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Upgrade to 2022-era LLVM LIBCXX
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Justine Tunney
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2f4ca71f26
commit
8e68384e15
2078 changed files with 165657 additions and 65010 deletions
281
third_party/libcxx/__random/poisson_distribution.h
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281
third_party/libcxx/__random/poisson_distribution.h
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//===----------------------------------------------------------------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#ifndef _LIBCPP___RANDOM_POISSON_DISTRIBUTION_H
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#define _LIBCPP___RANDOM_POISSON_DISTRIBUTION_H
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#include <__config>
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#include <__random/clamp_to_integral.h>
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#include <__random/exponential_distribution.h>
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#include <__random/is_valid.h>
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#include <__random/normal_distribution.h>
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#include <__random/uniform_real_distribution.h>
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#include <cmath>
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#include <iosfwd>
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#include <limits>
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#if !defined(_LIBCPP_HAS_NO_PRAGMA_SYSTEM_HEADER)
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# pragma GCC system_header
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#endif
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_LIBCPP_PUSH_MACROS
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#include <__undef_macros>
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_LIBCPP_BEGIN_NAMESPACE_STD
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template<class _IntType = int>
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class _LIBCPP_TEMPLATE_VIS poisson_distribution
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{
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static_assert(__libcpp_random_is_valid_inttype<_IntType>::value, "IntType must be a supported integer type");
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public:
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// types
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typedef _IntType result_type;
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class _LIBCPP_TEMPLATE_VIS param_type
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{
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double __mean_;
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double __s_;
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double __d_;
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double __l_;
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double __omega_;
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double __c0_;
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double __c1_;
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double __c2_;
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double __c3_;
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double __c_;
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public:
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typedef poisson_distribution distribution_type;
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_LIBCPP_HIDE_FROM_ABI explicit param_type(double __mean = 1.0);
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_LIBCPP_INLINE_VISIBILITY
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double mean() const {return __mean_;}
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friend _LIBCPP_INLINE_VISIBILITY
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bool operator==(const param_type& __x, const param_type& __y)
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{return __x.__mean_ == __y.__mean_;}
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friend _LIBCPP_INLINE_VISIBILITY
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bool operator!=(const param_type& __x, const param_type& __y)
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{return !(__x == __y);}
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friend class poisson_distribution;
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};
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private:
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param_type __p_;
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public:
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// constructors and reset functions
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#ifndef _LIBCPP_CXX03_LANG
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_LIBCPP_INLINE_VISIBILITY
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poisson_distribution() : poisson_distribution(1.0) {}
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_LIBCPP_INLINE_VISIBILITY
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explicit poisson_distribution(double __mean)
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: __p_(__mean) {}
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#else
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_LIBCPP_INLINE_VISIBILITY
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explicit poisson_distribution(double __mean = 1.0)
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: __p_(__mean) {}
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#endif
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_LIBCPP_INLINE_VISIBILITY
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explicit poisson_distribution(const param_type& __p) : __p_(__p) {}
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_LIBCPP_INLINE_VISIBILITY
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void reset() {}
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// generating functions
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template<class _URNG>
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_LIBCPP_INLINE_VISIBILITY
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result_type operator()(_URNG& __g)
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{return (*this)(__g, __p_);}
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template<class _URNG>
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_LIBCPP_HIDE_FROM_ABI result_type operator()(_URNG& __g, const param_type& __p);
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// property functions
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_LIBCPP_INLINE_VISIBILITY
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double mean() const {return __p_.mean();}
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_LIBCPP_INLINE_VISIBILITY
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param_type param() const {return __p_;}
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_LIBCPP_INLINE_VISIBILITY
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void param(const param_type& __p) {__p_ = __p;}
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_LIBCPP_INLINE_VISIBILITY
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result_type min() const {return 0;}
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_LIBCPP_INLINE_VISIBILITY
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result_type max() const {return numeric_limits<result_type>::max();}
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friend _LIBCPP_INLINE_VISIBILITY
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bool operator==(const poisson_distribution& __x,
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const poisson_distribution& __y)
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{return __x.__p_ == __y.__p_;}
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friend _LIBCPP_INLINE_VISIBILITY
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bool operator!=(const poisson_distribution& __x,
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const poisson_distribution& __y)
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{return !(__x == __y);}
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};
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template<class _IntType>
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poisson_distribution<_IntType>::param_type::param_type(double __mean)
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// According to the standard `inf` is a valid input, but it causes the
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// distribution to hang, so we replace it with the maximum representable
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// mean.
