predictions = multilabel.predict
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
Reading datafile = train-sets/multilabel
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
2.000000 2.000000            1            1.0      0 1                 2
1.500000 1.000000            2            2.0      1 2      0 1        2
1.750000 2.000000            4            4.0      3 4      1 2        2
1.875000 2.000000            8            8.0        8                 2

finished run
number of examples = 10
weighted example sum = 10.000000
weighted label sum = 0.000000
average loss = 1.800000
total feature number = 20
