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 #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::exponential_distribution<> D; 37 typedef D::param_type P; 38 typedef std::mt19937 G; 39 G g; 40 D d(.75); 41 P p(2); 42 const int N = 1000000; 43 std::vector<D::result_type> u; 44 for (int i = 0; i < N; ++i) 45 { 46 D::result_type v = d(g, p); 47 assert(d.min() < v); 48 u.push_back(v); 49 } 50 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); 51 double var = 0; 52 double skew = 0; 53 double kurtosis = 0; 54 for (std::size_t i = 0; i < u.size(); ++i) 55 { 56 double dbl = (u[i] - mean); 57 double d2 = sqr(dbl); 58 var += d2; 59 skew += dbl * d2; 60 kurtosis += d2 * d2; 61 } 62 var /= u.size(); 63 double dev = std::sqrt(var); 64 skew /= u.size() * dev * var; 65 kurtosis /= u.size() * var * var; 66 kurtosis -= 3; 67 double x_mean = 1/p.lambda(); 68 double x_var = 1/sqr(p.lambda()); 69 double x_skew = 2; 70 double x_kurtosis = 6; 71 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 72 assert(std::abs((var - x_var) / x_var) < 0.01); 73 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 74 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 75 } 76 } 77
