polish

4 files changed, 0 insertions, 830 deletions

diff --git a/dtrain/test/mira_update/Hildreth.cpp b/dtrain/test/mira_update/Hildreth.cpp
deleted file mode 100644
index 0e67eb15..00000000
--- a/dtrain/test/mira_update/Hildreth.cpp
+++ /dev/null
@@ -1,187 +0,0 @@
-#include "Hildreth.h"
-#include "sparse_vector.h"
-
-using namespace std;
-
-namespace Mira {
-   vector<double> Hildreth::optimise (vector< SparseVector<double> >& a, vector<double>& b) {
-
-    size_t i;
-    int max_iter = 10000;
-    double eps = 0.00000001;
-    double zero = 0.000000000001;
-
-    vector<double> alpha ( b.size() );
-    vector<double> F ( b.size() );
-    vector<double> kkt ( b.size() );
-
-    double max_kkt = -1e100;
-
-    size_t K = b.size();
-
-    double A[K][K];
-    bool is_computed[K];
-    for ( i = 0; i < K; i++ )
-    {
-      A[i][i] = a[i].dot(a[i]);
-      is_computed[i] = false;
-    }
-
-    int max_kkt_i = -1;
-
-
-    for ( i = 0; i < b.size(); i++ )
-    {
-      F[i] = b[i];
-      kkt[i] = F[i];
-      if ( kkt[i] > max_kkt )
-      {
-        max_kkt = kkt[i];
-        max_kkt_i = i;
-      }
-    }
-
-    int iter = 0;
-    double diff_alpha;
-    double try_alpha;
-    double add_alpha;
-
-    while ( max_kkt >= eps && iter < max_iter )
-    {
-
-      diff_alpha = A[max_kkt_i][max_kkt_i] <= zero ? 0.0 : F[max_kkt_i]/A[max_kkt_i][max_kkt_i];
-      try_alpha = alpha[max_kkt_i] + diff_alpha;
-      add_alpha = 0.0;
-
-      if ( try_alpha < 0.0 )
-        add_alpha = -1.0 * alpha[max_kkt_i];
-      else
-        add_alpha = diff_alpha;
-
-      alpha[max_kkt_i] = alpha[max_kkt_i] + add_alpha;
-
-      if ( !is_computed[max_kkt_i] )
-      {
-        for ( i = 0; i < K; i++ )
-        {
-          A[i][max_kkt_i] = a[i].dot(a[max_kkt_i] ); // for version 1
-          //A[i][max_kkt_i] = 0; // for version 1
-          is_computed[max_kkt_i] = true;
-        }
-      }
-
-      for ( i = 0; i < F.size(); i++ )
-      {
-        F[i] -= add_alpha * A[i][max_kkt_i];
-        kkt[i] = F[i];
-        if ( alpha[i] > zero )
-          kkt[i] = abs ( F[i] );
-      }
-      max_kkt = -1e100;
-      max_kkt_i = -1;
-      for ( i = 0; i < F.size(); i++ )
-        if ( kkt[i] > max_kkt )
-        {
-          max_kkt = kkt[i];
-          max_kkt_i = i;
-        }
-
-      iter++;
-    }
-
-    return alpha;
-  }
-
-  vector<double> Hildreth::optimise (vector< SparseVector<double> >& a, vector<double>& b, double C) {
-
-    size_t i;
-    int max_iter = 10000;
-    double eps = 0.00000001;
-    double zero = 0.000000000001;
-
-    vector<double> alpha ( b.size() );
-    vector<double> F ( b.size() );
-    vector<double> kkt ( b.size() );
-
-    double max_kkt = -1e100;
-
-    size_t K = b.size();
-
-    double A[K][K];
-    bool is_computed[K];
-    for ( i = 0; i < K; i++ )
-    {
-      A[i][i] = a[i].dot(a[i]);
-      is_computed[i] = false;
-    }
-
-    int max_kkt_i = -1;
-
-
-    for ( i = 0; i < b.size(); i++ )
-    {
-      F[i] = b[i];
-      kkt[i] = F[i];
-      if ( kkt[i] > max_kkt )
-      {
-        max_kkt = kkt[i];
-        max_kkt_i = i;
-      }
-    }
-
-    int iter = 0;
-    double diff_alpha;
-    double try_alpha;
-    double add_alpha;
-
-    while ( max_kkt >= eps && iter < max_iter )
-    {
-
-      diff_alpha = A[max_kkt_i][max_kkt_i] <= zero ? 0.0 : F[max_kkt_i]/A[max_kkt_i][max_kkt_i];
