Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
Reading datafile = train-sets/multiclass
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
0.000000 0.000000            1            1.0        1        1        2
0.500000 1.000000            2            2.0        2        1        2
0.750000 1.000000            4            4.0        4        3        2
0.875000 1.000000            8            8.0        8        7        2

finished run
number of examples = 10
weighted example sum = 10.000000
weighted label sum = 0.000000
average loss = 0.900000
total feature number = 20
