On 03/12/2015 02:42 PM, Robert Kern wrote: > On Thu, Mar 12, 2015 at 1:31 PM, Johannes Kulick > <johannes.kul...@ipvs.uni-stuttgart.de > <mailto:johannes.kul...@ipvs.uni-stuttgart.de>> wrote: >> >> Hello, >> >> I wonder if it would be worth to enhance max, min, argmax and argmin > (more?) >> with a tie breaking parameter: If multiple entries have the same value > the first >> value is returned by now. It would be useful to have a parameter to > alter this >> behavior to an arbitrary tie-breaking. I would propose, that the > tie-breaking >> function gets a list with all indices of the max/mins. >> >> Example: >> >>> a = np.array([ 1, 2, 5, 5, 2, 1]) >> >>> np.argmax(a, tie_breaking=random.choice) >> 3 >> >> >>> np.argmax(a, tie_breaking=random.choice) >> 2 >> >> >>> np.argmax(a, tie_breaking=random.choice) >> 2 >> >> >>> np.argmax(a, tie_breaking=random.choice) >> 2 >> >> >>> np.argmax(a, tie_breaking=random.choice) >> 3 >> >> Especially for some randomized experiments it is necessary that not > always the >> first maximum is returned, but a random optimum. Thus I end up writing > these >> things over and over again. >> >> I understand, that max and min are crucial functions, which shouldn't > be slowed >> down by the proposed changes. Adding new functions instead of altering the >> existing ones would be a good option. >> >> Are there any concerns against me implementing these things and > sending a pull >> request? Should such a function better be included in scipy for example? > > On the whole, I think I would prefer new functions for this. I assume > you only need variants for argmin() and argmax() and not min() and > max(), since all of the tied values for the latter two would be > identical, so returning the first one is just as good as any other. >
is this such a common usecase that its worth a numpy function to replace one liners like this? np.random.choice(np.where(a == a.max())[0]) its also not that inefficient if the number of equal elements is not too large. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
