use new union api

1 files changed, 0 insertions, 716 deletions

diff --git a/extools/featurize_grammar.cc b/extools/featurize_grammar.cc
deleted file mode 100644
index 78175202..00000000
--- a/extools/featurize_grammar.cc
+++ /dev/null
@@ -1,716 +0,0 @@
-/*
- * Featurize a grammar in striped format
- */
-#include <iostream>
-#include <sstream>
-#include <string>
-#include <map>
-#include <vector>
-#include <utility>
-#include <cstdlib>
-#include <tr1/unordered_map>
-
-#include "lex_trans_tbl.h"
-#include "sparse_vector.h"
-#include "sentence_pair.h"
-#include "extract.h"
-#include "fdict.h"
-#include "tdict.h"
-#include "filelib.h"
-#include "striped_grammar.h"
-
-#include <boost/tuple/tuple.hpp>
-#include <boost/shared_ptr.hpp>
-#include <boost/functional/hash.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-using namespace std;
-using namespace std::tr1;
-using boost::shared_ptr;
-namespace po = boost::program_options;
-
-static string aligned_corpus;
-static const size_t MAX_LINE_LENGTH = 64000000;
-
-// Data structures for indexing and counting rules
-//typedef boost::tuple< WordID, vector<WordID>, vector<WordID> > RuleTuple;
-struct RuleTuple {
-  RuleTuple(const WordID& lhs, const vector<WordID>& s, const vector<WordID>& t)
-  : m_lhs(lhs), m_source(s), m_target(t) {
-    hash_value();
-    m_dirty = false;
-  }
-
-  size_t hash_value() const {
-//    if (m_dirty) {
-      size_t hash = 0;
-      boost::hash_combine(hash, m_lhs);
-      boost::hash_combine(hash, m_source);
-      boost::hash_combine(hash, m_target);
-//    }
-//    m_dirty = false;
-    return hash;
-  }
-
-  bool operator==(RuleTuple const& b) const
-  { return m_lhs == b.m_lhs && m_source == b.m_source && m_target == b.m_target; }
-
-  WordID& lhs() { m_dirty=true; return m_lhs; }
-  vector<WordID>& source() { m_dirty=true; return m_source; }
-  vector<WordID>& target() { m_dirty=true; return m_target; }
-  const WordID& lhs() const { return m_lhs; }
-  const vector<WordID>& source() const { return m_source; }
-  const vector<WordID>& target() const { return m_target; }
-
-//  mutable size_t m_hash;
-private:
-  WordID m_lhs;
-  vector<WordID> m_source, m_target;
-  mutable bool m_dirty;
-};
-std::size_t hash_value(RuleTuple const& b) { return b.hash_value(); }
-bool operator<(RuleTuple const& l, RuleTuple const& r) {
-  if (l.lhs() < r.lhs()) return true;
-  else if (l.lhs() == r.lhs()) {
-    if (l.source() < r.source()) return true;
-    else if (l.source() == r.source()) {
-      if (l.target() < r.target()) return true;
-    }
-  }
-  return false;
-}
-
-ostream& operator<<(ostream& o, RuleTuple const& r) {
-  o << "(" << r.lhs() << "-->" << "<";
-  for (vector<WordID>::const_iterator it=r.source().begin(); it!=r.source().end(); ++it)
-    o << TD::Convert(*it) << " ";
-  o << "|||";
-  for (vector<WordID>::const_iterator it=r.target().begin(); it!=r.target().end(); ++it)
-    o << " " << TD::Convert(*it);
-  o << ">)";
-  return o;
-}
-
-template <typename Key>
-struct FreqCount {
-  //typedef unordered_map<Key, int, boost::hash<Key> > Counts;
-  typedef map<Key, int> Counts;
-  Counts counts;
-
-  int inc(const Key& r, int c=1) {
-    pair<typename Counts::iterator,bool> itb
-      = counts.insert(make_pair(r,c));
-    if (!itb.second)
-      itb.first->second += c;
-    return itb.first->second;
-  }
-
-  int inc_if_exists(const Key& r, int c=1) {
-    typename Counts::iterator it = counts.find(r);
-    if (it != counts.end())
-      it->second += c;
