def setUp(self): # Set up an example mealy machine states = [MealyState(f'{i}') for i in range(100)] for state_a, state_b in [ states[i:i + 2] for i in range(len(states) - 1) ]: state_a.add_edge('a', state_a.name, state_b) state_a.add_edge('b', 'loop', state_a) state_a.add_edge('c', 'loop', state_a) state_a.add_edge('d', 'loop', state_a) states[-1].add_edge('a', states[-1].name, states[0]) states[-1].add_edge('b', 'loop', states[-1]) states[-1].add_edge('c', 'loop', states[-1]) states[-1].add_edge('d', 'loop', states[-1]) self.mm = MealyMachine(states[0])
def build_dfa(self): # Gather states from S S = self.S # The rows can function as index to the 'state' objects state_rows = set([tuple(self._get_row(s)) for s in S]) initial_state_row = tuple(self._get_row(tuple())) # Generate state names for convenience state_names = { state_row: f's{n + 1}' for (n, state_row) in enumerate(state_rows) } # Build the state objects and get the initial and accepting states states: Dict[Tuple, MealyState] = { state_row: MealyState(state_names[state_row]) for state_row in state_rows } initial_state = states[initial_state_row] # Add the connections between states A = [a for (a, ) in self.A] # Keep track of states already visited visited_rows = [] for s in S: s_row = tuple(self._get_row(s)) if s_row not in visited_rows: for a in A: sa_row = tuple(self._get_row(s + (a, ))) if sa_row in states.keys(): try: cur_output = self.query(s + (a, )) states[s_row].add_edge(a, cur_output, states[sa_row]) except: # Can't add the same edge twice pass else: visited_rows.append(s_row) return MealyMachine(initial_state)
def load_mealy_dot( path, parse_rules=industrial_mealy_parser): # industrial_mealy_parser): # Parse the dot file context = {'nodes': [], 'edges': []} with open(path, 'r') as file: for line in file.readlines(): _parse(parse_rules, line, context) # Build the mealy graph nodes = {name: MealyState(name) for (name, _) in context['nodes']} for (frm, to), edge_properties in context['edges']: input, output = edge_properties['label'].strip('"').split('/') nodes[frm].add_edge(input, output, nodes[to]) if 'start' in context: startnode = nodes[context['start']] else: startnode = nodes["0"] return MealyMachine(startnode)
def MakeRandomMealyMachine(n_states, A_in, A_out, minimize=True): states = [MealyState(f's{x + 1}') for x in range(n_states)] def get_reachable(start_state, states): to_visit = [start_state] visited = [] while len(to_visit) > 0: cur_state = to_visit.pop() if cur_state not in visited: visited.append(cur_state) for action, (other_state, output) in cur_state.edges.items(): if other_state not in visited and other_state not in to_visit: to_visit.append(other_state) return visited, list(set(states).difference(set(visited))) def fix_missing(states): for state in states: for a in A_in: if a not in state.edges.keys(): state.add_edge(a, "error", state) reached, unreached = get_reachable(states[0], states) while len(unreached) > 0: x = random.choice(reached) y = random.choice(unreached) a = random.choice(A_in) o = random.choice(A_out) x.add_edge(a, o, y, override=True) reached, unreached = get_reachable(states[0], states) fix_missing(states) return _minimize(MealyMachine(states[0])) if minimize else MealyMachine( states[0])
with q.open('w') as f: for a in seq: f.write(f'{a} ') f.write('0') # Do we need state cover or transition cover?? # TODO def get_transition_cover_set(fsm): pass states = fsm.get_states() alphabet = fsm.get_alphabet() if __name__ == "__main__": s1 = MealyState('1') s2 = MealyState('2') s3 = MealyState('3') s4 = MealyState('4') s5 = MealyState('5') s1.add_edge('a', 'nice', s2) s1.add_edge('b', 'nice', s3) s2.add_edge('a', 'nice!', s4) s2.add_edge('b', 'back', s1) s3.add_edge('a', 'nice', s4) s3.add_edge('b', 'back', s1) s4.add_edge('a', 'nice', s5)
import tempfile from stmlearn.equivalencecheckers import WmethodEquivalenceChecker from stmlearn.learners import LStarMealyLearner from stmlearn.suls import MealyState, MealyMachine from stmlearn.teachers import Teacher # Set up an example mealy machine s1 = MealyState('1') s2 = MealyState('2') s3 = MealyState('3') s1.add_edge('a', 'nice', s2) s1.add_edge('b', 'B', s1) s2.add_edge('a', 'nice', s3) s2.add_edge('b', 'back', s1) s3.add_edge('a', 'A', s3) s3.add_edge('b', 'back', s1) mm = MealyMachine(s1) mm.render_graph(tempfile.mktemp('.gv')) # Use the W method equivalence checker eqc = WmethodEquivalenceChecker(mm) teacher = Teacher(mm, eqc) # We are learning a mealy machine learner = LStarMealyLearner(teacher) hyp = learner.run()
def setUp(self): # Set up an example mealy machine s1 = MealyState('1') s2 = MealyState('2') s3 = MealyState('3') s1.add_edge('a', '1', s2) s1.add_edge('b', 'next', s1) s2.add_edge('a', '2', s3) s2.add_edge('b', 'next', s1) s3.add_edge('a', '3', s3) s3.add_edge('b', 'next', s1) self.mm = MealyMachine(s1)
def setUp(self): s1 = MealyState('1') s2 = MealyState('2') s3 = MealyState('3') s1.add_edge('a', 'nice', s2) s1.add_edge('b', 'B', s1) s2.add_edge('a', 'nice', s3) s2.add_edge('b', 'back', s1) s3.add_edge('a', 'A', s3) s3.add_edge('b', 'back', s1) self.mm = MealyMachine(s1)