def A_star_graph_search(graph: Maze, start, goal):
if goal == start:
return [], []
path = []
path.append(start)
path_heuristic = graph.manhattan_heuristic_calculator(start)
expanded = []
frontier = []
frontier.append(Evaluation(path_heuristic, path))
while True:
item = ExploreFrontier(frontier) # find the best next node to explore
if item is None:
break
current_node = item.getFurthestNode()
current_path = list(item.getPath())
current_cost = item.getCost() - graph.manhattan_heuristic_calculator(
current_node)
expanded.append(current_node)
# if goal was in frontier
if current_node == goal:
return expanded, current_path
nearby_nodes = (graph.getNode(current_node)).adjacent_list()
for node in nearby_nodes:
new_path = list(current_path)
new_path.append(node)
new_cost = current_cost + step_cost + graph.manhattan_heuristic_calculator(
node)
new_item = Evaluation(new_cost, new_path)
location_in_frontier = is_in_frontier(node, frontier)
if location_in_frontier is None:
if node not in expanded:
frontier.append(new_item)
else:
# replace an existing frontier node which have higher cost
if frontier[location_in_frontier].getCost() > new_cost:
frontier.pop(location_in_frontier)
frontier.append(new_item)
# no need to sort frontier 'cause ExploreFrontier can handle it
return None, None
def Breadth_first_search(graph: Maze, start, goal):
expanded = []
expanded_with_parent = []
frontier = []
# dict format: { "Node": 2, "Parent": 1 },
init = {"Node": start, "Parent": None}
frontier.append(init)
# return nothing if goal is start
if start == goal:
return [goal], [goal]
while frontier:
current_node = frontier.pop(0)
node = current_node["Node"]
if node not in expanded:
neighbours = (graph.getNode(node)).adjacent_list()
for neighbour in neighbours:
if neighbour == goal:
expanded.append(node)
expanded.append(goal)
expanded_with_parent.append(current_node)
expanded_with_parent.append({"Node": goal, "Parent": node})
path_to_node = tracePath(expanded_with_parent, start, neighbour)
return expanded, path_to_node
else:
new_node = {"Node": int(neighbour), "Parent": node}
frontier.append(new_node)
expanded.append(node)
expanded_with_parent.append(current_node)
return expanded, None
def A_star_graph_search(graph: Maze, start, goal):
if goal == start:
return [goal], [goal]
start_heuristic = graph.manhattan_heuristic_calculator(start)
init = {"Node": start, "Parent": None, "Cost": start_heuristic}
expanded = []
expanded_with_parent = []
frontier = []
frontier.append(init)
while True:
item = ExploreFrontier(frontier)# find the best next node to explore
if item is None:
#print(expanded)
break
expanded.append(item["Node"])
expanded_with_parent.append(item)
# if goal was in frontier
if item["Node"] == goal:
current_path = tracePath(expanded_with_parent, start, item["Node"])
return expanded, current_path
current_cost = item["Cost"] - graph.manhattan_heuristic_calculator(item["Node"])
nearby_nodes = (graph.getNode(item["Node"])).adjacent_list()
for node in nearby_nodes:
new_cost = current_cost + step_cost + graph.manhattan_heuristic_calculator(node)
new_item = {"Node": node, "Parent": item["Node"], "Cost": new_cost}
location_in_frontier = is_in_frontier(node, frontier)
if location_in_frontier is None:
if node not in expanded:
frontier.append(new_item)
else:
temp = frontier[location_in_frontier]
if temp["Cost"] > new_item["Cost"]:
frontier.pop(location_in_frontier)
frontier.append(new_item)
# no need to sort frontier 'cause ExploreFrontier can handle it
return expanded, None
def Iterative_deepening_search(graph: Maze, start, goal):
# Repeatedly depth-limit search till the maximum depth
expanded_by_depth = []
result_path = []
size = graph.getSize()
max_depth = size * size
for lim in range(0, max_depth):
expanded = []
init_node = {"Node":start, "Parent": None}
is_right, path = recursive_depth_limited_search(graph, init_node, goal, lim, expanded)
expanded_by_depth.append(expanded)
if is_right:
result_path = list(path)
break
return expanded_by_depth, result_path
def Uniform_cost_search(graph: Maze, start, goal):
if goal == start:
return [], []
path_cost = 0
path = []
path.append(start)
expanded = []
frontier = []
frontier.append(Evaluation(path_cost, path))
while True:
item = ExploreFrontier(frontier)
if item is None:
break
current_node = item.getFurthestNode()
current_path = list(item.getPath())
current_cost = item.getCost()
# print(current_path)
expanded.append(current_node)
# if goal was in frontier
if current_node == goal:
return expanded, current_path
nearby_nodes = (graph.getNode(current_node)).adjacent_list()
for node in nearby_nodes:
new_path = list(current_path)
new_path.append(node)
new_cost = current_cost + step_cost
new_item = Evaluation(new_cost, new_path)
in_frontier = is_in_frontier(node, frontier)
if in_frontier is None:
if node not in expanded:
frontier.append(new_item)
else:
if frontier[in_frontier].getCost() > new_cost:
frontier.pop(in_frontier)
frontier.append(new_item)
# no need to sort frontier 'cause ExploreFrontier can handle it
return None, None
def recursive_depth_limited_search(graph: Maze, current_node: dict, goal, limit, expanded):
expanded.append(current_node["Node"])
if(current_node["Node"] == goal):
return True, [current_node["Node"]]
