nervestaple/vectorious

A high performance linear algebra library, written in JavaScript and optimized with C++ bindings to BLAS.

Usage

In node.js

$ npm install vectorious

var v = require('vectorious'),
    Matrix = v.Matrix,
    Vector = v.Vector,
    BLAS = v.BLAS; // access BLAS routines

Will use your local BLAS copy (if any). Some notes for different operating systems:

OSX - by default included in the Accelerate framework
Debian/Ubuntu - different options, easiest is to apt-get install libblas-dev
Windows - https://icl.cs.utk.edu/lapack-for-windows/

In browser

Download a release and use it like this:

<script src="vectorious.min.js"></script>

Or if you prefer to use a CDN:

<script src="https://cdnjs.cloudflare.com/ajax/libs/vectorious/4.8.1/vectorious.min.js"></script>

<script>
  var A = new Matrix([[1], [2], [3]]),
      B = new Matrix([[1, 3, 5]]),
      C = A.multiply(B);

  console.log('C:', C.toArray());
  /* C: [
    [1, 3, 5],
    [2, 6, 10],
    [3, 9, 15]
  ] */
</script>

Examples

Basic

Machine learning

Documentation

The documentation is located in the wiki section of this repository.

Go to wiki.

Benchmarks

Internal benchmarks are located in the wiki section of this repository.

Go to wiki.

Compared to other libraries

Note: I'm in the process of creating better benchmarks and plots. Until then, below you'll find a simple comparison between vectorious and three other popular linear algebra libraries.

The following benchmarks compare Vectorious 4.1.0 with three popular matrix/vector libraries:

The graphs show operations per second on the vertical (y) axis.

Below is a graph comparing the vector operations add, angle, dot, magnitude (aka L2-norm), normalize and scale.

The operations were performed on vectors generated with Vector.random(1048576).