Merge branch 'master' of https://github.com/redpony/cdec

50 files changed, 0 insertions, 9460 deletions

diff --git a/gi/pf/Makefile.am b/gi/pf/Makefile.am
deleted file mode 100644
index 86f8e07b..00000000
--- a/gi/pf/Makefile.am
+++ /dev/null
@@ -1,44 +0,0 @@
-bin_PROGRAMS = cbgi brat dpnaive pfbrat pfdist itg pfnaive condnaive align-lexonly-pyp learn_cfg pyp_lm nuisance_test align-tl pf_test bayes_lattice_score
-
-noinst_LIBRARIES = libpf.a
-
-libpf_a_SOURCES = base_distributions.cc reachability.cc cfg_wfst_composer.cc corpus.cc unigrams.cc ngram_base.cc transliterations.cc backward.cc hpyp_tm.cc pyp_tm.cc
-
-bayes_lattice_score_SOURCES = bayes_lattice_score.cc
-bayes_lattice_score_LDADD = libpf.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a $(top_srcdir)/klm/lm/libklm.a $(top_srcdir)/klm/util/libklm_util.a -lz
-
-pf_test_SOURCES = pf_test.cc
-pf_test_LDADD = libpf.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a $(top_srcdir)/klm/lm/libklm.a $(top_srcdir)/klm/util/libklm_util.a -lz
-
-nuisance_test_SOURCES = nuisance_test.cc
-nuisance_test_LDADD = libpf.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a $(top_srcdir)/klm/lm/libklm.a $(top_srcdir)/klm/util/libklm_util.a -lz
-
-align_lexonly_pyp_SOURCES = align-lexonly-pyp.cc
-align_lexonly_pyp_LDADD = libpf.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a $(top_srcdir)/klm/lm/libklm.a $(top_srcdir)/klm/util/libklm_util.a -lz
-
-align_tl_SOURCES = align-tl.cc
-align_tl_LDADD = libpf.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a $(top_srcdir)/klm/lm/libklm.a $(top_srcdir)/klm/util/libklm_util.a -lz
-
-itg_SOURCES = itg.cc
-
-pyp_lm_SOURCES = pyp_lm.cc
-
-learn_cfg_SOURCES = learn_cfg.cc
-
-condnaive_SOURCES = condnaive.cc
-
-dpnaive_SOURCES = dpnaive.cc
-
-pfdist_SOURCES = pfdist.cc
-
-pfnaive_SOURCES = pfnaive.cc
-
-cbgi_SOURCES = cbgi.cc
-
-brat_SOURCES = brat.cc
-
-pfbrat_SOURCES = pfbrat.cc
-
-AM_CPPFLAGS = -W -Wall -Wno-sign-compare -funroll-loops -I$(top_srcdir)/utils $(GTEST_CPPFLAGS) -I$(top_srcdir)/decoder -I$(top_srcdir)/klm
-
-AM_LDFLAGS = libpf.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/klm/lm/libklm.a $(top_srcdir)/klm/util/libklm_util.a $(top_srcdir)/utils/libutils.a -lz
diff --git a/gi/pf/README b/gi/pf/README
deleted file mode 100644
index 62e47541..00000000
--- a/gi/pf/README
+++ /dev/null
@@ -1,2 +0,0 @@
-Experimental Bayesian alignment tools. Nothing to see here.
-
diff --git a/gi/pf/align-lexonly-pyp.cc b/gi/pf/align-lexonly-pyp.cc
deleted file mode 100644
index e7509f57..00000000
--- a/gi/pf/align-lexonly-pyp.cc
+++ /dev/null
@@ -1,243 +0,0 @@
-#include <iostream>
-#include <queue>
-
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "tdict.h"
-#include "stringlib.h"
-#include "filelib.h"
-#include "array2d.h"
-#include "sampler.h"
-#include "corpus.h"
-#include "pyp_tm.h"
-#include "hpyp_tm.h"
-#include "quasi_model2.h"
-
-using namespace std;
-namespace po = boost::program_options;
-
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
-  po::options_description opts("Configuration options");
-  opts.add_options()
-        ("samples,s",po::value<unsigned>()->default_value(1000),"Number of samples")
-        ("infer_alignment_hyperparameters,I", "Infer alpha and p_null, otherwise fixed values will be assumed")
-        ("p_null,0", po::value<double>()->default_value(0.08), "probability of aligning to null")
-        ("align_alpha,a", po::value<double>()->default_value(4.0), "how 'tight' is the bias toward be along the diagonal?")
-        ("input,i",po::value<string>(),"Read parallel data from")
-        ("random_seed,S",po::value<uint32_t>(), "Random seed");
-  po::options_description clo("Command line options");
-  clo.add_options()
-        ("config", po::value<string>(), "Configuration file")
-        ("help,h", "Print this help message and exit");
-  po::options_description dconfig_options, dcmdline_options;
-  dconfig_options.add(opts);
-  dcmdline_options.add(opts).add(clo);
-  
-  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
-  if (conf->count("config")) {
-    ifstream config((*conf)["config"].as<string>().c_str());
-    po::store(po::parse_config_file(config, dconfig_options), *conf);
-  }
-  po::notify(*conf);
-
-  if (conf->count("help") || (conf->count("input") == 0)) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-MT19937* prng;
-
-struct LexicalAlignment {
-  unsigned char src_index;
-  bool is_transliteration;
-  vector<pair<short, short> > derivation;
-};
-
-struct AlignedSentencePair {
-  vector<WordID> src;
-  vector<WordID> trg;
-  vector<LexicalAlignment> a;
-  Array2D<short> posterior;
-};
-
-template <class LexicalTranslationModel>
-struct Aligner {
-  Aligner(const vector<vector<WordID> >& lets,
-          int vocab_size,
-          int num_letters,
-          const po::variables_map& conf,
-          vector<AlignedSentencePair>* c) :
-      corpus(*c),
-      paj_model(conf["align_alpha"].as<double>(), conf["p_null"].as<double>()),
-      infer_paj(conf.count("infer_alignment_hyperparameters") > 0),
-      model(lets, vocab_size, num_letters),
-      kNULL(TD::Convert("NULL")) {
-    assert(lets[kNULL].size() == 0);
-  }
-
-  vector<AlignedSentencePair>& corpus;
-  QuasiModel2 paj_model;
-  const bool infer_paj;
-  LexicalTranslationModel model;
-  const WordID kNULL;
-
-  void ResampleHyperparameters() {
-    model.ResampleHyperparameters(prng);
-    if (infer_paj) paj_model.ResampleHyperparameters(prng);
-  }
-
-  void InitializeRandom() {
-    cerr << "Initializing with random alignments ...\n";
-    for (unsigned i = 0; i < corpus.size(); ++i) {
-      AlignedSentencePair& asp = corpus[i];
-      asp.a.resize(asp.trg.size());
-      for (unsigned j = 0; j < asp.trg.size(); ++j) {
-        unsigned char& a_j = asp.a[j].src_index;
-        a_j = prng->next() * (1 + asp.src.size());
-        const WordID f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
-        model.Increment(f_a_j, asp.trg[j], &*prng);
-        paj_model.Increment(a_j, j, asp.src.size(), asp.trg.size());
-      }
-    }
-    cerr << "Corpus intialized randomly." << endl;
-    cerr << "LLH = " << Likelihood() << "    \t(Amodel=" << paj_model.Likelihood()
-         << " TModel=" << model.Likelihood() << ") contexts=" << model.UniqueConditioningContexts() << endl;
-  }
-
-  void ResampleCorpus() {
-    for (unsigned i = 0; i < corpus.size(); ++i) {
-      AlignedSentencePair& asp = corpus[i];
-      SampleSet<prob_t> ss; ss.resize(asp.src.size() + 1);
-      for (unsigned j = 0; j < asp.trg.size(); ++j) {
-        unsigned char& a_j = asp.a[j].src_index;
-        const WordID e_j = asp.trg[j];
-        WordID f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
-        model.Decrement(f_a_j, e_j, prng);
-        paj_model.Decrement(a_j, j, asp.src.size(), asp.trg.size());
-
-        for (unsigned prop_a_j = 0; prop_a_j <= asp.src.size(); ++prop_a_j) {
-          const WordID prop_f = (prop_a_j ? asp.src[prop_a_j - 1] : kNULL);
-          ss[prop_a_j] = model.Prob(prop_f, e_j);
-          ss[prop_a_j] *= paj_model.Prob(prop_a_j, j, asp.src.size(), asp.trg.size());
-        }
-        a_j = prng->SelectSample(ss);
-        f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
-        model.Increment(f_a_j, e_j, prng);
-        paj_model.Increment(a_j, j, asp.src.size(), asp.trg.size());
-      }
-    }
-  }
-
-  prob_t Likelihood() const {
-    return model.Likelihood() * paj_model.Likelihood();
-  }
-};
-
-void ExtractLetters(const set<WordID>& v, vector<vector<WordID> >* l, set<WordID>* letset = NULL) {
-  for (set<WordID>::const_iterator it = v.begin(); it != v.end(); ++it) {
-    vector<WordID>& letters = (*l)[*it];
-    if (letters.size()) continue;   // if e and f have the same word
-
-    const string& w = TD::Convert(*it);
-    
-    size_t cur = 0;
-    while (cur < w.size()) {
-      const size_t len = UTF8Len(w[cur]);
-      letters.push_back(TD::Convert(w.substr(cur, len)));
-      if (letset) letset->insert(letters.back());
-      cur += len;
-    }
-  }
-}
-
-void Debug(const AlignedSentencePair& asp) {
-  cerr << TD::GetString(asp.src) << endl << TD::GetString(asp.trg) << endl;
-  Array2D<bool> a(asp.src.size(), asp.trg.size());
-  for (unsigned j = 0; j < asp.trg.size(); ++j) {
-    assert(asp.a[j].src_index <= asp.src.size());
-    if (asp.a[j].src_index) a(asp.a[j].src_index - 1, j) = true;
-  }
-  cerr << a << endl;
-}
-
-void AddSample(AlignedSentencePair* asp) {
-  for (unsigned j = 0; j < asp->trg.size(); ++j)
-    asp->posterior(asp->a[j].src_index, j)++;
-}
-
-void WriteAlignments(const AlignedSentencePair& asp) {
-  bool first = true;
-  for (unsigned j = 0; j < asp.trg.size(); ++j) {
-    int src_index = -1;
-    int mc = -1;
-    for (unsigned i = 0; i <= asp.src.size(); ++i) {
-      if (asp.posterior(i, j) > mc) {
-        mc = asp.posterior(i, j);
-        src_index = i;
-      }
-    }
-
-    if (src_index) {
-      if (first) first = false; else cout << ' ';
-      cout << (src_index - 1) << '-' << j;
-    }
-  }
-  cout << endl;
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-
-  if (conf.count("random_seed"))
-    prng = new MT19937(conf["random_seed"].as<uint32_t>());
-  else
-    prng = new MT19937;
-
-  vector<vector<int> > corpuse, corpusf;
-  set<int> vocabe, vocabf;
-  corpus::ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe);
-  cerr << "f-Corpus size: " << corpusf.size() << " sentences\n";
-  cerr << "f-Vocabulary size: " << vocabf.size() << " types\n";
-  cerr << "f-Corpus size: " << corpuse.size() << " sentences\n";
-  cerr << "f-Vocabulary size: " << vocabe.size() << " types\n";
-  assert(corpusf.size() == corpuse.size());
-
-  vector<AlignedSentencePair> corpus(corpuse.size());
-  for (unsigned i = 0; i < corpuse.size(); ++i) {
-    corpus[i].src.swap(corpusf[i]);
-    corpus[i].trg.swap(corpuse[i]);
-    corpus[i].posterior.resize(corpus[i].src.size() + 1, corpus[i].trg.size());
-  }
-  corpusf.clear(); corpuse.clear();
-
-  vocabf.insert(TD::Convert("NULL"));
-  vector<vector<WordID> > letters(TD::NumWords());
-  set<WordID> letset;
-  ExtractLetters(vocabe, &letters, &letset);
-  ExtractLetters(vocabf, &letters, NULL);
-  letters[TD::Convert("NULL")].clear();
-
-  //Aligner<PYPLexicalTranslation> aligner(letters, vocabe.size(), letset.size(), conf, &corpus);
-  Aligner<HPYPLexicalTranslation> aligner(letters, vocabe.size(), letset.size(), conf, &corpus);
-  aligner.InitializeRandom();
-
-  const unsigned samples = conf["samples"].as<unsigned>();
-  for (int i = 0; i < samples; ++i) {
-    for (int j = 65; j < 67; ++j) Debug(corpus[j]);
-    if (i % 10 == 9) {
-      aligner.ResampleHyperparameters();
-      cerr << "LLH = " << aligner.Likelihood() << "    \t(Amodel=" << aligner.paj_model.Likelihood()
-           << " TModel=" << aligner.model.Likelihood() << ") contexts=" << aligner.model.UniqueConditioningContexts() << endl;
-    }
-    aligner.ResampleCorpus();
-    if (i > (samples / 5) && (i % 6 == 5)) for (int j = 0; j < corpus.size(); ++j) AddSample(&corpus[j]);
-  }
-  for (unsigned i = 0; i < corpus.size(); ++i)
-    WriteAlignments(corpus[i]);
-  aligner.model.Summary();
-
-  return 0;
-}
diff --git a/gi/pf/align-tl.cc b/gi/pf/align-tl.cc
deleted file mode 100644
index f6608f1d..00000000
--- a/gi/pf/align-tl.cc
+++ /dev/null
@@ -1,339 +0,0 @@
-#include <iostream>
-#include <tr1/memory>
-#include <queue>
-
-#include <boost/multi_array.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "backward.h"
-#include "array2d.h"
-#include "base_distributions.h"
-#include "monotonic_pseg.h"
-#include "conditional_pseg.h"
-#include "trule.h"
-#include "tdict.h"
-#include "stringlib.h"
-#include "filelib.h"
-#include "dict.h"
-#include "sampler.h"
-#include "mfcr.h"
-#include "corpus.h"
-#include "ngram_base.h"
-#include "transliterations.h"
-
-using namespace std;
-using namespace tr1;
-namespace po = boost::program_options;
-
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
-  po::options_description opts("Configuration options");
-  opts.add_options()
-        ("samples,s",po::value<unsigned>()->default_value(1000),"Number of samples")
-        ("input,i",po::value<string>(),"Read parallel data from")
-        ("s2t", po::value<string>(), "character level source-to-target prior transliteration probabilities")
-        ("t2s", po::value<string>(), "character level target-to-source prior transliteration probabilities")
-        ("max_src_chunk", po::value<unsigned>()->default_value(4), "Maximum size of translitered chunk in source")
-        ("max_trg_chunk", po::value<unsigned>()->default_value(4), "Maximum size of translitered chunk in target")
-        ("expected_src_to_trg_ratio", po::value<double>()->default_value(1.0), "If a word is transliterated, what is the expected length ratio from source to target?")
-        ("random_seed,S",po::value<uint32_t>(), "Random seed");
-  po::options_description clo("Command line options");
-  clo.add_options()
-        ("config", po::value<string>(), "Configuration file")
-        ("help,h", "Print this help message and exit");
-  po::options_description dconfig_options, dcmdline_options;
-  dconfig_options.add(opts);
-  dcmdline_options.add(opts).add(clo);
-  
-  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
-  if (conf->count("config")) {
-    ifstream config((*conf)["config"].as<string>().c_str());
-    po::store(po::parse_config_file(config, dconfig_options), *conf);
-  }
-  po::notify(*conf);
-
-  if (conf->count("help") || (conf->count("input") == 0)) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-boost::shared_ptr<MT19937> prng;
-
-struct LexicalAlignment {
-  unsigned char src_index;
-  bool is_transliteration;
-  vector<pair<short, short> > derivation;
-};
-
-struct AlignedSentencePair {
-  vector<WordID> src;
-  vector<WordID> trg;
-  vector<LexicalAlignment> a;
-  Array2D<short> posterior;
-};
-
-struct HierarchicalWordBase {
-  explicit HierarchicalWordBase(const unsigned vocab_e_size) :
-      base(prob_t::One()), r(1,1,1,1,0.66,50.0), u0(-log(vocab_e_size)), l(1,prob_t::One()), v(1, prob_t::Zero()) {}
-
-  void ResampleHyperparameters(MT19937* rng) {
-    r.resample_hyperparameters(rng);
-  }
-
-  inline double logp0(const vector<WordID>& s) const {
-    return Md::log_poisson(s.size(), 7.5) + s.size() * u0;
-  }
-
-  // return p0 of rule.e_
-  prob_t operator()(const TRule& rule) const {
-    v[0].logeq(logp0(rule.e_));
-    return r.prob(rule.e_, v.begin(), l.begin());
-  }
-
-  void Increment(const TRule& rule) {
-    v[0].logeq(logp0(rule.e_));
-    if (r.increment(rule.e_, v.begin(), l.begin(), &*prng).count) {
-      base *= v[0] * l[0];
-    }
-  }
-
-  void Decrement(const TRule& rule) {
-    if (r.decrement(rule.e_, &*prng).count) {
-      base /= prob_t(exp(logp0(rule.e_)));
-    }
-  }
-
-  prob_t Likelihood() const {
-    prob_t p; p.logeq(r.log_crp_prob());
-    p *= base;
-    return p;
-  }
-
-  void Summary() const {
-    cerr << "NUMBER OF CUSTOMERS: " << r.num_customers() << "  (d=" << r.discount() << ",s=" << r.strength() << ')' << endl;
-    for (MFCR<1,vector<WordID> >::const_iterator it = r.begin(); it != r.end(); ++it)
-      cerr << "   " << it->second.total_dish_count_ << " (on " << it->second.table_counts_.size() << " tables) " << TD::GetString(it->first) << endl;
-  }
-
-  prob_t base;
-  MFCR<1,vector<WordID> > r;
-  const double u0;
-  const vector<prob_t> l;
-  mutable vector<prob_t> v;
-};
-
-struct BasicLexicalAlignment {
-  explicit BasicLexicalAlignment(const vector<vector<WordID> >& lets,
-                                 const unsigned words_e,
-                                 const unsigned letters_e,
-                                 vector<AlignedSentencePair>* corp) :
-      letters(lets),
-      corpus(*corp),
-      //up0(words_e),
-      //up0("en.chars.1gram", letters_e),
-      //up0("en.words.1gram"),
-      up0(letters_e),
-      //up0("en.chars.2gram"),
-      tmodel(up0) {
-  }
-
-  void InstantiateRule(const WordID src,
-                       const WordID trg,
-                       TRule* rule) const {
-    static const WordID kX = TD::Convert("X") * -1;
-    rule->lhs_ = kX;
-    rule->e_ = letters[trg];
-    rule->f_ = letters[src];
-  }
-
-  void InitializeRandom() {
-    const WordID kNULL = TD::Convert("NULL");
-    cerr << "Initializing with random alignments ...\n";
-    for (unsigned i = 0; i < corpus.size(); ++i) {
-      AlignedSentencePair& asp = corpus[i];
-      asp.a.resize(asp.trg.size());
-      for (unsigned j = 0; j < asp.trg.size(); ++j) {
-        const unsigned char a_j = prng->next() * (1 + asp.src.size());
-        const WordID f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
-        TRule r;
-        InstantiateRule(f_a_j, asp.trg[j], &r);
-        asp.a[j].is_transliteration = false;
-        asp.a[j].src_index = a_j;
-        if (tmodel.IncrementRule(r, &*prng))
-          up0.Increment(r);
-      }
-    }
-    cerr << "  LLH = " << Likelihood() << endl;
-  }
-
-  prob_t Likelihood() const {
-    prob_t p = tmodel.Likelihood();
-    p *= up0.Likelihood();
-    return p;
-  }
-
-  void ResampleHyperparemeters() {
-    tmodel.ResampleHyperparameters(&*prng);
-    up0.ResampleHyperparameters(&*prng);
-    cerr << "  (base d=" << up0.r.discount() << ",s=" << up0.r.strength() << ")\n";
-  }
-
-  void ResampleCorpus();
-
-  const vector<vector<WordID> >& letters; // spelling dictionary
-  vector<AlignedSentencePair>& corpus;
-  //PhraseConditionalUninformativeBase up0;
-  //PhraseConditionalUninformativeUnigramBase up0;
-  //UnigramWordBase up0;
-  //HierarchicalUnigramBase up0;
-  HierarchicalWordBase up0;
-  //CompletelyUniformBase up0;
-  //FixedNgramBase up0;
-  //ConditionalTranslationModel<PhraseConditionalUninformativeBase> tmodel;
-  //ConditionalTranslationModel<PhraseConditionalUninformativeUnigramBase> tmodel;
-  //ConditionalTranslationModel<UnigramWordBase> tmodel;
-  //ConditionalTranslationModel<HierarchicalUnigramBase> tmodel;
-  MConditionalTranslationModel<HierarchicalWordBase> tmodel;
-  //ConditionalTranslationModel<FixedNgramBase> tmodel;
-  //ConditionalTranslationModel<CompletelyUniformBase> tmodel;
-};
-
-void BasicLexicalAlignment::ResampleCorpus() {
-  static const WordID kNULL = TD::Convert("NULL");
-  for (unsigned i = 0; i < corpus.size(); ++i) {
-    AlignedSentencePair& asp = corpus[i];
-    SampleSet<prob_t> ss; ss.resize(asp.src.size() + 1);
-    for (unsigned j = 0; j < asp.trg.size(); ++j) {
-      TRule r;
-      unsigned char& a_j = asp.a[j].src_index;
-      WordID f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
-      InstantiateRule(f_a_j, asp.trg[j], &r);
-      if (tmodel.DecrementRule(r, &*prng))
-        up0.Decrement(r);
-
-      for (unsigned prop_a_j = 0; prop_a_j <= asp.src.size(); ++prop_a_j) {
-        const WordID prop_f = (prop_a_j ? asp.src[prop_a_j - 1] : kNULL);
-        InstantiateRule(prop_f, asp.trg[j], &r);
-        ss[prop_a_j] = tmodel.RuleProbability(r);
-      }
-      a_j = prng->SelectSample(ss);
-      f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
-      InstantiateRule(f_a_j, asp.trg[j], &r);
-      if (tmodel.IncrementRule(r, &*prng))
-        up0.Increment(r);
-    }
-  }
-  cerr << "  LLH = " << Likelihood() << endl;
-}
-
-void ExtractLetters(const set<WordID>& v, vector<vector<WordID> >* l, set<WordID>* letset = NULL) {
-  for (set<WordID>::const_iterator it = v.begin(); it != v.end(); ++it) {
-    vector<WordID>& letters = (*l)[*it];
-    if (letters.size()) continue;   // if e and f have the same word
-
-    const string& w = TD::Convert(*it);
-    
-    size_t cur = 0;
-    while (cur < w.size()) {
-      const size_t len = UTF8Len(w[cur]);
-      letters.push_back(TD::Convert(w.substr(cur, len)));
-      if (letset) letset->insert(letters.back());
-      cur += len;
-    }
-  }
-}
-
-void Debug(const AlignedSentencePair& asp) {
-  cerr << TD::GetString(asp.src) << endl << TD::GetString(asp.trg) << endl;
-  Array2D<bool> a(asp.src.size(), asp.trg.size());
-  for (unsigned j = 0; j < asp.trg.size(); ++j)
-    if (asp.a[j].src_index) a(asp.a[j].src_index - 1, j) = true;
-  cerr << a << endl;
-}
-
-void AddSample(AlignedSentencePair* asp) {
-  for (unsigned j = 0; j < asp->trg.size(); ++j)
-    asp->posterior(asp->a[j].src_index, j)++;
-}
-
-void WriteAlignments(const AlignedSentencePair& asp) {
-  bool first = true;
-  for (unsigned j = 0; j < asp.trg.size(); ++j) {
-    int src_index = -1;
-    int mc = -1;
-    for (unsigned i = 0; i <= asp.src.size(); ++i) {
-      if (asp.posterior(i, j) > mc) {
-        mc = asp.posterior(i, j);
-        src_index = i;
-      }
-    }
-
-    if (src_index) {
-      if (first) first = false; else cout << ' ';
-      cout << (src_index - 1) << '-' << j;
-    }
-  }
-  cout << endl;
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-
-  if (conf.count("random_seed"))
-    prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
-  else
-    prng.reset(new MT19937);
-//  MT19937& rng = *prng;
-
-  vector<vector<int> > corpuse, corpusf;
-  set<int> vocabe, vocabf;
-  corpus::ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe);
-  cerr << "f-Corpus size: " << corpusf.size() << " sentences\n";
-  cerr << "f-Vocabulary size: " << vocabf.size() << " types\n";
-  cerr << "f-Corpus size: " << corpuse.size() << " sentences\n";
-  cerr << "f-Vocabulary size: " << vocabe.size() << " types\n";
-  assert(corpusf.size() == corpuse.size());
-
-  vector<AlignedSentencePair> corpus(corpuse.size());
-  for (unsigned i = 0; i < corpuse.size(); ++i) {
-    corpus[i].src.swap(corpusf[i]);
-    corpus[i].trg.swap(corpuse[i]);
-    corpus[i].posterior.resize(corpus[i].src.size() + 1, corpus[i].trg.size());
-  }
-  corpusf.clear(); corpuse.clear();
-
-  vocabf.insert(TD::Convert("NULL"));
-  vector<vector<WordID> > letters(TD::NumWords() + 1);
-  set<WordID> letset;
-  ExtractLetters(vocabe, &letters, &letset);
-  ExtractLetters(vocabf, &letters, NULL);
-  letters[TD::Convert("NULL")].clear();
-
-  // TODO configure this
-  const int max_src_chunk = conf["max_src_chunk"].as<unsigned>();
-  const int max_trg_chunk = conf["max_trg_chunk"].as<unsigned>();
-  const double s2t_rat = conf["expected_src_to_trg_ratio"].as<double>();
-  const BackwardEstimator be(conf["s2t"].as<string>(), conf["t2s"].as<string>());
-  Transliterations tl(max_src_chunk, max_trg_chunk, s2t_rat, be); 
-
-  cerr << "Initializing transliteration graph structures ...\n";
-  for (int i = 0; i < corpus.size(); ++i) {
-    const vector<int>& src = corpus[i].src;
-    const vector<int>& trg = corpus[i].trg;
-    for (int j = 0; j < src.size(); ++j) {
-      const vector<int>& src_let = letters[src[j]];
-      for (int k = 0; k < trg.size(); ++k) {
-        const vector<int>& trg_let = letters[trg[k]];
-        tl.Initialize(src[j], src_let, trg[k], trg_let);
-        //if (src_let.size() < min_trans_src)
-        //  tl.Forbid(src[j], src_let, trg[k], trg_let);
-      }
-    }
-  }
-  cerr << endl;
-  tl.GraphSummary();
-
-  return 0;
-}
diff --git a/gi/pf/backward.cc b/gi/pf/backward.cc
deleted file mode 100644
index b92629fd..00000000
--- a/gi/pf/backward.cc
+++ /dev/null
@@ -1,89 +0,0 @@
-#include "backward.h"
-
-#include <queue>
-#include <utility>
-
-#include "array2d.h"
-#include "reachability.h"
-#include "base_distributions.h"
-
-using namespace std;
-
-BackwardEstimator::BackwardEstimator(const string& s2t,
-                    const string& t2s) : m1(new Model1(s2t)), m1inv(new Model1(t2s)) {}
-
-BackwardEstimator::~BackwardEstimator() {
-  delete m1; m1 = NULL;
-  delete m1inv; m1inv = NULL;
-}
-
-float BackwardEstimator::ComputeBackwardProb(const std::vector<WordID>& src,
-                                             const std::vector<WordID>& trg,
-                                             unsigned src_covered,
-                                             unsigned trg_covered,
-                                             double s2t_ratio) const {
-  if (src_covered == src.size() || trg_covered == trg.size()) {
-    assert(src_covered == src.size());
-    assert(trg_covered == trg.size());
-    return 0;
-  }
-  static const WordID kNULL = TD::Convert("<eps>");
-  const prob_t uniform_alignment(1.0 / (src.size() - src_covered + 1));
-  // TODO factor in expected length ratio
-  prob_t e; e.logeq(Md::log_poisson(trg.size() - trg_covered, (src.size() - src_covered) * s2t_ratio)); // p(trg len remaining | src len remaining)
-  for (unsigned j = trg_covered; j < trg.size(); ++j) {
-    prob_t p = (*m1)(kNULL, trg[j]) + prob_t(1e-12);
-    for (unsigned i = src_covered; i < src.size(); ++i)
-      p += (*m1)(src[i], trg[j]);
-    if (p.is_0()) {
-      cerr << "ERROR: p(" << TD::Convert(trg[j]) << " | " << TD::GetString(src) << ") = 0!\n";
-      assert(!"failed");
-    }
-    p *= uniform_alignment;
-    e *= p;
-  }
-  // TODO factor in expected length ratio
-  const prob_t inv_uniform(1.0 / (trg.size() - trg_covered + 1.0));
-  prob_t inv;
-  inv.logeq(Md::log_poisson(src.size() - src_covered, (trg.size() - trg_covered) / s2t_ratio));
-  for (unsigned i = src_covered; i < src.size(); ++i) {
-    prob_t p = (*m1inv)(kNULL, src[i]) + prob_t(1e-12);
-    for (unsigned j = trg_covered; j < trg.size(); ++j)
-      p += (*m1inv)(trg[j], src[i]);
-    if (p.is_0()) {
-      cerr << "ERROR: p_inv(" << TD::Convert(src[i]) << " | " << TD::GetString(trg) << ") = 0!\n";
-      assert(!"failed");
-    }
-    p *= inv_uniform;
-    inv *= p;
-  }
-  return (log(e) + log(inv)) / 2;
-}
-
-void BackwardEstimator::InitializeGrid(const vector<WordID>& src,
-                      const vector<WordID>& trg,
-                      const Reachability& r,
-                      double s2t_ratio,
-                      float* grid) const {
-  queue<pair<int,int> > q;
-  q.push(make_pair(0,0));
-  Array2D<bool> done(src.size()+1, trg.size()+1, false);
-  //cerr << TD::GetString(src) << " ||| " << TD::GetString(trg) << endl;
-  while(!q.empty()) {
-    const pair<int,int> n = q.front();
-    q.pop();
-    if (done(n.first,n.second)) continue;
-    done(n.first,n.second) = true;
-
-    float lp = ComputeBackwardProb(src, trg, n.first, n.second, s2t_ratio);
-    if (n.first == 0 && n.second == 0) grid[0] = lp;
-    //cerr << "  " << n.first << "," << n.second << "\t" << lp << endl;
-
-    if (n.first == src.size() || n.second == trg.size()) continue;
-    const vector<pair<short,short> >& edges = r.valid_deltas[n.first][n.second];
-    for (int i = 0; i < edges.size(); ++i)
-      q.push(make_pair(n.first + edges[i].first, n.second + edges[i].second));
-  }
-  //static int cc = 0; ++cc; if (cc == 80) exit(1);
-}
-
diff --git a/gi/pf/backward.h b/gi/pf/backward.h
deleted file mode 100644
index e67eff0c..00000000
--- a/gi/pf/backward.h
+++ /dev/null
@@ -1,33 +0,0 @@
-#ifndef _BACKWARD_H_
-#define _BACKWARD_H_
-
-#include <vector>
-#include <string>
-#include "wordid.h"
-
-struct Reachability;
-struct Model1;
-
-struct BackwardEstimator {
-  BackwardEstimator(const std::string& s2t,
-                    const std::string& t2s);
-  ~BackwardEstimator();
-
-  void InitializeGrid(const std::vector<WordID>& src,
-                      const std::vector<WordID>& trg,
-                      const Reachability& r,
-                      double src2trg_ratio,
-                      float* grid) const;
-
- private:
-  float ComputeBackwardProb(const std::vector<WordID>& src,
-                            const std::vector<WordID>& trg,
-                            unsigned src_covered,
-                            unsigned trg_covered,
-                            double src2trg_ratio) const;
-
-  Model1* m1;
-  Model1* m1inv;
-};
-
-#endif
diff --git a/gi/pf/base_distributions.cc b/gi/pf/base_distributions.cc
deleted file mode 100644
index 57e0bbe1..00000000
--- a/gi/pf/base_distributions.cc
+++ /dev/null
@@ -1,241 +0,0 @@
-#include "base_distributions.h"
-
-#include <iostream>
-
-#include "filelib.h"
-
-using namespace std;
-
-TableLookupBase::TableLookupBase(const string& fname) {
-  cerr << "TableLookupBase reading from " << fname << " ..." << endl;
-  ReadFile rf(fname);
-  istream& in = *rf.stream();
-  string line;
-  unsigned lc = 0;
-  const WordID kDIV = TD::Convert("|||");
-  vector<WordID> tmp;
-  vector<int> le, lf;
-  TRule x;
-  x.lhs_ = -TD::Convert("X");
-  bool flag = false;
-  while(getline(in, line)) {
-    ++lc;
-    if (lc % 1000000 == 0) { cerr << " [" << lc << ']' << endl; flag = false; }
-    else if (lc % 25000 == 0) { cerr << '.' << flush; flag = true; }
-    tmp.clear();
-    TD::ConvertSentence(line, &tmp);
-    x.f_.clear();
-    x.e_.clear();
-    size_t pos = 0;
-    int cc = 0;
-    while(pos < tmp.size()) {
-      const WordID cur = tmp[pos++];
-      if (cur == kDIV) {
-        ++cc;
-      } else if (cc == 0) {
-        x.f_.push_back(cur);    
-      } else if (cc == 1) {
-        x.e_.push_back(cur);
-      } else if (cc == 2) {
-        table[x].logeq(atof(TD::Convert(cur).c_str()));
-        ++cc;
-      } else {
-        if (flag) cerr << endl;
-        cerr << "Bad format in " << lc << ": " << line << endl; abort();
-      }
-    }
-    if (cc != 3) {
-      if (flag) cerr << endl;
-      cerr << "Bad format in " << lc << ": " << line << endl; abort();
-    }
-  }
-  if (flag) cerr << endl;
-  cerr << " read " << lc << " entries\n";
-}
-
-prob_t PhraseConditionalUninformativeUnigramBase::p0(const vector<WordID>& vsrc,
-                                                     const vector<WordID>& vtrg,
-                                                     int start_src, int start_trg) const {
-  const int flen = vsrc.size() - start_src;
-  const int elen = vtrg.size() - start_trg;
-  prob_t p;
-  p.logeq(Md::log_poisson(elen, flen + 0.01));       // elen | flen          ~Pois(flen + 0.01)
-  //p.logeq(log_poisson(elen, 1));       // elen | flen          ~Pois(flen + 0.01)
-  for (int i = 0; i < elen; ++i)
-    p *= u(vtrg[i + start_trg]);                        // draw e_i             ~Uniform
-  return p;
-}
-
-prob_t PhraseConditionalUninformativeBase::p0(const vector<WordID>& vsrc,
-                                              const vector<WordID>& vtrg,
-                                              int start_src, int start_trg) const {
-  const int flen = vsrc.size() - start_src;
-  const int elen = vtrg.size() - start_trg;
-  prob_t p;
-  //p.logeq(log_poisson(elen, flen + 0.01));       // elen | flen          ~Pois(flen + 0.01)
-  p.logeq(Md::log_poisson(elen, 1));       // elen | flen          ~Pois(flen + 0.01)
-  for (int i = 0; i < elen; ++i)
-    p *= kUNIFORM_TARGET;                        // draw e_i             ~Uniform
-  return p;
-}
-
-void Model1::LoadModel1(const string& fname) {
-  cerr << "Loading Model 1 parameters from " << fname << " ..." << endl;
-  ReadFile rf(fname);
-  istream& in = *rf.stream();
-  string line;
-  unsigned lc = 0;
-  while(getline(in, line)) {
-    ++lc;
-    int cur = 0;
-    int start = 0;
-    while(cur < line.size() && line[cur] != ' ') { ++cur; }
-    assert(cur != line.size());
-    line[cur] = 0;
-    const WordID src = TD::Convert(&line[0]);
-    ++cur;
-    start = cur;
-    while(cur < line.size() && line[cur] != ' ') { ++cur; }
-    assert(cur != line.size());
-    line[cur] = 0;
-    WordID trg = TD::Convert(&line[start]);
-    const double logprob = strtod(&line[cur + 1], NULL);
-    if (src >= ttable.size()) ttable.resize(src + 1);
-    ttable[src][trg].logeq(logprob);
-  }
-  cerr << "  read " << lc << " parameters.\n";
-}
-
-prob_t PhraseConditionalBase::p0(const vector<WordID>& vsrc,
-                                 const vector<WordID>& vtrg,
-                                 int start_src, int start_trg) const {
-  const int flen = vsrc.size() - start_src;
-  const int elen = vtrg.size() - start_trg;
-  prob_t uniform_src_alignment; uniform_src_alignment.logeq(-log(flen + 1));
-  prob_t p;
-  p.logeq(Md::log_poisson(elen, flen + 0.01));       // elen | flen          ~Pois(flen + 0.01)
-  for (int i = 0; i < elen; ++i) {               // for each position i in e-RHS
-    const WordID trg = vtrg[i + start_trg];
-    prob_t tp = prob_t::Zero();
-    for (int j = -1; j < flen; ++j) {
-      const WordID src = j < 0 ? 0 : vsrc[j + start_src];
-      tp += kM1MIXTURE * model1(src, trg);
-      tp += kUNIFORM_MIXTURE * kUNIFORM_TARGET;
-    }
-    tp *= uniform_src_alignment;                 //     draw a_i         ~uniform
-    p *= tp;                                     //     draw e_i         ~Model1(f_a_i) / uniform
-  }
-  if (p.is_0()) {
-    cerr << "Zero! " << vsrc << "\nTRG=" << vtrg << endl;
-    abort();
-  }
-  return p;
-}
-
-prob_t PhraseJointBase::p0(const vector<WordID>& vsrc,
-                           const vector<WordID>& vtrg,
-                           int start_src, int start_trg) const {
-  const int flen = vsrc.size() - start_src;
-  const int elen = vtrg.size() - start_trg;
-  prob_t uniform_src_alignment; uniform_src_alignment.logeq(-log(flen + 1));
-  prob_t p;
-  p.logeq(Md::log_poisson(flen, 1.0));               // flen                 ~Pois(1)
-                                                 // elen | flen          ~Pois(flen + 0.01)
-  prob_t ptrglen; ptrglen.logeq(Md::log_poisson(elen, flen + 0.01));
-  p *= ptrglen;
-  p *= kUNIFORM_SOURCE.pow(flen);                // each f in F ~Uniform
-  for (int i = 0; i < elen; ++i) {               // for each position i in E
-    const WordID trg = vtrg[i + start_trg];
-    prob_t tp = prob_t::Zero();
-    for (int j = -1; j < flen; ++j) {
-      const WordID src = j < 0 ? 0 : vsrc[j + start_src];
-      tp += kM1MIXTURE * model1(src, trg);
-      tp += kUNIFORM_MIXTURE * kUNIFORM_TARGET;
-    }
-    tp *= uniform_src_alignment;                 //     draw a_i         ~uniform
-    p *= tp;                                     //     draw e_i         ~Model1(f_a_i) / uniform
-  }
-  if (p.is_0()) {
-    cerr << "Zero! " << vsrc << "\nTRG=" << vtrg << endl;
-    abort();
-  }
-  return p;
-}
-
-prob_t PhraseJointBase_BiDir::p0(const vector<WordID>& vsrc,
-                                 const vector<WordID>& vtrg,
-                                 int start_src, int start_trg) const {
-  const int flen = vsrc.size() - start_src;
-  const int elen = vtrg.size() - start_trg;
-  prob_t uniform_src_alignment; uniform_src_alignment.logeq(-log(flen + 1));
-  prob_t uniform_trg_alignment; uniform_trg_alignment.logeq(-log(elen + 1));
-
-  prob_t p1;
-  p1.logeq(Md::log_poisson(flen, 1.0));               // flen                 ~Pois(1)
-                                                 // elen | flen          ~Pois(flen + 0.01)
-  prob_t ptrglen; ptrglen.logeq(Md::log_poisson(elen, flen + 0.01));
-  p1 *= ptrglen;
-  p1 *= kUNIFORM_SOURCE.pow(flen);                // each f in F ~Uniform
-  for (int i = 0; i < elen; ++i) {               // for each position i in E
-    const WordID trg = vtrg[i + start_trg];
-    prob_t tp = prob_t::Zero();
-    for (int j = -1; j < flen; ++j) {
-      const WordID src = j < 0 ? 0 : vsrc[j + start_src];
-      tp += kM1MIXTURE * model1(src, trg);
-      tp += kUNIFORM_MIXTURE * kUNIFORM_TARGET;
-    }
-    tp *= uniform_src_alignment;                 //     draw a_i         ~uniform
-    p1 *= tp;                                     //     draw e_i         ~Model1(f_a_i) / uniform
-  }
-  if (p1.is_0()) {
-    cerr << "Zero! " << vsrc << "\nTRG=" << vtrg << endl;
-    abort();
-  }
-
-  prob_t p2;
-  p2.logeq(Md::log_poisson(elen, 1.0));               // elen                 ~Pois(1)
-                                                 // flen | elen          ~Pois(flen + 0.01)
-  prob_t psrclen; psrclen.logeq(Md::log_poisson(flen, elen + 0.01));
-  p2 *= psrclen;
-  p2 *= kUNIFORM_TARGET.pow(elen);                // each f in F ~Uniform
-  for (int i = 0; i < flen; ++i) {               // for each position i in E
-    const WordID src = vsrc[i + start_src];
-    prob_t tp = prob_t::Zero();
-    for (int j = -1; j < elen; ++j) {
-      const WordID trg = j < 0 ? 0 : vtrg[j + start_trg];
-      tp += kM1MIXTURE * invmodel1(trg, src);
-      tp += kUNIFORM_MIXTURE * kUNIFORM_SOURCE;
-    }
-    tp *= uniform_trg_alignment;                 //     draw a_i         ~uniform
-    p2 *= tp;                                     //     draw e_i         ~Model1(f_a_i) / uniform
-  }
-  if (p2.is_0()) {
-    cerr << "Zero! " << vsrc << "\nTRG=" << vtrg << endl;
-    abort();
-  }
-
-  static const prob_t kHALF(0.5);
-  return (p1 + p2) * kHALF;
-}
-
-JumpBase::JumpBase() : p(200) {
-  for (unsigned src_len = 1; src_len < 200; ++src_len) {
-    map<int, prob_t>& cpd = p[src_len];
-    int min_jump = 1 - src_len;
-    int max_jump = src_len;
-    prob_t z;
-    for (int j = min_jump; j <= max_jump; ++j) {
-      prob_t& cp = cpd[j];
-      if (j < 0)
-        cp.logeq(Md::log_poisson(1.5-j, 1));
-      else if (j > 0)
-        cp.logeq(Md::log_poisson(j, 1));
-      cp.poweq(0.2);
-      z += cp;
-    }
-    for (int j = min_jump; j <= max_jump; ++j) {
-      cpd[j] /= z;
-    }
-  }
-}
-
diff --git a/gi/pf/base_distributions.h b/gi/pf/base_distributions.h
deleted file mode 100644
index 41b513f8..00000000
--- a/gi/pf/base_distributions.h
+++ /dev/null
@@ -1,238 +0,0 @@
-#ifndef _BASE_MEASURES_H_
-#define _BASE_MEASURES_H_
-
-#include <vector>
-#include <map>
-#include <string>
-#include <cmath>
-#include <iostream>
-#include <cassert>
-
-#include "unigrams.h"
-#include "trule.h"
-#include "prob.h"
-#include "tdict.h"
-#include "sampler.h"
-#include "m.h"
-#include "os_phrase.h"
-
-struct Model1 {
-  explicit Model1(const std::string& fname) :
-      kNULL(TD::Convert("<eps>")),
-      kZERO() {
-    LoadModel1(fname);
-  }
-
-  void LoadModel1(const std::string& fname);
-
-  // returns prob 0 if src or trg is not found
-  const prob_t& operator()(WordID src, WordID trg) const {
-    if (src == 0) src = kNULL;
-    if (src < ttable.size()) {
-      const std::map<WordID, prob_t>& cpd = ttable[src];
-      const std::map<WordID, prob_t>::const_iterator it = cpd.find(trg);
-      if (it != cpd.end())
-        return it->second;
-    }
-    return kZERO;
-  }
-
-  const WordID kNULL;
-  const prob_t kZERO;
-  std::vector<std::map<WordID, prob_t> > ttable;
-};
-
-struct PoissonUniformUninformativeBase {
-  explicit PoissonUniformUninformativeBase(const unsigned ves) : kUNIFORM(1.0 / ves) {}
-  prob_t operator()(const TRule& r) const {
-    prob_t p; p.logeq(Md::log_poisson(r.e_.size(), 1.0));
-    prob_t q = kUNIFORM; q.poweq(r.e_.size());
-    p *= q;
-    return p;
-  }
-  void Summary() const {}
-  void ResampleHyperparameters(MT19937*) {}
-  void Increment(const TRule&) {}
-  void Decrement(const TRule&) {}
-  prob_t Likelihood() const { return prob_t::One(); }
-  const prob_t kUNIFORM;
-};
-
-struct CompletelyUniformBase {
-  explicit CompletelyUniformBase(const unsigned ves) : kUNIFORM(1.0 / ves) {}
-  prob_t operator()(const TRule&) const {
-    return kUNIFORM;
-  }
-  void Summary() const {}
-  void ResampleHyperparameters(MT19937*) {}
-  void Increment(const TRule&) {}
-  void Decrement(const TRule&) {}
-  prob_t Likelihood() const { return prob_t::One(); }
-  const prob_t kUNIFORM;
-};
-
-struct UnigramWordBase {
-  explicit UnigramWordBase(const std::string& fname) : un(fname) {}
-  prob_t operator()(const TRule& r) const {
-    return un(r.e_);
-  }
-  const UnigramWordModel un;
-};
-
-struct RuleHasher {
-  size_t operator()(const TRule& r) const {
-    return hash_value(r);
-  }
-};
-
-struct TableLookupBase {
-  TableLookupBase(const std::string& fname);
-
-  prob_t operator()(const TRule& rule) const {
-    const std::tr1::unordered_map<TRule,prob_t,RuleHasher>::const_iterator it = table.find(rule);
-    if (it == table.end()) {
-      std::cerr << rule << " not found\n";
-      abort();
-    }
-    return it->second;
-  }
-
-  void ResampleHyperparameters(MT19937*) {}
-  void Increment(const TRule&) {}
-  void Decrement(const TRule&) {}
-  prob_t Likelihood() const { return prob_t::One(); }
-  void Summary() const {}
-
-  std::tr1::unordered_map<TRule,prob_t,RuleHasher> table;
-};
-
-struct PhraseConditionalUninformativeBase {
-  explicit PhraseConditionalUninformativeBase(const unsigned vocab_e_size) :
-      kUNIFORM_TARGET(1.0 / vocab_e_size) {
-    assert(vocab_e_size > 0);
-  }
-
-  // return p0 of rule.e_ | rule.f_
-  prob_t operator()(const TRule& rule) const {
-    return p0(rule.f_, rule.e_, 0, 0);
-  }
-
-  prob_t p0(const std::vector<WordID>& vsrc, const std::vector<WordID>& vtrg, int start_src, int start_trg) const;
-
-  void Summary() const {}
-  void ResampleHyperparameters(MT19937*) {}
-  void Increment(const TRule&) {}
-  void Decrement(const TRule&) {}
-  prob_t Likelihood() const { return prob_t::One(); }
-  const prob_t kUNIFORM_TARGET;
-};
-
-struct PhraseConditionalUninformativeUnigramBase {
-  explicit PhraseConditionalUninformativeUnigramBase(const std::string& file, const unsigned vocab_e_size) : u(file, vocab_e_size) {}
-
-  // return p0 of rule.e_ | rule.f_
-  prob_t operator()(const TRule& rule) const {
-    return p0(rule.f_, rule.e_, 0, 0);
-  }
-
-  prob_t p0(const std::vector<WordID>& vsrc, const std::vector<WordID>& vtrg, int start_src, int start_trg) const;
-
-  const UnigramModel u;
-};
-
-struct PhraseConditionalBase {
-  explicit PhraseConditionalBase(const Model1& m1, const double m1mixture, const unsigned vocab_e_size) :
-      model1(m1),
-      kM1MIXTURE(m1mixture),
-      kUNIFORM_MIXTURE(1.0 - m1mixture),
-      kUNIFORM_TARGET(1.0 / vocab_e_size) {
-    assert(m1mixture >= 0.0 && m1mixture <= 1.0);
-    assert(vocab_e_size > 0);
-  }
-
-  // return p0 of rule.e_ | rule.f_
-  prob_t operator()(const TRule& rule) const {
-    return p0(rule.f_, rule.e_, 0, 0);
-  }
-
-  prob_t p0(const std::vector<WordID>& vsrc, const std::vector<WordID>& vtrg, int start_src, int start_trg) const;
-
-  const Model1& model1;
-  const prob_t kM1MIXTURE;  // Model 1 mixture component
-  const prob_t kUNIFORM_MIXTURE; // uniform mixture component
-  const prob_t kUNIFORM_TARGET;
-};
-
-struct PhraseJointBase {
-  explicit PhraseJointBase(const Model1& m1, const double m1mixture, const unsigned vocab_e_size, const unsigned vocab_f_size) :
-      model1(m1),
-      kM1MIXTURE(m1mixture),
-      kUNIFORM_MIXTURE(1.0 - m1mixture),
-      kUNIFORM_SOURCE(1.0 / vocab_f_size),
-      kUNIFORM_TARGET(1.0 / vocab_e_size) {
-    assert(m1mixture >= 0.0 && m1mixture <= 1.0);
-    assert(vocab_e_size > 0);
-  }
-
-  // return p0 of rule.e_ , rule.f_
-  prob_t operator()(const TRule& rule) const {
-    return p0(rule.f_, rule.e_, 0, 0);
-  }
-
-  prob_t p0(const std::vector<WordID>& vsrc, const std::vector<WordID>& vtrg, int start_src, int start_trg) const;
-
-  const Model1& model1;
-  const prob_t kM1MIXTURE;  // Model 1 mixture component
-  const prob_t kUNIFORM_MIXTURE; // uniform mixture component
-  const prob_t kUNIFORM_SOURCE;
-  const prob_t kUNIFORM_TARGET;
-};
-
-struct PhraseJointBase_BiDir {
-  explicit PhraseJointBase_BiDir(const Model1& m1,
-                                 const Model1& im1,
-                                 const double m1mixture,
-                                 const unsigned vocab_e_size,
-                                 const unsigned vocab_f_size) :
-      model1(m1),
-      invmodel1(im1),
-      kM1MIXTURE(m1mixture),
-      kUNIFORM_MIXTURE(1.0 - m1mixture),
-      kUNIFORM_SOURCE(1.0 / vocab_f_size),
-      kUNIFORM_TARGET(1.0 / vocab_e_size) {
-    assert(m1mixture >= 0.0 && m1mixture <= 1.0);
-    assert(vocab_e_size > 0);
-  }
-
-  // return p0 of rule.e_ , rule.f_
-  prob_t operator()(const TRule& rule) const {
-    return p0(rule.f_, rule.e_, 0, 0);
-  }
-
-  prob_t p0(const std::vector<WordID>& vsrc, const std::vector<WordID>& vtrg, int start_src, int start_trg) const;
-
-  const Model1& model1;
-  const Model1& invmodel1;
