Hi!

how do i get to the source code of kpca or even better predict.kpca(which it tells me doesn't exist but should) ? 

(And if anyone has too much time:
Now if i got that right, the @pcv attribute consists of the principal components, and for kpca, these are defined as projections of some random point x, which was transformed into the other feature space -> f(x), projected onto the actual PC (eigenvector of Covariance). This can be computed as the sum of the (eigenvectors of the Kernel matrix * the kernel function(sample_i,x))

Now assume i have some new points and want to project them, how can i do that with only having @pcv?
Wouldn't i rather need the eigenvectors of K?
)







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