add generative word alignment model and primitive EM trainer. Model 1 and HMM are supported, without NULL source words

1 files changed, 0 insertions, 270 deletions

diff --git a/training/mr_em_train.cc b/training/mr_em_train.cc
deleted file mode 100644
index a15fbe4c..00000000
--- a/training/mr_em_train.cc
+++ /dev/null
@@ -1,270 +0,0 @@
-#include <iostream>
-#include <vector>
-#include <cassert>
-#include <cmath>
-
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "config.h"
-#ifdef HAVE_BOOST_DIGAMMA
-#include <boost/math/special_functions/digamma.hpp>
-using boost::math::digamma;
-#endif
-
-#include "tdict.h"
-#include "filelib.h"
-#include "trule.h"
-#include "fdict.h"
-#include "weights.h"
-#include "sparse_vector.h"
-
-using namespace std;
-using boost::shared_ptr;
-namespace po = boost::program_options;
-
-#ifndef HAVE_BOOST_DIGAMMA
-#warning Using Mark Johnson's digamma()
-double digamma(double x) {
-  double result = 0, xx, xx2, xx4;
-  assert(x > 0);
-  for ( ; x < 7; ++x)
-    result -= 1/x;
-  x -= 1.0/2.0;
-  xx = 1.0/x;
-  xx2 = xx*xx;
-  xx4 = xx2*xx2;
-  result += log(x)+(1./24.)*xx2-(7.0/960.0)*xx4+(31.0/8064.0)*xx4*xx2-(127.0/30720.0)*xx4*xx4;
-  return result;
-}
-#endif
-
-void SanityCheck(const vector<double>& w) {
-  for (int i = 0; i < w.size(); ++i) {
-    assert(!isnan(w[i]));
-  }
-}
-
-struct FComp {
-  const vector<double>& w_;
-  FComp(const vector<double>& w) : w_(w) {}
-  bool operator()(int a, int b) const {
-    return w_[a] > w_[b];
-  }
-};
-
-void ShowLargestFeatures(const vector<double>& w) {
-  vector<int> fnums(w.size() - 1);
-  for (int i = 1; i < w.size(); ++i)
-    fnums[i-1] = i;
-  vector<int>::iterator mid = fnums.begin();
-  mid += (w.size() > 10 ? 10 : w.size()) - 1;
-  partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
-  cerr << "MOST PROBABLE:";
-  for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
-    cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
-  }
-  cerr << endl;
-}
-
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
-  po::options_description opts("Configuration options");
-  opts.add_options()
-        ("output,o",po::value<string>()->default_value("-"),"Output log probs file")
-        ("grammar,g",po::value<vector<string> >()->composing(),"SCFG grammar file(s)")
-        ("optimization_method,m", po::value<string>()->default_value("em"), "Optimization method (em, vb)")
-        ("input_format,f",po::value<string>()->default_value("b64"),"Encoding of the input (b64 or text)");
-  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("grammar")) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-// describes a multinomial or multinomial with a prior
-// does not contain the parameters- just the list of events
-// and any hyperparameters
-struct MultinomialInfo {
- MultinomialInfo() : alpha(1.0) {}
- vector<int> events;      // the events that this multinomial generates
- double alpha;            // hyperparameter for (optional) Dirichlet prior
-};
-
-typedef map<WordID, MultinomialInfo> ModelDefinition;
-
-void LoadModelEvents(const po::variables_map& conf, ModelDefinition* pm) {
-  ModelDefinition& m = *pm;
-  m.clear();
-  vector<string> gfiles = conf["grammar"].as<vector<string> >();
-  for (int i = 0; i < gfiles.size(); ++i) {
-    ReadFile rf(gfiles[i]);
-    istream& in = *rf.stream();
-    int lc = 0;
-    while(in) {
-      string line;
-      getline(in, line);
-      if (line.empty()) continue;
-      ++lc;
-      TRule r(line, true);
-      const SparseVector<double>& f = r.GetFeatureValues();
-      if (f.num_active() == 0) {
-        cerr << "[WARNING] no feature found in " << gfiles[i] << ':' << lc << endl;
-        continue;
-      }
-      if (f.num_active() > 1) {
-        cerr << "[ERROR] more than one feature found in " << gfiles[i] << ':' << lc << endl;
-        exit(1);
-      }
-      SparseVector<double>::const_iterator it = f.begin();
-      if (it->second != 1.0) {
-        cerr << "[ERROR] feature with value != 1 found in " << gfiles[i] << ':' << lc << endl;
-        exit(1);
-      }
-      m[r.GetLHS()].events.push_back(it->first);
