On 11/08/2011 08:35 PM, Andreas Mueller wrote:
On 11/08/2011 08:18 PM, Matt Henderson wrote:
So if I have a custom kernel function: CustomKernel(x, y) - I should
do something like:
M = CustomKernel(model.support_vectors_, X)
# if X is a matrix where the rows are test vectors
results = model.predict_proba(M)
Actually, I haven't done this with sklearn yet ;)
But I think that should do it.
As an afterthought, I think you might also have to give the indices to
the support vectors in the matrix.
Maybe someone else knows it, otherwise look in the code.
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