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
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