ignoring namespaces beginning with: w 
scanned dictionary 'dictionary_test.dict' from 'train-sets\dictionary_test.dict', hash=3226e82e3d58b6b2
dictionary dictionary_test.dict contains 4 items
scanned dictionary 'dictionary_test.dict.gz' from 'train-sets\dictionary_test.dict.gz', hash=3226e82e3d58b6b2
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
creating cache_file = train-sets/dictionary_test.dat.cache
Reading datafile = train-sets/dictionary_test.dat
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
1.000000 1.000000            1            1.0   1.0000  -1.0000        3
1.000000 1.000000            2            2.0  -1.0000   1.0000        3
0.500000 0.000000            4            4.0  -1.0000  -1.0000        3
0.250000 0.000000            8            8.0  -1.0000  -1.0000        3
0.125000 0.000000           16           16.0  -1.0000  -1.0000        3
0.062500 0.000000           32           32.0  -1.0000  -1.0000        3
0.031250 0.000000           64           64.0  -1.0000  -1.0000        3
0.015625 0.000000          128          128.0  -1.0000  -1.0000        3

finished run
number of examples per pass = 4
passes used = 32
weighted example sum = 128.000000
weighted label sum = 0.000000
average loss = 0.015625
best constant = 0.000000
best constant's loss = 1.000000
total feature number = 384
