On Wed, Oct 26, 2011 at 03:02:28PM +0200, SK Sn wrote: > Hi there, I am trying to apply and test several dimension reduction methods > on 20Newsgroup data. However, I got errors, which I did not get how, on all > of them except RandomPCA. Would you please help me to get a better > understand of the issue?
> X = Vectorizer(max_features=10000).fit_transform(data_set.data) I think that your problem is that X (returned by the Vectorizer) is a sparse matrix, and that the different methods other than the RandomizedPCA do not accept sparse matrices as inputs. You can make the data dense using X = X.todense() This will consume much more memory, and might not be an option, though. HTH, Gaël ------------------------------------------------------------------------------ The demand for IT networking professionals continues to grow, and the demand for specialized networking skills is growing even more rapidly. Take a complimentary Learning@Cisco Self-Assessment and learn about Cisco certifications, training, and career opportunities. http://p.sf.net/sfu/cisco-dev2dev _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
