def test_board_equivalence(self):
size = 9
cache = BitBoardCache("../cache/9-magics", size=9, win_chain_length=5, force_build_win_checks=False)
bitboard = BitBoard(cache, size=9, win_chain_length=5)
normal_board = Board(size=9, win_chain_length=5)
fboard_1 = FeatureBoard_v1_1(bitboard)
fboard_2 = FeatureBoard_v1_1(normal_board)
for i in range(1000):
if np.random.rand() < 0.5:
normal_board.make_random_move()
bitboard.move(normal_board.get_last_move())
if normal_board.game_over():
break
fboard_1.move(normal_board.get_last_move())
fboard_2.move(normal_board.get_last_move())
elif normal_board.get_last_move():
normal_board.unmove()
bitboard.unmove()
fboard_1.unmove()
fboard_2.unmove()
self.verify_sync(normal_board, fboard_2)
self.verify_sync(bitboard, fboard_1)
def test_mass_play(self):
for i in range(1000):
board = Board(size=7, win_chain_length=4)
board.make_random_move()
if board.game_over():
self.assertTrue(board.game_won() or board.game_assume_drawn())
if board.game_assume_drawn():
self.assertTrue(board.game_over())
def test_thoughtboard_consistency(self):
board = Board(size=9, win_chain_length=5)
fb = FeatureBoard_v1_1(board)
tb = ThoughtBoard(board, FeatureBoard_v1_1)
fb.move(15)
movelist = MoveList((), []).append(15)
self.assertTrue(
np.equal(fb.get_p_features(),
tb.get_p_features_after(movelist)).all())
movelist_2 = movelist.append(32)
fb.move(32)
self.assertTrue(
np.equal(fb.get_p_features(),
tb.get_p_features_after(movelist_2)).all())
fb.unmove()
self.assertTrue(
np.equal(fb.get_p_features(),
tb.get_p_features_after(movelist)).all())
fb.move(32)
self.assertTrue(
np.equal(fb.get_p_features(),
tb.get_p_features_after(movelist_2)).all())
tb.make_move(15)
tb.make_move(32)
self.assertTrue(
np.equal(fb.get_p_features(), tb.get_q_features()).all())
def replay(self):
board = Board.load(self.initial_state)
print(board.pprint())
for i, move in enumerate(self.moves):
print('Q', self.q_assessments[i])
board.move(move)
print(board.pprint())
def test_thoughtboard_root_win(self):
trivial_board = Board(size=9, win_chain_length=5)
trivial_board.set_to_one_move_from_win()
tb = ThoughtBoard(trivial_board, FeatureBoard_v1_1)
trivial_board.move(36)
print(trivial_board.pprint())
move_list = MoveList((), []).append(36)
self.assertTrue(trivial_board.game_won())
self.assertTrue(tb.game_over_after(move_list))
def test_move_available_vector(self):
board = Board(size=9, win_chain_length=5)
fb = FeatureBoard_v1_1(board)
tb = ThoughtBoard(board, FeatureBoard_v1_1)
self.assertEqual(np.sum(tb.get_available_move_vector_after([34, 45])),
79)
fb.move(15)
self.assertEqual(np.sum(fb.get_init_available_move_vector()), 81)
self.assertEqual(np.sum(tb.get_available_move_vector_after([18, 25])),
79)
def test_export_parse(self):
board = Board(size=9, win_chain_length=5)
for i in range(10):
board.make_random_move()
parsed_board = Board.load(board.export())
self.assertEqual(board.pprint(lastmove_highlight=False), parsed_board.pprint(lastmove_highlight=False))
self.assertFalse(parsed_board.game_over())
self.assertFalse(parsed_board.game_won())
self.assertTrue(np.equal(parsed_board._matrix, board._matrix).all())
def iterate_on(self, record):
board = Board.load(record.get_initial_state())
q_assessments = record.get_q_assessments()
