predictions = multilabel_ldf.predict
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
Reading datafile = train-sets/cs_test_multilabel.ldf
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
1.000000 1.000000            1            1.0    known        0.....        9
0.500000 0.000000            2            2.0    known        1.....        6
1.250000 2.000000            4            4.0    known        1.....        9
0.875000 0.500000            8            8.0    known        2.....        9

finished run
number of examples = 8
weighted example sum = 8.000000
weighted label sum = 0.000000
average loss = 0.875000
total feature number = 24
