def test_valid_move(self):
"""
STS-1 Test 1
"""
s = State()
request = s.to_dict()
request.pop('winner', None)
request['piece'] = 1
request['target'] = 18
result = self.app.post('/move',
data=json.dumps(request),
headers={'content-type': 'application/json'})
self.assertEqual(200, result.status_code, 'Status is OK')
expected = s.get_child(1 << 1, 1 << 18)
expected = expected.to_dict()
jd = json.loads(result.data)
jd['prev_move'] = tuple(jd['prev_move'])
jd['can_castle'] = tuple(jd['can_castle'])
# subset because we're not considering jd['valid_moves']
self.assertTrue(expected.items() <= jd.items(), 'Returned response')
def test_search(self):
s = State((0, 0, 0, 0, 2 << 16, 4 << 16), (0, 0, 0, 0, 0, 1),
turn='w',
in_check=False)
agent = SampleMinimaxAgent()
selected_move = agent.select_move(s)
self.assertEqual(selected_move, (2 << 16, 2 << 8), 'Checkmate in 1')
def test_select_move_playout(self):
agent = RandomPlayoutAgent(max_time=1, max_depth=2)
s = State((0, 0, 0, 0, 2 << 16, 4 << 16), (0, 0, 0, 0, 0, 1),
turn='w',
in_check=False)
random.seed(124915)
selected_move = agent.select_move(s)
self.assertEqual(selected_move, (2 << 16, 2 << 8), 'Checkmate in 1')
def test_select_move_random(self):
agent = RandomMoveAgent()
s = State()
random.seed(124915)
moves = set()
for i in range(10):
moves.add(agent.select_move(s))
self.assertTrue(len(moves) > 1)
def test_gradients(self):
state_list = [State(), State(turn='b')]
reward = GameResult.P1_WINS
np.random.seed(185192)
a1 = ValueNetworkAgent()
a1.update(state_list, reward, use_numerical=False)
actual_dwo, actual_dwh = a1.wo, a1.wh
np.random.seed(185192)
a2 = ValueNetworkAgent()
a2.update(state_list, reward, use_numerical=True)
expected_dwo, expected_dwh = a1.wo, a1.wh
dwo_equal = np.allclose(actual_dwo, expected_dwo, 1e-8)
dwh_equal = np.allclose(actual_dwh, expected_dwh, 1e-8)
self.assertTrue(dwo_equal, 'dW_o almost equal')
self.assertTrue(dwh_equal, 'dW_h almost equal')
def test_select_move_black(self):
s = State(turn='b')
a = ValueNetworkAgent()
move = a.select_move(s)
is_legal = True
try:
s.get_child(*move)
except IllegalMoveException:
is_legal = False
self.assertTrue(is_legal, 'White select move')
def test_reset(self):
s = State()
result = self.app.get('/reset')
jd = json.loads(result.data)
jd['can_castle'] = tuple(jd['can_castle'])
actual = State.from_dict(jd['pieces'], jd['turn'], jd['in_check'],
jd['can_castle'], jd['prev_move'])
self.assertEqual(s, actual, 'Reset board state')
def play_and_update(self):
self.setup_iteration()
n_moves = 0
result = GameResult.NONTERMINAL
states = []
state = State()
while n_moves < self.max_iter and result == GameResult.NONTERMINAL:
states.append(state)
move = self.select_move(state)
state = state.get_child(*move)
result = state.is_terminal()
n_moves += 1
self.update(states, result)
self.teardown_iteration()
def test_winning(self):
s = State((0, 0, 0, 0, 2 << 16, 4 << 16), (0, 0, 0, 0, 0, 1),
turn='w',
in_check=False)
request = s.to_dict()
request.pop('winner', None)
request['piece'] = 17
request['target'] = 9
result = self.app.post('/moveai',
data=json.dumps(request),
headers={'content-type': 'application/json'})
self.assertEqual(200, result.status_code, 'Status is OK')
data = json.loads(result.data)
self.assertEqual(data['winner'], 'P1_WINS', 'White wins')
def reset():
s = State()
