only testing
predictions = 0001_ftrl.predict
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
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
0.725667 0.725667            1            1.0   1.0000   0.1481       51
0.374067 0.022466            2            2.0   0.0000   0.1499      104
0.197718 0.021369            4            4.0   0.0000   0.1463      135
0.197180 0.196643            8            8.0   0.0000   0.1496      146
0.240375 0.283569           16           16.0   1.0000   0.1517       24
0.262741 0.285108           32           32.0   0.0000   0.1458       32
0.273560 0.284380           64           64.0   0.0000   0.1458       61
0.312129 0.350697          128          128.0   1.0000   0.1463      106

finished run
number of examples = 200
weighted example sum = 200.000000
weighted label sum = 91.000000
average loss = 0.340300
best constant = 0.455000
best constant's loss = 0.247975
total feature number = 15482
