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 
22 template <class T>
23 inline
24 T
25 sqr(T x)
26 {
27     return x * x;
28 }
29 
30 int 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