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 exponential_distribution
14
15// template<class _URNG> result_type operator()(_URNG& g);
16
17#include <random>
18#include <cassert>
19#include <vector>
20#include <numeric>
21
22template <class T>
23inline
24T
25sqr(T x)
26{
27    return x * x;
28}
29
30int main()
31{
32    {
33        typedef std::exponential_distribution<> D;
34        typedef D::param_type P;
35        typedef std::mt19937 G;
36        G g;
37        D d(.75);
38        const int N = 1000000;
39        std::vector<D::result_type> u;
40        for (int i = 0; i < N; ++i)
41        {
42            D::result_type v = d(g);
43            assert(d.min() < v);
44            u.push_back(v);
45        }
46        double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
47        double var = 0;
48        double skew = 0;
49        double kurtosis = 0;
50        for (int i = 0; i < u.size(); ++i)
51        {
52            double d = (u[i] - mean);
53            double d2 = sqr(d);
54            var += d2;
55            skew += d * d2;
56            kurtosis += d2 * d2;
57        }
58        var /= u.size();
59        double dev = std::sqrt(var);
60        skew /= u.size() * dev * var;
61        kurtosis /= u.size() * var * var;
62        kurtosis -= 3;
63        double x_mean = 1/d.lambda();
64        double x_var = 1/sqr(d.lambda());
65        double x_skew = 2;
66        double x_kurtosis = 6;
67        assert(std::abs((mean - x_mean) / x_mean) < 0.01);
68        assert(std::abs((var - x_var) / x_var) < 0.01);
69        assert(std::abs((skew - x_skew) / x_skew) < 0.01);
70        assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
71    }
72    {
73        typedef std::exponential_distribution<> D;
74        typedef D::param_type P;
75        typedef std::mt19937 G;
76        G g;
77        D d(1);
78        const int N = 1000000;
79        std::vector<D::result_type> u;
80        for (int i = 0; i < N; ++i)
81        {
82            D::result_type v = d(g);
83            assert(d.min() < v);
84            u.push_back(v);
85        }
86        double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
87        double var = 0;
88        double skew = 0;
89        double kurtosis = 0;
90        for (int i = 0; i < u.size(); ++i)
91        {
92            double d = (u[i] - mean);
93            double d2 = sqr(d);
94            var += d2;
95            skew += d * d2;
96            kurtosis += d2 * d2;
97        }
98        var /= u.size();
99        double dev = std::sqrt(var);
100        skew /= u.size() * dev * var;
101        kurtosis /= u.size() * var * var;
102        kurtosis -= 3;
103        double x_mean = 1/d.lambda();
104        double x_var = 1/sqr(d.lambda());
105        double x_skew = 2;
106        double x_kurtosis = 6;
107        assert(std::abs((mean - x_mean) / x_mean) < 0.01);
108        assert(std::abs((var - x_var) / x_var) < 0.01);
109        assert(std::abs((skew - x_skew) / x_skew) < 0.01);
110        assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
111    }
112    {
113        typedef std::exponential_distribution<> D;
114        typedef D::param_type P;
115        typedef std::mt19937 G;
116        G g;
117        D d(10);
118        const int N = 1000000;
119        std::vector<D::result_type> u;
120        for (int i = 0; i < N; ++i)
121        {
122            D::result_type v = d(g);
123            assert(d.min() < v);
124            u.push_back(v);
125        }
126        double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
127        double var = 0;
128        double skew = 0;
129        double kurtosis = 0;
130        for (int i = 0; i < u.size(); ++i)
131        {
132            double d = (u[i] - mean);
133            double d2 = sqr(d);
134            var += d2;
135            skew += d * d2;
136            kurtosis += d2 * d2;
137        }
138        var /= u.size();
139        double dev = std::sqrt(var);
140        skew /= u.size() * dev * var;
141        kurtosis /= u.size() * var * var;
142        kurtosis -= 3;
143        double x_mean = 1/d.lambda();
144        double x_var = 1/sqr(d.lambda());
145        double x_skew = 2;
146        double x_kurtosis = 6;
147        assert(std::abs((mean - x_mean) / x_mean) < 0.01);
148        assert(std::abs((var - x_var) / x_var) < 0.01);
149        assert(std::abs((skew - x_skew) / x_skew) < 0.01);
150        assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
151    }
152}
153