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Samee Zahur commented on MAHOUT24:

Actually I do have some Unit tests in there, albeit rudimentary. And the reason I didn't comment
it was, like I said, it is a skeletal implementation, which might need to change significantly
before being commited.
As for mapping the concepts of the nips paper, it simply calculates the sum(x[i]x[j]) mentioned
in the nips paper, nothing else. So to the actual set of regression equations is this:
x[i] = x[ind] * output[i]/output[ind]
where ind is the index of the independent variable, x[ind] is the test input at which we want
to use the regression equation for predictions, and x[i] is the ith component of the predicted
output. output[i] represents the ith line in the output file.
For example, in the 2D case, the input file is supposed to contain a list of points along
with their weights. If the output file results in something like this:
0: 2
1: 4
then the regression equation is
y = x*4/2
or, y=2x
where 4=output[0], 2=output[1], x is x[ind]=x[0], y is x[1].
When I complete the implementation, I will have all the documentations ready to clear this
up. But first
Samee
> Skeletal LWLR implementation
> 
>
> Key: MAHOUT24
> URL: https://issues.apache.org/jira/browse/MAHOUT24
> 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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