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