1 files changed, 113 insertions, 0 deletions
diff --git a/gi/pf/pyp_tm.cc b/gi/pf/pyp_tm.cc
new file mode 100644
index 00000000..94cbe7c3
--- /dev/null
+++ b/gi/pf/pyp_tm.cc
@@ -0,0 +1,113 @@
+#include "pyp_tm.h"
+
+#include <tr1/unordered_map>
+#include <iostream>
+#include <queue>
+
+#include "base_distributions.h"
+#include "monotonic_pseg.h"
+#include "conditional_pseg.h"
+#include "tdict.h"
+#include "ccrp.h"
+#include "pyp_word_model.h"
+
+using namespace std;
+using namespace std::tr1;
+
+template <typename Base>
+struct ConditionalPYPWordModel {
+  ConditionalPYPWordModel(Base* b) : base(*b) {}
+
+  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.total_dish_count_ << '\t' << TD::GetString(i2->first) << endl;
+    }
+  }
+
+  void ResampleHyperparameters(MT19937* rng) {
+    for (RuleModelHash::iterator it = r.begin(); it != r.end(); ++it)
+      it->second.resample_hyperparameters(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> >(1,1,1,1,0.5,1.0))).first;
+    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);
+      if (it->second.num_customers() == 0)
+        r.erase(it);
+    }
+  }
+
+  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();
+  }
+
+  Base& base;
+  typedef unordered_map<WordID, CCRP<vector<WordID> > > RuleModelHash;
+  RuleModelHash r;
+};
+
+PYPLexicalTranslation::PYPLexicalTranslation(const vector<vector<WordID> >& lets,
+                                             const unsigned num_letters) :
+    letters(lets),
+    up0(new PYPWordModel(num_letters)),
+    tmodel(new ConditionalPYPWordModel<PYPWordModel>(up0)),
+    kX(-TD::Convert("X")) {}
+
+prob_t PYPLexicalTranslation::Likelihood() const {
+  prob_t p = up0->Likelihood();
+  p *= tmodel->Likelihood();
+  return p;
+}
+
+void PYPLexicalTranslation::ResampleHyperparameters(MT19937* rng) {
+  tmodel->ResampleHyperparameters(rng);
+  up0->ResampleHyperparameters(rng);
+}
+
+unsigned PYPLexicalTranslation::UniqueConditioningContexts() const {
+  return tmodel->UniqueConditioningContexts();
+}
+
+prob_t PYPLexicalTranslation::Prob(WordID src, WordID trg) const {
+  return tmodel->Prob(src, letters[trg]);
+}
+
+void PYPLexicalTranslation::Increment(WordID src, WordID trg, MT19937* rng) {
+  tmodel->Increment(src, letters[trg], rng);
+}
+
+void PYPLexicalTranslation::Decrement(WordID src, WordID trg, MT19937* rng) {
+  tmodel->Decrement(src, letters[trg], rng);
+}
+