add support to rampion for accumulating k-best lists

3 files changed, 22 insertions, 15 deletions

diff --git a/training/candidate_set.cc b/training/candidate_set.cc
index e2ca9ad2..8c086ece 100644
--- a/training/candidate_set.cc
+++ b/training/candidate_set.cc
@@ -112,7 +112,7 @@ void CandidateSet::WriteToFile(const string& file) const {
   for (unsigned i = 0; i < cs.size(); ++i) {
     out << TD::GetString(cs[i].ewords) << endl;
     out << cs[i].fmap << endl;
-    cs[i].score_stats.Encode(&ss);
+    cs[i].eval_feats.Encode(&ss);
     out << ss << endl;
   }
 }
@@ -131,7 +131,7 @@ void CandidateSet::ReadFromFile(const string& file) {
     cs.push_back(Candidate());
     TD::ConvertSentence(cand, &cs.back().ewords);
     ParseSparseVector(feats, 0, &cs.back().fmap);
-    cs.back().score_stats = SufficientStats(ss);
+    cs.back().eval_feats = SufficientStats(ss);
   }
   cerr << "  read " << cs.size() << " candidates\n";
 }
@@ -160,7 +160,7 @@ void CandidateSet::AddKBestCandidates(const Hypergraph& hg, size_t kbest_size, c
     if (!d) break;
     cs.push_back(Candidate(d->yield, d->feature_values));
     if (scorer)
-      scorer->Evaluate(d->yield, &cs.back().score_stats);
+      scorer->Evaluate(d->yield, &cs.back().eval_feats);
   }
   Dedup();
 }
diff --git a/training/candidate_set.h b/training/candidate_set.h
index 824a4de2..9d326ed0 100644
--- a/training/candidate_set.h
+++ b/training/candidate_set.h
@@ -15,16 +15,25 @@ namespace training {
 struct Candidate {
   Candidate() {}
   Candidate(const std::vector<WordID>& e, const SparseVector<double>& fm) :
-    ewords(e),
-    fmap(fm) {}
-  std::vector<WordID> ewords;
-  SparseVector<double> fmap;
-  SufficientStats score_stats;
+      ewords(e),
+      fmap(fm) {}
+  Candidate(const std::vector<WordID>& e,
+            const SparseVector<double>& fm,
+            const SegmentEvaluator& se) :
+      ewords(e),
+      fmap(fm) {
+    se.Evaluate(ewords, &eval_feats);
+  }
+
   void swap(Candidate& other) {
-    score_stats.swap(other.score_stats);
+    eval_feats.swap(other.eval_feats);
     ewords.swap(other.ewords);
     fmap.swap(other.fmap);
   }
+
+  std::vector<WordID> ewords;
+  SparseVector<double> fmap;
+  SufficientStats eval_feats;
 };
 
 // represents some kind of collection of translation candidates, e.g.
diff --git a/training/mpi_flex_optimize.cc b/training/mpi_flex_optimize.cc
index a9197208..a9ead018 100644
--- a/training/mpi_flex_optimize.cc
+++ b/training/mpi_flex_optimize.cc
@@ -179,18 +179,16 @@ double ApplyRegularizationTerms(const double C,
                                 const double T,
                                 const vector<double>& weights,
                                 const vector<double>& prev_weights,
-                                vector<double>* g) {
-  assert(weights.size() == g->size());
+                                double* g) {
   double reg = 0;
   for (size_t i = 0; i < weights.size(); ++i) {
     const double prev_w_i = (i < prev_weights.size() ? prev_weights[i] : 0.0);
     const double& w_i = weights[i];
-    double& g_i = (*g)[i];
     reg += C * w_i * w_i;
-    g_i += 2 * C * w_i;
+    g[i] += 2 * C * w_i;
 
     reg += T * (w_i - prev_w_i) * (w_i - prev_w_i);
-    g_i += 2 * T * (w_i - prev_w_i);
+    g[i] += 2 * T * (w_i - prev_w_i);
   }
   return reg;
 }
@@ -365,7 +363,7 @@ int main(int argc, char** argv) {
                                 time_series_strength, // * (iter == 0 ? 0.0 : 1.0),
                                 cur_weights,
                                 prev_weights,
-                                &gg);
+                                &gg[0]);
           obj += r;
           if (mi == 0 || mi == (minibatch_iterations - 1)) {
             if (!mi) cerr << iter << ' '; else cerr << ' ';