-    return it->second;
-  }
-
-  int count(const Key& r) const {
-    typename Counts::const_iterator it = counts.find(r);
-    if (it == counts.end()) return 0;
-    return it->second;
-  }
-
-  int operator()(const Key& r) const { return count(r); }
-};
-typedef FreqCount<RuleTuple> RuleFreqCount;
-
-class FeatureExtractor;
-class FERegistry;
-struct FEFactoryBase {
-  virtual ~FEFactoryBase() {}
-  virtual boost::shared_ptr<FeatureExtractor> Create() const = 0;
-};
-
-
-class FERegistry {
-  friend class FEFactoryBase;
- public:
-  FERegistry() {}
-  boost::shared_ptr<FeatureExtractor> Create(const std::string& ffname) const {
-    map<string, boost::shared_ptr<FEFactoryBase> >::const_iterator it = reg_.find(ffname);
-    boost::shared_ptr<FeatureExtractor> res;
-    if (it == reg_.end()) {
-      cerr << "I don't know how to create feature " << ffname << endl;
-    } else {
-      res = it->second->Create();
-    }
-    return res;
-  }
-  void DisplayList(ostream* out) const {
-    bool first = true;
-    for (map<string, boost::shared_ptr<FEFactoryBase> >::const_iterator it = reg_.begin();
-        it != reg_.end(); ++it) {
-      if (first) {first=false;} else {*out << ' ';}
-      *out << it->first;
-    }
-  }
-
-  void Register(const std::string& ffname, FEFactoryBase* factory) {
-    if (reg_.find(ffname) != reg_.end()) {
-      cerr << "Duplicate registration of FeatureExtractor with name " << ffname << "!\n";
-      exit(1);
-    }
-    reg_[ffname].reset(factory);
-  }
-
- private:
-  std::map<std::string, boost::shared_ptr<FEFactoryBase> > reg_;
-};
-
-template<class FE>
-class FEFactory : public FEFactoryBase {
-  boost::shared_ptr<FeatureExtractor> Create() const {
-    return boost::shared_ptr<FeatureExtractor>(new FE);
-  }
-};
-
-void InitCommandLine(const FERegistry& r, int argc, char** argv, po::variables_map* conf) {
-  po::options_description opts("Configuration options");
-  ostringstream feats;
-  feats << "[multiple] Features to extract (";
-  r.DisplayList(&feats);
-  feats << ")";
-  opts.add_options()
-        ("filtered_grammar,g", po::value<string>(), "Grammar to add features to")
-        ("list_features,L", "List extractable features")
-        ("feature,f", po::value<vector<string> >()->composing(), feats.str().c_str())
-        ("aligned_corpus,c", po::value<string>(), "Aligned corpus (single line format)")
-        ("help,h", "Print this help message and exit");
-  po::options_description clo("Command line options");
-  po::options_description dcmdline_options;
-  dcmdline_options.add(opts);
-
-  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
-  po::notify(*conf);
-
-  if (conf->count("help") || conf->count("aligned_corpus")==0 || conf->count("feature") == 0) {
-    cerr << "\nUsage: featurize_grammar -g FILTERED-GRAMMAR.gz -c ALIGNED_CORPUS.fr-en-al -f Feat1 -f Feat2 ... < UNFILTERED-GRAMMAR\n";
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-static const bool DEBUG = false;
-
-void LexTranslationTable::createTTable(const char* buf){
-  AnnotatedParallelSentence sent;
-  sent.ParseInputLine(buf);
-
-  //iterate over the alignment to compute aligned words
-
-  for(int i =0;i<sent.aligned.width();i++)
-    {
-      for (int j=0;j<sent.aligned.height();j++)
-        {
-          if (DEBUG) cerr << sent.aligned(i,j) << " ";
-          if( sent.aligned(i,j))
-            {
-              if (DEBUG) cerr << TD::Convert(sent.f[i])  << " aligned to " << TD::Convert(sent.e[j]);
