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https://issues.apache.org/jira/browse/SPARK-16566?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joseph K. Bradley closed SPARK-16566.
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Resolution: Duplicate
Linking existing JIRA which this one is duplicating. Could you please work
under the other JIRA instead of this one? Thanks!
> Bug in SparseMatrix multiplication with SparseVector
> ----------------------------------------------------
>
> Key: SPARK-16566
> URL: https://issues.apache.org/jira/browse/SPARK-16566
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 1.6.2
> Reporter: Wilson
>
> In the org.apache.spark.mllib.linalg.BLAS.scala, the multiplication between
> SparseMatrix (sm) and SparseVector (sv) when sm is not transposed assume that
> the indices is sorted, but there is no validation to make sure that is the
> case, making the result returned wrongly.
> This can be replicated simply by using spark-shell and entering these
> commands:
> import org.apache.spark.mllib.linalg.SparseMatrix
> import org.apache.spark.mllib.linalg.SparseVector
> import org.apache.spark.mllib.linalg.DenseVector
> import scala.collection.mutable.ArrayBuffer
> val vectorIndices = Array(3,2)
> val vectorValues = Array(0.1,0.2)
> val size = 4
> val sm = new SparseMatrix(size, size, Array(0, 0, 0, 1, 1), Array(0),
> Array(1.0))
> val dm = sm.toDense
> val sv = new SparseVector(size, vectorIndices, vectorValues)
> val dv = new DenseVector(s.toArray)
> sm.multiply(dv) == sm.multiply(sv)
> sm.multiply(dv)
> sm.multiply(sv)
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