def breadthFirstSearch(problem):
"Search the shallowest nodes in the search tree first. [p 74]"
"*** YOUR CODE HERE ***"
from util import Queue
#util.raiseNotDefined()
currentState = problem.getStartState()
setOfExploredNodes = set([currentState])
listofMoves = []
successorStack = Queue()
counter = 0;
while not problem.isGoalState(currentState):
for successor in problem.getSuccessors(currentState):
if successor[0] not in setOfExploredNodes:
#print successor
setOfExploredNodes.add(successor[0])
successorStack.push((successor[0], listofMoves + [successor[1]]))
currentState = successorStack.pop()
#setOfExploredNodes.add(currentState[0])
listofMoves = currentState[1]
currentState = currentState[0]
counter += 1
if counter % 100 == 0:
print counter
print listofMoves
return listofMoves
def breadthFirstSearch(problem):
"Search the shallowest nodes in the search tree first. [p 74]"
"*** YOUR CODE HERE ***"
from util import Queue
currentState = problem.getStartState()
setOfExploredNodes = set([currentState])
listofMoves = []
successorStack = Queue()
while not problem.isGoalState(currentState):
for successor in problem.getSuccessors(currentState):
if successor[0] not in setOfExploredNodes:
if problem.isGoalState(successor[0]):
#print listofMoves + [successor[1]]
return listofMoves + [successor[1]]
setOfExploredNodes.add(successor[0])
successorStack.push((successor[0], listofMoves + [successor[1]]))
currentState = successorStack.pop()
listofMoves = currentState[1]
currentState = currentState[0]
return None
def breadthFirstSearch(problem):
successor_idx = {'dir': 1, 'state': 0, 'cost': -1}
MAX_ITER = int(20000)
stateQ = Queue()
actQ = Queue() # queues action for backtracking
visitedSet = set()
curState = problem.getStartState()
visitedSet.add(curState)
actionList = [] # add actions to get to the state, use pop to remove last one
for it in range(MAX_ITER):
if problem.isGoalState(curState):
return actionList
successors = problem.getSuccessors(curState)
for node in successors:
if node[successor_idx['state']] not in visitedSet:
stateQ.push(node[successor_idx['state']])
actQ.push(actionList + [node[successor_idx['dir']]])
visitedSet.add(node[successor_idx['state']])
actionList = actQ.pop()
curState = stateQ.pop()
return []
def breadthFirstSearch(problem):
"""Search the shallowest nodes in the search tree first."""
"*** YOUR CODE HERE ***"
# Imports for tools
from util import Queue
from game import Directions
import copy
path = Queue()
path.push((problem.getStartState(), []))
visited = set([problem.getStartState()])
while not path.isEmpty():
info = path.pop()
currentState = info[0]
actions = info[1]
if problem.isGoalState(currentState):
return actions
for successor in problem.getSuccessors(currentState):
if not successor[0] in visited:
visited.add(successor[0])
if ("North" in successor):
actions.append(Directions.NORTH)
elif ("East" in successor):
actions.append(Directions.EAST)
elif ("South" in successor):
actions.append(Directions.SOUTH)
elif ("West" in successor):
actions.append(Directions.WEST)
path.push((successor[0], copy.copy(actions)))
actions.pop()
def breadthFirstSearch(problem):
"Search the shallowest nodes in the search tree first. [p 74]"
"*** YOUR CODE HERE ***"
from util import Queue
successorsExplored = {}
statesExplored = set()
bfsQueue = Queue()
for successor in problem.getSuccessors(problem.getStartState()):
bfsQueue.push((successor, None))
statesExplored.add(problem.getStartState())
if(problem.isGoalState(problem.getStartState())):
return []
while(not bfsQueue.isEmpty()):
currentSuccessorPair = bfsQueue.pop()
if(currentSuccessorPair[0][0] in statesExplored):
continue
successorsExplored[currentSuccessorPair[0]] = currentSuccessorPair[1]
statesExplored.add(currentSuccessorPair[0][0])
if(problem.isGoalState(currentSuccessorPair[0][0])):
return reconstructPath(successorsExplored, currentSuccessorPair[0])
for successor in problem.getSuccessors(currentSuccessorPair[0][0]):
if(successor[0] not in statesExplored):
bfsQueue.push((successor, currentSuccessorPair[0]))
return None
def breadthFirstSearch(problem):
"""
Search the shallowest nodes in the search tree first.
"""
closed = []
closedSet = sets.Set(closed)
fringe = Queue()
for child in problem.getSuccessors(problem.getStartState()):
path = [child[1]]
fringe.push((child[0], path, child[2]))
closedSet.add(problem.getStartState())
while( not(fringe.isEmpty()) ):
nodePos, nodeDir, nodeCost = fringe.pop()
if (problem.isGoalState(nodePos)):
return nodeDir
if (nodePos not in closedSet):
successors = problem.getSuccessors(nodePos)
closedSet.add(nodePos)
path = [nodeDir]
for childNode in successors:
#cost = nodeCost + childNode[2]
path = nodeDir + [childNode[1]]
childNode = [childNode[0], path , child[2]]
fringe.push(childNode)
return -1
def breadthFirstSearch(problem):
"""Search the shallowest nodes in the search tree first."""
"*** YOUR CODE HERE ***"
from util import Queue
startState = problem.getStartState()
# Visited
visited = []
# Declare variable for queue node
queueNode = Queue()
# Push start state into queue
queueNode.push((startState,[]))
while(not queueNode.isEmpty()):
currentState, actions = queueNode.pop()
#if currentState not in visited:
#visited.append(currentState)
seccessors = problem.getSuccessors(currentState)
print "\t" , seccessors
for state,action,cost in seccessors:
if state not in visited:
if problem.isGoalState(currentState):
print actions
return actions
visited.append(state)
queueNode.push((state,actions + [action]))
return []
def breadthFirstSearch(problem):
"Search the shallowest nodes in the search tree first. [p 81]"
"*** YOUR CODE HERE ***"
from util import Queue
queue = Queue()
exploredSet = set()
pathConstructDict = {}
#print "Start's successors:", problem.getSuccessors(problem.getStartState())
#add start to exploredSet, and check if it is the goal
exploredSet.add(problem.getStartState())
if(problem.isGoalState(problem.getStartState())):
return []
for successor in problem.getSuccessors(problem.getStartState()):
#print successor
queue.push((successor, None))
while(not queue.isEmpty()):
expansion = queue.pop()
if(expansion[0][0] in exploredSet):
continue
exploredSet.add(expansion[0][0])
pathConstructDict[expansion[0]] = expansion[1]
if(problem.isGoalState(expansion[0][0])):
return pathConstructor(expansion[0], pathConstructDict)
for successor in problem.getSuccessors(expansion[0][0]):
queue.push((successor, expansion[0]))
def breadthFirstSearch(problem):
"Search the shallowest nodes in the search tree first. [p 81]"
"*** YOUR CODE HERE ***"
from util import Queue
from game import Directions
state = 0
action = 1
cstate = problem.getStartState()
if (problem.isGoalState(cstate)):
return []
q = Queue()
visited = []
q.push((cstate, []))
while(not q.isEmpty()):
cstate, path = q.pop()
visited.append(cstate)
for x in problem.getSuccessors(cstate):
npath = path + [x[action]]
if(visited.count(x[state]) != 0):
continue
if (problem.isGoalState(x[state])):
return npath
nstate = x[state]
visited.append(x[state])
q.push((nstate, npath))
print("Path is not found")
def breadthFirstSearch(problem):
"""
Search the shallowest nodes in the search tree first.
