def __init__(self,
featureDimension,
alpha,
lambda_,
createTime,
NoiseScale,
delta_1,
delta_2,
init="zero"):
LinUCBUserStruct.__init__(self,
featureDimension=featureDimension,
alpha=alpha,
lambda_=lambda_,
NoiseScale=NoiseScale,
init=init)
self.alpha = alpha
self.fail = 0.0
self.success = 0.0
self.failList = []
self.plays = 0.0
self.updates = 0.0
self.emp_loss = 1.0
self.createTime = createTime
self.time = createTime
self.lambda_ = lambda_
self.NoiseScale = NoiseScale
self.sigma = 1.e-2 #Used in the high probability bound, i.e, with probability at least (1 - sigma) the confidence bound. So sigma should be very small
self.model_id = createTime
self.ratio = 0.0
self.badness = 0.0
self.badness_CB = 0.5
self.active = True
self.update_num = 0.0
self.delta_1 = delta_1
self.delta_2 = delta_2
self.history = []
self.clicks = []
def __init__(self,
featureDimension,
lambda_,
RankoneInverse=False,
nIn=25,
nOut=25):
LinUCBUserStruct.__init__(self,
featureDimension=featureDimension,
lambda_=lambda_,
RankoneInverse=RankoneInverse)
self.reward = 0
self.W = theano.shared(value=numpy.identity(
nIn, dtype=theano.config.floatX),
name='W',
borrow=True)
self.Bias = theano.shared(value=numpy.zeros(
(nOut, ), dtype=theano.config.floatX),
name='Bias',
borrow=True)
# 计算加入权重矩阵之后的评分预估值
self.estimateReward = []
# 取出预估评分值中的最高值,作为预测item分值
self.recommendReward = 0
# parameters of the model
self.params = [self.W, self.Bias]
# keep track of model input
# self.inputMean = inputMean
# self.inputBias = inputBias
# learningRate 我随机取的一个数字,应该还需要随后继续做调整
self.learningRate = 0.13
def __init__(self, featureDimension, lambda_):
LinUCBUserStruct.__init__(self,
featureDimension=featureDimension,
lambda_=lambda_)
self.learn_stats = articleAccess()
def __init__(self, featureDimension, lambda_):
LinUCBUserStruct.__init__(self, featureDimension= featureDimension, lambda_ = lambda_)
self.reward = 0
def __init__(self, featureDimension, lambda_): LinUCBUserStruct.__init__(self, featureDimension= featureDimension, lambda_ = lambda_) self.learn_stats = articleAccess()
def __init__(self, featureDimension, lambda_, RankoneInverse=False):
LinUCBUserStruct.__init__(self, featureDimension=featureDimension, lambda_=lambda_,
RankoneInverse=RankoneInverse)
self.reward = 0
def __init__(self, featureDimension, lambda_, RankoneInverse = False):
LinUCBUserStruct.__init__(self, featureDimension= featureDimension, lambda_ = lambda_, RankoneInverse = RankoneInverse)
self.reward = 0
def __init__(self, featureDimension, lambda_, RankoneInverse = False): LinUCBUserStruct.__init__(self, featureDimension= featureDimension, lambda_ = lambda_, RankoneInverse = RankoneInverse) self.learn_stats = articleAccess()