-  const prob_t kM1MIXTURE;  // Model 1 mixture component
-  const prob_t kUNIFORM_MIXTURE; // uniform mixture component
-  const prob_t kUNIFORM_SOURCE;
-  const prob_t kUNIFORM_TARGET;
-};
-
-// base distribution for jump size multinomials
-// basically p(0) = 0 and then, p(1) is max, and then
-// you drop as you move to the max jump distance
-struct JumpBase {
-  JumpBase();
-
-  const prob_t& operator()(int jump, unsigned src_len) const {
-    assert(jump != 0);
-    const std::map<int, prob_t>::const_iterator it = p[src_len].find(jump);
-    assert(it != p[src_len].end());
-    return it->second;
-  }
-  std::vector<std::map<int, prob_t> > p;
-};
-
-
-#endif
diff --git a/gi/pf/bayes_lattice_score.cc b/gi/pf/bayes_lattice_score.cc
deleted file mode 100644
index 70cb8dc2..00000000
--- a/gi/pf/bayes_lattice_score.cc
+++ /dev/null
@@ -1,309 +0,0 @@
-#include <iostream>
-#include <queue>
-
-#include <boost/functional.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "inside_outside.h"
-#include "hg.h"
-#include "hg_io.h"
-#include "bottom_up_parser.h"
-#include "fdict.h"
-#include "grammar.h"
-#include "m.h"
-#include "trule.h"
-#include "tdict.h"
-#include "filelib.h"
-#include "dict.h"
-#include "sampler.h"
-#include "ccrp.h"
-#include "ccrp_onetable.h"
-
-using namespace std;
-using namespace tr1;
-namespace po = boost::program_options;
-
-boost::shared_ptr<MT19937> prng;
-
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
-  po::options_description opts("Configuration options");
-  opts.add_options()
-        ("samples,s",po::value<unsigned>()->default_value(1000),"Number of samples")
-        ("input,i",po::value<string>(),"Read parallel data from")
-        ("random_seed,S",po::value<uint32_t>(), "Random seed");
-  po::options_description clo("Command line options");
-  clo.add_options()
-        ("config", po::value<string>(), "Configuration file")
-        ("help", "Print this help message and exit");
-  po::options_description dconfig_options, dcmdline_options;
-  dconfig_options.add(opts);
-  dcmdline_options.add(opts).add(clo);
-  
-  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
-  if (conf->count("config")) {
-    ifstream config((*conf)["config"].as<string>().c_str());
-    po::store(po::parse_config_file(config, dconfig_options), *conf);
-  }
-  po::notify(*conf);
-
-  if (conf->count("help") || (conf->count("input") == 0)) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-unsigned ReadCorpus(const string& filename,
-                    vector<Lattice>* e,
-                    set<WordID>* vocab_e) {
-  e->clear();
-  vocab_e->clear();
-  ReadFile rf(filename);
-  istream* in = rf.stream();
-  assert(*in);
-  string line;
-  unsigned toks = 0;
-  while(*in) {
-    getline(*in, line);
-    if (line.empty() && !*in) break;
-    e->push_back(Lattice());
-    Lattice& le = e->back();
-    LatticeTools::ConvertTextOrPLF(line, & le);
-    for (unsigned i = 0; i < le.size(); ++i)
-      for (unsigned j = 0; j < le[i].size(); ++j)
-        vocab_e->insert(le[i][j].label);
-    toks += le.size();
-  }
-  return toks;
-}
-
-struct BaseModel {
-  explicit BaseModel(unsigned tc) :
-      unif(1.0 / tc), p(prob_t::One()) {}
-  prob_t prob(const TRule& r) const {
-    return unif;
-  }
-  void increment(const TRule& r, MT19937* rng) {
-    p *= prob(r);
-  }
-  void decrement(const TRule& r, MT19937* rng) {
-    p /= prob(r);
-  }
-  prob_t Likelihood() const {
-    return p;
-  }
-  const prob_t unif;
-  prob_t p;
-};
-
-struct UnigramModel {
-  explicit UnigramModel(unsigned tc) : base(tc), crp(1,1,1,1), glue(1,1,1,1) {}
-  BaseModel base;
-  CCRP<TRule> crp;
-  CCRP<TRule> glue;
-
-  prob_t Prob(const TRule& r) const {
-    if (r.Arity() != 0) {
-      return glue.prob(r, prob_t(0.5));
-    }
-    return crp.prob(r, base.prob(r));
-  }
-
-  int Increment(const TRule& r, MT19937* rng) {
-    if (r.Arity() != 0) {
-      glue.increment(r, 0.5, rng);
-      return 0;
-    } else {
-      if (crp.increment(r, base.prob(r), rng)) {
-        base.increment(r, rng);
-        return 1;
-      }
-      return 0;
-    }
-  }
-
-  int Decrement(const TRule& r, MT19937* rng) {
-    if (r.Arity() != 0) {
-      glue.decrement(r, rng);
-      return 0;
-    } else {
-      if (crp.decrement(r, rng)) {
-        base.decrement(r, rng);
-        return -1;
-      }
-      return 0;
-    }
-  }
-
-  prob_t Likelihood() const {
-    prob_t p;
-    p.logeq(crp.log_crp_prob() + glue.log_crp_prob());
-    p *= base.Likelihood();
-    return p;
-  }
-
-  void ResampleHyperparameters(MT19937* rng) {
-    crp.resample_hyperparameters(rng);
-    glue.resample_hyperparameters(rng);
-    cerr << " d=" << crp.discount() << ", s=" << crp.strength() << "\t STOP d=" << glue.discount() << ", s=" << glue.strength() << endl;
-  }
-};
-
-UnigramModel* plm;
-
-void SampleDerivation(const Hypergraph& hg, MT19937* rng, vector<unsigned>* sampled_deriv) {
-  vector<prob_t> node_probs;
-  Inside<prob_t, EdgeProb>(hg, &node_probs);
-  queue<unsigned> q;
-  q.push(hg.nodes_.size() - 2);
-  while(!q.empty()) {
-    unsigned cur_node_id = q.front();
-//    cerr << "NODE=" << cur_node_id << endl;
-    q.pop();
-    const Hypergraph::Node& node = hg.nodes_[cur_node_id];
-    const unsigned num_in_edges = node.in_edges_.size();
-    unsigned sampled_edge = 0;
-    if (num_in_edges == 1) {
-      sampled_edge = node.in_edges_[0];
-    } else {
-      //prob_t z;
-      assert(num_in_edges > 1);
-      SampleSet<prob_t> ss;
-      for (unsigned j = 0; j < num_in_edges; ++j) {
-        const Hypergraph::Edge& edge = hg.edges_[node.in_edges_[j]];
-        prob_t p = edge.edge_prob_;
-        for (unsigned k = 0; k < edge.tail_nodes_.size(); ++k)
-          p *= node_probs[edge.tail_nodes_[k]];
-        ss.add(p);
-//        cerr << log(ss[j]) << " ||| " << edge.rule_->AsString() << endl;
-        //z += p;
-      }
-//      for (unsigned j = 0; j < num_in_edges; ++j) {
-//        const Hypergraph::Edge& edge = hg.edges_[node.in_edges_[j]];
-//        cerr << exp(log(ss[j] / z)) << " ||| " << edge.rule_->AsString() << endl;
-//      }
-//      cerr << " --- \n";
-      sampled_edge = node.in_edges_[rng->SelectSample(ss)];
-    }
-    sampled_deriv->push_back(sampled_edge);
-    const Hypergraph::Edge& edge = hg.edges_[sampled_edge];
-    for (unsigned j = 0; j < edge.tail_nodes_.size(); ++j) {
-      q.push(edge.tail_nodes_[j]);
-    }
-  }
-//  for (unsigned i = 0; i < sampled_deriv->size(); ++i) {
-//    cerr << *hg.edges_[(*sampled_deriv)[i]].rule_ << endl;
-//  }
-}
-
-void IncrementDerivation(const Hypergraph& hg, const vector<unsigned>& d, UnigramModel* plm, MT19937* rng) {
-  for (unsigned i = 0; i < d.size(); ++i)
-    plm->Increment(*hg.edges_[d[i]].rule_, rng);
-}
-
-void DecrementDerivation(const Hypergraph& hg, const vector<unsigned>& d, UnigramModel* plm, MT19937* rng) {
-  for (unsigned i = 0; i < d.size(); ++i)
-    plm->Decrement(*hg.edges_[d[i]].rule_, rng);
-}
-
-prob_t TotalProb(const Hypergraph& hg) {
-  return Inside<prob_t, EdgeProb>(hg);
-}
-
-void IncrementLatticePath(const Hypergraph& hg, const vector<unsigned>& d, Lattice* pl) {
-  Lattice& lat = *pl;
-  for (int i = 0; i < d.size(); ++i) {
-    const Hypergraph::Edge& edge = hg.edges_[d[i]];
-    if (edge.rule_->Arity() != 0) continue;
-    WordID sym = edge.rule_->e_[0];
-    vector<LatticeArc>& las = lat[edge.i_];
-    int dist = edge.j_ - edge.i_;
-    assert(dist > 0);
-    for (int j = 0; j < las.size(); ++j) {
-      if (las[j].dist2next == dist &&
-          las[j].label == sym) {
-        las[j].cost += 1;
-      }
-    }
-  }
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-
-  InitCommandLine(argc, argv, &conf);
-  vector<GrammarPtr> grammars(2);
-  grammars[0].reset(new GlueGrammar("S","X"));
-  const unsigned samples = conf["samples"].as<unsigned>();
-
-  if (conf.count("random_seed"))
-    prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
-  else
-    prng.reset(new MT19937);
-  MT19937& rng = *prng;
-  vector<Lattice> corpuse;
-  set<WordID> vocabe;
-  cerr << "Reading corpus...\n";
-  const unsigned toks = ReadCorpus(conf["input"].as<string>(), &corpuse, &vocabe);
-  cerr << "E-corpus size: " << corpuse.size() << " lattices\t (" << vocabe.size() << " word types)\n";
-  UnigramModel lm(vocabe.size());
-  vector<Hypergraph> hgs(corpuse.size());
-  vector<vector<unsigned> > derivs(corpuse.size());
-  for (int i = 0; i < corpuse.size(); ++i) {
-    grammars[1].reset(new PassThroughGrammar(corpuse[i], "X"));
-    ExhaustiveBottomUpParser parser("S", grammars);
-    bool res = parser.Parse(corpuse[i], &hgs[i]);  // exhaustive parse
-    assert(res);
-  }
-
-  double csamples = 0;
-  for (int SS=0; SS < samples; ++SS) {
-    const bool is_last = ((samples - 1) == SS);
-    prob_t dlh = prob_t::One();
-    bool record_sample = (SS > (samples * 1 / 3) && (SS % 5 == 3));
-    if (record_sample) csamples++;
-    for (int ci = 0; ci < corpuse.size(); ++ci) {
-      Lattice& lat = corpuse[ci];
-      Hypergraph& hg = hgs[ci];
-      vector<unsigned>& d = derivs[ci];
-      if (!is_last) DecrementDerivation(hg, d, &lm, &rng);
-      for (unsigned i = 0; i < hg.edges_.size(); ++i) {
-        TRule& r = *hg.edges_[i].rule_;
-        if (r.Arity() != 0)
-          hg.edges_[i].edge_prob_ = prob_t::One();
-        else
-          hg.edges_[i].edge_prob_ = lm.Prob(r);
-      }
-      if (!is_last) {
-        d.clear();
-        SampleDerivation(hg, &rng, &d);
-        IncrementDerivation(hg, derivs[ci], &lm, &rng);
-      } else {
-        prob_t p = TotalProb(hg);
-        dlh *= p;
-        cerr << " p(sentence) = " << log(p) << "\t" << log(dlh) << endl;
-      }
-      if (record_sample) IncrementLatticePath(hg, derivs[ci], &lat);
-    }
-    double llh = log(lm.Likelihood());
-    cerr << "LLH=" << llh << "\tENTROPY=" << (-llh / log(2) / toks) << "\tPPL=" << pow(2, -llh / log(2) / toks) << endl;
-    if (SS % 10 == 9) lm.ResampleHyperparameters(&rng);
-    if (is_last) {
-      double z = log(dlh);
-      cerr << "TOTAL_PROB=" << z << "\tENTROPY=" << (-z / log(2) / toks) << "\tPPL=" << pow(2, -z / log(2) / toks) << endl;
-    }
-  }
-  cerr << lm.crp << endl;
-  cerr << lm.glue << endl;
-  for (int i = 0; i < corpuse.size(); ++i) {
-    for (int j = 0; j < corpuse[i].size(); ++j)
-      for (int k = 0; k < corpuse[i][j].size(); ++k) {
-        corpuse[i][j][k].cost /= csamples;
-        corpuse[i][j][k].cost += 1e-3;
-        corpuse[i][j][k].cost = log(corpuse[i][j][k].cost);
-      }
-    cout << HypergraphIO::AsPLF(corpuse[i]) << endl;
-  }
-  return 0;
-}
-
diff --git a/gi/pf/brat.cc b/gi/pf/brat.cc
deleted file mode 100644
index 832f22cf..00000000
--- a/gi/pf/brat.cc
+++ /dev/null
@@ -1,543 +0,0 @@
-#include <iostream>
-#include <tr1/memory>
-#include <queue>
-
-#include <boost/functional.hpp>
-#include <boost/multi_array.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "viterbi.h"
-#include "hg.h"
-#include "trule.h"
-#include "tdict.h"
-#include "filelib.h"
-#include "dict.h"
-#include "sampler.h"
-#include "ccrp_nt.h"
-#include "cfg_wfst_composer.h"
-
-using namespace std;
-using namespace tr1;
-namespace po = boost::program_options;
-
-static unsigned kMAX_SRC_PHRASE;
-static unsigned kMAX_TRG_PHRASE;
-struct FSTState;
-
-double log_poisson(unsigned x, const double& lambda) {
-  assert(lambda > 0.0);
-  return log(lambda) * x - lgamma(x + 1) - lambda;
-}
-
-struct ConditionalBase {
-  explicit ConditionalBase(const double m1mixture, const unsigned vocab_e_size, const string& model1fname) :
-      kM1MIXTURE(m1mixture),
-      kUNIFORM_MIXTURE(1.0 - m1mixture),
-      kUNIFORM_TARGET(1.0 / vocab_e_size),
-      kNULL(TD::Convert("<eps>")) {
-    assert(m1mixture >= 0.0 && m1mixture <= 1.0);
-    assert(vocab_e_size > 0);
-    LoadModel1(model1fname);
-  }
-
-  void LoadModel1(const string& fname) {
-    cerr << "Loading Model 1 parameters from " << fname << " ..." << endl;
-    ReadFile rf(fname);
-    istream& in = *rf.stream();
-    string line;
-    unsigned lc = 0;
-    while(getline(in, line)) {
-      ++lc;
-      int cur = 0;
-      int start = 0;
-      while(cur < line.size() && line[cur] != ' ') { ++cur; }
-      assert(cur != line.size());
-      line[cur] = 0;
-      const WordID src = TD::Convert(&line[0]);
-      ++cur;
-      start = cur;
-      while(cur < line.size() && line[cur] != ' ') { ++cur; }
-      assert(cur != line.size());
-      line[cur] = 0;
-      WordID trg = TD::Convert(&line[start]);
-      const double logprob = strtod(&line[cur + 1], NULL);
-      if (src >= ttable.size()) ttable.resize(src + 1);
-      ttable[src][trg].logeq(logprob);
-    }
-    cerr << "  read " << lc << " parameters.\n";
-  }
-
-  // return logp0 of rule.e_ | rule.f_
-  prob_t operator()(const TRule& rule) const {
-    const int flen = rule.f_.size();
-    const int elen = rule.e_.size();
-    prob_t uniform_src_alignment; uniform_src_alignment.logeq(-log(flen + 1));
-    prob_t p;
-    p.logeq(log_poisson(elen, flen + 0.01));       // elen | flen          ~Pois(flen + 0.01)
-    for (int i = 0; i < elen; ++i) {               // for each position i in e-RHS
-      const WordID trg = rule.e_[i];
-      prob_t tp = prob_t::Zero();
-      for (int j = -1; j < flen; ++j) {
-        const WordID src = j < 0 ? kNULL : rule.f_[j];
-        const map<WordID, prob_t>::const_iterator it = ttable[src].find(trg);
-        if (it != ttable[src].end()) {
-          tp += kM1MIXTURE * it->second;
-        }
-        tp += kUNIFORM_MIXTURE * kUNIFORM_TARGET;
-      }
-      tp *= uniform_src_alignment;                 //     draw a_i         ~uniform
-      p *= tp;                                     //     draw e_i         ~Model1(f_a_i) / uniform
-    }
-    return p;
-  }
-
-  const prob_t kM1MIXTURE;  // Model 1 mixture component
-  const prob_t kUNIFORM_MIXTURE; // uniform mixture component
-  const prob_t kUNIFORM_TARGET;
-  const WordID kNULL;
-  vector<map<WordID, prob_t> > ttable;
-};
-
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
-  po::options_description opts("Configuration options");
-  opts.add_options()
-        ("samples,s",po::value<unsigned>()->default_value(1000),"Number of samples")
-        ("input,i",po::value<string>(),"Read parallel data from")
-        ("max_src_phrase",po::value<unsigned>()->default_value(3),"Maximum length of source language phrases")
-        ("max_trg_phrase",po::value<unsigned>()->default_value(3),"Maximum length of target language phrases")
-        ("model1,m",po::value<string>(),"Model 1 parameters (used in base distribution)")
-        ("model1_interpolation_weight",po::value<double>()->default_value(0.95),"Mixing proportion of model 1 with uniform target distribution")
-        ("random_seed,S",po::value<uint32_t>(), "Random seed");
-  po::options_description clo("Command line options");
-  clo.add_options()
-        ("config", po::value<string>(), "Configuration file")
-        ("help,h", "Print this help message and exit");
-  po::options_description dconfig_options, dcmdline_options;
-  dconfig_options.add(opts);
-  dcmdline_options.add(opts).add(clo);
-  
-  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
-  if (conf->count("config")) {
-    ifstream config((*conf)["config"].as<string>().c_str());
-    po::store(po::parse_config_file(config, dconfig_options), *conf);
-  }
-  po::notify(*conf);
-
-  if (conf->count("help") || (conf->count("input") == 0)) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-void ReadParallelCorpus(const string& filename,
-                vector<vector<WordID> >* f,
-                vector<vector<int> >* e,
-                set<int>* vocab_f,
-                set<int>* vocab_e) {
-  f->clear();
-  e->clear();
-  vocab_f->clear();
-  vocab_e->clear();
-  istream* in;
-  if (filename == "-")
-    in = &cin;
-  else
-    in = new ifstream(filename.c_str());
-  assert(*in);
-  string line;
-  const WordID kDIV = TD::Convert("|||");
-  vector<WordID> tmp;
-  while(*in) {
-    getline(*in, line);
-    if (line.empty() && !*in) break;
-    e->push_back(vector<int>());
-    f->push_back(vector<int>());
-    vector<int>& le = e->back();
-    vector<int>& lf = f->back();
-    tmp.clear();
-    TD::ConvertSentence(line, &tmp);
-    bool isf = true;
-    for (unsigned i = 0; i < tmp.size(); ++i) {
-      const int cur = tmp[i];
-      if (isf) {
-        if (kDIV == cur) { isf = false; } else {
-          lf.push_back(cur);
-          vocab_f->insert(cur);
-        }
-      } else {
-        assert(cur != kDIV);
-        le.push_back(cur);
-        vocab_e->insert(cur);
-      }
-    }
-    assert(isf == false);
-  }
-  if (in != &cin) delete in;
-}
-
-struct UniphraseLM {
-  UniphraseLM(const vector<vector<int> >& corpus,
-              const set<int>& vocab,
-              const po::variables_map& conf) :
-    phrases_(1,1),
-    gen_(1,1),
-    corpus_(corpus),
-    uniform_word_(1.0 / vocab.size()),
-    gen_p0_(0.5),
-    p_end_(0.5),
-    use_poisson_(conf.count("poisson_length") > 0) {}
-
-  void ResampleHyperparameters(MT19937* rng) {
-    phrases_.resample_hyperparameters(rng);
-    gen_.resample_hyperparameters(rng);
-    cerr << " " << phrases_.alpha();
-  }
-
-  CCRP_NoTable<vector<int> > phrases_;
-  CCRP_NoTable<bool> gen_;
-  vector<vector<bool> > z_;   // z_[i] is there a phrase boundary after the ith word
-  const vector<vector<int> >& corpus_;
-  const double uniform_word_;
-  const double gen_p0_;
-  const double p_end_; // in base length distribution, p of the end of a phrase
-  const bool use_poisson_;
-};
-
-struct Reachability {
-  boost::multi_array<bool, 4> edges;  // edges[src_covered][trg_covered][x][trg_delta] is this edge worth exploring?
-  boost::multi_array<short, 2> max_src_delta; // msd[src_covered][trg_covered] -- the largest src delta that's valid
-
-  Reachability(int srclen, int trglen, int src_max_phrase_len, int trg_max_phrase_len) :
-      edges(boost::extents[srclen][trglen][src_max_phrase_len+1][trg_max_phrase_len+1]),
-      max_src_delta(boost::extents[srclen][trglen]) {
-    ComputeReachability(srclen, trglen, src_max_phrase_len, trg_max_phrase_len);
-  }
-
- private:
-  struct SState {
-    SState() : prev_src_covered(), prev_trg_covered() {}
-    SState(int i, int j) : prev_src_covered(i), prev_trg_covered(j) {}
-    int prev_src_covered;
-    int prev_trg_covered;
-  };
-
-  struct NState {
-    NState() : next_src_covered(), next_trg_covered() {}
-    NState(int i, int j) : next_src_covered(i), next_trg_covered(j) {}
-    int next_src_covered;
-    int next_trg_covered;
-  };
-
-  void ComputeReachability(int srclen, int trglen, int src_max_phrase_len, int trg_max_phrase_len) {
-    typedef boost::multi_array<vector<SState>, 2> array_type;
-    array_type a(boost::extents[srclen + 1][trglen + 1]);
-    a[0][0].push_back(SState());
-    for (int i = 0; i < srclen; ++i) {
-      for (int j = 0; j < trglen; ++j) {
-        if (a[i][j].size() == 0) continue;
-        const SState prev(i,j);
-        for (int k = 1; k <= src_max_phrase_len; ++k) {
-          if ((i + k) > srclen) continue;
-          for (int l = 1; l <= trg_max_phrase_len; ++l) {
-            if ((j + l) > trglen) continue;
-            a[i + k][j + l].push_back(prev);
-          }
-        }
-      }
-    }
-    a[0][0].clear();
-    cerr << "Final cell contains " << a[srclen][trglen].size() << " back pointers\n";
-    assert(a[srclen][trglen].size() > 0);
-
-    typedef boost::multi_array<bool, 2> rarray_type;
-    rarray_type r(boost::extents[srclen + 1][trglen + 1]);
-//    typedef boost::multi_array<vector<NState>, 2> narray_type;
-//    narray_type b(boost::extents[srclen + 1][trglen + 1]);
-    r[srclen][trglen] = true;
-    for (int i = srclen; i >= 0; --i) {
-      for (int j = trglen; j >= 0; --j) {
-        vector<SState>& prevs = a[i][j];
-        if (!r[i][j]) { prevs.clear(); }
-//        const NState nstate(i,j);
-        for (int k = 0; k < prevs.size(); ++k) {
-          r[prevs[k].prev_src_covered][prevs[k].prev_trg_covered] = true;
-          int src_delta = i - prevs[k].prev_src_covered;
-          edges[prevs[k].prev_src_covered][prevs[k].prev_trg_covered][src_delta][j - prevs[k].prev_trg_covered] = true;
-          short &msd = max_src_delta[prevs[k].prev_src_covered][prevs[k].prev_trg_covered];
-          if (src_delta > msd) msd = src_delta;
-//          b[prevs[k].prev_src_covered][prevs[k].prev_trg_covered].push_back(nstate);
-        }
-      }
-    }
-    assert(!edges[0][0][1][0]);
-    assert(!edges[0][0][0][1]);
-    assert(!edges[0][0][0][0]);
-    cerr << "  MAX SRC DELTA[0][0] = " << max_src_delta[0][0] << endl;
-    assert(max_src_delta[0][0] > 0);
-    //cerr << "First cell contains " << b[0][0].size() << " forward pointers\n";
-    //for (int i = 0; i < b[0][0].size(); ++i) {
-    //  cerr << "  -> (" << b[0][0][i].next_src_covered << "," << b[0][0][i].next_trg_covered << ")\n";
-    //}
-  }
-};
-
-ostream& operator<<(ostream& os, const FSTState& q);
-struct FSTState {
-  explicit FSTState(int src_size) :
-      trg_covered_(),
-      src_covered_(),
-      src_coverage_(src_size) {}
-
-  FSTState(short trg_covered, short src_covered, const vector<bool>& src_coverage, const vector<short>& src_prefix) :
-      trg_covered_(trg_covered),
-      src_covered_(src_covered),
-      src_coverage_(src_coverage),
-      src_prefix_(src_prefix) {
-    if (src_coverage_.size() == src_covered) {
-      assert(src_prefix.size() == 0);
-    }
-  }
-
-  // if we extend by the word at src_position, what are
-  // the next states that are reachable and lie on a valid
-  // path to the final state?
-  vector<FSTState> Extensions(int src_position, int src_len, int trg_len, const Reachability& r) const {
-    assert(src_position < src_coverage_.size());
-    if (src_coverage_[src_position]) {
-      cerr << "Trying to extend " << *this << " with position " << src_position << endl;
-      abort();
-    }
-    vector<bool> ncvg = src_coverage_;
-    ncvg[src_position] = true;
-
-    vector<FSTState> res;
-    const int trg_remaining = trg_len - trg_covered_;
-    if (trg_remaining <= 0) {
-      cerr << "Target appears to have been covered: " << *this << " (trg_len=" << trg_len << ",trg_covered=" << trg_covered_ << ")" << endl;
-      abort();
-    }
-    const int src_remaining = src_len - src_covered_;
-    if (src_remaining <= 0) {
-      cerr << "Source appears to have been covered: " << *this << endl;
-      abort();
-    }
-
-    for (int tc = 1; tc <= kMAX_TRG_PHRASE; ++tc) {
-      if (r.edges[src_covered_][trg_covered_][src_prefix_.size() + 1][tc]) {
-        int nc = src_prefix_.size() + 1 + src_covered_;
-        res.push_back(FSTState(trg_covered_ + tc, nc, ncvg, vector<short>()));
-      }
-    }
-
-    if ((src_prefix_.size() + 1) < r.max_src_delta[src_covered_][trg_covered_]) {
-      vector<short> nsp = src_prefix_;
-      nsp.push_back(src_position);
-      res.push_back(FSTState(trg_covered_, src_covered_, ncvg, nsp));
-    }
-
-    if (res.size() == 0) {
-      cerr << *this << " can't be extended!\n";
-      abort();
-    }
-    return res;
-  }
-
-  short trg_covered_, src_covered_;
-  vector<bool> src_coverage_;
-  vector<short> src_prefix_;
-};
-bool operator<(const FSTState& q, const FSTState& r) {
-  if (q.trg_covered_ != r.trg_covered_) return q.trg_covered_ < r.trg_covered_;
-  if (q.src_covered_!= r.src_covered_) return q.src_covered_ < r.src_covered_;
-  if (q.src_coverage_ != r.src_coverage_) return q.src_coverage_ < r.src_coverage_;
-  return q.src_prefix_ < r.src_prefix_;
-}
-
-ostream& operator<<(ostream& os, const FSTState& q) {
-  os << "[" << q.trg_covered_ << " : ";
-  for (int i = 0; i < q.src_coverage_.size(); ++i)
-    os << q.src_coverage_[i];
-  os << " : <";
-  for (int i = 0; i < q.src_prefix_.size(); ++i) {
-    if (i != 0) os << ' ';
-    os << q.src_prefix_[i];
-  }
-  return os << ">]";
-}
-
-struct MyModel {
-  MyModel(ConditionalBase& rcp0) : rp0(rcp0) {}
-  typedef unordered_map<vector<WordID>, CCRP_NoTable<TRule>, boost::hash<vector<WordID> > > SrcToRuleCRPMap;
-
-  void DecrementRule(const TRule& rule) {
-    SrcToRuleCRPMap::iterator it = rules.find(rule.f_);
-    assert(it != rules.end());
-    it->second.decrement(rule);
-    if (it->second.num_customers() == 0) rules.erase(it);
-  }
-
-  void IncrementRule(const TRule& rule) {
-    SrcToRuleCRPMap::iterator it = rules.find(rule.f_);
-    if (it == rules.end()) {
-      CCRP_NoTable<TRule> crp(1,1);
-      it = rules.insert(make_pair(rule.f_, crp)).first;
-    }
-    it->second.increment(rule);
-  }
-
-  // conditioned on rule.f_
-  prob_t RuleConditionalProbability(const TRule& rule) const {
-    const prob_t base = rp0(rule);
-    SrcToRuleCRPMap::const_iterator it = rules.find(rule.f_);
-    if (it == rules.end()) {
-      return base;
-    } else {
-      const double lp = it->second.logprob(rule, log(base));
-      prob_t q; q.logeq(lp);
-      return q;
-    }
-  }
-
-  const ConditionalBase& rp0;
-  SrcToRuleCRPMap rules;
-};
-
-struct MyFST : public WFST {
-  MyFST(const vector<WordID>& ssrc, const vector<WordID>& strg, MyModel* m) :
-      src(ssrc), trg(strg),
-      r(src.size(),trg.size(),kMAX_SRC_PHRASE, kMAX_TRG_PHRASE),
-      model(m) {
-    FSTState in(src.size());
-    cerr << " INIT: " << in << endl;
-    init = GetNode(in);
-    for (int i = 0; i < in.src_coverage_.size(); ++i) in.src_coverage_[i] = true;
-    in.src_covered_ = src.size();
-    in.trg_covered_ = trg.size();
-    cerr << "FINAL: " << in << endl;
-    final = GetNode(in);
-  }
-  virtual const WFSTNode* Final() const;
-  virtual const WFSTNode* Initial() const;
-
-  const WFSTNode* GetNode(const FSTState& q);
-  map<FSTState, boost::shared_ptr<WFSTNode> > m;
-  const vector<WordID>& src;
-  const vector<WordID>& trg;
-  Reachability r;
-  const WFSTNode* init;
-  const WFSTNode* final;
-  MyModel* model;
-};
-
-struct MyNode : public WFSTNode {
-  MyNode(const FSTState& q, MyFST* fst) : state(q), container(fst) {}
-  virtual vector<pair<const WFSTNode*, TRulePtr> > ExtendInput(unsigned srcindex) const;
-  const FSTState state;
-  mutable MyFST* container;
-};
-
-vector<pair<const WFSTNode*, TRulePtr> > MyNode::ExtendInput(unsigned srcindex) const {
-  cerr << "EXTEND " << state << " with " << srcindex << endl;
-  vector<FSTState> ext = state.Extensions(srcindex, container->src.size(), container->trg.size(), container->r);
-  vector<pair<const WFSTNode*,TRulePtr> > res(ext.size());
-  for (unsigned i = 0; i < ext.size(); ++i) {
-    res[i].first = container->GetNode(ext[i]);
-    if (ext[i].src_prefix_.size() == 0) {
-      const unsigned trg_from = state.trg_covered_;
-      const unsigned trg_to = ext[i].trg_covered_;
-      const unsigned prev_prfx_size = state.src_prefix_.size();
-      res[i].second.reset(new TRule);
-      res[i].second->lhs_ = -TD::Convert("X");
-      vector<WordID>& src = res[i].second->f_;
-      vector<WordID>& trg = res[i].second->e_;
-      src.resize(prev_prfx_size + 1);
-      for (unsigned j = 0; j < prev_prfx_size; ++j)
-        src[j] = container->src[state.src_prefix_[j]];
-      src[prev_prfx_size] = container->src[srcindex];
-      for (unsigned j = trg_from; j < trg_to; ++j)
-        trg.push_back(container->trg[j]);
-      res[i].second->scores_.set_value(FD::Convert("Proposal"), log(container->model->RuleConditionalProbability(*res[i].second)));
-    }
-  }
-  return res;
-}
-
-const WFSTNode* MyFST::GetNode(const FSTState& q) {
-  boost::shared_ptr<WFSTNode>& res = m[q];
-  if (!res) {
-    res.reset(new MyNode(q, this));
-  }
-  return &*res;
-}
-
-const WFSTNode* MyFST::Final() const {
-  return final;
-}
-
-const WFSTNode* MyFST::Initial() const {
-  return init;
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-  kMAX_TRG_PHRASE = conf["max_trg_phrase"].as<unsigned>();
-  kMAX_SRC_PHRASE = conf["max_src_phrase"].as<unsigned>();
-
-  if (!conf.count("model1")) {
-    cerr << argv[0] << "Please use --model1 to specify model 1 parameters\n";
-    return 1;
-  }
-  boost::shared_ptr<MT19937> prng;
-  if (conf.count("random_seed"))
-    prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
-  else
-    prng.reset(new MT19937);
-  MT19937& rng = *prng;
-
-  vector<vector<int> > corpuse, corpusf;
-  set<int> vocabe, vocabf;
-  ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe);
-  cerr << "f-Corpus size: " << corpusf.size() << " sentences\n";
-  cerr << "f-Vocabulary size: " << vocabf.size() << " types\n";
-  cerr << "f-Corpus size: " << corpuse.size() << " sentences\n";
-  cerr << "f-Vocabulary size: " << vocabe.size() << " types\n";
-  assert(corpusf.size() == corpuse.size());
-
-  ConditionalBase lp0(conf["model1_interpolation_weight"].as<double>(),
-                      vocabe.size(),
-                      conf["model1"].as<string>());
-  MyModel m(lp0);
-
-  TRule x("[X] ||| kAnwntR myN ||| at the convent ||| 0");
-  m.IncrementRule(x);
-  TRule y("[X] ||| nY dyN ||| gave ||| 0");
-  m.IncrementRule(y);
-
-
-  MyFST fst(corpusf[0], corpuse[0], &m);
-  ifstream in("./kimura.g");
-  assert(in);
-  CFG_WFSTComposer comp(fst);
-  Hypergraph hg;
-  bool succeed = comp.Compose(&in, &hg);
-  hg.PrintGraphviz();
-  if (succeed) { cerr << "SUCCESS.\n"; } else { cerr << "FAILURE REPORTED.\n"; }
-
-#if 0
-  ifstream in2("./amnabooks.g");
-  assert(in2);
-  MyFST fst2(corpusf[1], corpuse[1], &m);
-  CFG_WFSTComposer comp2(fst2);
-  Hypergraph hg2;
-  bool succeed2 = comp2.Compose(&in2, &hg2);
-  if (succeed2) { cerr << "SUCCESS.\n"; } else { cerr << "FAILURE REPORTED.\n"; }
-#endif
-
-  SparseVector<double> w; w.set_value(FD::Convert("Proposal"), 1.0);
-  hg.Reweight(w);
-  cerr << ViterbiFTree(hg) << endl;
-  return 0;
-}
-
diff --git a/gi/pf/cbgi.cc b/gi/pf/cbgi.cc
deleted file mode 100644
index 97f1ba34..00000000
--- a/gi/pf/cbgi.cc
+++ /dev/null
@@ -1,330 +0,0 @@
-#include <queue>
-#include <sstream>
-#include <iostream>
-
-#include <boost/unordered_map.hpp>
-#include <boost/functional/hash.hpp>
-
-#include "sampler.h"
-#include "filelib.h"
-#include "hg_io.h"
-#include "hg.h"
-#include "ccrp_nt.h"
-#include "trule.h"
-#include "inside_outside.h"
-
-using namespace std;
-using namespace std::tr1;
-
-double log_poisson(unsigned x, const double& lambda) {
-  assert(lambda > 0.0);
-  return log(lambda) * x - lgamma(x + 1) - lambda;
-}
-
-double log_decay(unsigned x, const double& b) {
-  assert(b > 1.0);
-  assert(x > 0);
-  return log(b - 1) - x * log(b);
-}
-
-struct SimpleBase {
-  SimpleBase(unsigned esize, unsigned fsize, unsigned ntsize = 144) :
-    uniform_e(-log(esize)),
-    uniform_f(-log(fsize)),
-    uniform_nt(-log(ntsize)) {
-  }
-
-  // binomial coefficient
-  static double choose(unsigned n, unsigned k) {
-    return exp(lgamma(n + 1) - lgamma(k + 1) - lgamma(n - k + 1));
-  }
-
-  // count the number of patterns of terminals and NTs in the rule, given elen and flen
-  static double log_number_of_patterns(const unsigned flen, const unsigned elen) {
-    static vector<vector<double> > counts;
-    if (elen >= counts.size()) counts.resize(elen + 1);
-    if (flen >= counts[elen].size()) counts[elen].resize(flen + 1);
-    double& count = counts[elen][flen];
-    if (count) return log(count);
-    const unsigned max_arity = min(elen, flen);
-    for (unsigned a = 0; a <= max_arity; ++a)
-      count += choose(elen, a) * choose(flen, a);
-    return log(count);
-  }
-
-  // return logp0 of rule | LHS
-  double operator()(const TRule& rule) const {
-    const unsigned flen = rule.f_.size();
-    const unsigned elen = rule.e_.size();
-#if 0
-    double p = 0;
-    p += log_poisson(flen, 0.5);                   // flen                 ~Pois(0.5)
-    p += log_poisson(elen, flen);                  // elen | flen          ~Pois(flen)
-    p -= log_number_of_patterns(flen, elen);       // pattern | flen,elen  ~Uniform
-    for (unsigned i = 0; i < flen; ++i) {          // for each position in f-RHS
-      if (rule.f_[i] <= 0)                         //   according to pattern
-        p += uniform_nt;                           //     draw NT          ~Uniform
-      else
-        p += uniform_f;                            //     draw f terminal  ~Uniform
-    }
-    p -= lgamma(rule.Arity() + 1);                 // draw permutation     ~Uniform 
-    for (unsigned i = 0; i < elen; ++i) {          // for each position in e-RHS
-      if (rule.e_[i] > 0)                          //   according to pattern
-        p += uniform_e;                            //     draw e|f term    ~Uniform
-        // TODO this should prob be model 1
-    }
-#else
-    double p = 0;
-    bool is_abstract = rule.f_[0] <= 0;
-    p += log(0.5);
-    if (is_abstract) {
-      if (flen == 2) p += log(0.99); else p += log(0.01);
-    } else {
-      p += log_decay(flen, 3);
-    }
-
-    for (unsigned i = 0; i < flen; ++i) {          // for each position in f-RHS
-      if (rule.f_[i] <= 0)                         //   according to pattern
-        p += uniform_nt;                           //     draw NT          ~Uniform
-      else
-        p += uniform_f;                            //     draw f terminal  ~Uniform
-    }
-#endif
-    return p;
-  }
-  const double uniform_e;
-  const double uniform_f;
-  const double uniform_nt;
-  vector<double> arities;
-};
-
-MT19937* rng = NULL;
-
-template <typename Base>
-struct MHSamplerEdgeProb {
-  MHSamplerEdgeProb(const Hypergraph& hg,
-                  const map<int, CCRP_NoTable<TRule> >& rdp,
-                  const Base& logp0,
-                  const bool exclude_multiword_terminals) : edge_probs(hg.edges_.size()) {
-    for (int i = 0; i < edge_probs.size(); ++i) {
-      const TRule& rule = *hg.edges_[i].rule_;
-      const map<int, CCRP_NoTable<TRule> >::const_iterator it = rdp.find(rule.lhs_);
-      assert(it != rdp.end());
-      const CCRP_NoTable<TRule>& crp = it->second;
-      edge_probs[i].logeq(crp.logprob(rule, logp0(rule)));
-      if (exclude_multiword_terminals && rule.f_[0] > 0 && rule.f_.size() > 1)
-        edge_probs[i] = prob_t::Zero();
-    }
-  }
-  inline prob_t operator()(const Hypergraph::Edge& e) const {
-    return edge_probs[e.id_];
-  }
-  prob_t DerivationProb(const vector<int>& d) const {
-    prob_t p = prob_t::One();
-    for (unsigned i = 0; i < d.size(); ++i)
-      p *= edge_probs[d[i]];
-    return p;
-  }
-  vector<prob_t> edge_probs;
-};
-
-template <typename Base>
-struct ModelAndData {
-  ModelAndData() :
-     base_lh(prob_t::One()),
-     logp0(10000, 10000),
-     mh_samples(),
-     mh_rejects() {}
-
-  void SampleCorpus(const string& hgpath, int i);
-  void ResampleHyperparameters() {
-    for (map<int, CCRP_NoTable<TRule> >::iterator it = rules.begin(); it != rules.end(); ++it)
-      it->second.resample_hyperparameters(rng);
-  }
-
-  CCRP_NoTable<TRule>& RuleCRP(int lhs) {
-    map<int, CCRP_NoTable<TRule> >::iterator it = rules.find(lhs);
-    if (it == rules.end()) {
-      rules.insert(make_pair(lhs, CCRP_NoTable<TRule>(1,1)));
-      it = rules.find(lhs);
-    }
-    return it->second;
-  }
-
-  void IncrementRule(const TRule& rule) {
-    CCRP_NoTable<TRule>& crp = RuleCRP(rule.lhs_);
-    if (crp.increment(rule)) {
-      prob_t p; p.logeq(logp0(rule));
-      base_lh *= p;
-    }
-  }
-
-  void DecrementRule(const TRule& rule) {
-    CCRP_NoTable<TRule>& crp = RuleCRP(rule.lhs_);
-    if (crp.decrement(rule)) {
-      prob_t p; p.logeq(logp0(rule));
-      base_lh /= p;
-    }
-  }
-
-  void DecrementDerivation(const Hypergraph& hg, const vector<int>& d) {
-    for (unsigned i = 0; i < d.size(); ++i) {
-      const TRule& rule = *hg.edges_[d[i]].rule_;
-      DecrementRule(rule);
-    }
-  }
-
-  void IncrementDerivation(const Hypergraph& hg, const vector<int>& d) {
-    for (unsigned i = 0; i < d.size(); ++i) {
-      const TRule& rule = *hg.edges_[d[i]].rule_;
-      IncrementRule(rule);
-    }
-  }
-
-  prob_t Likelihood() const {
-    prob_t p = prob_t::One();
-    for (map<int, CCRP_NoTable<TRule> >::const_iterator it = rules.begin(); it != rules.end(); ++it) {
-      prob_t q; q.logeq(it->second.log_crp_prob());
-      p *= q;
-    }
-    p *= base_lh;
-    return p;
-  }
-
-  void ResampleDerivation(const Hypergraph& hg, vector<int>* sampled_derivation);
-
-  map<int, CCRP_NoTable<TRule> > rules;  // [lhs] -> distribution over RHSs
-  prob_t base_lh;
-  SimpleBase logp0;
-  vector<vector<int> > samples;   // sampled derivations
-  unsigned int mh_samples;
-  unsigned int mh_rejects;
-};
-
-template <typename Base>
-void ModelAndData<Base>::SampleCorpus(const string& hgpath, int n) {
-  vector<Hypergraph> hgs(n); hgs.clear();
-  boost::unordered_map<TRule, unsigned> acc;
-  map<int, unsigned> tot;
-  for (int i = 0; i < n; ++i) {
-    ostringstream os;
-    os << hgpath << '/' << i << ".json.gz";
-    if (!FileExists(os.str())) continue;
-    hgs.push_back(Hypergraph());
-    ReadFile rf(os.str());
-    HypergraphIO::ReadFromJSON(rf.stream(), &hgs.back());
-  }
-  cerr << "Read " << hgs.size() << " alignment hypergraphs.\n";
-  samples.resize(hgs.size());
-  const unsigned SAMPLES = 2000;
-  const unsigned burnin = 3 * SAMPLES / 4;
-  const unsigned every = 20;
-  for (unsigned s = 0; s < SAMPLES; ++s) {
-    if (s % 10 == 0) {
-      if (s > 0) { cerr << endl; ResampleHyperparameters(); }
-      cerr << "[" << s << " LLH=" << log(Likelihood()) << " REJECTS=" << ((double)mh_rejects / mh_samples) << " LHS's=" << rules.size() << " base=" << log(base_lh) << "] ";
-    }
-    cerr << '.';
-    for (unsigned i = 0; i < hgs.size(); ++i) {
-      ResampleDerivation(hgs[i], &samples[i]);
-      if (s > burnin && s % every == 0) {
-        for (unsigned j = 0; j < samples[i].size(); ++j) {
-          const TRule& rule = *hgs[i].edges_[samples[i][j]].rule_;
-          ++acc[rule];
-          ++tot[rule.lhs_];
-        }
-      }
-    }
-  }
-  cerr << endl;
-  for (boost::unordered_map<TRule,unsigned>::iterator it = acc.begin(); it != acc.end(); ++it) {
-    cout << it->first << " MyProb=" << log(it->second)-log(tot[it->first.lhs_]) << endl;
-  }
-}
-
-template <typename Base>
-void ModelAndData<Base>::ResampleDerivation(const Hypergraph& hg, vector<int>* sampled_deriv) {
-  vector<int> cur;
-  cur.swap(*sampled_deriv);
-
-  const prob_t p_cur = Likelihood();
-  DecrementDerivation(hg, cur);
-  if (cur.empty()) {
-    // first iteration, create restaurants
-    for (int i = 0; i < hg.edges_.size(); ++i)
-      RuleCRP(hg.edges_[i].rule_->lhs_);
-  }
-  MHSamplerEdgeProb<SimpleBase> wf(hg, rules, logp0, cur.empty());
-//  MHSamplerEdgeProb<SimpleBase> wf(hg, rules, logp0, false);
-  const prob_t q_cur = wf.DerivationProb(cur);
-  vector<prob_t> node_probs;
-  Inside<prob_t, MHSamplerEdgeProb<SimpleBase> >(hg, &node_probs, wf);
-  queue<unsigned> q;
-  q.push(hg.nodes_.size() - 3);
-  while(!q.empty()) {
-    unsigned cur_node_id = q.front();
-//    cerr << "NODE=" << cur_node_id << endl;
-    q.pop();
-    const Hypergraph::Node& node = hg.nodes_[cur_node_id];
-    const unsigned num_in_edges = node.in_edges_.size();
-    unsigned sampled_edge = 0;
-    if (num_in_edges == 1) {
-      sampled_edge = node.in_edges_[0];
-    } else {
-      prob_t z;
-      assert(num_in_edges > 1);
-      SampleSet<prob_t> ss;
-      for (unsigned j = 0; j < num_in_edges; ++j) {
-        const Hypergraph::Edge& edge = hg.edges_[node.in_edges_[j]];
-        prob_t p = wf.edge_probs[edge.id_];             // edge proposal prob
-        for (unsigned k = 0; k < edge.tail_nodes_.size(); ++k)
-          p *= node_probs[edge.tail_nodes_[k]];
-        ss.add(p);
-//        cerr << log(ss[j]) << " ||| " << edge.rule_->AsString() << endl;
-        z += p;
-      }
-//      for (unsigned j = 0; j < num_in_edges; ++j) {
-//        const Hypergraph::Edge& edge = hg.edges_[node.in_edges_[j]];
-//        cerr << exp(log(ss[j] / z)) << " ||| " << edge.rule_->AsString() << endl;
-//      }
-//      cerr << " --- \n";
-      sampled_edge = node.in_edges_[rng->SelectSample(ss)];
-    }
-    sampled_deriv->push_back(sampled_edge);
-    const Hypergraph::Edge& edge = hg.edges_[sampled_edge];
-    for (unsigned j = 0; j < edge.tail_nodes_.size(); ++j) {
-      q.push(edge.tail_nodes_[j]);
-    }
-  }
-  IncrementDerivation(hg, *sampled_deriv);
-
-//  cerr << "sampled derivation contains " << sampled_deriv->size() << " edges\n";
-//  cerr << "DERIV:\n";
-//  for (int i = 0; i < sampled_deriv->size(); ++i) {
-//    cerr << "  " << hg.edges_[(*sampled_deriv)[i]].rule_->AsString() << endl;
-//  }
-
-  if (cur.empty()) return;  // accept first sample
-
-  ++mh_samples;
-  // only need to do MH if proposal is different to current state
-  if (cur != *sampled_deriv) {
-    const prob_t q_prop = wf.DerivationProb(*sampled_deriv);
-    const prob_t p_prop = Likelihood();
-    if (!rng->AcceptMetropolisHastings(p_prop, p_cur, q_prop, q_cur)) {
-      ++mh_rejects;
-      DecrementDerivation(hg, *sampled_deriv);
-      IncrementDerivation(hg, cur);
-      swap(cur, *sampled_deriv);
-    }
-  }
-}
-
-int main(int argc, char** argv) {
-  rng = new MT19937;
-  ModelAndData<SimpleBase> m;
-  m.SampleCorpus("./hgs", 50);
-  // m.SampleCorpus("./btec/hgs", 5000);
-  return 0;
-}
-
diff --git a/gi/pf/cfg_wfst_composer.cc b/gi/pf/cfg_wfst_composer.cc
deleted file mode 100644
index 21d5ec5b..00000000
--- a/gi/pf/cfg_wfst_composer.cc
+++ /dev/null
@@ -1,731 +0,0 @@
-#include "cfg_wfst_composer.h"
-
-#include <iostream>
-#include <fstream>
-#include <map>
-#include <queue>
-#include <tr1/unordered_map>
-#include <tr1/unordered_set>
-
-#include <boost/shared_ptr.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-#include "fast_lexical_cast.hpp"
-
-#include "phrasetable_fst.h"
-#include "sparse_vector.h"
-#include "tdict.h"
-#include "hg.h"
-#include "hg_remove_eps.h"
-
-namespace po = boost::program_options;
-using namespace std;
-using namespace std::tr1;
-
-WFSTNode::~WFSTNode() {}
-WFST::~WFST() {}
-
-// Define the following macro if you want to see lots of debugging output
-// when you run the chart parser
-#undef DEBUG_CHART_PARSER
-
-// A few constants used by the chart parser ///////////////
-static const int kMAX_NODES = 2000000;
-static const string kPHRASE_STRING = "X";
-static bool constants_need_init = true;
-static WordID kUNIQUE_START;
-static WordID kPHRASE;
-static TRulePtr kX1X2;
-static TRulePtr kX1;
-static WordID kEPS;
-static TRulePtr kEPSRule;
-
-static void InitializeConstants() {
-  if (constants_need_init) {
-    kPHRASE = TD::Convert(kPHRASE_STRING) * -1;
-    kUNIQUE_START = TD::Convert("S") * -1;
-    kX1X2.reset(new TRule("[X] ||| [X,1] [X,2] ||| [X,1] [X,2]"));
-    kX1.reset(new TRule("[X] ||| [X,1] ||| [X,1]"));
-    kEPSRule.reset(new TRule("[X] ||| <eps> ||| <eps>"));
-    kEPS = TD::Convert("<eps>");
-    constants_need_init = false;
-  }
-}
-////////////////////////////////////////////////////////////
-
-class EGrammarNode {
-  friend bool CFG_WFSTComposer::Compose(const Hypergraph& src_forest, Hypergraph* trg_forest);
-  friend void AddGrammarRule(const string& r, map<WordID, EGrammarNode>* g);
- public:
-#ifdef DEBUG_CHART_PARSER
-  string hint;
-#endif
-  EGrammarNode() : is_some_rule_complete(false), is_root(false) {}
-  const map<WordID, EGrammarNode>& GetTerminals() const { return tptr; }
-  const map<WordID, EGrammarNode>& GetNonTerminals() const { return ntptr; }
-  bool HasNonTerminals() const { return (!ntptr.empty()); }
-  bool HasTerminals() const { return (!tptr.empty()); }
-  bool RuleCompletes() const {
-    return (is_some_rule_complete || (ntptr.empty() && tptr.empty()));
-  }
-  bool GrammarContinues() const {
-    return !(ntptr.empty() && tptr.empty());
-  }
-  bool IsRoot() const {
-    return is_root;
-  }
-  // these are the features associated with the rule from the start
-  // node up to this point.  If you use these features, you must
-  // not Extend() this rule.
