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 // class bernoulli_distribution 13 14 // template<class _URNG> result_type operator()(_URNG& g, const param_type& parm); 15 16 #include <random> 17 #include <numeric> 18 #include <vector> 19 #include <cassert> 20 21 template <class T> 22 inline 23 T 24 sqr(T x) 25 { 26 return x * x; 27 } 28 29 int main() 30 { 31 { 32 typedef std::bernoulli_distribution D; 33 typedef D::param_type P; 34 typedef std::minstd_rand G; 35 G g; 36 D d(.75); 37 P p(.25); 38 const int N = 100000; 39 std::vector<D::result_type> u; 40 for (int i = 0; i < N; ++i) 41 u.push_back(d(g, p)); 42 double mean = std::accumulate(u.begin(), u.end(), 43 double(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 = p.p(); 61 double x_var = p.p()*(1-p.p()); 62 double x_skew = (1 - 2 * p.p())/std::sqrt(x_var); 63 double x_kurtosis = (6 * sqr(p.p()) - 6 * p.p() + 1)/x_var; 64 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 65 assert(std::abs((var - x_var) / x_var) < 0.01); 66 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 67 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.02); 68 } 69 { 70 typedef std::bernoulli_distribution D; 71 typedef D::param_type P; 72 typedef std::minstd_rand G; 73 G g; 74 D d(.25); 75 P p(.75); 76 const int N = 100000; 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(), 81 double(0)) / u.size(); 82 double var = 0; 83 double skew = 0; 84 double kurtosis = 0; 85 for (int i = 0; i < u.size(); ++i) 86 { 87 double d = (u[i] - mean); 88 double d2 = sqr(d); 89 var += d2; 90 skew += d * d2; 91 kurtosis += d2 * d2; 92 } 93 var /= u.size(); 94 double dev = std::sqrt(var); 95 skew /= u.size() * dev * var; 96 kurtosis /= u.size() * var * var; 97 kurtosis -= 3; 98 double x_mean = p.p(); 99 double x_var = p.p()*(1-p.p()); 100 double x_skew = (1 - 2 * p.p())/std::sqrt(x_var); 101 double x_kurtosis = (6 * sqr(p.p()) - 6 * p.p() + 1)/x_var; 102 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 103 assert(std::abs((var - x_var) / x_var) < 0.01); 104 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 105 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.02); 106 } 107 } 108
