Uses a kd-tree to find the p number of near neighbours for each point in an
input/output dataset. The advantage of the kd-tree is that it runs in O(M log
M) time.
An M x d data.frame or matrix, where each of the
M rows is a point or a (column) vector (where d=1).
query
A set of N x d points that will be queried against
data. d, the number of columns, must be the same as
data. If missing, defaults to data.
k
The maximum number of nearest neighbours to compute. The default
value is set to the smaller of the number of columsn in data
treetype
Character vector specifying the standard 'kd' tree or a
'bd' (box-decomposition, AMNSW98) tree which may perform better for
larger point sets
searchtype
See details
radius
Radius of search for searchtype='radius'
eps
Error bound: default of 0.0 implies exact nearest neighbour search
Details
The RANN package utilizes the Approximate Near Neighbor (ANN) C++
library, which can give the exact near neighbours or (as the name suggests)
approximate near neighbours to within a specified error bound. For more
information on the ANN library please visit
http://www.cs.umd.edu/~mount/ANN/.
Search types: priority visits cells in increasing order of distance
from the query point, and hence, should converge more rapidly on the true
nearest neighbour, but standard is usually faster for exact searches.
radius only searches for neighbours within a specified radius of the
point. If there are no neighbours then nn.idx will contain 0 and nn.dists
will contain 1.340781e+154 for that point.
Value
A list of length 2 with elements:
nn.idx
A N x k integer matrix returning the near
neighbour indices.
nn.dists
A N x kmatrix returning the near
neighbour Euclidean distances.
Author(s)
Gregory Jefferis based on earlier code by Samuel E. Kemp (knnFinder
package)
References
Bentley J. L. (1975), Multidimensional binary search trees used
for associative search. Communication ACM, 18:309-517.
Arya S. and Mount D. M. (1993), Approximate nearest neighbor searching,
Proc. 4th Ann. ACM-SIAM Symposium on Discrete Algorithms (SODA'93), 271-280.
Arya S., Mount D. M., Netanyahu N. S., Silverman R. and Wu A. Y (1998), An
optimal algorithm for approximate nearest neighbor searching, Journal of the
ACM, 45, 891-923.
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(RANN)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/RANN/nn2.Rd_%03d_medium.png", width=480, height=480)
> ### Name: nn2
> ### Title: Nearest Neighbour Search
> ### Aliases: nn2
> ### Keywords: nonparametric
>
> ### ** Examples
>
> x1 <- runif(100, 0, 2*pi)
> x2 <- runif(100, 0,3)
> DATA <- data.frame(x1, x2)
> nearest <- nn2(DATA,DATA)
>
>
>
>
>
> dev.off()
null device
1
>