Merge pull request #63 from kho/const_reorder_2

Soft linguistic reordering constraints from http://www.aclweb.org/anthology/P14-1106

1 files changed, 1127 insertions, 0 deletions

diff --git a/utils/maxent.cpp b/utils/maxent.cpp
new file mode 100644
index 00000000..fd772e08
--- /dev/null
+++ b/utils/maxent.cpp
@@ -0,0 +1,1127 @@
+/*
+ * $Id: maxent.cpp,v 1.1.1.1 2007/05/15 08:30:35 kyoshida Exp $
+ */
+
+#include "maxent.h"
+
+#include <vector>
+#include <iostream>
+#include <cmath>
+#include <cstdio>
+
+using namespace std;
+
+namespace maxent {
+double ME_Model::FunctionGradient(const vector<double>& x,
+                                  vector<double>& grad) {
+  assert((int)_fb.Size() == x.size());
+  for (size_t i = 0; i < x.size(); i++) {
+    _vl[i] = x[i];
+  }
+
+  double score = update_model_expectation();
+
+  if (_l2reg == 0) {
+    for (size_t i = 0; i < x.size(); i++) {
+      grad[i] = -(_vee[i] - _vme[i]);
+    }
+  } else {
+    const double c = _l2reg * 2;
+    for (size_t i = 0; i < x.size(); i++) {
+      grad[i] = -(_vee[i] - _vme[i] - c * _vl[i]);
+    }
+  }
+
+  return -score;
+}
+
+int ME_Model::perform_GIS(int C) {
+  cerr << "C = " << C << endl;
+  C = 1;
+  cerr << "performing AGIS" << endl;
+  vector<double> pre_v;
+  double pre_logl = -999999;
+  for (int iter = 0; iter < 200; iter++) {
+
+    double logl = update_model_expectation();
+    fprintf(stderr, "iter = %2d  C = %d  f = %10.7f  train_err = %7.5f", iter,
+            C, logl, _train_error);
+    if (_heldout.size() > 0) {
+      double hlogl = heldout_likelihood();
+      fprintf(stderr, "  heldout_logl(err) = %f (%6.4f)", hlogl,
+              _heldout_error);
+    }
+    cerr << endl;
+
+    if (logl < pre_logl) {
+      C += 1;
+      _vl = pre_v;
+      iter--;
+      continue;
+    }
+    if (C > 1 && iter % 10 == 0) C--;
+
+    pre_logl = logl;
+    pre_v = _vl;
+    for (int i = 0; i < _fb.Size(); i++) {
+      double coef = _vee[i] / _vme[i];
+      _vl[i] += log(coef) / C;
+    }
+  }
+  cerr << endl;
+
+  return 0;
+}
+
+int ME_Model::perform_QUASI_NEWTON() {
+  const int dim = _fb.Size();
+  vector<double> x0(dim);
+
+  for (int i = 0; i < dim; i++) {
+    x0[i] = _vl[i];
+  }
+
+  vector<double> x;
+  if (_l1reg > 0) {
+    cerr << "performing OWLQN" << endl;
+    x = perform_OWLQN(x0, _l1reg);
+  } else {
+    cerr << "performing LBFGS" << endl;
+    x = perform_LBFGS(x0);
+  }
+
+  for (int i = 0; i < dim; i++) {
+    _vl[i] = x[i];
+  }
+
+  return 0;
+}
+
+int ME_Model::conditional_probability(const Sample& s,
+                                      std::vector<double>& membp) const {
+  // int num_classes = membp.size();
+  double sum = 0;
+  int max_label = 0;
+  //  double maxp = 0;
+
+  vector<double> powv(_num_classes, 0.0);
+  for (vector<int>::const_iterator j = s.positive_features.begin();
+       j != s.positive_features.end(); j++) {
+    for (vector<int>::const_iterator k = _feature2mef[*j].begin();
+         k != _feature2mef[*j].end(); k++) {
+      powv[_fb.Feature(*k).label()] += _vl[*k];
+    }
+  }
+  for (vector<pair<int, double> >::const_iterator j = s.rvfeatures.begin();
+       j != s.rvfeatures.end(); j++) {
+    for (vector<int>::const_iterator k = _feature2mef[j->first].begin();
+         k != _feature2mef[j->first].end(); k++) {
+      powv[_fb.Feature(*k).label()] += _vl[*k] * j->second;
+    }
+  }
+
+  std::vector<double>::const_iterator pmax =
+      max_element(powv.begin(), powv.end());
+  double offset = max(0.0, *pmax - 700);  // to avoid overflow
+  for (int label = 0; label < _num_classes; label++) {
+    double pow = powv[label] - offset;
+    double prod = exp(pow);
+    //      cout << pow << " " << prod << ", ";
+    //      if (_ref_modelp != NULL) prod *= _train_refpd[n][label];
+    if (_ref_modelp != NULL) prod *= s.ref_pd[label];
+    assert(prod != 0);
+    membp[label] = prod;
+    sum += prod;
+  }
+  for (int label = 0; label < _num_classes; label++) {
+    membp[label] /= sum;
+    if (membp[label] > membp[max_label]) max_label = label;
+  }
+  return max_label;
+}
+
+int ME_Model::make_feature_bag(const int cutoff) {
+  int max_num_features = 0;
+
+// count the occurrences of features
+#ifdef USE_HASH_MAP
+  typedef std::unordered_map<unsigned int, int> map_type;
+#else
+  typedef std::map<unsigned int, int> map_type;
+#endif
+  map_type count;
+  if (cutoff > 0) {
+    for (std::vector<Sample>::const_iterator i = _vs.begin(); i != _vs.end();
+         i++) {
+      for (std::vector<int>::const_iterator j = i->positive_features.begin();
+           j != i->positive_features.end(); j++) {
+        count[ME_Feature(i->label, *j).body()]++;
+      }
+      for (std::vector<pair<int, double> >::const_iterator j =
+               i->rvfeatures.begin();
+           j != i->rvfeatures.end(); j++) {
