def test_local_beam_search():
initial = 0
goal = 10
limits = (-goal, goal)
p = AnnotatedProblem(
EasyProblem(initial, initial_cost=initial, extra=limits))
sol = next(local_beam_search(p, graph=False))
assert abs(sol.state_node.state - limits[0]) <= 0.1
p2 = HillProblem(initial, goal=goal, initial_cost=initial, extra=limits)
sol = next(local_beam_search(p2))
assert abs(sol.state_node.state - goal) <= 0.1
p3 = HillProblemNoGoalTest(initial,
goal=goal,
initial_cost=initial,
extra=limits)
sol = next(local_beam_search(p3))
assert abs(sol.state_node.state - goal) <= 0.1
p4 = AnnotatedProblem(
HillProblem(initial, goal=initial, initial_cost=initial, extra=limits))
sol = next(local_beam_search(p4))
assert p4.nodes_expanded == 0
assert p4.goal_tests == 1
assert abs(sol.state_node.state - initial) <= 0.1
p5 = AnnotatedProblem(PlateauProblem(initial))
sol = next(local_beam_search(p5, beam_width=2))
def test_local_beam_search():
initial = 0
goal = 10
limits = (-goal, goal)
p = AnnotatedProblem(EasyProblem(initial, initial_cost=initial, extra=limits))
sol = next(local_beam_search(p, graph_search=False))
assert abs(sol.state - limits[0]) <= 0.1
def beam2(problem):
return local_beam_search(problem, beam_width=2)
def local_beam_width2(problem):
return local_beam_search(problem, beam_width=2)
def beam2(problem):
return local_beam_search(problem, beam_width=2, cost_limit=0)
def beam1(problem):
return local_beam_search(problem, beam_width=1)
