cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 133 deletions

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);
-}
-