polynomial-regression

Model generator based on the method of least squares.

Usage

There is a unique entry-point, the createModel method.

Once we have created our model we can fit it by feeding it with data and specifying the desired degree/s of our resulting model/s.

The API is designed in a way that allows creating different models based on each degree at the same time. Instead of having a fixed estimation function, we have an internal object where we are going to store the corresponding coefficients for each degree. This way we can easily compare which degree suits best our problem by calling the estimate function specifying the different degrees.

Let's illustrate its usage with a simple example. We are given these data points. We have them stored as an array of [x,y] values:

const data = [ [ 1, 2.4 ],  [ 1.5, 2.6 ], [ 2, 3 ], [ 2.5, 3.2 ] ... ];

We want to find a model that allows us to interpolate or estimate unknown values according to this information. In this example, we are going to use a low degree (3) and a higher one (20).

The code would look as follows:

const { createModel } = require('polynomial-regression');
const { data } = require('./example_dataset');

const model = createModel();

model.fit(data, [3,20]);

model.estimate(3,unknownXValue);
model.estimate(20,unknownXValue);

model.saveExpressions('./expressionsForGraphs.json');

The file saved looks like this:

{
  "3": "+1.5062596662599177*x^0+1.425302045761659*x^1 ... "
  "20": "+66.40442892021944*x^0-204.3486735913864*x^1 ... "
}

And we can easily copy&paste those strings and plot the corresponding functions.

API Reference

Method	Description	Arguments to be passed
fit	Calculates coefficients for each degree provided and stores them internally	Data <Array<[x,y]>>, Degrees <Array<Number>>
estimate	Returns the estimated value	Degree <Number>, xValue <Number>
loadParams	Loads precalculated coefficients merging them into the current model internal store	Path <string>
saveParams	Save the current model coefficients in a JSON file	Path <string>
saveExpressions	Turn the current model coefficients into reusable expressions and save them in a JSON file	Path <string>
expressions	Turn the current model coefficients into reusable expressions and return them as a variable	None