A high performance linear algebra library, written in JavaScript and optimized with C++ bindings to BLAS, SPBLAS, LAPACK.
Forked from mateogianolio/vectorious, added sparse matrix/vector support (by using SPBLAS, nodejs only) and boost of AX=B solve (by using LAPACK).
todo: documment new featues
Also see nblas-plus

Usage

In node.js

First please read and apply Preinstall section of nblas-plus

Windows is not tested.

Then install:

$ npm install vectorious-plus

var v = require('vectorious'),
    Matrix = v.Matrix,
    Vector = v.Vector,
    SpVector = v.SpVector,
    SpMatrix = v.Matrix,
    BLAS = v.BLAS; // access BLAS routines (and also SPBLAS, LAPACK)

In browser

Download a release and use it like this:

<script src="vectorious-4.x.x.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

• Solving linear systems of equations
• Using low-level BLAS routines

Machine learning

• Neural network (by @lucidrains)
• Logistic regression

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

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

• Numeric.js
• Sylvester
• Math.js

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).

Below is a graph comparing the matrix operations add, scale and transpose.

The operations were performed on matrices generated with Matrix.random(512, 512).