def performance_test():
"""
the performance test borrowed from
http://www.codeskulptor.org/#user41_rAX7Nukse9WzOOV.py
"""
time.time()
start = time.time()
provided.play_game(mc_move, NTRIALS, False)
print str(provided.play_game)+": \t" + str( (time.time() - start) )
mc_update_scores(scores, board1, player)
num -= 1
next_move = get_best_move(board, scores)
return next_move
# Test game with the console or the GUI.
# Uncomment whichever you prefer.
# Both should be commented out when you submit for
# testing to save time.
provided.play_game(mc_move, NTRIALS, False)
# poc_ttt_gui.run_gui(3, provided.PLAYERX, mc_move, NTRIALS, False)
#import user35_PIk21NjpAa_0 as tests
#tests.test_mc_trial(mc_trial) # tests for mc_trial
#tests.test_mc_update_scores(mc_update_scores, MCMATCH, MCOTHER) # tests for mc_update_scores
#tests.test_get_best_move(get_best_move) # tests for get_best_move
#tests.test_mc_move(mc_move, NTRIALS)
if player == provided.PLAYERX:
if score < result[0]:
score = max(score, result[0])
final_move = _move
else:
if score > result[0]:
score = min(score, result[0])
final_move = _move
return score, final_move
def move_wrapper(board, player, trials):
"""
Wrapper to allow the use of the same infrastructure that was used
for Monte Carlo Tic-Tac-Toe.
"""
move = mm_move(board, player)
#assert move[1] != (-1, -1), "returned illegal move (-1, -1)"
return move[1]
# Test game with the console or the GUI.
# Uncomment whichever you prefer.
# Both should be commented out when you submit for
# testing to save time.
provided.play_game(move_wrapper, 1, False)
#poc_ttt_gui.run_gui(3, provided.PLAYERO, move_wrapper, 1, False)
all_move =[]
for each in board.get_empty_squares():
temp = board.clone()
temp.move(each[0], each[1], player)
all_move.append([minmax(temp, provided.switch_player(player)), each])
if player == provided.PLAYERX:
all_move.sort(key = lambda x: x[0], reverse=True)
else:
all_move.sort(key = lambda x: x[0])
return all_move[0][0], all_move[0][1]
def move_wrapper(board, player, trials):
"""
Wrapper to allow the use of the same infrastructure that was used
for Monte Carlo Tic-Tac-Toe.
"""
move = mm_move(board, player)
assert move[1] != (-1, -1), "returned illegal move (-1, -1)"
return move[1]
# Test game with the console or the GUI.
# Uncomment whichever you prefer.
# Both should be commented out when you submit for
# testing to save time.
provided.play_game(move_wrapper, 1, False)
poc_ttt_gui.run_gui(3, provided.PLAYERO, move_wrapper, 1, False)
"""
dimension = board.get_dim()
scores = [[0 for dummy in range(dimension)] for dummy in range(dimension)]
for dummy in range(trials):
board_copy = board.clone()
mc_trial(board_copy, player)
mc_update_scores(scores, board_copy, player)
return get_best_move(board, scores)
# Test game with the console or the GUI Uncomment whichever
# you prefer. Both should be commented out when you submit
# for testing to save time.
provided.play_game(mc_move, NTRIALS, False)
poc_ttt_gui.run_gui(3, provided.PLAYERX, mc_move, NTRIALS, False)
# # Testing code
# test_game = provided.TTTBoard(3)
# test_dim = test_game.get_dim()
# test_scores = [[0 for emptycol in range(test_dim)]
# for emptyrow in range(test_dim)]
# # test mc_trial()
# mc_trial(test_game, provided.PLAYERX)
# print str(test_game)
# print "Winner:", test_game.check_win(), "\n"
# # test mc_update_scores
# mc_update_scores(test_scores, test_game, provided.PLAYERX)
return res
###Test game with the console or the GUI.
AI_VS_AI_GAMES = 20
winners = []
lookup = {provided.PLAYERX: "Monte carol", provided.PLAYERO: "minMax"}
win1 = 0
win2 = 0
tie = 0
a1_total_t = 0
a2_total_t = 0
for i in range(AI_VS_AI_GAMES):
# two AI test
print("test", i)
winner, a1_time, a2_time = provided.play_game(mc_move, NTRIALS, minMaxMove,
DEPTH, False)
a1_total_t += a1_time
a2_total_t += a2_time
if winner == provided.PLAYERX:
win1 += 1
elif winner == provided.PLAYERO:
win2 += 1
else:
tie += 1
print("%d %d %d\n" % (win1, tie, win2))
# poc_ttt_gui.run_gui(3, provided.PLAYERX, mc_move, NTRIALS, False)
# just run minmax
# poc_ttt_gui.run_gui(3, provided.PLAYERX, minMaxMove, DEPTH, False)