Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
Reading datafile = train-sets/audit.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        1
2.500000 4.000000            2            2.0   2.0000   0.0000        1
7.500000 12.500000            4            4.0   4.0000   0.0000        1

finished run
number of examples = 4
weighted example sum = 4.000000
weighted label sum = 10.000000
average loss = 7.500000
best constant = 2.500000
total feature number = 4
