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// <random>
11
12// template<class RealType = double>
13// class normal_distribution
14
15// template<class _URNG> result_type operator()(_URNG& g, const param_type& parm);
16
17#include <random>
18#include <cassert>
19#include <vector>
20#include <numeric>
21
22template <class T>
23inline
24T
25sqr(T x)
26{
27    return x * x;
28}
29
30int main()
31{
32    {
33        typedef std::normal_distribution<> D;
34        typedef D::param_type P;
35        typedef std::minstd_rand G;
36        G g;
37        D d(5, 4);
38        P p(50, .5);
39        const int N = 1000000;
40        std::vector<D::result_type> u;
41        for (int i = 0; i < N; ++i)
42            u.push_back(d(g, p));
43        double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
44        double var = 0;
45        double skew = 0;
46        double kurtosis = 0;
47        for (int i = 0; i < u.size(); ++i)
48        {
49            double d = (u[i] - mean);
50            double d2 = sqr(d);
51            var += d2;
52            skew += d * d2;
53            kurtosis += d2 * d2;
54        }
55        var /= u.size();
56        double dev = std::sqrt(var);
57        skew /= u.size() * dev * var;
58        kurtosis /= u.size() * var * var;
59        kurtosis -= 3;
60        double x_mean = p.mean();
61        double x_var = sqr(p.stddev());
62        double x_skew = 0;
63        double x_kurtosis = 0;
64        assert(std::abs((mean - x_mean) / x_mean) < 0.01);
65        assert(std::abs((var - x_var) / x_var) < 0.01);
66        assert(std::abs(skew - x_skew) < 0.01);
67        assert(std::abs(kurtosis - x_kurtosis) < 0.01);
68    }
69}
70