dtrain: removed old stuff

48 files changed, 0 insertions, 1251 deletions

diff --git a/training/dtrain/examples/parallelized/README b/training/dtrain/examples/parallelized/README
deleted file mode 100644
index 89715105..00000000
--- a/training/dtrain/examples/parallelized/README
+++ /dev/null
@@ -1,5 +0,0 @@
-run for example
-  ../../parallelize.rb ./dtrain.ini 4 false 2 2 ./in ./refs
-
-final weights will be in the file work/weights.3
-
diff --git a/training/dtrain/examples/parallelized/cdec.ini b/training/dtrain/examples/parallelized/cdec.ini
deleted file mode 100644
index 5773029a..00000000
--- a/training/dtrain/examples/parallelized/cdec.ini
+++ /dev/null
@@ -1,22 +0,0 @@
-formalism=scfg
-add_pass_through_rules=true
-intersection_strategy=cube_pruning
-cubepruning_pop_limit=200
-scfg_max_span_limit=15
-feature_function=WordPenalty
-feature_function=KLanguageModel ../standard//nc-wmt11.en.srilm.gz
-#feature_function=ArityPenalty
-#feature_function=CMR2008ReorderingFeatures
-#feature_function=Dwarf
-#feature_function=InputIndicator
-#feature_function=LexNullJump
-#feature_function=NewJump
-#feature_function=NgramFeatures
-#feature_function=NonLatinCount
-#feature_function=OutputIndicator
-#feature_function=RuleIdentityFeatures
-#feature_function=RuleNgramFeatures
-#feature_function=RuleShape
-#feature_function=SourceSpanSizeFeatures
-#feature_function=SourceWordPenalty
-#feature_function=SpanFeatures
diff --git a/training/dtrain/examples/parallelized/dtrain.ini b/training/dtrain/examples/parallelized/dtrain.ini
deleted file mode 100644
index 0b0932d6..00000000
--- a/training/dtrain/examples/parallelized/dtrain.ini
+++ /dev/null
@@ -1,14 +0,0 @@
-k=100
-N=4
-learning_rate=0.0001
-gamma=0
-loss_margin=1.0
-epochs=1
-scorer=stupid_bleu
-sample_from=kbest
-filter=uniq
-pair_sampling=XYX
-hi_lo=0.1
-select_weights=last
-print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PhraseModel_5 PhraseModel_6 PassThrough
-decoder_config=cdec.ini
diff --git a/training/dtrain/examples/parallelized/grammar/grammar.out.0.gz b/training/dtrain/examples/parallelized/grammar/grammar.out.0.gz
deleted file mode 100644
index 1e28a24b..00000000
--- a/training/dtrain/examples/parallelized/grammar/grammar.out.0.gz
+++ /dev/null
diff --git a/training/dtrain/examples/parallelized/grammar/grammar.out.1.gz b/training/dtrain/examples/parallelized/grammar/grammar.out.1.gz
deleted file mode 100644
index 372f5675..00000000
--- a/training/dtrain/examples/parallelized/grammar/grammar.out.1.gz
+++ /dev/null
diff --git a/training/dtrain/examples/parallelized/grammar/grammar.out.2.gz b/training/dtrain/examples/parallelized/grammar/grammar.out.2.gz
deleted file mode 100644
index 145d0dc0..00000000
--- a/training/dtrain/examples/parallelized/grammar/grammar.out.2.gz
+++ /dev/null
diff --git a/training/dtrain/examples/parallelized/grammar/grammar.out.3.gz b/training/dtrain/examples/parallelized/grammar/grammar.out.3.gz
deleted file mode 100644
index 105593ff..00000000
--- a/training/dtrain/examples/parallelized/grammar/grammar.out.3.gz
+++ /dev/null
diff --git a/training/dtrain/examples/parallelized/grammar/grammar.out.4.gz b/training/dtrain/examples/parallelized/grammar/grammar.out.4.gz
deleted file mode 100644
index 30781f48..00000000
--- a/training/dtrain/examples/parallelized/grammar/grammar.out.4.gz
+++ /dev/null
diff --git a/training/dtrain/examples/parallelized/grammar/grammar.out.5.gz b/training/dtrain/examples/parallelized/grammar/grammar.out.5.gz
deleted file mode 100644
index 834ee759..00000000
--- a/training/dtrain/examples/parallelized/grammar/grammar.out.5.gz
+++ /dev/null
diff --git a/training/dtrain/examples/parallelized/grammar/grammar.out.6.gz b/training/dtrain/examples/parallelized/grammar/grammar.out.6.gz
deleted file mode 100644
index 2e76f348..00000000
--- a/training/dtrain/examples/parallelized/grammar/grammar.out.6.gz
+++ /dev/null
diff --git a/training/dtrain/examples/parallelized/grammar/grammar.out.7.gz b/training/dtrain/examples/parallelized/grammar/grammar.out.7.gz
deleted file mode 100644
index 3741a887..00000000
--- a/training/dtrain/examples/parallelized/grammar/grammar.out.7.gz
+++ /dev/null
diff --git a/training/dtrain/examples/parallelized/grammar/grammar.out.8.gz b/training/dtrain/examples/parallelized/grammar/grammar.out.8.gz
deleted file mode 100644
index ebf6bd0c..00000000
--- a/training/dtrain/examples/parallelized/grammar/grammar.out.8.gz
+++ /dev/null
diff --git a/training/dtrain/examples/parallelized/grammar/grammar.out.9.gz b/training/dtrain/examples/parallelized/grammar/grammar.out.9.gz
deleted file mode 100644
index c1791059..00000000
--- a/training/dtrain/examples/parallelized/grammar/grammar.out.9.gz
+++ /dev/null
diff --git a/training/dtrain/examples/parallelized/in b/training/dtrain/examples/parallelized/in
deleted file mode 100644
index 51d01fe7..00000000
--- a/training/dtrain/examples/parallelized/in
+++ /dev/null
@@ -1,10 +0,0 @@
-<seg grammar="grammar/grammar.out.0.gz" id="0">europas nach rassen geteiltes haus</seg>
-<seg grammar="grammar/grammar.out.1.gz" id="1">ein gemeinsames merkmal aller extremen rechten in europa ist ihr rassismus und die tatsache , daß sie das einwanderungsproblem als politischen hebel benutzen .</seg>
-<seg grammar="grammar/grammar.out.2.gz" id="2">der lega nord in italien , der vlaams block in den niederlanden , die anhänger von le pens nationaler front in frankreich , sind beispiele für parteien oder bewegungen , die sich um das gemeinsame thema : ablehnung der zuwanderung gebildet haben und um forderung nach einer vereinfachten politik , um sie zu regeln .</seg>
-<seg grammar="grammar/grammar.out.3.gz" id="3">während individuen wie jörg haidar und jean @-@ marie le pen kommen und ( leider nicht zu bald ) wieder gehen mögen , wird die rassenfrage aus der europäischer politik nicht so bald verschwinden .</seg>
-<seg grammar="grammar/grammar.out.4.gz" id="4">eine alternde einheimische bevölkerung und immer offenere grenzen vermehren die rassistische zersplitterung in den europäischen ländern .</seg>
-<seg grammar="grammar/grammar.out.5.gz" id="5">die großen parteien der rechten und der linken mitte haben sich dem problem gestellt , in dem sie den kopf in den sand gesteckt und allen aussichten zuwider gehofft haben , es möge bald verschwinden .</seg>
-<seg grammar="grammar/grammar.out.6.gz" id="6">das aber wird es nicht , wie die geschichte des rassismus in amerika deutlich zeigt .</seg>
-<seg grammar="grammar/grammar.out.7.gz" id="7">die beziehungen zwischen den rassen standen in den usa über jahrzehnte - und tun das noch heute - im zentrum der politischen debatte . das ging so weit , daß rassentrennung genauso wichtig wie das einkommen wurde , - wenn nicht sogar noch wichtiger - um politische zuneigungen und einstellungen zu bestimmen .</seg>
-<seg grammar="grammar/grammar.out.8.gz" id="8">der erste schritt , um mit der rassenfrage umzugehen ist , ursache und folgen rassistischer feindseligkeiten zu verstehen , auch dann , wenn das bedeutet , unangenehme tatsachen aufzudecken .</seg>
-<seg grammar="grammar/grammar.out.9.gz" id="9">genau das haben in den usa eine große anzahl an forschungsvorhaben in wirtschaft , soziologie , psychologie und politikwissenschaft geleistet . diese forschungen zeigten , daß menschen unterschiedlicher rasse einander deutlich weniger vertrauen .</seg>
diff --git a/training/dtrain/examples/parallelized/refs b/training/dtrain/examples/parallelized/refs
deleted file mode 100644
index 632e27b0..00000000
--- a/training/dtrain/examples/parallelized/refs
+++ /dev/null
@@ -1,10 +0,0 @@
-europe 's divided racial house
-a common feature of europe 's extreme right is its racism and use of the immigration issue as a political wedge .
