def single_gbfs(inputFile):
full_state = FullState(inputFile)
maze = full_state.get_maze()
dynamic_state_1 = DynamicState(full_state.get_mouse_loc(),
full_state.get_cheese_loc())
prize_loc = dynamic_state_1.get_cheese_loc()[0] #single prize
root_node = Node(dynamic_state_1)
gbf_tree = InformedSearchTree()
gbf_tree.add_to_frontier(root_node)
while gbf_tree.get_frontier() != []:
node_to_exp = gbf_tree.pop_frontier_min() #node to expand
if node_to_exp.get_state().goal_test(): #goal achieved
goal_node = node_to_exp
break
if node_to_exp.get_state() in gbf_tree.get_expanded_states():
continue
gbf_tree.add_to_expanded_states(node_to_exp.get_state())
greedy_expand(node_to_exp, gbf_tree, maze, prize_loc)
try:
print_sin_solution(maze, goal_node)
print("The path cost is: ", goal_node.get_path_cost())
except NameError: #goal_node is not defined
print('Goal not reached')
print("The number of nodes expanded is: ",
len(gbf_tree.get_expanded_states()))
def single_astar(inputFile):
full_state = FullState(inputFile)
maze = full_state.get_maze()
dynamic_state_1 = DynamicState(full_state.get_mouse_loc(),
full_state.get_cheese_loc())
prize_loc = dynamic_state_1.get_cheese_loc()[0] #single prize
root_node = Node(dynamic_state_1)
astar_tree = InformedSearchTree()
astar_tree.add_to_frontier(root_node)
while astar_tree.get_frontier() != []:
node_to_exp = astar_tree.pop_frontier_min() #node to expand
if node_to_exp.get_state() in astar_tree.get_expanded_states():
continue
# because there are duplicates in the frontier,
# we need to check if node_to_exp is already in expanded_states to avoid expanding the same node twice:
if node_to_exp.get_state().goal_test():
goal_node = node_to_exp
break
astar_tree.add_to_expanded_states(node_to_exp.get_state())
astar_expand(node_to_exp, astar_tree, maze, prize_loc)
try:
print_sin_solution(maze, goal_node)
print("The path cost is: ", goal_node.get_path_cost())
except NameError: #goal_node is not defined
print('Goal not reached')
print("The number of nodes expanded is: ",
len(astar_tree.get_expanded_states()))
def single_bfs(inputFile):
full_state = FullState(inputFile)
maze = full_state.get_maze()
dynamic_state_1 = DynamicState(full_state.get_mouse_loc(), full_state.get_cheese_loc())
root_node = Node(dynamic_state_1)
bf_tree = BfSearchTree()
bf_tree.add_to_frontier(root_node)
while bf_tree.get_frontier() != deque():
node_to_exp = bf_tree.deque_frontier() #node to expand
if node_to_exp.get_state().goal_test(): #goal achieved
goal_node = node_to_exp
break
# because there are duplicates in the frontier,
# we need to check if node_to_exp is already in expanded_states to avoid expanding the same node twice:
if node_to_exp.get_state() in bf_tree.get_expanded_states():
continue
bf_tree.add_to_expanded_states(node_to_exp.get_state())
bf_expand(node_to_exp, bf_tree, maze)
try:
print_sin_solution(maze, goal_node)
print("The path cost is: ", goal_node.get_path_cost())
except NameError: #goal_node is not defined
print('Goal not reached')
print("The number of nodes expanded is: ", len(bf_tree.get_expanded_states()))
def bf_expand(parent_node, search_tree, maze): for action in ['N', 'E', 'S', 'W']: child_state = transition_model(maze, parent_node.get_state(), action) child_node = Node(child_state, parent_node, action, parent_node.get_path_cost() + 1) if child_state not in search_tree.get_expanded_states(): search_tree.add_to_frontier(child_node)
def single_dfs(inputFile):
full_state = FullState(inputFile)
maze = full_state.get_maze()
dynamic_state_1 = DynamicState(full_state.get_mouse_loc(), full_state.get_cheese_loc())
root_node = Node(dynamic_state_1)
df_tree = DfSearchTree()
df_tree.add_to_frontier(root_node)
while df_tree.get_frontier() != []:
node_to_exp = df_tree.pop_frontier() #node to expand
if node_to_exp.get_state().goal_test(): #goal achieved
goal_node = node_to_exp
break
df_tree.add_to_expanded_states(node_to_exp.get_state())
df_expand(node_to_exp, df_tree, maze)
try:
print_sin_solution(maze, goal_node)
print("The path cost is: ", goal_node.get_path_cost())
except NameError: #goal_node is not defined
print('Goal not reached')
print("The number of nodes expanded is: ", len(df_tree.get_expanded_states()))
def greedy_expand(parent_node, search_tree, maze, prize_loc):
for action in ['N', 'E', 'S', 'W']:
child_state = transition_model(maze, parent_node.get_state(), action)
if child_state in search_tree.get_expanded_states(
): #if it's already expanded
continue
child_priority = single_heuristic(child_state, prize_loc)
child_node = Node(child_state, parent_node, action,
parent_node.get_path_cost() + 1, child_priority)
search_tree.add_to_frontier(child_node)
def multi_astar_expand(parent_node, search_tree, maze):
for action in ['N', 'E', 'S', 'W']:
child_state = transition_model(maze, parent_node.get_state(), action)
if child_state in search_tree.get_expanded_states():
continue
child_path_cost = parent_node.get_path_cost() + 1
child_priority = child_path_cost + multi_heuristic(child_state)
child_node = Node(child_state, parent_node, action, child_path_cost,
child_priority)
search_tree.add_to_frontier(child_node)