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final_regressor = models/sequencespan_data.model
Num weight bits = 18
learning rate = 10
initial_t = 1
power_t = 0.5
decay_learning_rate = 1
creating cache_file = train-sets/sequencespan_data.cache
Reading datafile = train-sets/sequencespan_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.
10.000000 10.000000 1 1.000000 [2 1 1 2 2 1 6 7 7 ..] [1 1 1 1 1 1 1 1 1 ..] 45 0 0 15 15
11.500000 13.000000 2 2.000000 [2 1 1 2 2 1 6 7 7 ..] [2 1 6 4 1 6 4 1 6 ..] 45 1 0 30 30
7.500000 3.500000 4 4.000000 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 45 3 0 60 60
3.750000 0.000000 8 8.000000 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 45 7 0 120 120
1.875000 0.000000 16 16.000000 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 45 15 0 240 240
finished run
number of examples per pass = 1
passes used = 20
weighted example sum = 20
weighted label sum = 0
average loss = 1.5
best constant = -0.0526316
total feature number = 900
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