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 the Levenshtein distance algorithm.

Features

• Levenshtein Distance: Calculates the minimum number of single-character edits (insertions, deletions, or substitutions) required to change one string into the other.
• Match Percentage: Returns the match percentage between two strings, indicating how similar they are.
• Case Sensitivity Option: Allows optional case sensitivity in string comparisons.

Installation

Add the following to your pubspec.yaml:

dependencies:
  text_comparison_score_codespark: ^0.0.4

Then run:

flutter pub get

Usage

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

import 'package:text_comparison_score_codespark/text_comparison_score_codespark.dart';

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

  double matchPercent = TextComparisonScore.matchPercentage(string1, string2);
  print("Match Percentage between '$string1' and '$string2': $matchPercent%");

  // Example 2: Identical strings
  String identical1 = "flutter";
  String identical2 = "flutter";

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

  // Example 3: Completely different strings
  String different1 = "apple";
  String different2 = "orange";

  double differentMatchPercent = TextComparisonScore.matchPercentage(different1, different2);
  print("Match Percentage between completely different strings '$different1' and '$different2': $differentMatchPercent%");

  // Example 4: One string is empty
  String emptyString = "";

  double emptyMatchPercent = TextComparisonScore.matchPercentage(string1, emptyString);
  print("Match Percentage between '$string1' and an empty string: $emptyMatchPercent%");

  // Example 5: Both strings are empty
  double bothEmptyMatchPercent = TextComparisonScore.matchPercentage(emptyString, emptyString);
  print("Match Percentage between two empty strings: $bothEmptyMatchPercent%");

  // Example 6: Case insensitive comparison
  String caseSensitive1 = "Hello";
  String caseSensitive2 = "hello";

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

Example Output

- **Match Percentage between** `'kitten'` **and** `'sitting'`: `57.14285714285714%`
- **Match Percentage between identical strings** `'flutter'` **and** `'flutter'`: `100.0%`
- **Match Percentage between completely different strings** `'apple'` **and** `'orange'`: `0.0%`
- **Match Percentage between** `'kitten'` **and an empty string**: `0.0%`
- **Match Percentage between two empty strings**: `100.0%`
- **Match Percentage between** `'Hello'` **and** `'hello'` **(case insensitive)**: `100.0%`

Future Updates

In future versions, this package will include:

1. Jaro-Winkler Distance: A string metric for measuring the edit distance between two sequences, giving more favorable ratings to strings that match from the beginning for a set prefix length.
2. Cosine Similarity: Measures the cosine of the angle between two vectors, which can be used for similarity between text strings.
3. Soundex: A phonetic algorithm for indexing names by sound, as pronounced in English.
4. Damerau-Levenshtein Distance: Extends Levenshtein distance by considering transpositions of two adjacent characters as a single edit.
5. Hamming Distance: Measures the number of differing bits between two binary strings.
6. Normalized Distance Measures: Provides normalized versions of distance metrics to return values between 0 and 1.
7. String Tokenization & N-grams: Support for splitting strings into tokens and analyzing n-grams.
8. Customizable Weighting: Allows users to assign custom weights to different types of edits.
9. Multi-Language Support: Ensures that algorithms work with various character sets and languages.
10. Threshold-based Matching: Returns whether the match percentage is above a user-defined threshold.
11. Performance Optimization for Large Texts: Implements efficient data structures and parallel processing to handle large texts.
12. Batch Comparison: Allows users to compare a single string against a batch of other strings, returning the most similar ones.
13. Detailed Comparison Report: Provides a detailed report with multiple similarity metrics between two strings.
14. 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.