Merge branch 'master' of https://github.com/pks/cdec-dtrain

11 files changed, 421 insertions, 71 deletions

diff --git a/dtrain/Makefile.am b/dtrain/Makefile.am
index f39d161e..64fef489 100644
--- a/dtrain/Makefile.am
+++ b/dtrain/Makefile.am
@@ -3,5 +3,5 @@ bin_PROGRAMS = dtrain
 dtrain_SOURCES = dtrain.cc score.cc
 dtrain_LDADD   = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
 
-AM_CPPFLAGS = -O3 -W -Wall -Wno-sign-compare -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval
+AM_CPPFLAGS = -W -Wall -Wno-sign-compare -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval
 
diff --git a/dtrain/README.md b/dtrain/README.md
index 9580df6d..7edabbf1 100644
--- a/dtrain/README.md
+++ b/dtrain/README.md
@@ -39,16 +39,6 @@ For an example of local usage (with the 'distributed' format)
 the see test/example/ . This expects dtrain to be built without
 DTRAIN_LOCAL.
 
-Next
-----
-+ (dtrain|decoder) more meta-parameters testing
-+ feature selection directly in dtrain
-+ feature template: target side rule ngrams
-+ sa-extract -> leave-one-out for grammar of training set?
-+ make svm doable; no subgradient?
-+ reranking while sgd?
-+ try PRO, mira emulations
-
 Legal
 -----
 Copyright (c) 2012 by Patrick Simianer <p@simianer.de>
diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc
index d9bce843..b3e62914 100644
--- a/dtrain/dtrain.cc
+++ b/dtrain/dtrain.cc
@@ -6,37 +6,39 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
 {
   po::options_description ini("Configuration File Options");
   ini.add_options()
-    ("input",             po::value<string>()->default_value("-"),                                                "input file")
-    ("output",            po::value<string>()->default_value("-"),                       "output weights file, '-' for STDOUT")
-    ("input_weights",     po::value<string>(),                             "input weights file (e.g. from previous iteration)")
-    ("decoder_config",    po::value<string>(),                                                   "configuration file for cdec")
-    ("print_weights",     po::value<string>(),                                            "weights to print on each iteration")
-    ("stop_after",        po::value<unsigned>()->default_value(0),                              "stop after X input sentences")
-    ("tmp",               po::value<string>()->default_value("/tmp"),                                        "temp dir to use")
-    ("keep",              po::value<bool>()->zero_tokens(),                            "keep weights files for each iteration")
-    ("hstreaming",        po::value<string>(),                                "run in hadoop streaming mode, arg is a task id")
-    ("epochs",            po::value<unsigned>()->default_value(10),                            "# of iterations T (per shard)")
-    ("k",                 po::value<unsigned>()->default_value(100),                         "how many translations to sample")
-    ("sample_from",       po::value<string>()->default_value("kbest"),  "where to sample translations from: 'kbest', 'forest'")
-    ("filter",            po::value<string>()->default_value("uniq"),                       "filter kbest list: 'not', 'uniq'")
-    ("pair_sampling",     po::value<string>()->default_value("XYX"),              "how to sample pairs: 'all', 'XYX' or 'PRO'")
-    ("hi_lo",             po::value<float>()->default_value(0.1),                "hi and lo (X) for XYX (default 0.1), <= 0.5")
-    ("pair_threshold",    po::value<score_t>()->default_value(0.),                      "bleu [0,1] threshold to filter pairs")
-    ("N",                 po::value<unsigned>()->default_value(4),                                       "N for Ngrams (BLEU)")
-    ("scorer",            po::value<string>()->default_value("stupid_bleu"),        "scoring: bleu, stupid_, smooth_, approx_")
-    ("learning_rate",     po::value<weight_t>()->default_value(0.0001),                                        "learning rate")
-    ("gamma",             po::value<weight_t>()->default_value(0.),                         "gamma for SVM (0 for perceptron)")
-    ("select_weights",    po::value<string>()->default_value("last"),  "output best, last, avg weights ('VOID' to throw away)")
-    ("rescale",           po::value<bool>()->zero_tokens(),                           "rescale weight vector after each input")
-    ("l1_reg",            po::value<string>()->default_value("none"),   "apply l1 regularization as in 'Tsuroka et al' (2010)")
-    ("l1_reg_strength",   po::value<weight_t>(),                                                  "l1 regularization strength")
-    ("fselect",           po::value<weight_t>()->default_value(-1), "TODO select top x percent (or by threshold) of features after each epoch")
-    ("approx_bleu_d",     po::value<score_t>()->default_value(0.9),                                "discount for approx. BLEU")
-    ("scale_bleu_diff",   po::value<bool>()->zero_tokens(),                   "learning rate <- bleu diff of a misranked pair")
+    ("input",             po::value<string>()->default_value("-"),                                                   "input file")
+    ("output",            po::value<string>()->default_value("-"),                          "output weights file, '-' for STDOUT")
+    ("input_weights",     po::value<string>(),                                "input weights file (e.g. from previous iteration)")
+    ("decoder_config",    po::value<string>(),                                                      "configuration file for cdec")
