def solve_by_greedy_best_first(self, head: TreeNode, target_pos: Pos):
"""
Solve the Bloxorz problem using A* algorithm.
:param head: head node.
:param target_pos: target position for heuristic estimates.
"""
# compute the heuristic cost from all valid positions to the target positions
heuristic_costs = self.compute_heuristic_costs(target_pos)
head.f_cost = self.min_h_cost(heuristic_costs, head)
self.set_cost_visited(head.brick.pos, 0)
expanded_nodes = list()
steps = 0
node = head
print("Step: {}, Depth: {}, Cost: {} - {}".format(
steps, self.get_node_depth(head),
self.get_cost_visited(head.brick.pos), str(head)))
self.show(head.brick)
while True:
for next_pos, direction in self.next_valid_move(node, []):
# new node and estimated cost.
new_node = TreeNode(Brick(next_pos))
h_cost = self.min_h_cost(heuristic_costs, new_node)
new_node.f_cost = h_cost
# set current node's child pointer.
setattr(node, direction.name.lower(),
new_node) # node.{left|right|up|down} -> new_node
# link new_node to the current node.
new_node.parent = node
new_node.dir_from_parent = direction
heappush(expanded_nodes, new_node)
self.debug("{:10s}: {:21s} - {} [f_cost: {:.2f}] ".format(
"added", "new", str(new_node), new_node.f_cost))
node = heappop(expanded_nodes)
self.debug("{:10s}: {:21s} - {}".format("removed", "frontier node",
str(node)))
# update cost of this node
self.set_cost_visited(
node.brick.pos,
self.get_cost_visited(node.parent.brick.pos) + 1)
steps += 1
print("Step: {}, Depth: {}, Cost: {} - {} [f_cost: {:.2f}]".format(
steps, self.get_node_depth(node),
self.get_cost_visited(node.brick.pos), str(node), node.f_cost))
self.show(node.brick)
# if goal state is dequeued, mark the search as completed.
if node.brick.pos == target_pos:
break
print("\nGreedy Best First SEARCH COMPLETED !")
return
def solve_by_astar(self, head: TreeNode, target_pos: Pos):
"""
Solve the Bloxorz problem using A* algorithm.
:param head: head node.
:param target_pos: target position for heuristic estimates.
"""
# compute the heuristic cost from all valid positions to the target positions
heuristic_costs = self.compute_heuristic_costs(target_pos)
head.f_cost = self.min_h_cost(heuristic_costs, head)
self.set_cost_visited(head.brick.pos, 0)
expanded_nodes = list()
steps = 0
node = head
print("Step: {}, Depth: {}, Cost: {} - {}".format(
steps, self.get_node_depth(head),
self.get_cost_visited(head.brick.pos), str(head)))
self.show(head.brick)
while True:
for next_pos, direction in self.next_valid_move(node, []):
g_cost = self.get_cost_visited(node.brick.pos) + 1
# if the node is not visited, add to expanded queue.
# if the node is visited, but has lower actual cost than previously recorded, add to expanded queue.
if next_pos not in self.cost_visited or g_cost < self.get_cost_visited(
next_pos):
# new node and estimated cost.
new_node = TreeNode(Brick(next_pos))
h_cost = self.min_h_cost(heuristic_costs, new_node)
new_node.f_cost = g_cost + h_cost
# set current node's child pointer.
setattr(node, direction.name.lower(),
new_node) # node.{left|right|up|down} -> new_node
# link new_node to the current node.
new_node.parent = node
new_node.dir_from_parent = direction
heappush(expanded_nodes, new_node)
self.debug(
"{:10s}: {:21s} - {} [f_cost: {:.2f} = {} + {:.2f}] ".
format("added", "new | visited & cheap", str(new_node),
new_node.f_cost, g_cost, h_cost))
else:
self.debug(
"{:10s}: {:21s} - [hash(Parent): {}, Parent->{}] [Cost now: {}, earlier: {}]"
.format("rejected", "visited & costly", hash(node),
direction.name.lower(), g_cost,
self.get_cost_visited(next_pos)))
node = heappop(expanded_nodes)
self.debug("{:10s}: {:21s} - {}".format("removed", "frontier node",
str(node)))
# update cost of this node
self.set_cost_visited(
node.brick.pos,
self.get_cost_visited(node.parent.brick.pos) + 1)
steps += 1
print("Step: {}, Depth: {}, Cost: {} - {} [f_cost: {:.2f}]".format(
steps, self.get_node_depth(node),
self.get_cost_visited(node.brick.pos), str(node), node.f_cost))
self.show(node.brick)
# if goal state is dequeued, mark the search as completed.
if node.brick.pos == target_pos:
break
print("\nA* SEARCH COMPLETED !")
print("Optimal path is as below -> \n")
self.show_optimal_path(node)
return
