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 // <random>
10 
11 // class bernoulli_distribution
12 
13 // template<class _URNG> result_type operator()(_URNG& g);
14 
15 #include <random>
16 #include <cassert>
17 #include <cmath>
18 #include <cstddef>
19 #include <numeric>
20 #include <vector>
21 
22 #include "test_macros.h"
23 
24 template <class T>
25 inline
26 T
27 sqr(T x)
28 {
29     return x * x;
30 }
31 
32 int main(int, char**)
33 {
34     {
35         typedef std::bernoulli_distribution D;
36         typedef std::minstd_rand G;
37         G g;
38         D d(.75);
39         const int N = 100000;
40         std::vector<D::result_type> u;
41         for (int i = 0; i < N; ++i)
42             u.push_back(d(g));
43         double mean = std::accumulate(u.begin(), u.end(),
44                                               double(0)) / u.size();
45         double var = 0;
46         double skew = 0;
47         double kurtosis = 0;
48         for (std::size_t i = 0; i < u.size(); ++i)
49         {
50             double dbl = (u[i] - mean);
51             double d2 = sqr(dbl);
52             var += d2;
53             skew += dbl * d2;
54             kurtosis += d2 * d2;
55         }
56         var /= u.size();
57         double dev = std::sqrt(var);
58         skew /= u.size() * dev * var;
59         kurtosis /= u.size() * var * var;
60         kurtosis -= 3;
61         double x_mean = d.p();
62         double x_var = d.p()*(1-d.p());
63         double x_skew = (1 - 2 * d.p())/std::sqrt(x_var);
64         double x_kurtosis = (6 * sqr(d.p()) - 6 * d.p() + 1)/x_var;
65         assert(std::abs((mean - x_mean) / x_mean) < 0.01);
66         assert(std::abs((var - x_var) / x_var) < 0.01);
67         assert(std::abs((skew - x_skew) / x_skew) < 0.02);
68         assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.05);
69     }
70     {
71         typedef std::bernoulli_distribution D;
72         typedef std::minstd_rand G;
73         G g;
74         D d(.25);
75         const int N = 100000;
76         std::vector<D::result_type> u;
77         for (int i = 0; i < N; ++i)
78             u.push_back(d(g));
79         double mean = std::accumulate(u.begin(), u.end(),
80                                               double(0)) / u.size();
81         double var = 0;
82         double skew = 0;
83         double kurtosis = 0;
84         for (std::size_t i = 0; i < u.size(); ++i)
85         {
86             double dbl = (u[i] - mean);
87             double d2 = sqr(dbl);
88             var += d2;
89             skew += dbl * d2;
90             kurtosis += d2 * d2;
91         }
92         var /= u.size();
93         double dev = std::sqrt(var);
94         skew /= u.size() * dev * var;
95         kurtosis /= u.size() * var * var;
96         kurtosis -= 3;
97         double x_mean = d.p();
98         double x_var = d.p()*(1-d.p());
99         double x_skew = (1 - 2 * d.p())/std::sqrt(x_var);
100         double x_kurtosis = (6 * sqr(d.p()) - 6 * d.p() + 1)/x_var;
101         assert(std::abs((mean - x_mean) / x_mean) < 0.01);
102         assert(std::abs((var - x_var) / x_var) < 0.01);
103         assert(std::abs((skew - x_skew) / x_skew) < 0.02);
104         assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.05);
105     }
106 
107   return 0;
108 }
109