BiocNeighbors 1.10.0
Another application of the KMKNN or VP tree algorithms is to identify all neighboring points within a certain distance1 The default here is Euclidean, but again, we can set distance="Manhattan" in the BNPARAM object if so desired. of the current point.
We first mock up some data:
nobs <- 10000
ndim <- 20
data <- matrix(runif(nobs*ndim), ncol=ndim)We apply the findNeighbors() function to data:
fout <- findNeighbors(data, threshold=1)
head(fout$index)## [[1]]
## [1] 974 1
##
## [[2]]
## [1] 112 2
##
## [[3]]
## [1] 3 3806 2144
##
## [[4]]
## [1] 4536 4
##
## [[5]]
## [1] 5 4002 5028
##
## [[6]]
## [1] 6562 5986 6 7199head(fout$distance)## [[1]]
## [1] 0.8606125 0.0000000
##
## [[2]]
## [1] 0.9093502 0.0000000
##
## [[3]]
## [1] 0.0000000 0.9494483 0.9976992
##
## [[4]]
## [1] 0.9793248 0.0000000
##
## [[5]]
## [1] 0.0000000 0.9666547 0.9851473
##
## [[6]]
## [1] 0.9609676 0.9543126 0.0000000 0.9654677Each entry of the index list corresponds to a point in data and contains the row indices in data that are within threshold.
For example, the 3rd point in data has the following neighbors:
fout$index[[3]]## [1] 3 3806 2144… with the following distances to those neighbors:
fout$distance[[3]]## [1] 0.0000000 0.9494483 0.9976992Note that, for this function, the reported neighbors are not sorted by distance. The order of the output is completely arbitrary and will vary depending on the random seed. However, the identity of the neighbors is fully deterministic.
The queryNeighbors() function is also provided for identifying all points within a certain distance of a query point.
Given a query data set:
nquery <- 1000
ndim <- 20
query <- matrix(runif(nquery*ndim), ncol=ndim)… we apply the queryNeighbors() function:
qout <- queryNeighbors(data, query, threshold=1)
length(qout$index)## [1] 1000… where each entry of qout$index corresponds to a row of query and contains its neighbors in data.
Again, the order of the output is arbitrary but the identity of the neighbors is deterministic.
Most of the options described for findKNN() are also applicable here.
For example:
subset to identify neighbors for a subset of points.get.distance to avoid retrieving distances when unnecessary.BPPARAM to parallelize the calculations across multiple workers.raw.index to return the raw indices from a precomputed index.Note that the argument for a precomputed index is precomputed:
pre <- buildIndex(data, BNPARAM=KmknnParam())
fout.pre <- findNeighbors(BNINDEX=pre, threshold=1)
qout.pre <- queryNeighbors(BNINDEX=pre, query=query, threshold=1)Users are referred to the documentation of each function for specific details.
sessionInfo()## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
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## BLAS: /home/biocbuild/bbs-3.13-bioc/R/lib/libRblas.so
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] BiocParallel_1.26.0 BiocNeighbors_1.10.0 knitr_1.33
## [4] BiocStyle_2.20.0
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## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.6 magrittr_2.0.1 BiocGenerics_0.38.0
## [4] lattice_0.20-44 R6_2.5.0 rlang_0.4.11
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