Hi Sklearn,
I'm using Kernel PCA with the rbf kernel for projecting data into 3
dimensions for viewing alongside normal PCA and a stereographic projection
class that I wrote myself. Both the PCA and SGP classes seem to be
functioning correctly on this data set, but when I get to the .fit() method
for the KPCA class it fails silently and raises no exception and I have no
idea why.
My code looks something like this:
from sklearn.decomposition import PCA, KernelPCA
transformer = KernelPCA(n_components=3, kernel='rbf')
print transformer
transformer.fit(data)
print "DONE"
Obviously it never outputs "DONE", but the transformer output is:
KernelPCA(alpha=1.0, coef0=1, degree=3, eigen_solver='auto',
fit_inverse_transform=False, gamma=None, kernel='rbf', kernel_params=None,
max_iter=None, n_components=3, remove_zero_eig=False, tol=0)
Any ideas?
Best,
Steve O'Neill
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