def search (self, initialState):
open = []
new_n = Node(initialState, None)
open.append((new_n, new_n.h()))
while (len(open) > 0):
#list sorted by h()
open.sort(key = sortFunction, reverse = True)
n = open.pop()[0]
if (n.state.is_goal()):
return n
for i in n.state.sucessors():
new_n = Node(i,n)
open.append((new_n, new_n.h()))
return None
def astar_search(graph, heuristics, src, dest):
open = []
explored = []
# Create the start node and the goal node
start = Node(src, None)
goal = Node(dest, None)
# Add the first node to the open list
open.append(start)
# Loop until the list is empty
while len(open) > 0:
# Keep the list sorted so we extract only the smallest element
open.sort()
# Get the node with the lowest cost
current_node = open.pop(0)
# Add the current node to the explored nodes list
explored.append(current_node)
# Check if the current node is the goal node
# If we reached the goal, return the path
if current_node == goal:
# Store the path from destination node to the source node
path = []
while current_node != start:
path.append(current_node.name)
current_node = current_node.parent
path.append(src)
# Return reversed path
return path[::-1]
# Get neighbors
neighbors = graph.get(current_node.name)
# Loop through neighbors
for key, value in neighbors.items():
# Create a neighbor node
neighbor = Node(key, current_node)
# Check if the neighbor is in the closed list
if(neighbor in explored):
continue
# Calculate full path cost
neighbor.g = current_node.g + graph.get(current_node.name, neighbor.name)
neighbor.h = heuristics[neighbor.name]
neighbor.f = neighbor.g + neighbor.h
# Check if neighbor is in open list and if it has a lower f value
if(add_to_open(open, neighbor) == True):
# Everything is green, add neighbor to open list
open.append(neighbor)
# Return None if no path is found
return None
