cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 144 deletions

diff --git a/rst_parser/rst_train.cc b/rst_parser/rst_train.cc
deleted file mode 100644
index a8b8dd84..00000000
--- a/rst_parser/rst_train.cc
+++ /dev/null
@@ -1,144 +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()
-        ("training_data,t",po::value<string>()->default_value("-"), "File containing training data (jsent format)")
-        ("q_weights,q",po::value<string>(), "Arc-factored weights for proposal distribution")
-        ("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")) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-  vector<weight_t> qweights(FD::NumFeats(), 0.0);
-  Weights::InitFromFile(conf["q_weights"].as<string>(), &qweights);
-  vector<TrainingInstance> corpus;
-  ArcFeatureFunctions ffs;
-  GlobalFeatureFunctions gff;
-  TrainingInstance::ReadTrainingCorpus(conf["training_data"].as<string>(), &corpus);
-  vector<ArcFactoredForest> forests(corpus.size());
-  vector<prob_t> zs(corpus.size());
-  SparseVector<double> empirical;
-  bool flag = false;
-  for (int i = 0; i < corpus.size(); ++i) {
-    TrainingInstance& cur = corpus[i];
-    if ((i+1) % 10 == 0) { cerr << '.' << flush; flag = true; }
-    if ((i+1) % 400 == 0) { cerr << " [" << (i+1) << "]\n"; flag = false; }
-    SparseVector<weight_t> efmap;
-    ffs.PrepareForInput(cur.ts);
-    gff.PrepareForInput(cur.ts);
-    for (int j = 0; j < cur.tree.h_m_pairs.size(); ++j) {
-      efmap.clear();
-      ffs.EdgeFeatures(cur.ts, cur.tree.h_m_pairs[j].first,
-                       cur.tree.h_m_pairs[j].second,
-                       &efmap);
-      cur.features += efmap;
-    }
-    for (int j = 0; j < cur.tree.roots.size(); ++j) {
-      efmap.clear();
-      ffs.EdgeFeatures(cur.ts, -1, cur.tree.roots[j], &efmap);
-      cur.features += efmap;
-    }
-    efmap.clear();
-    gff.Features(cur.ts, cur.tree, &efmap);
-    cur.features += efmap;
-    empirical += cur.features;
-    forests[i].resize(cur.ts.words.size());
-    forests[i].ExtractFeatures(cur.ts, ffs);
-    forests[i].Reweight(qweights);
-    forests[i].EdgeMarginals(&zs[i]);
-    zs[i] = prob_t::One() / zs[i];
-    // cerr << zs[i] << endl;
-    forests[i].Reweight(qweights);    // EdgeMarginals overwrites edge_prob
-  }
-  if (flag) cerr << endl;
-  MT19937 rng;
-  SparseVector<double> model_exp;
-  SparseVector<double> weights;
-  Weights::InitSparseVector(qweights, &weights);
-  int samples = conf["samples"].as<unsigned>();
-  for (int i = 0; i < corpus.size(); ++i) {
-#if 0
-    forests[i].EdgeMarginals();
-    model_exp.clear();
-    for (int h = -1; h < num_words; ++h) {
-      for (int m = 0; m < num_words; ++m) {
-        if (h == m) continue;
-        const ArcFactoredForest::Edge& edge = forests[i](h,m);
-        const SparseVector<weight_t>& fmap = edge.features;
-        double prob = edge.edge_prob.as_float();
-        model_exp += fmap * prob;
-      }
-    }
-    cerr << "TRUE EXP: " << model_exp << endl;
-    forests[i].Reweight(weights);
-#endif
-
-    TreeSampler ts(forests[i]);
-    prob_t zhat = prob_t::Zero();
-    SparseVector<prob_t> sampled_exp;
-    for (int n = 0; n < samples; ++n) {
-      EdgeSubset tree;
-      ts.SampleRandomSpanningTree(&tree, &rng);
-      SparseVector<double> qfeats, gfeats;
-      tree.ExtractFeatures(corpus[i].ts, ffs, &qfeats);
-      prob_t u; u.logeq(qfeats.dot(qweights));
-      const prob_t q = u / zs[i];  // proposal mass
-      gff.Features(corpus[i].ts, tree, &gfeats);
-      SparseVector<double> tot_feats = qfeats + gfeats;
-      u.logeq(tot_feats.dot(weights));
-      prob_t w = u / q;
-      zhat += w;
-      for (SparseVector<double>::iterator it = tot_feats.begin(); it != tot_feats.end(); ++it)
-        sampled_exp.add_value(it->first, w * prob_t(it->second));
-    }
-    sampled_exp /= zhat;
-    SparseVector<double> tot_m;
-    for (SparseVector<prob_t>::iterator it = sampled_exp.begin(); it != sampled_exp.end(); ++it)
-      tot_m.add_value(it->first, it->second.as_float());
-    //cerr << "DIFF: " << (tot_m - corpus[i].features) << endl;
-    const double eta = 0.03;
-    weights -= (tot_m - corpus[i].features) * eta;
-  }
-  cerr << "WEIGHTS.\n";
-  cerr << weights << endl;
-  return 0;
-}
-