On Tue, Mar 26, 2013 at 3:28 PM, Gael Varoquaux <gael.varoqu...@normalesup.org> wrote:
> * For matrix factorization to be useful in the context of recomender > systems, there needs to be an API for recomender systems. While I'd > love to see this, I am afraid that it might be premature and should > probably happen after the release of 1.0. It seems to me that inverse_transform would do the job: X_transformed = estimator.fit_transform(X) # X contains missing values X = estimator.inverse_transform(X_transformed) # missing values were imputed Mathieu ------------------------------------------------------------------------------ Own the Future-Intel® Level Up Game Demo Contest 2013 Rise to greatness in Intel's independent game demo contest. Compete for recognition, cash, and the chance to get your game on Steam. $5K grand prize plus 10 genre and skill prizes. Submit your demo by 6/6/13. http://p.sf.net/sfu/intel_levelupd2d _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
