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