def run(self):
pq = PriorityQueue()
for q in self.queues:
t = q.reset()
pq.push(t, q)
while not pq.empty():
t = max(t, pq.key())
q = pq.value()
pq.pop()
next_t = q.process(t)
if next_t >= 0:
pq.push(next_t, q)
return t
def aStar(start, goal, neighbor_func, distance_func, heuristic_func):
"""Returns a sequence of nodes that optmizes for the least cost
from the start node to the goal.
Let's describe the data that we pass to this function:
start: the start of the search.
goal: the goal of the search.
neighbor_func: a function that, given a state, returns a list of
neighboring states.
distance_func: a function that takes two nodes, and returns
the distance between them.
heuristic_func: a function that takes two nodes, and returns
the heuristic distance between them.
Each state mush be hashable --- each state must support the
hash() function.
"""
pqueue = PriorityQueue()
g_costs = {start : 1}
parents = {start : start}
pqueue.push(heuristic_func(start, goal), start)
while not pqueue.isEmpty():
next_cost, next_node = pqueue.pop()
g_costs[next_node] = g_costs[parents[next_node]] \
+ distance_func(next_node, parents[next_node])
if next_node == goal: break
children = neighbor_func(next_node)
for child in children:
updateChild(goal, distance_func, heuristic_func,
child, next_node, parents, g_costs, pqueue)
return getPathToGoal(start, goal, parents)
class TestPriorityQueue(unittest.TestCase):
def setUp(self):
self.pq = PriorityQueue(":memory:")
def test_empty(self):
result = self.pq.is_empty()
self.assertTrue(result)
def test_order(self):
self.assertEqual("hello", self.pq.push(10, "hello"))
self.pq.push(1, "foo")
self.pq.push(30, "bar")
self.pq.push(5, "baz")
self.assertEqual("bar", self.pq.pop())
self.assertEqual("hello", self.pq.pop())
self.assertEqual("baz", self.pq.pop())
self.assertEqual("foo", self.pq.pop())
class TestPriorityQueue(unittest.TestCase):
def setUp(self):
self.pq = PriorityQueue(":memory:")
def test_empty(self):
result = self.pq.is_empty()
self.assertTrue(result)
def test_order(self):
self.assertEqual("hello", self.pq.push(10, "hello"))
self.pq.push(1, "foo")
self.pq.push(30, "bar")
self.pq.push(5, "baz")
self.assertEqual("bar", self.pq.pop())
self.assertEqual("hello", self.pq.pop())
self.assertEqual("baz", self.pq.pop())
self.assertEqual("foo", self.pq.pop())
def UCS(
initial_state,
avoid_backtrack=False,
filtering=False,
cutoff=INF,
state_callback_fn=lambda state:
False, # A callback function for extended states. If it returns True, terminate
counter={
'num_enqueues': 0,
'num_extends': 0
}): # A counter for
frontier = PriorityQueue()
frontier.append(initial_state, initial_state.get_path_cost())
extended_filter = set()
while frontier: # frontier is False when it is empty. So just keep going until out of places to go...
# choose next state to "extend" from frontier
ext_node = frontier.pop()
if (filtering and ext_node.get_all_features() in extended_filter):
continue
extended_filter.add(ext_node.get_all_features())
counter['num_extends'] += 1
# are we there? If so, return the node.
if ext_node.is_goal_state():