-    }
-  }
-  for (ModelDefinition::iterator it = m.begin(); it != m.end(); ++it) {
-    const vector<int>& v = it->second.events;
-    cerr << "Multinomial [" << TD::Convert(it->first*-1) << "]\n";
-    if (v.size() < 1000) {
-      cerr << "  generates:";    
-      for (int i = 0; i < v.size(); ++i) {
-        cerr << " " << FD::Convert(v[i]);
-      }
-      cerr << endl;
-    }
-  }
-}
-
-void Maximize(const ModelDefinition& m, const bool use_vb, vector<double>* counts) {
-  for (ModelDefinition::const_iterator it = m.begin(); it != m.end(); ++it) {
-    const MultinomialInfo& mult_info = it->second;
-    const vector<int>& events = mult_info.events;
-    cerr << "Multinomial [" << TD::Convert(it->first*-1) << "]";
-    double tot = 0;
-    for (int i = 0; i < events.size(); ++i)
-      tot += (*counts)[events[i]];
-    cerr << " = " << tot << endl;
-    assert(tot > 0.0);
-    double ltot = log(tot);
-    if (use_vb)
-      ltot = digamma(tot + events.size() * mult_info.alpha);
-    for (int i = 0; i < events.size(); ++i) {
-      if (use_vb) {
-        (*counts)[events[i]] = digamma((*counts)[events[i]] + mult_info.alpha) - ltot;
-      } else {
-        (*counts)[events[i]] = log((*counts)[events[i]]) - ltot;
-      }
-    }
-    if (events.size() < 50) {
-      for (int i = 0; i < events.size(); ++i) {
-        cerr << " p(" << FD::Convert(events[i]) << ")=" << exp((*counts)[events[i]]);
-      }
-      cerr << endl;
-    }
-  }
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-
-  const bool use_b64 = conf["input_format"].as<string>() == "b64";
-  const bool use_vb = conf["optimization_method"].as<string>() == "vb";
-  if (use_vb)
-    cerr << "Using variational Bayes, make sure alphas are set\n";
-
-  ModelDefinition model_def;
-  LoadModelEvents(conf, &model_def);
-
-  const string s_obj = "**OBJ**";
-  int num_feats = FD::NumFeats();
-  cerr << "Number of features: " << num_feats << endl;
-
-  vector<double> counts(num_feats, 0);
-  double logprob = 0;
-  // 0<TAB>**OBJ**=12.2;Feat1=2.3;Feat2=-0.2;
-  // 0<TAB>**OBJ**=1.1;Feat1=1.0;
-
-  // E-step
-  while(cin) {
-    string line;
-    getline(cin, line);
-    if (line.empty()) continue;
-    int feat;
-    double val;
-    size_t i = line.find("\t");
-    assert(i != string::npos);
-    ++i;
-    if (use_b64) {
-      SparseVector<double> g;
-      double obj;
-      if (!B64::Decode(&obj, &g, &line[i], line.size() - i)) {
-        cerr << "B64 decoder returned error, skipping!\n";
-        continue;
-      }
-      logprob += obj;
-      const SparseVector<double>& cg = g;
-      for (SparseVector<double>::const_iterator it = cg.begin(); it != cg.end(); ++it) {
-        if (it->first >= num_feats) {
-	  cerr << "Unexpected feature: " << FD::Convert(it->first) << endl;
-	  abort();
-        }
-        counts[it->first] += it->second;
-      }
-    } else {       // text encoding - your counts will not be accurate!
-      while (i < line.size()) {
-        size_t start = i;
-        while (line[i] != '=' && i < line.size()) ++i;
-        if (i == line.size()) { cerr << "FORMAT ERROR\n"; break; }
-        string fname = line.substr(start, i - start);
-        if (fname == s_obj) {
-          feat = -1;
-        } else {
-          feat = FD::Convert(line.substr(start, i - start));
-          if (feat >= num_feats) {
-	    cerr << "Unexpected feature: " << line.substr(start, i - start) << endl;
-	    abort();
-	  }
-        }
-        ++i;
-        start = i;
-        while (line[i] != ';' && i < line.size()) ++i;
-        if (i - start == 0) continue;
-        val = atof(line.substr(start, i - start).c_str());
-        ++i;
-        if (feat == -1) {
-          logprob += val;
-        } else {
-          counts[feat] += val;
-        }
-      }
-    }
-  }
-
-  cerr << "LOGPROB: " << logprob << endl;
-  // M-step
-  Maximize(model_def, use_vb, &counts);
-
-  SanityCheck(counts);
-  ShowLargestFeatures(counts);
-  Weights weights;
-  weights.InitFromVector(counts);
-  weights.WriteToFile(conf["output"].as<string>(), false);
-
-  return 0;
-}