"""
from util import Queue
from game import Directions
actions = []
frontier = Queue()
frontier.push((problem.getStartState(), [], 0))
visited = []
while (frontier.isEmpty() == False):
(currentS, currentP, currentC) = frontier.pop()
if (problem.isGoalState(currentS) == True):
actions = currentP
break
if (visited.count(currentS) == 0):
visited.append(currentS)
successors = problem.getSuccessors(currentS)
for i in range(0,len(successors)):
(neighbor, direction, cost) = successors[i]
if (visited.count(neighbor) == 0):
frontier.push((neighbor, (currentP +[direction]), (currentC + cost)))
print actions
return actions
util.raiseNotDefined()
def breadthFirstSearch(problem):
"""
Search the shallowest nodes in the search tree first.
"""
"*** YOUR CODE HERE ***"
queue = Queue()
explored = []
queue.push((problem.getStartState(), [])) #what in the queue is the (state, path)
explored.append(problem.getStartState())
while not queue.isEmpty():
tuple = queue.pop()
currentPath = tuple[1]
if problem.isGoalState(tuple[0]):
return currentPath
suc = problem.getSuccessors(tuple[0])
for triple in suc:
if explored.__contains__(triple[0]):
continue
explored.append(triple[0])
path = currentPath + [triple[1]]
queue.push((triple[0], path))
def breadthFirstSearch(problem):
"Search the shallowest nodes in the search tree first. [p 81]"
from util import Queue
BFS1 = Queue()
Moves = []
Visited = []
Final = []
NewState = (0, (problem.getStartState(), 'Start', 0))
#print CurrentState
BFS1.push([NewState])
while not BFS1.isEmpty():
NewState = BFS1.pop()
if problem.isGoalState(NewState[0][1][0]):
Final = NewState
break
if Visited.count(NewState[0][1][0]) == 0:
#print NewState
for item in enumerate(problem.getSuccessors(NewState[0][1][0])):
#print item
BFS1.push([item] + NewState)
Visited.append(NewState[0][1][0])
for nodes in Final:
Moves.append(nodes[1][1])
Moves.reverse()
Moves.remove('Start')
#print Moves
return Moves
"""def breadthFirstSearch(problem):
def buildMazeDistanceMap(position, gameState):
"""
Use BFS to build a map that stores maze distances between position and each point in the layout
"""
x, y = position
walls = gameState.getWalls()
assert not walls[x][y], 'position is a wall: ' + str(position)
# initialization
distanceMap = {}
queue = Queue()
distance = 0
queue.push(position)
while not queue.isEmpty():
currPos = queue.pop()
if currPos not in distanceMap:
distanceMap[currPos] = distance
for pos in Actions.getLegalNeighbors(currPos, walls):
queue.push(pos)
distance += 1
return distanceMap
def breadthFirstSearch(problem):
"""
Search the shallowest nodes in the search tree first.
[2nd Edition: p 73, 3rd Edition: p 82]
"""
"*** YOUR CODE HERE ***"
from util import Queue
fila = Queue()
colorir = []
acoes = []
inicial = (problem.getStartState(),None,None,0)
"Tupla formada por (Estado atual, Estado Pai, Aчуo, Custo)"
fila.push(inicial)
colorir.append(inicial)
while fila.isEmpty() == False:
noAtual = fila.pop()
if problem.isGoalState(noAtual[0]):
return gerarCaminho(noAtual)
else:
for item in problem.getSuccessors(noAtual[0]):
est = item[0]
aco = item[1]
cus = item[2]
if est not in colorir:
colorir.append(est)
tupla = (est,noAtual,aco,cus)
fila.push(tupla)
def breadthFirstSearch(problem):
"""
Search the shallowest nodes in the search tree first.
"""
"*** YOUR CODE HERE ***"
from util import Queue
# fringe
fringe = Queue()
# closed set
closed = set([])
fringe.push((problem.getStartState(), []))
while True:
if fringe.isEmpty() == True:
print 'fail to find a path to the goal state'
return []
else:
node = fringe.pop()
if problem.isGoalState(node[0]) == True:
return node[1]
if node[0] not in closed:
closed.add(node[0])
actionsSoFar = node[1]
for successor in problem.getSuccessors(node[0]):
newActions = actionsSoFar[:]
newActions.append(successor[1])
fringe.push((successor[0], newActions))
def breadthFirstSearch(problem):
"""Search the shallowest nodes in the search tree first."""
"*** YOUR CODE HERE ***"
from util import Queue
queueOpen = Queue()
rootNode = SearchNode(problem.getStartState(), None, None, 0, 0)
if problem.isGoalState(rootNode.position):
return []
queueOpen.push(rootNode)
visited = {}
#visited;
#cvorovi = [pocetni];
while not queueOpen.isEmpty():
#ako je cvor u visited -> continue
#stavi cvor u visited
#za svakog sljedbenika: ako nije u visited, dodaj ga u cvorove
currentNode = queueOpen.pop()
if currentNode.position in visited:
continue
if problem.isGoalState(currentNode.position):
return currentNode.backtrack()
visited[currentNode.position] = True
for succ in expand(problem, currentNode):
if succ.position not in visited:
temp = SearchNode(succ.position, currentNode, succ.transition, succ.cost, 0)
queueOpen.push(temp)
def breadthFirstSearch(problem):
"""Search the shallowest nodes in the search tree first."""
