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        "author": "Sebastian Bank",
        "author_email": "sebastian.bank@uni-leipzig.de",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Scientific/Engineering :: Visualization"
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        "description": "Concepts\r\n========\r\n\r\n|PyPI version| |License| |Supported Python| |Format| |Docs|\r\n\r\n|Travis| |Codecov|\r\n\r\nConcepts is a simple Python implementation of **Formal Concept Analysis**\r\n(FCA_).\r\n\r\nFCA provides a mathematical model for describing a set of **objects** (e.g. King\r\nArthur, Sir Robin, and the holy grail) with a set of **properties** (e.g. human,\r\nknight, king, and mysterious) which each of the objects either has or not. A\r\ntable called *formal context* defines which objects have a given property and\r\nvice versa which properties a given object has.\r\n\r\n\r\nLinks\r\n-----\r\n\r\n- GitHub: https://github.com/xflr6/concepts\r\n- PyPI: https://pypi.org/project/concepts/\r\n- Documentation: https://concepts.readthedocs.io\r\n- Changelog: https://concepts.readthedocs.io/en/latest/changelog.html\r\n- Issue Tracker: https://github.com/xflr6/concepts/issues\r\n- Download: https://pypi.org/project/concepts/#files\r\n\r\n\r\nInstallation\r\n------------\r\n\r\nThis package runs under Python 2.7 and 3.5+, use pip_ to install:\r\n\r\n.. code:: bash\r\n\r\n    $ pip install concepts\r\n\r\nThis will also install the bitsets_ and graphviz_ packages from PyPI as\r\nrequired dependencies.\r\n\r\nRendering lattice graphs depends on the `Graphviz software`_. Make sure its\r\n``dot`` executable is on your systems' path.\r\n\r\n\r\nQuickstart\r\n----------\r\n\r\nCreate a **formal context** defining which object has which property, e.g. from\r\na simple ASCII-art style cross-table with *object* rows and *property* columns\r\n(alternatively load a CXT or CSV file):\r\n\r\n.. code:: python\r\n\r\n    >>> from concepts import Context\r\n\r\n    >>> c = Context.fromstring('''\r\n    ...            |human|knight|king |mysterious|\r\n    ... King Arthur|  X  |  X   |  X  |          |\r\n    ... Sir Robin  |  X  |  X   |     |          |\r\n    ... holy grail |     |      |     |     X    |\r\n    ... ''')\r\n    >>> c  # doctest: +ELLIPSIS\r\n    <Context object mapping 3 objects to 4 properties [dae7402a] at 0x...>\r\n\r\n\r\nQuery **common properties** of objects or **common objects** of properties\r\n(*derivation*):\r\n\r\n.. code:: python\r\n\r\n    >>> c.intension(['King Arthur', 'Sir Robin'])\r\n    ('human', 'knight')\r\n\r\n    >>> c.extension(['knight', 'mysterious'])\r\n    ()\r\n\r\nGet the closest matching **objects-properties pair** of objects or properties\r\n(*formal concepts*):\r\n\r\n.. code:: python\r\n\r\n    >>> c['Sir Robin', 'holy grail']\r\n    (('King Arthur', 'Sir Robin', 'holy grail'), ())\r\n\r\n    >>> c['king',]\r\n    (('King Arthur',), ('human', 'knight', 'king'))\r\n\r\nIterate over the **concept lattice** of all objects-properties pairs:\r\n\r\n.. code:: python\r\n\r\n    >>> for extent, intent in c.lattice:\r\n    ...     print('%r %r' % (extent, intent))\r\n    () ('human', 'knight', 'king', 'mysterious')\r\n    ('King Arthur',) ('human', 'knight', 'king')\r\n    ('holy grail',) ('mysterious',)\r\n    ('King Arthur', 'Sir Robin') ('human', 'knight')\r\n    ('King Arthur', 'Sir Robin', 'holy grail') ()\r\n\r\nMake a Graphviz visualization of the lattice (use ``.graphviz(view=True)`` to\r\ndirectly render it and display the resulting PDF):\r\n\r\n.. code:: python\r\n\r\n    >>> c.lattice.graphviz()  # doctest: +ELLIPSIS\r\n    <graphviz.dot.Digraph object at 0x...