def __repr__(self):
level = 0
q = Queue()
visit_order = list()
node = self.get_root()
q.enqueue( (node,level) )
while q.size() > 0:
node, level = q.dequeue()
if node == None:
visit_order.append( ("<empty>", level))
continue
visit_order.append( (node, level) )
if node.has_left_child():
q.enqueue( (node.get_left_child(), level +1 ))
else:
q.enqueue( (None, level +1) )
if node.has_right_child():
q.enqueue( (node.get_right_child(), level +1 ))
else:
q.enqueue( (None, level +1) )
s = "Tree\n"
previous_level = -1
for i in range(len(visit_order)):
node, level = visit_order[i]
if level == previous_level:
s += " | " + str(node)
else:
s += "\n" + str(node)
previous_level = level
return s
def test_queue_size():
q = Queue()
q.enqueue(1)
q.enqueue(2)
q.enqueue(3)
assert q.size() == 3
def bft(self, starting_vertex_id):
"""
Traverse and print each vertex in breadth-first order (a BFT) beginning from starting_vertex.
"""
# Initialize empty queue and set of visited nodes:
queue = Queue()
visited = set()
# Check if provided starting vertex is in our graph:
if starting_vertex_id in self.vertices.keys():
# If so, add starting vertex to queue:
queue.enqueue(starting_vertex_id)
else:
return IndexError(
f"Provided starting vertex {starting_vertex_id} does not exist in graph!"
)
# Process all vertices via BFT:
while queue.size() > 0:
# Get next vertex to process as first item in queue:
current_vertex = queue.dequeue()
# If the current vertex has not already been visited+processed, process it:
if current_vertex not in visited:
# Process current vertex:
print(current_vertex)
# Add adjacent vertices to queue for future processing:
for adjacent_vertex in self.get_neighbors(current_vertex):
queue.enqueue(adjacent_vertex)
# Mark current vertex as "visited" by adding to our set of visited vertices:
visited.add(current_vertex)
def josephusProblem(namelist, num):
simqueue = Queue()
for name in namelist:
simqueue.enqueue(name)
while simqueue.size() > 1:
for i in range(num):
simqueue.enqueue(simqueue.dequeue())
simqueue.dequeue()
return simqueue.dequeue()
def bfs(self, starting_room, target_room):
"""
Return a list containing the shortest path from starting_room to destination_room,
after searching for and finding it with a breadth-first search (BFS) algorithm.
"""
# Initialize empty queue and set of BFS-"visited" rooms:
queue = Queue()
visited_bfs = set()
# Initialize path (we will add the rest of the path from starting room to target room below):
path_rooms = [starting_room]
path_directions = []
# Check if provided starting room is in our map:
if starting_room in self.rooms.keys():
# If so, add starting room to queue:
queue.enqueue((path_rooms, path_directions))
else:
return IndexError(
f"Provided starting room {starting_room} does not exist in map!"
)
# Search all rooms via BFS, logging their paths (from starting_room --> room) as we go:
while queue.size() > 0:
# Get next room to process as first item in queue:
current_path_rooms, current_path_directions = queue.dequeue()
current_room = current_path_rooms[-1]
# Check if it is the target --> if so, return its full path:
if current_room == target_room:
return current_path_directions # Alternative: return (current_path_rooms, current_path_directions)
# If the current room has not already been visited+processed, check and process it:
if current_room not in visited_bfs and current_room in self.rooms:
# If not, then get its neighbor rooms and add their paths to the queue for future processing:s
neighbors = self.get_neighbors(current_room)
for adjacent_room_direction in neighbors:
adjacent_room_path_rooms = current_path_rooms + [
neighbors[adjacent_room_direction]
]
adjacent_room_path_directions = current_path_directions + [
adjacent_room_direction
]
queue.enqueue((adjacent_room_path_rooms,
adjacent_room_path_directions))
# Mark current room as "visited" by adding to our set of visited rooms:
visited_bfs.add(current_room)
# If no path found in entire map, return None:
return None
def simulation(seconds, ppm):
printer = Printer(ppm)
print_queue = Queue()
waiting_times = []
for sec in range(seconds):
if is_new_task():
task = Task(sec)
print_queue.enqueue(task)
if not printer.busy() and not print_queue.is_empty():
next_task = print_queue.dequeue()
waiting_times.append(next_task.wait_time(sec))
printer.start_next(next_task)
printer.tick()
average_wait = sum(waiting_times) / len(waiting_times)
print("Average Wait %6.2f secs %3d tasks remaining." %
(average_wait, print_queue.size()))
def simulation(num_seconds, pages_per_minute, num_students):
lab_printer = Printer(pages_per_minute)
print_queue = Queue()
waiting_times = []
for current_second in range(num_seconds):
if new_print_task(num_students):
task = Task(current_second)
print_queue.enqueue(task)
if (not lab_printer.busy()) and (not print_queue.is_empty()):
nexttask = print_queue.dequeue()
waiting_times.append(nexttask.wait_time(current_second))
lab_printer.start_next(nexttask)
lab_printer.tick()
average_wait = sum(waiting_times) / len(waiting_times)
print("Average Wait %6.2f secs %3d tasks remaining." %
(average_wait, print_queue.size()))
def bfs(self, starting_vertex, target_vertex):
"""
Return a list containing the shortest path from starting_vertex to destination_vertex,
after searching for and finding it with a breadth-first search (BFS) algorithm.
"""
# Initialize empty queue and set of visited nodes:
queue = Queue()
visited = set()
# Initialize path (we will add the rest of the path from starting vertex to target vertex below):
path = [starting_vertex]
# Check if provided starting vertex is in our graph:
if starting_vertex in self.vertices.keys():
# If so, add starting vertex to queue:
queue.enqueue(path)
else:
return IndexError(
f"Provided starting vertex {starting_vertex} does not exist in graph!"
)
# Process all vertices via BFT:
while queue.size() > 0:
# Get next vertex to process as first item in queue:
current_path = queue.dequeue()
current_vertex = current_path[-1]
# If the current vertex has not already been visited+processed, check and process it:
if current_vertex not in visited:
# Check if it is the target --> if so, return its full path:
if current_vertex == target_vertex:
return current_path
# If not, then get its neighbor vertices and add their paths to the queue for future processing:
for adjacent_vertex in self.get_neighbors(current_vertex):
adjacent_vertex_path = current_path + [adjacent_vertex]
queue.enqueue(adjacent_vertex_path)
# Mark current vertex as "visited" by adding to our set of visited vertices:
visited.add(current_vertex)
# If no path found in entire graph, return None:
return None
def simulation(numSeconds, numStudents, pagesPerMinute):
labprinter = Printer(pagesPerMinute)
printQueue = Queue()
waitingtimes = []
for currentSecond in range(numSeconds):
if newPrintTask(numStudents):
task = Task(currentSecond)
printQueue.enqueue(task)
if (not labprinter.busy()) and (not printQueue.isEmpty()):
nexttask = printQueue.dequeue()
waitingtimes.append(nexttask.waitTime(currentSecond))
labprinter.startNext(nexttask)
labprinter.tick()
averageWait = sum(waitingtimes) / len(waitingtimes)
print("Average Wait %6.2f secs %3d tasks remaining." %
(averageWait, printQueue.size()))
return averageWait
class TestQueue(unittest.TestCase): # FIFO
def setUp(self):
self.emptyQueue = Queue()
self.Queue = Queue()
for i in range(5):
self.Queue.enqueue(i)
def test_repr(self):
'''Test returning the queue as a string literal.'''
self.assertEqual(repr(self.emptyQueue), str(()))
tup = (1, 3.14, 'foo', True)
for i in tup:
self.emptyQueue.enqueue(i)
self.assertEqual(repr(self.emptyQueue), str(tup))
def test_len_size(self):
'''Test returning the size of the queue.'''
for i in range(100):
self.emptyQueue.enqueue(i)
self.assertEqual(len(self.emptyQueue), 100)
self.assertEqual(self.emptyQueue.size(), 100)
self.emptyQueue.dequeue()
self.assertEqual(len(self.emptyQueue), 99)
self.assertEqual(self.emptyQueue.size(), 99)
def test_tuple(self):
'''Test returning the queue as a tuple literal.'''
self.assertEqual(tuple(self.emptyQueue), ())
tup = (1, 3.14, 'foo', True)
for i in tup:
self.emptyQueue.enqueue(i)
self.assertEqual(tuple(self.emptyQueue), tup)
def test_list(self):
'''Test returning the queue as a list literal.'''
self.assertEqual(list(self.emptyQueue), [])
li = [1, 3.14, 'foo', True]
for i in li:
self.emptyQueue.enqueue(i)
self.assertEqual(list(self.emptyQueue), li)
def test_enqueue(self):
'''Test adding items to the front of the queue.'''
self.Queue.enqueue(True)
self.assertEqual(tuple(self.Queue), (0, 1, 2, 3, 4, True))
def test_dequeue(self):
'''Test removing items from the front of the queue.'''
self.assertEqual(self.Queue.dequeue(), 0)
self.assertEqual(tuple(self.Queue), (1, 2, 3, 4))
self.assertEqual(self.Queue.dequeue(), 1)
self.assertEqual(self.Queue.dequeue(), 2)
self.assertEqual(self.Queue.dequeue(), 3)
self.assertEqual(self.Queue.dequeue(), 4)
self.assertEqual(tuple(self.Queue), ())
with self.assertRaises(ValueError):
self.Queue.dequeue()
# test enqueuing after dequeuing
self.Queue.enqueue(0)
self.Queue.enqueue(True)
self.assertEqual(tuple(self.Queue), (0, True))
def test_peek(self):
'''Test peeking at the first enqueued item w/o modifying the queue.'''
self.assertEqual(self.Queue.peek(), 0)
with self.assertRaises(ValueError):
self.emptyQueue.peek()
print(stack)
for i in range(5):
stack.push(i)
print(stack)
for i in range(stack.size()):
stack.pop()
print(stack)
print("*** Queue ***")
print(queue)
for i in range(5):
queue.enqueue(i)
print(queue)
for i in range(queue.size()):
queue.dequeue()
print(queue)
print("*** Deque - Stack operation ***")
print(deque)
for i in range(5):
deque.add_rear(i)
print(deque)
for i in range(deque.size()):
deque.remove_rear()
print(deque)
print("*** Deque - Queue operation ***")
print(deque)