-      try_alpha = alpha[max_kkt_i] + diff_alpha;
-      add_alpha = 0.0;
-
-      if ( try_alpha < 0.0 )
-        add_alpha = -1.0 * alpha[max_kkt_i];
-      else if (try_alpha > C)
-				add_alpha = C - alpha[max_kkt_i];
-      else
-        add_alpha = diff_alpha;
-
-      alpha[max_kkt_i] = alpha[max_kkt_i] + add_alpha;
-
-      if ( !is_computed[max_kkt_i] )
-      {
-        for ( i = 0; i < K; i++ )
-        {
-          A[i][max_kkt_i] = a[i].dot(a[max_kkt_i] ); // for version 1
-          //A[i][max_kkt_i] = 0; // for version 1
-          is_computed[max_kkt_i] = true;
-        }
-      }
-
-      for ( i = 0; i < F.size(); i++ )
-      {
-        F[i] -= add_alpha * A[i][max_kkt_i];
-        kkt[i] = F[i];
-        if (alpha[i] > C - zero)
-					kkt[i]=-kkt[i];
-				else if (alpha[i] > zero)
-					kkt[i] = abs(F[i]);
-
-      }
-      max_kkt = -1e100;
-      max_kkt_i = -1;
-      for ( i = 0; i < F.size(); i++ )
-        if ( kkt[i] > max_kkt )
-        {
-          max_kkt = kkt[i];
-          max_kkt_i = i;
-        }
-
-      iter++;
-    }
-
-    return alpha;
-  }
-}
diff --git a/dtrain/test/mira_update/Hildreth.h b/dtrain/test/mira_update/Hildreth.h
deleted file mode 100644
index 8d791085..00000000
--- a/dtrain/test/mira_update/Hildreth.h
+++ /dev/null
@@ -1,10 +0,0 @@
-#include "sparse_vector.h"
-
-namespace Mira {
-  class Hildreth {
-  public :
-    static std::vector<double> optimise(std::vector< SparseVector<double> >& a, std::vector<double>& b);
-    static std::vector<double> optimise(std::vector< SparseVector<double> >& a, std::vector<double>& b, double C);
-  };
-}
-
diff --git a/dtrain/test/mira_update/dtrain.cc b/dtrain/test/mira_update/dtrain.cc
deleted file mode 100644
index 933417a4..00000000
--- a/dtrain/test/mira_update/dtrain.cc
+++ /dev/null
@@ -1,532 +0,0 @@
-#include "common.h"
-#include "kbestget.h"
-#include "util.h"
-#include "sample.h"
-#include "Hildreth.h"
-
-#include "ksampler.h"
-
-// boost compression
-#include <boost/iostreams/device/file.hpp> 
-#include <boost/iostreams/filtering_stream.hpp>
-#include <boost/iostreams/filter/gzip.hpp>
-//#include <boost/iostreams/filter/zlib.hpp>
-//#include <boost/iostreams/filter/bzip2.hpp>
-using namespace boost::iostreams;
-
-
-#ifdef DTRAIN_DEBUG
-#include "tests.h"
-#endif
-
-
-/*
- * init
- *
- */
-bool
-init(int argc, char** argv, po::variables_map* cfg)
-{
-  po::options_description conff( "Configuration File Options" );
-  size_t k, N, T, stop, n_pairs;
-  string s, f, update_type;
-  conff.add_options()
-    ( "decoder_config", po::value<string>(),                            "configuration file for cdec" )
-    ( "kbest",          po::value<size_t>(&k)->default_value(DTRAIN_DEFAULT_K),         "k for kbest" )
-    ( "ngrams",         po::value<size_t>(&N)->default_value(DTRAIN_DEFAULT_N),        "N for Ngrams" )
-    ( "filter",         po::value<string>(&f)->default_value("unique"),           "filter kbest list" )
-    ( "epochs",         po::value<size_t>(&T)->default_value(DTRAIN_DEFAULT_T),   "# of iterations T" ) 
-    ( "input",          po::value<string>(),                                             "input file" )
-    ( "scorer",         po::value<string>(&s)->default_value(DTRAIN_DEFAULT_SCORER), "scoring metric" )
-    ( "output",         po::value<string>(),                                    "output weights file" )