-  const SparseVector<double>& GetCFGProductionFeatures() const {
-    return input_features;
-  }
-
-  const EGrammarNode* Extend(const WordID& t) const {
-    if (t < 0) {
-      map<WordID, EGrammarNode>::const_iterator it = ntptr.find(t);
-      if (it == ntptr.end()) return NULL;
-      return &it->second;
-    } else {
-      map<WordID, EGrammarNode>::const_iterator it = tptr.find(t);
-      if (it == tptr.end()) return NULL;
-      return &it->second;
-    }
-  }
-
- private:
-  map<WordID, EGrammarNode> tptr;
-  map<WordID, EGrammarNode> ntptr;
-  SparseVector<double> input_features;
-  bool is_some_rule_complete;
-  bool is_root;
-};
-typedef map<WordID, EGrammarNode> EGrammar;    // indexed by the rule LHS
-
-// edges are immutable once created
-struct Edge {
-#ifdef DEBUG_CHART_PARSER
-  static int id_count;
-  const int id;
-#endif
-  const WordID cat;                   // lhs side of rule proved/being proved
-  const EGrammarNode* const dot;      // dot position
-  const WFSTNode* const q;             // start of span
-  const WFSTNode* const r;             // end of span
-  const Edge* const active_parent;    // back pointer, NULL for PREDICT items
-  const Edge* const passive_parent;   // back pointer, NULL for SCAN and PREDICT items
-  TRulePtr tps;   // translations
-  boost::shared_ptr<SparseVector<double> > features; // features from CFG rule
-
-  bool IsPassive() const {
-    // when a rule is completed, this value will be set
-    return static_cast<bool>(features);
-  }
-  bool IsActive() const { return !IsPassive(); }
-  bool IsInitial() const {
-    return !(active_parent || passive_parent);
-  }
-  bool IsCreatedByScan() const {
-    return active_parent && !passive_parent && !dot->IsRoot();
-  }
-  bool IsCreatedByPredict() const {
-    return dot->IsRoot();
-  }
-  bool IsCreatedByComplete() const {
-    return active_parent && passive_parent;
-  }
-
-  // constructor for PREDICT
-  Edge(WordID c, const EGrammarNode* d, const WFSTNode* q_and_r) :
-#ifdef DEBUG_CHART_PARSER
-    id(++id_count),
-#endif
-    cat(c), dot(d), q(q_and_r), r(q_and_r), active_parent(NULL), passive_parent(NULL), tps() {}
-  Edge(WordID c, const EGrammarNode* d, const WFSTNode* q_and_r, const Edge* act_parent) :
-#ifdef DEBUG_CHART_PARSER
-    id(++id_count),
-#endif
-    cat(c), dot(d), q(q_and_r), r(q_and_r), active_parent(act_parent), passive_parent(NULL), tps() {}
-
-  // constructors for SCAN
-  Edge(WordID c, const EGrammarNode* d, const WFSTNode* i, const WFSTNode* j,
-       const Edge* act_par, const TRulePtr& translations) :
-#ifdef DEBUG_CHART_PARSER
-    id(++id_count),
-#endif
-    cat(c), dot(d), q(i), r(j), active_parent(act_par), passive_parent(NULL), tps(translations) {}
-
-  Edge(WordID c, const EGrammarNode* d, const WFSTNode* i, const WFSTNode* j,
-       const Edge* act_par, const TRulePtr& translations,
-       const SparseVector<double>& feats) :
-#ifdef DEBUG_CHART_PARSER
-    id(++id_count),
-#endif
-    cat(c), dot(d), q(i), r(j), active_parent(act_par), passive_parent(NULL), tps(translations),
-    features(new SparseVector<double>(feats)) {}
-
-  // constructors for COMPLETE
-  Edge(WordID c, const EGrammarNode* d, const WFSTNode* i, const WFSTNode* j,
-       const Edge* act_par, const Edge *pas_par) :
-#ifdef DEBUG_CHART_PARSER
-    id(++id_count),
-#endif
-    cat(c), dot(d), q(i), r(j), active_parent(act_par), passive_parent(pas_par), tps() {
-      assert(pas_par->IsPassive());
-      assert(act_par->IsActive());
-    }
-
-  Edge(WordID c, const EGrammarNode* d, const WFSTNode* i, const WFSTNode* j,
-       const Edge* act_par, const Edge *pas_par, const SparseVector<double>& feats) :
-#ifdef DEBUG_CHART_PARSER
-    id(++id_count),
-#endif
-    cat(c), dot(d), q(i), r(j), active_parent(act_par), passive_parent(pas_par), tps(),
-    features(new SparseVector<double>(feats)) {
-      assert(pas_par->IsPassive());
-      assert(act_par->IsActive());
-    }
-
-  // constructor for COMPLETE query
-  Edge(const WFSTNode* _r) :
-#ifdef DEBUG_CHART_PARSER
-    id(0),
-#endif
-    cat(0), dot(NULL), q(NULL),
-    r(_r), active_parent(NULL), passive_parent(NULL), tps() {}
-  // constructor for MERGE quere
-  Edge(const WFSTNode* _q, int) :
-#ifdef DEBUG_CHART_PARSER
-    id(0),
-#endif
-    cat(0), dot(NULL), q(_q),
-    r(NULL), active_parent(NULL), passive_parent(NULL), tps() {}
-};
-#ifdef DEBUG_CHART_PARSER
-int Edge::id_count = 0;
-#endif
-
-ostream& operator<<(ostream& os, const Edge& e) {
-  string type = "PREDICT";
-  if (e.IsCreatedByScan())
-    type = "SCAN";
-  else if (e.IsCreatedByComplete())
-    type = "COMPLETE";
-  os << "["
-#ifdef DEBUG_CHART_PARSER
-     << '(' << e.id << ") "
-#else
-     << '(' << &e << ") "
-#endif
-     << "q=" << e.q << ", r=" << e.r
-     << ", cat="<< TD::Convert(e.cat*-1) << ", dot="
-     << e.dot
-#ifdef DEBUG_CHART_PARSER
-     << e.dot->hint
-#endif
-     << (e.IsActive() ? ", Active" : ", Passive")
-     << ", " << type;
-#ifdef DEBUG_CHART_PARSER
-  if (e.active_parent) { os << ", act.parent=(" << e.active_parent->id << ')'; }
-  if (e.passive_parent) { os << ", psv.parent=(" << e.passive_parent->id << ')'; }
-#endif
-  if (e.tps) { os << ", tps=" << e.tps->AsString(); }
-  return os << ']';
-}
-
-struct Traversal {
-  const Edge* const edge;      // result from the active / passive combination
-  const Edge* const active;
-  const Edge* const passive;
-  Traversal(const Edge* me, const Edge* a, const Edge* p) : edge(me), active(a), passive(p) {}
-};
-
-struct UniqueTraversalHash {
-  size_t operator()(const Traversal* t) const {
-    size_t x = 5381;
-    x = ((x << 5) + x) ^ reinterpret_cast<size_t>(t->active);
-    x = ((x << 5) + x) ^ reinterpret_cast<size_t>(t->passive);
-    x = ((x << 5) + x) ^ t->edge->IsActive();
-    return x;
-  }
-};
-
-struct UniqueTraversalEquals {
-  size_t operator()(const Traversal* a, const Traversal* b) const {
-    return (a->passive == b->passive && a->active == b->active && a->edge->IsActive() == b->edge->IsActive());
-  }
-};
-
-struct UniqueEdgeHash {
-  size_t operator()(const Edge* e) const {
-    size_t x = 5381;
-    if (e->IsActive()) {
-      x = ((x << 5) + x) ^ reinterpret_cast<size_t>(e->dot);
-      x = ((x << 5) + x) ^ reinterpret_cast<size_t>(e->q);
-      x = ((x << 5) + x) ^ reinterpret_cast<size_t>(e->r);
-      x = ((x << 5) + x) ^ static_cast<size_t>(e->cat);
-      x += 13;
-    } else {  // with passive edges, we don't care about the dot
-      x = ((x << 5) + x) ^ reinterpret_cast<size_t>(e->q);
-      x = ((x << 5) + x) ^ reinterpret_cast<size_t>(e->r);
-      x = ((x << 5) + x) ^ static_cast<size_t>(e->cat);
-    }
-    return x;
-  }
-};
-
-struct UniqueEdgeEquals {
-  bool operator()(const Edge* a, const Edge* b) const {
-    if (a->IsActive() != b->IsActive()) return false;
-    if (a->IsActive()) {
-      return (a->cat == b->cat) && (a->dot == b->dot) && (a->q == b->q) && (a->r == b->r);
-    } else {
-      return (a->cat == b->cat) && (a->q == b->q) && (a->r == b->r);
-    }
-  }
-};
-
-struct REdgeHash {
-  size_t operator()(const Edge* e) const {
-    size_t x = 5381;
-    x = ((x << 5) + x) ^ reinterpret_cast<size_t>(e->r);
-    return x;
-  }
-};
-
-struct REdgeEquals {
-  bool operator()(const Edge* a, const Edge* b) const {
-    return (a->r == b->r);
-  }
-};
-
-struct QEdgeHash {
-  size_t operator()(const Edge* e) const {
-    size_t x = 5381;
-    x = ((x << 5) + x) ^ reinterpret_cast<size_t>(e->q);
-    return x;
-  }
-};
-
-struct QEdgeEquals {
-  bool operator()(const Edge* a, const Edge* b) const {
-    return (a->q == b->q);
-  }
-};
-
-struct EdgeQueue {
-  queue<const Edge*> q;
-  EdgeQueue() {}
-  void clear() { while(!q.empty()) q.pop(); }
-  bool HasWork() const { return !q.empty(); }
-  const Edge* Next() { const Edge* res = q.front(); q.pop(); return res; }
-  void AddEdge(const Edge* s) { q.push(s); }
-};
-
-class CFG_WFSTComposerImpl {
- public:
-  CFG_WFSTComposerImpl(WordID start_cat,
-                       const WFSTNode* q_0,
-                       const WFSTNode* q_final) : start_cat_(start_cat), q_0_(q_0), q_final_(q_final) {}
-
-  // returns false if the intersection is empty
-  bool Compose(const EGrammar& g, Hypergraph* forest) {
-    goal_node = NULL;
-    EGrammar::const_iterator sit = g.find(start_cat_);
-    forest->ReserveNodes(kMAX_NODES);
-    assert(sit != g.end());
-    Edge* init = new Edge(start_cat_, &sit->second, q_0_);
-    assert(IncorporateNewEdge(init));
-    while (exp_agenda.HasWork() || agenda.HasWork()) {
-      while(exp_agenda.HasWork()) {
-        const Edge* edge = exp_agenda.Next();
-        FinishEdge(edge, forest);
-      }
-      if (agenda.HasWork()) {
-        const Edge* edge = agenda.Next();
-#ifdef DEBUG_CHART_PARSER
-        cerr << "processing (" << edge->id << ')' << endl;
-#endif
-        if (edge->IsActive()) {
-          if (edge->dot->HasTerminals())
-            DoScan(edge);
-          if (edge->dot->HasNonTerminals()) {
-            DoMergeWithPassives(edge);
-            DoPredict(edge, g);
-          }
-        } else {
-          DoComplete(edge);
-        }
-      }
-    }
-    if (goal_node) {
-      forest->PruneUnreachable(goal_node->id_);
-      RemoveEpsilons(forest, kEPS);
-    }
-    FreeAll();
-    return goal_node;
-  }
-
-  void FreeAll() {
-    for (int i = 0; i < free_list_.size(); ++i)
-      delete free_list_[i];
-    free_list_.clear();
-    for (int i = 0; i < traversal_free_list_.size(); ++i)
-      delete traversal_free_list_[i];
-    traversal_free_list_.clear();
-    all_traversals.clear();
-    exp_agenda.clear();
-    agenda.clear();
-    tps2node.clear();
-    edge2node.clear();
-    all_edges.clear();
-    passive_edges.clear();
-    active_edges.clear();
-  }
-
-  ~CFG_WFSTComposerImpl() {
-    FreeAll();
-  }
-
-  // returns the total number of edges created during composition
-  int EdgesCreated() const {
-    return free_list_.size();
-  }
-
- private:
-  void DoScan(const Edge* edge) {
-    // here, we assume that the FST will potentially have many more outgoing
-    // edges than the grammar, which will be just a couple.  If you want to
-    // efficiently handle the case where both are relatively large, this code
-    // will need to change how the intersection is done.  The best general
-    // solution would probably be the Baeza-Yates double binary search.
-
-    const EGrammarNode* dot = edge->dot;
-    const WFSTNode* r = edge->r;
-    const map<WordID, EGrammarNode>& terms = dot->GetTerminals();
-    for (map<WordID, EGrammarNode>::const_iterator git = terms.begin();
-         git != terms.end(); ++git) {
-
-      if (!(TD::Convert(git->first)[0] >= '0' && TD::Convert(git->first)[0] <= '9')) {
-        std::cerr << "TERMINAL SYMBOL: " << TD::Convert(git->first) << endl;
-        abort();
-      }
-      std::vector<std::pair<const WFSTNode*, TRulePtr> > extensions = r->ExtendInput(atoi(TD::Convert(git->first).c_str()));
-      for (unsigned nsi = 0; nsi < extensions.size(); ++nsi) {
-        const WFSTNode* next_r = extensions[nsi].first;
-        const EGrammarNode* next_dot = &git->second;
-        const bool grammar_continues = next_dot->GrammarContinues();
-        const bool rule_completes    = next_dot->RuleCompletes();
-        if (extensions[nsi].second)
-          cerr << "!!! " << extensions[nsi].second->AsString() << endl;
-        // cerr << "  rule completes: " << rule_completes << " after consuming " << TD::Convert(git->first) << endl;
-        assert(grammar_continues || rule_completes);
-        const SparseVector<double>& input_features = next_dot->GetCFGProductionFeatures();
-        if (rule_completes)
-          IncorporateNewEdge(new Edge(edge->cat, next_dot, edge->q, next_r, edge, extensions[nsi].second, input_features));
-        if (grammar_continues)
-          IncorporateNewEdge(new Edge(edge->cat, next_dot, edge->q, next_r, edge, extensions[nsi].second));
-      }
-    }
-  }
-
-  void DoPredict(const Edge* edge, const EGrammar& g) {
-    const EGrammarNode* dot = edge->dot;
-    const map<WordID, EGrammarNode>& non_terms = dot->GetNonTerminals();
-    for (map<WordID, EGrammarNode>::const_iterator git = non_terms.begin();
-         git != non_terms.end(); ++git) {
-      const WordID nt_to_predict = git->first;
-      //cerr << edge->id << " -- " << TD::Convert(nt_to_predict*-1) << endl;
-      EGrammar::const_iterator egi = g.find(nt_to_predict);
-      if (egi == g.end()) {
-        cerr << "[ERROR] Can't find any grammar rules with a LHS of type "
-             << TD::Convert(-1*nt_to_predict) << '!' << endl;
-        continue;
-      }
-      assert(edge->IsActive());
-      const EGrammarNode* new_dot = &egi->second;
-      Edge* new_edge = new Edge(nt_to_predict, new_dot, edge->r, edge);
-      IncorporateNewEdge(new_edge);
-    }
-  }
-
-  void DoComplete(const Edge* passive) {
-#ifdef DEBUG_CHART_PARSER
-    cerr << "  complete: " << *passive << endl;
-#endif
-    const WordID completed_nt = passive->cat;
-    const WFSTNode* q = passive->q;
-    const WFSTNode* next_r = passive->r;
-    const Edge query(q);
-    const pair<unordered_multiset<const Edge*, REdgeHash, REdgeEquals>::iterator,
-         unordered_multiset<const Edge*, REdgeHash, REdgeEquals>::iterator > p =
-      active_edges.equal_range(&query);
-    for (unordered_multiset<const Edge*, REdgeHash, REdgeEquals>::iterator it = p.first;
-         it != p.second; ++it) {
-      const Edge* active = *it;
-#ifdef DEBUG_CHART_PARSER
-      cerr << "    pos: " << *active << endl;
-#endif
-      const EGrammarNode* next_dot = active->dot->Extend(completed_nt);
-      if (!next_dot) continue;
-      const SparseVector<double>& input_features = next_dot->GetCFGProductionFeatures();
-      // add up to 2 rules
-      if (next_dot->RuleCompletes())
-        IncorporateNewEdge(new Edge(active->cat, next_dot, active->q, next_r, active, passive, input_features));
-      if (next_dot->GrammarContinues())
-        IncorporateNewEdge(new Edge(active->cat, next_dot, active->q, next_r, active, passive));
-    }
-  }
-
-  void DoMergeWithPassives(const Edge* active) {
-    // edge is active, has non-terminals, we need to find the passives that can extend it
-    assert(active->IsActive());
-    assert(active->dot->HasNonTerminals());
-#ifdef DEBUG_CHART_PARSER
-    cerr << "  merge active with passives: ACT=" << *active << endl;
-#endif
-    const Edge query(active->r, 1);
-    const pair<unordered_multiset<const Edge*, QEdgeHash, QEdgeEquals>::iterator,
-         unordered_multiset<const Edge*, QEdgeHash, QEdgeEquals>::iterator > p =
-      passive_edges.equal_range(&query);
-    for (unordered_multiset<const Edge*, QEdgeHash, QEdgeEquals>::iterator it = p.first;
-         it != p.second; ++it) {
-      const Edge* passive = *it;
-      const EGrammarNode* next_dot = active->dot->Extend(passive->cat);
-      if (!next_dot) continue;
-      const WFSTNode* next_r = passive->r;
-      const SparseVector<double>& input_features = next_dot->GetCFGProductionFeatures();
-      if (next_dot->RuleCompletes())
-        IncorporateNewEdge(new Edge(active->cat, next_dot, active->q, next_r, active, passive, input_features));
-      if (next_dot->GrammarContinues())
-        IncorporateNewEdge(new Edge(active->cat, next_dot, active->q, next_r, active, passive));
-    }
-  }
-
-  // take ownership of edge memory, add to various indexes, etc
-  // returns true if this edge is new
-  bool IncorporateNewEdge(Edge* edge) {
-    free_list_.push_back(edge);
-    if (edge->passive_parent && edge->active_parent) {
-      Traversal* t = new Traversal(edge, edge->active_parent, edge->passive_parent);
-      traversal_free_list_.push_back(t);
-      if (all_traversals.find(t) != all_traversals.end()) {
-        return false;
-      } else {
-        all_traversals.insert(t);
-      }
-    }
-    exp_agenda.AddEdge(edge);
-    return true;
-  }
-
-  bool FinishEdge(const Edge* edge, Hypergraph* hg) {
-    bool is_new = false;
-    if (all_edges.find(edge) == all_edges.end()) {
-#ifdef DEBUG_CHART_PARSER
-      cerr << *edge << " is NEW\n";
-#endif
-      all_edges.insert(edge);
-      is_new = true;
-      if (edge->IsPassive()) passive_edges.insert(edge);
-      if (edge->IsActive()) active_edges.insert(edge);
-      agenda.AddEdge(edge);
-    } else {
-#ifdef DEBUG_CHART_PARSER
-      cerr << *edge << " is NOT NEW.\n";
-#endif
-    }
-    AddEdgeToTranslationForest(edge, hg);
-    return is_new;
-  }
-
-  // build the translation forest
-  void AddEdgeToTranslationForest(const Edge* edge, Hypergraph* hg) {
-    assert(hg->nodes_.size() < kMAX_NODES);
-    Hypergraph::Node* tps = NULL;
-    // first add any target language rules
-    if (edge->tps) {
-      Hypergraph::Node*& node = tps2node[(size_t)edge->tps.get()];
-      if (!node) {
-        // cerr << "Creating phrases for " << edge->tps << endl;
-        const TRulePtr& rule = edge->tps;
-        node = hg->AddNode(kPHRASE);
-        Hypergraph::Edge* hg_edge = hg->AddEdge(rule, Hypergraph::TailNodeVector());
-        hg_edge->feature_values_ += rule->GetFeatureValues();
-        hg->ConnectEdgeToHeadNode(hg_edge, node);
-      }
-      tps = node;
-    }
-    Hypergraph::Node*& head_node = edge2node[edge];
-    if (!head_node)
-      head_node = hg->AddNode(kPHRASE);
-    if (edge->cat == start_cat_ && edge->q == q_0_ && edge->r == q_final_ && edge->IsPassive()) {
-      assert(goal_node == NULL || goal_node == head_node);
-      goal_node = head_node;
-    }
-    Hypergraph::TailNodeVector tail;
-    SparseVector<double> extra;
-    if (edge->IsCreatedByPredict()) {
-      // extra.set_value(FD::Convert("predict"), 1);
-    } else if (edge->IsCreatedByScan()) {
-      tail.push_back(edge2node[edge->active_parent]->id_);
-      if (tps) {
-        tail.push_back(tps->id_);
-      }
-      //extra.set_value(FD::Convert("scan"), 1);
-    } else if (edge->IsCreatedByComplete()) {
-      tail.push_back(edge2node[edge->active_parent]->id_);
-      tail.push_back(edge2node[edge->passive_parent]->id_);
-      //extra.set_value(FD::Convert("complete"), 1);
-    } else {
-      assert(!"unexpected edge type!");
-    }
-    //cerr << head_node->id_ << "<--" << *edge << endl;
-
-#ifdef DEBUG_CHART_PARSER
-      for (int i = 0; i < tail.size(); ++i)
-        if (tail[i] == head_node->id_) {
-          cerr << "ERROR: " << *edge << "\n   i=" << i << endl;
-          if (i == 1) { cerr << "\tP: " << *edge->passive_parent << endl; }
-          if (i == 0) { cerr << "\tA: " << *edge->active_parent << endl; }
-          assert(!"self-loop found!");
-        }
-#endif
-    Hypergraph::Edge* hg_edge = NULL;
-    if (tail.size() == 0) {
-      hg_edge = hg->AddEdge(kEPSRule, tail);
-    } else if (tail.size() == 1) {
-      hg_edge = hg->AddEdge(kX1, tail);
-    } else if (tail.size() == 2) {
-      hg_edge = hg->AddEdge(kX1X2, tail);
-    }
-    if (edge->features)
-      hg_edge->feature_values_ += *edge->features;
-    hg_edge->feature_values_ += extra;
-    hg->ConnectEdgeToHeadNode(hg_edge, head_node);
-  }
-
-  Hypergraph::Node* goal_node;
-  EdgeQueue exp_agenda;
-  EdgeQueue agenda;
-  unordered_map<size_t, Hypergraph::Node*> tps2node;
-  unordered_map<const Edge*, Hypergraph::Node*, UniqueEdgeHash, UniqueEdgeEquals> edge2node;
-  unordered_set<const Traversal*, UniqueTraversalHash, UniqueTraversalEquals> all_traversals;
-  unordered_set<const Edge*, UniqueEdgeHash, UniqueEdgeEquals> all_edges;
-  unordered_multiset<const Edge*, QEdgeHash, QEdgeEquals> passive_edges;
-  unordered_multiset<const Edge*, REdgeHash, REdgeEquals> active_edges;
-  vector<Edge*> free_list_;
-  vector<Traversal*> traversal_free_list_;
-  const WordID start_cat_;
-  const WFSTNode* const q_0_;
-  const WFSTNode* const q_final_;
-};
-
-#ifdef DEBUG_CHART_PARSER
-static string TrimRule(const string& r) {
-  size_t start = r.find(" |||") + 5;
-  size_t end = r.rfind(" |||");
-  return r.substr(start, end - start);
-}
-#endif
-
-void AddGrammarRule(const string& r, EGrammar* g) {
-  const size_t pos = r.find(" ||| ");
-  if (pos == string::npos || r[0] != '[') {
-    cerr << "Bad rule: " << r << endl;
-    return;
-  }
-  const size_t rpos = r.rfind(" ||| ");
-  string feats;
-  string rs = r;
-  if (rpos != pos) {
-    feats = r.substr(rpos + 5);
-    rs = r.substr(0, rpos);
-  }
-  string rhs = rs.substr(pos + 5);
-  string trule = rs + " ||| " + rhs + " ||| " + feats;
-  TRule tr(trule);
-  cerr << "X: " << tr.e_[0] << endl;
-#ifdef DEBUG_CHART_PARSER
-  string hint_last_rule;
-#endif
-  EGrammarNode* cur = &(*g)[tr.GetLHS()];
-  cur->is_root = true;
-  for (int i = 0; i < tr.FLength(); ++i) {
-    WordID sym = tr.f()[i];
-#ifdef DEBUG_CHART_PARSER
-    hint_last_rule = TD::Convert(sym < 0 ? -sym : sym);
-    cur->hint += " <@@> (*" + hint_last_rule + ") " + TrimRule(tr.AsString());
-#endif
-    if (sym < 0)
-      cur = &cur->ntptr[sym];
-    else
-      cur = &cur->tptr[sym];
-  }
-#ifdef DEBUG_CHART_PARSER
-  cur->hint += " <@@> (" + hint_last_rule + "*) " + TrimRule(tr.AsString());
-#endif
-  cur->is_some_rule_complete = true;
-  cur->input_features = tr.GetFeatureValues();
-}
-
-CFG_WFSTComposer::~CFG_WFSTComposer() {
-  delete pimpl_;
-}
-
-CFG_WFSTComposer::CFG_WFSTComposer(const WFST& wfst) {
-  InitializeConstants();
-  pimpl_ = new CFG_WFSTComposerImpl(kUNIQUE_START, wfst.Initial(), wfst.Final());
-}
-
-bool CFG_WFSTComposer::Compose(const Hypergraph& src_forest, Hypergraph* trg_forest) {
-  // first, convert the src forest into an EGrammar
-  EGrammar g;
-  const int nedges = src_forest.edges_.size();
-  const int nnodes = src_forest.nodes_.size();
-  vector<int> cats(nnodes);
-  bool assign_cats = false;
-  for (int i = 0; i < nnodes; ++i)
-    if (assign_cats) {
-      cats[i] = TD::Convert("CAT_" + boost::lexical_cast<string>(i)) * -1;
-    } else {
-      cats[i] = src_forest.nodes_[i].cat_;
-    }
-  // construct the grammar
-  for (int i = 0; i < nedges; ++i) {
-    const Hypergraph::Edge& edge = src_forest.edges_[i];
-    const vector<WordID>& src = edge.rule_->f();
-    EGrammarNode* cur = &g[cats[edge.head_node_]];
-    cur->is_root = true;
-    int ntc = 0;
-    for (int j = 0; j < src.size(); ++j) {
-      WordID sym = src[j];
-      if (sym <= 0) {
-        sym = cats[edge.tail_nodes_[ntc]];
-        ++ntc;
-        cur = &cur->ntptr[sym];
-      } else {
-        cur = &cur->tptr[sym];
-      }
-    }
-    cur->is_some_rule_complete = true;
-    cur->input_features = edge.feature_values_;
-  }
-  EGrammarNode& goal_rule = g[kUNIQUE_START];
-  assert((goal_rule.ntptr.size() == 1 && goal_rule.tptr.size() == 0) ||
-         (goal_rule.ntptr.size() == 0 && goal_rule.tptr.size() == 1));
-
-  return pimpl_->Compose(g, trg_forest);
-}
-
-bool CFG_WFSTComposer::Compose(istream* in, Hypergraph* trg_forest) {
-  EGrammar g;
-  while(*in) {
-    string line;
-    getline(*in, line);
-    if (line.empty()) continue;
-    AddGrammarRule(line, &g);
-  }
-
-  return pimpl_->Compose(g, trg_forest);
-}
diff --git a/gi/pf/cfg_wfst_composer.h b/gi/pf/cfg_wfst_composer.h
deleted file mode 100644
index cf47f459..00000000
--- a/gi/pf/cfg_wfst_composer.h
+++ /dev/null
@@ -1,46 +0,0 @@
-#ifndef _CFG_WFST_COMPOSER_H_
-#define _CFG_WFST_COMPOSER_H_
-
-#include <iostream>
-#include <vector>
-#include <utility>
-
-#include "trule.h"
-#include "wordid.h"
-
-class CFG_WFSTComposerImpl;
-class Hypergraph;
-
-struct WFSTNode {
-  virtual ~WFSTNode();
-  // returns the next states reachable by consuming srcindex (which identifies a word)
-  // paired with the output string generated by taking that transition.
-  virtual std::vector<std::pair<const WFSTNode*,TRulePtr> > ExtendInput(unsigned srcindex) const = 0;
-};
-
-struct WFST {
-  virtual ~WFST();
-  virtual const WFSTNode* Final() const = 0;
-  virtual const WFSTNode* Initial() const = 0;
-};
-
-class CFG_WFSTComposer {
- public:
-  ~CFG_WFSTComposer();
-  explicit CFG_WFSTComposer(const WFST& wfst);
-  bool Compose(const Hypergraph& in_forest, Hypergraph* trg_forest);
-
-  // reads the grammar from a file. There must be a single top-level
-  // S -> X rule.  Anything else is possible. Format is:
-  // [S] ||| [SS,1]
-  // [SS] ||| [NP,1] [VP,2] ||| Feature1=0.2 Feature2=-2.3
-  // [SS] ||| [VP,1] [NP,2] ||| Feature1=0.8
-  // [NP] ||| [DET,1] [N,2] ||| Feature3=2
-  // ...
