bayes bayes bayes

1 files changed, 25 insertions, 21 deletions

diff --git a/gi/pf/pyp_word_model.h b/gi/pf/pyp_word_model.h
index ff366865..224a9034 100644
--- a/gi/pf/pyp_word_model.h
+++ b/gi/pf/pyp_word_model.h
@@ -11,48 +11,52 @@
 #include "os_phrase.h"
 
 // PYP(d,s,poisson-uniform) represented as a CRP
+template <class Base>
 struct PYPWordModel {
-  explicit PYPWordModel(const unsigned vocab_e_size, const double mean_len = 5) :
-      base(prob_t::One()), r(1,1,1,1,0.66,50.0), u0(-std::log(vocab_e_size)), mean_length(mean_len) {}
-
-  void ResampleHyperparameters(MT19937* rng);
+  explicit PYPWordModel(Base* b) :
+      base(*b),
+      r(1,1,1,1,0.66,50.0)
+    {}
+
+  void ResampleHyperparameters(MT19937* rng) {
+    r.resample_hyperparameters(rng);
+    std::cerr << " PYPWordModel(d=" << r.discount() << ",s=" << r.strength() << ")\n";
+  }
 
   inline prob_t operator()(const std::vector<WordID>& s) const {
-    return r.prob(s, p0(s));
+    return r.prob(s, base(s));
   }
 
   inline void Increment(const std::vector<WordID>& s, MT19937* rng) {
-    if (r.increment(s, p0(s), rng))
-      base *= p0(s);
+    if (r.increment(s, base(s), rng))
+      base.Increment(s, rng);
   }
 
   inline void Decrement(const std::vector<WordID>& s, MT19937 *rng) {
     if (r.decrement(s, rng))
-      base /= p0(s);
+      base.Decrement(s, rng);
   }
 
   inline prob_t Likelihood() const {
     prob_t p; p.logeq(r.log_crp_prob());
-    p *= base;
+    p *= base.Likelihood();
     return p;
   }
 
-  void Summary() const;
-
- private:
-  inline double logp0(const std::vector<WordID>& s) const {
-    return Md::log_poisson(s.size(), mean_length) + s.size() * u0;
+  void Summary() const {
+    std::cerr << "PYPWordModel: generations=" << r.num_customers()
+         << " PYP(d=" << r.discount() << ",s=" << r.strength() << ')' << std::endl;
+    for (typename CCRP<std::vector<WordID> >::const_iterator it = r.begin(); it != r.end(); ++it) {
+      std::cerr << "   " << it->second.total_dish_count_
+                << " (on " << it->second.table_counts_.size() << " tables) "
+                << TD::GetString(it->first) << std::endl;
+    }
   }
 
-  inline prob_t p0(const std::vector<WordID>& s) const {
-    prob_t p; p.logeq(logp0(s));
-    return p;
-  }
+ private:
 
-  prob_t base;  // keeps track of the draws from the base distribution
+  Base& base;  // keeps track of the draws from the base distribution
   CCRP<std::vector<WordID> > r;
-  const double u0;  // uniform log prob of generating a letter
-  const double mean_length;  // mean length of a word in the base distribution
 };
 
 #endif