def test_simulated_annealing_landscape(self):
height = 8
landscape = [2, 3, 4, 3, 2, 3, 5, 6, 5, 4, 5,
4, 3, 2, 3, 4, 5, 6, 7, 6, 5, 4]
state = 0
def actions(state):
actions = []
if state > 0: actions.append(-1)
if state < len(landscape) - 1: actions.append(1)
return actions
result = lambda state, action: state + action
step_cost = lambda state, action: 1
heuristic = lambda state: (height - landscape[state] - 1) / (height - 1)
goal_state = lambda state: heuristic(state) == 0
factory = problem.ProblemFactory()
pr = factory.from_functions(state, actions, step_cost,
result, goal_state)
solution = search.simulated_annealing(pr, heuristic,
local_minima_acceptable=True)
self.assertNotEqual(solution, problem.FAILURE)
final_state = solution[-1][0]
# Assert that SA has found at least the second best local minima
self.assertLessEqual(heuristic(final_state), 1)
def test_simulated_annealing_landscape(self):
height = 8
landscape = [
2, 3, 4, 3, 2, 3, 5, 6, 5, 4, 5, 4, 3, 2, 3, 4, 5, 6, 7, 6, 5, 4
]
state = 0
def actions(state):
actions = []
if state > 0: actions.append(-1)
if state < len(landscape) - 1: actions.append(1)
return actions
result = lambda state, action: state + action
step_cost = lambda state, action: 1
heuristic = lambda state: (height - landscape[state] - 1) / (height - 1
)
goal_state = lambda state: heuristic(state) == 0
factory = problem.ProblemFactory()
pr = factory.from_functions(state, actions, step_cost, result,
goal_state)
solution = search.simulated_annealing(pr,
heuristic,
local_minima_acceptable=True)
self.assertNotEqual(solution, problem.FAILURE)
final_state = solution[-1][0]
# Assert that SA has found at least the second best local minima
self.assertLessEqual(heuristic(final_state), 1)
def test_simulated_annealing_success_ratio(self):
factory = problem.ProblemFactory()
queens8_gen = functools.partial(factory.from_nqueens, 8)
heuristic = factory.heuristic_for(queens8_gen())
problem_count = 10
expected_solutions = 0.7 * problem_count
found_solutions = 0
for _ in range(problem_count):
solution = search.simulated_annealing(queens8_gen(), heuristic)
found_solutions += 1 if solution != problem.FAILURE else 0
self.assertGreaterEqual(found_solutions, expected_solutions)
def test_simulated_annealing_success_ratio(self):
factory = problem.ProblemFactory()
queens8_gen = functools.partial(factory.from_nqueens, 8)
heuristic = factory.heuristic_for(queens8_gen())
problem_count = 10
expected_solutions = 0.7 * problem_count
found_solutions = 0
for _ in range(problem_count):
solution = search.simulated_annealing(queens8_gen(), heuristic)
found_solutions += 1 if solution != problem.FAILURE else 0
self.assertGreaterEqual(found_solutions, expected_solutions)
def main():
height = 8
landscape = [2, 3, 4, 3, 2, 3, 5, 6, 5, 4, 5,
4, 3, 2, 3, 4, 5, 6, 7, 6, 5, 4]
width = len(landscape)
state = 0
def actions(state):
actions = []
if state > 0:
actions.append(-1)
if state < len(landscape) - 1:
actions.append(1)
return actions
def print_landscape(state):
for row_index in range(height):
row = [' ' if landscape[i] > row_index else
('|' if landscape[i] < row_index
else ('Y' if i == state else 'T'))
for i in range(width)]
print("".join(row))
sleep(0.4)
result = lambda state, action: state + action
step_cost = lambda state, action: 1
heuristic = lambda state: (height - landscape[state] - 1) / (height - 1)
goal_state = lambda state: heuristic(state) == 0
factory = problem.ProblemFactory()
pr = factory.from_functions(state, actions, step_cost, result, goal_state)
solution = search.simulated_annealing(pr, heuristic,
local_minima_acceptable=True,
min_temperature=0.1,
print_state=print_landscape)
print("Done")
