cdec cleanup, remove bayesian stuff, parsing stuff

6 files changed, 0 insertions, 767 deletions

diff --git a/phrasinator/Jamfile b/phrasinator/Jamfile
deleted file mode 100644
index 1fc34f79..00000000
--- a/phrasinator/Jamfile
+++ /dev/null
@@ -1,4 +0,0 @@
-exe gibbs_train_plm : gibbs_train_plm.cc ..//utils ..//z ..//boost_program_options ;
-exe gibbs_train_plm_notables : gibbs_train_plm.notables.cc ..//utils ..//z ..//boost_program_options ;
-
-alias programs : gibbs_train_plm gibbs_train_plm_notables ;
diff --git a/phrasinator/Makefile.am b/phrasinator/Makefile.am
deleted file mode 100644
index 3ddd1934..00000000
--- a/phrasinator/Makefile.am
+++ /dev/null
@@ -1,14 +0,0 @@
-bin_PROGRAMS = gibbs_train_plm gibbs_train_plm_notables
-
-#head_bigram_model
-
-gibbs_train_plm_notables_SOURCES = gibbs_train_plm.notables.cc
-gibbs_train_plm_notables_LDADD = $(top_srcdir)/utils/libutils.a -lz
-
-gibbs_train_plm_SOURCES = gibbs_train_plm.cc
-gibbs_train_plm_LDADD = $(top_srcdir)/utils/libutils.a -lz
-
-#head_bigram_model_SOURCES = head_bigram_model.cc
-#head_bigram_model_LDADD = $(top_srcdir)/utils/libutils.a -lz
-
-AM_CPPFLAGS = -funroll-loops -ffast-math -W -Wall -I$(top_srcdir)/utils
diff --git a/phrasinator/README b/phrasinator/README
deleted file mode 100644
index fb5b93ef..00000000
--- a/phrasinator/README
+++ /dev/null
@@ -1,16 +0,0 @@
-The "phrasinator" uses a simple Bayesian nonparametric model to segment
-text into chunks. The inferred model is then saved so that it can rapidly
-predict segments in new (but related) texts.
-
-  Input will be a corpus of sentences, e.g.:
-
-    economists have argued that real interest rates have fallen .
-
-  The output will be a model that, when run with cdec, will produce
-  a segmentation into phrasal units, e.g.:
-
-    economists have argued that real_interest_rates have fallen .
-
-
-To train a model, run ./train-phrasinator.pl and follow instructions.
-
diff --git a/phrasinator/gibbs_train_plm.cc b/phrasinator/gibbs_train_plm.cc
deleted file mode 100644
index 7847a460..00000000
--- a/phrasinator/gibbs_train_plm.cc
+++ /dev/null
@@ -1,309 +0,0 @@
-#include <iostream>
-#include <tr1/memory>
-
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "filelib.h"
-#include "dict.h"
-#include "sampler.h"
-#include "ccrp.h"
-#include "m.h"
-
-using namespace std;
-using namespace std::tr1;
-namespace po = boost::program_options;
-
-Dict d; // global dictionary
-
-string Join(char joiner, const vector<int>& phrase) {
-  ostringstream os;
-  for (unsigned i = 0; i < phrase.size(); ++i) {
-    if (i > 0) os << joiner;
-    os << d.Convert(phrase[i]);
-  }
-  return os.str();
-}
-
-ostream& operator<<(ostream& os, const vector<int>& phrase) {
-  for (unsigned i = 0; i < phrase.size(); ++i)
-    os << (i == 0 ? "" : " ") << d.Convert(phrase[i]);
-  return os;
-}
-
-struct UnigramLM {
-  explicit UnigramLM(const string& fname) {
-    ifstream in(fname.c_str());
-    assert(in);
-  }
-
-  double logprob(unsigned word) const {
-    assert(word < freqs_.size());
-    return freqs_[word];
-  }
-
-  vector<double> freqs_;
-};
-
-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 file from")
-        ("random_seed,S",po::value<uint32_t>(), "Random seed")
-        ("write_cdec_grammar,g", po::value<string>(), "Write cdec grammar to this file")
