[Numpy-discussion] Re: Proposal to Extend NumPy Broadcasting for (D₁, D₂, ..., N, M) → (D₁, D₂, ..., K, M) When K is a Multiple of N (K % N == 0)

Dear Shashang, Like the others, I fear that this super-broadcasting would be confusing. In the end, if you have a.shape = (2, 3) and b.shape = (4, 3), it is unclear why element 0 of a should broadcast against 0 and 1 of B rather than 0 and 2. And numpy should refuse the temptation to guess! I th

[Numpy-discussion] Re: Proposal to Extend NumPy Broadcasting for (D₁, D₂, ..., N, M) → (D₁, D₂, ..., K, M) When K is a Multiple of N (K % N == 0)

I think one aspect that would be hard to think about is how the tiling would happen when broadcasting from K -> N where N/K is an integer. There are at least 2 different ways to tile that would produce different results. Suppose you have the array [[1, 2, 3]] which is (1,3). If you wanted to bro

[Numpy-discussion] Re: Proposal to Extend NumPy Broadcasting for (D₁, D₂, ..., N, M) → (D₁, D₂, ..., K, M) When K is a Multiple of N (K % N == 0)

Shasang, My main concern is that if there is a legitimate bug in someone's code that is causing mismatched array sizes, this can mask that bug in certain situations where the mismatch just so happens to produce arrays of certain shapes. I am intrigued by the idea, though. Ben Root On Tue, Mar 25

[Numpy-discussion] Proposal to Extend NumPy Broadcasting for (D₁, D₂, ..., N, M) → (D₁, D₂, ..., K, M) When K is a Multiple of N (K % N == 0)

Dear NumPy Developers, I hope you are doing well. I am writing to propose an enhancement to NumPy’s broadcasting mechanism that could make the library even more powerful, intuitive, and flexible while maintaining its memory efficiency. ## **Current Broadcasting Rule and Its Limitation** As per N