def fix_g(self, old_state, new_parent):
state = self.tree.get_node(old_state.__str__())
old_parent = self.tree.get_node(state.predecessor(
self.tree.identifier))
new_parent = self.tree.get_node(new_parent.__str__())
if self.seen_comparison(new_parent, old_parent):
self.tree.move_node(state.identifier, new_parent.identifier)
new_g = new_parent.data.g + \
Utils.n_dim_distance(self.tree.get_node(state.identifier).data.pos, new_parent.data.pos)
self.tree.get_node(state.identifier).data.f = self.tree.get_node(state.identifier).data.f - \
self.tree.get_node(state.identifier).data.g + new_g
self.tree.get_node(state.identifier).data.g = new_g
for index, data in enumerate(self.open_list):
if np.all(data.pos == state.data.pos):
self.open_list = np.delete(self.open_list, index)
self.insert_to_open_list(
self.tree.get_node(state.identifier).data)
break
def expend(self, state):
move_index = np.zeros(self.number_of_agent).astype(int)
for i in range(LOS**number_of_agent):
for j in range(number_of_agent):
i, index = divmod(i, LOS)
move_index[j] = index
neighbor = self.world.get_one_neighbor(state, move_index)
if self.world.in_bund(neighbor) and self.world.is_obstical(
neighbor):
new_g = mwrp.tree.get_node(
state.__str__()).data.g + Utils.n_dim_distance(
state, neighbor)
if not self.tree.contains(neighbor.__str__()):
h = self.heuristic(state)
new_node = Node(neighbor, new_g, new_g + h)
self.tree.create_node(neighbor.__str__(),
neighbor.__str__(),
parent=(state.__str__()),
data=new_node)
self.insert_to_open_list(new_node)
else:
if new_g < self.tree.get_node(neighbor.__str__()).data.g:
self.fix_g(neighbor, state)
self.move_from_open_to_close()