Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
creating cache_file = train-sets/wsj_small.dparser.vw.gz.cache
Reading datafile = train-sets/wsj_small.dparser.vw.gz
num sources = 1
average    since      instance            current true      current predicted   cur   cur   predic    cache  examples          
loss       last        counter           output prefix          output prefix  pass   pol     made     hits    gener  beta    
88.000000  88.000000         1  [43:1 5:2 5:2 5:2 1..] [0:8 1:1 2:1 3:1 4:..]     0     0      144        0      144  0.014199
48.000000  8.000000          2  [2:2 3:5 0:8 3:7 3:4 ] [0:8 1:1 2:1 3:3 1:4 ]     0     0      157        0      156  0.015381
28.750000  9.500000          4  [2:2 3:5 0:8 3:7 3:4 ] [2:2 3:5 0:8 3:7 3:4 ]     1     0      319        0      312  0.030623
14.625000  0.500000          8  [2:2 3:5 0:8 3:7 3:4 ] [2:2 3:5 0:8 3:7 3:4 ]     3     0      642        0      624  0.060402

finished run
number of examples per pass = 2
passes used = 6
weighted example sum = 12
weighted label sum = 0
average loss = 9.91667
total feature number = 275092