+        count[ME_Feature(i->label, j->first).body()]++;
+      }
+    }
+  }
+
+  int n = 0;
+  for (std::vector<Sample>::const_iterator i = _vs.begin(); i != _vs.end();
+       i++, n++) {
+    max_num_features =
+        max(max_num_features, (int)(i->positive_features.size()));
+    for (std::vector<int>::const_iterator j = i->positive_features.begin();
+         j != i->positive_features.end(); j++) {
+      const ME_Feature feature(i->label, *j);
+      //      if (cutoff > 0 && count[feature.body()] < cutoff) continue;
+      if (cutoff > 0 && count[feature.body()] <= cutoff) continue;
+      _fb.Put(feature);
+      //      cout << i->label << "\t" << *j << "\t" << id << endl;
+      //      feature2sample[id].push_back(n);
+    }
+    for (std::vector<pair<int, double> >::const_iterator j =
+             i->rvfeatures.begin();
+         j != i->rvfeatures.end(); j++) {
+      const ME_Feature feature(i->label, j->first);
+      //      if (cutoff > 0 && count[feature.body()] < cutoff) continue;
+      if (cutoff > 0 && count[feature.body()] <= cutoff) continue;
+      _fb.Put(feature);
+    }
+  }
+  count.clear();
+
+  //  cerr << "num_classes = " << _num_classes << endl;
+  //  cerr << "max_num_features = " << max_num_features << endl;
+
+  init_feature2mef();
+
+  return max_num_features;
+}
+
+double ME_Model::heldout_likelihood() {
+  double logl = 0;
+  int ncorrect = 0;
+  for (std::vector<Sample>::const_iterator i = _heldout.begin();
+       i != _heldout.end(); i++) {
+    vector<double> membp(_num_classes);
+    int l = classify(*i, membp);
+    logl += log(membp[i->label]);
+    if (l == i->label) ncorrect++;
+  }
+  _heldout_error = 1 - (double)ncorrect / _heldout.size();
+
+  return logl /= _heldout.size();
+}
+
+double ME_Model::update_model_expectation() {
+  double logl = 0;
+  int ncorrect = 0;
+
+  _vme.resize(_fb.Size());
+  for (int i = 0; i < _fb.Size(); i++) _vme[i] = 0;
+
+  int n = 0;
+  for (vector<Sample>::const_iterator i = _vs.begin(); i != _vs.end();
+       i++, n++) {
+    vector<double> membp(_num_classes);
+    int max_label = conditional_probability(*i, membp);
+
+    logl += log(membp[i->label]);
+    //    cout << membp[*i] << " " << logl << " ";
+    if (max_label == i->label) ncorrect++;
+
+    // model_expectation
+    for (vector<int>::const_iterator j = i->positive_features.begin();
+         j != i->positive_features.end(); j++) {
+      for (vector<int>::const_iterator k = _feature2mef[*j].begin();
+           k != _feature2mef[*j].end(); k++) {
+        _vme[*k] += membp[_fb.Feature(*k).label()];
+      }
+    }
+    for (vector<pair<int, double> >::const_iterator j = i->rvfeatures.begin();
+         j != i->rvfeatures.end(); j++) {
+      for (vector<int>::const_iterator k = _feature2mef[j->first].begin();
+           k != _feature2mef[j->first].end(); k++) {
+        _vme[*k] += membp[_fb.Feature(*k).label()] * j->second;
+      }
+    }
+  }
+
+  for (int i = 0; i < _fb.Size(); i++) {
+    _vme[i] /= _vs.size();
+  }
+
+  _train_error = 1 - (double)ncorrect / _vs.size();
+
+  logl /= _vs.size();
+
+  if (_l2reg > 0) {
+    const double c = _l2reg;
+    for (int i = 0; i < _fb.Size(); i++) {
+      logl -= _vl[i] * _vl[i] * c;
+    }
+  }
+
+  // logl /= _vs.size();
+
+  //  fprintf(stderr, "iter =%3d  logl = %10.7f  train_acc = %7.5f\n", iter,
+  // logl, (double)ncorrect/train.size());
+  //  fprintf(stderr, "logl = %10.7f  train_acc = %7.5f\n", logl,
+  // (double)ncorrect/_train.size());
+
+  return logl;
+}
+
+int ME_Model::train(const vector<ME_Sample>& vms) {
+  _vs.clear();
+  for (vector<ME_Sample>::const_iterator i = vms.begin(); i != vms.end(); i++) {
+    add_training_sample(*i);
+  }
+
+  return train();
+}
+
+void ME_Model::add_training_sample(const ME_Sample& mes) {
+  Sample s;
+  s.label = _label_bag.Put(mes.label);
+  if (s.label > ME_Feature::MAX_LABEL_TYPES) {
+    cerr << "error: too many types of labels." << endl;
+    exit(1);
+  }
+  for (vector<string>::const_iterator j = mes.features.begin();
+       j != mes.features.end(); j++) {
+    s.positive_features.push_back(_featurename_bag.Put(*j));
+  }
+  for (vector<pair<string, double> >::const_iterator j = mes.rvfeatures.begin();
+       j != mes.rvfeatures.end(); j++) {
+    s.rvfeatures.push_back(
+        pair<int, double>(_featurename_bag.Put(j->first), j->second));
+  }
+  if (_ref_modelp != NULL) {
+    ME_Sample tmp = mes;
+    ;
+    s.ref_pd = _ref_modelp->classify(tmp);
+  }
+  //  cout << s.label << "\t";
+  //  for (vector<int>::const_iterator j = s.positive_features.begin(); j !=
+  // s.positive_features.end(); j++){
+  //    cout << *j << " ";
+  //  }
+  //  cout << endl;
+
+  _vs.push_back(s);
+}
+
+int ME_Model::train() {
+  if (_l1reg > 0 && _l2reg > 0) {
+    cerr << "error: L1 and L2 regularizers cannot be used simultaneously."
