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https://issues.apache.org/jira/browse/MAHOUT-6?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12571923#action_12571923
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Ted Dunning commented on MAHOUT-6:
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Paul,
Can you amplify a bit on how you see the difference between interfaces and
abstract classes?
It is absolutely true that interfaces can only be changed delicately, but at
this stage I think that everybody understands that. But why is a public
abstract class any different?
I should also say that the interface is definitely incomplete as it says
nothing about labeling of rows and columns, nor does it have any way to find
out if a matrix is sparse, nor whether a sparse matrix has fast column or row
viewing. All are pretty important, but all are additions to this API, rather
than changes.
Finally, I have a little problem in viewing a lucene index itself as a single
matrix. I would propose an interface in which a lucene index is a factory
which constructs matrices that are linear combinations of fields of the lucene
matrix. There should also be some way to view the native retrieval operation
of the lucene index as matrix multiplication.
> Need a matrix implementation
> ----------------------------
>
> Key: MAHOUT-6
> URL: https://issues.apache.org/jira/browse/MAHOUT-6
> Project: Mahout
> Issue Type: New Feature
> Reporter: Ted Dunning
> Attachments: MAHOUT-6a.diff, MAHOUT-6b.diff
>
>
> We need matrices for Mahout.
> An initial set of basic requirements includes:
> a) sparse and dense support are required
> b) row and column labels are important
> c) serialization for hadoop use is required
> d) reasonable floating point performance is required, but awesome FP is not
> e) the API should be simple enough to understand
> f) it should be easy to carve out sub-matrices for sending to different
> reducers
> g) a reasonable set of matrix operations should be supported, these should
> eventually include:
> simple matrix-matrix and matrix-vector and matrix-scalar linear algebra
> operations, A B, A + B, A v, A + x, v + x, u + v, dot(u, v)
> row and column sums
> generalized level 2 and 3 BLAS primitives, alpha A B + beta C and A u +
> beta v
> h) easy and efficient iteration constructs, especially for sparse matrices
> i) easy to extend with new implementations
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