(self, doc_topic_matrix: np.ndarray)
return doc_topic_matrix.sum(axis=0) / doc_topic_matrix.sum(axis=0).sum()
Get the overall weight of topics across an entire corpus. Note: Values depend
        on whether topic weights per document in ``doc_topic_matrix`` were normalized,
        or not. I suppose either way makes sense... o_O

        Args:
            doc_topic_matrix: Document-topic matrix with shape (n_docs, n_topics),
                the result of calling :meth:`TopicModel.get_doc_topic_matrix()`

        Returns:
            Array, where the ith element is the ith topic's overall weight.
Get the overall weight of topics across an entire corpus. Note: Values depend
        on whether topic weights per document in ``doc_topic_matrix`` were normalized,
        or not. I suppose either way makes sense... o_O
Get the overall weight of topics across an entire corpus . Note : Values depend on whether topic weights per document in doc_topic_matrix were normalized or not . I suppose either way makes sense ... o_O
def topic_weights(self, doc_topic_matrix: np.ndarray) -> np.ndarray:
        """
        Get the overall weight of topics across an entire corpus. Note: Values depend
        on whether topic weights per document in ``doc_topic_matrix`` were normalized,
        or not. I suppose either way makes sense... o_O

        Args:
            doc_topic_matrix: Document-topic matrix with shape (n_docs, n_topics),
                the result of calling :meth:`TopicModel.get_doc_topic_matrix()`

        Returns:
            Array, where the ith element is the ith topic's overall weight.
        """
        return doc_topic_matrix.sum(axis=0) / doc_topic_matrix.sum(axis=0).sum()
def topic_weights ( self , doc_topic_matrix : np . ndarray ) -> np . ndarray : return doc_topic_matrix . sum ( axis = 0 ) / doc_topic_matrix . sum ( axis = 0 ) . sum ( )