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This lightweight npm package can be used to calculate the similarity of strings. It supports both the best known Levenshtein distance and the slightly more accurate Sørensen dice coefficient.
Using Node.js install the package using shell command:
npm install cmpstr
Load the package into your project:
const cmpstr = require( 'cmpstr' );
Sample of how to use the package in your code:
let str1 = 'kitten';
let str2 = 'sitting';
let distance = cmpstr.levenshteinDistance( str1, str2 );
// expected 3
let dice = cmpstr.diceCoefficient( str1, str2 );
// expected 0.3636363636363636
let closest = cmpstr.diceClosest( 'best', [
'better', 'bestest', 'well', 'good'
] );
// expected bestest
The npm package cmpstr
supports two different methods for determining the similarity of two strings. The Levenshtein distance, as the minimum number of inserting, deleting and replacing operations to convert one string into another, and the Sørensen-Dice coefficient to measure the similarity of two samples.
Learn more about both by visiting these links:
levenshteinDistance( a, b )
Calculates the difference between two strings a
and b
and returns the Levenshtein distance as an integer value.
levenshtein( a, b )
Returns the match percentage of two strings a
and b
. The output value is in the range 0..1
as a floating point number.
levenshteinClosest( str, arr )
Returns the best match of the string str
against the array arr
of passed strings. The function returns the most closely matched string found in the array.
diceCoefficient( a, b )
This function evaluates the similarity of two given strings a
and b
as percentage value according to the Sørensen-Dice coefficient and returns the result as floating point number.
diceClosest( str, arr )
As another way to find the best match between the string str
and a given array arr
of samples, this function uses the Sørensen-Dice coefficient. It returns the most matching string as well.
FAQs
CmpStr is a lightweight, fast and well performing package for calculating string similarity
We found that cmpstr demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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