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https://issues.apache.org/jira/browse/MAHOUT-1197?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13641163#comment-13641163
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Ted Dunning commented on MAHOUT-1197:
-------------------------------------
Actually, I would imagine that this would work fine. Vector.times should be a
sparse op and the assign should be just as sparse (since there is nothing there
to begin with.
As long as matrixLike returns the right type, this code should be pretty much
fine aside from cons'ing up a too many copies of the data. That will only
impose a constant cost.
Is there something I am not seeing?
> AbstractVector#cross is only appropriately efficient for dense vectors
> ----------------------------------------------------------------------
>
> Key: MAHOUT-1197
> URL: https://issues.apache.org/jira/browse/MAHOUT-1197
> Project: Mahout
> Issue Type: Bug
> Components: Math
> Affects Versions: 0.6
> Reporter: Jake Mannix
> Fix For: 0.8
>
>
> Nobody overrides this implementation:
> [code]
> @Override
> public Matrix cross(Vector other) {
> Matrix result = matrixLike(size, other.size());
> for (int row = 0; row < size; row++) {
> result.assignRow(row, other.times(getQuick(row)));
> }
> return result;
> }
> [code]
> I think you can imagine what kind of performance this has on sparse vectors
> (k non-zeroes) with high cardinality (N) - scales as O(N^2) instead of O(k^2).
> I think the right approach is to *not* implement this in AbstractVector at
> all, and force concrete implementations to properly implement it performantly.
> Alternatively, killing this method entirely might be appropriate. If anyone
> was using it (and uses sparse vectors), they'd have complained about this by
> now.
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