creating cubic features for triples: abc 
final_regressor = models/xxor.model
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
creating cache_file = train-sets/xxor.dat.cache
Reading datafile = train-sets/xxor.dat
num sources = 1
average    since         example     example  current  current  current
loss       last          counter      weight    label  predict features
0.422559   0.422559            3         3.0   0.0000   0.3239        5
0.527830   0.633101            6         6.0   1.0000   0.1236        5
0.406989   0.261980           11        11.0   0.0000   0.4429        5
0.287058   0.167128           22        22.0   1.0000   0.6241        5
0.171319   0.055579           44        44.0   1.0000   0.8228        5
0.091564   0.009955           87        87.0   1.0000   0.9574        5
0.046018   0.000472          174       174.0   1.0000   0.9966        5
0.023010   0.000002          348       348.0   1.0000   1.0000        5
0.011505   0.000000          696       696.0   0.0000   0.0000        5

finished run
number of examples = 800
weighted example sum = 800
weighted label sum = 400
average loss = 0.01001
best constant = 0.5
best constant's loss = 0.25
total feature number = 4000