-  bool Compose(std::istream* grammar_file, Hypergraph* trg_forest);
-
- private:
-  CFG_WFSTComposerImpl* pimpl_;
-};
-
-#endif
diff --git a/gi/pf/conditional_pseg.h b/gi/pf/conditional_pseg.h
deleted file mode 100644
index 81ddb206..00000000
--- a/gi/pf/conditional_pseg.h
+++ /dev/null
@@ -1,275 +0,0 @@
-#ifndef _CONDITIONAL_PSEG_H_
-#define _CONDITIONAL_PSEG_H_
-
-#include <vector>
-#include <tr1/unordered_map>
-#include <boost/functional/hash.hpp>
-#include <iostream>
-
-#include "m.h"
-#include "prob.h"
-#include "ccrp_nt.h"
-#include "mfcr.h"
-#include "trule.h"
-#include "base_distributions.h"
-#include "tdict.h"
-
-template <typename ConditionalBaseMeasure>
-struct MConditionalTranslationModel {
-  explicit MConditionalTranslationModel(ConditionalBaseMeasure& rcp0) :
-    rp0(rcp0), d(0.5), strength(1.0), lambdas(1, prob_t::One()), p0s(1) {}
-
-  void Summary() const {
-    std::cerr << "Number of conditioning contexts: " << r.size() << std::endl;
-    for (RuleModelHash::const_iterator it = r.begin(); it != r.end(); ++it) {
-      std::cerr << TD::GetString(it->first) << "   \t(d=" << it->second.discount() << ",s=" << it->second.strength() << ") --------------------------" << std::endl;
-      for (MFCR<1,TRule>::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
-        std::cerr << "   " << i2->second.total_dish_count_ << '\t' << i2->first << std::endl;
-    }
-  }
-
-  double log_likelihood(const double& dd, const double& aa) const {
-    if (aa <= -dd) return -std::numeric_limits<double>::infinity();
-    //double llh = Md::log_beta_density(dd, 10, 3) + Md::log_gamma_density(aa, 1, 1);
-    double llh = Md::log_beta_density(dd, 1, 1) +
-                 Md::log_gamma_density(dd + aa, 1, 1);
-    typename std::tr1::unordered_map<std::vector<WordID>, MFCR<1,TRule>, boost::hash<std::vector<WordID> > >::const_iterator it;
-    for (it = r.begin(); it != r.end(); ++it)
-      llh += it->second.log_crp_prob(dd, aa);
-    return llh;
-  }
-
-  struct DiscountResampler {
-    DiscountResampler(const MConditionalTranslationModel& m) : m_(m) {}
-    const MConditionalTranslationModel& m_;
-    double operator()(const double& proposed_discount) const {
-      return m_.log_likelihood(proposed_discount, m_.strength);
-    }
-  };
-
-  struct AlphaResampler {
-    AlphaResampler(const MConditionalTranslationModel& m) : m_(m) {}
-    const MConditionalTranslationModel& m_;
-    double operator()(const double& proposed_strength) const {
-      return m_.log_likelihood(m_.d, proposed_strength);
-    }
-  };
-
-  void ResampleHyperparameters(MT19937* rng) {
-    typename std::tr1::unordered_map<std::vector<WordID>, MFCR<1,TRule>, boost::hash<std::vector<WordID> > >::iterator it;
-#if 1
-    for (it = r.begin(); it != r.end(); ++it) {
-      it->second.resample_hyperparameters(rng);
-    }
-#else
-    const unsigned nloop = 5;
-    const unsigned niterations = 10;
-    DiscountResampler dr(*this);
-    AlphaResampler ar(*this);
-    for (int iter = 0; iter < nloop; ++iter) {
-      strength = slice_sampler1d(ar, strength, *rng, -d + std::numeric_limits<double>::min(),
-                              std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
-      double min_discount = std::numeric_limits<double>::min();
-      if (strength < 0.0) min_discount -= strength;
-      d = slice_sampler1d(dr, d, *rng, min_discount,
-                          1.0, 0.0, niterations, 100*niterations);
-    }
-    strength = slice_sampler1d(ar, strength, *rng, -d,
-                            std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
-    std::cerr << "MConditionalTranslationModel(d=" << d << ",s=" << strength << ") = " << log_likelihood(d, strength) << std::endl;
-    for (it = r.begin(); it != r.end(); ++it) {
-      it->second.set_discount(d);
-      it->second.set_strength(strength);
-    }
-#endif
-  }
-
-  int DecrementRule(const TRule& rule, MT19937* rng) {
-    RuleModelHash::iterator it = r.find(rule.f_);
-    assert(it != r.end());
-    const TableCount delta = it->second.decrement(rule, rng);
-    if (delta.count) {
-      if (it->second.num_customers() == 0) r.erase(it);
-    }
-    return delta.count;
-  }
-
-  int IncrementRule(const TRule& rule, MT19937* rng) {
-    RuleModelHash::iterator it = r.find(rule.f_);
-    if (it == r.end()) {
-      //it = r.insert(make_pair(rule.f_, MFCR<1,TRule>(d, strength))).first;
-      it = r.insert(make_pair(rule.f_, MFCR<1,TRule>(1,1,1,1,0.6, -0.12))).first;
-    }
-    p0s[0] = rp0(rule); 
-    TableCount delta = it->second.increment(rule, p0s.begin(), lambdas.begin(), rng);
-    return delta.count;
-  }
-
-  prob_t RuleProbability(const TRule& rule) const {
-    prob_t p;
-    RuleModelHash::const_iterator it = r.find(rule.f_);
-    if (it == r.end()) {
-      p = rp0(rule);
-    } else {
-      p0s[0] = rp0(rule);
-      p = it->second.prob(rule, p0s.begin(), lambdas.begin());
-    }
-    return p;
-  }
-
-  prob_t Likelihood() const {
-    prob_t p; p.logeq(log_likelihood(d, strength));
-    return p;
-  }
-
-  const ConditionalBaseMeasure& rp0;
-  typedef std::tr1::unordered_map<std::vector<WordID>,
-                                  MFCR<1, TRule>,
-                                  boost::hash<std::vector<WordID> > > RuleModelHash;
-  RuleModelHash r;
-  double d, strength;
-  std::vector<prob_t> lambdas;
-  mutable std::vector<prob_t> p0s;
-};
-
-template <typename ConditionalBaseMeasure>
-struct ConditionalTranslationModel {
-  explicit ConditionalTranslationModel(ConditionalBaseMeasure& rcp0) :
-    rp0(rcp0) {}
-
-  void Summary() const {
-    std::cerr << "Number of conditioning contexts: " << r.size() << std::endl;
-    for (RuleModelHash::const_iterator it = r.begin(); it != r.end(); ++it) {
-      std::cerr << TD::GetString(it->first) << "   \t(\\alpha = " << it->second.alpha() << ") --------------------------" << std::endl;
-      for (CCRP_NoTable<TRule>::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
-        std::cerr << "   " << i2->second << '\t' << i2->first << std::endl;
-    }
-  }
-
-  void ResampleHyperparameters(MT19937* rng) {
-    for (RuleModelHash::iterator it = r.begin(); it != r.end(); ++it)
-      it->second.resample_hyperparameters(rng);
-  } 
-
-  int DecrementRule(const TRule& rule) {
-    RuleModelHash::iterator it = r.find(rule.f_);
-    assert(it != r.end());    
-    int count = it->second.decrement(rule);
-    if (count) {
-      if (it->second.num_customers() == 0) r.erase(it);
-    }
-    return count;
-  }
-
-  int IncrementRule(const TRule& rule) {
-    RuleModelHash::iterator it = r.find(rule.f_);
-    if (it == r.end()) {
-      it = r.insert(make_pair(rule.f_, CCRP_NoTable<TRule>(1.0, 1.0, 8.0))).first;
-    } 
-    int count = it->second.increment(rule);
-    return count;
-  }
-
-  void IncrementRules(const std::vector<TRulePtr>& rules) {
-    for (int i = 0; i < rules.size(); ++i)
-      IncrementRule(*rules[i]);
-  }
-
-  void DecrementRules(const std::vector<TRulePtr>& rules) {
-    for (int i = 0; i < rules.size(); ++i)
-      DecrementRule(*rules[i]);
-  }
-
-  prob_t RuleProbability(const TRule& rule) const {
-    prob_t p;
-    RuleModelHash::const_iterator it = r.find(rule.f_);
-    if (it == r.end()) {
-      p.logeq(log(rp0(rule)));
-    } else {
-      p.logeq(it->second.logprob(rule, log(rp0(rule))));
-    }
-    return p;
-  }
-
-  prob_t Likelihood() const {
-    prob_t p = prob_t::One();
-    for (RuleModelHash::const_iterator it = r.begin(); it != r.end(); ++it) {
-      prob_t q; q.logeq(it->second.log_crp_prob());
-      p *= q;
-      for (CCRP_NoTable<TRule>::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
-        p *= rp0(i2->first);
-    }
-    return p;
-  }
-
-  const ConditionalBaseMeasure& rp0;
-  typedef std::tr1::unordered_map<std::vector<WordID>,
-                                  CCRP_NoTable<TRule>,
-                                  boost::hash<std::vector<WordID> > > RuleModelHash;
-  RuleModelHash r;
-};
-
-template <typename ConditionalBaseMeasure>
-struct ConditionalParallelSegementationModel {
-  explicit ConditionalParallelSegementationModel(ConditionalBaseMeasure& rcp0) :
-    tmodel(rcp0), base(prob_t::One()), aligns(1,1) {}
-
-  ConditionalTranslationModel<ConditionalBaseMeasure> tmodel;
-
-  void DecrementRule(const TRule& rule) {
-    tmodel.DecrementRule(rule);
-  }
-
-  void IncrementRule(const TRule& rule) {
-    tmodel.IncrementRule(rule);
-  }
-
-  void IncrementRulesAndAlignments(const std::vector<TRulePtr>& rules) {
-    tmodel.IncrementRules(rules);
-    for (int i = 0; i < rules.size(); ++i) {
-      IncrementAlign(rules[i]->f_.size());
-    }
-  }
-
-  void DecrementRulesAndAlignments(const std::vector<TRulePtr>& rules) {
-    tmodel.DecrementRules(rules);
-    for (int i = 0; i < rules.size(); ++i) {
-      DecrementAlign(rules[i]->f_.size());
-    }
-  }
-
-  prob_t RuleProbability(const TRule& rule) const {
-    return tmodel.RuleProbability(rule);
-  }
-
-  void IncrementAlign(unsigned span) {
-    if (aligns.increment(span)) {
-      // TODO
-    }
-  }
-
-  void DecrementAlign(unsigned span) {
-    if (aligns.decrement(span)) {
-      // TODO
-    }
-  }
-
-  prob_t AlignProbability(unsigned span) const {
-    prob_t p;
-    p.logeq(aligns.logprob(span, Md::log_poisson(span, 1.0)));
-    return p;
-  }
-
-  prob_t Likelihood() const {
-    prob_t p; p.logeq(aligns.log_crp_prob());
-    p *= base;
-    p *= tmodel.Likelihood();
-    return p;
-  }
-
-  prob_t base;
-  CCRP_NoTable<unsigned> aligns;
-};
-
-#endif
-
diff --git a/gi/pf/condnaive.cc b/gi/pf/condnaive.cc
deleted file mode 100644
index 419731ac..00000000
--- a/gi/pf/condnaive.cc
+++ /dev/null
@@ -1,298 +0,0 @@
-#include <iostream>
-#include <tr1/memory>
-#include <queue>
-
-#include <boost/multi_array.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "base_distributions.h"
-#include "monotonic_pseg.h"
-#include "conditional_pseg.h"
-#include "trule.h"
-#include "tdict.h"
-#include "filelib.h"
-#include "dict.h"
-#include "sampler.h"
-#include "ccrp_nt.h"
-#include "corpus.h"
-
-using namespace std;
-using namespace std::tr1;
-namespace po = boost::program_options;
-
-static unsigned kMAX_SRC_PHRASE;
-static unsigned kMAX_TRG_PHRASE;
-
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
-  po::options_description opts("Configuration options");
-  opts.add_options()
-        ("samples,s",po::value<unsigned>()->default_value(1000),"Number of samples")
-        ("input,i",po::value<string>(),"Read parallel data from")
-        ("max_src_phrase",po::value<unsigned>()->default_value(4),"Maximum length of source language phrases")
-        ("max_trg_phrase",po::value<unsigned>()->default_value(4),"Maximum length of target language phrases")
-        ("model1,m",po::value<string>(),"Model 1 parameters (used in base distribution)")
-        ("model1_interpolation_weight",po::value<double>()->default_value(0.95),"Mixing proportion of model 1 with uniform target distribution")
-        ("random_seed,S",po::value<uint32_t>(), "Random seed");
-  po::options_description clo("Command line options");
-  clo.add_options()
-        ("config", po::value<string>(), "Configuration file")
-        ("help,h", "Print this help message and exit");
-  po::options_description dconfig_options, dcmdline_options;
-  dconfig_options.add(opts);
-  dcmdline_options.add(opts).add(clo);
-  
-  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
-  if (conf->count("config")) {
-    ifstream config((*conf)["config"].as<string>().c_str());
-    po::store(po::parse_config_file(config, dconfig_options), *conf);
-  }
-  po::notify(*conf);
-
-  if (conf->count("help") || (conf->count("input") == 0)) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-boost::shared_ptr<MT19937> prng;
-
-struct ModelAndData {
-  explicit ModelAndData(ConditionalParallelSegementationModel<PhraseConditionalBase>& m, const vector<vector<int> >& ce, const vector<vector<int> >& cf, const set<int>& ve, const set<int>& vf) :
-     model(m),
-     rng(&*prng),
-     corpuse(ce),
-     corpusf(cf),
-     vocabe(ve),
-     vocabf(vf),
-     mh_samples(),
-     mh_rejects(),
-     kX(-TD::Convert("X")),
-     derivations(corpuse.size()) {}
-
-  void ResampleHyperparameters() {
-  }
-
-  void InstantiateRule(const pair<short,short>& from,
-                       const pair<short,short>& to,
-                       const vector<int>& sentf,
-                       const vector<int>& sente,
-                       TRule* rule) const {
-    rule->f_.clear();
-    rule->e_.clear();
-    rule->lhs_ = kX;
-    for (short i = from.first; i < to.first; ++i)
-      rule->f_.push_back(sentf[i]);
-    for (short i = from.second; i < to.second; ++i)
-      rule->e_.push_back(sente[i]);
-  }
-
-  void DecrementDerivation(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) {
-    if (d.size() < 2) return;
-    TRule x;
-    for (int i = 1; i < d.size(); ++i) {
-      InstantiateRule(d[i], d[i-1], sentf, sente, &x);
-      model.DecrementRule(x);
-      model.DecrementAlign(x.f_.size());
-    }
-  }
-
-  void PrintDerivation(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) {
-    if (d.size() < 2) return;
-    TRule x;
-    for (int i = 1; i < d.size(); ++i) {
-      InstantiateRule(d[i], d[i-1], sentf, sente, &x);
-      cerr << i << '/' << (d.size() - 1) << ": " << x << endl;
-    }
-  }
-
-  void IncrementDerivation(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) {
-    if (d.size() < 2) return;
-    TRule x;
-    for (int i = 1; i < d.size(); ++i) {
-      InstantiateRule(d[i], d[i-1], sentf, sente, &x);
-      model.IncrementRule(x);
-      model.IncrementAlign(x.f_.size());
-    }
-  }
-
-  prob_t Likelihood() const {
-    return model.Likelihood();
-  }
-
-  prob_t DerivationProposalProbability(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) const {
-    prob_t p = prob_t::One();
-    TRule x;
-    for (int i = 1; i < d.size(); ++i) {
-      InstantiateRule(d[i], d[i-1], sentf, sente, &x);
-      p *= model.RuleProbability(x);
-      p *= model.AlignProbability(x.f_.size());
-    }
-    return p;
-  }
-
-  void Sample();
-
-  ConditionalParallelSegementationModel<PhraseConditionalBase>& model;
-  MT19937* rng;
-  const vector<vector<int> >& corpuse, corpusf;
-  const set<int>& vocabe, vocabf;
-  unsigned mh_samples, mh_rejects;
-  const int kX;
-  vector<vector<pair<short, short> > > derivations;
-};
-
-void ModelAndData::Sample() {
-  unsigned MAXK = kMAX_SRC_PHRASE;
-  unsigned MAXL = kMAX_TRG_PHRASE;
-  TRule x;
-  x.lhs_ = -TD::Convert("X");
-
-  for (int samples = 0; samples < 1000; ++samples) {
-    if (samples % 1 == 0 && samples > 0) {
-      //ResampleHyperparameters();
-      cerr << " [" << samples << " LLH=" << log(Likelihood()) << " MH=" << ((double)mh_rejects / mh_samples) << "]\n";
-      for (int i = 0; i < 10; ++i) {
-        cerr << "SENTENCE: " << TD::GetString(corpusf[i]) << " ||| " << TD::GetString(corpuse[i]) << endl;
-        PrintDerivation(derivations[i], corpusf[i], corpuse[i]);
-      }
-      static TRule xx("[X] ||| w n ||| s h ||| X=0");
-      const CCRP_NoTable<TRule>& dcrp = model.tmodel.r.find(xx.f_)->second;
-      for (CCRP_NoTable<TRule>::const_iterator it = dcrp.begin(); it != dcrp.end(); ++it) {
-        cerr << "\t" << it->second << "\t" << it->first << endl;
-      }
-    }
-    cerr << '.' << flush;
-    for (int s = 0; s < corpuse.size(); ++s) {
-      const vector<int>& sentf = corpusf[s];
-      const vector<int>& sente = corpuse[s];
-//      cerr << "  CUSTOMERS: " << rules.num_customers() << endl;
-//      cerr << "SENTENCE: " << TD::GetString(sentf) << " ||| " << TD::GetString(sente) << endl;
-
-      vector<pair<short, short> >& deriv = derivations[s];
-      const prob_t p_cur = Likelihood();
-      DecrementDerivation(deriv, sentf, sente);
-
-      boost::multi_array<prob_t, 2> a(boost::extents[sentf.size() + 1][sente.size() + 1]);
-      boost::multi_array<prob_t, 4> trans(boost::extents[sentf.size() + 1][sente.size() + 1][MAXK][MAXL]);
-      a[0][0] = prob_t::One();
-      for (int i = 0; i < sentf.size(); ++i) {
-        for (int j = 0; j < sente.size(); ++j) {
-          const prob_t src_a = a[i][j];
-          x.f_.clear();
-          for (int k = 1; k <= MAXK; ++k) {
-            if (i + k > sentf.size()) break;
-            x.f_.push_back(sentf[i + k - 1]);
-            x.e_.clear();
-            const prob_t p_span = model.AlignProbability(k);  // prob of consuming this much source
-            for (int l = 1; l <= MAXL; ++l) {
-              if (j + l > sente.size()) break;
-              x.e_.push_back(sente[j + l - 1]);
-              trans[i][j][k - 1][l - 1] = model.RuleProbability(x) * p_span;
-              a[i + k][j + l] += src_a * trans[i][j][k - 1][l - 1];
-            }
-          }
-        }
-      }
-//      cerr << "Inside: " << log(a[sentf.size()][sente.size()]) << endl;
-      const prob_t q_cur = DerivationProposalProbability(deriv, sentf, sente);
-
-      vector<pair<short,short> > newderiv;
-      int cur_i = sentf.size();
-      int cur_j = sente.size();
-      while(cur_i > 0 && cur_j > 0) {
-        newderiv.push_back(pair<short,short>(cur_i, cur_j));
-//        cerr << "NODE: (" << cur_i << "," << cur_j << ")\n";
-        SampleSet<prob_t> ss;
-        vector<pair<short,short> > nexts;
-        for (int k = 1; k <= MAXK; ++k) {
-          const int hyp_i = cur_i - k;
-          if (hyp_i < 0) break;
-          for (int l = 1; l <= MAXL; ++l) {
-            const int hyp_j = cur_j - l;
-            if (hyp_j < 0) break;
-            const prob_t& inside = a[hyp_i][hyp_j];
-            if (inside == prob_t::Zero()) continue;
-            const prob_t& transp = trans[hyp_i][hyp_j][k - 1][l - 1];
-            if (transp == prob_t::Zero()) continue;
-            const prob_t p = inside * transp;
-            ss.add(p);
-            nexts.push_back(pair<short,short>(hyp_i, hyp_j));
-//            cerr << "    (" << hyp_i << "," << hyp_j << ")  <--- " << log(p) << endl;
-          }
-        }
-//        cerr << "  sample set has " << nexts.size() << " elements.\n";
-        const int selected = rng->SelectSample(ss);
-        cur_i = nexts[selected].first;
-        cur_j = nexts[selected].second;
-      }
-      newderiv.push_back(pair<short,short>(0,0));
-      const prob_t q_new = DerivationProposalProbability(newderiv, sentf, sente);
-      IncrementDerivation(newderiv, sentf, sente);
-//      cerr << "SANITY: " << q_new << "  " <<log(DerivationProposalProbability(newderiv, sentf, sente)) << endl;
-      if (deriv.empty()) { deriv = newderiv; continue; }
-      ++mh_samples;
-
-      if (deriv != newderiv) {
-        const prob_t p_new = Likelihood();
-//        cerr << "p_cur=" << log(p_cur) << "\t p_new=" << log(p_new) << endl;
-//        cerr << "q_cur=" << log(q_cur) << "\t q_new=" << log(q_new) << endl;
-        if (!rng->AcceptMetropolisHastings(p_new, p_cur, q_new, q_cur)) {
-          ++mh_rejects;
-          DecrementDerivation(newderiv, sentf, sente);
-          IncrementDerivation(deriv, sentf, sente);
-        } else {
-//          cerr << "  ACCEPT\n";
-          deriv = newderiv;
-        }
-      }
-    }
-  }
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-  kMAX_TRG_PHRASE = conf["max_trg_phrase"].as<unsigned>();
-  kMAX_SRC_PHRASE = conf["max_src_phrase"].as<unsigned>();
-
-  if (!conf.count("model1")) {
-    cerr << argv[0] << "Please use --model1 to specify model 1 parameters\n";
-    return 1;
-  }
-  if (conf.count("random_seed"))
-    prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
-  else
-    prng.reset(new MT19937);
-//  MT19937& rng = *prng;
-
-  vector<vector<int> > corpuse, corpusf;
-  set<int> vocabe, vocabf;
-  corpus::ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe);
-  cerr << "f-Corpus size: " << corpusf.size() << " sentences\n";
-  cerr << "f-Vocabulary size: " << vocabf.size() << " types\n";
-  cerr << "f-Corpus size: " << corpuse.size() << " sentences\n";
-  cerr << "f-Vocabulary size: " << vocabe.size() << " types\n";
-  assert(corpusf.size() == corpuse.size());
-
-  Model1 m1(conf["model1"].as<string>());
-
-  PhraseConditionalBase pcb0(m1, conf["model1_interpolation_weight"].as<double>(), vocabe.size());
-  ConditionalParallelSegementationModel<PhraseConditionalBase> x(pcb0);  
-
-  ModelAndData posterior(x, corpuse, corpusf, vocabe, vocabf);
-  posterior.Sample();
-
-  TRule r1("[X] ||| x ||| l e ||| X=0");
-  TRule r2("[X] ||| A ||| a d ||| X=0");
-  TRule r3("[X] ||| n ||| e r ||| X=0");
-  TRule r4("[X] ||| x A n ||| b l a g ||| X=0");
-
-  PhraseConditionalUninformativeBase u0(vocabe.size());
-
-  cerr << (pcb0(r1)*pcb0(r2)*pcb0(r3)) << endl;
-  cerr << (u0(r4)) << endl;
-
-  return 0;
-}
-
diff --git a/gi/pf/corpus.cc b/gi/pf/corpus.cc
deleted file mode 100644
index cb6e4ed7..00000000
--- a/gi/pf/corpus.cc
+++ /dev/null
@@ -1,62 +0,0 @@
-#include "corpus.h"
-
-#include <set>
-#include <vector>
-#include <string>
-
-#include "tdict.h"
-#include "filelib.h"
-
-using namespace std;
-
-namespace corpus {
-
-void ReadParallelCorpus(const string& filename,
-                vector<vector<WordID> >* f,
-                vector<vector<WordID> >* e,
-                set<WordID>* vocab_f,
-                set<WordID>* vocab_e) {
-  f->clear();
-  e->clear();
-  vocab_f->clear();
-  vocab_e->clear();
-  ReadFile rf(filename);
-  istream* in = rf.stream();
-  assert(*in);
-  string line;
-  unsigned lc = 0;
-  const WordID kDIV = TD::Convert("|||");
-  vector<WordID> tmp;
-  while(getline(*in, line)) {
-    ++lc;
-    e->push_back(vector<int>());
-    f->push_back(vector<int>());
-    vector<int>& le = e->back();
-    vector<int>& lf = f->back();
-    tmp.clear();
-    TD::ConvertSentence(line, &tmp);
-    bool isf = true;
-    for (unsigned i = 0; i < tmp.size(); ++i) {
-      const int cur = tmp[i];
-      if (isf) {
-        if (kDIV == cur) {
-          isf = false;
-        } else {
-          lf.push_back(cur);
-          vocab_f->insert(cur);
-        }
-      } else {
-        if (cur == kDIV) {
-          cerr << "ERROR in " << lc << ": " << line << endl << endl;
-          abort();
-        }
-        le.push_back(cur);
-        vocab_e->insert(cur);
-      }
-    }
-    assert(isf == false);
-  }
-}
-
-}
-
diff --git a/gi/pf/corpus.h b/gi/pf/corpus.h
deleted file mode 100644
index e7febdb7..00000000
--- a/gi/pf/corpus.h
+++ /dev/null
@@ -1,19 +0,0 @@
-#ifndef _CORPUS_H_
-#define _CORPUS_H_
-
-#include <string>
-#include <vector>
-#include <set>
-#include "wordid.h"
-
-namespace corpus {
-
-void ReadParallelCorpus(const std::string& filename,
-                std::vector<std::vector<WordID> >* f,
-                std::vector<std::vector<WordID> >* e,
-                std::set<WordID>* vocab_f,
-                std::set<WordID>* vocab_e);
-
-}
-
-#endif
diff --git a/gi/pf/dpnaive.cc b/gi/pf/dpnaive.cc
deleted file mode 100644
index 75ccad72..00000000
--- a/gi/pf/dpnaive.cc
+++ /dev/null
@@ -1,301 +0,0 @@
-#include <iostream>
-#include <tr1/memory>
-#include <queue>
-
-#include <boost/multi_array.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "base_distributions.h"
-#include "monotonic_pseg.h"
-#include "trule.h"
-#include "tdict.h"
-#include "filelib.h"
-#include "dict.h"
-#include "sampler.h"
-#include "ccrp_nt.h"
-#include "corpus.h"
-
-using namespace std;
-using namespace std::tr1;
-namespace po = boost::program_options;
-
-static unsigned kMAX_SRC_PHRASE;
-static unsigned kMAX_TRG_PHRASE;
-
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
-  po::options_description opts("Configuration options");
-  opts.add_options()
-        ("samples,s",po::value<unsigned>()->default_value(1000),"Number of samples")
-        ("input,i",po::value<string>(),"Read parallel data from")
-        ("max_src_phrase",po::value<unsigned>()->default_value(4),"Maximum length of source language phrases")
-        ("max_trg_phrase",po::value<unsigned>()->default_value(4),"Maximum length of target language phrases")
-        ("model1,m",po::value<string>(),"Model 1 parameters (used in base distribution)")
-        ("inverse_model1,M",po::value<string>(),"Inverse Model 1 parameters (used in base distribution)")
-        ("model1_interpolation_weight",po::value<double>()->default_value(0.95),"Mixing proportion of model 1 with uniform target distribution")
-        ("random_seed,S",po::value<uint32_t>(), "Random seed");
-  po::options_description clo("Command line options");
-  clo.add_options()
-        ("config", po::value<string>(), "Configuration file")
-        ("help,h", "Print this help message and exit");
-  po::options_description dconfig_options, dcmdline_options;
-  dconfig_options.add(opts);
-  dcmdline_options.add(opts).add(clo);
-  
-  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
-  if (conf->count("config")) {
-    ifstream config((*conf)["config"].as<string>().c_str());
-    po::store(po::parse_config_file(config, dconfig_options), *conf);
-  }
-  po::notify(*conf);
-
-  if (conf->count("help") || (conf->count("input") == 0)) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-boost::shared_ptr<MT19937> prng;
-
-template <typename Base>
-struct ModelAndData {
-  explicit ModelAndData(MonotonicParallelSegementationModel<PhraseJointBase_BiDir>& m, const Base& b, const vector<vector<int> >& ce, const vector<vector<int> >& cf, const set<int>& ve, const set<int>& vf) :
-     model(m),
-     rng(&*prng),
-     p0(b),
-     baseprob(prob_t::One()),
-     corpuse(ce),
-     corpusf(cf),
-     vocabe(ve),
-     vocabf(vf),
-     mh_samples(),
-     mh_rejects(),
-     kX(-TD::Convert("X")),
-     derivations(corpuse.size()) {}
-
-  void ResampleHyperparameters() {
-  }
-
-  void InstantiateRule(const pair<short,short>& from,
-                       const pair<short,short>& to,
-                       const vector<int>& sentf,
-                       const vector<int>& sente,
-                       TRule* rule) const {
-    rule->f_.clear();
-    rule->e_.clear();
-    rule->lhs_ = kX;
-    for (short i = from.first; i < to.first; ++i)
-      rule->f_.push_back(sentf[i]);
-    for (short i = from.second; i < to.second; ++i)
-      rule->e_.push_back(sente[i]);
-  }
-
-  void DecrementDerivation(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) {
-    if (d.size() < 2) return;
-    TRule x;
-    for (int i = 1; i < d.size(); ++i) {
-      InstantiateRule(d[i], d[i-1], sentf, sente, &x);
-      model.DecrementRule(x);
-      model.DecrementContinue();
-    }
-    model.DecrementStop();
-  }
-
-  void PrintDerivation(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) {
-    if (d.size() < 2) return;
-    TRule x;
-    for (int i = 1; i < d.size(); ++i) {
-      InstantiateRule(d[i], d[i-1], sentf, sente, &x);
-      cerr << i << '/' << (d.size() - 1) << ": " << x << endl;
-    }
-  }
-
-  void IncrementDerivation(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) {
-    if (d.size() < 2) return;
-    TRule x;
-    for (int i = 1; i < d.size(); ++i) {
-      InstantiateRule(d[i], d[i-1], sentf, sente, &x);
-      model.IncrementRule(x);
-      model.IncrementContinue();
-    }
-    model.IncrementStop();
-  }
-
-  prob_t Likelihood() const {
-    return model.Likelihood();
-  }
-
-  prob_t DerivationProposalProbability(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) const {
-    prob_t p = model.StopProbability();
-    if (d.size() < 2) return p;
-    TRule x;
-    const prob_t p_cont = model.ContinueProbability();
-    for (int i = 1; i < d.size(); ++i) {
-      InstantiateRule(d[i], d[i-1], sentf, sente, &x);
-      p *= p_cont;
-      p *= model.RuleProbability(x);
-    }
-    return p;
-  }
-
-  void Sample();
-
-  MonotonicParallelSegementationModel<PhraseJointBase_BiDir>& model;
-  MT19937* rng;
-  const Base& p0;
-  prob_t baseprob; // cached value of generating the table table labels from p0
-                   // this can't be used if we go to a hierarchical prior!
-  const vector<vector<int> >& corpuse, corpusf;
-  const set<int>& vocabe, vocabf;
-  unsigned mh_samples, mh_rejects;
-  const int kX;
-  vector<vector<pair<short, short> > > derivations;
-};
-
-template <typename Base>
-void ModelAndData<Base>::Sample() {
-  unsigned MAXK = kMAX_SRC_PHRASE;
-  unsigned MAXL = kMAX_TRG_PHRASE;
-  TRule x;
-  x.lhs_ = -TD::Convert("X");
-  for (int samples = 0; samples < 1000; ++samples) {
-    if (samples % 1 == 0 && samples > 0) {
-      //ResampleHyperparameters();
-      cerr << " [" << samples << " LLH=" << log(Likelihood()) << " MH=" << ((double)mh_rejects / mh_samples) << "]\n";
-      for (int i = 0; i < 10; ++i) {
-        cerr << "SENTENCE: " << TD::GetString(corpusf[i]) << " ||| " << TD::GetString(corpuse[i]) << endl;
-        PrintDerivation(derivations[i], corpusf[i], corpuse[i]);
-      }
-    }
-    cerr << '.' << flush;
-    for (int s = 0; s < corpuse.size(); ++s) {
-      const vector<int>& sentf = corpusf[s];
-      const vector<int>& sente = corpuse[s];
-//      cerr << "  CUSTOMERS: " << rules.num_customers() << endl;
-//      cerr << "SENTENCE: " << TD::GetString(sentf) << " ||| " << TD::GetString(sente) << endl;
-
-      vector<pair<short, short> >& deriv = derivations[s];
-      const prob_t p_cur = Likelihood();
-      DecrementDerivation(deriv, sentf, sente);
-
-      boost::multi_array<prob_t, 2> a(boost::extents[sentf.size() + 1][sente.size() + 1]);
-      boost::multi_array<prob_t, 4> trans(boost::extents[sentf.size() + 1][sente.size() + 1][MAXK][MAXL]);
-      a[0][0] = prob_t::One();
-      const prob_t q_stop = model.StopProbability();
-      const prob_t q_cont = model.ContinueProbability();
-      for (int i = 0; i < sentf.size(); ++i) {
-        for (int j = 0; j < sente.size(); ++j) {
-          const prob_t src_a = a[i][j];
-          x.f_.clear();
-          for (int k = 1; k <= MAXK; ++k) {
-            if (i + k > sentf.size()) break;
-            x.f_.push_back(sentf[i + k - 1]);
-            x.e_.clear();
-            for (int l = 1; l <= MAXL; ++l) {
-              if (j + l > sente.size()) break;
-              x.e_.push_back(sente[j + l - 1]);
-              const bool stop_now = ((j + l) == sente.size()) && ((i + k) == sentf.size());
-              const prob_t& cp = stop_now ? q_stop : q_cont;
-              trans[i][j][k - 1][l - 1] = model.RuleProbability(x) * cp;
-              a[i + k][j + l] += src_a * trans[i][j][k - 1][l - 1];
-            }
-          }
-        }
-      }
-//      cerr << "Inside: " << log(a[sentf.size()][sente.size()]) << endl;
-      const prob_t q_cur = DerivationProposalProbability(deriv, sentf, sente);
-
-      vector<pair<short,short> > newderiv;
-      int cur_i = sentf.size();
-      int cur_j = sente.size();
-      while(cur_i > 0 && cur_j > 0) {
-        newderiv.push_back(pair<short,short>(cur_i, cur_j));
-//        cerr << "NODE: (" << cur_i << "," << cur_j << ")\n";
-        SampleSet<prob_t> ss;
-        vector<pair<short,short> > nexts;
-        for (int k = 1; k <= MAXK; ++k) {
-          const int hyp_i = cur_i - k;
-          if (hyp_i < 0) break;
-          for (int l = 1; l <= MAXL; ++l) {
-            const int hyp_j = cur_j - l;
-            if (hyp_j < 0) break;
-            const prob_t& inside = a[hyp_i][hyp_j];
-            if (inside == prob_t::Zero()) continue;
-            const prob_t& transp = trans[hyp_i][hyp_j][k - 1][l - 1];
-            if (transp == prob_t::Zero()) continue;
-            const prob_t p = inside * transp;
-            ss.add(p);
-            nexts.push_back(pair<short,short>(hyp_i, hyp_j));
-//            cerr << "    (" << hyp_i << "," << hyp_j << ")  <--- " << log(p) << endl;
-          }
-        }
-//        cerr << "  sample set has " << nexts.size() << " elements.\n";
-        const int selected = rng->SelectSample(ss);
-        cur_i = nexts[selected].first;
-        cur_j = nexts[selected].second;
-      }
-      newderiv.push_back(pair<short,short>(0,0));
-      const prob_t q_new = DerivationProposalProbability(newderiv, sentf, sente);
-      IncrementDerivation(newderiv, sentf, sente);
-//      cerr << "SANITY: " << q_new << "  " <<log(DerivationProposalProbability(newderiv, sentf, sente)) << endl;
-      if (deriv.empty()) { deriv = newderiv; continue; }
-      ++mh_samples;
-
-      if (deriv != newderiv) {
-        const prob_t p_new = Likelihood();
-//        cerr << "p_cur=" << log(p_cur) << "\t p_new=" << log(p_new) << endl;
-//        cerr << "q_cur=" << log(q_cur) << "\t q_new=" << log(q_new) << endl;
-        if (!rng->AcceptMetropolisHastings(p_new, p_cur, q_new, q_cur)) {
-          ++mh_rejects;
-          DecrementDerivation(newderiv, sentf, sente);
-          IncrementDerivation(deriv, sentf, sente);
-        } else {
-//          cerr << "  ACCEPT\n";
-          deriv = newderiv;
-        }
-      }
-    }
-  }
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-  kMAX_TRG_PHRASE = conf["max_trg_phrase"].as<unsigned>();
-  kMAX_SRC_PHRASE = conf["max_src_phrase"].as<unsigned>();
-
-  if (!conf.count("model1")) {
-    cerr << argv[0] << "Please use --model1 to specify model 1 parameters\n";
-    return 1;
-  }
-  if (!conf.count("inverse_model1")) {
-    cerr << argv[0] << "Please use --inverse_model1 to specify inverse model 1 parameters\n";
-    return 1;
-  }
-  if (conf.count("random_seed"))
-    prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
-  else
-    prng.reset(new MT19937);
-//  MT19937& rng = *prng;
-
-  vector<vector<int> > corpuse, corpusf;
-  set<int> vocabe, vocabf;
-  corpus::ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe);
-  cerr << "f-Corpus size: " << corpusf.size() << " sentences\n";
-  cerr << "f-Vocabulary size: " << vocabf.size() << " types\n";
-  cerr << "f-Corpus size: " << corpuse.size() << " sentences\n";
-  cerr << "f-Vocabulary size: " << vocabe.size() << " types\n";
-  assert(corpusf.size() == corpuse.size());
-
-  Model1 m1(conf["model1"].as<string>());
-  Model1 invm1(conf["inverse_model1"].as<string>());
-//  PhraseJointBase lp0(m1, conf["model1_interpolation_weight"].as<double>(), vocabe.size(), vocabf.size());
-  PhraseJointBase_BiDir alp0(m1, invm1, conf["model1_interpolation_weight"].as<double>(), vocabe.size(), vocabf.size());
-  MonotonicParallelSegementationModel<PhraseJointBase_BiDir> m(alp0);
-
-  ModelAndData<PhraseJointBase_BiDir> posterior(m, alp0, corpuse, corpusf, vocabe, vocabf);
-  posterior.Sample();
-
-  return 0;
-}
-
diff --git a/gi/pf/guess-translits.pl b/gi/pf/guess-translits.pl
deleted file mode 100755
index d00c2168..00000000
--- a/gi/pf/guess-translits.pl
+++ /dev/null
@@ -1,72 +0,0 @@
-#!/usr/bin/perl -w
-use strict;
-use utf8;
-
-my $MIN_PMI = -3;
-
-my %fs;
-my %es;
-my %ef;
-
-die "Usage: $0 < input.utf8.txt\n" if scalar @ARGV > 0;
-
-binmode(STDIN,":utf8");
-binmode(STDOUT,":utf8");
-binmode(STDERR,":utf8");
-
-my $tot = 0;
-print STDERR "Reading alignments from STDIN ...\n";
-while(<STDIN>) {
-  chomp;
-  my ($fsent, $esent, $alsent) = split / \|\|\| /;
-  die "Format should be 'foreign sentence ||| english sentence ||| 0-0 1-1 ...'\n" unless defined $fsent && defined $esent && defined $alsent;
-
-  my @fws = split /\s+/, $fsent;  
-  my @ews = split /\s+/, $esent;
-  my @as = split /\s+/, $alsent;
-  my %a2b;
-  my %b2a;
-  for my $ap (@as) {
-    my ($a,$b) = split /-/, $ap;
-    die "BAD INPUT: $_\n" unless defined $a && defined $b;
-    $a2b{$a}->{$b} = 1;
-    $b2a{$b}->{$a} = 1;
-  }
-  for my $a (keys %a2b) {
-    my $bref = $a2b{$a};
-    next unless scalar keys %$bref < 2;
-    my $b = (keys %$bref)[0];
-    next unless scalar keys %{$b2a{$b}} < 2;
-    my $f = $fws[$a];
-    next unless defined $f;
-    next unless length($f) > 3;
-    my $e = $ews[$b];
-    next unless defined $e;
-    next unless length($e) > 3;
-
-    $ef{$f}->{$e}++;
-    $es{$e}++;
-    $fs{$f}++;
-    $tot++;
-  }  
-}
-my $ltot = log($tot);
-my $num = 0;
-print STDERR "Extracting pairs for PMI > $MIN_PMI ...\n";
-for my $f (keys %fs) {
-  my $logf = log($fs{$f});
-  my $esref = $ef{$f};
-  for my $e (keys %$esref) {
-    my $loge = log($es{$e});
-    my $ef = $esref->{$e};
-    my $logef = log($ef);
-    my $pmi = $logef - ($loge + $logf);
-    next if $pmi < $MIN_PMI;
-    my @flets = split //, $f;
-    my @elets = split //, $e;
-    print "@flets ||| @elets\n";
-    $num++;
-  }
-}
-print STDERR "Extracted $num pairs.\n";
-print STDERR "Recommend running:\n   ../../training/model1 -v -d -t -99999 output.txt\n";
diff --git a/gi/pf/hpyp_tm.cc b/gi/pf/hpyp_tm.cc
deleted file mode 100644
index f362d3f8..00000000
--- a/gi/pf/hpyp_tm.cc
+++ /dev/null
@@ -1,133 +0,0 @@
-#include "hpyp_tm.h"
-
-#include <tr1/unordered_map>
-#include <iostream>
-#include <queue>
-
-#include "tdict.h"
-#include "ccrp.h"
-#include "pyp_word_model.h"
-#include "tied_resampler.h"
-
-using namespace std;
-using namespace std::tr1;
-
-struct FreqBinner {
-  FreqBinner(const std::string& fname) { fd_.Load(fname); }
-  unsigned NumberOfBins() const { return fd_.Max() + 1; }
-  unsigned Bin(const WordID& w) const { return fd_.LookUp(w); }
-  FreqDict<unsigned> fd_;
-};
-
-template <typename Base, class Binner = FreqBinner>
-struct ConditionalPYPWordModel {
-  ConditionalPYPWordModel(Base* b, const Binner* bnr = NULL) :
-      base(*b),
-      binner(bnr),
-      btr(binner ? binner->NumberOfBins() + 1u : 2u) {}
-
-  void Summary() const {
-    cerr << "Number of conditioning contexts: " << r.size() << endl;
-    for (RuleModelHash::const_iterator it = r.begin(); it != r.end(); ++it) {
-      cerr << TD::Convert(it->first) << "   \tPYP(d=" << it->second.discount() << ",s=" << it->second.strength() << ") --------------------------" << endl;
-      for (CCRP<vector<WordID> >::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
-        cerr << "   " << i2->second << endl;
-    }
-  }
-
-  void ResampleHyperparameters(MT19937* rng) {
-    btr.ResampleHyperparameters(rng);
-  } 
-
-  prob_t Prob(const WordID src, const vector<WordID>& trglets) const {
-    RuleModelHash::const_iterator it = r.find(src);
-    if (it == r.end()) {
-      return base(trglets);
-    } else {
-      return it->second.prob(trglets, base(trglets));
-    }
-  }
-
-  void Increment(const WordID src, const vector<WordID>& trglets, MT19937* rng) {
-    RuleModelHash::iterator it = r.find(src);
-    if (it == r.end()) {
-      it = r.insert(make_pair(src, CCRP<vector<WordID> >(0.5,1.0))).first;
-      static const WordID kNULL = TD::Convert("NULL");
-      unsigned bin = (src == kNULL ? 0 : 1);
-      if (binner && bin) { bin = binner->Bin(src) + 1; }
-      btr.Add(bin, &it->second);
-    }
-    if (it->second.increment(trglets, base(trglets), rng))
-      base.Increment(trglets, rng);
-  }
-
-  void Decrement(const WordID src, const vector<WordID>& trglets, MT19937* rng) {
-    RuleModelHash::iterator it = r.find(src);
-    assert(it != r.end());
-    if (it->second.decrement(trglets, rng)) {
-      base.Decrement(trglets, rng);
-    }
-  }
-
-  prob_t Likelihood() const {
-    prob_t p = prob_t::One();
-    for (RuleModelHash::const_iterator it = r.begin(); it != r.end(); ++it) {
-      prob_t q; q.logeq(it->second.log_crp_prob());
-      p *= q;
-    }
-    return p;
-  }
-
-  unsigned UniqueConditioningContexts() const {
-    return r.size();
-  }
-
-  // TODO tie PYP hyperparameters based on source word frequency bins
-  Base& base;
-  const Binner* binner;
-  BinTiedResampler<CCRP<vector<WordID> > > btr;
-  typedef unordered_map<WordID, CCRP<vector<WordID> > > RuleModelHash;
-  RuleModelHash r;
-};
-
-HPYPLexicalTranslation::HPYPLexicalTranslation(const vector<vector<WordID> >& lets,
-                                               const unsigned vocab_size,
-                                               const unsigned num_letters) :
-    letters(lets),
-    base(vocab_size, num_letters, 5),
-    up0(new PYPWordModel<PoissonUniformWordModel>(&base)),
-    tmodel(new ConditionalPYPWordModel<PYPWordModel<PoissonUniformWordModel> >(up0, new FreqBinner("10k.freq"))),
-    kX(-TD::Convert("X")) {}
-
-void HPYPLexicalTranslation::Summary() const {
-  tmodel->Summary();
-  up0->Summary();
-}
-
-prob_t HPYPLexicalTranslation::Likelihood() const {
-  prob_t p = up0->Likelihood();
-  p *= tmodel->Likelihood();
-  return p;
-}
-
-void HPYPLexicalTranslation::ResampleHyperparameters(MT19937* rng) {
-  tmodel->ResampleHyperparameters(rng);
-  up0->ResampleHyperparameters(rng);
-}
-
-unsigned HPYPLexicalTranslation::UniqueConditioningContexts() const {
-  return tmodel->UniqueConditioningContexts();
-}
-
-prob_t HPYPLexicalTranslation::Prob(WordID src, WordID trg) const {
-  return tmodel->Prob(src, letters[trg]);
-}
-
-void HPYPLexicalTranslation::Increment(WordID src, WordID trg, MT19937* rng) {
-  tmodel->Increment(src, letters[trg], rng);
-}
-
-void HPYPLexicalTranslation::Decrement(WordID src, WordID trg, MT19937* rng) {
-  tmodel->Decrement(src, letters[trg], rng);
-}
-
diff --git a/gi/pf/hpyp_tm.h b/gi/pf/hpyp_tm.h
deleted file mode 100644
index af3215ba..00000000
--- a/gi/pf/hpyp_tm.h
+++ /dev/null
@@ -1,38 +0,0 @@
-#ifndef HPYP_LEX_TRANS
-#define HPYP_LEX_TRANS
-
-#include <vector>
-#include "wordid.h"
-#include "prob.h"
-#include "sampler.h"
-#include "freqdict.h"
-#include "poisson_uniform_word_model.h"
-
-struct FreqBinner;
-template <class B> struct PYPWordModel;
-template <typename T, class B> struct ConditionalPYPWordModel;
-
-struct HPYPLexicalTranslation {
-  explicit HPYPLexicalTranslation(const std::vector<std::vector<WordID> >& lets,
-                                 const unsigned vocab_size,
-                                 const unsigned num_letters);
-
-  prob_t Likelihood() const;
-
-  void ResampleHyperparameters(MT19937* rng);
-  prob_t Prob(WordID src, WordID trg) const;  // return p(trg | src)
-  void Summary() const;
-  void Increment(WordID src, WordID trg, MT19937* rng);
-  void Decrement(WordID src, WordID trg, MT19937* rng);
-  unsigned UniqueConditioningContexts() const;
-
- private:
-  const std::vector<std::vector<WordID> >& letters;   // spelling dictionary
-  PoissonUniformWordModel base;  // "generator" of English types
-  PYPWordModel<PoissonUniformWordModel>* up0;  // model English lexicon
-  ConditionalPYPWordModel<PYPWordModel<PoissonUniformWordModel>, FreqBinner>* tmodel;  // translation distributions
-                      // (model English word | French word)
-  const WordID kX;
-};
-
-#endif
diff --git a/gi/pf/itg.cc b/gi/pf/itg.cc
deleted file mode 100644
index 29ec3860..00000000
--- a/gi/pf/itg.cc
+++ /dev/null
@@ -1,275 +0,0 @@
-#include <iostream>
-#include <tr1/memory>
-#include <queue>
-
-#include <boost/functional.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "viterbi.h"
-#include "hg.h"
-#include "trule.h"
-#include "tdict.h"
-#include "filelib.h"
-#include "dict.h"
-#include "sampler.h"
-#include "ccrp_nt.h"
-#include "ccrp_onetable.h"
-
-using namespace std;
-using namespace tr1;
-namespace po = boost::program_options;
-
-ostream& operator<<(ostream& os, const vector<WordID>& p) {
-  os << '[';
-  for (int i = 0; i < p.size(); ++i)
-    os << (i==0 ? "" : " ") << TD::Convert(p[i]);
-  return os << ']';
-}
-
-struct UnigramModel {
-  explicit UnigramModel(const string& fname, unsigned vocab_size, double p0null = 0.05) :
-      use_uniform_(fname.size() == 0),
-      p0null_(p0null),
-      uniform_((1.0 - p0null) / vocab_size),
-      probs_(TD::NumWords() + 1) {
-    if (fname.size() > 0) LoadUnigrams(fname);
-    probs_[0] = p0null_;
-  }
-
-// 
-// \data\
-// ngram 1=9295
-// 
-// \1-grams:
-// -3.191193	"
-
-  void LoadUnigrams(const string& fname) {
-    cerr << "Loading unigram probabilities from " << fname << " ..." << endl;
-    ReadFile rf(fname);
-    string line;
-    istream& in = *rf.stream();
-    assert(in);
-    getline(in, line);
-    assert(line.empty());
-    getline(in, line);
-    assert(line == "\\data\\");
-    getline(in, line);
-    size_t pos = line.find("ngram 1=");
-    assert(pos == 0);
-    assert(line.size() > 8);
-    const size_t num_unigrams = atoi(&line[8]);
-    getline(in, line);
-    assert(line.empty());
-    getline(in, line);
-    assert(line == "\\1-grams:");
-    for (size_t i = 0; i < num_unigrams; ++i) {
-      getline(in, line);
-      assert(line.size() > 0);
-      pos = line.find('\t');
-      assert(pos > 0);
-      assert(pos + 1 < line.size());
-      const WordID w = TD::Convert(line.substr(pos + 1));
-      line[pos] = 0;
-      float p = atof(&line[0]);
-      const prob_t pnon_null(1.0 - p0null_.as_float());
-      if (w < probs_.size()) probs_[w].logeq(p * log(10) + log(pnon_null)); else abort();
-    }
-  }
-
-  const prob_t& operator()(const WordID& w) const {
-    if (!w) return p0null_;
-    if (use_uniform_) return uniform_;
-    return probs_[w];
-  }
-
-  const bool use_uniform_;
-  const prob_t p0null_;
-  const prob_t uniform_;
-  vector<prob_t> probs_;
-};
-
-struct Model1 {
-  explicit Model1(const string& fname) :
-      kNULL(TD::Convert("<eps>")),
-      kZERO() {
-    LoadModel1(fname);
-  }
-
-  void LoadModel1(const string& fname) {
-    cerr << "Loading Model 1 parameters from " << fname << " ..." << endl;
-    ReadFile rf(fname);
-    istream& in = *rf.stream();
-    string line;
-    unsigned lc = 0;
-    while(getline(in, line)) {
-      ++lc;
-      int cur = 0;
-      int start = 0;
-      while(cur < line.size() && line[cur] != ' ') { ++cur; }
-      assert(cur != line.size());
-      line[cur] = 0;
-      const WordID src = TD::Convert(&line[0]);
-      ++cur;
-      start = cur;
-      while(cur < line.size() && line[cur] != ' ') { ++cur; }
-      assert(cur != line.size());
-      line[cur] = 0;
-      WordID trg = TD::Convert(&line[start]);
-      const double logprob = strtod(&line[cur + 1], NULL);
-      if (src >= ttable.size()) ttable.resize(src + 1);
-      ttable[src][trg].logeq(logprob);
-    }
-    cerr << "  read " << lc << " parameters.\n";
-  }
-
-  // returns prob 0 if src or trg is not found!