"*** YOUR CODE HERE ***"
path = list()
parentChild = list()
print "Problem: ", problem
print "Start:", problem.getStartState()
print "Is the start a goal?", problem.isGoalState(problem.getStartState())
print "Start's successors:", problem.getSuccessors(problem.getStartState())
if problem.isGoalState(problem.getStartState()):
return None
explored = list()
frontier = Queue()
frontier.push(problem.getStartState())
while (not frontier.isEmpty()):
state = frontier.pop()
#print "Current state: ", state
explored.append(state)
if (problem.isGoalState(state)):
#print "Found..."
path = backtracking(problem, state, parentChild)
return path
for successor in problem.getSuccessors(state):
#print "Successor: ", successor
if (not successor[0] in explored):
parentChild.append((state, successor[1], successor[0]))
frontier.push(successor[0])
return None
def breadthFirstSearchNormal2(problem):
"""Search the shallowest nodes in the search tree first."""
"*** YOUR CODE HERE ***"
from util import Queue
actionListMap = []
visitedList = {}
startState = problem.getStartState()
q = Queue()
q.push(startState)
visitedList[startState] = [None, None]
while not q.isEmpty():
targetState = q.pop()
isGoal = problem.isGoalState(targetState)
if isGoal == None:
actionList = buildActionListFromBFSResult(visitedList,startState, targetState)
actionListMap.append((targetState, actionList))
elif isGoal:
actionList = buildActionListFromBFSResult(visitedList,startState, targetState)
actionListMap.append((targetState, actionList))
print "Meet Goal"
return actionListMap
else:
for successor in problem.getSuccessors(targetState):
state = successor[0]
action = successor[1]
if state not in visitedList.keys():
visitedList[state] = [targetState, action]
q.push(state)
def __init__(self, position, scaredTimer, game_state, n):
self.ds = Queue()
self.position = position
self.scaredTimer = scaredTimer
self.game_state = game_state
self.n = n
self.visited = {}
for i in range(n):
self.visited[i] = []
def breadthFirstSearch(problem):
from collections import defaultdict
from util import Queue
initial_node=problem.getStartState()
current_node=defaultdict(list)
current_node[initial_node].append("No parent")
current_node[initial_node].append("No direction")
queue=Queue()
queue.push(current_node)
current_node=queue.pop()
direction_list={}
Child=current_node.keys()
Parent=current_node.values()[0]
visited_list={}
while (problem.isGoalState(Child[0]) is not True):
if(Child[0] in visited_list.keys()):
current_node=queue.pop()
Child=current_node.keys()
Parent=current_node.values()[0]
else:
visited_list[Child[0]]=Parent[0]
direction_list[Child[0]]=Parent[1]
Successor_node=problem.getSuccessors(Child[0])
for index in range(len(Successor_node)):
temp_dict=defaultdict(list)
temp_dict[Successor_node[index][0]].append(Child[0])
temp_dict[Successor_node[index][0]].append(Successor_node[index][1])
queue.push(temp_dict)
current_node=queue.pop()
Child=current_node.keys()
Parent=current_node.values()[0]
visited_list[Child[0]]= Parent[0]
direction_list[Child[0]]=Parent[1]
backtracking =[]
path = Child[0]
backtracking.append(direction_list[path])
path = visited_list[path]
print "The path is",path
while (path!= problem.getStartState()):
backtracking.append(direction_list[path])
path = visited_list[path]
backtracking.reverse()
return backtracking
util.raiseNotDefined()
def breadthFirstSearch(problem):
"""
Search the shallowest nodes in the search tree first.
"""
"*** YOUR CODE HERE ***"
from util import Queue
#: lista de nodos visitados
closed = []
#: pila que hace la funcion de frontera
frontier = Queue()
# Recogemos el primer estado, creamos un nodo y lo
# insertamos en la pila
frontier.push(Node(problem.getStartState()))
# iteramos hasta que no hayan nodos en la pila
# o hayamos encontrado el objetivo
while not frontier.isEmpty():
#: siguiente nodo a expandir
node = frontier.pop()
#print "\nNodo actual: ", node.state
#print "\nCola: ", frontier.imprime()
#print "\nVisitados: ", visited
#while (raw_input(">>> ") != ""):
# pass
# comprobamos si el estado actual nos cumple el objetivo
if problem.isGoalState(node.state):
# si lo cumple, salimos del bucle
break
if node.state not in closed:
# insertamos el estado en la lista de visitados
closed.append(node.state)
# recuperamos los estados sucesores
for successor in problem.getSuccessors(node.state):
# si el estado sucesor no esta en la frontera, lo encapsulamos
# y lo itroducimos
frontier.push(Node(
successor[0],
node,
successor[1],
successor[2]))
#print frontier
#: acciones para llegar al objetivo
actions = []
# recorremos mientras haya un action en el nodo previo
while node.action:
actions.append(node.action)
node = node.previous
#mostramos el resultado antes de devolverlo
#print actions[::-1]
return actions[::-1]
def breadthFirstSearch(problem):
"""
Search the shallowest nodes in the search tree first.
[2nd Edition: p 73, 3rd Edition: p 82]
"""
"*** YOUR CODE HERE ***"
from util import Queue
fringe = Queue()
visited = set()
startNode = problem.getStartState()
startNode = (startNode,"",0)
start = Child()
start.create(startNode,[],0,None)
fringe.push(start)
while fringe.list:
node = fringe.pop()
if problem.isGoalState(node.getCoord()):
return node.getPath()
visited.add(node.getNode()[0])
children = problem.getSuccessors(node.getCoord())
for childNode in children:
child = Child()
child.create(childNode,node.getPath(),1,node)
child.addElmt(child.getNode()[1])
if child.getCoord() not in visited and child.getCoord() not in map(Child.getCoord,fringe.list):
fringe.push(child)
return None
def breadthFirstSearch(problem):
"""
Search the shallowest nodes in the search tree first.
[2nd Edition: p 73, 3rd Edition: p 82]
"""
"*** YOUR CODE HERE ***"
from util import Queue
fringe = Queue()
successors = []
state = (problem.getStartState(), "")
fringe.push(state)
visited = set()
while problem.isGoalState(state[0]) == False:
#print "state = %r, visited = %r " % (state[0],visited)
if state[0] not in visited:
successors = problem.getSuccessors(state[0])
visited.add(state[0])
for successor in successors:
pos, dir, cost = successor
#if pos not in visited:
fringe.push((pos,state[1]+dir+' '))
state = fringe.pop()
#print state[0], successors
return state[1].split()
def breadthFirstSearch(problem):
"""Search the shallowest nodes in the search tree first."""
