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, const param_type& parm);
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 D::param_type P;
37 typedef std::minstd_rand G;
38 G g;
39 D d(.75);
40 P p(.25);
41 const int N = 100000;
42 std::vector<D::result_type> u;
43 for (int i = 0; i < N; ++i)
44 u.push_back(d(g, p));
45 double mean = std::accumulate(u.begin(), u.end(),
46 double(0)) / u.size();
47 double var = 0;
48 double skew = 0;
49 double kurtosis = 0;
50 for (std::size_t i = 0; i < u.size(); ++i)
51 {
52 double dbl = (u[i] - mean);
53 double d2 = sqr(dbl);
54 var += d2;
55 skew += dbl * d2;
56 kurtosis += d2 * d2;
57 }
58 var /= u.size();
59 double dev = std::sqrt(x: var);
60 skew /= u.size() * dev * var;
61 kurtosis /= u.size() * var * var;
62 kurtosis -= 3;
63 double x_mean = p.p();
64 double x_var = p.p()*(1-p.p());
65 double x_skew = (1 - 2 * p.p())/std::sqrt(x: x_var);
66 double x_kurtosis = (6 * sqr(p.p()) - 6 * p.p() + 1)/x_var;
67 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
68 assert(std::abs((var - x_var) / x_var) < 0.01);
69 assert(std::abs((skew - x_skew) / x_skew) < 0.02);
70 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.05);
71 }
72 {
73 typedef std::bernoulli_distribution D;
74 typedef D::param_type P;
75 typedef std::minstd_rand G;
76 G g;
77 D d(.25);
78 P p(.75);
79 const int N = 100000;
80 std::vector<D::result_type> u;
81 for (int i = 0; i < N; ++i)
82 u.push_back(d(g, p));
83 double mean = std::accumulate(u.begin(), u.end(),
84 double(0)) / u.size();
85 double var = 0;
86 double skew = 0;
87 double kurtosis = 0;
88 for (std::size_t i = 0; i < u.size(); ++i)
89 {
90 double dbl = (u[i] - mean);
91 double d2 = sqr(dbl);
92 var += d2;
93 skew += dbl * d2;
94 kurtosis += d2 * d2;
95 }
96 var /= u.size();
97 double dev = std::sqrt(x: var);
98 skew /= u.size() * dev * var;
99 kurtosis /= u.size() * var * var;
100 kurtosis -= 3;
101 double x_mean = p.p();
102 double x_var = p.p()*(1-p.p());
103 double x_skew = (1 - 2 * p.p())/std::sqrt(x: x_var);
104 double x_kurtosis = (6 * sqr(p.p()) - 6 * p.p() + 1)/x_var;
105 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
106 assert(std::abs((var - x_var) / x_var) < 0.01);
107 assert(std::abs((skew - x_skew) / x_skew) < 0.02);
108 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.05);
109 }
110
111 return 0;
112}
113

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