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predictions = confidence.preds
Num weight bits = 18
learning rate = 0.5
initial_t = 0
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
0.893728 0.787455            2            2.0  -1.0000  -0.1126       44
0.905122 0.916517            4            4.0  -1.0000  -0.1701      190
0.924790 0.944458            8            8.0   1.0000  -0.0231       34

finished run
number of examples per pass = 10
passes used = 1
weighted example sum = 10.000000
weighted label sum = -2.000000
average loss = 0.874088
best constant = -0.200000
best constant's loss = 0.960000
total feature number = 979