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""""""
old_training_data = self.training_data
self.training_data = {x: [] for x in self.sm_vector}
for t in self.smi_vector:
src_state = t[:-1]
symbol = t[-1:]
found = False
for dst_state in self.sm_vector:
if self.observation_table[dst_state] == self.observation_table[t]:
self._add_training_data(src_state, dst_state, symbol)
found = True
break
if not found:
return False, t
assert self.training_data != old_training_data, \
""No update happened from previous round. The algo will loop infinetely""
return True, None"
536,"def _fill_table_entry(self, row, col):
""""""""""
Fill an entry of the observation table.
Args:
row (str): The row of the observation table
col (str): The column of the observation table
Returns:
None
""""""
self.observation_table[row, col] = self._membership_query(row + col)"
537,"def _run_in_hypothesis(self, mma, w_string, index):
""""""""""
Run the string in the hypothesis automaton for index steps and then
return the access string for the state reached concatanated with the
rest of the string w.
Args:
mma (DFA): The hypothesis automaton
w_string (str): The examined string to be consumed
index (int): The index value for selecting the prefix of w
Return:
str: The access string
""""""
state = mma.states[0]
s_index = 0
for i in range(index):
for arc in state:
if arc.guard.is_sat(w_string[i]):
state = mma.states[arc.dst_state]
s_index = arc.dst_state
# The id of the state is its index inside the Sm list
access_string = self.observation_table.sm_vector[s_index]
logging.debug(
'Access string for %d: %s - %d ',
index,
access_string,
s_index)
return access_string"
538,"def _process_counter_example(self, mma, w_string):
""""""""
Process a counterexample in the Rivest-Schapire way.
Args:
mma (DFA): The hypothesis automaton
w_string (str): The examined string to be consumed
Return:
None
""""""
if len(w_string) == 1:
self.observation_table.smi_vector.append(w_string)
for exp in self.observation_table.em_vector:
self._fill_table_entry(w_string, exp)
diff = len(w_string)
same = 0
membership_answer = self._membership_query(w_string)
while True:
i = (same + diff) / 2
access_string = self._run_in_hypothesis(mma, w_string, i)
if membership_answer != self._membership_query(access_string + w_string[i:]):
diff = i
else:
same = i
if diff - same == 1:
break
# First check if the transition is part of our training data.
access_string = self._run_in_hypothesis(mma, w_string, diff - 1)
wrong_transition = access_string + w_string[diff - 1]
if wrong_transition not in self.observation_table.smi_vector:
# If transition is not part of our training data add s_ib to Smi and
# return to checking table closedness.
self.observation_table.smi_vector.append(wrong_transition)
for exp in self.observation_table.em_vector:
self._fill_table_entry(wrong_transition, exp)
return
# This point presents a tradeoff between equivalence and membership
# queries. If the transition in the counterexample'input_string breakpoint is not
# part of our current training data (i.e. s_ib is not part of our Smi
# set), then we assume a wrong transition and return to checking table
# closure by adding s_ib to our training data. This saves a number of
# membership queries since we don't add a row in our table unless