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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/argmax_data.cache
Reading datafile = train-sets/argmax_data
num sources = 1
average since instance current true current predicted cur cur predic cache examples
loss last counter output prefix output prefix pass pol made hits gener beta
10.000000 10.000000 1 [2 ] [1 ] 0 0 5 0 5 0.000000
5.500000 1.000000 2 [1 ] [2 ] 0 0 9 0 9 0.000000
5.250000 5.000000 4 [2 ] [1 ] 0 0 15 0 15 0.000000
2.875000 0.500000 8 [2 ] [2 ] 1 0 30 0 30 0.000000
1.687500 0.500000 16 [2 ] [2 ] 3 0 60 0 60 0.000001
1.093750 0.500000 32 [2 ] [2 ] 7 0 120 0 120 0.000001
0.796875 0.500000 64 [2 ] [2 ] 15 0 240 0 240 0.000002
finished run
number of examples per pass = 4
passes used = 20
weighted example sum = 80
weighted label sum = 0
average loss = 0.7375
best constant = 0
total feature number = 900
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