R: kNN search

find_knn {destiny}

R Documentation

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.

[Package destiny version 3.2.0 Index]