knn: kNN search

In destiny: Creates diffusion maps

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.

destiny documentation built on Nov. 8, 2020, 7:38 p.m.