for i, move in enumerate(record.get_moves()):
if record.get_winning_player() == board.get_player_to_move():
self.make_feature_tensors(board, move, q_assessments[i][0],
q_assessments[i][1])
# learn drawn positions
elif record.get_winning_player() == Player.NONE:
self.make_feature_tensors(board, move, q_assessments[i][0],
q_assessments[i][1])
board.move(move)
def test_parse(self):
board = Board(size=9)
board.move_coord(5, 5)
record = GameRecord.create(board)
record.add_move(35, 0, 0)
record.set_winner(1)
with self.assertRaises(ValueError):
record.add_move(45, 0, 0)
board = Board(size=9)
record2 = GameRecord.create(board)
record2.add_move(45, 0, 0)
record2.add_move(35, 0, 0)
record2.set_winner(1)
def test_even_search(self):
mind = PExpMind_v3(size=9, init=False, search_params=None)
mind.load_net('../../models/voldmaster_' + str(0))
board = Board(size=9, win_chain_length=5)
searcher = PEvenSearch(board,
mind.policy_est,
mind.value_est,
max_iterations=2,
p_batch_size=1024,
verbose=True,
validations=True)
searcher.p_expand()
self.assertGreater(len(searcher.expandable_nodes), 0)
for node in searcher.expandable_nodes:
self.assertEqual(list(node.get_parents())[0], searcher.root_node)
self.assertLess(node.log_total_p, 0)
searcher.q_eval()
self.assertNotEqual(searcher.root_node.get_principal_variation(), None)
def test_rotator(self):
rot = BoardTransform(size=9)
for i in range(9 * 9):
x, y = rot.index_to_coordinate(i)
# print(x, y, i)
self.assertEqual(i, rot.coordinate_to_index(x, y))
board = Board(size=9, win_chain_length=5)
moves = [(0, 0), (2, 1), (5, 5), (4, 4)]
for i, move in enumerate(moves):
board.move_coord(move[0], move[1])
print(board.pprint())
index = rot.coordinate_to_index(move[0], move[1])
rotated_matrices = rot.get_rotated_matrices(board._matrix.reshape((board.get_size(), board.get_size(), 1)))
self.assertFalse(np.equal(rotated_matrices[0], rotated_matrices[2]).all())
for point, mat in zip(rot.get_rotated_points(index), rotated_matrices):
x, y = rot.index_to_coordinate(point)
self.assertEqual(mat[x][y][0], board.get_player_last_move().value)
def test_deepcopy(self):
board = Board(size=7, win_chain_length=4)
import copy
copied_board = copy.deepcopy(board)
board.move(22)
self.assertNotEqual(board.get_spot(22), copied_board.get_spot(22))
def test_double_serialize(self):
board = Board(size=9, win_chain_length=5)
json.loads(json.dumps(board.export()))
def test_odd_board_bug(self):
board = Board(size=9, win_chain_length=5)
board.move_coord(4, 0)
board.move_coord(4, 1)
board.move_coord(3, 0)
board.move_coord(3, 1)
board.move_coord(2, 0)
board.move_coord(2, 1)
board.move_coord(1, 0)
board.move_coord(1, 1)
board.move_coord(0, 0)
self.assertTrue(board.game_won())
def test_precomputed_checklengths(self):
board = Board(size=9, win_chain_length=5)
self.assertEqual(board._check_locations[(0, -1, 0)], ())
self.assertEqual(board._check_locations[(0, 1, 0)], (1, 2, 3, 4))
self.assertEqual(board._check_locations[(0, 0, 1)], (9, 18, 27, 36))
def run():
mind = pexp_mind.PExpMind(size=SIZE, init=False, channels=4)
mind.load_net('../models/9_4_4')
rs = RandomState(42)
for i in range(50):
board = Board(size=SIZE, win_chain_length=5, draw_point=50)
print('Game', i)
# randomize the board a bit
for j in range(rs.randint(0, 10)):
