Hi Ted, Thanks a lot for the trick.
Just implemented it and the SingularValueDecomposition class now accepts matrices for which numRows()<numCols(). I have also implemented the svd covariance. As a consequence, all tests are now ok. There is no patch on JIRA yet. Can you do the integration of the code from the web source (there are only new files and nothing to merge) or do you prefer an "official" patch ? Cheers, Nicolas On Wed, Jul 21, 2010 at 7:19 PM, Ted Dunning <[email protected]> wrote: > Is this available as a patch on a JIRA in addition to your web hosted source > code? I think that this is very close to ready to commit. The missing test > can be handled pretty easily because svd(A') = V D U' so all that is needed > is to detect the problematic shape, decompose the transposed matrix and > reverse the roles of U and V. > > On Wed, Jul 21, 2010 at 1:09 AM, Nicolas Maillot <[email protected]> wrote: > >> Ted, Robin, >> >> Thanks for your remarks. I have taken them into account. >> >> I have added a set of unit tests for the SVD (based on >> org.apache.commons.math.linear.SingularValueDecompositionImplTest): >> >> http://www.nicolas-maillot.net/MahoutContribs/TestSingularValueDecomposition.java >> Note that I had to deactivate some tests due to limitations of the >> CERN implementation: >> -Tests where the input matrix has strictly fewer rows than columns >> -Tests on svd covariance which is not (yet) implemented >> >> Otherwise all the "valid" tests are running fine. >> >> I have also fixed the parameter management. >> >> Nicolas >> >> >> >> >> >> On Mon, Jul 19, 2010 at 11:08 PM, Ted Dunning <[email protected]> >> wrote: >> > Nicolas, >> > >> > This was quick work. Very nice to see. >> > >> > To finish, the migration, we need to add test cases as well. >> > >> > A good source of test cases for the SingularValueDecomposition would >> > be package >> org.apache.commons.math.linear.SingularValueDecompositionImplTest >> > >> > On Mon, Jul 19, 2010 at 1:08 PM, Nicolas Maillot <[email protected]> >> wrote: >> > >> >> As you can see, I have migrated SingularValueDecomposition (useful to >> >> tackle the inverse covariance problem) to use the Matrix Class. >> >> >> > >> >