+         << endl;
+    return 0;
+  }
+  if (_vs.size() == 0) {
+    cerr << "error: no training data." << endl;
+    return 0;
+  }
+  if (_nheldout >= (int)_vs.size()) {
+    cerr << "error: too much heldout data. no training data is available."
+         << endl;
+    return 0;
+  }
+  //  if (_nheldout > 0) random_shuffle(_vs.begin(), _vs.end());
+
+  int max_label = 0;
+  for (std::vector<Sample>::const_iterator i = _vs.begin(); i != _vs.end();
+       i++) {
+    max_label = max(max_label, i->label);
+  }
+  _num_classes = max_label + 1;
+  if (_num_classes != _label_bag.Size()) {
+    cerr << "warning: _num_class != _label_bag.Size()" << endl;
+  }
+
+  if (_ref_modelp != NULL) {
+    cerr << "setting reference distribution...";
+    for (int i = 0; i < _ref_modelp->num_classes(); i++) {
+      _label_bag.Put(_ref_modelp->get_class_label(i));
+    }
+    _num_classes = _label_bag.Size();
+    for (vector<Sample>::iterator i = _vs.begin(); i != _vs.end(); i++) {
+      set_ref_dist(*i);
+    }
+    cerr << "done" << endl;
+  }
+
+  for (int i = 0; i < _nheldout; i++) {
+    _heldout.push_back(_vs.back());
+    _vs.pop_back();
+  }
+
+  sort(_vs.begin(), _vs.end());
+
+  int cutoff = 0;
+  if (cutoff > 0) cerr << "cutoff threshold = " << cutoff << endl;
+  if (_l1reg > 0) cerr << "L1 regularizer = " << _l1reg << endl;
+  if (_l2reg > 0) cerr << "L2 regularizer = " << _l2reg << endl;
+
+  // normalize
+  _l1reg /= _vs.size();
+  _l2reg /= _vs.size();
+
+  cerr << "preparing for estimation...";
+  make_feature_bag(cutoff);
+  //  _vs.clear();
+  cerr << "done" << endl;
+  cerr << "number of samples = " << _vs.size() << endl;
+  cerr << "number of features = " << _fb.Size() << endl;
+
+  cerr << "calculating empirical expectation...";
+  _vee.resize(_fb.Size());
+  for (int i = 0; i < _fb.Size(); i++) {
+    _vee[i] = 0;
+  }
+  for (int n = 0; n < (int)_vs.size(); n++) {
+    const Sample* i = &_vs[n];
+    for (vector<int>::const_iterator j = i->positive_features.begin();
+         j != i->positive_features.end(); j++) {
+      for (vector<int>::const_iterator k = _feature2mef[*j].begin();
+           k != _feature2mef[*j].end(); k++) {
+        if (_fb.Feature(*k).label() == i->label) _vee[*k] += 1.0;
+      }
+    }
+
+    for (vector<pair<int, double> >::const_iterator j = i->rvfeatures.begin();
+         j != i->rvfeatures.end(); j++) {
+      for (vector<int>::const_iterator k = _feature2mef[j->first].begin();
+           k != _feature2mef[j->first].end(); k++) {
+        if (_fb.Feature(*k).label() == i->label) _vee[*k] += j->second;
+      }
+    }
+  }
+  for (int i = 0; i < _fb.Size(); i++) {
+    _vee[i] /= _vs.size();
+  }
+  cerr << "done" << endl;
+
+  _vl.resize(_fb.Size());
+  for (int i = 0; i < _fb.Size(); i++) _vl[i] = 0.0;
+
+  if (_optimization_method == SGD) {
+    perform_SGD();
+  } else {
+    perform_QUASI_NEWTON();
+  }
+
+  int num_active = 0;
+  for (int i = 0; i < _fb.Size(); i++) {
+    if (_vl[i] != 0) num_active++;
+  }
+  cerr << "number of active features = " << num_active << endl;
+
+  return 0;
+}
+
+void ME_Model::get_features(list<pair<pair<string, string>, double> >& fl) {
+  fl.clear();
+  //  for (int i = 0; i < _fb.Size(); i++) {
+  //    ME_Feature f = _fb.Feature(i);
+  //    fl.push_back( make_pair(make_pair(_label_bag.Str(f.label()),
+  // _featurename_bag.Str(f.feature())), _vl[i]));
+  //  }
+  for (MiniStringBag::map_type::const_iterator i = _featurename_bag.begin();
+       i != _featurename_bag.end(); i++) {
+    for (int j = 0; j < _label_bag.Size(); j++) {
+      string label = _label_bag.Str(j);
+      string history = i->first;
+      int id = _fb.Id(ME_Feature(j, i->second));
+      if (id < 0) continue;
+      fl.push_back(make_pair(make_pair(label, history), _vl[id]));
+    }
+  }
+}
+
+void ME_Model::clear() {
+  _vl.clear();
+  _label_bag.Clear();
+  _featurename_bag.Clear();
+  _fb.Clear();
+  _feature2mef.clear();
+  _vee.clear();
+  _vme.clear();
+  _vs.clear();
