A linear algebra library, written in TypeScript and accelerated with C++ bindings to BLAS and LAPACK.
Follow the installation instructions in nlapack and nblas to get maximum performance.
# with C++ bindings
$ npm install vectorious
# or, if you don't want C++ bindings
$ npm install vectorious --no-optional
There are three output bundles exposed in this package.
A node.js bundle, can be found in dist/index.js
and imported with the require()
syntax:
const v = require('vectorious');
A browser bundle, can be found in dist/index.browser.js
and imported with the <script>
tag:
<script src="dist/index.browser.js" />
It exposes a global variable named v
in the window
object and can be accessed like this:
<script>
const x = v.array([1, 2, 3]);
</script>
Added in version 6.1.0, vectorious exposes an ES module bundle at dist/index.esm.js
which can be imported using the import
syntax:
import { array } from 'vectorious';
const x = array([1, 2, 3]);
import { array, random, range } from 'vectorious';
// Create a random 2x2 matrix
const x = random(2, 2);
/*
array([
[
0.26472008228302,
0.4102575480937958
],
[
0.4068726599216461,
0.4589384198188782
]
], dtype=float64)
*/
// Create a one-dimensional vector with values from
// 0 through 8 and reshape it into a 3x3 matrix
const y = v.range(0, 9).reshape(3, 3);
/*
array([
[ 0, 1, 2 ],
[ 3, 4, 5 ],
[ 6, 7, 8 ]
], dtype=float64)
*/
// Add the second row of x to the first row of x
y.slice(0, 1).add(y.slice(1, 2))
/*
array([
[ 3, 5, 7 ],
[ 3, 4, 5 ],
[ 6, 7, 8 ]
], dtype=float64)
*/
// Swap the first and second rows of x
y.swap(0, 1);
/*
array([
[ 3, 4, 5 ],
[ 3, 5, 7 ],
[ 6, 7, 8 ]
], dtype=float64)
*/
// Create a 2x2x1 tensor
const z = v.array([[[1], [2]], [[3], [4]]]);
/*
array([
[ [ 1 ], [ 2 ] ],
[ [ 3 ], [ 4 ] ]
], dtype=float64)
*/
Basic
Machine learning
All functions are accompanied with a .spec.ts
file.
The Jest testing framework is used for testing and the whole test suite can be run using a single command:
$ npm test
All functions are accompanied with a .bench.ts
file.
Run all benchmarks with:
$ npm run benchmark
Or for a single function with:
$ npx ts-node src/core/abs.bench.ts