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 // <random> 11 12 // template<class RealType = double> 13 // class student_t_distribution 14 15 // template<class _URNG> result_type operator()(_URNG& g, const param_type& parm); 16 17 #include <random> 18 #include <cassert> 19 #include <vector> 20 #include <numeric> 21 22 template <class T> 23 inline 24 T 25 sqr(T x) 26 { 27 return x * x; 28 } 29 30 int main() 31 { 32 { 33 typedef std::student_t_distribution<> D; 34 typedef D::param_type P; 35 typedef std::minstd_rand G; 36 G g; 37 D d; 38 P p(5.5); 39 const int N = 1000000; 40 std::vector<D::result_type> u; 41 for (int i = 0; i < N; ++i) 42 u.push_back(d(g, p)); 43 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 44 double var = 0; 45 double skew = 0; 46 double kurtosis = 0; 47 for (int i = 0; i < u.size(); ++i) 48 { 49 double d = (u[i] - mean); 50 double d2 = sqr(d); 51 var += d2; 52 skew += d * d2; 53 kurtosis += d2 * d2; 54 } 55 var /= u.size(); 56 double dev = std::sqrt(var); 57 skew /= u.size() * dev * var; 58 kurtosis /= u.size() * var * var; 59 kurtosis -= 3; 60 double x_mean = 0; 61 double x_var = p.n() / (p.n() - 2); 62 double x_skew = 0; 63 double x_kurtosis = 6 / (p.n() - 4); 64 assert(std::abs(mean - x_mean) < 0.01); 65 assert(std::abs((var - x_var) / x_var) < 0.01); 66 assert(std::abs(skew - x_skew) < 0.01); 67 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.2); 68 } 69 { 70 typedef std::student_t_distribution<> D; 71 typedef D::param_type P; 72 typedef std::minstd_rand G; 73 G g; 74 D d; 75 P p(10); 76 const int N = 1000000; 77 std::vector<D::result_type> u; 78 for (int i = 0; i < N; ++i) 79 u.push_back(d(g, p)); 80 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 81 double var = 0; 82 double skew = 0; 83 double kurtosis = 0; 84 for (int i = 0; i < u.size(); ++i) 85 { 86 double d = (u[i] - mean); 87 double d2 = sqr(d); 88 var += d2; 89 skew += d * d2; 90 kurtosis += d2 * d2; 91 } 92 var /= u.size(); 93 double dev = std::sqrt(var); 94 skew /= u.size() * dev * var; 95 kurtosis /= u.size() * var * var; 96 kurtosis -= 3; 97 double x_mean = 0; 98 double x_var = p.n() / (p.n() - 2); 99 double x_skew = 0; 100 double x_kurtosis = 6 / (p.n() - 4); 101 assert(std::abs(mean - x_mean) < 0.01); 102 assert(std::abs((var - x_var) / x_var) < 0.01); 103 assert(std::abs(skew - x_skew) < 0.01); 104 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.04); 105 } 106 { 107 typedef std::student_t_distribution<> D; 108 typedef D::param_type P; 109 typedef std::minstd_rand G; 110 G g; 111 D d; 112 P p(100); 113 const int N = 1000000; 114 std::vector<D::result_type> u; 115 for (int i = 0; i < N; ++i) 116 u.push_back(d(g, p)); 117 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 118 double var = 0; 119 double skew = 0; 120 double kurtosis = 0; 121 for (int i = 0; i < u.size(); ++i) 122 { 123 double d = (u[i] - mean); 124 double d2 = sqr(d); 125 var += d2; 126 skew += d * d2; 127 kurtosis += d2 * d2; 128 } 129 var /= u.size(); 130 double dev = std::sqrt(var); 131 skew /= u.size() * dev * var; 132 kurtosis /= u.size() * var * var; 133 kurtosis -= 3; 134 double x_mean = 0; 135 double x_var = p.n() / (p.n() - 2); 136 double x_skew = 0; 137 double x_kurtosis = 6 / (p.n() - 4); 138 assert(std::abs(mean - x_mean) < 0.01); 139 assert(std::abs((var - x_var) / x_var) < 0.01); 140 assert(std::abs(skew - x_skew) < 0.01); 141 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.02); 142 } 143 } 144