-        ("write_cdec_weights,w", po::value<string>(), "Write cdec weights to this file")
-        ("poisson_length,p", "Use a Poisson distribution as the length of a phrase in the base distribuion")
-        ("no_hyperparameter_inference,N", "Disable hyperparameter inference");
-  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 ReadCorpus(const string& filename, vector<vector<int> >* c, set<int>* vocab) {
-  c->clear();
-  istream* in;
-  if (filename == "-")
-    in = &cin;
-  else
-    in = new ifstream(filename.c_str());
-  assert(*in);
-  string line;
-  while(*in) {
-    getline(*in, line);
-    if (line.empty() && !*in) break;
-    c->push_back(vector<int>());
-    vector<int>& v = c->back();
-    d.ConvertWhitespaceDelimitedLine(line, &v);
-    for (unsigned i = 0; i < v.size(); ++i) vocab->insert(v[i]);
-  }
-  if (in != &cin) delete in;
-}
-
-struct UniphraseLM {
-  UniphraseLM(const vector<vector<int> >& corpus,
-              const set<int>& vocab,
-              const po::variables_map& conf) :
-    phrases_(1,1,1,1),
-    gen_(1,1,1,1),
-    corpus_(corpus),
-    uniform_word_(1.0 / vocab.size()),
-    gen_p0_(0.5),
-    p_end_(0.5),
-    use_poisson_(conf.count("poisson_length") > 0) {}
-
-  double p0(const vector<int>& phrase) const {
-    static vector<double> p0s(10000, 0.0);
-    assert(phrase.size() < 10000);
-    double& p = p0s[phrase.size()];
-    if (p) return p;
-    p = exp(log_p0(phrase));
-    if (!p) {
-      cerr << "0 prob phrase: " << phrase << "\nAssigning std::numeric_limits<double>::min()\n";
-      p = std::numeric_limits<double>::min();
-    }
-    return p;
-  }
-
-  double log_p0(const vector<int>& phrase) const {
-    double len_logprob;
-    if (use_poisson_)
-      len_logprob = Md::log_poisson(phrase.size(), 1.0);
-    else
-      len_logprob = log(1 - p_end_) * (phrase.size() -1) + log(p_end_);
-    return log(uniform_word_) * phrase.size() + len_logprob;
-  }
-
-  double llh() const {
-    double llh = gen_.log_crp_prob();
-    llh += gen_.num_tables(false) * log(gen_p0_) +
-           gen_.num_tables(true) * log(1 - gen_p0_);
-    double llhr = phrases_.log_crp_prob();
-    for (CCRP<vector<int> >::const_iterator it = phrases_.begin(); it != phrases_.end(); ++it) {
-      llhr += phrases_.num_tables(it->first) * log_p0(it->first);
-      //llhr += log_p0(it->first);
-      if (!isfinite(llh)) {
-        cerr << it->first << endl;
-        cerr << log_p0(it->first) << endl;
-        abort();
-      }
-    }
-    return llh + llhr;
-  }
-
-  void Sample(unsigned int samples, bool hyp_inf, MT19937* rng) {
-    cerr << "Initializing...\n";
-    z_.resize(corpus_.size());
-    int tc = 0;
-    for (unsigned i = 0; i < corpus_.size(); ++i) {
-      const vector<int>& line = corpus_[i];
-      const int ls = line.size();
-      const int last_pos = ls - 1;
-      vector<bool>& z = z_[i];
-      z.resize(ls);
-      int prev = 0;
-      for (int j = 0; j < ls; ++j) {
-        z[j] = rng->next() < 0.5;
-        if (j == last_pos) z[j] = true;  // break phrase at the end of the sentence
-        if (z[j]) {
-          const vector<int> p(line.begin() + prev, line.begin() + j + 1);
-          phrases_.increment(p, p0(p), rng);
-          //cerr << p << ": " << p0(p) << endl;
-          prev = j + 1;
-          gen_.increment(false, gen_p0_, rng);
