DiSi

Library of Distance and Similarity (and more) functions.

How to use

Require this package via npm, then:

1. In a node application:

    const Disi = require('disi');
   
    let v = [1,2];
    let u = [3,2];
    let euclidian = Disi.euclidian(u, v));
    console.log(euclidian);
   

2. For use in web pages

    <script src="path/to/build/disi.js"></script>
   
    <script>
        let v = [1,2];
        let u = [3,2];
        let euclidian = Disi.euclidian(u, v));
        alert(euclidian);
    </script>
   

You can refer to the examples folder for complete examples.

Important:

Some functionality is still being implemented or not existent at all, in the following sections, the functions preceded by a [WIP] are either not fully or not implemented at all.

Distance measures:

• Euclidian --> Disi.euclidian(vector1, vector2)
• Manhattan --> Disi.manhattan(vector1, vector2)
• Supremum --> Disi.supremum(vector1, vector2)
• Minkowski --> Disi.minkowski(vector1, vector2, rank)
• [WIP] Mahalanobis --> Disi.mahalanobis(vector1, vector2, covariance)

Similarity measures:

• Simple Matching Coefficient --> Disi.sm(vector1, vector2)
• Jaccard Coefficient --> Disi.jc(vector1, vector2)
• Extended Jaccard Coefficient (executes Tanimoto) --> Disi.ejc(vector1, vector2)
• Tanimoto --> Disi.tanimoto(vector1, vector2)
• Dice Coefficient --> Disi.dice(vector1, vector2)
• Generalized Jaccard Coefficient --> Disi.gjc(vector1, vector2)
• Cosine similarity --> Disi.cosine(vector1, vector2)

Additionally:

• [WIP] Chi-Square test --> Disi.chi(vector1, vector2)
• [WIP] Person correlation --> Disi.person(vector1, vector2)
• [WIP] Covariance --> Disi.covariance([vector1, vector2, vector3, ...])