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https://issues.apache.org/jira/browse/MAHOUT-1464?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14031165#comment-14031165
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ASF GitHub Bot commented on MAHOUT-1464:
----------------------------------------
Github user pferrel commented on a diff in the pull request:
https://github.com/apache/mahout/pull/18#discussion_r13771632
--- Diff:
math-scala/src/main/scala/org/apache/mahout/math/scalabindings/MatrixOps.scala
---
@@ -188,8 +188,8 @@ object MatrixOps {
def apply(f: Vector): Double = f.sum
}
- private def vectorCountFunc = new VectorFunction {
- def apply(f: Vector): Double = f.aggregate(Functions.PLUS,
Functions.greater(0))
+ private def vectorCountNonZeroElementsFunc = new VectorFunction {
+ def apply(f: Vector): Double = f.aggregate(Functions.PLUS,
Functions.notEqual(0))
--- End diff --
This is creating a Vector of non-zero counts per column, just like colSums
is summing the column's values. The function is simply needed in the
aggregateColumns. If you are suggesting another way to do this you'll have to
be more explicit.
#17 has nothing to do with that afaict. It is about finding non-zero
elements in a Vector.
> Cooccurrence Analysis on Spark
> ------------------------------
>
> Key: MAHOUT-1464
> URL: https://issues.apache.org/jira/browse/MAHOUT-1464
> Project: Mahout
> Issue Type: Improvement
> Components: Collaborative Filtering
> Environment: hadoop, spark
> Reporter: Pat Ferrel
> Assignee: Pat Ferrel
> Fix For: 1.0
>
> Attachments: MAHOUT-1464.patch, MAHOUT-1464.patch, MAHOUT-1464.patch,
> MAHOUT-1464.patch, MAHOUT-1464.patch, MAHOUT-1464.patch, run-spark-xrsj.sh
>
>
> Create a version of Cooccurrence Analysis (RowSimilarityJob with LLR) that
> runs on Spark. This should be compatible with Mahout Spark DRM DSL so a DRM
> can be used as input.
> Ideally this would extend to cover MAHOUT-1422. This cross-cooccurrence has
> several applications including cross-action recommendations.
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