get rid of bad Weights class so it no longer keeps a copy of a vector inside it

1 files changed, 14 insertions, 48 deletions

diff --git a/mira/kbest_mira.cc b/mira/kbest_mira.cc
index 6918a9a1..459a5e6f 100644
--- a/mira/kbest_mira.cc
+++ b/mira/kbest_mira.cc
@@ -32,21 +32,6 @@ namespace po = boost::program_options;
 bool invert_score;
 boost::shared_ptr<MT19937> rng;
 
-void SanityCheck(const vector<double>& w) {
-  for (int i = 0; i < w.size(); ++i) {
-    assert(!isnan(w[i]));
-    assert(!isinf(w[i]));
-  }
-}
-
-struct FComp {
-  const vector<double>& w_;
-  FComp(const vector<double>& w) : w_(w) {}
-  bool operator()(int a, int b) const {
-    return fabs(w_[a]) > fabs(w_[b]);
-  }
-};
-
 void RandomPermutation(int len, vector<int>* p_ids) {
   vector<int>& ids = *p_ids;
   ids.resize(len);
@@ -58,21 +43,6 @@ void RandomPermutation(int len, vector<int>* p_ids) {
   }  
 }
 
-void ShowLargestFeatures(const vector<double>& w) {
-  vector<int> fnums(w.size());
-  for (int i = 0; i < w.size(); ++i)
-    fnums[i] = i;
-  vector<int>::iterator mid = fnums.begin();
-  mid += (w.size() > 10 ? 10 : w.size());
-  partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
-  cerr << "TOP FEATURES:";
-  --mid;
-  for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
-    cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
-  }
-  cerr << endl;
-}
-
 bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
   po::options_description opts("Configuration options");
   opts.add_options()
@@ -209,14 +179,16 @@ int main(int argc, char** argv) {
     cerr << "Mismatched number of references (" << ds.size() << ") and sources (" << corpus.size() << ")\n";
     return 1;
   }
-  // load initial weights
-  Weights weights;
-  weights.InitFromFile(conf["input_weights"].as<string>());
-  SparseVector<double> lambdas;
-  weights.InitSparseVector(&lambdas);
 
   ReadFile ini_rf(conf["decoder_config"].as<string>());
   Decoder decoder(ini_rf.stream());
+
+  // load initial weights
+  vector<weight_t>& dense_weights = decoder.CurrentWeightVector();
+  SparseVector<weight_t> lambdas;
+  Weights::InitFromFile(conf["input_weights"].as<string>(), &dense_weights);
+  Weights::InitSparseVector(dense_weights, &lambdas);
+
   const double max_step_size = conf["max_step_size"].as<double>();
   const double mt_metric_scale = conf["mt_metric_scale"].as<double>();
 
@@ -230,7 +202,6 @@ int main(int argc, char** argv) {
   double tot_loss = 0;
   int dots = 0;
   int cur_pass = 0;
-  vector<double> dense_weights;
   SparseVector<double> tot;
   tot += lambdas;          // initial weights
   normalizer++;            // count for initial weights
@@ -240,27 +211,22 @@ int main(int argc, char** argv) {
   vector<int> order;
   RandomPermutation(corpus.size(), &order);
   while (lcount <= max_iteration) {
-    dense_weights.clear();
-    weights.InitFromVector(lambdas);
-    weights.InitVector(&dense_weights);
-    decoder.SetWeights(dense_weights);
+    lambdas.init_vector(&dense_weights);
     if ((cur_sent * 40 / corpus.size()) > dots) { ++dots; cerr << '.'; }
     if (corpus.size() == cur_sent) {
       cerr << " [AVG METRIC LAST PASS=" << (tot_loss / corpus.size()) << "]\n";
-      ShowLargestFeatures(dense_weights);
+      Weights::ShowLargestFeatures(dense_weights);
       cur_sent = 0;
       tot_loss = 0;
       dots = 0;
       ostringstream os;
       os << "weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << ".gz";
-      weights.WriteToFile(os.str(), true, &msg);
       SparseVector<double> x = tot;
       x /= normalizer;
       ostringstream sa;
       sa << "weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << "-avg.gz";
-      Weights ww;
-      ww.InitFromVector(x);
-      ww.WriteToFile(sa.str(), true, &msga);
+      x.init_vector(&dense_weights);
+      Weights::WriteToFile(os.str(), dense_weights, true, &msg);
       ++cur_pass;
       RandomPermutation(corpus.size(), &order);
     }
@@ -294,11 +260,11 @@ int main(int argc, char** argv) {
     ++cur_sent;
   }
   cerr << endl;
-  weights.WriteToFile("weights.mira-final.gz", true, &msg);
+  Weights::WriteToFile("weights.mira-final.gz", dense_weights, true, &msg);
   tot /= normalizer;
-  weights.InitFromVector(tot);
+  tot.init_vector(dense_weights);
   msg = "# MIRA tuned weights (averaged vector)";
-  weights.WriteToFile("weights.mira-final-avg.gz", true, &msg);
+  Weights::WriteToFile("weights.mira-final-avg.gz", dense_weights, true, &msg);
   cerr << "Optimization complete.\nAVERAGED WEIGHTS: weights.mira-final-avg.gz\n";
   return 0;
 }