return ext_node
# Update our caller (e.g. GUI) with the state we're extending.
# Terminate search early if True is returned.
if (state_callback_fn(ext_node)):
break
### Update frontier with next states
for state in ext_node.generate_next_states():
if (avoid_backtrack and ext_node.get_parent() == state):
continue
if (filtering and state.get_all_features() in extended_filter):
continue
if (cutoff != INF and state.get_path_length() > cutoff):
continue
frontier.append(state, state.get_path_cost())
counter['num_enqueues'] += 1
# if loop breaks before finding goal, search is failure; return None
return None
def search(self, start):
node = Node(self.problem.initial, action=self.problem.cur_dir)
if self.problem.goal_test(node.state):
return node
frontier = PriorityQueue(self.f)
frontier.append(node)
while frontier:
node = frontier.pop()
finish = pygame.time.get_ticks()
#se demorou 90 % do tempo
if finish - start >= 0.9 * self.problem.time:
frontier.append(node)
return node
if self.problem.goal_test(node.state):
return node
self.explored.add(node.state)
for child in node.expand(self.problem):
if child.path_cost == 1 and child.state in self.problem.get_adv_next_p_head(
):
continue
if child.state not in self.explored and child not in frontier:
frontier.append(child)
# caso o node expandido ja estiver na frontier
# fica na frontier o que tiver menor função de avaliação
elif child in frontier:
incumbent = frontier[child]
if self.f(child) < self.f(incumbent):
del frontier[incumbent]
return None
def a_search(graph, start, goals):
my_heap = PriorityQueue(
) #not really a heap just easier to call it that than queue
my_heap.push(start, 0)
pathway = []
cost_so_far = {}
came_from = {}
cost_so_far[start] = 0
came_from[start] = None
the_sum = 0
ordered_goals = []
while not my_heap.empty():
current = my_heap.pop()
#print("here")
if current in goals:
ordered_goals.append(current)
goals.remove(current)
for i in graph:
if i.parent:
the_sum += 1
while current.parent:
pathway.append(current)
x = current.parent
current.parent = None
current = x
if len(goals) == 0:
break
for i in current.edges:
new_score = current.gscore + 1 #increment g(n)
if i not in cost_so_far or new_score < i.gscore:
cost_so_far[i] = new_score
i.gscore = new_score
i.hscore = findMST(i, goals)
#print(i.hscore, current)
i.fscore = i.gscore + i.hscore
my_heap.push(i, i.fscore)
if not i.parent:
i.parent = current
return pathway, the_sum, ordered_goals
def a_star_graph_search(map,start,goal):
""" Frontier and explored must be either a hash or tree for fast
membership testing
In this implementation node doesn't need to be hashable because it is
not used in membership testing, a dic is used to associate keys and values.
it may be better to create a Node class"""
node = create_node(start, map, goal)
if goal == start:
node["path"].append(start)
return node
frontier = PriorityQueue()
frontier.append(node)
explored = set()
while frontier:
node = frontier.pop()
state = node["state"]
if goal == state:
return node
explored.add(state)
for action in map.roads[state]:
"""child_node is not created here to not be called if in explored"""
if action not in explored and action not in frontier:
child_node = create_node(action, map, goal, node)
frontier.append(child_node)
elif action in frontier:
child_node = create_node(action, map, goal, node)
"""frontier[child_node] = node with same state as child_node"""
if child_node['f'] < frontier[child_node]['f']:
del frontier[frontier[child_node]]
frontier.append(child_node)
return None
pointList.append(((8,12), None))
pointList.append(((16,9), None))
pointList.append(((3,5), None))
pointList.append(((6,1), None))
sites = PriorityQueue(pointList[:200])
#Test
counter = 1
# Go through sites
# while sites and counter < 20:
while sites:
# try:
s = sites.pop()
#Check if site event
