code: def get_new_term_doc_mat(self, doc_domains):
  		'''
  		Combines documents together that are in the same domain
  
  		Parameters
  		----------
  		doc_domains : array-like
  
  		Returns
  		-------
  		scipy.sparse.csr_matrix
  
  		'''
  		assert len(doc_domains) == self.term_doc_matrix.get_num_docs()
  		doc_domain_set = set(doc_domains)
  		num_terms = self.term_doc_matrix.get_num_terms()
  		num_domains = len(doc_domain_set)
  		domain_mat = lil_matrix((num_domains, num_terms), dtype=int)
  		X = self.term_doc_matrix.get_term_doc_mat()
  		for i, domain in enumerate(doc_domain_set):
  			domain_mat[i, :] = X[np.array(doc_domains == domain)].sum(axis=0)
  		return domain_mat.tocsr(f get_new_term_doc_mat(self, doc_domains):
  		'''
  		Combines documents together that are in the same domain
  
  		Parameters
  		----------
  		doc_domains : array-like
  
  		Returns
  		-------
  		scipy.sparse.csr_matrix
  
  		'''
  		assert len(doc_domains) == self.term_doc_matrix.get_num_docs()
  		doc_domain_set = set(doc_domains)
  		num_terms = self.term_doc_matrix.get_num_terms()
  		num_domains = len(doc_domain_set)
  		domain_mat = lil_matrix((num_domains, num_terms), dtype=int)
  		X = self.term_doc_matrix.get_term_doc_mat()
  		for i, domain in enumerate(doc_domain_set):
  			domain_mat[i, :] = X[np.array(doc_domains == domain)].sum(axis=0)
  		return domain_mat.tocsr()
docs: Combines documents together that are in the same domain
  
  		Parameters
  		----------
  		doc_domains : array-like
  
  		Returns
  		-------
  		scipy.sparse.csr_matrix