Generating 2-grams for all namespaces.
Generating 4-skips for all namespaces.
Num weight bits = 24
learning rate = 0.125
initial_t = 0
power_t = 0.5
using no cache
Reading datafile = train-sets/rcv1_raw_cb_small.vw
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
2.000000 2.000000            1            1.0    known        1      280
1.000000 0.000000            2            2.0    known        2      598
0.500000 0.000000            4            4.0    known        2      784
0.250000 0.000000            8            8.0    known        2      850
0.500000 0.750000           16           16.0    known        2      118
0.312500 0.125000           32           32.0    known        1      166
0.343750 0.375000           64           64.0    known        1      340
0.312500 0.281250          128          128.0    known        1      610
0.257812 0.203125          256          256.0    known        2      712
0.218750 0.179688          512          512.0    known        1      424

finished run
number of examples = 1000
weighted example sum = 1000.000000
weighted label sum = 0.000000
average loss = 0.166000
total feature number = 440020
