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 exponential_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::exponential_distribution<> D; 36 typedef D::param_type P; 37 typedef std::mt19937 G; 38 G g; 39 D d(.75); 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 assert(d.min() < v); 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 (int i = 0; i < u.size(); ++i) 53 { 54 double d = (u[i] - mean); 55 double d2 = sqr(d); 56 var += d2; 57 skew += d * 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 = 1/d.lambda(); 66 double x_var = 1/sqr(d.lambda()); 67 double x_skew = 2; 68 double x_kurtosis = 6; 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 typedef std::exponential_distribution<> D; 76 typedef D::param_type P; 77 typedef std::mt19937 G; 78 G g; 79 D d(1); 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 assert(d.min() < v); 86 u.push_back(v); 87 } 88 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 89 double var = 0; 90 double skew = 0; 91 double kurtosis = 0; 92 for (int i = 0; i < u.size(); ++i) 93 { 94 double d = (u[i] - mean); 95 double d2 = sqr(d); 96 var += d2; 97 skew += d * d2; 98 kurtosis += d2 * d2; 99 } 100 var /= u.size(); 101 double dev = std::sqrt(var); 102 skew /= u.size() * dev * var; 103 kurtosis /= u.size() * var * var; 104 kurtosis -= 3; 105 double x_mean = 1/d.lambda(); 106 double x_var = 1/sqr(d.lambda()); 107 double x_skew = 2; 108 double x_kurtosis = 6; 109 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 110 assert(std::abs((var - x_var) / x_var) < 0.01); 111 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 112 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 113 } 114 { 115 typedef std::exponential_distribution<> D; 116 typedef D::param_type P; 117 typedef std::mt19937 G; 118 G g; 119 D d(10); 120 const int N = 1000000; 121 std::vector<D::result_type> u; 122 for (int i = 0; i < N; ++i) 123 { 124 D::result_type v = d(g); 125 assert(d.min() < v); 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 (int i = 0; i < u.size(); ++i) 133 { 134 double d = (u[i] - mean); 135 double d2 = sqr(d); 136 var += d2; 137 skew += d * 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 = 1/d.lambda(); 146 double x_var = 1/sqr(d.lambda()); 147 double x_skew = 2; 148 double x_kurtosis = 6; 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
