def cat_dog_generation(): cat_dog_text = simple_generation(999) sp = StreamPredictor() sp.pop_manager.add_pop_string('cat') sp.pop_manager.add_pop_string('dog') sp.pop_manager.add_pop_string(' ate food. ') sp.pop_manager.add_pop_string('The ') for i in range(3): sp.train_characters(cat_dog_text) sp.pop_manager.save_pb_plain('PatternStore/cat_dog_' + str(i) + '.txt') generated = sp.generate_stream(100) print(generated)
def fruit_generalization(): print('hello') sp = StreamPredictor() sp.pop_manager.add_pop_string('apple') sp.pop_manager.add_pop_string('banana') text = data_fetcher.get_clean_text_from_file('data/Experimental/case.txt', 100000) sp.train_characters(text) sp.pop_manager.refactor() # sp.generalize() # sp.train(text) # sp.generalize() # sp.train(text) # sp.generalize() print([i.belongs_to_category.__repr__() for i in list(sp.pop_manager.patterns_collection.values()) if i.belongs_to_category is not None]) sp.pop_manager.save_pb_plain('PatternStore/fruit_experiment.txt') sp.pop_manager.load_pb_plain('PatternStore/fruit_experiment.txt')
def cat_dog_train_category(): cat_dog_text = simple_generation(999) sp = StreamPredictor() sp.pop_manager.load_pb_plain('PatternStore/cat_dog_gen.txt') sp.train_characters(cat_dog_text) sp.pop_manager.save_pb_plain('PatternStore/cat_dog_gen2.txt')