Find K nearest neighbours for multiple query points

Usage

knn(data, query = data, k, eps = 0, searchtype = 1L, radius = 0)

Arguments

data: Mxd matrix of M target points with dimension d
query: Nxd matrix of N query points with dimension d (nb data and query must have same dimension). If missing defaults to data i.e. a self-query.
k: an integer number of nearest neighbours to find
eps: An approximate error bound. The default of 0 implies exact matching.
searchtype: A character vector or integer indicating the search type. The default value of 1L is equivalent to "auto". See details.
radius: Maximum radius search bound. The default of 0 implies no radius bound.

Value

A list with elements nn.idx (1-indexed indices) and nn.dists (distances), both of which are N x k matrices. See details for the results obtained with1 invalid inputs.

Details

If searchtype="auto", the default, knn uses a k-d tree with a linear heap when k < 30 nearest neighbours are requested (equivalent to searchtype="kd_linear_heap"), a k-d tree with a tree heap otherwise (equivalent to searchtype="kd_tree_heap"). searchtype="brute" checks all point combinations and is intended for validation only.

Integer values of searchtype should be the 1-indexed position in the vector c("auto", "brute", "kd_linear_heap", "kd_tree_heap"), i.e. a value between 1L and 4L.

The underlying libnabo does not have a signalling value to identify indices for invalid query points (e.g. those containing an NA). In this situation, the index returned by libnabo will be 0 and knn will therefore return an index of 1. However the distance will be Inf signalling a failure to find a nearest neighbour.

When radius>0.0 and no point is found within the search bound, the index returned will be 0 but the reported distance will be Inf (in contrast RANN::nn2 returns 1.340781e+154).

Examples

## Basic usage
# load sample data consisting of list of 3 separate 3d pointets
data(kcpoints)

# Nearest neighbour in first pointset of all points in second pointset 
nn1 <- knn(data=kcpoints[[1]], query=kcpoints[[2]], k=1)
str(nn1)
#> List of 2
#>  $ nn.idx  : int [1:297, 1] 42 42 42 42 42 42 9 9 9 9 ...
#>  $ nn.dists: num [1:297, 1] 16.3 10.9 16.2 12.1 10.7 ...

# 5 nearest neighbours
nn5 <-knn(data=kcpoints[[1]], query=kcpoints[[2]], k=5)
str(nn5)
#> List of 2
#>  $ nn.idx  : int [1:297, 1:5] 42 42 42 42 42 42 9 9 9 9 ...
#>  $ nn.dists: num [1:297, 1:5] 16.3 10.9 16.2 12.1 10.7 ...

# Self match within first pointset, all distances will be 0
nnself1 <- knn(data=kcpoints[[1]], k=1)
str(nnself1)
#> List of 2
#>  $ nn.idx  : int [1:284, 1] 1 2 3 4 5 6 7 8 9 10 ...
#>  $ nn.dists: num [1:284, 1] 0 0 0 0 0 0 0 0 0 0 ...

# neighbour 2 will be the nearest point 
nnself2 <- knn(data=kcpoints[[1]], k=2)

## Advanced usage
# nearest neighbour with radius bound
nn1.rad <- knn(data=kcpoints[[1]], query=kcpoints[[2]], k=1, radius=5)
str(nn1.rad)
#> List of 2
#>  $ nn.idx  : int [1:297, 1] 0 0 0 0 0 0 0 0 0 0 ...
#>  $ nn.dists: num [1:297, 1] Inf Inf Inf Inf Inf ...

# approximate nearest neighbour with 10% error bound
nn1.approx <- knn(data=kcpoints[[1]], query=kcpoints[[2]], k=1, eps=0.1)
str(nn1.approx)
#> List of 2
#>  $ nn.idx  : int [1:297, 1] 42 42 42 42 42 42 9 9 9 9 ...
#>  $ nn.dists: num [1:297, 1] 16.3 10.9 16.2 12.1 10.7 ...

# 5 nearest neighbours, brute force search
nn5.b <-knn(data=kcpoints[[1]], query=kcpoints[[2]], k=5, searchtype='brute')
stopifnot(all.equal(nn5.b, nn5))

# 5 nearest neighbours, brute force search (specified by int)
nn5.b2 <-knn(data=kcpoints[[1]], query=kcpoints[[2]], k=5, searchtype=2L)
stopifnot(all.equal(nn5.b2, nn5.b))