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 IntType = int> 15 // class poisson_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 int main() 33 { 34 { 35 typedef std::poisson_distribution<> D; 36 typedef D::param_type P; 37 typedef std::minstd_rand G; 38 G g; 39 D d(.75); 40 P p(2); 41 const int N = 100000; 42 std::vector<double> u; 43 for (int i = 0; i < N; ++i) 44 { 45 D::result_type v = d(g, p); 46 assert(d.min() <= v && v <= d.max()); 47 u.push_back(v); 48 } 49 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 50 double var = 0; 51 double skew = 0; 52 double kurtosis = 0; 53 for (unsigned i = 0; i < u.size(); ++i) 54 { 55 double dbl = (u[i] - mean); 56 double d2 = sqr(dbl); 57 var += d2; 58 skew += dbl * d2; 59 kurtosis += d2 * d2; 60 } 61 var /= u.size(); 62 double dev = std::sqrt(var); 63 skew /= u.size() * dev * var; 64 kurtosis /= u.size() * var * var; 65 kurtosis -= 3; 66 double x_mean = p.mean(); 67 double x_var = p.mean(); 68 double x_skew = 1 / std::sqrt(x_var); 69 double x_kurtosis = 1 / x_var; 70 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 71 assert(std::abs((var - x_var) / x_var) < 0.01); 72 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 73 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03); 74 } 75 { 76 typedef std::poisson_distribution<> D; 77 typedef D::param_type P; 78 typedef std::minstd_rand G; 79 G g; 80 D d(2); 81 P p(.75); 82 const int N = 100000; 83 std::vector<double> u; 84 for (int i = 0; i < N; ++i) 85 { 86 D::result_type v = d(g, p); 87 assert(d.min() <= v && v <= d.max()); 88 u.push_back(v); 89 } 90 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 91 double var = 0; 92 double skew = 0; 93 double kurtosis = 0; 94 for (unsigned i = 0; i < u.size(); ++i) 95 { 96 double dbl = (u[i] - mean); 97 double d2 = sqr(dbl); 98 var += d2; 99 skew += dbl * d2; 100 kurtosis += d2 * d2; 101 } 102 var /= u.size(); 103 double dev = std::sqrt(var); 104 skew /= u.size() * dev * var; 105 kurtosis /= u.size() * var * var; 106 kurtosis -= 3; 107 double x_mean = p.mean(); 108 double x_var = p.mean(); 109 double x_skew = 1 / std::sqrt(x_var); 110 double x_kurtosis = 1 / x_var; 111 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 112 assert(std::abs((var - x_var) / x_var) < 0.01); 113 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 114 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.04); 115 } 116 { 117 typedef std::poisson_distribution<> D; 118 typedef D::param_type P; 119 typedef std::mt19937 G; 120 G g; 121 D d(2); 122 P p(20); 123 const int N = 1000000; 124 std::vector<double> u; 125 for (int i = 0; i < N; ++i) 126 { 127 D::result_type v = d(g, p); 128 assert(d.min() <= v && v <= d.max()); 129 u.push_back(v); 130 } 131 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 132 double var = 0; 133 double skew = 0; 134 double kurtosis = 0; 135 for (unsigned i = 0; i < u.size(); ++i) 136 { 137 double dbl = (u[i] - mean); 138 double d2 = sqr(dbl); 139 var += d2; 140 skew += dbl * d2; 141 kurtosis += d2 * d2; 142 } 143 var /= u.size(); 144 double dev = std::sqrt(var); 145 skew /= u.size() * dev * var; 146 kurtosis /= u.size() * var * var; 147 kurtosis -= 3; 148 double x_mean = p.mean(); 149 double x_var = p.mean(); 150 double x_skew = 1 / std::sqrt(x_var); 151 double x_kurtosis = 1 / x_var; 152 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 153 assert(std::abs((var - x_var) / x_var) < 0.01); 154 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 155 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 156 } 157 } 158
