fixed conflicts

23 files changed, 0 insertions, 2081 deletions

diff --git a/dtrain/Makefile.am b/dtrain/Makefile.am
deleted file mode 100644
index 64fef489..00000000
--- a/dtrain/Makefile.am
+++ /dev/null
@@ -1,7 +0,0 @@
-bin_PROGRAMS = dtrain
-
-dtrain_SOURCES = dtrain.cc score.cc
-dtrain_LDADD   = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
-
-AM_CPPFLAGS = -W -Wall -Wno-sign-compare -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval
-
diff --git a/dtrain/README.md b/dtrain/README.md
deleted file mode 100644
index 7edabbf1..00000000
--- a/dtrain/README.md
+++ /dev/null
@@ -1,48 +0,0 @@
-This is a simple (and parallelizable) tuning method for cdec
-which is able to train the weights of very many (sparse) features.
-It was used here:
-  "Joint Feature Selection in Distributed Stochastic
-   Learning for Large-Scale Discriminative Training in
-   SMT"
-(Simianer, Riezler, Dyer; ACL 2012)
-
-
-Building
---------
-Builds when building cdec, see ../BUILDING .
-To build only parts needed for dtrain do
-```
-  autoreconf -ifv
-  ./configure [--disable-gtest]
-  cd dtrain/; make
-```
-
-Running
--------
-To run this on a dev set locally:
-```
-    #define DTRAIN_LOCAL
-```
-otherwise remove that line or undef, then recompile. You need a single
-grammar file or input annotated with per-sentence grammars (psg) as you
-would use with cdec. Additionally you need to give dtrain a file with
-references (--refs) when running locally.
-
-The input for use with hadoop streaming looks like this:
-```
-    <sid>\t<source>\t<ref>\t<grammar rules separated by \t>
-```
-To convert a psg to this format you need to replace all "\n"
-by "\t". Make sure there are no tabs in your data.
-
-For an example of local usage (with the 'distributed' format)
-the see test/example/ . This expects dtrain to be built without
-DTRAIN_LOCAL.
-
-Legal
------
-Copyright (c) 2012 by Patrick Simianer <p@simianer.de>
-
-See the file ../LICENSE.txt for the licensing terms that this software is
-released under.
-
diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc
deleted file mode 100644
index b7a4bb6f..00000000
--- a/dtrain/dtrain.cc
+++ /dev/null
@@ -1,657 +0,0 @@
-#include "dtrain.h"
-
-
-bool
-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_, 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")
-#endif
-    ("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")
-    ("quiet,q",          po::value<bool>()->zero_tokens(),           "be quiet")
-    ("verbose,v",        po::value<bool>()->zero_tokens(),         "be verbose");
-  cl.add(ini);
-  po::store(parse_command_line(argc, argv, cl), *cfg);
-  if (cfg->count("config")) {
-    ifstream ini_f((*cfg)["config"].as<string>().c_str());
-    po::store(po::parse_config_file(ini_f, ini), *cfg);
-  }
-  po::notify(*cfg);
-  if (!cfg->count("decoder_config")) {
-    cerr << cl << endl;
-    return false;
-  }
-  if (cfg->count("hstreaming") && (*cfg)["output"].as<string>() != "-") {
-    cerr << "When using 'hstreaming' the 'output' param should be '-'." << endl;
-    return false;
-  }
-#ifdef DTRAIN_LOCAL
-  if ((*cfg)["input"].as<string>() == "-") {
-    cerr << "Can't use stdin as input with this binary. Recompile without DTRAIN_LOCAL" << endl;
-    return false;
-  }
-#endif
-  if ((*cfg)["sample_from"].as<string>() != "kbest"
-       && (*cfg)["sample_from"].as<string>() != "forest") {
-    cerr << "Wrong 'sample_from' param: '" << (*cfg)["sample_from"].as<string>() << "', use 'kbest' or 'forest'." << endl;
-    return false;
-  }
-  if ((*cfg)["sample_from"].as<string>() == "kbest" && (*cfg)["filter"].as<string>() != "uniq" &&
-        (*cfg)["filter"].as<string>() != "not") {
-    cerr << "Wrong 'filter' param: '" << (*cfg)["filter"].as<string>() << "', use 'uniq' or 'not'." << endl;
-    return false;
-  }
-  if ((*cfg)["pair_sampling"].as<string>() != "all" && (*cfg)["pair_sampling"].as<string>() != "XYX" &&
-        (*cfg)["pair_sampling"].as<string>() != "PRO") {
-    cerr << "Wrong 'pair_sampling' param: '" << (*cfg)["pair_sampling"].as<string>() << "'." << endl;
-    return false;
-  }
-  if(cfg->count("hi_lo") && (*cfg)["pair_sampling"].as<string>() != "XYX") {
-    cerr << "Warning: hi_lo only works with pair_sampling XYX." << endl;
-  }
-  if((*cfg)["hi_lo"].as<float>() > 0.5 || (*cfg)["hi_lo"].as<float>() < 0.01) {
-    cerr << "hi_lo must lie in [0.01, 0.5]" << endl;
-    return false;
-  }
-  if ((*cfg)["pair_threshold"].as<score_t>() < 0) {
-    cerr << "The threshold must be >= 0!" << endl;
-    return false;
-  }
-  if ((*cfg)["select_weights"].as<string>() != "last" && (*cfg)["select_weights"].as<string>() != "best" &&
-        (*cfg)["select_weights"].as<string>() != "avg" && (*cfg)["select_weights"].as<string>() != "VOID") {
-    cerr << "Wrong 'select_weights' param: '" << (*cfg)["select_weights"].as<string>() << "', use 'last' or 'best'." << endl;
-    return false;
-  }
-  return true;
-}
-
-int
-main(int argc, char** argv)
-{
-  // handle most parameters
-  po::variables_map cfg;
-  if (!dtrain_init(argc, argv, &cfg)) exit(1); // something is wrong
-  bool quiet = false;
-  if (cfg.count("quiet")) quiet = true;
-  bool verbose = false;
-  if (cfg.count("verbose")) verbose = true;
-  bool noup = false;
-  if (cfg.count("noup")) noup = true;
