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