README

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.

Requirements

PHP 8.3+
Composer

Installation

Use the library via Composer:

composer require edgaras/strsim

Include the Composer autoloader:

require __DIR__ . '/vendor/autoload.php';

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

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

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::distance("dixon", "dicksonx");  // Returns: 0.767

// Matching names with common prefixes
JaroWinkler::distance("martha", "marhta");  // Returns: 0.961

// 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

// 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::distance("привет", "привет");  // Returns: 1.0
JaroWinkler::distance("عربي", "عربى");  // Returns: 0.9

// ZWJ sequences and combining marks
Levenshtein::distance("👨‍👩‍👧‍👦", "👨👩👧👦");  // Returns: 3
Levenshtein::distance("é", "e\u{0301}");  // Returns: 2

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::distance("prefix_test", "prefix_demo", 0.2);  // Custom scale factor

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."
}

edgaras / strsim

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