def do_mstep(self, rho, other):
        """
        M step: use linear interpolation between the existing topics and
        collected sufficient statistics in `other` to update the topics.

        """
        logger.debug("updating topics")
        # update self with the new blend; also keep track of how much did
        # the topics change through this update, to assess convergence
        diff = numpy.log(self.expElogbeta)
        self.state.blend(rho, other)
        del other
        diff -= self.state.get_Elogbeta()
        self.sync_state()
        self.print_topics(15) # print out some debug info at the end of each EM iteration
        logger.info((numpy.mean(numpy.abs(diff)), rho) % "topic diff=%f, rho=%f")
        self.num_updates += 1
dataset/ETHPy150Open largelymfs/topical_word_embeddings/TWE-1/gensim/models/ldamodel.py/LdaModel.do_mstep/swapped operands of `%`