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 = 60.000000
weighted label sum = 0.000000
average loss = 0.095275
best constant = 0.000000
best constant's loss = 1.000000
total feature number = 180
