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https://issues.apache.org/jira/browse/SYSTEMML-516?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Mike Dusenberry updated SYSTEMML-516:
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Description:
DML allows for index slicing of matrices for specified ranges, as in {{X[1:4,
2:6]}}. However, this currently requires that *both* a lower *and* upper bound
be specified for a given row or column range.
It would be useful to be able to specify *either* a lower *or* upper bound,
with the missing bound implicitly added internally. This would allow for
scenarios such as selecting all columns *except* the first one, as in
{code}
data = rand(rows=10, cols=20, min=0, max=1, pdf="uniform", sparsity=0.2)
X = X[1:4, 2:] # select rows 1 to 4, and columns 2 to ncol(X)
{code}.
was:
DML allows for index slicing of matrices for specified ranges, as in {{X[1:4,
2:6]}}. However, this currently requires that *both* a lower *and* upper bound
be specified for a given row or column range.
It would be useful to be able to specify *either* a lower *or* upper bound,
with the missing bound implicitly added internally. This would allow for
scenarios such as selecting all columns *except* the first one, as in
{code}
data = rand(rows=10, cols=20, min=0, max=1, pdf="uniform", sparsity=0.2)
X = X[1:4, 2:] # select rows 1 to 4, and columns 2 to numColumns
{code}.
> Index Range Slicing Should Allow Implicit Upper Or Lower Bounds
> ---------------------------------------------------------------
>
> Key: SYSTEMML-516
> URL: https://issues.apache.org/jira/browse/SYSTEMML-516
> Project: SystemML
> Issue Type: Improvement
> Reporter: Mike Dusenberry
>
> DML allows for index slicing of matrices for specified ranges, as in {{X[1:4,
> 2:6]}}. However, this currently requires that *both* a lower *and* upper
> bound be specified for a given row or column range.
> It would be useful to be able to specify *either* a lower *or* upper bound,
> with the missing bound implicitly added internally. This would allow for
> scenarios such as selecting all columns *except* the first one, as in
> {code}
> data = rand(rows=10, cols=20, min=0, max=1, pdf="uniform", sparsity=0.2)
> X = X[1:4, 2:] # select rows 1 to 4, and columns 2 to ncol(X)
> {code}.
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