libcxx/test/std/numerics/rand/rand.dist/rand.dist.pois/rand.dist.pois.poisson/eval_param.pass.cpp

   1 //===----------------------------------------------------------------------===//
   2 //
   3 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
   4 // See https://llvm.org/LICENSE.txt for license information.
   5 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
   6 //
   7 //===----------------------------------------------------------------------===//
   8 //
   9 // REQUIRES: long_tests
  10
  11 // <random>
  12
  13 // template<class IntType = int>
  14 // class poisson_distribution
  15
  16 // template<class _URNG> result_type operator()(_URNG& g, const param_type& parm);
  17
  18 #include <random>
  19 #include <cassert>
  20 #include <vector>
  21 #include <numeric>
  22
  23 #include "test_macros.h"
  24
  25 template <class T>
  26 inline
  27 T
  28 sqr(T x)
  29 {
  30     return x * x;
  31 }
  32
  33 int main(int, char**)
  34 {
  35     {
  36         typedef std::poisson_distribution<> D;
  37         typedef D::param_type P;
  38         typedef std::minstd_rand G;
  39         G g;
  40         D d(.75);
  41         P p(2);
  42         const int N = 100000;
  43         std::vector<double> u;
  44         for (int i = 0; i < N; ++i)
  45         {
  46             D::result_type v = d(g, p);
  47             assert(d.min() <= v && v <= d.max());
  48             u.push_back(v);
  49         }
  50         double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
  51         double var = 0;
  52         double skew = 0;
  53         double kurtosis = 0;
  54         for (unsigned i = 0; i < u.size(); ++i)
  55         {
  56             double dbl = (u[i] - mean);
  57             double d2 = sqr(dbl);
  58             var += d2;
  59             skew += dbl * d2;
  60             kurtosis += d2 * d2;
  61         }
  62         var /= u.size();
  63         double dev = std::sqrt(var);
  64         skew /= u.size() * dev * var;
  65         kurtosis /= u.size() * var * var;
  66         kurtosis -= 3;
  67         double x_mean = p.mean();
  68         double x_var = p.mean();
  69         double x_skew = 1 / std::sqrt(x_var);
  70         double x_kurtosis = 1 / x_var;
  71         assert(std::abs((mean - x_mean) / x_mean) < 0.01);
  72         assert(std::abs((var - x_var) / x_var) < 0.01);
  73         assert(std::abs((skew - x_skew) / x_skew) < 0.01);
  74         assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03);
  75     }
  76     {
  77         typedef std::poisson_distribution<> D;
  78         typedef D::param_type P;
  79         typedef std::minstd_rand G;
  80         G g;
  81         D d(2);
  82         P p(.75);
  83         const int N = 100000;
  84         std::vector<double> u;
  85         for (int i = 0; i < N; ++i)
  86         {
  87             D::result_type v = d(g, p);
  88             assert(d.min() <= v && v <= d.max());
  89             u.push_back(v);
  90         }
  91         double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
  92         double var = 0;
  93         double skew = 0;
  94         double kurtosis = 0;
  95         for (unsigned i = 0; i < u.size(); ++i)
  96         {
  97             double dbl = (u[i] - mean);
  98             double d2 = sqr(dbl);
  99             var += d2;
 100             skew += dbl * d2;
 101             kurtosis += d2 * d2;
 102         }
 103         var /= u.size();
 104         double dev = std::sqrt(var);
 105         skew /= u.size() * dev * var;
 106         kurtosis /= u.size() * var * var;
 107         kurtosis -= 3;
 108         double x_mean = p.mean();
 109         double x_var = p.mean();
 110         double x_skew = 1 / std::sqrt(x_var);
 111         double x_kurtosis = 1 / x_var;
 112         assert(std::abs((mean - x_mean) / x_mean) < 0.01);
 113         assert(std::abs((var - x_var) / x_var) < 0.01);
 114         assert(std::abs((skew - x_skew) / x_skew) < 0.01);
 115         assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.04);
 116     }
 117     {
 118         typedef std::poisson_distribution<> D;
 119         typedef D::param_type P;
 120         typedef std::mt19937 G;
 121         G g;
 122         D d(2);
 123         P p(20);
 124         const int N = 1000000;
 125         std::vector<double> u;
 126         for (int i = 0; i < N; ++i)
 127         {
 128             D::result_type v = d(g, p);
 129             assert(d.min() <= v && v <= d.max());
 130             u.push_back(v);
 131         }
 132         double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
 133         double var = 0;
 134         double skew = 0;
 135         double kurtosis = 0;
 136         for (unsigned i = 0; i < u.size(); ++i)
 137         {
 138             double dbl = (u[i] - mean);
 139             double d2 = sqr(dbl);
 140             var += d2;
 141             skew += dbl * d2;
 142             kurtosis += d2 * d2;
 143         }
 144         var /= u.size();
 145         double dev = std::sqrt(var);
 146         skew /= u.size() * dev * var;
 147         kurtosis /= u.size() * var * var;
 148         kurtosis -= 3;
 149         double x_mean = p.mean();
 150         double x_var = p.mean();
 151         double x_skew = 1 / std::sqrt(x_var);
 152         double x_kurtosis = 1 / x_var;
 153         assert(std::abs((mean - x_mean) / x_mean) < 0.01);
 154         assert(std::abs((var - x_var) / x_var) < 0.01);
 155         assert(std::abs((skew - x_skew) / x_skew) < 0.01);
 156         assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
 157     }
 158
 159   return 0;
 160 }