| //===----------------------------------------------------------------------===// |
| // |
| // The LLVM Compiler Infrastructure |
| // |
| // This file is distributed under the University of Illinois Open Source |
| // License. See LICENSE.TXT for details. |
| // |
| //===----------------------------------------------------------------------===// |
| |
| // <random> |
| |
| // template<class RealType = double> |
| // class weibull_distribution |
| |
| // template<class _URNG> result_type operator()(_URNG& g, const param_type& parm); |
| |
| #include <random> |
| #include <cassert> |
| #include <vector> |
| #include <numeric> |
| |
| template <class T> |
| inline |
| T |
| sqr(T x) |
| { |
| return x * x; |
| } |
| |
| int main() |
| { |
| { |
| typedef std::weibull_distribution<> D; |
| typedef D::param_type P; |
| typedef std::mt19937 G; |
| G g; |
| D d(0.5, 2); |
| P p(1, .5); |
| const int N = 100000; |
| std::vector<D::result_type> u; |
| for (int i = 0; i < N; ++i) |
| u.push_back(d(g, p)); |
| D::result_type mean = std::accumulate(u.begin(), u.end(), |
| D::result_type(0)) / u.size(); |
| D::result_type var = 0; |
| for (int i = 0; i < u.size(); ++i) |
| var += sqr(u[i] - mean); |
| var /= u.size(); |
| D::result_type x_mean = p.b() * std::tgamma(1 + 1/p.a()); |
| D::result_type x_var = sqr(p.b()) * std::tgamma(1 + 2/p.a()) - sqr(x_mean); |
| assert(std::abs(mean - x_mean) / x_mean < 0.02); |
| assert(std::abs(var - x_var) / x_var < 0.02); |
| } |
| { |
| typedef std::weibull_distribution<> D; |
| typedef D::param_type P; |
| typedef std::mt19937 G; |
| G g; |
| D d(1, .5); |
| P p(2, 3); |
| const int N = 100000; |
| std::vector<D::result_type> u; |
| for (int i = 0; i < N; ++i) |
| u.push_back(d(g, p)); |
| D::result_type mean = std::accumulate(u.begin(), u.end(), |
| D::result_type(0)) / u.size(); |
| D::result_type var = 0; |
| for (int i = 0; i < u.size(); ++i) |
| var += sqr(u[i] - mean); |
| var /= u.size(); |
| D::result_type x_mean = p.b() * std::tgamma(1 + 1/p.a()); |
| D::result_type x_var = sqr(p.b()) * std::tgamma(1 + 2/p.a()) - sqr(x_mean); |
| assert(std::abs(mean - x_mean) / x_mean < 0.02); |
| assert(std::abs(var - x_var) / x_var < 0.02); |
| } |
| { |
| typedef std::weibull_distribution<> D; |
| typedef D::param_type P; |
| typedef std::mt19937 G; |
| G g; |
| D d(2, 3); |
| P p(.5, 2); |
| const int N = 100000; |
| std::vector<D::result_type> u; |
| for (int i = 0; i < N; ++i) |
| u.push_back(d(g, p)); |
| D::result_type mean = std::accumulate(u.begin(), u.end(), |
| D::result_type(0)) / u.size(); |
| D::result_type var = 0; |
| for (int i = 0; i < u.size(); ++i) |
| var += sqr(u[i] - mean); |
| var /= u.size(); |
| D::result_type x_mean = p.b() * std::tgamma(1 + 1/p.a()); |
| D::result_type x_var = sqr(p.b()) * std::tgamma(1 + 2/p.a()) - sqr(x_mean); |
| assert(std::abs(mean - x_mean) / x_mean < 0.02); |
| assert(std::abs(var - x_var) / x_var < 0.02); |
| } |
| } |