-  bool hstreaming = false;
-  string task_id;
-  if (cfg.count("hstreaming")) {
-    hstreaming = true;
-    quiet = true;
-    task_id = cfg["hstreaming"].as<string>();
-    cerr.precision(17);
-  }
-  bool rescale = false;
-  if (cfg.count("rescale")) rescale = true;
-  HSReporter rep(task_id);
-  bool keep = false;
-  if (cfg.count("keep")) keep = true;
-
-  const unsigned k = cfg["k"].as<unsigned>();
-  const unsigned N = cfg["N"].as<unsigned>();
-  const unsigned T = cfg["epochs"].as<unsigned>();
-  const unsigned stop_after = cfg["stop_after"].as<unsigned>();
-  const string filter_type = cfg["filter"].as<string>();
-  const string sample_from = cfg["sample_from"].as<string>();
-  const string pair_sampling = cfg["pair_sampling"].as<string>();
-  const score_t pair_threshold = cfg["pair_threshold"].as<score_t>();
-  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;
-  if (select_weights == "avg")
-    average = true;
-  vector<string> print_weights;
-  if (cfg.count("print_weights"))
-    boost::split(print_weights, cfg["print_weights"].as<string>(), boost::is_any_of(" "));
-
-  // setup decoder
-  register_feature_functions();
-  SetSilent(true);
-  ReadFile ini_rf(cfg["decoder_config"].as<string>());
-  if (!quiet)
-    cerr << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl;
-  Decoder decoder(ini_rf.stream());
-
-  // scoring metric/scorer
-  string scorer_str = cfg["scorer"].as<string>();
-  LocalScorer* scorer;
-  if (scorer_str == "bleu") {
-    scorer = dynamic_cast<BleuScorer*>(new BleuScorer);
-  } else if (scorer_str == "stupid_bleu") {
-    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);
-  }
-  vector<score_t> bleu_weights;
-  scorer->Init(N, bleu_weights);
-
-  // setup decoder observer
-  MT19937 rng; // random number generator, only for forest sampling
-  HypSampler* observer;
-  if (sample_from == "kbest")
-    observer = dynamic_cast<KBestGetter*>(new KBestGetter(k, filter_type));
-  else
-    observer = dynamic_cast<KSampler*>(new KSampler(k, &rng));
-  observer->SetScorer(scorer);
-
-  // init weights
-  vector<weight_t>& dense_weights = decoder.CurrentWeightVector();
-  SparseVector<weight_t> lambdas, cumulative_penalties, w_average;
-  if (cfg.count("input_weights")) Weights::InitFromFile(cfg["input_weights"].as<string>(), &dense_weights);
-  Weights::InitSparseVector(dense_weights, &lambdas);
-
-  // meta params for perceptron, SVM
-  weight_t eta = cfg["learning_rate"].as<weight_t>();
-  weight_t gamma = cfg["gamma"].as<weight_t>();
-
-  // l1 regularization
-  bool l1naive = false;
-  bool l1clip = false;
-  bool l1cumul = false;
-  weight_t l1_reg = 0;
-  if (cfg["l1_reg"].as<string>() != "none") {
-    string s = cfg["l1_reg"].as<string>();
-    if (s == "naive") l1naive = true;
-    else if (s == "clip") l1clip = true;
-    else if (s == "cumul") l1cumul = true;
-    l1_reg = cfg["l1_reg_strength"].as<weight_t>();
-  }
-
-  // output
-  string output_fn = cfg["output"].as<string>();
-  // input
-  string input_fn = cfg["input"].as<string>();
-  ReadFile input(input_fn);
-  // buffer input for t > 0
-  vector<string> src_str_buf;          // source strings (decoder takes only strings)
-  vector<vector<WordID> > ref_ids_buf; // references as WordID vecs
-  // where temp files go
-  string tmp_path = cfg["tmp"].as<string>();
-#ifdef DTRAIN_LOCAL
-  string refs_fn = cfg["refs"].as<string>();
-  ReadFile refs(refs_fn);
-#else
-  string grammar_buf_fn = gettmpf(tmp_path, "dtrain-grammars");
-  ogzstream grammar_buf_out;
-  grammar_buf_out.open(grammar_buf_fn.c_str());
-#endif
-
-  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;
-  float overall_time = 0.;
-
-  // output cfg
-  if (!quiet) {
-    cerr << _p5;
-    cerr << endl << "dtrain" << endl << "Parameters:" << endl;
-    cerr << setw(25) << "k " << k << endl;
-    cerr << setw(25) << "N " << N << endl;
-    cerr << setw(25) << "T " << T << endl;
-    cerr << setw(25) << "scorer '" << scorer_str << "'" << endl;
-    if (scorer_str == "approx_bleu")
-      cerr << setw(25) << "approx. B discount " << approx_bleu_d << endl;
-    cerr << setw(25) << "sample from " << "'" << sample_from << "'" << endl;
-    if (sample_from == "kbest")
-      cerr << setw(25) << "filter " << "'" << filter_type << "'" << endl;
-    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;
-    cerr << setw(25) << "pair threshold " << pair_threshold << endl;
-    cerr << setw(25) << "select weights " << "'" << select_weights << "'" << endl;
-    if (cfg.count("l1_reg"))
-      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
-    cerr << setw(25) << "refs " << "'" << refs_fn << "'" << endl;
-#endif
-    cerr << setw(25) << "output " << "'" << output_fn << "'" << endl;
-    if (cfg.count("input_weights"))
-      cerr << setw(25) << "weights in " << "'" << cfg["input_weights"].as<string>() << "'" << endl;
-    if (stop_after > 0)
-      cerr << setw(25) << "stop_after " << stop_after << endl;
-    if (!verbose) cerr << "(a dot represents " << DTRAIN_DOTS << " inputs)" << endl;
-  }
-
-
-  for (unsigned t = 0; t < T; t++) // T epochs
-  {
-
-  if (hstreaming) cerr << "reporter:status:Iteration #" << t+1 << " of " << T << endl;
-
-  time_t start, end;
-  time(&start);
-#ifndef DTRAIN_LOCAL
