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 uniform_real_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
sqr(T x)25 sqr(T x)
26 {
27     return x * x;
28 }
29 
main()30 int main()
31 {
32     {
33         typedef std::uniform_real_distribution<> D;
34         typedef std::minstd_rand G;
35         typedef D::param_type P;
36         G g;
37         D d(5.5, 25);
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         D::result_type mean = std::accumulate(u.begin(), u.end(),
48                                               D::result_type(0)) / u.size();
49         D::result_type var = 0;
50         D::result_type skew = 0;
51         D::result_type kurtosis = 0;
52         for (int i = 0; i < u.size(); ++i)
53         {
54             D::result_type d = (u[i] - mean);
55             D::result_type d2 = sqr(d);
56             var += d2;
57             skew += d * d2;
58             kurtosis += d2 * d2;
59         }
60         var /= u.size();
61         D::result_type dev = std::sqrt(var);
62         skew /= u.size() * dev * var;
63         kurtosis /= u.size() * var * var;
64         kurtosis -= 3;
65         D::result_type x_mean = (p.a() + p.b()) / 2;
66         D::result_type x_var = sqr(p.b() - p.a()) / 12;
67         D::result_type x_skew = 0;
68         D::result_type x_kurtosis = -6./5;
69         assert(std::abs((mean - x_mean) / x_mean) < 0.01);
70         assert(std::abs((var - x_var) / x_var) < 0.01);
71         assert(std::abs(skew - x_skew) < 0.01);
72         assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
73     }
74 }
75