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.dat.gz.cache
Reading datafile = train-sets/wsj_small.dat.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    
30.000000  30.000000         1  [1 2 3 1 4 5 6 7 8 ..] [1 1 1 1 1 1 1 1 1 ..]     0     0       37        0       37  0.000036
23.500000  17.000000         2  [11 2 3 11 11 11 15..] [1 2 1 1 4 1 2 1 1 ..]     0     0       64        0       64  0.000063
16.000000  8.500000          4  [3 4 6 3 1 2 3 1 4 ..] [11 11 2 3 1 2 3 1 ..]     1     0      134        0      134  0.000133
8.000000   0.000000          8  [11 2 3 11 11 11 15..] [11 2 3 11 11 11 15..]     2     0      258        0      258  0.000257
4.000000   0.000000         16  [3 4 6 3 1 2 3 1 4 ..] [3 4 6 3 1 2 3 1 4 ..]     5     0      522        0      522  0.000521

finished run
number of examples per pass = 3
passes used = 6
weighted example sum = 19
weighted label sum = 0
average loss = 3.36842
total feature number = 52110
