modify comment in LdaModel.inference() to be more explicit

[yocen]

One comment in LdaModel.inference() confused me:
# The optimal phi_{dwk} is proportional to expElogthetad_k * expElogbetad_w.
And I think it should modified to this so the comment would be more accurate:
# The optimal phi_{dwk} is proportional to expElogthetad_k * expElogbetad_kw.

Reasons:
1.
According to Algorithm 1 on paper:
Online Learning for Latent Dirichlet Allocation, NIPS 2010 http://www.cs.princeton.edu/~mdhoffma,
phi_dwk is proportional to exp(Elogtheta_dk + Elogbeta_kw).
2.
phi_{dwk} is a scalar, expElogthetad_k is a scalar, and expElogbetad_w is a vector with K elements, so expElogthetad_k * expElogbetad_w will return a vector with K elements, which does not match the type of phi_{dwk}. If we use expElogthetad_k * expElogbetad_kw instead, in which expElogbetad_kw is a scalar, it matches.

[mpenkov]

Thank you for pointing this out to us!

[jonaschn]

@yocen Thanks for pointing that out.
@olavurmortensen implemented an efficient version of computing the phinorm for the Author-Topic Model.
I am not sure if the phinorm is the same for LDA and the author-topic model.
If this is the case it would make sense to "backport" @olavurmortensen idea to the LDAModel.

https://github.com/RaRe-Technologies/gensim/blob/2feef89a24c222e4e0fc6e32ac7c6added752c26/gensim/models/atmodel.py#L377