def show_exemplar(model, x, x_as_onehot, y):
"""Picks a random example from the data."""
i = np.random.randint(0, x.shape[0])
example = x[i, :]
example_as_onehot = x_as_onehot[i]
score = y[i]
example_as_single_example = np.reshape(example_as_onehot,
(1, WIDTH * HEIGHT, MAX_TILE))
prediction = model.predict(example_as_single_example)
board = Board.from_vector(example)
board.pretty_print()
print("Actual score: %d; predicted score: %s" % (score, prediction))
def add_n_examples(self, strategy, rnd, n,
starting_positions_dataset=None):
"""Runs games and adds them to the dataset until at least @p n
examples have been added. Returns the number of examples added.
If @p starting_positions_dataset is set, games will be started from
a randomly selected position from that dataset rather than from a
blank board."""
print("Adding", n, "examples to dataset.")
added = 0
while added < n:
starting_game = None
if starting_positions_dataset:
random_position = starting_positions_dataset.nth_example(
rnd.randint(0,
starting_positions_dataset.num_examples() - 1))
starting_game = Game(Board.from_vector(random_position))
if not starting_game.board().can_move():
continue
num_added = self.add_game(strategy, rnd, starting_game)
if (added // 10000) != ((num_added + added) // 10000):
print("Added %d so far..." % (num_added + added))
added += num_added
return added
def test_encoding(self):
board = self.realistic_board
encoding = board.as_vector()
self.assertEqual(encoding.size, Board.vector_width())
decoding = Board.from_vector(encoding)
self.assertEqual(board, decoding)