"*** YOUR CODE HERE ***"
start = problem.getStartState()
from util import Queue
fringe = Queue()
fringe.push([(start, 'Start', 1)])
result = None
expanded = [problem.getStartState()]
while result == None:
currentPath = fringe.pop()
currentState = currentPath[-1]
if problem.isGoalState(currentState[0]):
result = currentPath
continue
succ = problem.getSuccessors(currentState[0])
for state in succ:
if state[0] not in expanded:
expanded.append(state[0])
new_list = currentPath[:]
new_list.append(state)
fringe.push(new_list)
steps = []
for state in result[1:]:
steps.append(state[1])
return steps
def getMinimaxValues(self, gameState, agentIndex, isRed, expandTimeout):
startTime = time.time()
if(PRINTDEBUG): print("Expanding nodes...")
# two queues to deepen minimax
curExpand = Queue()
nextExpand = Queue()
# create and enqueue the first node
rootNode = MinimaxNode(None, gameState, agentIndex, self.delegate, self.ignoreIndices)
curExpand.push(rootNode)
nodesExpanded = 1
depthExpanded = 0
minimaxValues = []
# bfs expanding nodes
while(True):
while(not curExpand.isEmpty()):
toExpand = curExpand.pop()
toExpand.expand()
for child in toExpand.children:
nextExpand.push(child)
nodesExpanded += 1
if(time.time() - startTime > expandTimeout):
break
if(time.time() - startTime > expandTimeout):
break
depthExpanded += 1
curExpand = nextExpand
nextExpand = Queue()
minimaxValues = []
for childNode in rootNode.children:
mmaxValue = childNode.calculateMinimaxValue(isRed, self.impatience)
minimaxValues.append((childNode.action, mmaxValue))
timeTaken = time.time() - startTime
if(PRINTDEBUG): print("Expanded " + str(nodesExpanded) + " nodes to a depth of " + str(depthExpanded) + "in " + str(timeTaken) + " seconds.")
return minimaxValues
def breadthFirstSearch(problem):
queue = Queue() ; parentNode = []; successors = []; visitedNodes = []
parentChildMapList = {}
currentState = problem.getStartState() #need to remove
while problem.isGoalState(currentState) is False and problem.isGoalState(currentState[0]) is False:
if(currentState == problem.getStartState()):
successors = problem.getSuccessors(currentState)
visitedNodes.append(currentState)
visitedNodes.append((currentState, ()))
else:
successors = problem.getSuccessors(currentState[0])
visitedNodes.append(currentState[0])
if successors != None and len(successors) > 0:
parentNode.append(currentState)
for node in successors:
if(visitedNodes.__contains__(node) == False and visitedNodes.__contains__(node[0]) == False):
queue.push(node)
parentChildMapList[node] = currentState
tempNode = queue.pop()
if((visitedNodes.__contains__(tempNode) == False and visitedNodes.__contains__(tempNode[0]) == False)):
currentState = tempNode
else:
currentState = queue.pop()
validDirections = []
firstState = currentState
while firstState != problem.getStartState():
validDirections.append(firstState[1])
firstState = parentChildMapList[firstState]
validDirections.reverse()
return validDirections
util.raiseNotDefined()
def breadthFirstSearch(problem):
"""
Search the shallowest nodes in the search tree first.
[2nd Edition: p 73, 3rd Edition: p 82]
"""
"*** YOUR CODE HERE ***"
from game import Directions
from util import Queue
Explored = {}
Frontier = Queue()
node = 0
current = problem.getStartState()
#print "bfs first state", current
succ = problem.getSuccessors(current)
for k in succ:
#print "initial push",k
Frontier.push([k]);
while not (Frontier.isEmpty()):
current = Frontier.pop()
node = current[-1][0]
#print "curr path and node",current,node,"explored status",(node in Explored),"\n"
# check if done
if (problem.isGoalState(node)):
break
Explored[node] = 1;
if (len(Explored)%100) == 0:
print "ex",len(Explored)
#print node
#Explored[node[0]] = 1;
succ = problem.getSuccessors(node)
for k in succ:
if not (k[0] in Explored):
if not (Frontier.isPresent(current + [k])):
Frontier.push(current + [k]);
sol = []
for k in current:
sol += [k[1]]
#print current
#print "action sol", sol
return sol
def breadthFirstSearch(problem):
from util import Queue
q = Queue()
q.push((problem.getStartState(),[]))
expanded = []
while not q.isEmpty():
top = q.pop()
if problem.isGoalState(top[0]):
return top[1]
successors = problem.getSuccessors(top[0])
for successor in successors:
if not successor[0] in expanded:
q.push((successor[0], top[1]+[successor[1]]))
expanded.append(top[0])
return []
def breadthFirstSearch(problem):
"""Search the shallowest nodes in the search tree first."""
loc_queue = Queue()
visited_node = {}
parent_child_map = {}
direction_list = []
start_node = problem.getStartState()
parent_child_map[start_node] = []
loc_queue.push(start_node)
def traverse_path(parent_node):
while True:
map_row = parent_child_map[parent_node]
if (len(map_row) == 2):
parent_node = map_row[0]
direction = map_row[1]
direction_list.append(direction)
else:
break
return direction_list
while (loc_queue.isEmpty() == False):
parent_node = loc_queue.pop()
if (problem.isGoalState(parent_node)):
pathlist = traverse_path(parent_node)
pathlist.reverse()
return pathlist
elif (visited_node.has_key(parent_node) == False):
visited_node[parent_node] = []
sucessor_list = problem.getSuccessors(parent_node)
no_of_child = len(sucessor_list)
if (no_of_child > 0):
temp = 0
while (temp < no_of_child):
child_nodes = sucessor_list[temp]
child_state = child_nodes[0];
child_action = child_nodes[1];
if (visited_node.has_key(child_state) == False):
loc_queue.push(child_state)
if (parent_child_map.has_key(child_state) == False):
parent_child_map[child_state] = [parent_node,child_action]
temp = temp + 1
def breadthFirstSearch(problem):
from game import Directions
from util import Queue
traces=[]
StartState=problem.getStartState()
fringe =Queue()
closed = set()
temp=problem.getSuccessors(StartState)
for i in temp:
fringe.push((i,[]))
closed.add(StartState)
solution=[]
s = Directions.SOUTH
w = Directions.WEST
n = Directions.NORTH
e = Directions.EAST
while fringe:
node=fringe.list[-1]
state=node[0][0]
if not problem.isGoalState(state):
fringe.pop()
'if state in corners:'
'traces=traces+[node[1]+[node[0]]]'
if not state in closed:
temp= problem.getSuccessors(state)
if temp==[]:
closed.add(state)
else:
for i in temp:
if not i[0][0] in closed:
fringe.push((i,node[1]+[node[0]]))
closed.add(state)
else:
path=node[1]+[node[0]]
for i in path:
if i[1]=='South':
solution.append(s)
else:
if i[1]=='West':
solution.append(w)
else:
if i[1]=='North':
solution.append(n)
else:
if i[1]=='East':
solution.append(e)
return solution
def get_all_social_paths(self, user_id):
"""
Takes a user's user_id as an argument
Returns a dictionary containing every user in that user's
extended network with the shortest friendship path between them.
