def schedule(tasks, num_processors): _cpy_list = [t for t in tasks] total_slice = 0 for i in tasks: total_slice += i _cpy = total_slice pq = PriorityQueue() temp_out_list = [0]*len(tasks) for i in range(len(tasks)): pq.put((1.0 / tasks[i], i)) iterations = 0 misses = 0 while pq.empty() == False: temp_out_c = 0 for i in range(num_processors): if pq.empty() == True: break else: temp_out_list[temp_out_c] = pq.get()[1] temp_out_c += 1 for i in range(temp_out_c): tasks[temp_out_list[i]] -= 1 if tasks[temp_out_list[i]] != 0: pq.put(((_cpy_list[temp_out_list[i]] - tasks[temp_out_list[i]]) * _cpy / _cpy_list[temp_out_list[i]], temp_out_list[i])) iterations += 1 #print iterations for i in range(len(tasks)): lag = 1.0 * _cpy_list[i] * iterations / _cpy - (_cpy_list[i] - tasks[i]) if lag > 0: misses += math.floor(lag) return misses
def get_matches(vector, num_matches):
best_matches = PriorityQueue(num_matches)
good_matches = PriorityQueue(num_matches)
ok_matches = PriorityQueue(num_matches)
matches_found = 0
vectors = get_vectors()
for v in vectors:
r = sim_coeff(vector, v)
print(r)
if r<.5:
print("not a match")
continue
if r<.7:
print("Ok Match Found")
ok_matches.put((r, v))
matches_found += 1
print(v)
if matches_found == num_matches:
break
else:
continue
if r<.9:
print("Good Match Found")
good_matches.put((r, v))
matches_found += 1
print(v)
if matches_found == num_matches:
break
else:
continue
if r<=1:
print("Great Match Found")
best_matches.put((r, v))
if not ok_matches.empty():
ok_matches.get()
else:
matches_found += 1
print(v)
if matches_found == num_matches:
break
else:
continue
if r>1:
print("Esthena is bad at math.")
pq = PriorityQueue()
while not best_matches.empty():
pq.put(best_matches.get())
while not good_matches.empty():
pq.put(good_matches.get())
while not (ok_matches.empty()):
pq.put(ok_matches.get())
print("Results: ")
matches = []
while not pq.empty():
matches.append(pq.get()[1])
return matches
class WorkersList(object):
"""docstring for WorkersList"""
def __init__(self):
super(WorkersList, self).__init__()
self.queue = PriorityQueue()
self.workers = []
def empty_queue(self):
while not self.queue.empty():
next_level = self.queue.get()
print 'Processing level:', next_level.schedule
def get_from_queue(self):
return self.queue.get()
def queue_size(self):
return self.queue.size()
def _collect_workers(self):
while not self.queue.empty():
next_job = self.queue.get()
worker = Worker(next_job, next_job.description)
self.workers.append(worker)
worker.start()
def start(self):
self._collect_workers()
for worker in self.workers:
worker.join()
def put_in_queue(self, job):
self.queue.put(job)
def stop_single_worker(self, value):
for worker in self.workers:
if worker.name == value:
worker.triggerStop = False
break
else:
raise WorkerException("Worker with name/id {}".format(value))
def stop_all_workers(self):
for worker in self.workers:
worker.triggerStop = False
def terminate_all_workers(self):
for worker in workers:
worker.terminate()
def terminate_single_worker(self, value):
for worker in self.workers:
if worker.name == value:
worker.terminate()
break
else:
raise WorkerException("Worker with name/id {}".format(value))
def search_schedule(plant):
# todo AI scheduling alg can base on this: heat priorities in q, caster priorities
# todo put index to the heats, delete heat+1, group+1
# todo AI: find a fastest schedule, then cal the adjustment room in time, then shift the EAF tasks
# todo AI: exchange the groups orders, see what happens with the final ECost
# todo aim for a ChemE journal paper
# currently, heat/group starting at 1, time starting at 0
"""Find a feasible schedule by matching heats and equipment units.
Main Idea:
Match available heats (intermediate products) to available equipment units.
Maintain availability by priority queues (priority index: available time).
"""
schedule = []
q_t_h = PriorityQueue() # ready time (priority), heat
for heat in range(1, plant.num_heats+1):
q_t_h.put((0, heat))
for stage in [1, 2, 3]:
q_t_u = PriorityQueue() # ready time (priority), unit
for unit, num in plant.stage2units[str(stage)].items():
for u_id in range(num):
q_t_u.put((0, unit, u_id))
q_t_h_next = PriorityQueue() # ready time of heat for next stage
while not q_t_h.empty():
t_heat, heat = q_t_h.get()
t_unit, unit, u_id = q_t_u.get()
t_start = max(t_heat, t_unit)
task = plant.tasks[unit][heat-1]
t_end_process = t_start + plant.task_duration[task]
q_t_u.put((t_end_process, unit, u_id))
# todo, RTN2
transport = plant.tasks['TR_S%d' % stage][heat-1]
t_end_trans = t_end_process + plant.task_duration[transport]
q_t_h_next.put((t_end_trans, heat))
schedule.append((stage, heat, unit, u_id, task, t_start, t_end_process, t_end_process))
schedule.append((stage, heat, 'TR_S%d' % stage, 0, transport, t_end_process, t_end_trans, t_end_trans))
q_t_h = q_t_h_next
heat2time = dict()
while not q_t_h.empty():
t, heat = q_t_h.get()
heat2time[heat] = t
q_t_caster = PriorityQueue() # ready time (priority), unit
for unit, num in plant.stage2units['4'].items():
for u_id in range(num):
q_t_caster.put((0, unit, u_id))
for group in range(1, plant.num_groups+1):
t_caster, caster, caster_id = q_t_caster.get()
t_group = max([heat2time[heat] - plant.heat_consume_time_in_group[caster][heat]
for heat in plant.group2heats[str(group)]])
t_start = max(t_group, t_caster)
task = plant.tasks[caster][group-1]
t_end_process = t_start + plant.task_duration[task]
t_cleanup = t_start + plant.task_cleanup_duration[task]
schedule.append((4, group, caster, caster_id, task, t_start, t_end_process, t_cleanup))
q_t_caster.put((t_cleanup, caster, caster_id))
return schedule
def get_matches(vector, num_matches):
best_matches = PriorityQueue(num_matches)
good_matches = PriorityQueue(num_matches)
ok_matches = PriorityQueue(num_matches)
matches_found = 0
vectors = get_vectors()
for v in vectors:
r = sim_coeff(vector, v)
print(r)
if r<.5:
print("not a match")
continue
if r<.7:
print("Ok Match Found")
ok_matches.put(v, r)
matches_found += 1
print(v)
if matches_found == num_matches:
break
else:
continue
if r<.9:
print("Good Match Found")
good_matches.put(v, r)
matches_found += 1
print(v)
if matches_found == num_matches:
break
else:
continue
if r<=1:
print("Great Match Found")
best_matches.put(v, r)
matches_found += 1
print(v)
if matches_found == num_matches:
break
else:
continue
if r>1:
print("Esthena is bad at math.")
