Polynomial Regression
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[![Test coverage][codecov-image]][codecov-url]
Polynomial Regression.
$ npm i ml-regression-polynomial
``js
import { PolynomialRegression } from 'ml-regression-polynomial';
const x = [50, 50, 50, 70, 70, 70, 80, 80, 80, 90, 90, 90, 100, 100, 100];
const y = [
3.3, 2.8, 2.9, 2.3, 2.6, 2.1, 2.5, 2.9, 2.4, 3.0, 3.1, 2.8, 3.3, 3.5, 3.0,
];
const degree = 5; // setup the maximum degree of the polynomial
const regression = new PolynomialRegression(x, y, degree);
console.log(regression.predict(80)); // Apply the model to some x value. Prints 2.6.
console.log(regression.coefficients); // Prints the coefficients in increasing order of power (from 0 to degree).
console.log(regression.toString(3)); // Prints a human-readable version of the function.
console.log(regression.toLaTeX());
console.log(regression.score(x, y));
`
An interceptAtZero option is available, to force $f(0) = 0$. Also, a "powers array" can be specified.
- Using interceptAtZero
`js`
const regression = new PolynomialRegression(x, y, degree, {
interceptAtZero: true,
});
- Using the powers array
`js`
const powers = [0, 1, 2, 3, 4, 5];
const regression = new PolynomialRegression(x, y, powers);
powers could also be [1,2,3,4,5]or[1,3,5] and so on.
For intercepting at zero using an array, skip the zero in the array (the option interceptAtZero` is ignored in this case.)
[npm-image]: https://img.shields.io/npm/v/ml-regression-polynomial.svg?style=flat-square
[npm-url]: https://npmjs.org/package/ml-regression-polynomial
[download-image]: https://img.shields.io/npm/dm/ml-regression-polynomial.svg?style=flat-square
[download-url]: https://npmjs.org/package/ml-regression-polynomial
[codecov-image]: https://img.shields.io/codecov/c/github/mljs/regression-polynomial.svg
[codecov-url]: https://codecov.io/gh/mljs/regression-polynomial
[ci-image]: https://github.com/mljs/regression-polynomial/workflows/Node.js%20CI/badge.svg?branch=main
[ci-url]: https://github.com/mljs/regression-polynomial/actions?query=workflow%3A%22Node.js+CI%22