diff options
Diffstat (limited to 'test/train-sets/ref/sequencespan_data.nonldf.train.stderr')
| -rw-r--r-- | test/train-sets/ref/sequencespan_data.nonldf.train.stderr | 18 |
1 files changed, 8 insertions, 10 deletions
diff --git a/test/train-sets/ref/sequencespan_data.nonldf.train.stderr b/test/train-sets/ref/sequencespan_data.nonldf.train.stderr index 649e61a5..1d7db07c 100644 --- a/test/train-sets/ref/sequencespan_data.nonldf.train.stderr +++ b/test/train-sets/ref/sequencespan_data.nonldf.train.stderr @@ -7,21 +7,19 @@ 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 +average since instance current true current predicted cur cur predic cache examples +loss last counter output prefix output prefix pass pol made hits gener beta +6.000000 6.000000 1 [2 1 1 2 2 1 6 7 7 ..] [1 1 1 1 1 1 1 1 1 ..] 0 0 15 0 15 0.000000 +8.500000 11.000000 2 [2 1 1 2 2 1 6 7 7 ..] [2 1 6 4 1 6 4 1 6 ..] 1 0 30 0 30 0.000000 +6.000000 3.500000 4 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 3 0 60 0 60 0.000001 +3.000000 0.000000 8 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 7 0 120 0 120 0.000001 +1.500000 0.000000 16 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 15 0 240 0 240 0.000002 finished run number of examples per pass = 1 passes used = 20 weighted example sum = 20 weighted label sum = 0 -average loss = 1.5 +average loss = 1.2 best constant = -0.0526316 total feature number = 900 |