-  const prob_t& operator()(WordID src, WordID trg) const {
-    if (src == 0) src = kNULL;
-    if (src < ttable.size()) {
-      const map<WordID, prob_t>& cpd = ttable[src];
-      const map<WordID, prob_t>::const_iterator it = cpd.find(trg);
-      if (it != cpd.end())
-        return it->second;
-    }
-    return kZERO;
-  }
-
-  const WordID kNULL;
-  const prob_t kZERO;
-  vector<map<WordID, prob_t> > ttable;
-};
-
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
-  po::options_description opts("Configuration options");
-  opts.add_options()
-        ("samples,s",po::value<unsigned>()->default_value(1000),"Number of samples")
-        ("particles,p",po::value<unsigned>()->default_value(25),"Number of particles")
-        ("input,i",po::value<string>(),"Read parallel data from")
-        ("model1,m",po::value<string>(),"Model 1 parameters (used in base distribution)")
-        ("inverse_model1,M",po::value<string>(),"Inverse Model 1 parameters (used in backward estimate)")
-        ("model1_interpolation_weight",po::value<double>()->default_value(0.95),"Mixing proportion of model 1 with uniform target distribution")
-        ("src_unigram,u",po::value<string>()->default_value(""),"Source unigram distribution; empty for uniform")
-        ("trg_unigram,U",po::value<string>()->default_value(""),"Target unigram distribution; empty for uniform")
-        ("random_seed,S",po::value<uint32_t>(), "Random seed");
-  po::options_description clo("Command line options");
-  clo.add_options()
-        ("config", po::value<string>(), "Configuration file")
-        ("help,h", "Print this help message and exit");
-  po::options_description dconfig_options, dcmdline_options;
-  dconfig_options.add(opts);
-  dcmdline_options.add(opts).add(clo);
-  
-  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
-  if (conf->count("config")) {
-    ifstream config((*conf)["config"].as<string>().c_str());
-    po::store(po::parse_config_file(config, dconfig_options), *conf);
-  }
-  po::notify(*conf);
-
-  if (conf->count("help") || (conf->count("input") == 0)) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-void ReadParallelCorpus(const string& filename,
-                vector<vector<WordID> >* f,
-                vector<vector<WordID> >* e,
-                set<WordID>* vocab_f,
-                set<WordID>* vocab_e) {
-  f->clear();
-  e->clear();
-  vocab_f->clear();
-  vocab_e->clear();
-  istream* in;
-  if (filename == "-")
-    in = &cin;
-  else
-    in = new ifstream(filename.c_str());
-  assert(*in);
-  string line;
-  const WordID kDIV = TD::Convert("|||");
-  vector<WordID> tmp;
-  while(*in) {
-    getline(*in, line);
-    if (line.empty() && !*in) break;
-    e->push_back(vector<int>());
-    f->push_back(vector<int>());
-    vector<int>& le = e->back();
-    vector<int>& lf = f->back();
-    tmp.clear();
-    TD::ConvertSentence(line, &tmp);
-    bool isf = true;
-    for (unsigned i = 0; i < tmp.size(); ++i) {
-      const int cur = tmp[i];
-      if (isf) {
-        if (kDIV == cur) { isf = false; } else {
-          lf.push_back(cur);
-          vocab_f->insert(cur);
-        }
-      } else {
-        assert(cur != kDIV);
-        le.push_back(cur);
-        vocab_e->insert(cur);
-      }
-    }
-    assert(isf == false);
-  }
-  if (in != &cin) delete in;
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-  const unsigned particles = conf["particles"].as<unsigned>();
-  const unsigned samples = conf["samples"].as<unsigned>();
-  TD::Convert("<s>");
-  TD::Convert("</s>");
-  TD::Convert("<unk>");
-  if (!conf.count("model1")) {
-    cerr << argv[0] << "Please use --model1 to specify model 1 parameters\n";
-    return 1;
-  }
-  boost::shared_ptr<MT19937> prng;
-  if (conf.count("random_seed"))
-    prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
-  else
-    prng.reset(new MT19937);
-  MT19937& rng = *prng;
-
-  vector<vector<WordID> > corpuse, corpusf;
-  set<WordID> vocabe, vocabf;
-  cerr << "Reading corpus...\n";
-  ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe);
-  cerr << "F-corpus size: " << corpusf.size() << " sentences\t (" << vocabf.size() << " word types)\n";
-  cerr << "E-corpus size: " << corpuse.size() << " sentences\t (" << vocabe.size() << " word types)\n";
-  assert(corpusf.size() == corpuse.size());
-  UnigramModel src_unigram(conf["src_unigram"].as<string>(), vocabf.size());
-  UnigramModel trg_unigram(conf["trg_unigram"].as<string>(), vocabe.size());
-  const prob_t kHALF(0.5);
-
-  const string kEMPTY = "NULL";
-  const int kLHS = -TD::Convert("X");
-  Model1 m1(conf["model1"].as<string>());
-  Model1 invm1(conf["inverse_model1"].as<string>());
-  for (int si = 0; si < conf["samples"].as<unsigned>(); ++si) {
-    cerr << '.' << flush;
-    for (int ci = 0; ci < corpusf.size(); ++ci) {
-      const vector<WordID>& trg = corpuse[ci];
-      const vector<WordID>& src = corpusf[ci];
-      for (int i = 0; i <= trg.size(); ++i) {
-        const WordID e_i = i > 0 ? trg[i-1] : 0;
-        for (int j = 0; j <= src.size(); ++j) {
-          const WordID f_j = j > 0 ? src[j-1] : 0;
-          if (e_i == 0 && f_j == 0) continue;
-          prob_t je = kHALF * src_unigram(f_j) * m1(f_j,e_i) + kHALF * trg_unigram(e_i) * invm1(e_i,f_j);
-          cerr << "p( " << (e_i ? TD::Convert(e_i) : kEMPTY) << " , " << (f_j ? TD::Convert(f_j) : kEMPTY) << " ) = " << log(je) << endl;
-          if (e_i && f_j)
-            cout << "[X] ||| " << TD::Convert(f_j) << " ||| " << TD::Convert(e_i) << " ||| LogProb=" << log(je) << endl;
-        }
-      }
-    }
-  }
-}
-
diff --git a/gi/pf/learn_cfg.cc b/gi/pf/learn_cfg.cc
deleted file mode 100644
index 1d5126e4..00000000
--- a/gi/pf/learn_cfg.cc
+++ /dev/null
@@ -1,428 +0,0 @@
-#include <iostream>
-#include <tr1/memory>
-#include <queue>
-
-#include <boost/functional.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "inside_outside.h"
-#include "hg.h"
-#include "bottom_up_parser.h"
-#include "fdict.h"
-#include "grammar.h"
-#include "m.h"
-#include "trule.h"
-#include "tdict.h"
-#include "filelib.h"
-#include "dict.h"
-#include "sampler.h"
-#include "ccrp.h"
-#include "ccrp_onetable.h"
-
-using namespace std;
-using namespace tr1;
-namespace po = boost::program_options;
-
-boost::shared_ptr<MT19937> prng;
-vector<int> nt_vocab;
-vector<int> nt_id_to_index;
-static unsigned kMAX_RULE_SIZE = 0;
-static unsigned kMAX_ARITY = 0;
-static bool kALLOW_MIXED = true;  // allow rules with mixed terminals and NTs
-static bool kHIERARCHICAL_PRIOR = false;
-
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
-  po::options_description opts("Configuration options");
-  opts.add_options()
-        ("samples,s",po::value<unsigned>()->default_value(1000),"Number of samples")
-        ("input,i",po::value<string>(),"Read parallel data from")
-        ("max_rule_size,m", po::value<unsigned>()->default_value(0), "Maximum rule size (0 for unlimited)")
-        ("max_arity,a", po::value<unsigned>()->default_value(0), "Maximum number of nonterminals in a rule (0 for unlimited)")
-        ("no_mixed_rules,M", "Do not mix terminals and nonterminals in a rule RHS")
-        ("nonterminals,n", po::value<unsigned>()->default_value(1), "Size of nonterminal vocabulary")
-        ("hierarchical_prior,h", "Use hierarchical prior")
-        ("random_seed,S",po::value<uint32_t>(), "Random seed");
-  po::options_description clo("Command line options");
-  clo.add_options()
-        ("config", po::value<string>(), "Configuration file")
-        ("help", "Print this help message and exit");
-  po::options_description dconfig_options, dcmdline_options;
-  dconfig_options.add(opts);
-  dcmdline_options.add(opts).add(clo);
-  
-  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
-  if (conf->count("config")) {
-    ifstream config((*conf)["config"].as<string>().c_str());
-    po::store(po::parse_config_file(config, dconfig_options), *conf);
-  }
-  po::notify(*conf);
-
-  if (conf->count("help") || (conf->count("input") == 0)) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-unsigned ReadCorpus(const string& filename,
-                    vector<vector<WordID> >* e,
-                    set<WordID>* vocab_e) {
-  e->clear();
-  vocab_e->clear();
-  istream* in;
-  if (filename == "-")
-    in = &cin;
-  else
-    in = new ifstream(filename.c_str());
-  assert(*in);
-  string line;
-  unsigned toks = 0;
-  while(*in) {
-    getline(*in, line);
-    if (line.empty() && !*in) break;
-    e->push_back(vector<int>());
-    vector<int>& le = e->back();
-    TD::ConvertSentence(line, &le);
-    for (unsigned i = 0; i < le.size(); ++i)
-      vocab_e->insert(le[i]);
-    toks += le.size();
-  }
-  if (in != &cin) delete in;
-  return toks;
-}
-
-struct Grid {
-  // a b c d e
-  // 0 - 0 - -
-  vector<int> grid;
-};
-
-struct BaseRuleModel {
-  explicit BaseRuleModel(unsigned term_size,
-                         unsigned nonterm_size = 1) :
-      unif_term(1.0 / term_size),
-      unif_nonterm(1.0 / nonterm_size) {}
-  prob_t operator()(const TRule& r) const {
-    prob_t p; p.logeq(Md::log_poisson(1.0, r.f_.size()));
-    const prob_t term_prob((2.0 + 0.01*r.f_.size()) / (r.f_.size() + 2));
-    const prob_t nonterm_prob(1.0 - term_prob.as_float());
-    for (unsigned i = 0; i < r.f_.size(); ++i) {
-      if (r.f_[i] <= 0) {     // nonterminal
-        if (kALLOW_MIXED) p *= nonterm_prob;
-        p *= unif_nonterm;
-      } else {                // terminal
-        if (kALLOW_MIXED) p *= term_prob;
-        p *= unif_term;
-      }
-    }
-    return p;
-  }
-  const prob_t unif_term, unif_nonterm;
-};
-
-struct HieroLMModel {
-  explicit HieroLMModel(unsigned vocab_size, unsigned num_nts = 1) :
-      base(vocab_size, num_nts),
-      q0(1,1,1,1),
-      nts(num_nts, CCRP<TRule>(1,1,1,1)) {}
-
-  prob_t Prob(const TRule& r) const {
-    return nts[nt_id_to_index[-r.lhs_]].prob(r, p0(r));
-  }
-
-  inline prob_t p0(const TRule& r) const {
-    if (kHIERARCHICAL_PRIOR)
-      return q0.prob(r, base(r));
-    else
-      return base(r);
-  }
-
-  int Increment(const TRule& r, MT19937* rng) {
-    const int delta = nts[nt_id_to_index[-r.lhs_]].increment(r, p0(r), rng);
-    if (kHIERARCHICAL_PRIOR && delta)
-      q0.increment(r, base(r), rng);
-    return delta;
-    // return x.increment(r);
-  }
-
-  int Decrement(const TRule& r, MT19937* rng) {
-    const int delta = nts[nt_id_to_index[-r.lhs_]].decrement(r, rng);
-    if (kHIERARCHICAL_PRIOR && delta)
-      q0.decrement(r, rng);
-    return delta;
-    //return x.decrement(r);
-  }
-
-  prob_t Likelihood() const {
-    prob_t p = prob_t::One();
-    for (unsigned i = 0; i < nts.size(); ++i) {
-      prob_t q; q.logeq(nts[i].log_crp_prob());
-      p *= q;
-      for (CCRP<TRule>::const_iterator it = nts[i].begin(); it != nts[i].end(); ++it) {
-        prob_t tp = p0(it->first);
-        tp.poweq(it->second.num_tables());
-        p *= tp;
-      }
-    }
-    if (kHIERARCHICAL_PRIOR) {
-      prob_t q; q.logeq(q0.log_crp_prob());
-      p *= q;
-      for (CCRP<TRule>::const_iterator it = q0.begin(); it != q0.end(); ++it) {
-        prob_t tp = base(it->first);
-        tp.poweq(it->second.num_tables());
-        p *= tp;
-      }
-    }
-    //for (CCRP_OneTable<TRule>::const_iterator it = x.begin(); it != x.end(); ++it)
-    //    p *= base(it->first);
-    return p;
-  }
-
-  void ResampleHyperparameters(MT19937* rng) {
-    for (unsigned i = 0; i < nts.size(); ++i)
-      nts[i].resample_hyperparameters(rng);
-    if (kHIERARCHICAL_PRIOR) {
-      q0.resample_hyperparameters(rng);
-      cerr << "[base d=" << q0.discount() << ", s=" << q0.strength() << "]";
-    }
-    cerr << " d=" << nts[0].discount() << ", s=" << nts[0].strength() << endl;
-  }
-
-  const BaseRuleModel base;
-  CCRP<TRule> q0;
-  vector<CCRP<TRule> > nts;
-  //CCRP_OneTable<TRule> x;
-};
-
-vector<GrammarIter* > tofreelist;
-
-HieroLMModel* plm;
-
-struct NPGrammarIter : public GrammarIter, public RuleBin {
-  NPGrammarIter() : arity() { tofreelist.push_back(this); }
-  NPGrammarIter(const TRulePtr& inr, const int a, int symbol) : arity(a) {
-    if (inr) {
-      r.reset(new TRule(*inr));
-    } else {
-      r.reset(new TRule);
-    }
-    TRule& rr = *r;
-    rr.lhs_ = nt_vocab[0];
-    rr.f_.push_back(symbol);
-    rr.e_.push_back(symbol < 0 ? (1-int(arity)) : symbol);
-    tofreelist.push_back(this);
-  }
-  inline static unsigned NextArity(int cur_a, int symbol) {
-    return cur_a + (symbol <= 0 ? 1 : 0);
-  }
-  virtual int GetNumRules() const {
-    if (r) return nt_vocab.size(); else return 0;
-  }
-  virtual TRulePtr GetIthRule(int i) const {
-    if (i == 0) return r;
-    TRulePtr nr(new TRule(*r));
-    nr->lhs_ = nt_vocab[i];
-    return nr;
-  }
-  virtual int Arity() const {
-    return arity;
-  }
-  virtual const RuleBin* GetRules() const {
-    if (!r) return NULL; else return this;
-  }
-  virtual const GrammarIter* Extend(int symbol) const {
-    const int next_arity = NextArity(arity, symbol);
-    if (kMAX_ARITY && next_arity > kMAX_ARITY)
-      return NULL;
-    if (!kALLOW_MIXED && r) {
-      bool t1 = r->f_.front() <= 0;
-      bool t2 = symbol <= 0;
-      if (t1 != t2) return NULL;
-    }
-    if (!kMAX_RULE_SIZE || !r || (r->f_.size() < kMAX_RULE_SIZE))
-      return new NPGrammarIter(r, next_arity, symbol);
-    else
-      return NULL;
-  }
-  const unsigned char arity;
-  TRulePtr r;
-};
-
-struct NPGrammar : public Grammar {
-  virtual const GrammarIter* GetRoot() const {
-    return new NPGrammarIter;
-  }
-};
-
-prob_t TotalProb(const Hypergraph& hg) {
-  return Inside<prob_t, EdgeProb>(hg);
-}
-
-void SampleDerivation(const Hypergraph& hg, MT19937* rng, vector<unsigned>* sampled_deriv) {
-  vector<prob_t> node_probs;
-  Inside<prob_t, EdgeProb>(hg, &node_probs);
-  queue<unsigned> q;
-  q.push(hg.nodes_.size() - 2);
-  while(!q.empty()) {
-    unsigned cur_node_id = q.front();
-//    cerr << "NODE=" << cur_node_id << endl;
-    q.pop();
-    const Hypergraph::Node& node = hg.nodes_[cur_node_id];
-    const unsigned num_in_edges = node.in_edges_.size();
-    unsigned sampled_edge = 0;
-    if (num_in_edges == 1) {
-      sampled_edge = node.in_edges_[0];
-    } else {
-      //prob_t z;
-      assert(num_in_edges > 1);
-      SampleSet<prob_t> ss;
-      for (unsigned j = 0; j < num_in_edges; ++j) {
-        const Hypergraph::Edge& edge = hg.edges_[node.in_edges_[j]];
-        prob_t p = edge.edge_prob_;
-        for (unsigned k = 0; k < edge.tail_nodes_.size(); ++k)
-          p *= node_probs[edge.tail_nodes_[k]];
-        ss.add(p);
-//        cerr << log(ss[j]) << " ||| " << edge.rule_->AsString() << endl;
-        //z += p;
-      }
-//      for (unsigned j = 0; j < num_in_edges; ++j) {
-//        const Hypergraph::Edge& edge = hg.edges_[node.in_edges_[j]];
-//        cerr << exp(log(ss[j] / z)) << " ||| " << edge.rule_->AsString() << endl;
-//      }
-//      cerr << " --- \n";
-      sampled_edge = node.in_edges_[rng->SelectSample(ss)];
-    }
-    sampled_deriv->push_back(sampled_edge);
-    const Hypergraph::Edge& edge = hg.edges_[sampled_edge];
-    for (unsigned j = 0; j < edge.tail_nodes_.size(); ++j) {
-      q.push(edge.tail_nodes_[j]);
-    }
-  }
-  for (unsigned i = 0; i < sampled_deriv->size(); ++i) {
-    cerr << *hg.edges_[(*sampled_deriv)[i]].rule_ << endl;
-  }
-}
-
-void IncrementDerivation(const Hypergraph& hg, const vector<unsigned>& d, HieroLMModel* plm, MT19937* rng) {
-  for (unsigned i = 0; i < d.size(); ++i)
-    plm->Increment(*hg.edges_[d[i]].rule_, rng);
-}
-
-void DecrementDerivation(const Hypergraph& hg, const vector<unsigned>& d, HieroLMModel* plm, MT19937* rng) {
-  for (unsigned i = 0; i < d.size(); ++i)
-    plm->Decrement(*hg.edges_[d[i]].rule_, rng);
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-
-  InitCommandLine(argc, argv, &conf);
-  nt_vocab.resize(conf["nonterminals"].as<unsigned>());
-  assert(nt_vocab.size() > 0);
-  assert(nt_vocab.size() < 26);
-  {
-    string nt = "X";
-    for (unsigned i = 0; i < nt_vocab.size(); ++i) {
-      if (nt_vocab.size() > 1) nt[0] = ('A' + i);
-      int pid = TD::Convert(nt);
-      nt_vocab[i] = -pid;
-      if (pid >= nt_id_to_index.size()) {
-        nt_id_to_index.resize(pid + 1, -1);
-      }
-      nt_id_to_index[pid] = i;
-    }
-  }
-  vector<GrammarPtr> grammars;
-  grammars.push_back(GrammarPtr(new NPGrammar));
-
-  const unsigned samples = conf["samples"].as<unsigned>();
-  kMAX_RULE_SIZE = conf["max_rule_size"].as<unsigned>();
-  if (kMAX_RULE_SIZE == 1) {
-    cerr << "Invalid maximum rule size: must be 0 or >1\n";
-    return 1;
-  }
-  kMAX_ARITY = conf["max_arity"].as<unsigned>();
-  if (kMAX_ARITY == 1) {
-    cerr << "Invalid maximum arity: must be 0 or >1\n";
-    return 1;
-  }
-  kALLOW_MIXED = !conf.count("no_mixed_rules");
-
-  kHIERARCHICAL_PRIOR = conf.count("hierarchical_prior");
-
-  if (conf.count("random_seed"))
-    prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
-  else
-    prng.reset(new MT19937);
-  MT19937& rng = *prng;
-  vector<vector<WordID> > corpuse;
-  set<WordID> vocabe;
-  cerr << "Reading corpus...\n";
-  const unsigned toks = ReadCorpus(conf["input"].as<string>(), &corpuse, &vocabe);
-  cerr << "E-corpus size: " << corpuse.size() << " sentences\t (" << vocabe.size() << " word types)\n";
-  HieroLMModel lm(vocabe.size(), nt_vocab.size());
-
-  plm = &lm;
-  ExhaustiveBottomUpParser parser(TD::Convert(-nt_vocab[0]), grammars);
-
-  Hypergraph hg;
-  const int kGoal = -TD::Convert("Goal");
-  const int kLP = FD::Convert("LogProb");
-  SparseVector<double> v; v.set_value(kLP, 1.0);
-  vector<vector<unsigned> > derivs(corpuse.size());
-  vector<Lattice> cl(corpuse.size());
-  for (int ci = 0; ci < corpuse.size(); ++ci) {
-    vector<int>& src = corpuse[ci];
-    Lattice& lat = cl[ci];
-    lat.resize(src.size());
-    for (unsigned i = 0; i < src.size(); ++i)
-      lat[i].push_back(LatticeArc(src[i], 0.0, 1));
-  }
-  for (int SS=0; SS < samples; ++SS) {
-    const bool is_last = ((samples - 1) == SS);
-    prob_t dlh = prob_t::One();
-    for (int ci = 0; ci < corpuse.size(); ++ci) {
-      const vector<int>& src = corpuse[ci];
-      const Lattice& lat = cl[ci];
-      cerr << TD::GetString(src) << endl;
-      hg.clear();
-      parser.Parse(lat, &hg);  // exhaustive parse
-      vector<unsigned>& d = derivs[ci];
-      if (!is_last) DecrementDerivation(hg, d, &lm, &rng);
-      for (unsigned i = 0; i < hg.edges_.size(); ++i) {
-        TRule& r = *hg.edges_[i].rule_;
-        if (r.lhs_ == kGoal)
-          hg.edges_[i].edge_prob_ = prob_t::One();
-        else
-          hg.edges_[i].edge_prob_ = lm.Prob(r);
-      }
-      if (!is_last) {
-        d.clear();
-        SampleDerivation(hg, &rng, &d);
-        IncrementDerivation(hg, derivs[ci], &lm, &rng);
-      } else {
-        prob_t p = TotalProb(hg);
-        dlh *= p;
-        cerr << " p(sentence) = " << log(p) << "\t" << log(dlh) << endl;
-      }
-      if (tofreelist.size() > 200000) {
-        cerr << "Freeing ... ";
-        for (unsigned i = 0; i < tofreelist.size(); ++i)
-          delete tofreelist[i];
-        tofreelist.clear();
-        cerr << "Freed.\n";
-      }
-    }
-    double llh = log(lm.Likelihood());
-    cerr << "LLH=" << llh << "\tENTROPY=" << (-llh / log(2) / toks) << "\tPPL=" << pow(2, -llh / log(2) / toks) << endl;
-    if (SS % 10 == 9) lm.ResampleHyperparameters(&rng);
-    if (is_last) {
-      double z = log(dlh);
-      cerr << "TOTAL_PROB=" << z << "\tENTROPY=" << (-z / log(2) / toks) << "\tPPL=" << pow(2, -z / log(2) / toks) << endl;
-    }
-  }
-  for (unsigned i = 0; i < nt_vocab.size(); ++i)
-    cerr << lm.nts[i] << endl;
-  return 0;
-}
-
diff --git a/gi/pf/make-freq-bins.pl b/gi/pf/make-freq-bins.pl
deleted file mode 100755
index fdcd3555..00000000
--- a/gi/pf/make-freq-bins.pl
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/usr/bin/perl -w
-use strict;
-
-my $BASE = 6;
-my $CUTOFF = 3;
-
-my %d;
-my $num = 0;
-while(<>){
- chomp;
- my @words = split /\s+/;
- for my $w (@words) {$d{$w}++; $num++;}
-}
-
-my @vocab = sort {$d{$b} <=> $d{$a}} keys %d;
-
-for (my $i=0; $i<scalar @vocab; $i++) {
-  my $most = $d{$vocab[$i]};
-  my $least = 1;
-
-  my $nl = -int(log($most / $num) / log($BASE) + $CUTOFF);
-  if ($nl < 0) { $nl = 0; }
-  print "$vocab[$i] $nl\n"
-}
-
-
diff --git a/gi/pf/mh_test.cc b/gi/pf/mh_test.cc
deleted file mode 100644
index 296e7285..00000000
--- a/gi/pf/mh_test.cc
+++ /dev/null
@@ -1,148 +0,0 @@
-#include "ccrp.h"
-
-#include <vector>
-#include <iostream>
-
-#include "tdict.h"
-#include "transliterations.h"
-
-using namespace std;
-
-MT19937 rng;
-
-static bool verbose = false;
-
-struct Model {
-
-  Model() : bp(), base(0.2, 0.6) , ccrps(5, CCRP<int>(0.8, 0.5)) {}
-
-  double p0(int x) const {
-    assert(x > 0);
-    assert(x < 5);
-    return 1.0/4.0;
-  }
-
-  double llh() const {
-    double lh = bp + base.log_crp_prob();
-    for (int ctx = 1; ctx < 5; ++ctx)
-      lh += ccrps[ctx].log_crp_prob();
-    return lh;
-  }
-
-  double prob(int ctx, int x) const {
-    assert(ctx > 0 && ctx < 5);
-    return ccrps[ctx].prob(x, base.prob(x, p0(x)));
-  }
-
-  void increment(int ctx, int x) {
-    assert(ctx > 0 && ctx < 5);
-    if (ccrps[ctx].increment(x, base.prob(x, p0(x)), &rng)) {
-      if (base.increment(x, p0(x), &rng)) {
-        bp += log(1.0 / 4.0);
-      }
-    }
-  }
-
-  // this is just a biased estimate
-  double est_base_prob(int x) {
-    return (x + 1) * x / 40.0;
-  }
-
-  void increment_is(int ctx, int x) {
-    assert(ctx > 0 && ctx < 5);
-    SampleSet<double> ss;
-    const int PARTICLES = 25;
-    vector<CCRP<int> > s1s(PARTICLES, CCRP<int>(0.5,0.5));
-    vector<CCRP<int> > sbs(PARTICLES, CCRP<int>(0.5,0.5));
-    vector<double> sp0s(PARTICLES);
-
-    CCRP<int> s1 = ccrps[ctx];
-    CCRP<int> sb = base;
-    double sp0 = bp;
-    for (int pp = 0; pp < PARTICLES; ++pp) {
-      if (pp > 0) {
-        ccrps[ctx] = s1;
-        base = sb;
-        bp = sp0;
-      }
-
-      double q = 1;
-      double gamma = 1;
-      double est_p = est_base_prob(x);
-      //base.prob(x, p0(x)) + rng.next() * 0.1;
-      if (ccrps[ctx].increment(x, est_p, &rng, &q)) {
-        gamma = q * base.prob(x, p0(x));
-        q *= est_p;
-        if (verbose) cerr << "(DP-base draw) ";
-        double qq = -1;
-        if (base.increment(x, p0(x), &rng, &qq)) {
-          if (verbose) cerr << "(G0 draw) ";
-          bp += log(p0(x));
-          qq *= p0(x);
-        }
-      } else { gamma = q; }
-      double w = gamma / q;
-      if (verbose)
-        cerr << "gamma=" << gamma << " q=" << q << "\tw=" << w << endl;
-      ss.add(w);
-      s1s[pp] = ccrps[ctx];
-      sbs[pp] = base;
-      sp0s[pp] = bp;
-    }
-    int ps = rng.SelectSample(ss);
-    ccrps[ctx] = s1s[ps];
-    base = sbs[ps];
-    bp = sp0s[ps];
-    if (verbose) {
-      cerr << "SELECTED: " << ps << endl;
-      static int cc = 0; cc++; if (cc ==10) exit(1);
-    }
-  }
-
-  void decrement(int ctx, int x) {
-    assert(ctx > 0 && ctx < 5);
-    if (ccrps[ctx].decrement(x, &rng)) {
-      if (base.decrement(x, &rng)) {
-        bp -= log(p0(x));
-      }
-    }
-  }
-
-  double bp;
-  CCRP<int> base;
-  vector<CCRP<int> > ccrps;
-
-};
-
-int main(int argc, char** argv) {
-  if (argc > 1) { verbose = true; }
-  vector<int> counts(15, 0);
-  vector<int> tcounts(15, 0);
-  int points[] = {1,2, 2,2, 3,2, 4,1, 3, 4, 3, 3, 2, 3, 4, 1, 4, 1, 3, 2, 1, 3, 1, 4, 0, 0};
-  double tlh = 0;
-  double tt = 0;
-  for (int n = 0; n < 1000; ++n) {
-    if (n % 10 == 0) cerr << '.';
-    if ((n+1) % 400 == 0) cerr << " [" << (n+1) << "]\n";
-    Model m;
-    for (int *x = points; *x; x += 2)
-      m.increment(x[0], x[1]);
-
-    for (int j = 0; j < 24; ++j) {
-      for (int *x = points; *x; x += 2) {
-        if (rng.next() < 0.8) {
-          m.decrement(x[0], x[1]);
-          m.increment_is(x[0], x[1]);
-        }
-      }
-    }
-    counts[m.base.num_customers()]++;
-    tcounts[m.base.num_tables()]++;
-    tlh += m.llh();
-    tt += 1.0;
-  }
-  cerr << "mean LLH = " << (tlh / tt) << endl;
-  for (int i = 0; i < 15; ++i)
-    cerr << i << ": " << (counts[i] / tt) << "\t" << (tcounts[i] / tt) << endl;
-}
-
diff --git a/gi/pf/monotonic_pseg.h b/gi/pf/monotonic_pseg.h
deleted file mode 100644
index 10d171fe..00000000
--- a/gi/pf/monotonic_pseg.h
+++ /dev/null
@@ -1,89 +0,0 @@
-#ifndef _MONOTONIC_PSEG_H_
-#define _MONOTONIC_PSEG_H_
-
-#include <vector>
-
-#include "prob.h"
-#include "ccrp_nt.h"
-#include "trule.h"
-#include "base_distributions.h"
-
-template <typename BaseMeasure>
-struct MonotonicParallelSegementationModel {
-  explicit MonotonicParallelSegementationModel(BaseMeasure& rcp0) :
-    rp0(rcp0), base(prob_t::One()), rules(1,1), stop(1.0) {}
-
-  void DecrementRule(const TRule& rule) {
-    if (rules.decrement(rule))
-      base /= rp0(rule);
-  }
-
-  void IncrementRule(const TRule& rule) {
-    if (rules.increment(rule))
-      base *= rp0(rule);
-  }
-
-  void IncrementRulesAndStops(const std::vector<TRulePtr>& rules) {
-    for (int i = 0; i < rules.size(); ++i)
-      IncrementRule(*rules[i]);
-    if (rules.size()) IncrementContinue(rules.size() - 1);
-    IncrementStop();
-  }
-
-  void DecrementRulesAndStops(const std::vector<TRulePtr>& rules) {
-    for (int i = 0; i < rules.size(); ++i)
-      DecrementRule(*rules[i]);
-    if (rules.size()) {
-      DecrementContinue(rules.size() - 1);
-      DecrementStop();
-    }
-  }
-
-  prob_t RuleProbability(const TRule& rule) const {
-    prob_t p; p.logeq(rules.logprob(rule, log(rp0(rule))));
-    return p;
-  }
-
-  prob_t Likelihood() const {
-    prob_t p = base;
-    prob_t q; q.logeq(rules.log_crp_prob());
-    p *= q;
-    q.logeq(stop.log_crp_prob());
-    p *= q;
-    return p;
-  }
-
-  void IncrementStop() {
-    stop.increment(true);
-  }
-
-  void IncrementContinue(int n = 1) {
-    for (int i = 0; i < n; ++i)
-      stop.increment(false);
-  }
-
-  void DecrementStop() {
-    stop.decrement(true);
-  }
-
-  void DecrementContinue(int n = 1) {
-    for (int i = 0; i < n; ++i)
-      stop.decrement(false);
-  }
-
-  prob_t StopProbability() const {
-    return prob_t(stop.prob(true, 0.5));
-  }
-
-  prob_t ContinueProbability() const {
-    return prob_t(stop.prob(false, 0.5));
-  }
-
-  const BaseMeasure& rp0;
-  prob_t base;
-  CCRP_NoTable<TRule> rules;
-  CCRP_NoTable<bool> stop;
-};
-
-#endif
-
diff --git a/gi/pf/ngram_base.cc b/gi/pf/ngram_base.cc
deleted file mode 100644
index 1299f06f..00000000
--- a/gi/pf/ngram_base.cc
+++ /dev/null
@@ -1,69 +0,0 @@
-#include "ngram_base.h"
-
-#include "lm/model.hh"
-#include "tdict.h"
-
-using namespace std;
-
-namespace {
-struct GICSVMapper : public lm::EnumerateVocab {
-  GICSVMapper(vector<lm::WordIndex>* out) : out_(out), kLM_UNKNOWN_TOKEN(0) { out_->clear(); }
-  void Add(lm::WordIndex index, const StringPiece &str) {
-    const WordID cdec_id = TD::Convert(str.as_string());
-    if (cdec_id >= out_->size())
-      out_->resize(cdec_id + 1, kLM_UNKNOWN_TOKEN);
-    (*out_)[cdec_id] = index;
-  }
-  vector<lm::WordIndex>* out_;
-  const lm::WordIndex kLM_UNKNOWN_TOKEN;
-};
-}
-
-struct FixedNgramBaseImpl {
-  FixedNgramBaseImpl(const string& param) {
-    GICSVMapper vm(&cdec2klm_map_);
-    lm::ngram::Config conf;
-    conf.enumerate_vocab = &vm;
-    cerr << "Reading character LM from " << param << endl;
-    model = new lm::ngram::ProbingModel(param.c_str(), conf);
-    order = model->Order();
-    kEOS = MapWord(TD::Convert("</s>"));
-    assert(kEOS > 0);
-  }
-
-  lm::WordIndex MapWord(const WordID w) const {
-    if (w < cdec2klm_map_.size()) return cdec2klm_map_[w];
-    return 0;
-  }
-
-  ~FixedNgramBaseImpl() { delete model; }
-
-  prob_t StringProbability(const vector<WordID>& s) const {
-    lm::ngram::State state = model->BeginSentenceState();
-    double prob = 0;
-    for (unsigned i = 0; i < s.size(); ++i) {
-      const lm::ngram::State scopy(state);
-      prob += model->Score(scopy, MapWord(s[i]), state);
-    }
-    const lm::ngram::State scopy(state);
-    prob += model->Score(scopy, kEOS, state);
-    prob_t p; p.logeq(prob * log(10));
-    return p;
-  }
-
-  lm::ngram::ProbingModel* model;
-  unsigned order;
-  vector<lm::WordIndex> cdec2klm_map_;
-  lm::WordIndex kEOS;
-};
-
-FixedNgramBase::~FixedNgramBase() { delete impl; }
-
-FixedNgramBase::FixedNgramBase(const string& lmfname) {
-  impl = new FixedNgramBaseImpl(lmfname);
-}
-
-prob_t FixedNgramBase::StringProbability(const vector<WordID>& s) const {
-  return impl->StringProbability(s);
-}
-
diff --git a/gi/pf/ngram_base.h b/gi/pf/ngram_base.h
deleted file mode 100644
index 4ea999f3..00000000
--- a/gi/pf/ngram_base.h
+++ /dev/null
@@ -1,25 +0,0 @@
-#ifndef _NGRAM_BASE_H_
-#define _NGRAM_BASE_H_
-
-#include <string>
-#include <vector>
-#include "trule.h"
-#include "wordid.h"
-#include "prob.h"
-
-struct FixedNgramBaseImpl;
-struct FixedNgramBase {
-  FixedNgramBase(const std::string& lmfname);
-  ~FixedNgramBase();
-  prob_t StringProbability(const std::vector<WordID>& s) const;
-
-  prob_t operator()(const TRule& rule) const {
-    return StringProbability(rule.e_);
-  }
-
- private:
-  FixedNgramBaseImpl* impl;
-
-};
-
-#endif
diff --git a/gi/pf/nuisance_test.cc b/gi/pf/nuisance_test.cc
deleted file mode 100644
index fc0af9cb..00000000
--- a/gi/pf/nuisance_test.cc
+++ /dev/null
@@ -1,161 +0,0 @@
-#include "ccrp.h"
-
-#include <vector>
-#include <iostream>
-
-#include "tdict.h"
-#include "transliterations.h"
-
-using namespace std;
-
-MT19937 rng;
-
-ostream& operator<<(ostream&os, const vector<int>& v) {
-  os << '[' << v[0];
-  if (v.size() == 2) os << ' ' << v[1];
-  return os << ']';
-}
-
-struct Base {
-  Base() : llh(), v(2), v1(1), v2(1), crp(0.25, 0.5) {}
-  inline double p0(const vector<int>& x) const {
-    double p = 0.75;
-    if (x.size() == 2) p = 0.25;
-    p *= 1.0 / 3.0;
-    if (x.size() == 2) p *= 1.0 / 3.0;
-    return p;
-  }
-  double est_deriv_prob(int a, int b, int seg) const {
-    assert(a > 0 && a < 4);  // a \in {1,2,3}
-    assert(b > 0 && b < 4);  // b \in {1,2,3}
-    assert(seg == 0 || seg == 1);   // seg \in {0,1}
-    if (seg == 0) {
-      v[0] = a;
-      v[1] = b;
-      return crp.prob(v, p0(v));
-    } else {
-      v1[0] = a;
-      v2[0] = b;
-      return crp.prob(v1, p0(v1)) * crp.prob(v2, p0(v2));
-    }
-  }
-  double est_marginal_prob(int a, int b) const {
-    return est_deriv_prob(a,b,0) + est_deriv_prob(a,b,1);
-  }
-  int increment(int a, int b, double* pw = NULL) {
-    double p1 = est_deriv_prob(a, b, 0);
-    double p2 = est_deriv_prob(a, b, 1);
-    //p1 = 0.5; p2 = 0.5;
-    int seg = rng.SelectSample(p1,p2);
-    double tmp = 0;
-    if (!pw) pw = &tmp;
-    double& w = *pw;
-    if (seg == 0) {
-      v[0] = a;
-      v[1] = b;
-      w = crp.prob(v, p0(v)) / p1;
-      if (crp.increment(v, p0(v), &rng)) {
-        llh += log(p0(v));
-      }
-    } else {
-      v1[0] = a;
-      w = crp.prob(v1, p0(v1)) / p2;
-      if (crp.increment(v1, p0(v1), &rng)) {
-        llh += log(p0(v1));
-      }
-      v2[0] = b;
-      w *= crp.prob(v2, p0(v2));
-      if (crp.increment(v2, p0(v2), &rng)) {
-        llh += log(p0(v2));
-      }
-    }
-    return seg;
-  }
-  void increment(int a, int b, int seg) {
-    if (seg == 0) {
-      v[0] = a;
-      v[1] = b;
-      if (crp.increment(v, p0(v), &rng)) {
-        llh += log(p0(v));
-      }
-    } else {
-      v1[0] = a;
-      if (crp.increment(v1, p0(v1), &rng)) {
-        llh += log(p0(v1));
-      }
-      v2[0] = b;
-      if (crp.increment(v2, p0(v2), &rng)) {
-        llh += log(p0(v2));
-      }
-    }
-  }
-  void decrement(int a, int b, int seg) {
-    if (seg == 0) {
-      v[0] = a;
-      v[1] = b;
-      if (crp.decrement(v, &rng)) {
-        llh -= log(p0(v));
-      }
-    } else {
-      v1[0] = a;
-      if (crp.decrement(v1, &rng)) {
-        llh -= log(p0(v1));
-      }
-      v2[0] = b;
-      if (crp.decrement(v2, &rng)) {
-        llh -= log(p0(v2));
-      }
-    }
-  }
-  double log_likelihood() const {
-    return llh + crp.log_crp_prob();
-  }
-  double llh;
-  mutable vector<int> v, v1, v2;
-  CCRP<vector<int> > crp;
-};
-
-int main(int argc, char** argv) {
-  double tl = 0;
-  const int ITERS = 1000;
-  const int PARTICLES = 20;
-  const int DATAPOINTS = 50;
-  WordID x = TD::Convert("souvenons");
-  WordID y = TD::Convert("remember");
-  vector<WordID> src; TD::ConvertSentence("s o u v e n o n s", &src);
-  vector<WordID> trg; TD::ConvertSentence("r e m e m b e r", &trg);
-//  Transliterations xx;
-//  xx.Initialize(x, src, y, trg);
-//  return 1;
-
- for (int j = 0; j < ITERS; ++j) {
-  Base b;
-  vector<int> segs(DATAPOINTS);
-  SampleSet<double> ss;
-  vector<int> sss;
-  for (int i = 0; i < DATAPOINTS; i++) {
-    ss.clear();
-    sss.clear();
-    int x = ((i / 10) % 3) + 1;
-    int y = (i % 3) + 1;
-    //double ep = b.est_marginal_prob(x,y);
-    //cerr << "est p(" << x << "," << y << ") = " << ep << endl;
-    for (int n = 0; n < PARTICLES; ++n) {
-      double w;
-      int seg = b.increment(x,y,&w);
-      //cerr << seg << " w=" << w << endl;
-      ss.add(w);
-      sss.push_back(seg);
-      b.decrement(x,y,seg);
-    }
-    int seg = sss[rng.SelectSample(ss)];
-    b.increment(x, y, seg);
-    //cerr << "Selected: " << seg << endl;
-    //return 1;
-    segs[i] = seg;
-  }
-  tl += b.log_likelihood();
- }
-  cerr << "LLH=" << tl / ITERS << endl;
-}
-
diff --git a/gi/pf/os_phrase.h b/gi/pf/os_phrase.h
deleted file mode 100644
index dfe40cb1..00000000
--- a/gi/pf/os_phrase.h
+++ /dev/null
@@ -1,15 +0,0 @@
-#ifndef _OS_PHRASE_H_
-#define _OS_PHRASE_H_
-
-#include <iostream>
-#include <vector>
-#include "tdict.h"
-
-inline std::ostream& operator<<(std::ostream& os, const std::vector<WordID>& p) {
-  os << '[';
-  for (int i = 0; i < p.size(); ++i)
-    os << (i==0 ? "" : " ") << TD::Convert(p[i]);
-  return os << ']';
-}
-
-#endif
diff --git a/gi/pf/pf.h b/gi/pf/pf.h
deleted file mode 100644
index ede7cda8..00000000
--- a/gi/pf/pf.h
+++ /dev/null
@@ -1,84 +0,0 @@
-#ifndef _PF_H_
-#define _PF_H_
-
-#include <cassert>
-#include <vector>
-#include "sampler.h"
-#include "prob.h"
-
-template <typename ParticleType>
-struct ParticleRenormalizer {
-  void operator()(std::vector<ParticleType>* pv) const {
-    if (pv->empty()) return;
-    prob_t z = prob_t::Zero();
-    for (unsigned i = 0; i < pv->size(); ++i)
-      z += (*pv)[i].weight;
-    assert(z > prob_t::Zero());
-    for (unsigned i = 0; i < pv->size(); ++i)
-      (*pv)[i].weight /= z;
-  }
-};
-
-template <typename ParticleType>
-struct MultinomialResampleFilter {
-  explicit MultinomialResampleFilter(MT19937* rng) : rng_(rng) {}
-
-  void operator()(std::vector<ParticleType>* pv) {
-    if (pv->empty()) return;
-    std::vector<ParticleType>& ps = *pv;
-    SampleSet<prob_t> ss;
-    for (int i = 0; i < ps.size(); ++i)
-      ss.add(ps[i].weight);
-    std::vector<ParticleType> nps; nps.reserve(ps.size());
-    const prob_t uniform_weight(1.0 / ps.size());
-    for (int i = 0; i < ps.size(); ++i) {
-      nps.push_back(ps[rng_->SelectSample(ss)]);
-      nps[i].weight = uniform_weight;
-    }
-    nps.swap(ps);
-  }
-
- private:
-  MT19937* rng_;
-};
-
-template <typename ParticleType>
-struct SystematicResampleFilter {
-  explicit SystematicResampleFilter(MT19937* rng) : rng_(rng), renorm_() {}
-
-  void operator()(std::vector<ParticleType>* pv) {
-    if (pv->empty()) return;
-    renorm_(pv);
-    std::vector<ParticleType>& ps = *pv;
-    std::vector<ParticleType> nps; nps.reserve(ps.size());
-    double lower = 0, upper = 0;
-    const double skip = 1.0 / ps.size();
-    double u_j = rng_->next() * skip;
-    //std::cerr << "u_0: " << u_j << std::endl;
-    int j = 0;
-    for (unsigned i = 0; i < ps.size(); ++i) {
-      upper += ps[i].weight.as_float();
-      //std::cerr << "lower: " << lower << " upper: " << upper << std::endl;
-      // how many children does ps[i] have?
-      while (u_j < lower) { u_j += skip; ++j; }
-      while (u_j >= lower && u_j <= upper) {
-        assert(j < ps.size());
-        nps.push_back(ps[i]);
-        u_j += skip;
-        //std::cerr << " add u_j=" << u_j << std::endl;
-        ++j;
-      }
-      lower = upper;
-    }
-    //std::cerr << ps.size() << " " << nps.size() << "\n";
-    assert(ps.size() == nps.size());
-    //exit(1);
-    ps.swap(nps);
-  }
-
- private:
-  MT19937* rng_;
-  ParticleRenormalizer<ParticleType> renorm_;
-};
-
-#endif
diff --git a/gi/pf/pf_test.cc b/gi/pf/pf_test.cc
deleted file mode 100644
index 296e7285..00000000
--- a/gi/pf/pf_test.cc
+++ /dev/null
@@ -1,148 +0,0 @@
-#include "ccrp.h"
-
-#include <vector>
-#include <iostream>
-
-#include "tdict.h"
-#include "transliterations.h"
-
-using namespace std;
-
-MT19937 rng;
-
-static bool verbose = false;
-
-struct Model {
-
-  Model() : bp(), base(0.2, 0.6) , ccrps(5, CCRP<int>(0.8, 0.5)) {}
-
-  double p0(int x) const {
-    assert(x > 0);
-    assert(x < 5);
-    return 1.0/4.0;
-  }
-
-  double llh() const {
-    double lh = bp + base.log_crp_prob();
-    for (int ctx = 1; ctx < 5; ++ctx)
-      lh += ccrps[ctx].log_crp_prob();
-    return lh;
-  }
-
-  double prob(int ctx, int x) const {
-    assert(ctx > 0 && ctx < 5);
-    return ccrps[ctx].prob(x, base.prob(x, p0(x)));
-  }
-
-  void increment(int ctx, int x) {
-    assert(ctx > 0 && ctx < 5);
-    if (ccrps[ctx].increment(x, base.prob(x, p0(x)), &rng)) {
-      if (base.increment(x, p0(x), &rng)) {
-        bp += log(1.0 / 4.0);
-      }
-    }
-  }
-
-  // this is just a biased estimate
-  double est_base_prob(int x) {
-    return (x + 1) * x / 40.0;
-  }
-
-  void increment_is(int ctx, int x) {
-    assert(ctx > 0 && ctx < 5);
-    SampleSet<double> ss;
-    const int PARTICLES = 25;
-    vector<CCRP<int> > s1s(PARTICLES, CCRP<int>(0.5,0.5));
-    vector<CCRP<int> > sbs(PARTICLES, CCRP<int>(0.5,0.5));
-    vector<double> sp0s(PARTICLES);
-
-    CCRP<int> s1 = ccrps[ctx];
-    CCRP<int> sb = base;
-    double sp0 = bp;
-    for (int pp = 0; pp < PARTICLES; ++pp) {
-      if (pp > 0) {
-        ccrps[ctx] = s1;
-        base = sb;
-        bp = sp0;
-      }
-
-      double q = 1;
-      double gamma = 1;
-      double est_p = est_base_prob(x);
-      //base.prob(x, p0(x)) + rng.next() * 0.1;
-      if (ccrps[ctx].increment(x, est_p, &rng, &q)) {
-        gamma = q * base.prob(x, p0(x));
-        q *= est_p;
-        if (verbose) cerr << "(DP-base draw) ";
-        double qq = -1;
-        if (base.increment(x, p0(x), &rng, &qq)) {
-          if (verbose) cerr << "(G0 draw) ";
-          bp += log(p0(x));
-          qq *= p0(x);
-        }
-      } else { gamma = q; }
-      double w = gamma / q;
-      if (verbose)
-        cerr << "gamma=" << gamma << " q=" << q << "\tw=" << w << endl;
-      ss.add(w);
-      s1s[pp] = ccrps[ctx];
-      sbs[pp] = base;
-      sp0s[pp] = bp;
-    }
-    int ps = rng.SelectSample(ss);
-    ccrps[ctx] = s1s[ps];
-    base = sbs[ps];
-    bp = sp0s[ps];
-    if (verbose) {
-      cerr << "SELECTED: " << ps << endl;
-      static int cc = 0; cc++; if (cc ==10) exit(1);
-    }
-  }
-
-  void decrement(int ctx, int x) {
-    assert(ctx > 0 && ctx < 5);
-    if (ccrps[ctx].decrement(x, &rng)) {
-      if (base.decrement(x, &rng)) {
-        bp -= log(p0(x));
-      }
-    }
-  }
-
-  double bp;
-  CCRP<int> base;
-  vector<CCRP<int> > ccrps;
-
-};
-
-int main(int argc, char** argv) {
-  if (argc > 1) { verbose = true; }
-  vector<int> counts(15, 0);
-  vector<int> tcounts(15, 0);
-  int points[] = {1,2, 2,2, 3,2, 4,1, 3, 4, 3, 3, 2, 3, 4, 1, 4, 1, 3, 2, 1, 3, 1, 4, 0, 0};
-  double tlh = 0;
-  double tt = 0;
-  for (int n = 0; n < 1000; ++n) {
-    if (n % 10 == 0) cerr << '.';
-    if ((n+1) % 400 == 0) cerr << " [" << (n+1) << "]\n";
-    Model m;
-    for (int *x = points; *x; x += 2)
-      m.increment(x[0], x[1]);
-
-    for (int j = 0; j < 24; ++j) {
-      for (int *x = points; *x; x += 2) {
-        if (rng.next() < 0.8) {
-          m.decrement(x[0], x[1]);
-          m.increment_is(x[0], x[1]);
-        }
-      }
-    }
-    counts[m.base.num_customers()]++;
-    tcounts[m.base.num_tables()]++;
-    tlh += m.llh();
-    tt += 1.0;
-  }
-  cerr << "mean LLH = " << (tlh / tt) << endl;
-  for (int i = 0; i < 15; ++i)
-    cerr << i << ": " << (counts[i] / tt) << "\t" << (tcounts[i] / tt) << endl;
-}
-
diff --git a/gi/pf/pfbrat.cc b/gi/pf/pfbrat.cc
deleted file mode 100644
index 832f22cf..00000000
--- a/gi/pf/pfbrat.cc
+++ /dev/null
@@ -1,543 +0,0 @@
-#include <iostream>
-#include <tr1/memory>
-#include <queue>
-
-#include <boost/functional.hpp>
-#include <boost/multi_array.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "viterbi.h"
-#include "hg.h"
-#include "trule.h"
-#include "tdict.h"
-#include "filelib.h"
-#include "dict.h"
-#include "sampler.h"
-#include "ccrp_nt.h"
-#include "cfg_wfst_composer.h"
-
-using namespace std;
-using namespace tr1;
-namespace po = boost::program_options;
-
-static unsigned kMAX_SRC_PHRASE;
-static unsigned kMAX_TRG_PHRASE;
-struct FSTState;
-
-double log_poisson(unsigned x, const double& lambda) {
-  assert(lambda > 0.0);
-  return log(lambda) * x - lgamma(x + 1) - lambda;
-}
-
-struct ConditionalBase {
-  explicit ConditionalBase(const double m1mixture, const unsigned vocab_e_size, const string& model1fname) :
-      kM1MIXTURE(m1mixture),
-      kUNIFORM_MIXTURE(1.0 - m1mixture),
-      kUNIFORM_TARGET(1.0 / vocab_e_size),
-      kNULL(TD::Convert("<eps>")) {
-    assert(m1mixture >= 0.0 && m1mixture <= 1.0);
-    assert(vocab_e_size > 0);
-    LoadModel1(model1fname);
-  }
-
-  void LoadModel1(const string& fname) {
-    cerr << "Loading Model 1 parameters from " << fname << " ..." << endl;
-    ReadFile rf(fname);
-    istream& in = *rf.stream();
-    string line;
-    unsigned lc = 0;
-    while(getline(in, line)) {
-      ++lc;
-      int cur = 0;
-      int start = 0;
-      while(cur < line.size() && line[cur] != ' ') { ++cur; }
-      assert(cur != line.size());
-      line[cur] = 0;
-      const WordID src = TD::Convert(&line[0]);
-      ++cur;
-      start = cur;
-      while(cur < line.size() && line[cur] != ' ') { ++cur; }
-      assert(cur != line.size());
-      line[cur] = 0;
-      WordID trg = TD::Convert(&line[start]);
-      const double logprob = strtod(&line[cur + 1], NULL);
-      if (src >= ttable.size()) ttable.resize(src + 1);
-      ttable[src][trg].logeq(logprob);
-    }
-    cerr << "  read " << lc << " parameters.\n";
-  }
-
-  // return logp0 of rule.e_ | rule.f_
-  prob_t operator()(const TRule& rule) const {
-    const int flen = rule.f_.size();
-    const int elen = rule.e_.size();
-    prob_t uniform_src_alignment; uniform_src_alignment.logeq(-log(flen + 1));
-    prob_t p;
-    p.logeq(log_poisson(elen, flen + 0.01));       // elen | flen          ~Pois(flen + 0.01)
-    for (int i = 0; i < elen; ++i) {               // for each position i in e-RHS
-      const WordID trg = rule.e_[i];
-      prob_t tp = prob_t::Zero();
-      for (int j = -1; j < flen; ++j) {
-        const WordID src = j < 0 ? kNULL : rule.f_[j];
-        const map<WordID, prob_t>::const_iterator it = ttable[src].find(trg);
-        if (it != ttable[src].end()) {
-          tp += kM1MIXTURE * it->second;
-        }
-        tp += kUNIFORM_MIXTURE * kUNIFORM_TARGET;
-      }
-      tp *= uniform_src_alignment;                 //     draw a_i         ~uniform
-      p *= tp;                                     //     draw e_i         ~Model1(f_a_i) / uniform
-    }
-    return p;
-  }
-
-  const prob_t kM1MIXTURE;  // Model 1 mixture component
-  const prob_t kUNIFORM_MIXTURE; // uniform mixture component
-  const prob_t kUNIFORM_TARGET;
-  const WordID kNULL;
-  vector<map<WordID, prob_t> > ttable;
-};
-
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
-  po::options_description opts("Configuration options");
-  opts.add_options()
-        ("samples,s",po::value<unsigned>()->default_value(1000),"Number of samples")
-        ("input,i",po::value<string>(),"Read parallel data from")
-        ("max_src_phrase",po::value<unsigned>()->default_value(3),"Maximum length of source language phrases")
-        ("max_trg_phrase",po::value<unsigned>()->default_value(3),"Maximum length of target language phrases")
-        ("model1,m",po::value<string>(),"Model 1 parameters (used in base distribution)")
-        ("model1_interpolation_weight",po::value<double>()->default_value(0.95),"Mixing proportion of model 1 with uniform target distribution")
-        ("random_seed,S",po::value<uint32_t>(), "Random seed");
-  po::options_description clo("Command line options");
-  clo.add_options()
-        ("config", po::value<string>(), "Configuration file")
-        ("help,h", "Print this help message and exit");
-  po::options_description dconfig_options, dcmdline_options;
-  dconfig_options.add(opts);
-  dcmdline_options.add(opts).add(clo);
-  
-  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
-  if (conf->count("config")) {
-    ifstream config((*conf)["config"].as<string>().c_str());
-    po::store(po::parse_config_file(config, dconfig_options), *conf);
-  }
-  po::notify(*conf);
-
-  if (conf->count("help") || (conf->count("input") == 0)) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-void ReadParallelCorpus(const string& filename,
-                vector<vector<WordID> >* f,
-                vector<vector<int> >* e,
-                set<int>* vocab_f,
-                set<int>* vocab_e) {
-  f->clear();
-  e->clear();
-  vocab_f->clear();
-  vocab_e->clear();
-  istream* in;
-  if (filename == "-")
-    in = &cin;
-  else
-    in = new ifstream(filename.c_str());
-  assert(*in);
-  string line;
-  const WordID kDIV = TD::Convert("|||");
-  vector<WordID> tmp;
-  while(*in) {
-    getline(*in, line);
-    if (line.empty() && !*in) break;
-    e->push_back(vector<int>());
-    f->push_back(vector<int>());
-    vector<int>& le = e->back();
-    vector<int>& lf = f->back();
-    tmp.clear();
-    TD::ConvertSentence(line, &tmp);
-    bool isf = true;
-    for (unsigned i = 0; i < tmp.size(); ++i) {
-      const int cur = tmp[i];
-      if (isf) {
-        if (kDIV == cur) { isf = false; } else {
-          lf.push_back(cur);
-          vocab_f->insert(cur);
-        }
-      } else {
-        assert(cur != kDIV);
-        le.push_back(cur);
-        vocab_e->insert(cur);
-      }
-    }
-    assert(isf == false);
-  }
-  if (in != &cin) delete in;
-}
-
-struct UniphraseLM {
-  UniphraseLM(const vector<vector<int> >& corpus,
-              const set<int>& vocab,
-              const po::variables_map& conf) :
-    phrases_(1,1),
-    gen_(1,1),
-    corpus_(corpus),
-    uniform_word_(1.0 / vocab.size()),
-    gen_p0_(0.5),
-    p_end_(0.5),
-    use_poisson_(conf.count("poisson_length") > 0) {}
-
-  void ResampleHyperparameters(MT19937* rng) {
-    phrases_.resample_hyperparameters(rng);
-    gen_.resample_hyperparameters(rng);
-    cerr << " " << phrases_.alpha();
-  }
-
-  CCRP_NoTable<vector<int> > phrases_;
-  CCRP_NoTable<bool> gen_;
-  vector<vector<bool> > z_;   // z_[i] is there a phrase boundary after the ith word
-  const vector<vector<int> >& corpus_;
-  const double uniform_word_;
-  const double gen_p0_;
-  const double p_end_; // in base length distribution, p of the end of a phrase
-  const bool use_poisson_;
-};
-
-struct Reachability {
-  boost::multi_array<bool, 4> edges;  // edges[src_covered][trg_covered][x][trg_delta] is this edge worth exploring?
