Hi again,
I want to select/access several columns from a sparse csc_matrix. The only
way I could think of is the following enormously inefficient algorithm which
basically initalizes a new lil_matrix (for assigments) and loops over all
the specified columns and does sparse -> dense -> sparse. All
Hi,
Ok, I got it to work now but - damn, it's ugly. I thought I'd have to watch
the differences between ndarray and matrix type but it turns out
sparseMatrix is yet again different from matrix in several respects when it
comes to certain operations. Is this intended or something that will be
mend
Alright, may all the trickery rest until that day.
One thing I need to do however is patch a column of "ones" onto a sparse
matrix of format n * d with n >> d. I tried "concatenate" and it didn't work
so I did like this:
def spInsCol(X):
"insert doc string"
n, d = shape(X)
X = X.tocsc(
Hi David,
> The "worst" problem I encountered is that sparse matrices do not
> seem to support the kind of indexing I need. At least I get
> "NotImplementedError: sequence indexing not yet fully supported"
> and " supports slices only of a single row"
> errors all the time.
I agree .. thi
Ok, I will bump this once ...
The "worst" problem I encountered is that sparse matrices do not seem to
support the kind of indexing I need. At least I get "NotImplementedError:
sequence indexing not yet fully supported" and "
supports slices only of a single row" errors all the time.
Any advice
Hi,
I implemented an algorithm in NumPy which assumes that the input is of type
ndarray, so elementwise multiplication is done as dot(x,y), equation solving
using linalg.solve etc. I now want to modify the whole thing to accept
scipy.sparse matrices (which for instance has linsolve.spsolve instea
