def get_type_m(self, curr: Cut) -> Cut:
pall = self.get_type_pall(curr)
if len(pall.pl) == 0:
return Cut()
final_cut = Cut()
final_cut.pl = pall.pl.copy()
self.random_one_more_edge(final_cut)
count = 0
while self.support_el(final_cut.cl) < self.support_count and \
count <= self.G[self.mini_idx].number_of_edges():
count += 1
final_cut.pl = pall.pl.copy()
self.random_one_more_edge(final_cut)
if count > self.G[self.mini_idx].number_of_edges():
return Cut()
cm_child = self.find_common_child(final_cut.cl, curr.cl)
final_cut.pl = final_cut.cl
final_cut.cl = cm_child
return final_cut
def get_type_s1(self, curr: Cut) -> Cut:
if len(curr.cl) == 0:
return Cut()
final_cut = Cut()
final_cut.cl = curr.cl.copy()
self.random_one_less_edge(final_cut)
count = 0
while self.support_el(final_cut.pl) >= self.support_count and \
count <= self.G[self.mini_idx].number_of_edges():
count += 1
final_cut.cl = curr.cl.copy()
self.random_one_less_edge(final_cut)
if count > self.G[self.mini_idx].number_of_edges():
return Cut()
final_cut.cl = final_cut.pl.copy()
grand_parents = self.one_less_edge(final_cut.cl)
for gp in grand_parents:
if self.support_el(gp) >= self.support_count:
final_cut.pl = gp
break
if len(final_cut.pl) == 0:
return Cut()
return final_cut
def random_replace_edge(self, cut: Cut, change_time: int) -> None:
final_cut = Cut()
final_cut.pl = cut.pl.copy()
list_cand_edge = list(set(self.G[self.mini_idx].edges) - set(cut.pl))
for _ in range(change_time):
step_cut = final_cut.copy()
chose_edge = None
p_graph = nx.Graph()
while len(list_cand_edge) > 0:
chose_edge = random.choice(list_cand_edge)
final_cut.pl = step_cut.pl.copy() + [chose_edge]
final_cut.pl = list(sorted(final_cut.pl))
p_graph = self.edgelist_to_graph(final_cut.pl)
if nx.is_connected(p_graph):
break
if not nx.classes.function.is_empty(
p_graph) and self.support_graph(
p_graph) >= self.support_count:
list_cand_edge.remove(chose_edge)
else:
final_cut.pl = step_cut.pl.copy()
break
final_cut.cl = final_cut.pl
step_cl = final_cut.cl.copy()
while len(list_cand_edge) > 0:
chose_edge = random.choice(list_cand_edge)
final_cut.cl = step_cl + [chose_edge]
final_cut.cl = list(sorted(final_cut.cl))
p_graph = self.edgelist_to_graph(final_cut.cl)
if nx.is_connected(p_graph):
list_cand_edge.remove(chose_edge)
step_cl = final_cut.cl.copy()
if self.support_graph(p_graph) < self.support_count:
break
cut.cl = final_cut.cl
cut.pl = final_cut.pl
def random_one_less_edge(self, cut: Cut) -> None:
while True:
remove_idx = np.random.randint(0, len(cut.cl))
cut.pl = cut.cl.copy()
del cut.pl[remove_idx]
p_graph = self.edgelist_to_graph(cut.pl)
if not nx.classes.function.is_empty(p_graph) and nx.is_connected(
p_graph):
break
def get_type_call(self, curr: Cut) -> Cut:
pall = self.get_type_pall(curr)
if len(pall.pl) == 0:
return Cut()
final_cut = Cut()
final_cut.pl = pall.pl.copy()
self.random_one_more_edge(final_cut)
count = 0
while self.support_el(final_cut.cl) >= self.support_count and \
count <= self.G[self.mini_idx].number_of_edges():
count += 1
pall = self.get_type_pall(curr)
final_cut.pl = pall.pl.copy()
self.random_one_more_edge(final_cut)
if count > self.G[self.mini_idx].number_of_edges():
return Cut()
return final_cut