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
0.508353   0.016707            2         2.0   0.0000   0.1293      104
0.346304   0.022206            3         3.0   0.0000   0.1490       57
0.209216   0.003583            5         5.0   0.0000   0.0590      131
0.242874   0.298972            8         8.0   0.0000   0.2086      146
0.229431   0.202545           12        12.0   0.0000   0.2576      209
0.254600   0.304939           18        18.0   0.0000   0.2418       29
0.250139   0.241216           27        27.0   0.0000   0.2297      197
0.229229   0.188902           41        41.0   0.0000   0.2481       20
0.234869   0.245881           62        62.0   0.0000   0.4625       96
0.216129   0.178649           93        93.0   1.0000   0.9731       58
0.216509   0.217260          140       140.0   0.0000   0.4171       82

finished run
number of examples per pass = 200
passes used = 1
weighted example sum = 200
weighted label sum = 91
average loss = 0.19509
best constant = 0.455
best constant's loss = 0.247975
total feature number = 15482