cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 470 deletions

diff --git a/gi/markov_al/ml.cc b/gi/markov_al/ml.cc
deleted file mode 100644
index 1e71edd6..00000000
--- a/gi/markov_al/ml.cc
+++ /dev/null
@@ -1,470 +0,0 @@
-#include <iostream>
-#include <tr1/unordered_map>
-
-#include <boost/shared_ptr.hpp>
-#include <boost/functional.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "tdict.h"
-#include "filelib.h"
-#include "sampler.h"
-#include "ccrp_onetable.h"
-#include "array2d.h"
-
-using namespace std;
-using namespace std::tr1;
-namespace po = boost::program_options;
-
-void PrintTopCustomers(const CCRP_OneTable<WordID>& crp) {
-  for (CCRP_OneTable<WordID>::const_iterator it = crp.begin(); it != crp.end(); ++it) {
-    cerr << "  " << TD::Convert(it->first) << " = " << it->second << endl;
-  }
-}
-
-void PrintAlignment(const vector<WordID>& src, const vector<WordID>& trg, const vector<unsigned char>& a) {
-  cerr << TD::GetString(src) << endl << TD::GetString(trg) << endl;
-  Array2D<bool> al(src.size(), trg.size());
-  for (int i = 0; i < a.size(); ++i)
-    if (a[i] != 255) al(a[i], i) = true;
-  cerr << al << endl;
-}
-
-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,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 Unigram;
-struct Bigram {
-  Bigram() : trg(), cond() {}
-  Bigram(WordID prev, WordID cur, WordID t) : trg(t) { cond.first = prev; cond.second = cur; }
-  const pair<WordID,WordID>& ConditioningPair() const {
-    return cond;
-  }
-  WordID& prev_src() { return cond.first; }
-  WordID& cur_src() { return cond.second; }
-  const WordID& prev_src() const { return cond.first; }
-  const WordID& cur_src() const { return cond.second; }
-  WordID trg;
- private:
-  pair<WordID, WordID> cond;
-};
-
-struct Unigram {
-  Unigram() : cur_src(), trg() {}
-  Unigram(WordID s, WordID t) : cur_src(s), trg(t) {}
-  WordID cur_src;
-  WordID trg;
-};
-
-ostream& operator<<(ostream& os, const Bigram& b) {
-  os << "( " << TD::Convert(b.trg) << " | " << TD::Convert(b.prev_src()) << " , " << TD::Convert(b.cur_src()) << " )";
-  return os;
-}
-
-ostream& operator<<(ostream& os, const Unigram& u) {
-  os << "( " << TD::Convert(u.trg) << " | " << TD::Convert(u.cur_src) << " )";
-  return os;
-}
-
-bool operator==(const Bigram& a, const Bigram& b) {
-  return a.trg == b.trg && a.cur_src() == b.cur_src() && a.prev_src() == b.prev_src();
-}
-
-bool operator==(const Unigram& a, const Unigram& b) {
-  return a.trg == b.trg && a.cur_src == b.cur_src;
-}
-
-size_t hash_value(const Bigram& b) {
-  size_t h = boost::hash_value(b.prev_src());
-  boost::hash_combine(h, boost::hash_value(b.cur_src()));
-  boost::hash_combine(h, boost::hash_value(b.trg));
-  return h;
-}
-
-size_t hash_value(const Unigram& u) {
-  size_t h = boost::hash_value(u.cur_src);
-  boost::hash_combine(h, boost::hash_value(u.trg));
-  return h;
-}
-
-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;
-}
-
-struct UnigramModel {
-  UnigramModel(size_t src_voc_size, size_t trg_voc_size) :
-    unigrams(TD::NumWords() + 1, CCRP_OneTable<WordID>(1,1,1,1)),
-    p0(1.0 / trg_voc_size) {}
-
-  void increment(const Bigram& b) {
-    unigrams[b.cur_src()].increment(b.trg);
-  }
-
-  void decrement(const Bigram& b) {
-    unigrams[b.cur_src()].decrement(b.trg);
-  }
-
-  double prob(const Bigram& b) const {
-    const double q0 = unigrams[b.cur_src()].prob(b.trg, p0);