-    ( "stop_after",     po::value<size_t>(&stop)->default_value(0),    "stop after X input sentences" )
-    ( "weights_file",   po::value<string>(),      "input weights file (e.g. from previous iteration)" )
-    ( "wprint",         po::value<string>(),                     "weights to print on each iteration" )
-    ( "noup",           po::value<bool>()->zero_tokens(),                     "do not update weights" );
-
-  po::options_description clo("Command Line Options");
-  clo.add_options()
-    ( "config,c",         po::value<string>(),              "dtrain config file" )
-    ( "quiet,q",          po::value<bool>()->zero_tokens(),           "be quiet" )
-    ( "update-type",      po::value<string>(&update_type)->default_value("mira"), "perceptron or mira" )
-    ( "n-pairs",          po::value<size_t>(&n_pairs)->default_value(10), "number of pairs used to compute update" )
-    ( "verbose,v",        po::value<bool>()->zero_tokens(),         "be verbose" )
-#ifndef DTRAIN_DEBUG
-    ;
-#else
-    ( "test", "run tests and exit");
-#endif
-  po::options_description config_options, cmdline_options;
-
-  config_options.add(conff);
-  cmdline_options.add(clo);
-  cmdline_options.add(conff);
-
-  po::store( parse_command_line(argc, argv, cmdline_options), *cfg );
-  if ( cfg->count("config") ) {
-    ifstream config( (*cfg)["config"].as<string>().c_str() );
-    po::store( po::parse_config_file(config, config_options), *cfg );
-  }
-  po::notify(*cfg);
-
-  if ( !cfg->count("decoder_config") || !cfg->count("input") ) { 
-    cerr << cmdline_options << endl;
-    return false;
-  }
-  if ( cfg->count("noup") && cfg->count("decode") ) {
-    cerr << "You can't use 'noup' and 'decode' at once." << endl;
-    return false;
-  }
-  if ( cfg->count("filter") && (*cfg)["filter"].as<string>() != "unique"
-       && (*cfg)["filter"].as<string>() != "no" ) {
-    cerr << "Wrong 'filter' type: '" << (*cfg)["filter"].as<string>() << "'." << endl;
-  }
-  #ifdef DTRAIN_DEBUG       
-  if ( !cfg->count("test") ) {
-    cerr << cmdline_options << endl;
-    return false;
-  }
-  #endif
-  return true;
-}
-
-
-// output formatting
-ostream& _nopos( ostream& out ) { return out << resetiosflags( ios::showpos ); }
-ostream& _pos( ostream& out ) { return out << setiosflags( ios::showpos ); }
-ostream& _prec2( ostream& out ) { return out << setprecision(2); }
-ostream& _prec5( ostream& out ) { return out << setprecision(5); }
-
-
-
-
-/*
- * dtrain
- *
- */
-int
-main( int argc, char** argv )
-{
-  cout << setprecision( 5 );
-  // handle most parameters
-  po::variables_map cfg;
-  if ( ! init(argc, argv, &cfg) ) exit(1); // something is wrong 
-#ifdef DTRAIN_DEBUG
-  if ( cfg.count("test") ) run_tests(); // run tests and exit 
-#endif
-  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;
-  const size_t k = cfg["kbest"].as<size_t>();
-  const size_t N = cfg["ngrams"].as<size_t>(); 
-  const size_t T = cfg["epochs"].as<size_t>();
-  const size_t stop_after = cfg["stop_after"].as<size_t>();
-  const string filter_type = cfg["filter"].as<string>();
-  const string update_type = cfg["update-type"].as<string>();
-  const size_t n_pairs = cfg["n-pairs"].as<size_t>();
-  const string output_file = cfg["output"].as<string>();
-  if ( !quiet ) {
-    cout << endl << "dtrain" << endl << "Parameters:" << endl;