-the lega nord in italy , the vlaams blok in the netherlands , the supporters of le pen 's national front in france , are all examples of parties or movements formed on the common theme of aversion to immigrants and promotion of simplistic policies to control them .
-while individuals like jorg haidar and jean @-@ marie le pen may come and ( never to soon ) go , the race question will not disappear from european politics anytime soon .
-an aging population at home and ever more open borders imply increasing racial fragmentation in european countries .
-mainstream parties of the center left and center right have confronted this prospect by hiding their heads in the ground , hoping against hope that the problem will disappear .
-it will not , as america 's racial history clearly shows .
-race relations in the us have been for decades - and remain - at the center of political debate , to the point that racial cleavages are as important as income , if not more , as determinants of political preferences and attitudes .
-the first step to address racial politics is to understand the origin and consequences of racial animosity , even if it means uncovering unpleasant truths .
-this is precisely what a large amount of research in economics , sociology , psychology and political science has done for the us .
diff --git a/training/dtrain/examples/parallelized/work/out.0.0 b/training/dtrain/examples/parallelized/work/out.0.0
deleted file mode 100644
index c559dd4d..00000000
--- a/training/dtrain/examples/parallelized/work/out.0.0
+++ /dev/null
@@ -1,62 +0,0 @@
-                cdec cfg 'cdec.ini'
-Loading the LM will be faster if you build a binary file.
-Reading ../standard//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
-****************************************************************************************************
-Seeding random number sequence to 405292278
-
-dtrain
-Parameters:
-                       k 100
-                       N 4
-                       T 1
-                  scorer 'stupid_bleu'
-             sample from 'kbest'
-                  filter 'uniq'
-           learning rate 0.0001
-                   gamma 0
-             loss margin 1
-       faster perceptron 0
-                   pairs 'XYX'
-                   hi lo 0.1
-          pair threshold 0
-          select weights 'last'
-                  l1 reg 0 'none'
-               max pairs 4294967295
-                cdec cfg 'cdec.ini'
-                   input 'work/shard.0.0.in'
-                    refs 'work/shard.0.0.refs'
-                  output 'work/weights.0.0'
-(a dot represents 10 inputs)
-Iteration #1 of 1.
-  5
-WEIGHTS
-              Glue = +0.2663
-       WordPenalty = -0.0079042
-     LanguageModel = +0.44782
- LanguageModel_OOV = -0.0401
-     PhraseModel_0 = -0.193
-     PhraseModel_1 = +0.71321
-     PhraseModel_2 = +0.85196
-     PhraseModel_3 = -0.43986
-     PhraseModel_4 = -0.44803
-     PhraseModel_5 = -0.0538
-     PhraseModel_6 = -0.1788
-       PassThrough = -0.1477
-        ---
-       1best avg score: 0.17521 (+0.17521)
- 1best avg model score: 21.556 (+21.556)
-           avg # pairs: 1671.2
-        avg # rank err: 1118.6
-     avg # margin viol: 552.6
-    non0 feature count: 12
-           avg list sz: 100
-           avg f count: 11.32
-(time 0.35 min, 4.2 s/S)
-
-Writing weights file to 'work/weights.0.0' ...
-done
-
----
-Best iteration: 1 [SCORE 'stupid_bleu'=0.17521].
-This took 0.35 min.
diff --git a/training/dtrain/examples/parallelized/work/out.0.1 b/training/dtrain/examples/parallelized/work/out.0.1
deleted file mode 100644
index 8bc7ea9c..00000000
--- a/training/dtrain/examples/parallelized/work/out.0.1
+++ /dev/null
@@ -1,63 +0,0 @@
-                cdec cfg 'cdec.ini'
-Loading the LM will be faster if you build a binary file.
-Reading ../standard//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
-****************************************************************************************************
-Seeding random number sequence to 43859692
-
-dtrain
-Parameters:
-                       k 100
-                       N 4
-                       T 1
-                  scorer 'stupid_bleu'
-             sample from 'kbest'
-                  filter 'uniq'
-           learning rate 0.0001
-                   gamma 0
-             loss margin 1
-       faster perceptron 0
-                   pairs 'XYX'
-                   hi lo 0.1
-          pair threshold 0
-          select weights 'last'
-                  l1 reg 0 'none'
-               max pairs 4294967295
-                cdec cfg 'cdec.ini'
-                   input 'work/shard.0.0.in'
-                    refs 'work/shard.0.0.refs'
-                  output 'work/weights.0.1'
-              weights in 'work/weights.0'
-(a dot represents 10 inputs)
-Iteration #1 of 1.
-  5
-WEIGHTS
-              Glue = -0.2699
-       WordPenalty = +0.080605
-     LanguageModel = -0.026572
- LanguageModel_OOV = -0.30025
-     PhraseModel_0 = -0.32076
-     PhraseModel_1 = +0.67451
-     PhraseModel_2 = +0.92
-     PhraseModel_3 = -0.36402
-     PhraseModel_4 = -0.592
-     PhraseModel_5 = -0.0269
-     PhraseModel_6 = -0.28755
-       PassThrough = -0.33285
-        ---
-       1best avg score: 0.26638 (+0.26638)
- 1best avg model score: 53.197 (+53.197)
-           avg # pairs: 2028.6
-        avg # rank err: 998.2
-     avg # margin viol: 918.8
-    non0 feature count: 12
-           avg list sz: 100
-           avg f count: 10.496
-(time 0.35 min, 4.2 s/S)
-
-Writing weights file to 'work/weights.0.1' ...
-done
-
----
-Best iteration: 1 [SCORE 'stupid_bleu'=0.26638].
-This took 0.35 min.
diff --git a/training/dtrain/examples/parallelized/work/out.1.0 b/training/dtrain/examples/parallelized/work/out.1.0
deleted file mode 100644
index 65d1e7dc..00000000
--- a/training/dtrain/examples/parallelized/work/out.1.0
+++ /dev/null
@@ -1,62 +0,0 @@
-                cdec cfg 'cdec.ini'
-Loading the LM will be faster if you build a binary file.
-Reading ../standard//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
-****************************************************************************************************
-Seeding random number sequence to 4126799437
-
-dtrain
-Parameters:
-                       k 100
-                       N 4
-                       T 1
-                  scorer 'stupid_bleu'
-             sample from 'kbest'
-                  filter 'uniq'
-           learning rate 0.0001
-                   gamma 0
-             loss margin 1
-       faster perceptron 0
-                   pairs 'XYX'
-                   hi lo 0.1
-          pair threshold 0
-          select weights 'last'
-                  l1 reg 0 'none'
-               max pairs 4294967295
-                cdec cfg 'cdec.ini'
-                   input 'work/shard.1.0.in'
-                    refs 'work/shard.1.0.refs'
-                  output 'work/weights.1.0'
-(a dot represents 10 inputs)
-Iteration #1 of 1.
