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 // REQUIRES: long_tests
11 
12 // <random>
13 
14 // template<class RealType = double>
15 // class exponential_distribution
16 
17 // template<class _URNG> result_type operator()(_URNG& g, const param_type& parm);
18 
19 #include <random>
20 #include <cassert>
21 #include <vector>
22 #include <numeric>
23 
24 template <class T>
25 inline
26 T
sqr(T x)27 sqr(T x)
28 {
29     return x * x;
30 }
31 
main()32 int main()
33 {
34     {
35         typedef std::exponential_distribution<> D;
36         typedef D::param_type P;
37         typedef std::mt19937 G;
38         G g;
39         D d(.75);
40         P p(2);
41         const int N = 1000000;
42         std::vector<D::result_type> u;
43         for (int i = 0; i < N; ++i)
44         {
45             D::result_type v = d(g, p);
46             assert(d.min() < v);
47             u.push_back(v);
48         }
49         double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
50         double var = 0;
51         double skew = 0;
52         double kurtosis = 0;
53         for (int i = 0; i < u.size(); ++i)
54         {
55             double d = (u[i] - mean);
56             double d2 = sqr(d);
57             var += d2;
58             skew += d * 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 = 1/p.lambda();
67         double x_var = 1/sqr(p.lambda());
68         double x_skew = 2;
69         double x_kurtosis = 6;
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) / x_skew) < 0.01);
73         assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
74     }
75 }
76