-  igzstream grammar_buf_in;
-  if (t > 0) grammar_buf_in.open(grammar_buf_fn.c_str());
-#endif
-  score_t score_sum = 0.;
-  score_t model_sum(0);
-  unsigned ii = 0, rank_errors = 0, margin_violations = 0, npairs = 0, f_count = 0, list_sz = 0;
-  if (!quiet) cerr << "Iteration #" << t+1 << " of " << T << "." << endl;
-
-  while(true)
-  {
-
-    string in;
-    bool next = false, stop = false; // next iteration or premature stop
-    if (t == 0) {
-      if(!getline(*input, in)) next = true;
-    } else {
-      if (ii == in_sz) next = true; // stop if we reach the end of our input
-    }
-    // stop after X sentences (but still go on for those)
-    if (stop_after > 0 && stop_after == ii && !next) stop = true;
-
-    // produce some pretty output
-    if (!quiet && !verbose) {
-      if (ii == 0) cerr << " ";
-      if ((ii+1) % (DTRAIN_DOTS) == 0) {
-        cerr << ".";
-        cerr.flush();
-      }
-      if ((ii+1) % (20*DTRAIN_DOTS) == 0) {
-        cerr << " " << ii+1 << endl;
-        if (!next && !stop) cerr << " ";
-      }
-      if (stop) {
-        if (ii % (20*DTRAIN_DOTS) != 0) cerr << " " << ii << endl;
-        cerr << "Stopping after " << stop_after << " input sentences." << endl;
-      } else {
-        if (next) {
-          if (ii % (20*DTRAIN_DOTS) != 0) cerr << " " << ii << endl;
-        }
-      }
-    }
-
-    // next iteration
-    if (next || stop) break;
-
-    // weights
-    lambdas.init_vector(&dense_weights);
-
-    // getting input
-    vector<WordID> ref_ids; // reference as vector<WordID>
-#ifndef DTRAIN_LOCAL
-    vector<string> in_split; // input: sid\tsrc\tref\tpsg
-    if (t == 0) {
-      // handling input
-      split_in(in, in_split);
-      if (hstreaming && ii == 0) cerr << "reporter:counter:" << task_id << ",First ID," << in_split[0] << endl;
-      // getting reference
-      vector<string> ref_tok;
-      boost::split(ref_tok, in_split[2], boost::is_any_of(" "));
-      register_and_convert(ref_tok, ref_ids);
-      ref_ids_buf.push_back(ref_ids);
-      // process and set grammar
-      bool broken_grammar = true; // ignore broken grammars
-      for (string::iterator it = in.begin(); it != in.end(); it++) {
-        if (!isspace(*it)) {
-          broken_grammar = false;
-          break;
-        }
-      }
-      if (broken_grammar) {
-        cerr << "Broken grammar for " << ii+1 << "! Ignoring this input." << endl;
-        continue;
-      }
-      boost::replace_all(in, "\t", "\n");
-      in += "\n";
-      grammar_buf_out << in << DTRAIN_GRAMMAR_DELIM << " " << in_split[0] << endl;
-      decoder.AddSupplementalGrammarFromString(in);
-      src_str_buf.push_back(in_split[1]);
-      // decode
-      observer->SetRef(ref_ids);
-      decoder.Decode(in_split[1], observer);
-    } else {
-      // get buffered grammar
-      string grammar_str;
-      while (true) {
-        string rule;
-        getline(grammar_buf_in, rule);
-        if (boost::starts_with(rule, DTRAIN_GRAMMAR_DELIM)) break;
-        grammar_str += rule + "\n";
-      }
-      decoder.AddSupplementalGrammarFromString(grammar_str);
-      // decode
-      observer->SetRef(ref_ids_buf[ii]);
-      decoder.Decode(src_str_buf[ii], observer);
-    }
-#else
-    if (t == 0) {
-      string r_;
-      getline(*refs, r_);
-      vector<string> ref_tok;
-      boost::split(ref_tok, r_, boost::is_any_of(" "));
-      register_and_convert(ref_tok, ref_ids);
-      ref_ids_buf.push_back(ref_ids);
-      src_str_buf.push_back(in);
-    } else {
-      ref_ids = ref_ids_buf[ii];
-    }
-    observer->SetRef(ref_ids);
-    if (t == 0)
-      decoder.Decode(in, observer);
-    else
-      decoder.Decode(src_str_buf[ii], observer);
-#endif
-
-    // get (scored) samples
-    vector<ScoredHyp>* samples = observer->GetSamples();
-
-    if (verbose) {
-      cerr << "--- ref for " << ii << ": ";
-      if (t > 0) printWordIDVec(ref_ids_buf[ii]);
-      else printWordIDVec(ref_ids);
-      cerr << endl;
-      for (unsigned u = 0; u < samples->size(); u++) {
-        cerr << _p2 << _np << "[" << u << ". '";
-        printWordIDVec((*samples)[u].w);
-        cerr << "'" << endl;
-        cerr << "SCORE=" << (*samples)[u].score << ",model="<< (*samples)[u].model << endl;
-        cerr << "F{" << (*samples)[u].f << "} ]" << endl << endl;
-      }
-    }
-
-    score_sum += (*samples)[0].score; // stats for 1best
-    model_sum += (*samples)[0].model;
-
-    f_count += observer->get_f_count();
-    list_sz += observer->get_sz();
-
-    // weight updates
-    if (!noup) {
-      // get pairs
-      vector<pair<ScoredHyp,ScoredHyp> > pairs;
-      if (pair_sampling == "all")
-        all_pairs(samples, pairs, pair_threshold, max_pairs);
-      if (pair_sampling == "XYX")
-        partXYX(samples, pairs, pair_threshold, max_pairs, hi_lo);
-      if (pair_sampling == "PRO")
-        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(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 || 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)
-            lambdas.plus_eq_v_times_s(lambdas, -2*gamma*eta*(1./npairs));
-        }
-      }
-
-      // l1 regularization
-      if (l1naive) {
-        for (unsigned d = 0; d < lambdas.size(); d++) {
-          weight_t v = lambdas.get(d);
