Fast implementation of the edit distance(Levenshtein distance)
Fast implementation of the edit distance(Levenshtein distance).
This library simply implements Levenshtein distance with C++ and Cython.
The algorithm used in this library is proposed by Heikki Hyyrö, “Explaining and extending the bit-parallel approximate string matching algorithm of Myers”, (2001)..
Tested on Python 2.7.5 and 3.3.2.
You can install via pip:
pip install editdistance
It’s quite simple:
>>> import editdistance >>> editdistance.eval('banana', 'bahama') 2L
With IPython, I tried several libraries:
On Python 2.7.5:
In : a = 'fsffvfdsbbdfvvdavavavavavava' In : b = 'fvdaabavvvvvadvdvavavadfsfsdafvvav' In : import pylev In : timeit pylev.levenshtein(a, b) 100 loops, best of 3: 7.48 ms per loop In : from pyxdameraulevenshtein import damerau_levenshtein_distance In : timeit damerau_levenshtein_distance(a, b) 100000 loops, best of 3: 11.4 µs per loop In : timeit editdistance.eval(a, b) # my library 100000 loops, best of 3: 3.5 µs per loop In : import Levenshtein In : timeit Levenshtein.distance(a, b) 100000 loops, best of 3: 3.21 µs per loop
Above libraries only support strings. But Sometimes other type of objects such as list of strings(words). I support any iterable, only requires hashable object of it:
In : Levenshtein.distance(['spam', 'egg'], ['spam', 'ham']) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-22-3e0b30d145ac> in <module>() ----> 1 Levenshtein.distance(['spam', 'egg'], ['spam', 'ham']) TypeError: distance expected two Strings or two Unicodes In : editdistance.eval(['spam', 'egg'], ['spam', 'ham']) Out: 1L
So if object’s hash is same, it’s same.
You can provide
__hash__ method to your object instances.
It is released under the MIT license.
Copyright (c) 2013 Hiroyuki Tanaka
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