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 <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
33void
34test1()
35{
36 typedef std::extreme_value_distribution<> D;
37 typedef D::param_type P;
38 typedef std::mt19937 G;
39 G g;
40 D d(-0.5, 1);
41 P p(0.5, 2);
42 const int N = 1000000;
43 std::vector<D::result_type> u;
44 for (int i = 0; i < N; ++i)
45 {
46 D::result_type v = d(g, p);
47 u.push_back(x: v);
48 }
49 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
50 double var = 0;
51 double skew = 0;
52 double kurtosis = 0;
53 for (unsigned i = 0; i < u.size(); ++i)
54 {
55 double dbl = (u[i] - mean);
56 double d2 = sqr(dbl);
57 var += d2;
58 skew += dbl * d2;
59 kurtosis += d2 * d2;
60 }
61 var /= u.size();
62 double dev = std::sqrt(x: var);
63 skew /= u.size() * dev * var;
64 kurtosis /= u.size() * var * var;
65 kurtosis -= 3;
66 double x_mean = p.a() + p.b() * 0.577215665;
67 double x_var = sqr(p.b()) * 1.644934067;
68 double x_skew = 1.139547;
69 double x_kurtosis = 12./5;
70 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
71 assert(std::abs((var - x_var) / x_var) < 0.01);
72 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
73 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
74}
75
76void
77test2()
78{
79 typedef std::extreme_value_distribution<> D;
80 typedef D::param_type P;
81 typedef std::mt19937 G;
82 G g;
83 D d(-0.5, 1);
84 P p(1, 2);
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 u.push_back(x: v);
91 }
92 double mean = std::accumulate(first: u.begin(), last: u.end(), init: 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(x: 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.a() + p.b() * 0.577215665;
110 double x_var = sqr(x: p.b()) * 1.644934067;
111 double x_skew = 1.139547;
112 double x_kurtosis = 12./5;
113 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
114 assert(std::abs((var - x_var) / x_var) < 0.01);
115 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
116 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
117}
118
119void
120test3()
121{
122 typedef std::extreme_value_distribution<> D;
123 typedef D::param_type P;
124 typedef std::mt19937 G;
125 G g;
126 D d(-0.5, 1);
127 P p(1.5, 3);
128 const int N = 1000000;
129 std::vector<D::result_type> u;
130 for (int i = 0; i < N; ++i)
131 {
132 D::result_type v = d(g, p);
133 u.push_back(x: v);
134 }
135 double mean = std::accumulate(first: u.begin(), last: u.end(), init: 0.0) / u.size();
136 double var = 0;
137 double skew = 0;
138 double kurtosis = 0;
139 for (unsigned i = 0; i < u.size(); ++i)
140 {
141 double dbl = (u[i] - mean);
142 double d2 = sqr(x: dbl);
143 var += d2;
144 skew += dbl * d2;
145 kurtosis += d2 * d2;
146 }
147 var /= u.size();
148 double dev = std::sqrt(x: var);
149 skew /= u.size() * dev * var;
150 kurtosis /= u.size() * var * var;
151 kurtosis -= 3;
152 double x_mean = p.a() + p.b() * 0.577215665;
153 double x_var = sqr(x: p.b()) * 1.644934067;
154 double x_skew = 1.139547;
155 double x_kurtosis = 12./5;
156 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
157 assert(std::abs((var - x_var) / x_var) < 0.01);
158 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
159 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
160}
161
162void
163test4()
164{
165 typedef std::extreme_value_distribution<> D;
166 typedef D::param_type P;
167 typedef std::mt19937 G;
168 G g;
169 D d(-0.5, 1);
170 P p(3, 4);
171 const int N = 1000000;
172 std::vector<D::result_type> u;
173 for (int i = 0; i < N; ++i)
174 {
175 D::result_type v = d(g, p);
176 u.push_back(x: v);
177 }
178 double mean = std::accumulate(first: u.begin(), last: u.end(), init: 0.0) / u.size();
179 double var = 0;
180 double skew = 0;
181 double kurtosis = 0;
182 for (unsigned i = 0; i < u.size(); ++i)
183 {
184 double dbl = (u[i] - mean);
185 double d2 = sqr(x: dbl);
186 var += d2;
187 skew += dbl * d2;
188 kurtosis += d2 * d2;
189 }
190 var /= u.size();
191 double dev = std::sqrt(x: var);
192 skew /= u.size() * dev * var;
193 kurtosis /= u.size() * var * var;
194 kurtosis -= 3;
195 double x_mean = p.a() + p.b() * 0.577215665;
196 double x_var = sqr(x: p.b()) * 1.644934067;
197 double x_skew = 1.139547;
198 double x_kurtosis = 12./5;
199 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
200 assert(std::abs((var - x_var) / x_var) < 0.01);
201 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
202 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
203}
204
205int main(int, char**)
206{
207 test1();
208 test2();
209 test3();
210 test4();
211
212 return 0;
213}
214

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