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 RealType = double>
14 // class normal_distribution
15 
16 // template<class _URNG> result_type operator()(_URNG& g);
17 
18 #include <random>
19 #include <cassert>
20 #include <vector>
21 #include <numeric>
22 #include <cstddef>
23 
24 #include "test_macros.h"
25 
26 template <class T>
27 inline
28 T
sqr(T x)29 sqr(T x)
30 {
31     return x * x;
32 }
33 
main(int,char **)34 int main(int, char**)
35 {
36     {
37         typedef std::normal_distribution<> D;
38         typedef std::minstd_rand G;
39         G g;
40         D d(5, 4);
41         const int N = 1000000;
42         std::vector<D::result_type> u;
43         for (int i = 0; i < N; ++i)
44             u.push_back(d(g));
45         double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
46         double var = 0;
47         double skew = 0;
48         double kurtosis = 0;
49         for (std::size_t i = 0; i < u.size(); ++i)
50         {
51             double dbl = (u[i] - mean);
52             double d2 = sqr(dbl);
53             var += d2;
54             skew += dbl * d2;
55             kurtosis += d2 * d2;
56         }
57         var /= u.size();
58         double dev = std::sqrt(var);
59         skew /= u.size() * dev * var;
60         kurtosis /= u.size() * var * var;
61         kurtosis -= 3;
62         double x_mean = d.mean();
63         double x_var = sqr(d.stddev());
64         double x_skew = 0;
65         double x_kurtosis = 0;
66         assert(std::abs((mean - x_mean) / x_mean) < 0.01);
67         assert(std::abs((var - x_var) / x_var) < 0.01);
68         assert(std::abs(skew - x_skew) < 0.01);
69         assert(std::abs(kurtosis - x_kurtosis) < 0.01);
70     }
71 
72   return 0;
73 }
74