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 student_t_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::student_t_distribution<> D;
36 typedef D::param_type P;
37 typedef std::minstd_rand G;
38 G g;
39 D d;
40 P p(5.5);
41 const int N = 1000000;
42 std::vector<D::result_type> u;
43 for (int i = 0; i < N; ++i)
44 u.push_back(d(g, p));
45 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
46 double var = 0;
47 double skew = 0;
48 double kurtosis = 0;
49 for (int i = 0; i < u.size(); ++i)
50 {
51 double d = (u[i] - mean);
52 double d2 = sqr(d);
53 var += d2;
54 skew += d * d2;
55 kurtosis += d2 * d2;
56 }
57 var /= u.size();
58 double dev = std::sqrt(var);
59 skew /= u.size() * dev * var;
60 kurtosis /= u.size() * var * var;
61 kurtosis -= 3;
62 double x_mean = 0;
63 double x_var = p.n() / (p.n() - 2);
64 double x_skew = 0;
65 double x_kurtosis = 6 / (p.n() - 4);
66 assert(std::abs(mean - x_mean) < 0.01);
67 assert(std::abs((var - x_var) / x_var) < 0.01);
68 assert(std::abs(skew - x_skew) < 0.01);
69 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.2);
70 }
71 {
72 typedef std::student_t_distribution<> D;
73 typedef D::param_type P;
74 typedef std::minstd_rand G;
75 G g;
76 D d;
77 P p(10);
78 const int N = 1000000;
79 std::vector<D::result_type> u;
80 for (int i = 0; i < N; ++i)
81 u.push_back(d(g, p));
82 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
83 double var = 0;
84 double skew = 0;
85 double kurtosis = 0;
86 for (int i = 0; i < u.size(); ++i)
87 {
88 double d = (u[i] - mean);
89 double d2 = sqr(d);
90 var += d2;
91 skew += d * d2;
92 kurtosis += d2 * d2;
93 }
94 var /= u.size();
95 double dev = std::sqrt(var);
96 skew /= u.size() * dev * var;
97 kurtosis /= u.size() * var * var;
98 kurtosis -= 3;
99 double x_mean = 0;
100 double x_var = p.n() / (p.n() - 2);
101 double x_skew = 0;
102 double x_kurtosis = 6 / (p.n() - 4);
103 assert(std::abs(mean - x_mean) < 0.01);
104 assert(std::abs((var - x_var) / x_var) < 0.01);
105 assert(std::abs(skew - x_skew) < 0.01);
106 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.04);
107 }
108 {
109 typedef std::student_t_distribution<> D;
110 typedef D::param_type P;
111 typedef std::minstd_rand G;
112 G g;
113 D d;
114 P p(100);
115 const int N = 1000000;
116 std::vector<D::result_type> u;
117 for (int i = 0; i < N; ++i)
118 u.push_back(d(g, p));
119 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
120 double var = 0;
121 double skew = 0;
122 double kurtosis = 0;
123 for (int i = 0; i < u.size(); ++i)
124 {
125 double d = (u[i] - mean);
126 double d2 = sqr(d);
127 var += d2;
128 skew += d * d2;
129 kurtosis += d2 * d2;
130 }
131 var /= u.size();
132 double dev = std::sqrt(var);
133 skew /= u.size() * dev * var;
134 kurtosis /= u.size() * var * var;
135 kurtosis -= 3;
136 double x_mean = 0;
137 double x_var = p.n() / (p.n() - 2);
138 double x_skew = 0;
139 double x_kurtosis = 6 / (p.n() - 4);
140 assert(std::abs(mean - x_mean) < 0.01);
141 assert(std::abs((var - x_var) / x_var) < 0.01);
142 assert(std::abs(skew - x_skew) < 0.01);
143 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.02);
144 }
145 }
146