On Wed, Dec 7, 2022 at 11:51 PM Ilhan Polat email@example.com wrote:
On matrix_transpose() : Every time this discussion brought up, there was a huge resistance to add more methods to array object or new functions (I have been involved in some of them on the pro .H side, links you have given and more in the mailing list) and now we are adding .mT and not .H? That is very surprising to me (and disappointing) after forcing people to write A.conj().T for years which is fundamentally the most common 2D operation regarding transpose and from a user experience perspective. Having a function name with the word "matrix" is already problematic but I can live with that. But adding .mT to the main namespace seems really going against all decisions made in the past. I also wish for cache-oblivious inplace transposition too that would make many linalg functions perform faster but I wouldn't dare to propose inplace_transpose() or .iT because it is not that important for *all* users. And in a way, neither is mT.
Again not trying to starting an old dumpster fire but surely there must have been some consideration for .H before we ended up with milliTranspose. In fact, as I am typing this, I am already regretting it.
In that case, I propose to not dive into .H here - it is a separate and more complicated topic that was not proposed here. Chuck brought it up in the community meeting, we had a chat about it. tl;dr "it is complicated".
That said, the thing that got a clearer thumbs up (both on GitHub and yesterday) is the `matrix_transpose` function, not the ndarray attribute. Aaron, given that you are looking for functionality that has value and can be implemented in a backwards compatible fashion, I suggest implementing `matrix_transpose`, and not deal with `.mT` right now.
Just a rant about typing too much conj().T lately I guess.
On Wed, Dec 7, 2022 at 10:26 PM Aaron Meurer firstname.lastname@example.org wrote:
As discussed in today's community meeting, I plan to start working on adding some useful functions to NumPy which are part of the array API standard https://data-apis.org/array-api/latest/index.html.
Although these are all things that will be needed for NumPy to be standard compliant, my focus for now at least is going to be on new functionality that is useful for NumPy independent of the standard. The things that I (and possibly others) plan on working on are:
- A new function matrix_transpose() and corresponding ndarray
attribute x.mT. Unlike transpose(), matrix_transpose() will require at least 2 dimensions and only operate on the last two dimensions (it's effectively an alias for swapaxes(x, -1, -2)). This was discussed in the past at https://github.com/numpy/numpy/issues/9530 and https://github.com/numpy/numpy/issues/13797. See
- namedtuple outputs for eigh, qr, slogdet and svd. This would only
apply to the instances where they currently return a tuple (e.g., svd(compute_uv=False) would still just return an array). See the corresponding pages at https://data-apis.org/array-api/latest/extensions/index.html for the namedtuple names. These four functions are the ones that are part of the array API spec, but if there are other functions that aren't part of the spec which we'd like to update to namedtuples as well for consistency, I can look into that.
- New functions matrix_norm() and vector_norm(), which split off the
behavior of norm() between vector and matrix specific functionalities. This is a cleaner API and would allow these functions to be proper gufuncs. See https://data-apis.org/array-api/latest/extensions/generated/signatures.linal... and https://data-apis.org/array-api/latest/extensions/generated/signatures.linal... .
- New function vecdot() which does a broadcasted 1-D dot product along
a specified axis
- New function svdvals(), which is equivalent to
svd(compute_uv=False). The idea here is that functions that have different return types depending on keyword arguments are problematic for various reasons (e.g., they are hard to type annotate), so it's cleaner to split these APIs. Functionality-wise there's not much new here, so this is lower priority than the rest.
- New function permute_dims(), which works just like transpose() but
it has a required axis argument. This is more explicit and can't be confused with doing a matrix transpose, which transpose() does not do for stacked matrices by default.
- Adding a copy argument to reshape(). This has already been discussed
at https://github.com/numpy/numpy/issues/9818. The main motivation is to replace the current usage of modifying array.shape inplace. (side note: this also still needs to be added to numpy.array_api)
You can see the source code of numpy.array_api for an idea of what pure Python implementations of these changes look like, but to be clear, the proposal here is to add these to the main NumPy namespace, not to numpy.array_api.
One question I have is which of the new functions proposed should be implemented as pure Python wrappers and which should be implemented in C as ufuncs/gufuncs?
Unless there are any objections, I plan to start working on implementing these right away.
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