Re: [SciPy-Dev] Sparse Matrix Prototype
On Tue, Mar 5, 2013 at 1:57 PM, Daniel Smith <smith.daniel.br@gmail.com> wrote:
Hi,
I've been working on adding fancy indexing to the LIL sparse matrix class and have run into a problem. Judging from the tests, no decision has been made as to what NumPy structure the class should replicate. In particular, some of the tests assume ndarray behavior, and others assume matrix behavior. The biggest difference is in row/column vector behavior. Take for example:
A = np.zeros((5, 5)) B = np.matrix(A)
A[:, 1] array([0, 0, 0, 0, 0]) B[:, 1] matrix([ [0], [0], [0], [0], [0] ])
Since NumPy is encouraging people to use ndarrays over matrices, it might make sense to reproduce the ndarray behavior. However, since the class has other matrix-specific behavior, e.g. being only two-dimensional, it might be confusing to have the class behave like an ndarray in other ways. Until a decision is made, no version of the class will pass all the tests as currently written. Any input would be greatly appreciated.
I find the "matrixness" of sparse matrices to be constantly annoying and a source of tons of special cases in code that wants to handle both sparse and dense matrices... but it is what it is. If they can't act like ndarrays, better they act like np.matrix's than like some weird mash-up of the two. And in particular the key thing for this indexing example is that we *can't* return a sparse 1-d ndarray-alike, right, because we have no structure to represent sparse 1-d things?
-n
We have at least one sparse 1-d ndarray like thing in a sparse column vector, a 1xN matrix. This code would output exactly what you want: B = sparse.lil_matrix(np.reshape(np.arange(25), (5, 5))) vector = np.squeeze(B[:, 0].A) vector np.array([0, 5, 10, 15, 20]) Another option is to leave the built-in __getitem__ and __setitem__ behavior like a matrix and add get_array, set_array which would mirror ndarray behavior. We already have the method .A which returns a dense ndarray to go along with .todense() which returns a dense matrix. Daniel
On Tue, Mar 5, 2013 at 2:47 PM, Daniel Smith <smith.daniel.br@gmail.com> wrote:
On Tue, Mar 5, 2013 at 1:57 PM, Daniel Smith <smith.daniel.br@gmail.com> wrote:
Hi,
I've been working on adding fancy indexing to the LIL sparse matrix class and have run into a problem. Judging from the tests, no decision has been made as to what NumPy structure the class should replicate. In particular, some of the tests assume ndarray behavior, and others assume matrix behavior. The biggest difference is in row/column vector behavior. Take for example:
A = np.zeros((5, 5)) B = np.matrix(A)
A[:, 1] array([0, 0, 0, 0, 0]) B[:, 1] matrix([ [0], [0], [0], [0], [0] ])
Since NumPy is encouraging people to use ndarrays over matrices, it might make sense to reproduce the ndarray behavior. However, since the class has other matrix-specific behavior, e.g. being only two-dimensional, it might be confusing to have the class behave like an ndarray in other ways. Until a decision is made, no version of the class will pass all the tests as currently written. Any input would be greatly appreciated.
I find the "matrixness" of sparse matrices to be constantly annoying and a source of tons of special cases in code that wants to handle both sparse and dense matrices... but it is what it is. If they can't act like ndarrays, better they act like np.matrix's than like some weird mash-up of the two. And in particular the key thing for this indexing example is that we *can't* return a sparse 1-d ndarray-alike, right, because we have no structure to represent sparse 1-d things?
-n
We have at least one sparse 1-d ndarray like thing in a sparse column vector, a 1xN matrix.
I thought the whole point of your example was that for the given sort of indexing, ndarray's return an object with shape (N,) and np.matrix's return an object with shape (N, 1), and the question was which should sparse matrices do? And my point was that there's no such thing as a sparse matrix with shape (N,) so the choice was sort of obvious? Maybe I'm just missing something. -n
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Daniel Smith -
Nathaniel Smith