Package Exports
- vectorious
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Readme

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@beta
# or, if you don't want C++ bindings
$ npm install vectorious@beta --no-optionalimport v from 'vectorious';
const x = v.random(2, 2);
/*
x: NDArray {
data: Float32Array [
0.38323071599006653,
0.9094724655151367,
0.8513918519020081,
0.2443944215774536
],
dtype: 'float32',
length: 4,
shape: [ 2, 2 ]
}
*/
const y = v.range(0, 9).reshape(3, 3);
/*
y: NDArray {
data: Float32Array [
0, 1, 2, 3, 4,
5, 6, 7, 8
],
dtype: 'float32',
length: 9,
shape: [ 3, 3 ]
}
*/
const z = v.array([[1, 2], [3, 4]]);
x.add(z);
/*
x: NDArray {
data: Float32Array [
1.3832306861877441,
2.9094724655151367,
3.8513917922973633,
4.244394302368164
],
dtype: 'float32',
length: 4,
shape: [ 2, 2 ]
}
*/In browser
Download dist/vectorious.min.js or search for vectorious on cdnjs.
<script src="vectorious.min.js"></script><script>
var A = v.array([[1], [2], [3]]),
B = v.array([[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
Benchmarks
Run benchmarks with
$ npm run benchmarkContributors
This project exists thanks to all the people who contribute. [Contribute].
Backers
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Sponsors
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