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[Utility & Helpers](/categories/utility) 4. / 5. bl4ckbon3/strsim ActiveLibrary[Utility & Helpers](/categories/utility) bl4ckbon3/strsim ================ Collection of string similarity and distance algorithms in PHP including Levenshtein, Damerau-Levenshtein, Jaro-Winkler, and more 1.0.0(6mo ago)038MITPHPPHP >=8.1 Since Oct 31Pushed 6mo agoCompare [ Source](https://github.com/bl4ckbon3/StrSim)[ Packagist](https://packagist.org/packages/bl4ckbon3/strsim)[ Docs](https://github.com/Edgaras0x4E/StrSim)[ RSS](/packages/bl4ckbon3-strsim/feed)WikiDiscussions master Synced 1mo ago READMEChangelog (1)Dependencies (1)Versions (2)Used By (0) StrSim v1.1.1 ============= [](#strsim-v111) A collection of string similarity and distance algorithms implemented in PHP with full Unicode and multibyte character support. This library provides standalone static methods for computing various similarity metrics, useful in natural language processing, fuzzy matching, spell checking, and bioinformatics. What's New in v1.1.1 -------------------- [](#whats-new-in-v111) ### 🔧 **Fixed Naming Issues** [](#-fixed-naming-issues) - **Fixed `Jaro::distance()`** - Previously returned similarity values (1.0 = identical), now correctly returns distance values (0.0 = identical) - **Fixed `JaroWinkler::distance()`** - Previously returned similarity values (1.0 = identical), now correctly returns distance values (0.0 = identical) ### ✨ **New Functions Added** [](#-new-functions-added) - **`Jaro::similarity()`** - Returns proper similarity values (1.0 = identical, 0.0 = completely different) - **`JaroWinkler::similarity()`** - Returns proper similarity values (1.0 = identical, 0.0 = completely different) ### 📚 **Improvements** [](#-improvements) - **Better MongeElkan** - Fixed edge cases for empty string comparisons ### 🔄 **Migration Guide** [](#-migration-guide) If you were using `Jaro::distance()` or `JaroWinkler::distance()` expecting similarity values (where 1.0 = identical): - **Before**: `Jaro::distance("hello", "hello")` returned `1.0` - **After**: Use `Jaro::similarity("hello", "hello")` to get `1.0`, or `Jaro::distance("hello", "hello")` returns `0.0` --- Requirements ------------ [](#requirements) - PHP 8.3+ - Composer Installation ------------ [](#installation) 1. Use the library via Composer: ``` composer require edgaras/strsim ``` 2. Include the Composer autoloader: ``` require __DIR__ . '/vendor/autoload.php'; ``` Features -------- [](#features) - **Full Unicode Support**: All algorithms handle multibyte characters, emoji, combining marks, and complex grapheme clusters - **UTF-8 Validation**: Automatic validation of input strings with clear error messages - **Error Handling**: Proper exception types with descriptive messages - **Code-Point Based**: Consistent behavior across all Unicode normalization forms - **Optimized Tokenization**: Smart whitespace handling for text-based algorithms - **Distance vs Similarity**: Clear distinction between distance measures (0 = identical) and similarity measures (1 = identical) Supported Algorithms -------------------- [](#supported-algorithms) ClassMethodReturn RangeDescription`Levenshtein``distance()`0 to ∞Number of insertions, deletions, or substitutions needed.`DamerauLevenshtein``distance()`0 to ∞Levenshtein with transpositions included.`Hamming``distance()`0 to ∞Number of differing positions (requires equal-length strings).`Jaro``similarity()`0.0 to 1.0Similarity based on character matches and transpositions.`Jaro``distance()`0.0 to 1.0Distance measure (1 - similarity).`JaroWinkler``similarity()`0.0 to 1.0Jaro with a prefix match boost for similar string starts.`JaroWinkler``distance()`0.0 to 1.0Distance measure (1 - similarity).`LCS``length()`0 to ∞Length of the longest common subsequence.`SmithWaterman``score()`0 to ∞Local alignment scoring for best-matching subsequences.`NeedlemanWunsch``score()`-∞ to ∞Global alignment scoring for entire string similarity.`Cosine``similarity()`0.0 to 1.0Similarity via character frequency vectors.`Cosine``similarityFromVectors()`-1.0 to 1.0Cosine similarity for numeric vector inputs.`Jaccard``index()`0.0 to 1.0Ratio of shared to total unique characters.