| //===----------------------------------------------------------------------===// |
| // |
| // The LLVM Compiler Infrastructure |
| // |
| // This file is dual licensed under the MIT and the University of Illinois Open |
| // Source Licenses. See LICENSE.TXT for details. |
| // |
| //===----------------------------------------------------------------------===// |
| // |
| // REQUIRES: long_tests |
| |
| // <random> |
| |
| // template<class RealType = double> |
| // class student_t_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::student_t_distribution<> D; |
| typedef D::param_type P; |
| typedef std::minstd_rand G; |
| G g; |
| D d; |
| P p(5.5); |
| const int N = 1000000; |
| std::vector<D::result_type> u; |
| for (int i = 0; i < N; ++i) |
| u.push_back(d(g, p)); |
| double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); |
| double var = 0; |
| double skew = 0; |
| double kurtosis = 0; |
| for (int i = 0; i < u.size(); ++i) |
| { |
| double d = (u[i] - mean); |
| double d2 = sqr(d); |
| var += d2; |
| skew += d * d2; |
| kurtosis += d2 * d2; |
| } |
| var /= u.size(); |
| double dev = std::sqrt(var); |
| skew /= u.size() * dev * var; |
| kurtosis /= u.size() * var * var; |
| kurtosis -= 3; |
| double x_mean = 0; |
| double x_var = p.n() / (p.n() - 2); |
| double x_skew = 0; |
| double x_kurtosis = 6 / (p.n() - 4); |
| assert(std::abs(mean - x_mean) < 0.01); |
| assert(std::abs((var - x_var) / x_var) < 0.01); |
| assert(std::abs(skew - x_skew) < 0.01); |
| assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.2); |
| } |
| { |
| typedef std::student_t_distribution<> D; |
| typedef D::param_type P; |
| typedef std::minstd_rand G; |
| G g; |
| D d; |
| P p(10); |
| const int N = 1000000; |
| std::vector<D::result_type> u; |
| for (int i = 0; i < N; ++i) |
| u.push_back(d(g, p)); |
| double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); |
| double var = 0; |
| double skew = 0; |
| double kurtosis = 0; |
| for (int i = 0; i < u.size(); ++i) |
| { |
| double d = (u[i] - mean); |
| double d2 = sqr(d); |
| var += d2; |
| skew += d * d2; |
| kurtosis += d2 * d2; |
| } |
| var /= u.size(); |
| double dev = std::sqrt(var); |
| skew /= u.size() * dev * var; |
| kurtosis /= u.size() * var * var; |
| kurtosis -= 3; |
| double x_mean = 0; |
| double x_var = p.n() / (p.n() - 2); |
| double x_skew = 0; |
| double x_kurtosis = 6 / (p.n() - 4); |
| assert(std::abs(mean - x_mean) < 0.01); |
| assert(std::abs((var - x_var) / x_var) < 0.01); |
| assert(std::abs(skew - x_skew) < 0.01); |
| assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.04); |
| } |
| { |
| typedef std::student_t_distribution<> D; |
| typedef D::param_type P; |
| typedef std::minstd_rand G; |
| G g; |
| D d; |
| P p(100); |
| const int N = 1000000; |
| std::vector<D::result_type> u; |
| for (int i = 0; i < N; ++i) |
| u.push_back(d(g, p)); |
| double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); |
| double var = 0; |
| double skew = 0; |
| double kurtosis = 0; |
| for (int i = 0; i < u.size(); ++i) |
| { |
| double d = (u[i] - mean); |
| double d2 = sqr(d); |
| var += d2; |
| skew += d * d2; |
| kurtosis += d2 * d2; |
| } |
| var /= u.size(); |
| double dev = std::sqrt(var); |
| skew /= u.size() * dev * var; |
| kurtosis /= u.size() * var * var; |
| kurtosis -= 3; |
| double x_mean = 0; |
| double x_var = p.n() / (p.n() - 2); |
| double x_skew = 0; |
| double x_kurtosis = 6 / (p.n() - 4); |
| assert(std::abs(mean - x_mean) < 0.01); |
| assert(std::abs((var - x_var) / x_var) < 0.01); |
| assert(std::abs(skew - x_skew) < 0.01); |
| assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.02); |
| } |
| } |