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https://issues.apache.org/jira/browse/SPARK-6244?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14356464#comment-14356464
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Sean Owen commented on SPARK-6244:
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You can reuse this JIRA but you would have to modify the title and description.
You should close your PR and make a new one.
At best we are talking about adding a few utility methods to {{Vectors}}, but
what is the use case for these? yes, I can imagine some, but would it refactor
some repeated usages in the code?
I don't think MLlib intends to contain a full suite of vector utility methods
as that is what Breeze is used for. I'd rather not add utility code just for
its own sake. But if there's a clear common use for these things it could make
sense.
> Implement VectorSpace to easy create a complicated feature vector
> -----------------------------------------------------------------
>
> Key: SPARK-6244
> URL: https://issues.apache.org/jira/browse/SPARK-6244
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: Kirill A. Korinskiy
> Priority: Minor
>
> VectorSpace is wrapper what implement three operation:
> - concat -- concat all vectors to single vector
> - sum -- sum of vectors
> - scaled -- multiple scalar to vector
>
> Example of usage:
> ```
> import org.apache.spark.mllib.linalg.Vectors
> import org.apache.spark.mllib.linalg.VectorSpace
> // Create a new Vector Space with one dense vector.
> val vs = VectorSpace.create(Vectors.dense(1.0, 0.0, 3.0))
> // Add a to vector space a scaled vector space
> val vs2 = vs.add(vs.scaled(-1d))
> // concat vectors from vector space, result: Vectors.dense(1.0, 0.0, 3.0,
> -1.0, 0.0, -3.0)
> val concat = vs2.concat
> // take a sum from vector space, result: Vectors.dense(0.0, 0.0, 0.0)
> val sum = vs2.sum
> ```
> This wrapper is very useful when create a complicated feature vector from
> structured objects.
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