def update_empty_intersection(self):
n = self.board_size * self.board_size
ownership = [UNDECIDED] * n
visited = BitSet(n)
adjacent_chains = BitSet(n)
for x in range(self.board_size):
for y in range(self.board_size):
z = x * self.board_size + y
if self._color[z] != EMPTY or visited.contains(z):
continue
self._chain_size[z] = 0
adjacent_to_black, adjacent_to_white = False, False
adjacent_chains.clear()
q = Queue(n)
q.enqueue((x, y))
visited.add(z)
while not q.is_empty():
x, y = q.dequeue()
for dx, dy in _directions:
if self.on_board(x + dx, y + dy):
if self.color(x + dx, y + dy) == BLACK:
adjacent_chains.add(self.find(x + dx, y + dy))
adjacent_to_black = True
elif self.color(x + dx, y + dy) == WHITE:
adjacent_chains.add(self.find(x + dx, y + dy))
adjacent_to_white = True
elif not visited.contains(
(x + dx) * self.board_size + y + dy):
q.enqueue((x + dx, y + dy))
visited.add((x + dx) * self.board_size + y +
dy)
self._parent[x * self.board_size + y] = z
self._chain_size[z] += 1
if (adjacent_to_black and adjacent_to_white) \
or len(adjacent_chains) == 0:
ownership[z] = UNDECIDED
elif len(adjacent_chains) == 1:
if adjacent_to_black:
ownership[z] = BLACK_EYE
else:
ownership[z] = WHITE_EYE
else:
if adjacent_to_black:
ownership[z] = BLACK_OWNED
else:
ownership[z] = WHITE_OWNED
return ownership
class TestQueue(unittest.TestCase):
@classmethod
def setUpClass(self):
self.initial_list = [i for i in range(5)]
@classmethod
def tearDownClass(self):
print("All tests for queue completed")
def setUp(self):
# print("Initializing queue")
self.queue = Queue(8, 0, initial_iter=self.initial_list)
def tearDown(self):
pass
def test_head(self):
"""
Test getting top element
"""
self.assertEqual(4, self.queue.head())
def test_tail(self):
"""
Test getting top element
"""
self.assertEqual(0, self.queue.tail())
def test_enqueue(self):
"""
Test pushing to stack top
"""
self.queue.enqueue(5)
self.assertEqual(5, self.queue.tail())
self.assertNotEqual(5, self.queue.head())
self.assertEqual(4, self.queue.head())
self.assertEqual(6, self.queue.size())
def test_dequeue(self):
"""
Test popping
"""
for x in range(4, -1, -1):
self.assertEqual(x, self.queue.dequeue())
self.assertEqual(0, self.queue.size())
def _remove(self, x, y):
self._liberties[self.find(x, y)] = None
n = self.board_size * self.board_size
z = x * self.board_size + y
color = self._color[z]
# insert (x, y) into the queue, and mark it as "visited but not
# processed" (color = _GRAY)
# we abuse the _color array to store the BFS state here:
# - color = EMPTY: processed
# - color = BLACK: not visited (for black stones)
# - color = WHITE: not visited (for white stones)
# - color = _GRAY: visited (i.e., in queue) but not processed
q = Queue(n)
q.enqueue((x, y))
self._color[z] = _GRAY
adjacent_opponent_chains = SmallSet(4)
while not q.is_empty():
x, y = q.dequeue()
adjacent_opponent_chains.clear()
for dx, dy in _directions:
if self.on_board(x + dx, y + dy):
# insert all the own adjacent stones that are not
# visited into the queue
if self.color(x + dx, y + dy) == color:
q.enqueue((x + dx, y + dy))
self._color[(x + dx) * self.board_size + y +
dy] = _GRAY
# save opponent's stone chains that are adjacent to
# this new empty intersection
if self.color(x + dx, y + dy) == -color:
adjacent_opponent_chains.add(self.find(x + dx, y + dy))
# the new empty intersection (x, y) provides liberty to its
# adjacent stone chains
z = x * self.board_size + y
for c in adjacent_opponent_chains:
if self._liberties[c] is None:
self._liberties[c] = BitSet(n)
self._liberties[c].add(z)
self._color[z] = EMPTY
class ResignManager:
def __init__(self, conf):
self._regret_frac = conf.RESIGN_REGRET_FRAC
self._histories = Queue(conf.NUM_RESIGN_SAMPLES)
self._threshold = -1.0
def threshold(self):
return -1.0 if not self._histories.is_full() else self._threshold
def add(self, history, result):
if len(history) == 0:
return
