diff options
Diffstat (limited to 'test/train-sets/ref')
| -rw-r--r-- | test/train-sets/ref/argmax_data.stderr | 20 | ||||
| -rw-r--r-- | test/train-sets/ref/searn_small.stderr | 0 | ||||
| -rw-r--r-- | test/train-sets/ref/searn_small.stdout | 0 | ||||
| -rw-r--r-- | test/train-sets/ref/searn_wsj.stderr | 0 | ||||
| -rw-r--r-- | test/train-sets/ref/searn_wsj.stdout | 0 | ||||
| -rw-r--r-- | test/train-sets/ref/searn_wsj2.dat.stderr | 0 | ||||
| -rw-r--r-- | test/train-sets/ref/searn_wsj2.dat.stdout | 0 | ||||
| -rw-r--r-- | test/train-sets/ref/sequence_data.ldf.test.stderr | 8 | ||||
| -rw-r--r-- | test/train-sets/ref/sequence_data.ldf.train.stderr | 22 | ||||
| -rw-r--r-- | test/train-sets/ref/sequence_data.nonldf.test.stderr | 8 | ||||
| -rw-r--r-- | test/train-sets/ref/sequence_data.nonldf.train.stderr | 16 | ||||
| -rw-r--r-- | test/train-sets/ref/sequencespan_data.nonldf-bilou.test.stderr | 8 | ||||
| -rw-r--r-- | test/train-sets/ref/sequencespan_data.nonldf-bilou.train.stderr | 18 | ||||
| -rw-r--r-- | test/train-sets/ref/sequencespan_data.nonldf.test.stderr | 8 | ||||
| -rw-r--r-- | test/train-sets/ref/sequencespan_data.nonldf.train.stderr | 18 |
15 files changed, 54 insertions, 72 deletions
diff --git a/test/train-sets/ref/argmax_data.stderr b/test/train-sets/ref/argmax_data.stderr index 6357dbac..84ca90df 100644 --- a/test/train-sets/ref/argmax_data.stderr +++ b/test/train-sets/ref/argmax_data.stderr @@ -6,17 +6,15 @@ decay_learning_rate = 1 creating cache_file = train-sets/argmax_data.cache Reading datafile = train-sets/argmax_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 ] 15 0 0 5 5 -5.500000 1.000000 2 2.000000 [1 ] [2 ] 12 0 0 9 9 -5.250000 5.000000 4 4.000000 [2 ] [1 ] 9 0 0 15 15 -2.875000 0.500000 8 8.000000 [2 ] [2 ] 9 1 0 30 30 -1.687500 0.500000 16 16.000000 [2 ] [2 ] 9 3 0 60 60 -1.093750 0.500000 32 32.000000 [2 ] [2 ] 9 7 0 120 120 -0.796875 0.500000 64 64.000000 [2 ] [2 ] 9 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 +10.000000 10.000000 1 [2 ] [1 ] 0 0 5 0 5 0.000000 +5.500000 1.000000 2 [1 ] [2 ] 0 0 9 0 9 0.000000 +5.250000 5.000000 4 [2 ] [1 ] 0 0 15 0 15 0.000000 +2.875000 0.500000 8 [2 ] [2 ] 1 0 30 0 30 0.000000 +1.687500 0.500000 16 [2 ] [2 ] 3 0 60 0 60 0.000001 +1.093750 0.500000 32 [2 ] [2 ] 7 0 120 0 120 0.000001 +0.796875 0.500000 64 [2 ] [2 ] 15 0 240 0 240 0.000002 finished run number of examples per pass = 4 diff --git a/test/train-sets/ref/searn_small.stderr b/test/train-sets/ref/searn_small.stderr deleted file mode 100644 index e69de29b..00000000 --- a/test/train-sets/ref/searn_small.stderr +++ /dev/null diff --git a/test/train-sets/ref/searn_small.stdout b/test/train-sets/ref/searn_small.stdout deleted file mode 100644 index e69de29b..00000000 --- a/test/train-sets/ref/searn_small.stdout +++ /dev/null diff --git a/test/train-sets/ref/searn_wsj.stderr b/test/train-sets/ref/searn_wsj.stderr deleted file mode 100644 index e69de29b..00000000 --- a/test/train-sets/ref/searn_wsj.stderr +++ /dev/null diff --git a/test/train-sets/ref/searn_wsj.stdout b/test/train-sets/ref/searn_wsj.stdout deleted file mode 100644 index e69de29b..00000000 --- a/test/train-sets/ref/searn_wsj.stdout +++ /dev/null diff --git a/test/train-sets/ref/searn_wsj2.dat.stderr