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only testing
Num weight bits = 18
learning rate = 10
initial_t = 1
power_t = 0.5
predictions = ftrl_001.predict.tmp
using no cache
Reading datafile = train-sets/0001.dat
num sources = 1
average since example example current current current
loss last counter weight label predict features
0.725668 0.725668 1 1.0 1.0000 0.1481 51
0.374067 0.022466 2 2.0 0.0000 0.1499 104
0.197718 0.021369 4 4.0 0.0000 0.1463 135
0.197180 0.196643 8 8.0 0.0000 0.1496 146
0.240375 0.283569 16 16.0 1.0000 0.1517 24
0.262741 0.285108 32 32.0 0.0000 0.1458 32
0.273560 0.284380 64 64.0 0.0000 0.1458 61
0.312129 0.350697 128 128.0 1.0000 0.1463 106
finished run
number of examples per pass = 200
passes used = 1
weighted example sum = 200
weighted label sum = 91
average loss = 0.3403
best constant = 0.455
best constant's loss = 0.247975
total feature number = 15482
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