knn: kNN search

In theislab/destiny: Creates diffusion maps

kNN search

Description

Approximate k nearest neighbor search with flexible distance function.

Usage

find_knn(
  data,
  k,
  ...,
  query = NULL,
  distance = c("euclidean", "cosine", "rankcor", "l2"),
  method = c("covertree", "hnsw"),
  sym = TRUE,
  verbose = FALSE
)

Arguments

data	Data matrix
k	Number of nearest neighbors
...	Parameters passed to hnsw_knn
query	Query matrix. Leave it out to use data as query
distance	Distance metric to use. Allowed measures: Euclidean distance (default), cosine distance (1-corr(c_1, c_2)) or rank correlation distance (1-corr(rank(c_1), rank(c_2)))
method	Method to use. 'hnsw' is tunable with ... but generally less exact than 'covertree' (default: 'covertree')
sym	Return a symmetric matrix (as long as query is NULL)?
verbose	Show a progressbar? (default: FALSE)

Value

A list with the entries:

index

A nrow(data) \times k integer matrix containing the indices of the k nearest neighbors for each cell.

dist

A nrow(data) \times k double matrix containing the distances to the k nearest neighbors for each cell.

dist_mat

A dgCMatrix if sym == TRUE, else a dsCMatrix (nrow(query) \times nrow(data)). Any zero in the matrix (except for the diagonal) indicates that the cells in the corresponding pair are close neighbors.

theislab/destiny documentation built on Nov. 19, 2024, 5:43 a.m.