-              ++word_translation[pair<WordID,WordID> (sent.f[i], sent.e[j])];
-              ++total_foreign[sent.f[i]];
-              ++total_english[sent.e[j]];
-            }
-        }
-      if (DEBUG)  cerr << endl;
-    }
-  if (DEBUG) cerr << endl;
-
-  const WordID NULL_ = TD::Convert("NULL");
-  //handle unaligned words - align them to null
-  for (int j =0; j < sent.e_len; j++) {
-    if (sent.e_aligned[j]) continue;
-    ++word_translation[pair<WordID,WordID> (NULL_, sent.e[j])];
-    ++total_foreign[NULL_];
-    ++total_english[sent.e[j]];
-  }
-
-  for (int i =0; i < sent.f_len; i++) {
-    if (sent.f_aligned[i]) continue;
-    ++word_translation[pair<WordID,WordID> (sent.f[i], NULL_)];
-    ++total_english[NULL_];
-    ++total_foreign[sent.f[i]];
-  }
-}
-
-inline float safenlog(float v) {
-  if (v == 1.0f) return 0.0f;
-  float res = -log(v);
-  if (res > 100.0f) res = 100.0f;
-  return res;
-}
-
-static bool IsZero(float f) { return (f > 0.999 && f < 1.001); }
-
-struct FeatureExtractor {
-  // create any keys necessary
-  virtual void ObserveFilteredRule(const WordID /* lhs */,
-                                   const vector<WordID>& /* src */,
-                                   const vector<WordID>& /* trg */) {}
-
-  // compute statistics over keys, the same lhs-src-trg tuple may be seen
-  // more than once
-  virtual void ObserveUnfilteredRule(const WordID /* lhs */,
-                                     const vector<WordID>& /* src */,
-                                     const vector<WordID>& /* trg */,
-                                     const RuleStatistics& /* info */) {}
-
-  // compute features, a unique lhs-src-trg tuple will be seen exactly once
-  virtual void ExtractFeatures(const WordID lhs,
-                               const vector<WordID>& src,
-                               const vector<WordID>& trg,
-                               const RuleStatistics& info,
-                               SparseVector<float>* result) const = 0;
-
-  virtual ~FeatureExtractor() {}
-};
-
-struct LogRuleCount : public FeatureExtractor {
-  LogRuleCount() :
-    fid_(FD::Convert("LogRuleCount")),
-    sfid_(FD::Convert("SingletonRule")),
-    kCFE(FD::Convert("CFE")) {}
-  virtual void ExtractFeatures(const WordID lhs,
-                               const vector<WordID>& src,
-                               const vector<WordID>& trg,
-                               const RuleStatistics& info,
-                               SparseVector<float>* result) const {
-    (void) lhs; (void) src; (void) trg;
-    //result->set_value(fid_, log(info.counts.get(kCFE)));
-    result->set_value(fid_, log(info.counts.get(kCFE)));
-    if (IsZero(info.counts.get(kCFE)))
-      result->set_value(sfid_, 1);
-  }
-  const int fid_;
-  const int sfid_;
-  const int kCFE;
-};
-
-struct RulePenalty : public FeatureExtractor {
-  RulePenalty() : fid_(FD::Convert("RulePenalty")) {}
-  virtual void ExtractFeatures(const WordID /*lhs*/,
-                               const vector<WordID>& /*src*/,
-                               const vector<WordID>& /*trg*/,
-                               const RuleStatistics& /*info*/,
-                               SparseVector<float>* result) const
-  { result->set_value(fid_, 1); }
-
-  const int fid_;
-};
-
-// The negative log of the condition rule probs
-// ignoring the identities of the  non-terminals.
-// i.e. the prob Hiero would assign.
-// Also extracts Labelled features.
