def load_ts_lyric_obfuscators(): lo0 = MarkovKeyState(16) lo1 = MarkovKeyState(16) with open(join("datasets", "ts_lyrics.lst"), "r") as f: text = f.read() lo0.learn_book(text) lo1.learn_book(text) return lo0, lo1
def test_splitting_and_joining(self): test_string = "test0 test1 test2\ntest3 test4 test5" split_string = MarkovKeyState.split_obfuscated_string(test_string) joined_string = MarkovKeyState.join_obfuscated_string(split_string) assert joined_string == test_string
parser.add_argument('-r', '--remote', default=None, type=str, action='append', help='Remote server to tunnel to') parser.add_argument('-p', '--port', default=9050, type=int, help='Port to listen on') parser.add_argument('-P', '--remoteport', default=9999, type=int, help='Port for remote server') args = parser.parse_args() logging.basicConfig(level=logging.DEBUG) # Regular expression to split our training files on split_regex = r'\.' # File/book to read for training the Markov model (will be read into memory) training_file = "datasets/98.txt" # Obfuscating Markov engine m = MarkovKeyState() # Read the shared key into memory logging.info("Reading {0}".format(training_file)) with open(training_file, "r") as f: text = f.read() import re # Split learning data into sentences, in this case, based on periods. logging.info("Teaching the Markov model") map(m.learn_sentence, re.split(split_regex, text)) if args.server: # We are the terminating server host = "0.0.0.0" port = int(args.remoteport)
def __init__(self, *args): MarkovKeyState.__init__(self, *args)