Hi Steve.
So by failing, you mean it never finishes?
Or the python process dies?
What is the shape of your data?
Andy
On 03/23/2015 02:14 PM, Stephen O'Neill wrote:
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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