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        "author": "Florian Scholz",
        "author_email": "livint@posteo.de",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
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        "description": "Install\n-------\n\nMyNLQ\u2019s source code hosted on `GitHub <https://github.com/livinter/MyNQL>`_.\n\n.. code-block:: bash\n\n   git clone https://github.com/livinter/MyNQL.git\n   python setup.py install\n\nor just\n\n.. code-block:: bash\n\n   pip install MyNQL\n\nTeach the Network\n-----------------\n\nFor example if a customer make a purchase of a product you assume a relation between ``customer.id`` and ``product.id``,\nso you connect them. Optional you can specify a distance between nodes, to represent how close the nodes are related.\n\n* ``connect`` - connect two nodes\n* ``delete`` - delete a connection\n\nNodes are created automatically when you do the connection, and removed if they do not have any more connections. So do not worry about them.\n\n\nAsk the Network\n---------------\n\nNow you can query all kinds of relations, not only the once you taught. With select you specify a starting point, like\n``customer.id`` and specify the category where you like to know its closes relation.\n\n* ``select`` - gives you the best related nodes from a specified category\n\nThe searching query takes into account all the different routes up to a radius you specify.\n\nExample\n-------\n\n\nLets imagine we already have a table *customer*\n\n\n.. list-table::\n   :header-rows: 1\n\n   * - Id\n     - Name\n     - ..\n   * - 101\n     - jose\n     - ...\n   * - 102\n     - maria\n     - ...\n   * - 103\n     - juan\n     - ...\n\nand you want to teach the network about recent purchases.\n\n.. code-block:: python\n\n   from MyNQL import MyNQL\n   mynql = MyNQL('store')\n\n   mynql.connect('customer.juan', 'product.jeans')\n   mynql.connect('customer.juan',  'product.socks')\n   mynql.connect('customer.maria', 'product.socks')\n\nIf the column ``Name`` is unique you can use it as a key, otherwise you would need column ``Id``\\ , and your code would look like this:\n\n.. code-block:: python\n\n   mynql.connect(\"customer.103', 'product.12')\n\nNow you can ask questions from other points of view. You always specify a starting point, and the category where you want to know the best matches:\n\n.. code-block:: python\n\n   >>> mynql.select('customer.maria', 'product')\n   ['socks', 'jeans']\n\nMaria is more connected to ``socks``, as she has a direct connection, but also a bit to ``jeans`` as there exist an indirect connection through Juan.\n\n.. code-block:: python\n\n   >>> mynql.select('product.jeans', 'product')\n   ['socks']\n\nAny combination is valid. For example you can ask about how one product is related to other. \n\n\nBackend\n-------\n\nStorage is done in memory, but if you want to use MySQL, SQLite or PostgreSQL as a backend take a look at ``test/pee_example.py``.\nThis will keep a copy of all updates in your database.",
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