[SciPy-user] Sparse matrix: division by vector
Tue Apr 29 18:05:54 CDT 2008
On 29/04/2008, Nathan Bell <email@example.com> wrote:
> On Tue, Apr 29, 2008 at 12:16 PM, Dinesh B Vadhia
> <firstname.lastname@example.org> wrote:
> > Sparse matrix A is in csr_matrix format and I want to divide each column
> > element j of A by A's column j sum (where colSum is a numpy vector) ie.
> > > A[:,j] = A[:,j]/colSum[j]
> > What is the most efficient way to achieve this apart from brute force ie.
> > > A[i,j] = A[i,j]/colSum[j] ?
> > I can initially create A in a different c**_matrix method if that helps but
> > the final A has to be in csr_matrix form.
> Assuing A is in CSR and colSum[j] stores the j-th column sum you can do
> A.data /= colSum[A.indices]
Is there any way to make the OP's approach work? Or even A /=
colsum[newaxis,:]? This seems like a basic expectation from sparse
matrices... Is there a document describing which basic numpy
operations work on sparse matrices and which don't?
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