Num weight bits = 20
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    
97.000000  97.000000         1  [43:1 5:2 5:2 5:2 1..] [0:1 0:1 0:1 0:1 0:..]     0     0       97        0       97  0.000960
52.500000  8.000000          2  [2:2 3:5 0:8 3:7 3:4 ] [2:2 0:8 4:2 2:3 2:4 ]     0     0      106        0      106  0.001049
26.250000  0.000000          4  [2:2 3:5 0:8 3:7 3:4 ] [2:2 3:5 0:8 3:7 3:4 ]     1     0      115        0      212  0.002108
13.125000  0.000000          8  [2:2 3:5 0:8 3:7 3:4 ] [2:2 3:5 0:8 3:7 3:4 ]     3     0      124        0      424  0.004221

finished run
number of examples per pass = 2
passes used = 4
weighted example sum = 8
weighted label sum = 0
average loss = undefined (no holdout)
total feature number = 32302