+  _heldout.clear();
+}
+
+bool ME_Model::load_from_file(const string& filename) {
+  FILE* fp = fopen(filename.c_str(), "r");
+  if (!fp) {
+    cerr << "error: cannot open " << filename << "!" << endl;
+    return false;
+  }
+
+  _vl.clear();
+  _label_bag.Clear();
+  _featurename_bag.Clear();
+  _fb.Clear();
+  char buf[1024];
+  while (fgets(buf, 1024, fp)) {
+    string line(buf);
+    string::size_type t1 = line.find_first_of('\t');
+    string::size_type t2 = line.find_last_of('\t');
+    string classname = line.substr(0, t1);
+    string featurename = line.substr(t1 + 1, t2 - (t1 + 1));
+    float lambda;
+    string w = line.substr(t2 + 1);
+    sscanf(w.c_str(), "%f", &lambda);
+
+    int label = _label_bag.Put(classname);
+    int feature = _featurename_bag.Put(featurename);
+    _fb.Put(ME_Feature(label, feature));
+    _vl.push_back(lambda);
+  }
+
+  _num_classes = _label_bag.Size();
+
+  init_feature2mef();
+
+  fclose(fp);
+
+  return true;
+}
+
+void ME_Model::init_feature2mef() {
+  _feature2mef.clear();
+  for (int i = 0; i < _featurename_bag.Size(); i++) {
+    vector<int> vi;
+    for (int k = 0; k < _num_classes; k++) {
+      int id = _fb.Id(ME_Feature(k, i));
+      if (id >= 0) vi.push_back(id);
+    }
+    _feature2mef.push_back(vi);
+  }
+}
+
+bool ME_Model::load_from_array(const ME_Model_Data data[]) {
+  _vl.clear();
+  for (int i = 0;; i++) {
+    if (string(data[i].label) == "///") break;
+    int label = _label_bag.Put(data[i].label);
+    int feature = _featurename_bag.Put(data[i].feature);
+    _fb.Put(ME_Feature(label, feature));
+    _vl.push_back(data[i].weight);
+  }
+  _num_classes = _label_bag.Size();
+
+  init_feature2mef();
+
+  return true;
+}
+
+bool ME_Model::save_to_file(const string& filename, const double th) const {
+  FILE* fp = fopen(filename.c_str(), "w");
+  if (!fp) {
+    cerr << "error: cannot open " << filename << "!" << endl;
+    return false;
+  }
+
+  //  for (int i = 0; i < _fb.Size(); i++) {
+  //    if (_vl[i] == 0) continue; // ignore zero-weight features
+  //    ME_Feature f = _fb.Feature(i);
+  //    fprintf(fp, "%s\t%s\t%f\n", _label_bag.Str(f.label()).c_str(),
+  // _featurename_bag.Str(f.feature()).c_str(), _vl[i]);
+  //  }
+  for (MiniStringBag::map_type::const_iterator i = _featurename_bag.begin();
+       i != _featurename_bag.end(); i++) {
+    for (int j = 0; j < _label_bag.Size(); j++) {
+      string label = _label_bag.Str(j);
+      string history = i->first;
+      int id = _fb.Id(ME_Feature(j, i->second));
+      if (id < 0) continue;
+      if (_vl[id] == 0) continue;        // ignore zero-weight features
+      if (fabs(_vl[id]) < th) continue;  // cut off low-weight features
+      fprintf(fp, "%s\t%s\t%f\n", label.c_str(), history.c_str(), _vl[id]);
+    }
+  }
+
+  fclose(fp);
+
+  return true;
+}
+
+void ME_Model::set_ref_dist(Sample& s) const {
+  vector<double> v0 = s.ref_pd;
+  vector<double> v(_num_classes);
+  for (unsigned int i = 0; i < v.size(); i++) {
+    v[i] = 0;
+    string label = get_class_label(i);
+    int id_ref = _ref_modelp->get_class_id(label);
+    if (id_ref != -1) {
+      v[i] = v0[id_ref];
+    }
+    if (v[i] == 0) v[i] = 0.001;  // to avoid -inf logl
+  }
+  s.ref_pd = v;
+}
+
+int ME_Model::classify(const Sample& nbs, vector<double>& membp) const {
+  //  vector<double> membp(_num_classes);
+  assert(_num_classes == (int)membp.size());
+  conditional_probability(nbs, membp);
+  int max_label = 0;
+  double max = 0.0;
+  for (int i = 0; i < (int)membp.size(); i++) {
+    //    cout << membp[i] << " ";
+    if (membp[i] > max) {
+      max_label = i;
+      max = membp[i];
+    }
+  }
+  //  cout << endl;
+  return max_label;
+}
+
+vector<double> ME_Model::classify(ME_Sample& mes) const {
+  Sample s;
+  for (vector<string>::const_iterator j = mes.features.begin();
+       j != mes.features.end(); j++) {
+    int id = _featurename_bag.Id(*j);
+    if (id >= 0) s.positive_features.push_back(id);
+  }
