https://github.com/Tom-Alexander/regression-js

Curve Fitting in JavaScript.
https://github.com/Tom-Alexander/regression-js

Last synced: 16 days ago
JSON representation

Curve Fitting in JavaScript.

• Host: GitHub
• URL: https://github.com/Tom-Alexander/regression-js
• Owner: Tom-Alexander
• License: mit
• Created: 2013-01-05T09:53:33.000Z (almost 12 years ago)
• Default Branch: master
• Last Pushed: 2022-12-06T19:46:27.000Z (almost 2 years ago)
• Last Synced: 2024-10-21T00:37:31.992Z (22 days ago)
• Language: JavaScript
• Homepage: http://tom-alexander.github.io/regression-js/
• Size: 1.67 MB
• Stars: 939
• Watchers: 30
• Forks: 131
• Open Issues: 45
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

Awesome Lists containing this project

README

        



regression-js





























regression-js is a JavaScript module containing a collection of linear least-squares fitting methods for simple data analysis.





## Installation

This module works on node and in the browser. It is available as the 'regression' package on [npm](https://www.npmjs.com/package/regression). It is also available on a [CDN](https://cdnjs.com/libraries/regression).

### npm

```

npm install --save regression

```

## Usage

```javascript

import regression from 'regression';

const result = regression.linear([[0, 1], [32, 67], [12, 79]]);

const gradient = result.equation[0];

const yIntercept = result.equation[1];

```

Data is passed into the model as an array. A second parameter can be used to configure the model. The configuration parameter is optional. `null` values are ignored. The precision option will set the number of significant figures the output is rounded to.

### Configuration options

Below are the default values for the configuration parameter.

```javascript

{

  order: 2,

  precision: 2,

}

```

### Properties

- `equation`: an array containing the coefficients of the equation

- `string`: A string representation of the equation

- `points`: an array containing the predicted data in the domain of the input

- `r2`: the coefficient of determination (R2)

- `predict(x)`: This function will return the predicted value

## API

### `regression.linear(data[, options])`

Fits the input data to a straight line with the equation ![y = mx + c](http://mathurl.com/ycqyhets.png). It returns the coefficients in the form `[m, c]`.

### `regression.exponential(data[, options])`

Fits the input data to a exponential curve with the equation ![y = ae^bx](http://mathurl.com/zuys53z.png). It returns the coefficients in the form `[a, b]`.

### `regression.logarithmic(data[, options])`

Fits the input data to a logarithmic curve with the equation ![y = a + b ln x](http://mathurl.com/zye394m.png). It returns the coefficients in the form `[a, b]`.

### `regression.power(data[, options])`

Fits the input data to a power law curve with the equation ![y = ax^b](http://mathurl.com/gojkazs.png). It returns the coefficients in the form `[a, b]`.

### `regression.polynomial(data[, options])`

Fits the input data to a polynomial curve with the equation ![anx^n ... + a1x + a0](http://mathurl.com/hxz543o.png). It returns the coefficients in the form `[an..., a1, a0]`. The order can be configure with the `order` option.

#### Example

```javascript

const data = [[0,1],[32, 67] .... [12, 79]];

const result = regression.polynomial(data, { order: 3 });

```

## Development

- Install the dependencies with `npm install`

- To build the assets in the `dist` directory, use `npm run build`

- You can run the tests with: `npm run test`.