print("Ok: ")
while not ok_matches.empty():
print(ok_matches.get())
print("Good: ")
while not good_matches.empty():
print(good_matches.get())
print("Best: ")
while not best_matches.empty():
print(best_matches.get())
def solve(N, L, start, goal):
"""search for minimum bit switches to make start == goal
NB: treat goal and start as sets of currents -- that is, order doesn't
matter in comparing start to goal and all currents in start/goal
are mutually unique"""
# BFS w/ priority queue; queue orders by min bits flipped;
# we don't use an 'explored' set because we may need to revisit the same
# configuration of currents several times as index i increments;
# instead, to constrain the search space, we use the is_consistent filter
# before placing a state in the queue in order to check that the
# configuration of currents in that state is consistent with goal at least
# up to bit i-1
start_state = (0, start, 0) # ( n_bits_flipped, state_of_currents, index )
queue = PriorityQueue()
queue.put( start_state )
while not queue.empty():
nchanges, state, index = queue.get()
if is_goal(state, goal):
return nchanges
for nch,s,i in successors(nchanges, state, index):
if is_consistent(s, goal, i):
# when i = len(goal)+1, s will be added to queue
# only if s == goal
queue.put([nch, s, i])
return 'NOT POSSIBLE'
def req_proxy(self, url):
from urlparse import urlparse
netloc = urlparse(url).netloc
busy_queue = PriorityQueue()
lazy_queue = PriorityQueue()
index = 0
now = datetime.utcnow()
while index < self.proxy_in_queue_count():
index += 1
proxy_url = self.get_proxy_from_queue()
if not proxy_url:
break
if proxy_url in self.proxy_meta_map:
proxy_meta = self.proxy_meta_map[proxy_url]
if proxy_meta.last_used_time and (now - proxy_meta.last_used_time).total_seconds() < self.settings["interval_second"]:
busy_queue.put_nowait((proxy_meta.last_used_time, proxy_url))
continue
if netloc in proxy_meta.latency and proxy_meta.latency[netloc][0] >= self.settings["max_unavailable_count"]:
import random
if random.randint(1, 10) > 1:
lazy_queue.put_nowait((proxy_meta.latency[netloc], proxy_url))
continue
proxy_meta.last_used_time = now
proxy_meta.master = netloc
return proxy_meta.proxy
while not lazy_queue.empty():
_, proxy_url = lazy_queue.get_nowait()
if proxy_url in self.proxy_meta_map:
proxy_meta = self.proxy_meta_map[proxy_url]
proxy_meta.last_used_time = now
proxy_meta.master = netloc
return proxy_meta.proxy
return None
def a_star(self):
# like BFS, but puts coords with lowest heuristic (path length + manhattan dist to goal) up front
pq = PriorityQueue(maxsize=0)
pq.put_nowait((self.manhattan_distance(self.currPos, self.goalPos), (self.currPos, [])))
visited = set()
bestPath = None
bestHeur = None
numNodes = 0
while not pq.empty():
priority, curr = pq.get_nowait()
coord, path = curr
visited.add(coord)
if bestPath is not None and priority >= bestHeur:
pass
elif self.getChar(coord) == '%': # wall
pass
else: # recursive case
if self.getChar(coord) == '.': # goal
print "Found a path:", path
if bestPath is None or len(path) < len(bestPath):
print "Is best path"
bestPath = path[:]
bestHeur = priority
for adj, direction in self.adjacent(coord):
if adj not in visited and self.getChar(adj) != '%':
numNodes += 1
heur = len(path + direction) + self.manhattan_distance(adj, self.goalPos)
if bestPath is None or heur < bestHeur: # preselect based on heuristic
pq.put_nowait((heur, (adj, path + direction)))
print "Num Nodes:", numNodes
print self.debug(bestPath) # debug
return bestPath
def dijkstra(self, adjacencies, start_point, end_point):
seen_so_far = defaultdict(float)
for k in adjacencies:
seen_so_far[k] = float('inf')
q = PriorityQueue()
start = Vertex(start_point, [], 0.0)
q.put(start)
seen_so_far[start_point] = 0
while not q.empty():
v = q.get()
if v.coords == end_point:
new_path = v.path
new_path.append(end_point)
# write to file
with open("output", "w") as out_file:
for point in new_path:
out_file.write('{} {}\n'.format(point[0], point[1]))
self.best_path = [(new_path[i], new_path[i+1]) for i in range(len(new_path)-1)]
return
for point in adjacencies[v.coords]:
# import pdb; pdb.set_trace()
new_cost = v.cost+distance(v.coords, point)
if seen_so_far[point]<new_cost:
continue
seen_so_far[point]=new_cost
new_path = deepcopy(v.path)
new_path.append(v.coords)
q.put(Vertex(point, new_path, new_cost))
def ucs(source, target, graph):
""" Uniform-cost graph search """
queue = PriorityQueue() # fringe
queue.put((0, source))
parent = {source:None}
visited = {}
while not queue.empty():
(d, v_in) = queue.get()
if v_in not in visited or d < visited[v_in]:
if v_in == target:
return (d, build_path(parent, target))
for v_out in graph.adj(v_in):
cost = graph.distance(v_in, v_out) + d
if v_out not in visited:
queue.put((cost, v_out))
parent[v_out] = v_in
visited[v_in] = cost
return None
class LazyQueue:
def __init__(self):
self._queue = PriorityQueue()
def enq(self, item):
self._queue.put(item)
def deq(self):
if self._queue.empty():
return None
else:
return self._queue.get()
def __len__(self):
return self._queue.qsize()
def mget(self, n):
if n > self._queue.qsize():
n = self._queue.qsize()
lst = []
for _ in xrange(n):
lst.append(self._queue.get())
return lst
def extend(self, iterable):
for ele in iterable:
self.enq(ele)
def append(self, item):
self.enq(item)
def segment(img) :
g = numpy.array(img.getdata())
g = g.reshape(img.size)
gx = g[:-1,1:] - g[:-1,:-1]
gy = g[1:,:-1] - g[:-1,:-1]
gms = gx * gx + gy * gy
seg = Image.new('L', gms.shape)
taken = numpy.zeros(seg.size)
#print 'original local mininum'
for i in range(seg.size[0]) :
for j in range(seg.size[1]) :
if localmin(gms, i, j) :
taken[i][j] = 1
seg.putpixel((j, i), img.getpixel((j, i)))
#print (j, i), img.getpixel((j, i))
q = PriorityQueue(-1)
#print 'expand'
for i in range(seg.size[0]) :
for j in range(seg.size[1]) :
if taken[i][j] > 0 :
addNeighbor(seg, gms, taken, i, j, q)
#print q.qsize()
while not q.empty() :
(i, j) = q.get()[1]
addNeighbor(seg, gms, taken, i, j, q)
#print q.qsize()
return seg
def main():
global CREATURE_COUNT
if process_command_line():
c0 = []
c1 = PriorityQueue()
print "[+]\tLoading DB..."
