crossKnn {N2R}

Perform fast approximate K-nearest neighbor search of rows input matrix mA in rows of matrix mB.

Description

Perform fast approximate K-nearest neighbor search of rows input matrix mA in rows of matrix mB.

Usage

crossKnn(
  mA,
  mB,
  k,
  nThreads = 10L,
  verbose = TRUE,
  indexType = "angular",
  M = 12L,
  MaxM0 = 24L,
  ef_search_multiplier = 50,
  quiet = FALSE
)

Arguments

mA

Input numeric matrix of data

mB

Input numeric matrix of data

k

Integer number of clusters

nThreads

Integer number of threads (default=10)

verbose

Boolean flag for verbose output (default=FALSE)

indexType

Metric distance type, which can be "angular" or "L2" (default="angular")

M

Integer number of connections (default=12) The NSW graph is constructed via consecutive insertion of elements in random order by bidirectionally connecting them to the M closest neighbors from the previously inserted elements.

MaxM0

Integer maximum number of connections that an element can have in the zero layer. (default=24) It is recommended that MaxM0 not exceed 2*M.

ef_search_multiplier

Integer multiplier to calculate candidate nearest neighbors, set to k*ef_search_multiplier (default=50). Refer to the parameters er and efConstruction in Malkov & Yashunin (2020) doi: 10.1109/TPAMI.2018.2889473

quiet

Boolean flag specifically for Rcpp warnings (default=FALSE)

Value

clusters per row in sparse Matrix of class "dgCMatrix" of dimensions mB rows by mA rows

Examples

data(iris)
iris_df = data.matrix(iris[-5]) ## convert to a numeric matrix 
crossKnn(mA=iris_df, mB=head(iris_df, 50), 4)