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 // template<class RealType = double> 13 // class exponential_distribution 14 15 // template<class _URNG> result_type operator()(_URNG& g, const param_type& parm); 16 17 #include <random> 18 #include <cassert> 19 #include <vector> 20 #include <numeric> 21 22 template <class T> 23 inline 24 T 25 sqr(T x) 26 { 27 return x * x; 28 } 29 30 int main() 31 { 32 { 33 typedef std::exponential_distribution<> D; 34 typedef D::param_type P; 35 typedef std::mt19937 G; 36 G g; 37 D d(.75); 38 P p(2); 39 const int N = 1000000; 40 std::vector<D::result_type> u; 41 for (int i = 0; i < N; ++i) 42 { 43 D::result_type v = d(g, p); 44 assert(d.min() < v); 45 u.push_back(v); 46 } 47 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 48 double var = 0; 49 double skew = 0; 50 double kurtosis = 0; 51 for (int i = 0; i < u.size(); ++i) 52 { 53 double d = (u[i] - mean); 54 double d2 = sqr(d); 55 var += d2; 56 skew += d * d2; 57 kurtosis += d2 * d2; 58 } 59 var /= u.size(); 60 double dev = std::sqrt(var); 61 skew /= u.size() * dev * var; 62 kurtosis /= u.size() * var * var; 63 kurtosis -= 3; 64 double x_mean = 1/p.lambda(); 65 double x_var = 1/sqr(p.lambda()); 66 double x_skew = 2; 67 double x_kurtosis = 6; 68 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 69 assert(std::abs((var - x_var) / x_var) < 0.01); 70 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 71 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 72 } 73 } 74
