cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 111 deletions

diff --git a/rst_parser/rst_parse.cc b/rst_parser/rst_parse.cc
deleted file mode 100644
index 9c42a8f4..00000000
--- a/rst_parser/rst_parse.cc
+++ /dev/null
@@ -1,111 +0,0 @@
-#include "arc_factored.h"
-
-#include <vector>
-#include <iostream>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "timing_stats.h"
-#include "arc_ff.h"
-#include "dep_training.h"
-#include "stringlib.h"
-#include "filelib.h"
-#include "tdict.h"
-#include "weights.h"
-#include "rst.h"
-#include "global_ff.h"
-
-using namespace std;
-namespace po = boost::program_options;
-
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
-  po::options_description opts("Configuration options");
-  string cfg_file;
-  opts.add_options()
-        ("input,i",po::value<string>()->default_value("-"), "File containing test data (jsent format)")
-        ("q_weights,q",po::value<string>(), "Arc-factored weights for proposal distribution (mandatory)")
-        ("p_weights,p",po::value<string>(), "Weights for target distribution (optional)")
-        ("samples,n",po::value<unsigned>()->default_value(1000), "Number of samples");
-  po::options_description clo("Command line options");
-  clo.add_options()
-        ("config,c", po::value<string>(&cfg_file), "Configuration file")
-        ("help,?", "Print this help message and exit");
-
-  po::options_description dconfig_options, dcmdline_options;
-  dconfig_options.add(opts);
-  dcmdline_options.add(dconfig_options).add(clo);
-  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
-  if (cfg_file.size() > 0) {
-    ReadFile rf(cfg_file);
-    po::store(po::parse_config_file(*rf.stream(), dconfig_options), *conf);
-  }
-  if (conf->count("help") || conf->count("q_weights") == 0) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-  vector<weight_t> qweights, pweights;
-  Weights::InitFromFile(conf["q_weights"].as<string>(), &qweights);
-  if (conf.count("p_weights"))
-    Weights::InitFromFile(conf["p_weights"].as<string>(), &pweights);
-  const bool global = pweights.size() > 0;
-  ArcFeatureFunctions ffs;
-  GlobalFeatureFunctions gff;
-  ReadFile rf(conf["input"].as<string>());
-  istream* in = rf.stream();
-  TrainingInstance sent;
-  MT19937 rng;
-  int samples = conf["samples"].as<unsigned>();
-  int totroot = 0, root_right = 0, tot = 0, cor = 0;
-  while(TrainingInstance::ReadInstance(in, &sent)) {
-    ffs.PrepareForInput(sent.ts);
-    if (global) gff.PrepareForInput(sent.ts);
-    ArcFactoredForest forest(sent.ts.pos.size());
-    forest.ExtractFeatures(sent.ts, ffs);
-    forest.Reweight(qweights);
-    TreeSampler ts(forest);
-    double best_score = -numeric_limits<double>::infinity();
-    EdgeSubset best_tree;
-    for (int n = 0; n < samples; ++n) {
-      EdgeSubset tree;
-      ts.SampleRandomSpanningTree(&tree, &rng);
-      SparseVector<double> qfeats, gfeats;
-      tree.ExtractFeatures(sent.ts, ffs, &qfeats);
-      double score = 0;
-      if (global) {
-        gff.Features(sent.ts, tree, &gfeats);
-        score = (qfeats + gfeats).dot(pweights);
-      } else {
-        score = qfeats.dot(qweights);
-      }
-      if (score > best_score) {
-        best_tree = tree;
-        best_score = score;
-      }
-    }
-    cerr << "BEST SCORE: " << best_score << endl;
-    cout << best_tree << endl;
-    const bool sent_has_ref = sent.tree.h_m_pairs.size() > 0;
-    if (sent_has_ref) {
-      map<pair<short,short>, bool> ref;
-      for (int i = 0; i < sent.tree.h_m_pairs.size(); ++i)
-        ref[sent.tree.h_m_pairs[i]] = true;
-      int ref_root = sent.tree.roots.front();
-      if (ref_root == best_tree.roots.front()) { ++root_right; }
-      ++totroot;
-      for (int i = 0; i < best_tree.h_m_pairs.size(); ++i) {
-        if (ref[best_tree.h_m_pairs[i]]) {
-          ++cor;
-        }
-        ++tot;
-      }
-    }
-  }
-  cerr << "F = " << (double(cor + root_right) / (tot + totroot)) << endl;
-  return 0;
-}
-