Public Member Functions
void	operator() (MatrixLayout layout, std::size_t n, std::size_t dim, const result_type x, const result_type w, result_type mean, result_type cov, MatrixLayout cov_layout=RowMajor, bool cov_upper=false, bool cov_packed=false)
	Compute the sample covariance matrix. More...

Detailed Description

Definition at line 142 of file covariance.hpp.

Member Typedef Documentation

template<typename RealType = double>

using vsmc::Covariance< RealType >::result_type = RealType

Definition at line 148 of file covariance.hpp.

template<typename RealType = double>

void vsmc::Covariance< RealType >::operator()	(	MatrixLayout	layout,
		std::size_t	n,
		std::size_t	dim,
		const result_type *	x,
		const result_type *	w,
		result_type *	mean,
		result_type *	cov,
		MatrixLayout	cov_layout = `RowMajor`,
		bool	cov_upper = `false`,
		bool	cov_packed = `false`
	)

inline

Compute the sample covariance matrix.

Parameters

layout	The storage layout of the data `x`. The data is considere to be `N` by `dim` matrix. In `RowMajor` storage.
n	Sample size. If `N == 0` then no computation is carried out.
dim	Dimension of the random variable. If `dim == 0` then no computation carried out.
x	The samples. If it is a null pointer, then no computation is carried out.
w	The weights. If it is a null pointer, then all samples are assigned weight 1.
mean	Output storage of the mean. If it is a null pointer, then it is ignored.
cov	Output storage of the covarianc matrix. If it is a null pointer, then it is ignored.
cov_layout	The storage layout of the covariance matrix.
cov_upper	If true, the upper triangular of the covariance matrix is packed, otherwise the lower triangular is packed. Ignored if `cov_pack` is `false`.
cov_packed	If true, the matrix is packed.

Definition at line 170 of file covariance.hpp.