From 0b598b997a7c1d2d9dc255cc2ff1bf9bb2c425a1 Mon Sep 17 00:00:00 2001
From: Chris Dyer <cdyer@cs.cmu.edu>
Date: Thu, 15 Mar 2012 22:47:04 -0400
Subject: bayes bayes bayes

---
 gi/pf/hpyp_tm.cc | 133 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 133 insertions(+)
 create mode 100644 gi/pf/hpyp_tm.cc

(limited to 'gi/pf/hpyp_tm.cc')

diff --git a/gi/pf/hpyp_tm.cc b/gi/pf/hpyp_tm.cc
new file mode 100644
index 00000000..784f9958
--- /dev/null
+++ b/gi/pf/hpyp_tm.cc
@@ -0,0 +1,133 @@
+#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.total_dish_count_ << '\t' << TD::GetString(i2->first) << 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);
+}
+
-- 
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