# load in creatures from DB
lc = load_DB()
# f = open('database', 'a')
# f.close()
# f = open('database')
# lc = f.read()
# f.close()
loaded_creatures = lc.split("\n")
# finish loading
print "[+]\tSuccess"
print "[+]\tCreating initial batch of creatures..."
cl = create_creatures(CREATURE_COUNT, GENOME_LENGTH, tools)
generation = 0
for i in cl:
c1.put((100, i))
for i in loaded_creatures:
c1.put((50, Creature(0, i)))
for ii in range(0, GENE_POOL_INFLUENCE - 1):
c1.put((50, Creature(0, mutate(i))))
print "[+]\tSuccess"
# print '[+]\tPre-breeeding in loaded creatures with the population' +\
# ' for great success'
# while not c1.empty():
# c = c1.get()[1]
# c0.append(c)
# c1 = breed_it(c0)
# c1 = c0
print "[+]\tSuccess"
exploit_found = 0
while exploit_found == 0:
generation += 1
CREATURE_COUNT = c1.qsize()
print "[>]\tRunning with creature_count %d,\tgeneration %d" % (CREATURE_COUNT, generation)
c2 = PriorityQueue(0)
cached_c = 0
total_c = 0
while not c1.empty():
c = c1.get()[1]
total_c += 1
if c.modified == 0:
cached_c += 1
if fitnessfunction(c) == 1:
exploit_found = 1
break
c2.put((c.score, c))
# print '[i]\tEfficiency %s, cached[%d], total[%d]' %\
# (str((total_c-cached_c) * 1.0 / total_c),cached_c,total_c)
c3 = cull_it(c2)
c4 = []
while not c3.empty():
c = c3.get()[1]
c4.append(c)
c1 = breed_it(c4)
print "[i]\tExploit found in %d seconds with %d requests" % (abs(int(start_time - time.time())), REQ_TOTAL)
def a_star_penalize(self, forwardPenalty, turnPenalty):
# part 1.2
# using euclidean heuristic (not manhattan)
pq = PriorityQueue(maxsize=0)
pq.put_nowait((self.manhattan_distance(self.currPos, self.goalPos), (self.currPos, [])))
visited = set()
bestPath = None
bestHeur = None
numNodes = 0
while not pq.empty():
priority, curr = pq.get_nowait()
coord, path = curr
visited.add(coord)
if bestPath is not None and priority >= bestHeur:
pass
elif self.getChar(coord) == '%': # wall
pass
else: # recursive case
if self.getChar(coord) == '.': # goal
print "Found a path:", path
if bestPath is None or len(path) < len(bestPath):
bestPath = path[:]
for adj, direction in self.adjacent(coord):
if adj not in visited and self.getChar(adj) != '%':
numNodes += 1
heur = self.calculate_penalty(path + direction, forwardPenalty, turnPenalty) + self.manhattan_distance(adj, self.goalPos) * forwardPenalty
if bestPath is None or heur < bestHeur: # preselect based on heuristic
pq.put_nowait((heur, (adj, path + direction)))
print "Num Nodes:", numNodes
print self.debug(bestPath) # debug
return bestPath
def Astar(start, goal, cost_matrix, strategy='minimize'):
mindist = min([min(dists.values()) for c, dists in cost_matrix.items()])
maxdist = max([max(dists.values()) for c, dists in cost_matrix.items()])
def cost_fn(path):
if strategy == 'minimize':
return path_cost(path, cost_matrix) + (len(cost_matrix.keys()) - len(path)) * 2 * mindist
else:
return -(path_cost(path, cost_matrix) + (len(cost_matrix.keys()) - len(path)) * 2 * maxdist)
abort_cost = 1 << 32
q = PriorityQueue()
q.put((cost_fn([start]), [start]))
best_path = []
while not q.empty():
total_cost, partial_path = q.get()
if total_cost > abort_cost:
break
if partial_path[-1] == goal and len(partial_path) == len(cost_matrix.keys()):
if total_cost < abort_cost:
abort_cost = total_cost
best_path = partial_path
else:
for c in cost_matrix[partial_path[-1]]:
if c not in partial_path:
new_path = partial_path + [c]
total_cost = cost_fn(new_path)
if total_cost < abort_cost:
q.put((total_cost, new_path))
return best_path
def calculatePathToPoint(self, start, goal):
# http://en.wikipedia.org/wiki/A*_search_algorithm#Pseudocode
closedSet = set()
queue = PriorityQueue(0)
queue.put((self._heuristic(start, goal), start))
cameFrom = {}
score = {}
score[start] = 0
while not queue.empty():
_, current = queue.get()
if current.x == goal.x and current.y == goal.y:
return self._reconstructPath(cameFrom, goal)
if current.flatten(800) in closedSet:
continue
closedSet.add(current.flatten(800))
for neighbor in self._getNeighborNodes(current):
if neighbor.flatten(800) in closedSet:
continue
turnPenalty = 0.5
if current.flatten(800) in cameFrom and \
current.x - neighbor.x == cameFrom[current.flatten(800)].x - current.x and \
current.y - neighbor.y == cameFrom[current.flatten(800)].y - current.y:
turnPenalty = 0
neighborScore = score[current] + 1 + turnPenalty
cameFrom[neighbor.flatten(800)] = current
score[neighbor] = neighborScore
queue.put((neighborScore + self._heuristic(neighbor, goal), neighbor))
return []
def trapRainWater(self, board):
"""
:type board: List[List[int]]
:rtype: int
"""
m = len(board)
if m == 0: return 0
n = len(board[0])
queue = PriorityQueue()
visited = [[False] * n for _ in range(m)]
for i in range(m):
visited[i][0] = True
queue.put((board[i][0], i, 0))
visited[i][n-1] = True
queue.put((board[i][n-1], i, n-1))
for j in range(n):
visited[0][j] = True
queue.put((board[0][j], 0, j))
visited[m-1][j] = True
queue.put((board[m-1][j], m-1, j))
dirs = [(-1,0),(1,0),(0,-1),(0,1)]
res = 0
while not queue.empty():
h, i, j = queue.get()
for di, dj in dirs:
ni, nj = i+di, j+dj
if ni >= 0 and ni < m and nj >= 0 and nj < n and not visited[ni][nj]:
visited[ni][nj] = True
res += max(0, h - board[ni][nj])
queue.put((max(h, board[ni][nj]), ni, nj))
return res
def astar(source, target, graph, heuristic=null_heuristic):
""" A* algorithm """
queue = PriorityQueue()
queue.put((0, source))
parent = {source:None}
visited = {}
while not queue.empty():
(d, v_in) = queue.get()
if v_in not in visited or d < visited[v_in]:
if v_in == target:
return (d, build_path(parent, target))
for v_out in graph.adj(v_in):
cost = graph.distance(v_in, v_out) + d