+  for (vector<pair<string, double> >::const_iterator j = mes.rvfeatures.begin();
+       j != mes.rvfeatures.end(); j++) {
+    int id = _featurename_bag.Id(j->first);
+    if (id >= 0) {
+      s.rvfeatures.push_back(pair<int, double>(id, j->second));
+    }
+  }
+  if (_ref_modelp != NULL) {
+    s.ref_pd = _ref_modelp->classify(mes);
+    set_ref_dist(s);
+  }
+
+  vector<double> vp(_num_classes);
+  int label = classify(s, vp);
+  mes.label = get_class_label(label);
+  return vp;
+}
+
+// template<class FuncGrad>
+// std::vector<double>
+// perform_LBFGS(FuncGrad func_grad, const std::vector<double> & x0);
+
+std::vector<double> perform_LBFGS(
+    double (*func_grad)(const std::vector<double> &, std::vector<double> &),
+    const std::vector<double> &x0);
+
+std::vector<double> perform_OWLQN(
+    double (*func_grad)(const std::vector<double> &, std::vector<double> &),
+    const std::vector<double> &x0, const double C);
+
+const int LBFGS_M = 10;
+
+const static int M = LBFGS_M;
+const static double LINE_SEARCH_ALPHA = 0.1;
+const static double LINE_SEARCH_BETA = 0.5;
+
+// stopping criteria
+int LBFGS_MAX_ITER = 300;
+const static double MIN_GRAD_NORM = 0.0001;
+
+// LBFGS
+
+double ME_Model::backtracking_line_search(const Vec& x0, const Vec& grad0,
+                                          const double f0, const Vec& dx,
+                                          Vec& x, Vec& grad1) {
+  double t = 1.0 / LINE_SEARCH_BETA;
+
+  double f;
+  do {
+    t *= LINE_SEARCH_BETA;
+    x = x0 + t * dx;
+    f = FunctionGradient(x.STLVec(), grad1.STLVec());
+    //        cout << "*";
+  } while (f > f0 + LINE_SEARCH_ALPHA * t * dot_product(dx, grad0));
+
+  return f;
+}
+
+//
+// Jorge Nocedal, "Updating Quasi-Newton Matrices With Limited Storage",
+// Mathematics of Computation, Vol. 35, No. 151, pp. 773-782, 1980.
+//
+Vec approximate_Hg(const int iter, const Vec& grad, const Vec s[],
+                   const Vec y[], const double z[]) {
+  int offset, bound;
+  if (iter <= M) {
+    offset = 0;
+    bound = iter;
+  } else {
+    offset = iter - M;
+    bound = M;
+  }
+
+  Vec q = grad;
+  double alpha[M], beta[M];
+  for (int i = bound - 1; i >= 0; i--) {
+    const int j = (i + offset) % M;
+    alpha[i] = z[j] * dot_product(s[j], q);
+    q += -alpha[i] * y[j];
+  }
+  if (iter > 0) {
+    const int j = (iter - 1) % M;
+    const double gamma = ((1.0 / z[j]) / dot_product(y[j], y[j]));
+    //    static double gamma;
+    //    if (gamma == 0) gamma = ((1.0 / z[j]) / dot_product(y[j], y[j]));
+    q *= gamma;
+  }
+  for (int i = 0; i <= bound - 1; i++) {
+    const int j = (i + offset) % M;
+    beta[i] = z[j] * dot_product(y[j], q);
+    q += s[j] * (alpha[i] - beta[i]);
+  }
+
+  return q;
+}
+
+vector<double> ME_Model::perform_LBFGS(const vector<double>& x0) {
+  const size_t dim = x0.size();
+  Vec x = x0;
+
+  Vec grad(dim), dx(dim);
+  double f = FunctionGradient(x.STLVec(), grad.STLVec());
+
+  Vec s[M], y[M];
+  double z[M];  // rho
+
+  for (int iter = 0; iter < LBFGS_MAX_ITER; iter++) {
+
+    fprintf(stderr, "%3d  obj(err) = %f (%6.4f)", iter + 1, -f, _train_error);
+    if (_nheldout > 0) {
+      const double heldout_logl = heldout_likelihood();
+      fprintf(stderr, "  heldout_logl(err) = %f (%6.4f)", heldout_logl,
+              _heldout_error);
+    }
+    fprintf(stderr, "\n");
+
+    if (sqrt(dot_product(grad, grad)) < MIN_GRAD_NORM) break;
+
+    dx = -1 * approximate_Hg(iter, grad, s, y, z);
+
+    Vec x1(dim), grad1(dim);
+    f = backtracking_line_search(x, grad, f, dx, x1, grad1);
+
+    s[iter % M] = x1 - x;
+    y[iter % M] = grad1 - grad;
+    z[iter % M] = 1.0 / dot_product(y[iter % M], s[iter % M]);
+    x = x1;
+    grad = grad1;
+  }
+
+  return x.STLVec();
+}
+
+// OWLQN
+
+// stopping criteria
+int OWLQN_MAX_ITER = 300;
+
+Vec approximate_Hg(const int iter, const Vec& grad, const Vec s[],
+                   const Vec y[], const double z[]);
+
+inline int sign(double x) {
+  if (x > 0) return 1;
