use template parameter inference to figure out what type to use for probability computations, templatatize number of floors in MFCR rather than compile-time set

3 files changed, 25 insertions, 25 deletions

diff --git a/gi/pf/align-lexonly-pyp.cc b/gi/pf/align-lexonly-pyp.cc
index 87f7f6b5..ac0590e0 100644
--- a/gi/pf/align-lexonly-pyp.cc
+++ b/gi/pf/align-lexonly-pyp.cc
@@ -68,7 +68,7 @@ struct AlignedSentencePair {
 
 struct HierarchicalWordBase {
   explicit HierarchicalWordBase(const unsigned vocab_e_size) :
-      base(prob_t::One()), r(1,1,1,25,25), u0(-log(vocab_e_size)), l(1,1.0), v(1, 0.0) {}
+      base(prob_t::One()), r(1,1,1,1), u0(-log(vocab_e_size)), l(1,prob_t::One()), v(1, prob_t::Zero()) {}
 
   void ResampleHyperparameters(MT19937* rng) {
     r.resample_hyperparameters(rng);
@@ -80,14 +80,14 @@ struct HierarchicalWordBase {
 
   // return p0 of rule.e_
   prob_t operator()(const TRule& rule) const {
-    v[0] = exp(logp0(rule.e_));
-    return prob_t(r.prob(rule.e_, v, l));
+    v[0].logeq(logp0(rule.e_));
+    return r.prob(rule.e_, v.begin(), l.begin());
   }
 
   void Increment(const TRule& rule) {
-    v[0] = exp(logp0(rule.e_));
-    if (r.increment(rule.e_, v, l, &*prng).count) {
-      base *= prob_t(v[0] * l[0]);
+    v[0].logeq(logp0(rule.e_));
+    if (r.increment(rule.e_, v.begin(), l.begin(), &*prng).count) {
+      base *= v[0] * l[0];
     }
   }
 
@@ -105,15 +105,15 @@ struct HierarchicalWordBase {
 
   void Summary() const {
     cerr << "NUMBER OF CUSTOMERS: " << r.num_customers() << "  (d=" << r.discount() << ",s=" << r.strength() << ')' << endl;
-    for (MFCR<vector<WordID> >::const_iterator it = r.begin(); it != r.end(); ++it)
+    for (MFCR<1,vector<WordID> >::const_iterator it = r.begin(); it != r.end(); ++it)
       cerr << "   " << it->second.total_dish_count_ << " (on " << it->second.table_counts_.size() << " tables)" << TD::GetString(it->first) << endl;
   }
 
   prob_t base;
-  MFCR<vector<WordID> > r;
+  MFCR<1,vector<WordID> > r;
   const double u0;
-  const vector<double> l;
-  mutable vector<double> v;
+  const vector<prob_t> l;
+  mutable vector<prob_t> v;
 };
 
 struct BasicLexicalAlignment {
diff --git a/gi/pf/conditional_pseg.h b/gi/pf/conditional_pseg.h
index 86403d8d..ef73e332 100644
--- a/gi/pf/conditional_pseg.h
+++ b/gi/pf/conditional_pseg.h
@@ -17,13 +17,13 @@
 template <typename ConditionalBaseMeasure>
 struct MConditionalTranslationModel {
   explicit MConditionalTranslationModel(ConditionalBaseMeasure& rcp0) :
-    rp0(rcp0), lambdas(1, 1.0), p0s(1) {}
+    rp0(rcp0), lambdas(1, prob_t::One()), p0s(1) {}
 
   void Summary() const {
     std::cerr << "Number of conditioning contexts: " << r.size() << std::endl;
     for (RuleModelHash::const_iterator it = r.begin(); it != r.end(); ++it) {
       std::cerr << TD::GetString(it->first) << "   \t(d=" << it->second.discount() << ",s=" << it->second.strength() << ") --------------------------" << std::endl;
-      for (MFCR<TRule>::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
+      for (MFCR<1,TRule>::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
         std::cerr << "   " << -1 << '\t' << i2->first << std::endl;
     }
   }
@@ -46,10 +46,10 @@ struct MConditionalTranslationModel {
   int IncrementRule(const TRule& rule, MT19937* rng) {
     RuleModelHash::iterator it = r.find(rule.f_);
     if (it == r.end()) {
-      it = r.insert(make_pair(rule.f_, MFCR<TRule>(1, 1.0, 1.0, 1.0, 1.0, 1e-9, 4.0))).first;
+      it = r.insert(make_pair(rule.f_, MFCR<1,TRule>(1.0, 1.0, 1.0, 1.0, 1e-9, 4.0))).first;
     }
-    p0s[0] = rp0(rule).as_float(); 
-    TableCount delta = it->second.increment(rule, p0s, lambdas, rng);
+    p0s[0] = rp0(rule); 
+    TableCount delta = it->second.increment(rule, p0s.begin(), lambdas.begin(), rng);
     return delta.count;
   }
 
@@ -57,10 +57,10 @@ struct MConditionalTranslationModel {
     prob_t p;
     RuleModelHash::const_iterator it = r.find(rule.f_);
     if (it == r.end()) {
-      p.logeq(log(rp0(rule)));
+      p = rp0(rule);
     } else {
-      p0s[0] = rp0(rule).as_float();
-      p = prob_t(it->second.prob(rule, p0s, lambdas));
+      p0s[0] = rp0(rule);
+      p = it->second.prob(rule, p0s.begin(), lambdas.begin());
     }
     return p;
   }
@@ -80,11 +80,11 @@ struct MConditionalTranslationModel {
 
   const ConditionalBaseMeasure& rp0;
   typedef std::tr1::unordered_map<std::vector<WordID>,
-                                  MFCR<TRule>,
+                                  MFCR<1, TRule>,
                                   boost::hash<std::vector<WordID> > > RuleModelHash;
   RuleModelHash r;
-  std::vector<double> lambdas;
-  mutable std::vector<double> p0s;
+  std::vector<prob_t> lambdas;
+  mutable std::vector<prob_t> p0s;
 };
 
 template <typename ConditionalBaseMeasure>
diff --git a/gi/pf/learn_cfg.cc b/gi/pf/learn_cfg.cc
index bf157828..ed1772bf 100644
--- a/gi/pf/learn_cfg.cc
+++ b/gi/pf/learn_cfg.cc
@@ -127,20 +127,20 @@ struct HieroLMModel {
       nts(num_nts, CCRP<TRule>(1,1,1,1)) {}
 
   prob_t Prob(const TRule& r) const {
-    return nts[nt_id_to_index[-r.lhs_]].probT<prob_t>(r, p0(r));
+    return nts[nt_id_to_index[-r.lhs_]].prob(r, p0(r));
   }
 
   inline prob_t p0(const TRule& r) const {
     if (kHIERARCHICAL_PRIOR)
-      return q0.probT<prob_t>(r, base(r));
+      return q0.prob(r, base(r));
     else
       return base(r);
   }
 
   int Increment(const TRule& r, MT19937* rng) {
-    const int delta = nts[nt_id_to_index[-r.lhs_]].incrementT<prob_t>(r, p0(r), rng);
+    const int delta = nts[nt_id_to_index[-r.lhs_]].increment(r, p0(r), rng);
     if (kHIERARCHICAL_PRIOR && delta)
-      q0.incrementT<prob_t>(r, base(r), rng);
+      q0.increment(r, base(r), rng);
     return delta;
     // return x.increment(r);
   }