Note: this module should be considered unsupported. It probably won't compile, and if it does, it may contain errors in some cases. See a more developed version of this functionality in scikit-learn: https://github.com/scikit-learn/scikit-learn/pull/1732/
Fast cython distance computations in a variety of metrics which expose pointers to C functions.
This is a framework that will allow general distance metrics to be incorporated into tree-based neighbors searches. The idea is that we need a fast way to compute the distance between two points under a given metric. In the basic framework here, this involves creating an object which exposes C-pointers to a function and a parameter structure so that the distance function can be called from python as normal, or alternatively can be called directly from cython without python overhead.
The code has functions which duplicate the behavior of scipy.spatial.distances.pdist and scipy.spatial.distances.cdist.
The code features a BallTree object which can quickly return nearest neighbors under any of the available metrics.
run bench.py for a comparison of runtimes between pyDistance and scipy.spatial for the available metrics. Times are comparable in most cases, much faster in a few cases, and slightly slower in a few cases.
Search TODO within distmetrics.pyx to see a list. One big one (which should be straightforward) is to make the distance metrics work with CSR matrices. This will involve writing an alternate version of each core distance function.