class TestPlanningGraphHeuristics(unittest.TestCase):
def setUp(self):
self.p = have_cake()
self.pg = PlanningGraph(self.p, self.p.initial)
def test_levelsum(self):
self.assertEqual(self.pg.h_levelsum(), 1)
def h_pg_levelsum(self, node: Node):
# uses the planning graph level-sum heuristic calculated
# from this node to the goal
# requires implementation in PlanningGraph
pg = PlanningGraph(self, node.state)
pg_levelsum = pg.h_levelsum()
return pg_levelsum
def h_pg_levelsum(self, node: Node):
"""This heuristic uses a planning graph representation of the problem
state space to estimate the sum of all actions that must be carried
out from the current state in order to satisfy each individual goal
condition.
"""
# requires implemented PlanningGraph class
pg = PlanningGraph(self, node.state)
pg_levelsum = pg.h_levelsum()
return pg_levelsum
def h_pg_levelsum(self, node: Node):
"""This heuristic uses a planning graph representation of the problem
state space to estimate the sum of all actions that must be carried
out from the current state in order to satisfy each individual goal
condition.
"""
# requires implemented PlanningGraph class
time_in = time()
pg = PlanningGraph(self, node.state)
time_mid = time()
pg_levelsum = pg.h_levelsum()
time_out = time()
print("LSH Timing: {} {} {}".format(time_in, time_mid, time_out))
print("LSH Create PG: {}".format(time_mid - time_in))
print("LSH Call LS: {}".format(time_out - time_mid))
return pg_levelsum
def h_pg_levelsum(self, node):
""" This heuristic uses a planning graph representation of the problem
state space to estimate the sum of the number of actions that must be
carried out from the current state in order to satisfy each individual
goal condition.
See Also
--------
Russell-Norvig 10.3.1 (3rd Edition)
"""
pg = PlanningGraph(self,
node.state,
serialize=True,
ignore_mutexes=True)
score = pg.h_levelsum()
return score
state space to estimate the sum of all actions that must be carried
out from the current state in order to satisfy each individual goal
condition.
<<<<<<< HEAD
<<<<<<< HEAD
"""
>>>>>>> dc9e870... Base Code
=======
'''
>>>>>>> 8d1ef1b... Submission_01
=======
'''
>>>>>>> a7568d9f54a58f4956b458fc6a3732a6565cdda0
# requires implemented PlanningGraph class
pg = PlanningGraph(self, node.state)
pg_levelsum = pg.h_levelsum()
return pg_levelsum
<<<<<<< HEAD
<<<<<<< HEAD
<<<<<<< HEAD
=======
>>>>>>> a7568d9f54a58f4956b458fc6a3732a6565cdda0
def h_ignore_preconditions(self, node: Node):
'''
This heuristic estimates the minimum number of actions that must be
carried out from the current state in order to satisfy all of the goal
conditions by ignoring the preconditions required for an action to be
executed.
'''
# TODO implement (see Russell-Norvig Ed-3 10.2.3 or Russell-Norvig Ed-2 11.2)
def h_pg_levelsum(self, node: Node):
pg = PlanningGraph(self, node.state)
pg_levelsum = pg.h_levelsum()
return pg_levelsum
