: It fits the library's design of returning a new matrix rather than modifying the original, maintaining "immutability". davidm/lua-matrix - GitHub
: 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
While the existing library supports basic arithmetic, a dedicated mapping function fills the gap for non-linear operations. matrix.lua
: 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, such as function(val) return math.max(0, val) end (ReLU activation). : It fits the library's design of returning
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, ...)
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,
: It prevents users from writing repetitive nested for loops, making the code cleaner and less error-prone.