On Fr, 2014-12-12 at 05:48 -0800, Jaime Fernández del Río wrote: > On Thu, Dec 11, 2014 at 10:53 AM, Stephan Hoyer <sho...@gmail.com> > wrote: > On Thu, Dec 11, 2014 at 8:17 AM, Sebastian Berg > <sebast...@sipsolutions.net> wrote: > One option > would also be to have something like: > > np.common_shape(*arrays) > np.broadcast_to(array, shape) > # (though I would like many arrays too) > > and then broadcast_ar rays could be implemented in > terms of these two. > > > It looks like np.broadcast let's us write the common_shape > function very easily; > > > def common_shape(*args): > return np.broadcast(*args).shape > > > And it's also very fast: > 1000000 loops, best of 3: 1.04 µs per loop > > So that does seem like a feasible refactor/simplification for > np.broadcast_arrays. > > > Sebastian -- if you're up for writing np.broadcast_to in C, > that's great! If you're not sure if you'll be able to get > around to that in the near future, I'll submit my PR with a > Python implementation (which will have tests that will be > useful in any case). > > > np.broadcast is the Python object of the old iterator. It may be a > better idea to write all of these functions using the new one, > np.nditer: > > > def common_shape(*args): > return np.nditer(args).shape[::-1] # Yes, you do need to reverse > it! > > > And in writing 'broadcast_to', rather than rewriting the broadcasting > logic, you could check the compatibility of the shape with something > like: > > > np.nditer((arr,), itershape=shape) # will raise ValueError if shapes > incompatible > > > > After that, all that would be left is some prepending of zero strides, > and some zeroing of strides of shape 1 dimensions before calling > as_strided >
Hahaha, right there is the 32 limitation, but you can also (ab)use it: np.nditer(np.arange(10), itershape=(5, 10)).itviews[0] - Sebastian > > Jaime > > > -- > (\__/) > ( O.o) > ( > <) Este es Conejo. Copia a Conejo en tu firma y ayúdale en sus > planes de dominación mundial. > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion
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