parameters: (self, topic_names=None, class_ids=None, model_name=None)
return_statement: return nd_array, tokens, topic_names
docstring: :Description: get phi matrix in dense format
  
          :param topic_names: list with topics or single topic to extract, None value means all topics
          :type topic_names: list of str or str or None
          :param class_ids: list with class_ids or single class_id to extract, None means all class ids
          :type class_ids: list of str or str or None
          :param str model_name: self.model_pwt by default, self.model_nwt is also\
                        reasonable to extract unnormalized counters
  
          :return:
            * a 3-tuple of (data, rows, columns), where
            * data --- numpy.ndarray with Phi data (i.e., p(w|t) values)
            * rows --- the tokens of topic model;
            * columns --- the names of topics in topic model;
docstring_summary: :Description: get phi matrix in dense format
docstring_tokens: : Description : get phi matrix in dense format
function: def get_phi_dense(self, topic_names=None, class_ids=None, model_name=None):
          """
          :Description: get phi matrix in dense format
  
          :param topic_names: list with topics or single topic to extract, None value means all topics
          :type topic_names: list of str or str or None
          :param class_ids: list with class_ids or single class_id to extract, None means all class ids
          :type class_ids: list of str or str or None
          :param str model_name: self.model_pwt by default, self.model_nwt is also\
                        reasonable to extract unnormalized counters
  
          :return:
            * a 3-tuple of (data, rows, columns), where
            * data --- numpy.ndarray with Phi data (i.e., p(w|t) values)
            * rows --- the tokens of topic model;
            * columns --- the names of topics in topic model;
          """
          if not self._initialized:
              raise RuntimeError('Model does not exist yet. Use ARTM.initialize()/ARTM.fit_*()')
  
          valid_model_name = self.model_pwt if model_name is None else model_name
  
          info = self.master.get_phi_info(valid_model_name)
  
          if isinstance(topic_names, string_types):
              topic_names = [topic_names]
          if isinstance(class_ids, string_types):
              class_ids = [class_ids]
  
          _, nd_array = self.master.get_phi_matrix(model=valid_model_name,
                                                   topic_names=topic_names,
                                                   class_ids=class_ids)
  
          tokens = [(c, t) for t, c in zip(info.token, info.class_id) if (class_ids is None or c in class_ids)]
          topic_names = [topic_name for topic_name in info.topic_name
                         if topic_names is None or topic_name in topic_names]
          return nd_array, tokens, topic_names
function_tokens: def get_phi_dense ( self , topic_names = None , class_ids = None , model_name = None ) : if not self . _initialized : raise RuntimeError ( 'Model does not exist yet. Use ARTM.initialize()/ARTM.fit_*()' ) valid_model_name = self . model_pwt if model_name is None else model_name info = self . master . get_phi_info ( valid_model_name ) if isinstance ( topic_names , string_types ) : topic_names = [ topic_names ] if isinstance ( class_ids , string_types ) : class_ids = [ class_ids ] _ , nd_array = self . master . get_phi_matrix ( model = valid_model_name , topic_names = topic_names , class_ids = class_ids ) tokens = [ ( c , t ) for t , c in zip ( info . token , info . class_id ) if ( class_ids is None or c in class_ids ) ] topic_names = [ topic_name for topic_name in info . topic_name if topic_names is None or topic_name in topic_names ] return nd_array , tokens , topic_names