switch to log domain for matrix operations

1 files changed, 21 insertions, 8 deletions

diff --git a/rst_parser/mst_train.cc b/rst_parser/mst_train.cc
index b5114726..c5cab6ec 100644
--- a/rst_parser/mst_train.cc
+++ b/rst_parser/mst_train.cc
@@ -23,7 +23,9 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
   string cfg_file;
   opts.add_options()
         ("training_data,t",po::value<string>()->default_value("-"), "File containing training data (jsent format)")
-        ("feature_function,F",po::value<vector<string> >()->composing(), "feature function")
+        ("feature_function,F",po::value<vector<string> >()->composing(), "feature function (multiple permitted)")
+        ("weights,w",po::value<string>(), "Optional starting weights")
+        ("output_every_i_iterations,I",po::value<unsigned>()->default_value(1), "Write weights every I iterations")
         ("regularization_strength,C",po::value<double>()->default_value(1.0), "Regularization strength")
         ("correction_buffers,m", po::value<int>()->default_value(10), "LBFGS correction buffers");
   po::options_description clo("Command line options");
@@ -161,9 +163,13 @@ int main(int argc, char** argv) {
   if (flag) cerr << endl;
   //cerr << "EMP: " << empirical << endl; //DE
   vector<weight_t> weights(FD::NumFeats(), 0.0);
+  if (conf.count("weights"))
+    Weights::InitFromFile(conf["weights"].as<string>(), &weights);
   vector<weight_t> g(FD::NumFeats(), 0.0);
   cerr << "features initialized\noptimizing...\n";
   boost::shared_ptr<BatchOptimizer> o;
+  int every = corpus.size() / 20;
+  if (!every) ++every;
   o.reset(new LBFGSOptimizer(g.size(), conf["correction_buffers"].as<int>()));
   int iterations = 1000;
   for (int iter = 0; iter < iterations; ++iter) {
@@ -174,11 +180,12 @@ int main(int argc, char** argv) {
     double obj = -empirical.dot(weights);
     // SparseVector<double> mfm;  //DE
     for (int i = 0; i < corpus.size(); ++i) {
+      if ((i + 1) % every == 0) cerr << '.' << flush;
       const int num_words = corpus[i].ts.words.size();
       forests[i].Reweight(weights);
-      double lz;
-      forests[i].EdgeMarginals(&lz);
-      obj -= lz;
+      prob_t z;
+      forests[i].EdgeMarginals(&z);
+      obj -= log(z);
       //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) {
@@ -202,14 +209,20 @@ int main(int argc, char** argv) {
       gnorm += g[i]*g[i];
     ostringstream ll;
     ll << "ITER=" << (iter+1) << "\tOBJ=" << (obj+r) << "\t[F=" << obj << " R=" << r << "]\tGnorm=" << sqrt(gnorm);
-    cerr << endl << ll.str() << endl;
+    cerr << ' ' << ll.str().substr(ll.str().find('\t')+1) << 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; }
+    const bool converged = o->HasConverged();
+    const char* ofname = converged ? "weights.final.gz" : "weights.cur.gz";
+    if (converged || ((iter+1) % conf["output_every_i_iterations"].as<unsigned>()) == 0) {
+      cerr << "writing..." << flush;
+      const string sl = ll.str();
+      Weights::WriteToFile(ofname, weights, true, &sl);
+      cerr << "done" << endl;
+    }
+    if (converged) { cerr << "CONVERGED\n"; break; }
   }
   forests[0].Reweight(weights);
   TreeSampler ts(forests[0]);