1//===----------------------------------------------------------------------===//
2//
3//                     The LLVM Compiler Infrastructure
4//
5// This file is dual licensed under the MIT and the University of Illinois Open
6// Source Licenses. See LICENSE.TXT for details.
7//
8//===----------------------------------------------------------------------===//
9//
10// REQUIRES: long_tests
11
12// <random>
13
14// template<class RealType = double>
15// class normal_distribution
16
17// template<class _URNG> result_type operator()(_URNG& g);
18
19#include <random>
20#include <cassert>
21#include <vector>
22#include <numeric>
23#include <cstddef>
24
25template <class T>
26inline
27T
28sqr(T x)
29{
30    return x * x;
31}
32
33int main()
34{
35    {
36        typedef std::normal_distribution<> D;
37        typedef std::minstd_rand G;
38        G g;
39        D d(5, 4);
40        const int N = 1000000;
41        std::vector<D::result_type> u;
42        for (int i = 0; i < N; ++i)
43            u.push_back(d(g));
44        double mean = std::accumulate(u.begin(), u.end(), 0.0) / 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.mean();
62        double x_var = sqr(d.stddev());
63        double x_skew = 0;
64        double x_kurtosis = 0;
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) < 0.01);
68        assert(std::abs(kurtosis - x_kurtosis) < 0.01);
69    }
70}
71