if s[1] == None:
#print (s)
handle_site(s[0])
else:
# node = s[1]
# print("len_node: " + str(len(node)))
handle_circle(s)
# except KeyError as err:
# print ("error")
# print err
# break
#Test
if s[1] is None:
class Node():
def __init__(self, node_id):
self.node_id = node_id
self.timestamp = 0
self.nodeudp = NodeUDP(node_id)
self.thread = threading.Thread(target=self.listen, args=())
self.thread.setDaemon(True)
self.lock = threading.Lock()
self.thread.start()
self.q = PriorityQueue()
self.q_lock = threading.Lock()
self.replied_list = []
self.reply_lock = threading.Lock()
def send_to(self, message, node_id):
self.lock.acquire()
self.timestamp += 1
message = "<{},{}>:".format(self.timestamp, self.node_id) + message
logger.debug("node {} sends '{}' to node {} at {}".format(
self.node_id, message, node_id, self.timestamp))
self.lock.release()
self.nodeudp.send(message, ("127.0.0.1", startPort + node_id))
def broadcast(self, message):
global node_num
self.lock.acquire()
self.timestamp += 1
request_str = "<{},{}>".format(self.timestamp, self.node_id)
message = "<{},{}>:".format(self.timestamp, self.node_id) + message
logger.debug("node {} broadcasts '{}' at {}".format(
self.node_id, message, self.timestamp))
self.lock.release()
for node_id in range(node_num):
if node_id == self.node_id:
continue
self.nodeudp.send(message, ("127.0.0.1", startPort + node_id))
return request_str
def request(self):
request_str = self.broadcast("request")
request_tuple = str2tuple(request_str)
self.q_lock.acquire()
self.q.push(request_tuple)
self.q_lock.release()
def reply(self, request_str, to):
self.send_to("reply:" + request_str, to)
def release(self):
self.broadcast("release")
self.reply_lock.acquire()
self.replied_list = []
self.reply_lock.release()
self.q_lock.acquire()
self.q.pop()
self.q_lock.release()
def run(self):
global enter_times
global current
global node_num
for _ in range(enter_times):
logger.info("node {} requests to enter CS".format(self.node_id))
self.request()
while True:
time.sleep(0.1)
self.q_lock.acquire()
top_request_tuple = self.q.get()
# self.q.put(top_request_tuple)
self.q_lock.release()
self.reply_lock.acquire()
reply_num = len(self.replied_list)
self.reply_lock.release()
if self.node_id == top_request_tuple[1] and reply_num == (
node_num - 1):
break
logger.info("node {} enters CS at {}".format(
self.node_id, self.timestamp))
time.sleep(random.randint(1, 5))
self.release()
logger.info("node {} leaves CS at {}".format(
self.node_id, self.timestamp))
time.sleep(1)
time.sleep(3)
def listen(self):
while True:
msg = self.nodeudp.recv()[0]
ts = int(msg.split(',')[0][1:])
node_id = int(msg.split(',')[1].split('>')[0])
if ts > self.timestamp:
self.lock.acquire()
self.timestamp = ts + 1
self.lock.release()
else:
self.lock.acquire()
self.timestamp += 1
self.lock.release()
logger.debug("node {} recieve '{}' from node {} at {}".format(
self.node_id, msg, node_id, self.timestamp))
msg_type = msg.split(":")[1]
if msg_type == "request":
request_str = msg.split(":")[0]
self.reply(request_str, node_id)
request_tuple = str2tuple(request_str)
self.q_lock.acquire()
self.q.push(request_tuple)
self.q_lock.release()
elif msg_type == "reply":
self.reply_lock.acquire()
self.replied_list.append(node_id)
self.reply_lock.release()
elif msg_type == "release":
self.q_lock.acquire()
self.q.pop()
self.q_lock.release()
#for a in range(0,200):
# pointList.append(((random.randint(0,1000), random.randint(0,1000)), None))
pointList.append(((8,12), None))
pointList.append(((16,9), None))
pointList.append(((3,5), None))
pointList.append(((6,1), None))
sites = PriorityQueue(pointList[:200])
#Test
counter = 1
# Go through sites
# while sites and counter < 20:
while sites:
# try:
s = sites.pop()
#Check if site event
if s[1] == None:
#print (s)
handle_site(s[0])
else:
