require(knitr) opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE) library(BiocNeighbors)

Another application of the KMKNN or VP tree algorithms is to identify all neighboring points within a certain distance^[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$index) head(fout$distance)

Each 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]]
```

... with the following distances to those neighbors:

```
fout$distance[[3]]
```

Note 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)

... 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()
```

LTLA/BiocNeighbors documentation built on Sept. 18, 2021, 8:19 p.m.

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