I did test sklearn SVM against libsvm, and the rho's agree. If anything, #1 questions the libsvm calculation of rho, not sklearn's implementation of libsvm.
At some point, I would like to run an experiment where either rho is used in prediction to determine which calculation has the greatest applied value in a toy problem. -Kevin Andy: > Hi. > did you have time to investigate 1) any more? > I'm sorry but there are currently a couple of issues still open. > If there is an error here we should really look into it before the next > release :-/ > > Cheers, > Andy > > On 01/27/2015 11:27 PM, kjs wrote: >> 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. 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