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The package is registered in the [`General`](https://github.com/JuliaRegistries/General) registry and so can be installed at the REPL with `] add StringDistances`.
## Supported Distances
The package defines two abstract types: `StringSemiMetric <: SemiMetric`, and `StringMetric <: Metric`.
String distances inherit from one of these two types. They act over any pair of iterators that define `length` (this includes `AbstractStrings`, but also `GraphemeIterators` or `AbstractVectors`)
String distances act over any pair of iterators that define `length` (e.g. `AbstractStrings`, `GraphemeIterators`, or `AbstractVectors`)
The available distances are:
- Edit Distances
- Hamming Distance `Hamming() <: SemiStringMetric`
- [Jaro and Jaro-Winkler Distance](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) `Jaro()` `JaroWinkler() <: SemiStringMetric`
- [Levenshtein Distance](https://en.wikipedia.org/wiki/Levenshtein_distance) `Levenshtein() <: StringMetric`
- [Optimal String Alignement Distance](https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance#Optimal_string_alignment_distance) (a.k.a. restricted Damerau-Levenshtein) `OptimalStringAlignement() <: SemiStringMetric`
- [Damerau-Levenshtein Distance](https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance#Distance_with_adjacent_transpositions) `DamerauLevenshtein() <: StringMetric`
- [RatcliffObershelp Distance](https://xlinux.nist.gov/dads/HTML/ratcliffObershelp.html) `RatcliffObershelp() <: SemiStringMetric`
- Q-gram distances compare the set of all substrings of length `q` in each string.
- QGram Distance `Qgram(q::Int) <: SemiStringMetric`
- [Cosine Distance](https://en.wikipedia.org/wiki/Cosine_similarity) `Cosine(q::Int) <: SemiStringMetric`
- [Jaccard Distance](https://en.wikipedia.org/wiki/Jaccard_index) `Jaccard(q::Int) <: SemiStringMetric`
- [Overlap Distance](https://en.wikipedia.org/wiki/Overlap_coefficient) `Overlap(q::Int) <: SemiStringMetric`
- [Sorensen-Dice Distance](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient) `SorensenDice(q::Int) <: SemiStringMetric`
- [MorisitaOverlap Distance](https://en.wikipedia.org/wiki/Morisita%27s_overlap_index) `MorisitaOverlap(q::Int) <: SemiStringMetric`
- [Normalized Multiset Distance](https://www.sciencedirect.com/science/article/pii/S1047320313001417) `NMD(q::Int) <: SemiStringMetric`
- Hamming Distance `Hamming() <: SemiMetric`
- [Jaro and Jaro-Winkler Distance](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) `Jaro()` `JaroWinkler() <: SemiMetric`
- [Levenshtein Distance](https://en.wikipedia.org/wiki/Levenshtein_distance) `Levenshtein() <: Metric`
- [Optimal String Alignement Distance](https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance#Optimal_string_alignment_distance) (a.k.a. restricted Damerau-Levenshtein) `OptimalStringAlignement() <: SemiMetric`
- [Damerau-Levenshtein Distance](https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance#Distance_with_adjacent_transpositions) `DamerauLevenshtein() <: Metric`
- [RatcliffObershelp Distance](https://xlinux.nist.gov/dads/HTML/ratcliffObershelp.html) `RatcliffObershelp() <: SemiMetric`
- Q-gram distances compare the set of all substrings of length `q` in each string (and which
- QGram Distance `Qgram(q::Int) <: SemiMetric`
- [Cosine Distance](https://en.wikipedia.org/wiki/Cosine_similarity) `Cosine(q::Int) <: SemiMetric`
- [Jaccard Distance](https://en.wikipedia.org/wiki/Jaccard_index) `Jaccard(q::Int) <: SemiMetric`
- [Overlap Distance](https://en.wikipedia.org/wiki/Overlap_coefficient) `Overlap(q::Int) <: SemiMetric`
- [Sorensen-Dice Distance](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient) `SorensenDice(q::Int) <: SemiMetric`
- [MorisitaOverlap Distance](https://en.wikipedia.org/wiki/Morisita%27s_overlap_index) `MorisitaOverlap(q::Int) <: SemiMetric`
- [Normalized Multiset Distance](https://www.sciencedirect.com/science/article/pii/S1047320313001417) `NMD(q::Int) <: SemiMetric`
## Basic Use
### distance
You can always compute a certain distance between two strings using the following syntax:
The distance between two strings can be computed using the following syntax:
```julia
evaluate(dist, s1, s2)
dist(s1, s2)
```
For instance, with the `Levenshtein` distance,
```julia
evaluate(Levenshtein(), "martha", "marhta")
Levenshtein()("martha", "marhta")
```
In contrast, the function `compare` returns the similarity score, defined as 1 minus the normalized distance between two strings. It always returns an element of type `Float64`. A value of 0.0 means completely different and a value of 1.0 means completely similar.
```julia
compare("martha", "martha", Levenshtein())
#> 1.0
```
### pairwise
`pairwise` returns the matrix of distance between two `AbstractVectors` of AbstractStrings (or iterators)
@ -70,6 +60,13 @@ The package also defines Distance "modifiers" that are defined in the Python pac
### find
The package also adds some convience function to find the element in a list that is closest to a given string
- The function `compare` returns the similarity score, defined as 1 minus the normalized distance between two strings. It always returns an element of type `Float64`. A value of 0.0 means completely different and a value of 1.0 means completely similar.
```julia
compare("martha", "martha", Levenshtein())
#> 1.0
```
- `findnearest` returns the value and index of the element in `itr` with the highest similarity score with `s`. Its syntax is:
```julia
findnearest(s, itr, dist::StringDistance)