On Fri, Apr 17, 2015 at 10:47 AM, <[email protected]> wrote: > On Fri, Apr 17, 2015 at 10:07 AM, Sebastian Berg > <[email protected]> wrote: > > On Do, 2015-04-16 at 15:28 -0700, Matthew Brett wrote: > >> Hi, > >> > > <snip> > >> > >> So, how about a slight modification of your proposal? > >> > >> 1) Raise deprecation warning for np.outer for non 1D arrays for a few > >> versions, with depraction in favor of np.multiply.outer, then > >> 2) Raise error for np.outer on non 1D arrays > >> > > > > I think that was Neil's proposal a bit earlier, too. +1 for it in any > > case, since at least for the moment I doubt outer is used a lot for non > > 1-d arrays. Possible step 3) make it work on higher dims after a long > > period. > > sounds ok to me > > Some random comments of what I remember or guess in terms of usage > > I think there are at most very few np.outer usages with 2d or higher > dimension. > (statsmodels has two models that switch between 2d and 1d > parameterization where we don't use outer but it has similar > characteristics. However, we need to control the ravel order, which > IIRC is Fortran) > > The current behavior of 0-D scalars in the initial post might be > useful if a numpy function returns a scalar instead of a 1-D array in > size=1. np.diag which is a common case, doesn't return a scalar (in my > version of numpy). > > I don't know any use case where I would ever want to have the 2d > behavior of np.multiply.outer. >
My use case is pretty simple. Given an input vector x, and a weight matrix W, and a model y=Wx, I calculate the gradient of the loss L with respect W. It is the outer product of x with the vector of gradients dL/dy. So the code is simply: W -= outer(x, dL_by_dy) Sometimes, I have some x_indices and y_indices. Now I want to do: W[x_indices, y_indices] -= outer(x[x_indices], dL_by_dy[y_indices]) Unfortunately, if x_indices or y_indices are "int" or slice in some way that removes a dimension, the left side will have fewer dimensions than the right. np.multipy.outer does the right thing without the ugly cases: if isinstance(x_indices, int): … # ugly hacks follow. I guess we will or would have applications for outer along an axis, > for example if x.shape = (100, 10), then we have > x[:,None, :] * x[:, :, None] (I guess) > Something like this shows up reasonably often in econometrics as > "Outer Product". However in most cases we can avoid constructing this > matrix and get the final results in a more memory efficient or faster > way. > (example an array of covariance matrices) > Not sure I see this. outer(a, b) should return something that has shape: (a.shape + b.shape). If you're doing it "along an axis", you mean you're reshuffling the resulting shape vector? > > Josef > > > > > > > > - Sebastian > > > > > >> Best, > >> > >> Matthew > >> _______________________________________________ > >> 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 >
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