On Fr, 2016-11-04 at 13:11 -0400, Matthew Harrigan wrote: > I was reading this and got thinking about if a ufunc could compute > the sum of squared differences in a single pass without a temporary > array. The python code below demonstrates a possible approach. > > import numpy as np > x = np.arange(10) > c = 1.0 > def add_square_diff(x1, x2): > return x1 + (x2-c)**2 > ufunc = np.frompyfunc(add_square_diff, 2, 1) > print(ufunc.reduce(x) - x[0] + (x[0]-c)**2) > print(np.sum(np.square(x-c))) > > I have (at least) 4 questions: > 1. Is it possible to pass run time constants to a ufunc written in C > for use in its inner loop, and if so how?
I don't think its anticipated, since a ufunc could in most cases use a third argument, but a 3 arg ufunc can't be reduced. Not sure if there might be some trickery possible. > 2. Is it possible to pass an initial value to reduce to avoid the > clean up required for the first element? This is the identity normally. But the identity can only be 0, 1 or -1 right now I think. The identity is what the output array gets initialized with (which effectively makes it the first value passed into the inner loop). > 3. Does that ufunc work, or are there special cases which cause it to > fall apart? > 4. Would a very specialized ufunc such as this be considered for > incorporating in numpy since it would help reduce time and memory of > functions already in numpy? > Might be mixing up things, however, IIRC the single pass approach has a bad numerical accuracy, so that I doubt that it is a good default algorithm. - Sebastian > Thank you, > Matt > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion
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