+    ("print_weights",     po::value<string>(),                                               "weights to print on each iteration")
+    ("stop_after",        po::value<unsigned>()->default_value(0),                                 "stop after X input sentences")
+    ("tmp",               po::value<string>()->default_value("/tmp"),                                           "temp dir to use")
+    ("keep",              po::value<bool>()->zero_tokens(),                               "keep weights files for each iteration")
+    ("hstreaming",        po::value<string>(),                                   "run in hadoop streaming mode, arg is a task id")
+    ("epochs",            po::value<unsigned>()->default_value(10),                               "# of iterations T (per shard)")
+    ("k",                 po::value<unsigned>()->default_value(100),                            "how many translations to sample")
+    ("sample_from",       po::value<string>()->default_value("kbest"),     "where to sample translations from: 'kbest', 'forest'")
+    ("filter",            po::value<string>()->default_value("uniq"),                          "filter kbest list: 'not', 'uniq'")
+    ("pair_sampling",     po::value<string>()->default_value("XYX"),                 "how to sample pairs: 'all', 'XYX' or 'PRO'")
+    ("hi_lo",             po::value<float>()->default_value(0.1),                   "hi and lo (X) for XYX (default 0.1), <= 0.5")
+    ("pair_threshold",    po::value<score_t>()->default_value(0.),                         "bleu [0,1] threshold to filter pairs")
+    ("N",                 po::value<unsigned>()->default_value(4),                                          "N for Ngrams (BLEU)")
+    ("scorer",            po::value<string>()->default_value("stupid_bleu"),      "scoring: bleu, stupid_, smooth_, approx_, lc_")
+    ("learning_rate",     po::value<weight_t>()->default_value(0.0001),                                           "learning rate")
+    ("gamma",             po::value<weight_t>()->default_value(0.),                            "gamma for SVM (0 for perceptron)")
+    ("select_weights",    po::value<string>()->default_value("last"),     "output best, last, avg weights ('VOID' to throw away)")
+    ("rescale",           po::value<bool>()->zero_tokens(),                              "rescale weight vector after each input")
+    ("l1_reg",            po::value<string>()->default_value("none"),      "apply l1 regularization as in 'Tsuroka et al' (2010)")
+    ("l1_reg_strength",   po::value<weight_t>(),                                                     "l1 regularization strength")
+    ("fselect",           po::value<weight_t>()->default_value(-1), "select top x percent (or by threshold) of features after each epoch NOT IMPL") // TODO
+    ("approx_bleu_d",     po::value<score_t>()->default_value(0.9),                                   "discount for approx. BLEU")
+    ("scale_bleu_diff",   po::value<bool>()->zero_tokens(),                      "learning rate <- bleu diff of a misranked pair")
+    ("loss_margin",       po::value<weight_t>()->default_value(0.),  "update if no error in pref pair but model scores this near")
+    ("max_pairs",         po::value<unsigned>()->default_value(std::numeric_limits<unsigned>::max()), "max. # of pairs per Sent.")
 #ifdef DTRAIN_LOCAL
-    ("refs,r",            po::value<string>(),                                                      "references in local mode")
+    ("refs,r",            po::value<string>(),                                                         "references in local mode")
 #endif
-    ("noup",              po::value<bool>()->zero_tokens(),                                            "do not update weights");
+    ("noup",              po::value<bool>()->zero_tokens(),                                               "do not update weights");
   po::options_description cl("Command Line Options");
   cl.add_options()
     ("config,c",         po::value<string>(),              "dtrain config file")
@@ -134,6 +136,9 @@ main(int argc, char** argv)
   const string select_weights = cfg["select_weights"].as<string>();
   const float hi_lo = cfg["hi_lo"].as<float>();
   const score_t approx_bleu_d = cfg["approx_bleu_d"].as<score_t>();
+  const unsigned max_pairs = cfg["max_pairs"].as<unsigned>();
+  weight_t loss_margin = cfg["loss_margin"].as<weight_t>();
+  if (loss_margin > 9998.) loss_margin = std::numeric_limits<float>::max();
   bool scale_bleu_diff = false;
   if (cfg.count("scale_bleu_diff")) scale_bleu_diff = true;
   bool average = false;
@@ -160,8 +165,16 @@ main(int argc, char** argv)
     scorer = dynamic_cast<StupidBleuScorer*>(new StupidBleuScorer);
   } else if (scorer_str == "smooth_bleu") {
     scorer = dynamic_cast<SmoothBleuScorer*>(new SmoothBleuScorer);
+  } else if (scorer_str == "sum_bleu") {
+    scorer = dynamic_cast<SumBleuScorer*>(new SumBleuScorer);
+  } else if (scorer_str == "sumexp_bleu") {
+    scorer = dynamic_cast<SumExpBleuScorer*>(new SumExpBleuScorer);
+  } else if (scorer_str == "sumwhatever_bleu") {
+    scorer = dynamic_cast<SumWhateverBleuScorer*>(new SumWhateverBleuScorer);
   } else if (scorer_str == "approx_bleu") {
     scorer = dynamic_cast<ApproxBleuScorer*>(new ApproxBleuScorer(N, approx_bleu_d));
+  } else if (scorer_str == "lc_bleu") {
+    scorer = dynamic_cast<LinearBleuScorer*>(new LinearBleuScorer(N));
   } else {
     cerr << "Don't know scoring metric: '" << scorer_str << "', exiting." << endl;
     exit(1);
@@ -220,7 +233,7 @@ main(int argc, char** argv)
   grammar_buf_out.open(grammar_buf_fn.c_str());
 #endif
 
