creating quadratic features for pairs: st 
final_regressor = models/library_train.w
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
creating cache_file = train-sets/library_train.cache
Reading datafile = train-sets/library_train
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
1.000000 1.000000            1            1.0    known        1       30
0.500000 0.000000            2            2.0    known        0       30
0.500000 0.500000            4            4.0    known        1       30
0.250000 0.000000            8            8.0    known        1       30
0.125000 0.000000           16           16.0    known        1       30
0.062500 0.000000           32           32.0    known        1       30
0.031250 0.000000           64           64.0    known        1       30
0.015625 0.000000          128          128.0    known        1       30

finished run
number of examples per pass = 2
passes used = 100
weighted example sum = 200.000000
weighted label sum = 0.000000
average loss = 0.010000
total feature number = 6000
