Fast Nearest Neighbour Search (Wraps ANN Library) Using L2 Metric
Finds the k nearest neighbours for every point in a given dataset
in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is
support for approximate as well as exact searches, fixed radius searches
and 'bd' as well as 'kd' trees. The distance is computed using the L2
(Euclidean) metric. Please see package 'RANN.L1' for the same
functionality using the L1 (Manhattan, taxicab) metric.
Finds the k nearest neighbours for every point in a given dataset in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is support for approximate as well as exact searches, fixed radius searches and bd as well as kd trees.
This package implements nearest neighbors for the Euclidean (L2) metric. For the Manhattan (L1) metric, install the RANN1 package.
For further details on the underlying ANN library, see http://www.cs.umd.edu/~mount/ANN.
The recommendation is to install the released version from CRAN by doing:
You can, however, download the tar ball, and run
R CMD INSTALL on it, or use the devtools package to install the development version:
# install.packages("devtools") devtools::install_github("jefferis/RANN")
Please feel free to:
- submit suggestions and bug-reports at: https://github.com/jefferis/RANN/issues
- send pull requests after forking: https://github.com/jefferis/RANN/
- e-mail the maintainer: email@example.com
Copyright and License
Functions in RANN
|nn2||Nearest Neighbour Search|
|RANN-package||Wrapper for Arya and Mount's Approximate Nearest Neighbours (ANN) C++ library|
Last month downloads
|Copyright||ANN library is copyright University of Maryland and Sunil Arya and David Mount. See file COPYRIGHT for details.|
|License||GPL (>= 3)|
|Packaged||2019-01-08 19:24:15 UTC; jefferis|
|Date/Publication||2019-01-08 20:00:04 UTC|
Include our badge in your README