I need to calculate a single value result by adding corresponding elements from both matrices and then summing everything up. The mathematical formula involves double summation over all i and j indices.
I know that np.add(matrix_x, matrix_y) gives me element-wise addition but returns another matrix instead of a single number.
Is there a built-in NumPy method to handle this calculation directly? If not, what’s the best way to loop through both arrays and apply the summation formula?
I’m a bit confused about how to properly index the i-th and j-th positions during iteration. Any guidance would be really helpful!
That approach works great, but I’ll share what I’ve learned working with huge datasets. When you chain np.sum(matrix_x + matrix_y), NumPy creates a temporary array that can eat up memory with massive matrices. You could try np.sum([matrix_x, matrix_y]) instead - it sums along the first axis but still makes a temporary 3D array. Honestly, I stick with simple addition then sum in production code because it’s way more readable. Also, both methods handle mixed data types well - NumPy automatically converts integers and floats to the right type during computation.
You can also use (matrix_x + matrix_y).sum() - basically the same thing others mentioned but feels more natural to me. I’ve done scientific computing for years and this pattern shows up everywhere. There’s also np.einsum('ij->', matrix_x + matrix_y) but that’s overkill for what you need. That double summation you mentioned? That’s exactly what happens when you sum all elements - sum columns first, then rows (or the other way around). Don’t overthink the indexing - NumPy handles it when you call sum() on the whole array.