./wip/ann, Library for Approximate Nearest Neighbor Searching

[ CVSweb ] [ Homepage ] [ RSS ] [ Required by ] [ Add to tracker ]


Branch: CURRENT, Version: 1.1.2, Package name: ann-1.1.2, Maintainer: jihbed.research

ANN is a library written in the C++ programming language to support both
exact and approximate nearest neighbor searching in spaces of various
dimensions. It was implemented by David M. Mount of the University of
Maryland, and Sunil Arya of the Hong Kong University of Science and
Technology. ANN (pronounced like the name ``Ann'') stands for
Approximate Nearest Neighbors. ANN is also a testbed containing
programs and procedures for generating data sets, collecting and
analyzing statistics on the performance of nearest neighbor algorithms
and data structures, and visualizing the geometric structure of these
data structures.


Required to build:
[pkgtools/cwrappers]

Master sites:

SHA1: 27ec04d55e244380ade3706a9b71c3d631e2ff1a
RMD160: 1b76b2f5c25c83c6d52a1a1e19e5b058ccf929d0
Filesize: 576.677 KB

Version history: (Expand)


CVS history: (Expand)


   2014-09-30 13:22:27 by Filip Hajny | Files touched by this commit (4)
Log message:
Remove platform restrictions, fix build on SunOS.
   2012-09-24 18:56:26 by Aleksej Saushev | Files touched by this commit (144)
Log message:
Drop superfluous PKG_DESTDIR_SUPPORT, "user-destdir" is default these days.
Mark packages that don't and might probably not have staged installation.
   2011-01-17 23:02:17 by Kamel Derouiche | Files touched by this commit (5) | Imported package
Log message:
Import ann-1.1.2 as wip/ann.

ANN is a library written in the C++ programming language to support both
exact and approximate nearest neighbor searching in spaces of various
dimensions.  It was implemented by David M. Mount of the University of
Maryland, and Sunil Arya of the Hong Kong University of Science and
Technology.  ANN (pronounced like the name ``Ann'') stands for
Approximate Nearest Neighbors.  ANN is also a testbed containing
programs and procedures for generating data sets, collecting and
analyzing statistics on the performance of nearest neighbor algorithms
and data structures, and visualizing the geometric structure of these
data structures.