def __init__(self):
self.template = None
self.start_time = time.time()
self.estimation_time = 0
self.number_of_images = len(const.FLAT_IMAGES)
self.alphas: np.ndarray = const.ALPHAS_INIT
self.betas = [const.BETAS_INIT] * self.number_of_images
self.sd2: int = const.SD_INIT
# KBP ARE SPARSE
self.kBps = \
list(map((lambda beta: func.calculate_kBp(beta)),
self.betas))
# self.Gamma: np.ndarray = const.SIGMA_G
self.Gamma_Inv = const.SPARSE_SIGMA_G_INV
self.images = const.FLAT_IMAGES
yty = list(
map((lambda image: func.faster_norm_squared(image)), self.images))
self.YTY = (1 / self.number_of_images) \
* sum(yty) # Many images
self.predictions = None
self.Gamma_update_count = 0
self.asd2_update_count = 0
def update_kBps(self): # Can be part of minimization?
self.kBps = list(
map((lambda beta: func.calculate_kBp(beta)), self.betas))