-          ++tc; // remove
-        }
-      }
-      ++tc;
-      gen_.increment(true, 1.0 - gen_p0_, rng); // end of utterance
-    }
-    cerr << "TC: " << tc << endl;
-    cerr << "Initial LLH: " << llh() << endl;
-    cerr << "Sampling...\n";
-    cerr << gen_ << endl;
-    for (unsigned s = 1; s < samples; ++s) {
-      cerr << '.';
-      if (s % 10 == 0) {
-        cerr << " [" << s;
-        if (hyp_inf) ResampleHyperparameters(rng);
-        cerr << " LLH=" << llh() << "]\n";
-        vector<int> z(z_[0].size(), 0);
-        //for (int j = 0; j < z.size(); ++j) z[j] = z_[0][j];
-        //SegCorpus::Write(corpus_[0], z, d);
-      }
-      for (unsigned i = 0; i < corpus_.size(); ++i) {
-        const vector<int>& line = corpus_[i];
-        const int ls = line.size();
-        const int last_pos = ls - 1;
-        vector<bool>& z = z_[i];
-        int prev = 0;
-        for (int j = 0; j < last_pos; ++j) { // don't resample last position
-          int next = j+1;  while(!z[next]) { ++next; }
-          const vector<int> p1p2(line.begin() + prev, line.begin() + next + 1);
-          const vector<int> p1(line.begin() + prev, line.begin() + j + 1);
-          const vector<int> p2(line.begin() + j + 1, line.begin() + next + 1);
-
-          if (z[j]) {
-            phrases_.decrement(p1, rng);
-            phrases_.decrement(p2, rng);
-            gen_.decrement(false, rng);
-            gen_.decrement(false, rng);
-          } else {
-            phrases_.decrement(p1p2, rng);
-            gen_.decrement(false, rng);
-          }
-
-          const double d1 = phrases_.prob(p1p2, p0(p1p2)) * gen_.prob(false, gen_p0_);
-          double d2 = phrases_.prob(p1, p0(p1)) * gen_.prob(false, gen_p0_);
-          phrases_.increment(p1, p0(p1), rng);
-          gen_.increment(false, gen_p0_, rng);
-          d2 *= phrases_.prob(p2, p0(p2)) * gen_.prob(false, gen_p0_);
-          phrases_.decrement(p1, rng);
-          gen_.decrement(false, rng);
-          z[j] = rng->SelectSample(d1, d2);
-
-          if (z[j]) {
-            phrases_.increment(p1, p0(p1), rng);
-            phrases_.increment(p2, p0(p2), rng);
-            gen_.increment(false, gen_p0_, rng);
-            gen_.increment(false, gen_p0_, rng);
-            prev = j + 1;
-          } else {
-            phrases_.increment(p1p2, p0(p1p2), rng);
-            gen_.increment(false, gen_p0_, rng);
-          }
-        }
-      }
-    }
-//    cerr << endl << endl << gen_ << endl << phrases_ << endl;
-    cerr << gen_.prob(false, gen_p0_) << " " << gen_.prob(true, 1 - gen_p0_) << endl;
-  }
-
-  void WriteCdecGrammarForCurrentSample(ostream* os) const {
-    CCRP<vector<int> >::const_iterator it = phrases_.begin();
-    for (; it != phrases_.end(); ++it) {
-      (*os) << "[X] ||| " << Join(' ', it->first) << " ||| "
-                          << Join('_', it->first) << " ||| C=1 P=" 
-                          << log(phrases_.prob(it->first, p0(it->first))) << endl;
-    }
-  }
-
-  double OOVUnigramLogProb() const {
-    vector<int> x(1,99999999);
-    return log(phrases_.prob(x, p0(x)));
-  }
-
-  void ResampleHyperparameters(MT19937* rng) {
-    phrases_.resample_hyperparameters(rng);
-    gen_.resample_hyperparameters(rng);
-    cerr << " d=" << phrases_.discount() << ",s=" << phrases_.strength();
