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 normal_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 sqr(T x)27sqr(T x) 28 { 29 return x * x; 30 } 31 main()32int main() 33 { 34 { 35 typedef std::normal_distribution<> D; 36 typedef D::param_type P; 37 typedef std::minstd_rand G; 38 G g; 39 D d(5, 4); 40 P p(50, .5); 41 const int N = 1000000; 42 std::vector<D::result_type> u; 43 for (int i = 0; i < N; ++i) 44 u.push_back(d(g, p)); 45 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 46 double var = 0; 47 double skew = 0; 48 double kurtosis = 0; 49 for (int i = 0; i < u.size(); ++i) 50 { 51 double d = (u[i] - mean); 52 double d2 = sqr(d); 53 var += d2; 54 skew += d * d2; 55 kurtosis += d2 * d2; 56 } 57 var /= u.size(); 58 double dev = std::sqrt(var); 59 skew /= u.size() * dev * var; 60 kurtosis /= u.size() * var * var; 61 kurtosis -= 3; 62 double x_mean = p.mean(); 63 double x_var = sqr(p.stddev()); 64 double x_skew = 0; 65 double x_kurtosis = 0; 66 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 67 assert(std::abs((var - x_var) / x_var) < 0.01); 68 assert(std::abs(skew - x_skew) < 0.01); 69 assert(std::abs(kurtosis - x_kurtosis) < 0.01); 70 } 71 } 72