RANN2

This package is an updated version of the RANN package, making use of the Rcpp package. For basic use, there is little difference with original RANN package although there are some small (typically 5-10%) speedups for certain query/target size combinations. RANN2 also includes experimental functionality via WANN objects to:

• keep ANN points in memory to avoid repeated copying
• keep the ANN k-d tree in memory to avoid repeated building
• separate building the k-d tree from allocating the points
• permit very fast self queries
• permit queries of the points from one ANN tree against a second tree

Installation

Currently there isn't a released version on CRAN, although we are considering a submission when the package develops sufficiently distinct functionality from the original RANN package.

Development version

You can use the devtools package to install the development version:

if (!require("devtools")) install.packages("devtools")
devtools::install_github("jefferis/RANN2")

Note: Windows users need Rtools and devtools to install this way.

Use

Basic use

The expectation is that for 90% of users the nn2 function should be the only way that the library is used. This takes a target matrix of R points, copies them into an array used by ANN and builds a k-d tree. It then iterates over the query points, searching the tree one at a time.

Advanced use

RANN2 adds WANN objects, which allow fine control of when the k-d tree is built and removed.

data(kcpoints)
w1=WANN(kcpoints[[1]])
library(microbenchmark)
microbenchmark(w1sq<-w1$selfQuery(k=1,eps=0))
microbenchmark(nn2(kcpoints[[1]],k=1))
w2=WANN(kcpoints[[2]])
# NB must pass the Cpp object not the reference class object
w1$queryWANN(w2$.CppObject)

WANN objects will primarily be useful if you make repeated queries. You can also delay building the k-d tree:

w1=WANN(kcpoints[[1]])
w1$querySelf(k=1,eps=0)
w1$build_tree()
w1$delete_tree()

if only a fraction of the objects will need to be searched; the tree will automatically be built when it is queried. You can also explicitly control when the tree is built or deleted (for memory management). The tree is wrapped in an R reference class (R5) object which imposes a significant performance penalty for building small trees (< ~ 1000 points).

Changing ANN data types

By default ANN uses doubles for both points and returned distances. You can save space by changing this if you want. To do to this you must recompile after setting either ANN_COORD_TYPE or ANN_DIST_TYPE in src/MAKEVARS or MAKEVARS.win as appropriate. e.g.

PKG_CPPFLAGS=-I. -IANN -DRANN -DANN_COORD_TYPE=float

would switch to the use of floats for the main ANN coordinate type. Note however that the k-d tree itself appears to occupy ~ 2x the space of the underlying double coordinates.

Linking and using ANN library

This package compiles a static library for ANN and provides the headers for it. Developers can directly include them in their C++ code / Rcpp based package.

Instructions

DESCRIPTION file:

    LinkingTo: RANN2

src/Makevars file:

    PKG_IMPORT=RANN2
    PKG_HOME=`${R_HOME}/bin/Rscript -e 'cat(system.file(package=\"$(PKG_IMPORT)\"))'`
    PKG_LIBS=-L$(PKG_HOME)/lib -l$(PKG_IMPORT)

src/Makevars.win file:

    PKG_IMPORT=RANN2
    PKG_HOME=`${R_HOME}/bin/Rscript -e 'cat(system.file(package=\"$(PKG_IMPORT)\"))'`
    PKG_LIBS+=-L$(PKG_HOME)/lib -l$(PKG_IMPORT)

    PKG_CPPFLAGS+=-DDLL_EXPORTS

Your C++ file:

    #include <ANN.h>

For usage example: src/nn.cpp