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only testing
Num weight bits = 18
learning rate = 10
initial_t = 1
power_t = 0.5
predictions = bs.prreg.predict
using no cache
Reading datafile = train-sets/0002.dat
num sources = 1
average since example example current current current
loss last counter weight label predict features
0.286797 0.286797 3 3.0 0.5498 0.0000 15
0.271153 0.255508 6 6.0 0.2681 0.0000 15
0.310037 0.356697 11 11.0 0.4315 0.0000 15
0.270070 0.230104 22 22.0 0.5519 0.0000 15
0.303996 0.337922 44 44.0 0.5514 0.0000 15
0.284588 0.264729 87 87.0 0.5140 0.0000 15
0.294585 0.304582 174 174.0 0.5596 0.0000 15
0.290007 0.285429 348 348.0 0.5475 0.0000 15
0.278993 0.267978 696 696.0 0.3421 0.0000 15
0.286305 0.293618 1392 1392.0 0.4996 0.0000 15
0.274909 0.263514 2784 2784.0 0.5090 0.0000 15
0.263663 0.252417 5568 5568.0 0.6413 0.0000 15
0.266312 0.268961 11135 11135.0 0.3869 0.0000 15
0.260244 0.254175 22269 22269.0 0.5063 0.0000 15
0.251980 0.243716 44537 44537.0 0.4905 0.0000 15
finished run
number of examples per pass = 74746
passes used = 1
weighted example sum = 69521
weighted label sum = 35113.3
average loss = 0.260912
best constant = 0.505067
best constant's loss = 0.249974
total feature number = 1119986
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