Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
final_regressor = models/remask.model
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
0.306614   0.306614            1         1.0   1.0000   0.4463       51
0.337427   0.368240            2         2.0   0.0000   0.6068      104
0.251029   0.164631            4         4.0   0.0000   0.2290      135
0.209231   0.167432            8         8.0   0.0000   0.1417      146
0.208108   0.206986           16        16.0   1.0000   0.5560       24
0.208851   0.209594           32        32.0   0.0000   0.1033       32
0.225538   0.242224           64        64.0   0.0000   0.0000       61
0.209837   0.194137          128       128.0   1.0000   0.7004      106

finished run
number of examples per pass = 200
passes used = 1
weighted example sum = 200
weighted label sum = 91
average loss = 0.189489
best constant = 0.455
best constant's loss = 0.247975
total feature number = 15482