+  if (x < 0) return -1;
+  return 0;
+};
+
+static Vec pseudo_gradient(const Vec& x, const Vec& grad0, const double C) {
+  Vec grad = grad0;
+  for (size_t i = 0; i < x.Size(); i++) {
+    if (x[i] != 0) {
+      grad[i] += C * sign(x[i]);
+      continue;
+    }
+    const double gm = grad0[i] - C;
+    if (gm > 0) {
+      grad[i] = gm;
+      continue;
+    }
+    const double gp = grad0[i] + C;
+    if (gp < 0) {
+      grad[i] = gp;
+      continue;
+    }
+    grad[i] = 0;
+  }
+
+  return grad;
+}
+
+double ME_Model::regularized_func_grad(const double C, const Vec& x,
+                                       Vec& grad) {
+  double f = FunctionGradient(x.STLVec(), grad.STLVec());
+  for (size_t i = 0; i < x.Size(); i++) {
+    f += C * fabs(x[i]);
+  }
+
+  return f;
+}
+
+double ME_Model::constrained_line_search(double C, const Vec& x0,
+                                         const Vec& grad0, const double f0,
+                                         const Vec& dx, Vec& x, Vec& grad1) {
+  // compute the orthant to explore
+  Vec orthant = x0;
+  for (size_t i = 0; i < orthant.Size(); i++) {
+    if (orthant[i] == 0) orthant[i] = -grad0[i];
+  }
+
+  double t = 1.0 / LINE_SEARCH_BETA;
+
+  double f;
+  do {
+    t *= LINE_SEARCH_BETA;
+    x = x0 + t * dx;
+    x.Project(orthant);
+    //    for (size_t i = 0; i < x.Size(); i++) {
+    //      if (x0[i] != 0 && sign(x[i]) != sign(x0[i])) x[i] = 0;
+    //    }
+
+    f = regularized_func_grad(C, x, grad1);
+    //        cout << "*";
+  } while (f > f0 + LINE_SEARCH_ALPHA * dot_product(x - x0, grad0));
+
+  return f;
+}
+
+vector<double> ME_Model::perform_OWLQN(const vector<double>& x0,
+                                       const double C) {
+  const size_t dim = x0.size();
+  Vec x = x0;
+
+  Vec grad(dim), dx(dim);
+  double f = regularized_func_grad(C, x, grad);
+
+  Vec s[M], y[M];
+  double z[M];  // rho
+
+  for (int iter = 0; iter < OWLQN_MAX_ITER; iter++) {
+    Vec pg = pseudo_gradient(x, grad, C);
+
+    fprintf(stderr, "%3d  obj(err) = %f (%6.4f)", iter + 1, -f, _train_error);
+    if (_nheldout > 0) {
+      const double heldout_logl = heldout_likelihood();
+      fprintf(stderr, "  heldout_logl(err) = %f (%6.4f)", heldout_logl,
+              _heldout_error);
+    }
+    fprintf(stderr, "\n");
+
+    if (sqrt(dot_product(pg, pg)) < MIN_GRAD_NORM) break;
+
+    dx = -1 * approximate_Hg(iter, pg, s, y, z);
+    if (dot_product(dx, pg) >= 0) dx.Project(-1 * pg);
+
+    Vec x1(dim), grad1(dim);
+    f = constrained_line_search(C, x, pg, f, dx, x1, grad1);
+
+    s[iter % M] = x1 - x;
+    y[iter % M] = grad1 - grad;
+    z[iter % M] = 1.0 / dot_product(y[iter % M], s[iter % M]);
+
+    x = x1;
+    grad = grad1;
+  }
+
+  return x.STLVec();
+}
+
+// SGD
+
+// const double SGD_ETA0 = 1;
+// const double SGD_ITER = 30;
+// const double SGD_ALPHA = 0.85;
+
+//#define FOLOS_NAIVE
+//#define FOLOS_LAZY
+#define SGD_CP
+
+inline void apply_l1_penalty(const int i, const double u, vector<double>& _vl,
+                             vector<double>& q) {
+  double& w = _vl[i];
+  const double z = w;
+  double& qi = q[i];
+  if (w > 0) {
+    w = max(0.0, w - (u + qi));
+  } else if (w < 0) {
+    w = min(0.0, w + (u - qi));
+  }
+  qi += w - z;
+}
+
+static double l1norm(const vector<double>& v) {
+  double sum = 0;
+  for (size_t i = 0; i < v.size(); i++) sum += abs(v[i]);
+  return sum;
+}
+
+inline void update_folos_lazy(const int iter_sample, const int k,
+                              vector<double>& _vl,
+                              const vector<double>& sum_eta,
+                              vector<int>& last_updated) {
+  const double penalty = sum_eta[iter_sample] - sum_eta[last_updated[k]];
+  double& x = _vl[k];
+  if (x > 0)
+    x = max(0.0, x - penalty);
+  else
+    x = min(0.0, x + penalty);
+  last_updated[k] = iter_sample;
+}
+
+int ME_Model::perform_SGD() {
+  if (_l2reg > 0) {
+    cerr << "error: L2 regularization is currently not supported in SGD mode."
