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 poisson_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::poisson_distribution<> D;
38 typedef D::param_type P;
39 typedef std::minstd_rand G;
40 G g;
41 D d(.75);
42 P p(2);
43 const int N = 100000;
44 std::vector<double> 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(), 0.0) / u.size();
52 double var = 0;
53 double skew = 0;
54 double kurtosis = 0;
55 for (unsigned i = 0; i < u.size(); ++i)
56 {
57 double dbl = (u[i] - mean);
58 double d2 = sqr(dbl);
59 var += d2;
60 skew += dbl * d2;
61 kurtosis += d2 * d2;
62 }
63 var /= u.size();
64 double dev = std::sqrt(x: var);
65 skew /= u.size() * dev * var;
66 kurtosis /= u.size() * var * var;
67 kurtosis -= 3;
68 double x_mean = p.mean();
69 double x_var = p.mean();
70 double x_skew = 1 / std::sqrt(x: x_var);
71 double x_kurtosis = 1 / x_var;
72 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
73 assert(std::abs((var - x_var) / x_var) < 0.01);
74 assert(std::abs((skew - x_skew) / x_skew) < 0.03);
75 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.2);
76 }
77 {
78 typedef std::poisson_distribution<> D;
79 typedef D::param_type P;
80 typedef std::minstd_rand G;
81 G g;
82 D d(2);
83 P p(.75);
84 const int N = 100000;
85 std::vector<double> u;
86 for (int i = 0; i < N; ++i)
87 {
88 D::result_type v = d(g, p);
89 assert(d.min() <= v && v <= d.max());
90 u.push_back(v);
91 }
92 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
93 double var = 0;
94 double skew = 0;
95 double kurtosis = 0;
96 for (unsigned i = 0; i < u.size(); ++i)
97 {
98 double dbl = (u[i] - mean);
99 double d2 = sqr(dbl);
100 var += d2;
101 skew += dbl * d2;
102 kurtosis += d2 * d2;
103 }
104 var /= u.size();
105 double dev = std::sqrt(x: var);
106 skew /= u.size() * dev * var;
107 kurtosis /= u.size() * var * var;
108 kurtosis -= 3;
109 double x_mean = p.mean();
110 double x_var = p.mean();
111 double x_skew = 1 / std::sqrt(x: x_var);
112 double x_kurtosis = 1 / x_var;
113 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
114 assert(std::abs((var - x_var) / x_var) < 0.02);
115 assert(std::abs((skew - x_skew) / x_skew) < 0.02);
116 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.09);
117 }
118 {
119 typedef std::poisson_distribution<> D;
120 typedef D::param_type P;
121 typedef std::mt19937 G;
122 G g;
123 D d(2);
124 P p(20);
125 const int N = 1000000;
126 std::vector<double> u;
127 for (int i = 0; i < N; ++i)
128 {
129 D::result_type v = d(g, p);
130 assert(d.min() <= v && v <= d.max());
131 u.push_back(v);
132 }
133 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
134 double var = 0;
135 double skew = 0;
136 double kurtosis = 0;
137 for (unsigned i = 0; i < u.size(); ++i)
138 {
139 double dbl = (u[i] - mean);
140 double d2 = sqr(dbl);
141 var += d2;
142 skew += dbl * d2;
143 kurtosis += d2 * d2;
144 }
145 var /= u.size();
146 double dev = std::sqrt(x: var);
147 skew /= u.size() * dev * var;
148 kurtosis /= u.size() * var * var;
149 kurtosis -= 3;
150 double x_mean = p.mean();
151 double x_var = p.mean();
152 double x_skew = 1 / std::sqrt(x: x_var);
153 double x_kurtosis = 1 / x_var;
154 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
155 assert(std::abs((var - x_var) / x_var) < 0.01);
156 assert(std::abs((skew - x_skew) / x_skew) < 0.02);
157 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.3);
158 }
159
160 return 0;
161}
162

source code of libcxx/test/std/numerics/rand/rand.dist/rand.dist.pois/rand.dist.pois.poisson/eval_param.pass.cpp