-  boost::multi_array<short, 2> max_src_delta; // msd[src_covered][trg_covered] -- the largest src delta that's valid
-
-  Reachability(int srclen, int trglen, int src_max_phrase_len, int trg_max_phrase_len) :
-      edges(boost::extents[srclen][trglen][src_max_phrase_len+1][trg_max_phrase_len+1]),
-      max_src_delta(boost::extents[srclen][trglen]) {
-    ComputeReachability(srclen, trglen, src_max_phrase_len, trg_max_phrase_len);
-  }
-
- private:
-  struct SState {
-    SState() : prev_src_covered(), prev_trg_covered() {}
-    SState(int i, int j) : prev_src_covered(i), prev_trg_covered(j) {}
-    int prev_src_covered;
-    int prev_trg_covered;
-  };
-
-  struct NState {
-    NState() : next_src_covered(), next_trg_covered() {}
-    NState(int i, int j) : next_src_covered(i), next_trg_covered(j) {}
-    int next_src_covered;
-    int next_trg_covered;
-  };
-
-  void ComputeReachability(int srclen, int trglen, int src_max_phrase_len, int trg_max_phrase_len) {
-    typedef boost::multi_array<vector<SState>, 2> array_type;
-    array_type a(boost::extents[srclen + 1][trglen + 1]);
-    a[0][0].push_back(SState());
-    for (int i = 0; i < srclen; ++i) {
-      for (int j = 0; j < trglen; ++j) {
-        if (a[i][j].size() == 0) continue;
-        const SState prev(i,j);
-        for (int k = 1; k <= src_max_phrase_len; ++k) {
-          if ((i + k) > srclen) continue;
-          for (int l = 1; l <= trg_max_phrase_len; ++l) {
-            if ((j + l) > trglen) continue;
-            a[i + k][j + l].push_back(prev);
-          }
-        }
-      }
-    }
-    a[0][0].clear();
-    cerr << "Final cell contains " << a[srclen][trglen].size() << " back pointers\n";
-    assert(a[srclen][trglen].size() > 0);
-
-    typedef boost::multi_array<bool, 2> rarray_type;
-    rarray_type r(boost::extents[srclen + 1][trglen + 1]);
-//    typedef boost::multi_array<vector<NState>, 2> narray_type;
-//    narray_type b(boost::extents[srclen + 1][trglen + 1]);
-    r[srclen][trglen] = true;
-    for (int i = srclen; i >= 0; --i) {
-      for (int j = trglen; j >= 0; --j) {
-        vector<SState>& prevs = a[i][j];
-        if (!r[i][j]) { prevs.clear(); }
-//        const NState nstate(i,j);
-        for (int k = 0; k < prevs.size(); ++k) {
-          r[prevs[k].prev_src_covered][prevs[k].prev_trg_covered] = true;
-          int src_delta = i - prevs[k].prev_src_covered;
-          edges[prevs[k].prev_src_covered][prevs[k].prev_trg_covered][src_delta][j - prevs[k].prev_trg_covered] = true;
-          short &msd = max_src_delta[prevs[k].prev_src_covered][prevs[k].prev_trg_covered];
-          if (src_delta > msd) msd = src_delta;
-//          b[prevs[k].prev_src_covered][prevs[k].prev_trg_covered].push_back(nstate);
-        }
-      }
-    }
-    assert(!edges[0][0][1][0]);
-    assert(!edges[0][0][0][1]);
-    assert(!edges[0][0][0][0]);
-    cerr << "  MAX SRC DELTA[0][0] = " << max_src_delta[0][0] << endl;
-    assert(max_src_delta[0][0] > 0);
-    //cerr << "First cell contains " << b[0][0].size() << " forward pointers\n";
-    //for (int i = 0; i < b[0][0].size(); ++i) {
-    //  cerr << "  -> (" << b[0][0][i].next_src_covered << "," << b[0][0][i].next_trg_covered << ")\n";
-    //}
-  }
-};
-
-ostream& operator<<(ostream& os, const FSTState& q);
-struct FSTState {
-  explicit FSTState(int src_size) :
-      trg_covered_(),
-      src_covered_(),
-      src_coverage_(src_size) {}
-
-  FSTState(short trg_covered, short src_covered, const vector<bool>& src_coverage, const vector<short>& src_prefix) :
-      trg_covered_(trg_covered),
-      src_covered_(src_covered),
-      src_coverage_(src_coverage),
-      src_prefix_(src_prefix) {
-    if (src_coverage_.size() == src_covered) {
-      assert(src_prefix.size() == 0);
-    }
-  }
-
-  // if we extend by the word at src_position, what are
-  // the next states that are reachable and lie on a valid
-  // path to the final state?
-  vector<FSTState> Extensions(int src_position, int src_len, int trg_len, const Reachability& r) const {
-    assert(src_position < src_coverage_.size());
-    if (src_coverage_[src_position]) {
-      cerr << "Trying to extend " << *this << " with position " << src_position << endl;
-      abort();
-    }
-    vector<bool> ncvg = src_coverage_;
-    ncvg[src_position] = true;
-
-    vector<FSTState> res;
-    const int trg_remaining = trg_len - trg_covered_;
-    if (trg_remaining <= 0) {
-      cerr << "Target appears to have been covered: " << *this << " (trg_len=" << trg_len << ",trg_covered=" << trg_covered_ << ")" << endl;
-      abort();
-    }
-    const int src_remaining = src_len - src_covered_;
-    if (src_remaining <= 0) {
-      cerr << "Source appears to have been covered: " << *this << endl;
-      abort();
-    }
-
-    for (int tc = 1; tc <= kMAX_TRG_PHRASE; ++tc) {
-      if (r.edges[src_covered_][trg_covered_][src_prefix_.size() + 1][tc]) {
-        int nc = src_prefix_.size() + 1 + src_covered_;
-        res.push_back(FSTState(trg_covered_ + tc, nc, ncvg, vector<short>()));
-      }
-    }
-
-    if ((src_prefix_.size() + 1) < r.max_src_delta[src_covered_][trg_covered_]) {
-      vector<short> nsp = src_prefix_;
-      nsp.push_back(src_position);
-      res.push_back(FSTState(trg_covered_, src_covered_, ncvg, nsp));
-    }
-
-    if (res.size() == 0) {
-      cerr << *this << " can't be extended!\n";
-      abort();
-    }
-    return res;
-  }
-
-  short trg_covered_, src_covered_;
-  vector<bool> src_coverage_;
-  vector<short> src_prefix_;
-};
-bool operator<(const FSTState& q, const FSTState& r) {
-  if (q.trg_covered_ != r.trg_covered_) return q.trg_covered_ < r.trg_covered_;
-  if (q.src_covered_!= r.src_covered_) return q.src_covered_ < r.src_covered_;
-  if (q.src_coverage_ != r.src_coverage_) return q.src_coverage_ < r.src_coverage_;
-  return q.src_prefix_ < r.src_prefix_;
-}
-
-ostream& operator<<(ostream& os, const FSTState& q) {
-  os << "[" << q.trg_covered_ << " : ";
-  for (int i = 0; i < q.src_coverage_.size(); ++i)
-    os << q.src_coverage_[i];
-  os << " : <";
-  for (int i = 0; i < q.src_prefix_.size(); ++i) {
-    if (i != 0) os << ' ';
-    os << q.src_prefix_[i];
-  }
-  return os << ">]";
-}
-
-struct MyModel {
-  MyModel(ConditionalBase& rcp0) : rp0(rcp0) {}
-  typedef unordered_map<vector<WordID>, CCRP_NoTable<TRule>, boost::hash<vector<WordID> > > SrcToRuleCRPMap;
-
-  void DecrementRule(const TRule& rule) {
-    SrcToRuleCRPMap::iterator it = rules.find(rule.f_);
-    assert(it != rules.end());
-    it->second.decrement(rule);
-    if (it->second.num_customers() == 0) rules.erase(it);
-  }
-
-  void IncrementRule(const TRule& rule) {
-    SrcToRuleCRPMap::iterator it = rules.find(rule.f_);
-    if (it == rules.end()) {
-      CCRP_NoTable<TRule> crp(1,1);
-      it = rules.insert(make_pair(rule.f_, crp)).first;
-    }
-    it->second.increment(rule);
-  }
-
-  // conditioned on rule.f_
-  prob_t RuleConditionalProbability(const TRule& rule) const {
-    const prob_t base = rp0(rule);
-    SrcToRuleCRPMap::const_iterator it = rules.find(rule.f_);
-    if (it == rules.end()) {
-      return base;
-    } else {
-      const double lp = it->second.logprob(rule, log(base));
-      prob_t q; q.logeq(lp);
-      return q;
-    }
-  }
-
-  const ConditionalBase& rp0;
-  SrcToRuleCRPMap rules;
-};
-
-struct MyFST : public WFST {
-  MyFST(const vector<WordID>& ssrc, const vector<WordID>& strg, MyModel* m) :
-      src(ssrc), trg(strg),
-      r(src.size(),trg.size(),kMAX_SRC_PHRASE, kMAX_TRG_PHRASE),
-      model(m) {
-    FSTState in(src.size());
-    cerr << " INIT: " << in << endl;
-    init = GetNode(in);
-    for (int i = 0; i < in.src_coverage_.size(); ++i) in.src_coverage_[i] = true;
-    in.src_covered_ = src.size();
-    in.trg_covered_ = trg.size();
-    cerr << "FINAL: " << in << endl;
-    final = GetNode(in);
-  }
-  virtual const WFSTNode* Final() const;
-  virtual const WFSTNode* Initial() const;
-
-  const WFSTNode* GetNode(const FSTState& q);
-  map<FSTState, boost::shared_ptr<WFSTNode> > m;
-  const vector<WordID>& src;
-  const vector<WordID>& trg;
-  Reachability r;
-  const WFSTNode* init;
-  const WFSTNode* final;
-  MyModel* model;
-};
-
-struct MyNode : public WFSTNode {
-  MyNode(const FSTState& q, MyFST* fst) : state(q), container(fst) {}
-  virtual vector<pair<const WFSTNode*, TRulePtr> > ExtendInput(unsigned srcindex) const;
-  const FSTState state;
-  mutable MyFST* container;
-};
-
-vector<pair<const WFSTNode*, TRulePtr> > MyNode::ExtendInput(unsigned srcindex) const {
-  cerr << "EXTEND " << state << " with " << srcindex << endl;
-  vector<FSTState> ext = state.Extensions(srcindex, container->src.size(), container->trg.size(), container->r);
-  vector<pair<const WFSTNode*,TRulePtr> > res(ext.size());
-  for (unsigned i = 0; i < ext.size(); ++i) {
-    res[i].first = container->GetNode(ext[i]);
-    if (ext[i].src_prefix_.size() == 0) {
-      const unsigned trg_from = state.trg_covered_;
-      const unsigned trg_to = ext[i].trg_covered_;
-      const unsigned prev_prfx_size = state.src_prefix_.size();
-      res[i].second.reset(new TRule);
-      res[i].second->lhs_ = -TD::Convert("X");
-      vector<WordID>& src = res[i].second->f_;
-      vector<WordID>& trg = res[i].second->e_;
-      src.resize(prev_prfx_size + 1);
-      for (unsigned j = 0; j < prev_prfx_size; ++j)
-        src[j] = container->src[state.src_prefix_[j]];
-      src[prev_prfx_size] = container->src[srcindex];
-      for (unsigned j = trg_from; j < trg_to; ++j)
-        trg.push_back(container->trg[j]);
-      res[i].second->scores_.set_value(FD::Convert("Proposal"), log(container->model->RuleConditionalProbability(*res[i].second)));
-    }
-  }
-  return res;
-}
-
-const WFSTNode* MyFST::GetNode(const FSTState& q) {
-  boost::shared_ptr<WFSTNode>& res = m[q];
-  if (!res) {
-    res.reset(new MyNode(q, this));
-  }
-  return &*res;
-}
-
-const WFSTNode* MyFST::Final() const {
-  return final;
-}
-
-const WFSTNode* MyFST::Initial() const {
-  return init;
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-  kMAX_TRG_PHRASE = conf["max_trg_phrase"].as<unsigned>();
-  kMAX_SRC_PHRASE = conf["max_src_phrase"].as<unsigned>();
-
-  if (!conf.count("model1")) {
-    cerr << argv[0] << "Please use --model1 to specify model 1 parameters\n";
-    return 1;
-  }
-  boost::shared_ptr<MT19937> prng;
-  if (conf.count("random_seed"))
-    prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
-  else
-    prng.reset(new MT19937);
-  MT19937& rng = *prng;
-
-  vector<vector<int> > corpuse, corpusf;
-  set<int> vocabe, vocabf;
-  ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe);
-  cerr << "f-Corpus size: " << corpusf.size() << " sentences\n";
-  cerr << "f-Vocabulary size: " << vocabf.size() << " types\n";
-  cerr << "f-Corpus size: " << corpuse.size() << " sentences\n";
-  cerr << "f-Vocabulary size: " << vocabe.size() << " types\n";
-  assert(corpusf.size() == corpuse.size());
-
-  ConditionalBase lp0(conf["model1_interpolation_weight"].as<double>(),
-                      vocabe.size(),
-                      conf["model1"].as<string>());
-  MyModel m(lp0);
-
-  TRule x("[X] ||| kAnwntR myN ||| at the convent ||| 0");
-  m.IncrementRule(x);
-  TRule y("[X] ||| nY dyN ||| gave ||| 0");
-  m.IncrementRule(y);
-
-
-  MyFST fst(corpusf[0], corpuse[0], &m);
-  ifstream in("./kimura.g");
-  assert(in);
-  CFG_WFSTComposer comp(fst);
-  Hypergraph hg;
-  bool succeed = comp.Compose(&in, &hg);
-  hg.PrintGraphviz();
-  if (succeed) { cerr << "SUCCESS.\n"; } else { cerr << "FAILURE REPORTED.\n"; }
-
-#if 0
-  ifstream in2("./amnabooks.g");
-  assert(in2);
-  MyFST fst2(corpusf[1], corpuse[1], &m);
-  CFG_WFSTComposer comp2(fst2);
-  Hypergraph hg2;
-  bool succeed2 = comp2.Compose(&in2, &hg2);
-  if (succeed2) { cerr << "SUCCESS.\n"; } else { cerr << "FAILURE REPORTED.\n"; }
-#endif
-
-  SparseVector<double> w; w.set_value(FD::Convert("Proposal"), 1.0);
-  hg.Reweight(w);
-  cerr << ViterbiFTree(hg) << endl;
-  return 0;
-}
-
diff --git a/gi/pf/pfdist.cc b/gi/pf/pfdist.cc
deleted file mode 100644
index a3e46064..00000000
--- a/gi/pf/pfdist.cc
+++ /dev/null
@@ -1,598 +0,0 @@
-#include <iostream>
-#include <tr1/memory>
-#include <queue>
-
-#include <boost/functional.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "pf.h"
-#include "base_distributions.h"
-#include "reachability.h"
-#include "viterbi.h"
-#include "hg.h"
-#include "trule.h"
-#include "tdict.h"
-#include "filelib.h"
-#include "dict.h"
-#include "sampler.h"
-#include "ccrp_nt.h"
-#include "ccrp_onetable.h"
-
-using namespace std;
-using namespace tr1;
-namespace po = boost::program_options;
-
-boost::shared_ptr<MT19937> prng;
-
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
-  po::options_description opts("Configuration options");
-  opts.add_options()
-        ("samples,s",po::value<unsigned>()->default_value(1000),"Number of samples")
-        ("particles,p",po::value<unsigned>()->default_value(30),"Number of particles")
-        ("filter_frequency,f",po::value<unsigned>()->default_value(5),"Number of time steps between filterings")
-        ("input,i",po::value<string>(),"Read parallel data from")
-        ("max_src_phrase",po::value<unsigned>()->default_value(5),"Maximum length of source language phrases")
-        ("max_trg_phrase",po::value<unsigned>()->default_value(5),"Maximum length of target language phrases")
-        ("model1,m",po::value<string>(),"Model 1 parameters (used in base distribution)")
-        ("inverse_model1,M",po::value<string>(),"Inverse Model 1 parameters (used in backward estimate)")
-        ("model1_interpolation_weight",po::value<double>()->default_value(0.95),"Mixing proportion of model 1 with uniform target distribution")
-        ("random_seed,S",po::value<uint32_t>(), "Random seed");
-  po::options_description clo("Command line options");
-  clo.add_options()
-        ("config", po::value<string>(), "Configuration file")
-        ("help,h", "Print this help message and exit");
-  po::options_description dconfig_options, dcmdline_options;
-  dconfig_options.add(opts);
-  dcmdline_options.add(opts).add(clo);
-  
-  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
-  if (conf->count("config")) {
-    ifstream config((*conf)["config"].as<string>().c_str());
-    po::store(po::parse_config_file(config, dconfig_options), *conf);
-  }
-  po::notify(*conf);
-
-  if (conf->count("help") || (conf->count("input") == 0)) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-void ReadParallelCorpus(const string& filename,
-                vector<vector<WordID> >* f,
-                vector<vector<WordID> >* e,
-                set<WordID>* vocab_f,
-                set<WordID>* vocab_e) {
-  f->clear();
-  e->clear();
-  vocab_f->clear();
-  vocab_e->clear();
-  istream* in;
-  if (filename == "-")
-    in = &cin;
-  else
-    in = new ifstream(filename.c_str());
-  assert(*in);
-  string line;
-  const WordID kDIV = TD::Convert("|||");
-  vector<WordID> tmp;
-  while(*in) {
-    getline(*in, line);
-    if (line.empty() && !*in) break;
-    e->push_back(vector<int>());
-    f->push_back(vector<int>());
-    vector<int>& le = e->back();
-    vector<int>& lf = f->back();
-    tmp.clear();
-    TD::ConvertSentence(line, &tmp);
-    bool isf = true;
-    for (unsigned i = 0; i < tmp.size(); ++i) {
-      const int cur = tmp[i];
-      if (isf) {
-        if (kDIV == cur) { isf = false; } else {
-          lf.push_back(cur);
-          vocab_f->insert(cur);
-        }
-      } else {
-        assert(cur != kDIV);
-        le.push_back(cur);
-        vocab_e->insert(cur);
-      }
-    }
-    assert(isf == false);
-  }
-  if (in != &cin) delete in;
-}
-
-#if 0
-struct MyConditionalModel {
-  MyConditionalModel(PhraseConditionalBase& rcp0) : rp0(&rcp0), base(prob_t::One()), src_phrases(1,1), src_jumps(200, CCRP_NoTable<int>(1,1)) {}
-
-  prob_t srcp0(const vector<WordID>& src) const {
-    prob_t p(1.0 / 3000.0);
-    p.poweq(src.size());
-    prob_t lenp; lenp.logeq(log_poisson(src.size(), 1.0));
-    p *= lenp;
-    return p;
-  }
-
-  void DecrementRule(const TRule& rule) {
-    const RuleCRPMap::iterator it = rules.find(rule.f_);
-    assert(it != rules.end());
-    if (it->second.decrement(rule)) {
-      base /= (*rp0)(rule);
-      if (it->second.num_customers() == 0)
-        rules.erase(it);
-    }
-    if (src_phrases.decrement(rule.f_))
-      base /= srcp0(rule.f_);
-  }
-
-  void IncrementRule(const TRule& rule) {
-    RuleCRPMap::iterator it = rules.find(rule.f_);
-    if (it == rules.end())
-      it = rules.insert(make_pair(rule.f_, CCRP_NoTable<TRule>(1,1))).first;
-    if (it->second.increment(rule)) {
-      base *= (*rp0)(rule);
-    }
-    if (src_phrases.increment(rule.f_))
-      base *= srcp0(rule.f_);
-  }
-
-  void IncrementRules(const vector<TRulePtr>& rules) {
-    for (int i = 0; i < rules.size(); ++i)
-      IncrementRule(*rules[i]);
-  }
-
-  void DecrementRules(const vector<TRulePtr>& rules) {
-    for (int i = 0; i < rules.size(); ++i)
-      DecrementRule(*rules[i]);
-  }
-
-  void IncrementJump(int dist, unsigned src_len) {
-    assert(src_len > 0);
-    if (src_jumps[src_len].increment(dist))
-      base *= jp0(dist, src_len);
-  }
-
-  void DecrementJump(int dist, unsigned src_len) {
-    assert(src_len > 0);
-    if (src_jumps[src_len].decrement(dist))
-      base /= jp0(dist, src_len);
-  }
-
-  void IncrementJumps(const vector<int>& js, unsigned src_len) {
-    for (unsigned i = 0; i < js.size(); ++i)
-      IncrementJump(js[i], src_len);
-  }
-
-  void DecrementJumps(const vector<int>& js, unsigned src_len) {
-    for (unsigned i = 0; i < js.size(); ++i)
-      DecrementJump(js[i], src_len);
-  }
-
-  // p(jump = dist | src_len , z)
-  prob_t JumpProbability(int dist, unsigned src_len) {
-    const prob_t p0 = jp0(dist, src_len);
-    const double lp = src_jumps[src_len].logprob(dist, log(p0));
-    prob_t q; q.logeq(lp);
-    return q;
-  }
-
-  // p(rule.f_ | z) * p(rule.e_ | rule.f_ , z)
-  prob_t RuleProbability(const TRule& rule) const {
-    const prob_t p0 = (*rp0)(rule);
-    prob_t srcp; srcp.logeq(src_phrases.logprob(rule.f_, log(srcp0(rule.f_))));
-    const RuleCRPMap::const_iterator it = rules.find(rule.f_);
-    if (it == rules.end()) return srcp * p0;
-    const double lp = it->second.logprob(rule, log(p0));
-    prob_t q; q.logeq(lp);
-    return q * srcp;
-  }
-
-  prob_t Likelihood() const {
-    prob_t p = base;
-    for (RuleCRPMap::const_iterator it = rules.begin();
-         it != rules.end(); ++it) {
-      prob_t cl; cl.logeq(it->second.log_crp_prob());
-      p *= cl;
-    }
-    for (unsigned l = 1; l < src_jumps.size(); ++l) {
-      if (src_jumps[l].num_customers() > 0) {
-        prob_t q;
-        q.logeq(src_jumps[l].log_crp_prob());
-        p *= q;
-      }
-    }
-    return p;
-  }
-
-  JumpBase jp0;
-  const PhraseConditionalBase* rp0;
-  prob_t base;
-  typedef unordered_map<vector<WordID>, CCRP_NoTable<TRule>, boost::hash<vector<WordID> > > RuleCRPMap;
-  RuleCRPMap rules;
-  CCRP_NoTable<vector<WordID> > src_phrases;
-  vector<CCRP_NoTable<int> > src_jumps;
-};
-
-#endif
-
-struct MyJointModel {
-  MyJointModel(PhraseJointBase& rcp0) :
-    rp0(rcp0), base(prob_t::One()), rules(1,1), src_jumps(200, CCRP_NoTable<int>(1,1)) {}
-
-  void DecrementRule(const TRule& rule) {
-    if (rules.decrement(rule))
-      base /= rp0(rule);
-  }
-
-  void IncrementRule(const TRule& rule) {
-    if (rules.increment(rule))
-      base *= rp0(rule);
-  }
-
-  void IncrementRules(const vector<TRulePtr>& rules) {
-    for (int i = 0; i < rules.size(); ++i)
-      IncrementRule(*rules[i]);
-  }
-
-  void DecrementRules(const vector<TRulePtr>& rules) {
-    for (int i = 0; i < rules.size(); ++i)
-      DecrementRule(*rules[i]);
-  }
-
-  void IncrementJump(int dist, unsigned src_len) {
-    assert(src_len > 0);
-    if (src_jumps[src_len].increment(dist))
-      base *= jp0(dist, src_len);
-  }
-
-  void DecrementJump(int dist, unsigned src_len) {
-    assert(src_len > 0);
-    if (src_jumps[src_len].decrement(dist))
-      base /= jp0(dist, src_len);
-  }
-
-  void IncrementJumps(const vector<int>& js, unsigned src_len) {
-    for (unsigned i = 0; i < js.size(); ++i)
-      IncrementJump(js[i], src_len);
-  }
-
-  void DecrementJumps(const vector<int>& js, unsigned src_len) {
-    for (unsigned i = 0; i < js.size(); ++i)
-      DecrementJump(js[i], src_len);
-  }
-
-  // p(jump = dist | src_len , z)
-  prob_t JumpProbability(int dist, unsigned src_len) {
-    const prob_t p0 = jp0(dist, src_len);
-    const double lp = src_jumps[src_len].logprob(dist, log(p0));
-    prob_t q; q.logeq(lp);
-    return q;
-  }
-
-  // p(rule.f_ | z) * p(rule.e_ | rule.f_ , z)
-  prob_t RuleProbability(const TRule& rule) const {
-    prob_t p; p.logeq(rules.logprob(rule, log(rp0(rule))));
-    return p;
-  }
-
-  prob_t Likelihood() const {
-    prob_t p = base;
-    prob_t q; q.logeq(rules.log_crp_prob());
-    p *= q;
-    for (unsigned l = 1; l < src_jumps.size(); ++l) {
-      if (src_jumps[l].num_customers() > 0) {
-        prob_t q;
-        q.logeq(src_jumps[l].log_crp_prob());
-        p *= q;
-      }
-    }
-    return p;
-  }
-
-  JumpBase jp0;
-  const PhraseJointBase& rp0;
-  prob_t base;
-  CCRP_NoTable<TRule> rules;
-  vector<CCRP_NoTable<int> > src_jumps;
-};
-
-struct BackwardEstimate {
-  BackwardEstimate(const Model1& m1, const vector<WordID>& src, const vector<WordID>& trg) :
-      model1_(m1), src_(src), trg_(trg) {
-  }
-  const prob_t& operator()(const vector<bool>& src_cov, unsigned trg_cov) const {
-    assert(src_.size() == src_cov.size());
-    assert(trg_cov <= trg_.size());
-    prob_t& e = cache_[src_cov][trg_cov];
-    if (e.is_0()) {
-      if (trg_cov == trg_.size()) { e = prob_t::One(); return e; }
-      vector<WordID> r(src_.size() + 1); r.clear();
-      r.push_back(0);  // NULL word
-      for (int i = 0; i < src_cov.size(); ++i)
-        if (!src_cov[i]) r.push_back(src_[i]);
-      const prob_t uniform_alignment(1.0 / r.size());
-      e.logeq(Md::log_poisson(trg_.size() - trg_cov, r.size() - 1)); // p(trg len remaining | src len remaining)
-      for (unsigned j = trg_cov; j < trg_.size(); ++j) {
-        prob_t p;
-        for (unsigned i = 0; i < r.size(); ++i)
-          p += model1_(r[i], trg_[j]);
-        if (p.is_0()) {
-          cerr << "ERROR: p(" << TD::Convert(trg_[j]) << " | " << TD::GetString(r) << ") = 0!\n";
-          abort();
-        }
-        p *= uniform_alignment;
-        e *= p;
-      }
-    }
-    return e;
-  }
-  const Model1& model1_;
-  const vector<WordID>& src_;
-  const vector<WordID>& trg_;
-  mutable unordered_map<vector<bool>, map<unsigned, prob_t>, boost::hash<vector<bool> > > cache_;
-};
-
-struct BackwardEstimateSym {
-  BackwardEstimateSym(const Model1& m1,
-                      const Model1& invm1, const vector<WordID>& src, const vector<WordID>& trg) :
-      model1_(m1), invmodel1_(invm1), src_(src), trg_(trg) {
-  }
-  const prob_t& operator()(const vector<bool>& src_cov, unsigned trg_cov) const {
-    assert(src_.size() == src_cov.size());
-    assert(trg_cov <= trg_.size());
-    prob_t& e = cache_[src_cov][trg_cov];
-    if (e.is_0()) {
-      if (trg_cov == trg_.size()) { e = prob_t::One(); return e; }
-      vector<WordID> r(src_.size() + 1); r.clear();
-      for (int i = 0; i < src_cov.size(); ++i)
-        if (!src_cov[i]) r.push_back(src_[i]);
-      r.push_back(0);  // NULL word
-      const prob_t uniform_alignment(1.0 / r.size());
-      e.logeq(Md::log_poisson(trg_.size() - trg_cov, r.size() - 1)); // p(trg len remaining | src len remaining)
-      for (unsigned j = trg_cov; j < trg_.size(); ++j) {
-        prob_t p;
-        for (unsigned i = 0; i < r.size(); ++i)
-          p += model1_(r[i], trg_[j]);
-        if (p.is_0()) {
-          cerr << "ERROR: p(" << TD::Convert(trg_[j]) << " | " << TD::GetString(r) << ") = 0!\n";
-          abort();
-        }
-        p *= uniform_alignment;
-        e *= p;
-      }
-      r.pop_back();
-      const prob_t inv_uniform(1.0 / (trg_.size() - trg_cov + 1.0));
-      prob_t inv;
-      inv.logeq(Md::log_poisson(r.size(), trg_.size() - trg_cov));
-      for (unsigned i = 0; i < r.size(); ++i) {
-        prob_t p;
-        for (unsigned j = trg_cov - 1; j < trg_.size(); ++j)
-          p += invmodel1_(j < trg_cov ? 0 : trg_[j], r[i]);
-        if (p.is_0()) {
-          cerr << "ERROR: p_inv(" << TD::Convert(r[i]) << " | " << TD::GetString(trg_) << ") = 0!\n";
-          abort();
-        }
-        p *= inv_uniform;
-        inv *= p;
-      }
-      prob_t x = pow(e * inv, 0.5);
-      e = x;
-      //cerr << "Forward: " << log(e) << "\tBackward: " << log(inv) << "\t prop: " << log(x) << endl;
-    }
-    return e;
-  }
-  const Model1& model1_;
-  const Model1& invmodel1_;
-  const vector<WordID>& src_;
-  const vector<WordID>& trg_;
-  mutable unordered_map<vector<bool>, map<unsigned, prob_t>, boost::hash<vector<bool> > > cache_;
-};
-
-struct Particle {
-  Particle() : weight(prob_t::One()), src_cov(), trg_cov(), prev_pos(-1) {}
-  prob_t weight;
-  prob_t gamma_last;
-  vector<int> src_jumps;
-  vector<TRulePtr> rules;
-  vector<bool> src_cv;
-  int src_cov;
-  int trg_cov;
-  int prev_pos;
-};
-
-ostream& operator<<(ostream& o, const vector<bool>& v) {
-  for (int i = 0; i < v.size(); ++i)
-    o << (v[i] ? '1' : '0');
-  return o;
-}
-ostream& operator<<(ostream& o, const Particle& p) {
-  o << "[cv=" << p.src_cv << "  src_cov=" << p.src_cov << " trg_cov=" << p.trg_cov << " last_pos=" << p.prev_pos << " num_rules=" << p.rules.size() << "  w=" << log(p.weight) << ']';
-  return o;
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-  const unsigned kMAX_TRG_PHRASE = conf["max_trg_phrase"].as<unsigned>();
-  const unsigned kMAX_SRC_PHRASE = conf["max_src_phrase"].as<unsigned>();
-  const unsigned particles = conf["particles"].as<unsigned>();
-  const unsigned samples = conf["samples"].as<unsigned>();
-  const unsigned rejuv_freq = conf["filter_frequency"].as<unsigned>();
-
-  if (!conf.count("model1")) {
-    cerr << argv[0] << "Please use --model1 to specify model 1 parameters\n";
-    return 1;
-  }
-  if (conf.count("random_seed"))
-    prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
-  else
-    prng.reset(new MT19937);
-  MT19937& rng = *prng;
-
-  vector<vector<WordID> > corpuse, corpusf;
-  set<WordID> vocabe, vocabf;
-  cerr << "Reading corpus...\n";
-  ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe);
-  cerr << "F-corpus size: " << corpusf.size() << " sentences\t (" << vocabf.size() << " word types)\n";
-  cerr << "E-corpus size: " << corpuse.size() << " sentences\t (" << vocabe.size() << " word types)\n";
-  assert(corpusf.size() == corpuse.size());
-
-  const int kLHS = -TD::Convert("X");
-  Model1 m1(conf["model1"].as<string>());
-  Model1 invm1(conf["inverse_model1"].as<string>());
-
-#if 0
-  PhraseConditionalBase lp0(m1, conf["model1_interpolation_weight"].as<double>(), vocabe.size());
-  MyConditionalModel m(lp0);
-#else
-  PhraseJointBase lp0(m1, conf["model1_interpolation_weight"].as<double>(), vocabe.size(), vocabf.size());
-  MyJointModel m(lp0);
-#endif
-
-  MultinomialResampleFilter<Particle> filter(&rng);
-  cerr << "Initializing reachability limits...\n";
-  vector<Particle> ps(corpusf.size());
-  vector<Reachability> reaches; reaches.reserve(corpusf.size());
-  for (int ci = 0; ci < corpusf.size(); ++ci)
-    reaches.push_back(Reachability(corpusf[ci].size(),
-                                   corpuse[ci].size(),
-                                   kMAX_SRC_PHRASE,
-                                   kMAX_TRG_PHRASE));
-  cerr << "Sampling...\n"; 
-  vector<Particle> tmp_p(10000);  // work space
-  SampleSet<prob_t> pfss;
-  for (int SS=0; SS < samples; ++SS) {
-    for (int ci = 0; ci < corpusf.size(); ++ci) {
-      vector<int>& src = corpusf[ci];
-      vector<int>& trg = corpuse[ci];
-      m.DecrementRules(ps[ci].rules);
-      m.DecrementJumps(ps[ci].src_jumps, src.size());
-
-      //BackwardEstimate be(m1, src, trg);
-      BackwardEstimateSym be(m1, invm1, src, trg);
-      const Reachability& r = reaches[ci];
-      vector<Particle> lps(particles);
-
-      for (int pi = 0; pi < particles; ++pi) {
-        Particle& p = lps[pi];
-        p.src_cv.resize(src.size(), false);
-      }
-
-      bool all_complete = false;
-      while(!all_complete) {
-        SampleSet<prob_t> ss;
-
-        // all particles have now been extended a bit, we will reweight them now
-        if (lps[0].trg_cov > 0)
-          filter(&lps);
-
-        // loop over all particles and extend them
-        bool done_nothing = true;
-        for (int pi = 0; pi < particles; ++pi) {
-          Particle& p = lps[pi];
-          int tic = 0;
-          while(p.trg_cov < trg.size() && tic < rejuv_freq) {
-            ++tic;
-            done_nothing = false;
-            ss.clear();
-            TRule x; x.lhs_ = kLHS;
-            prob_t z;
-            int first_uncovered = src.size();
-            int last_uncovered = -1;
-            for (int i = 0; i < src.size(); ++i) {
-              const bool is_uncovered = !p.src_cv[i];
-              if (i < first_uncovered && is_uncovered) first_uncovered = i;
-              if (is_uncovered && i > last_uncovered) last_uncovered = i;
-            }
-            assert(last_uncovered > -1);
-            assert(first_uncovered < src.size());
-
-            for (int trg_len = 1; trg_len <= kMAX_TRG_PHRASE; ++trg_len) {
-              x.e_.push_back(trg[trg_len - 1 + p.trg_cov]);
-              for (int src_len = 1; src_len <= kMAX_SRC_PHRASE; ++src_len) {
-                if (!r.edges[p.src_cov][p.trg_cov][src_len][trg_len]) continue;
-
-                const int last_possible_start = last_uncovered - src_len + 1;
-                assert(last_possible_start >= 0);
-                //cerr << src_len << "," << trg_len << " is allowed. E=" << TD::GetString(x.e_) << endl;
-                //cerr << "  first_uncovered=" << first_uncovered << "  last_possible_start=" << last_possible_start << endl;
-                for (int i = first_uncovered; i <= last_possible_start; ++i) {
-                  if (p.src_cv[i]) continue;
-                  assert(ss.size() < tmp_p.size());  // if fails increase tmp_p size
-                  Particle& np = tmp_p[ss.size()];
-                  np = p;
-                  x.f_.clear();
-                  int gap_add = 0;
-                  bool bad = false;
-                  prob_t jp = prob_t::One();
-                  int prev_pos = p.prev_pos;
-                  for (int j = 0; j < src_len; ++j) {
-                    if ((j + i + gap_add) == src.size()) { bad = true; break; }
-                    while ((i+j+gap_add) < src.size() && p.src_cv[i + j + gap_add]) { ++gap_add; }
-                    if ((j + i + gap_add) == src.size()) { bad = true; break; }
-                    np.src_cv[i + j + gap_add] = true;
-                    x.f_.push_back(src[i + j + gap_add]);
-                    jp *= m.JumpProbability(i + j + gap_add - prev_pos, src.size());
-                    int jump = i + j + gap_add - prev_pos;
-                    assert(jump != 0);
-                    np.src_jumps.push_back(jump);
-                    prev_pos = i + j + gap_add;
-                  }
-                  if (bad) continue;
-                  np.prev_pos = prev_pos;
-                  np.src_cov += x.f_.size();
-                  np.trg_cov += x.e_.size();
-                  if (x.f_.size() != src_len) continue;
-                  prob_t rp = m.RuleProbability(x);
-                  np.gamma_last = rp * jp;
-                  const prob_t u = pow(np.gamma_last * be(np.src_cv, np.trg_cov), 0.2);
-                  //cerr << "**rule=" << x << endl;
-                  //cerr << "  u=" << log(u) << "  rule=" << rp << " jump=" << jp << endl;
-                  ss.add(u);
-                  np.rules.push_back(TRulePtr(new TRule(x)));
-                  z += u;
-
-                  const bool completed = (p.trg_cov == trg.size());
-                  if (completed) {
-                    int last_jump = src.size() - p.prev_pos;
-                    assert(last_jump > 0);
-                    p.src_jumps.push_back(last_jump);
-                    p.weight *= m.JumpProbability(last_jump, src.size());
-                  }
-                }
-              }
-            }
-            cerr << "number of edges to consider: " << ss.size() << endl;
-            const int sampled = rng.SelectSample(ss);
-            prob_t q_n = ss[sampled] / z;
-            p = tmp_p[sampled];
-            //m.IncrementRule(*p.rules.back());
-            p.weight *= p.gamma_last / q_n;
-            cerr << "[w=" << log(p.weight) << "]\tsampled rule: " << p.rules.back()->AsString() << endl;
-            cerr << p << endl;
-          }
-        } // loop over particles (pi = 0 .. particles)
-        if (done_nothing) all_complete = true;
-      }
-      pfss.clear();
-      for (int i = 0; i < lps.size(); ++i)
-        pfss.add(lps[i].weight);
-      const int sampled = rng.SelectSample(pfss);
-      ps[ci] = lps[sampled];
-      m.IncrementRules(lps[sampled].rules);
-      m.IncrementJumps(lps[sampled].src_jumps, src.size());
-      for (int i = 0; i < lps[sampled].rules.size(); ++i) { cerr << "S:\t" << lps[sampled].rules[i]->AsString() << "\n"; }
-      cerr << "tmp-LLH: " << log(m.Likelihood()) << endl;
-    }
-    cerr << "LLH: " << log(m.Likelihood()) << endl;
-    for (int sni = 0; sni < 5; ++sni) {
-      for (int i = 0; i < ps[sni].rules.size(); ++i) { cerr << "\t" << ps[sni].rules[i]->AsString() << endl; }
-    }
-  }
-  return 0;
-}
-
diff --git a/gi/pf/pfdist.new.cc b/gi/pf/pfdist.new.cc
deleted file mode 100644
index 3169eb75..00000000
--- a/gi/pf/pfdist.new.cc
+++ /dev/null
@@ -1,620 +0,0 @@
-#include <iostream>
-#include <tr1/memory>
-#include <queue>
-
-#include <boost/functional.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "base_measures.h"
-#include "reachability.h"
-#include "viterbi.h"
-#include "hg.h"
-#include "trule.h"
-#include "tdict.h"
-#include "filelib.h"
-#include "dict.h"
-#include "sampler.h"
-#include "ccrp_nt.h"
-#include "ccrp_onetable.h"
-
-using namespace std;
-using namespace tr1;
-namespace po = boost::program_options;
-
-shared_ptr<MT19937> prng;
-
-size_t hash_value(const TRule& r) {
-  size_t h = boost::hash_value(r.e_);
-  boost::hash_combine(h, -r.lhs_);
-  boost::hash_combine(h, boost::hash_value(r.f_));
-  return h;
-}
-
-bool operator==(const TRule& a, const TRule& b) {
-  return (a.lhs_ == b.lhs_ && a.e_ == b.e_ && a.f_ == b.f_);
-}
-
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
-  po::options_description opts("Configuration options");
-  opts.add_options()
-        ("samples,s",po::value<unsigned>()->default_value(1000),"Number of samples")
-        ("particles,p",po::value<unsigned>()->default_value(25),"Number of particles")
-        ("input,i",po::value<string>(),"Read parallel data from")
-        ("max_src_phrase",po::value<unsigned>()->default_value(5),"Maximum length of source language phrases")
-        ("max_trg_phrase",po::value<unsigned>()->default_value(5),"Maximum length of target language phrases")
-        ("model1,m",po::value<string>(),"Model 1 parameters (used in base distribution)")
-        ("inverse_model1,M",po::value<string>(),"Inverse Model 1 parameters (used in backward estimate)")
-        ("model1_interpolation_weight",po::value<double>()->default_value(0.95),"Mixing proportion of model 1 with uniform target distribution")
-        ("random_seed,S",po::value<uint32_t>(), "Random seed");
-  po::options_description clo("Command line options");
-  clo.add_options()
-        ("config", po::value<string>(), "Configuration file")
-        ("help,h", "Print this help message and exit");
-  po::options_description dconfig_options, dcmdline_options;
-  dconfig_options.add(opts);
-  dcmdline_options.add(opts).add(clo);
-  
-  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
-  if (conf->count("config")) {
-    ifstream config((*conf)["config"].as<string>().c_str());
