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Num weight bits = 19
learning rate = 10
initial_t = 1
power_t = 0.5
decay_learning_rate = 1
creating cache_file = train-sets/wsj_small.dat.gz.cache
Reading from train-sets/wsj_small.dat.gz
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.
0.810811 0.810811 1 37.000000 [ 1 2 3 1 4 ] [ 1 1 1 1 1 ] 1654 0 0 37 0
0.750000 0.666667 2 64.000000 [ 11 2 3 11 11 ] [ 1 2 11 12 9 ] 1194 0 0 64 37
0.709677 0.620690 3 93.000000 [ 14 10 13 9 1 ] [ 11 15 9 1 10 ] 1286 0 0 93 64
0.782946 0.972222 4 129.000000 [ 3 4 6 3 1 ] [ 11 11 11 11 11 ] 1608 0 0 129 93
0.768750 0.709677 5 160.000000 [ 19 3 10 2 1 ] [ 14 10 1 2 1 ] 1378 0 0 160 129
0.734694 0.583333 6 196.000000 [ 19 2 22 4 3 ] [ 19 11 11 11 11 ] 1608 0 0 196 160
0.761702 0.897436 7 235.000000 [ 10 2 3 1 10 ] [ 19 2 11 11 11 ] 1746 0 0 235 196
0.702550 0.584746 12 353.000000 [ 5 12 11 11 21 ] [ 11 12 29 21 21 ] 1102 0 0 353 328
0.563920 0.424501 25 704.000000 [ 10 13 22 4 9 ] [ 10 13 22 4 28 ] 1148 0 0 704 678
0.457687 0.352941 57 1418.000000 [ 19 1 4 6 36 ] [ 19 14 4 6 5 ] 2252 0 0 1418 1368
0.298962 0.135273 110 2793.000000 [ 9 1 10 21 2 ] [ 9 1 10 21 2 ] 1792 1 1 37578 2753
finished run
number of examples = 156
weighted example sum = 3864
weighted label sum = 0
average loss = 0.2246
best constant = -0.0002589
total feature number = 170256
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