def test_sensor_cells(self):
beer = beerworld.Beer()
cells = beer.sensor_cells()
print
if 1 in cells:
for x in beer.board:
print x
print cells
def test_spawn_agent(self):
beer = beerworld.Beer()
counter = 0
for x in beer.board:
for y in x:
if y == "A":
counter += 1
assert counter == 5
def test_spawn_object(self):
beer = beerworld.Beer()
counter = 0
for x in beer.board:
for y in x:
if y in "SB":
counter += 1
assert 0 < counter < 7
def test_modify_on_action(self):
beer = beerworld.Beer()
beer.modify_on_action(-4)
counter = 0
for x in beer.board:
for y in x:
if y == "A":
counter += 1
assert counter == 5
def simulate(generation):
nn = NeuralNet(generation.best_parameters)
beer = beerworld.Beer()
for _ in range(generation.timesteps):
stim = beer.sensor_cells()
output = nn.act_on_input(stim)
output = int(round((output[0] - output[1]) * 4.0))
beer.modify_on_action(output)
yield beer.board
def __init__(self, candidate, size, timesteps, max_generations,
probability, num_elites, run_type):
self.candidate = candidate
self.size = size
self.timesteps = timesteps
self.max_generations = max_generations
self.population = self.initialize_population()
self.probability = probability
self.num_elites = num_elites
self.run_type = run_type
self.beer = beerworld.Beer()
def test_graphics_from_beerworld(self):
beer = beerworld.Beer()
def test_fall(self):
beer = beerworld.Beer()
beer.fall()
assert "S" in beer.board[1] or "B" in beer.board[1]