Merge branch 'master' of https://github.com/redpony/cdec

1 files changed, 0 insertions, 275 deletions

diff --git a/gi/pf/itg.cc b/gi/pf/itg.cc
deleted file mode 100644
index 29ec3860..00000000
--- a/gi/pf/itg.cc
+++ /dev/null
@@ -1,275 +0,0 @@
-#include <iostream>
-#include <tr1/memory>
-#include <queue>
-
-#include <boost/functional.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "viterbi.h"
-#include "hg.h"
-#include "trule.h"
-#include "tdict.h"
-#include "filelib.h"
-#include "dict.h"
-#include "sampler.h"
-#include "ccrp_nt.h"
-#include "ccrp_onetable.h"
-
-using namespace std;
-using namespace tr1;
-namespace po = boost::program_options;
-
-ostream& operator<<(ostream& os, const vector<WordID>& p) {
-  os << '[';
-  for (int i = 0; i < p.size(); ++i)
-    os << (i==0 ? "" : " ") << TD::Convert(p[i]);
-  return os << ']';
-}
-
-struct UnigramModel {
-  explicit UnigramModel(const string& fname, unsigned vocab_size, double p0null = 0.05) :
-      use_uniform_(fname.size() == 0),
-      p0null_(p0null),
-      uniform_((1.0 - p0null) / vocab_size),
-      probs_(TD::NumWords() + 1) {
-    if (fname.size() > 0) LoadUnigrams(fname);
-    probs_[0] = p0null_;
-  }
-
-// 
-// \data\
-// ngram 1=9295
-// 
-// \1-grams:
-// -3.191193	"
-
-  void LoadUnigrams(const string& fname) {
-    cerr << "Loading unigram probabilities from " << fname << " ..." << endl;
-    ReadFile rf(fname);
-    string line;
-    istream& in = *rf.stream();
-    assert(in);
-    getline(in, line);
-    assert(line.empty());
-    getline(in, line);
-    assert(line == "\\data\\");
-    getline(in, line);
-    size_t pos = line.find("ngram 1=");
-    assert(pos == 0);
-    assert(line.size() > 8);
-    const size_t num_unigrams = atoi(&line[8]);
-    getline(in, line);
-    assert(line.empty());
-    getline(in, line);
-    assert(line == "\\1-grams:");
-    for (size_t i = 0; i < num_unigrams; ++i) {
-      getline(in, line);
-      assert(line.size() > 0);
-      pos = line.find('\t');
-      assert(pos > 0);
-      assert(pos + 1 < line.size());
-      const WordID w = TD::Convert(line.substr(pos + 1));
-      line[pos] = 0;
-      float p = atof(&line[0]);
-      const prob_t pnon_null(1.0 - p0null_.as_float());
-      if (w < probs_.size()) probs_[w].logeq(p * log(10) + log(pnon_null)); else abort();
-    }
-  }
-
-  const prob_t& operator()(const WordID& w) const {
-    if (!w) return p0null_;
-    if (use_uniform_) return uniform_;
-    return probs_[w];
-  }
-
-  const bool use_uniform_;
-  const prob_t p0null_;
-  const prob_t uniform_;
-  vector<prob_t> probs_;
-};
-
-struct Model1 {
-  explicit Model1(const string& fname) :
-      kNULL(TD::Convert("<eps>")),
-      kZERO() {
-    LoadModel1(fname);
-  }
-
-  void LoadModel1(const string& fname) {
-    cerr << "Loading Model 1 parameters from " << fname << " ..." << endl;
-    ReadFile rf(fname);
-    istream& in = *rf.stream();
-    string line;
-    unsigned lc = 0;
-    while(getline(in, line)) {
-      ++lc;
-      int cur = 0;
-      int start = 0;
-      while(cur < line.size() && line[cur] != ' ') { ++cur; }
-      assert(cur != line.size());
-      line[cur] = 0;
-      const WordID src = TD::Convert(&line[0]);
-      ++cur;
-      start = cur;
-      while(cur < line.size() && line[cur] != ' ') { ++cur; }
-      assert(cur != line.size());
-      line[cur] = 0;
-      WordID trg = TD::Convert(&line[start]);
-      const double logprob = strtod(&line[cur + 1], NULL);
-      if (src >= ttable.size()) ttable.resize(src + 1);
-      ttable[src][trg].logeq(logprob);
-    }
-    cerr << "  read " << lc << " parameters.\n";
-  }
-
-  // returns prob 0 if src or trg is not found!
