On Thu, Apr 26, 2018 at 12:03 PM Joseph Fox-Rabinovitz < jfoxrabinov...@gmail.com> wrote:
> Would it be useful to have a short-circuited version of the function that > is not a ufunc? > Yes definitely. I could use numba as suggested by Hameer but I'd rather not add a new runtime dependency. I could use cython or C but I'd need to deal with the packaging headaches of including C code in your package. I guess I could also create a new project that just implements the functions I need in cython, deal with the packaging headaches there, and then depend on that package. At least that way others won't need to deal with the pain :) > - Joe > > On Thu, Apr 26, 2018 at 12:51 PM, Hameer Abbasi <einstein.edi...@gmail.com > > wrote: > >> Hi Nathan, >> >> np.any and np.all call np.or.reduce and np.and.reduce respectively, and >> unfortunately the underlying function (ufunc.reduce) has no way of >> detecting that the value isn’t going to change anymore. It’s also used for >> (for example) np.sum (np.add.reduce), np.prod (np.multiply.reduce), >> np.min(np.minimum.reduce), np.max(np.maximum.reduce). >> >> You can find more information about this on the ufunc doc page >> <https://docs.scipy.org/doc/numpy/reference/ufuncs.html>. I don’t think >> it’s worth it to break this machinery for any and all, as it has numerous >> other advantages (such as being able to override in duck arrays, etc) >> >> Best regards, >> Hameer Abbasi >> Sent from Astro <https://www.helloastro.com> for Mac >> >> On Apr 26, 2018 at 18:45, Nathan Goldbaum <nathan12...@gmail.com> wrote: >> >> >> Hi all, >> >> I was surprised recently to discover that both np.any and np.all() do not >> have a way to exit early: >> >> In [1]: import numpy as np >> >> In [2]: data = np.arange(1e6) >> >> In [3]: print(data[:10]) >> [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.] >> >> In [4]: %timeit np.any(data) >> 724 us +- 42.4 us per loop (mean +- std. dev. of 7 runs, 1000 loops each) >> >> In [5]: data = np.zeros(int(1e6)) >> >> In [6]: %timeit np.any(data) >> 732 us +- 52.9 us per loop (mean +- std. dev. of 7 runs, 1000 loops each) >> >> I don't see any discussions about this on the NumPy issue tracker but >> perhaps I'm missing something. >> >> I'm curious if there's a way to get a fast early-terminating search in >> NumPy? Perhaps there's another package I can depend on that does this? I >> guess I could also write a bit of cython code that does this but so far >> this project is pure python and I don't want to deal with the packaging >> headache of getting wheels built and conda-forge packages set up on all >> platforms. >> >> Thanks for your help! >> >> -Nathan >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@python.org >> https://mail.python.org/mailman/listinfo/numpy-discussion >> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@python.org >> https://mail.python.org/mailman/listinfo/numpy-discussion >> >> > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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