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Num weight bits = 18
learning rate = 10
initial_t = 1
power_t = 0.5
decay_learning_rate = 1
creating cache_file = train-sets/seq_small.cache
Reading from train-sets/seq_small
num sources = 1
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.
0.666667 0.666667 1 6.000000 [ 1 3 2 1 4 3 ] [ 1 1 1 1 1 1 ] 12 0 0 6 0
0.333333 0.000000 2 12.000000 [ 1 3 2 1 4 3 ] [ 1 3 2 1 4 3 ] 12 1 0 12 6
0.222222 0.000000 3 18.000000 [ 1 3 2 1 4 3 ] [ 1 3 2 1 4 3 ] 12 2 0 18 12
0.166667 0.000000 4 24.000000 [ 1 3 2 1 4 3 ] [ 1 3 2 1 4 3 ] 12 3 0 24 18
0.083333 0.000000 8 48.000000 [ 1 3 2 1 4 3 ] [ 1 3 2 1 4 3 ] 12 7 1 51 42
finished run
number of examples = 12
weighted example sum = 72
weighted label sum = 0
average loss = 0.05556
best constant = -0.01408
total feature number = 144
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