creating quadratic features for pairs: MF 
final_regressor = topk.model
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
creating cache_file = topk-train.cache
Reading datafile = train-sets/topk.vw
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
9.000000 9.000000            1            1.0   3.0000   0.0000        4
4.590362 0.180723            2            2.0   0.0000   0.4251        4
3.928039 2.603395            4            4.0  unknown   0.2876        1
3.523584 3.119128            8            8.0  unknown   0.4184        1
2.610412 1.697241           16           16.0  unknown   0.6151        1
1.917275 1.224138           32           32.0  unknown   0.7335        1
1.246961 0.576646           64           64.0  unknown   0.8100        1
0.784439 0.321916          128          128.0  unknown   0.8650        1
0.439552 0.094665          256          256.0  unknown   0.9058        1
0.226776 0.014000          512          512.0  unknown   0.9328        1
0.113599 0.000422         1024         1024.0  unknown   0.9396        1

finished run
number of examples per pass = 12
passes used = 100
weighted example sum = 1200.000000
weighted label sum = 1500.000000
average loss = 0.096938
best constant = 1.666667
total feature number = 3900