-  5
-WEIGHTS
-              Glue = -0.3815
-       WordPenalty = +0.20064
-     LanguageModel = +0.95304
- LanguageModel_OOV = -0.264
-     PhraseModel_0 = -0.22362
-     PhraseModel_1 = +0.12254
-     PhraseModel_2 = +0.26328
-     PhraseModel_3 = +0.38018
-     PhraseModel_4 = -0.48654
-     PhraseModel_5 = +0
-     PhraseModel_6 = -0.3645
-       PassThrough = -0.2216
-        ---
-       1best avg score: 0.10863 (+0.10863)
- 1best avg model score: -4.9841 (-4.9841)
-           avg # pairs: 1345.4
-        avg # rank err: 822.4
-     avg # margin viol: 501
-    non0 feature count: 11
-           avg list sz: 100
-           avg f count: 11.814
-(time 0.43 min, 5.2 s/S)
-
-Writing weights file to 'work/weights.1.0' ...
-done
-
----
-Best iteration: 1 [SCORE 'stupid_bleu'=0.10863].
-This took 0.43333 min.
diff --git a/training/dtrain/examples/parallelized/work/out.1.1 b/training/dtrain/examples/parallelized/work/out.1.1
deleted file mode 100644
index f479fbbc..00000000
--- a/training/dtrain/examples/parallelized/work/out.1.1
+++ /dev/null
@@ -1,63 +0,0 @@
-                cdec cfg 'cdec.ini'
-Loading the LM will be faster if you build a binary file.
-Reading ../standard//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
-****************************************************************************************************
-Seeding random number sequence to 2112412848
-
-dtrain
-Parameters:
-                       k 100
-                       N 4
-                       T 1
-                  scorer 'stupid_bleu'
-             sample from 'kbest'
-                  filter 'uniq'
-           learning rate 0.0001
-                   gamma 0
-             loss margin 1
-       faster perceptron 0
-                   pairs 'XYX'
-                   hi lo 0.1
-          pair threshold 0
-          select weights 'last'
-                  l1 reg 0 'none'
-               max pairs 4294967295
-                cdec cfg 'cdec.ini'
-                   input 'work/shard.1.0.in'
-                    refs 'work/shard.1.0.refs'
-                  output 'work/weights.1.1'
-              weights in 'work/weights.0'
-(a dot represents 10 inputs)
-Iteration #1 of 1.
-  5
-WEIGHTS
-              Glue = -0.3178
-       WordPenalty = +0.11092
-     LanguageModel = +0.17269
- LanguageModel_OOV = -0.13485
-     PhraseModel_0 = -0.45371
-     PhraseModel_1 = +0.38789
-     PhraseModel_2 = +0.75311
-     PhraseModel_3 = -0.38163
-     PhraseModel_4 = -0.58817
-     PhraseModel_5 = -0.0269
-     PhraseModel_6 = -0.27315
-       PassThrough = -0.16745
-        ---
-       1best avg score: 0.13169 (+0.13169)
- 1best avg model score: 24.226 (+24.226)
-           avg # pairs: 1951.2
-        avg # rank err: 985.4
-     avg # margin viol: 951
-    non0 feature count: 12
-           avg list sz: 100
-           avg f count: 11.224
-(time 0.45 min, 5.4 s/S)
-
-Writing weights file to 'work/weights.1.1' ...
-done
-
----
-Best iteration: 1 [SCORE 'stupid_bleu'=0.13169].
-This took 0.45 min.
diff --git a/training/dtrain/examples/parallelized/work/shard.0.0.in b/training/dtrain/examples/parallelized/work/shard.0.0.in
deleted file mode 100644
index 92f9c78e..00000000
--- a/training/dtrain/examples/parallelized/work/shard.0.0.in
+++ /dev/null
@@ -1,5 +0,0 @@
-<seg grammar="grammar/grammar.out.0.gz" id="0">europas nach rassen geteiltes haus</seg>
-<seg grammar="grammar/grammar.out.1.gz" id="1">ein gemeinsames merkmal aller extremen rechten in europa ist ihr rassismus und die tatsache , daß sie das einwanderungsproblem als politischen hebel benutzen .</seg>
-<seg grammar="grammar/grammar.out.2.gz" id="2">der lega nord in italien , der vlaams block in den niederlanden , die anhänger von le pens nationaler front in frankreich , sind beispiele für parteien oder bewegungen , die sich um das gemeinsame thema : ablehnung der zuwanderung gebildet haben und um forderung nach einer vereinfachten politik , um sie zu regeln .</seg>
-<seg grammar="grammar/grammar.out.3.gz" id="3">während individuen wie jörg haidar und jean @-@ marie le pen kommen und ( leider nicht zu bald ) wieder gehen mögen , wird die rassenfrage aus der europäischer politik nicht so bald verschwinden .</seg>
-<seg grammar="grammar/grammar.out.4.gz" id="4">eine alternde einheimische bevölkerung und immer offenere grenzen vermehren die rassistische zersplitterung in den europäischen ländern .</seg>
diff --git a/training/dtrain/examples/parallelized/work/shard.0.0.refs b/training/dtrain/examples/parallelized/work/shard.0.0.refs
deleted file mode 100644
index bef68fee..00000000
--- a/training/dtrain/examples/parallelized/work/shard.0.0.refs
+++ /dev/null
@@ -1,5 +0,0 @@
-europe 's divided racial house
-a common feature of europe 's extreme right is its racism and use of the immigration issue as a political wedge .
-the lega nord in italy , the vlaams blok in the netherlands , the supporters of le pen 's national front in france , are all examples of parties or movements formed on the common theme of aversion to immigrants and promotion of simplistic policies to control them .
-while individuals like jorg haidar and jean @-@ marie le pen may come and ( never to soon ) go , the race question will not disappear from european politics anytime soon .
-an aging population at home and ever more open borders imply increasing racial fragmentation in european countries .
diff --git a/training/dtrain/examples/parallelized/work/shard.1.0.in b/training/dtrain/examples/parallelized/work/shard.1.0.in
deleted file mode 100644
index b7695ce7..00000000
--- a/training/dtrain/examples/parallelized/work/shard.1.0.in
+++ /dev/null
@@ -1,5 +0,0 @@
-<seg grammar="grammar/grammar.out.5.gz" id="5">die großen parteien der rechten und der linken mitte haben sich dem problem gestellt , in dem sie den kopf in den sand gesteckt und allen aussichten zuwider gehofft haben , es möge bald verschwinden .</seg>
-<seg grammar="grammar/grammar.out.6.gz" id="6">das aber wird es nicht , wie die geschichte des rassismus in amerika deutlich zeigt .</seg>
-<seg grammar="grammar/grammar.out.7.gz" id="7">die beziehungen zwischen den rassen standen in den usa über jahrzehnte - und tun das noch heute - im zentrum der politischen debatte . das ging so weit , daß rassentrennung genauso wichtig wie das einkommen wurde , - wenn nicht sogar noch wichtiger - um politische zuneigungen und einstellungen zu bestimmen .</seg>
-<seg grammar="grammar/grammar.out.8.gz" id="8">der erste schritt , um mit der rassenfrage umzugehen ist , ursache und folgen rassistischer feindseligkeiten zu verstehen , auch dann , wenn das bedeutet , unangenehme tatsachen aufzudecken .</seg>
-<seg grammar="grammar/grammar.out.9.gz" id="9">genau das haben in den usa eine große anzahl an forschungsvorhaben in wirtschaft , soziologie , psychologie und politikwissenschaft geleistet . diese forschungen zeigten , daß menschen unterschiedlicher rasse einander deutlich weniger vertrauen .</seg>
diff --git a/training/dtrain/examples/parallelized/work/shard.1.0.refs b/training/dtrain/examples/parallelized/work/shard.1.0.refs
deleted file mode 100644
index 6076f6d5..00000000
--- a/training/dtrain/examples/parallelized/work/shard.1.0.refs
+++ /dev/null
@@ -1,5 +0,0 @@
-mainstream parties of the center left and center right have confronted this prospect by hiding their heads in the ground , hoping against hope that the problem will disappear .
-it will not , as america 's racial history clearly shows .
-race relations in the us have been for decades - and remain - at the center of political debate , to the point that racial cleavages are as important as income , if not more , as determinants of political preferences and attitudes .
-the first step to address racial politics is to understand the origin and consequences of racial animosity , even if it means uncovering unpleasant truths .
