JSPM – vectorious@5.3.0

Package Exports

vectorious

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Readme

version travis

A linear algebra library, written in TypeScript and accelerated with C++ bindings to BLAS.

Usage

In node.js

# with BLAS bindings
$ npm install vectorious

# or, if you don't want BLAS bindings
$ npm install vectorious --no-optional

import { Matrix, Vector } from 'vectorious';

Matrix.random(2, 2);
/*
Matrix {
  shape: [ 2, 2 ],
  data:
   Float64Array [
     0.7041265660588281,
     0.6186458305857421,
     0.032954109874604454,
     0.5198025534810546 ],
  type: [Function: Float64Array] }
*/

Vector.random(4, -5, 5, Int8Array);
/*
Vector {
  type: [Function: Int8Array],
  data: Int8Array [ -2, 2, 2, -1 ],
  length: 4 }
*/

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.

Contributors

This project exists thanks to all the people who contribute. [Contribute].

Backers

Thank you to all our backers! 🙏 [Become a backer]

vectorious