def main():
#TODO delete code from here
go_state_obj = GoStateObject()
from search import DeepLearningSearch
from forward_prop_network import ForwardPropNetwork
import tensorflow as tf
import time
#search_algorithm = DeepLearningSearch()
sess = tf.Session()
#forward_prop_network = ForwardPropNetwork(sess)
import copy
search_algorithm = MontecarloSearch()
go_state_obj = GoStateObject()
args = sys.argv
user = '******'
# NNGS set up
nngs = NNGS()
# connect nngs
nngs.connect_nngs()
# login
nngs.login(user)
# match
if len(args) == 3 and args[2] in ('B', 'W'):
nngs.match(args[1], args[2])
# wait
nngs.nngs_wait()
init_time = time.time()
sum_time = 0
# Go, Player set up
rule = Go()
players = [
Player(0, nngs.player_name['B']),
Player(1, nngs.player_name['W'])
]
players[0].next_player = players[1]
players[1].next_player = players[0]
player = players[0]
#last_opponent_move=None
last_opponent_move = None
while True:
print(rule.print_board(go_state_obj))
if player.player_name == user:
my_start_time = time.time()
(go_state_obj,
move) = search_algorithm.next_move(go_state_obj, player,
1800 - (float(sum_time)),
last_opponent_move)
print("next_move:", move)
#go_state_obj = rule.move_and_return_state(go_state_obj, player, move)
nngs.send_data(move2nngs(move, rule))
my_end_time = time.time()
sum_time = sum_time + my_end_time - my_start_time
print("Sum time" + str(sum_time))
#search_algorithm.thread_close()
else:
z = nngs.nngs_wait()
if z == None:
print("None! None! なん！")
exit(0)
if z < 0: continue
#go_state_obj = rule.move_and_return_state(go_state_obj,player, nngs2move(rule, player, z))
nn_move = nngs2move(rule, player, z)
print(nn_move)
if nn_move == 0 or nn_move == 3:
last_opponent_move = rule._PASS
else:
last_opponent_move = nn_move
go_state_obj = rule.move_and_return_state(
go_state_obj, player, nn_move)
player = player.next_player
nngs.close_nngs()
def main():
# TODO delete code from here
go_state_obj = GoStateObject()
from search import DeepLearningSearch
from forward_prop_network import ForwardPropNetwork
import tensorflow as tf
search_algorithm = DeepLearningSearch()
sess = tf.Session()
forward_prop_network = ForwardPropNetwork(sess)
import copy
# search_algorithm=MontecarloSearch()
go_state_obj = GoStateObject()
args = sys.argv
user = '******'
# NNGS set up
nngs = NNGS()
# connect nngs
nngs.connect_nngs()
# login
nngs.login(user)
# match
if len(args) == 3 and args[2] in ('B', 'W'):
nngs.match(args[1], args[2])
# wait
nngs.nngs_wait()
# Go, Player set up
rule = Go()
players = [
Player(0, nngs.player_name['B']),
Player(1, nngs.player_name['W'])
]
players[0].next_player = players[1]
players[1].next_player = players[0]
player = players[0]
last_opponent_move = None
while True:
print(rule.print_board(go_state_obj))
if player.player_name == user:
(go_state_obj,
move) = search_algorithm.next_move(forward_prop_network, sess,
go_state_obj, player,
last_opponent_move)
print("next_move:", move)
# go_state_obj = rule.move_and_return_state(go_state_obj, player, move)
if go_state_obj.turns_num >= 500:
print("over 10 PASS in nngs_client_pass.py")
move = rule._PASS
nngs.send_data(move2nngs(move, rule))
else:
z = nngs.nngs_wait()
if z < 0: continue
nn_move = nngs2move(rule, player, z)
print(nn_move)
if nn_move == 0 or nn_move == 3:
last_opponent_move = rule._PASS
else:
last_opponent_move = nn_move
go_state_obj = rule.move_and_return_state(
go_state_obj, player, nn_move)
player = player.next_player
nngs.close_nngs()
