# brainstem

> brainstem-js is machine learning library for javascript

Latest version **0.0.2** (published 2020-12-17) · ISC license · 0 weekly downloads

## Install

```sh
npm install brainstem
pnpm add brainstem
yarn add brainstem
bun add brainstem
```

## Health

**Score 15/100 (F)** — status: abandoned.

Positive: no vulnerabilities.

Warnings: low downloads; no types; no esm support; pre 1.0.

Negative: abandoned; low maintenance score.

## Facts

| | |
|---|---|
| Version | 0.0.2 |
| Published | 2020-12-17 |
| First published | 2020-12-13 |
| Weekly downloads | 0 |
| License | ISC |
| TypeScript types | none |
| Module format | CommonJS |
| Dependencies | 2 |
| Unpacked size | 52.6 KB |
| Known vulnerabilities | 0 |
| Install scripts | no |
| Maintainers | son_xx |
| Keywords | machine-learning, supervise-learning, brainstemjs, ml, artificial-intelligence |

## Links

- npm: https://www.npmjs.com/package/brainstem
- Repository: https://github.com/rizki4106/brainstemjs
- Issues: https://github.com/rizki4106/brainstemjs/issues
- npm.io page: https://npm.io/package/brainstem

## Dependencies (2)

- [mathjs](https://npm.io/package/mathjs.md) ^8.1.0
- [sort-json-array](https://npm.io/package/sort-json-array.md) ^0.1.7

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## Recent versions

- 0.0.2 (latest) — 2020-12-17
- 0.0.1 — 2020-12-13
- 1.0.0 — 2020-12-13

## README

![](https://img.shields.io/badge/version-0.0.1-red.svg) ![](https://img.shields.io/badge/licence-MIT-green.svg) ![](https://img.shields.io/badge/maintener-github.com/rizki4106-blue.svg) ![](https://img.shields.io/badge/status-BETA-dfd3d3.svg)


[![brainstemjs.png](https://i.postimg.cc/wjQmM336/brainstemjs.png)](https://postimg.cc/cv6H5xQj)

### Brainstemjs
brainstemjs is machine learning library for javascript.

```bash
npm install brainstem
```

### Available Algorithm
| Name |
|------|
|K Nearest Neighbor (KNN)|
|Linear Regression Polynomial Order 1 and Order 2|

### Example
K Nearest Neighbors Algorithm

*try on google colab [https://colab.research.google.com/drive/1dYQ6m1bSl-6EROI5kqAXwBoaXrPx8IyS?usp=sharing](https://colab.research.google.com/drive/1dYQ6m1bSl-6EROI5kqAXwBoaXrPx8IyS?usp=sharing)*
```javascript
const {KNNeighborClassifier} = require("brainstem")
const {accuracy_score} = require("brainstem/metrics")
const {train_test_split} = require("brainstem/selection")

const x = [40, 65, 75, 80]
const y = ["E","D","B","A"]

const knn = new KNNeighborClassifier(n_neighbors = 1, distance = "manhattan")
const [x_train, x_test, y_train, y_test] = train_test_split(x,y, test_size=0.3)
knn.fit(x_train,y_train)

const y_pred = knn.predict(x_test)
console.log(accuracy_score(y_test, y_pred))
```
available distance for K Nearest Neighbors
| Name |
|------|
| Euclidean Distance |
| Manhattan Distance |
<hr/>
Linear Regression Polynomial Order 1

```javascript
const {PolynomialOrderOne} = require("brainstem")
const x = [1,2,3,4,5]
const y = [5,4,3,2,1]

const regression = new PolynomialOrderOne()
regression.fit(x,y)
const result = regression.getResult()
const pred = regression.predict([5])

console.log(result)
console.log(pred)
```
Linear Regression Polynomial Order 2

```javascript
const {PolynomialOrderTwo} = require("brainstem")
const x = [1,2,3,4,5]
const y = [5,4,3,2,1]

const regression = new PolynomialOrderTwo()
regression.fit(x,y)
const result = regression.getResult()
const pred = regression.predict([5])

console.log(result)
console.log(pred)
```

### Real Data

If you want test with real data like read csv file, json or anything else you have to use another library like [danfo.js](https://danfo.jsdata.org/).

*"Danfo.js is an open-source, JavaScript library providing high-performance, intuitive, and easy-to-use data structures for manipulating and processing structured data **Danfo.js** is heavily inspired by the **Pandas** library and provides a similar interface and API. This means users familiar with the Pandas API can easily use Danfo.js."* - https://danfo.jsdata.org/

---
_Source: https://npm.io/package/brainstem · Machine-readable twin of the npm.io package page. Health data is recomputed on every publish._