
__is >> __n;
std::vector<double> __p(__n);
vector<double> __p(__n);
for (size_t __i = 0; __i < __n; ++__i)
__is >> __p[__i];
if (!__is.fail())
@@ -5543,6 +5616,300 @@ operator>>(basic_istream<_CharT, _Traits>& __is,
return __is;
}
// piecewise_constant_distribution
template<class _RealType = double>
class piecewise_constant_distribution
{
public:
// types
typedef _RealType result_type;
class param_type
{
vector<double> __p_;
vector<result_type> __b_;
public:
typedef piecewise_constant_distribution distribution_type;
param_type();
template<class _InputIteratorB, class _InputIteratorW>
param_type(_InputIteratorB __fB, _InputIteratorB __lB,
_InputIteratorW __fW);
template<class _UnaryOperation>
param_type(initializer_list<result_type> __bl, _UnaryOperation __fw);
template<class _UnaryOperation>
param_type(size_t __nw, result_type __xmin, result_type __xmax,
_UnaryOperation __fw);
vector<result_type> intervals() const {return __b_;}
vector<double> densities() const;
friend bool operator==(const param_type& __x, const param_type& __y)
{return __x.__p_ == __y.__p_ && __x.__b_ == __y.__b_;}
friend bool operator!=(const param_type& __x, const param_type& __y)
{return !(__x == __y);}
private:
void __init();
friend class piecewise_constant_distribution;
template <class _CharT, class _Traits, class _RT>
friend
basic_ostream<_CharT, _Traits>&
operator<<(basic_ostream<_CharT, _Traits>& __os,
const piecewise_constant_distribution<_RT>& __x);
template <class _CharT, class _Traits, class _RT>
friend
basic_istream<_CharT, _Traits>&
operator>>(basic_istream<_CharT, _Traits>& __is,
piecewise_constant_distribution<_RT>& __x);
};
private:
param_type __p_;
public:
// constructor and reset functions
piecewise_constant_distribution() {}
template<class _InputIteratorB, class _InputIteratorW>
piecewise_constant_distribution(_InputIteratorB __fB,
_InputIteratorB __lB,
_InputIteratorW __fW)
: __p_(__fB, __lB, __fW) {}
template<class _UnaryOperation>
piecewise_constant_distribution(initializer_list<result_type> __bl,
_UnaryOperation __fw)
: __p_(__bl, __fw) {}
template<class _UnaryOperation>
piecewise_constant_distribution(size_t __nw, result_type __xmin,
result_type __xmax, _UnaryOperation __fw)
: __p_(__nw, __xmin, __xmax, __fw) {}
explicit piecewise_constant_distribution(const param_type& __p)
: __p_(__p) {}
void reset() {}
// generating functions
template<class _URNG> result_type operator()(_URNG& __g)
{return (*this)(__g, __p_);}
template<class _URNG> result_type operator()(_URNG& __g, const param_type& __p);
// property functions
vector<result_type> intervals() const {return __p_.intervals();}
vector<double> densities() const {return __p_.densities();}
param_type param() const {return __p_;}
void param(const param_type& __p) {__p_ = __p;}
result_type min() const {return __p_.__b_.front();}
result_type max() const {return __p_.__b_.back();}
friend bool operator==(const piecewise_constant_distribution& __x,
const piecewise_constant_distribution& __y)
{return __x.__p_ == __y.__p_;}
friend bool operator!=(const piecewise_constant_distribution& __x,
const piecewise_constant_distribution& __y)
{return !(__x == __y);}
template <class _CharT, class _Traits, class _RT>
friend
basic_ostream<_CharT, _Traits>&
operator<<(basic_ostream<_CharT, _Traits>& __os,
const piecewise_constant_distribution<_RT>& __x);
template <class _CharT, class _Traits, class _RT>
friend
basic_istream<_CharT, _Traits>&
operator>>(basic_istream<_CharT, _Traits>& __is,
piecewise_constant_distribution<_RT>& __x);
};
template<class _RealType>
void
piecewise_constant_distribution<_RealType>::param_type::__init()
{
if (!__p_.empty())
{
if (__p_.size() > 1)
{
double __s = _STD::accumulate(__p_.begin(), __p_.end(), 0.0);
for (_STD::vector<double>::iterator __i = __p_.begin(), __e = __p_.end();
__i < __e; ++__i)
*__i /= __s;
vector<double> __t(__p_.size() - 1);
_STD::partial_sum(__p_.begin(), __p_.end() - 1, __t.begin());
swap(__p_, __t);
}
else
{
__p_.clear();
__p_.shrink_to_fit();
}
}
}
template<class _RealType>
