pyANN
=====

pyANN provides Python bindings for David Mount and Sunil Arya's ANN_ library for
approximate nearest neighbor searching.

.. _ANN:        http://www.cs.umd.edu/~mount/ANN/


Example
-------

.. parsed-literal::

    >>> import pyANN
    >>> kdtree = pyANN.KDTree([[1,1,1], [1,2,3], [4,5,6]])
    >>> kdtree.kSearch([0,0,0], 2, 0)
    ([0, 1], [3.0, 14.0])


Download
--------

The easiest way to obtain pyANN is to check it out from my Mercurial_
repository:

.. parsed-literal::

    hg clone http://hg.mrzv.org/pyANN

Alternatively, one can download the tarball_.

.. _Mercurial:  http://www.selenic.com/mercurial/wiki/
.. _tarball:    http://hg.mrzv.org/pyANN/archive/tip.tar.gz


Building and Installing
-----------------------

First of all, one needs to patch ANN_ to make it compile with more recent
versions of GCC_ and to make it build shared libraries under Linux. If you are
not under Linux and have an older version of GCC_ (below 4.3), you can safely
skip these steps.

.. _GCC:                    http://gcc.gnu.org/

Download the gcc43.patch_ and shared-libs.patch_. Within the directory where
you've extracted ANN_, run::

    patch -p1 < .../gcc43.patch   
    patch -p1 < .../shared-libs.patch
    make linux-g++-sl

(assuming you are on Linux).    

.. note::

    One can get a distribution of ANN_ with the above patches applied as well as
    other changes (e.g. reentrant `annkSearch()` method) from my repository:
        
        .. parsed-literal::
        
            hg clone http://hg.mrzv.org/ANN

.. _gcc43.patch:            http://aur.archlinux.org/packages/ann/ann/gcc43.patch
.. _shared-libs.patch:      http://aur.archlinux.org/packages/ann/ann/shared-libs.patch

Besides ANN_, pyANN **depends** on Boost.Python_. To compile and install the
bindings, if you have waf_ installed, run::

    waf configure
    waf build
    waf install

Otherwise, there is the original simple ``Makefile``, which you can tweak (e.g.,
adjust paths), and then ``make`` will compile the bindings. You will have to
manually put them somewhere on your ``PYTHONPATH``.

.. _Boost.Python:           http://www.boost.org/doc/libs/1_39_0/libs/python/doc/index.html
.. _waf:                    http://code.google.com/p/waf/


KDTree class
------------

The main content of pyANN is the class `KDTree`. The class is initialized with a
list of points, and provides three key methods:

    `__init__(lst)`
        Initialize `KDTree` with a list of points (each point is a list of
        coordinates).

    `kSearch(q,k,eps)`
        Find `k` nearest neighbors of the query point `q` with an error of `eps`.
        Returns a pair of lists: `(idxs, dists)`. The first is the list of
        indices of the nearest neighbors; the second is the list of squared 
        distances to the corresponding points from the query point.
        
    `kPriSearch(q,k,eps)`
        Same as above using priority search.

    `kFRSearch(q, sqRad, k, eps)`
        Fixed radius search. Find at most `k` nearest neighbors of the query point
        `q` within radius `sqRad`, with an allowed error of `eps`. Returns
        `(idx,dists,k)`; the first two are the same as above, and the last is
        the number of neighbors found.

    `__len__()`
        Returns number of points in the `KDTree`.

    `dim()`
        Dimensions of the point set.

Additional auxiliary (global) function mimicks ANN's functionality:

    `max_pts_visit(maxPts)`
        Maximum number of points to visit before terminating (will override
        larger values of `k` in the above search functions).