CoCalc -- poisson_distribution.h

GitHub Repository: freebsd/freebsd-src
Path: blob/main/contrib/llvm-project/libcxx/include/__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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  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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    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_HIDE_FROM_ABI double mean() const { return __mean_; }
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    friend _LIBCPP_HIDE_FROM_ABI bool operator==(const param_type& __x, const param_type& __y) {
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      return __x.__mean_ == __y.__mean_;
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    }
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    friend _LIBCPP_HIDE_FROM_ABI bool operator!=(const param_type& __x, const param_type& __y) { 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_HIDE_FROM_ABI poisson_distribution() : poisson_distribution(1.0) {}
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  _LIBCPP_HIDE_FROM_ABI explicit poisson_distribution(double __mean) : __p_(__mean) {}
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#else
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  _LIBCPP_HIDE_FROM_ABI explicit poisson_distribution(double __mean = 1.0) : __p_(__mean) {}
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#endif
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  _LIBCPP_HIDE_FROM_ABI explicit poisson_distribution(const param_type& __p) : __p_(__p) {}
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  _LIBCPP_HIDE_FROM_ABI void reset() {}
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  // generating functions
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  template <class _URNG>
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  _LIBCPP_HIDE_FROM_ABI result_type operator()(_URNG& __g) {
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    return (*this)(__g, __p_);
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  }
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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_HIDE_FROM_ABI double mean() const { return __p_.mean(); }
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  _LIBCPP_HIDE_FROM_ABI param_type param() const { return __p_; }
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  _LIBCPP_HIDE_FROM_ABI void param(const param_type& __p) { __p_ = __p; }
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  _LIBCPP_HIDE_FROM_ABI result_type min() const { return 0; }
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  _LIBCPP_HIDE_FROM_ABI result_type max() const { return numeric_limits<result_type>::max(); }
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  friend _LIBCPP_HIDE_FROM_ABI bool operator==(const poisson_distribution& __x, const poisson_distribution& __y) {
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    return __x.__p_ == __y.__p_;
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  }
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  friend _LIBCPP_HIDE_FROM_ABI bool operator!=(const poisson_distribution& __x, const poisson_distribution& __y) {
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    return !(__x == __y);
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  }
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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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  if (__mean_ < 10) {
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    __s_     = 0;
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    __d_     = 0;
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    __l_     = std::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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  } else {
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    __s_        = std::sqrt(__mean_);
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    __d_        = 6 * __mean_ * __mean_;
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    __l_        = std::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 poisson_distribution<_IntType>::operator()(_URNG& __urng, const param_type& __pr) {
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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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    __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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  } else {
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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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      __tx = std::trunc(__g);
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      if (__tx >= __pr.__l_)
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        return std::__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 std::__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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      double __e;
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      if (__using_exp_dist || __g <= 0) {
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        double __t;
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        do {
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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             = std::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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        const double __fac[] = {1, 1, 2, 6, 24, 120, 720, 5040, 40320, 362880};
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        __px                 = -__pr.__mean_;
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        __py                 = std::pow(__pr.__mean_, (double)__tx) / __fac[static_cast<int>(__tx)];
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      } else {
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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 (std::abs(__v) > 0.25)
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          __px = __tx * std::log(1 + __v) - __difmuk - __del;
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        else
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          __px = __tx * __v * __v *
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                     (((((((.1250060 * __v + -.1384794) * __v + .1421878) * __v + -.1661269) * __v + .2000118) * __v +
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                        -.2500068) *
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                           __v +
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                       .3333333) *
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                          __v +
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                      -.5) -
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                 __del;
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        __py = .3989423 / std::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_) * __r2 + __pr.__c1_) * __r2 + __pr.__c0_);
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      if (__using_exp_dist) {
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        if (__pr.__c_ * std::abs(__u) <= __py * std::exp(__px + __e) - __fy * std::exp(__fx + __e))
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          break;
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      } else {
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        if (__fy - __u * __fy <= __py * std::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 std::__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, const poisson_distribution<_IntType>& __x) {
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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 | _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, poisson_distribution<_IntType>& __x) {
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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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