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 void 33 test1() 34 { 35 typedef std::extreme_value_distribution<> D; 36 typedef std::mt19937 G; 37 G g; 38 D d(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); 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 (unsigned i = 0; i < u.size(); ++i) 51 { 52 double dbl = (u[i] - mean); 53 double d2 = sqr(dbl); 54 var += d2; 55 skew += dbl * 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 = d.a() + d.b() * 0.577215665; 64 double x_var = sqr(d.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 void 74 test2() 75 { 76 typedef std::extreme_value_distribution<> D; 77 typedef std::mt19937 G; 78 G g; 79 D d(1, 2); 80 const int N = 1000000; 81 std::vector<D::result_type> u; 82 for (int i = 0; i < N; ++i) 83 { 84 D::result_type v = d(g); 85 u.push_back(v); 86 } 87 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 88 double var = 0; 89 double skew = 0; 90 double kurtosis = 0; 91 for (unsigned i = 0; i < u.size(); ++i) 92 { 93 double dbl = (u[i] - mean); 94 double d2 = sqr(dbl); 95 var += d2; 96 skew += dbl * d2; 97 kurtosis += d2 * d2; 98 } 99 var /= u.size(); 100 double dev = std::sqrt(var); 101 skew /= u.size() * dev * var; 102 kurtosis /= u.size() * var * var; 103 kurtosis -= 3; 104 double x_mean = d.a() + d.b() * 0.577215665; 105 double x_var = sqr(d.b()) * 1.644934067; 106 double x_skew = 1.139547; 107 double x_kurtosis = 12./5; 108 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 109 assert(std::abs((var - x_var) / x_var) < 0.01); 110 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 111 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 112 } 113 114 void 115 test3() 116 { 117 typedef std::extreme_value_distribution<> D; 118 typedef std::mt19937 G; 119 G g; 120 D d(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); 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 (unsigned i = 0; i < u.size(); ++i) 133 { 134 double dbl = (u[i] - mean); 135 double d2 = sqr(dbl); 136 var += d2; 137 skew += dbl * 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 = d.a() + d.b() * 0.577215665; 146 double x_var = sqr(d.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 void 156 test4() 157 { 158 typedef std::extreme_value_distribution<> D; 159 typedef std::mt19937 G; 160 G g; 161 D d(3, 4); 162 const int N = 1000000; 163 std::vector<D::result_type> u; 164 for (int i = 0; i < N; ++i) 165 { 166 D::result_type v = d(g); 167 u.push_back(v); 168 } 169 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 170 double var = 0; 171 double skew = 0; 172 double kurtosis = 0; 173 for (unsigned i = 0; i < u.size(); ++i) 174 { 175 double dbl = (u[i] - mean); 176 double d2 = sqr(dbl); 177 var += d2; 178 skew += dbl * d2; 179 kurtosis += d2 * d2; 180 } 181 var /= u.size(); 182 double dev = std::sqrt(var); 183 skew /= u.size() * dev * var; 184 kurtosis /= u.size() * var * var; 185 kurtosis -= 3; 186 double x_mean = d.a() + d.b() * 0.577215665; 187 double x_var = sqr(d.b()) * 1.644934067; 188 double x_skew = 1.139547; 189 double x_kurtosis = 12./5; 190 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 191 assert(std::abs((var - x_var) / x_var) < 0.01); 192 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 193 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 194 } 195 196 int main() 197 { 198 test1(); 199 test2(); 200 test3(); 201 test4(); 202 } 203
