predictions = oaa_scores.predict
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
Reading datafile = train-sets/probabilities.dat
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        1       51
0.500000 1.000000            2            2.0        2        1      104
0.750000 1.000000            4            4.0        2        3      135
0.500000 0.250000            8            8.0        2        2      146
0.562500 0.625000           16           16.0        1        2       24
0.531250 0.500000           32           32.0        2        2       32
0.593750 0.656250           64           64.0        2        2       61
0.515625 0.437500          128          128.0        1        1      106

finished run
number of examples = 200
weighted example sum = 200.000000
weighted label sum = 0.000000
average loss = 0.445000
total feature number = 15482
