def dataReceived(self, data):
print "Received: %r" % data
line = data.strip()
items = line.split("\n")
if items[-1] == "TEAM":
self.transport.write(self.name)
elif items[-1] == "MOVE":
if items[0] == "RESTART":
self.reset()
del items[0]
for item in items[:-1]:
parts = item.split()
# think for a random time 1-5 seconds, for testing only
# time.sleep(random.randint(1, 5))
player, x, y = parts[0], int(parts[1]), int(parts[2])
# make a random move
self.prev_moves.append((x, y))
move = self.make_random_move()
print "making move %r" % str(move)
scores = genVoronoi.get_scores(2)
print scores
self.transport.write("{0} {1}".format(move[0], move[1]))
elif items[-1] == "END":
self.transport.loseConnection()
elif items[-1] == "RESTART":
self.reset()
def thinkNextMove(self, board, deep, deepSet, points, Moves, OppMoves):
if Moves==0 and OppMoves ==0:
return (500,500)
if deep > deepSet:
genVoronoi.generate_voronoi_diagram(2, Moves, points, self.colors, None, 0, 0)
scores = genVoronoi.get_scores(2)
myScore = scores[self.playerIdx]
oppScore = scores[self.oppIdx]
return myScore
legalMoves = self.legal_plays(board)
if deep%2 == 1:
randSampling = 10
else:
randSampling = 10
randIdx = np.random.choice(len(legalMoves[0]), randSampling)
tmp = []
for idx in randIdx:
tmp.append((legalMoves[0][idx], legalMoves[1][idx]))
legalMoves = tmp
score = -sys.maxint - 1
bestMove = ()
for eachMove in legalMoves:
# print deep, eachMove
myBoard = np.copy(board)
myPoints = np.copy(points)
myMoves = copy.copy(Moves)
myOppMoves = copy.copy(OppMoves)
if deep%2 == 1: # it's me
myPoints[self.playerIdx][myMoves][0] = eachMove[0]
myPoints[self.playerIdx][myMoves][1] = eachMove[1]
tmp_val = self.thinkNextMove(board, deep+1, deepSet, myPoints, myMoves+1, myOppMoves)
board[eachMove[0]][eachMove[1]] = self.playerIdx
else:
myPoints[self.oppIdx][myOppMoves][0] = eachMove[0]
myPoints[self.oppIdx][myOppMoves][1] = eachMove[1]
tmp_val = self.thinkNextMove(board, deep+1, deepSet, myPoints, myMoves, myOppMoves+1)
board[eachMove[0]][eachMove[1]] = self.oppIdx
# tmp_val = self.thinkNextMove(board, deep, deepSet, myPoints, myMoves, myOppMoves)
if not isinstance(tmp_val, tuple) and tmp_val > score:
score = tmp_val
bestMove = eachMove
print "result in thinkNextMove"
print score, bestMove, self.myMoves, deep
if deep == 2:
return score
else:
return bestMove
def winner(self, myMoves, myOppMoves, myPoints):
# print "winner", myMoves, myOppMoves
if myMoves == myOppMoves and myOppMoves == self.numMoves-1:
genVoronoi.generate_voronoi_diagram(2, myMoves, myPoints, self.colors, None, 0, 0)
scores = genVoronoi.get_scores(2)
myScore = scores[self.playerIdx]
oppScore = scores[self.oppIdx]
print "myScore", myScore
if myScore >= oppScore:
return self.playerIdx
else:
return self.oppIdx
else:
return 0
def findBestLastOne(self, board, deep, deepSet, points, Moves, OppMoves):
legalMoves = self.legal_plays(board)
randIdx = np.random.choice(len(legalMoves[0]), 100)
tmp = []
for idx in randIdx:
tmp.append((legalMoves[0][idx], legalMoves[1][idx]))
legalMoves = tmp
score = -sys.maxint - 1
bestMove = ()
for eachMove in legalMoves:
points[self.playerIdx][Moves+1][0] = eachMove[0]
points[self.playerIdx][Moves+1][1] = eachMove[1]
genVoronoi.generate_voronoi_diagram(2, Moves+1, points, self.colors, None, 0, 0)
scores = genVoronoi.get_scores(2)
myScore = scores[self.playerIdx]
print "myScore", myScore, eachMove
if score < myScore:
score = myScore
bestMove = eachMove
points[self.playerIdx][Moves+1][0] = -1
points[self.playerIdx][Moves+1][1] = -1
return bestMove
def get_scores(self):
scores_np = genVoronoi.get_scores(num_players)
scores = [scores_np[p] for p in range(num_players)]
return scores
def get_score(self):
scores = genVoronoi.get_scores(2)
return scores[self.playerIdx], scores[self.oppIdx]
