def a_star(graph, heuristic):
""" Performs an A* search of the graph.
Input:
graph -- a graph implemented with the Graph.py module
Output:
Writes to searched.txt and path.txt
"""
searched_file = open('searched.txt', 'w')
path_file = open('path.txt', 'w')
visited = set()
root = 0
goal = len(graph) - 1
pq.add_task(root, 0)
visited.add(root)
# Keeps track of parents to build the path
# path_dict[child_node] = parent_node
path_dict = {}
path_dict[root] = None
while pq.pq:
# Get the minimum cost node
(node, cost) = pq.pop_task()
searched_file.write(str(node) + '\n')
if node == goal: break
visited.add(node)
children = graph.neighbors(node)
for child in sorted(children):
frontier = pq.get_tasks()
if child not in visited:
# Need to subtract the previous heuristic so we don't double-count
new_cost = ((cost - heuristic[node][goal]) + graph.get_weight_of_edge(node, child)
+ heuristic[child][goal])
if child not in frontier:
pq.add_task(child, new_cost)
path_dict[child] = node
else:
# Need to find the child in the frontier and replace
old_cost = pq.get_priority(child)
if old_cost > new_cost:
pq.remove_task(child)
pq.add_task(child, new_cost)
path_dict[child] = node
# Now, I will backtrace through the path_dict to get the path
node = goal
path = []
while node is not None:
path.insert(0, node)
node = path_dict[node]
for node in path:
path_file.write(str(node) + '\n')
def uniform_cost(graph):
""" Implements a Uniform-Cost Search using a Priority Queue module"""
searched_file = open('searched.txt', 'w')
path_file = open('path.txt', 'w')
visited = set()
root = 0
goal = len(graph) - 1
pq.add_task(root, 0)
visited.add(root)
# Keeps track of parents to build the path
# path_dict[child_node] = parent_node
path_dict = {}
path_dict[root] = None
while pq.pq:
# Get the minimum cost node
(node, cost) = pq.pop_task()
searched_file.write(str(node) + '\n')
if node == goal: break
visited.add(node)
children = graph.neighbors(node)
for child in sorted(children):
frontier = pq.get_tasks()
if child not in visited:
new_cost = cost + graph.get_weight_of_edge(node, child)
if child not in frontier:
pq.add_task(child, new_cost)
path_dict[child] = node
else:
# Need to find the child in the frontier and replace
old_cost = pq.get_priority(child)
if old_cost > new_cost:
pq.remove_task(child)
pq.add_task(child, new_cost)
path_dict[child] = node
# Now, I will backtrace through the path_dict to get the path
node = goal
path = []
while node is not None:
path.insert(0, node)
node = path_dict[node]
for node in path:
path_file.write(str(node) + '\n')
