def __init__(self, epsilon_nfa: EpsilonNFA):
edges = []
dfa = epsilon_nfa.to_deterministic()
state_to_idx = dict([(state, index)
for index, state in enumerate(dfa.states)])
for state_from, transitions in epsilon_nfa.to_deterministic().to_dict(
).items():
for label, state_to in transitions.items():
edges.append(
Edge(node_from=state_to_idx[state_from],
node_to=state_to_idx[state_to],
label=label))
self.dfa = dfa
self.dfa_state_to_idx = state_to_idx
start_states = [
self.dfa_state_to_idx[state] for state in self.dfa.start_states
]
final_states = [
self.dfa_state_to_idx[state] for state in self.dfa.final_states
]
super().__init__(edges, set(start_states), set(final_states))
def graph_to_dfa(g: Graph):
nfa = EpsilonNFA()
state_count = 0
for tup in g:
sub, pred, obj = map(str, tup)
# nfa.add_transition(State(sub), Symbol(pred), State(obj))
nfa.add_start_state(State(sub))
nfa.add_start_state(State(obj))
nfa.add_final_state(State(sub))
nfa.add_final_state(State(obj))
nfa.add_transition(State(sub), Epsilon(), State(state_count))
for c in pred:
nfa.add_transition(State(state_count),
Symbol(c),
State(state_count+1))
state_count += 1
nfa.add_transition(State(state_count),
Epsilon(),
State(obj))
return nfa.to_deterministic()