It was moved to mllib.linalg.distributed.RowMatrix. With RowMatrix, you can compute column summary statistics, gram matrix, covariance, SVD, and PCA. We will provide multiplication for distributed matrices, but not in v1.0. -Xiangrui
On Fri, Apr 11, 2014 at 9:12 PM, wxhsdp <wxh...@gmail.com> wrote: > Hi, all > the code under > https://github.com/apache/spark/tree/master/mllib/src/main/scala/org/apache/spark/mllib/linalg > has changed. previous matrix classes are all removed, like MatrixEntry, > MatrixSVD. Instead breeze matrix definition appears. Do we move to Breeze > Linear Algebra when do linear algorithm? > > another question, are there any matrix multiplication optimized codes in > spark? > i only see the outer product method in the removed SVD.scala > > // Compute A^T A, assuming rows are sparse enough to fit in memory > val rows = data.map(entry => > (entry.i, (entry.j, entry.mval))).groupByKey() > val emits = rows.flatMap{ case (rowind, cols) => > cols.flatMap{ case (colind1, mval1) => > cols.map{ case (colind2, mval2) => > ((colind1, colind2), > mval1*mval2) } }//colind1: col index, colind2: > row index > }.reduceByKey(_ + _) > > thank you! > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/SVD-under-spark-mllib-linalg-tp4156.html > Sent from the Apache Spark User List mailing list archive at Nabble.com.
