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 _IntType = int>
13// class uniform_int_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#include <cstddef>
22
23template <class T>
24inline
25T
26sqr(T x)
27{
28    return x * x;
29}
30
31int main()
32{
33    {
34        typedef std::uniform_int_distribution<> D;
35        typedef std::minstd_rand G;
36        typedef D::param_type P;
37        G g;
38        D d(5, 100);
39        P p(-10, 20);
40        const int N = 100000;
41        std::vector<D::result_type> u;
42        for (int i = 0; i < N; ++i)
43        {
44            D::result_type v = d(g, p);
45            assert(p.a() <= v && v <= p.b());
46            u.push_back(v);
47        }
48        double mean = std::accumulate(u.begin(), u.end(),
49                                              double(0)) / u.size();
50        double var = 0;
51        double skew = 0;
52        double kurtosis = 0;
53        for (std::size_t i = 0; i < u.size(); ++i)
54        {
55            double dbl = (u[i] - mean);
56            double d2 = sqr(dbl);
57            var += d2;
58            skew += dbl * d2;
59            kurtosis += d2 * d2;
60        }
61        var /= u.size();
62        double dev = std::sqrt(var);
63        skew /= u.size() * dev * var;
64        kurtosis /= u.size() * var * var;
65        kurtosis -= 3;
66        double x_mean = ((double)p.a() + p.b()) / 2;
67        double x_var = (sqr((double)p.b() - p.a() + 1) - 1) / 12;
68        double x_skew = 0;
69        double x_kurtosis = -6. * (sqr((double)p.b() - p.a() + 1) + 1) /
70                            (5. * (sqr((double)p.b() - p.a() + 1) - 1));
71        assert(std::abs((mean - x_mean) / x_mean) < 0.01);
72        assert(std::abs((var - x_var) / x_var) < 0.01);
73        assert(std::abs(skew - x_skew) < 0.01);
74        assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
75    }
76}
77