cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 339 deletions

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;
-}