# If reached the depth limit, stop recursing.
if limit <= 0 :
return False, []
node: Node = graph.getNode(current_node["Node"])
nearby_nodes = node.adjacent_list()
path = [current_node["Node"]]
# Recur for all the vertices adjacent to this vertex
for item in nearby_nodes:
if item != current_node["Parent"]:
next_node = {"Node" : item, "Parent" : current_node["Node"]}
res, child_path = recursive_depth_limited_search(graph, next_node, goal, limit - 1, expanded)
if(res):
return True, path + child_path
return False, []
def Breadth_first_search(graph: Maze, start, goal):
# keep track of explored nodes
expanded_list = []
# keep track of all the paths to be checked
path_list = [[start]]
# return nothing if goal is start
if start == goal:
return [], []
# test run all given paths until path_list is empty
while path_list:
# pop the oldest path from the path_list -> FIFO
path = path_list.pop(0)
# get the furthest node in path
node = path[-1]
if node not in expanded_list:
# get node's adjacent_list
neighbours = (graph.getNode(node)).adjacent_list()
# if input data is wrong (no node exist at given location)
if neighbours is None:
# mark node as explored
expanded_list.append(node)
continue
# check go through all neighbour nodes, create a new path and add it into the testing_paths
for neighbour in neighbours:
new_path = list(path)
new_path.append(neighbour)
path_list.append(new_path)
# return path if neighbour is goal
if neighbour == goal:
expanded_list.append(node)
expanded_list.append(goal) # task requirement
return expanded_list, new_path
# mark node as explored
expanded_list.append(node)
# if no result can be found
return None, None
def recursive_depth_limited_search(graph: Maze, current_node: int, goal, limit):
if(goal == None):
return True, Node
return None
# If reached the depth limit, stop recursing.
if limit <= 0 :
return False, None
node = graph.getNode(current_node)
nearby_nodes = node.adjacent_list()
path = []
path.append(current_node)
# Recur for all the vertices adjacent to this vertex
for item in nearby_nodes:
res, _path = recursive_depth_limited_search(graph, item, goal, limit - 1)
if(res):
return True, path.append(path)
return False, None
file_name = choose_input_files("..\INPUT")
if file_name is not None:
input_list = ImportData(file_name)
if len(input_list) < 3:
print("No data was imported")
print(input_list)
sys.exit()
size = int(input_list.pop(0))
goal = int(input_list.pop(-1))
if (goal < 0) or (goal > int(size * size)):
print(
"\n[Warning]: Goal doesn't exist in Maze!\n->This might result in long runtime and uncompleted result!!\n"
)
board = Maze(size, input_list, goal)
# start = input("Enter the number of starting point: ")
# start = int(start)
start = 0
print("Starting point:\t", start)
print("Goal:\t\t\t", goal)
algorithms = [(Breadth_first_search), (Uniform_cost_search),
(Iterative_deepening_search), (Greedy_best_first_search),
(A_star_graph_search)]
for method in algorithms:
result = method(board, start, goal)
#print(method.__name__ + " completed\n")
OutputData("..\OUTPUT\ ", method.__name__, result)