moves = [i.prev_move for i in s.get_children()]
legal_move_dict = {}
for piece, target in moves:
legal_move_dict[piece.bit_length() - 1] = legal_move_dict.get(
piece.bit_length() - 1, []) + [target.bit_length() - 1]
d = s.to_dict()
d['legal_moves'] = legal_move_dict
response = jsonify(d)
response.status_code = 200
return response
return response
def test_stalemate(self):
s = State((0, 0, 0, 0, 1 << 24, 4 << 16), (0, 0, 0, 0, 0, 2),
turn='w',
in_check=False)
request = s.to_dict()
request.pop('winner', None)
request['piece'] = 24
request['target'] = 16
result = self.app.post('/moveai',
data=json.dumps(request),
headers={'content-type': 'application/json'})
self.assertEqual(200, result.status_code, 'Status is OK')
data = json.loads(result.data)
self.assertEqual(data['winner'], 'DRAW',
'White moves, stalemate ensues')
def test_invalid_move(self):
"""
STS-1 Test 2
"""
s = State()
request = s.to_dict()
request.pop('winner', None)
request['piece'] = 1
request['target'] = 20
result = self.app.post('/move',
data=json.dumps(request),
headers={'content-type': 'application/json'})
self.assertEqual(400, result.status_code,
'Illegal request throws error')
result_message = json.loads(result.data)['message']
self.assertEqual(result_message, 'Completely and utterly illegal move',
'Illegal move message')
def test_capture(self):
s = State(wp=0x0800F000, bp=0x1000000000)
request = s.to_dict()
request.pop('winner', None)
request['piece'] = 27
request['target'] = 36
result = self.app.post('/move',
data=json.dumps(request),
headers={'content-type': 'application/json'})
self.assertEqual(200, result.status_code, 'Status is OK')
expected = s.get_child(1 << 27, 1 << 36)
expected = expected.to_dict()
jd = json.loads(result.data)
jd['prev_move'] = tuple(jd['prev_move'])
jd['can_castle'] = tuple(jd['can_castle'])
self.assertTrue(expected.items() <= jd.items(), 'Returned response')
def test_valid_nowin(self):
s = State()
request = s.to_dict()
request.pop('winner', None)
request['piece'] = 1
request['target'] = 18
result = self.app.post('/moveai',
data=json.dumps(request),
headers={'content-type': 'application/json'})
self.assertEqual(200, result.status_code, 'Status is OK')
move_one = s.get_child(1 << 1, 1 << 18)
possible_children = set(move_one.get_children())
data = json.loads(result.data)
actual = State.from_dict(data['pieces'], data['turn'],
data['in_check'], data['can_castle'],
data['prev_move'])
self.assertTrue(actual in possible_children, 'Valid responding move')
@property
def max_depth(self):
return self._max_depth
def select_move(self, state: 'State'):
self.n_ab = self.n_heuristic = 0
return super(CountingMinimaxAgent, self).select_move(state)
def _alpha_beta(self, state: 'State', depth: int, alpha: float, beta: float,
maxer: bool):
self.n_ab += 1
return super(CountingMinimaxAgent, self)._alpha_beta(state, depth,
alpha, beta, maxer)
def heuristic(self, state: 'State'):
self.n_heuristic += 1
return super(CountingMinimaxAgent, self).heuristic(state)
if __name__ == '__main__':
a = CountingMinimaxAgent(6)
s = State()
start = time.time()
a.select_move(s)
end = time.time()
heuristic_per_second = a.n_heuristic / (end - start)
ab_per_second = a.n_ab / (end - start)
print('Heuristic Evaluations per second: %.1f' % heuristic_per_second)
print('Alpha Beta Evaluations per second: %.1f' % ab_per_second)