pyTree : a collection of python codes for tree-based neighbor search
author : Jake Vanderplas <vanderplas@astro.washington.edu>
This is mainly a test-bed for codes to be included in scikit-learn.
As such, it's not set up to be a fully-fledged package, but to be installed
in-place.
Compiling
---------
This is set-up for in-place compilation. Compile with the command
python setup.py build_ext --inplace
Contents
--------
cpp_balltree :
This is the C++ BallTree code which I submitted to scikit-learn. The
cython wrapper and a few tweaks to the C++ source are due to some of
the scikit-learn contributers
npy_balltree :
This is an attempt to speed-up and streamline the ball tree implementation
Its approach is to use no dynamic memory allocation, but instead store
all the info for the built ball tree in a collection of numpy arrays.
I'm also making the ball tree more flexible (any minkowski p-distance
can be used).
This is a work in progress. Currently initializing the tree is
approximately 8-10 times faster than in the C++ version. The
query() function is about 50% faster. The query_radius()
function has not yet been implemented.