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https://issues.apache.org/jira/browse/SPARK-4900?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mike Beyer updated SPARK-4900:
------------------------------
Description:
java.lang.reflect.InvocationTargetException
...
Caused by: java.lang.IllegalStateException: ARPACK returns non-zero info = 3
Please refer ARPACK user guide for error message.
at
org.apache.spark.mllib.linalg.EigenValueDecomposition$.symmetricEigs(EigenValueDecomposition.scala:120)
at
org.apache.spark.mllib.linalg.distributed.RowMatrix.computeSVD(RowMatrix.scala:235)
at
org.apache.spark.mllib.linalg.distributed.RowMatrix.computeSVD(RowMatrix.scala:171)
...
was:
java.lang.reflect.InvocationTargetException
...
Caused by: java.lang.IllegalStateException: ARPACK returns non-zero info = 3
Please refer ARPACK user guide for error message.
at
org.apache.spark.mllib.linalg.EigenValueDecomposition$.symmetricEigs(EigenValueDecomposition.scala:120)
at
org.apache.spark.mllib.linalg.distributed.RowMatrix.computeSVD(RowMatrix.scala:235)
at
org.apache.spark.mllib.linalg.distributed.RowMatrix.computeSVD(RowMatrix.scala:171)
...
Environment:
Ubuntu 1410, Java HotSpot(TM) 64-Bit Server VM (build 25.25-b02, mixed mode)
spark local mode
was:Ubuntu 1410, Java HotSpot(TM) 64-Bit Server VM (build 25.25-b02, mixed
mode)
Affects Version/s: 1.2.0
Fix Version/s: (was: 1.2.0)
this exception occurs for various numbers of rows, columns, and k
> MLlib SingularValueDecomposition ARPACK IllegalStateException
> --------------------------------------------------------------
>
> Key: SPARK-4900
> URL: https://issues.apache.org/jira/browse/SPARK-4900
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 1.1.1, 1.2.0
> Environment: Ubuntu 1410, Java HotSpot(TM) 64-Bit Server VM (build
> 25.25-b02, mixed mode)
> spark local mode
> Reporter: Mike Beyer
>
> java.lang.reflect.InvocationTargetException
> ...
> Caused by: java.lang.IllegalStateException: ARPACK returns non-zero info = 3
> Please refer ARPACK user guide for error message.
> at
> org.apache.spark.mllib.linalg.EigenValueDecomposition$.symmetricEigs(EigenValueDecomposition.scala:120)
> at
> org.apache.spark.mllib.linalg.distributed.RowMatrix.computeSVD(RowMatrix.scala:235)
> at
> org.apache.spark.mllib.linalg.distributed.RowMatrix.computeSVD(RowMatrix.scala:171)
> ...
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