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https://issues.apache.org/jira/browse/MAHOUT-24?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Samee Zahur updated MAHOUT-24:
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Attachment: LWLR.patch.tar.bz2
Output format:
it outputs n lines where n is the number of dimensions
ith line = sum(x[i]*x[ind]) where ind is the index of the independent variable
For the classical 2D case, this means the regression line's gradient is given
by (1st line value)/(2nd line value).
Many other improvements are definitely possible, including a host of commonly
used options (lines not passing through the origin, weight function as a
function of x rather than being specified on file etc.). But all those are
doable, and shouldn't take too long.
Samee
> Skeletal LWLR implementation
> ----------------------------
>
> Key: MAHOUT-24
> URL: https://issues.apache.org/jira/browse/MAHOUT-24
> Project: Mahout
> Issue Type: New Feature
> Environment: n/a
> Reporter: Samee Zahur
> Attachments: LWLR.patch.tar.bz2
>
>
> This is a very skeletal but functional implementation for LWLR. It outputs n
> lines where n is the number of dimensions. ith line = sum(x[i]*x[ind]) where
> ind is the index of independant variable. So the actual gradient = 2nd
> line/1st line for the classical 2D.
> Contains a single small test case for demonstration.
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