cataloteca.LevenshteinDistance 1.0.1

dotnet add package cataloteca.LevenshteinDistance --version 1.0.1
NuGet\Install-Package cataloteca.LevenshteinDistance -Version 1.0.1
This command is intended to be used within the Package Manager Console in Visual Studio, as it uses the NuGet module's version of Install-Package.
<PackageReference Include="cataloteca.LevenshteinDistance" Version="1.0.1" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add cataloteca.LevenshteinDistance --version 1.0.1
#r "nuget: cataloteca.LevenshteinDistance, 1.0.1"
#r directive can be used in F# Interactive and Polyglot Notebooks. Copy this into the interactive tool or source code of the script to reference the package.
// Install cataloteca.LevenshteinDistance as a Cake Addin
#addin nuget:?package=cataloteca.LevenshteinDistance&version=1.0.1

// Install cataloteca.LevenshteinDistance as a Cake Tool
#tool nuget:?package=cataloteca.LevenshteinDistance&version=1.0.1

Levenshtein Distance

Algorithm used to calculate the distance and similarity between two strings.

How to use Levenshtein Distance Algorithm?

  • Use Leveshtein project
	using cataloteca.algorithms.LevenshteinDistance;
  • Define your reference, ie the string you want to compare with your data.
	string reference = "Example";
  • Define your targets, ie the data you want to compare with the reference.
	List<string> exampleList = new List<string>(new string[] {
        "example 1",
        "example 2",
        "example 3"
    });
  • Use the method to calculate distance and similarity. By default the method returns the distance and similarity values, but you may not return one of the metrics

	// Method that calculates string distance and similarity
    var bothCalc = new LevenshteinInput(reference, exampleList);
    var data = bothCalc.CallLevenstheinCalc(similarity:true, distance:true);

	// Console Response Sample

	// example
	// -------------------
	// example 1
	// Distance: 2,00 | Similarity: 77,78%

	// example 2
	// Distance: 2,00 | Similarity: 77,78%

	// example 3
	// Distance: 2,00 | Similarity: 77,78%
  • Use the method to calculate similarity only

	// Method that calculates similarity only
		var similarityCalc = new LevenshteinInput(reference, exampleList);
		var data = similarityCalc.CallLevenstheinCalc(similarity:true, distance:false);


	// Console Response Sample

	// example
	// -------------------
	// example 1
	// Similarity: 77,78%

	// example 2
	// Similarity: 77,78%

	// example 3
	// Similarity: 77,78%

  • Use the method to calculate distance only
	// Method that calculates only the distance
	var distanceCalc = new LevenshteinInput(reference, exampleList);
	var data = distanceCalc.CallLevenstheinCalc(similarity:false, distance:true);

	// Console Response Sample

	// example
	// -------------------
	// example 1
	// Distance: 2,00

	// example 2
	// Distance: 2,00

	// example 3
	// Distance: 2,00

Product Compatible and additional computed target framework versions.
.NET net5.0 was computed.  net5.0-windows was computed.  net6.0 was computed.  net6.0-android was computed.  net6.0-ios was computed.  net6.0-maccatalyst was computed.  net6.0-macos was computed.  net6.0-tvos was computed.  net6.0-windows was computed.  net7.0 was computed.  net7.0-android was computed.  net7.0-ios was computed.  net7.0-maccatalyst was computed.  net7.0-macos was computed.  net7.0-tvos was computed.  net7.0-windows was computed.  net8.0 was computed.  net8.0-android was computed.  net8.0-browser was computed.  net8.0-ios was computed.  net8.0-maccatalyst was computed.  net8.0-macos was computed.  net8.0-tvos was computed.  net8.0-windows was computed. 
.NET Core netcoreapp2.2 is compatible.  netcoreapp3.0 was computed.  netcoreapp3.1 was computed. 
Compatible target framework(s)
Included target framework(s) (in package)
Learn more about Target Frameworks and .NET Standard.

NuGet packages

This package is not used by any NuGet packages.

GitHub repositories

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Version Downloads Last updated
1.0.1 778 9/28/2019
1.0.0 466 9/19/2019