Arbitrary dense matrix, with underlying geometry. More...
#include <Kernel.hpp>
Public Member Functions | |
DenseKernel (DenseMatrix< scalar_t > &data, DenseMatrix< scalar_t > &A, scalar_t lambda) | |
scalar_t | eval (std::size_t i, std::size_t j) const override |
void | permute () override |
Public Member Functions inherited from strumpack::kernel::Kernel< scalar_t > | |
Kernel (DenseM_t &data, scalar_t lambda) | |
virtual | ~Kernel ()=default |
std::size_t | n () const |
std::size_t | d () const |
void | operator() (const std::vector< std::size_t > &I, const std::vector< std::size_t > &J, DenseMatrix< real_t > &B) const |
void | operator() (const std::vector< std::size_t > &I, const std::vector< std::size_t > &J, DenseMatrix< std::complex< real_t >> &B) const |
DenseM_t | fit_HSS (std::vector< scalar_t > &labels, const HSS::HSSOptions< scalar_t > &opts) |
std::vector< scalar_t > | predict (const DenseM_t &test, const DenseM_t &weights) const |
DistM_t | fit_HSS (const BLACSGrid &grid, std::vector< scalar_t > &labels, const HSS::HSSOptions< scalar_t > &opts) |
std::vector< scalar_t > | predict (const DenseM_t &test, const DistM_t &weights) const |
DenseM_t | fit_HODLR (const MPIComm &c, std::vector< scalar_t > &labels, const HODLR::HODLROptions< scalar_t > &opts) |
const DenseM_t & | data () const |
DenseM_t & | data () |
std::vector< int > & | permutation () |
const std::vector< int > & | permutation () const |
Arbitrary dense matrix, with underlying geometry.
This is a subclass of Kernel. It overrides the eval routine, unlike other kernel classes (Gauss and Laplace), which only implement the kernel function.
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inline |
Constructor of the dense matrix kernel object.
data | Coordinates of the points used to generate the matrix. |
A | The actual dense matrix |
lambda | Regularization parameter, added to the diagonal |
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inlineoverridevirtual |
Evaluate an entry of the kernel matrix.
i | row coordinate of entry to evaluate |
j | column coordinate of entry to evaluate |
Reimplemented from strumpack::kernel::Kernel< scalar_t >.