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
*estimate* *estimate*                                                avglossreg last pred  last correct
2.000000   2.000000          1      1.0    known        1      280   1.000000   0.000000   1.000000  
1.000000   0.000000          2      2.0    known        2      598   0.500397   0.028172   0.000000  
0.500000   0.000000          4      4.0    known        2      784   0.250681   0.023444   0.000000  
0.250000   0.000000          8      8.0    known        2      850   0.247231   0.064843   0.000000  
0.620405   0.990810         16     16.0    known        2      118   0.473622   0.062276   1.000000  
0.320962   0.021519         32     32.0    known        1      166   0.391822   0.037413   0.000000  
0.334178   0.347393         64     64.0    known        1      340   0.400066   0.213940   1.000000  
0.288107   0.242037        128    128.0    known        1      610   0.326892   0.213630   1.000000  
0.234279   0.180451        256    256.0    known        2      712   0.306077   0.517904   0.000000  
0.204043   0.173807        512    512.0    known        1      424   0.275817   0.297387   1.000000  
0.148564   0.093086       1024   1024.0    known        1      574   0.231517   0.755462   1.000000  
0.120964   0.093363       2048   2048.0    known        2      166   0.190603   0.217041   0.000000  
0.111878   0.102792       4096   4096.0    known        1      664   0.156218   0.363329   0.000000  
0.096249   0.080620       8192   8192.0    known        2      598   0.125803   0.838514   0.000000  

finished run
number of examples per pass = 10000
passes used = 1
weighted example sum = 10000
weighted label sum = 0
average loss = 0.0934776
best constant = 0
total feature number = 4476364