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: __mean_(isinf(__mean) ? numeric_limits<double>::max() : __mean)
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{
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if (__mean_ < 10)
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{
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__s_ = 0;
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__d_ = 0;
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__l_ = _VSTD::exp(-__mean_);
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__omega_ = 0;
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__c3_ = 0;
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__c2_ = 0;
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__c1_ = 0;
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__c0_ = 0;
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__c_ = 0;
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}
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else
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{
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__s_ = _VSTD::sqrt(__mean_);
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__d_ = 6 * __mean_ * __mean_;
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__l_ = _VSTD::trunc(__mean_ - 1.1484);
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__omega_ = .3989423 / __s_;
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double __b1 = .4166667E-1 / __mean_;
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double __b2 = .3 * __b1 * __b1;
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__c3_ = .1428571 * __b1 * __b2;
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__c2_ = __b2 - 15. * __c3_;
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__c1_ = __b1 - 6. * __b2 + 45. * __c3_;
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__c0_ = 1. - __b1 + 3. * __b2 - 15. * __c3_;
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__c_ = .1069 / __mean_;
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}
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}
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template <class _IntType>
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template<class _URNG>
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_IntType
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poisson_distribution<_IntType>::operator()(_URNG& __urng, const param_type& __pr)
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{
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static_assert(__libcpp_random_is_valid_urng<_URNG>::value, "");
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double __tx;
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uniform_real_distribution<double> __urd;
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if (__pr.__mean_ < 10)
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{
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__tx = 0;
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for (double __p = __urd(__urng); __p > __pr.__l_; ++__tx)
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__p *= __urd(__urng);
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}
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else
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{
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double __difmuk;
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double __g = __pr.__mean_ + __pr.__s_ * normal_distribution<double>()(__urng);
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double __u;
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if (__g > 0)
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{
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__tx = _VSTD::trunc(__g);
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if (__tx >= __pr.__l_)
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return _VSTD::__clamp_to_integral<result_type>(__tx);
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__difmuk = __pr.__mean_ - __tx;
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__u = __urd(__urng);
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if (__pr.__d_ * __u >= __difmuk * __difmuk * __difmuk)
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return _VSTD::__clamp_to_integral<result_type>(__tx);
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}
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exponential_distribution<double> __edist;
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for (bool __using_exp_dist = false; true; __using_exp_dist = true)
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{
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double __e;
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if (__using_exp_dist || __g <= 0)
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{
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double __t;
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do
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{
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__e = __edist(__urng);
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__u = __urd(__urng);
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__u += __u - 1;
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__t = 1.8 + (__u < 0 ? -__e : __e);
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} while (__t <= -.6744);
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__tx = _VSTD::trunc(__pr.__mean_ + __pr.__s_ * __t);
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__difmuk = __pr.__mean_ - __tx;
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__using_exp_dist = true;
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}
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double __px;
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double __py;
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if (__tx < 10 && __tx >= 0)
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{
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const double __fac[] = {1, 1, 2, 6, 24, 120, 720, 5040,
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40320, 362880};
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__px = -__pr.__mean_;
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__py = _VSTD::pow(__pr.__mean_, (double)__tx) / __fac[static_cast<int>(__tx)];
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}
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else
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{
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double __del = .8333333E-1 / __tx;
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__del -= 4.8 * __del * __del * __del;
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double __v = __difmuk / __tx;
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if (_VSTD::abs(__v) > 0.25)
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__px = __tx * _VSTD::log(1 + __v) - __difmuk - __del;
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else
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__px = __tx * __v * __v * (((((((.1250060 * __v + -.1384794) *
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__v + .1421878) * __v + -.1661269) * __v + .2000118) *
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__v + -.2500068) * __v + .3333333) * __v + -.5) - __del;
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__py = .3989423 / _VSTD::sqrt(__tx);
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}
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double __r = (0.5 - __difmuk) / __pr.__s_;
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double __r2 = __r * __r;
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double __fx = -0.5 * __r2;
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double __fy = __pr.__omega_ * (((__pr.__c3_ * __r2 + __pr.__c2_) *
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__r2 + __pr.__c1_) * __r2 + __pr.__c0_);
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if (__using_exp_dist)
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{
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if (__pr.__c_ * _VSTD::abs(__u) <= __py * _VSTD::exp(__px + __e) -
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__fy * _VSTD::exp(__fx + __e))
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break;
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}
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else
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{
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if (__fy - __u * __fy <= __py * _VSTD::exp(__px - __fx))
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break;
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}
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}
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}
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return _VSTD::__clamp_to_integral<result_type>(__tx);
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}
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template <class _CharT, class _Traits, class _IntType>
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_LIBCPP_HIDE_FROM_ABI basic_ostream<_CharT, _Traits>&
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operator<<(basic_ostream<_CharT, _Traits>& __os,
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const poisson_distribution<_IntType>& __x)
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{
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__save_flags<_CharT, _Traits> __lx(__os);
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typedef basic_ostream<_CharT, _Traits> _OStream;
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__os.flags(_OStream::dec | _OStream::left | _OStream::fixed |
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_OStream::scientific);
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return __os << __x.mean();
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}
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template <class _CharT, class _Traits, class _IntType>
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_LIBCPP_HIDE_FROM_ABI basic_istream<_CharT, _Traits>&
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operator>>(basic_istream<_CharT, _Traits>& __is,
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poisson_distribution<_IntType>& __x)
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{
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typedef poisson_distribution<_IntType> _Eng;
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typedef typename _Eng::param_type param_type;
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__save_flags<_CharT, _Traits> __lx(__is);
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typedef basic_istream<_CharT, _Traits> _Istream;
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__is.flags(_Istream::dec | _Istream::skipws);
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double __mean;
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__is >> __mean;
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if (!__is.fail())
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__x.param(param_type(__mean));
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return __is;
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}
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_LIBCPP_END_NAMESPACE_STD
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_LIBCPP_POP_MACROS
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#endif // _LIBCPP___RANDOM_POISSON_DISTRIBUTION_H
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