I'm trying to play with the implementation of least square solver (Ax = b) in mlmatrix.TSQR where A is a 50000*1024 matrix and b a 50000*10 matrix. It works but I notice that it's 8 times slower than the implementation given in the latest ampcamp : http://ampcamp.berkeley.edu/5/exercises/image-classification-with-pipelines.html . As far as I know these two implementations come from the same basis. What is the difference between these two codes ?
On Tue, Mar 3, 2015 at 8:02 PM, Shivaram Venkataraman < shiva...@eecs.berkeley.edu> wrote: > There are couple of solvers that I've written that is part of the AMPLab > ml-matrix repo [1,2]. These aren't part of MLLib yet though and if you are > interested in porting them I'd be happy to review it > > Thanks > Shivaram > > > [1] > https://github.com/amplab/ml-matrix/blob/master/src/main/scala/edu/berkeley/cs/amplab/mlmatrix/TSQR.scala > [2] > https://github.com/amplab/ml-matrix/blob/master/src/main/scala/edu/berkeley/cs/amplab/mlmatrix/NormalEquations.scala > > On Tue, Mar 3, 2015 at 9:01 AM, Jaonary Rabarisoa <jaon...@gmail.com> > wrote: > >> Dear all, >> >> Is there a least square solver based on DistributedMatrix that we can use >> out of the box in the current (or the master) version of spark ? >> It seems that the only least square solver available in spark is private >> to recommender package. >> >> >> Cheers, >> >> Jao >> > >
