def nfa_to_dfa(old_machine, debug=None):
"""
Given a nondeterministic Machine, return a new equivalent
Machine which is deterministic.
"""
# We build a new machine whose states correspond to sets of states
# in the old machine. Initially we add a new state corresponding to
# the epsilon-closure of each initial old state. Then we give transitions
# to each new state which are the union of all transitions out of any
# of the corresponding old states. The new state reached on a given
# character is the one corresponding to the set of states reachable
# on that character from any of the old states. As new combinations of
# old states are created, new states are added as needed until closure
# is reached.
new_machine = machines.FastMachine()
state_map = StateMap(new_machine)
# Seed the process using the initial states of the old machine.
# Make the corresponding new states into initial states of the new
# machine with the same names.
for (key, old_state) in old_machine.initial_states.items():
new_state = state_map.old_to_new(epsilon_closure(old_state))
new_machine.make_initial_state(key, new_state)
# Tricky bit here: we add things to the end of this list while we're
# iterating over it. The iteration stops when closure is achieved.
for new_state in new_machine.states:
transitions = TransitionMap()
for old_state in state_map.new_to_old(new_state).keys():
for event, old_target_states in old_state.transitions.items():
if event and old_target_states:
transitions.add_set(event,
set_epsilon_closure(old_target_states))
for event, old_states in transitions.items():
new_machine.add_transitions(new_state, event,
state_map.old_to_new(old_states))
if debug:
debug.write("\n===== State Mapping =====\n")
state_map.dump(debug)
return new_machine
def __init__(self):
# Preinitialise the list of empty transitions, because
# the nfa-to-dfa algorithm needs it
#self.transitions = {'':[]}
self.transitions = TransitionMap()
self.action_priority = LOWEST_PRIORITY