-    po::store(po::parse_config_file(config, dconfig_options), *conf);
-  }
-  po::notify(*conf);
-
-  if (conf->count("help") || (conf->count("input") == 0)) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-void ReadParallelCorpus(const string& filename,
-                vector<vector<WordID> >* f,
-                vector<vector<WordID> >* e,
-                set<WordID>* vocab_f,
-                set<WordID>* vocab_e) {
-  f->clear();
-  e->clear();
-  vocab_f->clear();
-  vocab_e->clear();
-  istream* in;
-  if (filename == "-")
-    in = &cin;
-  else
-    in = new ifstream(filename.c_str());
-  assert(*in);
-  string line;
-  const WordID kDIV = TD::Convert("|||");
-  vector<WordID> tmp;
-  while(*in) {
-    getline(*in, line);
-    if (line.empty() && !*in) break;
-    e->push_back(vector<int>());
-    f->push_back(vector<int>());
-    vector<int>& le = e->back();
-    vector<int>& lf = f->back();
-    tmp.clear();
-    TD::ConvertSentence(line, &tmp);
-    bool isf = true;
-    for (unsigned i = 0; i < tmp.size(); ++i) {
-      const int cur = tmp[i];
-      if (isf) {
-        if (kDIV == cur) { isf = false; } else {
-          lf.push_back(cur);
-          vocab_f->insert(cur);
-        }
-      } else {
-        assert(cur != kDIV);
-        le.push_back(cur);
-        vocab_e->insert(cur);
-      }
-    }
-    assert(isf == false);
-  }
-  if (in != &cin) delete in;
-}
-
-#if 0
-struct MyConditionalModel {
-  MyConditionalModel(PhraseConditionalBase& rcp0) : rp0(&rcp0), base(prob_t::One()), src_phrases(1,1), src_jumps(200, CCRP_NoTable<int>(1,1)) {}
-
-  prob_t srcp0(const vector<WordID>& src) const {
-    prob_t p(1.0 / 3000.0);
-    p.poweq(src.size());
-    prob_t lenp; lenp.logeq(log_poisson(src.size(), 1.0));
-    p *= lenp;
-    return p;
-  }
-
-  void DecrementRule(const TRule& rule) {
-    const RuleCRPMap::iterator it = rules.find(rule.f_);
-    assert(it != rules.end());
-    if (it->second.decrement(rule)) {
-      base /= (*rp0)(rule);
-      if (it->second.num_customers() == 0)
-        rules.erase(it);
-    }
-    if (src_phrases.decrement(rule.f_))
-      base /= srcp0(rule.f_);
-  }
-
-  void IncrementRule(const TRule& rule) {
-    RuleCRPMap::iterator it = rules.find(rule.f_);
-    if (it == rules.end())
-      it = rules.insert(make_pair(rule.f_, CCRP_NoTable<TRule>(1,1))).first;
-    if (it->second.increment(rule)) {
-      base *= (*rp0)(rule);
-    }
-    if (src_phrases.increment(rule.f_))
-      base *= srcp0(rule.f_);
-  }
-
-  void IncrementRules(const vector<TRulePtr>& rules) {
-    for (int i = 0; i < rules.size(); ++i)
-      IncrementRule(*rules[i]);
-  }
-
-  void DecrementRules(const vector<TRulePtr>& rules) {
-    for (int i = 0; i < rules.size(); ++i)
-      DecrementRule(*rules[i]);
-  }
-
-  void IncrementJump(int dist, unsigned src_len) {
-    assert(src_len > 0);
-    if (src_jumps[src_len].increment(dist))
-      base *= jp0(dist, src_len);
-  }
-
-  void DecrementJump(int dist, unsigned src_len) {
-    assert(src_len > 0);
-    if (src_jumps[src_len].decrement(dist))
-      base /= jp0(dist, src_len);
-  }
-
-  void IncrementJumps(const vector<int>& js, unsigned src_len) {
-    for (unsigned i = 0; i < js.size(); ++i)
-      IncrementJump(js[i], src_len);
-  }
-
-  void DecrementJumps(const vector<int>& js, unsigned src_len) {
-    for (unsigned i = 0; i < js.size(); ++i)
-      DecrementJump(js[i], src_len);
-  }
-
-  // p(jump = dist | src_len , z)
-  prob_t JumpProbability(int dist, unsigned src_len) {
-    const prob_t p0 = jp0(dist, src_len);
-    const double lp = src_jumps[src_len].logprob(dist, log(p0));
-    prob_t q; q.logeq(lp);
-    return q;
-  }
-
-  // p(rule.f_ | z) * p(rule.e_ | rule.f_ , z)
-  prob_t RuleProbability(const TRule& rule) const {
-    const prob_t p0 = (*rp0)(rule);
-    prob_t srcp; srcp.logeq(src_phrases.logprob(rule.f_, log(srcp0(rule.f_))));
-    const RuleCRPMap::const_iterator it = rules.find(rule.f_);
-    if (it == rules.end()) return srcp * p0;
-    const double lp = it->second.logprob(rule, log(p0));
-    prob_t q; q.logeq(lp);
-    return q * srcp;
-  }
-
-  prob_t Likelihood() const {
-    prob_t p = base;
-    for (RuleCRPMap::const_iterator it = rules.begin();
-         it != rules.end(); ++it) {
-      prob_t cl; cl.logeq(it->second.log_crp_prob());
-      p *= cl;
-    }
-    for (unsigned l = 1; l < src_jumps.size(); ++l) {
-      if (src_jumps[l].num_customers() > 0) {
-        prob_t q;
-        q.logeq(src_jumps[l].log_crp_prob());
-        p *= q;
-      }
-    }
-    return p;
-  }
-
-  JumpBase jp0;
-  const PhraseConditionalBase* rp0;
-  prob_t base;
-  typedef unordered_map<vector<WordID>, CCRP_NoTable<TRule>, boost::hash<vector<WordID> > > RuleCRPMap;
-  RuleCRPMap rules;
-  CCRP_NoTable<vector<WordID> > src_phrases;
-  vector<CCRP_NoTable<int> > src_jumps;
-};
-
-#endif
-
-struct MyJointModel {
-  MyJointModel(PhraseJointBase& rcp0) :
-    rp0(rcp0), base(prob_t::One()), rules(1,1), src_jumps(200, CCRP_NoTable<int>(1,1)) {}
-
-  void DecrementRule(const TRule& rule) {
-    if (rules.decrement(rule))
-      base /= rp0(rule);
-  }
-
-  void IncrementRule(const TRule& rule) {
-    if (rules.increment(rule))
-      base *= rp0(rule);
-  }
-
-  void IncrementRules(const vector<TRulePtr>& rules) {
-    for (int i = 0; i < rules.size(); ++i)
-      IncrementRule(*rules[i]);
-  }
-
-  void DecrementRules(const vector<TRulePtr>& rules) {
-    for (int i = 0; i < rules.size(); ++i)
-      DecrementRule(*rules[i]);
-  }
-
-  void IncrementJump(int dist, unsigned src_len) {
-    assert(src_len > 0);
-    if (src_jumps[src_len].increment(dist))
-      base *= jp0(dist, src_len);
-  }
-
-  void DecrementJump(int dist, unsigned src_len) {
-    assert(src_len > 0);
-    if (src_jumps[src_len].decrement(dist))
-      base /= jp0(dist, src_len);
-  }
-
-  void IncrementJumps(const vector<int>& js, unsigned src_len) {
-    for (unsigned i = 0; i < js.size(); ++i)
-      IncrementJump(js[i], src_len);
-  }
-
-  void DecrementJumps(const vector<int>& js, unsigned src_len) {
-    for (unsigned i = 0; i < js.size(); ++i)
-      DecrementJump(js[i], src_len);
-  }
-
-  // p(jump = dist | src_len , z)
-  prob_t JumpProbability(int dist, unsigned src_len) {
-    const prob_t p0 = jp0(dist, src_len);
-    const double lp = src_jumps[src_len].logprob(dist, log(p0));
-    prob_t q; q.logeq(lp);
-    return q;
-  }
-
-  // p(rule.f_ | z) * p(rule.e_ | rule.f_ , z)
-  prob_t RuleProbability(const TRule& rule) const {
-    prob_t p; p.logeq(rules.logprob(rule, log(rp0(rule))));
-    return p;
-  }
-
-  prob_t Likelihood() const {
-    prob_t p = base;
-    prob_t q; q.logeq(rules.log_crp_prob());
-    p *= q;
-    for (unsigned l = 1; l < src_jumps.size(); ++l) {
-      if (src_jumps[l].num_customers() > 0) {
-        prob_t q;
-        q.logeq(src_jumps[l].log_crp_prob());
-        p *= q;
-      }
-    }
-    return p;
-  }
-
-  JumpBase jp0;
-  const PhraseJointBase& rp0;
-  prob_t base;
-  CCRP_NoTable<TRule> rules;
-  vector<CCRP_NoTable<int> > src_jumps;
-};
-
-struct BackwardEstimate {
-  BackwardEstimate(const Model1& m1, const vector<WordID>& src, const vector<WordID>& trg) :
-      model1_(m1), src_(src), trg_(trg) {
-  }
-  const prob_t& operator()(const vector<bool>& src_cov, unsigned trg_cov) const {
-    assert(src_.size() == src_cov.size());
-    assert(trg_cov <= trg_.size());
-    prob_t& e = cache_[src_cov][trg_cov];
-    if (e.is_0()) {
-      if (trg_cov == trg_.size()) { e = prob_t::One(); return e; }
-      vector<WordID> r(src_.size() + 1); r.clear();
-      r.push_back(0);  // NULL word
-      for (int i = 0; i < src_cov.size(); ++i)
-        if (!src_cov[i]) r.push_back(src_[i]);
-      const prob_t uniform_alignment(1.0 / r.size());
-      e.logeq(log_poisson(trg_.size() - trg_cov, r.size() - 1)); // p(trg len remaining | src len remaining)
-      for (unsigned j = trg_cov; j < trg_.size(); ++j) {
-        prob_t p;
-        for (unsigned i = 0; i < r.size(); ++i)
-          p += model1_(r[i], trg_[j]);
-        if (p.is_0()) {
-          cerr << "ERROR: p(" << TD::Convert(trg_[j]) << " | " << TD::GetString(r) << ") = 0!\n";
-          abort();
-        }
-        p *= uniform_alignment;
-        e *= p;
-      }
-    }
-    return e;
-  }
-  const Model1& model1_;
-  const vector<WordID>& src_;
-  const vector<WordID>& trg_;
-  mutable unordered_map<vector<bool>, map<unsigned, prob_t>, boost::hash<vector<bool> > > cache_;
-};
-
-struct BackwardEstimateSym {
-  BackwardEstimateSym(const Model1& m1,
-                      const Model1& invm1, const vector<WordID>& src, const vector<WordID>& trg) :
-      model1_(m1), invmodel1_(invm1), src_(src), trg_(trg) {
-  }
-  const prob_t& operator()(const vector<bool>& src_cov, unsigned trg_cov) const {
-    assert(src_.size() == src_cov.size());
-    assert(trg_cov <= trg_.size());
-    prob_t& e = cache_[src_cov][trg_cov];
-    if (e.is_0()) {
-      if (trg_cov == trg_.size()) { e = prob_t::One(); return e; }
-      vector<WordID> r(src_.size() + 1); r.clear();
-      for (int i = 0; i < src_cov.size(); ++i)
-        if (!src_cov[i]) r.push_back(src_[i]);
-      r.push_back(0);  // NULL word
-      const prob_t uniform_alignment(1.0 / r.size());
-      e.logeq(log_poisson(trg_.size() - trg_cov, r.size() - 1)); // p(trg len remaining | src len remaining)
-      for (unsigned j = trg_cov; j < trg_.size(); ++j) {
-        prob_t p;
-        for (unsigned i = 0; i < r.size(); ++i)
-          p += model1_(r[i], trg_[j]);
-        if (p.is_0()) {
-          cerr << "ERROR: p(" << TD::Convert(trg_[j]) << " | " << TD::GetString(r) << ") = 0!\n";
-          abort();
-        }
-        p *= uniform_alignment;
-        e *= p;
-      }
-      r.pop_back();
-      const prob_t inv_uniform(1.0 / (trg_.size() - trg_cov + 1.0));
-      prob_t inv;
-      inv.logeq(log_poisson(r.size(), trg_.size() - trg_cov));
-      for (unsigned i = 0; i < r.size(); ++i) {
-        prob_t p;
-        for (unsigned j = trg_cov - 1; j < trg_.size(); ++j)
-          p += invmodel1_(j < trg_cov ? 0 : trg_[j], r[i]);
-        if (p.is_0()) {
-          cerr << "ERROR: p_inv(" << TD::Convert(r[i]) << " | " << TD::GetString(trg_) << ") = 0!\n";
-          abort();
-        }
-        p *= inv_uniform;
-        inv *= p;
-      }
-      prob_t x = pow(e * inv, 0.5);
-      e = x;
-      //cerr << "Forward: " << log(e) << "\tBackward: " << log(inv) << "\t prop: " << log(x) << endl;
-    }
-    return e;
-  }
-  const Model1& model1_;
-  const Model1& invmodel1_;
-  const vector<WordID>& src_;
-  const vector<WordID>& trg_;
-  mutable unordered_map<vector<bool>, map<unsigned, prob_t>, boost::hash<vector<bool> > > cache_;
-};
-
-struct Particle {
-  Particle() : weight(prob_t::One()), src_cov(), trg_cov(), prev_pos(-1) {}
-  prob_t weight;
-  prob_t gamma_last;
-  vector<int> src_jumps;
-  vector<TRulePtr> rules;
-  vector<bool> src_cv;
-  int src_cov;
-  int trg_cov;
-  int prev_pos;
-};
-
-ostream& operator<<(ostream& o, const vector<bool>& v) {
-  for (int i = 0; i < v.size(); ++i)
-    o << (v[i] ? '1' : '0');
-  return o;
-}
-ostream& operator<<(ostream& o, const Particle& p) {
-  o << "[cv=" << p.src_cv << "  src_cov=" << p.src_cov << " trg_cov=" << p.trg_cov << " last_pos=" << p.prev_pos << " num_rules=" << p.rules.size() << "  w=" << log(p.weight) << ']';
-  return o;
-}
-
-void FilterCrapParticlesAndReweight(vector<Particle>* pps) {
-  vector<Particle>& ps = *pps;
-  SampleSet<prob_t> ss;
-  for (int i = 0; i < ps.size(); ++i)
-    ss.add(ps[i].weight);
-  vector<Particle> nps; nps.reserve(ps.size());
-  const prob_t uniform_weight(1.0 / ps.size());
-  for (int i = 0; i < ps.size(); ++i) {
-    nps.push_back(ps[prng->SelectSample(ss)]);
-    nps[i].weight = uniform_weight;
-  }
-  nps.swap(ps);
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-  const unsigned kMAX_TRG_PHRASE = conf["max_trg_phrase"].as<unsigned>();
-  const unsigned kMAX_SRC_PHRASE = conf["max_src_phrase"].as<unsigned>();
-  const unsigned particles = conf["particles"].as<unsigned>();
-  const unsigned samples = conf["samples"].as<unsigned>();
-
-  if (!conf.count("model1")) {
-    cerr << argv[0] << "Please use --model1 to specify model 1 parameters\n";
-    return 1;
-  }
-  if (conf.count("random_seed"))
-    prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
-  else
-    prng.reset(new MT19937);
-  MT19937& rng = *prng;
-
-  vector<vector<WordID> > corpuse, corpusf;
-  set<WordID> vocabe, vocabf;
-  cerr << "Reading corpus...\n";
-  ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe);
-  cerr << "F-corpus size: " << corpusf.size() << " sentences\t (" << vocabf.size() << " word types)\n";
-  cerr << "E-corpus size: " << corpuse.size() << " sentences\t (" << vocabe.size() << " word types)\n";
-  assert(corpusf.size() == corpuse.size());
-
-  const int kLHS = -TD::Convert("X");
-  Model1 m1(conf["model1"].as<string>());
-  Model1 invm1(conf["inverse_model1"].as<string>());
-
-#if 0
-  PhraseConditionalBase lp0(m1, conf["model1_interpolation_weight"].as<double>(), vocabe.size());
-  MyConditionalModel m(lp0);
-#else
-  PhraseJointBase lp0(m1, conf["model1_interpolation_weight"].as<double>(), vocabe.size(), vocabf.size());
-  MyJointModel m(lp0);
-#endif
-
-  cerr << "Initializing reachability limits...\n";
-  vector<Particle> ps(corpusf.size());
-  vector<Reachability> reaches; reaches.reserve(corpusf.size());
-  for (int ci = 0; ci < corpusf.size(); ++ci)
-    reaches.push_back(Reachability(corpusf[ci].size(),
-                                   corpuse[ci].size(),
-                                   kMAX_SRC_PHRASE,
-                                   kMAX_TRG_PHRASE));
-  cerr << "Sampling...\n"; 
-  vector<Particle> tmp_p(10000);  // work space
-  SampleSet<prob_t> pfss;
-  for (int SS=0; SS < samples; ++SS) {
-    for (int ci = 0; ci < corpusf.size(); ++ci) {
-      vector<int>& src = corpusf[ci];
-      vector<int>& trg = corpuse[ci];
-      m.DecrementRules(ps[ci].rules);
-      m.DecrementJumps(ps[ci].src_jumps, src.size());
-
-      //BackwardEstimate be(m1, src, trg);
-      BackwardEstimateSym be(m1, invm1, src, trg);
-      const Reachability& r = reaches[ci];
-      vector<Particle> lps(particles);
-
-      for (int pi = 0; pi < particles; ++pi) {
-        Particle& p = lps[pi];
-        p.src_cv.resize(src.size(), false);
-      }
-
-      bool all_complete = false;
-      while(!all_complete) {
-        SampleSet<prob_t> ss;
-
-        // all particles have now been extended a bit, we will reweight them now
-        if (lps[0].trg_cov > 0)
-          FilterCrapParticlesAndReweight(&lps);
-
-        // loop over all particles and extend them
-        bool done_nothing = true;
-        for (int pi = 0; pi < particles; ++pi) {
-          Particle& p = lps[pi];
-          int tic = 0;
-          const int rejuv_freq = 1;
-          while(p.trg_cov < trg.size() && tic < rejuv_freq) {
-            ++tic;
-            done_nothing = false;
-            ss.clear();
-            TRule x; x.lhs_ = kLHS;
-            prob_t z;
-            int first_uncovered = src.size();
-            int last_uncovered = -1;
-            for (int i = 0; i < src.size(); ++i) {
-              const bool is_uncovered = !p.src_cv[i];
-              if (i < first_uncovered && is_uncovered) first_uncovered = i;
-              if (is_uncovered && i > last_uncovered) last_uncovered = i;
-            }
-            assert(last_uncovered > -1);
-            assert(first_uncovered < src.size());
-
-            for (int trg_len = 1; trg_len <= kMAX_TRG_PHRASE; ++trg_len) {
-              x.e_.push_back(trg[trg_len - 1 + p.trg_cov]);
-              for (int src_len = 1; src_len <= kMAX_SRC_PHRASE; ++src_len) {
-                if (!r.edges[p.src_cov][p.trg_cov][src_len][trg_len]) continue;
-
-                const int last_possible_start = last_uncovered - src_len + 1;
-                assert(last_possible_start >= 0);
-                //cerr << src_len << "," << trg_len << " is allowed. E=" << TD::GetString(x.e_) << endl;
-                //cerr << "  first_uncovered=" << first_uncovered << "  last_possible_start=" << last_possible_start << endl;
-                for (int i = first_uncovered; i <= last_possible_start; ++i) {
-                  if (p.src_cv[i]) continue;
-                  assert(ss.size() < tmp_p.size());  // if fails increase tmp_p size
-                  Particle& np = tmp_p[ss.size()];
-                  np = p;
-                  x.f_.clear();
-                  int gap_add = 0;
-                  bool bad = false;
-                  prob_t jp = prob_t::One();
-                  int prev_pos = p.prev_pos;
-                  for (int j = 0; j < src_len; ++j) {
-                    if ((j + i + gap_add) == src.size()) { bad = true; break; }
-                    while ((i+j+gap_add) < src.size() && p.src_cv[i + j + gap_add]) { ++gap_add; }
-                    if ((j + i + gap_add) == src.size()) { bad = true; break; }
-                    np.src_cv[i + j + gap_add] = true;
-                    x.f_.push_back(src[i + j + gap_add]);
-                    jp *= m.JumpProbability(i + j + gap_add - prev_pos, src.size());
-                    int jump = i + j + gap_add - prev_pos;
-                    assert(jump != 0);
-                    np.src_jumps.push_back(jump);
-                    prev_pos = i + j + gap_add;
-                  }
-                  if (bad) continue;
-                  np.prev_pos = prev_pos;
-                  np.src_cov += x.f_.size();
-                  np.trg_cov += x.e_.size();
-                  if (x.f_.size() != src_len) continue;
-                  prob_t rp = m.RuleProbability(x);
-                  np.gamma_last = rp * jp;
-                  const prob_t u = pow(np.gamma_last * be(np.src_cv, np.trg_cov), 0.2);
-                  //cerr << "**rule=" << x << endl;
-                  //cerr << "  u=" << log(u) << "  rule=" << rp << " jump=" << jp << endl;
-                  ss.add(u);
-                  np.rules.push_back(TRulePtr(new TRule(x)));
-                  z += u;
-
-                  const bool completed = (p.trg_cov == trg.size());
-                  if (completed) {
-                    int last_jump = src.size() - p.prev_pos;
-                    assert(last_jump > 0);
-                    p.src_jumps.push_back(last_jump);
-                    p.weight *= m.JumpProbability(last_jump, src.size());
-                  }
-                }
-              }
-            }
-            cerr << "number of edges to consider: " << ss.size() << endl;
-            const int sampled = rng.SelectSample(ss);
-            prob_t q_n = ss[sampled] / z;
-            p = tmp_p[sampled];
-            //m.IncrementRule(*p.rules.back());
-            p.weight *= p.gamma_last / q_n;
-            cerr << "[w=" << log(p.weight) << "]\tsampled rule: " << p.rules.back()->AsString() << endl;
-            cerr << p << endl;
-          }
-        } // loop over particles (pi = 0 .. particles)
-        if (done_nothing) all_complete = true;
-      }
-      pfss.clear();
-      for (int i = 0; i < lps.size(); ++i)
-        pfss.add(lps[i].weight);
-      const int sampled = rng.SelectSample(pfss);
-      ps[ci] = lps[sampled];
-      m.IncrementRules(lps[sampled].rules);
-      m.IncrementJumps(lps[sampled].src_jumps, src.size());
-      for (int i = 0; i < lps[sampled].rules.size(); ++i) { cerr << "S:\t" << lps[sampled].rules[i]->AsString() << "\n"; }
-      cerr << "tmp-LLH: " << log(m.Likelihood()) << endl;
-    }
-    cerr << "LLH: " << log(m.Likelihood()) << endl;
-    for (int sni = 0; sni < 5; ++sni) {
-      for (int i = 0; i < ps[sni].rules.size(); ++i) { cerr << "\t" << ps[sni].rules[i]->AsString() << endl; }
-    }
-  }
-  return 0;
-}
-
diff --git a/gi/pf/pfnaive.cc b/gi/pf/pfnaive.cc
deleted file mode 100644
index 958ec4e2..00000000
--- a/gi/pf/pfnaive.cc
+++ /dev/null
@@ -1,284 +0,0 @@
-#include <iostream>
-#include <tr1/memory>
-#include <queue>
-
-#include <boost/functional.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "pf.h"
-#include "base_distributions.h"
-#include "monotonic_pseg.h"
-#include "reachability.h"
-#include "viterbi.h"
-#include "hg.h"
-#include "trule.h"
-#include "tdict.h"
-#include "filelib.h"
-#include "dict.h"
-#include "sampler.h"
-#include "ccrp_nt.h"
-#include "ccrp_onetable.h"
-#include "corpus.h"
-
-using namespace std;
-using namespace tr1;
-namespace po = boost::program_options;
-
-boost::shared_ptr<MT19937> prng;
-
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
-  po::options_description opts("Configuration options");
-  opts.add_options()
-        ("samples,s",po::value<unsigned>()->default_value(1000),"Number of samples")
-        ("particles,p",po::value<unsigned>()->default_value(30),"Number of particles")
-        ("filter_frequency,f",po::value<unsigned>()->default_value(5),"Number of time steps between filterings")
-        ("input,i",po::value<string>(),"Read parallel data from")
-        ("max_src_phrase",po::value<unsigned>()->default_value(5),"Maximum length of source language phrases")
-        ("max_trg_phrase",po::value<unsigned>()->default_value(5),"Maximum length of target language phrases")
-        ("model1,m",po::value<string>(),"Model 1 parameters (used in base distribution)")
-        ("inverse_model1,M",po::value<string>(),"Inverse Model 1 parameters (used in backward estimate)")
-        ("model1_interpolation_weight",po::value<double>()->default_value(0.95),"Mixing proportion of model 1 with uniform target distribution")
-        ("random_seed,S",po::value<uint32_t>(), "Random seed");
-  po::options_description clo("Command line options");
-  clo.add_options()
-        ("config", po::value<string>(), "Configuration file")
-        ("help,h", "Print this help message and exit");
-  po::options_description dconfig_options, dcmdline_options;
-  dconfig_options.add(opts);
-  dcmdline_options.add(opts).add(clo);
-  
-  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
-  if (conf->count("config")) {
-    ifstream config((*conf)["config"].as<string>().c_str());
-    po::store(po::parse_config_file(config, dconfig_options), *conf);
-  }
-  po::notify(*conf);
-
-  if (conf->count("help") || (conf->count("input") == 0)) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-struct BackwardEstimateSym {
-  BackwardEstimateSym(const Model1& m1,
-                      const Model1& invm1, const vector<WordID>& src, const vector<WordID>& trg) :
-      model1_(m1), invmodel1_(invm1), src_(src), trg_(trg) {
-  }
-  const prob_t& operator()(unsigned src_cov, unsigned trg_cov) const {
-    assert(src_cov <= src_.size());
-    assert(trg_cov <= trg_.size());
-    prob_t& e = cache_[src_cov][trg_cov];
-    if (e.is_0()) {
-      if (trg_cov == trg_.size()) { e = prob_t::One(); return e; }
-      vector<WordID> r(src_.size() + 1); r.clear();
-      for (int i = src_cov; i < src_.size(); ++i)
-        r.push_back(src_[i]);
-      r.push_back(0);  // NULL word
-      const prob_t uniform_alignment(1.0 / r.size());
-      e.logeq(Md::log_poisson(trg_.size() - trg_cov, r.size() - 1)); // p(trg len remaining | src len remaining)
-      for (unsigned j = trg_cov; j < trg_.size(); ++j) {
-        prob_t p;
-        for (unsigned i = 0; i < r.size(); ++i)
-          p += model1_(r[i], trg_[j]);
-        if (p.is_0()) {
-          cerr << "ERROR: p(" << TD::Convert(trg_[j]) << " | " << TD::GetString(r) << ") = 0!\n";
-          abort();
-        }
-        p *= uniform_alignment;
-        e *= p;
-      }
-      r.pop_back();
-      const prob_t inv_uniform(1.0 / (trg_.size() - trg_cov + 1.0));
-      prob_t inv;
-      inv.logeq(Md::log_poisson(r.size(), trg_.size() - trg_cov));
-      for (unsigned i = 0; i < r.size(); ++i) {
-        prob_t p;
-        for (unsigned j = trg_cov - 1; j < trg_.size(); ++j)
-          p += invmodel1_(j < trg_cov ? 0 : trg_[j], r[i]);
-        if (p.is_0()) {
-          cerr << "ERROR: p_inv(" << TD::Convert(r[i]) << " | " << TD::GetString(trg_) << ") = 0!\n";
-          abort();
-        }
-        p *= inv_uniform;
-        inv *= p;
-      }
-      prob_t x = pow(e * inv, 0.5);
-      e = x;
-      //cerr << "Forward: " << log(e) << "\tBackward: " << log(inv) << "\t prop: " << log(x) << endl;
-    }
-    return e;
-  }
-  const Model1& model1_;
-  const Model1& invmodel1_;
-  const vector<WordID>& src_;
-  const vector<WordID>& trg_;
-  mutable unordered_map<unsigned, map<unsigned, prob_t> > cache_;
-};
-
-struct Particle {
-  Particle() : weight(prob_t::One()), src_cov(), trg_cov() {}
-  prob_t weight;
-  prob_t gamma_last;
-  vector<TRulePtr> rules;
-  int src_cov;
-  int trg_cov;
-};
-
-ostream& operator<<(ostream& o, const vector<bool>& v) {
-  for (int i = 0; i < v.size(); ++i)
-    o << (v[i] ? '1' : '0');
-  return o;
-}
-ostream& operator<<(ostream& o, const Particle& p) {
-  o << "[src_cov=" << p.src_cov << " trg_cov=" << p.trg_cov << " num_rules=" << p.rules.size() << "  w=" << log(p.weight) << ']';
-  return o;
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-  const unsigned kMAX_TRG_PHRASE = conf["max_trg_phrase"].as<unsigned>();
-  const unsigned kMAX_SRC_PHRASE = conf["max_src_phrase"].as<unsigned>();
-  const unsigned particles = conf["particles"].as<unsigned>();
-  const unsigned samples = conf["samples"].as<unsigned>();
-  const unsigned rejuv_freq = conf["filter_frequency"].as<unsigned>();
-
-  if (!conf.count("model1")) {
-    cerr << argv[0] << "Please use --model1 to specify model 1 parameters\n";
-    return 1;
-  }
-  if (conf.count("random_seed"))
-    prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
-  else
-    prng.reset(new MT19937);
-  MT19937& rng = *prng;
-
-  vector<vector<WordID> > corpuse, corpusf;
-  set<WordID> vocabe, vocabf;
-  cerr << "Reading corpus...\n";
-  corpus::ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe);
-  cerr << "F-corpus size: " << corpusf.size() << " sentences\t (" << vocabf.size() << " word types)\n";
-  cerr << "E-corpus size: " << corpuse.size() << " sentences\t (" << vocabe.size() << " word types)\n";
-  assert(corpusf.size() == corpuse.size());
-
-  const int kLHS = -TD::Convert("X");
-  Model1 m1(conf["model1"].as<string>());
-  Model1 invm1(conf["inverse_model1"].as<string>());
-
-  PhraseJointBase lp0(m1, conf["model1_interpolation_weight"].as<double>(), vocabe.size(), vocabf.size());
-  PhraseJointBase_BiDir alp0(m1, invm1, conf["model1_interpolation_weight"].as<double>(), vocabe.size(), vocabf.size());
-  MonotonicParallelSegementationModel<PhraseJointBase_BiDir> m(alp0);
-  TRule xx("[X] ||| ms. kimura ||| MS. KIMURA ||| X=0");
-  cerr << xx << endl << lp0(xx) << " " << alp0(xx) << endl;
-  TRule xx12("[X] ||| . ||| PHARMACY . ||| X=0");
-  TRule xx21("[X] ||| pharmacy . ||| . ||| X=0");
-//  TRule xx22("[X] ||| . ||| . ||| X=0");
-  TRule xx22("[X] ||| . ||| THE . ||| X=0");
-  cerr << xx12 << "\t" << lp0(xx12) << " " << alp0(xx12) << endl;
-  cerr << xx21 << "\t" << lp0(xx21) << " " << alp0(xx21) << endl;
-  cerr << xx22 << "\t" << lp0(xx22) << " " << alp0(xx22) << endl;
-
-  cerr << "Initializing reachability limits...\n";
-  vector<Particle> ps(corpusf.size());
-  vector<Reachability> reaches; reaches.reserve(corpusf.size());
-  for (int ci = 0; ci < corpusf.size(); ++ci)
-    reaches.push_back(Reachability(corpusf[ci].size(),
-                                   corpuse[ci].size(),
-                                   kMAX_SRC_PHRASE,
-                                   kMAX_TRG_PHRASE));
-  cerr << "Sampling...\n"; 
-  vector<Particle> tmp_p(10000);  // work space
-  SampleSet<prob_t> pfss;
-  SystematicResampleFilter<Particle> filter(&rng);
-  // MultinomialResampleFilter<Particle> filter(&rng);
-  for (int SS=0; SS < samples; ++SS) {
-    for (int ci = 0; ci < corpusf.size(); ++ci) {
-      vector<int>& src = corpusf[ci];
-      vector<int>& trg = corpuse[ci];
-      m.DecrementRulesAndStops(ps[ci].rules);
-      const prob_t q_stop = m.StopProbability();
-      const prob_t q_cont = m.ContinueProbability();
-      cerr << "P(stop)=" << q_stop << "\tP(continue)=" <<q_cont << endl;
-
-      BackwardEstimateSym be(m1, invm1, src, trg);
-      const Reachability& r = reaches[ci];
-      vector<Particle> lps(particles);
-
-      bool all_complete = false;
-      while(!all_complete) {
-        SampleSet<prob_t> ss;
-
-        // all particles have now been extended a bit, we will reweight them now
-        if (lps[0].trg_cov > 0)
-          filter(&lps);
-
-        // loop over all particles and extend them
-        bool done_nothing = true;
-        for (int pi = 0; pi < particles; ++pi) {
-          Particle& p = lps[pi];
-          int tic = 0;
-          while(p.trg_cov < trg.size() && tic < rejuv_freq) {
-            ++tic;
-            done_nothing = false;
-            ss.clear();
-            TRule x; x.lhs_ = kLHS;
-            prob_t z;
-
-            for (int trg_len = 1; trg_len <= kMAX_TRG_PHRASE; ++trg_len) {
-              x.e_.push_back(trg[trg_len - 1 + p.trg_cov]);
-              for (int src_len = 1; src_len <= kMAX_SRC_PHRASE; ++src_len) {
-                if (!r.edges[p.src_cov][p.trg_cov][src_len][trg_len]) continue;
-
-                int i = p.src_cov;
-                assert(ss.size() < tmp_p.size());  // if fails increase tmp_p size
-                Particle& np = tmp_p[ss.size()];
-                np = p;
-                x.f_.clear();
-                for (int j = 0; j < src_len; ++j)
-                  x.f_.push_back(src[i + j]);
-                np.src_cov += x.f_.size();
-                np.trg_cov += x.e_.size();
-                const bool stop_now = (np.src_cov == src_len && np.trg_cov == trg_len);
-                prob_t rp = m.RuleProbability(x) * (stop_now ? q_stop : q_cont);
-                np.gamma_last = rp;
-                const prob_t u = pow(np.gamma_last * pow(be(np.src_cov, np.trg_cov), 1.2), 0.1);
-                //cerr << "**rule=" << x << endl;
-                //cerr << "  u=" << log(u) << "  rule=" << rp << endl;
-                ss.add(u);
-                np.rules.push_back(TRulePtr(new TRule(x)));
-                z += u;
-              }
-            }
-            //cerr << "number of edges to consider: " << ss.size() << endl;
-            const int sampled = rng.SelectSample(ss);
-            prob_t q_n = ss[sampled] / z;
-            p = tmp_p[sampled];
-            //m.IncrementRule(*p.rules.back());
-            p.weight *= p.gamma_last / q_n;
-            //cerr << "[w=" << log(p.weight) << "]\tsampled rule: " << p.rules.back()->AsString() << endl;
-            //cerr << p << endl;
-          }
-        } // loop over particles (pi = 0 .. particles)
-        if (done_nothing) all_complete = true;
-        prob_t wv = prob_t::Zero();
-        for (int pp = 0; pp < lps.size(); ++pp)
-          wv += lps[pp].weight;
-        for (int pp = 0; pp < lps.size(); ++pp)
-          lps[pp].weight /= wv;
-      }
-      pfss.clear();
-      for (int i = 0; i < lps.size(); ++i)
-        pfss.add(lps[i].weight);
-      const int sampled = rng.SelectSample(pfss);
-      ps[ci] = lps[sampled];
-      m.IncrementRulesAndStops(lps[sampled].rules);
-      for (int i = 0; i < lps[sampled].rules.size(); ++i) { cerr << "S:\t" << lps[sampled].rules[i]->AsString() << "\n"; }
-      cerr << "tmp-LLH: " << log(m.Likelihood()) << endl;
-    }
-    cerr << "LLH: " << log(m.Likelihood()) << endl;
-  }
-  return 0;
-}
-
diff --git a/gi/pf/poisson_uniform_word_model.h b/gi/pf/poisson_uniform_word_model.h
deleted file mode 100644
index 76204a0e..00000000
--- a/gi/pf/poisson_uniform_word_model.h
+++ /dev/null
@@ -1,50 +0,0 @@
-#ifndef _POISSON_UNIFORM_WORD_MODEL_H_
-#define _POISSON_UNIFORM_WORD_MODEL_H_
-
-#include <cmath>
-#include <vector>
-#include "prob.h"
-#include "m.h"
-
-// len ~ Poisson(lambda)
-//   for (1..len)
-//     e_i ~ Uniform({Vocabulary})
-struct PoissonUniformWordModel {
-  explicit PoissonUniformWordModel(const unsigned vocab_size,
-                                   const unsigned alphabet_size,
-                                   const double mean_len = 5) :
-    lh(prob_t::One()),
-    v0(-std::log(vocab_size)),
-    u0(-std::log(alphabet_size)),
-    mean_length(mean_len) {}
-
-  void ResampleHyperparameters(MT19937*) {}
-
-  inline prob_t operator()(const std::vector<WordID>& s) const {
-    prob_t p;
-    p.logeq(Md::log_poisson(s.size(), mean_length) + s.size() * u0);
-    //p.logeq(v0);
-    return p;
-  }
-
-  inline void Increment(const std::vector<WordID>& w, MT19937*) {
-    lh *= (*this)(w);
-  }
-
-  inline void Decrement(const std::vector<WordID>& w, MT19937 *) {
-    lh /= (*this)(w);
-  }
-
-  inline prob_t Likelihood() const { return lh; }
-
-  void Summary() const {}
-
- private:
-
-  prob_t lh;  // keeps track of the draws from the base distribution
-  const double v0;  // uniform log prob of generating a word
-  const double u0;  // uniform log prob of generating a letter
-  const double mean_length;  // mean length of a word in the base distribution
-};
-
-#endif
diff --git a/gi/pf/pyp_lm.cc b/gi/pf/pyp_lm.cc
deleted file mode 100644
index 605d8206..00000000
--- a/gi/pf/pyp_lm.cc
+++ /dev/null
@@ -1,273 +0,0 @@
-#include <iostream>
-#include <tr1/memory>
-#include <queue>
-
-#include <boost/functional.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "gamma_poisson.h"
-#include "corpus_tools.h"
-#include "m.h"
-#include "tdict.h"
-#include "sampler.h"
-#include "ccrp.h"
-#include "tied_resampler.h"
-
-// A not very memory-efficient implementation of an N-gram LM based on PYPs
-// as described in Y.-W. Teh. (2006) A Hierarchical Bayesian Language Model
-// based on Pitman-Yor Processes. In Proc. ACL.
-
-// I use templates to handle the recursive formalation of the prior, so
-// the order of the model has to be specified here, at compile time:
-#define kORDER 3
-
-using namespace std;
-using namespace tr1;
-namespace po = boost::program_options;
-
-boost::shared_ptr<MT19937> prng;
-
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
-  po::options_description opts("Configuration options");
-  opts.add_options()
-        ("samples,n",po::value<unsigned>()->default_value(300),"Number of samples")
-        ("train,i",po::value<string>(),"Training data file")
-        ("test,T",po::value<string>(),"Test data file")
-        ("discount_prior_a,a",po::value<double>()->default_value(1.0), "discount ~ Beta(a,b): a=this")
-        ("discount_prior_b,b",po::value<double>()->default_value(1.0), "discount ~ Beta(a,b): b=this")
-        ("strength_prior_s,s",po::value<double>()->default_value(1.0), "strength ~ Gamma(s,r): s=this")
-        ("strength_prior_r,r",po::value<double>()->default_value(1.0), "strength ~ Gamma(s,r): r=this")
-        ("random_seed,S",po::value<uint32_t>(), "Random seed");
-  po::options_description clo("Command line options");
-  clo.add_options()
-        ("config", po::value<string>(), "Configuration file")
-        ("help", "Print this help message and exit");
-  po::options_description dconfig_options, dcmdline_options;
-  dconfig_options.add(opts);
-  dcmdline_options.add(opts).add(clo);
-  
-  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
-  if (conf->count("config")) {
-    ifstream config((*conf)["config"].as<string>().c_str());
-    po::store(po::parse_config_file(config, dconfig_options), *conf);
-  }
-  po::notify(*conf);
-
-  if (conf->count("help") || (conf->count("train") == 0)) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-// uniform distribution over a fixed vocabulary
-struct UniformVocabulary {
-  UniformVocabulary(unsigned vs, double, double, double, double) : p0(1.0 / vs), draws() {}
-  void increment(WordID, const vector<WordID>&, MT19937*) { ++draws; }
-  void decrement(WordID, const vector<WordID>&, MT19937*) { --draws; assert(draws >= 0); }
-  double prob(WordID, const vector<WordID>&) const { return p0; }
-  void resample_hyperparameters(MT19937*) {}
-  double log_likelihood() const { return draws * log(p0); }
-  const double p0;
-  int draws;
-};
-
-// Lord Rothschild. 1986. THE DISTRIBUTION OF ENGLISH DICTIONARY WORD LENGTHS.
