Enabling FTRL based optimization
Algorithm used: Proximal-FTRL
ftrl_alpha = 0.005
ftrl_beta = 0.1
Num weight bits = 20
learning rate = 0.5
initial_t = 0
power_t = 0.5
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    
89.000000  89.000000         1  [43:1 5:2 5:2 5:2 1..] [20:9 20:9 20:9 20:..]     0     0       96        0       96  0.000950
48.500000  8.000000          2  [2:2 3:5 0:8 3:7 3:4 ] [0:8 1:2 2:2 3:2 4:2 ]     0     0      104        0      104  0.001029

finished run
number of examples = 2
weighted example sum = 2.000000
weighted label sum = 0.000000
average loss = 48.500000
total feature number = 29203
