JaroWinkler¶
Functions¶
distance¶
- rapidfuzz.distance.JaroWinkler.distance(s1, s2, *, prefix_weight=0.1, processor=None, score_cutoff=None)¶
Calculates the jaro winkler distance
- Parameters:
s1 (Sequence[Hashable]) – First string to compare.
s2 (Sequence[Hashable]) – Second string to compare.
prefix_weight (float, optional) – Weight used for the common prefix of the two strings. Has to be between 0 and 0.25. Default is 0.1.
processor (callable, optional) – Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour.
score_cutoff (float, optional) – Optional argument for a score threshold as a float between 0 and 1.0. For ratio < score_cutoff 0 is returned instead. Default is None, which deactivates this behaviour.
- Returns:
distance – distance between s1 and s2 as a float between 1.0 and 0.0
- Return type:
float
- Raises:
ValueError – If prefix_weight is invalid
normalized_distance¶
- rapidfuzz.distance.JaroWinkler.normalized_distance(s1, s2, *, prefix_weight=0.1, processor=None, score_cutoff=None)¶
Calculates the normalized jaro winkler distance
- Parameters:
s1 (Sequence[Hashable]) – First string to compare.
s2 (Sequence[Hashable]) – Second string to compare.
prefix_weight (float, optional) – Weight used for the common prefix of the two strings. Has to be between 0 and 0.25. Default is 0.1.
processor (callable, optional) – Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour.
score_cutoff (float, optional) – Optional argument for a score threshold as a float between 0 and 1.0. For ratio < score_cutoff 0 is returned instead. Default is None, which deactivates this behaviour.
- Returns:
normalized distance – normalized distance between s1 and s2 as a float between 1.0 and 0.0
- Return type:
float
- Raises:
ValueError – If prefix_weight is invalid
similarity¶
- rapidfuzz.distance.JaroWinkler.similarity(s1, s2, *, prefix_weight=0.1, processor=None, score_cutoff=None)¶
Calculates the jaro winkler similarity
- Parameters:
s1 (Sequence[Hashable]) – First string to compare.
s2 (Sequence[Hashable]) – Second string to compare.
prefix_weight (float, optional) – Weight used for the common prefix of the two strings. Has to be between 0 and 0.25. Default is 0.1.
processor (callable, optional) – Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour.
score_cutoff (float, optional) – Optional argument for a score threshold as a float between 0 and 1.0. For ratio < score_cutoff 0 is returned instead. Default is None, which deactivates this behaviour.
- Returns:
similarity – similarity between s1 and s2 as a float between 0 and 1.0
- Return type:
float
- Raises:
ValueError – If prefix_weight is invalid
normalized_similarity¶
- rapidfuzz.distance.JaroWinkler.normalized_similarity(s1, s2, *, prefix_weight=0.1, processor=None, score_cutoff=None)¶
Calculates the normalized jaro winkler similarity
- Parameters:
s1 (Sequence[Hashable]) – First string to compare.
s2 (Sequence[Hashable]) – Second string to compare.
prefix_weight (float, optional) – Weight used for the common prefix of the two strings. Has to be between 0 and 0.25. Default is 0.1.
processor (callable, optional) – Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour.
score_cutoff (float, optional) – Optional argument for a score threshold as a float between 0 and 1.0. For ratio < score_cutoff 0 is returned instead. Default is None, which deactivates this behaviour.
- Returns:
normalized similarity – normalized similarity between s1 and s2 as a float between 0 and 1.0
- Return type:
float
- Raises:
ValueError – If prefix_weight is invalid
Performance¶
The following image shows a benchmark of the Jaro-Winkler similarity in RapidFuzz
and jellyfish. Jellyfish uses an implementation with a time complexity of O(NM)
,
while RapidFuzz has a time complexity of O([N/64]M)
.