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
24template <class T>
25inline
26T
27sqr(T x)
28{
29    return x * x;
30}
31
32int 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