def perform_simulation(self, state):
'''
Performs the simulation until legal moves are available.
If simulation ends by finding a solution, a root state starting from this simulation is returned
'''
solution_tiles_order = []
depth = 0
simulation_root_state = state # in case simulation ends in solution; these states are the solution
if len(state.tiles) == 0:
print('perform_simulation called with empty tiles')
return ALL_TILES_USED, simulation_root_state, solution_tiles_order
val = self.val
while True:
val += 1
if len(state.tiles) == 0:
print('solution found in simulation')
return ALL_TILES_USED, simulation_root_state, solution_tiles_order
valid_moves = SolutionChecker.get_valid_next_moves(state, state.tiles, val=val, colorful_states=self.colorful_states)
if not valid_moves:
return depth, simulation_root_state, solution_tiles_order
next_random_tile_index = random.randint(0, len(valid_moves) -1)
success, new_board = SolutionChecker.get_next_turn(
state, valid_moves[next_random_tile_index], val, destroy_state=True,
colorful_states=self.colorful_states
)
self.n_tiles_placed += 1
solution_tiles_order.append(valid_moves[next_random_tile_index])
if success == ALL_TILES_USED:
print('grid is full')
# no LFB on grid; probably means grid is full
solution_tiles_order.append(valid_moves[next_random_tile_index])
return ALL_TILES_USED, simulation_root_state, solution_tiles_order
elif success == NO_NEXT_POSITION_TILES_UNUSED:
print('no next position with unused tiles')
return depth, simulation_root_state, solution_tiles_order
elif success == TILE_CANNOT_BE_PLACED:
# cannot place the tile. return depth reached
return depth, simulation_root_state, solution_tiles_order
else:
new_tiles = SolutionChecker.eliminate_pair_tiles(state.tiles, valid_moves[next_random_tile_index])
new_state = State(board=new_board, tiles=new_tiles, parent=state)
new_state.score = -1 # because no choice is performed for sequent actions
state.children.append(new_state)
state = new_state
depth += 1
return depth, simulation_root_state, solution_tiles_order
def play_using_prediction(nnet,
width,
height,
tiles,
grid,
n_tiles,
dg,
predict_move_index=False,
verbose=False,
scalar_tiles=False):
if scalar_tiles:
tiles = tiles
else:
tiles = state_to_tiles_dims(tiles, dg)
while True:
tiles_left = len(tiles)
if tiles_left == 0:
print(
f"Success: game ended with {tiles_left / ORIENTATIONS} tiles left unplaced."
)
return 0
_tiles_ints = SolutionChecker.get_possible_tile_actions_given_grid(
grid, tiles)
if len(_tiles_ints) == 0 and tiles_left:
print(
f"game ended with {tiles_left / ORIENTATIONS} tiles left unplaced."
)
return tiles_left / ORIENTATIONS
np.random.shuffle(_tiles_ints)
if scalar_tiles:
_tiles = tiles_to_np_array(
SolutionChecker.pad_tiles_with_zero_scalars(
_tiles_ints, ORIENTATIONS * n_tiles - len(_tiles_ints)))
# state = state.squeeze()
prediction = nnet.predict([grid, tiles_to_np_array(_tiles)])
else:
tiles_in_matrix_shape = dg._transform_instance_to_matrix(
_tiles_ints, only_one_orientation=True)
tiles_in_matrix_shape = pad_tiles_with_zero_matrices(
tiles_in_matrix_shape,
n_tiles * ORIENTATIONS - tiles_in_matrix_shape.shape[2], width,
height)
state = np.dstack((np.expand_dims(grid,
axis=2), tiles_in_matrix_shape))
prediction = nnet.predict(state)
if not predict_move_index:
if len(prediction) == 2: # if we are also predicting v
prediction, v = prediction
prediction = np.reshape(prediction, (width, height))
# get the probability matrix
prediction = get_prediction_masked(prediction, state[:, :, 0])
if verbose:
print('-' * 50)
print(grid, prediction)
print(tiles)
if scalar_tiles:
_ttiles = tiles
else:
_ttiles = state_to_tiles_dims(state, dg)
solution_tile = get_best_tile_by_prediction(
grid,
_ttiles,
prediction,
dg,
predict_move_index=predict_move_index)
if verbose:
print(solution_tile)
if solution_tile is None:
print(
f"game ended with {tiles_left / ORIENTATIONS} tiles left unplaced."