-          lambdas.set_value(d, v - sign(v) * l1_reg);
-        }
-      } else if (l1clip) {
-        for (unsigned d = 0; d < lambdas.size(); d++) {
-          if (lambdas.nonzero(d)) {
-            weight_t v = lambdas.get(d);
-            if (v > 0) {
-              lambdas.set_value(d, max(0., v - l1_reg));
-            } else {
-              lambdas.set_value(d, min(0., v + l1_reg));
-            }
-          }
-        }
-      } else if (l1cumul) {
-        weight_t acc_penalty = (ii+1) * l1_reg; // ii is the index of the current input
-        for (unsigned d = 0; d < lambdas.size(); d++) {
-          if (lambdas.nonzero(d)) {
-            weight_t v = lambdas.get(d);
-            weight_t penalty = 0;
-            if (v > 0) {
-              penalty = max(0., v-(acc_penalty + cumulative_penalties.get(d)));
-            } else {
-              penalty = min(0., v+(acc_penalty - cumulative_penalties.get(d)));
-            }
-            lambdas.set_value(d, penalty);
-            cumulative_penalties.set_value(d, cumulative_penalties.get(d)+penalty);
-          }
-        }
-      }
-
-    }
-
-    if (rescale) lambdas /= lambdas.l2norm();
-
-    ++ii;
-
-    if (hstreaming) {
-      rep.update_counter("Seen #"+boost::lexical_cast<string>(t+1), 1u);
-      rep.update_counter("Seen", 1u);
-    }
-
-  } // input loop
-
-  if (average) w_average += lambdas;
-
-  if (scorer_str == "approx_bleu" || scorer_str == "lc_bleu") scorer->Reset();
-
-  if (t == 0) {
-    in_sz = ii; // remember size of input (# lines)
-    if (hstreaming) {
-      rep.update_counter("|Input|", ii);
-      rep.update_gcounter("|Input|", ii);
-      rep.update_gcounter("Shards", 1u);
-    }
-  }
-
-#ifndef DTRAIN_LOCAL
-  if (t == 0) {
-    grammar_buf_out.close();
-  } else {
-    grammar_buf_in.close();
-  }
-#endif
-
-  // print some stats
-  score_t score_avg = score_sum/(score_t)in_sz;
-  score_t model_avg = model_sum/(score_t)in_sz;
-  score_t score_diff, model_diff;
-  if (t > 0) {
-    score_diff = score_avg - all_scores[t-1].first;
-    model_diff = model_avg - all_scores[t-1].second;
-  } else {
-    score_diff = score_avg;
-    model_diff = model_avg;
-  }
-
-  unsigned nonz = 0;
-  if (!quiet || hstreaming) nonz = (unsigned)lambdas.num_nonzero();
-
-  if (!quiet) {
-    cerr << _p5 << _p << "WEIGHTS" << endl;
-    for (vector<string>::iterator it = print_weights.begin(); it != print_weights.end(); it++) {
-      cerr << setw(18) << *it << " = " << lambdas.get(FD::Convert(*it)) << endl;
-    }
-    cerr << "        ---" << endl;
-    cerr << _np << "       1best avg score: " << score_avg;
-    cerr << _p << " (" << score_diff << ")" << endl;
-    cerr << _np << " 1best avg model score: " << model_avg;
-    cerr << _p << " (" << model_diff << ")" << endl;
-    cerr << "           avg # pairs: ";
-    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;
-  }
-
-  if (hstreaming) {
-    rep.update_counter("Score 1best avg #"+boost::lexical_cast<string>(t+1), (unsigned)(score_avg*DTRAIN_SCALE));
-    rep.update_counter("Model 1best avg #"+boost::lexical_cast<string>(t+1), (unsigned)(model_avg*DTRAIN_SCALE));
-    rep.update_counter("Pairs avg #"+boost::lexical_cast<string>(t+1), (unsigned)((npairs/(weight_t)in_sz)*DTRAIN_SCALE));
-    rep.update_counter("Rank errors avg #"+boost::lexical_cast<string>(t+1), (unsigned)((rank_errors/(weight_t)in_sz)*DTRAIN_SCALE));
-    rep.update_counter("Margin violations avg #"+boost::lexical_cast<string>(t+1), (unsigned)((margin_violations/(weight_t)in_sz)*DTRAIN_SCALE));
-    rep.update_counter("Non zero feature count #"+boost::lexical_cast<string>(t+1), nonz);
-    rep.update_gcounter("Non zero feature count #"+boost::lexical_cast<string>(t+1), nonz);
-  }
-
-  pair<score_t,score_t> remember;
-  remember.first = score_avg;
-  remember.second = model_avg;
-  all_scores.push_back(remember);
-  if (score_avg > max_score) {
-    max_score = score_avg;
-    best_it = t;
-  }
-  time (&end);
-  float time_diff = difftime(end, start);
-  overall_time += time_diff;
-  if (!quiet) {
-    cerr << _p2 << _np << "(time " << time_diff/60. << " min, ";
-    cerr << time_diff/in_sz << " s/S)" << endl;
-  }
-  if (t+1 != T && !quiet) cerr << endl;
-
-  if (noup) break;
-
-  // write weights to file
-  if (select_weights == "best" || keep) {
-    lambdas.init_vector(&dense_weights);
-    string w_fn = "weights." + boost::lexical_cast<string>(t) + ".gz";
-    Weights::WriteToFile(w_fn, dense_weights, true);
-  }
-
-  } // outer loop
-
-  if (average) w_average /= (weight_t)T;
-
-#ifndef DTRAIN_LOCAL
-  unlink(grammar_buf_fn.c_str());
-#endif
-
-  if (!noup) {
-    if (!quiet) cerr << endl << "Writing weights file to '" << output_fn << "' ..." << endl;
-    if (select_weights == "last" || average) { // last, average
-      WriteFile of(output_fn); // works with '-'
-      ostream& o = *of.stream();
-      o.precision(17);
-      o << _np;
-      if (average) {
-        for (SparseVector<weight_t>::iterator it = w_average.begin(); it != w_average.end(); ++it) {
-	      if (it->second == 0) continue;
-          o << FD::Convert(it->first) << '\t' << it->second << endl;
-        }
-      } else {
-        for (SparseVector<weight_t>::iterator it = lambdas.begin(); it != lambdas.end(); ++it) {
