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https://issues.apache.org/jira/browse/SPARK-14707?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joseph K. Bradley updated SPARK-14707:
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Summary: Linear algebra: clarify light vs heavy constructors and accessors
(was: Linear algebra: clarify light vs heavy constructors and accesors)
> Linear algebra: clarify light vs heavy constructors and accessors
> -----------------------------------------------------------------
>
> Key: SPARK-14707
> URL: https://issues.apache.org/jira/browse/SPARK-14707
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Reporter: Joseph K. Bradley
>
> MLlib linear algebra provides methods for constructing Vectors and Matrices
> and for accessing the vector/matrix data. There are currently 2 types of
> these constructors and accessors:
> * light: avoid data copy and validation, useful for converting between MLlib
> types and numpy, Breeze, etc.
> * heavy: copy or validate data, useful for constructing MLlib types from user
> inputs
> MLlib is not very consistent about these and does not document which ops are
> light vs. heavy. This JIRA is for:
> * First, discussing which ops should be light vs heavy to choose a consistent
> API
> * Next, creating subtasks for Scala and Python for updating the
> implementations and clarifying the docs
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