Matrix.lua -

: Allows you to pass any anonymous function, such as function(val) return math.max(0, val) end (ReLU activation).

-- 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

: 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 matrix.lua

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

: By integrating this into the core library, you can ensure it handles table indexing in a way that minimizes overhead, which is a common bottleneck for large matrices (e.g., 250x250 or larger). : Allows you to pass any anonymous function,

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, ...)

: It fits the library's design of returning a new matrix rather than modifying the original, maintaining "immutability". davidm/lua-matrix - GitHub Implementation Concept This feature applies a given function

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