strutil
strutil provides a collection of string metrics for calculating string similarity as well as
other string utility functions.
Full documentation can be found at https://pkg.go.dev/github.com/adrg/strutil.
Installation
go get github.com/adrg/strutil
String metrics
- Hamming
- Levenshtein
- Jaro
- Jaro-Winkler
- Smith-Waterman-Gotoh
- Sorensen-Dice
- Jaccard
- Overlap Coefficient
The package defines the StringMetric interface, which is implemented by all
the string metrics. The interface is used with the Similarity function, which
calculates the similarity between the specified strings, using the provided
string metric.
type StringMetric interface {
Compare(a, b string) float64
}
func Similarity(a, b string, metric StringMetric) float64 {
}All defined string metrics can be found in the metrics package.
Hamming
Calculate similarity.
similarity := strutil.Similarity("text", "test", metrics.NewHamming())
fmt.Printf("%.2f\n", similarity) // Output: 0.75Calculate distance.
ham := metrics.NewHamming()
fmt.Printf("%d\n", ham.Distance("one", "once")) // Output: 2More information and additional examples can be found on pkg.go.dev.
Levenshtein
Calculate similarity using default options.
similarity := strutil.Similarity("graph", "giraffe", metrics.NewLevenshtein())
fmt.Printf("%.2f\n", similarity) // Output: 0.43Configure edit operation costs.
lev := metrics.NewLevenshtein()
lev.CaseSensitive = false
lev.InsertCost = 1
lev.ReplaceCost = 2
lev.DeleteCost = 1
similarity := strutil.Similarity("make", "Cake", lev)
fmt.Printf("%.2f\n", similarity) // Output: 0.50Calculate distance.
lev := metrics.NewLevenshtein()
fmt.Printf("%d\n", lev.Distance("graph", "giraffe")) // Output: 4More information and additional examples can be found on pkg.go.dev.
Jaro
similarity := strutil.Similarity("think", "tank", metrics.NewJaro())
fmt.Printf("%.2f\n", similarity) // Output: 0.78More information and additional examples can be found on pkg.go.dev.
Jaro-Winkler
similarity := strutil.Similarity("think", "tank", metrics.NewJaroWinkler())
fmt.Printf("%.2f\n", similarity) // Output: 0.80More information and additional examples can be found on pkg.go.dev.
Smith-Waterman-Gotoh
Calculate similarity using default options.
swg := metrics.NewSmithWatermanGotoh()
similarity := strutil.Similarity("times roman", "times new roman", swg)
fmt.Printf("%.2f\n", similarity) // Output: 0.82Customize gap penalty and substitution function.
swg := metrics.NewSmithWatermanGotoh()
swg.CaseSensitive = false
swg.GapPenalty = -0.1
swg.Substitution = metrics.MatchMismatch {
Match: 1,
Mismatch: -0.5,
}
similarity := strutil.Similarity("Times Roman", "times new roman", swg)
fmt.Printf("%.2f\n", similarity) // Output: 0.96More information and additional examples can be found on pkg.go.dev.
Sorensen-Dice
Calculate similarity using default options.
sd := metrics.NewSorensenDice()
similarity := strutil.Similarity("time to make haste", "no time to waste", sd)
fmt.Printf("%.2f\n", similarity) // Output: 0.62Customize n-gram size.
sd := metrics.NewSorensenDice()
sd.CaseSensitive = false
sd.NgramSize = 3
similarity := strutil.Similarity("Time to make haste", "no time to waste", sd)
fmt.Printf("%.2f\n", similarity) // Output: 0.53More information and additional examples can be found on pkg.go.dev.
Jaccard
Calculate similarity using default options.
j := metrics.NewJaccard()
similarity := strutil.Similarity("time to make haste", "no time to waste", j)
fmt.Printf("%.2f\n", similarity) // Output: 0.45Customize n-gram size.
j := metrics.NewJaccard()
j.CaseSensitive = false
j.NgramSize = 3
similarity := strutil.Similarity("Time to make haste", "no time to waste", j)
fmt.Printf("%.2f\n", similarity) // Output: 0.36The input of the Sorensen-Dice example is the same as the one of Jaccard because the metrics bear a resemblance to each other. In fact, each of the coefficients can be used to calculate the other one.
Sorensen-Dice to Jaccard.
J = SD/(2-SD)
where SD is the Sorensen-Dice coefficient and J is the Jaccard index.
Jaccard to Sorensen-Dice.
SD = 2*J/(1+J)
where SD is the Sorensen-Dice coefficient and J is the Jaccard index.
More information and additional examples can be found on pkg.go.dev.
Overlap Coefficient
Calculate similarity using default options.
oc := metrics.NewOverlapCoefficient()
similarity := strutil.Similarity("time to make haste", "no time to waste", oc)
fmt.Printf("%.2f\n", similarity) // Output: 0.67Customize n-gram size.
oc := metrics.NewOverlapCoefficient()
oc.CaseSensitive = false
oc.NgramSize = 3
similarity := strutil.Similarity("Time to make haste", "no time to waste", oc)
fmt.Printf("%.2f\n", similarity) // Output: 0.57More information and additional examples can be found on pkg.go.dev.
References
For more information see:
- Hamming distance
- Levenshtein distance
- Jaro-Winkler distance
- Smith-Waterman algorithm
- Sorensen-Dice coefficient
- Jaccard index
- Overlap coefficient
Stargazers over time
Contributing
Contributions in the form of pull requests, issues or just general feedback,
are always welcome.
See CONTRIBUTING.MD.
License
Copyright (c) 2019 Adrian-George Bostan.
This project is licensed under the MIT license. See LICENSE for more details.