only testing
predictions = poisson.normalized.preds
Num weight bits = 2
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
Reading datafile = train-sets/poisson.dat
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
95.590408 95.590408            1            1.0  12.0000   9.2347        3
82.029816 68.469223            2            2.0  11.0000  15.2623        3
70.361785 58.693754            4            4.0  13.0000   9.6882        3
66.002784 61.643783            8            8.0  14.0000  10.2895        3
73.706988 81.411191           16           16.0   9.0000   5.4146        3
54.857522 36.008057           32           32.0   6.0000   8.2880        3
71.853323 88.849125           64           64.0   6.0000   6.4692        3

finished run
number of examples per pass = 100
passes used = 1
weighted example sum = 100.000000
weighted label sum = 916.000000
average loss = 74.371941
best constant = 9.160000
total feature number = 276
