Also, to answer your question about searching for C and gamma, look at GridSearchCV and friends. http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html
The whole grid_search module may be worth looking at for your needs - I am assuming your cost to score will be something like RMSE? On Thu, Mar 27, 2014 at 9:52 AM, Kyle Kastner <[email protected]> wrote: > This may be an obvious question - but did you try applying a simple > Hamming, Blackman-Harris, etc. window to the data? Before trying EMD? > > Pretty much every transform (FFT included) has edge effect problems if the > signal is not exactly at a periodic boundary, and it sounds like the SVR > prediction would be used to create a kind of "custom" window function for > very strange data, but the mirroring process is still assuming it is > periodic in some way (by basically wrapping the function, the predicting > that) > > I don't know enough about EMD to know whether you are supposed to window > or not, but the slides I just glanced through definitely had tapers at the > edges. You may also try moving the "black region" forward until it reaches > 0 again - this looks like the natural periodic point of your data, and may > greatly improve your prediction even though it is kind of cheating... > unless it is always possible to find good "periodic points" and use those > (maybe by measuring cyclostationarity/autocorrelation?) > > Also, is this testing data a good representation of your real dataset? It > looks EKG-ish to my eyes. > > This is cool stuff - thanks for sharing. EMD seems worth investigating... > > Kyle > > > > On Thu, Mar 27, 2014 at 8:53 AM, Jaidev Deshpande < > [email protected]> wrote: > >> >> >> >> On Thu, Mar 27, 2014 at 7:16 PM, Nabil Freij <[email protected]>wrote: >> >>> Hey, >>> >>> I've been attempting to create an Empirical Mode Decomposition (EMD) >>> code and I came across a paper that removed the edge effects by using SVR >>> to predict the signal and then mirror that signal. >>> >>> I've created an IPython Notebook with background and my example code >>> trying to reproduce the SVR prediction. I've also linked the paper but it >>> might be behind a paywall, so I can provide the PDF >>> as needed. >>> >>> See: >>> >>> >>> http://nbviewer.ipython.org/urls/raw.githubusercontent.com/nabobalis/pyhht/master/Ipython%20Examples/SVM%20Regression%20Fitting.ipynb?create=1 >>> >>> Thanks, >>> Nabil >>> >>> >>> ------------------------------------------------------------------------------ >>> >>> _______________________________________________ >>> Scikit-learn-general mailing list >>> [email protected] >>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>> >>> >> Hi Nabil, >> >> This is very interesting. Can you also show how the SVR prediction fits >> into the EMD process? I mean, can you show how to go through the entire EMD >> pipeline while using this method to remove the edge effects? >> >> Thanks. >> >> -- >> JD >> >> >> ------------------------------------------------------------------------------ >> >> _______________________________________________ >> Scikit-learn-general mailing list >> [email protected] >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> >> >
------------------------------------------------------------------------------
_______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
