Metadata-Version: 1.2
Name: sympound
Version: 0.2.0
Summary: Python implementation of SymSpell Compound
Home-page: https://github.com/Esukhia/sympound-python
Author: Esukhia development team
Author-email: esukhiadev@gmail.com
License: MIT
Project-URL: Source, https://github.com/Esukhia/sympound-python
Project-URL: Tracker, https://github.com/Esukhia/sympound-python/issues
Description: sympound-python
        ===============
        
        This library is an implementation of the
        `SymSpellCompound <https://github.com/wolfgarbe/SymSpell>`__ algorithm
        in Python. It was initially forked from
        `rcourivaud/symspellcompound <https://github.com/rcourivaud/symspellcompound>`__
        although most of the code has been rewritten.
        
        Installation
        ============
        
        ::
        
           pip install sympound
        
        Documentation
        =============
        
        If you want a quick complete example, see `example.py <example.py>`__.
        
        Creating the sympound object
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        The first step is to create an ``sympound`` object, the constructor
        takes two main arguments: - ``distancefun`` is a function that will be
        used to compute the distance between two strings. It takes two arguments
        (the two strings to compare). You typically want to use a function
        computing the `Damerau-Levenshtein
        distance <https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance>`__,
        but you can get more creative and use keyboard distances. -
        ``maxDictionaryEditDistance`` is the maximum distance that will be
        pre-computed. Increasing this parameter will return more suggestions,
        but also make the memory print much larger
        
        adding dictionaries
        ~~~~~~~~~~~~~~~~~~~
        
        Then some dictionaries can be added through the ``load_dictionary``
        function, typically taking a file path as argument. The format of the
        dictionary is typically either a list of words (one per line), or a list
        of word and frequency (separated by a space). See
        `example-dict.txt <example-dict.txt>`__ for an example.
        
        You can also add entries directly with
        ``create_dictionary_entry(key, count)`` where ``key`` is the valid
        string and ``count`` the frequency associated with it. This is the
        advised method to use if your data is not in a simple format like the
        previously described dictionary.
        
        A lot of computations happen at this stage and adding a large number of
        entries can easily take more than one minute, so we provide two
        functions to save the analyzed ductionaries as a pickle: ``save_pickle``
        and ``load_pickle``, both taking a file path as argument. Note that the
        pickled is gzipped.
        
        Lookup
        ~~~~~~
        
        Once the dictionaries are loaded, you can get suggestions for a string
        by calling ``lookup_compound(str, edit_distance_max)``, where ``str`` is
        the string you want to analyze and ``edit_distance_max`` is the maximum
        distance you want suggestions for.
        
        The function returns a sorted list of ``SuggestItem``\ s, containing
        three fields: - ``term`` being the suggested fixed string - ``distance``
        being the distance with the original string - ``count`` being the
        frequency if given in the dictionary
        
        Copyright
        =========
        
        The code is Copyright Esukhia, 2018, and is distributed under the `MIT
        License <LICENSE>`__.
        
Keywords: spell check
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Text Processing :: Linguistic
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3
