it's in Mahout - 0.9. It should be in very final stages now.
On Fri, Jan 3, 2014 at 10:51 AM, Debasish Das <[email protected]>wrote: > Hi Dmitri, > > We have a mahout mirror from github but I don't see any of the math-scala > code. > > Where do I see the math-scala code ? I thought github mirror is updated > with svn repo. > > Thanks. > Deb > > > > On Fri, Jan 3, 2014 at 10:43 AM, Dmitriy Lyubimov <[email protected]>wrote: > >> >> >> >> On Fri, Jan 3, 2014 at 10:28 AM, Sebastian Schelter <[email protected]>wrote: >> >>> > I wonder if anyone might have recommendation on scala native >>> implementation >>> > of SVD. >>> >>> Mahout has a scala implementation of an SVD variant called Stochastic >>> SVD: >>> >>> >>> https://svn.apache.org/viewvc/mahout/trunk/math-scala/src/main/scala/org/apache/mahout/math/scalabindings/SSVD.scala?view=markup >> >> >> Mahout also has SVD and Eigen decompositions mapped to scala as svd() >> and eigen(). Unfortunately i have not put it on wiki yet but the summary is >> available here https://issues.apache.org/jira/browse/MAHOUT-1297 >> >> Mahout also has distributed PCA implementation (which is based on >> distributed Stochastic SVD and has a special provisions for sparse matrix >> cases). Unfortunately our wiki is in flux now due to migration off >> confluence to CMS so the SSVD page has not yet been migrated to CMS so >> confluence version is here >> https://cwiki.apache.org/confluence/display/MAHOUT/Stochastic+Singular+Value+Decomposition >> >> >>> >>> Otherwise, all the major java math libraries (mahout math, jblas, >>> commons-math) should provide an implementation that you can use in scala. >>> >>> --sebastian >>> >>> > C >>> > >>> > >>> > >>> > >>> > On Thu, Jan 2, 2014 at 7:06 PM, Ameet Talwalkar < >>> [email protected]>wrote: >>> > >>> >> Hi Deb, >>> >> >>> >> Thanks for your email. We currently do not have a DSGD >>> implementation in >>> >> MLlib. Also, just to clarify, DSGD is not a variant of ALS, but >>> rather a >>> >> different algorithm for solving the same the same bi-convex objective >>> >> function. >>> >> >>> >> It would be a good thing to do add, but to the best of my knowledge, >>> no >>> >> one is actively working on this right now. >>> >> >>> >> Also, as you mentioned, the ALS implementation in mllib is more >>> >> robust/scalable than the one in spark.examples. >>> >> >>> >> -Ameet >>> >> >>> >> >>> >> On Thu, Jan 2, 2014 at 3:16 PM, Debasish Das < >>> [email protected]>wrote: >>> >> >>> >>> Hi, >>> >>> >>> >>> I am not noticing any DSGD implementation of ALS in Spark. >>> >>> >>> >>> There are two ALS implementations. >>> >>> >>> >>> org.apache.spark.examples.SparkALS does not run on large matrices and >>> >>> seems more like a demo code. >>> >>> >>> >>> org.apache.spark.mllib.recommendation.ALS looks feels more robust >>> version >>> >>> and I am experimenting with it. >>> >>> >>> >>> References here are Jellyfish, Twitter's implementation of Jellyfish >>> >>> called Scalafish, Google paper called Sparkler and similar idea put >>> forward >>> >>> by IBM paper by Gemulla et al. (large-scale matrix factorization with >>> >>> distributed stochastic gradient descent) >>> >>> >>> >>> https://github.com/azymnis/scalafish >>> >>> >>> >>> Are there any plans of adding DSGD in Spark or there are any existing >>> >>> JIRA ? >>> >>> >>> >>> Thanks. >>> >>> Deb >>> >>> >>> >>> >>> >> >>> > >>> > >>> >>> >> >
