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, 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
34void
35test1()
36{
37 typedef std::extreme_value_distribution<> D;
38 typedef D::param_type P;
39 typedef std::mt19937 G;
40 G g;
41 D d(-0.5, 1);
42 P p(0.5, 2);
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 u.push_back(x: v);
49 }
50 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
51 double var = 0;
52 double skew = 0;
53 double kurtosis = 0;
54 for (unsigned i = 0; i < u.size(); ++i)
55 {
56 double dbl = (u[i] - mean);
57 double d2 = sqr(dbl);
58 var += d2;
59 skew += dbl * d2;
60 kurtosis += d2 * d2;
61 }
62 var /= u.size();
63 double dev = std::sqrt(x: var);
64 skew /= u.size() * dev * var;
65 kurtosis /= u.size() * var * var;
66 kurtosis -= 3;
67 double x_mean = p.a() + p.b() * 0.577215665;
68 double x_var = sqr(p.b()) * 1.644934067;
69 double x_skew = 1.139547;
70 double x_kurtosis = 12./5;
71 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
72 assert(std::abs((var - x_var) / x_var) < 0.01);
73 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
74 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03);
75}
76
77void
78test2()
79{
80 typedef std::extreme_value_distribution<> D;
81 typedef D::param_type P;
82 typedef std::mt19937 G;
83 G g;
84 D d(-0.5, 1);
85 P p(1, 2);
86 const int N = 1000000;
87 std::vector<D::result_type> u;
88 for (int i = 0; i < N; ++i)
89 {
90 D::result_type v = d(g, p);
91 u.push_back(x: v);
92 }
93 double mean = std::accumulate(first: u.begin(), last: u.end(), init: 0.0) / u.size();
94 double var = 0;
95 double skew = 0;
96 double kurtosis = 0;
97 for (unsigned i = 0; i < u.size(); ++i)
98 {
99 double dbl = (u[i] - mean);
100 double d2 = sqr(x: dbl);
101 var += d2;
102 skew += dbl * d2;
103 kurtosis += d2 * d2;
104 }
105 var /= u.size();
106 double dev = std::sqrt(x: var);
107 skew /= u.size() * dev * var;
108 kurtosis /= u.size() * var * var;
109 kurtosis -= 3;
110 double x_mean = p.a() + p.b() * 0.577215665;
111 double x_var = sqr(x: p.b()) * 1.644934067;
112 double x_skew = 1.139547;
113 double x_kurtosis = 12./5;
114 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
115 assert(std::abs((var - x_var) / x_var) < 0.01);
116 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
117 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03);
118}
119
120void
121test3()
122{
123 typedef std::extreme_value_distribution<> D;
124 typedef D::param_type P;
125 typedef std::mt19937 G;
126 G g;
127 D d(-0.5, 1);
128 P p(1.5, 3);
129 const int N = 1000000;
130 std::vector<D::result_type> u;
131 for (int i = 0; i < N; ++i)
132 {
133 D::result_type v = d(g, p);
134 u.push_back(x: v);
135 }
136 double mean = std::accumulate(first: u.begin(), last: u.end(), init: 0.0) / 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(x: 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 = p.a() + p.b() * 0.577215665;
154 double x_var = sqr(x: p.b()) * 1.644934067;
155 double x_skew = 1.139547;
156 double x_kurtosis = 12./5;
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
163void
164test4()
165{
166 typedef std::extreme_value_distribution<> D;
167 typedef D::param_type P;
168 typedef std::mt19937 G;
169 G g;
170 D d(-0.5, 1);
171 P p(3, 4);
172 const int N = 1000000;
173 std::vector<D::result_type> u;
174 for (int i = 0; i < N; ++i)
175 {
176 D::result_type v = d(g, p);
177 u.push_back(x: v);
178 }
179 double mean = std::accumulate(first: u.begin(), last: u.end(), init: 0.0) / u.size();
180 double var = 0;
181 double skew = 0;
182 double kurtosis = 0;
183 for (unsigned i = 0; i < u.size(); ++i)
184 {
185 double dbl = (u[i] - mean);
186 double d2 = sqr(x: dbl);
187 var += d2;
188 skew += dbl * d2;
189 kurtosis += d2 * d2;
190 }
191 var /= u.size();
192 double dev = std::sqrt(x: var);
193 skew /= u.size() * dev * var;
194 kurtosis /= u.size() * var * var;
195 kurtosis -= 3;
196 double x_mean = p.a() + p.b() * 0.577215665;
197 double x_var = sqr(x: p.b()) * 1.644934067;
198 double x_skew = 1.139547;
199 double x_kurtosis = 12./5;
200 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
201 assert(std::abs((var - x_var) / x_var) < 0.01);
202 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
203 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03);
204}
205
206int main(int, char**)
207{
208 test1();
209 test2();
210 test3();
211 test4();
212
213 return 0;
214}
215

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