class_docstr: The storage structure of LDA model's inference result.
      
class_docstr_tok: ["The","storage","structure","of","LDA","model","'s","inference","result","."]
docstr: Return the distribution of document topics in dense format,
  taking into account the prior parameter alpha.
docstr_tok: ["Return","the","distribution","of","document","topics","in","dense","format",",","taking","into","account","the","prior","parameter","alpha","."]
code: def dense_topic_dist(self):
      """Return the distribution of document topics in dense format,
      taking into account the prior parameter alpha.
      """
      dense_dist = np.zeros(self._num_topics)
      if self.size() == 0:
          return dense_dist
      dense_dist = (
          self._accum_topic_sum * 1.0 / self._num_accum + self._alpha
      ) / (self.size() + self._alpha * self._num_topics)
      return dense_dist
  
imports: import numpy
label_desc: Use image node_id 10 for calling the LDADoc obj's underlying member method code with example usage: obj.dense_topic_dist() and returns: dense_dist, dense_dist