predictions = confidence.preds
Num weight bits = 18
learning rate = 0.5
initial_t = 0.1
power_t = 0.5
using no cache
Reading datafile = ./train-sets/rcv1_micro.dat
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
1.000000 1.000000            1            1.0  -1.0000   0.0000      128
0.929134 0.858268            2            2.0  -1.0000  -0.0736       44
0.929115 0.929096            4            4.0  -1.0000  -0.1457      190
0.936975 0.944835            8            8.0   1.0000  -0.0284       34

finished run
number of examples = 10
weighted example sum = 10.000000
weighted label sum = -2.000000
average loss = 0.900808
best constant = -0.200000
best constant's loss = 0.960000
total feature number = 979