-    cout << setw(25) << "k " << k << endl;
-    cout << setw(25) << "N " << N << endl;
-    cout << setw(25) << "T " << T << endl;
-    if ( cfg.count("stop-after") )
-      cout << setw(25) << "stop_after " << stop_after << endl;
-    if ( cfg.count("weights") )
-      cout << setw(25) << "weights " << cfg["weights"].as<string>() << endl;
-    cout << setw(25) << "input " << "'" << cfg["input"].as<string>() << "'" << endl;
-    cout << setw(25) << "filter " << "'" << filter_type << "'" << endl;
-  }
-
-  vector<string> wprint;
-  if ( cfg.count("wprint") ) {
-    boost::split( wprint, cfg["wprint"].as<string>(), boost::is_any_of(" ") );
-  }
-
-  // setup decoder, observer
-  register_feature_functions();
-  SetSilent(true);
-  ReadFile ini_rf( cfg["decoder_config"].as<string>() );
-  if ( !quiet )
-    cout << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl;
-  Decoder decoder( ini_rf.stream() );
-  //KBestGetter observer( k, filter_type );
-  MT19937 rng;
-  KSampler observer( k, &rng );
-
-  // scoring metric/scorer
-  string scorer_str = cfg["scorer"].as<string>();
-  double (*scorer)( NgramCounts&, const size_t, const size_t, size_t, vector<float> );
-  if ( scorer_str == "bleu" ) {
-    scorer = &bleu;
-  } else if ( scorer_str == "stupid_bleu" ) {
-    scorer = &stupid_bleu;
-  } else if ( scorer_str == "smooth_bleu" ) {
-    scorer = &smooth_bleu;
-  } else if ( scorer_str == "approx_bleu" ) {
-    scorer = &approx_bleu;
-  } else {
-    cerr << "Don't know scoring metric: '" << scorer_str << "', exiting." << endl;
-    exit(1);
-  }
-  // for approx_bleu
-  NgramCounts global_counts( N ); // counts for 1 best translations
-  size_t global_hyp_len = 0;      // sum hypothesis lengths
-  size_t global_ref_len = 0;      // sum reference lengths
-  // this is all BLEU implmentations
-  vector<float> bleu_weights; // we leave this empty -> 1/N; TODO? 
-  if ( !quiet ) cout << setw(26) << "scorer '" << scorer_str << "'" << endl << endl;
-
-  // init weights
-  Weights weights;
-  if ( cfg.count("weights") ) weights.InitFromFile( cfg["weights"].as<string>() );
-  SparseVector<double> lambdas;
-  weights.InitSparseVector( &lambdas );
-  vector<double> dense_weights;
-
-  // input
-  if ( !quiet && !verbose )
-    cout << "(a dot represents " << DTRAIN_DOTS << " lines of input)" << endl;
-  string input_fn = cfg["input"].as<string>();
-  ifstream input;
-  if ( input_fn != "-" ) input.open( input_fn.c_str() );
-  string in;
-  vector<string> in_split; // input: src\tref\tpsg
-  vector<string> ref_tok;  // tokenized reference
-  vector<WordID> ref_ids;  // reference as vector of WordID
-  string grammar_str;
-
-  // buffer input for t > 0
-  vector<string> src_str_buf;           // source strings, TODO? memory
-  vector<vector<WordID> > ref_ids_buf;  // references as WordID vecs
-  filtering_ostream grammar_buf;        // written to compressed file in /tmp
-  // this is for writing the grammar buffer file
-  grammar_buf.push( gzip_compressor() );
-  char grammar_buf_tmp_fn[] = DTRAIN_TMP_DIR"/dtrain-grammars-XXXXXX";
-  mkstemp( grammar_buf_tmp_fn );
-  grammar_buf.push( file_sink(grammar_buf_tmp_fn, ios::binary | ios::trunc) );
-  
-  size_t sid = 0, in_sz = 99999999; // sentence id, input size
-  double acc_1best_score = 0., acc_1best_model = 0.;
-  vector<vector<double> > scores_per_iter;
