predictions = oaa_probabilities.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(25%)       51
0.500000 1.000000            2            2.0        2   1(31%)      104
0.500000 0.500000            4            4.0        2   2(27%)      135
0.375000 0.250000            8            8.0        2   2(55%)      146
0.500000 0.625000           16           16.0        1   2(38%)       24
0.500000 0.500000           32           32.0        2   2(35%)       32
0.578125 0.656250           64           64.0        2   2(62%)       61
0.515625 0.453125          128          128.0        1   1(53%)      106

finished run
number of examples = 200
weighted example sum = 200.000000
weighted label sum = 0.000000
average loss = 0.450000
average multiclass log loss = 1.161185
total feature number = 15482
