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https://issues.apache.org/jira/browse/MAHOUT-6?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12576371#action_12576371
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Jason Rennie commented on MAHOUT-6:
-----------------------------------
Re: Jeff
Sounds good. It think I might actually have some time to do this. One thing I
didn't see when looking through the last patch was basic matrix/vector
operations. I'll go ahead and include a dot-product method to exhibit how it'd
work and do some speed comparisons vs. a HashMap impl.
Yeah, would definitely be good to get this stuff in trunk, if only to make it
easier to read/access! :)
Re: Ted
Didn't realize HashMaps were so fast. Will be good to revisit the testing I
did earlier. Agreed on the CRS benefits.
One way I get around the sorted constraint while constructing a sparse vector
is a SparseVectorBuilder class. It basically has two methods: void add(int
_idx, double _val), and SparseVector build(). Avoids having to keep state
within the SparseVector.
> 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, MAHOUT-6c.diff,
> MAHOUT-6d.diff, MAHOUT-6e.diff, MAHOUT-6f.diff, MAHOUT-6g.diff,
> MAHOUT-6h.patch, MAHOUT-6i.diff, MAHOUT-6j.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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