Approximate port of scikit-learn neighbors using Numba.

If you want to install/modify (recommented at this point):

git clone https://github.com/jackd/numba-neighbors.git
pip install -e numba-neighbors

Quick-start:

pip install git+git://github.com/jackd/ifp-sample.git

You may see performance benefits from fastmath by installing Intel's short vector math library (SVML).

conda install -c numba icc_rt

Debugging is often simpler without jitting. To disable numba,

export NUMBA_DISABLE_JIT=1

and re-enable with

export NUMBA_DISABLE_JIT=0

Be wary of using os.environ["NUMBA_DISABLE_JIT"] = "1" from python code - this must be set above imports.

1. All operations are done using reduced distances. E.g. provided KDTree implementations use squared distances rather than actual distances both for inputs and outputs.
2. query_radius-like functions must specify a maximum number of neighbors. Over-estimating this is fairly cheap - it just means we allocate more data than necessary - but if the limit is reached the first max_count neighbors that are found are returned. These aren't necessarily the closest max_count neighbors.
3. Query outputs aren't sorted, though can be using binary_tree.simultaneous_sort_partial.
4. Use of Interl's short vector math library (SVML) if instaled. This makes computation faster, but may result in very small errors.

• n_nodes is inconsistent with scikit-learn implementation... - scikit bug?
• Port NeighborsHeap from scikit learn.
• query implementations.