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