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/imageclassificationwithpipelines.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 <
shivaram@eecs.berkeley.edu> wrote:
> There are couple of solvers that I've written that is part of the AMPLab
> mlmatrix 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/mlmatrix/blob/master/src/main/scala/edu/berkeley/cs/amplab/mlmatrix/TSQR.scala
> [2]
> https://github.com/amplab/mlmatrix/blob/master/src/main/scala/edu/berkeley/cs/amplab/mlmatrix/NormalEquations.scala
>
> On Tue, Mar 3, 2015 at 9:01 AM, Jaonary Rabarisoa <jaonary@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
>>
>
>
