def test_ko_undo(self):
gs, moves = parseboard.parse(". B . . . . .|"
"B W B . . . .|"
"W k W . . . .|"
". W . . . . .|"
". . . . . . .|"
". . . . a . .|"
". . . . . . .|")
gs.set_current_player(go.BLACK)
# Trigger ko at (1, 1)
gs.do_move(moves['k'])
ko = gs.get_ko_location()
self.assertIsNotNone(ko)
copy = gs.copy()
self.equality_checks(gs, copy)
with copy.try_stone(flatten_idx(moves['a'], gs.get_size())):
self.inequality_checks(gs, copy)
# Doing move at 'a' clears ko
self.assertIsNone(copy.get_ko_location())
self.equality_checks(gs, copy)
# Undoing move at 'a' resets ko
self.assertEqual(copy.get_ko_location(), ko)
def _select_moves_and_normalize(self, nn_output, moves, size):
"""helper function to normalize a distribution over the given list of moves
and return a list of (move, prob) tuples
"""
if len(moves) == 0:
return []
move_indices = [flatten_idx(m, size) for m in moves]
# get network activations at legal move locations
distribution = nn_output[move_indices]
distribution = distribution / distribution.sum()
return zip(moves, distribution)
def _select_moves_and_normalize(self, nn_output, moves, size): """helper function to normalize a distribution over the given list of moves and return a list of (move, prob) tuples """ if len(moves) == 0: return [] move_indices = [flatten_idx(m, size) for m in moves] # get network activations at legal move locations distribution = nn_output[move_indices] distribution = distribution / distribution.sum() return zip(moves, distribution)
def do_move(states, states_prev, moves, X_list, y_list, player_color): bsize_flat = bsize * bsize for st, st_prev, mv, X, y in zip(states, states_prev, moves, X_list, y_list): if not st.is_end_of_game: # Only do more moves if not end of game already st.do_move(mv) if st.current_player != player_color and mv is not go.PASS_MOVE: # Convert move to one-hot state_1hot = preprocessor.state_to_tensor(st_prev) move_1hot = np.zeros(bsize_flat) move_1hot[flatten_idx(mv, bsize)] = 1 X.append(state_1hot) y.append(move_1hot) return states, X_list, y_list
def do_move(states, states_prev, moves, X_list, y_list, player_color):
bsize_flat = bsize * bsize
for st, st_prev, mv, X, y in zip(states, states_prev, moves, X_list,
y_list):
if not st.is_end_of_game:
# Only do more moves if not end of game already
st.do_move(mv)
if st.current_player != player_color and mv is not go.PASS_MOVE:
# Convert move to one-hot
state_1hot = preprocessor.state_to_tensor(st_prev)
move_1hot = np.zeros(bsize_flat)
move_1hot[flatten_idx(mv, bsize)] = 1
X.append(state_1hot)
y.append(move_1hot)
return states, X_list, y_list
def eval_state(self, state, moves=None):
"""Given a GameState object, returns a list of (action, probability) pairs
according to the network outputs
If a list of moves is specified, only those moves are kept in the distribution
"""
tensor = self.preprocessor.state_to_tensor(state)
# run the tensor through the network
network_output = self.forward(tensor)
moves = moves or state.get_legal_moves()
move_indices = [flatten_idx(m, state.size) for m in moves]
# get network activations at legal move locations
# note: may not be a proper distribution by ignoring illegal moves
distribution = network_output[0][move_indices]
distribution = distribution / distribution.sum()
return zip(moves, distribution)
def test_merge_and_capture_undo(self):
gs, moves = parseboard.parse(". . B B B . .|"
". B W W W B .|"
". B W B W B .|"
". B W c W B .|"
". B W B W B .|"
". B W W W B .|"
". . B B B . .|")
gs.set_current_player(go.BLACK)
copy = gs.copy()
# Initial equality checks
self.assertTrue(copy.sanity_check_groups())
self.equality_checks(gs, copy)
with copy.try_stone(flatten_idx(moves['c'], gs.get_size())):
self.assertTrue(copy.sanity_check_groups())
self.inequality_checks(gs, copy)
# Move should now be undone - retry equality checks from above
self.assertTrue(copy.sanity_check_groups())
self.equality_checks(gs, copy)
def test_hash_update_matches_actual_hash(self):
gs = GameState(size=7)
gs, moves = parseboard.parse("a x b . . . .|"
"z c d . . . .|"
". . . . . . .|"
". . . y . . .|"
". . . . . . .|"
". . . . . . .|"
". . . . . . .|")
# a,b,c,d are black, x,y,z,x are white
move_order = ['a', 'x', 'b', 'y', 'c', 'z', 'd', 'x']
for m in move_order:
move_1d = flatten_idx(moves[m], gs.get_size())
# 'Try' move and get hash
with gs.try_stone(move_1d):
hash1 = gs.get_hash()
# Actually do move and get hash
gs.do_move(moves[m])
hash2 = gs.get_hash()
self.assertEqual(hash1, hash2)
def _make_training_pair(st, mv, preprocessor):
# Convert move to one-hot
st_tensor = preprocessor.state_to_tensor(st)
mv_tensor = np.zeros((1, st.get_size() * st.get_size()))
mv_tensor[(0, flatten_idx(mv, st.get_size()))] = 1
return (st_tensor, mv_tensor)
