def bfs(problem):
"""
Breadth first graph search algorithm
returns a solution for the given search problem
:param
problem (a Problem object) representing the quest
see Problem class definition in spartanquest.py)
:return: list of actions representing the solution to the quest
or None if there is no solution
"""
closed = set()
fringe = data_structures.Queue()
state = problem.start_state()
root = data_structures.Node(state)
fringe.push(root)
while True:
if fringe.is_empty():
return None
node = fringe.pop()
if problem.is_goal(node.state):
return node.solution()
if node.state not in closed:
closed.add(node.state)
for child_state, action, action_cost in problem.successors(
node.state):
child_node = data_structures.Node(child_state, node, action)
fringe.push(child_node)
def bfs(problem):
"""
Breadth first graph search algorithm
returns a solution for the given search problem
:param
problem (a Problem object) representing the quest
see Problem class definition in spartanquest.py)
:return: list of actions representing the solution to the quest
or None if there is no solution
"""
# Enter your code here and remove the pass statement below
closed = set() # keep track of our explored states
fringe = data_structures.Queue() # for BFS, the fringe is a Queue
state = problem.start_state()
root = data_structures.Node(state)
fringe.push(root)
while True:
if fringe.is_empty():
return None # Failure - fringe is empty and no solution was found
node = fringe.pop()
if problem.is_goal(node.state):
return node.solution()
if node.state not in closed: # we are implementing graph search
closed.add(node.state)
for child_state, action, action_cost in problem.successors(
node.state):
child_node = data_structures.Node(child_state, node, action)
fringe.push(child_node)
def bfs(problem):
"""
Breadth first graph search algorithm
returns a solution for the given search problem
:param
problem (a Problem object) representing the quest
see Problem class definition in spartanquest.py)
:return: list of actions representing the solution to the quest
or None if there is no solution
"""
closed = set() # keep track of our explored states
fringe = data_structures.Queue() # for bfs, the fringe is a queue
state = problem.start_state()
root = data_structures.Node(state, None, None)
fringe.push(root)
while not fringe.is_empty():
node = fringe.pop()
if problem.is_goal(node.state):
return node.solution() # we found a solution
if node.state not in closed: # we are implementing graph search
closed.add(node.state)
for child_state, action, action_cost in problem.expand(node.state):
child_node = data_structures.Node(child_state, node, action)
fringe.push(child_node)
return None
def test_dequeue_empty(self):
test_queue = data_structures.Queue()
try:
dequeued = test_queue.dequeue()
except:
pass
else:
assert False, "Expected an exception; instead got a Node with value '{}'.".format(
dequeued.data)
def test_queue(self):
x = data_structures.Queue()
x.enqueue('lorem')
x.enqueue('dim')
x.enqueue('sum')
x.enqueue('Cha')
x.enqueue('siu')
x.enqueue('bao')
return x
assert stackwithwrapper.pop() == 3
assert stackwithwrapper.peek() == 2
assert stackwithwrapper.size() == 3
# test object reference is returned
stackwithobj = ds.Stack([[0, 1], [2, 3], [4, 5]]) # stack of lists
(stackwithobj.peek())[0] = 6
assert (stackwithobj.peek())[0] == 6
assert (stackwithobj.pop())[0] == 6
#==================================
# QUEUE TESTS
#==================================
# test initialisation conditions
queue = ds.Queue()
assert queue.size() == 0
assert queue.isEmpty()
# test push() conditions
queue.offer('abc')
assert queue.size() == 1
assert not queue.isEmpty()
# test pop() conditions
assert queue.poll() == 'abc'
assert queue.size() == 0
assert queue.isEmpty()
# test null cases
assert queue.peek() == None