-  unsigned in_sz = UINT_MAX; // input index, input size
+  unsigned in_sz = std::numeric_limits<unsigned>::max(); // input index, input size
   vector<pair<score_t, score_t> > all_scores;
   score_t max_score = 0.;
   unsigned best_it = 0;
@@ -242,6 +255,7 @@ main(int argc, char** argv)
     if (!scale_bleu_diff) cerr << setw(25) << "learning rate " << eta << endl;
     else cerr << setw(25) << "learning rate " << "bleu diff" << endl;
     cerr << setw(25) << "gamma " << gamma << endl;
+    cerr << setw(25) << "loss margin " << loss_margin << endl;
     cerr << setw(25) << "pairs " << "'" << pair_sampling << "'" << endl;
     if (pair_sampling == "XYX")
       cerr << setw(25) << "hi lo " << hi_lo << endl;
@@ -251,6 +265,7 @@ main(int argc, char** argv)
       cerr << setw(25) << "l1 reg " << l1_reg << " '" << cfg["l1_reg"].as<string>() << "'" << endl;
     if (rescale)
       cerr << setw(25) << "rescale " << rescale << endl;
+    cerr << setw(25) << "max pairs " << max_pairs << endl;
     cerr << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl;
     cerr << setw(25) << "input " << "'" << input_fn << "'" << endl;
 #ifdef DTRAIN_LOCAL
@@ -415,21 +430,27 @@ main(int argc, char** argv)
       // get pairs
       vector<pair<ScoredHyp,ScoredHyp> > pairs;
       if (pair_sampling == "all")
-        all_pairs(samples, pairs, pair_threshold);
+        all_pairs(samples, pairs, pair_threshold, max_pairs);
       if (pair_sampling == "XYX")
-        partXYX(samples, pairs, pair_threshold, hi_lo);
+        partXYX(samples, pairs, pair_threshold, max_pairs, hi_lo);
       if (pair_sampling == "PRO")
-        PROsampling(samples, pairs, pair_threshold);
+        PROsampling(samples, pairs, pair_threshold, max_pairs);
       npairs += pairs.size();
 
       for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin();
            it != pairs.end(); it++) {
+#ifdef DTRAIN_FASTER_PERCEPTRON
+        bool rank_error = true; // pair sampling already did this for us
+        rank_errors++;
+        score_t margin = std::numeric_limits<float>::max();
+#else
         bool rank_error = it->first.model <= it->second.model;
         if (rank_error) rank_errors++;
-        score_t margin = fabs(it->first.model - it->second.model);
-        if (!rank_error && margin < 1) margin_violations++;
+        score_t margin = fabs(fabs(it->first.model) - fabs(it->second.model));
+        if (!rank_error && margin < loss_margin) margin_violations++;
+#endif
         if (scale_bleu_diff) eta = it->first.score - it->second.score;
-        if (rank_error || (gamma && margin<1)) {
+        if (rank_error || margin < loss_margin) {
           SparseVector<weight_t> diff_vec = it->first.f - it->second.f;
           lambdas.plus_eq_v_times_s(diff_vec, eta);
           if (gamma)
@@ -486,7 +507,7 @@ main(int argc, char** argv)
 
   if (average) w_average += lambdas;
 