`MongeElkan``similarity()`0.0 to 1.0Average best-word similarity using Jaro-Winkler internally.Understanding Distance vs Similarity ------------------------------------ [](#understanding-distance-vs-similarity) This library provides both **distance** and **similarity** measures for certain algorithms: - **Distance measures**: Return `0.0` for identical strings and higher values for more different strings - Examples: `Levenshtein::distance()`, `Hamming::distance()`, `Jaro::distance()`, `JaroWinkler::distance()` - **Similarity measures**: Return `1.0` for identical strings and lower values for more different strings - Examples: `Cosine::similarity()`, `Jaccard::index()`, `Jaro::similarity()`, `JaroWinkler::similarity()` For Jaro and Jaro-Winkler algorithms, both functions are available: - `similarity()` returns values from 0.0 (completely different) to 1.0 (identical) - `distance()` returns values from 0.0 (identical) to 1.0 (completely different) - The relationship is: `distance = 1.0 - similarity` Usage ----- [](#usage) ### Basic Usage [](#basic-usage) ``` use Edgaras\StrSim\Levenshtein; use Edgaras\StrSim\DamerauLevenshtein; use Edgaras\StrSim\Hamming; use Edgaras\StrSim\Jaro; use Edgaras\StrSim\JaroWinkler; use Edgaras\StrSim\LCS; use Edgaras\StrSim\SmithWaterman; use Edgaras\StrSim\NeedlemanWunsch; use Edgaras\StrSim\Cosine; use Edgaras\StrSim\Jaccard; use Edgaras\StrSim\MongeElkan; // Detecting spelling error distance in user input Levenshtein::distance("kitten", "sitting"); // Returns: 3 // Detecting typo distance with transposition correction DamerauLevenshtein::distance("abcd", "acbd"); // Returns: 1 // Bit-level error detection (equal-length only) Hamming::distance("1011101", "1001001"); // Returns: 2 // Comparing short strings with transposition support Jaro::similarity("dixon", "dicksonx"); // Returns: 0.767 (similarity) Jaro::distance("dixon", "dicksonx"); // Returns: 0.233 (distance = 1 - similarity) // Matching names with common prefixes JaroWinkler::similarity("martha", "marhta"); // Returns: 0.961 (similarity) JaroWinkler::distance("martha", "marhta"); // Returns: 0.039 (distance = 1 - similarity) // Finding common subsequence in DNA fragments LCS::length("ACCGGTCGAGTGCGCGGAAGCCGGCCGAA", "GTCGTTCGGAATGCCGTTGCTCTGTAAA"); // Returns: 13 // Local alignment score for substring match SmithWaterman::score("ACACACTA", "AGCACACA"); // Returns: 11 // Global alignment score for complete sequence match NeedlemanWunsch::score("GATTACA", "GCATGCU"); // Returns: 0 // Comparing word frequency in short texts Cosine::similarity("night", "nacht"); // Returns: 0.6 // Comparing embedding vectors from NLP model Cosine::similarityFromVectors([0.1, 0.2, 0.3], [0.1, 0.3, 0.4]); // Returns: 0.925 // Comparing token overlap in short strings Jaccard::index("abc", "bcd"); // Returns: 0.5 // Fuzzy match between two multi-word names MongeElkan::similarity("john smith", "jon smythe"); // Returns: 0.822 ``` ### Unicode and Multibyte Examples [](#unicode-and-multibyte-examples) ``` // All algorithms support Unicode characters Levenshtein::distance("café", "caffe"); // Returns: 2 Levenshtein::distance("こんにちは", "こんにちわ"); // Returns: 1 // Emoji and complex characters Levenshtein::distance("🚀🌟", "🚀⭐"); // Returns: 1 Hamming::distance("👍🏽", "👍🏾"); // Returns: 1 // Different scripts and languages Jaro::similarity("привет", "привет"); // Returns: 1.0 (identical) Jaro::distance("привет", "привет"); // Returns: 0.0 (no distance) JaroWinkler::similarity("عربي", "عربى"); // Returns: 0.9 (high similarity) JaroWinkler::distance("عربي", "عربى"); // Returns: 0.1 (low distance) // ZWJ sequences and combining marks Levenshtein::distance("👨‍👩‍👧‍👦", "👨👩👧👦"); // Returns: 3 Levenshtein::distance("é", "e\u{0301}"); // Returns: 2 ``` ### Custom Scoring [](#custom-scoring) ``` // Smith-Waterman with custom scoring SmithWaterman::score("ACGT", "ACGT", match: 5, mismatch: -2, gap: -1); // Returns: 20 // Needleman-Wunsch with custom parameters NeedlemanWunsch::score("ACGT", "ACGT", match: 3, mismatch: -1, gap: -2); // Returns: 12 // Jaro-Winkler with custom prefix scaling JaroWinkler::similarity("prefix_test", "prefix_demo", 0.2); // Custom