-  double max_score = 0.;
-  size_t best_t = 0;
-  bool next = false, stop = false;
-  double score = 0.;
-  size_t cand_len = 0;
-  double overall_time = 0.;
-
-  // for the perceptron/SVM; TODO as params
-  double eta = 0.0005;
-  double gamma = 0.;//01; // -> SVM
-  lambdas.add_value( FD::Convert("__bias"), 0 );
-  
-  // for random sampling
-  srand ( time(NULL) );
-
-
-  for ( size_t t = 0; t < T; t++ ) // T epochs
-  {
-
-  time_t start, end;  
-  time( &start );
-
-  // actually, we need only need this if t > 0 FIXME
-  ifstream grammar_file( grammar_buf_tmp_fn, ios_base::in | ios_base::binary );
-  filtering_istream grammar_buf_in;
-  grammar_buf_in.push( gzip_decompressor() );
-  grammar_buf_in.push( grammar_file );
-
-  // reset average scores
-  acc_1best_score = acc_1best_model = 0.;
-  
-  // reset sentence counter
-  sid = 0;
-  
-  if ( !quiet ) cout << "Iteration #" << t+1 << " of " << T << "." << endl;
-  
-  while( true )
-  {
-
-    // get input from stdin or file
-    in.clear();
-    next = stop = false; // next iteration, premature stop
-    if ( t == 0 ) {    
-      if ( input_fn == "-" ) {
-        if ( !getline(cin, in) ) next = true;
-      } else {
-        if ( !getline(input, in) ) next = true; 
-      }
-    } else {
-      if ( sid == in_sz ) next = true; // stop if we reach the end of our input
-    }
-    // stop after X sentences (but still iterate for those)
-    if ( stop_after > 0 && stop_after == sid && !next ) stop = true;
-    
-    // produce some pretty output
-    if ( !quiet && !verbose ) {
-        if ( sid == 0 ) cout << " ";
-        if ( (sid+1) % (DTRAIN_DOTS) == 0 ) {
-            cout << ".";
-            cout.flush();
-        }
-        if ( (sid+1) % (20*DTRAIN_DOTS) == 0) {
-            cout << " " << sid+1 << endl;
-            if ( !next && !stop ) cout << " ";
-        }
-        if ( stop ) {
-          if ( sid % (20*DTRAIN_DOTS) != 0 ) cout << " " << sid << endl;
-          cout << "Stopping after " << stop_after << " input sentences." << endl;
-        } else {
-          if ( next ) {
-            if ( sid % (20*DTRAIN_DOTS) != 0 ) {
-              cout << " " << sid << endl;
-            }
-          }
-        }
-    }
-    
-    // next iteration
-    if ( next || stop ) break;
-
-    // weights
-    dense_weights.clear();
-    weights.InitFromVector( lambdas );
-    weights.InitVector( &dense_weights );
-    decoder.SetWeights( dense_weights );
-
-    if ( t == 0 ) {
-      // handling input
-      in_split.clear();
-      boost::split( in_split, in, boost::is_any_of("\t") ); // in_split[0] is id
-      // getting reference
-      ref_tok.clear(); ref_ids.clear();
-      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;
-      for ( string::iterator ti = in_split[3].begin(); ti != in_split[3].end(); ti++ ) {
-        if ( !isspace(*ti) ) {
-          broken_grammar = false;
-          break;
-        }
-      }
-      if ( broken_grammar ) continue;
-      grammar_str = boost::replace_all_copy( in_split[3], " __NEXT__RULE__ ", "\n" ) + "\n"; // FIXME copy, __
-      grammar_buf << grammar_str << DTRAIN_GRAMMAR_DELIM << endl;
-      decoder.SetSentenceGrammarFromString( grammar_str );
-      // decode, kbest
-      src_str_buf.push_back( in_split[1] );
-      decoder.Decode( in_split[1], &observer );
-    } else {
-      // get buffered grammar
