def __getitem__(self, bow):
        # if the input vector is in fact a corpus, return a transformed corpus
        is_corpus, bow = utils.is_corpus(bow)
        if is_corpus:
            return self._apply(bow)

        # unknown (new) terms will be given zero weight (NOT infinity/huge)
        vector = [(term_id, math.log(tf + 1) * self.entr.get(term_id))
                  for term_id, tf in bow if term_id in self.entr]
        if self.normalize:
            vector = matutils.unitvec(vector)
        return vector
Return log entropy representation of the input vector and/or corpus.
dataset/ETHPy150Open largelymfs/topical_word_embeddings/TWE-1/gensim/models/logentropy_model.py/LogEntropyModel.__getitem__