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final_regressor = models/0001.model
creating cache_file = train-sets/0001.dat.cache
Reading from train-sets/0001.dat
num sources = 1
Num weight bits = 17
learning rate = 2.56e+06
initial_t = 128000
power_t = 1
decay_learning_rate = 1
learning_rate set to 2.56e+06
average since example example current current current
loss last counter weight label predict features
1.000000 1.000000 3 3.0 0.0000 1.0000 326
0.666667 0.333333 6 6.0 1.0000 0.0000 170
0.407598 0.096716 11 11.0 0.0000 0.4840 212
0.465796 0.523995 22 22.0 1.0000 0.0000 512
0.468838 0.471879 44 44.0 0.0000 1.0000 380
0.391612 0.312590 87 87.0 1.0000 0.0000 350
0.356712 0.321812 174 174.0 1.0000 1.0000 140
0.226064 0.095417 348 348.0 1.0000 1.0000 152
finished run
number of examples = 400
weighted example sum = 400
weighted label sum = 182
average loss = 0.2017
best constant = -0.001426
total feature number = 179384
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