A linear algebra library, written in TypeScript and accelerated with C++ bindings to BLAS and LAPACK.
Usage
Follow the installation instructions in nlapack and nblas to get maximum performance.
In node.js
# with C++ bindings
$ npm install vectorious
# or, if you don't want C++ bindings
$ npm install vectorious --no-optional
import v = require('vectorious');
const x = v.random(2, 2);
/*
array([
[
0.26472008228302,
0.4102575480937958
],
[
0.4068726599216461,
0.4589384198188782
]
], dtype=float32)
*/
const y = v.range(0, 9).reshape(3, 3);
/*
array([
[ 0, 1, 2 ],
[ 3, 4, 5 ],
[ 6, 7, 8 ]
], dtype=float32)
*/
const z = v.array([[1, 2], [3, 4]]);
/*
array([ [ 1, 2 ], [ 3, 4 ] ], dtype=float32)
*/
x.add(z);
/*
array([
[
1.26472008228302,
2.410257577896118
],
[
3.4068727493286133,
4.4589385986328125
]
], dtype=float32)
*/
In browser
Download dist/vectorious.min.js or search for vectorious on cdnjs.
<script src="vectorious.min.js"></script>
<script>
const A = v.array([[1], [2], [3]]);
const B = v.array();
const 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
Benchmarks
Run benchmarks with
$ npm run benchmark