{
    "info": {
        "author": "orsinium",
        "author_email": "master_fess@mail.ru",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Environment :: Plugins",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering :: Human Machine Interfaces"
        ],
        "description": "TextDistance\n============\n\n.. figure:: logo.png\n   :alt: TextDistance logo\n\n   TextDistance logo\n\n|Build Status| |PyPI version| |Status| |Code size| |License|\n\n**TextDistance** -- python library for comparing distance between two or\nmore sequences by many algorithms.\n\nFeatures:\n\n-  30+ algorithms\n-  Pure python implementation\n-  Simple usage\n-  More than two sequences comparing\n-  Some algorithms have more than one implementation in one class.\n-  Optional numpy usage for maximum speed.\n\nAlgorithms\n----------\n\nEdit based\n~~~~~~~~~~\n\n+------------------------------------------------------------------------------------------------+--------------------------+------------------------------+\n| Algorithm                                                                                      | Class                    | Functions                    |\n+================================================================================================+==========================+==============================+\n| `Hamming <https://en.wikipedia.org/wiki/Hamming_distance>`__                                   | ``Hamming``              | ``hamming``                  |\n+------------------------------------------------------------------------------------------------+--------------------------+------------------------------+\n| `MLIPNS <http://www.sial.iias.spb.su/files/386-386-1-PB.pdf>`__                                | ``Mlipns``               | ``mlipns``                   |\n+------------------------------------------------------------------------------------------------+--------------------------+------------------------------+\n| `Levenshtein <https://en.wikipedia.org/wiki/Levenshtein_distance>`__                           | ``Levenshtein``          | ``levenshtein``              |\n+------------------------------------------------------------------------------------------------+--------------------------+------------------------------+\n| `Damerau-Levenshtein <https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance>`__   | ``DamerauLevenshtein``   | ``damerau_levenshtein``      |\n+------------------------------------------------------------------------------------------------+--------------------------+------------------------------+\n| `Jaro-Winkler <https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance>`__                 | ``JaroWinkler``          | ``jaro_winkler``, ``jaro``   |\n+------------------------------------------------------------------------------------------------+--------------------------+------------------------------+\n| `Strcmp95 <http://cpansearch.perl.org/src/SCW/Text-JaroWinkler-0.1/strcmp95.c>`__              | ``StrCmp95``             | ``strcmp95``                 |\n+------------------------------------------------------------------------------------------------+--------------------------+------------------------------+\n| `Needleman-Wunsch <https://en.wikipedia.org/wiki/Needleman%E2%80%93Wunsch_algorithm>`__        | ``NeedlemanWunsch``      | ``needleman_wunsch``         |\n+------------------------------------------------------------------------------------------------+--------------------------+------------------------------+\n| `Gotoh <https://www.cs.umd.edu/class/spring2003/cmsc838t/papers/gotoh1982.pdf>`__              | ``Gotoh``                | ``gotoh``                    |\n+------------------------------------------------------------------------------------------------+--------------------------+------------------------------+\n| `Smith-Waterman <https://en.wikipedia.org/wiki/Smith%E2%80%93Waterman_algorithm>`__            | ``SmithWaterman``        | ``smith_waterman``           |\n+------------------------------------------------------------------------------------------------+--------------------------+------------------------------+\n\nToken based\n~~~~~~~~~~~\n\n+---------------------------------------------------------------------------------------------------------------------------+------------------+---------------------------------------------+\n| Algorithm                                                                                                                 | Class            | Functions                                   |\n+===========================================================================================================================+==================+=============================================+\n| `Jaccard index <https://en.wikipedia.org/wiki/Jaccard_index>`__                                                           | ``Jaccard``      | ``jaccard``                                 |\n+---------------------------------------------------------------------------------------------------------------------------+------------------+---------------------------------------------+\n| `S\u00f8rensen\u2013Dice coefficient <https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient>`__                      | ``Sorensen``     | ``sorensen``, ``sorensen_dice``, ``dice``   |\n+---------------------------------------------------------------------------------------------------------------------------+------------------+---------------------------------------------+\n| `Tversky index <https://en.wikipedia.org/wiki/Tversky_index>`__                                                           | ``Tversky``      | ``tversky``                                 |\n+---------------------------------------------------------------------------------------------------------------------------+------------------+---------------------------------------------+\n| `Overlap coefficient <https://en.wikipedia.org/wiki/Overlap_coefficient>`__                                               | ``Overlap``      | ``overlap``                                 |\n+---------------------------------------------------------------------------------------------------------------------------+------------------+---------------------------------------------+\n| `Tanimoto distance <https://en.wikipedia.org/wiki/Jaccard_index#Tanimoto_similarity_and_distance>`__                      | ``Tanimoto``     | ``tanimoto``                                |\n+---------------------------------------------------------------------------------------------------------------------------+------------------+---------------------------------------------+\n| `Cosine similarity <https://en.wikipedia.org/wiki/Cosine_similarity>`__                                                   | ``Cosine``       | ``cosine``                                  |\n+---------------------------------------------------------------------------------------------------------------------------+------------------+---------------------------------------------+\n| `Monge-Elkan <https://www.academia.edu/200314/Generalized_Monge-Elkan_Method_for_Approximate_Text_String_Comparison>`__   | ``MongeElkan``   | ``monge_elkan``                             |\n+---------------------------------------------------------------------------------------------------------------------------+------------------+---------------------------------------------+\n| `Bag distance <https://github.com/Yomguithereal/talisman/blob/master/src/metrics/distance/bag.js>`__                      | ``Bag``          | ``bag``                                     |\n+---------------------------------------------------------------------------------------------------------------------------+------------------+---------------------------------------------+\n\nSequence based\n~~~~~~~~~~~~~~\n\n+-----------------------------------------------------------------------------------------------------------------------------------------------+-------------------------+--------------------------+\n| Algorithm                                                                                                                                     | Class                   | Functions                |\n+===============================================================================================================================================+=========================+==========================+\n| `longest common subsequence similarity <https://en.wikipedia.org/wiki/Longest_common_subsequence_problem>`__                                  | ``LCSSeq``              | ``lcsseq``               |\n+-----------------------------------------------------------------------------------------------------------------------------------------------+-------------------------+--------------------------+\n| `longest common substring similarity <https://docs.python.org/2/library/difflib.html#difflib.SequenceMatcher>`__                              | ``LCSStr``              | ``lcsstr``               |\n+-----------------------------------------------------------------------------------------------------------------------------------------------+-------------------------+--------------------------+\n| `Ratcliff-Obershelp similarity <http://collaboration.cmc.ec.gc.ca/science/rpn/biblio/ddj/Website/articles/DDJ/1988/8807/8807c/8807c.htm>`__   | ``RatcliffObershelp``   | ``ratcliff_obershelp``   |\n+-----------------------------------------------------------------------------------------------------------------------------------------------+-------------------------+--------------------------+\n\nCompression based\n~~~~~~~~~~~~~~~~~\n\n`Normalized compression\ndistance <https://en.wikipedia.org/wiki/Normalized_compression_distance#Normalized_compression_distance>`__\nwith different compression algorithms.