def main():
numbers = read_numbers()
three = numbers[3][0]
place = [[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0]]
for i in three:
print(i)
print()
BUBBLE_SIZE = 7
q = hello(three, place)
print("SIZE: ", len(q), len(q[0]))
for i in q:
print(i)
bubbled_place = make_bubble(place, BUBBLE_SIZE)
out_matrix(bubbled_place)
out_matrix(zip_bubbled_data(bubbled_place, BUBBLE_SIZE))
# важно чтобы id клеток разных регионов не пересекались!!!
r = Region(len(merge_input([make_bubble(place, BUBBLE_SIZE), hello(three, place)])), 5, 0)
for step in range(200):
s = 0
x = -1
y = -1
for i, I in enumerate(place):
for j, J in enumerate(I):
if place[i][j] == 1:
x, y = i, j
break
print("-----" * 10)
for i in place:
print(i)
print("-----" * 10)
for i, I in enumerate(place):
for j, J in enumerate(I):
place[i][j] = 0
if (x == -1 and y == -1) or step < 10:
while 1:
x = randrange(len(place))
y = randrange(len(place))
place[x][y] = 1
if get_sum(hello(three, place)):
break
place[x][y] = 0
else:
place[x][y] = 1
r.step_forward(merge_input([make_bubble(place, BUBBLE_SIZE), hello(three, place)]))
r.out_prediction()
new_place = zip_bubbled_data(do_cut(r.get_binary_prediction(), len(make_bubble(place, BUBBLE_SIZE))),
BUBBLE_SIZE)
place = new_place
cur_reward = 0
x, y = env.cur_position_discrete
finish = env.cur_task.finish
# print("start:", x, y)
# if current_episode * 2 > episode_count:
# print("BEGIN" * 5)
P = [[0 for ___ in range(size)] for _____ in range(size)]
a = np.zeros(shape=(10, 10))
tx, ty = env.cur_position_discrete
a[tx, ty] = 1
temporal_pooler_region.step_forward(a)
for current_step in range(max_steps):
p = temporal_pooler_region.get_binary_prediction()
p_actions = []
x, y = env.cur_position_discrete
P[x][y] = 1
for i in BY_PIXEL_ACTION_DIFFS:
dx, dy = BY_PIXEL_ACTION_DIFFS[i]
try:
if p[x + dx][y + dy]:
p_actions.append(i)
except IndexError:
pass
if not len(p_actions) or (
current_episode < 0.7 * episode_count and numpy.random.randint(max_steps) / 4 < current_step
):
action = numpy.random.randint(agent.number_of_actions)
