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Num weight bits = 18
learning rate = 10
initial_t = 1
power_t = 0.5
decay_learning_rate = 1
creating cache_file = train-sets/multiclass.cache
Reading datafile = train-sets/multiclass
num sources = 1
average since example example current current current
loss last counter weight label predict features
0.000000 0.000000 1 1.0 1 1 2
0.500000 1.000000 2 2.0 2 1 2
0.750000 1.000000 4 4.0 4 3 2
0.875000 1.000000 8 8.0 8 7 2
0.687500 0.500000 16 16.0 6 6 2
0.343750 0.000000 32 32.0 2 2 2
0.171875 0.000000 64 64.0 4 4 2
finished run
number of examples per pass = 10
passes used = 10
weighted example sum = 100
weighted label sum = 0
average loss = 0.11
best constant = -0.010101
total feature number = 200
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