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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
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