# node = s[1]
# print("len_node: " + str(len(node)))
handle_circle(s)
# except KeyError as err:
# print ("error")
# print err
# break
#Test
if s[1] is None:
class AStar():
'''
Properties:
public:
- world: 2D array of Nodes
internal:
- size: (width, height) tuple of world
- open: Nodes queue to evaluate (heap-based priority queue)
'''
#----------------------------------------------------------------------
def __init__(self, world):
self.world = world
self.size = (len(world), len(world[0]))
# self.open = SortedList()
self.open = PriorityQueue()
self.openValue = 1
self.closedValue = 2
#----------------------------------------------------------------------
def initSearch(self, start, goal, obstacles):
''' first, check we can achieve the goal'''
if goal.type in obstacles:
return False
''' clear open list and setup new open/close value state to avoid the clearing of a closed list'''
self.open.clear()
self.openValue += 2
self.closedValue += 2
''' then init search variables'''
self.start = start
self.goal = goal
self.obstacles = obstacles
self.start.cost = 0
self.addToOpen(self.start)
self.goal.parent = None
return True
#----------------------------------------------------------------------
def search(self):
while not self.openIsEmpty():
current = self.popFromOpen()
if current == self.goal:
break
self.removeFromOpen(current)
self.addToClosed(current)
''' generator passes : look at the 8 neighbours around the current node from open'''
for (di, dj) in [(-1,-1), (-1,0), (-1,1), (0,-1), (0,1), (1,-1), (1,0), (1,1)]:
neighbour = self.getNode(current.i + di, current.j + dj)
if (not neighbour) or (neighbour.type in self.obstacles):
continue
'''the cost to get to this node is the current cost plus the movement
cost to reach this node. Note that the heuristic value is only used
in the open list'''
nextStepCost = current.cost + self.getNeighbourCost(current, neighbour)
'''if the new cost we've determined for this node is lower than
it has been previously makes sure the node has not been
determined that there might have been a better path to get to
this node, so it needs to be re-evaluated'''
if nextStepCost < neighbour.cost and (self.inOpenList(neighbour) or self.inClosedList(neighbour)):
self.invalidateState(neighbour)
'''if the node hasn't already been processed and discarded then
step (i.e. to the open list)'''
if (not self.inOpenList(neighbour)) and (not self.inClosedList(neighbour)):
neighbour.cost = nextStepCost
neighbour.heuristic = self.getHeuristicCost(neighbour, self.goal)
neighbour.parent = current
self.addToOpen(neighbour)
''' exit with None = path not yet found'''
yield None
'''since we've run out of search
there was no path. Just return'''
if self.goal.parent is None:
return
'''At this point we've definitely found a path so we can uses the parent
references of the nodes to find out way from the target location back
to the start recording the nodes on the way.'''
path = []
goal = self.goal
while goal is not self.start:
path.insert(0, (goal.i, goal.j))
goal = goal.parent
''' done, exit with path'''
yield path
#-----------------------------------------------------------------------------
def getNode(self, i, j):
if i >=0 and i < self.size[0] and j >= 0 and j < self.size[1]:
return self.world[i][j]
else:
return None
#----------------------------------------------------------------------
def getNeighbourCost(self, n1, n2):
return (abs(n2.i - n1.i) + abs(n2.j - n1.j))
#----------------------------------------------------------------------
def getHeuristicCost(self, n1, n2):
return (abs(n2.i - n1.i) + abs(n2.j - n1.j))
#----------------------------------------------------------------------
def invalidateState(self, node):
node.state = 0
#----------------------------------------------------------------------
def popFromOpen(self):