The key is the friend's ID and the value is the path.
"""
# implement BFS where id is the destination
print("start of paths")
visited = {} # Note that this is a dictionary, not a set
# !!!! IMPLEMENT ME
if user_id not in self.friendships:
return visited
qq = Queue()
qq.enqueue([user_id])
# While queue is not empty:
while qq.size() > 0:
# dequeue the first vertex
path = qq.dequeue()
v = path[-1]
# check if it is visited
# if not visited
if v not in visited:
visited[v] = path
# enqueue all neighbors
for friend in self.friendships[v]:
path_copy = path.copy()
path_copy.append(friend)
qq.enqueue(path_copy)
# enqueue the copy
return visited
def earliest_ancestor(ancestors, starting_node):
queue = Queue()
current_node = starting_node
relationships = {}
for node in ancestors:
if node[1] not in relationships:
relationships[node[1]] = set()
relationships[node[1]].add(node[0])
if starting_node in relationships:
queue.enqueue(relationships[current_node])
else:
return -1
while True:
relations = queue.dequeue()
current_node = min(relations)
if current_node not in relationships:
return current_node
else:
queue.enqueue(relationships[current_node])
def bfs(self, starting_vertex, destination_vertex):
"""
Return a list containing the shortest path from
starting_vertex to destination_vertex in
breath-first order.
"""
# pass # TODO
# QUEUE
# Create a queue
q = Queue()
# Enqueue A PATH TO the starting vertex
q.enqueue([starting_vertex])
# Create a set to store visited vertices
visited = set()
# While the queue is not empty...
while q.size() > 0:
# Dequeue the first PATH
path = q.dequeue()
# GRAB THE VERTEX FROM THE END OF THE PATH
v = path[-1]
# Check if it's been visited
# If it hasn't been visited...
if v not in visited:
# CHECK IF IT'S THE TARGET
if v == destination_vertex:
# IF SO, RETURN THE PATH
return path
# Mark it as visited
visited.add(v)
# Enqueue all it's neighbors to back of the queue
for neighbor in self.get_neighbors(v):
# MAKE A COPY OF THE PATH
copy = path.copy()
# ADD NEIGHBOR TO BACK OF PATH
copy.append(neighbor)
# ENQUEUE THE COPY
q.enqueue(copy)
def find_new_room(self):
# print('find_new_room')
# implement bfs to find path to nearest unseen room
# def bfs(self, starting_vertex, destination_vertex):
# """
# Return a list containing the shortest path from
# starting_vertex to destination_vertex in
# breath-first order.
# """
# # initialize queue with starting vertex
q = Queue()
q.enqueue((self.current_room, []))
# # set to keep track of vertexes already seen
visited = set()
# # while queue is not empty
while q.size() > 0:
# # get path and vertex
room, path = q.dequeue()
# print(path)
# print(path)
# print(type(path))
# # if room has not been seen, return path
if room.id not in self.seen:
return path
# else, add vertex to visited
elif room.id not in visited:
visited.add(room.id)
# and add paths to the queue for each edge
for exit in room.get_exits():
path_copy = path.copy()
path_copy.append(exit)
q.enqueue((room.get_room_in_direction(exit), path_copy))
raise ValueError('Room not found')
def bft(self, starting_vertex):
"""
Print each vertex in breadth-first order
beginning from starting_vertex.
"""
# # Create a queue and enqueue starting vertex
# qq = Queue()
# qq.enqueue([starting_vertex])
# # Create a set of traversed vertices
# visited = set()
# # While queue is not empty:
# while qq.size() > 0:
# # dequeue/pop the first vertex
# path = qq.dequeue()
# # if not visited
# if path[-1] not in visited:
# # DO THE THING!
# print(path[-1])
# # mark as visited
# visited.add(path[-1])
# # enqueue all neighbors
# for next_vert in self.get_neighbors(path[-1]):
# new_path = list(path)
# new_path.append(next_vert)
# qq.enqueue(new_path)
# Why not do this instead?
# After doing the later functions I see....
qq = Queue()
qq.enqueue(starting_vertex)
visited = set()
while qq.size() > 0:
vertex = qq.dequeue()
if vertex not in visited:
print(vertex)
visited.add(vertex)
for next_vert in self.get_neighbors(vertex):
qq.enqueue(next_vert)
def breadthFirstSearch(problem):
from util import Queue
frontier = Queue()
explored = []
actions = []
class node:
def __init__(self, path, dad, action):
self.path = path
self.dad = dad
self.action = action
start = node(problem.getStartState(), '', '')
frontier.push(start)
while frontier.isEmpty() == False:
path = frontier.pop()
successors = problem.getSuccessors(path.path)
explored.append(path)
for vertex in successors:
achou = False
for path_ex in explored:
if vertex[0] == path_ex.path:
achou = True
if achou == False:
successor = node(vertex[0], path.path, vertex[1])
frontier.push(successor)
if problem.isGoalState(successor.path):
while len(explored) > 0:
ant = explored.pop()
if ant.path == successor.dad:
actions.append(successor.action)
successor = ant
actions.reverse()
return actions
def get_all_social_paths(self, user_id):
"""
Takes a user's user_id as an argument
Returns a dictionary containing every user in that user's
extended network with the shortest friendship path between them.
The key is the friend's ID and the value is the path.
"""
visited = {} # Note that this is a dictionary, not a set
#visited = set()
# Create an empty queue and enqueue A PATH TO the starting vertex ID.
q = Queue()
# Create a Set to store visited vertices.
path = [user_id]
q.enqueue(path)
# While the queue is not empty...
while q.size() > 0:
