maxjhandsome
/
llvm-project
forked from OSchip/llvm-project
1
0
Fork 0
Code Issues Pull Requests Packages Releases Wiki Activity
llvm-project/libcxx/include/__random/poisson_distribution.h

281 lines
9.0 KiB
C++
Raw Blame History

//===----------------------------------------------------------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
#ifndef _LIBCPP___RANDOM_POISSON_DISTRIBUTION_H
#define _LIBCPP___RANDOM_POISSON_DISTRIBUTION_H
#include <__config>
#include <__random/clamp_to_integral.h>
#include <__random/exponential_distribution.h>
#include <__random/is_valid.h>
#include <__random/normal_distribution.h>
#include <__random/uniform_real_distribution.h>
#include <cmath>
#include <iosfwd>
#include <limits>
#if !defined(_LIBCPP_HAS_NO_PRAGMA_SYSTEM_HEADER)
# pragma GCC system_header
#endif
_LIBCPP_PUSH_MACROS
#include <__undef_macros>
_LIBCPP_BEGIN_NAMESPACE_STD
template<class _IntType = int>
class _LIBCPP_TEMPLATE_VIS poisson_distribution
{
static_assert(__libcpp_random_is_valid_inttype<_IntType>::value, "IntType must be an integer type larger than char");
public:
// types
typedef _IntType result_type;
class _LIBCPP_TEMPLATE_VIS param_type
{
double __mean_;
double __s_;
double __d_;
double __l_;
double __omega_;
double __c0_;
double __c1_;
double __c2_;
double __c3_;
double __c_;
public:
typedef poisson_distribution distribution_type;
explicit param_type(double __mean = 1.0);
_LIBCPP_INLINE_VISIBILITY
double mean() const {return __mean_;}
friend _LIBCPP_INLINE_VISIBILITY
bool operator==(const param_type& __x, const param_type& __y)
{return __x.__mean_ == __y.__mean_;}
friend _LIBCPP_INLINE_VISIBILITY
bool operator!=(const param_type& __x, const param_type& __y)
{return !(__x == __y);}
friend class poisson_distribution;
};
private:
param_type __p_;
public:
// constructors and reset functions
#ifndef _LIBCPP_CXX03_LANG
_LIBCPP_INLINE_VISIBILITY
poisson_distribution() : poisson_distribution(1.0) {}
_LIBCPP_INLINE_VISIBILITY
explicit poisson_distribution(double __mean)
: __p_(__mean) {}
#else
_LIBCPP_INLINE_VISIBILITY
explicit poisson_distribution(double __mean = 1.0)
: __p_(__mean) {}
#endif
_LIBCPP_INLINE_VISIBILITY
explicit poisson_distribution(const param_type& __p) : __p_(__p) {}
_LIBCPP_INLINE_VISIBILITY
void reset() {}
// generating functions
template<class _URNG>
_LIBCPP_INLINE_VISIBILITY
result_type operator()(_URNG& __g)
{return (*this)(__g, __p_);}
template<class _URNG> result_type operator()(_URNG& __g, const param_type& __p);
// property functions
_LIBCPP_INLINE_VISIBILITY
double mean() const {return __p_.mean();}
_LIBCPP_INLINE_VISIBILITY
param_type param() const {return __p_;}
_LIBCPP_INLINE_VISIBILITY
void param(const param_type& __p) {__p_ = __p;}
_LIBCPP_INLINE_VISIBILITY
result_type min() const {return 0;}
_LIBCPP_INLINE_VISIBILITY
result_type max() const {return numeric_limits<result_type>::max();}
friend _LIBCPP_INLINE_VISIBILITY
bool operator==(const poisson_distribution& __x,
const poisson_distribution& __y)
{return __x.__p_ == __y.__p_;}
friend _LIBCPP_INLINE_VISIBILITY
bool operator!=(const poisson_distribution& __x,
const poisson_distribution& __y)
{return !(__x == __y);}
};
template<class _IntType>
poisson_distribution<_IntType>::param_type::param_type(double __mean)
// According to the standard `inf` is a valid input, but it causes the
// distribution to hang, so we replace it with the maximum representable
// mean.
