predictions = mwt_learn_exclude.preds
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
Reading datafile = train-sets/cb_eval
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
0.000000 0.000000            1            1.0  unknown        1        3
0.000000 0.000000            2            2.0  unknown        1        3
0.000000 0.000000            4            4.0  unknown        1        3
0.000000 0.000000            8            8.0  unknown        1        3

finished run
number of examples = 8
weighted example sum = 8.000000
weighted label sum = 0.000000
average loss = 0.000000
total feature number = 24