-this is precisely what a large amount of research in economics , sociology , psychology and political science has done for the us .
diff --git a/training/dtrain/examples/parallelized/work/weights.0 b/training/dtrain/examples/parallelized/work/weights.0
deleted file mode 100644
index ddd595a8..00000000
--- a/training/dtrain/examples/parallelized/work/weights.0
+++ /dev/null
@@ -1,12 +0,0 @@
-LanguageModel	0.7004298992212881
-PhraseModel_2	0.5576194336478857
-PhraseModel_1	0.41787318415343155
-PhraseModel_4	-0.46728502545635164
-PhraseModel_3	-0.029839521598455515
-Glue	-0.05760000000000068
-PhraseModel_6	-0.2716499999999978
-PhraseModel_0	-0.20831031065605327
-LanguageModel_OOV	-0.15205000000000077
-PassThrough	-0.1846500000000006
-WordPenalty	0.09636994553433414
-PhraseModel_5	-0.026900000000000257
diff --git a/training/dtrain/examples/parallelized/work/weights.0.0 b/training/dtrain/examples/parallelized/work/weights.0.0
deleted file mode 100644
index c9370b18..00000000
--- a/training/dtrain/examples/parallelized/work/weights.0.0
+++ /dev/null
@@ -1,12 +0,0 @@
-WordPenalty	-0.0079041595706392243
-LanguageModel	0.44781580828279532
-LanguageModel_OOV	-0.04010000000000042
-Glue	0.26629999999999948
-PhraseModel_0	-0.19299677809125185
-PhraseModel_1	0.71321026861732773
-PhraseModel_2	0.85195540993310537
-PhraseModel_3	-0.43986310822842656
-PhraseModel_4	-0.44802855630415955
-PhraseModel_5	-0.053800000000000514
-PhraseModel_6	-0.17879999999999835
-PassThrough	-0.14770000000000036
diff --git a/training/dtrain/examples/parallelized/work/weights.0.1 b/training/dtrain/examples/parallelized/work/weights.0.1
deleted file mode 100644
index 8fad3de8..00000000
--- a/training/dtrain/examples/parallelized/work/weights.0.1
+++ /dev/null
@@ -1,12 +0,0 @@
-WordPenalty	0.080605055841244472
-LanguageModel	-0.026571720531022844
-LanguageModel_OOV	-0.30024999999999141
-Glue	-0.26989999999999842
-PhraseModel_2	0.92000295209089566
-PhraseModel_1	0.67450748692470841
-PhraseModel_4	-0.5920000014976784
-PhraseModel_3	-0.36402437203127397
-PhraseModel_6	-0.28754999999999603
-PhraseModel_0	-0.32076244202907672
-PassThrough	-0.33284999999999004
-PhraseModel_5	-0.026900000000000257
diff --git a/training/dtrain/examples/parallelized/work/weights.1 b/training/dtrain/examples/parallelized/work/weights.1
deleted file mode 100644
index 03058a16..00000000
--- a/training/dtrain/examples/parallelized/work/weights.1
+++ /dev/null
@@ -1,12 +0,0 @@
-PhraseModel_2	0.8365578543552836
-PhraseModel_4	-0.5900840266009169
-PhraseModel_1	0.5312000609786991
-PhraseModel_0	-0.3872342271319619
-PhraseModel_3	-0.3728279676912084
-Glue	-0.2938500000000036
-PhraseModel_6	-0.2803499999999967
-PassThrough	-0.25014999999999626
-LanguageModel_OOV	-0.21754999999999702
-LanguageModel	0.07306061161169894
-WordPenalty	0.09576193325966899
-PhraseModel_5	-0.026900000000000257
diff --git a/training/dtrain/examples/parallelized/work/weights.1.0 b/training/dtrain/examples/parallelized/work/weights.1.0
deleted file mode 100644
index 6a6a65c1..00000000
--- a/training/dtrain/examples/parallelized/work/weights.1.0
+++ /dev/null
@@ -1,11 +0,0 @@
-WordPenalty	0.20064405063930751
-LanguageModel	0.9530439901597807
-LanguageModel_OOV	-0.26400000000000112
-Glue	-0.38150000000000084
-PhraseModel_0	-0.22362384322085468
-PhraseModel_1	0.12253609968953538
-PhraseModel_2	0.26328345736266612
-PhraseModel_3	0.38018406503151553
-PhraseModel_4	-0.48654149460854373
-PhraseModel_6	-0.36449999999999722
-PassThrough	-0.22160000000000085
diff --git a/training/dtrain/examples/parallelized/work/weights.1.1 b/training/dtrain/examples/parallelized/work/weights.1.1
deleted file mode 100644
index f56ea4a2..00000000
--- a/training/dtrain/examples/parallelized/work/weights.1.1
+++ /dev/null
@@ -1,12 +0,0 @@
-WordPenalty	0.1109188106780935
-LanguageModel	0.17269294375442074
-LanguageModel_OOV	-0.13485000000000266
-Glue	-0.3178000000000088
-PhraseModel_2	0.75311275661967159
-PhraseModel_1	0.38789263503268989
-PhraseModel_4	-0.58816805170415531
-PhraseModel_3	-0.38163156335114284
-PhraseModel_6	-0.27314999999999739
-PhraseModel_0	-0.45370601223484697
-PassThrough	-0.16745000000000249
-PhraseModel_5	-0.026900000000000257
diff --git a/training/dtrain/examples/standard/README b/training/dtrain/examples/standard/README
deleted file mode 100644
index ce37d31a..00000000
--- a/training/dtrain/examples/standard/README
+++ /dev/null
@@ -1,2 +0,0 @@
-Call `dtrain` from this folder with ../../dtrain -c dtrain.ini .
-
diff --git a/training/dtrain/examples/standard/cdec.ini b/training/dtrain/examples/standard/cdec.ini
deleted file mode 100644
index 3330dd71..00000000
--- a/training/dtrain/examples/standard/cdec.ini
+++ /dev/null
@@ -1,27 +0,0 @@
-formalism=scfg
-add_pass_through_rules=true
-scfg_max_span_limit=15
-intersection_strategy=cube_pruning
-cubepruning_pop_limit=200
-grammar=nc-wmt11.grammar.gz
-feature_function=WordPenalty
-feature_function=KLanguageModel ./nc-wmt11.en.srilm.gz
-# all currently working feature functions for translation:
-# (with those features active that were used in the ACL paper)
-#feature_function=ArityPenalty
-#feature_function=CMR2008ReorderingFeatures
-#feature_function=Dwarf
-#feature_function=InputIndicator
-#feature_function=LexNullJump
-#feature_function=NewJump
-#feature_function=NgramFeatures
-#feature_function=NonLatinCount
-#feature_function=OutputIndicator
-feature_function=RuleIdentityFeatures
-feature_function=RuleSourceBigramFeatures
-feature_function=RuleTargetBigramFeatures
-feature_function=RuleShape
-feature_function=LexicalFeatures 1 1 1
-#feature_function=SourceSpanSizeFeatures
-#feature_function=SourceWordPenalty
-#feature_function=SpanFeatures
diff --git a/training/dtrain/examples/standard/dtrain.ini b/training/dtrain/examples/standard/dtrain.ini
deleted file mode 100644
index a515db02..00000000
--- a/training/dtrain/examples/standard/dtrain.ini
+++ /dev/null
@@ -1,27 +0,0 @@
-#input=./nc-wmt11.de.gz
-#refs=./nc-wmt11.en.gz
-bitext=./nc-wmt11.gz
-output=-                  # a weights file (add .gz for gzip compression) or STDOUT '-'
-select_weights=avg        # output average (over epochs) weight vector
-decoder_config=./cdec.ini # config for cdec
-# weights for these features will be printed on each iteration
-print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PhraseModel_5 PhraseModel_6 PassThrough
-# newer version of the grammar extractor use different feature names: 
-#print_weights= EgivenFCoherent SampleCountF CountEF MaxLexFgivenE MaxLexEgivenF IsSingletonF IsSingletonFE Glue WordPenalty PassThrough LanguageModel LanguageModel_OOV
-stop_after=10 # stop epoch after 10 inputs
-
-# interesting stuff
-epochs=3                 # run over input 3 times
-k=100                    # use 100best lists
-N=4                      # optimize (approx) BLEU4
-scorer=fixed_stupid_bleu # use 'stupid' BLEU+1
-learning_rate=0.1        # learning rate, don't care if gamma=0 (perceptron) and loss_margin=0 (not margin perceptron)
-gamma=0                  # use SVM reg
-sample_from=kbest        # use kbest lists (as opposed to forest)
-filter=uniq              # only unique entries in kbest (surface form)
-pair_sampling=XYX        #
-hi_lo=0.1                # 10 vs 80 vs 10 and 80 vs 10 here
-pair_threshold=0         # minimum distance in BLEU (here: > 0)
-loss_margin=0            # update if correctly ranked, but within this margin
-repeat=1                 # repeat training on a kbest list 1 times 
-#batch=true              # batch tuning, update after accumulating over all sentences and all kbest lists
diff --git a/training/dtrain/examples/standard/expected-output b/training/dtrain/examples/standard/expected-output
deleted file mode 100644
index 2460cfbb..00000000
--- a/training/dtrain/examples/standard/expected-output
+++ /dev/null
@@ -1,123 +0,0 @@
-                cdec cfg './cdec.ini'
-Loading the LM will be faster if you build a binary file.