# return self.open.first()
return self.open.pop()
#----------------------------------------------------------------------
def addToOpen(self, node):
# self.open.add(node)
self.open.insert(node)
node.state = self.openValue
#----------------------------------------------------------------------
def inOpenList(self, node):
return node.state is self.openValue
#----------------------------------------------------------------------
def removeFromOpen(self, node):
# self.open.remove(node)
self.open.remove(node)
node.state = 0
#----------------------------------------------------------------------
def openIsEmpty(self):
# return not self.open.size()
return self.open.isEmpty()
#----------------------------------------------------------------------
def addToClosed(self, node):
node.state = self.closedValue
#----------------------------------------------------------------------
def inClosedList(self, node):
return node.state is self.closedValue
def sweep_line_algorithm(self):
self.current = Point()
pointsPQ = PriorityQueue()
tree = TreeSet()
pointsPQ.pushAll([seg.p for seg in self.segments])
pointsPQ.pushAll([seg.q for seg in self.segments])
res = 0
#print [str(x) for x in pointsPQ]
while not pointsPQ.isEmpty():
self.current.__update__(pointsPQ.pop())
#print "Round", current
if self.current.status == 'left':
#print "Adding", self.current.segment
low, high = tree.add_high_low(self.current.segment)
low = tree.lower(self.current.segment)
high = tree.higher(self.current.segment)
#print "Actual:", self.current.segment
#print "Low:", low, self.current.segment.intersect(low) if low else False
#print "High:", high, self.current.segment.intersect(high) if high else False
if low:
if self.current.segment.intersect(low):
a = self.current.segment.intersection_point(low)
#print "Adding a:", a, self.current.segment, low
pointsPQ.push(a)
if high:
if self.current.segment.intersect(high):
a = self.current.segment.intersection_point(high)
#print "Adding 2:", a, self.current.segment, high
pointsPQ.push(a)
elif self.current.status == "right":
low = tree.lower(self.current.segment)
high = tree.higher(self.current.segment)
if low and high:
if low.intersect(high):
a = low.intersection_point(high)
#print "Adding 3:", a, low, high
pointsPQ.push(a)
tree.remove(self.current.segment)
#print "Removing", self.current.segment
elif self.current.status == "int":
# exchange the position in tree of the two segments intersecting in current
s1, s2 = self.current.segment
#print "Between, swapping:", str(s1), str(s2)
tree.swap(s1, s2)
#print "After swap:", s1, s2, s1 is tree.lower(s2), s2 is tree.lower(s1)
#print "Modifying segments starts"
old_s1 = s1.p.node
old_s2 = s2.p.node
s1.set_p_node(self.current.node)
s2.set_p_node(self.current.node)
#print "Tree after modification:", [str(x) for x in tree]
# s1
if s1 is tree.lower(s2):
#print "... s1, s2, ..."
low = tree.lower(s1)
#print "s1:", s1, "low:", low, s1.intersect(low) if low else False
if low is not None:
if s1.intersect(low):
pointsPQ.push(s1.intersection_point(low))
high = tree.higher(s2)
#print "s2:", s2, "high:", high, s2.intersect(high) if high else False
if high is not None:
if s2.intersect(high):
pointsPQ.push(s2.intersection_point(high))
elif s2 is tree.lower(s1):
#print "... s2, s1, ..."
high = tree.higher(s1)
#print "s1:", s1, "high:", high, s1.intersect(high) if high else False
if high is not None:
if s1.intersect(high):
pointsPQ.push(s1.intersection_point(high))
low = tree.lower(s2)
#print "s2:", s2, "low:", low, s2.intersect(low) if low else False
if low is not None:
if s2.intersect(low):
pointsPQ.push(s2.intersection_point(low))
else:
print "Error" #raise SweepPlaneException("Intersection point error!")
res += 1
s1.set_p_node(old_s1)
s2.set_p_node(old_s2)
else:
print "Error 2" #raise SweepPlaneException("Node without status!")
