Source: ann Maintainer: Debian Science Maintainers Uploaders: Teemu Ikonen Section: libs Priority: optional Build-Depends: autoconf, automake, libtool, debhelper (>= 11~), autoconf-archive Standards-Version: 4.1.3 Vcs-Browser: https://salsa.debian.org/science-team/ann Vcs-Git: https://salsa.debian.org/science-team/ann.git Homepage: http://www.cs.umd.edu/~mount/ANN/ Package: libann-dev Architecture: any Section: libdevel Depends: libann0 (= ${binary:Version}), ${misc:Depends} Description: 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. Package: libann0 Architecture: any Depends: ${shlibs:Depends}, ${misc:Depends} Description: 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. Package: ann-tools Architecture: any Section: math Depends: ${shlibs:Depends}, ${misc:Depends} Description: 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.