def main():
tasks = Dispenser(after)
time = 0
cmd, args = get_cmd( COMMANDS )
while cmd != QUIT_CMD:
if cmd == TICK_CMD:
time += 1
if not tasks.isEmpty():
current = tasks.peek()
current.time_left -= 1
if current.time_left == 0:
print( "\nTask '" + current.name + \
"' completed at time " + str( time ) + "." )
tasks.remove()
if not tasks.isEmpty():
print( "New task is '" + tasks.peek().name + "'." )
else:
print( "Nothing else to do." )
else:
print( "Nothing to do." )
elif cmd == ADD_CMD:
new_task = Task( args[ 0 ], int( args[ 1 ] ) )
tasks.insert( new_task )
print( "\nAdded. Current task is '" + tasks.peek().name + \
"'." )
else:
assert True, "PROGRAM ERROR"
cmd, args = get_cmd( COMMANDS )
print( "\nTerminating the simulation." )
def main():
tasks = Dispenser(after)
time = 0
cmd, args = get_cmd(COMMANDS)
while cmd != QUIT_CMD:
if cmd == TICK_CMD:
time += 1
if not tasks.isEmpty():
current = tasks.peek()
current.time_left -= 1
if current.time_left == 0:
print( "\nTask '" + current.name + \
"' completed at time " + str( time ) + "." )
tasks.remove()
if not tasks.isEmpty():
print("New task is '" + tasks.peek().name + "'.")
else:
print("Nothing else to do.")
else:
print("Nothing to do.")
elif cmd == ADD_CMD:
new_task = Task(args[0], int(args[1]))
tasks.insert(new_task)
print( "\nAdded. Current task is '" + tasks.peek().name + \
"'." )
else:
assert True, "PROGRAM ERROR"
cmd, args = get_cmd(COMMANDS)
print("\nTerminating the simulation.")
def __init__(self,k,x,y):
"""
x and y is a list of tuples or lists -> (score,obj1,obj2)
"""
self.x = x
self.y = y
self.C = PriorityQueue()
self.p = []
self.k = k
self.tmp = []
class KBest:
def __init__(self,k,x,y):
"""
x and y is a list of tuples or lists -> (score,obj1,obj2)
"""
self.x = x
self.y = y
self.C = PriorityQueue()
self.p = []
self.k = k
self.tmp = []
def _insert_queue(self,i,j):
self.C.put((self.x[i][0]+self.y[j][0],[i,j]))
self.tmp.append([i,j])
def mult(self,k=None):
if k != None:
self.k = k
self._insert_queue(0,0)
while len(self.p) < self.k and not self.C.empty():
self._append_next()
return self.p
def _append_next(self):
s,v = self.C.get()
self.tmp.remove(v)
self.p.append((s,v))
for i in range(2):
vc = copy.deepcopy(v)
vc[i] += 1
if vc[0] < len(self.x) and vc[1] < len(self.y) and vc not in self.tmp:
self._insert_queue(vc[0],vc[1])
def test_update_vertex_helper(update_vertex_helper):
print("Testing update_vertex_helper...")
# Test 1: triangle problem; A not in queue, don't insert
g = dict(A=9, B=10, C=2)
g_original = g.copy()
rhs = dict(A="value_to_be_overwritten")
graph = graph_triangle.copy()
queue = PriorityQueue() #empty
update_vertex_helper("A", g, rhs, "non-existent_goal_node", graph, queue)
assert g == g_original #not modified
assert rhs == dict(A=9)
assert graph == graph_triangle #not modified
assert queue == PriorityQueue()
# Test 2: triangle problem; remove A from queue and don't re-insert
g = dict(A=9, B=10, C=2)
g_original = g.copy()
rhs = dict(A="value_to_be_overwritten")
graph = graph_triangle.copy()
queue = PriorityQueue() #empty
queue.extend(list("XYAZ"))
expected_queue = queue.copy()