-Reading ./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
-****************************************************************************************************
-  Example feature: Shape_S00000_T00000
-T=1 I=1 D=1
-Seeding random number sequence to 2327685089
-
-dtrain
-Parameters:
-                       k 100
-                       N 4
-                       T 3
-                   batch 0
-                  scorer 'fixed_stupid_bleu'
-             sample from 'kbest'
-                  filter 'uniq'
-           learning rate 0.1
-                   gamma 0
-             loss margin 0
-       faster perceptron 1
-                   pairs 'XYX'
-                   hi lo 0.1
-          pair threshold 0
-          select weights 'avg'
-                  l1 reg 0 'none'
-                    pclr no
-               max pairs 4294967295
-                  repeat 1
-                cdec cfg './cdec.ini'
-                   input './nc-wmt11.gz'
-                  output '-'
-              stop_after 10
-(a dot represents 10 inputs)
-Iteration #1 of 3.
- . 10
-Stopping after 10 input sentences.
-WEIGHTS
-              Glue = +6.9
-       WordPenalty = -46.426
-     LanguageModel = +535.12
- LanguageModel_OOV = -123.5
-     PhraseModel_0 = -160.73
-     PhraseModel_1 = -350.13
-     PhraseModel_2 = -187.81
-     PhraseModel_3 = +172.04
-     PhraseModel_4 = +0.90108
-     PhraseModel_5 = +21.6
-     PhraseModel_6 = +67.2
-       PassThrough = -149.7
-        ---
-       1best avg score: 0.23327 (+0.23327)
- 1best avg model score: -9084.9 (-9084.9)
-           avg # pairs: 780.7
-        avg # rank err: 0 (meaningless)
-     avg # margin viol: 0
-       k-best loss imp: 100%
-    non0 feature count: 1389
-           avg list sz: 91.3
-           avg f count: 146.2
-(time 0.37 min, 2.2 s/S)
-
-Iteration #2 of 3.
- . 10
-WEIGHTS
-              Glue = -43
-       WordPenalty = -22.019
-     LanguageModel = +591.53
- LanguageModel_OOV = -252.1
-     PhraseModel_0 = -120.21
-     PhraseModel_1 = -43.589
-     PhraseModel_2 = +73.53
-     PhraseModel_3 = +113.7
-     PhraseModel_4 = -223.81
-     PhraseModel_5 = +64
-     PhraseModel_6 = +54.8
-       PassThrough = -331.1
-        ---
-       1best avg score: 0.29568 (+0.062413)
- 1best avg model score: -15879 (-6794.1)
-           avg # pairs: 566.1
-        avg # rank err: 0 (meaningless)
-     avg # margin viol: 0
-       k-best loss imp: 100%
-    non0 feature count: 1931
-           avg list sz: 91.3
-           avg f count: 139.89
-(time 0.33 min, 2 s/S)
-
-Iteration #3 of 3.
- . 10
-WEIGHTS
-              Glue = -44.3
-       WordPenalty = -131.85
-     LanguageModel = +230.91
- LanguageModel_OOV = -285.4
-     PhraseModel_0 = -194.27
-     PhraseModel_1 = -294.83
-     PhraseModel_2 = -92.043
-     PhraseModel_3 = -140.24
-     PhraseModel_4 = +85.613
-     PhraseModel_5 = +238.1
-     PhraseModel_6 = +158.7
-       PassThrough = -359.6
-        ---
-       1best avg score: 0.37375 (+0.078067)
- 1best avg model score: -14519 (+1359.7)
-           avg # pairs: 545.4
-        avg # rank err: 0 (meaningless)
-     avg # margin viol: 0
-       k-best loss imp: 100%
-    non0 feature count: 2218
-           avg list sz: 91.3
-           avg f count: 137.77
-(time 0.35 min, 2.1 s/S)
-
-Writing weights file to '-' ...
-done
-
----
-Best iteration: 3 [SCORE 'fixed_stupid_bleu'=0.37375].
-This took 1.05 min.
diff --git a/training/dtrain/examples/standard/nc-wmt11.de.gz b/training/dtrain/examples/standard/nc-wmt11.de.gz
deleted file mode 100644
index 0741fd92..00000000
--- a/training/dtrain/examples/standard/nc-wmt11.de.gz
+++ /dev/null
diff --git a/training/dtrain/examples/standard/nc-wmt11.en.gz b/training/dtrain/examples/standard/nc-wmt11.en.gz
deleted file mode 100644
index 1c0bd401..00000000
--- a/training/dtrain/examples/standard/nc-wmt11.en.gz
+++ /dev/null
diff --git a/training/dtrain/examples/standard/nc-wmt11.en.srilm.gz b/training/dtrain/examples/standard/nc-wmt11.en.srilm.gz
deleted file mode 100644
index 7ce81057..00000000
--- a/training/dtrain/examples/standard/nc-wmt11.en.srilm.gz
+++ /dev/null
diff --git a/training/dtrain/examples/standard/nc-wmt11.grammar.gz b/training/dtrain/examples/standard/nc-wmt11.grammar.gz
deleted file mode 100644
index ce4024a1..00000000
--- a/training/dtrain/examples/standard/nc-wmt11.grammar.gz
+++ /dev/null
diff --git a/training/dtrain/examples/standard/nc-wmt11.gz b/training/dtrain/examples/standard/nc-wmt11.gz
deleted file mode 100644
index c39c5aef..00000000
--- a/training/dtrain/examples/standard/nc-wmt11.gz
+++ /dev/null
diff --git a/training/dtrain/examples/toy/cdec.ini b/training/dtrain/examples/toy/cdec.ini
deleted file mode 100644
index e6c19abe..00000000
--- a/training/dtrain/examples/toy/cdec.ini
+++ /dev/null
@@ -1,4 +0,0 @@
-formalism=scfg
-add_pass_through_rules=true
-grammar=grammar.gz
-#add_extra_pass_through_features=6
diff --git a/training/dtrain/examples/toy/dtrain.ini b/training/dtrain/examples/toy/dtrain.ini
deleted file mode 100644
index ef956df7..00000000
--- a/training/dtrain/examples/toy/dtrain.ini
+++ /dev/null
@@ -1,13 +0,0 @@
-decoder_config=cdec.ini
-input=src
-refs=tgt
-output=-
-print_weights=logp shell_rule house_rule small_rule little_rule PassThrough PassThrough_1 PassThrough_2 PassThrough_3 PassThrough_4 PassThrough_5 PassThrough_6
-k=4
-N=4
-epochs=2
-scorer=bleu
-sample_from=kbest
-filter=uniq
-pair_sampling=all
-learning_rate=1
diff --git a/training/dtrain/examples/toy/expected-output b/training/dtrain/examples/toy/expected-output
deleted file mode 100644
index 1da2aadd..00000000
--- a/training/dtrain/examples/toy/expected-output
+++ /dev/null
@@ -1,77 +0,0 @@
-Warning: hi_lo only works with pair_sampling XYX.
