On Do, 2016-04-07 at 11:56 -0400, josef.p...@gmail.com wrote: > > <snip>
> > I don't think numpy treats 1d arrays as row vectors. numpy has C > -order for axis preference which coincides in many cases with row > vector behavior. > Well, broadcasting rules, are that (n,) should typically behave similar to (1, n). However, for dot/matmul and @ the rules are stretched to mean "the one dimensional thing that gives an inner product" (using matmul since my python has no @ yet): In [12]: a = np.arange(20) In [13]: b = np.arange(20) In [14]: np.matmul(a, b) Out[14]: 2470 In [15]: np.matmul(a, b[:, None]) Out[15]: array([2470]) In [16]: np.matmul(a[None, :], b) Out[16]: array([2470]) In [17]: np.matmul(a[None, :], b[:, None]) Out[17]: array([[2470]]) which indeed gives us a fun thing, because if you look at the last line, the outer product equivalent would be: outer = np.matmul(a[None, :].T, b[:, None].T) Now if I go back to the earlier example: a.T @ b Does not achieve the outer product at all with using T2, since a.T2 @ b.T2 # only correct for a, but not for b a.T2 @ b # b attempts to be "inner", so does not work It almost seems to me that the example is a counter example, because on first sight the `T2` attribute would still leave you with no shorthand for `b`. I understand the pain of having to write (and parse get into the depth of) things like `arr[:, np.newaxis]` or reshape. I also understand the idea of a shorthand for vectorized matrix operations. That is, an argument for a T2 attribute which errors on 1D arrays (not sure I like it, but that is a different issue). However, it seems that implicit adding of an axis which only works half the time does not help too much? I have to admit I don't write these things too much, but I wonder if it would not help more if we just provided some better information/link to longer examples in the "dimension mismatch" error message? In the end it is quite simple, as Nathaniel, I think I would like to see some example code, where the code obviously looks easier then before? With the `@` operator that was the case, with the "dimension adding logic" I am not so sure, plus it seems it may add other pitfalls. - Sebastian > >>> np.concatenate(([[1,2,3]], [4,5,6])) > Traceback (most recent call last): > File "<pyshell#63>", line 1, in <module> > np.concatenate(([[1,2,3]], [4,5,6])) > ValueError: arrays must have same number of dimensions > > It's not an uncommon exception for me. > > Josef > > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@scipy.org > > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion
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