cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 298 deletions

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;
-}
-