4 files changed, 15 insertions, 15 deletions

diff --git a/gi/pf/align-lexonly-pyp.cc b/gi/pf/align-lexonly-pyp.cc
index 4ce7cf62..87f7f6b5 100644
--- a/gi/pf/align-lexonly-pyp.cc
+++ b/gi/pf/align-lexonly-pyp.cc
@@ -104,7 +104,7 @@ struct HierarchicalWordBase {
   }
 
   void Summary() const {
-    cerr << "NUMBER OF CUSTOMERS: " << r.num_customers() << "  (d=" << r.discount() << ",\\alpha=" << r.alpha() << ')' << endl;
+    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)
       cerr << "   " << it->second.total_dish_count_ << " (on " << it->second.table_counts_.size() << " tables)" << TD::GetString(it->first) << endl;
   }
diff --git a/gi/pf/conditional_pseg.h b/gi/pf/conditional_pseg.h
index f9841cbf..86403d8d 100644
--- a/gi/pf/conditional_pseg.h
+++ b/gi/pf/conditional_pseg.h
@@ -22,7 +22,7 @@ struct MConditionalTranslationModel {
   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() << ",\\alpha = " << it->second.alpha() << ") --------------------------" << std::endl;
+      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)
         std::cerr << "   " << -1 << '\t' << i2->first << std::endl;
     }
diff --git a/gi/pf/learn_cfg.cc b/gi/pf/learn_cfg.cc
index 5b748311..bf157828 100644
--- a/gi/pf/learn_cfg.cc
+++ b/gi/pf/learn_cfg.cc
@@ -183,9 +183,9 @@ struct HieroLMModel {
       nts[i].resample_hyperparameters(rng);
     if (kHIERARCHICAL_PRIOR) {
       q0.resample_hyperparameters(rng);
-      cerr << "[base d=" << q0.discount() << ", alpha=" << q0.alpha() << "]";
+      cerr << "[base d=" << q0.discount() << ", s=" << q0.strength() << "]";
     }
-    cerr << " d=" << nts[0].discount() << ", alpha=" << nts[0].alpha() << endl;
+    cerr << " d=" << nts[0].discount() << ", s=" << nts[0].strength() << endl;
   }
 
   const BaseRuleModel base;
diff --git a/gi/pf/pyp_lm.cc b/gi/pf/pyp_lm.cc
index e5c44c8b..7ebada13 100644
--- a/gi/pf/pyp_lm.cc
+++ b/gi/pf/pyp_lm.cc
@@ -78,14 +78,14 @@ template <unsigned N> struct PYPLM {
       backoff(vs, da, db, ss, sr),
       discount_a(da), discount_b(db),
       strength_s(ss), strength_r(sr),
-      d(0.8), alpha(1.0), lookup(N-1) {}
+      d(0.8), strength(1.0), lookup(N-1) {}
   void increment(WordID w, const vector<WordID>& context, MT19937* rng) {
     const double bo = backoff.prob(w, context);
     for (unsigned i = 0; i < N-1; ++i)
       lookup[i] = context[context.size() - 1 - i];
     typename unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > >::iterator it = p.find(lookup);
     if (it == p.end())
-      it = p.insert(make_pair(lookup, CCRP<WordID>(d,alpha))).first;
+      it = p.insert(make_pair(lookup, CCRP<WordID>(d,strength))).first;
     if (it->second.increment(w, bo, rng))
       backoff.increment(w, context, rng);
   }
@@ -107,7 +107,7 @@ template <unsigned N> struct PYPLM {
   }
 
   double log_likelihood() const {
-    return log_likelihood(d, alpha) + backoff.log_likelihood();
+    return log_likelihood(d, strength) + backoff.log_likelihood();
   }
 
   double log_likelihood(const double& dd, const double& aa) const {
@@ -125,15 +125,15 @@ template <unsigned N> struct PYPLM {
     DiscountResampler(const PYPLM& m) : m_(m) {}
     const PYPLM& m_;
     double operator()(const double& proposed_discount) const {
-      return m_.log_likelihood(proposed_discount, m_.alpha);
+      return m_.log_likelihood(proposed_discount, m_.strength);
     }
   };
 
   struct AlphaResampler {
     AlphaResampler(const PYPLM& m) : m_(m) {}
     const PYPLM& m_;
-    double operator()(const double& proposed_alpha) const {
-      return m_.log_likelihood(m_.d, proposed_alpha);
+    double operator()(const double& proposed_strength) const {
+      return m_.log_likelihood(m_.d, proposed_strength);
     }
   };
 
@@ -141,25 +141,25 @@ template <unsigned N> struct PYPLM {
     DiscountResampler dr(*this);
     AlphaResampler ar(*this);
     for (int iter = 0; iter < nloop; ++iter) {
-      alpha = slice_sampler1d(ar, alpha, *rng, 0.0,
+      strength = slice_sampler1d(ar, strength, *rng, 0.0,
                               std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
       d = slice_sampler1d(dr, d, *rng, std::numeric_limits<double>::min(),
                           1.0, 0.0, niterations, 100*niterations);
     }
-    alpha = slice_sampler1d(ar, alpha, *rng, 0.0,
+    strength = slice_sampler1d(ar, strength, *rng, 0.0,
                             std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
     typename unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > >::iterator it;
-    cerr << "PYPLM<" << N << ">(d=" << d << ",a=" << alpha << ") = " << log_likelihood(d, alpha) << endl;
+    cerr << "PYPLM<" << N << ">(d=" << d << ",a=" << strength << ") = " << log_likelihood(d, strength) << endl;
     for (it = p.begin(); it != p.end(); ++it) {
       it->second.set_discount(d);
-      it->second.set_alpha(alpha);
+      it->second.set_strength(strength);
     }
     backoff.resample_hyperparameters(rng, nloop, niterations);
   }
 
   PYPLM<N-1> backoff;
   double discount_a, discount_b, strength_s, strength_r;
-  double d, alpha;
+  double d, strength;
   mutable vector<WordID> lookup;  // thread-local
   unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > > p;
 };