def test_bfs():
# First create and initialize the environment
env = GridEnvironment(10, 10)
env.initialize()
# Get the start and goal
start = GridState(0, 0)
goal = GridState(5, 5)
# Solve the problem
path = BFS.solve(env, start, goal)
if path[0] is None:
raise AssertionError(
"BFS could not find feasible path in solvable environment")
def test_depth_limited_dfs(depth):
# First create and initialize the environment
env = GridEnvironment(10, 10)
env.initialize()
# Get the start and goal
start = GridState(0, 0)
goal = GridState(5, 5)
# Solve the problem
path = DepthLimitedDFS.solve(env, start, goal, depth_limit=depth)
if depth < 11 and path[0] is not None:
raise AssertionError(
f"Depth-Limited DFS went too deep: Depth {depth}!")
elif depth >= 11 and path[0] is None:
raise AssertionError(
f"DFS could not find feasible path in solvable environment. Depth {depth}"
)
def test_idastar_heuristic():
# First create and initialize the environment
env = GridEnvironment(10, 10)
env.initialize()
# Get the start and goal
start = GridState(0, 0)
goal = GridState(5, 5)
def h_fn(x: GridState, y: GridState):
return 1000 * math.sqrt((x.location[0] - y.location[0])**2 +
(x.location[1] - y.location[1])**2)
# Solve the problem
path = IDAStar.solve(env, start, goal, heuristic=h_fn)
if path[0] is None:
raise AssertionError(
"BFS could not find feasible path in solvable environment")