matrix

Introduction to the ml-matrix Project

Overview

The ml-matrix project is a comprehensive library designed to facilitate matrix manipulation and computation. It is maintained by Zakodium, a platform dedicated to delivering quality tools for mathematical and scientific computations. This library is readily available for installation via npm and can be seamlessly integrated into JavaScript projects as either an ES module or a CommonJS module.

Installation

To install the ml-matrix library, simply run the following command in your terminal:

$ npm install ml-matrix

Usage

ES Modules

For projects using ES modules, import the Matrix class using:

import { Matrix } from 'ml-matrix';

const matrix = Matrix.ones(5, 5);

CommonJS Modules

For projects using CommonJS, require the Matrix class as follows:

const { Matrix } = require('ml-matrix');

const matrix = Matrix.ones(5, 5);

API Documentation

Comprehensive API documentation can be found here.

Examples and Features

The library offers a wide array of functionalities, catering to basic matrix operations to more complex mathematical computations:

Standard Operations

Basic operations such as addition, subtraction, multiplication, and others can be performed directly on matrices. For instance:

const { Matrix } = require('ml-matrix');

var A = new Matrix([
  [1, 1],
  [2, 2],
]);

var B = new Matrix([
  [3, 3],
  [1, 1],
]);

const addition = Matrix.add(A, B); 

Inplace Operations

These operations modify the original matrix without creating a new one:

C.add(A); // Modifies C by adding A to it

Mathematical Functions

The library supports a wide range of mathematical functions such as sine, cosine, exponential, and logarithms:

var exponential = Matrix.exp(A);

Matrix Manipulation

Manipulating matrix properties is straightforward. You can get the number of rows, dimensions, and even transpose matrices easily:

var transpose = A.transpose();

Advanced Mathematical Computations

The library includes functionalities for handling inverse matrices, pseudo-inverses, least square problems, and various kinds of decompositions, such as QR and LU decompositions, all essential for advanced linear algebra and computations. For example:

Inverse and Pseudo-inverse

Calculating the inverse or the pseudo-inverse of a matrix:

var inverseA = inverse(A);

Decompositions

Decompositions like QR, LU, Cholesky, and eigenvalue decompositions can be efficiently computed, enabling you to undertake complex linear algebra tasks:

var QR = new QrDecomposition(A);

Conclusion

The ml-matrix library is a powerful tool for anyone looking to perform matrix calculations in JavaScript. With its ease of use and extensive documentation, it is suitable for both beginners and experts in mathematical programming. Its robust features enhance capabilities in scientific computing and data analysis, making it a valuable asset in the field of computational mathematics.