only testing
predictions = 0022.predict
Num weight bits = 16
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
Reading datafile = train-sets/3parity
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
0.000000 0.000000            1            1.0  -1.0000  -1.0000        4
0.000000 0.000000            2            2.0  -1.0000  -1.0000        4
0.000000 0.000000            4            4.0  -1.0000  -1.0000        4
0.000000 0.000000            8            8.0   1.0000   1.0000        4

finished run
number of examples = 8
weighted example sum = 8.000000
weighted label sum = 0.000000
average loss = 0.000000
best constant = 0.000000
best constant's loss = 1.000000
total feature number = 32
