StringDistance

A fuzzy matching string distance library for Scala and Java that includes Levenshtein distance, Jaro distance, Jaro-Winkler distance, Dice coefficient, N-Gram similarity, Cosine similarity, Jaccard similarity, Longest common subsequence, Hamming distance, and more.

Works with generalized arrays.

For more detailed information, please refer to the API Documentation.

Requires: Java 8+ or Scala 2.11+

1. Add it to your project ...

Using sbt:

In build.sbt:

libraryDependencies += "com.github.vickumar1981" %% "stringdistance" % "1.2.7"

Using gradle:

In build.gradle:

dependencies {
    compile 'com.github.vickumar1981:stringdistance_2.13:1.2.7'
}

Using Maven:

In pom.xml:

<dependency>
    <groupId>com.github.vickumar1981</groupId>
    <artifactId>stringdistance_2.13</artifactId>
    <version>1.2.7</version>
</dependency>

Notes:

For Scala 2.12, please use the stringdistance_2.12 artifact as a dependency instead.
For Scala 2.11, please use the stringdistance_2.11 artifact as a dependency instead.

2. Scala Usage

Example.scala:

// Scala example
import com.github.vickumar1981.stringdistance.StringDistance._
import com.github.vickumar1981.stringdistance.StringSound._
import com.github.vickumar1981.stringdistance.impl.{ConstantGap, LinearGap}

// Cosine Similarity
val cosSimilarity: Double = Cosine.score("hello", "chello")  // 0.935

// Damerau-Levenshtein Distance
val damerauDist: Int = Damerau.distance("martha", "marhta")  // 1
val damerau: Double = Damerau.score("martha", "marhta")  // 0.833

// Dice Coefficient
val diceCoefficient: Double = DiceCoefficient.score("martha", "marhta")  // 0.4

// Hamming Distance
val hammingDist: Int = Hamming.distance("martha", "marhta")  // 2
val hamming: Double = Hamming.score("martha", "marhta")  // 0.667

// Jaccard Similarity
val jaccard: Double = Jaccard.score("karolin", "kathrin", 1)

// Jaro and Jaro Winkler
val jaro: Double = Jaro.score("martha", "marhta")  // 0.944
val jaroWinkler: Double = JaroWinkler.score("martha", "marhta", 0.1)  // 0.961

// Levenshtein Distance
val levenshteinDist: Int = Levenshtein.distance("martha", "marhta")  // 2
val levenshtein: Double = Levenshtein.score("martha", "marhta")  // 0.667

// Longest Common Subsequence
val longestCommonSubSeq: Int = LongestCommonSeq.distance("martha", "marhta")  // 5

// Needleman Wunsch
val needlemanWunsch: Double = NeedlemanWunsch.score("martha", "marhta", ConstantGap())  // 0.667

// N-Gram Similarity and Distance
val ngramDist: Int = NGram.distance("karolin", "kathrin", 1)  // 5
val bigramDist: Int = NGram.distance("karolin", "kathrin", 2)  // 2
val ngramSimilarity: Double = NGram.score("karolin", "kathrin", 1)  // 0.714
val bigramSimilarity: Double = NGram.score("karolin", "kathrin", 2)  // 0.333

// N-Gram tokens, returns a List[String]
val tokens: List[String] = NGram.tokens("martha", 2)  // List("ma", "ar", "rt", "th", "ha")

// Overlap Similarity
val overlap: Double = Overlap.score("karolin", "kathrin", 1)  // 0.286
val overlapBiGram: Double = Overlap.score("karolin", "kathrin", 2)  // 0.667

// Smith Waterman Similarities
val smithWaterman: Double = SmithWaterman.score("martha", "marhta", (LinearGap(gapValue = -1), Integer.MAX_VALUE))
val smithWatermanGotoh: Double = SmithWatermanGotoh.score("martha", "marhta", ConstantGap())

// Tversky Similarity
val tversky: Double = Tversky.score("karolin", "kathrin", 0.5)  // 0.333

// Phonetic Similarity
val metaphone: Boolean = Metaphone.score("merci", "mercy")  // true
val soundex: Boolean = Soundex.score("merci", "mercy")  // true

3. Scala: Use with Implicits

To use implicits and extend the String class: import com.github.vickumar1981.stringdistance.StringConverter._

Example.scala

// Scala example using implicits
import com.github.vickumar1981.stringdistance.StringConverter._

// Scores between two strings
val cosSimilarity: Double = "hello".cosine("chello")
val damerau: Double = "martha".damerau("marhta")
val diceCoefficient: Double = "martha".diceCoefficient("marhta")
val hamming: Double = "martha".hamming("marhta")
val jaccard: Double = "karolin".jaccard("kathrin")
val jaro: Double = "martha".jaro("marhta")
val jaroWinkler: Double = "martha".jaroWinkler("marhta")
val levenshtein: Double = "martha".levenshtein("marhta")
val needlemanWunsch: Double = "martha".needlemanWunsch("marhta")
val ngramSimilarity: Double = "karolin".nGram("kathrin")
val bigramSimilarity: Double = "karolin".nGram("kathrin", 2)
val overlap: Double = "karolin".overlap("kathrin")
val overlapBiGram: Double = "karolin".overlap("kathrin", 2)
val smithWaterman: Double = "martha".smithWaterman("marhta")
val smithWatermanGotoh: Double = "martha".smithWatermanGotoh("marhta")
val tversky: Double = "karolin".tversky("kathrin", 0.5)