-                cdec cfg 'cdec.ini'
-Seeding random number sequence to 1664825829
-
-dtrain
-Parameters:
-                       k 4
-                       N 4
-                       T 2
-                  scorer 'bleu'
-             sample from 'kbest'
-                  filter 'uniq'
-           learning rate 1
-                   gamma 0
-             loss margin 0
-                   pairs 'all'
-          pair threshold 0
-          select weights 'last'
-                  l1 reg 0 'none'
-               max pairs 4294967295
-                cdec cfg 'cdec.ini'
-                   input 'src'
-                    refs 'tgt'
-                  output '-'
-(a dot represents 10 inputs)
-Iteration #1 of 2.
-  2
-WEIGHTS
-              logp = +0
-        shell_rule = -1
-        house_rule = +2
-        small_rule = -2
-       little_rule = +3
-       PassThrough = -5
-        ---
-       1best avg score: 0.5 (+0.5)
- 1best avg model score: 2.5 (+2.5)
-           avg # pairs: 4
-        avg # rank err: 1.5
-     avg # margin viol: 0
-    non0 feature count: 6
-           avg list sz: 4
-           avg f count: 2.875
-(time 0 min, 0 s/S)
-
-Iteration #2 of 2.
-  2
-WEIGHTS
-              logp = +0
-        shell_rule = -1
-        house_rule = +2
-        small_rule = -2
-       little_rule = +3
-       PassThrough = -5
-        ---
-       1best avg score: 1 (+0.5)
- 1best avg model score: 5 (+2.5)
-           avg # pairs: 5
-        avg # rank err: 0
-     avg # margin viol: 0
-    non0 feature count: 6
-           avg list sz: 4
-           avg f count: 3
-(time 0 min, 0 s/S)
-
-Writing weights file to '-' ...
-house_rule	2
-little_rule	3
-Glue	-4
-PassThrough	-5
-small_rule	-2
-shell_rule	-1
-done
-
----
-Best iteration: 2 [SCORE 'bleu'=1].
-This took 0 min.
diff --git a/training/dtrain/examples/toy/grammar.gz b/training/dtrain/examples/toy/grammar.gz
deleted file mode 100644
index 8eb0d29e..00000000
--- a/training/dtrain/examples/toy/grammar.gz
+++ /dev/null
diff --git a/training/dtrain/examples/toy/src b/training/dtrain/examples/toy/src
deleted file mode 100644
index 87e39ef2..00000000
--- a/training/dtrain/examples/toy/src
+++ /dev/null
@@ -1,2 +0,0 @@
-ich sah ein kleines haus
-ich fand ein kleines haus
diff --git a/training/dtrain/examples/toy/tgt b/training/dtrain/examples/toy/tgt
deleted file mode 100644
index 174926b3..00000000
--- a/training/dtrain/examples/toy/tgt
+++ /dev/null
@@ -1,2 +0,0 @@
-i saw a little house
-i found a little house
diff --git a/training/dtrain/kbestget.h b/training/dtrain/kbestget.h
deleted file mode 100644
index 85252db3..00000000
--- a/training/dtrain/kbestget.h
+++ /dev/null
@@ -1,88 +0,0 @@
-#ifndef _DTRAIN_KBESTGET_H_
-#define _DTRAIN_KBESTGET_H_
-
-#include "kbest.h"
-
-namespace dtrain
-{
-
-
-struct KBestGetter : public HypSampler
-{
-  const unsigned k_;
-  const string filter_type_;
-  vector<ScoredHyp> s_;
-  unsigned src_len_;
-
-  KBestGetter(const unsigned k, const string filter_type) :
-    k_(k), filter_type_(filter_type) {}
-
-  virtual void
-  NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg)
-  {
-    src_len_ = smeta.GetSourceLength();
-    KBestScored(*hg);
-  }
-
-  vector<ScoredHyp>* GetSamples() { return &s_; }
-
-  void
-  KBestScored(const Hypergraph& forest)
-  {
-    if (filter_type_ == "uniq") {
-      KBestUnique(forest);
-    } else if (filter_type_ == "not") {
-      KBestNoFilter(forest);
-    }
-  }
-
-  void
-  KBestUnique(const Hypergraph& forest)
-  {
-    s_.clear(); sz_ = f_count_ = 0;
-    KBest::KBestDerivations<vector<WordID>, ESentenceTraversal,
-      KBest::FilterUnique, prob_t, EdgeProb> kbest(forest, k_);
-    for (unsigned i = 0; i < k_; ++i) {
-      const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal, KBest::FilterUnique,
-              prob_t, EdgeProb>::Derivation* d =
-            kbest.LazyKthBest(forest.nodes_.size() - 1, i);
-      if (!d) break;
-      ScoredHyp h;
-      h.w = d->yield;
-      h.f = d->feature_values;
-      h.model = log(d->score);
-      h.rank = i;
-      h.score = scorer_->Score(h.w, *ref_, i, src_len_);
-      s_.push_back(h);
-      sz_++;
-      f_count_ += h.f.size();
-    }
-  }
-
-  void
-  KBestNoFilter(const Hypergraph& forest)
-  {
-    s_.clear(); sz_ = f_count_ = 0;
-    KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(forest, k_);
-    for (unsigned i = 0; i < k_; ++i) {
-      const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
-            kbest.LazyKthBest(forest.nodes_.size() - 1, i);
-      if (!d) break;
-      ScoredHyp h;
-      h.w = d->yield;
-      h.f = d->feature_values;
-      h.model = log(d->score);
-      h.rank = i;
-      h.score = scorer_->Score(h.w, *ref_, i, src_len_);
-      s_.push_back(h);
-      sz_++;
-      f_count_ += h.f.size();
-    }
-  }
-};
-
-
-} // namespace
-
-#endif
-
diff --git a/training/dtrain/ksampler.h b/training/dtrain/ksampler.h
deleted file mode 100644
index 29dab667..00000000
--- a/training/dtrain/ksampler.h
+++ /dev/null
@@ -1,60 +0,0 @@
-#ifndef _DTRAIN_KSAMPLER_H_
-#define _DTRAIN_KSAMPLER_H_
-
-#include "hg_sampler.h"
-
-namespace dtrain
-{
-
-
-bool
-cmp_hyp_by_model_d(ScoredHyp a, ScoredHyp b)
-{
-  return a.model > b.model;
-}
-
-struct KSampler : public HypSampler
-{
-  const unsigned k_;
-  vector<ScoredHyp> s_;
-  MT19937* prng_;
-  score_t (*scorer)(NgramCounts&, const unsigned, const unsigned, unsigned, vector<score_t>);
-  unsigned src_len_;
-
-  explicit KSampler(const unsigned k, MT19937* prng) :
-    k_(k), prng_(prng) {}
-
-  virtual void
-  NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg)
-  {
-    src_len_ = smeta.GetSourceLength();
-    ScoredSamples(*hg);
-  }
-
-  vector<ScoredHyp>* GetSamples() { return &s_; }
-
-  void ScoredSamples(const Hypergraph& forest) {
-    s_.clear(); sz_ = f_count_ = 0;
-    std::vector<HypergraphSampler::Hypothesis> samples;
-    HypergraphSampler::sample_hypotheses(forest, k_, prng_, &samples);
-    for (unsigned i = 0; i < k_; ++i) {
-      ScoredHyp h;
-      h.w = samples[i].words;
-      h.f = samples[i].fmap;
-      h.model = log(samples[i].model_score);
-      h.rank = i;
-      h.score = scorer_->Score(h.w, *ref_, i, src_len_);
-      s_.push_back(h);
-      sz_++;
-      f_count_ += h.f.size();
-    }
-    sort(s_.begin(), s_.end(), cmp_hyp_by_model_d);
-    for (unsigned i = 0; i < s_.size(); i++) s_[i].rank = i;
-  }
-};
-
-
-} // namespace
-
-#endif
-
diff --git a/training/dtrain/pairsampling.h b/training/dtrain/pairsampling.h