fn = cost + heuristic(v_out, graph) # only diference in retion to UCS
if v_out not in visited:
queue.put((fn, v_out))
parent[v_out] = v_in
visited[v_in] = cost
return None
def run(graph,start,goal):
frontier = PriorityQueue()
frontier.put((0,start))
came_from = {}
came_from[start] = None
cost_so_far = {}
cost_so_far[start] = 0
while not frontier.empty():
current = frontier.get()[1]
if current == goal:
break
for nextNode in graph.neighbors(current):
new_cost = cost_so_far[current] + graph.cost(current,nextNode)
if nextNode not in cost_so_far or new_cost < cost_so_far[nextNode]:
frontier.put((new_cost,nextNode))
cost_so_far[nextNode] = new_cost;
came_from[nextNode] = current
#for x in came_from:
# print (x,came_from[x],),
print len(came_from)
current = goal
path = [current]
while current != start:
current = came_from[current]
path.append(current)
print path
def a_star(self, start, goal):
""""""
# Init priority queue.
q = PriorityQueue()
q.put(start, 0)
came_from = {start: None}
# The known cost needed to travel from start to a point.
visited = {start: 0}
while not q.empty():
# pop the point with lowest priority(cost).
# The idea is always that points tends to be closer to goal first.
p = q.get()
if p == goal:
# Reach the goal.
self.reconstruct_path(came_from, goal)
return
for neighbor in self.neighbors(p):
tentative = visited[p] + self.heuristic(p, neighbor)
if neighbor not in visited or tentative < visited[neighbor]:
came_from[neighbor] = p
visited[neighbor] = tentative
# Priority is the estimated cost that it took to travel from
# neighbor to goal. In this case, it's just the Manhattan
# distance assuming that there are no obstacles between them.
priority = tentative + self.heuristic(neighbor, goal)
q.put(neighbor, priority)
def aStar(initialState, heuristic):
#stores a list of visited coords, the direction and whether the tile was affected by demolish
visited = []
fringe = PriorityQueue()
#add the start node to the fringe
fringe.put((-(initialState.score - heuristic(initialState)), initialState))
while True:
if fringe.empty():
print "No Solution"
return None
nextState = fringe.get()[1]
#check if the tile has been affected by demolish
if len(nextState.actionList) > 0 and nextState.actionList[-1] is nextState.act_demolish:
if (nextState.posX, nextState.posY, "s") in nextState.demolishedTiles:
tileDemolished = "S"
else:
if (nextState.posX, nextState.posY) in nextState.demolishedTiles:
tileDemolished = "D"
else:
tileDemolished = "N"
if nextState.isGoalState():
#we have reached the goal. Return the relevant stats
return (nextState.actionList, nextState.score, len(visited))
elif (nextState.posX, nextState.posY, nextState.direction, tileDemolished) not in visited:
visited.append((nextState.posX, nextState.posY, nextState.direction, tileDemolished))
for successorState in nextState.getSuccessors():
fringe.put((-(successorState.score - heuristic(successorState)), successorState))
class MinimumWeightMatchingWithEdges:
"""Find a minimum weight matching using a greedy method.
Attributes
----------
graph : input undirected graph
mate : dict with nodes (values are edges or None)
cardinality : number
"""
# Bedzie potrzebne do problemu chinskiego listonosza.
def __init__(self, graph):
"""The algorithm initialization."""
if graph.is_directed():
raise ValueError("the graph is directed")
self.graph = graph
self.mate = dict((node, None) for node in self.graph.iternodes())
self.cardinality = 0
self._pq = PriorityQueue()
def run(self):
"""Executable pseudocode."""
for edge in self.graph.iteredges():
self._pq.put((edge.weight, edge))
while not self._pq.empty():
_, edge = self._pq.get()
if (self.mate[edge.source] is None and
self.mate[edge.target] is None):
self.mate[edge.source] = edge
self.mate[edge.target] = ~edge
self.cardinality += 1
def find_path(startkey, goalkey, world):
frontier = PriorityQueue()
frontier.put({"key": startkey, "cost": 0}, 0)
came_from = {startkey: None}
cost_so_far = {startkey: 0}
step = 0
while not frontier.empty() and step < 100000:
step += 1 # just safety
current = frontier.get()
if current["key"] == goalkey:
break
for neighbor in get_neighbors(current["key"], world):
new_cost = cost_so_far[current["key"]] + neighbor["cost"]
if new_cost < cost_so_far.get(neighbor["key"], 10000000):
cost_so_far[neighbor["key"]] = new_cost
neighbor["cost"] += heuristic(neighbor["key"], goalkey)
frontier.put(neighbor)
came_from[neighbor["key"]] = current["key"]
if current["key"] != goalkey:
return None
key = goalkey
path = []
step = 0
while key and step < 1000:
key = came_from[key]
path.insert(0, key)
return path
def run(graph,start,goal):
frontier = PriorityQueue()
frontier.put((0,start))
came_from = {}
came_from[start] = None
while not frontier.empty():
current = frontier.get()[1]
if current == goal:
break
for next in graph.neighbors(current):
new_cost = graph.gcost(next,goal)
if next not in came_from:
frontier.put((new_cost,next))
came_from[next] = current
#for x in came_from:
# print x,came_from[x]
print len(came_from)
current = goal
path = [current]
while current != start:
current = came_from[current]
path.append(current)
print path
class EventManager(object):
""" Keeps track of and triggers events in our network simulation
Instance Properties:
Q - A priority queue of events ordered by time
"""
def __init__(self):
self._Q = PriorityQueue()
self.t = 0
def add_event(self, t, event, args):
""" Add an event onto the event queue
:param t: The time of the event
:param event: The event to add
:param args: A list of args for the event
"""
self._Q.put((t, event, args))
def pop_event(self):
""" Getting the first event off of the event queue.
