def gen_hypothesis(self) -> Onfsm:
"""
Generate automaton based on the values found in the abstracted observation table.
Returns:
Current abstracted hypothesis
"""
state_distinguish = dict()
states_dict = dict()
initial = None
unified_S = self.S + self.S_dot_A
stateCounter = 0
for prefix in self.S:
state_id = f's{stateCounter}'
states_dict[prefix] = OnfsmState(state_id)
states_dict[prefix].prefix = prefix
state_distinguish[self.row_to_hashable(
prefix)] = states_dict[prefix]
if prefix == self.S[0]:
initial = states_dict[prefix]
stateCounter += 1
for prefix in self.S:
similar_rows = []
for row in unified_S:
if self.row_to_hashable(row) == self.row_to_hashable(prefix):
similar_rows.append(row)
for row in similar_rows:
for a in self.A:
for t in self.observation_table.T[row][a]:
if (row[0] + a, row[1] + tuple([t])) in unified_S:
state_in_S = state_distinguish[
self.row_to_hashable(
(row[0] + a, row[1] + tuple([t])))]
if (t, state_in_S
) not in states_dict[prefix].transitions[a[0]]:
states_dict[prefix].transitions[a[0]].append(
(t, state_in_S))
assert initial
automaton = Onfsm(initial, [s for s in states_dict.values()])
automaton.characterization_set = self.E
return automaton
def gen_hypothesis(self) -> Automaton:
"""
Generate automaton based on the values found in the observation table.
Returns:
Current hypothesis
"""
state_distinguish = dict()
states_dict = dict()
initial = None
stateCounter = 0
for prefix in self.S:
state_id = f's{stateCounter}'
states_dict[prefix] = OnfsmState(state_id)
states_dict[prefix].prefix = prefix
state_distinguish[self.row_to_hashable(
prefix)] = states_dict[prefix]
if prefix == self.S[0]:
initial = states_dict[prefix]
stateCounter += 1
for prefix in self.S:
curr_node = self.sul.pta.get_to_node(prefix[0], prefix[1])
for a in self.A:
trace = self.sul.pta.get_all_traces(curr_node, a)
for t in trace:
reached_row = (prefix[0] + a, prefix[1] + (t[-1], ))
if self.row_to_hashable(
reached_row) not in state_distinguish.keys():
print('reeee')
state_in_S = state_distinguish[self.row_to_hashable(
reached_row)]
assert state_in_S # shouldn't be necessary because of the if condition
states_dict[prefix].transitions[a[0]].append(
(t[-1], state_in_S))
assert initial
automaton = Onfsm(initial, [s for s in states_dict.values()])
automaton.characterization_set = self.E
return automaton
def gen_hypothesis(self) -> Automaton:
"""
Generate automaton based on the values found in the observation table.
Returns:
Current hypothesis
"""
state_distinguish = dict()
states_dict = dict()
initial = None
stateCounter = 0
for prefix in self.S:
state_id = f's{stateCounter}'
states_dict[prefix] = OnfsmState(state_id)
states_dict[prefix].prefix = prefix
state_distinguish[self.row_to_hashable(
prefix)] = states_dict[prefix]
if prefix == self.S[0]:
initial = states_dict[prefix]
stateCounter += 1
for prefix in self.S:
for a in self.A:
for t in self.T[prefix][a]:
state_in_S = state_distinguish[self.row_to_hashable(
(prefix[0] + a, prefix[1] + tuple([t])))]
assert state_in_S
states_dict[prefix].transitions[a[0]].append(
(t, state_in_S))
assert initial
automaton = Onfsm(initial, [s for s in states_dict.values()])
automaton.characterization_set = self.E
return automaton