>\r\n\r\n.. image:: https://raw.github.com/xflr6/concepts/master/docs/holy-grail.png\r\n    :align: center\r\n\r\n\r\nFurther reading\r\n---------------\r\n\r\n- https://en.wikipedia.org/wiki/Formal_concept_analysis\r\n- http://www.upriss.org.uk/fca/\r\n\r\nThe generation of the concept lattice is based on the algorithm from C. Lindig.\r\n`Fast Concept Analysis`_. In Gerhard Stumme, editors, Working with Conceptual\r\nStructures - Contributions to ICCS 2000, Shaker Verlag, Aachen, Germany, 2000.\r\n\r\nThe included example ``CXT`` files are taken from Uta Priss' `FCA homepage`_\r\n\r\n\r\nSee also\r\n--------\r\n\r\nThe implementation is based on these Python packages:\r\n\r\n- bitsets_ |--| Ordered subsets over a predefined domain\r\n- graphviz_ |--| Simple Python interface for Graphviz\r\n\r\nThe following package is build on top of concepts:\r\n\r\n- features_ |--| Feature set algebra for linguistics\r\n\r\nIf you want to apply FCA to bigger data sets, you might want to consider `other\r\nimplementations`_ based on `more sophisticated algorithms`_ like In-Close_\r\nor Fcbo_.\r\n\r\n\r\nLicense\r\n-------\r\n\r\nConcepts is distributed under the `MIT license`_.\r\n\r\n\r\n.. _FCA: https://en.wikipedia.org/wiki/Formal_concept_analysis\r\n.. _Fast Concept Analysis: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.948\r\n.. _FCA homepage: http://www.upriss.org.uk/fca/examples.html\r\n\r\n.. _pip: https://pip.readthedocs.io\r\n.. _Graphviz software: http://www.graphviz.org\r\n\r\n.. _bitsets: https://pypi.org/project/bitsets/\r\n.. _graphviz: https://pypi.org/project/graphviz/\r\n.. _features: https://pypi.org/project/features/\r\n\r\n.. _other implementations: http://www.upriss.org.uk/fca/fcasoftware.html\r\n.. _more sophisticated algorithms: http://www.upriss.org.uk/fca/fcaalgorithms.html\r\n.. _In-Close: https://sourceforge.net/projects/inclose/\r\n.. _Fcbo: http://fcalgs.sourceforge.net\r\n\r\n.. _MIT license: https://opensource.org/licenses/MIT\r\n\r\n\r\n.. |--| unicode:: U+2013\r\n\r\n\r\n.. |PyPI version| image:: https://img.shields.io/pypi/v/concepts.svg\r\n    :target: https://pypi.org/project/concepts/\r\n    :alt: Latest PyPI Version\r\n.. |License| image:: https://img.shields.io/pypi/l/concepts.svg\r\n    :target: https://pypi.org/project/concepts/\r\n    :alt: License\r\n.. |Supported Python| image:: https://img.shields.io/pypi/pyversions/concepts.svg\r\n    :target: https://pypi.org/project/concepts/\r\n    :alt: Supported Python Versions\r\n.. |Format| image:: https://img.shields.io/pypi/format/concepts.svg\r\n    :target: https://pypi.org/project/concepts/\r\n    :alt: Format\r\n.. |Docs| image:: https://readthedocs.org/projects/concepts/badge/?version=stable\r\n    :target: https://concepts.readthedocs.io/en/stable/\r\n    :alt: Readthedocs\r\n.. |Travis| image:: https://img.shields.io/travis/xflr6/concepts.svg\r\n    :target: https://travis-ci.org/xflr6/concepts\r\n    :alt: Travis\r\n.. |Codecov| image:: https://codecov.io/gh/xflr6/concepts/branch/master/graph/badge.svg\r\n    :target: https://codecov.io/gh/xflr6/concepts\r\n    :alt: Codecov\r\n\r\n\r\n",
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