-  const prob_t& operator()(WordID src, WordID trg) const {
-    if (src == 0) src = kNULL;
-    if (src < ttable.size()) {
-      const map<WordID, prob_t>& cpd = ttable[src];
-      const map<WordID, prob_t>::const_iterator it = cpd.find(trg);
-      if (it != cpd.end())
-        return it->second;
-    }
-    return kZERO;
-  }
-
-  const WordID kNULL;
-  const prob_t kZERO;
-  vector<map<WordID, prob_t> > ttable;
-};
-
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
-  po::options_description opts("Configuration options");
-  opts.add_options()
-        ("samples,s",po::value<unsigned>()->default_value(1000),"Number of samples")
-        ("particles,p",po::value<unsigned>()->default_value(25),"Number of particles")
-        ("input,i",po::value<string>(),"Read parallel data from")
-        ("model1,m",po::value<string>(),"Model 1 parameters (used in base distribution)")
-        ("inverse_model1,M",po::value<string>(),"Inverse Model 1 parameters (used in backward estimate)")
-        ("model1_interpolation_weight",po::value<double>()->default_value(0.95),"Mixing proportion of model 1 with uniform target distribution")
-        ("src_unigram,u",po::value<string>()->default_value(""),"Source unigram distribution; empty for uniform")
-        ("trg_unigram,U",po::value<string>()->default_value(""),"Target unigram distribution; empty for uniform")
-        ("random_seed,S",po::value<uint32_t>(), "Random seed");
-  po::options_description clo("Command line options");
-  clo.add_options()
-        ("config", po::value<string>(), "Configuration file")
-        ("help,h", "Print this help message and exit");
-  po::options_description dconfig_options, dcmdline_options;
-  dconfig_options.add(opts);
-  dcmdline_options.add(opts).add(clo);
-  
-  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
-  if (conf->count("config")) {
-    ifstream config((*conf)["config"].as<string>().c_str());
-    po::store(po::parse_config_file(config, dconfig_options), *conf);
-  }
-  po::notify(*conf);
-
-  if (conf->count("help") || (conf->count("input") == 0)) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-void ReadParallelCorpus(const string& filename,
-                vector<vector<WordID> >* f,
-                vector<vector<WordID> >* e,
-                set<WordID>* vocab_f,
-                set<WordID>* vocab_e) {
-  f->clear();
-  e->clear();
-  vocab_f->clear();
-  vocab_e->clear();
-  istream* in;
-  if (filename == "-")
-    in = &cin;
-  else
-    in = new ifstream(filename.c_str());
-  assert(*in);
-  string line;
-  const WordID kDIV = TD::Convert("|||");
-  vector<WordID> tmp;
-  while(*in) {
-    getline(*in, line);
-    if (line.empty() && !*in) break;
-    e->push_back(vector<int>());
-    f->push_back(vector<int>());
-    vector<int>& le = e->back();
-    vector<int>& lf = f->back();
-    tmp.clear();
-    TD::ConvertSentence(line, &tmp);
-    bool isf = true;
-    for (unsigned i = 0; i < tmp.size(); ++i) {
-      const int cur = tmp[i];
-      if (isf) {
-        if (kDIV == cur) { isf = false; } else {
-          lf.push_back(cur);
-          vocab_f->insert(cur);
-        }
-      } else {
-        assert(cur != kDIV);
-        le.push_back(cur);
-        vocab_e->insert(cur);
-      }
-    }
-    assert(isf == false);
-  }
-  if (in != &cin) delete in;
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-  const unsigned particles = conf["particles"].as<unsigned>();
-  const unsigned samples = conf["samples"].as<unsigned>();
-  TD::Convert("<s>");
-  TD::Convert("</s>");
-  TD::Convert("<unk>");
-  if (!conf.count("model1")) {
-    cerr << argv[0] << "Please use --model1 to specify model 1 parameters\n";
-    return 1;
-  }
-  boost::shared_ptr<MT19937> prng;
-  if (conf.count("random_seed"))
-    prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
-  else
-    prng.reset(new MT19937);
-  MT19937& rng = *prng;
-
-  vector<vector<WordID> > corpuse, corpusf;
-  set<WordID> vocabe, vocabf;
-  cerr << "Reading corpus...\n";
-  ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe);
-  cerr << "F-corpus size: " << corpusf.size() << " sentences\t (" << vocabf.size() << " word types)\n";
-  cerr << "E-corpus size: " << corpuse.size() << " sentences\t (" << vocabe.size() << " word types)\n";
-  assert(corpusf.size() == corpuse.size());
-  UnigramModel src_unigram(conf["src_unigram"].as<string>(), vocabf.size());
-  UnigramModel trg_unigram(conf["trg_unigram"].as<string>(), vocabe.size());
-  const prob_t kHALF(0.5);
-
-  const string kEMPTY = "NULL";
-  const int kLHS = -TD::Convert("X");
-  Model1 m1(conf["model1"].as<string>());
-  Model1 invm1(conf["inverse_model1"].as<string>());
-  for (int si = 0; si < conf["samples"].as<unsigned>(); ++si) {
-    cerr << '.' << flush;
-    for (int ci = 0; ci < corpusf.size(); ++ci) {
-      const vector<WordID>& trg = corpuse[ci];
-      const vector<WordID>& src = corpusf[ci];
-      for (int i = 0; i <= trg.size(); ++i) {
-        const WordID e_i = i > 0 ? trg[i-1] : 0;
-        for (int j = 0; j <= src.size(); ++j) {
-          const WordID f_j = j > 0 ? src[j-1] : 0;
-          if (e_i == 0 && f_j == 0) continue;
-          prob_t je = kHALF * src_unigram(f_j) * m1(f_j,e_i) + kHALF * trg_unigram(e_i) * invm1(e_i,f_j);
-          cerr << "p( " << (e_i ? TD::Convert(e_i) : kEMPTY) << " , " << (f_j ? TD::Convert(f_j) : kEMPTY) << " ) = " << log(je) << endl;
-          if (e_i && f_j)
-            cout << "[X] ||| " << TD::Convert(f_j) << " ||| " << TD::Convert(e_i) << " ||| LogProb=" << log(je) << endl;
-        }
-      }
-    }
-  }
-}
-