parsed 3 named labels
final_regressor = models/test_named_csoaa.model
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
creating cache_file = train-sets/test_named_csoaa.cache
Reading datafile = train-sets/test_named_csoaa
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
0.000000 0.000000            1            1.0    known      det        2
0.500000 1.000000            2            2.0    known      det        2
0.666667 1.000000            4            4.0  unknown      det        2
0.571429 0.500000            8            8.0    known     noun        2
0.307692 0.000000           16           16.0  unknown      det        2
0.148148 0.000000           32           32.0    known     noun        2

finished run
number of examples per pass = 6
passes used = 10
weighted example sum = 60.000000
weighted label sum = 0.000000
average loss = 0.080000
total feature number = 120
