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https://issues.apache.org/jira/browse/MAHOUT-1837?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15270296#comment-15270296
]
ASF GitHub Bot commented on MAHOUT-1837:
----------------------------------------
Github user andrewpalumbo commented on the pull request:
https://github.com/apache/mahout/pull/228#issuecomment-216776902
Changed only this test (when testing samples a full matrix) with a density
threshold of .3 rows/matrix containing, .30% nonZeroElements/row and a sample
size or .25 (with a minimum of one row to test). It seems that the test
*should* be returning a `DenseMatrix` (there is only a single missing element
in the entire matrix.
```scala
test("DRM blockify sparse -> SRM") {
val inCoreA = sparse(
(1, 2, 3),
0 -> 3 :: 2 -> 5 :: Nil
)
val drmA = drmParallelize(inCoreA, numPartitions = 2)
(inCoreA - drmA.mapBlock() {
case (keys, block) =>
--> // if (!block.isInstanceOf[SparseRowMatrix])
if (block.isInstanceOf[SparseRowMatrix])
throw new AssertionError("Block must be dense.")
keys -> block
}).norm should be < 1e-4
}
```
> Sparse/Dense Matrix analysis for Matrix Multiplication
> ------------------------------------------------------
>
> Key: MAHOUT-1837
> URL: https://issues.apache.org/jira/browse/MAHOUT-1837
> Project: Mahout
> Issue Type: Improvement
> Components: Math
> Affects Versions: 0.12.0
> Reporter: Andrew Palumbo
> Assignee: Andrew Palumbo
> Fix For: 0.12.1
>
>
> In matrix multiplication, Sparse Matrices can easily turn dense and bloat
> memory, one fully dense column and one fully dense row can cause a sparse
> %*% sparse operation have a dense result.
> There are two issues here one with a quick Fix and one a bit more involved:
> # in {{ABt.Scala}} use check the `MatrixFlavor` of the combiner and use
> the flavor of the Block as the resulting Sparse or Dense matrix type:
> {code}
> val comb = if (block.getFlavor == MatrixFlavor.SPARSELIKE) {
> new SparseMatrix(prodNCol, block.nrow).t
> } else {
> new DenseMatrix(prodNCol, block.nrow).t
> }
> {code}
> a simlar check needs to be made in the {{blockify}} transformation.
>
> # More importantly, and more involved is to do an actual analysis of the
> resulting matrix data in the in-core {{mmul}} class and use a matrix of the
> appropriate Structure as a result.
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