Did I find the right examples for you? yes no      Crawl my project      Python Jobs

# dameraulevenshtein.dameraulevenshtein

All Samples(2)  |  Call(1)  |  Derive(0)  |  Import(1)
```Calculate the Damerau-Levenshtein distance between sequences.

This distance is the number of additions, deletions, substitutions,
and transpositions needed to transform the first sequence into the
second. Although generally used with strings, any sequences of
comparable objects will work.

Transpositions are exchanges of *consecutive* characters; all other
operations are self-explanatory.
(more...)
```

```        def dameraulevenshtein(seq1, seq2):
"""Calculate the Damerau-Levenshtein distance between sequences.

This distance is the number of additions, deletions, substitutions,
and transpositions needed to transform the first sequence into the
second. Although generally used with strings, any sequences of
comparable objects will work.

Transpositions are exchanges of *consecutive* characters; all other
operations are self-explanatory.

This implementation is O(N*M) time and O(M) space, for N and M the
lengths of the two sequences.

>>> dameraulevenshtein('ba', 'abc')
2
>>> dameraulevenshtein('fee', 'deed')
2

It works with arbitrary sequences too:
>>> dameraulevenshtein('abcd', ['b', 'a', 'c', 'd', 'e'])
2
"""
# codesnippet:D0DE4716-B6E6-4161-9219-2903BF8F547F
# Conceptually, this is based on a len(seq1) + 1 * len(seq2) + 1 matrix.
# However, only the current and two previous rows are needed at once,
# so we only store those.
oneago = None
thisrow = range(1, len(seq2) + 1) + 
for x in xrange(len(seq1)):
# Python lists wrap around for negative indices, so put the
# leftmost column at the *end* of the list. This matches with
# the zero-indexed strings and saves extra calculation.
twoago, oneago, thisrow = oneago, thisrow,  * len(seq2) + [x + 1]
for y in xrange(len(seq2)):
delcost = oneago[y] + 1
addcost = thisrow[y - 1] + 1
subcost = oneago[y - 1] + (seq1[x] != seq2[y])
# This block deals with transpositions
if (x > 0 and y > 0 and seq1[x] == seq2[y - 1]
and seq1[x-1] == seq2[y] and seq1[x] != seq2[y]):
thisrow[y] = min(thisrow[y], twoago[y - 2] + 1)
return thisrow[len(seq2) - 1]
```

```# example usage using badwords.txt (not for the easily offended, but seriously, you're from the internet sooo...)

from dameraulevenshtein import dameraulevenshtein as dl_distance
import string
```
```    """
word = word.lower()
dl = lambda x: dl_distance(x, word) <= max_distance
return any( map(dl, swear_list) )

```