-  }
-
-  CCRP<vector<int> > phrases_;
-  CCRP<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_;
-};
-
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-  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> > corpus;
-  set<int> vocab;
-  ReadCorpus(conf["input"].as<string>(), &corpus, &vocab);
-  cerr << "Corpus size: " << corpus.size() << " sentences\n";
-  cerr << "Vocabulary size: " << vocab.size() << " types\n";
-
-  UniphraseLM ulm(corpus, vocab, conf);
-  ulm.Sample(conf["samples"].as<unsigned>(), conf.count("no_hyperparameter_inference") == 0, &rng);
-  cerr << "OOV unigram prob: " << ulm.OOVUnigramLogProb() << endl;
-
-  for (unsigned i = 0; i < corpus.size(); ++i)
-//    SegCorpus::Write(corpus[i], shmmlm.z_[i], d);
- ;
-  if (conf.count("write_cdec_grammar")) {
-    string fname = conf["write_cdec_grammar"].as<string>();
-    cerr << "Writing model to " << fname << " ...\n";
-    WriteFile wf(fname);
-    ulm.WriteCdecGrammarForCurrentSample(wf.stream());
-  }
-
-  if (conf.count("write_cdec_weights")) {
-    string fname = conf["write_cdec_weights"].as<string>();
-    cerr << "Writing weights to " << fname << " .\n";
-    WriteFile wf(fname);
-    ostream& os = *wf.stream();
-    os << "# make C smaller to use more phrases\nP 1\nPassThrough " << ulm.OOVUnigramLogProb() << "\nC -3\n";
-  }
-
-  return 0;
-}
-
diff --git a/phrasinator/gibbs_train_plm.notables.cc b/phrasinator/gibbs_train_plm.notables.cc
deleted file mode 100644
index 4526eaa6..00000000
--- a/phrasinator/gibbs_train_plm.notables.cc
+++ /dev/null
@@ -1,335 +0,0 @@
-#include <iostream>
-#include <tr1/memory>
-
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "filelib.h"
-#include "dict.h"
-#include "sampler.h"
-#include "ccrp.h"
-#include "ccrp_nt.h"
-
-using namespace std;
-using namespace std::tr1;
-namespace po = boost::program_options;
-
-Dict d; // global dictionary
-
-string Join(char joiner, const vector<int>& phrase) {
-  ostringstream os;
-  for (unsigned i = 0; i < phrase.size(); ++i) {
-    if (i > 0) os << joiner;
-    os << d.Convert(phrase[i]);
-  }
-  return os.str();
-}
-
-template <typename BType>
-void WriteSeg(const vector<int>& line, const vector<BType>& label, const Dict& d) {
-  assert(line.size() == label.size());
-  assert(label.back());
-  unsigned prev = 0;
-  unsigned cur = 0;
-  while (cur < line.size()) {
-    if (label[cur]) {
-      if (prev) cout << ' ';
-      cout << "{{";
-      for (unsigned i = prev; i <= cur; ++i)
-        cout << (i == prev ? "" : " ") << d.Convert(line[i]);
-      cout << "}}:" << label[cur];
-      prev = cur + 1;
-    }
-    ++cur;
-  }
-  cout << endl;
-}
-
-ostream& operator<<(ostream& os, const vector<int>& phrase) {
-  for (unsigned i = 0; i < phrase.size(); ++i)
-    os << (i == 0 ? "" : " ") << d.Convert(phrase[i]);
-  return os;
-}
-
-struct UnigramLM {
-  explicit UnigramLM(const string& fname) {
-    ifstream in(fname.c_str());
-    assert(in);
-  }
-
-  double logprob(unsigned word) const {
-    assert(word < freqs_.size());
-    return freqs_[word];
-  }
-
-  vector<double> freqs_;
-};
-
-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 file from")
-        ("random_seed,S",po::value<uint32_t>(), "Random seed")
-        ("write_cdec_grammar,g", po::value<string>(), "Write cdec grammar to this file")
