While the existing library supports basic arithmetic, a dedicated mapping function fills the gap for non-linear operations.
: It prevents users from writing repetitive nested for loops, making the code cleaner and less error-prone. matrix.lua
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, ...) While the existing library supports basic arithmetic, a
-- 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 This allows you to apply complex logic across
: 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).
This feature applies a given function to every element in a matrix, returning a new matrix with the results.