-struct XFeatures: public FeatureExtractor {
-  XFeatures() :
-    fid_xfe(FD::Convert("XFE")),
-    fid_xef(FD::Convert("XEF")),
-    fid_labelledfe(FD::Convert("LabelledFE")),
-    fid_labelledef(FD::Convert("LabelledEF")),
-    fid_xesingleton(FD::Convert("XE_Singleton")),
-    fid_xfsingleton(FD::Convert("XF_Singleton")),
-    kCFE(FD::Convert("CFE")) {}
-  virtual void ObserveFilteredRule(const WordID /*lhs*/,
-                                   const vector<WordID>& src,
-                                   const vector<WordID>& trg) {
-    RuleTuple r(-1, src, trg);
-    map_rule(r);
-    rule_counts.inc(r, 0);
-    source_counts.inc(r.source(), 0);
-    target_counts.inc(r.target(), 0);
-  }
-
-  virtual void ObserveUnfilteredRule(const WordID /*lhs*/,
-                                     const vector<WordID>& src,
-                                     const vector<WordID>& trg,
-                                     const RuleStatistics& info) {
-    RuleTuple r(-1, src, trg);
-    map_rule(r);
-    const int count = info.counts.get(kCFE);
-    assert(count > 0);
-    rule_counts.inc_if_exists(r, count);
-    source_counts.inc_if_exists(r.source(), count);
-    target_counts.inc_if_exists(r.target(), count);
-  }
-
-  virtual void ExtractFeatures(const WordID /*lhs*/,
-                               const vector<WordID>& src,
-                               const vector<WordID>& trg,
-                               const RuleStatistics& info,
-                               SparseVector<float>* result) const {
-    RuleTuple r(-1, src, trg);
-    map_rule(r);
-    double l_r_freq = log(rule_counts(r));
-
-    const int t_c = target_counts(r.target());
-    assert(t_c > 0);
-    result->set_value(fid_xfe, log(t_c) - l_r_freq);
-    result->set_value(fid_labelledfe, log(t_c) - log(info.counts.get(kCFE)));
-//    if (t_c == 1)
-//      result->set_value(fid_xesingleton, 1.0);
-
-    const int s_c = source_counts(r.source());
-    assert(s_c > 0);
-    result->set_value(fid_xef, log(s_c) - l_r_freq);
-    result->set_value(fid_labelledef, log(s_c) - log(info.counts.get(kCFE)));
-//    if (s_c == 1)
-//      result->set_value(fid_xfsingleton, 1.0);
-  }
-
-  void map_rule(RuleTuple& r) const {
-    vector<WordID> indexes; int i=0;
-    for (vector<WordID>::iterator it = r.target().begin(); it != r.target().end(); ++it) {
-      if (*it <= 0)
-        indexes.push_back(*it);
-    }
-    for (vector<WordID>::iterator it = r.source().begin(); it != r.source().end(); ++it) {
-      if (*it <= 0)
-        *it = indexes.at(i++);
-    }
-  }
-
-  const int fid_xfe, fid_xef;
-  const int fid_labelledfe, fid_labelledef;
-  const int fid_xesingleton, fid_xfsingleton;
-  const int kCFE;
-  RuleFreqCount rule_counts;
-  FreqCount< vector<WordID> > source_counts, target_counts;
-};
-
-
-struct LabelledRuleConditionals: public FeatureExtractor {
-  LabelledRuleConditionals() :
-    fid_fe(FD::Convert("LabelledFE")),
-    fid_ef(FD::Convert("LabelledEF")),
-    kCFE(FD::Convert("CFE")) {}
-  virtual void ObserveFilteredRule(const WordID lhs,
-                                   const vector<WordID>& src,
-                                   const vector<WordID>& trg) {
-    RuleTuple r(lhs, src, trg);
-    rule_counts.inc(r, 0);
-    source_counts.inc(r.source(), 0);
-
-    target_counts.inc(r.target(), 0);
-  }
-
-  virtual void ObserveUnfilteredRule(const WordID lhs,
-                                     const vector<WordID>& src,
-                                     const vector<WordID>& trg,
-                                     const RuleStatistics& info) {
-    RuleTuple r(lhs, src, trg);
-    rule_counts.inc_if_exists(r, info.counts.get(kCFE));
-    source_counts.inc_if_exists(r.source(), info.counts.get(kCFE));
-
-    target_counts.inc_if_exists(r.target(), info.counts.get(kCFE));
-  }
-
-  virtual void ExtractFeatures(const WordID lhs,
-                               const vector<WordID>& src,
-                               const vector<WordID>& trg,
-                               const RuleStatistics& /*info*/,
-                               SparseVector<float>* result) const {
-    RuleTuple r(lhs, src, trg);
-    double l_r_freq = log(rule_counts(r));
-    result->set_value(fid_fe, log(target_counts(r.target())) - l_r_freq);
