def dist_top_doc(self, topic_or_topics, weights=[], filter_words=[],
                     print_len=10, as_strings=True, label_fn=def_label_fn, 
                     filter_nan=True, dist_fn=JS_dist, order='i'):
        Q = self.model.top_doc / self.model.top_doc.sum(0)

        d_arr = dist_top_doc(topic_or_topics, Q, self.corpus,   
                               self.model.context_type, weights=weights, 
                               print_len=print_len, as_strings=False, 
                               label_fn=label_fn, filter_nan=filter_nan, 
                               dist_fn=dist_fn)

        topics = res_top_type(topic_or_topics)

        if len(filter_words) > 0:
            white = set()
            for w in filter_words:
                l = self.word_topics(w, as_strings=False)
                d = l['i'][np.in1d(l['value'], topics)]
                white.update(d)
            
            d_arr = d_arr[(np.in1d(d_arr['i'], white))]

        if as_strings:
            md = self.corpus.view_metadata(self.model.context_type)
            docs = label_fn(md)
            d_arr = map_strarr(d_arr, docs, k='i', new_k='doc')

    	return d_arr
Determine the requested response content-type.
dataset/ETHPy150Open inpho/vsm/vsm/viewer/ldacgsviewer.py/LdaCgsViewer.dist_top_doc--crossover with--dataset/ETHPy150Open openstack/rack/rack/api/wsgi.py/Request.best_match_content_type