Generating 2-grams for all namespaces.
Generating 4-skips for all namespaces.
only testing
Num weight bits = 24
learning rate = 0.5
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
0.161820 0.161820            1            1.0    known        2      280
-0.213881 -0.589583            2            2.0    known        1      598
-0.257713 -0.301545            4            4.0    known        1      784
-0.170628 -0.083542            8            8.0    known        1      850
0.049008 0.268643           16           16.0    known        2      118
0.029790 0.010573           32           32.0    known        1      166
0.108944 0.188098           64           64.0    known        1      340
0.079725 0.050506          128          128.0    known        2      610
0.094953 0.110182          256          256.0    known        2      712
0.114991 0.135028          512          512.0    known        1      424

finished run
number of examples = 1000
weighted example sum = 1000.000000
weighted label sum = 0.000000
average loss = 0.117742
total feature number = 440020
