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 1072059181

dtrain
Parameters:
                       k 100
                       N 4
                       T 3
                 scorer 'stupid_bleu'
             sample from 'kbest'
                  filter 'uniq'
           learning rate 0.0001
                   gamma 0
             loss margin 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 10
(a dot represents 10 inputs)
Iteration #1 of 3.
 . 10
Stopping after 10 input sentences.
WEIGHTS
              Glue = -0.0293
       WordPenalty = +0.049075
     LanguageModel = +0.24345
 LanguageModel_OOV = -0.2029
     PhraseModel_0 = +0.0084102
     PhraseModel_1 = +0.021729
     PhraseModel_2 = +0.014922
     PhraseModel_3 = +0.104
     PhraseModel_4 = -0.14308
     PhraseModel_5 = +0.0247
     PhraseModel_6 = -0.012
       PassThrough = -0.2161
        ---
       1best avg score: 0.16872 (+0.16872)
 1best avg model score: -1.8276 (-1.8276)
           avg # pairs: 1121.1
        avg # rank err: 555.6
     avg # margin viol: 0
    non0 feature count: 277
           avg list sz: 77.2
           avg f count: 90.96
(time 0.1 min, 0.6 s/S)

Iteration #2 of 3.
 . 10
WEIGHTS
              Glue = -0.3526
       WordPenalty = +0.067576
     LanguageModel = +1.155
 LanguageModel_OOV = -0.2728
     PhraseModel_0 = -0.025529
     PhraseModel_1 = +0.095869
     PhraseModel_2 = +0.094567
     PhraseModel_3 = +0.12482
     PhraseModel_4 = -0.36533
     PhraseModel_5 = +0.1068
     PhraseModel_6 = -0.1517
       PassThrough = -0.286
        ---
       1best avg score: 0.18394 (+0.015221)
 1best avg model score: 3.205 (+5.0326)
           avg # pairs: 1168.3
        avg # rank err: 594.8
     avg # margin viol: 0
    non0 feature count: 543
           avg list sz: 77.5
           avg f count: 85.916
(time 0.083 min, 0.5 s/S)

Iteration #3 of 3.
 . 10
WEIGHTS
              Glue = -0.392
       WordPenalty = +0.071963
     LanguageModel = +0.81266
 LanguageModel_OOV = -0.4177
     PhraseModel_0 = -0.2649
     PhraseModel_1 = -0.17931
     PhraseModel_2 = +0.038261
     PhraseModel_3 = +0.20261
     PhraseModel_4 = -0.42621
     PhraseModel_5 = +0.3198
     PhraseModel_6 = -0.1437
       PassThrough = -0.4309
        ---
       1best avg score: 0.2962 (+0.11225)
 1best avg model score: -36.274 (-39.479)
           avg # pairs: 1109.6
        avg # rank err: 515.9
     avg # margin viol: 0
    non0 feature count: 741
           avg list sz: 77
           avg f count: 88.982
(time 0.083 min, 0.5 s/S)

Writing weights file to '-' ...
done

---
Best iteration: 3 [SCORE 'stupid_bleu'=0.2962].
This took 0.26667 min.