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
12.813775 12.813775            1            1.0  unknown   0.0000      201
12.946867 13.079960            2            2.0  unknown   0.0000      220
13.718190 14.489512            4            4.0  unknown   0.0000      136
14.787531 15.856872            8            8.0  unknown   0.0000      371
15.934851 17.082171           16           16.0  unknown   0.0000      138
17.209260 18.483669           32           32.0  unknown   0.0000      276
17.163311 17.117362           64           64.0  unknown   0.0000       55
16.494666 15.826021          128          128.0  unknown   0.0000      131
15.918044 15.341422          256          256.0  unknown   0.0000      433
15.281656 14.645268          512          512.0  unknown   0.0000       61

finished run
number of examples per pass = 500
passes used = 2
weighted example sum = 1000.000000
weighted label sum = 0.000000
average loss = 14.297952
total feature number = 193156
