On Tue, Jul 5, 2022 at 5:49 PM Aaron Meurer <asmeu...@gmail.com> wrote: > > The vecdot() function in the array API should be what you are looking > for (note that the current implementation in numpy.array_api is > incorrect, which I'm fixing at > https://github.com/numpy/numpy/pull/21928). It works like dot() but it > always applies the 1-D dot product case with broadcasting, and lets > you specify the axis. We'd want this function to be added to the main > numpy namespace as well.
See https://data-apis.org/array-api/latest/API_specification/generated/signatures.linear_algebra_functions.vecdot.html Aaron Meurer > > Aaron Meurer > > On Tue, Jul 5, 2022 at 5:39 PM <rmccampbe...@gmail.com> wrote: > > > > Maybe I wasn't clear, I'm talking about the 1-dimensional vector product, > > but applied to N-D arrays of vectors. Certainly dot products can be > > realized as matrix products, and often are in mathematics for convenience, > > but matrices and vectors are not the same thing, theoretically or coding > > wise. If I have two (M, N, k) arrays a and b where k is the vector > > dimension, to dot product them using matrix notation I have to do: > > > > (a[:, :, np.newaxis, :] @ b[:, :, :, np.newaxis])[:, :, 0, 0] > > > > Which I certainly don't find readable (I always have to scratch my head a > > little bit to figure out whether the newaxis's are in the right places). If > > this is a common operation in larger expressions, then it basically has to > > be written as a separate function, which then someone reading the code may > > have to look at for the semantics. It also breaks down if you want to write > > generic vector functions that may be applied along different axes; then you > > need to do something like > > > > np.squeeze(np.expand_dims(a, axis=axis) @ np.expand_dims(b, axis=axis+1), > > (axis, axis+1)) > > > > (after normalizing the axis; if it's negative you'd need to do axis-1 and > > axis instead). > > > > Compare this to the simplicity, composability and consistency of: > > > > a.dot(b, axis=-1) * np.cross(c, d, axis=-1).dot(e, axis=-1) / > > np.linalg.norm(f, axis=-1) > > > > (the cross and norm operators already support an axis parameter) > > _______________________________________________ > > NumPy-Discussion mailing list -- numpy-discussion@python.org > > To unsubscribe send an email to numpy-discussion-le...@python.org > > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > > Member address: asmeu...@gmail.com _______________________________________________ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
