fixed conflicts

1 files changed, 553 insertions, 0 deletions

diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc
new file mode 100644
index 00000000..149f87d4
--- /dev/null
+++ b/training/dtrain/dtrain.cc
@@ -0,0 +1,553 @@
+#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 (src)")
+    ("refs,r",            po::value<string>(),                                                                       "references")
+    ("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")
+    ("keep",              po::value<bool>()->zero_tokens(),                               "keep weights files for each iteration")
+    ("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(1.0),                                              "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) UNTESTED")
+    ("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 IMPLEMENTED") // 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.")
+    ("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)["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 rescale = false;
+  if (cfg.count("rescale")) rescale = true;
+  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 == "fixed_stupid_bleu") {
+    scorer = dynamic_cast<FixedStupidBleuScorer*>(new FixedStupidBleuScorer);
+  } 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>();
+
+  // faster perceptron: consider only misranked pairs, see
+  // DO NOT ENABLE  WITH SVM (gamma > 0) OR loss_margin!
+  bool faster_perceptron = false;
+  if (gamma==0 && loss_margin==0) faster_perceptron = true;
+
+  // 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
+  string refs_fn = cfg["refs"].as<string>();
+  ReadFile refs(refs_fn);
+
+  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(26) << "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) << "faster perceptron " << faster_perceptron << 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;
+    cerr << setw(25) << "refs " << "'" << refs_fn << "'" << endl;
+    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
+  {
+
+  time_t start, end;
+  time(&start);
+  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>
+    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);
+
+    // 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, faster_perceptron);
+      if (pair_sampling == "XYX")
+        partXYX(samples, pairs, pair_threshold, max_pairs, faster_perceptron, 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++) {
+        bool rank_error;
+        score_t margin;
+        if (faster_perceptron) { // we only have considering misranked pairs
+          rank_error = true; // pair sampling already did this for us
+          margin = std::numeric_limits<float>::max();
+        } else {
+          rank_error = it->first.model <= it->second.model;
+          margin = fabs(fabs(it->first.model) - fabs(it->second.model));
+          if (!rank_error && margin < loss_margin) margin_violations++;
+        }
+        if (rank_error) rank_errors++;
+        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
+      // please note that this penalizes _all_ weights
+      // (contrary to only the ones changed by the last update)
+      // after a _sentence_ (not after each example/pair)
+      if (l1naive) {
+        FastSparseVector<weight_t>::iterator it = lambdas.begin();
+        for (; it != lambdas.end(); ++it) {
+          it->second -= sign(it->second) * l1_reg;
+        }
+      } else if (l1clip) {
+        FastSparseVector<weight_t>::iterator it = lambdas.begin();
+        for (; it != lambdas.end(); ++it) {
+          if (it->second != 0) {
+            weight_t v = it->second;
+            if (v > 0) {
+              it->second = max(0., v - l1_reg);
+            } else {
+              it->second = min(0., v + l1_reg);
+            }
+          }
+        }
+      } else if (l1cumul) {
+        weight_t acc_penalty = (ii+1) * l1_reg; // ii is the index of the current input
+        FastSparseVector<weight_t>::iterator it = lambdas.begin();
+        for (; it != lambdas.end(); ++it) {
+          if (it->second != 0) {
+            weight_t v = it->second;
+            weight_t penalized = 0.;
+            if (v > 0) {
+              penalized = max(0., v-(acc_penalty + cumulative_penalties.get(it->first)));
+            } else {
+              penalized = min(0., v+(acc_penalty - cumulative_penalties.get(it->first)));
+            }
+            it->second = penalized;
+            cumulative_penalties.set_value(it->first, cumulative_penalties.get(it->first)+penalized);
+          }
+        }
+      }
+
+    }
+
+    if (rescale) lambdas /= lambdas.l2norm();
+
+    ++ii;
+
+  } // 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)
+  }
+
+  // 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) 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;
+    if (faster_perceptron) cerr << " (meaningless)";
+    cerr << endl;
+    cerr << "        avg # rank err: ";
+    cerr << rank_errors/(float)in_sz << endl;
+    cerr << "     avg # margin viol: ";
+    cerr << margin_violations/(float)in_sz << endl;
+    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;
+  }
+
+  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;
+
+  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 (!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;
+  }
+}
+