-    result->set_value(fid_ef, log(source_counts(r.source())) - l_r_freq);
-  }
-
-  const int fid_fe, fid_ef;
-  const int kCFE;
-  RuleFreqCount rule_counts;
-  FreqCount< vector<WordID> > source_counts, target_counts;
-};
-
-struct LHSProb: public FeatureExtractor {
-  LHSProb() : fid_(FD::Convert("LHSProb")), kCFE(FD::Convert("CFE")), total_count(0) {}
-
-  virtual void ObserveUnfilteredRule(const WordID lhs,
-                                     const vector<WordID>& /*src*/,
-                                     const vector<WordID>& /*trg*/,
-                                     const RuleStatistics& info) {
-    int count = info.counts.get(kCFE);
-    total_count += count;
-    lhs_counts.inc(lhs, count);
-  }
-
-  virtual void ExtractFeatures(const WordID lhs,
-                               const vector<WordID>& /*src*/,
-                               const vector<WordID>& /*trg*/,
-                               const RuleStatistics& /*info*/,
-                               SparseVector<float>* result) const {
-    double lhs_log_prob =  log(total_count) - log(lhs_counts(lhs));
-    result->set_value(fid_, lhs_log_prob);
-  }
-
-  const int fid_;
-  const int kCFE;
-  int total_count;
-  FreqCount<WordID> lhs_counts;
-};
-
-// Proper rule generative probability: p( s,t | lhs)
-struct GenerativeProb: public FeatureExtractor {
-  GenerativeProb() :
-    fid_(FD::Convert("GenerativeProb")),
-    kCFE(FD::Convert("CFE")) {}
-
-  virtual void ObserveUnfilteredRule(const WordID lhs,
-                                     const vector<WordID>& /*src*/,
-                                     const vector<WordID>& /*trg*/,
-                                     const RuleStatistics& info)
-  { lhs_counts.inc(lhs, info.counts.get(kCFE)); }
-
-  virtual void ExtractFeatures(const WordID lhs,
-                               const vector<WordID>& /*src*/,
-                               const vector<WordID>& /*trg*/,
-                               const RuleStatistics& info,
-                               SparseVector<float>* result) const {
-    double log_prob = log(lhs_counts(lhs)) - log(info.counts.get(kCFE));
-    result->set_value(fid_, log_prob);
-  }
-
-  const int fid_;
-  const int kCFE;
-  FreqCount<WordID> lhs_counts;
-};
-
-// remove terminals from the rules before estimating the conditional prob
-struct LabellingShape: public FeatureExtractor {
-  LabellingShape() : fid_(FD::Convert("LabellingShape")), kCFE(FD::Convert("CFE")) {}
-
-  virtual void ObserveFilteredRule(const WordID /*lhs*/,
-                                   const vector<WordID>& src,
-                                   const vector<WordID>& trg) {
-    RuleTuple r(-1, src, trg);
-    map_rule(r);
-    rule_counts.inc(r, 0);
-    source_counts.inc(r.source(), 0);
-  }
-
-  virtual void ObserveUnfilteredRule(const WordID /*lhs*/,
-                                     const vector<WordID>& src,
-                                     const vector<WordID>& trg,
-                                     const RuleStatistics& info) {
-    RuleTuple r(-1, src, trg);
-    map_rule(r);
-    rule_counts.inc_if_exists(r, info.counts.get(kCFE));
-    source_counts.inc_if_exists(r.source(), info.counts.get(kCFE));
-  }
-
-  virtual void ExtractFeatures(const WordID /*lhs*/,
-                               const vector<WordID>& src,
-                               const vector<WordID>& trg,
-                               const RuleStatistics& /*info*/,
-                               SparseVector<float>* result) const {
-    RuleTuple r(-1, src, trg);
-    map_rule(r);
-    double l_r_freq = log(rule_counts(r));
-    result->set_value(fid_, log(source_counts(r.source())) - l_r_freq);
-  }
-
-  // Replace all terminals with generic -1
-  void map_rule(RuleTuple& r) const {
-    for (vector<WordID>::iterator it = r.target().begin(); it != r.target().end(); ++it)
-      if (*it <= 0) *it = -1;
-    for (vector<WordID>::iterator it = r.source().begin(); it != r.source().end(); ++it)
-      if (*it <= 0) *it = -1;
-  }
-
-  const int fid_, kCFE;
-  RuleFreqCount rule_counts;
-  FreqCount< vector<WordID> > source_counts;
-};
-
-
-// this extracts the lexical translation prob features
-// in BOTH directions.
