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    "info": {
        "author": "Marius Muja & Gefu Tang, Maintainer: Russi Chatterjee",
        "author_email": "root@ixaxaar.in",
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        "classifiers": [
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6"
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        "description": "pyflann\n=======\n\n1. Introduction\n~~~~~~~~~~~~~~~\n\npyflann is the python bindings for `FLANN - Fast Library for Approximate\nNearest Neighbors <http://www.cs.ubc.ca/research/flann/>`__.\n\n2. Install\n~~~~~~~~~~\n\nFor python2\n^^^^^^^^^^^\n\nThis package uses distutils, which is the default way of installing\npython modules. To install in your home directory, securely run the\nfollowing:\n\n::\n\n    git clone https://github.com/primetang/pyflann.git\n    cd pyflann\n    [sudo] python setup.py install\n\nOr directly through ``pip`` to install it:\n\n::\n\n    [sudo] pip install pyflann\n\nFor python3\n^^^^^^^^^^^\n\nPlease refer to `this\nissuse <https://github.com/primetang/pyflann/issues/1>`__ to modify the\ncode.\n\n3. Usage\n~~~~~~~~\n\nUse it just like the following code:\n\n.. code:: python\n\n    from pyflann import *\n    import numpy as np\n\n    dataset = np.array(\n        [[1., 1, 1, 2, 3],\n         [10, 10, 10, 3, 2],\n         [100, 100, 2, 30, 1]\n         ])\n    testset = np.array(\n        [[1., 1, 1, 1, 1],\n         [90, 90, 10, 10, 1]\n         ])\n    flann = FLANN()\n    result, dists = flann.nn(\n        dataset, testset, 2, algorithm=\"kmeans\", branching=32, iterations=7, checks=16)\n    print result\n    print dists\n\n    dataset = np.random.rand(10000, 128)\n    testset = np.random.rand(1000, 128)\n    flann = FLANN()\n    result, dists = flann.nn(\n        dataset, testset, 5, algorithm=\"kmeans\", branching=32, iterations=7, checks=16)\n    print result\n    print dists\n\n\n",
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        "keywords": "Approximate Nearest Neighbors",
        "license": "BSD",
        "maintainer": "",
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        "name": "flann",
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