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 <vector>
21 #include <numeric>
22
23 #include "test_macros.h"
24
25 template <class T>
26 inline
27 T
sqr(T x)28 sqr(T x)
29 {
30 return x * x;
31 }
32
33 void
test1()34 test1()
35 {
36 typedef std::extreme_value_distribution<> D;
37 typedef std::mt19937 G;
38 G g;
39 D d(0.5, 2);
40 const int N = 1000000;
41 std::vector<D::result_type> u;
42 for (int i = 0; i < N; ++i)
43 {
44 D::result_type v = d(g);
45 u.push_back(v);
46 }
47 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
48 double var = 0;
49 double skew = 0;
50 double kurtosis = 0;
51 for (unsigned i = 0; i < u.size(); ++i)
52 {
53 double dbl = (u[i] - mean);
54 double d2 = sqr(dbl);
55 var += d2;
56 skew += dbl * d2;
57 kurtosis += d2 * d2;
58 }
59 var /= u.size();
60 double dev = std::sqrt(var);
61 skew /= u.size() * dev * var;
62 kurtosis /= u.size() * var * var;
63 kurtosis -= 3;
64 double x_mean = d.a() + d.b() * 0.577215665;
65 double x_var = sqr(d.b()) * 1.644934067;
66 double x_skew = 1.139547;
67 double x_kurtosis = 12./5;
68 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
69 assert(std::abs((var - x_var) / x_var) < 0.01);
70 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
71 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
72 }
73
74 void
test2()75 test2()
76 {
77 typedef std::extreme_value_distribution<> D;
78 typedef std::mt19937 G;
79 G g;
80 D d(1, 2);
81 const int N = 1000000;
82 std::vector<D::result_type> u;
83 for (int i = 0; i < N; ++i)
84 {
85 D::result_type v = d(g);
86 u.push_back(v);
87 }
88 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
89 double var = 0;
90 double skew = 0;
91 double kurtosis = 0;
92 for (unsigned i = 0; i < u.size(); ++i)
93 {
94 double dbl = (u[i] - mean);
95 double d2 = sqr(dbl);
96 var += d2;
97 skew += dbl * d2;
98 kurtosis += d2 * d2;
99 }
100 var /= u.size();
101 double dev = std::sqrt(var);
102 skew /= u.size() * dev * var;
103 kurtosis /= u.size() * var * var;
104 kurtosis -= 3;
105 double x_mean = d.a() + d.b() * 0.577215665;
106 double x_var = sqr(d.b()) * 1.644934067;
107 double x_skew = 1.139547;
108 double x_kurtosis = 12./5;
109 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
110 assert(std::abs((var - x_var) / x_var) < 0.01);
111 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
112 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
113 }
114
115 void
test3()116 test3()
117 {
118 typedef std::extreme_value_distribution<> D;
119 typedef std::mt19937 G;
120 G g;
121 D d(1.5, 3);
122 const int N = 1000000;
123 std::vector<D::result_type> u;
124 for (int i = 0; i < N; ++i)
125 {
126 D::result_type v = d(g);
127 u.push_back(v);
128 }
129 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
130 double var = 0;
131 double skew = 0;
132 double kurtosis = 0;
133 for (unsigned i = 0; i < u.size(); ++i)
134 {
135 double dbl = (u[i] - mean);
136 double d2 = sqr(dbl);
137 var += d2;
138 skew += dbl * d2;
139 kurtosis += d2 * d2;
140 }
141 var /= u.size();
142 double dev = std::sqrt(var);
143 skew /= u.size() * dev * var;
144 kurtosis /= u.size() * var * var;
145 kurtosis -= 3;
146 double x_mean = d.a() + d.b() * 0.577215665;
147 double x_var = sqr(d.b()) * 1.644934067;
148 double x_skew = 1.139547;
149 double x_kurtosis = 12./5;
150 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
151 assert(std::abs((var - x_var) / x_var) < 0.01);
152 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
153 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
154 }
155
156 void
test4()157 test4()
158 {
159 typedef std::extreme_value_distribution<> D;
160 typedef std::mt19937 G;
161 G g;
162 D d(3, 4);
163 const int N = 1000000;
164 std::vector<D::result_type> u;
165 for (int i = 0; i < N; ++i)
166 {
167 D::result_type v = d(g);
168 u.push_back(v);
169 }
170 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
171 double var = 0;
172 double skew = 0;
173 double kurtosis = 0;
174 for (unsigned i = 0; i < u.size(); ++i)
175 {
176 double dbl = (u[i] - mean);
177 double d2 = sqr(dbl);
178 var += d2;
179 skew += dbl * d2;
180 kurtosis += d2 * d2;
181 }
182 var /= u.size();
183 double dev = std::sqrt(var);
184 skew /= u.size() * dev * var;
185 kurtosis /= u.size() * var * var;
186 kurtosis -= 3;
187 double x_mean = d.a() + d.b() * 0.577215665;
188 double x_var = sqr(d.b()) * 1.644934067;
189 double x_skew = 1.139547;
190 double x_kurtosis = 12./5;
191 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
192 assert(std::abs((var - x_var) / x_var) < 0.01);
193 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
194 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
195 }
196
main(int,char **)197 int main(int, char**)
198 {
199 test1();
200 test2();
201 test3();
202 test4();
203
204 return 0;
205 }
206