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 // <random>
11
12 // template<class RealType = double>
13 // class extreme_value_distribution
14
15 // template<class _URNG> result_type operator()(_URNG& g, const param_type& parm);
16
17 #include <random>
18 #include <cassert>
19 #include <vector>
20 #include <numeric>
21
22 template <class T>
23 inline
24 T
sqr(T x)25 sqr(T x)
26 {
27 return x * x;
28 }
29
main()30 int main()
31 {
32 {
33 typedef std::extreme_value_distribution<> D;
34 typedef D::param_type P;
35 typedef std::mt19937 G;
36 G g;
37 D d(-0.5, 1);
38 P p(0.5, 2);
39 const int N = 1000000;
40 std::vector<D::result_type> u;
41 for (int i = 0; i < N; ++i)
42 {
43 D::result_type v = d(g, p);
44 u.push_back(v);
45 }
46 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
47 double var = 0;
48 double skew = 0;
49 double kurtosis = 0;
50 for (int i = 0; i < u.size(); ++i)
51 {
52 double d = (u[i] - mean);
53 double d2 = sqr(d);
54 var += d2;
55 skew += d * d2;
56 kurtosis += d2 * d2;
57 }
58 var /= u.size();
59 double dev = std::sqrt(var);
60 skew /= u.size() * dev * var;
61 kurtosis /= u.size() * var * var;
62 kurtosis -= 3;
63 double x_mean = p.a() + p.b() * 0.577215665;
64 double x_var = sqr(p.b()) * 1.644934067;
65 double x_skew = 1.139547;
66 double x_kurtosis = 12./5;
67 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
68 assert(std::abs((var - x_var) / x_var) < 0.01);
69 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
70 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
71 }
72 {
73 typedef std::extreme_value_distribution<> D;
74 typedef D::param_type P;
75 typedef std::mt19937 G;
76 G g;
77 D d(-0.5, 1);
78 P p(1, 2);
79 const int N = 1000000;
80 std::vector<D::result_type> u;
81 for (int i = 0; i < N; ++i)
82 {
83 D::result_type v = d(g, p);
84 u.push_back(v);
85 }
86 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
87 double var = 0;
88 double skew = 0;
89 double kurtosis = 0;
90 for (int i = 0; i < u.size(); ++i)
91 {
92 double d = (u[i] - mean);
93 double d2 = sqr(d);
94 var += d2;
95 skew += d * d2;
96 kurtosis += d2 * d2;
97 }
98 var /= u.size();
99 double dev = std::sqrt(var);
100 skew /= u.size() * dev * var;
101 kurtosis /= u.size() * var * var;
102 kurtosis -= 3;
103 double x_mean = p.a() + p.b() * 0.577215665;
104 double x_var = sqr(p.b()) * 1.644934067;
105 double x_skew = 1.139547;
106 double x_kurtosis = 12./5;
107 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
108 assert(std::abs((var - x_var) / x_var) < 0.01);
109 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
110 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
111 }
112 {
113 typedef std::extreme_value_distribution<> D;
114 typedef D::param_type P;
115 typedef std::mt19937 G;
116 G g;
117 D d(-0.5, 1);
118 P p(1.5, 3);
119 const int N = 1000000;
120 std::vector<D::result_type> u;
121 for (int i = 0; i < N; ++i)
122 {
123 D::result_type v = d(g, p);
124 u.push_back(v);
125 }
126 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
127 double var = 0;
128 double skew = 0;
129 double kurtosis = 0;
130 for (int i = 0; i < u.size(); ++i)
131 {
132 double d = (u[i] - mean);
133 double d2 = sqr(d);
134 var += d2;
135 skew += d * d2;
136 kurtosis += d2 * d2;
137 }
138 var /= u.size();
139 double dev = std::sqrt(var);
140 skew /= u.size() * dev * var;
141 kurtosis /= u.size() * var * var;
142 kurtosis -= 3;
143 double x_mean = p.a() + p.b() * 0.577215665;
144 double x_var = sqr(p.b()) * 1.644934067;
145 double x_skew = 1.139547;
146 double x_kurtosis = 12./5;
147 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
148 assert(std::abs((var - x_var) / x_var) < 0.01);
149 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
150 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
151 }
152 {
153 typedef std::extreme_value_distribution<> D;
154 typedef D::param_type P;
155 typedef std::mt19937 G;
156 G g;
157 D d(-0.5, 1);
158 P p(3, 4);
159 const int N = 1000000;
160 std::vector<D::result_type> u;
161 for (int i = 0; i < N; ++i)
162 {
163 D::result_type v = d(g, p);
164 u.push_back(v);
165 }
166 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
167 double var = 0;
168 double skew = 0;
169 double kurtosis = 0;
170 for (int i = 0; i < u.size(); ++i)
171 {
172 double d = (u[i] - mean);
173 double d2 = sqr(d);
174 var += d2;
175 skew += d * d2;
176 kurtosis += d2 * d2;
177 }
178 var /= u.size();
179 double dev = std::sqrt(var);
180 skew /= u.size() * dev * var;
181 kurtosis /= u.size() * var * var;
182 kurtosis -= 3;
183 double x_mean = p.a() + p.b() * 0.577215665;
184 double x_var = sqr(p.b()) * 1.644934067;
185 double x_skew = 1.139547;
186 double x_kurtosis = 12./5;
187 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
188 assert(std::abs((var - x_var) / x_var) < 0.01);
189 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
190 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
191 }
192 }
193