final_regressor = models/sr.model
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
creating cache_file = train-sets/0001.dat.cache
Reading datafile = train-sets/0001.dat
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
0.235697 0.235697           50           50.0   1.0000   0.4783       21
0.220809 0.205921          100          100.0   1.0000   0.6434       41
0.210683 0.190432          150          150.0   1.0000   0.4742       38
0.182728 0.182728          200          200.0   1.0000   1.0000       88 h
0.167471 0.100341          250          250.0   0.0000   0.0920       41 h
0.175111 0.209492          300          300.0   0.0000   0.0677       14 h
0.179518 0.208603          350          350.0   0.0000   0.0000      122 h

finished run
number of examples per pass = 180
passes used = 2
weighted example sum = 360.000000
weighted label sum = 160.000000
average loss = 0.153097 h
best constant = 0.444444
best constant's loss = 0.246914
total feature number = 27566
