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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
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