Issues calculating the dot product of two 2D arrays with different shapes in NumPy 1.25
I'm having a hard time understanding I'm trying to compute the dot product of two 2D NumPy arrays, but I'm running into a shape mismatch scenario that I need to quite understand. I have two arrays, `A` and `B`, where `A` has a shape of `(3, 4)` and `B` has a shape of `(4, 2)`. According to the rules of matrix multiplication, their inner dimensions match, so I expected the dot product to work without any issues. Here's the code I'm using: ```python import numpy as np A = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) B = np.array([[1, 2], [3, 4], [5, 6], [7, 8]]) try: result = np.dot(A, B) print(result) except ValueError as e: print(f"behavior: {e}") ``` When I run this code, I get the following behavior message: ``` behavior: shapes (3,4) and (4,2) not aligned: 4 (dim 1) != 4 (dim 0) ``` I've double-checked the shapes of both arrays, and I think they should be compatible for the dot product. I even tried using `np.matmul()` instead of `np.dot()`, but I received the same behavior. Is there something I'm missing here? Could it be the way I'm defining or initializing the arrays? Any insights would be appreciated! What am I doing wrong?