Find the Fixed Radius Nearest Neighbors
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, ...)
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!
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 |
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/.
data(iris)
x <- iris[, -5]
# Example 1: Find fixed radius nearest neighbors for each point
nn <- frNN(x, eps = .5)
# Number of neighbors
hist(sapply(adjacencylist(nn), length),
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.