nn2: Nearest Neighbour Search

In RANN: Fast Nearest Neighbour Search (Wraps ANN Library) Using L2 Metric

Nearest Neighbour Search

Description

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.

Usage

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

Arguments

data	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 columnns 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 https://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 k matrix 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.

Examples

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

RANN documentation built on Sept. 11, 2024, 5:32 p.m.