# Dequeue from the front of the line, this is our current path.
current_path = q.dequeue()
# current_node is the last thing in the path
current_node = current_path[-1]
# Check if we've visited yet, if not:
if current_node not in visited:
# mark as visited
#visited.add(current_node)
visited[current_node] = current_path
# get the current node's friends
friends = self.get_friends(current_node)
# iterate over the friends
for friend in friends:
# add the neighbor to the path
friend_path = current_path.copy()
friend_path.append(friend)
# enqueue the neighbor's path
q.enqueue(friend_path)
return visited
def earliest_ancestor(ancestors, starting_node):
g = Graph()
for pair in ancestors:
g.add_vertex(pair[0])
g.add_vertex(pair[1])
g.add_edge(pair[1], pair[0])
# do a BFS (storing the path)
q = Queue()
# enqueue a path to starting node
q.enqueue([starting_node])
# creating a set to store visited
visited = set()
# no parents set to -1
# initializing parents
earliest_ancestor = -1
while q.size() > 0:
# gets the first path in the queue
path = q.dequeue()
# getting last node in the path
v = path[-1]
# check if it has been visited, and if not ->
if v not in visited:
#mark it as visited
visited.add(v)
# check if path(v) is less than parent meaning if there was no path it would be the parent
# or length is longer than 1
if ((v < earliest_ancestor) or (len(path) > 1)):
# update V with the new NODE
earliest_ancestor = v
# enqueue a path to all its neighbors
for neighbor in g.get_neighbors(v):
# make a copy of the path
copy = path.copy()
# append the neighbor
copy.append(neighbor)
# enqueue the copy
q.enqueue(copy)
return earliest_ancestor
def bfs(self, starting_vertex, destination_vertex):
"""
Return a list containing the shortest path from
starting_vertex to destination_vertex in
breath-first order.
"""
# Create an empty queue
# Add A PATH TO the starting vertex_id to the queue
# Create an empty set to store visited nodes
# While the queue is not empty...
# Dequeue, the first PATH
# GRAB THE LAST VERTEX FROM THE PATH
# CHECK IF IT'S THE TARGET
# IF SO, RETURN THE PATH
# Check if it's been visited
# If it has not been visited...
# Mark it as visited
# Then add A PATH TO all neighbors to the back of the queue
# (Make a copy of the path before adding)
queue = Queue()
queue.enqueue([starting_vertex])
visited = set()
while queue.size() > 0:
path = queue.dequeue()
vertex = path[-1]
if vertex not in visited:
if vertex == destination_vertex:
return path
visited.add(vertex)
for next_vertex in self.get_neighbors(vertex):
new_path = list(path)
new_path.append(next_vertex)
queue.enqueue(new_path)
def breadthFirstSearch(problem):
"""Search the shallowest nodes in the search tree first."""
"*** YOUR CODE HERE ***"
explored = set()
rootNode = [problem.getStartState(), list()]
from util import Queue
queueNode = Queue()
queueNode.push(rootNode)
while not queueNode.isEmpty():
currentNode = queueNode.pop()
currentState = currentNode[0]
currentPath = currentNode[1]
explored.add(currentState)
if problem.isGoalState(currentState):
return currentPath
else:
children = problem.getSuccessors(currentState)
for child in children:
childState = child[0]
# Converts a tuple into list.
childPath = list(currentPath)
childPath.append(child[1])
if childState not in explored:
queueNode.push([child[0], childPath])
raise Exception, 'No solutions found'
def find_nearest_unvisited(player, visited):
room_id = player.cur_room
neighbors = g.get_neighbors(room_id)
q = Queue()
# FORMAT OF QUEUE: [ [directions], [room_ids] ]
# Initialize queue
for direction in neighbors:
if neighbors[direction] == '-1':
# Return to main calling function
print('Found next unvisited room 1 move away.')
return [direction]
else:
# [directions to new location, new room id]
room_id = neighbors[direction]
q.enqueue([[direction], [room_id]])
# START SEARCH
while q.size() > 0:
directions, room_ids = q.dequeue()
last_room_id = room_ids[-1]
# Get neighbors
neighbors = g.get_neighbors(last_room_id)
neighbors_keys = list(neighbors.keys())
random.shuffle(neighbors_keys)
for direction in neighbors_keys:
r_id = neighbors[direction]
if r_id == '-1':
directions.append(direction)
print(
f"Found next unvisited room {len(directions)} moves away.")
return directions
elif r_id not in room_ids:
new_directions = list(directions)
new_directions.append(direction)
new_room_ids = list(room_ids)
new_room_ids.append(r_id)
q.enqueue([new_directions, new_room_ids])
def get_all_social_paths(self, user_id):
"""
Takes a user's user_id as an argument
Returns a dictionary containing every user in that user's
extended network with the shortest friendship path between them.
The key is the friend's ID and the value is the path.
"""
# Uper
visited = {} # Note that this is a dictionary, not a set
queue = Queue()
# add the user_id to an array and throw it into the queue
queue.enqueue([user_id])
# traverse through the graph
while queue.size() > 0:
cur_path = queue.dequeue()
node = cur_path[-1]
# check if we've visisted the node
if node not in visited:
# mapping visisted node w/current path to dict
visited[node] = cur_path
# grabbing all the neighbors
friends = self.friendships[node]
for friend in friends:
# add friend to path
copy_path = [*cur_path, friend]
# enqueue copy of current path + new node
queue.enqueue(copy_path)
# returns list of users
return visited
def earliest_ancestor(ancestors, starting_node):
# build a graph
graph = Graph()
# go over a set of vertices and make nodes
for pair in ancestors:
# create the vertexes
# for the specific id
# create and check to see if its already in
if pair[0] not in graph.vertices:
graph.add_vertex(pair[0])
if pair[1] not in graph.vertices:
graph.add_vertex(pair[1])
# create the edges
graph.add_edge(pair[1], pair[0])
# initialize queue
queue = Queue()
queue.enqueue([starting_node])
# keep track of the longest path to figure out
longest_path = []
while queue.size() > 0:
vertex_path = queue.dequeue()
if len(vertex_path) > len(longest_path):
longest_path = vertex_path
if len(vertex_path) == len(longest_path):
if vertex_path[-1] < longest_path[-1]:
longest_path = vertex_path
for neighbors in graph.get_neighbors(vertex_path[-1]):
temp_path = vertex_path.copy()
temp_path.append(neighbors)
queue.enqueue(temp_path)
if len(longest_path) <= 1:
return -1
else:
return longest_path[-1]
def bfs(self, starting_vertex, destination_vertex):
"""
Return a list containing the shortest path from
starting_vertex to destination_vertex in
breath-first order.
