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| FASTANN: A library for fast approximate nearest neighbours |
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| James Philbin <philbinj@gmail.com> |
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The plan is to support a number of different approximate nearest
neighbour routines and possibly different distance norms. At the
moment only the Euclidean distance is supported with the following
methods:
- Exact NN
- Approximate k-d trees
Both methods use some fairly optimized distance functions (though
these can be improved).
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| INSTALLATION |
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Before installation the following requirements should be met:
- Linux GCC or Cygwin (native windows coming soon)
Build the library
> make
Test the library (everything should say PASSED)
> make test
Install the library to /usr/include and /usr/lib
> make install
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| USAGE |
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See examples/
C++ example:
#include <fastann/fastann.hpp>
float* pnts = ...; // Points to search over go here
fastann::nn_obj<float>* nno =
fastann::nn_obj_build_kdtree(pnts, npoints, ndims, 8, 768);
float* qus = ...; // Points to query go here
float* mins[nqueries];
unsigned* argmins[nqueries];
nno->search_nn(qus, nqueries, argmins, mins);
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| TODO |
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In no particular order:
- C interface (for easy use with Python / Matlab)
- Improved distance functions (gcc makes a cockup of some of the
intrinsics based ones like the double precision ones.
- Better use of cache in kdtree. This might involve using prefetches,
re-ordering the points in some way or even placing the point data in
the nodes.
- Other types of approximate search such as LSH and Spectral Hashing.
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| CHANGELOG |
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v0.1
- Initial checkin.
- Support for exact NN and approximate k-d trees.