Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
Reading datafile = train-sets/rcv1_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    known        2       50
0.000000 0.000000            2            2.0    known        2      103
0.000000 0.000000            4            4.0    known        2      134
0.000000 0.000000            8            8.0    known        2      145
0.475577 0.951154           16           16.0    known        2       23
0.497601 0.519625           32           32.0    known        2       31
0.578183 0.658765           64           64.0    known        2       60
0.483010 0.387836          128          128.0    known        2      105
0.480136 0.477262          256          256.0    known        2      122
0.519469 0.558803          512          512.0    known        2       74

finished run
number of examples = 1000
weighted example sum = 1000.000000
weighted label sum = 0.000000
average loss = 0.508142
total feature number = 76670
