frNN | R Documentation |
This function uses a kd-tree to find the fixed radius nearest neighbors (including distances) fast.
frNN(
x,
eps,
query = NULL,
sort = TRUE,
search = "kdtree",
bucketSize = 10,
splitRule = "suggest",
approx = 0
)
## S3 method for class 'frNN'
sort(x, decreasing = FALSE, ...)
## S3 method for class 'frNN'
adjacencylist(x, ...)
## S3 method for class 'frNN'
print(x, ...)
x |
a data matrix, a dist object or a frNN object. |
eps |
neighbors radius. |
query |
a data matrix with the points to query. If query is not
specified, the NN for all the points in |
sort |
sort the neighbors by distance? This is expensive and can be
done later using |
search |
nearest neighbor search strategy (one of |
bucketSize |
max size of the kd-tree leafs. |
splitRule |
rule to split the kd-tree. One of |
approx |
use approximate nearest neighbors. All NN up to a distance of
a factor of |
decreasing |
sort in decreasing order? |
... |
further arguments |
If x
is specified as a data matrix, then Euclidean distances an fast
nearest neighbor lookup using a kd-tree are used.
To create a frNN object from scratch, you need to supply at least the
elements id
with a list of integer vectors with the nearest neighbor
ids for each point and eps
(see below).
Self-matches: Self-matches are not returned!
frNN()
returns an object of class frNN (subclass of
NN) containing a list with the following components:
id |
a list of integer vectors. Each vector contains the ids of the fixed radius nearest neighbors. |
dist |
a list with distances (same structure as
|
eps |
neighborhood radius |
metric |
used distance metric. |
adjacencylist()
returns a list with one entry per data point in x
. Each entry
contains the id of the nearest neighbors.
Michael Hahsler
David M. Mount and Sunil Arya (2010). ANN: A Library for Approximate Nearest Neighbor Searching, http://www.cs.umd.edu/~mount/ANN/.
Other NN functions:
NN
,
comps()
,
kNN()
,
kNNdist()
,
sNN()
data(iris)
x <- iris[, -5]
# Example 1: Find fixed radius nearest neighbors for each point
nn <- frNN(x, eps = .5)
nn
# Number of neighbors
hist(lengths(adjacencylist(nn)),
xlab = "k", main="Number of Neighbors",
sub = paste("Neighborhood size eps =", nn$eps))
# Explore neighbors of point i = 10
i <- 10
nn$id[[i]]
nn$dist[[i]]
plot(x, col = ifelse(1:nrow(iris) %in% nn$id[[i]], "red", "black"))
# get an adjacency list
head(adjacencylist(nn))
# plot the fixed radius neighbors (and then reduced to a radius of .3)
plot(nn, x)
plot(frNN(nn, eps = .3), x)
## Example 2: find fixed-radius NN for query points
q <- x[c(1,100),]
nn <- frNN(x, eps = .5, query = q)
plot(nn, x, col = "grey")
points(q, pch = 3, lwd = 2)
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