optional hierarchical prior

1 files changed, 40 insertions, 6 deletions

diff --git a/gi/pf/learn_cfg.cc b/gi/pf/learn_cfg.cc
index 6e574035..b2ca029a 100644
--- a/gi/pf/learn_cfg.cc
+++ b/gi/pf/learn_cfg.cc
@@ -30,6 +30,7 @@ vector<int> nt_id_to_index;
 static unsigned kMAX_RULE_SIZE = 0;
 static unsigned kMAX_ARITY = 0;
 static bool kALLOW_MIXED = true;  // allow rules with mixed terminals and NTs
+static bool kHIERARCHICAL_PRIOR = false;
 
 void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
   po::options_description opts("Configuration options");
@@ -40,11 +41,12 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
         ("max_arity,a", po::value<unsigned>()->default_value(0), "Maximum number of nonterminals in a rule (0 for unlimited)")
         ("no_mixed_rules,M", "Do not mix terminals and nonterminals in a rule RHS")
         ("nonterminals,n", po::value<unsigned>()->default_value(1), "Size of nonterminal vocabulary")
+        ("hierarchical_prior,h", "Use hierarchical prior")
         ("random_seed,S",po::value<uint32_t>(), "Random seed");
   po::options_description clo("Command line options");
   clo.add_options()
         ("config", po::value<string>(), "Configuration file")
-        ("help,h", "Print this help message and exit");
+        ("help", "Print this help message and exit");
   po::options_description dconfig_options, dcmdline_options;
   dconfig_options.add(opts);
   dcmdline_options.add(opts).add(clo);
@@ -119,19 +121,35 @@ struct BaseRuleModel {
 };
 
 struct HieroLMModel {
-  explicit HieroLMModel(unsigned vocab_size, unsigned num_nts = 1) : p0(vocab_size, num_nts), nts(num_nts, CCRP<TRule>(1,1,1,1)) {}
+  explicit HieroLMModel(unsigned vocab_size, unsigned num_nts = 1) :
+      base(vocab_size, num_nts),
+      q0(1,1,1,1),
+      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));
   }
 
+  inline prob_t p0(const TRule& r) const {
+    if (kHIERARCHICAL_PRIOR)
+      return q0.probT<prob_t>(r, base(r));
+    else
+      return base(r);
+  }
+
   int Increment(const TRule& r, MT19937* rng) {
-    return nts[nt_id_to_index[-r.lhs_]].incrementT<prob_t>(r, p0(r), rng);
+    const int delta = nts[nt_id_to_index[-r.lhs_]].incrementT<prob_t>(r, p0(r), rng);
+    if (kHIERARCHICAL_PRIOR && delta)
+      q0.incrementT<prob_t>(r, base(r), rng);
+    return delta;
     // return x.increment(r);
   }
 
   int Decrement(const TRule& r, MT19937* rng) {
-    return nts[nt_id_to_index[-r.lhs_]].decrement(r, rng);
+    const int delta = nts[nt_id_to_index[-r.lhs_]].decrement(r, rng);
+    if (kHIERARCHICAL_PRIOR && delta)
+      q0.decrement(r, rng);
+    return delta;
     //return x.decrement(r);
   }
 
@@ -146,18 +164,32 @@ struct HieroLMModel {
         p *= tp;
       }
     }
+    if (kHIERARCHICAL_PRIOR) {
+      prob_t q; q.logeq(q0.log_crp_prob());
+      p *= q;
+      for (CCRP<TRule>::const_iterator it = q0.begin(); it != q0.end(); ++it) {
+        prob_t tp = base(it->first);
+        tp.poweq(it->second.table_counts_.size());
+        p *= tp;
+      }
+    }
     //for (CCRP_OneTable<TRule>::const_iterator it = x.begin(); it != x.end(); ++it)
-    //    p *= p0(it->first);
+    //    p *= base(it->first);
     return p;
   }
 
   void ResampleHyperparameters(MT19937* rng) {
     for (unsigned i = 0; i < nts.size(); ++i)
       nts[i].resample_hyperparameters(rng);
+    if (kHIERARCHICAL_PRIOR) {
+      q0.resample_hyperparameters(rng);
+      cerr << "[base d=" << q0.discount() << ", alpha=" << q0.discount() << "]";
+    }
     cerr << " d=" << nts[0].discount() << ", alpha=" << nts[0].concentration() << endl;
   }
 
-  const BaseRuleModel p0;
+  const BaseRuleModel base;
+  CCRP<TRule> q0;
   vector<CCRP<TRule> > nts;
   //CCRP_OneTable<TRule> x;
 };
@@ -316,6 +348,8 @@ int main(int argc, char** argv) {
   }
   kALLOW_MIXED = !conf.count("no_mixed_rules");
 
+  kHIERARCHICAL_PRIOR = conf.count("hierarchical_prior");
+
   if (conf.count("random_seed"))
     prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
   else