-      grammar_str.clear();
-      int i = 1;
-      while ( true ) {
-        string g;  
-        getline( grammar_buf_in, g );
-        if ( g == DTRAIN_GRAMMAR_DELIM ) break;
-        grammar_str += g+"\n";
-        i += 1;
-      }
-      decoder.SetSentenceGrammarFromString( grammar_str );
-      // decode, kbest
-      decoder.Decode( src_str_buf[sid], &observer );
-    }
-
-    // get kbest list
-    KBestList* kb;
-    //if ( ) { // TODO get from forest
-      kb = observer.GetKBest();
-    //}
-
-    // scoring kbest
-    if ( t > 0 ) ref_ids = ref_ids_buf[sid];
-    for ( size_t i = 0; i < kb->GetSize(); i++ ) {
-      NgramCounts counts = make_ngram_counts( ref_ids, kb->sents[i], N );
-      // this is for approx bleu
-      if ( scorer_str == "approx_bleu" ) {
-        if ( i == 0 ) { // 'context of 1best translations'
-          global_counts  += counts;
-          global_hyp_len += kb->sents[i].size();
-          global_ref_len += ref_ids.size();
-          counts.reset();
-          cand_len = 0;
-        } else {
-            cand_len = kb->sents[i].size();
-        }
-        NgramCounts counts_tmp = global_counts + counts;
-        // TODO as param
-        score = 0.9 * scorer( counts_tmp,
-                              global_ref_len,
-                              global_hyp_len + cand_len, N, bleu_weights );
-      } else {
-        // other scorers
-        cand_len = kb->sents[i].size();
-        score = scorer( counts,
-                        ref_ids.size(),
-                        kb->sents[i].size(), N, bleu_weights );
-      }
-
-      kb->scores.push_back( score );
-
-      if ( i == 0 ) {
-        acc_1best_score += score;
-        acc_1best_model += kb->model_scores[i];
-      }
-
-      if ( verbose ) {
-        if ( i == 0 ) cout << "'" << TD::GetString( ref_ids ) << "' [ref]" << endl;
-        cout << _prec5 << _nopos << "[hyp " << i << "] " << "'" << TD::GetString( kb->sents[i] ) << "'";
-        cout << " [SCORE=" << score << ",model="<< kb->model_scores[i] << "]" << endl;
-        cout << kb->feats[i] << endl; // this is maybe too verbose
-      }
-    } // Nbest loop
-
-    if ( verbose ) cout << endl;
-
-
-    // UPDATE WEIGHTS
-    if ( !noup ) {
-
-      TrainingInstances pairs;
-      sample_all( kb, pairs, n_pairs );
-       
-      vector< SparseVector<double> > featureValueDiffs;
-      vector<double> lossMinusModelScoreDiffs;
-      for ( TrainingInstances::iterator ti = pairs.begin();
-            ti != pairs.end(); ti++ ) {
-
-        SparseVector<double> dv;
-        if ( ti->first_score - ti->second_score < 0 ) {
-          dv = ti->second - ti->first;
-          dv.add_value( FD::Convert("__bias"), -1 );
-        
-	  featureValueDiffs.push_back(dv);
-	  double lossMinusModelScoreDiff = ti->loss_diff - ti->model_score_diff;
-	  lossMinusModelScoreDiffs.push_back(lossMinusModelScoreDiff);
-
-	  if (update_type == "perceptron") {
-	    lambdas += dv * eta;
-	    cerr << "after perceptron update: " << lambdas << endl << endl;
-	  }
-
-          if ( verbose ) {
-            cout << "{{ f("<< ti->first_rank <<") > f(" << ti->second_rank << ") but g(i)="<< ti->first_score <<" < g(j)="<< ti->second_score << " so update" << endl;
-            cout << " i  " << TD::GetString(kb->sents[ti->first_rank]) << endl;
-            cout << "    " << kb->feats[ti->first_rank] << endl;
