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https://github.com/clementreiffers/clementreiffers-linear-regression
a npm package to compute linear regression in javascript
https://github.com/clementreiffers/clementreiffers-linear-regression
javascript linear-regression machine-learning nodejs nodejs-modules npm npm-package ramda ramdajs regression regression-algorithms regression-model regression-models regression-testing regressions
Last synced: about 2 months ago
JSON representation
a npm package to compute linear regression in javascript
- Host: GitHub
- URL: https://github.com/clementreiffers/clementreiffers-linear-regression
- Owner: clementreiffers
- Created: 2022-10-10T14:12:12.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2022-12-21T17:53:40.000Z (about 2 years ago)
- Last Synced: 2024-04-24T13:21:59.100Z (8 months ago)
- Topics: javascript, linear-regression, machine-learning, nodejs, nodejs-modules, npm, npm-package, ramda, ramdajs, regression, regression-algorithms, regression-model, regression-models, regression-testing, regressions
- Language: JavaScript
- Homepage: https://www.npmjs.com/package/clementreiffers-linear-regression
- Size: 206 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# linear-regression
[](https://developer.mozilla.org/fr/docs/Web/JavaScript)
[](https://nodejs.dev/en/)
[](https://www.npmjs.com/package/clementreiffers-linear-regression)
Simple linear regression made in JavaScript.
## How to install
`npm i clementreiffers-linear-regression` or `yarn add clementreiffers-linear-regression` if you
use yarn instead of npm.
## How to use
### Linear Regression
#### The lightest function
the lightest function is very usefull if you're interested in getting only the essential
parameters
```js
import { linearRegression, predict } from "clementreiffers-linear-regression";
// import { computeLightLinearRegression } from "clementreiffers-linear-regression";
const x = [1, 2, 3, 4];
const y = [1, 2, 3, 4];
const lr = linearRegression(x, y, true); // if you want values into an Object
// executed only if true in linearRegression Function, it gives the same result as above
// computeLightLinearRegression(x, y);
const pred1 = predict([1, 2], lr);
const pred2 = predict(6, lr);
console.log(lr); // to show the object which represents the linear regression
```
by trying this example above, you will have :
```text
{ parameters: { a: 1, b: 0 } }
```
#### The loudest function
it will compute all necessary calculations and put it into the same json.
```js
import { linearRegression, predict } from "clementreiffers-linear-regression";
// import { computeLoudLinearRegression } from "clementreiffers-linear-regression";
const x = [1, 2, 3, 4];
const y = [1, 2, 3, 4];
const lr = linearRegression(x, y); // if you want values into an Object
// executed by default, it gives the same result as above
// computeLoudLinearRegression(x, y);
const pred1 = predict([1, 2], lr);
const pred2 = predict(6, lr);
console.log(lr); // to show the object which represents the linear regression
```
by trying this example above, you will have :
```text
{
parameters: { a: 1, b: 0 },
trainData: { x: [ 1, 2, 3, 4 ], y: [ 1, 2, 3, 4 ] },
trainCurvePredict: [ 1, 2, 3, 4 ],
statistics: { r2: 0.9999999999999996, cost: 0, pearson: 0.9999999999999998 }
}
```
### score
the score represents the capacity to do a linear regression with the data given.
```js
import { score } from "clementreiffers-linear-regression";
const x = [1, 2, 3, 4];
const y = [1, 2, 3, 4];
console.log(score(x, y));
```
by executing this code you will have :
```text
0.9999999999999996
```
### cost function
```js
import { linearRegression, costFunction } from "clementreiffers-linear-regression";
const x = [1, 2, 3, 4];
const y = [1, 2, 3, 4];
const lr = linearRegression(x, y);
const pred = predict(lr, x);
const cost = costFunction(y, pred);
console.log(cost);
```
by executing this function you will have :
```text
0
```
## How it is calculated
this package use the Covariance and Variance to calculate the linear regression,
see here : https://en.wikipedia.org/wiki/Linear_regression
## Contacts
any idea to improve this package ?
- email me to : [[email protected]](mailto://[email protected])
- do a git issue on [](https://github.com/clementreiffers/clementreiffers-linear-regression/issues)
- contact me on linkedin : https://www.linkedin.com/in/cl%C3%A9ment-reiffers-bb8983185/
## Links
See the source code on [](https://github.com/clementreiffers/clementreiffers-linear-regression)
See the package on [](https://www.npmjs.com/package/clementreiffers-linear-regression)