-	      if (it->second == 0) continue;
-          o << FD::Convert(it->first) << '\t' << it->second << endl;
-        }
-      }
-    } else if (select_weights == "VOID") { // do nothing with the weights
-    } else { // best
-      if (output_fn != "-") {
-        CopyFile("weights."+boost::lexical_cast<string>(best_it)+".gz", output_fn);
-      } else {
-        ReadFile bestw("weights."+boost::lexical_cast<string>(best_it)+".gz");
-        string o;
-        cout.precision(17);
-        cout << _np;
-        while(getline(*bestw, o)) cout << o << endl;
-      }
-      if (!keep) {
-        for (unsigned i = 0; i < T; i++) {
-          string s = "weights." + boost::lexical_cast<string>(i) + ".gz";
-          unlink(s.c_str());
-        }
-      }
-    }
-    if (output_fn == "-" && hstreaming) cout << "__SHARD_COUNT__\t1" << endl;
-    if (!quiet) cerr << "done" << endl;
-  }
-
-  if (!quiet) {
-    cerr << _p5 << _np << endl << "---" << endl << "Best iteration: ";
-    cerr << best_it+1 << " [SCORE '" << scorer_str << "'=" << max_score << "]." << endl;
-    cerr << "This took " << overall_time/60. << " min." << endl;
-  }
-}
-
diff --git a/dtrain/dtrain.h b/dtrain/dtrain.h
deleted file mode 100644
index 7e084a79..00000000
--- a/dtrain/dtrain.h
+++ /dev/null
@@ -1,98 +0,0 @@
-#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>
-
-#include <boost/algorithm/string.hpp>
-#include <boost/program_options.hpp>
-
-#include "ksampler.h"
-#include "pairsampling.h"
-
-#include "filelib.h"
-
-
-
-using namespace std;
-using namespace dtrain;
-namespace po = boost::program_options;
-
-inline void register_and_convert(const vector<string>& strs, vector<WordID>& ids)
-{
-  vector<string>::const_iterator it;
-  for (it = strs.begin(); it < strs.end(); it++)
-    ids.push_back(TD::Convert(*it));
-}
-
-inline string gettmpf(const string path, const string infix)
-{
-  char fn[path.size() + infix.size() + 8];
-  strcpy(fn, path.c_str());
-  strcat(fn, "/");
-  strcat(fn, infix.c_str());
-  strcat(fn, "-XXXXXX");
-  if (!mkstemp(fn)) {
-    cerr << "Cannot make temp file in" << path << " , exiting." << endl;
-    exit(1);
-  }
-  return string(fn);
-}
-
-inline void split_in(string& s, vector<string>& parts)
-{
-  unsigned f = 0;
-  for(unsigned i = 0; i < 3; i++) {
-    unsigned e = f;
-    f = s.find("\t", f+1);
-    if (e != 0) parts.push_back(s.substr(e+1, f-e-1));
-    else parts.push_back(s.substr(0, f));
-  }
-  s.erase(0, f+1);
-}
-
-struct HSReporter
-{
-  string task_id_;
-
-  HSReporter(string task_id) : task_id_(task_id) {}
-
-  inline void update_counter(string name, unsigned amount) {
-    cerr << "reporter:counter:" << task_id_ << "," << name << "," << amount << endl;
-  }
-  inline void update_gcounter(string name, unsigned amount) {
-    cerr << "reporter:counter:Global," << name << "," << amount << endl;
-  }
-};
-
-inline ostream& _np(ostream& out) { return out << resetiosflags(ios::showpos); }
-inline ostream& _p(ostream& out)  { return out << setiosflags(ios::showpos); }
-inline ostream& _p2(ostream& out) { return out << setprecision(2); }
-inline ostream& _p5(ostream& out) { return out << setprecision(5); }
-
-inline void printWordIDVec(vector<WordID>& v)
-{
-  for (unsigned i = 0; i < v.size(); i++) {
-    cerr << TD::Convert(v[i]);
-    if (i < v.size()-1) cerr << " ";
-  }
-}
-
-template<typename T>
-inline T sign(T z)
-{
-  if (z == 0) return 0;
-  return z < 0 ? -1 : +1;
-}
-
-#endif
-
diff --git a/dtrain/hstreaming/avg.rb b/dtrain/hstreaming/avg.rb
deleted file mode 100755
index 2599c732..00000000
--- a/dtrain/hstreaming/avg.rb
+++ /dev/null
@@ -1,32 +0,0 @@
-#!/usr/bin/env ruby
-# first arg may be an int of custom shard count
-
-shard_count_key = "__SHARD_COUNT__"
-
-STDIN.set_encoding 'utf-8'
-STDOUT.set_encoding 'utf-8'
-
-w = {}
-c = {}
-w.default = 0
-c.default = 0
-while line = STDIN.gets
-  key, val = line.split /\s/
-  w[key] += val.to_f
-  c[key] += 1
-end
-
-if ARGV.size == 0
-  shard_count = w["__SHARD_COUNT__"]
-else
-  shard_count = ARGV[0].to_f
-end
-w.each_key { |k|
-  if k == shard_count_key
-    next
-  else
-    puts "#{k}\t#{w[k]/shard_count}"
-    #puts "# #{c[k]}"
-  end
-}
-
diff --git a/dtrain/hstreaming/cdec.ini b/dtrain/hstreaming/cdec.ini
deleted file mode 100644
index d4f5cecd..00000000
--- a/dtrain/hstreaming/cdec.ini
+++ /dev/null
@@ -1,22 +0,0 @@
-formalism=scfg
-add_pass_through_rules=true
-scfg_max_span_limit=15
-intersection_strategy=cube_pruning
-cubepruning_pop_limit=30
-feature_function=WordPenalty
-feature_function=KLanguageModel 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/dtrain/hstreaming/dtrain.ini b/dtrain/hstreaming/dtrain.ini
deleted file mode 100644
index a2c219a1..00000000
--- a/dtrain/hstreaming/dtrain.ini
+++ /dev/null
@@ -1,15 +0,0 @@
-input=-
-output=-
-decoder_config=cdec.ini
-tmp=/var/hadoop/mapred/local/
-epochs=1
-k=100
-N=4
-learning_rate=0.0001
-gamma=0
-scorer=stupid_bleu
-sample_from=kbest
-filter=uniq
-pair_sampling=XYX
-pair_threshold=0
-select_weights=last
diff --git a/dtrain/hstreaming/dtrain.sh b/dtrain/hstreaming/dtrain.sh
deleted file mode 100755
index 877ff94c..00000000
--- a/dtrain/hstreaming/dtrain.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-# script to run dtrain with a task id
-
-pushd . &>/dev/null
-cd ..
