David Koch wrote:
> On 3/27/07, Robert Cimrman <[EMAIL PROTECTED]> wrote:
>>
>>
>> ok. now which version of scipy (scipy.__version__) do you use (you may
>> have posted it, but I missed it)? Not so long ago, there was an effort
>> by Nathan Bell and others reimplementing sparsetools + scipy.sparse
On 3/27/07, Robert Cimrman <[EMAIL PROTECTED]> wrote:
ok. now which version of scipy (scipy.__version__) do you use (you may
have posted it, but I missed it)? Not so long ago, there was an effort
by Nathan Bell and others reimplementing sparsetools + scipy.sparse to
get better usability and per
David Koch wrote:
> Ok,
>
> I did and the results are:
> csc * csc: 372.601957083
> csc * csc: 3.90811300278
a typo here? which one is csr?
> csr * csc: 15.3202679157
> csr * csr: 3.84498214722
>
> Mhm, quite insightful. Note, that in an operation X.transpose() * X,
> where X
> is csc_matrix,
Ok,
I did and the results are:
csc * csc: 372.601957083
csc * csc: 3.90811300278
csr * csc: 15.3202679157
csr * csr: 3.84498214722
Mhm, quite insightful. Note, that in an operation X.transpose() * X, where X
is csc_matrix, then X.tranpose() is automatically cast to csr_matrix. A
re-cast to csc
David Koch wrote:
> On 3/26/07, Robert Cimrman <[EMAIL PROTECTED]> wrote:
>>
>> Could you be more specific on which type of the sparse matrix storage
>> did you use?
>
>
>
> Hi Robert,
>
> I used csc_matrix.
OK, good. Would you mind measuring csc * csr, csc * csc, csr * csc and
csr * csr? I am
On 3/26/07, Robert Cimrman <[EMAIL PROTECTED]> wrote:
Could you be more specific on which type of the sparse matrix storage
did you use?
Hi Robert,
I used csc_matrix.
/David
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David Koch wrote:
> Hi,
>
> I ran some tests on the very same matrices in Matlab/Numpy and it seems
> that
> for sparse matrix multipilcation to be faster than dense multiplication -
> the degree of sparsity has to be much higher in Numpy than in Matlab. Is
> there anything I can tune in the under
Hi,
I ran some tests on the very same matrices in Matlab/Numpy and it seems that
for sparse matrix multipilcation to be faster than dense multiplication -
the degree of sparsity has to be much higher in Numpy than in Matlab. Is
there anything I can tune in the underlying routines? I need good
per
