On Thu, Apr 7, 2016 at 4:03 PM, Stéfan van der Walt <stef...@berkeley.edu> wrote:
> On 7 April 2016 at 11:17, Chris Barker <chris.bar...@noaa.gov> wrote: > > np.col_vector(arr) > > > > which would be a synonym for np.reshape(arr, (-1,1)) > > > > would that make anyone happy? > > I'm curious to see use cases where this doesn't solve the problem. > > The most common operations that I run into: > > colvec = lambda x: np.c_[x] > > x = np.array([1, 2, 3]) > A = np.arange(9).reshape((3, 3)) > > > 1) x @ x (equivalent to x @ colvec(x)) > 2) A @ x (equivalent to A @ colvec(x), apart from the shape) > 3) x @ A > 4) x @ colvec(x) -- gives an error, but perhaps this should work and > be equivalent to np.dot(colvec(x), rowvec(x)) ? > > If (4) were changed, 1D arrays would mostly* be interpreted as row > vectors, and there would be no need for a rowvec function. And we > already do that kind of magic for (2). > Apropos column/row vectors, I've toyed a bit with the idea of adding a flag to numpy arrays to indicate that the last index is one or the other, and maybe neither. Chuck
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
