DiSi
Library of Distance and Similarity (and more) functions.
How to use
Require this package via npm, then:
-
In a node application:
const Disi = ;let v = 12;let u = 32;let euclidian = Disi);console; -
For use in web pages
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, ...])