"""
#Create a queue
#Enqueue a path to the starting vertex
#Create a set to store visited vertices
#While the queue is not empty
#Dequeue the first path
#Grab the vertex from the end of the path
#Check if it has been visited
#Check if its the target if so return the path
#if it hasnt been visited
#mark it as visited
#enqueue a path to all its neighbors
#make a copy of the path
#enqueue the copy
queue = Queue()
visited = set()
path = [starting_vertex]
queue.enqueue(path)
while queue.size() > 0:
path = queue.dequeue()
vertex = path[-1]
if vertex == destination_vertex:
return path
if vertex not in visited:
visited.add(vertex)
for neighbor in self.get_neighbors(vertex):
new_path = path[:]
new_path.append(neighbor)
queue.enqueue(new_path)
def bfs(self, starting_vertex, destination_vertex):
"""
Return a list containing the shortest path from
starting_vertex to destination_vertex in
breath-first order.
plan --
new queue
add starting vert to queue
make a set for visited verts
while set is not empty
deque the vert
if this vert has not been visited
add it to the set
add neighbors to the back of the queue
"""
the_queue = Queue()
the_queue.enqueue([starting_vertex])
visited_verts = set()
while the_queue.size() > 0:
the_path_2destination = the_queue.dequeue()
vertex = the_path_2destination[-1]
if vertex not in visited_verts:
if vertex == destination_vertex:
return the_path_2destination
visited_verts.add(vertex)
for next_vert in self.vertices[vertex]:
new_path = list(the_path_2destination)
new_path.append(next_vert)
the_queue.enqueue(new_path)
def traverse_all_rooms(player):
visited = {}
visited_path = []
queue = Queue()
queue.enqueue(player.current_room.id)
while queue.size() > 0:
if len(visited) == len(world.rooms):
return visited
room_id = queue.dequeue()
visited = add_to_visited(room_id, visited)
if len(visited_path) > 0:
prev_room_id = visited_path[-1][0]
prev_room_dir = reverse_direction(visited_path[-1][1])
visited[room_id][prev_room_dir] = prev_room_id
choices = possible_exits(room_id, player, visited)
if choices:
choice = random.choice(choices)
player.travel(choice)
visited[room_id][choice] = player.current_room.id
visited_path.append([room_id, choice])
else:
choice = prev_room_dir
player.travel(choice)
if visited_path:
visited_path.pop()
else:
traversal_path.append(choice)
return visited
traversal_path.append(choice)
queue.enqueue(player.current_room.id)
print(visited_path)
def get_all_social_paths(self, user_id):
"""
Takes a user's user_id as an argument
Returns a dictionary containing every user in that user's
extended network with the shortest friendship path between them.
The key is the friend's ID and the value is the path.
Implemented as a bfs since we want shortest paths.
"""
visited = {} # Note that this is a dictionary, not a set
# instantiate the queue
q = Queue()
# intialize the path list and put it on the queue
# per rubric, path consisting of only the user is valid
path = [user_id]
q.enqueue(path)
# while the queue is not empty
while q.size() > 0:
# get the first path from the queue
cur_path = q.dequeue()
# and its last entry
new_user_id = cur_path[-1]
# search paths of unvisited users
if new_user_id not in visited:
visited[new_user_id] = cur_path
friends = self.get_friends(new_user_id)
for friend in friends:
path_copy = list(cur_path)
path_copy.append(friend)
q.enqueue(path_copy)
return visited
def bfs(graph, starting_vertex):
"""
Return a list containing the longest path
from starting_vertex
"""
earliest_an = None
# Create an empty queue and enqueue A PATH TO the starting vertex ID
q = Queue()
initial_path = [starting_vertex]
q.enqueue(initial_path)
# Create a Set to store visited vertices
visited = set()
# While the queue is not empty...
while q.size() > 0:
# Dequeue the first PATH
path = q.dequeue()
# save the length of the path
path_length = len(path)
# Grab the last vertex from the PATH
last_vert = path[-1]
# If that vertex has not been visited...
if last_vert not in visited:
# Mark it as visited...
visited.add(last_vert)
# Then add A PATH TO its neighbors to the back of the
for v in graph.vertices[last_vert]:
# COPY THE PATH
path_copy = path[:]
# APPEND THE NEIGHOR TO THE BACK
path_copy.append(v)
q.enqueue(path_copy)
# If the new path is longer than previous path
# Save the longer path as earliest_ancestor
if len(path_copy) > path_length:
earliest_an = path_copy
if earliest_an:
return earliest_an[-1]
return -1
def bfs(self, starting_vertex, destination_vertex):
"""
Return a list containing the shortest path from
starting_vertex to destination_vertex in
breath-first order.
"""
# Create an empty queue and enqueue A PATH TO the starting vertex ID
q = Queue()
path = [starting_vertex]
q.enqueue(path)
# Create a Set to store visited vertices
visited = set()
# While the queue is not empty...
while q.size() > 0:
# Dequeue the first PATH
path = q.dequeue()
# Grab the last vertex from the PATH
last_vert = path[-1]
# If that vertex has not been visited...
if last_vert not in visited:
# CHECK IF IT'S THE TARGET
if last_vert == destination_vertex:
# IF SO, RETURN PATH
return path
# Mark it as visited...
visited.add(last_vert)
# Then add A PATH TO its neighbors to the back of the queue
for next_vert in self.get_neighbors(last_vert):
# COPY THE PATH
new_path = [x for x in path]
new_path.append(next_vert)
# APPEND THE NEIGHOR TO THE BACK
q.enqueue(new_path)
def bfs(self, starting_vertex, destination_vertex):
# Breadth First can find shortest path
# create the queue, breadth first traversal requires a queue
# enqueue the starting vertex in a list
# create the visited set to keep track of visited nodes
q = Queue()
q.enqueue([starting_vertex])
visited = set()
# while the queue still has items
while q.size() > 0:
# grab the first item in the queue
path = q.dequeue()
# and grab the vertex from the last index in the path
vert = path[-1]
# if the vertex hasn't been visited
if vert not in visited:
# if the vertex equals our destination value,
# return the path, we have our answer
if vert == destination_vertex:
return path
# else add the vertex to visited
visited.add(vert)
# loop through all remaining neighbors and
# create a copy of the path,
# append the new vertex for all neighbors to the path,
# and enqueue the new paths
for next_vert in self.get_neighbors(vert):
path_copy = list(path)
path_copy.append(next_vert)
q.enqueue(path_copy)
# if we get here, there was no path from start to destination
return None
def get_all_social_paths(self, user_id):
"""
Takes a user's user_id as an argument
Returns a dictionary containing every user in that user's
extended network with the shortest friendship path between them.
The key is the friend's ID and the value is the path.
"""
visited = {} # Note that this is a dictionary, not a set
# use queue
qq = Queue()
# add user to queue
qq.enqueue([user_id])
# while there's a queue
while qq.size() > 0:
# dequeue first vert
path = qq.dequeue()
# if not visited
other = path[-1]
if other not in visited:
# PROCESS IT
visited[other] = path
# full copy the path
james = self.friendships[other]
for who in james:
new_path = list(path)
new_path.append(who)
qq.enqueue(new_path)
return visited
def get_all_social_paths(self, user_id):
"""
Takes a user's user_id as an argument
Returns a dictionary containing every user in that user's
extended network with the shortest friendship path between them.
