predictions = explore_eval.predict
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
Reading datafile = train-sets/cb_test.ldf
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
1.733333 1.733333            1            1.0    known        0:0.866667...        9
0.866667 0.000000            2            2.0    known        1:0.9...        6

finished run
number of examples = 3
weighted example sum = 3.000000
weighted label sum = 0.000000
average loss = 0.866667
total feature number = 21
update count = 2
violation count = 2
final multiplier = 0.555556