-ID=$(basename $(pwd)) # attempt_...
-popd &>/dev/null
-./dtrain -c dtrain.ini --hstreaming $ID
-
diff --git a/dtrain/hstreaming/hadoop-streaming-job.sh b/dtrain/hstreaming/hadoop-streaming-job.sh
deleted file mode 100755
index 92419956..00000000
--- a/dtrain/hstreaming/hadoop-streaming-job.sh
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/bin/sh
-
-EXP=a_simple_test
-
-# change these vars to fit your hadoop installation
-HADOOP_HOME=/usr/lib/hadoop-0.20
-JAR=contrib/streaming/hadoop-streaming-0.20.2-cdh3u1.jar
-HSTREAMING="$HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/$JAR"
-
- IN=input_on_hdfs
-OUT=output_weights_on_hdfs
-
-# you can -reducer to NONE if you want to
-# do feature selection/averaging locally (e.g. to
-# keep weights of all epochs)
-$HSTREAMING \
-    -mapper "dtrain.sh" \
-    -reducer "ruby lplp.rb l2 select_k 100000" \
-    -input $IN \
-    -output $OUT \
-    -file dtrain.sh \
-    -file lplp.rb \
-    -file ../dtrain \
-    -file dtrain.ini \
-    -file cdec.ini \
-    -file ../test/example/nc-wmt11.en.srilm.gz \
-    -jobconf mapred.reduce.tasks=30 \
-    -jobconf mapred.max.map.failures.percent=0 \
-    -jobconf mapred.job.name="dtrain $EXP"
-
diff --git a/dtrain/hstreaming/lplp.rb b/dtrain/hstreaming/lplp.rb
deleted file mode 100755
index f0cd58c5..00000000
--- a/dtrain/hstreaming/lplp.rb
+++ /dev/null
@@ -1,131 +0,0 @@
-# lplp.rb
-
-# norms
-def l0(feature_column, n)
-  if feature_column.size >= n then return 1 else return 0 end
-end
-
-def l1(feature_column, n=-1)
-  return feature_column.map { |i| i.abs }.reduce { |sum,i| sum+i }
-end
-
-def l2(feature_column, n=-1)
-  return Math.sqrt feature_column.map { |i| i.abs2 }.reduce { |sum,i| sum+i }
-end
-
-def linfty(feature_column, n=-1)
-  return feature_column.map { |i| i.abs }.max
-end
-
-# stats
-def median(feature_column, n)
-  return feature_column.concat(0.step(n-feature_column.size-1).map{|i|0}).sort[feature_column.size/2]
-end
-
-def mean(feature_column, n)
-  return feature_column.reduce { |sum, i| sum+i } / n
-end
-
-# selection
-def select_k(weights, norm_fun, n, k=10000)
-  weights.sort{|a,b| norm_fun.call(b[1], n) <=> norm_fun.call(a[1], n)}.each { |p|
-    puts "#{p[0]}\t#{mean(p[1], n)}"
-    k -= 1
-    if k == 0 then break end
-  }
-end
-
-def cut(weights, norm_fun, n, epsilon=0.0001)
-  weights.each { |k,v|
-    if norm_fun.call(v, n).abs >= epsilon
-      puts "#{k}\t#{mean(v, n)}"
-    end
-  }
-end
-
-# test
-def _test()
-  puts
-  w = {}
-  w["a"] = [1, 2, 3]
-  w["b"] = [1, 2]
-  w["c"] = [66]
-  w["d"] = [10, 20, 30]
-  n = 3
-  puts w.to_s
-  puts
-  puts "select_k"
-  puts "l0 expect ad"
-  select_k(w, method(:l0), n, 2)
-  puts "l1 expect cd"
-  select_k(w, method(:l1), n, 2)
-  puts "l2 expect c"
-  select_k(w, method(:l2), n, 1)
-  puts
-  puts "cut"
-  puts "l1 expect cd"
-  cut(w, method(:l1), n, 7)
-  puts
-  puts "median"
-  a = [1,2,3,4,5]
-  puts a.to_s
-  puts median(a, 5)
-  puts
-  puts "#{median(a, 7)} <- that's because we add missing 0s:"
-  puts a.concat(0.step(7-a.size-1).map{|i|0}).to_s
-  puts
-  puts "mean expect bc"
-  w.clear
-  w["a"] = [2]
-  w["b"] = [2.1]
-  w["c"] = [2.2]
-  cut(w, method(:mean), 1, 2.05)
- exit
-end
-#_test()
-
-# actually do something
-def usage()
-  puts "lplp.rb <l0,l1,l2,linfty,mean,median> <cut|select_k> <k|threshold> [n] < <input>"
-  puts "   l0...: norms for selection"
-  puts "select_k: only output top k (according to the norm of their column vector) features"
-  puts "     cut: output features with weight >= threshold"
-  puts "       n: if we do not have a shard count use this number for averaging"
-  exit
-end
-
-if ARGV.size < 3 then usage end
-norm_fun = method(ARGV[0].to_sym)
-type = ARGV[1]
-x = ARGV[2].to_f
-
-shard_count_key = "__SHARD_COUNT__"
-
-STDIN.set_encoding 'utf-8'
-STDOUT.set_encoding 'utf-8'
-
-w = {}
-shard_count = 0
-while line = STDIN.gets
-  key, val = line.split /\s+/
-  if key == shard_count_key
-    shard_count += 1
-    next
-  end
-  if w.has_key? key
-    w[key].push val.to_f
-  else
-    w[key] = [val.to_f]
-  end
-end
-
-if ARGV.size == 4 then shard_count = ARGV[3].to_f end
-
-if type == 'cut'
-  cut(w, norm_fun, shard_count, x)
-elsif type == 'select_k'
-  select_k(w, norm_fun, shard_count, x)
-else
-  puts "oh oh"
-end
-
diff --git a/dtrain/hstreaming/red-test b/dtrain/hstreaming/red-test
deleted file mode 100644
index 2623d697..00000000
--- a/dtrain/hstreaming/red-test
+++ /dev/null
@@ -1,9 +0,0 @@
-a	1
-b	2
-c	3.5
-a	1
-b	2
-c	3.5
-d	1
-e	2
-__SHARD_COUNT__	2
diff --git a/dtrain/kbestget.h b/dtrain/kbestget.h
deleted file mode 100644
index dd8882e1..00000000
--- a/dtrain/kbestget.h
+++ /dev/null
@@ -1,152 +0,0 @@
-#ifndef _DTRAIN_KBESTGET_H_
-#define _DTRAIN_KBESTGET_H_
-
-#include "kbest.h" // cdec
-#include "sentence_metadata.h"
-
-#include "verbose.h"
-#include "viterbi.h"
-#include "ff_register.h"
-#include "decoder.h"
-#include "weights.h"
-#include "logval.h"
-
-using namespace std;
-
-namespace dtrain
-{
-
-
-typedef double score_t;
-
-struct ScoredHyp
-{
-  vector<WordID> w;
-  SparseVector<double> f;
-  score_t model;
-  score_t score;
-  unsigned rank;
-};
-
-struct LocalScorer
-{
-  unsigned N_;
-  vector<score_t> w_;
-
-  virtual score_t
-  Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned rank, const unsigned src_len)=0;
-
-  void Reset() {} // only for approx bleu
-
-  inline void
-  Init(unsigned N, vector<score_t> weights)
-  {
-    assert(N > 0);
-    N_ = N;
-    if (weights.empty()) for (unsigned i = 0; i < N_; i++) w_.push_back(1./N_);
-    else w_ = weights;
-  }
-
-  inline score_t
-  brevity_penalty(const unsigned hyp_len, const unsigned ref_len)
-  {
-    if (hyp_len > ref_len) return 1;
-    return exp(1 - (score_t)ref_len/hyp_len);
-  }
-};
-
-struct HypSampler : public DecoderObserver
-{
-  LocalScorer* scorer_;
-  vector<WordID>* ref_;
-  unsigned f_count_, sz_;
-  virtual vector<ScoredHyp>* GetSamples()=0;
