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https://issues.apache.org/jira/browse/MAHOUT-1464?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14031751#comment-14031751
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ASF GitHub Bot commented on MAHOUT-1464:
----------------------------------------
Github user tdunning commented on a diff in the pull request:
https://github.com/apache/mahout/pull/18#discussion_r13783816
--- 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 --
The issue I have is with the rowAggregation and columnAggregation API. It
enforces row by row evaluation. A map-reduce API could evaluate in many
different orders and could iterate by rows or by columns for either aggregation
and wouldn't require the a custom VectorFunction for simple aggregations.
> 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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