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 make the whole operation faster. It's still about 1000 times slower than Matlab but 4 times faster than before. Note, that <sp_mat>.transpose already switches the matrix On 3/26/07, Robert Cimrman <[EMAIL PROTECTED]> wrote:
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 curious how this will compare. r. ps: this thread might be more appropriate for scipy-user or scipy-dev... _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
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