find_knn {destiny} | R Documentation |
Approximate k nearest neighbor search with flexible distance function.
find_knn( data, k, ..., query = NULL, distance = c("euclidean", "cosine", "rankcor", "l2"), method = c("covertree", "hnsw"), sym = TRUE, verbose = FALSE )
data |
Data matrix |
k |
Number of nearest neighbors |
... |
Parameters passed to |
query |
Query matrix. Leave it out to use |
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. |
sym |
Return a symmetric matrix (as long as query is NULL)? |
verbose |
Show a progressbar? (default: FALSE) |
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.