only testing
Num weight bits = 18
learning rate = 10
initial_t = 1
power_t = 0.5
predictions = bs.prvote.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  -1.0000       51
1.000000   1.000000            2         2.0   0.0000  -1.0000      104
1.000000   1.000000            4         4.0   0.0000  -1.0000      135
1.000000   1.000000            8         8.0   0.0000  -1.0000      146
1.000000   1.000000           16        16.0   1.0000  -1.0000       24
1.000000   1.000000           32        32.0   0.0000  -1.0000       32
0.968750   0.937500           64        64.0   0.0000  -1.0000       61
0.953125   0.937500          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 = 0.945
best constant = -0.00502513
total feature number = 15482