def __init__(self, N):
self.N = N
self.CacheRecency = Disk(N)
self.CacheFrequecy = priorityqueue(N)
self.Hist1 = Disk(N)
self.Hist2 = priorityqueue(N)
## Config variables
self.decayRate = 1
self.epsilon = 0.05
self.lamb = 0.05
self.learning_phase = N / 2
# self.error_discount_rate = (0.005)**(1.0/N) ## TODO ADD BACK
self.error_discount_rate = 1
##
self.learning = True
self.policy = 0
self.evictionTime = {}
self.policyUsed = {}
self.weightsUsed = {}
self.freq = {}
## TODO add decay_time and decay_factor
self.decay_time = N
self.decay_factor = 1
## Accounting variables
self.time = 0
self.W = np.array([.5, .5], dtype=np.float32)
self.X = np.array([], dtype=np.int32)
self.Y1 = np.array([])
self.Y2 = np.array([])
def __init__(self, N, decay=1):
self.N = N
self.PQ = priorityqueue(N)
self.unique = {}
self.unique_cnt = 0
self.pollution_dat_x = []
self.pollution_dat_y = []
self.time = 0
def __init__(self, N):
self.N = N
self.CacheRecency = Disk(N)
self.CacheFrequecy = priorityqueue(N)
self.Hist1 = Disk(N)
self.Hist2 = priorityqueue(N)
## Config variables
self.decayRate = 1
self.epsilon = 0.90
self.lamb = 0.05
self.learning_phase = N/2
self.error_discount_rate = (0.005)**(1.0/N)
# self.error_discount_rate = 1
##
self.learning = True
self.policy = 0
self.evictionTime = {}
self.policyUsed = {}
self.weightsUsed = {}
self.freq = {}
## TODO add decay_time and decay_factor
self.decay_time = N
self.decay_factor = 1
## Accounting variables
self.time = 0
self.W = np.zeros((10,2))
self.W[:,:] = 0.5
self.X = np.array([],dtype=np.int32)
self.Y1 = np.array([])
self.Y2 = np.array([])
###
self.q = Queue.Queue()
self.sum = 0
self.NewPages = []
def __init__(self, param):
assert 'cache_size' in param
self.N = int(param['cache_size'])
self.PQ = priorityqueue(self.N)
self.unique = {}
self.unique_cnt = 0
self.pollution_dat_x = []
self.pollution_dat_y = []
self.time = 0
def __init__(self, N,decay=1):
self.N = N
self.PQ = priorityqueue(N)
self.time = 0
