Num weight bits = 18
learning rate = 10
initial_t = 1
power_t = 0.5
decay_learning_rate = 1
final_regressor = models/sequence_data.model
creating cache_file = train-sets/sequence_data.cache
Reading datafile = train-sets/sequence_data
num sources = 1
average    since         example     example  current  current  current
loss       last          counter      weight    label  predict features
average    since      sequence         example            current label      current predicted  current   cur   cur         predic.        examples
loss       last        counter          weight          sequence prefix        sequence prefix features  pass   pol            made          gener.
4.000000   4.000000          1        1.000000   [5 4 3 2 1           ] [1 1 1 1 1           ]       50     0     0              25               5
2.000000   0.000000          2        2.000000   [5 4 3 2 1           ] [5 4 3 2 1           ]       50     1     0              50              10
1.000000   0.000000          4        4.000000   [5 4 3 2 1           ] [5 4 3 2 1           ]       50     3     0             100              20
0.500000   0.000000          8        8.000000   [5 4 3 2 1           ] [5 4 3 2 1           ]       50     7     0             200              40
0.250000   0.000000         16       16.000000   [5 4 3 2 1           ] [5 4 3 2 1           ]       50    15     0             400              80

finished run
number of examples per pass = 1
passes used = 20
weighted example sum = 20
weighted label sum = 0
average loss = 0.2
best constant = -0.0526316
total feature number = 1000