class_docstr: Estimate Dynamic Topic Model parameters based on a training corpus.
class_docstr_tok: ["Estimate","Dynamic","Topic","Model","parameters","based","on","a","training","corpus","."]
docstr: Get the topic mixture for a document.
  
  Uses the priors for the dirichlet distribution that approximates the true posterior with the optimal
  lower bound, and therefore requires the model to be already trained.
  
  
  Parameters
  ----------
  doc_number : int
      Index of the document for which the mixture is returned.
  
  Returns
  -------
  list of length `self.num_topics`
      Probability for each topic in the mixture (essentially a point in the `self.num_topics - 1` simplex.
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code: def doc_topics(self, doc_number):
      """Get the topic mixture for a document.
  
      Uses the priors for the dirichlet distribution that approximates the true posterior with the optimal
      lower bound, and therefore requires the model to be already trained.
  
  
      Parameters
      ----------
      doc_number : int
          Index of the document for which the mixture is returned.
  
      Returns
      -------
      list of length `self.num_topics`
          Probability for each topic in the mixture (essentially a point in the `self.num_topics - 1` simplex.
  
      """
      doc_topic = self.gammas / self.gammas.sum(axis=1)[:, np.newaxis]
      return doc_topic[doc_number]
  
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 11 for calling the LdaSeqModel obj's underlying member method code with example usage: obj.doc_topics(doc_number) and returns: doc_topic