The key is the friend's ID and the value is the path.
"""
# Create a Queue
q = Queue()
# Enqueue a path to user_id
q.enqueue([user_id])
# Create a dictionary for key/value pairs
visited = {} # Note that this is a dictionary, not a set
# While the queue is not empty...
while q.size() > 0:
# Dequeue the first path
path = q.dequeue()
# Grab the vertex from the end of the path
v = path[-1]
# Check if its been visited
# IF it has not been visited...
if v not in visited:
if v is not user_id:
# Do the thing
visited[v] = path
# Enqueue a path to all its neighbors
for neighbor in self.friendships[v]:
if neighbor not in visited:
# Make a copy of the path
copy = path.copy()
# Append the neighbor
copy.append(neighbor)
# Enqueue the copy
q.enqueue(copy)
return visited
def find_words(starting_word, ending_word):
visited = set()
pending = Queue()
pending.enqueue([starting_word])
while len(pending) > 0:
path = pending.dequeue()
vert = path[-1]
if vert == ending_word:
return path
if vert not in visited:
visited.add(vert)
for neighbor in get_neighbors(vert):
new_path = list(path)
new_path.append(neighbor)
pending.enqueue(new_path)
def bfs(self, starting_vertex, destination_vertex):
"""
Return a list containing the shortest path from
starting_vertex to destination_vertex in
breath-first order.
"""
qq, path = Queue(), []
qq.enqueue(starting_vertex)
while qq:
vertex = qq.dequeue()
if vertex in path:
continue
path.append(vertex)
for adj_vertex in self.vertices[vertex]:
qq.enqueue(adj_vertex)
if vertex == destination_vertex:
return path
return path
def bft(self, starting_vertex):
"""
Print each vertex in breadth-first order
beginning from starting_vertex.
"""
self.vertex_search(starting_vertex)
pending = Queue()
visited = set()
if starting_vertex in self.vertices:
pending.enqueue(starting_vertex)
while len(pending.queue) > 0:
vertex = pending.dequeue()
if vertex and vertex not in visited:
visited.add(vertex)
print(vertex)
for neighbor in self.get_neighbors(vertex):
pending.enqueue(neighbor)
def breadthFirstSearch(problem):
"""Search the shallowest nodes in the search tree first."""
"*** YOUR CODE HERE ***"
# create a stack to bfs and save the path
bfs = Queue()
rs = []
# to vistie node
visited = []
#Get start state
startState = problem.getStartState()
# print(startState)
if(problem.isGoalState(startState)):
return []
bfs.push((startState,[]))
while(not bfs.isEmpty()):
pos,rs = bfs.pop()
# if current is goal
if (problem.isGoalState(pos)):
return rs
if (pos not in visited):
visited.append(pos)
#get next states
next = problem.getSuccessors(pos)
#push next states into queue to wait to visite
for item in next:
if(item[0] not in visited):
bfs.push((item[0],rs + [item[1]]))
#if no solution:
return []
def traverseBack():
q = Queue()
roomID = player.currentRoom.id
visited = [roomID]
exits = graph[roomID]
for exit in exits.items():
q.enqueue([exit[1]])
while True:
temp = q.dequeue()
visited.append(temp[-1])
roomID = temp[-1]
exits = graph[roomID]
if '?' in exits.values():
executePath(temp)
return
for exit in exits.items():
if exit[1] not in visited:
q.enqueue([*temp, exit[1]])
def get_all_social_paths(self, user_id):
"""
Takes a user's user_id as an argument
Returns a dictionary containing every user in that user's
extended network with the shortest friendship path between them.
The key is the friend's ID and the value is the path.
"""
visited = {} # Note that this is a dictionary, not a set
# !!!! IMPLEMENT ME
# instantiate a queue for BFS
q = Queue()
# enqueue starting user
q.enqueue([user_id])
# while the queue is not empty:
while q.size() > 0:
# dequeue the first path
path = q.dequeue()
# grab the last user from the path
user = path[-1]
# if that user has not been visited...
if user not in visited:
# add the user as a key to the visted dictionary, and add its
#. path as the value
visited[user] = path
# then add a path to its neighbors
for friend in self.friendships[user]:
# make a copy of the path
new_path = path.copy()
# append the neighbor to the back of the new path
new_path.append(friend)
# enqueue the new path
q.enqueue(new_path)
return visited
def get_all_social_paths(self, user_id):
""" Takes a user's user_id as an argument. Returns a dictionary containing every user in that user's extended network with the shortest friendship path between them. The key is the friend's ID and the value is the path."""
# use BFT to get to each person in the connected component
# use BFS to get to the shortest path to each component
def get_shortest_path(origin, destination):
"""Helper function that gets the shortest path to a destination node via BFS"""
q2 = Queue()
q2.enqueue([origin])
visited = set()
while q.size() > 0:
path = q2.dequeue()
vertex = path[-1]
if vertex not in visited:
visited.add(vertex)
if vertex == destination:
return path
else:
for neighbor in self.friendships[vertex]:
q2.enqueue([*path, neighbor])
return [origin, destination]
q = Queue()
q.enqueue(self.friendships[user_id])
visited = {} # Note that this is a dictionary, not a set
while q.size() > 0:
friendships = q.dequeue()
# print(f'current friendships: {friendships}')
# for each friend in friendships
for friend in friendships:
# friendships is a set of integers
if friend not in visited and friend != user_id:
visited[friend] = get_shortest_path(user_id, friend)
q.enqueue(self.friendships[friend])
# looks like you'll have to do a traversal of that connected component
return visited
def calcMazeDistance(point_1, point_2, walls):
x1, y1 = point_1
x2, y2 = point_2
assert not walls[x1][y1], 'point1 is a wall: ' + str(point_1)
assert not walls[x2][y2], 'point2 is a wall: ' + str(point_2)
from util import Queue
from game import Actions
state_queue = Queue()
initial_path = []
state_queue.push((point_1, initial_path))
visited_states = []
visited_states.append(point_1)
while state_queue.isEmpty() == False:
state = state_queue.pop()
if state[0] == point_2:
return len(state[1])
successors = []
for action in [
Directions.NORTH, Directions.SOUTH, Directions.EAST,
Directions.WEST
]:
x, y = state[0]
dx, dy = Actions.directionToVector(action)
nextx, nexty = int(x + dx), int(y + dy)
if not walls[nextx][nexty]:
nextState = (nextx, nexty)
cost = 1
successors.append((nextState, action, cost))
for successor in successors:
if successor[0] in visited_states:
continue
visited_states.append(successor[0])
new_path = state[1][:]
new_path.append(successor[1])
state_queue.push((successor[0], new_path))
return 0