cdec cfg 'test/example/cdec.ini'
Loading the LM will be faster if you build a binary file.
Reading test/example/nc-wmt11.en.srilm.gz
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****************************************************************************************************
  Example feature: Shape_S00000_T00000
Seeding random number sequence to 2912000813

dtrain
Parameters:
                       k 100
                       N 4
                       T 2
                 scorer 'stupid_bleu'
             sample from 'kbest'
                  filter 'uniq'
           learning rate 1
                   gamma 0
             loss margin 0
                   pairs 'XYX'
                   hi lo 0.1
          pair threshold 0
          select weights 'VOID'
                  l1 reg 0 'none'
               max pairs 4294967295
                cdec cfg 'test/example/cdec.ini'
                   input 'test/example/nc-wmt11.1k.gz'
                  output '-'
              stop_after 10
(a dot represents 10 inputs)
Iteration #1 of 2.
 . 10
Stopping after 10 input sentences.
WEIGHTS
              Glue = -637
       WordPenalty = +1064
     LanguageModel = +1175.3
 LanguageModel_OOV = -1437
     PhraseModel_0 = +1935.6
     PhraseModel_1 = +2499.3
     PhraseModel_2 = +964.96
     PhraseModel_3 = +1410.8
     PhraseModel_4 = -5977.9
     PhraseModel_5 = +522
     PhraseModel_6 = +1089
       PassThrough = -1308
        ---
       1best avg score: 0.16963 (+0.16963)
 1best avg model score: 64485 (+64485)
           avg # pairs: 1494.4
        avg # rank err: 702.6
     avg # margin viol: 0
    non0 feature count: 528
           avg list sz: 85.7
           avg f count: 102.75
(time 0.083 min, 0.5 s/S)

Iteration #2 of 2.
 . 10
WEIGHTS
              Glue = -1196
       WordPenalty = +809.52
     LanguageModel = +3112.1
 LanguageModel_OOV = -1464
     PhraseModel_0 = +3895.5
     PhraseModel_1 = +4683.4
     PhraseModel_2 = +1092.8
     PhraseModel_3 = +1079.6
     PhraseModel_4 = -6827.7
     PhraseModel_5 = -888
     PhraseModel_6 = +142
       PassThrough = -1335
        ---
       1best avg score: 0.277 (+0.10736)
 1best avg model score: -3110.5 (-67595)
           avg # pairs: 1144.2
        avg # rank err: 529.1
     avg # margin viol: 0
    non0 feature count: 859
           avg list sz: 74.9
           avg f count: 112.84
(time 0.067 min, 0.4 s/S)

Writing weights file to '-' ...
done

---
Best iteration: 2 [SCORE 'stupid_bleu'=0.277].
This took 0.15 min.