deleted file mode 100644
index 1a3c498c..00000000
--- a/training/dtrain/pairsampling.h
+++ /dev/null
@@ -1,141 +0,0 @@
-#ifndef _DTRAIN_PAIRSAMPLING_H_
-#define _DTRAIN_PAIRSAMPLING_H_
-
-namespace dtrain
-{
-
-
-bool
-accept_pair(score_t a, score_t b, score_t threshold)
-{
-  if (fabs(a - b) < threshold) return false;
-  return true;
-}
-
-bool
-cmp_hyp_by_score_d(ScoredHyp a, ScoredHyp b)
-{
-  return a.score > b.score;
-}
-
-inline void
-all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, unsigned max, bool misranked_only, float _unused=1)
-{
-  sort(s->begin(), s->end(), cmp_hyp_by_score_d);
-  unsigned sz = s->size();
-  bool b = false;
-  unsigned count = 0;
-  for (unsigned i = 0; i < sz-1; i++) {
-    for (unsigned j = i+1; j < sz; j++) {
-      if (misranked_only && !((*s)[i].model <= (*s)[j].model)) continue;
-      if (threshold > 0) {
-        if (accept_pair((*s)[i].score, (*s)[j].score, threshold))
-          training.push_back(make_pair((*s)[i], (*s)[j]));
-      } else {
-        if ((*s)[i].score != (*s)[j].score)
-          training.push_back(make_pair((*s)[i], (*s)[j]));
-      }
-      if (++count == max) {
-        b = true;
-        break;
-      }
-    }
-    if (b) break;
-  }
-}
-
-/*
- * multipartite ranking
- *  sort (descending) by bleu
- *  compare top X to middle Y and low X
- *  cmp middle Y to low X
- */
-
-inline void
-partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, unsigned max, bool misranked_only, float hi_lo)
-{
-  unsigned sz = s->size();
-  if (sz < 2) return;
-  sort(s->begin(), s->end(), cmp_hyp_by_score_d);
-  unsigned sep = round(sz*hi_lo);
-  unsigned sep_hi = sep;
-  if (sz > 4) while (sep_hi < sz && (*s)[sep_hi-1].score == (*s)[sep_hi].score) ++sep_hi;
-  else sep_hi = 1;
-  bool b = false;
-  unsigned count = 0;
-  for (unsigned i = 0; i < sep_hi; i++) {
-    for (unsigned j = sep_hi; j < sz; j++) {
-      if (misranked_only && !((*s)[i].model <= (*s)[j].model)) continue;
-      if (threshold > 0) {
-        if (accept_pair((*s)[i].score, (*s)[j].score, threshold))
-          training.push_back(make_pair((*s)[i], (*s)[j]));
-      } else {
-        if ((*s)[i].score != (*s)[j].score)
-          training.push_back(make_pair((*s)[i], (*s)[j]));
-      }
-      if (++count == max) {
-        b = true;
-        break;
-      }
-    }
-    if (b) break;
-  }
-  unsigned sep_lo = sz-sep;
-  while (sep_lo > 0 && (*s)[sep_lo-1].score == (*s)[sep_lo].score) --sep_lo;
-  for (unsigned i = sep_hi; i < sz-sep_lo; i++) {
-    for (unsigned j = sz-sep_lo; j < sz; j++) {
-      if (misranked_only && !((*s)[i].model <= (*s)[j].model)) continue;
-      if (threshold > 0) {
-        if (accept_pair((*s)[i].score, (*s)[j].score, threshold))
-          training.push_back(make_pair((*s)[i], (*s)[j]));
-      } else {
-        if ((*s)[i].score != (*s)[j].score)
-          training.push_back(make_pair((*s)[i], (*s)[j]));
-      }
-      if (++count == max) return;
-    }
-  }
-}
-
-/*
- * pair sampling as in
- * 'Tuning as Ranking' (Hopkins & May, 2011)
- *     count = 5000
- * threshold = 5% BLEU (0.05 for param 3)
- *       cut = top 50
- */
-bool
-_PRO_cmp_pair_by_diff_d(pair<ScoredHyp,ScoredHyp> a, pair<ScoredHyp,ScoredHyp> b)
-{
-  return (fabs(a.first.score - a.second.score)) > (fabs(b.first.score - b.second.score));
-}
-inline void
-PROsampling(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, unsigned max, bool _unused=false, float _also_unused=0)
-{
-  sort(s->begin(), s->end(), cmp_hyp_by_score_d);
-  unsigned max_count = 5000, count = 0, sz = s->size();
-  bool b = false;
-  for (unsigned i = 0; i < sz-1; i++) {
-    for (unsigned j = i+1; j < sz; j++) {
-      if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) {
-        training.push_back(make_pair((*s)[i], (*s)[j]));
-        if (++count == max_count) {
-          b = true;
-          break;
-        }
-      }
-    }
-    if (b) break;
-  }
-  if (training.size() > 50) {
-    sort(training.begin(), training.end(), _PRO_cmp_pair_by_diff_d);
-    training.erase(training.begin()+50, training.end());
-  }
-  return;
-}
-
-
-} // namespace
-
-#endif
-
diff --git a/training/dtrain/score.cc b/training/dtrain/score.cc
deleted file mode 100644
index 127f34d2..00000000
--- a/training/dtrain/score.cc
+++ /dev/null
@@ -1,283 +0,0 @@
-#include "score.h"
-
-namespace dtrain
-{
-
-
-/*
- * bleu
- *
- * as in "BLEU: a Method for Automatic Evaluation
- *        of Machine Translation"
- * (Papineni et al. '02)
- *
- * NOTE: 0 if for one n \in {1..N} count is 0
- */
-score_t
-BleuScorer::Bleu(NgramCounts& counts, const unsigned hyp_len, const unsigned ref_len)
-{
-  if (hyp_len == 0 || ref_len == 0) return 0.;
-  unsigned M = N_;
-  vector<score_t> v = w_;
-  if (ref_len < N_) {
-    M = ref_len;
-    for (unsigned i = 0; i < M; i++) v[i] = 1/((score_t)M);
-  }
-  score_t sum = 0;
-  for (unsigned i = 0; i < M; i++) {
-    if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) return 0.;
-    sum += v[i] * log((score_t)counts.clipped_[i]/counts.sum_[i]);
-  }
-  return brevity_penalty(hyp_len, ref_len) * exp(sum);
-}
-
-score_t
-BleuScorer::Score(const vector<WordID>& hyp, const vector<WordID>& ref,
-                  const unsigned /*rank*/, const unsigned /*src_len*/)
-{
-  unsigned hyp_len = hyp.size(), ref_len = ref.size();
-  if (hyp_len == 0 || ref_len == 0) return 0.;
-  NgramCounts counts = make_ngram_counts(hyp, ref, N_);
-  return Bleu(counts, hyp_len, ref_len);
-}
-
-/*
- * 'stupid' bleu
- *
- * as in "ORANGE: a Method for Evaluating
- *        Automatic Evaluation Metrics
- *        for Machine Translation"
- * (Lin & Och '04)
- *
- * NOTE: 0 iff no 1gram match ('grounded')
- */
-score_t
-StupidBleuScorer::Score(const vector<WordID>& hyp, const vector<WordID>& ref,
-                        const unsigned /*rank*/, const unsigned /*src_len*/)
-{
-  unsigned hyp_len = hyp.size(), ref_len = ref.size();
-  if (hyp_len == 0 || ref_len == 0) return 0.;
-  NgramCounts counts = make_ngram_counts(hyp, ref, N_);
-  unsigned M = N_;
-  vector<score_t> v = w_;
-  if (ref_len < N_) {
-    M = ref_len;
-    for (unsigned i = 0; i < M; i++) v[i] = 1/((score_t)M);
-  }
-  score_t sum = 0, add = 0;
-  for (unsigned i = 0; i < M; i++) {
-    if (i == 0 && (counts.sum_[i] == 0 || counts.clipped_[i] == 0)) return 0.;
-    if (i == 1) add = 1;
-    sum += v[i] * log(((score_t)counts.clipped_[i] + add)/((counts.sum_[i] + add)));
-  }
