using l1 regularization = 2
final_regressor = models/0001_ftrl.model
Enabling FTRL based optimization
Algorithm used: Proximal-FTRL
ftrl_alpha = 0.01
ftrl_beta = 0
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
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
1.000000 1.000000            1            1.0   1.0000   0.0000       51
0.500000 0.000000            2            2.0   0.0000   0.0000      104
0.250000 0.000000            4            4.0   0.0000   0.0000      135
0.250012 0.250025            8            8.0   0.0000   0.0070      146
0.308000 0.365988           16           16.0   1.0000   0.0173       24
0.330613 0.353225           32           32.0   0.0000   0.0413       32
0.334181 0.337749           64           64.0   0.0000   0.0670       61
0.363877 0.393573          128          128.0   1.0000   0.1077      106

finished run
number of examples = 200
weighted example sum = 200.000000
weighted label sum = 91.000000
average loss = 0.379717
best constant = 0.455000
best constant's loss = 0.247975
total feature number = 15482
