On 21/08/07, Timothy Hochberg <[EMAIL PROTECTED]> wrote: > This is just a general comment on recent threads of this type and not > directed specifically at Chuck or anyone else. > > IMO, the emphasis on avoiding FOR loops at all costs is misplaced. It is > often more memory friendly and thus faster to vectorize only the inner loop > and leave outer loops alone. Everything varies with the specific case of > course, but trying to avoid FOR loops on principle is not a good strategy.
Yes and no. From a performance point of view, you are certainly right; vectorizing is definitely not always a speedup. But for me, the main advantage of vectorized operations is generally clarity: C = A*B is clearer and simpler than C = [a*b for (a,b) in zip(A,B)]. When it's not clearer and simpler, I feel no compunction about falling back to list comprehensions and for loops. That said, it would often be nice to have something like map(f,arange(10)) for arrays; the best I've found is vectorize(f)(arange(10)). vectorize, of course, is a good example of my point above: it really just loops, in python IIRC, but conceptually it's extremely handy for doing exactly what the OP wanted. Unfortunately vectorize() does not yield a sufficiently ufunc-like object to support .outer(), as that would be extremely tidy. Anne _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
