Installing via NuGet

Install-Package Bit.Simila

What is Simila?

Are Color and Colour equal? No!

if("Color"=="Coluor")// Always falseif("The Candy Shop"=="The Kandi Schap")// Always false

But they are Similar in Simila!

if(simila.AreSimilar("Color","Colour"))// It's true now!if(simila.AreSimilar("The Candy Shop","The Kandi Schap"));// It's true now!

How to use

varsimila=newSimila();// Comparing Wordssimila.AreSimilar("Lamborghini","Lanborgini");// True// Comparing Expressionssimila.AreSimilar("Lamborghini is some great car","Lanborgini is some graet kar");// True

Customizing Simila

Treshold

You set the sensivity of similarity by setting Treshold. If not set, default value is 0.6 which means it considers similar if they are 60% similar

// Are similar if their at least 50% similar.varsimilaEasy=newSimila(){Treshold=0.5};// considered as similar.similaEasy.IsSimilar("Lamborghini","Lanborgni");// True, They are 50% similar.// Are similar if their at least 80% similar.varsimilaTough=newSimila(){Treshold=0.8};// considered as NOT similar!similaEasy.AreSimilar("Lamborghini","Lanborgni");// False, Not 80% similar.

Similarity Resolver

Similarity Resolvers are different algorithms which Simila can use for similarity checking. Each algorithm works fine it is being used in its proper scenario.

There are 3 types of similarity resolvers available in Simila:

• Levenshtein (Default): It works good if we need them to look similar. You can read more about Levenshtein here: Levenshtein Algorithm
• Soundex: It works good if we need them to sound similar. You can read more about Soundex here: Soundex Algorithm
• SharedPair: It works good if we need them to structured similar.

You can configure simila to use a specific algorithm. We call them Resolvers.

Using Soudex Resolver

varsimilaSounedx=newSimila(){Resolver=newSoundexSimilarityResolver()};

Using SharedPair Resolver

varsimilaSharedPair=newSimila(){Resolver=newSharedPairSimilarityResolver()};

Using Levenshtein Resolver

Levenshtein is even more configurable. You can set the accepted mistakes both character level and word level. In this example we told Simila to consider color and colour words similar.

varsimila=newSimila(){Resolver=newPhraseSimilarityResolver(newWordSimilarityResolver(newMistakeRepository<Word>(newMistake<Word>[]{("color","colour",1)})))};

Also you can add some character level accepted mistakes. In this example we told Simila to not only consider color and colour similar, but also consider c and k similar too.

varsimila=newSimila(){Resolver=newPhraseSimilarityResolver(newWordSimilarityResolver(newMistakeRepository<Word>(newMistake<Word>[]{("color","colour",1)}),newCharacterSimilarityResolver(newMistakeRepository<char>(newMistake<char>[]{('c','k',1)}))))};