:return: Returns a tuple consisting of the time, event, and the args
for the event
"""
return self._Q.get()
def has_events(self):
""" Whether or not the manager has any more events on its queue
:return: True or False based on whether its empty
"""
return not self._Q.empty()
def greedy(self):
# like DFS, but puts coords closest to goal up front
pq = PriorityQueue(maxsize=0)
pq.put_nowait((self.manhattan_distance(self.currPos, self.goalPos), (self.currPos, [])))
visited = set()
bestPath = None
numNodes = 0
while not pq.empty():
priority, curr = pq.get_nowait()
coord, path = curr
visited.add(coord)
if bestPath is not None and len(path) >= len(bestPath):
pass
elif self.getChar(coord) == '%': # wall
pass
else: # recursive case
if self.getChar(coord) == '.': # goal
print "Num Nodes:", numNodes
print self.debug(path)
return path # return on first path found
for adj, direction in self.adjacent(coord):
if adj not in visited and self.getChar(adj) != '%':
numNodes += 1
heur = self.manhattan_distance(adj, self.goalPos)
if bestPath is None: # preselect based on heuristic
pq.put_nowait((heur, (adj, path + direction)))
return [] # impossible
def travel(self):
visitedNodes = 0
optimalPath = []
priorityQueue = PriorityQueue()
optimalPathLen = -1
priorityQueue.put(self.addNode(0, []))
startTime = time.time()
while not priorityQueue.empty():
currentPath = priorityQueue.get()[1]
if optimalPath:
pathLen = self.graph.getPathLength(optimalPath)
upperBound = self.graph.upperBound(self.graph.getPathLength(currentPath), currentPath)
optimalPathLen = self.graph.getPathLength(optimalPath)
# Jesli nie mamy sciezki lub jest ona gorsza od UB
if not optimalPath or upperBound < pathLen:
visitedNodes += 1
# Jesli odwiedzono wszystkie miasta
if len(currentPath) >= self.graph.size:
# Jesli mozna wrocic do miasta poczatkowego z ostatniego punktu sciezki
if self.graph.distance(currentPath[len(currentPath) - 1], 0) > 0:
currentPath = self.addNode(0, currentPath)[1]
# Jesli nie mamy sciezki lub zmodyfikowan jest lepsza niz dotychczasowe
if not optimalPath or optimalPathLen > self.graph.getPathLength(currentPath):
optimalPath = currentPath
else:
for i in range(self.graph.size):
# Jesli rozwazane miasto nie bylo odwiedzone i istnieje prowadzaca do niego droga
if i not in currentPath and self.graph.distance(currentPath[len(currentPath) - 1], i) > 0:
newPath = copy.deepcopy(currentPath)
bound, newPath = self.addNode(i, newPath)
if not optimalPath or bound < optimalPathLen:
priorityQueue.put((bound, newPath))
return [optimalPath, self.graph.getPathLength(optimalPath), visitedNodes, time.time() - startTime]
def getMRV(self):
q = PriorityQueue()
for blank in self.blanks:
possible = self.getPossibleValues(blank, True)
q.put((len(possible), blank))
blanks = []
blanks.append(q.get())
minVal = blanks[0][0]
while not q.empty(): #Get all equally-prioritized blanks
next = q.get()
if next[0] == minVal:
blanks.append(next)
else:
break
maxDeg = len(self.getNeighborBlanks(blanks[0][1]))
maxDegBlank = blanks[0]
for blank in blanks:
degree = len(self.getNeighborBlanks(blank[1]))
if degree > maxDeg:
maxDegBlank = blank
maxDeg = degree
return maxDegBlank[1]
def main():
seed = (0, str(467959))
unique = set()
unique.add(seed)
nextOnes = PriorityQueue()
nextOnes.put(seed)
_prev = list(open('./visited.txt', 'r').read())
for item in _prev:
unique.add(item)
global beforeNext
while (not nextOnes.empty()):
summID = nextOnes.get()
summID = summID[1]
url = makeURL(summID)
json = performReq(url)
matches = getMatches(json)
matches.sort(reverse=True)
if matches == list():
nextOnes.put(summID)
continue
printMatches(matches)
scrapeSummIDs = getSummonerIds(matches)
for summId in scrapeSummIDs:
if summId[1] not in unique:
nextOnes.put(summId)
unique.add(summId[1])
beforeNext = 0
def IDAsearch(state, goalBoard, N): Q = PriorityQueue() initial_cost = Heuristic(state[1], N) Q.put((state, initial_cost), initial_cost) current_threshold = initial_cost next_threshold = initial_cost visited = [] while(Q.empty() != True): current_threshold = next_threshold next_threshold = maxint visit = Q.get() print visit con = 0 if(check(visit[0][1], goalBoard, N) == 0): #print "f**k yeah, found it" return visit[0][2] elif(visit[1] <= current_threshold): if visit[0][1] not in visited: con = 0 else: con = 1 if(con == 0): children = getNextState(visit[0], N) CQ = PriorityQueue() for child in children: b = Heuristic(child[1], N) h = b + 1 if( h < next_threshold): next_threshold = h if child[1] not in visited: Q.put((child, h), h) visited.append(visit[0][1])
def mergeKLists(self, lists):
"""
:type lists: List[ListNode]
:rtype: ListNode
"""
if not lists:
return None
dummy = res = ListNode(0)
tmp = PriorityQueue()
for l in lists:
if l:
tmp.put((l.val,l))
while not tmp.empty():
val, node = tmp.get()
res.next = ListNode(val)
res = res.next
node = node.next
if node:
tmp.put((node.val, node))
return dummy.next
def getLeastNumbers(self, arr, k):
"""
:type arr: List[int]
:type k: int
:rtype: List[int]
"""
if k <= 0 or (not arr) or len(arr) == 0:
return []
Q = PriorityQueue()
for x in arr:
Q.put(-x)
while Q.qsize() > k:
Q.get()
ans = []
while not Q.empty():
ans.append(-Q.get())
return ans
def minMeetingRooms(self, intervals):
"""
:type intervals: List[Interval]
:rtype: int
"""
from Queue import PriorityQueue
# O(nlgn)
Q = PriorityQueue()
intervals.sort(key=lambda itm: itm.start)
for i, interval in enumerate(intervals):
if Q.empty():
Q.put(interval.end)
else:
head = Q.get()
if head <= interval.start:
Q.put(interval.end)
else:
Q.put(interval.end)
Q.put(head)
return Q.qsize()
def mergeKLists(self, lists):
"""
:type lists: List[ListNode]
:rtype: ListNode
"""
head = current = ListNode(0)
q = PriorityQueue()
for l in lists:
if l:
q.put((l.val, l))
while not q.empty():
val, node = q.get()
current.next = ListNode(val)
current = current.next
node = node.next
if node:
q.put((node.val, node))
return head.next
def mergeKLists(self, lists):
"""
:type lists: List[ListNode]
:rtype: ListNode
"""
m_queue = PriorityQueue()
for node in lists:
if node:
m_queue.put((node.val, node))
tail = ListNode(0)
head = tail
while not m_queue.empty():
now_val, now_node = m_queue.get()
p = ListNode(now_val)
tail.next = p
tail = p
now_node = now_node.next
if (now_node):
m_queue.put((now_node.val, now_node))
return head.next
def aStar(state):
frontier = PriorityQueue()
for move in state.getTransitions():
frontier.put((0, move))
