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 #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 D::param_type P; 38 typedef std::minstd_rand G; 39 G g; 40 D d(5, 4); 41 P p(50, .5); 42 const int N = 1000000; 43 std::vector<D::result_type> u; 44 for (int i = 0; i < N; ++i) 45 u.push_back(d(g, p)); 46 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 47 double var = 0; 48 double skew = 0; 49 double kurtosis = 0; 50 for (std::size_t i = 0; i < u.size(); ++i) 51 { 52 double dbl = (u[i] - mean); 53 double d2 = sqr(dbl); 54 var += d2; 55 skew += dbl * d2; 56 kurtosis += d2 * d2; 57 } 58 var /= u.size(); 59 double dev = std::sqrt(var); 60 skew /= u.size() * dev * var; 61 kurtosis /= u.size() * var * var; 62 kurtosis -= 3; 63 double x_mean = p.mean(); 64 double x_var = sqr(p.stddev()); 65 double x_skew = 0; 66 double x_kurtosis = 0; 67 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 68 assert(std::abs((var - x_var) / x_var) < 0.01); 69 assert(std::abs(skew - x_skew) < 0.01); 70 assert(std::abs(kurtosis - x_kurtosis) < 0.01); 71 } 72 } 73