-            cout << " j  " << TD::GetString(kb->sents[ti->second_rank]) << endl;
-            cout << "    " << kb->feats[ti->second_rank] << endl; 
-            cout << " diff vec: " << dv << endl;
-            cout << " lambdas after update: " << lambdas << endl;
-            cout << "}}" << endl;
-          }
-        } else {
-          //SparseVector<double> reg;
-          //reg = lambdas * ( 2 * gamma );
-          //lambdas += reg * ( -eta );
-        }
-      }
-      cerr << "Collected " << featureValueDiffs.size() << " constraints." << endl;
-
-      double slack = 0.01;
-      if (update_type == "mira") {
-	if (featureValueDiffs.size() > 0) {
-	  vector<double> alphas;
-	  if (slack != 0) {
-	    alphas = Mira::Hildreth::optimise(featureValueDiffs, lossMinusModelScoreDiffs, slack);
-	  } else {
-	    alphas = Mira::Hildreth::optimise(featureValueDiffs, lossMinusModelScoreDiffs);
-	  }
-	  
-	  for (size_t k = 0; k < featureValueDiffs.size(); ++k) {
-	    lambdas += featureValueDiffs[k] * alphas[k];
-	  }
-	  //	  cerr << "after mira update: " << lambdas << endl << endl;
-	}      
-      }
-    }
-
-    ++sid;
-
-  } // input loop
-
-  if ( t == 0 ) in_sz = sid; // remember size (lines) of input
-
-  // print some stats
-  double avg_1best_score = acc_1best_score/(double)in_sz;
-  double avg_1best_model = acc_1best_model/(double)in_sz;
-  double avg_1best_score_diff, avg_1best_model_diff;
-  if ( t > 0 ) {
-    avg_1best_score_diff = avg_1best_score - scores_per_iter[t-1][0];
-    avg_1best_model_diff = avg_1best_model - scores_per_iter[t-1][1];
-  } else {
-    avg_1best_score_diff = avg_1best_score;
-    avg_1best_model_diff = avg_1best_model;
-  }
-  cout << _prec5 << _pos << "WEIGHTS" << endl;
-  for (vector<string>::iterator it = wprint.begin(); it != wprint.end(); it++) {
-    cout << setw(16) << *it << " = " << dense_weights[FD::Convert( *it )] << endl;
-  }
-
-  cout << "        ---" << endl;
-  cout << _nopos << "      avg score: " << avg_1best_score;
-  cout << _pos << " (" << avg_1best_score_diff << ")" << endl;
-  cout << _nopos << "avg model score: " << avg_1best_model;
-  cout << _pos << " (" << avg_1best_model_diff << ")" << endl;
-  vector<double> remember_scores;
-  remember_scores.push_back( avg_1best_score );
-  remember_scores.push_back( avg_1best_model );
-  scores_per_iter.push_back( remember_scores );
-  if ( avg_1best_score > max_score ) {
-    max_score = avg_1best_score;
-    best_t = t;
-  }
-
-  // close open files
-  if ( input_fn != "-" ) input.close();
-  close( grammar_buf );
-  grammar_file.close();
-
-  time ( &end );
-  double time_dif = difftime( end, start );
-  overall_time += time_dif;
-  if ( !quiet ) {
-    cout << _prec2 << _nopos << "(time " << time_dif/60. << " min, ";
-    cout << time_dif/(double)in_sz<< " s/S)" << endl;
-  }
-  
-  if ( t+1 != T ) cout << endl;
-
-  if ( noup ) break;
-
-  // write weights after every epoch                                                                                                                                               
-  std::string s;
-  std::stringstream out;
-  out << t;
-  s = out.str();
-  string weights_file = output_file + "." + s;
-  weights.WriteToFile(weights_file, true );
-
-  } // outer loop
-
-  unlink( grammar_buf_tmp_fn );
-  if ( !noup ) {
