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creating quadratic features for pairs: MF 
predictions = topk-rec.predict
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
Reading datafile = train-sets/topk.vw
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
0.000003 0.000003            1            1.0   3.0000   2.9983        4
0.000004 0.000005            2            2.0   0.0000   0.0022        4
0.000003 0.000000            3            3.0   2.0000   2.0000        4
0.000002 0.000000            4            4.0  unknown   0.0000        1
0.000002 0.000003            5            5.0   0.0000   0.0018        4
0.000004 0.000011            6            6.0   3.0000   2.9968        4
0.000003 0.000001            7            7.0   1.0000   1.0007        4
0.000003 0.000000            8            8.0  unknown   0.0000        1
0.000002 0.000000            9            9.0   2.0000   2.0004        4
0.000002 0.000000           10           10.0   1.0000   1.0003        4
0.000002 0.000000           11           11.0   3.0000   2.9995        4
0.000002 0.000000           12           12.0  unknown   0.0000        1

finished run
number of examples per pass = 12
passes used = 1
weighted example sum = 12.000000
weighted label sum = 15.000000
average loss = 0.000002
best constant = 1.250000
total feature number = 39