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 get_benchmark_ONFSM():
"""
Returns ONFSM presented in 'Learning Finite State Models of Observable Nondeterministic Systems in a Testing
Context'.
"""
from aalpy.automata import Onfsm, OnfsmState
a = OnfsmState('q0')
b = OnfsmState('q1')
c = OnfsmState('g2')
d = OnfsmState('q3')
a.transitions['a'].append((0, b))
a.transitions['b'].append((2, a))
a.transitions['b'].append((0, c))
b.transitions['a'].append((2, a))
b.transitions['b'].append((3, b))
c.transitions['a'].append((2, d))
c.transitions['b'].append((0, c))
c.transitions['b'].append((3, c))
d.transitions['a'].append((2, b))
d.transitions['b'].append((3, d))
return Onfsm(a, [a, b, c, d])
def get_benchmark_ONFSM():
"""
Returns ONFSM presented in 'Learning Finite State Models of Observable Nondeterministic Systems in a Testing
Context'.
"""
a = OnfsmState('a')
b = OnfsmState('b')
c = OnfsmState('c')
d = OnfsmState('d')
a.transitions['a'].append((0, b))
a.transitions['b'].append((2, a))
a.transitions['b'].append((0, c))
b.transitions['a'].append((2, a))
b.transitions['b'].append((3, b))
c.transitions['a'].append((2, d))
c.transitions['b'].append((0, c))
c.transitions['b'].append((3, c))
d.transitions['a'].append((2, b))
d.transitions['b'].append((3, d))
return Onfsm(a, [a, b, c, d])
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 cex_processing(self, cex: tuple, hypothesis: Onfsm):
"""
Add counterexample to the observation table. If the counterexample leads to a state where an output of the same equivalence class already exists, the prefixes of the counterexample are added to S.A. Otherwise, the postfixes of counterexample are added to E.
Args:
cex: counterexample that should be added to the observation table
hypothesis: onfsm that implements the counterexample
"""
cex_len = len(cex[0])
hypothesis.reset_to_initial()
for step in range(0, cex_len - 1):
hypothesis.step_to(cex[0][step], cex[1][step])
possible_outputs = hypothesis.outputs_on_input(cex[0][cex_len - 1])
equivalent_output = False
for out in possible_outputs:
abstracted_out = self.get_abstraction(out)
abstracted_out_cex = self.get_abstraction(cex[1][cex_len - 1])
if abstracted_out_cex == abstracted_out:
equivalent_output = True
break
if equivalent_output:
# add prefixes of cex to S_dot_A
cex_prefixes = [(tuple(cex[0][0:i + 1]), tuple(cex[1][0:i + 1])) for i in range(0, len(cex[0]))]
prefixes_to_extend = self.extend_S_dot_A(cex_prefixes)
# CHANGED: REMOVED
# self.observation_table.S_dot_A.extend(prefixes_to_extend)
self.update_obs_table(s_set=prefixes_to_extend)
else:
# add distinguishing suffixes of cex to E
# CHANGED CEX PROX
# TODO: this will now not work as cex processing was changed
# cex_suffixes = non_det_longest_prefix_cex_processing(self.observation_table, cex)
cex_suffixes = all_suffixes(cex[0])
added_suffixes = extend_set(self.observation_table.E, cex_suffixes)
self.update_obs_table(e_set=added_suffixes)
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
def generate_random_ONFSM(num_states,
num_inputs,
num_outputs,
multiple_out_prob=0.1):
"""
Randomly generate an observable non-deterministic finite-state machine.
Args:
num_states: number of states
num_inputs: number of inputs
num_outputs: number of outputs
multiple_out_prob: probability that state will have multiple outputs (Default value = 0.1)
Returns:
randomly generated ONFSM
"""
inputs = [
random_string_generator(random.randint(1, 3))
for _ in range(num_inputs)
]
outputs = [
random_string_generator(random.randint(3, 7))
for _ in range(num_outputs)
]
states = []
for i in range(num_states):
state = OnfsmState(f's{i}')
states.append(state)
for state in states:
for i in inputs:
state_outputs = 1
if random.random() <= multiple_out_prob and num_outputs > 1:
state_outputs = random.randint(2, num_outputs)
random_out = random.sample(outputs, state_outputs)
for index in range(state_outputs):
state.transitions[i].append(
(random_out[index], random.choice(states)))
return Onfsm(states[0], states)
def generate_random_ONFSM(num_states,
num_inputs,
num_outputs,
multiple_out_prob=0.1):
"""
Randomly generate an observable non-deterministic finite-state machine.
Args:
num_states: number of states
num_inputs: number of inputs
num_outputs: number of outputs
multiple_out_prob: probability that state will have multiple outputs (Default value = 0.5)
Returns:
randomly generated ONFSM
"""
inputs = [f'i{i+1}' for i in range(num_inputs)]
outputs = [f'o{i+1}' for i in range(num_outputs)]
states = []
for i in range(num_states):
state = OnfsmState(f's{i}')
states.append(state)
state_buffer = states.copy()
for state in states:
for i in inputs:
state_outputs = 1
if random.random() <= multiple_out_prob and num_outputs >= 2:
state_outputs = random.randint(2, num_outputs)
random_out = random.sample(outputs, state_outputs)
for index in range(state_outputs):
if state_buffer:
new_state = random.choice(state_buffer)
state_buffer.remove(new_state)
else:
new_state = random.choice(states)
state.transitions[i].append((random_out[index], new_state))
return Onfsm(states[0], states)
def get_ONFSM():
"""
Returns example of an ONFSM.