// Distances between two strings
val damerauDist: Int = "martha".damerauDist("marhta")  // 1
val hammingDist: Int = "martha".hammingDist("marhta")
val levenshteinDist: Int = "martha".levenshteinDist("marhta")
val longestCommonSeq: Int = "martha".longestCommonSeq("marhta")
val ngramDist: Int = "karolin".nGramDist("kathrin")
val bigramDist: Int = "karolin".nGramDist("kathrin", 2)

// N-Gram tokens, returns a List[String]
val tokens: List[String] = "martha".tokens(2)  // List("ma", "ar", "rt", "th", "ha")

// Phonetic similarity of two strings
val metaphone: Boolean = "merci".metaphone("mercy")
val soundex: Boolean = "merci".soundex("mercy")

4. Java Usage

To use in Java: import com.github.vickumar1981.stringdistance.util.StringDistance

Example.java

// Java example
import com.github.vickumar1981.stringdistance.util.StringDistance;
import com.github.vickumar1981.stringdistance.util.StringSound;

// Scores between two strings
Double cosSimilarity = StringDistance.cosine("hello", "chello");
Double damerau = StringDistance.damerau("martha", "marhta");
Double diceCoefficient = StringDistance.diceCoefficient("martha", "marhta");
Double hamming = StringDistance.hamming("martha", "marhta");
Double jaccard = StringDistance.jaccard("karolin", "kathrin");
Double jaro = StringDistance.jaro("martha", "marhta");
Double jaroWinkler = StringDistance.jaroWinkler("martha", "marhta");
Double levenshtein = StringDistance.levenshtein("martha", "marhta");
Double needlemanWunsch = StringDistance.needlemanWunsch("martha", "marhta");
Double ngramSimilarity = StringDistance.nGram("karolin", "kathrin");
Double bigramSimilarity = StringDistance.nGram("karolin", "kathrin", 2);
Double overlap = StringDistance.overlap("karolin", "kathrin");
Double overlapBiGram = StringDistance.overlap("karolin", "kathrin", 2);
Double smithWaterman = StringDistance.smithWaterman("martha", "marhta");
Double smithWatermanGotoh = StringDistance.smithWatermanGotoh("martha", "marhta");
Double tversky = StringDistance.tversky("karolin", "kathrin", 0.5);

// Distances between two strings
Integer damerauDist = StringDistance.damerauDist("martha", "marhta");
Integer hammingDist = StringDistance.hammingDist("martha", "marhta");
Integer levenshteinDist = StringDistance.levenshteinDist("martha", "marhta");
Integer longestCommonSeq = StringDistance.longestCommonSeq("martha", "marhta");
Integer ngramDist = StringDistance.nGramDist("karolin", "kathrin");
Integer bigramDist = StringDistance.nGramDist("karolin", "kathrin", 2);

// N-Gram tokens, returns a List<String>
List<String> tokens = StringDistance.nGramTokens(2)  // List("ma", "ar", "rt", "th", "ha")

// Phonetic similarity of two strings
Boolean metaphone = StringSound.metaphone("merci", "mercy");
Boolean soundex = StringSound.soundex("merci", "mercy");

5. Using with Arrays

You can use the ArrayDistance class just like the StringDistance class, except using a generic array - Array[T] for Scala and T[] for Java.
Make sure your classes are comparable using == for Scala or .equals for Java

Scala Sample Code:

import com.github.vickumar1981.stringdistance.ArrayDistance._

// Example Levenshtein Distance and Score
val levenshteinDist = Levenshtein.distance(Array("m", "a", "r", "t", "h", "a"), Array("m", "a", "r", "h", "t", "a")) // 2
val levenshtein = Levenshtein.score(Array("m", "a", "r", "t", "h", "a"), Array("m", "a", "r", "h", "t", "a")) // 0.667

Java Example Code:

6. Adding your own Distance or Scoring Algorithm

Create a marker trait that extends StringMetricAlgorithm:

trait CustomAlgorithm extends StringMetricAlgorithm

Create an implementation for that algorithm using an implicit object. Override either the score or the distance method, depending upon whether the object extends DistanceAlgorithm or ScoringAlgorithm.

implicit object CustomDistance extends DistanceAlgorithm[CustomAlgorithm] {
    override def distance(s1: String, s2: String): Int = {
        // Implement distance between s1 and s2
    }
}

implicit object CustomScore extends ScoringAlgorithm[CustomAlgorithm] {
    override def score(s1: String, s2: String): Double = {
        // Implement fuzzy score between s1 and s2
    }
}

Create an object that extends StringMetric using your algorithm as the type parameter, and use the score and distance methods defined in the implicit object.

object CustomMetric extends StringMetric[CustomAlgorithm]

val customScore: Double = CustomMetric.score("hello", "hello2")
val customDist: Int = CustomMetric.distance("hello", "hello2")

7. Reporting an Issue

Please report any issues or bugs to the Github issues page.

8. Contributing

Please view the contributing guidelines

9. License

This project is licensed under the Apache 2 License.

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