-  if (scorer_str == "approx_bleu") scorer->Reset();
+  if (scorer_str == "approx_bleu" || scorer_str == "lc_bleu") scorer->Reset();
 
   if (t == 0) {
     in_sz = ii; // remember size of input (# lines)
@@ -534,8 +555,10 @@ main(int argc, char** argv)
     cerr << _np << npairs/(float)in_sz << endl;
     cerr << "        avg # rank err: ";
     cerr << rank_errors/(float)in_sz << endl;
+#ifndef DTRAIN_FASTER_PERCEPTRON
     cerr << "     avg # margin viol: ";
     cerr << margin_violations/(float)in_sz << endl;
+#endif
     cerr << "    non0 feature count: " <<  nonz << endl;
     cerr << "           avg list sz: " << list_sz/(float)in_sz << endl;
     cerr << "           avg f count: " << f_count/(float)list_sz << endl;
diff --git a/dtrain/dtrain.h b/dtrain/dtrain.h
index 94d149ce..7e084a79 100644
--- a/dtrain/dtrain.h
+++ b/dtrain/dtrain.h
@@ -1,6 +1,14 @@
 #ifndef _DTRAIN_H_
 #define _DTRAIN_H_
 
+#undef DTRAIN_FASTER_PERCEPTRON // only look at misranked pairs
+                                 // DO NOT USE WITH SVM!
+#define DTRAIN_LOCAL
+#define DTRAIN_DOTS 10 // after how many inputs to display a '.'
+#define DTRAIN_GRAMMAR_DELIM "########EOS########"
+#define DTRAIN_SCALE 100000
+
+
 #include <iomanip>
 #include <climits>
 #include <string.h>
@@ -13,11 +21,7 @@
 
 #include "filelib.h"
 
-#undef DTRAIN_LOCAL
 
-#define DTRAIN_DOTS 10 // after how many inputs to display a '.'
-#define DTRAIN_GRAMMAR_DELIM "########EOS########"
-#define DTRAIN_SCALE 100000
 
 using namespace std;
 using namespace dtrain;
@@ -32,7 +36,7 @@ inline void register_and_convert(const vector<string>& strs, vector<WordID>& ids
 
 inline string gettmpf(const string path, const string infix)
 {
-  char fn[1024];
+  char fn[path.size() + infix.size() + 8];
   strcpy(fn, path.c_str());
   strcat(fn, "/");
   strcat(fn, infix.c_str());
diff --git a/dtrain/ksampler.h b/dtrain/ksampler.h
index f52fb649..bc2f56cd 100644
--- a/dtrain/ksampler.h
+++ b/dtrain/ksampler.h
@@ -8,6 +8,11 @@
 namespace dtrain
 {
 
+bool
+cmp_hyp_by_model_d(ScoredHyp a, ScoredHyp b)
+{
+  return a.model > b.model;
+}
 
 struct KSampler : public HypSampler
 {
@@ -44,6 +49,8 @@ struct KSampler : public HypSampler
       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;
   }
 };
 
diff --git a/dtrain/pairsampling.h b/dtrain/pairsampling.h
index bac132c6..84be1efb 100644
--- a/dtrain/pairsampling.h
+++ b/dtrain/pairsampling.h
@@ -19,10 +19,12 @@ cmp_hyp_by_score_d(ScoredHyp a, ScoredHyp b)
 }
 
 inline void
-all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, float _unused=1)
+all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, unsigned max, 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 (threshold > 0) {
@@ -32,7 +34,12 @@ all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, sc
         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;
   }
 }
 
