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); 18 19 #include <random> 20 #include <cassert> 21 #include <vector> 22 #include <numeric> 23 24 template <class T> 25 inline 26 T 27 sqr(T x) 28 { 29 return x * x; 30 } 31 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, 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 (int i = 0; i < u.size(); ++i) 52 { 53 double d = (u[i] - mean); 54 double d2 = sqr(d); 55 var += d2; 56 skew += d * 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 typedef std::extreme_value_distribution<> D; 75 typedef D::param_type P; 76 typedef std::mt19937 G; 77 G g; 78 D d(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); 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 = d.a() + d.b() * 0.577215665; 104 double x_var = sqr(d.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(1.5, 3); 118 const int N = 1000000; 119 std::vector<D::result_type> u; 120 for (int i = 0; i < N; ++i) 121 { 122 D::result_type v = d(g); 123 u.push_back(v); 124 } 125 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 126 double var = 0; 127 double skew = 0; 128 double kurtosis = 0; 129 for (int i = 0; i < u.size(); ++i) 130 { 131 double d = (u[i] - mean); 132 double d2 = sqr(d); 133 var += d2; 134 skew += d * d2; 135 kurtosis += d2 * d2; 136 } 137 var /= u.size(); 138 double dev = std::sqrt(var); 139 skew /= u.size() * dev * var; 140 kurtosis /= u.size() * var * var; 141 kurtosis -= 3; 142 double x_mean = d.a() + d.b() * 0.577215665; 143 double x_var = sqr(d.b()) * 1.644934067; 144 double x_skew = 1.139547; 145 double x_kurtosis = 12./5; 146 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 147 assert(std::abs((var - x_var) / x_var) < 0.01); 148 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 149 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 150 } 151 { 152 typedef std::extreme_value_distribution<> D; 153 typedef D::param_type P; 154 typedef std::mt19937 G; 155 G g; 156 D d(3, 4); 157 const int N = 1000000; 158 std::vector<D::result_type> u; 159 for (int i = 0; i < N; ++i) 160 { 161 D::result_type v = d(g); 162 u.push_back(v); 163 } 164 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 165 double var = 0; 166 double skew = 0; 167 double kurtosis = 0; 168 for (int i = 0; i < u.size(); ++i) 169 { 170 double d = (u[i] - mean); 171 double d2 = sqr(d); 172 var += d2; 173 skew += d * d2; 174 kurtosis += d2 * d2; 175 } 176 var /= u.size(); 177 double dev = std::sqrt(var); 178 skew /= u.size() * dev * var; 179 kurtosis /= u.size() * var * var; 180 kurtosis -= 3; 181 double x_mean = d.a() + d.b() * 0.577215665; 182 double x_var = sqr(d.b()) * 1.644934067; 183 double x_skew = 1.139547; 184 double x_kurtosis = 12./5; 185 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 186 assert(std::abs((var - x_var) / x_var) < 0.01); 187 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 188 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 189 } 190 } 191
