parser.add_argument('-t', '--threshold', type=float, default=0.0) args = parser.parse_args() for key in vars(args).keys(): print(key, ":", vars(args)[key]) print("--- Result ---") if (args.build == True): model = Lecture2Vec() model.build(vocab=args.vocab, corpus=args.corpus, name=args.name) if (args.pred == True): pred = Predictor(name=args.name) lecture_file = open(args.lecture, 'r') lecture_list = lecture_file.read().splitlines() lectures = [] for l in lecture_list: tokens = l.split() vector = pred.get_vector(words=tokens) lectures.append((l, vector)) most_similars = {} for lecture in lectures: most_similars[lecture[0]] = pred.most_similar( target=lecture, lectures=lectures, threshold=args.threshold) for k in most_similars.keys(): print(k) print(most_similars[k]) print("-" * 10)