@@ -44,13 +51,22 @@ all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, sc
  */
 
 inline void
-partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, float hi_lo)
+partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, unsigned max, float hi_lo)
 {
-  sort(s->begin(), s->end(), cmp_hyp_by_score_d);
   unsigned sz = s->size();
+  if (sz < 2) return;
+  sort(s->begin(), s->end(), cmp_hyp_by_score_d);
   unsigned sep = round(sz*hi_lo);
-  for (unsigned i = 0; i < sep; i++) {
-    for (unsigned j = sep; j < sz; j++) {
+  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++) {
+#ifdef DTRAIN_FASTER_PERCEPTRON
+      if ((*s)[i].model <= (*s)[j].model) {
+#endif
       if (threshold > 0) {
         if (accept_pair((*s)[i].score, (*s)[j].score, threshold))
           training.push_back(make_pair((*s)[i], (*s)[j]));
@@ -58,10 +74,23 @@ partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, scor
         if ((*s)[i].score != (*s)[j].score)
           training.push_back(make_pair((*s)[i], (*s)[j]));
       }
+      if (++count == max) {
+        b = true;
+        break;
+      }
+#ifdef DTRAIN_FASTER_PERCEPTRON
+      }
+#endif
     }
+    if (b) break;
   }
-  for (unsigned i = sep; i < sz-sep; i++) {
-    for (unsigned j = sz-sep; j < sz; j++) {
+  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++) {
+#ifdef DTRAIN_FASTER_PERCEPTRON
+      if ((*s)[i].model <= (*s)[j].model) {
+#endif
       if (threshold > 0) {
         if (accept_pair((*s)[i].score, (*s)[j].score, threshold))
           training.push_back(make_pair((*s)[i], (*s)[j]));
@@ -69,6 +98,10 @@ partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, scor
         if ((*s)[i].score != (*s)[j].score)
           training.push_back(make_pair((*s)[i], (*s)[j]));
       }
+      if (++count == max) return;
+#ifdef DTRAIN_FASTER_PERCEPTRON
+      }
+#endif
     }
   }
 }
@@ -86,7 +119,7 @@ _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, float _unused=1)
+PROsampling(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, unsigned max, float _unused=1)
 {
   unsigned max_count = 5000, count = 0, sz = s->size();
   bool b = false;
diff --git a/dtrain/score.cc b/dtrain/score.cc
index 7b1f6be4..4a7cac6e 100644
--- a/dtrain/score.cc
+++ b/dtrain/score.cc
@@ -80,7 +80,7 @@ StupidBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
  *        to Machine Translation"
  * (Liang et al. '06)
  *
- * NOTE: max is 0.9375
+ * NOTE: max is 0.9375 (with N=4)
  */
 score_t
 SmoothBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
@@ -103,7 +103,83 @@ SmoothBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
         i_bleu[j] += (1/((score_t)j+1)) * i_ng;
       }
     }
-    sum += exp(i_bleu[i])/(pow(2.0, static_cast<double>(N_-i)));
+    sum += exp(i_bleu[i])/(pow(2.0, N_-i));
+  }
+  return brevity_penalty(hyp_len, ref_len) * sum;
+}
+
+/*
+ * 'sum' bleu
+ *
+ * sum up Ngram precisions
+ */
+score_t
+SumBleuScorer::Score(vector<WordID>& hyp, 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., N_-j+1);
+    j++;
+  }
+  return brevity_penalty(hyp_len, ref_len) * sum;
+}
+
+/*
+ * 'sum' (exp) bleu
+ *
+ * sum up exp(Ngram precisions)
+ */
+score_t
+SumExpBleuScorer::Score(vector<WordID>& hyp, 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., 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(vector<WordID>& hyp, 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., N_-j+1);
+    j++;
   }
   return brevity_penalty(hyp_len, ref_len) * sum;
 }
@@ -115,7 +191,8 @@ SmoothBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
  *        and Structural Translation Features"
  * (Chiang et al. '08)
  *
- * NOTE: needs some more code in dtrain.cc
+ * NOTE: Needs some more code in dtrain.cc .
+ *       No scaling by src len.
  */
 score_t
 ApproxBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
@@ -137,7 +214,39 @@ ApproxBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
     glob_ref_len_ = discount_ * (glob_ref_len_ + ref_len);
     glob_src_len_ = discount_ * (glob_src_len_ + src_len);
   }
-  return (score_t)glob_src_len_ * score;
+  return score;
+}
+
+/*
+ * Linear (Corpus) Bleu
+ *
+ * as in "Lattice Minimum Bayes-Risk Decoding
+ *        for Statistical Machine Translation"
+ * (Tromble et al. '08)
+ *
+ */
+score_t
+LinearBleuScorer::Score(vector<WordID>& hyp, 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;
 }
 