-    return q0;
-  }
-
-  double LogLikelihood() const {
-    double llh = 0;
-    for (unsigned i = 0; i < unigrams.size(); ++i) {
-      const CCRP_OneTable<WordID>& crp = unigrams[i];
-      if (crp.num_customers() > 0) {
-        llh += crp.log_crp_prob();
-        llh += crp.num_tables() * log(p0);
-      }
-    }
-    return llh;
-  }
-
-  void ResampleHyperparameters(MT19937* rng) {
-    for (unsigned i = 0; i < unigrams.size(); ++i)
-      unigrams[i].resample_hyperparameters(rng);
-  }
-
-  vector<CCRP_OneTable<WordID> > unigrams;  // unigrams[src].prob(trg, p0) = p(trg|src)
-
-  const double p0;
-};
-
-struct BigramModel {
-  BigramModel(size_t src_voc_size, size_t trg_voc_size) :
-    unigrams(TD::NumWords() + 1, CCRP_OneTable<WordID>(1,1,1,1)),
-    p0(1.0 / trg_voc_size) {}
-
-  void increment(const Bigram& b) {
-    BigramMap::iterator it = bigrams.find(b.ConditioningPair());
-    if (it == bigrams.end()) {
-      it = bigrams.insert(make_pair(b.ConditioningPair(), CCRP_OneTable<WordID>(1,1,1,1))).first;
-    }
-    if (it->second.increment(b.trg))
-      unigrams[b.cur_src()].increment(b.trg);
-  }
-
-  void decrement(const Bigram& b) {
-    BigramMap::iterator it = bigrams.find(b.ConditioningPair());
-    assert(it != bigrams.end());
-    if (it->second.decrement(b.trg)) {
-      unigrams[b.cur_src()].decrement(b.trg);
-      if (it->second.num_customers() == 0)
-        bigrams.erase(it);
-    }
-  }
-
-  double prob(const Bigram& b) const {
-    const double q0 = unigrams[b.cur_src()].prob(b.trg, p0);
-    const BigramMap::const_iterator it = bigrams.find(b.ConditioningPair());
-    if (it == bigrams.end()) return q0;
-    return it->second.prob(b.trg, q0);
-  }
-
-  double LogLikelihood() const {
-    double llh = 0;
-    for (unsigned i = 0; i < unigrams.size(); ++i) {
-      const CCRP_OneTable<WordID>& crp = unigrams[i];
-      if (crp.num_customers() > 0) {
-        llh += crp.log_crp_prob();
-        llh += crp.num_tables() * log(p0);
-      }
-    }
-    for (BigramMap::const_iterator it = bigrams.begin(); it != bigrams.end(); ++it) {
-      const CCRP_OneTable<WordID>& crp = it->second;
-      const WordID cur_src = it->first.second;
-      llh += crp.log_crp_prob();
-      for (CCRP_OneTable<WordID>::const_iterator bit = crp.begin(); bit != crp.end(); ++bit) {
-        llh += log(unigrams[cur_src].prob(bit->second, p0));
-      }
-    }
-    return llh;
-  }
-
-  void ResampleHyperparameters(MT19937* rng) {
-    for (unsigned i = 0; i < unigrams.size(); ++i)
-      unigrams[i].resample_hyperparameters(rng);
-    for (BigramMap::iterator it = bigrams.begin(); it != bigrams.end(); ++it)
-      it->second.resample_hyperparameters(rng);
-  }
-
-  typedef unordered_map<pair<WordID,WordID>, CCRP_OneTable<WordID>, boost::hash<pair<WordID,WordID> > > BigramMap;
-  BigramMap bigrams;  // bigrams[(src-1,src)].prob(trg, q0) = p(trg|src,src-1)
-  vector<CCRP_OneTable<WordID> > unigrams;  // unigrams[src].prob(trg, p0) = p(trg|src)
-
-  const double p0;
-};
-
-struct BigramAlignmentModel {
-  BigramAlignmentModel(size_t src_voc_size, size_t trg_voc_size) : bigrams(TD::NumWords() + 1, CCRP_OneTable<WordID>(1,1,1,1)), p0(1.0 / src_voc_size) {}
-  void increment(WordID prev, WordID next) {
-    bigrams[prev].increment(next);  // hierarchy?
-  }
-  void decrement(WordID prev, WordID next) {
-    bigrams[prev].decrement(next);  // hierarchy?
