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https://issues.apache.org/jira/browse/MATH-278?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12722001#action_12722001
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Sebb commented on MATH-278:
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serialVersionUID should be private
The patch includes an unrelated change to AbstractIntegrator.java.
It would be useful to add a constructor which had parameters for bandwidth and
roubustnessIterators, and drop the corresponding setxxx() methods
The fields could then be made final, and the class would then be immutable and
thus thread-safe.
The SVN keyword $Date$ causes problems when checking releases, so I'd recommend
that it is removed.
> Robust locally weighted regression (Loess / Lowess)
> ---------------------------------------------------
>
> Key: MATH-278
> URL: https://issues.apache.org/jira/browse/MATH-278
> Project: Commons Math
> Issue Type: New Feature
> Reporter: Eugene Kirpichov
> Attachments: loess.patch
>
>
> Attached is a patch that implements the robust Loess procedure for smoothing
> univariate scatterplots with local linear regression (
> http://en.wikipedia.org/wiki/Local_regression) described by William Cleveland
> in http://www.math.tau.ac.il/~yekutiel/MA%20seminar/Cleveland%201979.pdf ,
> with tests.
> (Also, the patch fixes one missing-javadoc checkstyle warning in the
> AbstractIntegrator class: I wanted to make it so that the code with my patch
> does not generate any checkstyle warnings at all)
> I propose to include the procedure into commons-math because commons-math, as
> of now, does not possess a method for robust smoothing of noisy data: there
> is interpolation (which virtually can't be used for noisy data at all) and
> there's regression, which has quite different goals.
> Loess allows one to build a smooth curve with a controllable degree of
> smoothness that approximates the overall shape of the data.
> I tried to follow the code requirements as strictly as possible: the tests
> cover the code completely, there are no checkstyle warnings, etc. The code is
> completely written by myself from scratch, with no borrowings of third-party
> licensed code.
> The method is pretty computationally intensive (10000 points with a bandwidth
> of 0.3 and 4 robustness iterations take about 3.7sec on my machine; generally
> the complexity is O(robustnessIters * n^2 * bandwidth)), but I don't know how
> to optimize it further; all implementations that I have found use exactly the
> same algorithm as mine for the unidimensional case.
> Some TODOs, in vastly increasing order of complexity:
> - Make the weight function customizable: according to Cleveland, this is
> needed in some exotic cases only, like, where the desired approximation is
> non-continuous, for example.
> - Make the degree of the locally fitted polynomial customizable: currently
> the algorithm does only a linear local regression; it might be useful to make
> it also use quadratic regression. Higher degrees are not worth it, according
> to Cleveland.
> - Generalize the algorithm to the multidimensional case: this will require A
> LOT of hard work.
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