scikits.ann 0.2.dev-r800

The ANN module provides a numpy-compatible python wrapper around the Approximate Nearest Neighbor library.

The Approximate Nearest Neighbor library is available from http://www.cs.umd.edu/~mount/ANN/.

* Installation *
Download and build the Approximate Nearest Neighbor library. Modify the ANN section of site.cfg so that ANN_ROOT is the path to the root of the Approximate Nearest Neighbor library include/lib tree.
If /usr/local/include contains the ANN/ include directory and /usr/local/lib contains libANN.a, then
ANN_ROOT = /usr/local

Run ::

python setup.py build
python setup.py test
sudo python setup.py install

from within the source directory.

* Usage *
scikits.ann exposes a single class, kdtree that wraps the Approximate Nearest Neighbor library's kd-tree implementation. kdtree has a single (non-constructor) method, knn that finds the indecies (of the points used to construct the kdtree) of the k-nearest neighbors and the squared distances to those points. A little example will probably be much more enlightening::
>>> import scikits.ann as ann

>>> import numpy as np

>>> k=ann.kdtree(np.array([[0.,0],[1,0],[1.5,2]]))

>>> k.knn([0,.2],1)
(array([[0]]), array([[ 0.04]]))

>>> k.knn([0,.2],2)
(array([[0, 1]]), array([[ 0.04, 1.04]]))

>>> k.knn([[0,.2],[.1,2],[3,1],[0,0]],2)
(array([[0, 1],
[2, 0],
[2, 1],
[1, 2]]), array([[ 0.04, 1.04],
[ 1.96, 4.01],
[ 3.25, 5. ],
[ 1. , 6.25]]))

>>> k.knn([[0,.2],[.1,2],[3,1],[0,0]],3)
(array([[ 0, 1, 2],
[ 2, 0, 1],
[ 2, 1, 0],
[ 1, 2, -1]]), array([[ 4.00000000e-002, 1.04000000e+000, 5.49000000e+000],
[ 1.96000000e+000, 4.01000000e+000, 4.81000000e+000],
[ 3.25000000e+000, 5.00000000e+000, 1.00000000e+001],
[ 1.00000000e+000, 6.25000000e+000, 1.79769313e+308]]))