expected_queue.remove("A")
update_vertex_helper("A", g, rhs, "non-existent_goal_node", graph, queue)
assert g == g_original #not modified
assert rhs == dict(A=9)
assert graph == graph_triangle #not modified
#problem here
assert queue == expected_queue
# Test 3: triangle problem; A not in queue, do insert
g = dict(A=8, B=10, C=2)
g_original = g.copy()
rhs = dict(A=0)
graph = graph_triangle.copy()
queue = PriorityQueue() #empty
queue.extend(list("XYZ"))
expected_queue = queue.copy()
expected_queue.insert("A")
update_vertex_helper("A", g, rhs, "non-existent_goal_node", graph, queue)
assert g == g_original #not modified
assert rhs == dict(A=9)
assert graph == graph_triangle #not modified
assert queue == expected_queue
# Test 4: triangle problem; A in queue, remove and re-insert
g = dict(A=20, B=10, C=2)
g_original = g.copy()
rhs = dict(B="other_stuff")
graph = graph_triangle.copy()
queue = PriorityQueue() #empty
queue.extend(list("XYAZ"))
expected_queue = queue.copy()
update_vertex_helper("A", g, rhs, "non-existent_goal_node", graph, queue)
assert g == g_original #not modified
assert rhs == dict(A=9, B="other_stuff")
assert graph == graph_triangle #not modified
#problem here
assert queue == expected_queue
# Test 5: triangle problem; B has only one successor
g = dict(A=1, B=100, C=10)
g_original = g.copy()
rhs = dict()
queue = PriorityQueue() #empty
expected_queue = queue.copy()
expected_queue.insert("B")
update_vertex_helper("B", g, rhs, "non-existent_goal_node",
graph_triangle.copy(), queue)
assert rhs == dict(B=13)
assert queue == expected_queue
# Test 6: triangle problem; B is goal
g = dict(A=1, B=0, C=10)
g_original = g.copy()
rhs = dict(B=0)
queue = PriorityQueue() #empty
update_vertex_helper("B", g, rhs, "B", graph_triangle.copy(), queue)
assert rhs == dict(B=0)
assert queue == PriorityQueue()
# Test 7: bidirectional graph from problem_simple
g = {
(1, 2): inf,
(0, 1): inf,
(3, 2): inf,
(0, 0): inf,
(3, 0): inf,
(3, 1): inf,
(2, 4): inf,
(1, 4): inf,
(0, 2): 2.0,
(2, 0): 0,
(1, 3): 5,
(2, 3): inf,
(2, 1): 1.0,
(2, 2): inf,
(1, 0): inf,
(4, 2): inf,
(0, 3): inf,
(0, 4): inf,
(4, 1): inf,
(1, 1): 1.0,
(4, 0): inf
}
rhs = {
(1, 2): inf,
(0, 1): 2.0,
(3, 2): inf,
(0, 0): 2.0,
(3, 0): inf,
(3, 1): inf,
(2, 4): inf,
(1, 4): inf,
(0, 2): 2.0,
(2, 0): 0,
(1, 3): 9,
(2, 3): inf,
(2, 1): 1.0,
(2, 2): 2.0,
(1, 0): 1.0,
(4, 2): inf,
(0, 3): inf,
(0, 4): inf,
(4, 1): inf,
(1, 1): 1.0,
(4, 0): inf
}
queue = PriorityQueue()
queue.extend([(1, 0), (0, 1), (2, 2), (0, 0)])
expected_rhs = {
(1, 2): inf,
(0, 1): 2.0,
(3, 2): inf,
(0, 0): 2.0,
(3, 0): inf,
(3, 1): inf,
(2, 4): inf,
(1, 4): inf,
(0, 2): 2.0,
(2, 0): 0,
(1, 3): inf,
(2, 3): inf,
(2, 1): 1.0,
(2, 2): 2.0,
(1, 0): 1.0,
(4, 2): inf,
(0, 3): inf,
(0, 4): inf,
(4, 1): inf,
(1, 1): 1.0,
(4, 0): inf
}
expected_queue = queue.copy()
expected_queue.insert((1, 3))
update_vertex_helper((1, 3), g, rhs, goal_simple, graph_simple, queue)
assert rhs == expected_rhs
assert queue == expected_queue
def test_compute_shortest_path_helper(compute_shortest_path_helper,
calc_key_helper):
print("Testing compute_shortest_path_helper...")
