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 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 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 (unsigned i = 0; i < u.size(); ++i) 53 { 54 double dbl = (u[i] - mean); 55 double d2 = sqr(dbl); 56 var += d2; 57 skew += dbl * 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 void 76 test2() 77 { 78 typedef std::extreme_value_distribution<> D; 79 typedef D::param_type P; 80 typedef std::mt19937 G; 81 G g; 82 D d(-0.5, 1); 83 P p(1, 2); 84 const int N = 1000000; 85 std::vector<D::result_type> u; 86 for (int i = 0; i < N; ++i) 87 { 88 D::result_type v = d(g, p); 89 u.push_back(v); 90 } 91 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 92 double var = 0; 93 double skew = 0; 94 double kurtosis = 0; 95 for (unsigned i = 0; i < u.size(); ++i) 96 { 97 double dbl = (u[i] - mean); 98 double d2 = sqr(dbl); 99 var += d2; 100 skew += dbl * d2; 101 kurtosis += d2 * d2; 102 } 103 var /= u.size(); 104 double dev = std::sqrt(var); 105 skew /= u.size() * dev * var; 106 kurtosis /= u.size() * var * var; 107 kurtosis -= 3; 108 double x_mean = p.a() + p.b() * 0.577215665; 109 double x_var = sqr(p.b()) * 1.644934067; 110 double x_skew = 1.139547; 111 double x_kurtosis = 12./5; 112 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 113 assert(std::abs((var - x_var) / x_var) < 0.01); 114 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 115 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 116 } 117 118 void 119 test3() 120 { 121 typedef std::extreme_value_distribution<> D; 122 typedef D::param_type P; 123 typedef std::mt19937 G; 124 G g; 125 D d(-0.5, 1); 126 P p(1.5, 3); 127 const int N = 1000000; 128 std::vector<D::result_type> u; 129 for (int i = 0; i < N; ++i) 130 { 131 D::result_type v = d(g, p); 132 u.push_back(v); 133 } 134 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 135 double var = 0; 136 double skew = 0; 137 double kurtosis = 0; 138 for (unsigned i = 0; i < u.size(); ++i) 139 { 140 double dbl = (u[i] - mean); 141 double d2 = sqr(dbl); 142 var += d2; 143 skew += dbl * d2; 144 kurtosis += d2 * d2; 145 } 146 var /= u.size(); 147 double dev = std::sqrt(var); 148 skew /= u.size() * dev * var; 149 kurtosis /= u.size() * var * var; 150 kurtosis -= 3; 151 double x_mean = p.a() + p.b() * 0.577215665; 152 double x_var = sqr(p.b()) * 1.644934067; 153 double x_skew = 1.139547; 154 double x_kurtosis = 12./5; 155 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 156 assert(std::abs((var - x_var) / x_var) < 0.01); 157 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 158 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 159 } 160 161 void 162 test4() 163 { 164 typedef std::extreme_value_distribution<> D; 165 typedef D::param_type P; 166 typedef std::mt19937 G; 167 G g; 168 D d(-0.5, 1); 169 P p(3, 4); 170 const int N = 1000000; 171 std::vector<D::result_type> u; 172 for (int i = 0; i < N; ++i) 173 { 174 D::result_type v = d(g, p); 175 u.push_back(v); 176 } 177 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 178 double var = 0; 179 double skew = 0; 180 double kurtosis = 0; 181 for (unsigned i = 0; i < u.size(); ++i) 182 { 183 double dbl = (u[i] - mean); 184 double d2 = sqr(dbl); 185 var += d2; 186 skew += dbl * d2; 187 kurtosis += d2 * d2; 188 } 189 var /= u.size(); 190 double dev = std::sqrt(var); 191 skew /= u.size() * dev * var; 192 kurtosis /= u.size() * var * var; 193 kurtosis -= 3; 194 double x_mean = p.a() + p.b() * 0.577215665; 195 double x_var = sqr(p.b()) * 1.644934067; 196 double x_skew = 1.139547; 197 double x_kurtosis = 12./5; 198 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 199 assert(std::abs((var - x_var) / x_var) < 0.01); 200 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 201 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 202 } 203 204 int main() 205 { 206 test1(); 207 test2(); 208 test3(); 209 test4(); 210 } 211