-  }
-  double prob(WordID prev, WordID next) {
-    return bigrams[prev].prob(next, p0);
-  }
-  double LogLikelihood() const {
-    double llh = 0;
-    for (unsigned i = 0; i < bigrams.size(); ++i) {
-      const CCRP_OneTable<WordID>& crp = bigrams[i];
-      if (crp.num_customers() > 0) {
-        llh += crp.log_crp_prob();
-        llh += crp.num_tables() * log(p0);
-      }
-    }
-    return llh;
-  }
-
-  vector<CCRP_OneTable<WordID> > bigrams;  // bigrams[prev].prob(next, p0) = p(next|prev)
-  const double p0;
-};
-
-struct Alignment {
-  vector<unsigned char> a;
-};
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-  const unsigned samples = conf["samples"].as<unsigned>();
-
-  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());
-  const size_t corpus_len = corpusf.size();
-  const WordID kNULL = TD::Convert("<eps>");
-  const WordID kBOS = TD::Convert("<s>");
-  const WordID kEOS = TD::Convert("</s>");
-  Bigram TT(kBOS, TD::Convert("我"), TD::Convert("i"));
-  Bigram TT2(kBOS, TD::Convert("要"), TD::Convert("i"));
-
-  UnigramModel model(vocabf.size(), vocabe.size());
-  vector<Alignment> alignments(corpus_len);
-  for (unsigned ci = 0; ci < corpus_len; ++ci) {
-    const vector<WordID>& src = corpusf[ci];
-    const vector<WordID>& trg = corpuse[ci];
-    vector<unsigned char>& alg = alignments[ci].a;
-    alg.resize(trg.size());
-    int lenp1 = src.size() + 1;
-    WordID prev_src = kBOS;
-    for (int j = 0; j < trg.size(); ++j) {
-      int samp = lenp1 * rng.next();
-      --samp;
-      if (samp < 0) samp = 255;
-      alg[j] = samp;
-      WordID cur_src = (samp == 255 ? kNULL : src[alg[j]]);
-      Bigram b(prev_src, cur_src, trg[j]);
-      model.increment(b);
-      prev_src = cur_src;
-    }
-    Bigram b(prev_src, kEOS, kEOS);
-    model.increment(b);
-  }
-  cerr << "Initial LLH: " << model.LogLikelihood() << endl;
-
-  SampleSet<double> ss;
-  for (unsigned si = 0; si < 50; ++si) {
-    for (unsigned ci = 0; ci < corpus_len; ++ci) {
-      const vector<WordID>& src = corpusf[ci];
-      const vector<WordID>& trg = corpuse[ci];
-      vector<unsigned char>& alg = alignments[ci].a;
-      WordID prev_src = kBOS;
-      for (unsigned j = 0; j < trg.size(); ++j) {
-        unsigned char& a_j = alg[j];
-        WordID cur_e_a_j = (a_j == 255 ? kNULL : src[a_j]);
-        Bigram b(prev_src, cur_e_a_j, trg[j]);
-        //cerr << "DEC: " << b << "\t" << nextb << endl;
-        model.decrement(b);
-        ss.clear();
-        for (unsigned i = 0; i <= src.size(); ++i) {
-          const WordID cur_src = (i ? src[i-1] : kNULL);
-          b.cur_src() = cur_src;
-          ss.add(model.prob(b));
-        }
-        int sampled_a_j = rng.SelectSample(ss);
-        a_j = (sampled_a_j ? sampled_a_j - 1 : 255);
-        cur_e_a_j = (a_j == 255 ? kNULL : src[a_j]);
-        b.cur_src() = cur_e_a_j;
-        //cerr << "INC: " << b << "\t" << nextb << endl;
-        model.increment(b);
-        prev_src = cur_e_a_j;
-      }
-    }
-    cerr << '.' << flush;
-    if (si % 10 == 9) {
-      cerr << "[LLH prev=" << model.LogLikelihood();
-      //model.ResampleHyperparameters(&rng);
-      cerr << " new=" << model.LogLikelihood() << "]\n";
-      //pair<WordID,WordID> xx = make_pair(kBOS, TD::Convert("我"));
-      //PrintTopCustomers(model.bigrams.find(xx)->second);
-      cerr << "p(" << TT << ") = " << model.prob(TT) << endl;
-      cerr << "p(" << TT2 << ") = " << model.prob(TT2) << endl;
-      PrintAlignment(corpusf[0], corpuse[0], alignments[0].a);
