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Posted to issues@systemml.apache.org by "Glenn Weidner (JIRA)" <ji...@apache.org> on 2017/09/09 03:33:00 UTC
[jira] [Updated] (SYSTEMML-1817) Full standard deviation fails due
to wrong data type
[ https://issues.apache.org/jira/browse/SYSTEMML-1817?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Glenn Weidner updated SYSTEMML-1817:
------------------------------------
Fix Version/s: (was: SystemML 1.0)
SystemML 0.15
> Full standard deviation fails due to wrong data type
> -----------------------------------------------------
>
> Key: SYSTEMML-1817
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1817
> Project: SystemML
> Issue Type: Bug
> Reporter: Matthias Boehm
> Assignee: Matthias Boehm
> Fix For: SystemML 0.15
>
>
> The following script currently fails due to incorrect hop construction of {{sd(Xi)}} at language level:
> {code}
> X = rand(rows=1000, cols=100);
> R = matrix(0, ncol(X), 4)
> parfor(i in 1:ncol(X), log=DEBUG) {
> Xi = X[,i];
> R[i,1] = mean(Xi); # mean
> R[i,2] = sd(Xi); # standard deviation
> R[i,3] = moment(Xi,3)/(sd(Xi)^3); # skewness
> R[i,4] = moment(Xi,4)/(sd(Xi)^4)-3; # kurtosis
> }
> {code}
> The stacktrace is as follows
> {code}
> Caused by: org.apache.sysml.runtime.DMLRuntimeException: Variable '_Var1703' does not exist in the symbol table.
> at org.apache.sysml.runtime.controlprogram.context.ExecutionContext.getMatrixObject(ExecutionContext.java:186)
> at org.apache.sysml.runtime.controlprogram.context.ExecutionContext.setMatrixOutput(ExecutionContext.java:451)
> at org.apache.sysml.runtime.instructions.cp.MatrixBuiltinCPInstruction.processInstruction(MatrixBuiltinCPInstruction.java:59)
> at org.apache.sysml.runtime.controlprogram.ProgramBlock.executeSingleInstruction(ProgramBlock.java:286)
> ... 6 more
> {code}
> which is cause by the following instruction which has an output type MATRIX although it should be SCALAR.
> {code}
> --------CP uavar _mVar3.MATRIX.DOUBLE _mVar5.MATRIX.DOUBLE 24
> {code}
> Interestingly, this only shows up when there are multiple consumers with matrix and scalar data types.
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