rst algorithm

1 files changed, 15 insertions, 6 deletions

diff --git a/rst_parser/mst_train.cc b/rst_parser/mst_train.cc
index def23edb..b5114726 100644
--- a/rst_parser/mst_train.cc
+++ b/rst_parser/mst_train.cc
@@ -13,6 +13,7 @@
 #include "picojson.h"
 #include "optimize.h"
 #include "weights.h"
+#include "rst.h"
 
 using namespace std;
 namespace po = boost::program_options;
@@ -173,12 +174,13 @@ int main(int argc, char** argv) {
     double obj = -empirical.dot(weights);
     // SparseVector<double> mfm;  //DE
     for (int i = 0; i < corpus.size(); ++i) {
+      const int num_words = corpus[i].ts.words.size();
       forests[i].Reweight(weights);
-      double logz;
-      forests[i].EdgeMarginals(&logz);
-      //cerr << " O = " << (-corpus[i].features.dot(weights)) << " D=" << -logz << "  OO= " << (-corpus[i].features.dot(weights) - logz) << endl;
-      obj -= logz;
-      int num_words = corpus[i].ts.words.size();
+      double lz;
+      forests[i].EdgeMarginals(&lz);
+      obj -= lz;
+      //cerr << " O = " << (-corpus[i].features.dot(weights)) << " D=" << -lz << "  OO= " << (-corpus[i].features.dot(weights) - lz) << endl;
+      //cerr << " ZZ = " << zz << endl;
       for (int h = -1; h < num_words; ++h) {
         for (int m = 0; m < num_words; ++m) {
           if (h == m) continue;
@@ -198,13 +200,20 @@ int main(int argc, char** argv) {
     double gnorm = 0;
     for (int i = 0; i < g.size(); ++i)
       gnorm += g[i]*g[i];
-    cerr << "OBJ=" << (obj+r) << "\t[F=" << obj << " R=" << r << "]\tGnorm=" << sqrt(gnorm) << endl;
+    ostringstream ll;
+    ll << "ITER=" << (iter+1) << "\tOBJ=" << (obj+r) << "\t[F=" << obj << " R=" << r << "]\tGnorm=" << sqrt(gnorm);
+    cerr << endl << ll.str() << endl;
     obj += r;
     assert(obj >= 0);
     o->Optimize(obj, g, &weights);
     Weights::ShowLargestFeatures(weights);
+    string sl = ll.str();
+    Weights::WriteToFile(o->HasConverged() ? "weights.final.gz" : "weights.cur.gz", weights, true, &sl);
     if (o->HasConverged()) { cerr << "CONVERGED\n"; break; }
   }
+  forests[0].Reweight(weights);
+  TreeSampler ts(forests[0]);
+  EdgeSubset tt; ts.SampleRandomSpanningTree(&tt);
   return 0;
 }