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 #include <cstddef> 24 25 template <class T> 26 inline 27 T 28 sqr(T x) 29 { 30 return x * x; 31 } 32 33 int main() 34 { 35 { 36 typedef std::exponential_distribution<> D; 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 (std::size_t 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 = 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 std::mt19937 G; 77 G g; 78 D d(1); 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 assert(d.min() < v); 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 (std::size_t 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 = 1/d.lambda(); 105 double x_var = 1/sqr(d.lambda()); 106 double x_skew = 2; 107 double x_kurtosis = 6; 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 typedef std::exponential_distribution<> D; 115 typedef std::mt19937 G; 116 G g; 117 D d(10); 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 assert(d.min() < v); 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 (std::size_t i = 0; i < u.size(); ++i) 131 { 132 double dbl = (u[i] - mean); 133 double d2 = sqr(dbl); 134 var += d2; 135 skew += dbl * 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 = 1/d.lambda(); 144 double x_var = 1/sqr(d.lambda()); 145 double x_skew = 2; 146 double x_kurtosis = 6; 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