+         << endl;
+    exit(1);
+  }
+
+  cerr << "performing SGD" << endl;
+
+  const double l1param = _l1reg;
+
+  const int d = _fb.Size();
+
+  vector<int> ri(_vs.size());
+  for (size_t i = 0; i < ri.size(); i++) ri[i] = i;
+
+  vector<double> grad(d);
+  int iter_sample = 0;
+  const double eta0 = SGD_ETA0;
+
+  //  cerr << "l1param = " << l1param << endl;
+  cerr << "eta0 = " << eta0 << " alpha = " << SGD_ALPHA << endl;
+
+  double u = 0;
+  vector<double> q(d, 0);
+  vector<int> last_updated(d, 0);
+  vector<double> sum_eta;
+  sum_eta.push_back(0);
+
+  for (int iter = 0; iter < SGD_ITER; iter++) {
+
+    random_shuffle(ri.begin(), ri.end());
+
+    double logl = 0;
+    int ncorrect = 0, ntotal = 0;
+    for (size_t i = 0; i < _vs.size(); i++, ntotal++, iter_sample++) {
+      const Sample& s = _vs[ri[i]];
+
+#ifdef FOLOS_LAZY
+      for (vector<int>::const_iterator j = s.positive_features.begin();
+           j != s.positive_features.end(); j++) {
+        for (vector<int>::const_iterator k = _feature2mef[*j].begin();
+             k != _feature2mef[*j].end(); k++) {
+          update_folos_lazy(iter_sample, *k, _vl, sum_eta, last_updated);
+        }
+      }
+#endif
+
+      vector<double> membp(_num_classes);
+      const int max_label = conditional_probability(s, membp);
+
+      const double eta =
+          eta0 * pow(SGD_ALPHA,
+                     (double)iter_sample / _vs.size());  // exponential decay
+      //      const double eta = eta0 / (1.0 + (double)iter_sample /
+      // _vs.size());
+
+      //      if (iter_sample % _vs.size() == 0) cerr << "eta = " << eta <<
+      // endl;
+      u += eta * l1param;
+
+      sum_eta.push_back(sum_eta.back() + eta * l1param);
+
+      logl += log(membp[s.label]);
+      if (max_label == s.label) ncorrect++;
+
+      // binary features
+      for (vector<int>::const_iterator j = s.positive_features.begin();
+           j != s.positive_features.end(); j++) {
+        for (vector<int>::const_iterator k = _feature2mef[*j].begin();
+             k != _feature2mef[*j].end(); k++) {
+          const double me = membp[_fb.Feature(*k).label()];
+          const double ee = (_fb.Feature(*k).label() == s.label ? 1.0 : 0);
+          const double grad = (me - ee);
+          _vl[*k] -= eta * grad;
+#ifdef SGD_CP
+          apply_l1_penalty(*k, u, _vl, q);
+#endif
+        }
+      }
+      // real-valued features
+      for (vector<pair<int, double> >::const_iterator j = s.rvfeatures.begin();
+           j != s.rvfeatures.end(); j++) {
+        for (vector<int>::const_iterator k = _feature2mef[j->first].begin();
+             k != _feature2mef[j->first].end(); k++) {
+          const double me = membp[_fb.Feature(*k).label()];
+          const double ee = (_fb.Feature(*k).label() == s.label ? 1.0 : 0);
+          const double grad = (me - ee) * j->second;
+          _vl[*k] -= eta * grad;
+#ifdef SGD_CP
+          apply_l1_penalty(*k, u, _vl, q);
+#endif
+        }
+      }
+
+#ifdef FOLOS_NAIVE
+      for (size_t j = 0; j < d; j++) {
+        double& x = _vl[j];
+        if (x > 0)
+          x = max(0.0, x - eta * l1param);
+        else
+          x = min(0.0, x + eta * l1param);
+      }
+#endif
+    }
+    logl /= _vs.size();
+//    fprintf(stderr, "%4d logl = %8.3f acc = %6.4f ", iter, logl,
+// (double)ncorrect / ntotal);
+
+#ifdef FOLOS_LAZY
+    if (l1param > 0) {
+      for (size_t j = 0; j < d; j++)
+        update_folos_lazy(iter_sample, j, _vl, sum_eta, last_updated);
+    }
+#endif
+
+    double f = logl;
+    if (l1param > 0) {
+      const double l1 =
+          l1norm(_vl);  // this is not accurate when lazy update is used
