https://github.com/robertmenke/js-polynomial-regression

A javascript library that predicts the value of a dependent variable using polynomial regression (Gaussian-Jordan elimination)
https://github.com/robertmenke/js-polynomial-regression

Last synced: 14 days ago
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A javascript library that predicts the value of a dependent variable using polynomial regression (Gaussian-Jordan elimination)

• Host: GitHub
• URL: https://github.com/robertmenke/js-polynomial-regression
• Owner: RobertMenke
• License: mit
• Created: 2017-04-16T17:38:54.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2023-01-04T03:58:42.000Z (almost 2 years ago)
• Last Synced: 2024-12-14T10:46:39.586Z (20 days ago)
• Language: JavaScript
• Size: 6.72 MB
• Stars: 23
• Watchers: 3
• Forks: 8
• Open Issues: 18
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
![alt tag](/example/polynomial_regression_example.png)

# About

This library can make predictions about data using a technique called polynomial regression.

Polynomial regression uses a technique called Gaussian-Jordan elimination, which creates a predictive model that more accurately fits non-linear data.

# How to use

Let's say you have your typical cartesian coordinates (x and y coordinates)

```javascript

const data = [

    {

        x : 5,

        y : 8

    },

    {

        x : 9,

        y : 12

    }

    // and so on...

];         

```

This library will read this data, and then make a prediction about a y value, given an x.

```javascript

//This library is a UMD module (thanks webpack!)

import PolynomialRegression from "js-polynomial-regression";

//Factory function - returns a PolynomialRegression instance. 2nd argument is the degree of the desired polynomial equation.

const model = PolynomialRegression.read(data, 3);

//terms is a list of coefficients for a polynomial equation. We'll feed these to predict y so that we don't have to re-compute them for every prediction.

const terms = model.getTerms();

//10 is just an example of an x value, the second argument is the independent variable being predicted.

const prediction = model.predictY(terms, 10);

```

That's it! I've created an example using random data in the example folder of this repo. Please use the issues section to communicate any bugs, questions, or feature requests.