def test_iterative_deepening_graph_search():
"""
Test iterative deepening graph search.
"""
for goal in range(1, 10):
p = AnnotatedProblem(EasyProblem(0, goal))
sol = next(iterative_deepening_search(p, graph=True))
assert sol.state_node.state == goal
assert p.nodes_expanded == sum([i * 2 for i in range(1, goal + 1)])
assert p.goal_tests == sum([i + 1 for i in range(1, goal + 1)]) + 1
p = EasyProblem(0, 10)
try:
next(iterative_deepening_search(p, graph=True, max_depth_limit=5))
assert False
except StopIteration:
pass
def test_iterative_deepening_graph_search():
"""
Test iterative deepening graph search.
"""
for goal in range(1, 10):
p = AnnotatedProblem(EasyProblem(0, goal))
sol = next(iterative_deepening_search(p, graph=True))
assert sol.state_node.state == goal
assert p.nodes_expanded == sum([i * 2 for i in range(1, goal + 1)])
assert p.goal_tests == sum([i + 1 for i in range(1, goal + 1)]) + 1
def test_iterative_deepening_tree_search():
"""
Test iterative deepening tree search.
"""
for goal in range(1, 10):
p = AnnotatedProblem(EasyProblem(0, goal))
sol = next(iterative_deepening_search(p, graph=False))
assert sol.state_node.state == goal
assert (p.nodes_expanded == sum(
[pow(2, i + 1) - 2 for i in range(1, goal)]) + (goal * 2))
assert (
p.goal_tests == sum([pow(2, i + 1) - 2
for i in range(1, goal)]) + (goal * 2) + 1)
p = EasyProblem(0, 10)
try:
next(iterative_deepening_search(p, graph=False, max_depth_limit=5))
assert False
except StopIteration:
pass
def fc_plan(self, goals, max_depth=5, epsilon=0.0):
"""
A strategy of expanding forward from the initial state.
"""
index = build_index(self.facts)
fc_problem = FC_Problem(initial=(frozenset(self.facts),
frozenset(goals)),
extra=(self.operators, index, epsilon))
for solution in iterative_deepening_search(fc_problem,
max_depth_limit=max_depth +
1):
yield solution
def iterative_deepening(self, x):
return iterative_deepening_search(x)