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 extreme_value_distribution
15
16// template<class _URNG> result_type operator()(_URNG& g);
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
34void
35test1()
36{
37 typedef std::extreme_value_distribution<> D;
38 typedef std::mt19937 G;
39 G g;
40 D d(0.5, 2);
41 const int N = 1000000;
42 std::vector<D::result_type> u;
43 for (int i = 0; i < N; ++i)
44 {
45 D::result_type v = d(g);
46 u.push_back(x: v);
47 }
48 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
49 double var = 0;
50 double skew = 0;
51 double kurtosis = 0;
52 for (unsigned i = 0; i < u.size(); ++i)
53 {
54 double dbl = (u[i] - mean);
55 double d2 = sqr(dbl);
56 var += d2;
57 skew += dbl * d2;
58 kurtosis += d2 * d2;
59 }
60 var /= u.size();
61 double dev = std::sqrt(x: var);
62 skew /= u.size() * dev * var;
63 kurtosis /= u.size() * var * var;
64 kurtosis -= 3;
65 double x_mean = d.a() + d.b() * 0.577215665;
66 double x_var = sqr(d.b()) * 1.644934067;
67 double x_skew = 1.139547;
68 double x_kurtosis = 12./5;
69 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
70 assert(std::abs((var - x_var) / x_var) < 0.01);
71 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
72 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03);
73}
74
75void
76test2()
77{
78 typedef std::extreme_value_distribution<> D;
79 typedef std::mt19937 G;
80 G g;
81 D d(1, 2);
82 const int N = 1000000;
83 std::vector<D::result_type> u;
84 for (int i = 0; i < N; ++i)
85 {
86 D::result_type v = d(g);
87 u.push_back(x: v);
88 }
89 double mean = std::accumulate(first: u.begin(), last: u.end(), init: 0.0) / u.size();
90 double var = 0;
91 double skew = 0;
92 double kurtosis = 0;
93 for (unsigned i = 0; i < u.size(); ++i)
94 {
95 double dbl = (u[i] - mean);
96 double d2 = sqr(x: dbl);
97 var += d2;
98 skew += dbl * d2;
99 kurtosis += d2 * d2;
100 }
101 var /= u.size();
102 double dev = std::sqrt(x: var);
103 skew /= u.size() * dev * var;
104 kurtosis /= u.size() * var * var;
105 kurtosis -= 3;
106 double x_mean = d.a() + d.b() * 0.577215665;
107 double x_var = sqr(x: d.b()) * 1.644934067;
108 double x_skew = 1.139547;
109 double x_kurtosis = 12./5;
110 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
111 assert(std::abs((var - x_var) / x_var) < 0.01);
112 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
113 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03);
114}
115
116void
117test3()
118{
119 typedef std::extreme_value_distribution<> D;
120 typedef std::mt19937 G;
121 G g;
122 D d(1.5, 3);
123 const int N = 1000000;
124 std::vector<D::result_type> u;
125 for (int i = 0; i < N; ++i)
126 {
127 D::result_type v = d(g);
128 u.push_back(x: v);
129 }
130 double mean = std::accumulate(first: u.begin(), last: u.end(), init: 0.0) / u.size();
131 double var = 0;
132 double skew = 0;
133 double kurtosis = 0;
134 for (unsigned i = 0; i < u.size(); ++i)
135 {
136 double dbl = (u[i] - mean);
137 double d2 = sqr(x: dbl);
138 var += d2;
139 skew += dbl * d2;
140 kurtosis += d2 * d2;
141 }
142 var /= u.size();
143 double dev = std::sqrt(x: var);
144 skew /= u.size() * dev * var;
145 kurtosis /= u.size() * var * var;
146 kurtosis -= 3;
147 double x_mean = d.a() + d.b() * 0.577215665;
148 double x_var = sqr(x: d.b()) * 1.644934067;
149 double x_skew = 1.139547;
150 double x_kurtosis = 12./5;
151 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
152 assert(std::abs((var - x_var) / x_var) < 0.01);
153 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
154 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03);
155}
156
157void
158test4()
159{
160 typedef std::extreme_value_distribution<> D;
161 typedef std::mt19937 G;
162 G g;
163 D d(3, 4);
164 const int N = 1000000;
165 std::vector<D::result_type> u;
166 for (int i = 0; i < N; ++i)
167 {
168 D::result_type v = d(g);
169 u.push_back(x: v);
170 }
171 double mean = std::accumulate(first: u.begin(), last: u.end(), init: 0.0) / u.size();
172 double var = 0;
173 double skew = 0;
174 double kurtosis = 0;
175 for (unsigned i = 0; i < u.size(); ++i)
176 {
177 double dbl = (u[i] - mean);
178 double d2 = sqr(x: dbl);
179 var += d2;
180 skew += dbl * d2;
181 kurtosis += d2 * d2;
182 }
183 var /= u.size();
184 double dev = std::sqrt(x: var);
185 skew /= u.size() * dev * var;
186 kurtosis /= u.size() * var * var;
187 kurtosis -= 3;
188 double x_mean = d.a() + d.b() * 0.577215665;
189 double x_var = sqr(x: d.b()) * 1.644934067;
190 double x_skew = 1.139547;
191 double x_kurtosis = 12./5;
192 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
193 assert(std::abs((var - x_var) / x_var) < 0.01);
194 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
195 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03);
196}
197
198int main(int, char**)
199{
200 test1();
201 test2();
202 test3();
203 test4();
204
205 return 0;
206}
207

source code of libcxx/test/std/numerics/rand/rand.dist/rand.dist.pois/rand.dist.pois.extreme/eval.pass.cpp