fast-levenshtein
's direct dependencies. Data on all dependencies, including transitive ones, is available via CSV download.Name | Version | Size | License | Type | Vulnerabilities |
---|---|---|---|---|---|
fastest-levenshtein | 1.0.16 | 5.96 kB | MIT | prod |
Fast-Levenshtein is a highly efficient JavaScript implementation of the Levenshtein algorithm. It provides locale-specific collator support and is designed to work both in node.js and in the browser. The Levenshtein algorithm is used for determining the smallest number of insertions, deletions, and substitutions required to change one string into another. Thus, Fast-Levenshtein helps developers calculate the "distance" between two strings, which is a crucial measure in many data science and natural language processing tasks. With extensive tests and high download counts, it's a reliable module for developers needing to harness the power of the Levenshtein algorithm.
Using Fast-Levenshtein is as simple as installing the package with a package manager like NPM and requiring it in your JavaScript code. You can install it using the command npm install fast-levenshtein
. To calculate the distance between two strings, you'd use the get
method, as shown below:
var levenshtein = require('fast-levenshtein');
var distance = levenshtein.get('back', 'book'); // 2
var distance = levenshtein.get('我愛你', '我叫你'); // 1
For locale-sensitive string comparisons, pass the { useCollator: true}
option like this:
var levenshtein = require('fast-levenshtein');
levenshtein.get('mikailovitch', 'Mikhaïlovitch', { useCollator: true}); // 1
Please remember these steps when you plan to calculate word transformation distances in your JavaScript code using Fast-Levenshtein.
The documentation for Fast-Levenshtein, containing all instructions on how to install and use the package, as well as other useful details, is available in the README file on the package's GitHub repository. The GitHub URL for the Fast-Levenshtein repository is https://github.com/hiddentao/fast-levenshtein. Please visit the GitHub page for the most in-depth guide on using Fast-Levenshtein for your text comparison needs.