while not frontier.empty():
dist, curr = tuple(frontier.get())
position = curr[0]
targets = list(curr[1])
if len(targets) == 0:
break
state.move(position, targets)
cost = len(state.currentPath)
state.costs[position][tuple(sorted(targets))] = cost
for neighbor, targets in state.getTransitions():
if len(targets) == 0:
return
if state.costs[neighbor][tuple(sorted(targets))] > cost + 1:
state.costs[neighbor][tuple(sorted(targets))] = cost + 1
priority = cost + heuristic(position, targets)
frontier.put((priority, (neighbor, targets)))
def mergeKLists(self, lists):
"""
:type lists: List[ListNode]
:rtype: ListNode
"""
sentinel = current = ListNode(0)
queue = PriorityQueue()
for node in lists:
if node:
queue.put((node.val, node))
while not queue.empty():
_, node = queue.get()
current.next = node
current = node
if node.next:
queue.put((node.next.val, node.next))
return sentinel.next
def branch_and_bound(graph, start, goal):
if start == goal:
return list(start)
paths = PriorityQueue()
for node in graph.get_connected_nodes(start):
paths.put_nowait((path_length(graph, [start, node]), [start, node]))
while not paths.empty():
path = paths.get_nowait()
if path[1][-1] == goal:
return path[1]
else:
cnodes = graph.get_connected_nodes(path[1][-1])
for node in cnodes:
if path[1].count(node) == 0:
epath = list(path[1])
epath.append(node)
paths.put_nowait((path_length(graph, epath), epath))
return []
def mergeKLists(self, lists):
"""
Two solutions provided, one runs in O(N log k) where N is the total number of nodes and k is the number of lists, the best runtime is 180ms, this should be the fastest way of solving this problem, however, my second solution, which is putting everything into a list and then sort it, then create a ListNode for each value, this one should runs in O(N logN) time, but when I tried on LeetCode, the best runtime is 84ms which is much faster than the first solution, I am guessing it because I used Python list.sort() method which is already optimized to run in shorter time whenever possible.
"""
pqueue = PriorityQueue()
for l in lists:
if l:
pqueue.put((l.val, l))
head = ListNode(-1)
cur_node = head
while not pqueue.empty():
val, node = pqueue.get()
cur_node.next = node
cur_node = node
node = node.next
if node:
pqueue.put((node.val, node))
return head.next
"""
def solve(initial_board):
fringe = PriorityQueue()
closed = {}
priority_1 = calculate_heuristic_1(initial_board, 0)
fringe.put((priority_1, (initial_board, "")))
while not fringe.empty():
(priority, (state, route_so_far)) = fringe.get()
closed[tuple(state)] = priority
#closed.append((priority,(state)))
if is_goal(state):
return (route_so_far)
for (succ, move) in successors(state):
route = str(route_so_far + " " + move)
cost_so_far = len(route_so_far.split())
priority_s = calculate_heuristic_1(succ, cost_so_far)
if tuple(succ) not in closed:
fringe.put((priority_s, (succ, route)))
elif tuple(succ) in closed and closed[tuple(succ)] < closed[tuple(
state)]:
fringe.put((priority_s, (succ, route)))
def ans(self, matrix, k):
if len(matrix) == 0 or len(matrix[0]) == 0 or k <= 0:
return None
from Queue import PriorityQueue
pq = PriorityQueue()
for i in range(len(matrix)):
for j in range(len(matrix[0])):
pq.put((matrix[i][j], matrix[i][j]))
i = 0
res = None
while i < k:
if not pq.empty():
res = pq.get()[1]
i += 1
return res
class queue:
def __init__(self):
self.list_queue = PriorityQueue()
def push(self, node):
self.list_queue.put(node)
def distance(self, pt1, pt2):
return np.sqrt((pt2[0] - pt1[0]) * (pt2[0] - pt1[0]) +
(pt2[1] - pt1[1]) * (pt2[1] - pt1[1]))
def pop(self):
node = self.list_queue.get()
return node
def leng(self):
return self.list_queue.qsize()
def isfull(self):
return np.invert(self.list_queue.empty())
def get_inferred_norms(self, topNorms=1):
call(["java", self.javaAppClass])
pq = PriorityQueue()
normProbabilities = {}
with open(self.oniOutputFileName, 'r') as fOutOni:
for line in fOutOni:
parts = line.split(
) # norm as a string of action chars, and then probability
if len(parts) > 0:
norm = ('eventually', parts[0][0]) if len(
parts[0]) == 1 else (parts[0][0], 'next', parts[0][1])
pq.put((float(parts[1]), norm))
normProbabilities[norm] = float(parts[1])
with open(self.pniOutputFileName, 'r') as fOutPni:
for line in fOutPni:
parts = line.split(
) # norm as a string of action chars, and then probability
if len(parts) > 0:
norm = ('never', parts[0][0]) if len(
parts[0]) == 1 else (parts[0][0], 'not next',
parts[0][1])
pq.put((float(parts[1]), norm))
normProbabilities[norm] = float(parts[1])
sorted_norms = []
while not pq.empty():
sorted_norms += [pq.get()[1]]
norms = [x for x in reversed(sorted_norms)]
# Check that we select either the topNorms, or the first ones with the same odds
for (i, n) in enumerate(norms):
if normProbabilities[n] == 0:
topNorms = i
break
if (i + 1 == topNorms):
tied = len(norms) > i + 1 and (
normProbabilities[n] == normProbabilities[norms[i + 1]])
if (tied):
topNorms += 1
else:
break
#endfor
return norms[0:topNorms]
def astar_multi(env,
starts,
goals,
constraint_fn=lambda node, lastnode, t: True):
starts = tuple(starts)
goals = tuple(goals)
pq = PriorityQueue()
cost = 0.0
t = 0.0
heur = sum(
[env.estimate(start, goal, t) for start, goal in zip(starts, goals)])
costmap = {starts: cost}
prevmap = {starts: None}
pq.put((heur + cost, starts, t))
while not pq.empty():
totcost, curr, t = pq.get()
at_goal = all([node == goal for node, goal in zip(curr, goals)])
if at_goal:
return construct_path_multi(prevmap, curr)
transitions = [env.next(node, t) for node in curr]
children = [[t[0] for t in node] for node in transitions]
step_costs = [[t[1] for t in node] for node in transitions]
children_combined = combine_actions(children)
step_costs_combined = combine_actions(step_costs)
for child, step_cost in zip(children_combined, step_costs_combined):
if len(set(child)) < len(child): # skip if there is a collision
continue
child_t = t + 1
if not constraint_fn(child, curr, child_t):
continue
child_cost = costmap.get(curr, float('inf')) + sum(step_cost)
if child_cost < costmap.get(child, float('inf')):
prevmap[child] = curr
costmap[child] = child_cost
child_totcost = sum([
env.estimate(child_i, goal_i, child_t)
for child_i, goal_i in zip(child, goals)
]) + child_cost
pq.put((child_totcost, child, child_t))
return None
def find_path(start, goal):
global xx
im = Image.open("output.png") #Can be many different formats.
