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Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
final_regressor = models/bs.vote.model
predictions = bs.vote.predict
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
1.000000 1.000000 1 1.0 1.0000 0.0000 51
0.500000 0.000000 2 2.0 0.0000 0.0000 104
0.250000 0.000000 4 4.0 0.0000 0.0000 135
0.500000 0.750000 8 8.0 0.0000 -1.0000 146
0.750000 1.000000 16 16.0 1.0000 -1.0000 24
0.875000 1.000000 32 32.0 0.0000 -1.0000 32
0.937500 1.000000 64 64.0 0.0000 -1.0000 61
0.968750 1.000000 128 128.0 1.0000 -1.0000 106
finished run
number of examples per pass = 200
passes used = 1
weighted example sum = 200
weighted label sum = 0
average loss = 0.98
best constant = 0
total feature number = 15482
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