def __init__(self):
AbstractBoard.__init__(self)
BacktrackingSolvable.__init__(self)
self._piecedict = { }
self._minx = 0
self._maxx = 0
self._miny = 0
self._maxy = 0
def randomGreedyGame(n):
A = AbstractBoard(4)
A.populateRandom(2)
isGame = True
moves = 0
score = 0
while isGame:
M = []
msc = -1
A_ = A.deepcopy()
for m in MOVES:
hasmoved,scp,_ = A.shift(m)
if hasmoved:
A_ = A.deepcopy()
if scp>msc:
msc = scp
M = [(scp,m)]
elif scp==msc:
M.append((scp,m))
scp,m = choice(M)
score+=scp
A.shift(m)
isGame = A.populateRandom(1)
moves+=1
return A,moves,score
def randomCycleGame(n):
A = AbstractBoard(4)
A.populateRandom(2)
isGame = True
moves = 0
score = 0
for m in cycle([(0,1),(-1,0),(0,-1),(1,0)]):
hasmoved,scp,_ = A.shift(m)
if hasmoved:
score+=scp
moves+=1
isGame = A.populateRandom(1)
if not isGame:break
return A,moves,score
def randomcyclePreferedMoves(F):
A = AbstractBoard(4)
A.populateRandom(2)
isGame = True
moves = 0
score = 0
c = 0
for m in cycle(F):
if c==len(F):
m = choice([i for i in MOVES if i not in F])
c+=1
hasmoved,scp,_ = A.shift(m)
if hasmoved:
score+=scp
moves+=1
isGame = A.populateRandom(1)
if not isGame:break
c=0
return A,moves,score
def randomPreferMoves(F):
A = AbstractBoard(4)
A.populateRandom(2)
isGame = True
moves = 0
score = 0
tested = set()
while True:
if len(tested)==len(F):
m = choice([i for i in MOVES if i not in F])
tested = set()
else:
m = choice(F)
hasmoved,scp,_ = A.shift(m)
if hasmoved:
score+=scp
moves+=1
isGame = A.populateRandom(1)
if not isGame:break
else:
tested.add(m)
return A,moves,score
def randomGame(n):
A = AbstractBoard(4)
A.populateRandom(2)
isGame = True
moves = 0
score = 0
while True:
MOVE_RED = MOVES[:]
while True:
m = choice(MOVE_RED)
hasmoved,scp,_ = A.shift(m)
score+=scp
if hasmoved:
break
MOVE_RED.remove(m)
if not MOVE_RED:
return A,moves,score
A.populateRandom(1)
moves+=1
isGame = A.populateRandom(1)
moves+=1
return A,moves,score
def randomBiasGame(n,(p1,p2,p3,p4)):
assert p1+p2+p3+p4==1
def iterateBiasedMoves():
while True:
x = random()
if x<p1:yield MOVES[0]
if x<p1+p2:yield MOVES[1]
if x<p1+p2+p3:yield MOVES[2]
yield MOVES[3]
A = AbstractBoard(4)
A.populateRandom(2)
isGame = True
moves = 0
score = 0
for m in iterateBiasedMoves():
hasmoved,scp,_ = A.shift(m)
if hasmoved:
score+=scp
moves+=1
isGame = A.populateRandom(1)
if not isGame:break
return A,moves,score
def randomPreferMoves(F):
A = AbstractBoard(4)
def __init__(self, gridlen): AbstractBoard.__init__(self) BacktrackingSolvable.__init__(self) self._piecedict = collections.defaultdict(lambda: collections.defaultdict(dict)) self._piececnt = 0 self._gridlen = gridlen