\n\nClassic compression algorithms:\n\n+---------------------------------------------------------------------------------+-----------------+------------------+\n| Algorithm                                                                       | Class           | Function         |\n+=================================================================================+=================+==================+\n| `Arithmetic coding <https://en.wikipedia.org/wiki/Arithmetic_coding>`__         | ``ArithNCD``    | ``arith_ncd``    |\n+---------------------------------------------------------------------------------+-----------------+------------------+\n| `RLE <https://en.wikipedia.org/wiki/Run-length_encoding>`__                     | ``RLENCD``      | ``rle_ncd``      |\n+---------------------------------------------------------------------------------+-----------------+------------------+\n| `BWT RLE <https://en.wikipedia.org/wiki/Burrows%E2%80%93Wheeler_transform>`__   | ``BWTRLENCD``   | ``bwtrle_ncd``   |\n+---------------------------------------------------------------------------------+-----------------+------------------+\n\nNormal compression algorithms:\n\n+----------------------------------------------------------------------------+------------------+-------------------+\n| Algorithm                                                                  | Class            | Function          |\n+============================================================================+==================+===================+\n| Square Root                                                                | ``SqrtNCD``      | ``sqrt_ncd``      |\n+----------------------------------------------------------------------------+------------------+-------------------+\n| `Entropy <https://en.wikipedia.org/wiki/Entropy_(information_theory)>`__   | ``EntropyNCD``   | ``entropy_ncd``   |\n+----------------------------------------------------------------------------+------------------+-------------------+\n\nWork in progress algorithms that compare two strings as array of bits:\n\n+-------------------------------------------------+---------------+----------------+\n| Algorithm                                       | Class         | Function       |\n+=================================================+===============+================+\n| `BZ2 <https://en.wikipedia.org/wiki/Bzip2>`__   | ``BZ2NCD``    | ``bz2_ncd``    |\n+-------------------------------------------------+---------------+----------------+\n| `LZMA <https://en.wikipedia.org/wiki/LZMA>`__   | ``LZMANCD``   | ``lzma_ncd``   |\n+-------------------------------------------------+---------------+----------------+\n| `ZLib <https://en.wikipedia.org/wiki/Zlib>`__   | ``ZLIBNCD``   | ``zlib_ncd``   |\n+-------------------------------------------------+---------------+----------------+\n\nSee `blog post <https://articles.life4web.ru/eng/ncd/>`__ for more\ndetails about NCD.\n\nPhonetic\n~~~~~~~~\n\n+-----------------------------------------------------------------------------------+--------------+--------------+\n| Algorithm                                                                         | Class        | Functions    |\n+===================================================================================+==============+==============+\n| `MRA <https://en.wikipedia.org/wiki/Match_rating_approach>`__                     | ``MRA``      | ``mra``      |\n+-----------------------------------------------------------------------------------+--------------+--------------+\n| `Editex <https://anhaidgroup.github.io/py_stringmatching/v0.3.x/Editex.html>`__   | ``Editex``   | ``editex``   |\n+-----------------------------------------------------------------------------------+--------------+--------------+\n\nSimple\n~~~~~~\n\n+-----------------------+----------------+----------------+\n| Algorithm             | Class          | Functions      |\n+=======================+================+================+\n| Prefix similarity     | ``Prefix``     | ``prefix``     |\n+-----------------------+----------------+----------------+\n| Postfix similarity    | ``Postfix``    | ``postfix``    |\n+-----------------------+----------------+----------------+\n| Length distance       | ``Length``     | ``length``     |\n+-----------------------+----------------+----------------+\n| Identity similarity   | ``Identity``   | ``identity``   |\n+-----------------------+----------------+----------------+\n| Matrix similarity     | ``Matrix``     | ``matrix``     |\n+-----------------------+----------------+----------------+\n\nInstallation\n------------\n\nStable\n~~~~~~\n\nOnly pure python implementation:\n\n.. code:: bash\n\n    pip install textdistance\n\nWith extra libraries for maximum speed:\n\n.. code:: bash\n\n    pip install \"textdistance[extras]\"\n\nWith all libraries (required for `benchmarking <#benchmarks>`__ and\n`testing <#test>`__):\n\n.. code:: bash\n\n    pip install \"textdistance[benchmark]\"\n\nWith algorithm specific extras:\n\n.. code:: bash\n\n    pip install \"textdistance[Hamming]\"\n\nAlgorithms with available extras: ``DamerauLevenshtein``, ``Hamming``,\n``Jaro``, ``JaroWinkler``, ``Levenshtein``.