On 16/04/2008, Alan G Isaac <[EMAIL PROTECTED]> wrote: > > The whole issue occurs because a Matrix is not a proper > > container. > > > Right. And *that* is the case because of the attempt to > treat matrices as containers of matrices instead of as > containers of 1d arrays. > > I can see no real advantage to having matrices be containers > of row vectors instead. A row vector would just be a matrix > (i.e., essentially 2d) that allowed 1d style indexing. > Again I ask, have you ever needed such a thing?
In the Matrix world, there is no such thing as an (N,) array -- which is why the current Matrix object behaves so strangely, and always returns a result with ndim > 1. Your proposal suggests that a Matrix be a container of arrays, but it does not address the slicing of column vectors, i.e. x[0] x[0,:] x[:,0] would then behave in completely different ways, whereas with ndarrays they don't. If you modified the proposal to say "any slicing operation that returns a (1,N) or (N,1) matrix should now return an (N,) ndarray", it would be more consistent, but then, wouldn't that break some other expected behaviour? I thought one of the powerful features of a matrix is that it preserves the orientation of a vector slice? The only way I've seen so far to have consistent slicing, is to have an N-dim Matrix object be a container of N-1 dim Matrices, up to the 2-D case, in which it becomes a container of Vectors which, in turn, contain scalars. > I agree this is a good perspective. (But *two* operators: * and **.) > It implies: there are deviations from array behavior that > are buying us nothing. This is my point. Yup, and fortunately we now have both those implemented for arrays as well :) Regards Stéfan _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
