Package Exports
- ml-regression
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Readme
ml-regression
Regression algorithms
Installation
$ npm install ml-regression
Examples
Simple linear regression
const SLR = require('ml-regression').SLR;
let inputs = [80, 60, 10, 20, 30];
let outputs = [20, 40, 30, 50, 60];
let regression = new SLR(inputs, outputs);
regression.toString(3) === 'f(x) = - 0.265 * x + 50.6';External links
Check this cool blog post for a detailed example: https://hackernoon.com/machine-learning-with-javascript-part-1-9b97f3ed4fe5
Polynomial regression
const PolynomialRegression = require('ml-regression').NLR.PolynomialRegression;
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 2.547.
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());