output = Image.new('RGBA', im.size)
pix = im.load()
dimensions = im.size #Get the width and hight of the image for iterating over
width = dimensions[0]
height = dimensions[1]
# initial parameters
frontier = PriorityQueue()
frontier.put(start, 0)
closed_set = []
costs_so_far = {}
came_from = {}
open_set = [start]
costs_so_far[start] = 0
came_from[start] = None
max_y = height
max_x = width
while not frontier.empty():
xx = xx + 1
current = frontier.get()
if current[0] == goal[0] and current[1] == goal[1]:
break
x = current[0]
y = current[1]
for i in range(-1, 2):
for j in range(-1, 2):
if x + i < max_x and y + j < max_y and x + i >= 0 and y + j >= 0 and x + i != x and y + j != y:
new_cost = costs_so_far[current] + pix[x + i, y + j][0]
if (x + i, y +
j) not in costs_so_far or new_cost < costs_so_far[
(x + i, y + j)]:
costs_so_far[(x + i, y + j)] = new_cost
priority = new_cost + heuristic(goal, (x + i, y + j))
frontier.put((x + i, y + j), priority)
came_from[(x + i, y + j)] = current
if xx > 1000:
break
#print current
return (came_from, costs_so_far)
class Dijkstra:
def __init__(self, graph):
self.graph = graph
# Shortest path tree as a dictionary.
self.parent = dict((node, None) for node in self.graph.iternodes())
self.distance = dict(
(node, float("inf")) for node in self.graph.iternodes())
self.source = None
self._in_queue = dict((node, True) for node in self.graph.iternodes())
self._pq = PriorityQueue()
def run(self, source):
self.source = source
self.distance[source] = 0
for node in self.graph.iternodes():
self._pq.put((self.distance[node], node))
while not self._pq.empty():
_, node = self._pq.get()
if self._in_queue[node]:
self._in_queue[node] = False
else:
continue
for edge in self.graph.iteroutedges(node):
if self._in_queue[edge.target] and self._relax(edge):
self._pq.put((self.distance[edge.target], edge.target))
def _relax(self, edge):
alt = self.distance[edge.source] + edge.cost
if self.distance[edge.target] > alt:
self.distance[edge.target] = alt
self.parent[edge.target] = edge.source
return True
return False
def path(self, target):
if self.source == target:
return [self.source]
elif self.parent[target] is None:
raise ValueError("no path to target")
else:
return self.path(self.parent[target]) + [target]
def search(targest_artist_profile, ogartist, connection):
underrated = PriorityQueue()
lokey = set()
fringe = q.Queue()
fringe.put(ogartist)
getRelatedArtists_bfs(ogartist, lokey, {ogartist[1]}, fringe, [])
quadrant_size = len(lokey) / 4
lokey_list = list(lokey)
processes = []
qu = mp.Queue()
for i in range(0, 4):
process = Process(
target=generateProfilePriority,
args=(lokey_list[0 + (quadrant_size * i):(quadrant_size * i) +
quadrant_size], targest_artist_profile, qu))
process.start()
processes += [process]
for process in processes:
process.join()
print '-------------results--------------'
while not qu.empty():
item = qu.get()
print item
underrated.put(item)
results = []
while not underrated.empty():
item = underrated.get()
results.append(item)
a = (json.dumps({"a": results})).encode()
json_file_size = str(len(a))
print(json_file_size)
connection.send(json_file_size)
connection.send("\n")
connection.send(a)
print(a)
def AStar(self, heuristic):
startTime = time()
expanded = 0
PQueue = PriorityQueue()
visited = set()
queueGrids = set() #Grids In Queue But Not Visited Yet
PQueue.put((heuristic(self.initialState), self.initialState))
while (not PQueue.empty()):
front = (PQueue.get())[1]
queueGrids.discard(front.grid)
if front.grid in visited: #Visited Before
continue
if front.grid in queueGrids: #Found in Queue
for node in PQueue.queue:
grid = node[1].grid
if grid == front.grid:
if node[0] > front.cost + heuristic(node[1]):
node[0] = front.cost + heuristic(node[1])
#Processing
if front.isGoal():
path = front.backTrack()
#self.drawGrids(path)
self.processResult(path, front.cost, expanded,
time() - startTime,
"AStar-" + heuristic.__name__[:9])
return path
visited.add(front.grid)
expanded += 1
queueGrids.add(front.grid)
children = front.generateChildren() #Expanding
for child in children:
PQueue.put((heuristic(child) + child.cost, child))
return False
def a_star_tsp(self, num_cities, g, s_city_name):
s_time = time.time()
num_nodes = 0
s = g[s_city_name]
q = PriorityQueue()
q.put((0, [[s_city_name], 0]))
num_nodes += 1
while not q.empty():
temp = q.get()[1]
path = temp[0]
prev_acc_cost = temp[1]
if time.time() - s_time > 300:
return path, prev_acc_cost, num_nodes, False
# Travelled all cities and then back to start
if len(path) == num_cities + 1:
return path, prev_acc_cost, num_nodes, True
# Travelled all cities
if len(path) == num_cities:
heur, acc_cost = self.calc_heur(g, path, s_city_name, s, prev_acc_cost)
new_path = path[:]
new_path.append(s_city_name)
q.put((heur, [new_path, acc_cost]))
num_nodes += 1
# Haven't travelled all cities
else:
not_visited = set(path).symmetric_difference(set(g.keys()))
for city in not_visited:
heur, acc_cost = self.calc_heur(g, path, city, s, prev_acc_cost)
new_path = path[:]
new_path.append(city)
q.put((heur, [new_path, acc_cost]))
