edgaras/strsim

Collection of string similarity and distance algorithms in PHP including Levenshtein, Damerau-Levenshtein, Jaro-Winkler, and more

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Details

github.com/Edgaras0x4E/StrSim

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v1.0.0 2025-05-25 13:30 UTC

Requires

  • php: >=8.3.0

Requires (Dev)

MIT ebe3da4a15815130382f893592ff25b04c7bfcad

textdistancestringAlgorithmcomparisonlevenshteinsimilarityfuzzylcsJaccardJarodamerau-levenshteinjaro-winklersmith-watermanneedleman-wunschcosinemonge-elkan

This package is auto-updated.

Last update: 2025-05-25 13:36:57 UTC

README

A collection of string similarity and distance algorithms implemented in PHP. This library provides standalone static methods for computing various similarity metrics, useful in natural language processing, fuzzy matching, spell checking, and bioinformatics.

Requirements

  • PHP 8.3+
  • Composer

Installation

  1. Use the library via Composer:
composer require edgaras/strsim
  1. Include the Composer autoloader:
require __DIR__ . '/vendor/autoload.php';

Supported Algorithms

Class Method Description
Levenshtein distance() Measures the number of insertions, deletions, or substitutions.
DamerauLevenshtein distance() Levenshtein with transpositions included.
Hamming distance() Counts differing positions (requires equal-length strings).
Jaro distance() Measures similarity based on character matches and transpositions.
JaroWinkler distance() Jaro with a prefix match boost for similar string starts.
LCS length() Returns the length of the longest common subsequence.
SmithWaterman score() Local alignment scoring for best-matching subsequences.
NeedlemanWunsch score() Global alignment scoring for entire string similarity.
Cosine similarity() Measures similarity via character frequency vectors.
Cosine similarityFromVectors() Computes cosine similarity for numeric vector inputs.
Jaccard index() Ratio of shared to total unique characters.
MongeElkan similarity() Average best-word similarity using Jaro-Winkler internally.

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");  

// Detecting typo distance with transposition correction
DamerauLevenshtein::distance("abcd", "acbd");  

// Bit-level error detection (equal-length only)
Hamming::distance("1011101", "1001001");  

// Comparing short strings with transposition support
Jaro::distance("dixon", "dicksonx");  

// Matching names with common prefixes
JaroWinkler::distance("martha", "marhta");  

// Finding common subsequence in DNA fragments
LCS::length("ACCGGTCGAGTGCGCGGAAGCCGGCCGAA", "GTCGTTCGGAATGCCGTTGCTCTGTAAA"); 

// Local alignment score for substring match
SmithWaterman::score("ACACACTA", "AGCACACA");  

// Global alignment score for complete sequence match
NeedlemanWunsch::score("GATTACA", "GCATGCU");  

// Comparing word frequency in short texts
Cosine::similarity("night", "nacht");  

// Comparing embedding vectors from NLP model
Cosine::similarityFromVectors([0.1, 0.2, 0.3], [0.1, 0.3, 0.4]);  

// Comparing token overlap in short strings
Jaccard::index("abc", "bcd"); 

// Fuzzy match between two multi-word names
MongeElkan::similarity("john smith", "jon smythe");

Useful links