Objects of this class realize the transformation between word-document co-occurence matrix (integers) into a locally/globally weighted matrix (positive floats).
This is done by combining the term frequency counts (the TF part) with inverse document frequency counts (the IDF part), optionally normalizing the resulting documents to unit length.
The main methods are:
>>> tfidf = TfidfModel(corpus)
>>> print = tfidf[some_doc]
>>> tfidf.save('/tmp/foo.tfidf_model')
Model persistency is achieved via its load/save methods.
normalize dictates whether the resulting vectors will be set to unit length.