{ "info": { "author": "Giorgos Tzampanakis", "author_email": "giorgos.tzampanakis@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "License :: OSI Approved :: GNU General Public License (GPL)", "Operating System :: OS Independent", "Programming Language :: Python", "Topic :: Text Processing :: Indexing" ], "description": "FastSS is a string similarity searching algorithm. For each query, which\n\t\tconsists of query string *s* and distance *d*, it retrieves all the words in a\n\t\tdictionary whose edit distance from *s* is equal or smaller than *d*. FastSS is an\n\t\toffline searching algorithm, as it needs to create an index of its dictionary\n\t\tbefore it can search. For more information on FastSS you can visit the website\n\t\tof its inventors at .\n\n\t\tHere are some of the characteristics of this implementation, to help you decide\n\t\twhether it fits your purposes:\n\n\t\t* Very fast searching at the cost of a rather large index size.\n\t\t* Indices are plain sqlite databases so they carry ACID guarantees.\n\t\t* Only unicode strings are supported.\n\n\t\tExample of usage: ::\n\n\t\t\t>>> import fastss\n\t\t\t>>> manager = fastss.FastSSManager('idx')\n\t\t\t>>> manager.create_index(False)\n\t\t\t# Insert lemmas in the index, using 20% of their length as the depth:\n\t\t\t>>> manager.update_index([u'Mike', u'Johnny', u'johnny'], lambda s: int(len(s)*.2))\n\t\t\t# Search:\n\t\t\t>>> for match in manager.search('Mike', 2): print match\n\t\t\t(u'Mike', 0)\n\t\t\t>>> for match in manager.search('johnny', 1): print match\n\t\t\t(u'johnny', 0)\n\t\t\t(u'Johnny', 1)\n\t\t\t>>> for match in manager.search('johnny', 1, nocase=True): print 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