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StringDistances.jl
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compathelper/new_version/2020-10-08-17-05-17-769-1797568811 v0.7.0
matthieugomez 2020-07-13 14:12:28 -07:00
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Project.toml
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name = "StringDistances"
uuid = "88034a9c-02f8-509d-84a9-84ec65e18404"
version = "0.6.5"
version = "0.7.0"
[deps]
Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"

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README.md
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@ -6,7 +6,7 @@ The package is registered in the [`General`](https://github.com/JuliaRegistries/
## Supported Distances
Distances are defined for `AbstractStrings`, but also on iterators that define a `length` function (e.g. `graphemes`, `AbstractVector`...)
Distances are defined for `AbstractStrings`, and any iterator that define `length()` (e.g. `graphemes`, `AbstractVector`...)
The available distances are:
@ -22,11 +22,11 @@ The available distances are:
- [Overlap Distance](https://en.wikipedia.org/wiki/Overlap_coefficient) `Overlap(q::Int)`
- [Sorensen-Dice Distance](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient) `SorensenDice(q::Int)`
- Distance "modifiers" that can be applied to any distance:
- [Winkler](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) diminishes the distance of strings with common prefixes. The Winkler adjustment was originally defined for the Jaro similarity score but it can be defined for any string distance.
- [Partial](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) returns the minimum 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 reording words alphabetically.
- [TokenSet](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) adjusts for differences in word orders and word numbers by comparing the intersection of two strings with each string.
- [TokenMax](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) combines scores using the base distance, 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/)
- [TokenMax](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) combines the normalized distance, 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/)
- [Winkler](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) diminishes the normalized distance of strings with common prefixes. The Winkler adjustment was originally defined for the Jaro similarity score but it can be defined for any string distance.
## Basic Use