b/test/train-sets/ref/searn_wsj2.dat.stderr deleted file mode 100644 index e69de29b..00000000 --- a/test/train-sets/ref/searn_wsj2.dat.stderr +++ /dev/null diff --git a/test/train-sets/ref/searn_wsj2.dat.stdout b/test/train-sets/ref/searn_wsj2.dat.stdout deleted file mode 100644 index e69de29b..00000000 --- a/test/train-sets/ref/searn_wsj2.dat.stdout +++ /dev/null diff --git a/test/train-sets/ref/sequence_data.ldf.test.stderr b/test/train-sets/ref/sequence_data.ldf.test.stderr index 3b66edd3..7deebd56 100644 --- a/test/train-sets/ref/sequence_data.ldf.test.stderr +++ b/test/train-sets/ref/sequence_data.ldf.test.stderr @@ -7,11 +7,9 @@ predictions = sequence_data.predict using no 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. -0.000000 0.000000 1 1.000000 [5 4 3 2 1 ] [5 4 3 2 1 ] 50 0 0 25 0 +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 +0.000000 0.000000 1 [5 4 3 2 1 ] [5 4 3 2 1 ] 0 0 5 0 0 0.000000 finished run number of examples per pass = 1 diff --git a/test/train-sets/ref/sequence_data.ldf.train.stderr b/test/train-sets/ref/sequence_data.ldf.train.stderr index 243e7c71..052ada4a 100644 --- a/test/train-sets/ref/sequence_data.ldf.train.stderr +++ b/test/train-sets/ref/sequence_data.ldf.train.stderr @@ -1,21 +1,19 @@ final_regressor = models/sequence_data.model Num weight bits = 18 -learning rate = 10 -initial_t = 1 +learning rate = 0.5 +initial_t = 0 power_t = 0.5 decay_learning_rate = 1 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 +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 +4.000000 4.000000 1 [5 4 3 2 1 ] [1 1 1 1 1 ] 0 0 5 0 25 0.000000 +2.000000 0.000000 2 [5 4 3 2 1 ] [5 4 3 2 1 ] 1 0 10 0 50 0.000000 +1.000000 0.000000 4 [5 4 3 2 1 ] [5 4 3 2 1 ] 3 0 20 0 100 0.000001 +0.500000 0.000000 8 [5 4 3 2 1 ] [5 4 3 2 1 ] 7 0 40 0 200 0.000002 +0.250000 0.000000 16 [5 4 3 2 1 ] [5 4 3 2 1 ] 15 0 80 0 400 0.000004 finished run number of examples per pass = 1 @@ -23,5 +21,5 @@ passes used = 20 weighted example sum = 20 weighted label sum = 0 average loss = 0.2 -best constant = -0.0526316 +best constant = 0 total feature number = 1000 diff --git a/test/train-sets/ref/sequence_data.nonldf.test.stderr b/test/train-sets/ref/sequence_data.nonldf.test.stderr index f2d1cd1c..c3409e1c 100644 --- a/test/train-sets/ref/sequence_data.nonldf.test.stderr +++ b/test/train-sets/ref/sequence_data.nonldf.test.stderr @@ -7,11 +7,9 @@ predictions = sequence_data.predict using no 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. -0.000000 0.000000 1 1.000000 [5 4 3 2 1 ] [5 4 3 2 1 ] 15 0 0 5 0 +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 +0.000000 0.000000 1 [5 4 3 2 1 ] [5 4 3 2 1 ] 0 0 5 0 0 0.000000 finished run number of examples per pass = 1 diff --git a/test/train-sets/ref/sequence_data.nonldf.train.stderr b/test/train-sets/ref/sequence_data.nonldf.train.stderr index d170aa86..2dbab703 100644 --- a/test/train-sets/ref/sequence_data.nonldf.train.stderr +++ b/test/train-sets/ref/sequence_data.nonldf.train.stderr @@ -7,15 +7,13 @@ decay_learning_rate = 1 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 ] 15 0 0 5 5 -4.000000 4.000000 2 2.000000 [5 4 3 2 1 ] [1 1 1 1 1 ] 15 1 0 10 10 -2.000000 0.000000 4 4.000000 [5 4 3 2 1 ] [5 4 