From fa80a23079d642a3e984712c9dfa9ac47d2457fa Mon Sep 17 00:00:00 2001
From: Chris Dyer <cdyer@cs.cmu.edu>
Date: Mon, 16 Apr 2012 22:42:24 -0400
Subject: refactor some code, simplify, fix typos

---
 rst_parser/rst_parse.cc | 126 ------------------------------------------------
 1 file changed, 126 deletions(-)
 delete mode 100644 rst_parser/rst_parse.cc

(limited to 'rst_parser/rst_parse.cc')

diff --git a/rst_parser/rst_parse.cc b/rst_parser/rst_parse.cc
deleted file mode 100644
index 9cc1359a..00000000
--- a/rst_parser/rst_parse.cc
+++ /dev/null
@@ -1,126 +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 "arc_ff_factory.h"
-#include "dep_training.h"
-#include "stringlib.h"
-#include "filelib.h"
-#include "tdict.h"
-#include "weights.h"
-#include "rst.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)")
-        ("feature_function,F",po::value<vector<string> >()->composing(), "feature function (multiple permitted)")
-        ("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);
-  ArcFactoredForest af(5);
-  ArcFFRegistry reg;
-  reg.Register("DistancePenalty", new ArcFFFactory<DistancePenalty>);
-  vector<TrainingInstance> corpus;
-  vector<boost::shared_ptr<ArcFeatureFunction> > ffs;
-  ffs.push_back(boost::shared_ptr<ArcFeatureFunction>(new DistancePenalty("")));
-  TrainingInstance::ReadTraining(conf["training_data"].as<string>(), &corpus);
-  vector<ArcFactoredForest> forests(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; }
-    for (int fi = 0; fi < ffs.size(); ++fi) {
-      ArcFeatureFunction& ff = *ffs[fi];
-      ff.PrepareForInput(cur.ts);
-      SparseVector<weight_t> efmap;
-      for (int j = 0; j < cur.tree.h_m_pairs.size(); ++j) {
-        efmap.clear();
-        ff.EgdeFeatures(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();
-        ff.EgdeFeatures(cur.ts, -1, cur.tree.roots[j], &efmap);
-        cur.features += efmap;
-      }
-    }
-    empirical += cur.features;
-    forests[i].resize(cur.ts.words.size());
-    forests[i].ExtractFeatures(cur.ts, ffs);
-  }
-  if (flag) cerr << endl;
-  vector<weight_t> weights(FD::NumFeats(), 0.0);
-  Weights::InitFromFile(conf["q_weights"].as<string>(), &weights);
-  MT19937 rng;
-  SparseVector<double> model_exp;
-  SparseVector<double> sampled_exp;
-  int samples = conf["samples"].as<unsigned>();
-  for (int i = 0; i < corpus.size(); ++i) {
-    const int num_words = corpus[i].ts.words.size();
-    forests[i].Reweight(weights);
-    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);
-    TreeSampler ts(forests[i]);
-    sampled_exp.clear();
-      //ostringstream os; os << "Samples_" << samples;
-      //Timer t(os.str());
-      for (int n = 0; n < samples; ++n) {
-        EdgeSubset tree;
-        ts.SampleRandomSpanningTree(&tree, &rng);
-        SparseVector<double> feats;
-        tree.ExtractFeatures(corpus[i].ts, ffs, &feats);
-        sampled_exp += feats;
-      }
-      sampled_exp /= samples;
-      cerr << "L2 norm of diff @ " << samples << " samples: " << (model_exp - sampled_exp).l2norm() << endl;
-  }
-  return 0;
-}
-
-- 
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