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
24template <class T>
25inline
26T
27sqr(T x)
28{
29 return x * x;
30}
31
32int 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(x: 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: 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(x: 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: 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

source code of libcxx/test/std/numerics/rand/rand.dist/rand.dist.bern/rand.dist.bern.bernoulli/eval.pass.cpp