{
    "info": {
        "author": "Kay Zhu",
        "author_email": "me@kayzhu.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Software Development :: Libraries"
        ],
        "description": "======\nLSHash\n======\n\n:Version: 0.0.4dev\n\nA fast Python implementation of locality sensitive hashing with persistance\nsupport.\n\nHighlights\n==========\n\n- Fast hash calculation for large amount of high dimensional data through the use of `numpy` arrays.\n- Built-in support for persistency through Redis.\n- Multiple hash indexes support.\n- Built-in support for common distance/objective functions for ranking outputs.\n\nInstallation\n============\n``LSHash`` depends on the following libraries:\n\n- numpy\n- redis (if persistency through Redis is needed)\n- bitarray (if hamming distance is used as distance function)\n\nTo install:\n\n.. code-block:: bash\n\n    $ pip install lshash\n\nQuickstart\n==========\nTo create 6-bit hashes for input data of 8 dimensions:\n\n.. code-block:: python\n\n    >>> from lshash import LSHash\n\n    >>> lsh = LSHash(6, 8)\n    >>> lsh.index([1,2,3,4,5,6,7,8])\n    >>> lsh.index([2,3,4,5,6,7,8,9])\n    >>> lsh.index([10,12,99,1,5,31,2,3])\n    >>> lsh.query([1,2,3,4,5,6,7,7])\n    [((1, 2, 3, 4, 5, 6, 7, 8), 1.0),\n     ((2, 3, 4, 5, 6, 7, 8, 9), 11)]\n\n\nMain Interface\n==============\n\n- To initialize a ``LSHash`` instance:\n\n.. code-block:: python\n\n    LSHash(hash_size, input_dim, num_of_hashtables=1, storage=None, matrices_filename=None, overwrite=False)\n\nparameters:\n\n``hash_size``:\n    The length of the resulting binary hash.\n``input_dim``:\n    The dimension of the input vector.\n``num_hashtables = 1``:\n    (optional) The number of hash tables used for multiple lookups.\n``storage = None``:\n    (optional) Specify the name of the storage to be used for the index\n    storage. Options include \"redis\".\n``matrices_filename = None``:\n    (optional) Specify the path to the .npz file random matrices are stored\n    or to be stored if the file does not exist yet\n``overwrite = False``:\n    (optional) Whether to overwrite the matrices file if it already exist\n\n- To index a data point of a given ``LSHash`` instance, e.g., ``lsh``:\n\n.. code-block:: python\n\n    lsh.index(input_point, extra_data=None):\n\nparameters:\n\n``input_point``:\n    The input data point is an array or tuple of numbers of input_dim.\n``extra_data = None``:\n    (optional) Extra data to be added along with the input_point.\n\n- To query a data point against a given ``LSHash`` instance, e.g., ``lsh``:\n\n.. code-block:: python\n\n    lsh.query(query_point, num_results=None, distance_func=\"euclidean\"):\n\nparameters:\n\n``query_point``:\n    The query data point is an array or tuple of numbers of input_dim.\n``num_results = None``:\n    (optional) The number of query results to return in ranked order. By\n    default all results will be returned.\n``distance_func = \"euclidean\"``:\n    (optional) Distance function to use to rank the candidates. By default\n    euclidean distance function will be used.",
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        "name": "lshash3",
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        "summary": "A fast Python implementation of locality sensitive hashing with persistance (Redis) support.",
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