1//===----------------------------------------------------------------------===//
2//
3// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
4// See https://llvm.org/LICENSE.txt for license information.
5// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
6//
7//===----------------------------------------------------------------------===//
8//
9// REQUIRES: long_tests
10
11// <random>
12
13// template<class RealType = double>
14// class student_t_distribution
15
16// template<class _URNG> result_type operator()(_URNG& g, const param_type& parm);
17
18#include <random>
19#include <cassert>
20#include <vector>
21#include <numeric>
22
23#include "test_macros.h"
24
25template <class T>
26inline
27T
28sqr(T x)
29{
30 return x * x;
31}
32
33int main(int, char**)
34{
35 {
36 typedef std::student_t_distribution<> D;
37 typedef D::param_type P;
38 typedef std::minstd_rand G;
39 G g;
40 D d;
41 P p(5.5);
42 const int N = 1000000;
43 std::vector<D::result_type> u;
44 for (int i = 0; i < N; ++i)
45 u.push_back(d(g, p));
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 (unsigned i = 0; i < u.size(); ++i)
51 {
52 double dbl = (u[i] - mean);
53 double d2 = sqr(dbl);
54 var += d2;
55 skew += dbl * d2;
56 kurtosis += d2 * d2;
57 }
58 var /= u.size();
59 double dev = std::sqrt(x: var);
60 skew /= u.size() * dev * var;
61 kurtosis /= u.size() * var * var;
62 kurtosis -= 3;
63 double x_mean = 0;
64 double x_var = p.n() / (p.n() - 2);
65 double x_skew = 0;
66 double x_kurtosis = 6 / (p.n() - 4);
67 assert(std::abs(mean - x_mean) < 0.01);
68 assert(std::abs((var - x_var) / x_var) < 0.01);
69 assert(std::abs(skew - x_skew) < 0.01);
70 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.2);
71 }
72 {
73 typedef std::student_t_distribution<> D;
74 typedef D::param_type P;
75 typedef std::minstd_rand G;
76 G g;
77 D d;
78 P p(10);
79 const int N = 1000000;
80 std::vector<D::result_type> u;
81 for (int i = 0; i < N; ++i)
82 u.push_back(d(g, p));
83 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
84 double var = 0;
85 double skew = 0;
86 double kurtosis = 0;
87 for (unsigned i = 0; i < u.size(); ++i)
88 {
89 double dbl = (u[i] - mean);
90 double d2 = sqr(dbl);
91 var += d2;
92 skew += dbl * d2;
93 kurtosis += d2 * d2;
94 }
95 var /= u.size();
96 double dev = std::sqrt(x: var);
97 skew /= u.size() * dev * var;
98 kurtosis /= u.size() * var * var;
99 kurtosis -= 3;
100 double x_mean = 0;
101 double x_var = p.n() / (p.n() - 2);
102 double x_skew = 0;
103 double x_kurtosis = 6 / (p.n() - 4);
104 assert(std::abs(mean - x_mean) < 0.01);
105 assert(std::abs((var - x_var) / x_var) < 0.01);
106 assert(std::abs(skew - x_skew) < 0.01);
107 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.04);
108 }
109 {
110 typedef std::student_t_distribution<> D;
111 typedef D::param_type P;
112 typedef std::minstd_rand G;
113 G g;
114 D d;
115 P p(100);
116 const int N = 1000000;
117 std::vector<D::result_type> u;
118 for (int i = 0; i < N; ++i)
119 u.push_back(d(g, p));
120 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
121 double var = 0;
122 double skew = 0;
123 double kurtosis = 0;
124 for (unsigned i = 0; i < u.size(); ++i)
125 {
126 double dbl = (u[i] - mean);
127 double d2 = sqr(dbl);
128 var += d2;
129 skew += dbl * d2;
130 kurtosis += d2 * d2;
131 }
132 var /= u.size();
133 double dev = std::sqrt(x: var);
134 skew /= u.size() * dev * var;
135 kurtosis /= u.size() * var * var;
136 kurtosis -= 3;
137 double x_mean = 0;
138 double x_var = p.n() / (p.n() - 2);
139 double x_skew = 0;
140 double x_kurtosis = 6 / (p.n() - 4);
141 assert(std::abs(mean - x_mean) < 0.01);
142 assert(std::abs((var - x_var) / x_var) < 0.01);
143 assert(std::abs(skew - x_skew) < 0.01);
144 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.02);
145 }
146
147 return 0;
148}
149

source code of libcxx/test/std/numerics/rand/rand.dist/rand.dist.norm/rand.dist.norm.t/eval_param.pass.cpp