eval_param.pass.cpp (4768B)
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 chi_squared_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 #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::chi_squared_distribution<> D; 37 typedef D::param_type P; 38 typedef std::minstd_rand G; 39 G g; 40 D d(0.5); 41 P p(1); 42 const int N = 1000000; 43 std::vector<D::result_type> u; 44 for (int i = 0; i < N; ++i) 45 { 46 D::result_type v = d(g, p); 47 assert(d.min() < v); 48 u.push_back(v); 49 } 50 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 51 double var = 0; 52 double skew = 0; 53 double kurtosis = 0; 54 for (std::size_t i = 0; i < u.size(); ++i) 55 { 56 double dbl = (u[i] - mean); 57 double d2 = sqr(dbl); 58 var += d2; 59 skew += dbl * d2; 60 kurtosis += d2 * d2; 61 } 62 var /= u.size(); 63 double dev = std::sqrt(var); 64 skew /= u.size() * dev * var; 65 kurtosis /= u.size() * var * var; 66 kurtosis -= 3; 67 double x_mean = p.n(); 68 double x_var = 2 * p.n(); 69 double x_skew = std::sqrt(8 / p.n()); 70 double x_kurtosis = 12 / p.n(); 71 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 72 assert(std::abs((var - x_var) / x_var) < 0.01); 73 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 74 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 75 } 76 { 77 typedef std::chi_squared_distribution<> D; 78 typedef D::param_type P; 79 typedef std::mt19937 G; 80 G g; 81 D d(1); 82 P p(2); 83 const int N = 1000000; 84 std::vector<D::result_type> u; 85 for (int i = 0; i < N; ++i) 86 { 87 D::result_type v = d(g, p); 88 assert(d.min() < v); 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 (std::size_t 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.n(); 109 double x_var = 2 * p.n(); 110 double x_skew = std::sqrt(8 / p.n()); 111 double x_kurtosis = 12 / p.n(); 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 typedef std::chi_squared_distribution<> D; 119 typedef D::param_type P; 120 typedef std::minstd_rand G; 121 G g; 122 D d(2); 123 P p(.5); 124 const int N = 1000000; 125 std::vector<D::result_type> u; 126 for (int i = 0; i < N; ++i) 127 { 128 D::result_type v = d(g, p); 129 assert(d.min() < v); 130 u.push_back(v); 131 } 132 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 133 double var = 0; 134 double skew = 0; 135 double kurtosis = 0; 136 for (std::size_t i = 0; i < u.size(); ++i) 137 { 138 double dbl = (u[i] - mean); 139 double d2 = sqr(dbl); 140 var += d2; 141 skew += dbl * d2; 142 kurtosis += d2 * d2; 143 } 144 var /= u.size(); 145 double dev = std::sqrt(var); 146 skew /= u.size() * dev * var; 147 kurtosis /= u.size() * var * var; 148 kurtosis -= 3; 149 double x_mean = p.n(); 150 double x_var = 2 * p.n(); 151 double x_skew = std::sqrt(8 / p.n()); 152 double x_kurtosis = 12 / p.n(); 153 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 154 assert(std::abs((var - x_var) / x_var) < 0.01); 155 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 156 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 157 } 158 }