10.02.2014 23:13, Alan G Isaac kirjoitti: > On 2/10/2014 4:03 PM, Pauli Virtanen wrote: >> What sparked this discussion (on Github) is that it is not >> possible to write duck-typed code that works correctly for: > > Do you mean one must start out with an 'asarray'? Or more than > that?
Starting with asarray won't work: sparse matrices are not subclasses of ndarray. Matrix-free linear operators are not such either. In Python code, you usually very seldom coerce your inputs to a specific type. The situation here is a bit as if there were two different stream object types in Python, and their .write() methods did completely different things, so that code doing I/O would need to always be careful with which type of a stream was in question. > As I detailed in past discussion, the one thing I really do not > like about the `matrix` design is that indexing always returns a > matrix. I speculate this is the primary problem you're running > into? The fact that reductions to 1D return 2D objects is also a problem, but the matrix multiplication vs. elementwise multiplication and division is also an issue. -- Pauli Virtanen _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
