: Extend it to accept multiple matrices of the same size, allowing for element-wise operations between them (like c[i][j] = f(a[i][j], b[i][j]) ). Implementation Concept

: It moves beyond standard arithmetic (add/sub/mul) to support any mathematical transformation.

-- Example: Apply a sigmoid function to all elements local sigmoid = function(x) return 1 / (1 + math.exp(-x)) end local activated_matrix = matrix.map(my_matrix, sigmoid) Use code with caution. Why this is a "Good" Feature

: It fits the library's design of returning a new matrix rather than modifying the original, maintaining "immutability". davidm/lua-matrix - GitHub

: It prevents users from writing repetitive nested for loops, making the code cleaner and less error-prone.

This feature applies a given function to every element in a matrix, returning a new matrix with the results.

While the existing library supports basic arithmetic, a dedicated mapping function fills the gap for non-linear operations.

A valuable feature for a matrix.lua library—such as the one found on GitHub by davidm —is a robust system. This allows you to apply complex logic across every cell of a matrix efficiently, which is critical for tasks like neural network activation or physics simulations. Feature: matrix.map(mtx, func, ...)

Matrix.lua Page

Matrix.lua Page

: Extend it to accept multiple matrices of the same size, allowing for element-wise operations between them (like c[i][j] = f(a[i][j], b[i][j]) ). Implementation Concept

: It moves beyond standard arithmetic (add/sub/mul) to support any mathematical transformation.

-- Example: Apply a sigmoid function to all elements local sigmoid = function(x) return 1 / (1 + math.exp(-x)) end local activated_matrix = matrix.map(my_matrix, sigmoid) Use code with caution. Why this is a "Good" Feature matrix.lua

: It fits the library's design of returning a new matrix rather than modifying the original, maintaining "immutability". davidm/lua-matrix - GitHub

: It prevents users from writing repetitive nested for loops, making the code cleaner and less error-prone. : Extend it to accept multiple matrices of

This feature applies a given function to every element in a matrix, returning a new matrix with the results.

While the existing library supports basic arithmetic, a dedicated mapping function fills the gap for non-linear operations. Why this is a "Good" Feature : It

A valuable feature for a matrix.lua library—such as the one found on GitHub by davidm —is a robust system. This allows you to apply complex logic across every cell of a matrix efficiently, which is critical for tasks like neural network activation or physics simulations. Feature: matrix.map(mtx, func, ...)