-  inline void SetScorer(LocalScorer* scorer) { scorer_ = scorer; }
-  inline void SetRef(vector<WordID>& ref) { ref_ = &ref; }
-  inline unsigned get_f_count() { return f_count_; }
-  inline unsigned get_sz() { return sz_; }
-};
-////////////////////////////////////////////////////////////////////////////////
-
-
-
-
-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/dtrain/ksampler.h b/dtrain/ksampler.h
deleted file mode 100644
index bc2f56cd..00000000
--- a/dtrain/ksampler.h
+++ /dev/null
@@ -1,61 +0,0 @@
-#ifndef _DTRAIN_KSAMPLER_H_
-#define _DTRAIN_KSAMPLER_H_
-
-#include "hg_sampler.h" // cdec
-#include "kbestget.h"
-#include "score.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/dtrain/pairsampling.h b/dtrain/pairsampling.h
deleted file mode 100644
index 84be1efb..00000000
--- a/dtrain/pairsampling.h
+++ /dev/null
@@ -1,149 +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, 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) {
-        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, 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++) {
-#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]));
-      } else {
-        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;
-  }
-  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]));
-      } else {
-        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
-    }
-  }
-}
-
-/*
- * 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, float _unused=1)
-{
-  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/dtrain/score.cc b/dtrain/score.cc
deleted file mode 100644
index 34fc86a9..00000000
--- a/dtrain/score.cc
+++ /dev/null
@@ -1,254 +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(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_);
-  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
- */
-score_t
-StupidBleuScorer::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, 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);
-}
-
-/*
- * 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(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.;
-  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(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(2.0, (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(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(2.0, (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(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(2.0, (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(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.;
-  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(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;
-}
-
-
-} // namespace
-
diff --git a/dtrain/score.h b/dtrain/score.h
deleted file mode 100644
index f317c903..00000000
--- a/dtrain/score.h
+++ /dev/null
@@ -1,212 +0,0 @@
-#ifndef _DTRAIN_SCORE_H_
-#define _DTRAIN_SCORE_H_
-
-#include "kbestget.h"
-
-using namespace std;
-
-namespace dtrain
-{
-
-
-struct NgramCounts
-{
-  unsigned N_;
-  map<unsigned, score_t> clipped_;
-  map<unsigned, score_t> sum_;
-
-  NgramCounts(const unsigned N) : N_(N) { Zero(); }
-
-  inline void
-  operator+=(const NgramCounts& rhs)
-  {
-    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;
-    }
-  }
-
-  inline const NgramCounts
-  operator+(const NgramCounts &other) const
-  {
-    NgramCounts result = *this;
-    result += other;
-    return result;
-  }
-
-  inline void
-  operator*=(const score_t rhs)
-  {
-    for (unsigned i = 0; i < N_; i++) {
-      this->clipped_[i] *= rhs;
-      this->sum_[i] *= rhs;
-    }
-  }
-
-  inline void
-  Add(const unsigned count, const unsigned ref_count, const unsigned i)
-  {
-    assert(i < N_);
-    if (count > ref_count) {
-      clipped_[i] += ref_count;
-    } else {
-      clipped_[i] += count;
-    }
-    sum_[i] += count;
-  }
-
-  inline void
-  Zero()
-  {
-    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++) {
-      cout << i+1 << "grams (clipped):\t" << clipped_[i] << endl;
-      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;
-
-inline Ngrams
-make_ngrams(const vector<WordID>& s, const unsigned N)
-{
-  Ngrams ngrams;
-  vector<WordID> ng;
-  for (size_t i = 0; i < s.size(); i++) {
-    ng.clear();
-    for (unsigned j = i; j < min(i+N, s.size()); j++) {
-      ng.push_back(s[j]);
-      ngrams[ng]++;
-    }
-  }
-  return ngrams;
-}
-
-inline NgramCounts
-make_ngram_counts(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned N)
-{
-  Ngrams hyp_ngrams = make_ngrams(hyp, N);
-  Ngrams ref_ngrams = make_ngrams(ref, N);
-  NgramCounts counts(N);
-  Ngrams::iterator it;
-  Ngrams::iterator ti;
-  for (it = hyp_ngrams.begin(); it != hyp_ngrams.end(); it++) {
-    ti = ref_ngrams.find(it->first);
-    if (ti != ref_ngrams.end()) {
-      counts.Add(it->second, ti->second, it->first.size() - 1);
-    } else {
-      counts.Add(it->second, 0, it->first.size() - 1);
-    }
-  }
-  return counts;
-}
-
-struct BleuScorer : public LocalScorer
-{
-  score_t Bleu(NgramCounts& counts, const unsigned hyp_len, const unsigned ref_len);
-  score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
-};
-
-struct StupidBleuScorer : public LocalScorer
-{
-  score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
-};
-
-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_;
-  unsigned glob_hyp_len_, glob_ref_len_, glob_src_len_;
-  score_t discount_;
-
-  ApproxBleuScorer(unsigned N, score_t d) : glob_onebest_counts_(NgramCounts(N)), discount_(d)
-  {
-    glob_hyp_len_ = glob_ref_len_ = glob_src_len_ = 0;
-  }
-
-  inline void Reset() {
-    glob_onebest_counts_.Zero();
-    glob_hyp_len_ = glob_ref_len_ = glob_src_len_ = 0.;
-  }
-
-  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
-
-#endif
-
diff --git a/dtrain/test/example/README b/dtrain/test/example/README
deleted file mode 100644
index 6937b11b..00000000
--- a/dtrain/test/example/README
+++ /dev/null
@@ -1,8 +0,0 @@
-Small example of input format for distributed training.
