implementing broadcasting when performing element-wise multiplication on arrays with different dimensions in NumPy 1.24
I'm working on a personal project and I'm trying to figure out I'm integrating two systems and Quick question that's been bugging me - I'm trying to perform element-wise multiplication between two NumPy arrays where one is 2D and the other is 1D. However, I keep running into broadcasting issues that I don't fully understand. Here's what I'm working with: I have a 2D array `A` with shape (3, 4) and a 1D array `B` with shape (4,). My goal is to multiply each row of `A` by the corresponding elements of `B`. However, when I attempt to do this: ```python import numpy as np A = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) B = np.array([10, 20, 30, 40]) C = A * B ``` I encounter the following behavior: ``` ValueError: shapes (3,4) and (4,) not aligned: 4 (dim 1) != 1 (dim 0) ``` I thought that NumPy would automatically broadcast the 1D array to match the dimensions of `A`, but it seems like that's not happening. I've looked at the documentation regarding broadcasting and tried reshaping `B` using `B.reshape(1, 4)` or `B[:, np.newaxis]`, but I still need to get it to work correctly. Could someone explain why this behavior occurs and how I can successfully perform this element-wise multiplication? I'm using NumPy version 1.24 and would appreciate any insights or alternative approaches to achieve my goal without reshaping manually if possible. I'm working on a API that needs to handle this. Cheers for any assistance! My team is using Python for this REST API. Is there a simpler solution I'm overlooking?