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Posted to dev@mahout.apache.org by "Dmitriy Lyubimov (JIRA)" <ji...@apache.org> on 2015/06/03 01:44:49 UTC
[jira] [Updated] (MAHOUT-1728) in-core functional assignments
[ https://issues.apache.org/jira/browse/MAHOUT-1728?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Dmitriy Lyubimov updated MAHOUT-1728:
-------------------------------------
Description:
in-core functional assignments (for vector and matrices)
mxA := { (x) => x * x}
mxA := { (row, col, x} => ... }
mxA ::={ (x) => ... }
mxA ::={ (row, col, x} => ...}
vec := { (x) => ...}
vec :={ (idx, x) => ..}
vec ::= { (x) => ...}
vec ::={ (ind, x) => ...}
the `:=` assignmentn applies the function to all elements of tensor.
the `::=` assignment ignores zero elements of the tensor to improve performance.
matrix functions iterations use matrix structural flavor to optimize traversal.
further examples.
mxA := exp _ (in-place exponent)
v ::= abs _
was:
in-core functional assignments (for vector and matrices)
mxA := { (x) => x * x}
mxA := { (row, col, x} => ... }
mxA ::={ (x) => ... }
mxA ::={ (row, col, x} => ...}
vec := { (x) => ...}
vec :={ (idx, x) => ..}
vec ::= { (x) => ...}
vec ::={ (ind, x) => ...}
the `:=` assignmentn applies the function to all elements of tensor.
the `::=` assignment ignores zero elements of the tensor to improve performance.
matrix functions iterations use matrix structural flavor to optimize traversal.
further examples.
mxA := exp _ (in-place exponent)
v := abs _
> in-core functional assignments
> ------------------------------
>
> Key: MAHOUT-1728
> URL: https://issues.apache.org/jira/browse/MAHOUT-1728
> Project: Mahout
> Issue Type: Improvement
> Reporter: Dmitriy Lyubimov
> Assignee: Dmitriy Lyubimov
> Fix For: 0.10.2
>
>
> in-core functional assignments (for vector and matrices)
> mxA := { (x) => x * x}
> mxA := { (row, col, x} => ... }
> mxA ::={ (x) => ... }
> mxA ::={ (row, col, x} => ...}
> vec := { (x) => ...}
> vec :={ (idx, x) => ..}
> vec ::= { (x) => ...}
> vec ::={ (ind, x) => ...}
> the `:=` assignmentn applies the function to all elements of tensor.
> the `::=` assignment ignores zero elements of the tensor to improve performance.
> matrix functions iterations use matrix structural flavor to optimize traversal.
> further examples.
> mxA := exp _ (in-place exponent)
> v ::= abs _
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