final_regressor = models/ftrl_pistol.model
Enabling FTRL based optimization
Algorithm used: PiSTOL
ftrl_alpha = 1
ftrl_beta = 0.5
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
1.000000 1.000000            2            2.0   0.0000   1.0000      104
0.604016 0.208033            4            4.0   0.0000   0.0000      135
0.427008 0.250000            8            8.0   0.0000   0.0000      146
0.388201 0.349394           16           16.0   1.0000   1.0000       24
0.398389 0.408578           32           32.0   0.0000   0.3512       32
0.337811 0.277233           64           64.0   0.0000   0.0000       61
0.313004 0.288197          128          128.0   1.0000   1.0000      106

finished run
number of examples = 200
weighted example sum = 200.000000
weighted label sum = 91.000000
average loss = 0.264456
best constant = 0.455000
best constant's loss = 0.247975
total feature number = 15482
