Metadata-Version: 2.0
Name: flann
Version: 1.6.13
Summary: flann is the python 3.6 bindings for FLANN - Fast Library for Approximate Nearest Neighbors.
Home-page: https://github.com/pypa/flann
Author: Marius Muja & Gefu Tang, Maintainer: Russi Chatterjee
Author-email: root@ixaxaar.in
License: BSD
Keywords: Approximate Nearest Neighbors
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3
Provides-Extra: dev
Provides-Extra: test
Requires-Dist: numpy
Provides-Extra: dev
Requires-Dist: check-manifest; extra == 'dev'
Provides-Extra: test
Requires-Dist: coverage; extra == 'test'

pyflann
=======

1. Introduction
~~~~~~~~~~~~~~~

pyflann is the python bindings for `FLANN - Fast Library for Approximate
Nearest Neighbors <http://www.cs.ubc.ca/research/flann/>`__.

2. Install
~~~~~~~~~~

For python2
^^^^^^^^^^^

This package uses distutils, which is the default way of installing
python modules. To install in your home directory, securely run the
following:

::

    git clone https://github.com/primetang/pyflann.git
    cd pyflann
    [sudo] python setup.py install

Or directly through ``pip`` to install it:

::

    [sudo] pip install pyflann

For python3
^^^^^^^^^^^

Please refer to `this
issuse <https://github.com/primetang/pyflann/issues/1>`__ to modify the
code.

3. Usage
~~~~~~~~

Use it just like the following code:

.. code:: python

    from pyflann import *
    import numpy as np

    dataset = np.array(
        [[1., 1, 1, 2, 3],
         [10, 10, 10, 3, 2],
         [100, 100, 2, 30, 1]
         ])
    testset = np.array(
        [[1., 1, 1, 1, 1],
         [90, 90, 10, 10, 1]
         ])
    flann = FLANN()
    result, dists = flann.nn(
        dataset, testset, 2, algorithm="kmeans", branching=32, iterations=7, checks=16)
    print result
    print dists

    dataset = np.random.rand(10000, 128)
    testset = np.random.rand(1000, 128)
    flann = FLANN()
    result, dists = flann.nn(
        dataset, testset, 5, algorithm="kmeans", branching=32, iterations=7, checks=16)
    print result
    print dists