-struct LexProbExtractor : public FeatureExtractor {
-  LexProbExtractor() :
-      e2f_(FD::Convert("LexE2F")), f2e_(FD::Convert("LexF2E")) {
-    ReadFile rf(aligned_corpus);
-    //create lexical translation table
-    cerr << "Computing lexical translation probabilities from " << aligned_corpus << "..." << endl;
-    char* buf = new char[MAX_LINE_LENGTH];
-    istream& alignment = *rf.stream();
-    while(alignment) {
-      alignment.getline(buf, MAX_LINE_LENGTH);
-      if (buf[0] == 0) continue;
-      table.createTTable(buf);
-    }
-    delete[] buf;
-  }
-
-  virtual void ExtractFeatures(const WordID /*lhs*/,
-                               const vector<WordID>& src,
-                               const vector<WordID>& trg,
-                               const RuleStatistics& info,
-                               SparseVector<float>* result) const {
-    map <WordID, pair<int, float> > foreign_aligned;
-    map <WordID, pair<int, float> > english_aligned;
-
-    //Loop over all the alignment points to compute lexical translation probability
-    const vector< pair<short,short> >& al = info.aligns;
-    vector< pair<short,short> >::const_iterator ita;
-    for (ita = al.begin(); ita != al.end(); ++ita) {
-            if (DEBUG) {
-              cerr << "\nA:" << ita->first << "," << ita->second << "::";
-              cerr <<  TD::Convert(src[ita->first]) << "-" << TD::Convert(trg[ita->second]);
-            }
-
-            //Lookup this alignment probability in the table
-            int temp = table.word_translation[pair<WordID,WordID> (src[ita->first],trg[ita->second])];
-            float f2e=0, e2f=0;
-            if ( table.total_foreign[src[ita->first]] != 0)
-              f2e = (float) temp / table.total_foreign[src[ita->first]];
-            if ( table.total_english[trg[ita->second]] !=0 )
-              e2f = (float) temp / table.total_english[trg[ita->second]];
-            if (DEBUG) printf (" %d %E %E\n", temp, f2e, e2f);
-
-            //local counts to keep track of which things haven't been aligned, to later compute their null alignment
-            if (foreign_aligned.count(src[ita->first])) {
-              foreign_aligned[ src[ita->first] ].first++;
-              foreign_aligned[ src[ita->first] ].second += e2f;
-            } else {
-              foreign_aligned[ src[ita->first] ] = pair<int,float> (1,e2f);
-            }
-
-            if (english_aligned.count( trg[ ita->second] )) {
-               english_aligned[ trg[ ita->second] ].first++;
-               english_aligned[ trg[ ita->second] ].second += f2e;
-            } else {
-               english_aligned[ trg[ ita->second] ] = pair<int,float> (1,f2e);
-            }
-          }
-
-          float final_lex_f2e=1, final_lex_e2f=1;
-          static const WordID NULL_ = TD::Convert("NULL");
-
-          //compute lexical weight P(F|E) and include unaligned foreign words
-           for(int i=0;i<src.size(); i++) {
-               if (!table.total_foreign.count(src[i])) continue;      //if we dont have it in the translation table, we won't know its lexical weight
-
-               if (foreign_aligned.count(src[i]))
-                 {
-                   pair<int, float> temp_lex_prob = foreign_aligned[src[i]];
-                   final_lex_e2f *= temp_lex_prob.second / temp_lex_prob.first;
-                 }
-               else //dealing with null alignment
-                 {
-                   int temp_count = table.word_translation[pair<WordID,WordID> (src[i],NULL_)];
-                   float temp_e2f = (float) temp_count / table.total_english[NULL_];
-                   final_lex_e2f *= temp_e2f;
-                 }
-
-             }
-
-           //compute P(E|F) unaligned english words
-           for(int j=0; j< trg.size(); j++) {
-               if (!table.total_english.count(trg[j])) continue;
-
-               if (english_aligned.count(trg[j]))
-                 {
-                   pair<int, float> temp_lex_prob = english_aligned[trg[j]];
