Enabling FTRL based optimization
Algorithm used: Proximal-FTRL
ftrl_alpha = 0.005
ftrl_beta = 0.1
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
Reading datafile = train-sets/0001.dat
num sources = 1
Regressor contains 1121 values
example      values         total   
counter      audited        progress
1                   36            3%
2                   85            7%
4                  151           13%
8                  233           20%
16                 344           30%
32                 512           45%
64                 713           63%
128                927           82%
200               1121          100%
