Soenneker.Utils.Strings.LevenshteinDistance 3.0.215

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dotnet add package Soenneker.Utils.Strings.LevenshteinDistance --version 3.0.215                
NuGet\Install-Package Soenneker.Utils.Strings.LevenshteinDistance -Version 3.0.215                
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<PackageReference Include="Soenneker.Utils.Strings.LevenshteinDistance" Version="3.0.215" />                
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paket add Soenneker.Utils.Strings.LevenshteinDistance --version 3.0.215                
#r "nuget: Soenneker.Utils.Strings.LevenshteinDistance, 3.0.215"                
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// Install Soenneker.Utils.Strings.LevenshteinDistance as a Cake Addin
#addin nuget:?package=Soenneker.Utils.Strings.LevenshteinDistance&version=3.0.215

// Install Soenneker.Utils.Strings.LevenshteinDistance as a Cake Tool
#tool nuget:?package=Soenneker.Utils.Strings.LevenshteinDistance&version=3.0.215                

alternate text is missing from this package README image alternate text is missing from this package README image alternate text is missing from this package README image

alternate text is missing from this package README image Soenneker.Utils.Strings.LevenshteinDistance

A utility library for comparing strings via the Levenshtein Distance algorithm

Installation

dotnet add package Soenneker.Utils.Strings.LevenshteinDistance

Why Levenshtein Distance?

Levenshtein Distance, also known as Edit Distance, is a widely used metric for measuring the similarity between two strings. It calculates the minimum number of operations required to transform one string into the other, where operations include:

  • Insertion of a character.
  • Deletion of a character.
  • Substitution of a character.

Levenshtein Distance is particularly useful in applications like:

Flexible String Comparison:

It handles strings of unequal length and allows for more flexible comparisons.

Real-World Scenarios:

It's well-suited for tasks like:

  • Fuzzy string matching.
  • Spelling correction.
  • DNA sequence analysis.
  • Natural language processing.

Position-Aware:

It captures positional changes and structural differences more effectively than set-based metrics like Jaccard or Hamming Distance.

Comprehensive Error Handling:

Unlike simpler metrics, it accounts for insertions and deletions, making it robust for strings with typos or omissions.


Usage

var text1 = "kitten";
var text2 = "sitting";

int distance = LevenshteinDistanceUtil.ComputeDistance(text1, text2); // 3
double similarityPercentage = LevenshteinDistanceUtil.CalculatePercentage(text1, text2); // ~57.14

This library is efficient, straightforward, and ideal for handling real-world string similarity comparisons where flexibility and accuracy are key.

Product Compatible and additional computed target framework versions.
.NET net9.0 is compatible.  net9.0-android was computed.  net9.0-browser was computed.  net9.0-ios was computed.  net9.0-maccatalyst was computed.  net9.0-macos was computed.  net9.0-tvos was computed.  net9.0-windows was computed. 
Compatible target framework(s)
Included target framework(s) (in package)
Learn more about Target Frameworks and .NET Standard.
  • net9.0

    • No dependencies.

NuGet packages

This package is not used by any NuGet packages.

GitHub repositories

This package is not used by any popular GitHub repositories.

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