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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/affix_test.dat.cache
Reading datafile = train-sets/affix_test.dat
num sources = 1
average since example example current current current
loss last counter weight label predict features
1.000000 1.000000 1 1.0 -1.0000 0.0000 3
1.211740 1.423480 2 2.0 1.0000 -0.1931 3
0.963641 0.715542 4 4.0 1.0000 0.0888 3
0.645542 0.327443 8 8.0 1.0000 0.5890 3
0.353358 0.061174 16 16.0 1.0000 0.8558 3
0.178619 0.003879 32 32.0 1.0000 0.9818 3
finished run
number of examples per pass = 6
passes used = 10
weighted example sum = 60
weighted label sum = 0
average loss = 0.0952751
best constant = 0
total feature number = 180
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