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 exponential_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 27 sqr(T x) 28 { 29 return x * x; 30 } 31 32 int main() 33 { 34 { 35 typedef std::exponential_distribution<> D; 36 typedef D::param_type P; 37 typedef std::mt19937 G; 38 G g; 39 D d(.75); 40 P p(2); 41 const int N = 1000000; 42 std::vector<D::result_type> u; 43 for (int i = 0; i < N; ++i) 44 { 45 D::result_type v = d(g, p); 46 assert(d.min() < v); 47 u.push_back(v); 48 } 49 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 50 double var = 0; 51 double skew = 0; 52 double kurtosis = 0; 53 for (int i = 0; i < u.size(); ++i) 54 { 55 double d = (u[i] - mean); 56 double d2 = sqr(d); 57 var += d2; 58 skew += d * d2; 59 kurtosis += d2 * d2; 60 } 61 var /= u.size(); 62 double dev = std::sqrt(var); 63 skew /= u.size() * dev * var; 64 kurtosis /= u.size() * var * var; 65 kurtosis -= 3; 66 double x_mean = 1/p.lambda(); 67 double x_var = 1/sqr(p.lambda()); 68 double x_skew = 2; 69 double x_kurtosis = 6; 70 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 71 assert(std::abs((var - x_var) / x_var) < 0.01); 72 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 73 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 74 } 75 } 76