: __mean_(isinf(__mean) ? numeric_limits<double>::max() : __mean)
{
if (__mean_ < 10)
{
__s_ = 0;
__d_ = 0;
__l_ = _VSTD::exp(-__mean_);
__omega_ = 0;
__c3_ = 0;
__c2_ = 0;
__c1_ = 0;
__c0_ = 0;
__c_ = 0;
}
else
{
__s_ = _VSTD::sqrt(__mean_);
__d_ = 6 * __mean_ * __mean_;
__l_ = _VSTD::trunc(__mean_ - 1.1484);
__omega_ = .3989423 / __s_;
double __b1_ = .4166667E-1 / __mean_;
double __b2_ = .3 * __b1_ * __b1_;
__c3_ = .1428571 * __b1_ * __b2_;
__c2_ = __b2_ - 15. * __c3_;
__c1_ = __b1_ - 6. * __b2_ + 45. * __c3_;
__c0_ = 1. - __b1_ + 3. * __b2_ - 15. * __c3_;
__c_ = .1069 / __mean_;
}
}
template <class _IntType>
template<class _URNG>
_IntType
poisson_distribution<_IntType>::operator()(_URNG& __urng, const param_type& __pr)
{
static_assert(__libcpp_random_is_valid_urng<_URNG>::value, "");
double __tx;
uniform_real_distribution<double> __urd;
if (__pr.__mean_ < 10)
{
__tx = 0;
for (double __p = __urd(__urng); __p > __pr.__l_; ++__tx)
__p *= __urd(__urng);
}
else
{
double __difmuk;
double __g = __pr.__mean_ + __pr.__s_ * normal_distribution<double>()(__urng);
double __u;
if (__g > 0)
{
__tx = _VSTD::trunc(__g);
if (__tx >= __pr.__l_)
return _VSTD::__clamp_to_integral<result_type>(__tx);
__difmuk = __pr.__mean_ - __tx;
__u = __urd(__urng);
if (__pr.__d_ * __u >= __difmuk * __difmuk * __difmuk)
return _VSTD::__clamp_to_integral<result_type>(__tx);
}
exponential_distribution<double> __edist;
for (bool __using_exp_dist = false; true; __using_exp_dist = true)
{
double __e;
if (__using_exp_dist || __g <= 0)
{
double __t;
do
{
__e = __edist(__urng);
__u = __urd(__urng);
__u += __u - 1;
__t = 1.8 + (__u < 0 ? -__e : __e);
} while (__t <= -.6744);
__tx = _VSTD::trunc(__pr.__mean_ + __pr.__s_ * __t);
__difmuk = __pr.__mean_ - __tx;
__using_exp_dist = true;
}
double __px;
double __py;
if (__tx < 10 && __tx >= 0)
{
const double __fac[] = {1, 1, 2, 6, 24, 120, 720, 5040,
40320, 362880};
__px = -__pr.__mean_;
__py = _VSTD::pow(__pr.__mean_, (double)__tx) / __fac[static_cast<int>(__tx)];
}
else
{
double __del = .8333333E-1 / __tx;
__del -= 4.8 * __del * __del * __del;
double __v = __difmuk / __tx;
if (_VSTD::abs(__v) > 0.25)
__px = __tx * _VSTD::log(1 + __v) - __difmuk - __del;
else
__px = __tx * __v * __v * (((((((.1250060 * __v + -.1384794) *
__v + .1421878) * __v + -.1661269) * __v + .2000118) *
__v + -.2500068) * __v + .3333333) * __v + -.5) - __del;
__py = .3989423 / _VSTD::sqrt(__tx);
}
double __r = (0.5 - __difmuk) / __pr.__s_;
double __r2 = __r * __r;
double __fx = -0.5 * __r2;
double __fy = __pr.__omega_ * (((__pr.__c3_ * __r2 + __pr.__c2_) *
__r2 + __pr.__c1_) * __r2 + __pr.__c0_);
if (__using_exp_dist)
{
if (__pr.__c_ * _VSTD::abs(__u) <= __py * _VSTD::exp(__px + __e) -
__fy * _VSTD::exp(__fx + __e))
break;
}
else
{
if (__fy - __u * __fy <= __py * _VSTD::exp(__px - __fx))
break;
}
}
}
return _VSTD::__clamp_to_integral<result_type>(__tx);
}
template <class _CharT, class _Traits, class _IntType>
basic_ostream<_CharT, _Traits>&
operator<<(basic_ostream<_CharT, _Traits>& __os,
const poisson_distribution<_IntType>& __x)
{
__save_flags<_CharT, _Traits> __lx(__os);
typedef basic_ostream<_CharT, _Traits> _OStream;
__os.flags(_OStream::dec | _OStream::left | _OStream::fixed |
_OStream::scientific);
return __os << __x.mean();
}
template <class _CharT, class _Traits, class _IntType>
basic_istream<_CharT, _Traits>&
operator>>(basic_istream<_CharT, _Traits>& __is,
poisson_distribution<_IntType>& __x)
{
typedef poisson_distribution<_IntType> _Eng;
typedef typename _Eng::param_type param_type;
__save_flags<_CharT, _Traits> __lx(__is);
typedef basic_istream<_CharT, _Traits> _Istream;
__is.flags(_Istream::dec | _Istream::skipws);
double __mean;
__is >> __mean;
if (!__is.fail())
__x.param(param_type(__mean));
return __is;
}
_LIBCPP_END_NAMESPACE_STD
_LIBCPP_POP_MACROS
#endif // _LIBCPP___RANDOM_POISSON_DISTRIBUTION_H
Reference in New Issue View Git Blame Copy Permalink