def main(articles_file, topics):
#get the topics
topics_model = ut.get_the_topics_lst(topics)
#get the training set
headers, articles, words_freqs, articles_freqs = ut.make_train_set(
articles_file)
#divide the words into cluster
words_into_clusters = ut.divide_clusters(articles)
#get the good wights for our model
w_model = em.run_em_algorithm(articles_freqs, words_freqs,
words_into_clusters, len(topics_model))
#create the confuision matrix
conf_matrix, clusters_and_topics, articles_of_clusters = ut.make_conf_matrix(
w_model, articles_freqs, topics_model, headers)
# conf_matrix_descending_order = sorted(conf_matrix, key=lambda line: line[-1], reverse=True)
print conf_matrix
#add the topic to the articles
articles_by_topic = ut.add_tag_to_articles(clusters_and_topics,
articles_of_clusters)
#print empty line
print "\n"
#compute the accuracy of the model
accuracy = ut.compute_accuracy(headers, articles_by_topic)
print "the accuracy of our model is- ", accuracy
