old_contents: from .base import TopycalBase
  from sklearn.decomposition import LatentDirichletAllocation
  
  class TopycalLDA(TopycalBase):
      
      def initialize(self):
          # Raw Count vectors
          tf_vectors, tf_vectorizer = self.vectorize_as_tf(self.naked_docs)
          lda = LatentDirichletAllocation(n_topics=self.num_topics, max_iter=5,
                                  learning_method='online',
                                  learning_offset=50.,
                                  random_state=0)
          lda.fit(tf_vectors)
          tf_feature_names = tf_vectorizer.get_feature_names()
          self.topics = self.get_topics(lda, tf_feature_names, self.num_topic_words)
          self.doc_topic_distrib = lda.transform(tf_vectors)
          self.model = lda
new_contents: from .base import TopycalBase
  from sklearn.decomposition import LatentDirichletAllocation
  
  class TopycalLDA(TopycalBase):
      
      def initialize(self):
          # Raw Count vectors
          tf_vectors, tf_vectorizer = self.vectorize_as_tf(self.naked_docs)
          lda = LatentDirichletAllocation(n_topics=self.num_topics, max_iter=5,
                                  learning_method='online',
                                  learning_offset=50.,
                                  random_state=0)
          lda.fit(tf_vectors)
          self.vectors = tf_vectors
          self.vectorizer = tf_vectorizer
          tf_feature_names = tf_vectorizer.get_feature_names()
          self.topics = self.get_topics(lda, tf_feature_names, self.num_topic_words)
          self.doc_topic_distrib = lda.transform(tf_vectors)
          self.model = lda
subject: save vectors/vectorizer in class