text_comparison_score_codespark 0.0.5

The TextComparisonScore package is a straightforward and efficient tool for comparing two strings and calculating their similarity score using the Levenshtein distance algorithm.

text_comparison_score_codespark

A simple yet powerful Dart package that allows you to compare two strings and determine the match percentage between them using multiple algorithms, including the Levenshtein distance and Jaro-Winkler distance.

Features

• Levenshtein Distance: Calculates the minimum number of single-character edits (insertions, deletions, or substitutions) required to change one string into the other.
• Jaro-Winkler Distance: Measures the similarity between two strings, taking into account the number of matching characters and transpositions, with a boost for common prefixes.
• Match Percentage: Returns the match percentage between two strings, indicating how similar they are.
• Case Sensitivity Option: Allows optional case sensitivity in string comparisons.
• Multiple Algorithms: Choose between different algorithms, such as Levenshtein and Jaro-Winkler, for your comparison needs.

Installation

Add the following to your pubspec.yaml:

dependencies:
  text_comparison_score_codespark: ^0.0.5

Then run:

flutter pub get

Usage

Here's how to use the TextComparisonScore class to calculate the match percentage between two strings using different algorithms:

import 'package:text_comparison_score_codespark/text_comparison_score_codespark.dart';

void main() {
  // Example 1: Simple Levenshtein comparison
  String string1 = "kitten";
  String string2 = "sitting";

  double matchPercent = TextComparisonScore.calculateScore(string1, string2, algorithm: ComparisonAlgorithm.levenshtein);
  print("Levenshtein Match Percentage between '$string1' and '$string2': $matchPercent%");

  // Example 2: Jaro-Winkler comparison
  double jaroMatchPercent = TextComparisonScore.calculateScore(string1, string2, algorithm: ComparisonAlgorithm.jaroWinkler);
  print("Jaro-Winkler Match Percentage between '$string1' and '$string2': $jaroMatchPercent%");

  // Example 3: Identical strings
  String identical1 = "flutter";
  String identical2 = "flutter";

  double identicalMatchPercent = TextComparisonScore.calculateScore(identical1, identical2);
  print("Match Percentage between identical strings '$identical1' and '$identical2': $identicalMatchPercent%");

  // Example 4: Case insensitive comparison
  String caseSensitive1 = "Hello";
  String caseSensitive2 = "hello";

  double caseSensitiveMatchPercent = TextComparisonScore.calculateScore(caseSensitive1, caseSensitive2, caseSensitive: false);
  print("Match Percentage between '$caseSensitive1' and '$caseSensitive2' (case insensitive): $caseSensitiveMatchPercent%");
}

Example Output

- **Levenshtein Match Percentage between** `'kitten'` **and** `'sitting'`: `57.14285714285714%`
- **Jaro-Winkler Match Percentage between** `'kitten'` **and** `'sitting'`: `74.74%`
- **Match Percentage between identical strings** `'flutter'` **and** `'flutter'`: `100.0%`
- **Match Percentage between** `'Hello'` **and** `'hello'` **(case insensitive)**: `100.0%`

Future Updates

In future versions, this package will include:

1. Cosine Similarity: Measures the cosine of the angle between two vectors, which can be used for similarity between text strings.
2. Soundex: A phonetic algorithm for indexing names by sound, as pronounced in English.
3. Damerau-Levenshtein Distance: Extends Levenshtein distance by considering transpositions of two adjacent characters as a single edit.
4. Hamming Distance: Measures the number of differing bits between two binary strings.
5. Normalized Distance Measures: Provides normalized versions of distance metrics to return values between 0 and 1.
6. String Tokenization & N-grams: Support for splitting strings into tokens and analyzing n-grams.
7. Customizable Weighting: Allows users to assign custom weights to different types of edits.
8. Multi-Language Support: Ensures that algorithms work with various character sets and languages.
9. Threshold-based Matching: Returns whether the match percentage is above a user-defined threshold.
10. Performance Optimization for Large Texts: Implements efficient data structures and parallel processing to handle large texts.
11. Batch Comparison: Allows users to compare a single string against a batch of other strings, returning the most similar ones.
12. Detailed Comparison Report: Provides a detailed report with multiple similarity metrics between two strings.
13. API for Custom Comparison Functions: Enables users to define and plug in their custom comparison functions.

License

This project is licensed under the MIT License.