-Call dtrain from cdec/dtrain/ with ./dtrain -c test/example/dtrain.ini .
-
-For this to work, undef 'DTRAIN_LOCAL' in dtrain.h
-and recompile.
-
-Data is here: http://simianer.de/#dtrain
-
diff --git a/dtrain/test/example/cdec.ini b/dtrain/test/example/cdec.ini
deleted file mode 100644
index 6642107f..00000000
--- a/dtrain/test/example/cdec.ini
+++ /dev/null
@@ -1,24 +0,0 @@
-formalism=scfg
-add_pass_through_rules=true
-scfg_max_span_limit=15
-intersection_strategy=cube_pruning
-cubepruning_pop_limit=30
-feature_function=WordPenalty
-feature_function=KLanguageModel test/example/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=RuleNgramFeatures
-feature_function=RuleShape
-#feature_function=SourceSpanSizeFeatures
-#feature_function=SourceWordPenalty
-#feature_function=SpanFeatures
diff --git a/dtrain/test/example/dtrain.ini b/dtrain/test/example/dtrain.ini
deleted file mode 100644
index c8ac7c3f..00000000
--- a/dtrain/test/example/dtrain.ini
+++ /dev/null
@@ -1,22 +0,0 @@
-input=test/example/nc-wmt11.1k.gz    # use '-' for STDIN
-output=-                             # a weights file (add .gz for gzip compression) or STDOUT '-'
-select_weights=VOID     # don't output weights
-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 10 inputs
-
-# interesting stuff
-epochs=3                # run over input 3 times
-k=100                   # use 100best lists
-N=4                     # optimize (approx) BLEU4
-scorer=stupid_bleu      # use 'stupid' BLEU+1
-learning_rate=0.0001    # learning rate
-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 (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
deleted file mode 100644
index 25d2c069..00000000
--- a/dtrain/test/example/expected-output
+++ /dev/null
@@ -1,125 +0,0 @@
-                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.
diff --git a/dtrain/test/toy/cdec.ini b/dtrain/test/toy/cdec.ini
deleted file mode 100644
index 98b02d44..00000000
--- a/dtrain/test/toy/cdec.ini
+++ /dev/null
@@ -1,2 +0,0 @@
-formalism=scfg
-add_pass_through_rules=true
diff --git a/dtrain/test/toy/dtrain.ini b/dtrain/test/toy/dtrain.ini
deleted file mode 100644
index a091732f..00000000
--- a/dtrain/test/toy/dtrain.ini
+++ /dev/null
@@ -1,12 +0,0 @@
-decoder_config=test/toy/cdec.ini
-input=test/toy/input
-output=-
-print_weights=logp shell_rule house_rule small_rule little_rule PassThrough
-k=4
-N=4
-epochs=2
-scorer=bleu
-sample_from=kbest
-filter=uniq
-pair_sampling=all
-learning_rate=1
diff --git a/dtrain/test/toy/input b/dtrain/test/toy/input
deleted file mode 100644
index 4d10a9ea..00000000
--- a/dtrain/test/toy/input
+++ /dev/null
@@ -1,2 +0,0 @@
-0	ich sah ein kleines haus	i saw a little house	[S] ||| [NP,1] [VP,2] ||| [1] [2] ||| logp=0	[NP] ||| ich ||| i ||| logp=0	[NP] ||| ein [NN,1] ||| a [1] ||| logp=0	[NN] ||| [JJ,1] haus ||| [1] house ||| logp=0 house_rule=1	[NN] ||| [JJ,1] haus ||| [1] shell ||| logp=0 shell_rule=1	[JJ] ||| kleines ||| small ||| logp=0 small_rule=1	[JJ] ||| kleines ||| little ||| logp=0 little_rule=1	[JJ] ||| grosses ||| big ||| logp=0	[JJ] ||| grosses ||| large ||| logp=0	[VP] ||| [V,1] [NP,2] ||| [1] [2] ||| logp=0	[V] ||| sah ||| saw ||| logp=0	[V] ||| fand ||| found ||| logp=0
-1	ich fand ein kleines haus	i found a little house	[S] ||| [NP,1] [VP,2] ||| [1] [2] ||| logp=0	[NP] ||| ich ||| i ||| logp=0	[NP] ||| ein [NN,1] ||| a [1] ||| logp=0	[NN] ||| [JJ,1] haus ||| [1] house ||| logp=0 house_rule=1	[NN] ||| [JJ,1] haus ||| [1] shell ||| logp=0 shell_rule=1	[JJ] ||| kleines ||| small ||| logp=0 small_rule=1	[JJ] ||| kleines ||| little ||| logp=0 little_rule=1	[JJ] ||| grosses ||| big ||| logp=0	[JJ] ||| grosses ||| large ||| logp=0	[VP] ||| [V,1] [NP,2] ||| [1] [2] ||| logp=0	[V] ||| sah ||| saw ||| logp=0	[V] ||| fand ||| found ||| logp=0