Actually, looking at the docs, numpy.outer is *only* defined for 1-d vectors. Should anyone who used it with multi-dimensional arrays have an expectation that it will keep working in the same way?
On Thu, Apr 16, 2015 at 10:53 AM, Neil Girdhar <[email protected]> wrote: > Would it be possible to deprecate np.outer's usage on non one-dimensional > vectors for a few versions, and then reintroduce it with definition > np.outer == np.multiply.outer? > > On Wed, Apr 15, 2015 at 8:02 PM, <[email protected]> wrote: > >> On Wed, Apr 15, 2015 at 6:40 PM, Nathaniel Smith <[email protected]> wrote: >> > On Wed, Apr 15, 2015 at 6:08 PM, <[email protected]> wrote: >> >> On Wed, Apr 15, 2015 at 5:31 PM, Neil Girdhar <[email protected]> >> wrote: >> >>> Does it work for you to set >> >>> >> >>> outer = np.multiply.outer >> >>> >> >>> ? >> >>> >> >>> It's actually faster on my machine. >> >> >> >> I assume it does because np.corrcoeff uses it, and it's the same type >> >> of use cases. >> >> However, I'm not using it very often (I prefer broadcasting), but I've >> >> seen it often enough when reviewing code. >> >> >> >> This is mainly to point out that it could be a popular function (that >> >> maybe shouldn't be deprecated) >> >> >> >> https://github.com/search?utf8=%E2%9C%93&q=np.outer >> >> 416914 >> > >> > For future reference, that's not the number -- you have to click >> > through to "Code" and then look at a single-language result to get >> > anything remotely meaningful. In this case b/c they're different by an >> > order of magnitude, and in general because sometimes the "top line" >> > number is completely made up (like it has no relation to the >> > per-language numbers on the left and then changes around randomly if >> > you simply reload the page). >> > >> > (So 29,397 is what you want in this case.) >> > >> > Also that count then tends to have tons of duplicates (e.g. b/c there >> > are hundreds of copies of numpy itself on github), so you need a big >> > grain of salt when looking at the absolute number, but it can be >> > useful, esp. for relative comparisons. >> >> My mistake, rushing too much. >> github show only 25 code references in numpy itself. >> >> in quotes, python only (namespace conscious packages on github) >> (I think github counts modules not instances) >> >> "np.cumsum" 11,022 >> "np.cumprod" 1,290 >> "np.outer" 6,838 >> >> statsmodels >> "np.cumsum" 21 >> "np.cumprod" 2 >> "np.outer" 15 >> >> Josef >> >> > >> > -n >> > _______________________________________________ >> > NumPy-Discussion mailing list >> > [email protected] >> > http://mail.scipy.org/mailman/listinfo/numpy-discussion >> _______________________________________________ >> NumPy-Discussion mailing list >> [email protected] >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> > >
_______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