-        ("write_cdec_weights,w", po::value<string>(), "Write cdec weights to this file")
-        ("poisson_length,p", "Use a Poisson distribution as the length of a phrase in the base distribuion")
-        ("no_hyperparameter_inference,N", "Disable hyperparameter inference");
-  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 ReadCorpus(const string& filename, vector<vector<int> >* c, set<int>* vocab) {
-  c->clear();
-  istream* in;
-  if (filename == "-")
-    in = &cin;
-  else
-    in = new ifstream(filename.c_str());
-  assert(*in);
-  string line;
-  while(*in) {
-    getline(*in, line);
-    if (line.empty() && !*in) break;
-    c->push_back(vector<int>());
-    vector<int>& v = c->back();
-    d.ConvertWhitespaceDelimitedLine(line, &v);
-    for (unsigned i = 0; i < v.size(); ++i) vocab->insert(v[i]);
-  }
-  if (in != &cin) delete in;
-}
-
-double log_poisson(unsigned x, const double& lambda) {
-  assert(lambda > 0.0);
-  return log(lambda) * x - lgamma(x + 1) - lambda;
-}
-
-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) {}
-
-  double p0(const vector<int>& phrase) const {
-    static vector<double> p0s(10000, 0.0);
-    assert(phrase.size() < 10000);
-    double& p = p0s[phrase.size()];
-    if (p) return p;
-    p = exp(log_p0(phrase));
-    if (!p) {
-      cerr << "0 prob phrase: " << phrase << "\nAssigning std::numeric_limits<double>::min()\n";
-      p = std::numeric_limits<double>::min();
-    }
-    return p;
-  }
-
-  double log_p0(const vector<int>& phrase) const {
-    double len_logprob;
-    if (use_poisson_)
-      len_logprob = log_poisson(phrase.size(), 1.0);
-    else
-      len_logprob = log(1 - p_end_) * (phrase.size() -1) + log(p_end_);
-    return log(uniform_word_) * phrase.size() + len_logprob;
-  }
-
-  double llh() const {
-    double llh = gen_.log_crp_prob();
-    llh += log(gen_p0_) + log(1 - gen_p0_);
-    double llhr = phrases_.log_crp_prob();
-    for (CCRP_NoTable<vector<int> >::const_iterator it = phrases_.begin(); it != phrases_.end(); ++it) {
-      llhr += log_p0(it->first);
-      //llhr += log_p0(it->first);
-      if (!isfinite(llh)) {
-        cerr << it->first << endl;
-        cerr << log_p0(it->first) << endl;
-        abort();
-      }
-    }
-    return llh + llhr;
-  }
-
-  void Sample(unsigned int samples, bool hyp_inf, MT19937* rng) {
-    cerr << "Initializing...\n";
-    z_.resize(corpus_.size());
-    int tc = 0;
-    for (unsigned i = 0; i < corpus_.size(); ++i) {
-      const vector<int>& line = corpus_[i];
-      const int ls = line.size();
-      const int last_pos = ls - 1;
-      vector<bool>& z = z_[i];
-      z.resize(ls);
-      int prev = 0;
-      for (int j = 0; j < ls; ++j) {
-        z[j] = rng->next() < 0.5;
-        if (j == last_pos) z[j] = true;  // break phrase at the end of the sentence
-        if (z[j]) {
-          const vector<int> p(line.begin() + prev, line.begin() + j + 1);
-          phrases_.increment(p);
-          //cerr << p << ": " << p0(p) << endl;
-          prev = j + 1;
-          gen_.increment(false);
-          ++tc; // remove
-        }
-      }
-      ++tc;
-      gen_.increment(true); // end of utterance
-    }
-    cerr << "TC: " << tc << endl;
-    cerr << "Initial LLH: " << llh() << endl;
-    cerr << "Sampling...\n";
-    cerr << gen_ << endl;
