c GddZdS)c$eZdZddZddZdZdS) BaseTopicModel chdd|||DS)asGet a single topic as a formatted string. Parameters ---------- topicno : int Topic id. topn : int Number of words from topic that will be used. Returns ------- str String representation of topic, like '-0.340 * "category" + 0.298 * "$M$" + 0.183 * "algebra" + ... '. z + c3*K|]\}}d||fzVdS)z %.3f*"%s"N).0kvs 7lib/python3.11/site-packages/gensim/models/basemodel.py z-BaseTopicModel.print_topic..s/ZZ41a+A.ZZZZZZ)join show_topic)selftopicnotopns r print_topiczBaseTopicModel.print_topics4 zzZZ4??7TX;Y;YZZZZZZr c2|||dS)a Get the most significant topics (alias for `show_topics()` method). Parameters ---------- num_topics : int, optional The number of topics to be selected, if -1 - all topics will be in result (ordered by significance). num_words : int, optional The number of words to be included per topics (ordered by significance). Returns ------- list of (int, list of (str, float)) Sequence with (topic_id, [(word, value), ... ]). T) num_topics num_wordslog) show_topics)rrrs r print_topicszBaseTopicModel.print_topicss :PTUUUr ct)zGet words X topics matrix. Returns -------- numpy.ndarray: The term topic matrix learned during inference, shape (`num_topics`, `vocabulary_size`). Raises ------ NotImplementedError )NotImplementedError)rs r get_topicszBaseTopicModel.get_topics's "!r N)r)rr)__name__ __module__ __qualname__rrrrr r rrsS[[[[$VVVV$ " " " " "r rN)rrr r r!s73"3"3"3"3"3"3"3"3"3"r