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