Philiprehberger.StringSimilarity

String similarity and phonetic matching with Levenshtein, Jaro-Winkler, Dice, Soundex, and fuzzy search.

Installation

dotnet add package Philiprehberger.StringSimilarity

Usage

Compare Strings

using Philiprehberger.StringSimilarity;

double levenshtein = Similarity.Levenshtein("kitten", "sitting");  // 0.571
double jaroWinkler = Similarity.JaroWinkler("kitten", "sitting");  // 0.746
double dice = Similarity.Dice("night", "nacht");                   // 0.25

Normalized Levenshtein

using Philiprehberger.StringSimilarity;

double score = Similarity.NormalizedLevenshtein("kitten", "sitting");  // 0.571

Best Match Search

using Philiprehberger.StringSimilarity;

var candidates = new[] { "apple", "application", "ape", "apex" };
var result = Similarity.BestMatch("app", candidates, threshold: 0.3);

if (result is not null)
{
    Console.WriteLine($"{result.Value} ({result.Score:F3}, {result.Algorithm})");
}

Find Top N Matches

using Philiprehberger.StringSimilarity;

var candidates = new[] { "apple", "application", "ape", "apex", "banana" };
var topMatches = Similarity.FindTopN("app", candidates, 3, SimilarityAlgorithm.Levenshtein);

foreach (var match in topMatches)
{
    Console.WriteLine($"{match.Value}: {match.Score:F3}");
}

Damerau-Levenshtein

using Philiprehberger.StringSimilarity;

int distance = Similarity.DamerauLevenshteinDistance("ab", "ba");      // 1 (transposition)
double score = Similarity.DamerauLevenshteinSimilarity("ab", "ba");    // 0.5

Fuzzy Search

using Philiprehberger.StringSimilarity;

var fuzzy = new FuzzySearch();
double score = fuzzy.Score("kitten", "sitting");

var candidates = new[] { "apple", "application", "ape", "banana" };
var results = fuzzy.Find("app", candidates, threshold: 0.3);
var topResults = fuzzy.FindTopN("app", candidates, 2);

// Custom weights
var custom = new FuzzySearch(new Dictionary<SimilarityAlgorithm, double>
{
    [SimilarityAlgorithm.JaroWinkler] = 2.0,
    [SimilarityAlgorithm.Dice] = 1.0
});

Phonetic Matching

using Philiprehberger.StringSimilarity;

bool similar = PhoneticMatcher.AreSimilar("Smith", "Smyth");  // true
double score = PhoneticMatcher.Score("Smith", "Smyth");       // 0.5-1.0

var candidates = new[] { "Smith", "Smyth", "Jones" };
var matches = PhoneticMatcher.FindMatches("Smith", candidates);

Soundex

using Philiprehberger.StringSimilarity;

string code = Soundex.Encode("Robert");           // "R163"
bool similar = Soundex.AreSimilar("Robert", "Rupert");  // true

Double Metaphone

using Philiprehberger.StringSimilarity;

var (primary, alternate) = DoubleMetaphone.Encode("Schmidt");
Console.WriteLine($"Primary: {primary}, Alternate: {alternate}");

Trigram Similarity

using Philiprehberger.StringSimilarity;

double score = Trigram.Similarity("night", "nacht");  // Jaccard index of trigram sets

Longest Common Subsequence

using Philiprehberger.StringSimilarity;

int length = LongestCommonSubsequence.Length("ABCBDAB", "BDCAB");     // 4
double score = LongestCommonSubsequence.Similarity("kitten", "sitting");  // 0.571

Overlap Coefficient

using Philiprehberger.StringSimilarity;

double score = OverlapCoefficient.Similarity("app", "application");  // 1.0
double partial = OverlapCoefficient.Similarity("night", "nacht");    // 0.25-0.75

Raw Edit Distance

using Philiprehberger.StringSimilarity;

int distance = Similarity.Distance("kitten", "sitting");  // 3

API

Similarity

Method	Return	Description
Levenshtein(a, b)	double	Normalized Levenshtein similarity (0--1)
JaroWinkler(a, b)	double	Jaro-Winkler similarity (0--1)
Dice(a, b)	double	Sorensen-Dice coefficient (0--1)
Distance(a, b)	int	Raw Levenshtein edit distance
DamerauLevenshteinDistance(a, b)	int	Damerau-Levenshtein distance with transpositions
DamerauLevenshteinSimilarity(a, b)	double	Normalized Damerau-Levenshtein similarity (0--1)
NormalizedLevenshtein(a, b)	double	Normalized Levenshtein similarity (0--1)
BestMatch(input, candidates, threshold)	MatchResult?	Best match across all algorithms
FindTopN(input, candidates, n, algorithm)	List<MatchResult>	Top N matches ranked by score

Soundex

Method	Return	Description
Encode(value)	string	4-character Soundex code
AreSimilar(a, b)	bool	Whether two strings share the same Soundex code

DoubleMetaphone

Method	Return	Description
Encode(value)	(string Primary, string Alternate)	Primary and alternate metaphone codes

DamerauLevenshtein

Method	Return	Description
Distance(a, b)	int	Damerau-Levenshtein distance with transpositions
Normalize(a, b)	double	Normalized similarity (0--1)

FuzzySearch

Method	Return	Description
Score(a, b)	double	Weighted average similarity (0--1)
Find(query, candidates, threshold)	List<MatchResult>	Candidates above threshold sorted by score
FindTopN(query, candidates, n)	List<MatchResult>	Top N candidates sorted by score

PhoneticMatcher

Method	Return	Description
AreSimilar(a, b)	bool	Whether strings sound alike (Soundex or Double Metaphone)
FindMatches(query, candidates)	List<string>	Candidates that sound similar to query
Score(a, b)	double	Phonetic similarity (0--1)

Trigram

Method	Return	Description
Similarity(a, b)	double	Jaccard index of trigram sets (0--1)

LongestCommonSubsequence

Method	Return	Description
Length(a, b)	int	Length of the longest common subsequence
Similarity(a, b)	double	Normalized LCS similarity (0--1)

OverlapCoefficient

Method	Return	Description
Similarity(a, b)	double	Overlap coefficient using bigrams (0--1)

SimilarityAlgorithm

Value	Description
Levenshtein	Normalized Levenshtein similarity
JaroWinkler	Jaro-Winkler similarity
Dice	Sorensen-Dice coefficient
Trigram	Trigram similarity (Jaccard index)
NormalizedLevenshtein	Normalized Levenshtein distance
DamerauLevenshtein	Damerau-Levenshtein with transpositions
LongestCommonSubsequence	LCS-based similarity
OverlapCoefficient	Overlap coefficient using bigrams

Development

dotnet build src/Philiprehberger.StringSimilarity.csproj --configuration Release

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License

MIT