Source header: cuvs/distance/distance.hpp
enum to tell how to compute distance
Values
Density kernel type for Kernel Density Estimation.
These are the smoothing kernels used in KDE — distinct from the dot-product kernels (RBF, Polynomial, etc.) in cuvs::distance::kernels used by SVMs.
Values
Parameters for kernel matrices.
The following kernels are implemented:
Fields
Compute pairwise distances for two matrices
Note: Only contiguous row- or column-major layouts supported currently.
Usage example:
Parameters
Returns
void
Additional overload: distance::pairwise_distance
Compute pairwise distances for two matrices
Note: Only contiguous row- or column-major layouts supported currently.
Usage example:
Parameters
Returns
void
Additional overload: distance::pairwise_distance
Compute pairwise distances for two matrices
Note: Only contiguous row- or column-major layouts supported currently.
Usage example:
Parameters
Returns
void
Additional overload: distance::pairwise_distance
Compute pairwise distances for two matrices
Note: Only contiguous row- or column-major layouts supported currently.
Usage example:
Parameters
Returns
void
Additional overload: distance::pairwise_distance
Compute pairwise distances for two matrices
Note: Only contiguous row- or column-major layouts supported currently.
Usage example:
Parameters
Returns
void
Additional overload: distance::pairwise_distance
Compute pairwise distances for two matrices
Note: Only contiguous row- or column-major layouts supported currently.
Usage example:
Parameters
Returns
void
Additional overload: distance::pairwise_distance
Compute sparse pairwise distances between x and y, using the provided input configuration and distance function.
Parameters
Returns
void
Additional overload: distance::pairwise_distance
Compute sparse pairwise distances between x and y, using the provided input configuration and distance function.
Parameters
Returns
void