Usage
ann(ref, target, k=1, eps=0.0, tree.type="kd",
search.type="standard", bucket.size=1, split.rule="sl_midpt",
shrink.rule="simple", verbose=TRUE, ...)Arguments
ref
an $n \times d$ matrix containing the reference point set
$S$. Each row in ref corresponds to a point in $d$-dimensional space.
target
an $m \times d$ matrix containing the points
for which $k$ nearest neighbor reference points are sought.
k
defines the number of nearest neighbors to find. The default
is $k$=1.
eps
the $i^{th}$ nearest neighbor is at most
(1+eps) from true $i^{th}$ nearest neighbor, where eps$\ge 0$ . Specifically, the true (not
squared) difference between the true $i^{th}$ and the
approximation of the $i^{th}$
tree.type
the data structures kd-tree or bd-tree as
quoted key words kd and bd, respectively. A brute force
search can be specified with the quoted key word brute. If
brute is specified, then all subsequent arguments
search.type
either standard or priority search in the kd-tree
or bd-tree, specified by quoted key words standard and priority,
respectively. The default is the standard search.
bucket.size
the maximum number of reference points in the leaf
nodes. The default is 1.
split.rule
is the strategy for the recursive splitting of those
nodes with more points than the bucket size. The splitting
rule applies to both the kd-tree and bd-tree. Splitting rule
options are the quoted key words:
[object Object],See supporting