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using no cache
Reading from train-sets/0001.dat
num sources = 1
Num weight bits = 17
learning rate = 10
initial_t = 1
power_t = 0.5
predictions = 001.predict.tmp
only testing
average since example example current current current
loss last counter weight label predict features
0.000000 0.000000 3 3.0 0.0000 0.0000 326
0.000000 0.000000 6 6.0 1.0000 1.0000 170
0.090909 0.200000 11 11.0 0.0000 0.0000 212
0.045455 0.000000 22 22.0 1.0000 1.0000 512
0.045455 0.045455 44 44.0 0.0000 0.0000 380
0.022989 0.000000 87 87.0 1.0000 1.0000 350
0.038096 0.053203 174 174.0 1.0000 1.0000 140
finished run
number of examples = 200
weighted example sum = 200
weighted label sum = 91
average loss = 0.03314
best constant = 0.4573
best constant's loss = 0.2482
total feature number = 89692
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