1 //===----------------------------------------------------------------------===//
2 //
3 // The LLVM Compiler Infrastructure
4 //
5 // This file is dual licensed under the MIT and the University of Illinois Open
6 // Source Licenses. See LICENSE.TXT for details.
7 //
8 //===----------------------------------------------------------------------===//
9 //
10 // REQUIRES: long_tests
11
12 // <random>
13
14 // template<class RealType = double>
15 // class extreme_value_distribution
16
17 // template<class _URNG> result_type operator()(_URNG& g, const param_type& parm);
18
19 #include <random>
20 #include <cassert>
21 #include <vector>
22 #include <numeric>
23
24 template <class T>
25 inline
26 T
sqr(T x)27 sqr(T x)
28 {
29 return x * x;
30 }
31
32 void
test1()33 test1()
34 {
35 typedef std::extreme_value_distribution<> D;
36 typedef D::param_type P;
37 typedef std::mt19937 G;
38 G g;
39 D d(-0.5, 1);
40 P p(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, p);
46 u.push_back(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(var);
62 skew /= u.size() * dev * var;
63 kurtosis /= u.size() * var * var;
64 kurtosis -= 3;
65 double x_mean = p.a() + p.b() * 0.577215665;
66 double x_var = sqr(p.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) < 0.01);
73 }
74
75 void
test2()76 test2()
77 {
78 typedef std::extreme_value_distribution<> D;
79 typedef D::param_type P;
80 typedef std::mt19937 G;
81 G g;
82 D d(-0.5, 1);
83 P p(1, 2);
84 const int N = 1000000;
85 std::vector<D::result_type> u;
86 for (int i = 0; i < N; ++i)
87 {
88 D::result_type v = d(g, p);
89 u.push_back(v);
90 }
91 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
92 double var = 0;
93 double skew = 0;
94 double kurtosis = 0;
95 for (unsigned i = 0; i < u.size(); ++i)
96 {
97 double dbl = (u[i] - mean);
98 double d2 = sqr(dbl);
99 var += d2;
100 skew += dbl * d2;
101 kurtosis += d2 * d2;
102 }
103 var /= u.size();
104 double dev = std::sqrt(var);
105 skew /= u.size() * dev * var;
106 kurtosis /= u.size() * var * var;
107 kurtosis -= 3;
108 double x_mean = p.a() + p.b() * 0.577215665;
109 double x_var = sqr(p.b()) * 1.644934067;
110 double x_skew = 1.139547;
111 double x_kurtosis = 12./5;
112 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
113 assert(std::abs((var - x_var) / x_var) < 0.01);
114 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
115 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
116 }
117
118 void
test3()119 test3()
120 {
121 typedef std::extreme_value_distribution<> D;
122 typedef D::param_type P;
123 typedef std::mt19937 G;
124 G g;
125 D d(-0.5, 1);
126 P p(1.5, 3);
127 const int N = 1000000;
128 std::vector<D::result_type> u;
129 for (int i = 0; i < N; ++i)
130 {
131 D::result_type v = d(g, p);
132 u.push_back(v);
133 }
134 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
135 double var = 0;
136 double skew = 0;
137 double kurtosis = 0;
138 for (unsigned i = 0; i < u.size(); ++i)
139 {
140 double dbl = (u[i] - mean);
141 double d2 = sqr(dbl);
142 var += d2;
143 skew += dbl * d2;
144 kurtosis += d2 * d2;
145 }
146 var /= u.size();
147 double dev = std::sqrt(var);
148 skew /= u.size() * dev * var;
149 kurtosis /= u.size() * var * var;
150 kurtosis -= 3;
151 double x_mean = p.a() + p.b() * 0.577215665;
152 double x_var = sqr(p.b()) * 1.644934067;
153 double x_skew = 1.139547;
154 double x_kurtosis = 12./5;
155 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
156 assert(std::abs((var - x_var) / x_var) < 0.01);
157 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
158 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
159 }
160
161 void
test4()162 test4()
163 {
164 typedef std::extreme_value_distribution<> D;
165 typedef D::param_type P;
166 typedef std::mt19937 G;
167 G g;
168 D d(-0.5, 1);
169 P p(3, 4);
170 const int N = 1000000;
171 std::vector<D::result_type> u;
172 for (int i = 0; i < N; ++i)
173 {
174 D::result_type v = d(g, p);
175 u.push_back(v);
176 }
177 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
178 double var = 0;
179 double skew = 0;
180 double kurtosis = 0;
181 for (unsigned i = 0; i < u.size(); ++i)
182 {
183 double dbl = (u[i] - mean);
184 double d2 = sqr(dbl);
185 var += d2;
186 skew += dbl * d2;
187 kurtosis += d2 * d2;
188 }
189 var /= u.size();
190 double dev = std::sqrt(var);
191 skew /= u.size() * dev * var;
192 kurtosis /= u.size() * var * var;
193 kurtosis -= 3;
194 double x_mean = p.a() + p.b() * 0.577215665;
195 double x_var = sqr(p.b()) * 1.644934067;
196 double x_skew = 1.139547;
197 double x_kurtosis = 12./5;
198 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
199 assert(std::abs((var - x_var) / x_var) < 0.01);
200 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
201 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
202 }
203
main()204 int main()
205 {
206 test1();
207 test2();
208 test3();
209 test4();
210 }
211