ann 1.1.2+doc-2ubuntu1 source package in Ubuntu

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ann (1.1.2+doc-2ubuntu1) precise; urgency=low

  * fix symbols to also exclude the non arm symbols under armhf
 -- Oliver Grawert <email address hidden>   Tue, 13 Dec 2011 12:54:15 +0100

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Uploaded by:
Oliver Grawert
Uploaded to:
Precise
Original maintainer:
Debian Science Team
Architectures:
any
Section:
libs
Urgency:
Low Urgency

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ann_1.1.2+doc-2ubuntu1.debian.tar.gz 21.8 KiB bef0d229e45ee0e40342f3779245b8a319658adbfacc06c17dad9b3dd40c2359
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Binary packages built by this source

ann-tools: Approximate Nearest Neighbor Searching library (tools)

 ANN is a library written in C++, which supports data structures and
 algorithms for both exact and approximate nearest neighbor searching
 in arbitrarily high dimensions. ANN assumes that distances
 are measured using any class of distance functions called Minkowski
 metrics. These include the well known Euclidean distance, Manhattan
 distance, and max distance. ANN performs quite efficiently for point
 sets ranging in size from thousands to hundreds of thousands, and in
 dimensions as high as 20.
 .
 This package contains the ann2fig (display ANN output in fig format)
 and the ann_sample (a sample demonstration for ANN) programs.

libann-dev: Approximate Nearest Neighbor Searching library (development files)

 ANN is a library written in C++, which supports data structures and
 algorithms for both exact and approximate nearest neighbor searching
 in arbitrarily high dimensions. ANN assumes that distances
 are measured using any class of distance functions called Minkowski
 metrics. These include the well known Euclidean distance, Manhattan
 distance, and max distance. ANN performs quite efficiently for point
 sets ranging in size from thousands to hundreds of thousands, and in
 dimensions as high as 20.
 .
 This package contains the header files for developing applications
 with the ANN library.

libann0: Approximate Nearest Neighbor Searching library

 ANN is a library written in C++, which supports data structures and
 algorithms for both exact and approximate nearest neighbor searching
 in arbitrarily high dimensions. ANN assumes that distances
 are measured using any class of distance functions called Minkowski
 metrics. These include the well known Euclidean distance, Manhattan
 distance, and max distance. ANN performs quite efficiently for point
 sets ranging in size from thousands to hundreds of thousands, and in
 dimensions as high as 20.