 
diff --git a/dtrain/score.h b/dtrain/score.h
index eb8ad912..f317c903 100644
--- a/dtrain/score.h
+++ b/dtrain/score.h
@@ -20,7 +20,7 @@ struct NgramCounts
   inline void
   operator+=(const NgramCounts& rhs)
   {
-    assert(N_ == rhs.N_);
+    if (rhs.N_ > N_) Resize(rhs.N_);
     for (unsigned i = 0; i < N_; i++) {
       this->clipped_[i] += rhs.clipped_.find(i)->second;
       this->sum_[i] += rhs.sum_.find(i)->second;
@@ -59,14 +59,22 @@ struct NgramCounts
   inline void
   Zero()
   {
-    unsigned i;
-    for (i = 0; i < N_; i++) {
+    for (unsigned i = 0; i < N_; i++) {
       clipped_[i] = 0.;
       sum_[i] = 0.;
     }
   }
 
   inline void
+  One()
+  {
+    for (unsigned i = 0; i < N_; i++) {
+      clipped_[i] = 1.;
+      sum_[i] = 1.;
+    }
+  }
+
+  inline void
   Print()
   {
     for (unsigned i = 0; i < N_; i++) {
@@ -74,6 +82,23 @@ struct NgramCounts
       cout << i+1 << "grams:\t\t\t" << sum_[i] << endl;
     }
   }
+
+  inline void Resize(unsigned N)
+  {
+    if (N == N_) return;
+    else if (N > N_) {
+      for (unsigned i = N_; i < N; i++) {
+        clipped_[i] = 0.;
+        sum_[i] = 0.;
+      }
+    } else { // N < N_
+      for (unsigned i = N_-1; i > N-1; i--) {
+        clipped_.erase(i);
+        sum_.erase(i);
+      }
+    }
+    N_ = N;
+  }
 };
 
 typedef map<vector<WordID>, unsigned> Ngrams;
@@ -128,6 +153,21 @@ struct SmoothBleuScorer : public LocalScorer
   score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
 };
 
+struct SumBleuScorer : public LocalScorer
+{
+   score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
+};
+
+struct SumExpBleuScorer : public LocalScorer
+{
+   score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
+};
+
+struct SumWhateverBleuScorer : public LocalScorer
+{
+   score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
+};
+
 struct ApproxBleuScorer : public BleuScorer
 {
   NgramCounts glob_onebest_counts_;
@@ -147,6 +187,24 @@ struct ApproxBleuScorer : public BleuScorer
   score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned rank, const unsigned src_len);
 };
 
+struct LinearBleuScorer : public BleuScorer
+{
+  unsigned onebest_len_;
+  NgramCounts onebest_counts_;
+
+  LinearBleuScorer(unsigned N) : onebest_len_(1), onebest_counts_(N)
+  {
+    onebest_counts_.One();
+  }
+
+  score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned rank, const unsigned /*src_len*/);
+
+  inline void Reset() {
+    onebest_len_ = 1;
+    onebest_counts_.One();
+  }
+};
+
 
 } // namespace
 
diff --git a/dtrain/test/example/README b/dtrain/test/example/README
index b3ea5f06..6937b11b 100644
--- a/dtrain/test/example/README
+++ b/dtrain/test/example/README
@@ -1,8 +1,8 @@
 Small example of input format for distributed training.
 Call dtrain from cdec/dtrain/ with ./dtrain -c test/example/dtrain.ini .
 
-For this to work, disable '#define DTRAIN_LOCAL' from dtrain.h
+For this to work, undef 'DTRAIN_LOCAL' in dtrain.h
 and recompile.
 
-Data is here: http://simianer.de/dtrain
+Data is here: http://simianer.de/#dtrain
 
diff --git a/dtrain/test/example/dtrain.ini b/dtrain/test/example/dtrain.ini
index f87ee9cf..c8ac7c3f 100644
--- a/dtrain/test/example/dtrain.ini
+++ b/dtrain/test/example/dtrain.ini
@@ -5,7 +5,7 @@ decoder_config=test/example/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
 tmp=/tmp
-stop_after=10 # stop epoch after 20 inputs
+stop_after=10 # stop epoch after 10 inputs
 
 # interesting stuff
 epochs=3                # run over input 3 times
@@ -19,3 +19,4 @@ 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 (this will still only use pairs with diff > 0)
+loss_margin=0
diff --git a/dtrain/test/example/expected-output b/dtrain/test/example/expected-output
new file mode 100644
index 00000000..25d2c069
--- /dev/null
+++ b/dtrain/test/example/expected-output
@@ -0,0 +1,125 @@
+                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.