Find K nearest neighbours for multiple query points

Find K nearest neighbours for multiple query points

knn_parallel(data, query = data, k, ..., ncores = bigparallelr::nb_cores())

Arguments

data

Mxd matrix of M target points with dimension d

query

Nxd matrix of N query points with dimension d (nb data and query must have same dimension). If missing defaults to data i.e. a self-query.

k

an integer number of nearest neighbours to find

...

Arguments passed on to nabor::knn

eps

An approximate error bound. The default of 0 implies exact matching.

searchtype

A character vector or integer indicating the search type. The default value of 1L is equivalent to "auto". See details.

radius

Maximum radius search bound. The default of 0 implies no radius bound.

ncores

Number of cores to use. Default uses bigparallelr::nb_cores().

Value

A list with elements nn.idx (1-indexed indices) and

nn.dists (distances), both of which are N x k matrices. See details for the results obtained with1 invalid inputs.

Examples

if (FALSE) knn_parallel(matrix(1:4, 2), k = 2, ncores = 2)