Update README.md
parent
78c3ec86f8
commit
059cab54b3
|
@ -75,6 +75,15 @@ The function `pairwise` is particularly optimized for QGram-distances (each elem
|
|||
The functions `findnearest` and `findall` are particularly optimized for `Levenshtein`, `DamerauLevenshtein` distances (these distances stop early if the distance is higher than a certain threshold).
|
||||
|
||||
|
||||
### distance modifiers
|
||||
The package also defines Distance "modifiers" that can be applied to any distance.
|
||||
- [Partial](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) returns the minimum of the distance between the shorter string and substrings of the longer string.
|
||||
- [TokenSort](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) adjusts for differences in word orders by returning the distance of the two strings, after re-ordering words alphabetically.
|
||||
- [TokenSet](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) adjusts for differences in word orders and word numbers by returning the distance between the intersection of two strings with each string.
|
||||
- [TokenMax](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) normalizes the distance, and combine the `Partial`, `TokenSort` and `TokenSet` modifiers, with penalty terms depending on string lengths. This is a good distance to match strings composed of multiple words, like addresses. `TokenMax(Levenshtein())` corresponds to the distance defined in [fuzzywuzzy](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/)
|
||||
|
||||
|
||||
|
||||
## References
|
||||
- [The stringdist Package for Approximate String Matching](https://journal.r-project.org/archive/2014-1/loo.pdf) Mark P.J. van der Loo
|
||||
- [fuzzywuzzy](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/)
|
||||
|
|
Loading…
Reference in New Issue