def case_insensitive_close_matches(word,
possibilities,
n=3,
cutoff=0.6,
excpt=None):
"""Use SequenceMatcher to return list of the best "good enough" matches.
word is a sequence for which close matches are desired (typically a
string).
possibilities is a list of sequences against which to match word
(typically a list of strings).
Optional arg n (default 3) is the maximum number of close matches to
return. n must be > 0.
Optional arg cutoff (default 0.6) is a float in [0, 1]. Possibilities
that don't score at least that similar to word are ignored.
The best (no more than n) matches among the possibilities are returned
in a list, sorted by similarity score, most similar first.
>>> get_close_matches("appel", ["ape", "apple", "peach", "puppy"])
['apple', 'ape']
>>> import keyword as _keyword
>>> get_close_matches("wheel", _keyword.kwlist)
['while']
>>> get_close_matches("Apple", _keyword.kwlist)
[]
>>> get_close_matches("accept", _keyword.kwlist)
['except']
"""
if not n > 0:
raise ValueError("n must be > 0: %r" % (n, ))
if not 0.0 <= cutoff <= 1.0:
raise ValueError("cutoff must be in [0.0, 1.0]: %r" % (cutoff, ))
result = []
s = _SequenceMatcher()
s.set_seq2(supercasefold(word))
for x in possibilities:
x_ = supercasefold(x)
s.set_seq1(x_)
if s.real_quick_ratio() >= cutoff and \
s.quick_ratio() >= cutoff and \
s.ratio() >= cutoff:
result.append((s.ratio(), x))
# Move the best scorers to head of list
result = _nlargest(n, result)
# Strip scores for the best n matches
ret = [x for score, x in result]
if not excpt is None:
did_you_mean = "'{}' not found, did you mean {}?".format(
word, " or ".join("'{}'".format(s) for s in ret))
raise excpt(did_you_mean)
return ret
def case_insensitive_close_matches(word, possibilities, n=3, cutoff=0.6, excpt=None):
"""Get a list of the best "good enough" matches.
Parameters
----------
word : str
A base string for which close matches are desired
possibilities : Collection[str]
Word list against which to match word.
n : int, default 3
Maximum number of close matches to return, must be > 0.
cutoff : float, default 0.6
A float in the range [0, 1]. Possibilities
that don't score at least that similar to `word` are ignored.
The best (no more than n) matches among the possibilities are returned
in a list, sorted by similarity score, most similar first.
Examples
--------
>>> case_insensitive_close_matches("appel", ["ape", "apple", "peach", "puppy"])
['apple', 'ape']
>>> import keyword
>>> case_insensitive_close_matches("wheel", keyword.kwlist)
['while']
>>> case_insensitive_close_matches("apples", keyword.kwlist)
[]
>>> case_insensitive_close_matches("Accept", keyword.kwlist)
['except']
>>> case_insensitive_close_matches("NonLocal", keyword.kwlist)
['nonlocal']
"""
if not n > 0:
raise ValueError("n must be > 0: %r" % (n,))
if not 0.0 <= cutoff <= 1.0:
raise ValueError("cutoff must be in [0.0, 1.0]: %r" % (cutoff,))
result = []
s = _SequenceMatcher()
s.set_seq2(supercasefold(word))
for x in possibilities:
x_ = supercasefold(x)
s.set_seq1(x_)
if s.real_quick_ratio() >= cutoff and \
s.quick_ratio() >= cutoff and \
s.ratio() >= cutoff:
result.append((s.ratio(), x))
# Move the best scorers to head of list
result = _nlargest(n, result)
# Strip scores for the best n matches
ret = [x for score, x in result]
if not excpt is None:
did_you_mean = "'{}' not found, did you mean {}?".format(word, " or ".join("'{}'".format(s) for s in ret))
raise excpt(did_you_mean)
return ret
def case_insensitive_close_matches(word, possibilities, n=3, cutoff=0.6, excpt=None):
"""Use SequenceMatcher to return list of the best "good enough" matches.
word is a sequence for which close matches are desired (typically a
string).
possibilities is a list of sequences against which to match word
(typically a list of strings).
Optional arg n (default 3) is the maximum number of close matches to
return. n must be > 0.
Optional arg cutoff (default 0.6) is a float in [0, 1]. Possibilities
that don't score at least that similar to word are ignored.
The best (no more than n) matches among the possibilities are returned
in a list, sorted by similarity score, most similar first.
>>> get_close_matches("appel", ["ape", "apple", "peach", "puppy"])
['apple', 'ape']
>>> import keyword as _keyword
>>> get_close_matches("wheel", _keyword.kwlist)
['while']
>>> get_close_matches("Apple", _keyword.kwlist)
[]
>>> get_close_matches("accept", _keyword.kwlist)
['except']
"""
if not n > 0:
raise ValueError("n must be > 0: %r" % (n,))
if not 0.0 <= cutoff <= 1.0:
raise ValueError("cutoff must be in [0.0, 1.0]: %r" % (cutoff,))
result = []
s = _SequenceMatcher()
s.set_seq2(supercasefold(word))
for x in possibilities:
x_ = supercasefold(x)
s.set_seq1(x_)
if s.real_quick_ratio() >= cutoff and \
s.quick_ratio() >= cutoff and \
s.ratio() >= cutoff:
result.append((s.ratio(), x))
# Move the best scorers to head of list
result = _nlargest(n, result)
# Strip scores for the best n matches
ret = [x for score, x in result]
if not excpt is None:
did_you_mean = "'{}' not found, did you mean {}?".format(word, " or ".join("'{}'".format(s) for s in ret))
raise excpt(did_you_mean)
return ret
def _standart(self, s1, s2):
matcher = _SequenceMatcher(a=s1, b=s2)
match = matcher.find_longest_match(0, len(s1), 0, len(s2))
return s1[match.a: match.a + match.size]
