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 IntType = int>
14// class geometric_distribution
15
16// template<class _URNG> result_type operator()(_URNG& g, const param_type& parm);
17
18#include <random>
19#include <cassert>
20#include <cmath>
21#include <numeric>
22#include <vector>
23
24#include "test_macros.h"
25
26template <class T>
27inline
28T
29sqr(T x)
30{
31 return x * x;
32}
33
34int main(int, char**)
35{
36 {
37 typedef std::geometric_distribution<> D;
38 typedef D::param_type P;
39 typedef std::mt19937 G;
40 G g;
41 D d(.75);
42 P p(.03125);
43 const int N = 1000000;
44 std::vector<D::result_type> u;
45 for (int i = 0; i < N; ++i)
46 {
47 D::result_type v = d(g, p);
48 assert(d.min() <= v && v <= d.max());
49 u.push_back(v);
50 }
51 double mean = std::accumulate(u.begin(), u.end(),
52 double(0)) / u.size();
53 double var = 0;
54 double skew = 0;
55 double kurtosis = 0;
56 for (unsigned i = 0; i < u.size(); ++i)
57 {
58 double dbl = (u[i] - mean);
59 double d2 = sqr(dbl);
60 var += d2;
61 skew += dbl * d2;
62 kurtosis += d2 * d2;
63 }
64 var /= u.size();
65 double dev = std::sqrt(x: var);
66 skew /= u.size() * dev * var;
67 kurtosis /= u.size() * var * var;
68 kurtosis -= 3;
69 double x_mean = (1 - p.p()) / p.p();
70 double x_var = x_mean / p.p();
71 double x_skew = (2 - p.p()) / std::sqrt((1 - p.p()));
72 double x_kurtosis = 6 + sqr(p.p()) / (1 - p.p());
73 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
74 assert(std::abs((var - x_var) / x_var) < 0.01);
75 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
76 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03);
77 }
78 {
79 typedef std::geometric_distribution<> D;
80 typedef D::param_type P;
81 typedef std::mt19937 G;
82 G g;
83 D d(.75);
84 P p(.25);
85 const int N = 1000000;
86 std::vector<D::result_type> u;
87 for (int i = 0; i < N; ++i)
88 {
89 D::result_type v = d(g, p);
90 assert(d.min() <= v && v <= d.max());
91 u.push_back(v);
92 }
93 double mean = std::accumulate(u.begin(), u.end(),
94 double(0)) / u.size();
95 double var = 0;
96 double skew = 0;
97 double kurtosis = 0;
98 for (unsigned i = 0; i < u.size(); ++i)
99 {
100 double dbl = (u[i] - mean);
101 double d2 = sqr(dbl);
102 var += d2;
103 skew += dbl * d2;
104 kurtosis += d2 * d2;
105 }
106 var /= u.size();
107 double dev = std::sqrt(x: var);
108 skew /= u.size() * dev * var;
109 kurtosis /= u.size() * var * var;
110 kurtosis -= 3;
111 double x_mean = (1 - p.p()) / p.p();
112 double x_var = x_mean / p.p();
113 double x_skew = (2 - p.p()) / std::sqrt((1 - p.p()));
114 double x_kurtosis = 6 + sqr(p.p()) / (1 - p.p());
115 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
116 assert(std::abs((var - x_var) / x_var) < 0.01);
117 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
118 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03);
119 }
120 {
121 typedef std::geometric_distribution<> D;
122 typedef D::param_type P;
123 typedef std::minstd_rand G;
124 G g;
125 D d(.5);
126 P p(.75);
127 const int N = 1000000;
128 std::vector<D::result_type> u;
129 for (int i = 0; i < N; ++i)
130 {
131 D::result_type v = d(g, p);
132 assert(d.min() <= v && v <= d.max());
133 u.push_back(v);
134 }
135 double mean = std::accumulate(u.begin(), u.end(),
136 double(0)) / u.size();
137 double var = 0;
138 double skew = 0;
139 double kurtosis = 0;
140 for (unsigned i = 0; i < u.size(); ++i)
141 {
142 double dbl = (u[i] - mean);
143 double d2 = sqr(dbl);
144 var += d2;
145 skew += dbl * d2;
146 kurtosis += d2 * d2;
147 }
148 var /= u.size();
149 double dev = std::sqrt(x: var);
150 skew /= u.size() * dev * var;
151 kurtosis /= u.size() * var * var;
152 kurtosis -= 3;
153 double x_mean = (1 - p.p()) / p.p();
154 double x_var = x_mean / p.p();
155 double x_skew = (2 - p.p()) / std::sqrt((1 - p.p()));
156 double x_kurtosis = 6 + sqr(p.p()) / (1 - p.p());
157 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
158 assert(std::abs((var - x_var) / x_var) < 0.01);
159 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
160 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03);
161 }
162
163 return 0;
164}
165

source code of libcxx/test/std/numerics/rand/rand.dist/rand.dist.bern/rand.dist.bern.geo/eval_param.pass.cpp