creating low rank quadratic features for pairs: aa3 
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
Reading datafile = train-sets/0080.dat
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
0.000000 0.000000            1            1.0   1.0000   1.0000        5
0.000000 0.000000            2            2.0   2.0000   2.0000        5
0.250000 0.500000            4            4.0   2.0000   2.0000        5

finished run
number of examples = 4
weighted example sum = 4.000000
weighted label sum = 6.000000
average loss = 0.250000
best constant = 1.500000
best constant's loss = 0.250000
total feature number = 18
