Tom-Alexander/regression-js

<div align="center">
<h1>regression-js</h1>
<a href="https://travis-ci.org/Tom-Alexander/regression-js">
<img src="https://travis-ci.org/Tom-Alexander/regression-js.svg?branch=master"/>
</a>
<a href="https://npmjs.com/package/regression">
<img src="https://img.shields.io/npm/v/regression.svg" alt="npm version" />
</a>
<a href="https://npmjs.com/package/regression">
<img src="https://img.shields.io/npm/dt/regression.svg" alt="npm downloads" />
</a>
<a href="https://codeclimate.com/github/Tom-Alexander/regression-js/coverage"><img src="https://codeclimate.com/github/Tom-Alexander/regression-js/badges/coverage.svg" /></a>
<br/>
<br/>
<p>
regression-js is a JavaScript module containing a collection of linear least-squares fitting methods for simple data analysis.
</p>
</div>

## 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 (<i>R</i><sup>2</sup>)
- `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`.