Metadata-Version: 1.1
Name: scikits.ann
Version: 0.2.dev-r803
Summary: Approximate Nearest Neighbor library wrapper for Numpy
Home-page: http://scipy.org/scipy/scikits/wiki/AnnWrapper
Author: Barry Wark
Author-email: barrywark@gmail.com
License: GNU Library or Lesser General Public License (LGPL)
Description: 
        The ANN module provides a numpy-compatible python wrapper around the 
        Approximate Nearest Neighbor library (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_ext --inplace build 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]]))
            
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires: numpy
