On Thu, Apr 7, 2016 at 1:20 PM, Sebastian Berg <sebast...@sipsolutions.net> wrote:
> 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`. > a.T2 @ b.T2.T (T2 as shortcut for creating a[:, None] that's neat, except if a is already 2D) Josef > > 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 > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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