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https://issues.apache.org/jira/browse/SPARK-4409?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng resolved SPARK-4409.
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Resolution: Fixed
Fix Version/s: 1.3.0
Issue resolved by pull request 3319
[https://github.com/apache/spark/pull/3319]
> Additional (but limited) Linear Algebra Utils
> ---------------------------------------------
>
> Key: SPARK-4409
> URL: https://issues.apache.org/jira/browse/SPARK-4409
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Reporter: Burak Yavuz
> Assignee: Burak Yavuz
> Fix For: 1.3.0
>
>
> This ticket is to discuss the addition of a very limited number of local
> matrix manipulation and generation methods that would be helpful in the
> further development for algorithms on top of BlockMatrix (SPARK-3974), such
> as Randomized SVD, and Multi Model Training (SPARK-1486).
> The proposed methods for addition are:
> For `Matrix`
> - map: maps the values in the matrix with a given function. Produces a new
> matrix.
> - update: the values in the matrix are updated with a given function.
> Occurs in place.
> Factory methods for `DenseMatrix`:
> - *zeros: Generate a matrix consisting of zeros
> - *ones: Generate a matrix consisting of ones
> - *eye: Generate an identity matrix
> - *rand: Generate a matrix consisting of i.i.d. uniform random numbers
> - *randn: Generate a matrix consisting of i.i.d. gaussian random numbers
> - *diag: Generate a diagonal matrix from a supplied vector
> *These methods already exist in the factory methods for `Matrices`, however
> for cases where we require a `DenseMatrix`, you constantly have to add
> `.asInstanceOf[DenseMatrix]` everywhere, which makes the code "dirtier". I
> propose moving these functions to factory methods for `DenseMatrix` where the
> putput will be a `DenseMatrix` and the factory methods for `Matrices` will
> call these functions directly and output a generic `Matrix`.
> Factory methods for `SparseMatrix`:
> - speye: Identity matrix in sparse format. Saves a ton of memory when
> dimensions are large, especially in Multi Model Training, where each row
> requires being multiplied by a scalar.
> - sprand: Generate a sparse matrix with a given density consisting of
> i.i.d. uniform random numbers.
> - sprandn: Generate a sparse matrix with a given density consisting of
> i.i.d. gaussian random numbers.
> - diag: Generate a diagonal matrix from a supplied vector, but is memory
> efficient, because it just stores the diagonal. Again, very helpful in Multi
> Model Training.
> Factory methods for `Matrices`:
> - Include all the factory methods given above, but return a generic
> `Matrix` rather than `SparseMatrix` or `DenseMatrix`.
> - horzCat: Horizontally concatenate matrices to form one larger matrix.
> Very useful in both Multi Model Training, and for the repartitioning of
> BlockMatrix.
> - vertCat: Vertically concatenate matrices to form one larger matrix. Very
> useful for the repartitioning of BlockMatrix.
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