piecewise_constant_distribution<_RealType>::param_type::param_type()
: __b_(2)
{
__b_[1] = 1;
}
template<class _RealType>
template<class _InputIteratorB, class _InputIteratorW>
piecewise_constant_distribution<_RealType>::param_type::param_type(
_InputIteratorB __fB, _InputIteratorB __lB, _InputIteratorW __fW)
: __b_(__fB, __lB)
{
if (__b_.size() < 2)
{
__b_.resize(2);
__b_[0] = 0;
__b_[1] = 1;
}
else
{
__p_.reserve(__b_.size() - 1);
for (size_t __i = 0; __i < __b_.size() - 1; ++__i, ++__fW)
__p_.push_back(*__fW);
__init();
}
}
template<class _RealType>
template<class _UnaryOperation>
piecewise_constant_distribution<_RealType>::param_type::param_type(
initializer_list<result_type> __bl, _UnaryOperation __fw)
: __b_(__bl.begin(), __bl.end())
{
if (__b_.size() < 2)
{
__b_.resize(2);
__b_[0] = 0;
__b_[1] = 1;
}
else
{
__p_.reserve(__b_.size() - 1);
for (size_t __i = 0; __i < __b_.size() - 1; ++__i)
__p_.push_back(__fw((__b_[__i+1] + __b_[__i])*.5));
__init();
}
}
template<class _RealType>
template<class _UnaryOperation>
piecewise_constant_distribution<_RealType>::param_type::param_type(
size_t __nw, result_type __xmin, result_type __xmax, _UnaryOperation __fw)
: __b_(__nw == 0 ? 2 : __nw + 1)
{
size_t __n = __b_.size() - 1;
result_type __d = (__xmax - __xmin) / __n;
__p_.reserve(__n);
for (size_t __i = 0; __i < __n; ++__i)
{
__b_[__i] = __xmin + __i * __d;
__p_.push_back(__fw(__b_[__i] + __d*.5));
}
__b_[__n] = __xmax;
__init();
}
template<class _RealType>
vector<double>
piecewise_constant_distribution<_RealType>::param_type::densities() const
{
const size_t __n = __b_.size() - 1;
vector<double> __d(__n);
if (__n == 1)
__d[0] = 1/(__b_[1] - __b_[0]);
else
{
__d[0] = __p_[0] / (__b_[1] - __b_[0]);
for (size_t __i = 1; __i < __n - 1; ++__i)
__d[__i] = (__p_[__i] - __p_[__i-1]) / (__b_[__i+1] - __b_[__i]);
__d[__n-1] = (1 - __p_[__n-2]) / (__b_[__n] - __b_[__n-1]);
}
return __d;
};
template<class _RealType>
template<class _URNG>
_RealType
piecewise_constant_distribution<_RealType>::operator()(_URNG& __g, const param_type& __p)
{
typedef uniform_real_distribution<result_type> _Gen;
if (__p.__b_.size() == 2)
return _Gen(__p.__b_[0], __p.__b_[1])(__g);
result_type __u = _Gen()(__g);
const vector<double>& __dd = __p.__p_;
size_t __k = static_cast<size_t>(_STD::upper_bound(__dd.begin(),
__dd.end(), static_cast<double>(__u)) - __dd.begin());
if (__k == 0)
return static_cast<result_type>(__u * (__p.__b_[1] - __p.__b_[0]) /
__dd[0] + __p.__b_[0]);
__u -= __dd[__k-1];
if (__k == __dd.size())
return static_cast<result_type>(__u * (__p.__b_[__k+1] - __p.__b_[__k]) /
(1 - __dd[__k-1]) + __p.__b_[__k]);
return static_cast<result_type>(__u * (__p.__b_[__k+1] - __p.__b_[__k]) /
(__dd[__k] - __dd[__k-1]) + __p.__b_[__k]);
}
template <class _CharT, class _Traits, class _RT>
basic_ostream<_CharT, _Traits>&
operator<<(basic_ostream<_CharT, _Traits>& __os,
const piecewise_constant_distribution<_RT>& __x)
{
__save_flags<_CharT, _Traits> _(__os);
__os.flags(ios_base::dec | ios_base::left);
_CharT __sp = __os.widen(' ');
__os.fill(__sp);
size_t __n = __x.__p_.__p_.size();
__os << __n;
for (size_t __i = 0; __i < __n; ++__i)
__os << __sp << __x.__p_.__p_[__i];
__n = __x.__p_.__b_.size();
__os << __sp << __n;
for (size_t __i = 0; __i < __n; ++__i)
__os << __sp << __x.__p_.__b_[__i];
return __os;
}
template <class _CharT, class _Traits, class _RT>
basic_istream<_CharT, _Traits>&
operator>>(basic_istream<_CharT, _Traits>& __is,
piecewise_constant_distribution<_RT>& __x)
{
typedef piecewise_constant_distribution<_RT> _Eng;
typedef typename _Eng::result_type result_type;
typedef typename _Eng::param_type param_type;
__save_flags<_CharT, _Traits> _(__is);
__is.flags(ios_base::dec | ios_base::skipws);
size_t __n;
__is >> __n;
vector<double> __p(__n);
for (size_t __i = 0; __i < __n; ++__i)
__is >> __p[__i];
__is >> __n;
vector<result_type> __b(__n);
for (size_t __i = 0; __i < __n; ++__i)
__is >> __b[__i];
if (!__is.fail())
{
swap(__x.__p_.__p_, __p);
swap(__x.__p_.__b_, __b);
}
return __is;
}
_LIBCPP_END_NAMESPACE_STD
#endif // _LIBCPP_RANDOM