#print "Tree", [str(x) for x in tree]
#print ""
self.nodes = self.nodes[:self.original_n_nodes]
return res
class AStar():
'''
Properties:
public:
- world: 2D array of Nodes
internal:
- size: (width, height) tuple of world
- open: Nodes queue to evaluate (heap-based priority queue)
'''
#----------------------------------------------------------------------
def __init__(self, world):
self.world = world
self.size = (len(world), len(world[0]))
# self.open = SortedList()
self.open = PriorityQueue()
self.openValue = 1
self.closedValue = 2
#----------------------------------------------------------------------
def initSearch(self, start, goal, obstacles):
''' first, check we can achieve the goal'''
if goal.type in obstacles:
return False
''' clear open list and setup new open/close value state to avoid the clearing of a closed list'''
self.open.clear()
self.openValue += 2
self.closedValue += 2
''' then init search variables'''
self.start = start
self.goal = goal
self.obstacles = obstacles
self.start.cost = 0
self.addToOpen(self.start)
self.goal.parent = None
return True
#----------------------------------------------------------------------
def search(self):
while not self.openIsEmpty():
current = self.popFromOpen()
if current == self.goal:
break
self.removeFromOpen(current)
self.addToClosed(current)
''' generator passes : look at the 8 neighbours around the current node from open'''
for (di, dj) in [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1),
(1, -1), (1, 0), (1, 1)]:
neighbour = self.getNode(current.i + di, current.j + dj)
if (not neighbour) or (neighbour.type in self.obstacles):
continue
'''the cost to get to this node is the current cost plus the movement
cost to reach this node. Note that the heuristic value is only used
in the open list'''
nextStepCost = current.cost + self.getNeighbourCost(
current, neighbour)
'''if the new cost we've determined for this node is lower than
it has been previously makes sure the node has not been
determined that there might have been a better path to get to
this node, so it needs to be re-evaluated'''
if nextStepCost < neighbour.cost and (
self.inOpenList(neighbour)
or self.inClosedList(neighbour)):
self.invalidateState(neighbour)
'''if the node hasn't already been processed and discarded then
step (i.e. to the open list)'''
if (not self.inOpenList(neighbour)) and (
not self.inClosedList(neighbour)):
neighbour.cost = nextStepCost
neighbour.heuristic = self.getHeuristicCost(
neighbour, self.goal)
neighbour.parent = current
self.addToOpen(neighbour)
''' exit with None = path not yet found'''
yield None
'''since we've run out of search
there was no path. Just return'''
if self.goal.parent is None:
return
'''At this point we've definitely found a path so we can uses the parent
references of the nodes to find out way from the target location back
to the start recording the nodes on the way.'''
path = []
goal = self.goal
while goal is not self.start:
path.insert(0, (goal.i, goal.j))
goal = goal.parent
''' done, exit with path'''
yield path
#-----------------------------------------------------------------------------
def getNode(self, i, j):
if i >= 0 and i < self.size[0] and j >= 0 and j < self.size[1]:
return self.world[i][j]
else:
return None
#----------------------------------------------------------------------
def getNeighbourCost(self, n1, n2):
return (abs(n2.i - n1.i) + abs(n2.j - n1.j))
#----------------------------------------------------------------------
def getHeuristicCost(self, n1, n2):
return (abs(n2.i - n1.i) + abs(n2.j - n1.j))
#----------------------------------------------------------------------
def invalidateState(self, node):
node.state = 0
#----------------------------------------------------------------------
def popFromOpen(self):
# return self.open.first()
return self.open.pop()
#----------------------------------------------------------------------
def addToOpen(self, node):
# self.open.add(node)
self.open.insert(node)
node.state = self.openValue
#----------------------------------------------------------------------
def inOpenList(self, node):
return node.state is self.openValue
#----------------------------------------------------------------------
def removeFromOpen(self, node):
# self.open.remove(node)
self.open.remove(node)
node.state = 0
#----------------------------------------------------------------------
def openIsEmpty(self):
# return not self.open.size()
return self.open.isEmpty()
#----------------------------------------------------------------------
def addToClosed(self, node):
node.state = self.closedValue
#----------------------------------------------------------------------
def inClosedList(self, node):
return node.state is self.closedValue