3 2 1 ] 15 3 0 20 20 -1.000000 0.000000 8 8.000000 [5 4 3 2 1 ] [5 4 3 2 1 ] 15 7 0 40 40 -0.500000 0.000000 16 16.000000 [5 4 3 2 1 ] [5 4 3 2 1 ] 15 15 0 80 80 +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 +4.000000 4.000000 1 [5 4 3 2 1 ] [1 1 1 1 1 ] 0 0 5 0 5 0.000000 +4.000000 4.000000 2 [5 4 3 2 1 ] [1 1 1 1 1 ] 1 0 10 0 10 0.000000 +2.000000 0.000000 4 [5 4 3 2 1 ] [5 4 3 2 1 ] 3 0 20 0 20 0.000000 +1.000000 0.000000 8 [5 4 3 2 1 ] [5 4 3 2 1 ] 7 0 40 0 40 0.000000 +0.500000 0.000000 16 [5 4 3 2 1 ] [5 4 3 2 1 ] 15 0 80 0 80 0.000001 finished run number of examples per pass = 1 diff --git a/test/train-sets/ref/sequencespan_data.nonldf-bilou.test.stderr b/test/train-sets/ref/sequencespan_data.nonldf-bilou.test.stderr index 7e008ec0..53c6b6ec 100644 --- a/test/train-sets/ref/sequencespan_data.nonldf-bilou.test.stderr +++ b/test/train-sets/ref/sequencespan_data.nonldf-bilou.test.stderr @@ -8,11 +8,9 @@ switching to BILOU encoding for sequence span labeling using no 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. -0.000000 0.000000 1 1.000000 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 45 0 0 15 0 +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 +0.000000 0.000000 1 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 0 0 15 0 0 0.000000 finished run number of examples per pass = 1 diff --git a/test/train-sets/ref/sequencespan_data.nonldf-bilou.train.stderr b/test/train-sets/ref/sequencespan_data.nonldf-bilou.train.stderr index bff3a58f..6b957e0e 100644 --- a/test/train-sets/ref/sequencespan_data.nonldf-bilou.train.stderr +++ b/test/train-sets/ref/sequencespan_data.nonldf-bilou.train.stderr @@ -8,21 +8,19 @@ switching to BILOU encoding for sequence span labeling 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 -7.500000 5.000000 2 2.000000 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 6 4 1 6 7 7 ..] 45 1 0 30 30 -3.750000 0.000000 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 -1.875000 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 -0.937500 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 +5.000000 4.000000 2 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 6 4 1 6 7 7 ..] 1 0 30 0 30 0.000000 +2.500000 0.000000 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 +1.250000 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 +0.625000 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 = 0.75 +average loss = 0.5 best constant = -0.0526316 total feature number = 900 diff --git a/test/train-sets/ref/sequencespan_data.nonldf.test.stderr b/test/train-sets/ref/sequencespan_data.nonldf.test.stderr index d52ea578..b21bbf15 100644 --- a/test/train-sets/ref/sequencespan_data.nonldf.test.stderr +++ b/test/train-sets/ref/sequencespan_data.nonldf.test.stderr @@ -7,11 +7,9 @@ predictions = sequencespan_data.predict using no 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. -0.000000 0.000000 1 1.000000 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 45 0 0 15 0 +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 +0.000000 0.000000 1 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 0 0 15 0 0 0.000000 finished run number of examples per pass = 1 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 |