scale factor for similarity JaroWinkler::distance("prefix_test", "prefix_demo", 0.2); // Custom scale factor for distance ``` ### Error Handling [](#error-handling) ``` try { // This will throw InvalidArgumentException for unequal lengths Hamming::distance("abc", "abcd"); } catch (InvalidArgumentException $e) { echo $e->getMessage(); // "Strings must be of equal length." } try { // This will throw InvalidArgumentException for invalid UTF-8 Levenshtein::distance("valid", "\xFF\xFF"); } catch (InvalidArgumentException $e) { echo $e->getMessage(); // "Input strings must be valid UTF-8." } try { // This will throw InvalidArgumentException for mismatched vector lengths Cosine::similarityFromVectors([1, 2], [1, 2, 3]); } catch (InvalidArgumentException $e) { echo $e->getMessage(); // "Vectors must be the same length." } ``` Useful links ------------ [](#useful-links) - [Levenshtein](https://en.wikipedia.org/wiki/Levenshtein_distance) - [Damerau–Levenshtein](https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance) - [Hamming](https://en.wikipedia.org/wiki/Hamming_distance) - [Jaro](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) - [Jaro–Winkler](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) - [Longest Common Subsequence (LCS)](https://en.wikipedia.org/wiki/Longest_common_subsequence) - [Smith–Waterman](https://en.wikipedia.org/wiki/Smith%E2%80%93Waterman_algorithm) - [Needleman–Wunsch](https://en.wikipedia.org/wiki/Needleman%E2%80%93Wunsch_algorithm) - [Cosine Similarity](https://en.wikipedia.org/wiki/Cosine_similarity) - [Jaccard Index](https://en.wikipedia.org/wiki/Jaccard_index) - [Monge–Elkan](https://en.wikipedia.org/wiki/Monge%E2%80%93Elkan_algorithm) ### Health Score 33 — LowBetter than 75% of packages Maintenance66 Regular maintenance activity Popularity8 Limited adoption so far Community6 Small or concentrated contributor base Maturity44 Maturing project, gaining track record Bus Factor1 Top contributor holds 100% of commits — single point of failure How is this calculated?**Maintenance (25%)** — Last commit recency, latest release date, and issue-to-star ratio. Uses a 2-year decay window. **Popularity (30%)** — Total and monthly downloads, GitHub stars, and forks. Logarithmic scaling prevents top-heavy scores. **Community (15%)** — Contributors, dependents, forks, watchers, and maintainers. Measures real ecosystem engagement. **Maturity (30%)** — Project age, version count, PHP version support, and release stability. ### Release Activity Cadence Unknown Total 1 Last Release 199d ago ### Community Maintainers ![](https://www.gravatar.com/avatar/c573df2af2e1cf9b81eda9b79fdfbed86db3631a2d121b7ad3c9a2994dc06ed7?d=identicon)[bl4ckbon3](/maintainers/bl4ckbon3) --- Top Contributors [![bl4ckbon3](https://avatars.githubusercontent.com/u/4352039?v=4)](https://github.com/bl4ckbon3 "bl4ckbon3 (2 commits)") --- Tags stringmultibytecomparisontextAlgorithmdistancesimilarityjaro winklerlevenshteinJarofuzzylcsJaccarddamerau-levenshteinsmith-watermanneedleman-wunschcosinemonge-elkan ### Code Quality TestsPHPUnit ### Embed Badge ![Health badge](/badges/bl4ckbon3-strsim/health.svg) ``` [![Health](https://phpackages.com/badges/bl4ckbon3-strsim/health.svg)](https://phpackages.com/packages/bl4ckbon3-strsim) ``` ### Alternatives [edgaras/strsim Collection of string similarity and distance algorithms in PHP including Levenshtein, Damerau-Levenshtein, Jaro-Winkler, and more 2423.0k](/packages/edgaras-strsim)[danielstjules/stringy A string manipulation library with multibyte support 2.4k26.0M191](/packages/danielstjules-stringy)[atomescrochus/laravel-string-similarities Compare two string and get a similarity percentage 70160.1k2](/packages/atomescrochus-laravel-string-similarities)[voku/stringy A string manipulation library with multibyte support 1783.8M19](/packages/voku-stringy)[oefenweb/damerau-levenshtein Get text similarity level with Damerau-Levenshtein distance 40265.8k4](/packages/oefenweb-damerau-levenshtein)[phootwork/lang Missing PHP language constructs 1224.8M8](/packages/phootwork-lang) PHPackages © 2026 [Directory](/)[Categories](/categories)[Trending](/trending)[Changelog](/changelog)[Analyze](/analyze)