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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
0.666667 0.666667 3 3.0 3 2 2
0.833333 1.000000 6 6.0 6 5 2
0.909091 1.000000 11 11.0 1 10 2
0.500000 0.090909 22 22.0 2 2 2
0.250000 0.000000 44 44.0 4 4 2
0.126437 0.000000 87 87.0 7 7 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 = -0.0101
total feature number = 200
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