# suppose the resignation values of a game are
# [v_0, v_1, v_2, ...], we say time t is resignation eligible
# if it is possible to choose an appropriate threshold such
# that the game resigns at t
# for example, t = 0 is always resignation eligible
# t = 1 is resignation eligible if and only if v_1 < v_0,
# otherwise it is impossible to find a threshold such that the
# game resigns at t = 1
# it is not difficult to see that if t is resignation eligible,
# the next resignation eligible time is the smallest t' such
# that t' > t and v_t' < v_t
# keep only the resignation eligible time and discard the rest
history_ = [history[0]]
for t in range(1, len(history)):
if history[t][0] < history_[-1][0]:
history_.append(history[t])
# update the regret
# regret = 1 means a false positive resignation, i.e., the game
# could have been won if the player had not resigned
# regret = 0 otherwise
for i in range(len(history_)):
# history_[i][1] originally stores the player
# after regret is calculated, the player information is no
# longer needed, we reuse the space to store regret
history_[i][1] = 1 if history_[i][1] == result else 0
# discard the earliest history and save the current history
if self._histories.is_full():
self._histories.dequeue()
self._histories.enqueue(history_)
# update the threshold when we get sufficient samples
if self._histories.is_full():
self._update_threshold()
def _update_threshold(self):
# sort all the resignation values in descending order
resign_values = []
for history in self._histories:
resign_values += [x[0] for x in history]
resign_values = sorted(resign_values, reverse=True)
# search for the threshold
pointers = [0] * len(self._histories)
i = 0
while i < len(resign_values):
threshold = resign_values[i]
# calculate the number of regretful resignations for this
# candidate threshold
regrets = 0
for j in range(len(self._histories)):
history = self._histories[j]
while pointers[j] < len(history) and \
history[pointers[j]][0] > threshold:
pointers[j] += 1
if pointers[j] < len(history):
regrets += history[pointers[j]][1]
if regrets <= self._regret_frac * len(self._histories):
self._threshold = threshold
return
# find the next candidate threshold
j = i + 1
while j < len(resign_values) and \
resign_values[j] == resign_values[i]:
j += 1
i = j
self._threshold = -1.0
def _level_order_traversal(self, starting_node=None, include_none=True):
"""
Traverses the binary tree from top level to bottom and saves data/height/depth for testing purpose
Args:
starting_node (TreeNode): traverse the subtree rooted at given starting node
include_none (bool): True if including None node in binary tree
Returns:
data_array ([3-tuple]): [(data saved in node, height, depth)]
"""
data_array = []
# returns empty list if the binary tree is empty
if self._root is None:
return data_array
# traverse from root if starting node is not given
if starting_node is None:
starting_node = self._root
queue = Queue()
# (data, height, depth, left child, right child)
queue.enqueue((starting_node.data(), starting_node.height(),
starting_node.depth(), starting_node.left_child(),
starting_node.right_child()))
while not queue.empty():
top_node = queue.dequeue()
if top_node[0] is not None:
# if it's a non-trival node, append data/height/depth
data_array.append((top_node[0], top_node[1], top_node[2]))
else:
data_array.append(None)
# if the node is not on the deepest level, left and right child can be added into queue and visited soon
if top_node[2] < self.depth():
top_node_left_child, top_node_right_child = top_node[
3], top_node[4]
entry = [None, top_node[1] - 1, top_node[2] + 1, None, None]
if top_node_left_child is not None:
entry[0] = top_node_left_child.data()
entry[3] = top_node_left_child.left_child()
entry[4] = top_node_left_child.right_child()
queue.enqueue(tuple(entry))
entry = [None, top_node[1] - 1, top_node[2] + 1, None, None]
if top_node_right_child is not None:
entry[0] = top_node_right_child.data()
entry[3] = top_node_right_child.left_child()
entry[4] = top_node_right_child.right_child()
queue.enqueue(tuple(entry))
# if include_none == False, filter out the None element
if not include_none:
data_array = list(filter(lambda x: x is not None, data_array))
# remove all None at the end of list since they are trival and not informative
while data_array[-1] is None:
data_array.pop()
return data_array