num_nodes += 1
def merge(lists):
# error checking
if not lists:
return []
lists = filter(lambda x: x != [], lists)
# initialize priority queue
pqueue = PriorityQueue()
for lst in lists:
if lst:
pqueue.put((lst[0], lst[1:]))
# merge the arrays
result = []
while not pqueue.empty():
elt = pqueue.get()
num, rest = elt[0], elt[1]
result.append(num)
if rest:
pqueue.put((rest[0], rest[1:]))
return result
def getP3(puzzle, pattern):
output = ''
tempMap = {}
pq = PriorityQueue()
for key in pattern:
tempMap[key] = 0
pq.put((key, key))
#print 'keyA', key
for i in range(len(puzzle)):
for j in range(len(puzzle)):
if puzzle[i][j] in pattern:
temp = ''
temp += str(i + 1)
temp += str(j + 1)
tempMap[puzzle[i][j]] = temp
#print 'num',puzzle[i][j],i,j
while not pq.empty():
key = pq.get()[1]
output += str(tempMap[key])
#print 'keyB', key, 'value', tempMap[key]
return output
def mergeKLists(self, lists):
"""
:type lists: List[ListNode]
:rtype: ListNode
"""
head = point = ListNode(0) # dummy node to point to new linked list
q = PriorityQueue()
# initial iteration through lists, if linked list not empty, put its head as a (value, node) pair in PQ
for l in lists:
if l: q.put((l.val, l))
# get the (value, node) pair at the front of the PQ and add a new node with the value to the linked list
# use node.next to get the next lowest element of the linked list to add to the PQ
while not q.empty():
val, node = q.get()
point.next = ListNode(val)
point = point.next
node = node.next
if node: q.put((node.val, node))
return head.next
def mergeKLists(self, lists):
priorityQueue = PriorityQueue()
for l in lists:
if l:
priorityQueue.put((l.val, l))
head = ListNode(-1)
pointer = head
while not priorityQueue.empty():
nodeVal, node = priorityQueue.get()
pointer.next = ListNode(nodeVal)
pointer = pointer.next
node = node.next
if node:
priorityQueue.put((node.val, node))
return head.next
def aStarSearch(graph, h, start, goal):
frontier = PriorityQueue()
frontier.put((0, start))
cost = {}
cost[start] = 0
parents = {}
while not frontier.empty():
_, current = frontier.get()
if current == goal:
return backtrace(parents, start, goal)
successors = graph[current]
for successor in successors:
cost[successor] = cost[current] + graph[current][successor]
parents[successor] = current
frontier.put((h[successor] + cost[successor], successor))
def mergeKLists(self, lists):
"""
:type lists: List[ListNode]
:rtype: ListNode
"""
from Queue import PriorityQueue
if not lists:
return None
pq = PriorityQueue()
for lst in lists:
if lst:
pq.put((lst.val, lst))
dummy = cur = ListNode(None)
while not pq.empty():
v, nd = pq.pop()
cur.next = nd
cur = cur.next
if nd.next:
pq.put((nd.next.val, nd.next))
return dummy.next
def AStar():
queue = PriorityQueue()
cur_node = Node(0, 0)
queue.put((get_FCost(cur_node), cur_node))
visited = [get_index(cur_node)]
path = [-1 for _ in xrange(width * height)]
while not queue.empty():
_, cur_node = queue.get()
for node in find_next_nodes(cur_node):
if get_index(node) in visited:
continue
path[get_index(node)] = get_index(cur_node)
if is_target(node):
print_path(path)
return
queue.put((get_FCost(node), node))
visited.append(get_index(node))
print 'not found'
def mergeKLists(self, lists):
"""
:type lists: List[ListNode]
:rtype: ListNode
"""
from Queue import PriorityQueue # in py3, use queue
head = p = ListNode(0)
q = PriorityQueue()
for l in lists:
if l:
# in py3, 当第一个值一样的时候，比较第二个值，所以若第二值不支持比较，就会报错。
q.put((l.val, l))
while not q.empty():
val, node = q.get()
p.next = node
p = p.next
if node.next:
q.put((node.next.val, node.next))
return head.next
def a_star_search(graph, start, goal):
frontier = PriorityQueue()
frontier.put(start, 0)
came_from = {start: None}
cost_so_far = {start: 0}
while not frontier.empty():
current = frontier.get()
if current == goal:
break
for next in graph.neighbors(current):
new_cost = cost_so_far[current] + graph.cost(current, next)
if next not in cost_so_far or new_cost < cost_so_far[next]:
cost_so_far[next] = new_cost
priority = new_cost + heuristic(goal, next)
frontier.put(next, priority)
came_from[next] = current
return came_from, cost_so_far
def lazy_prim_simplified(self):
"""
wrap a method visit() to make it more compact
:return:
"""
processed = {}
pq = PriorityQueue()
# start from 0
pq = self.visit(0, pq, processed)
while not pq.empty():
cur_node_w, cur_node_from, cur_node_to = pq.get()
# if processed, do nothing
if processed.get(cur_node_to) is True:
continue
# if not processed yet, it is one edge in MST
self.mst.append((cur_node_from, cur_node_to, cur_node_w))
# visit the other node in current minimum weight edge
pq = self.visit(cur_node_to, pq, processed)
self.mst_weight = self._calc_weight()
def mergeKLists(self, lists):
"""
:type lists: List[ListNode]
:rtype: ListNode
"""
dummy = cur = ListNode(0)
queue = PriorityQueue()
# O(k)
for l in lists:
if l: # there may be empty linked list
queue.put((l.val, l))
# O(nlogk),因为一共有n个元素
while not queue.empty():
# O(logk)
val, node = queue.get()
cur.next = node
cur = cur.next
node = node.next
if node:
queue.put((node.val, node))
return dummy.next