\n\nDev\n~~~\n\nVia pip:\n\n.. code:: bash\n\n    pip install -e git+https://github.com/orsinium/textdistance.git#egg=textdistance\n\nOr clone repo and install with some extras:\n\n.. code:: bash\n\n    git clone https://github.com/orsinium/textdistance.git\n    pip install -e \".[benchmark]\"\n\nUsage\n-----\n\nAll algorithms have 2 interfaces:\n\n1. Class with algorithm-specific params for customizing.\n2. Class instance with default params for quick and simple usage.\n\nAll algorithms have some common methods:\n\n1. ``.distance(*sequences)`` -- calculate distance between sequences.\n2. ``.similarity(*sequences)`` -- calculate similarity for sequences.\n3. ``.maximum(*sequences)`` -- maximum possible value for distance and\n   similarity. For any sequence: ``distance + similarity == maximum``.\n4. ``.normalized_distance(*sequences)`` -- normalized distance between\n   sequences. The return value is a float between 0 and 1, where 0 means\n   equal, and 1 totally different.\n5. ``.normalized_similarity(*sequences)`` -- normalized similarity for\n   sequences. The return value is a float between 0 and 1, where 0 means\n   totally different, and 1 equal.\n\nMost common init arguments:\n\n1. ``qval`` -- q-value for split sequences into q-grams. Possible\n   values:\n\n   -  1 (default) -- compare sequences by chars.\n   -  2 or more -- transform sequences to q-grams.\n   -  None -- split sequences by words.\n\n2. ``as_set`` -- for token-based algorithms:\n\n   -  True -- ``t`` and ``ttt`` is equal.\n   -  False (default) -- ``t`` and ``ttt`` is different.\n\nExample\n-------\n\nFor example, `Hamming\ndistance <https://en.wikipedia.org/wiki/Hamming_distance>`__:\n\n.. code:: python\n\n    import textdistance\n\n    textdistance.hamming('test', 'text')\n    # 1\n\n    textdistance.hamming.distance('test', 'text')\n    # 1\n\n    textdistance.hamming.similarity('test', 'text')\n    # 3\n\n    textdistance.hamming.normalized_distance('test', 'text')\n    # 0.25\n\n    textdistance.hamming.normalized_similarity('test', 'text')\n    # 0.75\n\n    textdistance.Hamming(qval=2).distance('test', 'text')\n    # 2\n\nAny other algorithms have same interface.\n\nExtra libraries\n---------------\n\nFor main algorithms textdistance try to call known external libraries\n(fastest first) if available (installed in your system) and possible\n(this implementation can compare this type of sequences).\n`Install <#installation>`__ textdistance with extras for this feature.\n\nYou can disable this by passing ``external=False`` argument on init:\n\n.. code:: python3\n\n    import textdistance\n    hamming = textdistance.Hamming(external=False)\n    hamming('text', 'testit')\n    # 3\n\nSupported libraries:\n\n1. `abydos <https://github.com/chrislit/abydos>`__\n2. `Distance <https://github.com/doukremt/distance>`__\n3. `jellyfish <https://github.com/jamesturk/jellyfish>`__\n4. `py\\_stringmatching <https://github.com/anhaidgroup/py_stringmatching>`__\n5. `pylev <https://github.com/toastdriven/pylev>`__\n6. `python-Levenshtein <https://github.com/ztane/python-Levenshtein>`__\n7. `pyxDamerauLevenshtein <https://github.com/gfairchild/pyxDamerauLevenshtein>`__\n\nAlgorithms:\n\n1. DamerauLevenshtein\n2. Hamming\n3. Jaro\n4. JaroWinkler\n5. Levenshtein\n\nBenchmarks\n----------\n\nWithout extras installation:\n\n+--------------+------------+-------------+---------+\n| algorithm    | library    | function    | time    |\n+==============+============+=============+=========+\n| DamerauLeven | jellyfish  | damerau\\_le | 0.00965 |\n| shtein       |            | venshtein\\_ | 294     |\n|              |            | distance    |         |\n+--------------+------------+-------------+---------+\n| DamerauLeven | pyxdamerau | damerau\\_le | 0.15137 |\n| shtein       | levenshtei | venshtein\\_ | 8       |\n|              | n          | distance    |         |\n+--------------+------------+-------------+---------+\n| DamerauLeven | pylev      | damerau\\_le | 0.76646 |\n| shtein       |            | venshtein   | 1       |\n+--------------+------------+-------------+---------+\n| DamerauLeven | **textdist | DamerauLeve | 4.13463 |\n| shtein       | ance**     | nshtein     |         |\n+--------------+------------+-------------+---------+\n| DamerauLeven | abydos     | damerau\\_le | 4.3831  |\n| shtein       |            | venshtein   |         |\n+--------------+------------+-------------+---------+\n| Hamming      | Levenshtei | hamming     | 0.00144 |\n|              | n          |             | 28      |\n+--------------+------------+-------------+---------+\n| Hamming      | jellyfish  | hamming\\_di | 0.00240 |\n|              |            | stance      | 262     |\n+--------------+------------+-------------+---------+\n| Hamming      | distance   | hamming     | 0.03625 |\n|              |            |             | 3       |\n+--------------+------------+-------------+---------+\n| Hamming      | abydos     | hamming     | 0.03839 |\n|              |            |             | 33      |\n+--------------+------------+-------------+---------+\n| Hamming      | **textdist | Hamming     | 0.17678 |\n|              | ance**     |             | 1       |\n+--------------+------------+-------------+---------+\n| Jaro         | Levenshtei | jaro        | 0.00313 |\n|              | n          |             | 561     |\n+--------------+------------+-------------+---------+\n| Jaro         | jellyfish  | jaro\\_dista | 0.00518 |\n|              |            | nce         | 85      |\n+--------------+------------+-------------+---------+\n| Jaro         | py\\_string | jaro        | 0.18062 |\n|              | matching   |             | 8       |\n+--------------+------------+-------------+---------+\n| Jaro         | **textdist | Jaro        | 0.27891 |\n|              | ance**     |             | 7       |\n+--------------+------------+-------------+---------+\n| JaroWinkler  | Levenshtei | jaro\\_winkl | 0.00319 |\n|              | n          | er          | 735     |\n+--------------+------------+-------------+---------+\n| JaroWinkler  | jellyfish  | jaro\\_winkl | 0.00540 |\n|              |            | er          | 443     |\n+--------------+------------+-------------+---------+\n| JaroWinkler  | **textdist | JaroWinkler | 0.28962 |\n|              | ance**     |             | 6       |\n+--------------+------------+-------------+---------+\n| Levenshtein  | Levenshtei | distance    | 0.00414 |\n|              | n          |             | 404     |\n+--------------+------------+-------------+---------+\n| Levenshtein  | jellyfish  | levenshtein | 0.00601 |\n|              |            | \\_distance  | 647     |\n+--------------+------------+-------------+---------+\n| Levenshtein  | py\\_string | levenshtein | 0.25290 |\n|              | matching   |             | 1       |\n+--------------+------------+-------------+---------+\n| Levenshtein  | pylev      | levenshtein | 0.56918 |\n|              |            |             | 2       |\n+--------------+------------+-------------+---------+\n| Levenshtein  | distance   | levenshtein | 1.15726 |\n+--------------+------------+-------------+---------+\n| Levenshtein  | abydos     | levenshtein | 3.68451 |\n+--------------+------------+-------------+---------+\n| Levenshtein  | **textdist | Levenshtein | 8.63674 |\n|              | ance**     |             |         |\n+--------------+------------+-------------+---------+\n\nTotal: 24 libs.\n\nYeah, so slow. Use TextDistance on production only with extras.\n\nTextdistance use benchmark's results for algorithm's optimization and\ntry to call fastest external lib first (if possible).\n\nYou can run benchmark manually on your system:\n\n.. code:: bash\n\n    pip install textdistance[benchmark]\n    python3 -m textdistance.benchmark\n\nTextDistance show benchmarks results table for your system and save\nlibraries priorities into ``libraries.json`` file in TextDistance's\nfolder. This file will be used by textdistance for calling fastest\nalgorithm implementation. Default\n`libraries.json <textdistance/libraries.json>`__ already included in\npackage.\n\nTest\n----\n\nYou can run tests via `tox <https://tox.readthedocs.io/en/latest/>`__:\n\n.. code:: bash\n\n    sudo pip3 install tox\n    tox\n\n.. |Build Status| image:: https://travis-ci.org/orsinium/textdistance.svg?branch=master\n   :target: https://travis-ci.org/orsinium/textdistance\n.. |PyPI version| image:: https://img.shields.io/pypi/v/textdistance.svg\n   :target: https://pypi.python.org/pypi/textdistance\n.. |Status| image:: https://img.shields.io/pypi/status/textdistance.svg\n   :target: https://pypi.python.org/pypi/textdistance\n.. |Code size| image:: https://img.shields.io/github/languages/code-size/orsinium/textdistance.svg\n   :target: https://github.com/orsinium/textdistance\n.. |License| image:: https://img.shields.io/pypi/l/textdistance.svg\n   :target: LICENSE\n\n\n",
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