findKNN {BiocNeighbors}R Documentation

Find k-nearest neighbors

Description

Find the k-nearest neighbors for each point in a data set, using exact or approximate algorithms.

Usage

findKNN(X, k, subset=NULL, get.index=TRUE, get.distance=TRUE,
    BPPARAM=SerialParam(), ..., BNINDEX, BNPARAM) 

Arguments

X

A numeric data matrix where rows are points and columns are dimensions. This can be missing if BNINDEX is supplied.

k

An integer scalar for the number of nearest neighbors.

subset

A vector specifying the subset of points in X to search.

get.index

A logical scalar indicating whether to return row indices of the neighbors.

get.distance

A logical scalar indicating whether to return distances to neighbors.

BPPARAM

a BiocParallelParam class for parallelization.

...

Further arguments to pass to individual methods.

BNINDEX

A BiocNeighborIndex object containing precomputed index information. This can be missing if BNPARAM is supplied, see Details.

BNPARAM

A BiocNeighborParam object specifying the algorithm to use. This can be missing if BNINDEX is supplied, see Details.

Details

The class of BNINDEX and BNPARAM will determine dispatch to specific methods. Only one of these arguments needs to be defined to resolve dispatch. However, if both are defined, they cannot specify different algorithms.

If BNINDEX is supplied, X does not need to be specified. In fact, any value of X will be ignored as all necessary information for the search is already present in BNINDEX. Similarly, any parameters in BNPARAM will be ignored.

If both BNINDEX and BNPARAM are missing, the function will default to the KMKNN algorithm by setting BNPARAM=KmknnParam().

Value

A list is returned containing:

If subset is not NULL, each row of the above matrices refers to a point in the subset, in the same order as supplied in subset.

Author(s)

Aaron Lun

See Also

findKmknn, findVptree, findAnnoy and findHnsw for specific methods.

Examples

Y <- matrix(rnorm(100000), ncol=20)
str(k.out <- findKNN(Y, k=10))
str(a.out <- findKNN(Y, k=10, BNPARAM=AnnoyParam()))

k.dex <- buildKmknn(Y)
str(k.out2 <- findKNN(Y, k=10, BNINDEX=k.dex))
str(k.out3 <- findKNN(Y, k=10, BNINDEX=k.dex, BNPARAM=KmknnParam()))

a.dex <- buildAnnoy(Y)
str(a.out2 <- findKNN(Y, k=10, BNINDEX=a.dex))
str(a.out3 <- findKNN(Y, k=10, BNINDEX=a.dex, BNPARAM=AnnoyParam()))

[Package BiocNeighbors version 1.2.0 Index]