-    }
-  }
-  {
-  // MODEL 2
-  BigramModel model(vocabf.size(), vocabe.size());
-  BigramAlignmentModel amodel(vocabf.size(), vocabe.size());
-  for (unsigned ci = 0; ci < corpus_len; ++ci) {
-    const vector<WordID>& src = corpusf[ci];
-    const vector<WordID>& trg = corpuse[ci];
-    vector<unsigned char>& alg = alignments[ci].a;
-    WordID prev_src = kBOS;
-    for (int j = 0; j < trg.size(); ++j) {
-      WordID cur_src = (alg[j] == 255 ? kNULL : src[alg[j]]);
-      Bigram b(prev_src, cur_src, trg[j]);
-      model.increment(b);
-      amodel.increment(prev_src, cur_src);
-      prev_src = cur_src;
-    }
-    amodel.increment(prev_src, kEOS);
-    Bigram b(prev_src, kEOS, kEOS);
-    model.increment(b);
-  }
-  cerr << "Initial LLH: " << model.LogLikelihood() << " " << amodel.LogLikelihood() << endl;
-
-  SampleSet<double> ss;
-  for (unsigned si = 0; si < samples; ++si) {
-    for (unsigned ci = 0; ci < corpus_len; ++ci) {
-      const vector<WordID>& src = corpusf[ci];
-      const vector<WordID>& trg = corpuse[ci];
-      vector<unsigned char>& alg = alignments[ci].a;
-      WordID prev_src = kBOS;
-      for (unsigned j = 0; j < trg.size(); ++j) {
-        unsigned char& a_j = alg[j];
-        WordID cur_e_a_j = (a_j == 255 ? kNULL : src[a_j]);
-        Bigram b(prev_src, cur_e_a_j, trg[j]);
-        WordID next_src = kEOS;
-        WordID next_trg = kEOS;
-        if (j < (trg.size() - 1)) {
-          next_src = (alg[j+1] == 255 ? kNULL : src[alg[j + 1]]);
-          next_trg = trg[j + 1];
-        }
-        Bigram nextb(cur_e_a_j, next_src, next_trg);
-        //cerr << "DEC: " << b << "\t" << nextb << endl;
-        model.decrement(b);
-        model.decrement(nextb);
-        amodel.decrement(prev_src, cur_e_a_j);
-        amodel.decrement(cur_e_a_j, next_src);
-        ss.clear();
-        for (unsigned i = 0; i <= src.size(); ++i) {
-          const WordID cur_src = (i ? src[i-1] : kNULL);
-          b.cur_src() = cur_src;
-          ss.add(model.prob(b) * model.prob(nextb) * amodel.prob(prev_src, cur_src) * amodel.prob(cur_src, next_src));
-          //cerr << log(ss[ss.size() - 1]) << "\t" << b << endl;
-        }
-        int sampled_a_j = rng.SelectSample(ss);
-        a_j = (sampled_a_j ? sampled_a_j - 1 : 255);
-        cur_e_a_j = (a_j == 255 ? kNULL : src[a_j]);
-        b.cur_src() = cur_e_a_j;
-        nextb.prev_src() = cur_e_a_j;
-        //cerr << "INC: " << b << "\t" << nextb << endl;
-        //exit(1);
-        model.increment(b);
-        model.increment(nextb);
-        amodel.increment(prev_src, cur_e_a_j);
-        amodel.increment(cur_e_a_j, next_src);
-        prev_src = cur_e_a_j;
-      }
-    }
-    cerr << '.' << flush;
-    if (si % 10 == 9) {
-      cerr << "[LLH prev=" << (model.LogLikelihood() + amodel.LogLikelihood());
-      //model.ResampleHyperparameters(&rng);
-      cerr << " new=" << model.LogLikelihood() << "]\n";
-      pair<WordID,WordID> xx = make_pair(kBOS, TD::Convert("我"));
-      cerr << "p(" << TT << ") = " << model.prob(TT) << endl;
-      cerr << "p(" << TT2 << ") = " << model.prob(TT2) << endl;
-      pair<WordID,WordID> xx2 = make_pair(kBOS, TD::Convert("要"));
-      PrintTopCustomers(model.bigrams.find(xx)->second);
-      //PrintTopCustomers(amodel.bigrams[TD::Convert("<s>")]);
-      //PrintTopCustomers(model.unigrams[TD::Convert("<eps>")]);
-      PrintAlignment(corpusf[0], corpuse[0], alignments[0].a);
-    }
-  }
-  }
-  return 0;
-}
-