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 get_examples(given_examples,
n_tiles,
height,
width,
dg,
from_file=False,
return_binary_mask=False,
predict_v=False,
predict_move_index=True,
scalar_tiles=False,
shuffle_tiles_times=20):
examples = []
for i, _example in enumerate(given_examples):
# print(f'{i}/{len(given_examples)}')
if scalar_tiles:
state, tiles, solution = _example
state = state.squeeze()
else:
state, solution = _example
grid = np.zeros([height, width])
for solution_index, solution_tile in enumerate(solution):
solution_copy = np.copy(solution)
solution_order = np.array(solution_copy[solution_index:])
solution_tile_dims = solution_tile[:2]
orig_solution_order = solution_order
for i in range(shuffle_tiles_times): # tile permutations
solution_order = np.copy(orig_solution_order)
_tiles_ints = [x[:ORIENTATIONS] for x in solution_order]
_tiles_ints = get_tiles_with_orientation(_tiles_ints)
_tiles_ints = SolutionChecker.get_possible_tile_actions_given_grid(
grid, _tiles_ints)
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)))
else:
tiles = dg._transform_instance_to_matrix(
_tiles_ints, only_one_orientation=True)
tiles = pad_tiles_with_zero_matrices(
tiles, ORIENTATIONS * n_tiles - tiles.shape[2], width,
height)
state = np.dstack((np.expand_dims(grid, axis=2), tiles))
pi = solution_to_solution_matrix(
solution_tile,
cols=width,
rows=height,
return_binary_mask=False).flatten()
# v = N_TILES - solution_index
v = 1
if solution_index == len(solution) - 1:
continue
if predict_move_index:
n_possible_tiles = SolutionChecker.get_n_nonplaced_tiles(
_tiles_ints)
if n_possible_tiles == 1: # if only one action-tile placement is possible
pass
else:
_tiles_ints = SolutionChecker.np_array_to_tiles(
_tiles_ints)
solution_index = _tiles_ints.index(solution_tile_dims)
if scalar_tiles:
if INDIVIDUAL_TILES:
split_tiles = np.array(tiles)
split_tiles = np.split(split_tiles,
split_tiles.shape[0])
else:
split_tiles = tiles
example = [
grid.copy(), split_tiles,
one_hot_encode(_tiles_ints, solution_tile_dims,
len(tiles))
]
else:
example = [
state,
one_hot_encode(_tiles_ints, solution_tile_dims,
state.shape[2] - 1)
]
examples.append(example)
# print(_tiles_ints, solution_tile_dims, one_hot_encode(_tiles_ints, solution_tile_dims, state))
else:
example = [state, pi]
if predict_v:
example.append(v)
examples.append(example)
success, grid = SolutionChecker.place_element_on_grid_given_grid(
solution_tile[:ORIENTATIONS],
solution_tile[2],
val=1,
grid=grid,
cols=width,
rows=height)
return examples
def test_pad(self):
tiles = [[1, 2], [2, 1], [4, 5]]
padded_tiles = SolutionChecker.pad_tiles_with_zero_scalars(tiles, 2)
self.assertEqual(len(padded_tiles), 5)
self.assertEqual([[1, 2], [2, 1], [4, 5], [0, 0], [0, 0]], padded_tiles)
