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#include <cstddef>
22
23template <class T>
24inline
25T
26sqr(T x)
27{
28    return x * x;
29}
30
31int main()
32{
33    {
34        typedef std::uniform_real_distribution<> D;
35        typedef std::minstd_rand G;
36        typedef D::param_type P;
37        G g;
38        D d(5.5, 25);
39        P p(-10, 20);
40        const int N = 100000;
41        std::vector<D::result_type> u;
42        for (int i = 0; i < N; ++i)
43        {
44            D::result_type v = d(g, p);
45            assert(p.a() <= v && v < p.b());
46            u.push_back(v);
47        }
48        D::result_type mean = std::accumulate(u.begin(), u.end(),
49                                              D::result_type(0)) / u.size();
50        D::result_type var = 0;
51        D::result_type skew = 0;
52        D::result_type kurtosis = 0;
53        for (std::size_t i = 0; i < u.size(); ++i)
54        {
55            D::result_type dbl = (u[i] - mean);
56            D::result_type d2 = sqr(dbl);
57            var += d2;
58            skew += dbl * d2;
59            kurtosis += d2 * d2;
60        }
61        var /= u.size();
62        D::result_type dev = std::sqrt(var);
63        skew /= u.size() * dev * var;
64        kurtosis /= u.size() * var * var;
65        kurtosis -= 3;
66        D::result_type x_mean = (p.a() + p.b()) / 2;
67        D::result_type x_var = sqr(p.b() - p.a()) / 12;
68        D::result_type x_skew = 0;
69        D::result_type x_kurtosis = -6./5;
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