# problem_simple, first step
g = {
(1, 2): inf,
(0, 1): inf,
(3, 2): inf,
(0, 0): inf,
(3, 0): inf,
(3, 1): inf,
(2, 4): inf,
(1, 4): inf,
(0, 2): inf,
(2, 0): inf,
(1, 3): inf,
(2, 3): inf,
(2, 1): inf,
(2, 2): inf,
(1, 0): inf,
(4, 2): inf,
(0, 3): inf,
(0, 4): inf,
(4, 1): inf,
(1, 1): inf,
(4, 0): inf
}
rhs = {
(1, 2): inf,
(0, 1): inf,
(3, 2): inf,
(0, 0): inf,
(3, 0): inf,
(3, 1): inf,
(2, 4): inf,
(1, 4): inf,
(0, 2): inf,
(2, 0): 0,
(1, 3): inf,
(2, 3): inf,
(2, 1): inf,
(2, 2): inf,
(1, 0): inf,
(4, 2): inf,
(0, 3): inf,
(0, 4): inf,
(4, 1): inf,
(1, 1): inf,
(4, 0): inf
}
graph = graph_simple.copy()
calc_key = lambda node: calc_key_helper(node, g, rhs, (0, 3), 0)
queue = PriorityQueue(f=lambda node: calc_key(node))
queue.insert(goal_simple)
g_expected = {
(1, 2): inf,
(0, 1): inf,
(3, 2): inf,
(0, 0): inf,
(3, 0): inf,
(3, 1): inf,
(2, 4): inf,
(1, 4): inf,
(0, 2): 2.0,
(2, 0): 0,
(1, 3): inf,
(2, 3): inf,
(2, 1): 1.0,
(2, 2): inf,
(1, 0): inf,
(4, 2): inf,
(0, 3): 3.0,
(0, 4): inf,
(4, 1): inf,
(1, 1): 1.0,
(4, 0): inf
}
rhs_expected = {
(1, 2): inf,
(0, 1): 2.0,
(3, 2): inf,
(0, 0): 2.0,
(3, 0): inf,
(3, 1): inf,
(2, 4): inf,
(1, 4): 4.0,
(0, 2): 2.0,
(2, 0): 0,
(1, 3): inf,
(2, 3): inf,
(2, 1): 1.0,
(2, 2): 2.0,
(1, 0): 1.0,
(4, 2): inf,
(0, 3): 3.0,
(0, 4): 4.0,
(4, 1): inf,
(1, 1): 1.0,
(4, 0): inf
}
compute_shortest_path_helper(g, rhs, (0, 3), goal_simple, 0, graph, queue)
assert graph == graph_simple #not modified
assert g == g_expected
assert rhs == rhs_expected
queue_expected = PriorityQueue(f=lambda node: calc_key(node))
queue_expected.extend([(1, 0), (0, 1), (2, 2), (0, 0), (0, 4), (1, 4)])
assert queue == queue_expected
# problem_simple, second step
g = {
(1, 2): inf,
(0, 1): inf,
(3, 2): inf,
(0, 0): inf,
(3, 0): inf,
(3, 1): inf,
(2, 4): inf,
(1, 4): inf,
(0, 2): 2.0,
(2, 0): 0,
(1, 3): inf,
(2, 3): inf,
(2, 1): 1.0,
(2, 2): inf,
(1, 0): inf,
(4, 2): inf,
(0, 3): 3.0,
(0, 4): inf,
(4, 1): inf,
(1, 1): 1.0,
(4, 0): inf
}
rhs = {
(1, 2): inf,
(0, 1): 3.0,
(3, 2): inf,
(0, 0): inf,
(3, 0): inf,
(3, 1): inf,
(2, 4): inf,
(1, 4): 4.0,
(0, 2): 4.0,
(2, 0): 0,
(1, 3): inf,
(2, 3): inf,
(2, 1): 1.0,
(2, 2): 2.0,
(1, 0): 1.0,
(4, 2): inf,
(0, 3): 3.0,
(0, 4): 4.0,
(4, 1): inf,
(1, 1): inf,
(4, 0): inf
}
graph = graph_simple_step2.copy()
calc_key = lambda node: calc_key_helper(node, g, rhs, (0, 2), 1)
queue = PriorityQueue(f=lambda node: calc_key(node))
queue.extend([(1, 1), (0, 2), (1, 0), (2, 2), (0, 1), (0, 4), (1, 4)])
g_expected = {
(1, 2): inf,
(0, 1): 2.0,
(3, 2): inf,
(0, 0): inf,
(3, 0): inf,
(3, 1): inf,
(2, 4): inf,
(1, 4): inf,
(0, 2): 3.0,
(2, 0): 0,
(1, 3): inf,
(2, 3): inf,
(2, 1): 1.0,
(2, 2): inf,
(1, 0): 1.0,
(4, 2): inf,
(0, 3): 3.0,
(0, 4): inf,
(4, 1): inf,
(1, 1): inf,
(4, 0): inf
}
rhs_expected = {
(1, 2): inf,
(0, 1): 2.0,
(3, 2): inf,
(0, 0): 2.0,
(3, 0): inf,
(3, 1): inf,
(2, 4): inf,
(1, 4): 4.0,
(0, 2): 3.0,
(2, 0): 0,
(1, 3): inf,
(2, 3): inf,
(2, 1): 1.0,
(2, 2): 2.0,
(1, 0): 1.0,
(4, 2): inf,
(0, 3): 4.0,
(0, 4): 4.0,
(4, 1): inf,
(1, 1): inf,
(4, 0): inf
}
compute_shortest_path_helper(g, rhs, (0, 2), goal_simple, 1, graph, queue)
assert graph == graph_simple_step2 #not modified
assert g == g_expected
assert rhs == rhs_expected
queue_expected = PriorityQueue(f=lambda node: calc_key(node))
queue_expected.extend([(0, 0), (2, 2), (0, 3), (0, 4), (1, 4)])
assert queue == queue_expected
