predictions = cs_test.ldf.wap.predict
Num weight bits = 18
learning rate = 10
initial_t = 1
power_t = 0.5
decay_learning_rate = 1
creating cache_file = train-sets/cs_test.ldf.cache
Reading datafile = train-sets/cs_test.ldf
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
1.000000 1.000000            1            1.0    known        1        9
0.500000 0.000000            2            2.0    known        0        6
0.500000 0.500000            4            4.0    known        0        9
0.375000 0.250000            8            8.0    known        0        6
0.375000 0.375000           16           16.0    known        0        9

finished run
number of examples per pass = 3
passes used = 10
weighted example sum = 30.000000
weighted label sum = 0.000000
average loss = 0.366667
total feature number = 210