-// Journal of Statistical Planning and Inference 14 (1986) 311-322
-struct PoissonLengthUniformCharWordModel {
-  explicit PoissonLengthUniformCharWordModel(unsigned vocab_size, double, double, double, double) : plen(5,5), uc(-log(95)), llh() {}
-  void increment(WordID w, const vector<WordID>& v, MT19937*) {
-    llh += log(prob(w, v)); // this isn't quite right
-    plen.increment(TD::Convert(w).size() - 1);
-  }
-  void decrement(WordID w, const vector<WordID>& v, MT19937*) {
-    plen.decrement(TD::Convert(w).size() - 1);
-    llh -= log(prob(w, v)); // this isn't quite right
-  }
-  double prob(WordID w, const vector<WordID>&) const {
-    const unsigned len = TD::Convert(w).size();
-    return plen.prob(len - 1) * exp(uc * len);
-  }
-  double log_likelihood() const { return llh; }
-  void resample_hyperparameters(MT19937*) {}
-  GammaPoisson plen;
-  const double uc;
-  double llh;
-};
-
-struct PYPAdaptedPoissonLengthUniformCharWordModel {
-  explicit PYPAdaptedPoissonLengthUniformCharWordModel(unsigned vocab_size, double, double, double, double) :
-    base(vocab_size,1,1,1,1),
-    crp(1,1,1,1) {}
-  void increment(WordID w, const vector<WordID>& v, MT19937* rng) {
-    double p0 = base.prob(w, v);
-    if (crp.increment(w, p0, rng))
-      base.increment(w, v, rng);
-  }
-  void decrement(WordID w, const vector<WordID>& v, MT19937* rng) {
-    if (crp.decrement(w, rng))
-      base.decrement(w, v, rng);
-  }
-  double prob(WordID w, const vector<WordID>& v) const {
-    double p0 = base.prob(w, v);
-    return crp.prob(w, p0);
-  }
-  double log_likelihood() const { return crp.log_crp_prob() + base.log_likelihood(); }
-  void resample_hyperparameters(MT19937* rng) { crp.resample_hyperparameters(rng); }
-  PoissonLengthUniformCharWordModel base;
-  CCRP<WordID> crp;
-};
-
-template <unsigned N> struct PYPLM;
-
-#if 1
-template<> struct PYPLM<0> : public UniformVocabulary {
-  PYPLM(unsigned vs, double a, double b, double c, double d) :
-    UniformVocabulary(vs, a, b, c, d) {}
-};
-#else
-#if 0
-template<> struct PYPLM<0> : public PoissonLengthUniformCharWordModel {
-  PYPLM(unsigned vs, double a, double b, double c, double d) :
-    PoissonLengthUniformCharWordModel(vs, a, b, c, d) {}
-};
-#else
-template<> struct PYPLM<0> : public PYPAdaptedPoissonLengthUniformCharWordModel {
-  PYPLM(unsigned vs, double a, double b, double c, double d) :
-    PYPAdaptedPoissonLengthUniformCharWordModel(vs, a, b, c, d) {}
-};
-#endif
-#endif
-
-// represents an N-gram LM
-template <unsigned N> struct PYPLM {
-  PYPLM(unsigned vs, double da, double db, double ss, double sr) :
-      backoff(vs, da, db, ss, sr),
-      tr(da, db, ss, sr, 0.8, 1.0),
-      lookup(N-1) {}
-  void increment(WordID w, const vector<WordID>& context, MT19937* rng) {
-    const double bo = backoff.prob(w, context);
-    for (unsigned i = 0; i < N-1; ++i)
-      lookup[i] = context[context.size() - 1 - i];
-    typename unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > >::iterator it = p.find(lookup);
-    if (it == p.end()) {
-      it = p.insert(make_pair(lookup, CCRP<WordID>(0.5,1))).first;
-      tr.Add(&it->second);  // add to resampler
-    }
-    if (it->second.increment(w, bo, rng))
-      backoff.increment(w, context, rng);
-  }
-  void decrement(WordID w, const vector<WordID>& context, MT19937* rng) {
-    for (unsigned i = 0; i < N-1; ++i)
-      lookup[i] = context[context.size() - 1 - i];
-    typename unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > >::iterator it = p.find(lookup);
-    assert(it != p.end());
-    if (it->second.decrement(w, rng))
-      backoff.decrement(w, context, rng);
-  }
-  double prob(WordID w, const vector<WordID>& context) const {
-    const double bo = backoff.prob(w, context);
-    for (unsigned i = 0; i < N-1; ++i)
-      lookup[i] = context[context.size() - 1 - i];
-    typename unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > >::const_iterator it = p.find(lookup);
-    if (it == p.end()) return bo;
-    return it->second.prob(w, bo);
-  }
-
-  double log_likelihood() const {
-    double llh = backoff.log_likelihood();
-    typename unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > >::const_iterator it;
-    for (it = p.begin(); it != p.end(); ++it)
-      llh += it->second.log_crp_prob();
-    llh += tr.LogLikelihood();
-    return llh;
-  }
-
-  void resample_hyperparameters(MT19937* rng) {
-    tr.ResampleHyperparameters(rng);
-    backoff.resample_hyperparameters(rng);
-  }
-
-  PYPLM<N-1> backoff;
-  TiedResampler<CCRP<WordID> > tr;
-  double discount_a, discount_b, strength_s, strength_r;
-  double d, strength;
-  mutable vector<WordID> lookup;  // thread-local
-  unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > > p;
-};
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-
-  InitCommandLine(argc, argv, &conf);
-  const unsigned samples = conf["samples"].as<unsigned>();
-  if (conf.count("random_seed"))
-    prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
-  else
-    prng.reset(new MT19937);
-  MT19937& rng = *prng;
-  vector<vector<WordID> > corpuse;
-  set<WordID> vocabe;
-  const WordID kEOS = TD::Convert("</s>");
-  cerr << "Reading corpus...\n";
-  CorpusTools::ReadFromFile(conf["train"].as<string>(), &corpuse, &vocabe);
-  cerr << "E-corpus size: " << corpuse.size() << " sentences\t (" << vocabe.size() << " word types)\n";
-  vector<vector<WordID> > test;
-  if (conf.count("test"))
-    CorpusTools::ReadFromFile(conf["test"].as<string>(), &test);
-  else
-    test = corpuse;
-  PYPLM<kORDER> lm(vocabe.size(),
-                   conf["discount_prior_a"].as<double>(),
-                   conf["discount_prior_b"].as<double>(),
-                   conf["strength_prior_s"].as<double>(),
-                   conf["strength_prior_r"].as<double>());
-  vector<WordID> ctx(kORDER - 1, TD::Convert("<s>"));
-  for (int SS=0; SS < samples; ++SS) {
-    for (int ci = 0; ci < corpuse.size(); ++ci) {
-      ctx.resize(kORDER - 1);
-      const vector<WordID>& s = corpuse[ci];
-      for (int i = 0; i <= s.size(); ++i) {
-        WordID w = (i < s.size() ? s[i] : kEOS);
-        if (SS > 0) lm.decrement(w, ctx, &rng);
-        lm.increment(w, ctx, &rng);
-        ctx.push_back(w);
-      }
-    }
-    if (SS % 10 == 9) {
-      cerr << " [LLH=" << lm.log_likelihood() << "]" << endl;
-      if (SS % 30 == 29) lm.resample_hyperparameters(&rng);
-    } else { cerr << '.' << flush; }
-  }
-  double llh = 0;
-  unsigned cnt = 0;
-  unsigned oovs = 0;
-  for (int ci = 0; ci < test.size(); ++ci) {
-    ctx.resize(kORDER - 1);
-    const vector<WordID>& s = test[ci];
-    for (int i = 0; i <= s.size(); ++i) {
-      WordID w = (i < s.size() ? s[i] : kEOS);
-      double lp = log(lm.prob(w, ctx)) / log(2);
-      if (i < s.size() && vocabe.count(w) == 0) {
-        cerr << "**OOV ";
-        ++oovs;
-        lp = 0;
-      }
-      cerr << "p(" << TD::Convert(w) << " |";
-      for (int j = ctx.size() + 1 - kORDER; j < ctx.size(); ++j)
-        cerr << ' ' << TD::Convert(ctx[j]);
-      cerr << ") = " << lp << endl;
-      ctx.push_back(w);
-      llh -= lp;
-      cnt++;
-    }
-  }
-  cerr << "  Log_10 prob: " << (-llh * log(2) / log(10)) << endl;
-  cerr << "        Count: " << cnt << endl;
-  cerr << "         OOVs: " << oovs << endl;
-  cerr << "Cross-entropy: " << (llh / cnt) << endl;
-  cerr << "   Perplexity: " << pow(2, llh / cnt) << endl;
-  return 0;
-}
-
-
diff --git a/gi/pf/pyp_tm.cc b/gi/pf/pyp_tm.cc
deleted file mode 100644
index 37b9a604..00000000
--- a/gi/pf/pyp_tm.cc
+++ /dev/null
@@ -1,128 +0,0 @@
-#include "pyp_tm.h"
-
-#include <tr1/unordered_map>
-#include <iostream>
-#include <queue>
-
-#include "tdict.h"
-#include "ccrp.h"
-#include "pyp_word_model.h"
-#include "tied_resampler.h"
-
-using namespace std;
-using namespace std::tr1;
-
-struct FreqBinner {
-  FreqBinner(const std::string& fname) { fd_.Load(fname); }
-  unsigned NumberOfBins() const { return fd_.Max() + 1; }
-  unsigned Bin(const WordID& w) const { return fd_.LookUp(w); }
-  FreqDict<unsigned> fd_;
-};
-
-template <typename Base, class Binner = FreqBinner>
-struct ConditionalPYPWordModel {
-  ConditionalPYPWordModel(Base* b, const Binner* bnr = NULL) :
-      base(*b),
-      binner(bnr),
-      btr(binner ? binner->NumberOfBins() + 1u : 2u) {}
-
-  void Summary() const {
-    cerr << "Number of conditioning contexts: " << r.size() << endl;
-    for (RuleModelHash::const_iterator it = r.begin(); it != r.end(); ++it) {
-      cerr << TD::Convert(it->first) << "   \tPYP(d=" << it->second.discount() << ",s=" << it->second.strength() << ") --------------------------" << endl;
-      for (CCRP<vector<WordID> >::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
-        cerr << "   " << i2->second << '\t' << TD::GetString(i2->first) << endl;
-    }
-  }
-
-  void ResampleHyperparameters(MT19937* rng) {
-    btr.ResampleHyperparameters(rng);
-  } 
-
-  prob_t Prob(const WordID src, const vector<WordID>& trglets) const {
-    RuleModelHash::const_iterator it = r.find(src);
-    if (it == r.end()) {
-      return base(trglets);
-    } else {
-      return it->second.prob(trglets, base(trglets));
-    }
-  }
-
-  void Increment(const WordID src, const vector<WordID>& trglets, MT19937* rng) {
-    RuleModelHash::iterator it = r.find(src);
-    if (it == r.end()) {
-      it = r.insert(make_pair(src, CCRP<vector<WordID> >(0.5,1.0))).first;
-      static const WordID kNULL = TD::Convert("NULL");
-      unsigned bin = (src == kNULL ? 0 : 1);
-      if (binner && bin) { bin = binner->Bin(src) + 1; }
-      btr.Add(bin, &it->second);
-    }
-    if (it->second.increment(trglets, base(trglets), rng))
-      base.Increment(trglets, rng);
-  }
-
-  void Decrement(const WordID src, const vector<WordID>& trglets, MT19937* rng) {
-    RuleModelHash::iterator it = r.find(src);
-    assert(it != r.end());
-    if (it->second.decrement(trglets, rng)) {
-      base.Decrement(trglets, rng);
-    }
-  }
-
-  prob_t Likelihood() const {
-    prob_t p = prob_t::One();
-    for (RuleModelHash::const_iterator it = r.begin(); it != r.end(); ++it) {
-      prob_t q; q.logeq(it->second.log_crp_prob());
-      p *= q;
-    }
-    return p;
-  }
-
-  unsigned UniqueConditioningContexts() const {
-    return r.size();
-  }
-
-  // TODO tie PYP hyperparameters based on source word frequency bins
-  Base& base;
-  const Binner* binner;
-  BinTiedResampler<CCRP<vector<WordID> > > btr;
-  typedef unordered_map<WordID, CCRP<vector<WordID> > > RuleModelHash;
-  RuleModelHash r;
-};
-
-PYPLexicalTranslation::PYPLexicalTranslation(const vector<vector<WordID> >& lets,
-                                             const unsigned vocab_size,
-                                             const unsigned num_letters) :
-    letters(lets),
-    base(vocab_size, num_letters, 5),
-    tmodel(new ConditionalPYPWordModel<PoissonUniformWordModel>(&base, new FreqBinner("10k.freq"))),
-    kX(-TD::Convert("X")) {}
-
-void PYPLexicalTranslation::Summary() const {
-  tmodel->Summary();
-}
-
-prob_t PYPLexicalTranslation::Likelihood() const {
-  return tmodel->Likelihood() * base.Likelihood();
-}
-
-void PYPLexicalTranslation::ResampleHyperparameters(MT19937* rng) {
-  tmodel->ResampleHyperparameters(rng);
-}
-
-unsigned PYPLexicalTranslation::UniqueConditioningContexts() const {
-  return tmodel->UniqueConditioningContexts();
-}
-
-prob_t PYPLexicalTranslation::Prob(WordID src, WordID trg) const {
-  return tmodel->Prob(src, letters[trg]);
-}
-
-void PYPLexicalTranslation::Increment(WordID src, WordID trg, MT19937* rng) {
-  tmodel->Increment(src, letters[trg], rng);
-}
-
-void PYPLexicalTranslation::Decrement(WordID src, WordID trg, MT19937* rng) {
-  tmodel->Decrement(src, letters[trg], rng);
-}
-
diff --git a/gi/pf/pyp_tm.h b/gi/pf/pyp_tm.h
deleted file mode 100644
index 2b076a25..00000000
--- a/gi/pf/pyp_tm.h
+++ /dev/null
@@ -1,36 +0,0 @@
-#ifndef PYP_LEX_TRANS
-#define PYP_LEX_TRANS
-
-#include <vector>
-#include "wordid.h"
-#include "prob.h"
-#include "sampler.h"
-#include "freqdict.h"
-#include "poisson_uniform_word_model.h"
-
-struct FreqBinner;
-template <typename T, class B> struct ConditionalPYPWordModel;
-
-struct PYPLexicalTranslation {
-  explicit PYPLexicalTranslation(const std::vector<std::vector<WordID> >& lets,
-                                 const unsigned vocab_size,
-                                 const unsigned num_letters);
-
-  prob_t Likelihood() const;
-
-  void ResampleHyperparameters(MT19937* rng);
-  prob_t Prob(WordID src, WordID trg) const;  // return p(trg | src)
-  void Summary() const;
-  void Increment(WordID src, WordID trg, MT19937* rng);
-  void Decrement(WordID src, WordID trg, MT19937* rng);
-  unsigned UniqueConditioningContexts() const;
-
- private:
-  const std::vector<std::vector<WordID> >& letters;   // spelling dictionary
-  PoissonUniformWordModel base;  // "generator" of English types
-  ConditionalPYPWordModel<PoissonUniformWordModel, FreqBinner>* tmodel;  // translation distributions
-                      // (model English word | French word)
-  const WordID kX;
-};
-
-#endif
diff --git a/gi/pf/pyp_word_model.h b/gi/pf/pyp_word_model.h
deleted file mode 100644
index 0bebb751..00000000
--- a/gi/pf/pyp_word_model.h
+++ /dev/null
@@ -1,61 +0,0 @@
-#ifndef _PYP_WORD_MODEL_H_
-#define _PYP_WORD_MODEL_H_
-
-#include <iostream>
-#include <cmath>
-#include <vector>
-#include "prob.h"
-#include "ccrp.h"
-#include "m.h"
-#include "tdict.h"
-#include "os_phrase.h"
-
-// PYP(d,s,poisson-uniform) represented as a CRP
-template <class Base>
-struct PYPWordModel {
-  explicit PYPWordModel(Base* b) :
-      base(*b),
-      r(1,1,1,1,0.66,50.0)
-    {}
-
-  void ResampleHyperparameters(MT19937* rng) {
-    r.resample_hyperparameters(rng);
-    std::cerr << " PYPWordModel(d=" << r.discount() << ",s=" << r.strength() << ")\n";
-  }
-
-  inline prob_t operator()(const std::vector<WordID>& s) const {
-    return r.prob(s, base(s));
-  }
-
-  inline void Increment(const std::vector<WordID>& s, MT19937* rng) {
-    if (r.increment(s, base(s), rng))
-      base.Increment(s, rng);
-  }
-
-  inline void Decrement(const std::vector<WordID>& s, MT19937 *rng) {
-    if (r.decrement(s, rng))
-      base.Decrement(s, rng);
-  }
-
-  inline prob_t Likelihood() const {
-    prob_t p; p.logeq(r.log_crp_prob());
-    p *= base.Likelihood();
-    return p;
-  }
-
-  void Summary() const {
-    std::cerr << "PYPWordModel: generations=" << r.num_customers()
-         << " PYP(d=" << r.discount() << ",s=" << r.strength() << ')' << std::endl;
-    for (typename CCRP<std::vector<WordID> >::const_iterator it = r.begin(); it != r.end(); ++it) {
-      std::cerr << "  " << it->second
-                << TD::GetString(it->first) << std::endl;
-    }
-  }
-
- private:
-
-  Base& base;  // keeps track of the draws from the base distribution
-  CCRP<std::vector<WordID> > r;
-};
-
-#endif
diff --git a/gi/pf/quasi_model2.h b/gi/pf/quasi_model2.h
deleted file mode 100644
index 4075affe..00000000
--- a/gi/pf/quasi_model2.h
+++ /dev/null
@@ -1,177 +0,0 @@
-#ifndef _QUASI_MODEL2_H_
-#define _QUASI_MODEL2_H_
-
-#include <vector>
-#include <cmath>
-#include <tr1/unordered_map>
-#include "boost/functional.hpp"
-#include "prob.h"
-#include "array2d.h"
-#include "slice_sampler.h"
-#include "m.h"
-#include "have_64_bits.h"
-
-struct AlignmentObservation {
-  AlignmentObservation() : src_len(), trg_len(), j(), a_j() {}
-  AlignmentObservation(unsigned sl, unsigned tl, unsigned tw, unsigned sw) :
-      src_len(sl), trg_len(tl), j(tw), a_j(sw) {}
-  unsigned short src_len;
-  unsigned short trg_len;
-  unsigned short j;
-  unsigned short a_j;
-};
-
-#ifdef HAVE_64_BITS
-inline size_t hash_value(const AlignmentObservation& o) {
-  return reinterpret_cast<const size_t&>(o);
-}
-inline bool operator==(const AlignmentObservation& a, const AlignmentObservation& b) {
-  return hash_value(a) == hash_value(b);
-}
-#else
-inline size_t hash_value(const AlignmentObservation& o) {
-  size_t h = 1;
-  boost::hash_combine(h, o.src_len);
-  boost::hash_combine(h, o.trg_len);
-  boost::hash_combine(h, o.j);
-  boost::hash_combine(h, o.a_j);
-  return h;
-}
-#endif
-
-struct QuasiModel2 {
-  explicit QuasiModel2(double alpha, double pnull = 0.1) :
-      alpha_(alpha),
-      pnull_(pnull),
-      pnotnull_(1 - pnull) {}
-
-  // a_j = 0 => NULL; src_len does *not* include null
-  prob_t Prob(unsigned a_j, unsigned j, unsigned src_len, unsigned trg_len) const {
-    if (!a_j) return pnull_;
-    return pnotnull_ *
-       prob_t(UnnormalizedProb(a_j, j, src_len, trg_len, alpha_) / GetOrComputeZ(j, src_len, trg_len));
-  }
-
-  void Increment(unsigned a_j, unsigned j, unsigned src_len, unsigned trg_len) {
-    assert(a_j <= src_len);
-    assert(j < trg_len);
-    ++obs_[AlignmentObservation(src_len, trg_len, j, a_j)];
-  }
-
-  void Decrement(unsigned a_j, unsigned j, unsigned src_len, unsigned trg_len) {
-    const AlignmentObservation ao(src_len, trg_len, j, a_j);
-    int &cc = obs_[ao];
-    assert(cc > 0);
-    --cc;
-    if (!cc) obs_.erase(ao);
-  }
-
-  struct PNullResampler {
-    PNullResampler(const QuasiModel2& m) : m_(m) {}
-    const QuasiModel2& m_;
-    double operator()(const double& proposed_pnull) const {
-      return log(m_.Likelihood(m_.alpha_, proposed_pnull));
-    }
-  };
-
-  struct AlphaResampler {
-    AlphaResampler(const QuasiModel2& m) : m_(m) {}
-    const QuasiModel2& m_;
-    double operator()(const double& proposed_alpha) const {
-      return log(m_.Likelihood(proposed_alpha, m_.pnull_.as_float()));
-    }
-  };
-
-  void ResampleHyperparameters(MT19937* rng, const unsigned nloop = 5, const unsigned niterations = 10) {
-    const PNullResampler dr(*this);
-    const AlphaResampler ar(*this);
-    for (unsigned i = 0; i < nloop; ++i) {
-      double pnull = slice_sampler1d(dr, pnull_.as_float(), *rng, 0.00000001,
-                            1.0, 0.0, niterations, 100*niterations);
-      pnull_ = prob_t(pnull);
-      alpha_ = slice_sampler1d(ar, alpha_, *rng, 0.00000001,
-                              std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
-    }
-    std::cerr << "QuasiModel2(alpha=" << alpha_ << ",p_null="
-              << pnull_.as_float() << ") = " << Likelihood() << std::endl;
-    zcache_.clear();
-  }
-
-  prob_t Likelihood() const {
-    return Likelihood(alpha_, pnull_.as_float());
-  }
-
-  prob_t Likelihood(double alpha, double ppnull) const {
-    const prob_t pnull(ppnull);
-    const prob_t pnotnull(1 - ppnull);
-
-    prob_t p;
-    p.logeq(Md::log_gamma_density(alpha, 0.1, 25));  // TODO configure
-    assert(!p.is_0());
-    prob_t prob_of_ppnull; prob_of_ppnull.logeq(Md::log_beta_density(ppnull, 2, 10));
-    assert(!prob_of_ppnull.is_0());
-    p *= prob_of_ppnull;
-    for (ObsCount::const_iterator it = obs_.begin(); it != obs_.end(); ++it) {
-      const AlignmentObservation& ao = it->first;
-      if (ao.a_j) {
-        prob_t u = XUnnormalizedProb(ao.a_j, ao.j, ao.src_len, ao.trg_len, alpha);
-        prob_t z = XComputeZ(ao.j, ao.src_len, ao.trg_len, alpha);
-        prob_t pa(u / z);
-        pa *= pnotnull;
-        pa.poweq(it->second);
-        p *= pa;
-      } else {
-        p *= pnull.pow(it->second);
-      }
-    }
-    return p;
-  }
-
- private:
-  static prob_t XUnnormalizedProb(unsigned a_j, unsigned j, unsigned src_len, unsigned trg_len, double alpha) {
-    prob_t p;
-    p.logeq(-fabs(double(a_j - 1) / src_len - double(j) / trg_len) * alpha);
-    return p;
-  }
-
-  static prob_t XComputeZ(unsigned j, unsigned src_len, unsigned trg_len, double alpha) {
-    prob_t z = prob_t::Zero();
-    for (int a_j = 1; a_j <= src_len; ++a_j)
-      z += XUnnormalizedProb(a_j, j, src_len, trg_len, alpha);
-    return z;
-  }
-
-  static double UnnormalizedProb(unsigned a_j, unsigned j, unsigned src_len, unsigned trg_len, double alpha) {
-    return exp(-fabs(double(a_j - 1) / src_len - double(j) / trg_len) * alpha);
-  }
-
-  static double ComputeZ(unsigned j, unsigned src_len, unsigned trg_len, double alpha) {
-    double z = 0;
-    for (int a_j = 1; a_j <= src_len; ++a_j)
-      z += UnnormalizedProb(a_j, j, src_len, trg_len, alpha);
-    return z;
-  }
-
-  const double& GetOrComputeZ(unsigned j, unsigned src_len, unsigned trg_len) const {
-    if (src_len >= zcache_.size())
-      zcache_.resize(src_len + 1);
-    if (trg_len >= zcache_[src_len].size())
-      zcache_[src_len].resize(trg_len + 1);
-    std::vector<double>& zv = zcache_[src_len][trg_len];
-    if (zv.size() == 0)
-      zv.resize(trg_len);
-    double& z = zv[j];
-    if (!z)
-      z = ComputeZ(j, src_len, trg_len, alpha_);
-    return z;
-  }
-
-  double alpha_;
-  prob_t pnull_;
-  prob_t pnotnull_;
-  mutable std::vector<std::vector<std::vector<double> > > zcache_;
-  typedef std::tr1::unordered_map<AlignmentObservation, int, boost::hash<AlignmentObservation> > ObsCount;
-  ObsCount obs_;
-};
-
-#endif
diff --git a/gi/pf/reachability.cc b/gi/pf/reachability.cc
deleted file mode 100644
index 7d0d04ac..00000000
--- a/gi/pf/reachability.cc
+++ /dev/null
@@ -1,74 +0,0 @@
-#include "reachability.h"
-
-#include <vector>
-#include <iostream>
-
-using namespace std;
-
-struct SState {
-  SState() : prev_src_covered(), prev_trg_covered() {}
-  SState(int i, int j) : prev_src_covered(i), prev_trg_covered(j) {}
-  int prev_src_covered;
-  int prev_trg_covered;
-};
-
-void Reachability::ComputeReachability(int srclen, int trglen, int src_max_phrase_len, int trg_max_phrase_len) {
-    typedef boost::multi_array<vector<SState>, 2> array_type;
-    array_type a(boost::extents[srclen + 1][trglen + 1]);
-    a[0][0].push_back(SState());
-    for (int i = 0; i < srclen; ++i) {
-      for (int j = 0; j < trglen; ++j) {
-        if (a[i][j].size() == 0) continue;
-        const SState prev(i,j);
-        for (int k = 1; k <= src_max_phrase_len; ++k) {
-          if ((i + k) > srclen) continue;
-          for (int l = 1; l <= trg_max_phrase_len; ++l) {
-            if ((j + l) > trglen) continue;
-            a[i + k][j + l].push_back(prev);
-          }
-        }
-      }
-    }
-    a[0][0].clear();
-    //cerr << srclen << "," << trglen << ": Final cell contains " << a[srclen][trglen].size() << " back pointers\n";
-    if (a[srclen][trglen].empty()) {
-      cerr << "Sequence pair with lengths (" << srclen << ',' << trglen << ") violates reachability constraints\n";
-      nodes = 0;
-      return;
-    }
-
-    typedef boost::multi_array<bool, 2> rarray_type;
-    rarray_type r(boost::extents[srclen + 1][trglen + 1]);
-    r[srclen][trglen] = true;
-    nodes = 0;
-    for (int i = srclen; i >= 0; --i) {
-      for (int j = trglen; j >= 0; --j) {
-        vector<SState>& prevs = a[i][j];
-        if (!r[i][j]) { prevs.clear(); }
-        for (int k = 0; k < prevs.size(); ++k) {
-          r[prevs[k].prev_src_covered][prevs[k].prev_trg_covered] = true;
-          int src_delta = i - prevs[k].prev_src_covered;
-          edges[prevs[k].prev_src_covered][prevs[k].prev_trg_covered][src_delta][j - prevs[k].prev_trg_covered] = true;
-          valid_deltas[prevs[k].prev_src_covered][prevs[k].prev_trg_covered].push_back(make_pair<short,short>(src_delta,j - prevs[k].prev_trg_covered));
-          short &msd = max_src_delta[prevs[k].prev_src_covered][prevs[k].prev_trg_covered];
-          if (src_delta > msd) msd = src_delta;
-        }
-      }
-    }
-    assert(!edges[0][0][1][0]);
-    assert(!edges[0][0][0][1]);
-    assert(!edges[0][0][0][0]);
-    assert(max_src_delta[0][0] > 0);
-    nodes = 0;
-    for (int i = 0; i < srclen; ++i) {
-      for (int j = 0; j < trglen; ++j) {
-        if (valid_deltas[i][j].size() > 0) {
-          node_addresses[i][j] = nodes++;
-        } else {
-          node_addresses[i][j] = -1;
-        }
-      }
-    }
-    cerr << "Sequence pair with lengths (" << srclen << ',' << trglen << ") has " << valid_deltas[0][0].size() << " out edges in its root node, " << nodes << " nodes in total, and outside estimate matrix will require " << sizeof(float)*nodes << " bytes\n";
-  }
-
diff --git a/gi/pf/reachability.h b/gi/pf/reachability.h
deleted file mode 100644
index 1e22c76a..00000000
--- a/gi/pf/reachability.h
+++ /dev/null
@@ -1,34 +0,0 @@
-#ifndef _REACHABILITY_H_
-#define _REACHABILITY_H_
-
-#include "boost/multi_array.hpp"
-
-// determines minimum and maximum lengths of outgoing edges from all
-// coverage positions such that the alignment path respects src and
-// trg maximum phrase sizes
-//
-// runs in O(n^2 * src_max * trg_max) time but should be relatively fast
-//
-// currently forbids 0 -> n and n -> 0 alignments
-
-struct Reachability {
-  unsigned nodes;
-  boost::multi_array<bool, 4> edges;  // edges[src_covered][trg_covered][src_delta][trg_delta] is this edge worth exploring?
-  boost::multi_array<short, 2> max_src_delta; // msd[src_covered][trg_covered] -- the largest src delta that's valid
-  boost::multi_array<short, 2> node_addresses; // na[src_covered][trg_covered] -- the index of the node in a one-dimensional array (of size "nodes")
-  boost::multi_array<std::vector<std::pair<short,short> >, 2> valid_deltas; // valid_deltas[src_covered][trg_covered] list of valid transitions leaving a particular node
-
-  Reachability(int srclen, int trglen, int src_max_phrase_len, int trg_max_phrase_len) :
-      nodes(),
-      edges(boost::extents[srclen][trglen][src_max_phrase_len+1][trg_max_phrase_len+1]),
-      max_src_delta(boost::extents[srclen][trglen]),
-      node_addresses(boost::extents[srclen][trglen]),
-      valid_deltas(boost::extents[srclen][trglen]) {
-    ComputeReachability(srclen, trglen, src_max_phrase_len, trg_max_phrase_len);
-  }
-
- private:
-  void ComputeReachability(int srclen, int trglen, int src_max_phrase_len, int trg_max_phrase_len);
-};
-
-#endif
diff --git a/gi/pf/tied_resampler.h b/gi/pf/tied_resampler.h
deleted file mode 100644
index a4f4af36..00000000
--- a/gi/pf/tied_resampler.h
+++ /dev/null
@@ -1,122 +0,0 @@
-#ifndef _TIED_RESAMPLER_H_
-#define _TIED_RESAMPLER_H_
-
-#include <set>
-#include <vector>
-#include "sampler.h"
-#include "slice_sampler.h"
-#include "m.h"
-
-template <class CRP>
-struct TiedResampler {
-  explicit TiedResampler(double da, double db, double ss, double sr, double d=0.5, double s=1.0) :
-      d_alpha(da),
-      d_beta(db),
-      s_shape(ss),
-      s_rate(sr),
-      discount(d),
-      strength(s) {}
-
-  void Add(CRP* crp) {
-    crps.insert(crp);
-    crp->set_discount(discount);
-    crp->set_strength(strength);
-    assert(!crp->has_discount_prior());
-    assert(!crp->has_strength_prior());
-  }
-
-  void Remove(CRP* crp) {
-    crps.erase(crp);
-  }
-
-  size_t size() const {
-    return crps.size();
-  }
-
-  double LogLikelihood(double d, double s) const {
-    if (s <= -d) return -std::numeric_limits<double>::infinity();
-    double llh = Md::log_beta_density(d, d_alpha, d_beta) +
-                 Md::log_gamma_density(d + s, s_shape, s_rate);
-    for (typename std::set<CRP*>::iterator it = crps.begin(); it != crps.end(); ++it)
-      llh += (*it)->log_crp_prob(d, s);
-    return llh;
-  }
-
-  double LogLikelihood() const {
-    return LogLikelihood(discount, strength);
-  }
-
-  struct DiscountResampler {
-    DiscountResampler(const TiedResampler& m) : m_(m) {}
-    const TiedResampler& m_;
-    double operator()(const double& proposed_discount) const {
-      return m_.LogLikelihood(proposed_discount, m_.strength);
-    }
-  };
-
-  struct AlphaResampler {
-    AlphaResampler(const TiedResampler& m) : m_(m) {}
-    const TiedResampler& m_;
-    double operator()(const double& proposed_strength) const {
-      return m_.LogLikelihood(m_.discount, proposed_strength);
-    }
-  };
-
-  void ResampleHyperparameters(MT19937* rng, const unsigned nloop = 5, const unsigned niterations = 10) {
-    if (size() == 0) { std::cerr << "EMPTY - not resampling\n"; return; }
-    const DiscountResampler dr(*this);
-    const AlphaResampler ar(*this);
-    for (int iter = 0; iter < nloop; ++iter) {
-      strength = slice_sampler1d(ar, strength, *rng, -discount + std::numeric_limits<double>::min(),
-                              std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
-      double min_discount = std::numeric_limits<double>::min();
-      if (strength < 0.0) min_discount -= strength;
-      discount = slice_sampler1d(dr, discount, *rng, min_discount,
-                          1.0, 0.0, niterations, 100*niterations);
-    }
-    strength = slice_sampler1d(ar, strength, *rng, -discount + std::numeric_limits<double>::min(),
-                            std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
-    std::cerr << "TiedCRPs(d=" << discount << ",s="
-              << strength << ") = " << LogLikelihood(discount, strength) << std::endl;
-    for (typename std::set<CRP*>::iterator it = crps.begin(); it != crps.end(); ++it)
-      (*it)->set_hyperparameters(discount, strength);
-  }
- private:
-  std::set<CRP*> crps;
-  const double d_alpha, d_beta, s_shape, s_rate;
-  double discount, strength;
-};
-
-// split according to some criterion
-template <class CRP>
-struct BinTiedResampler {
-  explicit BinTiedResampler(unsigned nbins) :
-      resamplers(nbins, TiedResampler<CRP>(1,1,1,1)) {}
-
-  void Add(unsigned bin, CRP* crp) {
-    resamplers[bin].Add(crp);
-  }
-
-  void Remove(unsigned bin, CRP* crp) {
-    resamplers[bin].Remove(crp);
-  }
-
-  void ResampleHyperparameters(MT19937* rng) {
-    for (unsigned i = 0; i < resamplers.size(); ++i) {
-      std::cerr << "BIN " << i << " (" << resamplers[i].size() << " CRPs): " << std::flush;
-      resamplers[i].ResampleHyperparameters(rng);
-    }
-  }
-
-  double LogLikelihood() const {
-    double llh = 0;
-    for (unsigned i = 0; i < resamplers.size(); ++i)
-      llh += resamplers[i].LogLikelihood();
-    return llh;
-  }
-
- private:
-  std::vector<TiedResampler<CRP> > resamplers;
-};
-
-#endif
diff --git a/gi/pf/tpf.cc b/gi/pf/tpf.cc
deleted file mode 100644
index 7348d21c..00000000
--- a/gi/pf/tpf.cc
+++ /dev/null
@@ -1,99 +0,0 @@
-#include <iostream>
-#include <tr1/memory>
-#include <queue>
-
-#include "sampler.h"
-
-using namespace std;
-using namespace tr1;
-
-shared_ptr<MT19937> prng;
-
-struct Particle {
-  Particle() : weight(prob_t::One()) {}
-  vector<int> states;
-  prob_t weight;
-  prob_t gamma_last;
-};
-
-ostream& operator<<(ostream& os, const Particle& p) {
-  os << "[";
-  for (int i = 0; i < p.states.size(); ++i) os << p.states[i] << ' ';
-  os << "| w=" << log(p.weight) << ']';
-  return os;
-}
-
-void Rejuvenate(vector<Particle>& pps) {
-  SampleSet<prob_t> ss;
-  vector<Particle> nps(pps.size());
-  for (int i = 0; i < pps.size(); ++i) {
-//    cerr << pps[i] << endl;
-    ss.add(pps[i].weight);
-  }
-//  cerr << "REJUVINATING...\n";
-  for (int i = 0; i < pps.size(); ++i) {
-    nps[i] = pps[prng->SelectSample(ss)];
-    nps[i].weight = prob_t(1.0 / pps.size());
-//    cerr << nps[i] << endl;
-  }
-  nps.swap(pps);
-//  exit(1);
-}
-
-int main(int argc, char** argv) {
-  const unsigned particles = 100;
-  prng.reset(new MT19937);
-  MT19937& rng = *prng;
-
-  // q(a) = 0.8
-  // q(b) = 0.8
-  // q(c) = 0.4
-  SampleSet<double> ssq;
-  ssq.add(0.4);
-  ssq.add(0.6);
-  ssq.add(0);
-  double qz = 1;
-
-  // p(a) = 0.2
-  // p(b) = 0.8
-  vector<double> p(3);
-  p[0] = 0.2;
-  p[1] = 0.8;
-  p[2] = 0;
-
-  vector<int> counts(3);
-  int tot = 0;
-
-  vector<Particle> pps(particles);
-  SampleSet<prob_t> ppss;
-  int LEN = 12;
-  int PP = 1;
-  while (pps[0].states.size() < LEN) {
-    for (int pi = 0; pi < particles; ++pi) {
-      Particle& prt = pps[pi];
-
-      bool redo = true;
-      const Particle savedp = prt;
-      while (redo) {
-        redo = false;
-        for (int i = 0; i < PP; ++i) {
-          int s = rng.SelectSample(ssq);
-          double gamma_last = p[s];
-          if (!gamma_last) { redo = true; break; }
-          double q = ssq[s] / qz;
-          prt.states.push_back(s);
-          prt.weight *= prob_t(gamma_last / q);
-        }
-        if (redo) { prt = savedp; continue; }
-      }
-    }
-    Rejuvenate(pps);
-  }
-  ppss.clear();
-  for (int i = 0; i < particles; ++i) { ppss.add(pps[i].weight); }
-  int sp = rng.SelectSample(ppss);
-  cerr << pps[sp] << endl;
-
-  return 0;
-}
-
diff --git a/gi/pf/transliterations.cc b/gi/pf/transliterations.cc
deleted file mode 100644
index b2996f65..00000000
--- a/gi/pf/transliterations.cc
+++ /dev/null
@@ -1,334 +0,0 @@
-#include "transliterations.h"
-
-#include <iostream>
-#include <vector>
-
-#include "boost/shared_ptr.hpp"
-
-#include "backward.h"
-#include "filelib.h"
-#include "tdict.h"
-#include "trule.h"
-#include "filelib.h"
-#include "ccrp_nt.h"
-#include "m.h"
-#include "reachability.h"
-
-using namespace std;
-using namespace std::tr1;
-
-struct TruncatedConditionalLengthModel {
-  TruncatedConditionalLengthModel(unsigned max_src_size, unsigned max_trg_size, double expected_src_to_trg_ratio) :
-      plens(max_src_size+1, vector<prob_t>(max_trg_size+1, 0.0)) {
-    for (unsigned i = 1; i <= max_src_size; ++i) {
-      prob_t z = prob_t::Zero();
-      for (unsigned j = 1; j <= max_trg_size; ++j)
-        z += (plens[i][j] = prob_t(0.01 + exp(Md::log_poisson(j, i * expected_src_to_trg_ratio))));
-      for (unsigned j = 1; j <= max_trg_size; ++j)
-        plens[i][j] /= z;
-      //for (unsigned j = 1; j <= max_trg_size; ++j)
-      //  cerr << "P(trg_len=" << j << " | src_len=" << i << ") = " << plens[i][j] << endl;
-    }
-  }
-
-  // return p(tlen | slen) for *chunks* not full words
-  inline const prob_t& operator()(int slen, int tlen) const {
-    return plens[slen][tlen];
-  }
-
-  vector<vector<prob_t> > plens;
-};
-
-struct CondBaseDist {
-  CondBaseDist(unsigned max_src_size, unsigned max_trg_size, double expected_src_to_trg_ratio) :
-    tclm(max_src_size, max_trg_size, expected_src_to_trg_ratio) {}
-
-  prob_t operator()(const vector<WordID>& src, unsigned sf, unsigned st,
-                    const vector<WordID>& trg, unsigned tf, unsigned tt) const {
-    prob_t p = tclm(st - sf, tt - tf);  // target len | source length ~ TCLM(source len)
-    assert(!"not impl");
-    return p;
-  }
-  inline prob_t operator()(const vector<WordID>& src, const vector<WordID>& trg) const {
-    return (*this)(src, 0, src.size(), trg, 0, trg.size());
-  }
-  TruncatedConditionalLengthModel tclm;
-};
-
-// represents transliteration phrase probabilities, e.g.
-//   p( a l - | A l ) , p( o | A w ) , ...
-struct TransliterationChunkConditionalModel {
-  explicit TransliterationChunkConditionalModel(const CondBaseDist& pp0) :
-      d(0.0),
-      strength(1.0),
-      rp0(pp0) {
-  }
-
-  void Summary() const {
-    std::cerr << "Number of conditioning contexts: " << r.size() << std::endl;
-    for (RuleModelHash::const_iterator it = r.begin(); it != r.end(); ++it) {
-      std::cerr << TD::GetString(it->first) << "   \t(\\alpha = " << it->second.alpha() << ") --------------------------" << std::endl;
-      for (CCRP_NoTable<TRule>::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
-        std::cerr << "   " << i2->second << '\t' << i2->first << std::endl;
-    }
-  }
-
-  int DecrementRule(const TRule& rule) {
-    RuleModelHash::iterator it = r.find(rule.f_);
-    assert(it != r.end());    
-    int count = it->second.decrement(rule);
-    if (count) {
-      if (it->second.num_customers() == 0) r.erase(it);
-    }
-    return count;
-  }
-
-  int IncrementRule(const TRule& rule) {
-    RuleModelHash::iterator it = r.find(rule.f_);
-    if (it == r.end()) {
-      it = r.insert(make_pair(rule.f_, CCRP_NoTable<TRule>(strength))).first;
-    } 
-    int count = it->second.increment(rule);
-    return count;
-  }
-
-  void IncrementRules(const std::vector<TRulePtr>& rules) {
-    for (int i = 0; i < rules.size(); ++i)
-      IncrementRule(*rules[i]);
-  }
-
-  void DecrementRules(const std::vector<TRulePtr>& rules) {
-    for (int i = 0; i < rules.size(); ++i)
-      DecrementRule(*rules[i]);
-  }
-
-  prob_t RuleProbability(const TRule& rule) const {
-    prob_t p;
-    RuleModelHash::const_iterator it = r.find(rule.f_);
-    if (it == r.end()) {
-      p = rp0(rule.f_, rule.e_);
-    } else {
-      p = it->second.prob(rule, rp0(rule.f_, rule.e_));
-    }
-    return p;
-  }
-
-  double LogLikelihood(const double& dd, const double& aa) const {
-    if (aa <= -dd) return -std::numeric_limits<double>::infinity();
-    //double llh = Md::log_beta_density(dd, 10, 3) + Md::log_gamma_density(aa, 1, 1);
-    double llh = //Md::log_beta_density(dd, 1, 1) +
-                 Md::log_gamma_density(dd + aa, 1, 1);
-    std::tr1::unordered_map<std::vector<WordID>, CCRP_NoTable<TRule>, boost::hash<std::vector<WordID> > >::const_iterator it;
-    for (it = r.begin(); it != r.end(); ++it)
-      llh += it->second.log_crp_prob(aa);
-    return llh;
-  }
-
-  struct AlphaResampler {
-    AlphaResampler(const TransliterationChunkConditionalModel& m) : m_(m) {}
-    const TransliterationChunkConditionalModel& m_;
-    double operator()(const double& proposed_strength) const {
-      return m_.LogLikelihood(m_.d, proposed_strength);
-    }
-  };
-
-  void ResampleHyperparameters(MT19937* rng) {
-    std::tr1::unordered_map<std::vector<WordID>, CCRP_NoTable<TRule>, boost::hash<std::vector<WordID> > >::iterator it;
-    //const unsigned nloop = 5;
-    const unsigned niterations = 10;
-    //DiscountResampler dr(*this);
-    AlphaResampler ar(*this);
-#if 0
-    for (int iter = 0; iter < nloop; ++iter) {
-      strength = slice_sampler1d(ar, strength, *rng, -d + std::numeric_limits<double>::min(),
-                              std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
-      double min_discount = std::numeric_limits<double>::min();
-      if (strength < 0.0) min_discount -= strength;
-      d = slice_sampler1d(dr, d, *rng, min_discount,
-                          1.0, 0.0, niterations, 100*niterations);
-    }
-#endif
-    strength = slice_sampler1d(ar, strength, *rng, -d,
-                            std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
-    std::cerr << "CTMModel(alpha=" << strength << ") = " << LogLikelihood(d, strength) << std::endl;
-    for (it = r.begin(); it != r.end(); ++it) {
-#if 0
-      it->second.set_discount(d);
-#endif
-      it->second.set_alpha(strength);
-    }
-  }
-
-  prob_t Likelihood() const {
-    prob_t p; p.logeq(LogLikelihood(d, strength));
-    return p;
-  }
-
-  const CondBaseDist& rp0;
-  typedef std::tr1::unordered_map<std::vector<WordID>,
-                                  CCRP_NoTable<TRule>,
-                                  boost::hash<std::vector<WordID> > > RuleModelHash;
-  RuleModelHash r;
-  double d, strength;
-};
-
-struct GraphStructure {
-  GraphStructure() : r() {}
-  // leak memory - these are basically static
-  const Reachability* r;
-  bool IsReachable() const { return r->nodes > 0; }
-};
-
-struct ProbabilityEstimates {
-  ProbabilityEstimates() : gs(), backward() {}
-  explicit ProbabilityEstimates(const GraphStructure& g) :
-      gs(&g), backward() {
-    if (g.r->nodes > 0)
-      backward = new float[g.r->nodes];
-  }
-  // leak memory, these are static
-
-  // returns an estimate of the marginal probability
-  double MarginalEstimate() const {
-    if (!backward) return 0;
-    return backward[0];
-  }
-
-  // returns an backward estimate
-  double Backward(int src_covered, int trg_covered) const {
-    if (!backward) return 0;
-    int ind = gs->r->node_addresses[src_covered][trg_covered];
-    if (ind < 0) return 0;
-    return backward[ind];
-  }
-
-  prob_t estp;
-  float* backward;
- private:
-  const GraphStructure* gs;
-};
-
-struct TransliterationsImpl {
-  TransliterationsImpl(int max_src, int max_trg, double sr, const BackwardEstimator& b) :
-      cp0(max_src, max_trg, sr),
-      tccm(cp0),
-      be(b),
-      kMAX_SRC_CHUNK(max_src),
-      kMAX_TRG_CHUNK(max_trg),
-      kS2T_RATIO(sr),
-      tot_pairs(), tot_mem() {
-  }
-  const CondBaseDist cp0;
-  TransliterationChunkConditionalModel tccm;
-  const BackwardEstimator& be;
-
-  void Initialize(WordID src, const vector<WordID>& src_lets, WordID trg, const vector<WordID>& trg_lets) {
-    const size_t src_len = src_lets.size();
-    const size_t trg_len = trg_lets.size();
-
-    // init graph structure
-    if (src_len >= graphs.size()) graphs.resize(src_len + 1);
-    if (trg_len >= graphs[src_len].size()) graphs[src_len].resize(trg_len + 1);
-    GraphStructure& gs = graphs[src_len][trg_len];
-    if (!gs.r) {
-      double rat = exp(fabs(log(trg_len / (src_len * kS2T_RATIO))));
-      if (rat > 1.5 || (rat > 2.4 && src_len < 6)) {
-        cerr << " ** Forbidding transliterations of size " << src_len << "," << trg_len << ": " << rat << endl;
-        gs.r = new Reachability(src_len, trg_len, 0, 0);
-      } else {
-        gs.r = new Reachability(src_len, trg_len, kMAX_SRC_CHUNK, kMAX_TRG_CHUNK);
-      }
-    }
-
-    const Reachability& r = *gs.r;
-
-    // init backward estimates
-    if (src >= ests.size()) ests.resize(src + 1);
-    unordered_map<WordID, ProbabilityEstimates>::iterator it = ests[src].find(trg);
-    if (it != ests[src].end()) return; // already initialized
-
-    it = ests[src].insert(make_pair(trg, ProbabilityEstimates(gs))).first;
-    ProbabilityEstimates& est = it->second;
-    if (!gs.r->nodes) return;  // not derivable subject to length constraints
-
-    be.InitializeGrid(src_lets, trg_lets, r, kS2T_RATIO, est.backward);
-    cerr << TD::GetString(src_lets) << " ||| " << TD::GetString(trg_lets) << " ||| " << (est.backward[0] / trg_lets.size()) << endl;
-    tot_pairs++;
-    tot_mem += sizeof(float) * gs.r->nodes;
-  }
-
-  void Forbid(WordID src, const vector<WordID>& src_lets, WordID trg, const vector<WordID>& trg_lets) {
-    const size_t src_len = src_lets.size();
-    const size_t trg_len = trg_lets.size();
-    // TODO
-  }
-
-  prob_t EstimateProbability(WordID s, const vector<WordID>& src, WordID t, const vector<WordID>& trg) const {
-    assert(src.size() < graphs.size());
-    const vector<GraphStructure>& tv = graphs[src.size()];
-    assert(trg.size() < tv.size());
-    const GraphStructure& gs = tv[trg.size()];
-    if (gs.r->nodes == 0)
-      return prob_t::Zero();
-    const unordered_map<WordID, ProbabilityEstimates>::const_iterator it = ests[s].find(t);
-    assert(it != ests[s].end());
-    return it->second.estp;
-  }
-
-  void GraphSummary() const {
-    double to = 0;
-    double tn = 0;
-    double tt = 0;
-    for (int i = 0; i < graphs.size(); ++i) {
-      const vector<GraphStructure>& vt = graphs[i];
-      for (int j = 0; j < vt.size(); ++j) {
-        const GraphStructure& gs = vt[j];
-        if (!gs.r) continue;
-        tt++;
-        for (int k = 0; k < i; ++k) {
-          for (int l = 0; l < j; ++l) {
-            size_t c = gs.r->valid_deltas[k][l].size();
-            if (c) {
-              tn += 1;
-              to += c;
-            }
-          }
-        }
-      }
-    }
-    cerr << "     Average nodes = " << (tn / tt) << endl;
-    cerr << "Average out-degree = " << (to / tn) << endl;
-    cerr << " Unique structures = " << tt << endl;
-    cerr << "      Unique pairs = " << tot_pairs << endl;
-    cerr << "          BEs size = " << (tot_mem / (1024.0*1024.0)) << " MB" << endl;
-  }
-
-  const int kMAX_SRC_CHUNK;
-  const int kMAX_TRG_CHUNK;
-  const double kS2T_RATIO;
-  unsigned tot_pairs;
-  size_t tot_mem;
-  vector<vector<GraphStructure> > graphs; // graphs[src_len][trg_len]
-  vector<unordered_map<WordID, ProbabilityEstimates> > ests; // ests[src][trg]
-};
-
-Transliterations::Transliterations(int max_src, int max_trg, double sr, const BackwardEstimator& be) :
-    pimpl_(new TransliterationsImpl(max_src, max_trg, sr, be)) {}
-Transliterations::~Transliterations() { delete pimpl_; }
-
-void Transliterations::Initialize(WordID src, const vector<WordID>& src_lets, WordID trg, const vector<WordID>& trg_lets) {
-  pimpl_->Initialize(src, src_lets, trg, trg_lets);
-}
-
-prob_t Transliterations::EstimateProbability(WordID s, const vector<WordID>& src, WordID t, const vector<WordID>& trg) const {
-  return pimpl_->EstimateProbability(s, src,t, trg);
-}
-
-void Transliterations::Forbid(WordID src, const vector<WordID>& src_lets, WordID trg, const vector<WordID>& trg_lets) {
-  pimpl_->Forbid(src, src_lets, trg, trg_lets);
-}
-
-void Transliterations::GraphSummary() const {
-  pimpl_->GraphSummary();
-}
-
diff --git a/gi/pf/transliterations.h b/gi/pf/transliterations.h
deleted file mode 100644
index 49d14684..00000000
--- a/gi/pf/transliterations.h
+++ /dev/null
@@ -1,24 +0,0 @@
-#ifndef _TRANSLITERATIONS_H_
-#define _TRANSLITERATIONS_H_
-
-#include <vector>
-#include "wordid.h"
-#include "prob.h"
-
-struct BackwardEstimator;
-struct TransliterationsImpl;
-struct Transliterations {
-  // max_src and max_trg indicate how big the transliteration phrases can be
-  // see reachability.h for information about filter_ratio
-  explicit Transliterations(int max_src, int max_trg, double s2t_rat, const BackwardEstimator& be);
-  ~Transliterations();
-  void Initialize(WordID src, const std::vector<WordID>& src_lets, WordID trg, const std::vector<WordID>& trg_lets);
-  void Forbid(WordID src, const std::vector<WordID>& src_lets, WordID trg, const std::vector<WordID>& trg_lets);
-  void GraphSummary() const;
-  prob_t EstimateProbability(WordID s, const std::vector<WordID>& src, WordID t, const std::vector<WordID>& trg) const;
- private:
-  TransliterationsImpl* pimpl_;
-};
-
-#endif
-
diff --git a/gi/pf/unigrams.cc b/gi/pf/unigrams.cc
deleted file mode 100644
index 40829775..00000000
--- a/gi/pf/unigrams.cc
+++ /dev/null
@@ -1,80 +0,0 @@
-#include "unigrams.h"
-
-#include <string>
-#include <cmath>
-
-#include "stringlib.h"
-#include "filelib.h"
-
-using namespace std;
-
-void UnigramModel::LoadUnigrams(const string& fname) {
-  cerr << "Loading unigram probabilities from " << fname << " ..." << endl;
-  ReadFile rf(fname);
-  string line;
-  istream& in = *rf.stream();
-  assert(in);
-  getline(in, line);
-  assert(line.empty());
-  getline(in, line);
-  assert(line == "\\data\\");
-  getline(in, line);
-  size_t pos = line.find("ngram 1=");
-  assert(pos == 0);
-  assert(line.size() > 8);
-  const size_t num_unigrams = atoi(&line[8]);
-  getline(in, line);
-  assert(line.empty());
-  getline(in, line);
-  assert(line == "\\1-grams:");
-  for (size_t i = 0; i < num_unigrams; ++i) {
-    getline(in, line);
-    assert(line.size() > 0);
-    pos = line.find('\t');
-    assert(pos > 0);
-    assert(pos + 1 < line.size());
-    const WordID w = TD::Convert(line.substr(pos + 1));
-    line[pos] = 0;
-    float p = atof(&line[0]);
-    if (w < probs_.size()) probs_[w].logeq(p * log(10)); else cerr << "WARNING: don't know about '" << TD::Convert(w) << "'\n";
-  }
-}
-
-void UnigramWordModel::LoadUnigrams(const string& fname) {
-  cerr << "Loading unigram probabilities from " << fname << " ..." << endl;
-  ReadFile rf(fname);
-  string line;
-  istream& in = *rf.stream();
-  assert(in);
-  getline(in, line);
-  assert(line.empty());
-  getline(in, line);
-  assert(line == "\\data\\");
-  getline(in, line);
-  size_t pos = line.find("ngram 1=");
-  assert(pos == 0);
-  assert(line.size() > 8);
-  const size_t num_unigrams = atoi(&line[8]);
-  getline(in, line);
-  assert(line.empty());
-  getline(in, line);
-  assert(line == "\\1-grams:");
-  for (size_t i = 0; i < num_unigrams; ++i) {
-    getline(in, line);
-    assert(line.size() > 0);
-    pos = line.find('\t');
-    assert(pos > 0);
-    assert(pos + 1 < line.size());
-    size_t cur = pos + 1;
-    vector<WordID> w;
-    while (cur < line.size()) {
-      const size_t len = UTF8Len(line[cur]);
-      w.push_back(TD::Convert(line.substr(cur, len)));
-      cur += len;
-    }
-    line[pos] = 0;
-    float p = atof(&line[0]);
-    probs_[w].logeq(p * log(10.0));
-  }
-}
-
diff --git a/gi/pf/unigrams.h b/gi/pf/unigrams.h
deleted file mode 100644
index 1660d1ed..00000000
--- a/gi/pf/unigrams.h
+++ /dev/null
@@ -1,69 +0,0 @@
-#ifndef _UNIGRAMS_H_
-#define _UNIGRAMS_H_
-
-#include <vector>
-#include <string>
-#include <tr1/unordered_map>
-#include <boost/functional.hpp>
-
-#include "wordid.h"
-#include "prob.h"
-#include "tdict.h"
-
-struct UnigramModel {
-  explicit UnigramModel(const std::string& fname, unsigned vocab_size) :
-      use_uniform_(fname.size() == 0),
-      uniform_(1.0 / vocab_size),
-      probs_() {
-    if (fname.size() > 0) {
-      probs_.resize(TD::NumWords() + 1);
-      LoadUnigrams(fname);
-    }
-  }
-
-  const prob_t& operator()(const WordID& w) const {
-    assert(w);
-    if (use_uniform_) return uniform_;
-    return probs_[w];
-  }
-
- private:
-  void LoadUnigrams(const std::string& fname);
-
-  const bool use_uniform_;
-  const prob_t uniform_;
-  std::vector<prob_t> probs_;
-};
-
-
-// reads an ARPA unigram file and converts words like 'cat' into a string 'c a t'
-struct UnigramWordModel {
-  explicit UnigramWordModel(const std::string& fname) :
-      use_uniform_(false),
-      uniform_(1.0),
-      probs_() {
-    LoadUnigrams(fname);
-  }
-
-  explicit UnigramWordModel(const unsigned vocab_size) :
-      use_uniform_(true),
-      uniform_(1.0 / vocab_size),
-      probs_() {}
-
-  const prob_t& operator()(const std::vector<WordID>& s) const {
-    if (use_uniform_) return uniform_;
-    const VectorProbHash::const_iterator it = probs_.find(s);
-    assert(it != probs_.end());
-    return it->second;
-  }
-
- private:
-  void LoadUnigrams(const std::string& fname);
-
-  const bool use_uniform_;
-  const prob_t uniform_;
-  typedef std::tr1::unordered_map<std::vector<WordID>, prob_t, boost::hash<std::vector<WordID> > > VectorProbHash;
-  VectorProbHash probs_;
-};
-
-#endif