Hi Igor.
Could you please say which version of sklearn you are using?
There was a minor change recently to improve consistency.
Now, the independent components of size [n_components, n_features are in
ICA.components_.
ICA.sources_ should contain the [n_samples, n_components] estimated
source signals for the data,
as it is described in the docs
<http://scikit-learn.org/dev/modules/generated/sklearn.decomposition.FastICA.html#sklearn.decomposition.FastICA>.
About the nans, I have no idea.
Have you tried to not do your own whitening, i.e. whiten=True?
Does it change the result at all (it shouldn't).
Cheers,
Andy
On 06/27/2012 10:50 PM, Igor Nikolskiy wrote:
Hello all,
I've got a FastICA question. I am interested in doing my own whitening
of the data before feeding it to fastICA, but I'm running into
trouble. Here's the code:
from pylab import *
from pandas import *
from sklearn.decomposition import FastICA, PCA
ms2= read_csv('L-Glu.csv', index_col= 0)
ms2_centered= ms2-ms2.mean()
ms2_whitened= PCA().fit(ms2_centered.T).transform(ms2_centered.T)
ica= FastICA(n_components= 2, whiten= False)
fit= ica.fit(ms2_whitened)
sources= abs(ica.transform(ms2_whitened))
The ms2_whitened matrix looks good - it is the right shape, and looks
reasonable with imshow. The problem is when I look at fit.sources_
there are more than 2 sources and all of the source values are nan.
Does anyone know what I could be doing wrong?
Thanks in advance,
Igor
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