only testing
predictions = 0001_ftrl_holdout_off.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
0.287783 0.287783            1            1.0   1.0000   0.4635       51
0.216831 0.145878            2            2.0   0.0000   0.3819      104
0.167839 0.118847            4            4.0   0.0000   0.3450      135
0.164572 0.161305            8            8.0   0.0000   0.3467      146
0.178211 0.191850           16           16.0   1.0000   0.4819       24
0.180740 0.183270           32           32.0   0.0000   0.3375       32
0.179899 0.179057           64           64.0   0.0000   0.3022       61
0.187408 0.194918          128          128.0   1.0000   0.4783      106

finished run
number of examples = 200
weighted example sum = 200.000000
weighted label sum = 91.000000
average loss = 0.190269
best constant = 0.455000
best constant's loss = 0.247975
total feature number = 15482