"""
from aalpy.automata import Onfsm, OnfsmState
q0 = OnfsmState('q0')
q1 = OnfsmState('q1')
q2 = OnfsmState('q2')
q3 = OnfsmState('q3')
q4 = OnfsmState('q4')
q5 = OnfsmState('q5')
q6 = OnfsmState('q6')
q7 = OnfsmState('q7')
q8 = OnfsmState('q8')
q0.transitions['a'].append((2, q1))
q0.transitions['b'].append((0, q0))
q1.transitions['a'].append((2, q0))
q1.transitions['b'].append((0, q2))
q2.transitions['a'].append((1, q2))
q2.transitions['b'].append((0, q3))
q3.transitions['a'].append((2, q8))
q3.transitions['b'].append((0, q4))
q4.transitions['a'].append((1, q4))
q4.transitions['b'].append((0, q5))
q5.transitions['a'].append((2, q6))
q5.transitions['b'].append((0, q7))
q6.transitions['a'].append((2, q5))
q6.transitions['b'].append((0, q6))
q7.transitions['a'].append((1, q7))
q7.transitions['b'].append(('O', q0))
q8.transitions['a'].append((2, q3))
q8.transitions['b'].append((0, q8))
return Onfsm(q0, [q0, q1, q2, q3, q4, q5, q6, q7, q8])
def load_automaton_from_file(path, automaton_type, compute_prefixes=False):
"""
Loads the automaton from the file.
Standard of the automatas strictly follows syntax found at: https://automata.cs.ru.nl/Syntax/Overview.
Args:
path: path to the file
automaton_type: type of the automaton, if not specified it will be automatically determined according,
one of ['dfa', 'mealy', 'moore', 'mdp', 'smm', 'onfsm']
compute_prefixes: it True, shortest path to reach every state will be computed and saved in the prefix of
the state. Useful when loading the model to use them as a equivalence oracle. (Default value = False)
Returns:
automaton
"""
graph = graph_from_dot_file(path)[0]
assert automaton_type in automaton_types
node = MealyState if automaton_type == 'mealy' else DfaState if automaton_type == 'dfa' else MooreState
node = MdpState if automaton_type == 'mdp' else StochasticMealyState if automaton_type == 'smm' else node
node = OnfsmState if automaton_type == 'onfsm' else node
assert node is not None
node_label_dict = dict()
for n in graph.get_node_list():
if n.get_name() == '__start0' or n.get_name() == '':
continue
label = None
if 'label' in n.get_attributes().keys():
label = n.get_attributes()['label']
label = _process_label(label)
node_name = n.get_name()
output = None
if automaton_type == 'moore' and label != "":
label_output = _process_label(label)
label = label_output.split('|')[0]
output = label_output.split('|')[1]
if automaton_type == 'mdp':
node_label_dict[node_name] = node(node_name, label)
else:
node_label_dict[node_name] = node(
label, output) if automaton_type == 'moore' else node(label)
if 'shape' in n.get_attributes().keys(
) and 'doublecircle' in n.get_attributes()['shape']:
node_label_dict[node_name].is_accepting = True
initial_node = None
for edge in graph.get_edge_list():
if edge.get_source() == '__start0':
initial_node = node_label_dict[edge.get_destination()]
continue
source = node_label_dict[edge.get_source()]
destination = node_label_dict[edge.get_destination()]
label = edge.get_attributes()['label']
label = _process_label(label)
if automaton_type == 'mealy':
inp = label.split('/')[0]
out = label.split('/')[1]
inp = int(inp) if inp.isdigit() else inp
out = int(out) if out.isdigit() else out
source.transitions[inp] = destination
source.output_fun[inp] = out
elif automaton_type == 'onfsm':
inp = label.split('/')[0]
out = label.split('/')[1]
inp = int(inp) if inp.isdigit() else inp
out = int(out) if out.isdigit() else out
source.transitions[inp].append((out, destination))
elif automaton_type == 'smm':
inp = label.split('/')[0]
out_prob = label.split('/')[1]
out = out_prob.split(':')[0]
prob = out_prob.split(':')[1]
inp = int(inp) if inp.isdigit() else inp
out = int(out) if out.isdigit() else out
source.transitions[inp].append((destination, out, float(prob)))
elif automaton_type == 'mdp':
inp = label.split(':')[0]
prob = label.split(':')[1]
inp = int(inp) if inp.isdigit() else inp
prob = float(prob)
source.transitions[inp].append((destination, prob))
else:
source.transitions[int(label) if label.isdigit(
) else label] = destination
if automaton_type == 'dfa':
automaton = Dfa(initial_node, list(node_label_dict.values()))
elif automaton_type == 'moore':
automaton = MooreMachine(initial_node, list(node_label_dict.values()))
elif automaton_type == 'mdp':
automaton = Mdp(initial_node, list(node_label_dict.values()))
elif automaton_type == 'smm':
automaton = StochasticMealyMachine(initial_node,
list(node_label_dict.values()))
elif automaton_type == 'onfsm':
automaton = Onfsm(initial_node, list(node_label_dict.values()))
else:
automaton = MealyMachine(initial_node, list(node_label_dict.values()))
assert automaton.is_input_complete()
if compute_prefixes:
for state in automaton.states:
state.prefix = automaton.get_shortest_path(automaton.initial_state,
state)
return automaton