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 25 sqr(T x) 26 { 27 return x * x; 28 } 29 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
