Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
creating cache_file = train-sets/lda-2pass-hang.dat.cache
Reading datafile = train-sets/lda-2pass-hang.dat
num sources = 1
average    since         example     example  current  current  current
loss       last          counter      weight    label  predict features
12.797082  12.797082           1         1.0  unknown   0.0000      201
12.934175  13.071269           2         2.0  unknown   0.0000      220
13.475964  14.017752           4         4.0  unknown   0.0000      136
14.728280  15.980597           8         8.0  unknown   0.0000      371
15.885340  17.042400          16        16.0  unknown   0.0000      138
17.174329  18.463318          32        32.0  unknown   0.0000      276
17.150571  17.126814          64        64.0  unknown   0.0000       55
16.497889  15.845206         128       128.0  unknown   0.0000      131
15.940465  15.383042         256       256.0  unknown   0.0000      433
15.306914  14.673363         512       512.0  unknown   0.0000       61

finished run
number of examples = 1000
weighted example sum = 1000
weighted label sum = 0
average loss = 14.3325
best constant = -nan
total feature number = 193156