-    for (unsigned s = 1; s < samples; ++s) {
-      cerr << '.';
-      if (s % 10 == 0) {
-        cerr << " [" << s;
-        if (hyp_inf) ResampleHyperparameters(rng);
-        cerr << " LLH=" << llh() << "]\n";
-        vector<int> z(z_[0].size(), 0);
-        //for (int j = 0; j < z.size(); ++j) z[j] = z_[0][j];
-        //SegCorpus::Write(corpus_[0], z, d);
-      }
-      for (unsigned i = 0; i < corpus_.size(); ++i) {
-        const vector<int>& line = corpus_[i];
-        const int ls = line.size();
-        const int last_pos = ls - 1;
-        vector<bool>& z = z_[i];
-        int prev = 0;
-        for (int j = 0; j < last_pos; ++j) { // don't resample last position
-          int next = j+1;  while(!z[next]) { ++next; }
-          const vector<int> p1p2(line.begin() + prev, line.begin() + next + 1);
-          const vector<int> p1(line.begin() + prev, line.begin() + j + 1);
-          const vector<int> p2(line.begin() + j + 1, line.begin() + next + 1);
-
-          if (z[j]) {
-            phrases_.decrement(p1);
-            phrases_.decrement(p2);
-            gen_.decrement(false);
-            gen_.decrement(false);
-          } else {
-            phrases_.decrement(p1p2);
-            gen_.decrement(false);
-          }
-
-          const double d1 = phrases_.prob(p1p2, p0(p1p2)) * gen_.prob(false, gen_p0_);
-          double d2 = phrases_.prob(p1, p0(p1)) * gen_.prob(false, gen_p0_);
-          phrases_.increment(p1);
-          gen_.increment(false);
-          d2 *= phrases_.prob(p2, p0(p2)) * gen_.prob(false, gen_p0_);
-          phrases_.decrement(p1);
-          gen_.decrement(false);
-          z[j] = rng->SelectSample(d1, d2);
-
-          if (z[j]) {
-            phrases_.increment(p1);
-            phrases_.increment(p2);
-            gen_.increment(false);
-            gen_.increment(false);
-            prev = j + 1;
-          } else {
-            phrases_.increment(p1p2);
-            gen_.increment(false);
-          }
-        }
-      }
-    }
-//    cerr << endl << endl << gen_ << endl << phrases_ << endl;
-    cerr << gen_.prob(false, gen_p0_) << " " << gen_.prob(true, 1 - gen_p0_) << endl;
-  }
-
-  void WriteCdecGrammarForCurrentSample(ostream* os) const {
-    CCRP_NoTable<vector<int> >::const_iterator it = phrases_.begin();
-    for (; it != phrases_.end(); ++it) {
-      (*os) << "[X] ||| " << Join(' ', it->first) << " ||| "
-                          << Join('_', it->first) << " ||| C=1 P=" 
-                          << log(phrases_.prob(it->first, p0(it->first))) << endl;
-    }
-  }
-
-  double OOVUnigramLogProb() const {
-    vector<int> x(1,99999999);
-    return log(phrases_.prob(x, p0(x)));
-  }
-
-  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_;
-};
-
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-  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> > corpus;
-  set<int> vocab;
-  ReadCorpus(conf["input"].as<string>(), &corpus, &vocab);
-  cerr << "Corpus size: " << corpus.size() << " sentences\n";
-  cerr << "Vocabulary size: " << vocab.size() << " types\n";
-
-  UniphraseLM ulm(corpus, vocab, conf);
-  ulm.Sample(conf["samples"].as<unsigned>(), conf.count("no_hyperparameter_inference") == 0, &rng);
-  cerr << "OOV unigram prob: " << ulm.OOVUnigramLogProb() << endl;
-
