def state_minimization(automaton):
partition = DisjointSet(*range(automaton.states))
## partition = { NON-FINALS | FINALS }
# Your code here
partition.merge([x for x in automaton.finals])
non_finals = [
nf for nf in filter(lambda x: not x in automaton.finals,
range(automaton.states))
]
partition.merge(non_finals)
while True:
new_partition = DisjointSet(*range(automaton.states))
## Split each group if needed (use distinguish_states(group, automaton, partition))
# Your code here
for gr in partition.groups:
for gr in distinguish_states(gr, automaton, partition):
new_partition.merge(gr)
if len(new_partition) == len(partition):
break
partition = new_partition
return partition
def state_minimization(automaton):
partition = DisjointSet(*range(automaton.states))
finals = list(automaton.finals)
non_finals = [
state for state in range(automaton.states)
if not state in automaton.finals
]
partition.merge(finals)
partition.merge(non_finals)
while True:
new_partition = DisjointSet(*range(automaton.states))
for group in partition.groups:
new_groups = distinguish_states(group, automaton, partition)
for new_group in new_groups:
new_partition.merge(new_group)
if len(new_partition) == len(partition):
break
partition = new_partition
return partition
def state_minimization(automaton):
K = DisjointSet(*ss(automaton.states))
K.merge(s for s in automaton.finals)
K.merge(s for s in ss(automaton.states) if s not in automaton.finals)
while KK:
c = DisjointSet(*ss(automaton.states))
for R in K.groups:
for h in distinguish_states(R, automaton, K):
c.merge(h)
if g(c) == g(K):
break
K = c
return K
def state_minimization(automaton):
partition = DisjointSet(*range(automaton.states))
## partition = { NON-FINALS | FINALS }
partition.merge(automaton.finals)
partition.merge(
[x for x in range(automaton.states) if x not in automaton.finals])
while True:
new_partition = DisjointSet(*range(automaton.states))
for group in partition.groups:
for subgroup in distinguish_states(group, automaton, partition):
new_partition.merge(subgroup)
if len(new_partition) == len(partition):
break
partition = new_partition
return partition
def from_lr1_to_lalr(lr1_automaton: State):
pending = [s for s in lr1_automaton]
ds = DisjointSet(*pending)
while pending:
to_merge = []
pivot = center_of(pending[0].state)
for i, x in enumerate(pending):
if center_of(x.state) == pivot:
to_merge.append(x)
pending[i] = None
pending = [x for x in pending if x]
ds.merge(to_merge)
for g in ds.groups:
r = g[0].representative
for x in [x for x in g if r != x]:
for prod in r.value.state:
for prod2 in x.value.state:
if prod.Center() == prod2.Center():
new_lookahead = {x for x in prod.lookaheads}
new_lookahead.update({x for x in prod2.lookaheads})
prod.lookaheads = frozenset(new_lookahead)
return build_automaton_from_ds(lr1_automaton, ds)
def state_minimization(automaton):
partition = DisjointSet(*range(automaton.states))
not_final = {s for s in range(automaton.states)}
final = {s for s in automaton.finals}
not_final.difference_update(final)
partition.merge(not_final)
partition.merge(final)
while True:
new_partition = DisjointSet(*range(automaton.states))
for group in partition.groups:
new_groups = distinguish_states(group, automaton, partition)
for g in new_groups:
new_partition.merge(g)
if len(new_partition) == len(partition):
break
partition = new_partition
return partition
def state_minimization(automaton):
partition = DisjointSet(*range(automaton.states))
## partition = { NON-FINALS | FINALS }
# Your code here
non_finals = [
state for state in range(automaton.states)
if state not in automaton.finals
]
finals = [state for state in automaton.finals]
partition.merge(non_finals)
partition.merge(finals)
while True:
new_partition = DisjointSet(*range(automaton.states))
## Split each group if needed (use distinguish_states(group, automaton, partition))
# Your code here
for group in partition.groups:
if len(group) > 1:
new_partition.merge([i.value for i in group])
aux_partition = DisjointSet(*range(automaton.states))
for group in new_partition.groups:
new_groups = distinguish_states(group, automaton, new_partition)
for i in new_groups:
aux_partition.merge(i)
new_partition = aux_partition
if len(new_partition) == len(partition):
break
partition = new_partition
return partition