warning: multiplicative --progress <float>: 0.5 is <= 1.0: adding 1.0
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
0.513618   0.027236            2         2.0   0.0000   0.1650      104
0.349751   0.022016            3         3.0   0.0000   0.1484       57
0.211121   0.003176            5         5.0   0.0000   0.0559      131
0.237739   0.282102            8         8.0   0.0000   0.2024      146
0.217918   0.178278           12        12.0   0.0000   0.2456      209
0.249520   0.312723           18        18.0   0.0000   0.2878       29
0.246782   0.241307           27        27.0   0.0000   0.2217      197
0.225381   0.184107           41        41.0   0.0000   0.2652       20
0.235017   0.253830           62        62.0   0.0000   0.4044       96
0.215733   0.177164           93        93.0   1.0000   0.9322       58
0.218306   0.223399          140       140.0   0.0000   0.3486       82

finished run
number of examples per pass = 200
passes used = 1
weighted example sum = 200
weighted label sum = 91
average loss = 0.19576
best constant = 0.455
best constant's loss = 0.247975
total feature number = 15482