Find K nearest neighbours for multiple query points
Find K nearest neighbours for multiple query points
knn(data, query = data, k, eps = 0, searchtype = 1L, radius = 0)
data |
Mxd matrix of M target points with dimension d |
query |
Nxd matrix of N query points with dimension d (nb |
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 |
radius |
Maximum radius search bound. The default of 0 implies no radius bound. |
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).
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.
## 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) # 5 nearest neighbours nn5 <-knn(data=kcpoints[[1]], query=kcpoints[[2]], k=5) str(nn5) # Self match within first pointset, all distances will be 0 nnself1 <- knn(data=kcpoints[[1]], k=1) str(nnself1) # 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) # approximate nearest neighbour with 10% error bound nn1.approx <- knn(data=kcpoints[[1]], query=kcpoints[[2]], k=1, eps=0.1) str(nn1.approx) # 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))
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