def main() -> None:
for comment in sub.stream.comments():
if comment.saved:
continue
if is_valid_command(comment):
comment.save()
load.save(userdata)
def change_background(self):
if self.scenery == "outside":
self.game.fill(BLACK)
self.sound.play()
req_egg_text.__init__(295, 100,
("Required eggs: %s" % chicken.req_egg),
WHITE, "helvetica", 32)
req_egg_text.draw(gameCanvas.game)
nightText.draw(gameCanvas.game)
nightGame.__init__(300, 30, 250, 450, foodBarFront.width)
pygame.display.update()
pygame.time.wait(2000)
self.file = pygame.image.load(
'graphics//background2.png').convert()
self.file2 = pygame.Surface([self.width, self.height],
pygame.SRCALPHA, 32)
timeBarFront.width += 75
self.scenery = "inside"
if timeBarFront.width <= 1:
if self.scenery == "inside":
self.game.fill(BLACK)
self.sound.play()
chicken.egg_highscore += chicken.egg
if chicken.egg < chicken.req_egg:
if chicken.egg_highscore > load.load():
load.save(chicken.egg_highscore)
scoreText.__init__(300, 120,
("Score: %s" % chicken.egg_highscore),
WHITE, "helvetica", 45)
scoreText.draw(gameCanvas.game)
highscoreText = text.Text(290, 400,
("Highscore: %s" % load.load()),
WHITE, "helvetica", 45)
highscoreText.draw(gameCanvas.game)
gameOverAnim()
else:
dayText.draw(gameCanvas.game)
chicken.egg = 0
chicken.req_egg += 2
pygame.display.update()
pygame.time.wait(2000)
self.file = pygame.image.load(
'graphics//background.png').convert()
self.file2 = pygame.image.load(
'graphics//hegn.png').convert_alpha()
timeBarFront.next_level()
foodBarFront.next_level()
timeBarFront.reset()
foodBarFront.reset()
self.scenery = "outside"
import sys
import cPickle as pickle
import numpy as np
import load
if __name__ == "__main__":
data = load.loadData("/home/liuy/obj/gist.ans.py.data.10000")
print "loadData"
X = np.matrix(data)
X = X.T
print X.shape
mean = X.mean(1)
X -= mean
X2 = X * X.T
Ml = load.loadData("/tmp/cifar.Ml")
X2 += Ml
print "adjust"
print "dump var"
print X2.shape
load.save(X2, "/tmp/cifar.var")
print "dump mean"
print mean.shape
load.save(mean, "/tmp/cifar.mean")
print "size ", X.shape
Sk = Slf
zeros = np.zeros((X.shape[0], 1))
beta = max([scipy.spatial.distance.euclidean(i, zeros) for i in X0.T])
alpha = 1 / beta * 0.3
print alpha
W = []
K = 24
for i in xrange(K):
print i, K
Mc = eta * X * X.T
print "Mc"
print "filter Matrix"
Mlf = Xlf * Slf * Xlf.T
M = Mlf + Mc
print "M"
eigVal, eigVec = np.linalg.eig(M)
wk = eigVec[0].T
print "eig"
Sk_tidle = Xlf.T * wk * wk.T * Xlf
Sk1 = Sk - alpha * T_func(Sk_tidle, Sk)
print "udpate S"
X = X - wk * wk.T * X
print "update X"
Sk = Sk1 # update
wk = wk.T
W.extend(wk.tolist())
W = np.matrix(W)
load.save(W, "/tmp/cifar.splh.w")
import load
import numpy as np
import sys
if __name__ == "__main__":
M = load.loadData("/tmp/cifar.adjustVar")
print M.shape
eigVal, _ = np.linalg.eig(M)
print min(eigVal)
rho = max(0, -min(eigVal))
print rho
rho *= 1.2
if rho == 0:
print "rho setting 0.1"
rho = 0.1
Q = np.eye(M.shape[0]) + 1 / rho * M
L = np.linalg.cholesky(Q)
U = L.T
load.save(L, "/tmp/cifar.nonor.L")
load.save(L, "/tmp/cifar.nonor.U")
