duncan smith wrote: > Hello, > I have several arrays that I need to combine elementwise in > various fashions. They are basically probability tables and there is a > mapping of axes to variables. I have code for transposing and reshaping > that aligns the variables / axes so the usual broadcasting rules achieve > the desired objective. But for a specific application I want to avoid > the transposing and reshaping. So I've specified arrays that contain the > full dimensionality (dimensions equal to the total number of variables). > e.g. > > Arrays with shape, > > [1,3,3] and [2,3,1] > > to represent probability tables with variables > > [B,C] and [A,B]. > > One operation that I need that is not elementwise is summing over axes, > but I can use numpy.sum with keepdims=True to retain the appropriate > shape. > > The problem I have is with slicing. This drops dimensions. Does anyone > know of a solution to this so that I can e.g. take an array with shape > [2,3,1] and generate a slice with shape [2,1,1]? I'm hoping to avoid > having to manually reshape it. Thanks.
Can you clarify your requirement or give an example of what you want? Given an array >>> a.shape (2, 3, 1) you can get a slice with shape (2,1,1) with (for example) >>> a[:,:1,:].shape (2, 1, 1) or even >>> newshape = (2, 1, 1) >>> a[tuple(slice(d) for d in newshape)].shape (2, 1, 1) but that's probably not what you are asking for... -- https://mail.python.org/mailman/listinfo/python-list
