RANN (version 2.6)

nn2: Nearest Neighbour Search


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.


nn2(data, query = data, k = min(10, nrow(data)), treetype = c("kd", "bd"),
  searchtype = c("standard", "priority", "radius"), radius = 0, eps = 0)



An M x d data.frame or matrix, where each of the M rows is a point or a (column) vector (where d=1).


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.


The maximum number of nearest neighbours to compute. The default value is set to the smaller of the number of columnns in data


Character vector specifying the standard 'kd' tree or a 'bd' (box-decomposition, AMNSW98) tree which may perform better for larger point sets


See details


Radius of search for searchtype='radius'


Error bound: default of 0.0 implies exact nearest neighbour search


A list of length 2 with elements:


A N x k integer matrix returning the near neighbour indices.


A N x k matrix returning the near neighbour Euclidean distances.


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.


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.


Run this code
x1 <- runif(100, 0, 2*pi)
x2 <- runif(100, 0,3)
DATA <- data.frame(x1, x2)
nearest <- nn2(DATA,DATA)
# }

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