-                   final_lex_f2e *= temp_lex_prob.second / temp_lex_prob.first;
-                 }
-               else //dealing with null
-                 {
-                   int temp_count = table.word_translation[pair<WordID,WordID> (NULL_,trg[j])];
-                   float temp_f2e = (float) temp_count / table.total_foreign[NULL_];
-                   final_lex_f2e *= temp_f2e;
-                 }
-           }
-     result->set_value(e2f_, safenlog(final_lex_e2f));
-     result->set_value(f2e_, safenlog(final_lex_f2e));
-  }
-  const int e2f_, f2e_;
-  mutable LexTranslationTable table;
-};
-
-struct Featurizer {
-  Featurizer(const vector<boost::shared_ptr<FeatureExtractor> >& ex) : extractors(ex) {
-  }
-  void Callback1(WordID lhs, const vector<WordID>& src, const ID2RuleStatistics& trgs) {
-    for (ID2RuleStatistics::const_iterator it = trgs.begin(); it != trgs.end(); ++it) {
-      for (int i = 0; i < extractors.size(); ++i)
-        extractors[i]->ObserveFilteredRule(lhs, src, it->first);
-    }
-  }
-  void Callback2(WordID lhs, const vector<WordID>& src, const ID2RuleStatistics& trgs) {
-    for (ID2RuleStatistics::const_iterator it = trgs.begin(); it != trgs.end(); ++it) {
-      for (int i = 0; i < extractors.size(); ++i)
-        extractors[i]->ObserveUnfilteredRule(lhs, src, it->first, it->second);
-    }
-  }
-  void Callback3(WordID lhs, const vector<WordID>& src, const ID2RuleStatistics& trgs) {
-    for (ID2RuleStatistics::const_iterator it = trgs.begin(); it != trgs.end(); ++it) {
-      SparseVector<float> feats;
-      for (int i = 0; i < extractors.size(); ++i)
-        extractors[i]->ExtractFeatures(lhs, src, it->first, it->second, &feats);
-      cout << '[' << TD::Convert(-lhs) << "] ||| ";
-      WriteNamed(src, &cout);
-      cout << " ||| ";
-      WriteAnonymous(it->first, &cout);
-      cout << " ||| ";
-      print(cout,feats,"=");
-      cout << endl;
-    }
-  }
- private:
-  vector<boost::shared_ptr<FeatureExtractor> > extractors;
-};
-
-void cb1(WordID lhs, const vector<WordID>& src_rhs, const ID2RuleStatistics& rules, void* extra) {
-  static_cast<Featurizer*>(extra)->Callback1(lhs, src_rhs, rules);
-}
-
-void cb2(WordID lhs, const vector<WordID>& src_rhs, const ID2RuleStatistics& rules, void* extra) {
-  static_cast<Featurizer*>(extra)->Callback2(lhs, src_rhs, rules);
-}
-
-void cb3(WordID lhs, const vector<WordID>& src_rhs, const ID2RuleStatistics& rules, void* extra) {
-  static_cast<Featurizer*>(extra)->Callback3(lhs, src_rhs, rules);
-}
-
-int main(int argc, char** argv){
-  FERegistry reg;
-  reg.Register("LogRuleCount", new FEFactory<LogRuleCount>);
-  reg.Register("LexProb", new FEFactory<LexProbExtractor>);
-  reg.Register("XFeatures", new FEFactory<XFeatures>);
-  reg.Register("LabelledRuleConditionals", new FEFactory<LabelledRuleConditionals>);
-  reg.Register("RulePenalty", new FEFactory<RulePenalty>);
-  reg.Register("LHSProb", new FEFactory<LHSProb>);
-  reg.Register("LabellingShape", new FEFactory<LabellingShape>);
-  reg.Register("GenerativeProb", new FEFactory<GenerativeProb>);
-  po::variables_map conf;
-  InitCommandLine(reg, argc, argv, &conf);
-  aligned_corpus = conf["aligned_corpus"].as<string>();  // GLOBAL VAR
-  ReadFile fg1(conf["filtered_grammar"].as<string>());
-
-  vector<string> feats = conf["feature"].as<vector<string> >();
-  vector<boost::shared_ptr<FeatureExtractor> > extractors(feats.size());
-  for (int i = 0; i < feats.size(); ++i)
-    extractors[i] = reg.Create(feats[i]);
-  Featurizer fizer(extractors);
-
-  cerr << "Reading filtered grammar to detect keys..." << endl;
-  StripedGrammarLexer::ReadStripedGrammar(fg1.stream(), cb1, &fizer);
-
-  cerr << "Reading unfiltered grammar..." << endl;
-  StripedGrammarLexer::ReadStripedGrammar(&cin, cb2, &fizer);
-
-  ReadFile fg2(conf["filtered_grammar"].as<string>());
-  cerr << "Reading filtered grammar and adding features..." << endl;
-  StripedGrammarLexer::ReadStripedGrammar(fg2.stream(), cb3, &fizer);
-
-  return 0;
-}
-