def test_create_board():
expected_spaces = np.array([[0, 0, 0],
[0, 0, 0],
[0, 0, 0]])
board = TicTacToeState()
assert np.array_equal(board.get_spaces(), expected_spaces)
def test_create_board_from_text():
text = """\
X..
.O.
...
"""
expected_spaces = np.array([[1, 0, 0],
[0, -1, 0],
[0, 0, 0]])
board = TicTacToeState(text)
assert np.array_equal(board.get_spaces(), expected_spaces)
def test_create_board_with_coordinates():
text = """\
ABC
1 X..
2 .O.
3 ...
"""
expected_spaces = np.array([[1, 0, 0],
[0, -1, 0],
[0, 0, 0]])
board = TicTacToeState(text)
assert np.array_equal(board.get_spaces(), expected_spaces)
def test_create_training_data():
start_state = TicTacToeState()
manager = SearchManager(start_state, FirstChoiceHeuristic())
expected_boards, expected_outputs = zip(
*[[
start_state.get_spaces(),
np.array([1., 0., 0., 0., 0., 0., 0., 0., 0., -1.])
],
[
TicTacToeState("""\
X..
...
...
""").get_spaces(),
np.array([0., 1., 0., 0., 0., 0., 0., 0., 0., 1.])
],
[
TicTacToeState("""\
XO.
...
...
""").get_spaces(),
np.array([0., 0., 1., 0., 0., 0., 0., 0., 0., -1.])
],
[
TicTacToeState("""\
XOX
...
...
""").get_spaces(),
np.array([0., 0., 0., 1., 0., 0., 0., 0., 0., 1.])
],
[
TicTacToeState("""\
XOX
O..
...
""").get_spaces(),
np.array([0., 0., 0., 0., 1., 0., 0., 0., 0., -1.])
],
[
TicTacToeState("""\
XOX
OX.
...
""").get_spaces(),
np.array([0., 0., 0., 0., 0., 1., 0., 0., 0., 1.])
],
[
TicTacToeState("""\
XOX
OXO
...
""").get_spaces(),
np.array([0., 0., 0., 0., 0., 0., 1., 0., 0., -1.])
]])
expected_boards = np.stack(expected_boards)
expected_outputs = np.stack(expected_outputs)
boards, outputs = manager.create_training_data(iterations=1, data_size=7)
assert repr(boards) == repr(expected_boards)
assert repr(outputs) == repr(expected_outputs)