def __init__(self, tp, agent_finish):
self.tp_level_one = tp
tp_level_one_settings = tp.temporal_settings
tp_level_one_settings.dendrite_permanence_dec_delta = tp_level_one_settings.dendrite_permanence_inc_delta = 0.0
tp_level_one_settings.passive_time_to_active_threshold = 10000000000000000000
dendrite_id_cnt = 0
self.id_to_dendrite_map = {}
self.id_to_Cell = {}
self.dendrites = []
for i, I in enumerate(self.tp_level_one.columns):
for j, J in enumerate(I):
for cell, Cell in enumerate(self.tp_level_one.columns[i][j].cells):
self.id_to_Cell[Cell.id] = Cell
Cell.position_x_y = [i, j]
for dendrite, Dendrite in enumerate(Cell.dendrites):
self.dendrites.append(Dendrite)
Dendrite.id = dendrite_id_cnt
Dendrite.id_to = Cell.id
dendrite_id_cnt += 1
Dendrite.position_x_y = [i, j]
self.id_to_dendrite_map[Dendrite.id] = Dendrite
self.used = {}
self.graph_edges = []
index = 0
os.system('rm -rf pic/')
if not os.path.exists('pic/'):
os.makedirs('pic/')
for current in self.dendrites:
self.edges = []
self.edges2 = []
# print("id" + str(current.id) + ":" + str(current.position_x_y))
active_cells = set()
for i in current.synapses:
if i.permanence > tp_level_one_settings.synapse_threshold:
active_cells.add(i.id_to)
if len(active_cells) < tp_level_one_settings.dendrite_activate_threshold:
continue
# print("acells:" + str(active_cells))
ans = []
for den in self.dendrites:
q = 0
for j in den.synapses:
if j.id_to in active_cells and j.permanence > tp_level_one_settings.synapse_threshold:
q += 1
if q >= tp_level_one_settings.dendrite_activate_threshold:
ans.append(den)
# print("---" * 5)
# for t in ans:
# print(t.position_x_y)
self.dfs(ans)
# if sys.platform == "linux":
size = self.tp_level_one.temporal_settings.region_size
p = [[0 for _ in range(size)] for _ in range(size)]
for i in range(len(self.edges2)):
for x in range(len(self.edges2[i])):
for y in range(len(self.edges2[i][x])):
if self.edges2[i][x][y]:
p[x][y] = 1
# if p[7][4]:
if len(self.edges) > 0:
p[agent_finish[0]][agent_finish[1]] = 2
# p[agent_finish[0]][agent_finish[1]] = 3
draw_image("pic/image" + str(index), p)
# draw_graph("pic/refactoring" + str(index), [(self.make_string_(p), "1")])
index += 1
inc_delta,
)
temporal_pooler_region.step_forward(a)
# if current_episode * 2 > episode_count:
# temporal_pooler_region.out_prediction()
inc_delta, temporal_pooler_region.temporal_settings.dendrite_permanence_inc_delta = (
temporal_pooler_region.temporal_settings.dendrite_permanence_inc_delta,
inc_delta,
)
dec_delta, temporal_pooler_region.temporal_settings.dendrite_permanence_dec_delta = (
temporal_pooler_region.temporal_settings.dendrite_permanence_dec_delta,
dec_delta,
)
if done == 100:
print(done)
print("finish:", x, y)
a = np.zeros(shape=(10, 10))
temporal_pooler_region.step_forward(a)
break
print("iter: ", current_episode, "reward: ", cur_reward)
if current_episode > episode_count - 50:
P[7][4] = 2
draw_image("pic2/last_path_on_grid" + str(current_episode), P)
if current_episode == episode_count - 1:
Foo(temporal_pooler_region, finish)
break