board.make_random_move()
# board.move(2, 2)
# board.move(0, 1)
# board.move(2, 3)
# board.move(2, 1)
# board.move(2, 4)
# board.move(3, 1)
# board.move(4, 3)
# board.move(3, 4)
# board.move(6, 6)
# board.move(4, 1)
# board.move(0, 0)
# board.move(1, 0)
# board.move(0, 4)
# board.move(2, 2)
# board.move(0, 7)
# board.move(3, 0)
# board.move(1, 2)
# board.move(3, 7)
# board.move(1, 6)
# board.move(4, 4)
# board.move(1, 8)
# board.move(4, 5)
# board.move(2, 0)
# board.move(4, 6)
# board.move(2, 4)
# board.move(4, 8)
# board.move(2, 8)
# board.move(5, 0)
# board.move(3, 1)
# board.move(5, 4)
# board.move(3, 6)
# board.move(5, 8)
# board.move(4, 0)
# board.move(6, 3)
# board.move(4, 3)
# board.move(7, 2)
# board.move(4, 7)
# board.move(7, 6)
# board.move(6, 6)
# board.move(8, 0)
# board.move(8, 1)
# board.move(8, 8)
# board.move(8, 7)
print(board)
current_player = board.get_player_to_move()
def expanding_p(depth, p):
return np.logical_or.reduce([
np.logical_and(depth < 2, p > -6),
np.logical_and(depth < 4, p > -4),
np.logical_and(depth < 6, p > -4),
np.logical_and(depth < np.inf, p > -3)
])
def permissive_expansion(depth):
if depth < 2:
return np.inf
if depth < 8:
return 5
return 3
mind.define_policies(expanding_p,
permissive_expansion,
convergence_count=5,
alpha=0.2,
q_exp_batch_size=SIZE**2,
p_exp_batch_size=SIZE**3,
required_depth=6,
max_iters=20)
while True:
result = mind.make_move(board,
as_player=current_player,
epsilon=0.1,
consistency_check=False,
verbose=True)
print(board.pprint())
if current_player == Board.FIRST_PLAYER:
current_player = Board.SECOND_PLAYER
else:
current_player = Board.FIRST_PLAYER
if result:
break
print('done')
def test_chain_length(self):
board = Board(size=9, win_chain_length=4)
self.assertEqual(board.get_spot_coord(0, 0), Player.NONE)
board.move_coord(0, 0)
self.assertEqual(board.get_spot_coord(0, 0), Player.FIRST)
self.assertFalse(board.is_move_available(0))
board.move_coord(0, 1)
board.move_coord(1, 1)
self.assertEqual(board.get_spot_coord(1, 1), Player.FIRST)
index00 = board._transformer.coordinate_to_index(0, 0)
index11 = board._transformer.coordinate_to_index(1, 1)
assert (board.chain_length(index11, -1, 0) == 1)
assert (board.chain_length(index11, -1, -1) == 2)
assert (board.chain_length(index00, 1, 1) == 2)
board.move_coord(1, 0)
board.move_coord(2, 2)
board.move_coord(2, 3)
board.move_coord(3, 3)
board.unmove()
board.move_coord(3, 3)
index33 = board._transformer.coordinate_to_index(3, 3)
self.assertEqual(board.chain_length(index33, 1, 1), 1)
self.assertEqual(board.get_spot_coord(2, 2), Player.FIRST)
assert (board.game_won())
if depth < 2:
return np.inf
if depth < 8:
return 5
return 3
mind.define_policies(expanding_p,
permissive_expansion,
convergence_count=5,
alpha=0.2,
q_exp_batch_size=SIZE**2,
p_exp_batch_size=SIZE**3,
required_depth=6,
max_iters=25)
board = Board(size=SIZE, win_chain_length=5)
# randomize the board a bit
for j in range(random.randint(0, int(SIZE * 2.5))):
board.make_random_move()
print(board.guide_print())
while True:
if board.get_player_to_move() == Board.FIRST_PLAYER:
inp = input("Input your move (i.e. \"3 5\"): ")
if len(inp.split(' ')) != 2:
print('Incorrect number of coordinates, please try again!')
continue
x, y = inp.split(' ')
try:
