class_docstr: Posterior values associated with each set of documents.
  
  TODO: use **Hoffman, Blei, Bach: Online Learning for Latent Dirichlet Allocation, NIPS 2010.**
  to update phi, gamma. End game would be to somehow replace LdaPost entirely with LdaModel.
class_docstr_tok: ["Posterior","values","associated","with","each","set","of","documents",".","TODO",":","use","*","*","Hoffman",",","Blei",",","Bach",":","Online","Learning","for","Latent","Dirichlet","Allocation",",","NIPS","2010",".","*","*","to","update","phi",",","gamma",".","End","game","would","be","to","somehow","replace","LdaPost","entirely","with","LdaModel","."]
docstr: Initialize variational posterior. 
docstr_tok: ["Initialize","variational","posterior","."]
code: def init_lda_post(self):
      """Initialize variational posterior."""
      total = sum(count for word_id, count in self.doc)
      self.gamma.fill(
          self.lda.alpha[0] + float(total) / self.lda.num_topics
      )
      self.phi[: len(self.doc), :] = 1.0 / self.lda.num_topics
  
imports: import logging
  import numpy
  from scipy.special import digamma, gammaln
  from scipy import optimize
  from gensim import utils, matutils
  from gensim.models import ldamodel
label_desc: Use image node_id 4 for calling the LdaPost obj's underlying member method code with example usage: obj.init_lda_post() without return types