-    if ( !quiet ) cout << endl << "writing weights file '" << cfg["output"].as<string>() << "' ...";
-    weights.WriteToFile( cfg["output"].as<string>(), true );
-    if ( !quiet ) cout << "done" << endl;
-  }
-  
-  if ( !quiet ) {
-    cout << _prec5 << _nopos << endl << "---" << endl << "Best iteration: ";
-    cout << best_t+1 << " [SCORE '" << scorer_str << "'=" << max_score << "]." << endl;
-    cout << _prec2 << "This took " << overall_time/60. << " min." << endl;
-  }
-
-  return 0;
-}
-
diff --git a/dtrain/test/mira_update/sample.h b/dtrain/test/mira_update/sample.h
deleted file mode 100644
index 5c331bba..00000000
--- a/dtrain/test/mira_update/sample.h
+++ /dev/null
@@ -1,101 +0,0 @@
-#ifndef _DTRAIN_SAMPLE_H_
-#define _DTRAIN_SAMPLE_H_
-
-
-#include "kbestget.h"
-
-
-namespace dtrain
-{
-
-
-struct TPair
-{
-  SparseVector<double> first, second;
-  size_t first_rank, second_rank;
-  double first_score, second_score;
-  double model_score_diff;
-  double loss_diff;
-};
-
-typedef vector<TPair> TrainingInstances;
-
-
-void
-  sample_all( KBestList* kb, TrainingInstances &training, size_t n_pairs )
-{
-  std::vector<double> loss_diffs;
-  TrainingInstances training_tmp;
-  for ( size_t i = 0; i < kb->GetSize()-1; i++ ) {
-    for ( size_t j = i+1; j < kb->GetSize(); j++ ) {
-      TPair p;
-      p.first = kb->feats[i];
-      p.second = kb->feats[j];
-      p.first_rank = i;
-      p.second_rank = j;
-      p.first_score = kb->scores[i];
-      p.second_score = kb->scores[j];
-
-      bool conservative = 1;
-      if ( kb->scores[i] - kb->scores[j] < 0 ) {
-	// j=hope, i=fear                                                                                                                         
-	p.model_score_diff = kb->model_scores[j] - kb->model_scores[i];
-        p.loss_diff = kb->scores[j] - kb->scores[i];
-        training_tmp.push_back(p);
-        loss_diffs.push_back(p.loss_diff);
-      }
-      else if (!conservative) {
-	// i=hope, j=fear
-	p.model_score_diff = kb->model_scores[i] - kb->model_scores[j];
-        p.loss_diff = kb->scores[i] - kb->scores[j];
-        training_tmp.push_back(p);
-        loss_diffs.push_back(p.loss_diff);
-      }
-    }
-  }
-  
-  if (training_tmp.size() > 0) {
-    double threshold;
-    std::sort(loss_diffs.begin(), loss_diffs.end());
-    std::reverse(loss_diffs.begin(), loss_diffs.end());
-    threshold = loss_diffs.size() >= n_pairs ? loss_diffs[n_pairs-1] : loss_diffs[loss_diffs.size()-1];
-    cerr << "threshold: " << threshold << endl;
-    size_t constraints = 0;
-    for (size_t i = 0; (i < training_tmp.size() && constraints < n_pairs); ++i) {
-      if (training_tmp[i].loss_diff >= threshold) {
-	training.push_back(training_tmp[i]);
-	constraints++;
-      }
-    }
-  }
-  else {
-    cerr << "No pairs selected." << endl;
-  }
-}
-
-void
-sample_rand( KBestList* kb, TrainingInstances &training )
-{
-  srand( time(NULL) );
-  for ( size_t i = 0; i < kb->GetSize()-1; i++ ) {
-    for ( size_t j = i+1; j < kb->GetSize(); j++ ) {
-      if ( rand() % 2 ) {
-        TPair p;
-        p.first = kb->feats[i];
-        p.second = kb->feats[j];
-        p.first_rank = i;
-        p.second_rank = j;
-        p.first_score = kb->scores[i];
-        p.second_score = kb->scores[j];
-        training.push_back( p );
-      }
-    }
-  }
-}
-
-
-} // namespace
-
-
-#endif
-