import load
import numpy as np
import sys
if __name__ == "__main__":
M = load.loadData("/tmp/cifar.adjustVar")
print M.shape
eigVal, _ = np.linalg.eig(M)
print min(eigVal)
rho = max(0, - min(eigVal))
print rho
rho *= 1.2
if rho == 0:
print "rho setting 0.1"
rho = 0.1
Q = np.eye(M.shape[0]) + 1/rho *M
L = np.linalg.cholesky(Q)
U = L.T
load.save(L, "/tmp/cifar.nonor.L")
load.save(L, "/tmp/cifar.nonor.U")
Sl = Sl[idxlst, :][:, idxlst]
return Xl, Sl
if __name__ == "__main__":
data = load.loadData("/home/liuy/obj/gist.ans.py.data.10000")
X = np.matrix(data)
X = X.T
mean = X.mean(1)
X -= mean
print "load X"
train = load.loadData("/home/liuy/obj/cifar-10-batches-py/data_batch_1")
labels = train["labels"]
print "load Label"
Sl = buildLabelMatrix(labels)
print "buildLabelMatrix"
Xl, Sl = filterMatrix(X, Sl)
print "filterMatrix"
print Xl.shape
print Sl.shape
Ml = Xl * Sl
Ml = Ml * Xl.T
print Ml.shape
var = load.loadData("/tmp/cifar.var")
adjustVar = var + Ml * conf.eta
print "adjust", adjustVar.shape
load.save(adjustVar, "/tmp/cifar.adjustVar")
def buildNonorW():
L = load.loadData("/tmp/cifar.nonor.L")
U = load.loadData("/tmp/cifar.nonor.U")
Uk = U[:, 0:conf.K]
W = L * Uk
return W.T
def buildOrthW():
W = load.loadData("/tmp/cifar.north.w")
return W[0:conf.K, :]
def buildSeqLHW():
W = load.loadData("/tmp/cifar.splh.w")
W = np.matrix(W)
return W[0:conf.K, :]
if __name__ == "__main__":
K = conf.K
W = buildSeqLHW()
data = load.loadData("/home/liuy/obj/gist.ans.py.data.10000")
X = np.matrix(data)
X = X.T
X -= X.mean(1)
_, col = X.shape
hashingArray = [hashingK(W, X[:, i]) for i in xrange(col)]
load.save(hashingArray, "/tmp/cifar.hashingArray")
import load
import numpy as np
import sys
if __name__ == "__main__":
M = load.loadData("/tmp/cifar.adjustVar")
print M.shape
V, W = np.linalg.eigh(M)
print V
print W
load.save(W, "/tmp/cifar.north.w")
return Xl, Sl
if __name__ == "__main__":
data = load.loadData("/home/liuy/obj/gist.ans.py.data.10000")
X = np.matrix(data)
X = X.T
mean = X.mean(1)
X -= mean
print "load X"
train = load.loadData("/home/liuy/obj/cifar-10-batches-py/data_batch_1")
labels = train["labels"]
print "load Label"
Sl = buildLabelMatrix(labels)
print "buildLabelMatrix"
Xl, Sl = filterMatrix(X, Sl)
print "filterMatrix"
print Xl.shape
print Sl.shape
Ml = Xl * Sl
Ml = Ml * Xl.T
print Ml.shape
var = load.loadData("/tmp/cifar.var")
adjustVar = var + Ml * conf.eta
print "adjust", adjustVar.shape
load.save(adjustVar, "/tmp/cifar.adjustVar")
L = load.loadData("/tmp/cifar.nonor.L")
U = load.loadData("/tmp/cifar.nonor.U")
Uk = U[:, 0:conf.K]
W = L * Uk
return W.T
def buildOrthW():
W = load.loadData("/tmp/cifar.north.w")
return W[0:conf.K, :]
def buildSeqLHW():
W = load.loadData("/tmp/cifar.splh.w")
W = np.matrix(W)
return W[0:conf.K, :]
if __name__ == "__main__":
K = conf.K
W = buildSeqLHW()
data = load.loadData("/home/liuy/obj/gist.ans.py.data.10000")
X = np.matrix(data)
X = X.T
X -= X.mean(1)
_, col = X.shape
hashingArray = [hashingK(W, X[:, i]) for i in xrange(col)]
load.save(hashingArray, "/tmp/cifar.hashingArray")
