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')