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https://issues.apache.org/jira/browse/MAHOUT-6?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Jeff Eastman updated MAHOUT-6:
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Attachment: MAHOUT-6f.diff
Here's another patch with like() and haveSharedCells() implemented and tested.
I also implemented an initial SparseDoubleVector using a Map<Integer, Double>
and tests for it. It uses a HashMap, is that ok with you?
Could you provide some more insight into the remaining functions? The
SparseBinaryVector seems pretty special purpose and I imagine you have a
proposal for its implementation. Could you please discribe that?
Both of these vector classes beg the question of why DenseMatrix1D is not
called DenseVector instead. Comments?
> 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
>
>
> 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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