-  return  brevity_penalty(hyp_len, ref_len) * exp(sum);
-}
-
-/*
- * fixed 'stupid' bleu
- *
- * as in "Optimizing for Sentence-Level BLEU+1
- *        Yields Short Translations"
- * (Nakov et al. '12)
- */
-score_t
-FixedStupidBleuScorer::Score(const vector<WordID>& hyp, const vector<WordID>& ref,
-                        const unsigned /*rank*/, const unsigned /*src_len*/)
-{
-  unsigned hyp_len = hyp.size(), ref_len = ref.size();
-  if (hyp_len == 0 || ref_len == 0) return 0.;
-  NgramCounts counts = make_ngram_counts(hyp, ref, N_);
-  unsigned M = N_;
-  vector<score_t> v = w_;
-  if (ref_len < N_) {
-    M = ref_len;
-    for (unsigned i = 0; i < M; i++) v[i] = 1/((score_t)M);
-  }
-  score_t sum = 0, add = 0;
-  for (unsigned i = 0; i < M; i++) {
-    if (i == 0 && (counts.sum_[i] == 0 || counts.clipped_[i] == 0)) return 0.;
-    if (i == 1) add = 1;
-    sum += v[i] * log(((score_t)counts.clipped_[i] + add)/((counts.sum_[i] + add)));
-  }
-  return  brevity_penalty(hyp_len, ref_len+1) * exp(sum); // <- fix
-}
-
-/*
- * smooth bleu
- *
- * as in "An End-to-End Discriminative Approach
- *        to Machine Translation"
- * (Liang et al. '06)
- *
- * NOTE: max is 0.9375 (with N=4)
- */
-score_t
-SmoothBleuScorer::Score(const vector<WordID>& hyp, const vector<WordID>& ref,
-                        const unsigned /*rank*/, const unsigned /*src_len*/)
-{
-  unsigned hyp_len = hyp.size(), ref_len = ref.size();
-  if (hyp_len == 0 || ref_len == 0) return 0.;
-  NgramCounts counts = make_ngram_counts(hyp, ref, N_);
-  unsigned M = N_;
-  if (ref_len < N_) M = ref_len;
-  score_t sum = 0.;
-  vector<score_t> i_bleu;
-  for (unsigned i = 0; i < M; i++) i_bleu.push_back(0.);
-  for (unsigned i = 0; i < M; i++) {
-    if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) {
-      break;
-    } else {
-      score_t i_ng = log((score_t)counts.clipped_[i]/counts.sum_[i]);
-      for (unsigned j = i; j < M; j++) {
-        i_bleu[j] += (1/((score_t)j+1)) * i_ng;
-      }
-    }
-    sum += exp(i_bleu[i])/pow(2.0, (double)(N_-i));
-  }
-  return brevity_penalty(hyp_len, ref_len) * sum;
-}
-
-/*
- * 'sum' bleu
- *
- * sum up Ngram precisions
- */
-score_t
-SumBleuScorer::Score(const vector<WordID>& hyp, const vector<WordID>& ref,
-                        const unsigned /*rank*/, const unsigned /*src_len*/)
-{
-  unsigned hyp_len = hyp.size(), ref_len = ref.size();
-  if (hyp_len == 0 || ref_len == 0) return 0.;
-  NgramCounts counts = make_ngram_counts(hyp, ref, N_);
-  unsigned M = N_;
-  if (ref_len < N_) M = ref_len;
-  score_t sum = 0.;
-  unsigned j = 1;
-  for (unsigned i = 0; i < M; i++) {
-    if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) break;
-    sum += ((score_t)counts.clipped_[i]/counts.sum_[i])/pow(2.0, (double) (N_-j+1));
-    j++;
-  }
-  return brevity_penalty(hyp_len, ref_len) * sum;
-}
-
-/*
- * 'sum' (exp) bleu
- *
- * sum up exp(Ngram precisions)
- */
-score_t
-SumExpBleuScorer::Score(const vector<WordID>& hyp, const vector<WordID>& ref,
-                        const unsigned /*rank*/, const unsigned /*src_len*/)
-{
-  unsigned hyp_len = hyp.size(), ref_len = ref.size();
-  if (hyp_len == 0 || ref_len == 0) return 0.;
-  NgramCounts counts = make_ngram_counts(hyp, ref, N_);
-  unsigned M = N_;
-  if (ref_len < N_) M = ref_len;
-  score_t sum = 0.;
-  unsigned j = 1;
-  for (unsigned i = 0; i < M; i++) {
-    if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) break;
-    sum += exp(((score_t)counts.clipped_[i]/counts.sum_[i]))/pow(2.0, (double) (N_-j+1));
-    j++;
-  }
-  return brevity_penalty(hyp_len, ref_len) * sum;
-}
-
-/*
- * 'sum' (whatever) bleu
- *
- * sum up exp(weight * log(Ngram precisions))
- */
-score_t
-SumWhateverBleuScorer::Score(const vector<WordID>& hyp, const vector<WordID>& ref,
-                        const unsigned /*rank*/, const unsigned /*src_len*/)
-{
-  unsigned hyp_len = hyp.size(), ref_len = ref.size();
-  if (hyp_len == 0 || ref_len == 0) return 0.;
-  NgramCounts counts = make_ngram_counts(hyp, ref, N_);
-  unsigned M = N_;
-  vector<score_t> v = w_;
-  if (ref_len < N_) {
-    M = ref_len;
-    for (unsigned i = 0; i < M; i++) v[i] = 1/((score_t)M);
-  }
-  score_t sum = 0.;
-  unsigned j = 1;
-  for (unsigned i = 0; i < M; i++) {
-    if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) break;
-    sum += exp(v[i] * log(((score_t)counts.clipped_[i]/counts.sum_[i])))/pow(2.0, (double) (N_-j+1));
-    j++;
-  }
-  return brevity_penalty(hyp_len, ref_len) * sum;
-}
-
-/*
- * approx. bleu
- *
- * as in "Online Large-Margin Training of Syntactic
- *        and Structural Translation Features"
- * (Chiang et al. '08)
- *
- * NOTE: Needs some more code in dtrain.cc .
- *       No scaling by src len.
- */
-score_t
-ApproxBleuScorer::Score(const vector<WordID>& hyp, const vector<WordID>& ref,
-                        const unsigned rank, const unsigned src_len)
-{
-  unsigned hyp_len = hyp.size(), ref_len = ref.size();
-  if (ref_len == 0) return 0.;
-  score_t score = 0.;
-  NgramCounts counts(N_);
-  if (hyp_len > 0) {
-    counts = make_ngram_counts(hyp, ref, N_);
-    NgramCounts tmp = glob_onebest_counts_ + counts;
-    score = Bleu(tmp, hyp_len, ref_len);
-  }
-  if (rank == 0) { // 'context of 1best translations'
-    glob_onebest_counts_ += counts;
-    glob_onebest_counts_ *= discount_;
-    glob_hyp_len_ = discount_ * (glob_hyp_len_ + hyp_len);
-    glob_ref_len_ = discount_ * (glob_ref_len_ + ref_len);
-    glob_src_len_ = discount_ * (glob_src_len_ + src_len);
-  }
-  return score;
-}
-
-/*
- * Linear (Corpus) Bleu
- *
- * as in "Lattice Minimum Bayes-Risk Decoding
- *        for Statistical Machine Translation"
- * (Tromble et al. '08)
- *
- */
-score_t
-LinearBleuScorer::Score(const vector<WordID>& hyp, const vector<WordID>& ref,
-                        const unsigned rank, const unsigned /*src_len*/)
-{
-  unsigned hyp_len = hyp.size(), ref_len = ref.size();
-  if (ref_len == 0) return 0.;
-  unsigned M = N_;
-  if (ref_len < N_) M = ref_len;
-  NgramCounts counts(M);
-  if (hyp_len > 0)
-    counts = make_ngram_counts(hyp, ref, M);
-  score_t ret = 0.;
-  for (unsigned i = 0; i < M; i++) {
-    if (counts.sum_[i] == 0 || onebest_counts_.sum_[i] == 0) break;
-    ret += counts.sum_[i]/onebest_counts_.sum_[i];
-  }
-  ret = -(hyp_len/(score_t)onebest_len_) + (1./M) * ret;
-  if (rank == 0) {
-    onebest_len_ += hyp_len;
-    onebest_counts_ += counts;
-  }
-  return ret;
-}
-
-
-} // namespace
-