Andy: > Hi Kevin. > Somehow I am sure there was a test computing that, but I can't find it > any more. > I'm pretty sure I wrote that at some point. > Btw, when I used a precomputed kernel using your implementation, I got > different results. > Not sure why that is. > > Cheers, > Andy >
My mistake, I was not accounting for gamma in the polynomial kernel. I have some new test code that calculates rho with high accuracy for the linear SVC and some accuracy for the polynomial and rbf kernel[0]. Two thoughts: 1) My calculation of rho is not exactly the same as the libsvm calculation, and I do not believe the difference can be attributed to a rounding error. Why/how does libsvm calculate rho as the average of G[i]*y[i] for all free support vectors in G and y? (I am not sure what the G or y arrays contain. G is described as the gradient in some comments and I assume y to contain the class labels.) 2) sklearn appears to me to report a positive rho in the linear SVC case and a negative rho in the kernel method case. If others agree, could we document this more clearly? -Kevin [0] http://pastebin.com/BXhVvH7y > > On 01/27/2015 11:55 AM, kjs wrote: >> Hi all, >> >> To gain better understanding of SVC methods, I am trying to train an SVC >> and then from the dual coefficients (in the kernel case) and the weights >> (in the linear case) to calculate rho and to make predictions on new >> feature vectors. Thus far, I am only successful in the linear case. I >> have posted some sample code to a paste bin for further clarity [0]. >> >> Please help me to understand where I am going wrong. My understanding is >> that rho, the constant term, should be the same for every support >> vector. However, in the code, I use the average of all hard-margin >> support vectors (with an absolute value less than C) to calculate rho. >> >> I have compared the sklearn SVC results with the libsvm SVC results. As >> per the documentation sklearn reports -rho from the libsvm trained SVC. >> >> Thanks much, >> Kevin >> >> [0] http://pastebin.com/5fqdh0CV >> >> >> ------------------------------------------------------------------------------ >> >> Dive into the World of Parallel Programming. The Go Parallel Website, >> sponsored by Intel and developed in partnership with Slashdot Media, >> is your >> hub for all things parallel software development, from weekly thought >> leadership blogs to news, videos, case studies, tutorials and more. >> Take a >> look and join the conversation now. http://goparallel.sourceforge.net/ >> >> >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > > > > ------------------------------------------------------------------------------ > Dive into the World of Parallel Programming. The Go Parallel Website, > sponsored by Intel and developed in partnership with Slashdot Media, is your > hub for all things parallel software development, from weekly thought > leadership blogs to news, videos, case studies, tutorials and more. Take a > look and join the conversation now. http://goparallel.sourceforge.net/ > > > > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >
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