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https://github.com/mateogianolio/vectorious
Linear algebra in TypeScript.
https://github.com/mateogianolio/vectorious
blas high-performance-computing javascript linear-algebra linear-algebra-library machine-learning matrix typescript vector
Last synced: 4 days ago
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Linear algebra in TypeScript.
- Host: GitHub
- URL: https://github.com/mateogianolio/vectorious
- Owner: mateogianolio
- License: mit
- Created: 2015-03-24T11:49:44.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2024-06-16T09:54:25.000Z (8 months ago)
- Last Synced: 2025-01-16T16:11:51.197Z (11 days ago)
- Topics: blas, high-performance-computing, javascript, linear-algebra, linear-algebra-library, machine-learning, matrix, typescript, vector
- Language: TypeScript
- Homepage: https://docs.vectorious.org
- Size: 42.8 MB
- Stars: 921
- Watchers: 18
- Forks: 44
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
A linear algebra library, written in TypeScript and accelerated with C++ bindings to BLAS and LAPACK.
### Installation
Follow the installation instructions in [nlapack](https://github.com/nperf/nlapack) and [nblas](https://github.com/nperf/nblas) to get maximum performance.
```bash
# 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.
#### CommonJS
A node.js bundle, can be found in `dist/index.js` and imported with the `require()` syntax:
```typescript
const v = require('vectorious');
```
#### Browser
A browser bundle, can be found in `dist/index.browser.js` and imported with the `` tag:
```html
<script src="dist/index.browser.js" />
```
It exposes a global variable named `v` in the `window` object and can be accessed like this:
```html
<script>
const x = v.array([1, 2, 3]);
```
#### ES module
Added in version 6.1.0, vectorious exposes an ES module bundle at `dist/index.mjs` which can be imported using the `import` syntax:
```typescript
import { array } from 'vectorious';
const x = array([1, 2, 3]);
```
### Usage
Unless stated otherwise, all operations are in-place, meaning that the result of the operation overwrites data in the current (or in the static case leftmost) array. To avoid this, an explicit `copy` call is needed before the operation (`copy(x)` or `x.copy()`).
```javascript
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 = 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 = array([
[[1], [2]],
[[3], [4]],
]);
/*
array([
[ [ 1 ], [ 2 ] ],
[ [ 3 ], [ 4 ] ]
], dtype=float64)
*/
```
### Documentation
- [**API Documentation**](https://docs.vectorious.org/vectorious/6.1.0/)
### Examples
**Basic**
- [**Solving linear systems of equations**](https://github.com/mateogianolio/vectorious/tree/master/examples/solve.ts)
- [**Using low-level BLAS routines**](https://github.com/mateogianolio/vectorious/tree/master/examples/blas.ts)
**Machine learning**
- [**Neural network**](https://github.com/mateogianolio/vectorious/tree/master/examples/neural-network.ts) (by [@lucidrains](https://github.com/lucidrains))
- [**Logistic regression**](https://github.com/mateogianolio/vectorious/tree/master/examples/logistic-regression.ts)
### Testing
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:
```sh
$ npm test
```
### Benchmarks
All functions are accompanied with a `.bench.ts` file.
Run all benchmarks with:
```bash
$ npm run benchmark
```
Or for a single function with:
```
$ npx ts-node src/core/abs.bench.ts
```