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        "author": "Barry Wark",
        "author_email": "barrywark@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development :: Libraries :: Python Modules"
        ],
        "description": "The ANN module provides a numpy-compatible python wrapper around the \nApproximate Nearest Neighbor library (http://www.cs.umd.edu/~mount/ANN/).\n\n* Installation *\nDownload and build the Approximate Nearest Neighbor library. Modify the ANN section of \nsite.cfg so that ANN_ROOT is the path to the root of the Approximate Nearest Neighbor \nlibrary include/lib tree.\nIf /usr/local/include contains the ANN/ include directory and /usr/local/lib contains \nlibANN.a, then\nANN_ROOT = /usr/local\n\nRun ::\n\n    python setup.py build_ext --inplace build test\n    sudo python setup.py install\n\nfrom within the source directory.\n\n* Usage *\nscikits.ann exposes a single class, kdtree that wraps the Approximate Nearest Neighbor \nlibrary's kd-tree implementation. kdtree has a single (non-constructor) method, knn that \nfinds the indecies (of the points used to construct the kdtree) of the k-nearest neighbors\n and the squared distances to those points. A little example will probably be much \n more enlightening::\n    >>> import scikits.ann as ann\n        \n    >>> import numpy as np\n\n    >>> k=ann.kdtree(np.array([[0.,0],[1,0],[1.5,2]]))\n\n    >>> k.knn([0,.2],1)\n    (array([[0]]), array([[ 0.04]]))\n\n    >>> k.knn([0,.2],2)\n    (array([[0, 1]]), array([[ 0.04,  1.04]]))\n\n    >>> k.knn([[0,.2],[.1,2],[3,1],[0,0]],2)\n    (array([[0, 1],\n           [2, 0],\n           [2, 1],\n           [1, 2]]), array([[ 0.04,  1.04],\n           [ 1.96,  4.01],\n           [ 3.25,  5.  ],\n           [ 1.  ,  6.25]]))\n\n    >>> k.knn([[0,.2],[.1,2],[3,1],[0,0]],3)\n    (array([[ 0,  1,  2],\n           [ 2,  0,  1],\n           [ 2,  1,  0],\n           [ 1,  2, -1]]), array([[  4.00000000e-002,   1.04000000e+000,   5.49000000e+000],\n           [  1.96000000e+000,   4.01000000e+000,   4.81000000e+000],\n           [  3.25000000e+000,   5.00000000e+000,   1.00000000e+001],\n           [  1.00000000e+000,   6.25000000e+000,   1.79769313e+308]]))",
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        "keywords": null,
        "license": "GNU Library or Lesser General Public License (LGPL)",
        "maintainer": null,
        "maintainer_email": null,
        "name": "scikits.ann",
        "package_url": "https://pypi.org/project/scikits.ann/",
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