4 ms·

Interesting, but I guess I'd have to learn APL or J to compare. Without understanding what those symbols mean, it seems much less like "math" than the numpy ver

Interesting, but I guess I'd have to learn APL or J to compare. Without understanding what those symbols mean, it seems much less like "math" than the numpy version. 1 + np.min(np.argwhere(running_sum[1:] == 0), axis=0)

rak1507 4y ago
⊃ is 'first', ⍸ is where, f⍤g is like function composition, and ⍤1 means apply to each rank 1 (vector), so ⊃⍤⍸⍤1 means 'the first true for each vector'
- mgraczyk 4y ago
  Interesting, thanks for explaining!