

def predict(self, resource):
    n = resource.get('n')
    key_list = []
    for i in range(n):
        key_list.append(('Xsum_' + str(i)))
        key_list.append(('T_' + str(i)))
    key_value_dict = resource.get_many(prec)
    sumX = []
    T = []
    for i in range(n):
        sumX.append(key_value_dict[('Xsum_' + str(i))])
        T.append(key_value_dict[('T_' + str(i))])
    mu = numpy.zeros(n)
    for i in range(n):
        if ((T[i] == 0) or (mu[i] == float('inf'))):
            mu[i] = (- 1)
        else:
            mu[i] = (sumX[i] / T[i])
    prec = [numpy.sqrt((1.0 / max(1, t))) for t in T]
    return (mu.tolist(), prec)

dataset/ETHPy150Open nextml/NEXT/next/apps/DuelingBanditsPureExploration/algs/BR_Thompson_b2/BR_Thompson_b2.py BR_Thompson_b2.predict/VarMisuse@71/91 `key_list`->`prec`