)
return tiles_left / ORIENTATIONS
success, grid = SolutionChecker.place_element_on_grid_given_grid(
solution_tile[0],
solution_tile[1],
val=1,
grid=grid,
cols=width,
rows=height)
if not success:
print(
f"game ended with {tiles_left / ORIENTATIONS} tiles left unplaced."
)
return tiles_left / ORIENTATIONS
if scalar_tiles:
tiles = [tuple(x) for x in tiles]
tiles = SolutionChecker.eliminate_pair_tiles(tiles, solution_tile[0])
return 0
def predict(self, temp=1, N=3000):
initial_state = self.state
state = self.state
available_tiles = state.tiles
prev_state = state
solution_found = False
depth = 0
self.val = 1
while len(state.tiles):
tile_placed = False
states = []
print(len(state.tiles))
best_score = 0
for i, tile in enumerate(state.tiles):
success, new_board = SolutionChecker.get_next_turn(state, tile, self.val, destroy_state=False)
self.n_tiles_placed += 1
if success == ALL_TILES_USED:
print('solution found!')
solution_found = True
return initial_state, depth, solution_found
if success == TILE_CANNOT_BE_PLACED:
# cannot place the tile. this branch will not be considered
states.append(None)
continue
else:
tile_placed = True
new_tiles = SolutionChecker.eliminate_pair_tiles(state.tiles, tile)
new_state = State(
board=new_board, tiles=new_tiles, parent=state)
state.children.append(new_state)
simulation_result, solution_tiles_order = self.perform_simulations(new_state, N=N, record_simulations=False)
if simulation_result == ALL_TILES_USED:
print('solution found in simulation!')
print(tile)
solution_found = True
# update scores of states found in simulation
new_state.score = len(state.tiles) / ORIENTATIONS - 1
_state = new_state.children[0]
while _state.children:
_state.score = (len(_state.tiles) / ORIENTATIONS) - 1
_state = _state.children[0]
if state.tile_placed:
self.solution_tiles_order.extend([state.tile_placed] + [tile] + solution_tiles_order)
else:
self.solution_tiles_order.extend([tile] + solution_tiles_order)
return initial_state, depth, solution_found
new_state.score = simulation_result
if new_state.score > best_score:
best_tile = tile
best_score = new_state.score
new_state.tile_placed = tile
state.solution_tiles_order.append(tile)
states.append(new_state)
if not tile_placed:
# no tile was placed, it's a dead end; end game
return initial_state, depth, solution_found
# PERFORMANCE:
# for visualization this can be changed
# all tiles will be 1 inside the frame for performance reasons
self.val += 1
depth += 1
best_action = get_max_index(states)
prev_state = state
new_state = states[best_action]
print(best_tile, prev_state.tile_placed)
if prev_state.tile_placed:
self.solution_tiles_order.append(prev_state.tile_placed)
state = new_state
print('Solution found!')
solution_found = True
return initial_state, depth, solution_found
def test_eliminate_pair_tiles_when_pair_is_not_present(self):
tiles_list = [(1, 8), (2, 1), (1, 1), (1, 1), (2, 1), (1, 1)]
with self.assertRaises(ValueError):
SolutionChecker.eliminate_pair_tiles(tiles_list, tile_to_remove=(1, 8))
def test_eliminate_pair_tiles_3(self):
tiles_list = [(1, 1), (2, 1), (1, 1), (1, 1), (2, 1), (1, 1)]
new_tiles = SolutionChecker.eliminate_pair_tiles(tiles_list, tile_to_remove=(1, 1))
self.assertEqual(new_tiles, [(2, 1), (1, 1), (2, 1), (1, 1)])