+      //      cerr << "f0 = " <<  update_model_expectation() - l1param * l1 << "
+      // ";
+      f -= l1param * l1;
+      int nonzero = 0;
+      for (int j = 0; j < d; j++)
+        if (_vl[j] != 0) nonzero++;
+      //      cerr << " f = " << f << " l1 = " << l1 << " nonzero_features = "
+      // << nonzero << endl;
+    }
+    //    fprintf(stderr, "%4d  obj = %7.3f acc = %6.4f", iter+1, f,
+    // (double)ncorrect/ntotal);
+    //    fprintf(stderr, "%4d  obj = %f", iter+1, f);
+    fprintf(stderr, "%3d  obj(err) = %f (%6.4f)", iter + 1, f,
+            1 - (double)ncorrect / ntotal);
+
+    if (_nheldout > 0) {
+      double heldout_logl = heldout_likelihood();
+      //      fprintf(stderr, "  heldout_logl = %f  acc = %6.4f\n",
+      // heldout_logl, 1 - _heldout_error);
+      fprintf(stderr, "  heldout_logl(err) = %f (%6.4f)", heldout_logl,
+              _heldout_error);
+    }
+    fprintf(stderr, "\n");
+  }
+
+  return 0;
+}
+
+}  // namespace maxent
+
+/*
+ * $Log: maxent.cpp,v $
+ * Revision 1.1.1.1  2007/05/15 08:30:35  kyoshida
+ * stepp tagger, by Okanohara and Tsuruoka
+ *
+ * Revision 1.28  2006/08/21 17:30:38  tsuruoka
+ * use MAX_LABEL_TYPES
+ *
+ * Revision 1.27  2006/07/25 13:19:53  tsuruoka
+ * sort _vs[]
+ *
+ * Revision 1.26  2006/07/18 11:13:15  tsuruoka
+ * modify comments
+ *
+ * Revision 1.25  2006/07/18 10:02:15  tsuruoka
+ * remove sample2feature[]
+ * speed up conditional_probability()
+ *
+ * Revision 1.24  2006/07/18 05:10:51  tsuruoka
+ * add ref_dist
+ *
+ * Revision 1.23  2005/12/24 07:05:32  tsuruoka
+ * modify conditional_probability() to avoid overflow
+ *
+ * Revision 1.22  2005/12/24 07:01:25  tsuruoka
+ * add cutoff for real-valued features
+ *
+ * Revision 1.21  2005/12/23 10:33:02  tsuruoka
+ * support real-valued features
+ *
+ * Revision 1.20  2005/12/23 09:15:29  tsuruoka
+ * modify _train to reduce memory consumption
+ *
+ * Revision 1.19  2005/10/28 13:10:14  tsuruoka
+ * fix for overflow (thanks to Ming Li)
+ *
+ * Revision 1.18  2005/10/28 13:03:07  tsuruoka
+ * add progress_bar
+ *
+ * Revision 1.17  2005/09/12 13:51:16  tsuruoka
+ * Sample: list -> vector
+ *
+ * Revision 1.16  2005/09/12 13:27:10  tsuruoka
+ * add add_training_sample()
+ *
+ * Revision 1.15  2005/04/27 11:22:27  tsuruoka
+ * bugfix
+ * ME_Sample: list -> vector
+ *
+ * Revision 1.14  2005/04/27 10:00:42  tsuruoka
+ * remove tmpfb
+ *
+ * Revision 1.13  2005/04/26 14:25:53  tsuruoka
+ * add MiniStringBag, USE_HASH_MAP
+ *
+ * Revision 1.12  2005/02/11 10:20:08  tsuruoka
+ * modify cutoff
+ *
+ * Revision 1.11  2004/10/04 05:50:25  tsuruoka
+ * add Clear()
+ *
+ * Revision 1.10  2004/08/26 16:52:26  tsuruoka
+ * fix load_from_file()
+ *
+ * Revision 1.9  2004/08/09 12:27:21  tsuruoka
+ * change messages
+ *
+ * Revision 1.8  2004/08/04 13:55:18  tsuruoka
+ * modify _sample2feature
+ *
+ * Revision 1.7  2004/07/28 13:42:58  tsuruoka
+ * add AGIS
+ *
+ * Revision 1.6  2004/07/28 05:54:13  tsuruoka
+ * get_class_name() -> get_class_label()
+ * ME_Feature: bugfix
+ *
+ * Revision 1.5  2004/07/27 16:58:47  tsuruoka
+ * modify the interface of classify()
+ *
+ * Revision 1.4  2004/07/26 17:23:46  tsuruoka
+ * _sample2feature: list -> vector
+ *
+ * Revision 1.3  2004/07/26 15:49:23  tsuruoka
+ * modify ME_Feature
+ *
+ * Revision 1.2  2004/07/26 13:52:18  tsuruoka
+ * modify cutoff
+ *
+ * Revision 1.1  2004/07/26 13:10:55  tsuruoka
+ * add files
+ *
+ * Revision 1.20  2004/07/22 08:34:45  tsuruoka
+ * modify _sample2feature[]
+ *
+ * Revision 1.19  2004/07/21 16:33:01  tsuruoka
+ * remove some comments
+ *
+ */