def step2(yChannels):
preprocessedImage = ndarray((84, 84, 4))
for imgCounter in xrange(len(yChannels)):
# TODO: look into bilinear reduction
preprocessedImage[:, :,
imgCounter] = imresize(yChannels[imgCounter],
(84, 84))
return preprocessedImage
def __gmmEm__(self):
self.mean = kmeans2(self.data, self.K)[0]
self.c = asarray([1.0/self.K]*self.K)
self.covm = asarray([identity(self.K)]*self.K)
self.p = ndarray((self.N,self.K),dtype='float32')
while self.it > 0:
self.it -=1
self.__calculateP__()
#self.__Estep__()
self.__Mstep__()
def __classify__(self, data):
labels = ndarray((data.shape[0],1),dtype=bool)
labels.fill(True)
return labels
