e if it's related to IDF normalization? > > How many dimensions do you have in your fitted model? > > >>> print len(vectorizer.vocabulary_) > > How many documents do you have in your training corpus? > > How many non-zeros do you have in your transformed document? > > >>> print vectorizer.transform([my_text_document])
In [30]: print vectorizer.transform([input_txt]).data.shape (110,) ------------------------------------------------------------------------------ Everyone hates slow websites. So do we. Make your web apps faster with AppDynamics Download AppDynamics Lite for free today: http://p.sf.net/sfu/appdyn_sfd2d_oct _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
