[ https://issues.apache.org/jira/browse/MAHOUT-1597?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14071004#comment-14071004 ]
ASF GitHub Bot commented on MAHOUT-1597: ---------------------------------------- Github user avati commented on the pull request: https://github.com/apache/mahout/pull/33#issuecomment-49807502 CbindAB had similar problems like A + B > A + 1.0 (element-wise scala operation) gives wrong result if rdd is missing > rows, Spark side > -------------------------------------------------------------------------------------------- > > Key: MAHOUT-1597 > URL: https://issues.apache.org/jira/browse/MAHOUT-1597 > Project: Mahout > Issue Type: Bug > Affects Versions: 0.9 > Reporter: Dmitriy Lyubimov > Assignee: Dmitriy Lyubimov > Fix For: 1.0 > > > {code} > // Concoct an rdd with missing rows > val aRdd: DrmRdd[Int] = sc.parallelize( > 0 -> dvec(1, 2, 3) :: > 3 -> dvec(3, 4, 5) :: Nil > ).map { case (key, vec) => key -> (vec: Vector)} > val drmA = drmWrap(rdd = aRdd) > val controlB = inCoreA + 1.0 > val drmB = drmA + 1.0 > (drmB -: controlB).norm should be < 1e-10 > {code} > should not fail. > it was failing due to elementwise scalar operator only evaluates rows > actually present in dataset. > In case of Int-keyed row matrices, there are implied rows that yet may not be > present in RDD. > Our goal is to detect the condition and evaluate missing rows prior to > physical operators that don't work with missing implied rows. -- This message was sent by Atlassian JIRA (v6.2#6252)