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
main()32 int main()
33 {
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 (int i = 0; i < u.size(); ++i)
53 {
54 double d = (u[i] - mean);
55 double d2 = sqr(d);
56 var += d2;
57 skew += d * 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 typedef std::extreme_value_distribution<> D;
76 typedef D::param_type P;
77 typedef std::mt19937 G;
78 G g;
79 D d(-0.5, 1);
80 P p(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, p);
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 (int i = 0; i < u.size(); ++i)
93 {
94 double d = (u[i] - mean);
95 double d2 = sqr(d);
96 var += d2;
97 skew += d * 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 = p.a() + p.b() * 0.577215665;
106 double x_var = sqr(p.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 typedef std::extreme_value_distribution<> D;
116 typedef D::param_type P;
117 typedef std::mt19937 G;
118 G g;
119 D d(-0.5, 1);
120 P p(1.5, 3);
121 const int N = 1000000;
122 std::vector<D::result_type> u;
123 for (int i = 0; i < N; ++i)
124 {
125 D::result_type v = d(g, p);
126 u.push_back(v);
127 }
128 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
129 double var = 0;
130 double skew = 0;
131 double kurtosis = 0;
132 for (int i = 0; i < u.size(); ++i)
133 {
134 double d = (u[i] - mean);
135 double d2 = sqr(d);
136 var += d2;
137 skew += d * d2;
138 kurtosis += d2 * d2;
139 }
140 var /= u.size();
141 double dev = std::sqrt(var);
142 skew /= u.size() * dev * var;
143 kurtosis /= u.size() * var * var;
144 kurtosis -= 3;
145 double x_mean = p.a() + p.b() * 0.577215665;
146 double x_var = sqr(p.b()) * 1.644934067;
147 double x_skew = 1.139547;
148 double x_kurtosis = 12./5;
149 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
150 assert(std::abs((var - x_var) / x_var) < 0.01);
151 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
152 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
153 }
154 {
155 typedef std::extreme_value_distribution<> D;
156 typedef D::param_type P;
157 typedef std::mt19937 G;
158 G g;
159 D d(-0.5, 1);
160 P p(3, 4);
161 const int N = 1000000;
162 std::vector<D::result_type> u;
163 for (int i = 0; i < N; ++i)
164 {
165 D::result_type v = d(g, p);
166 u.push_back(v);
167 }
168 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
169 double var = 0;
170 double skew = 0;
171 double kurtosis = 0;
172 for (int i = 0; i < u.size(); ++i)
173 {
174 double d = (u[i] - mean);
175 double d2 = sqr(d);
176 var += d2;
177 skew += d * d2;
178 kurtosis += d2 * d2;
179 }
180 var /= u.size();
181 double dev = std::sqrt(var);
182 skew /= u.size() * dev * var;
183 kurtosis /= u.size() * var * var;
184 kurtosis -= 3;
185 double x_mean = p.a() + p.b() * 0.577215665;
186 double x_var = sqr(p.b()) * 1.644934067;
187 double x_skew = 1.139547;
188 double x_kurtosis = 12./5;
189 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
190 assert(std::abs((var - x_var) / x_var) < 0.01);
191 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
192 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
193 }
194 }
195