cdec cfg 'test/example/cdec.ini'
feature: WordPenalty (no config parameters)
State is 0 bytes for feature WordPenalty
feature: KLanguageModel (with config parameters 'test/example/nc-wmt11.en.srilm.gz')
Loading the LM will be faster if you build a binary file.
Reading test/example/nc-wmt11.en.srilm.gz
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
****************************************************************************************************
Loaded 5-gram KLM from test/example/nc-wmt11.en.srilm.gz (MapSize=49581)
State is 98 bytes for feature KLanguageModel test/example/nc-wmt11.en.srilm.gz
feature: RuleIdentityFeatures (no config parameters)
State is 0 bytes for feature RuleIdentityFeatures
feature: RuleNgramFeatures (no config parameters)
State is 0 bytes for feature RuleNgramFeatures
feature: RuleShape (no config parameters)
  Example feature: Shape_S00000_T00000
State is 0 bytes for feature RuleShape
Seeding random number sequence to 380245307

dtrain
Parameters:
                       k 100
                       N 4
                       T 3
                 scorer 'stupid_bleu'
             sample from 'kbest'
                  filter 'uniq'
           learning rate 0.0001
                   gamma 0
                   pairs 'XYX'
                   hi lo 0.1
          pair threshold 0
          select weights 'VOID'
                  l1 reg 0 'none'
                cdec cfg 'test/example/cdec.ini'
                   input 'test/example/nc-wmt11.1k.gz'
                  output '-'
              stop_after 20
(a dot represents 10 inputs)
Iteration #1 of 3.
 .. 20
Stopping after 20 input sentences.
WEIGHTS
              Glue = -0.1015
       WordPenalty = -0.0152
     LanguageModel = +0.21493
 LanguageModel_OOV = -0.3257
     PhraseModel_0 = -0.050844
     PhraseModel_1 = +0.25074
     PhraseModel_2 = +0.27944
     PhraseModel_3 = -0.038384
     PhraseModel_4 = -0.12041
     PhraseModel_5 = +0.1047
     PhraseModel_6 = -0.1289
       PassThrough = -0.3094
        ---
       1best avg score: 0.17508 (+0.17508)
 1best avg model score: -1.2392 (-1.2392)
           avg # pairs: 1329.8
        avg # rank err: 649.1
     avg # margin viol: 677.5
    non0 feature count: 874
           avg list sz: 88.6
           avg f count: 85.643
(time 0.25 min, 0.75 s/S)

Iteration #2 of 3.
 .. 20
WEIGHTS
              Glue = -0.0792
       WordPenalty = -0.056198
     LanguageModel = +0.31038
 LanguageModel_OOV = -0.4011
     PhraseModel_0 = +0.072188
     PhraseModel_1 = +0.11473
     PhraseModel_2 = +0.049774
     PhraseModel_3 = -0.18448
     PhraseModel_4 = -0.12092
     PhraseModel_5 = +0.1599
     PhraseModel_6 = -0.0606
       PassThrough = -0.3848
        ---
       1best avg score: 0.24015 (+0.065075)
 1best avg model score: -10.131 (-8.8914)
           avg # pairs: 1324.7
        avg # rank err: 558.65
     avg # margin viol: 752.85
    non0 feature count: 1236
           avg list sz: 84.9
           avg f count: 88.306
(time 0.22 min, 0.65 s/S)

Iteration #3 of 3.
 .. 20
WEIGHTS
              Glue = -0.051
       WordPenalty = -0.077956
     LanguageModel = +0.33699
 LanguageModel_OOV = -0.4726
     PhraseModel_0 = +0.040228
     PhraseModel_1 = +0.18
     PhraseModel_2 = +0.15618
     PhraseModel_3 = -0.098908
     PhraseModel_4 = -0.036555
     PhraseModel_5 = +0.1619
     PhraseModel_6 = +0.0078
       PassThrough = -0.4563
        ---
       1best avg score: 0.25527 (+0.015113)
 1best avg model score: -13.906 (-3.7756)
           avg # pairs: 1356.3
        avg # rank err: 562.1
     avg # margin viol: 757.35
    non0 feature count: 1482
           avg list sz: 86.65
           avg f count: 87.475
(time 0.23 min, 0.7 s/S)

Writing weights file to '-' ...
done

---
Best iteration: 3 [SCORE 'stupid_bleu'=0.25527].
This took 0.7 min.