-  for (unsigned i = 0; i < corpus.size(); ++i)
-    WriteSeg(corpus[i], ulm.z_[i], d);
-
-  if (conf.count("write_cdec_grammar")) {
-    string fname = conf["write_cdec_grammar"].as<string>();
-    cerr << "Writing model to " << fname << " ...\n";
-    WriteFile wf(fname);
-    ulm.WriteCdecGrammarForCurrentSample(wf.stream());
-  }
-
-  if (conf.count("write_cdec_weights")) {
-    string fname = conf["write_cdec_weights"].as<string>();
-    cerr << "Writing weights to " << fname << " .\n";
-    WriteFile wf(fname);
-    ostream& os = *wf.stream();
-    os << "# make C smaller to use more phrases\nP 1\nPassThrough " << ulm.OOVUnigramLogProb() << "\nC -3\n";
-  }
-
-  
-
-  return 0;
-}
-
diff --git a/phrasinator/train-phrasinator.pl b/phrasinator/train-phrasinator.pl
deleted file mode 100755
index c50b8e68..00000000
--- a/phrasinator/train-phrasinator.pl
+++ /dev/null
@@ -1,89 +0,0 @@
-#!/usr/bin/perl -w
-use strict;
-my $script_dir; BEGIN { use Cwd qw/ abs_path cwd /; use File::Basename; $script_dir = dirname(abs_path($0)); push @INC, $script_dir; }
-use Getopt::Long;
-use File::Spec qw (rel2abs);
-
-my $DECODER = "$script_dir/../decoder/cdec";
-my $TRAINER = "$script_dir/gibbs_train_plm_notables";
-
-die "Can't find $TRAINER" unless -f $TRAINER;
-die "Can't execute $TRAINER" unless -x $TRAINER;
-
-if (!GetOptions(
-	"decoder=s" => \$DECODER,
-)) { usage(); }
-
-die "Can't find $DECODER" unless -f $DECODER;
-die "Can't execute $DECODER" unless -x $DECODER;
-if (scalar @ARGV != 2) { usage(); }
-my $INFILE = shift @ARGV;
-my $OUTDIR = shift @ARGV;
-$OUTDIR = File::Spec->rel2abs($OUTDIR);
-print STDERR "      Input file: $INFILE\n";
-print STDERR "Output directory: $OUTDIR\n";
-open F, "<$INFILE" or die "Failed to open $INFILE for reading: $!";
-close F;
-die "Please remove existing directory $OUTDIR\n" if (-f $OUTDIR || -d $OUTDIR);
-
-my $CMD = "mkdir $OUTDIR";
-safesystem($CMD) or die "Failed to create directory $OUTDIR\n$!";
-
-my $grammar="$OUTDIR/grammar.gz";
-my $weights="$OUTDIR/weights";
-$CMD = "$TRAINER -w $weights -g $grammar -i $INFILE";
-safesystem($CMD) or die "Failed to train model!\n";
-my $cdecini = "$OUTDIR/cdec.ini";
-open C, ">$cdecini" or die "Failed to open $cdecini for writing: $!";
-
-print C <<EOINI;
-quiet=true
-formalism=scfg
-grammar=$grammar
-add_pass_through_rules=true
-weights=$OUTDIR/weights
-EOINI
-
-close C;
-
-print <<EOT;
-
-Model trained successfully.  Text can be decoded into phrasal units with
-the following command:
-
-  $DECODER -c $OUTDIR/cdec.ini < FILE.TXT
-
-EOT
-exit(0);
-
-sub usage {
-  print <<EOT;
-Usage: $0 [options] INPUT.TXT OUTPUT-DIRECTORY
-
-  Infers a phrasal segmentation model from the tokenized text in INPUT.TXT
-  and writes it to OUTPUT-DIRECTORY/ so that it can be applied to other
-  text or have its granularity altered.
-
-EOT
-  exit(1);
-}
-
-sub safesystem {
-  print STDERR "Executing: @_\n";
-  system(@_);
-  if ($? == -1) {
-      print STDERR "ERROR: Failed to execute: @_\n  $!\n";
-      exit(1);
-  }
-  elsif ($? & 127) {
-      printf STDERR "ERROR: Execution of: @_\n  died with signal %d, %s coredump\n",
-          ($? & 127),  ($? & 128) ? 'with' : 'without';
-      exit(1);
-  }
-  else {
-    my $exitcode = $? >> 8;
-    print STDERR "Exit code: $exitcode\n" if $exitcode;
-    return ! $exitcode;
-  }
-}
-