Apply clang-format

1 files changed, 148 insertions, 143 deletions

diff --git a/utils/synutils/maxent-3.0/maxent.cpp b/utils/synutils/maxent-3.0/maxent.cpp
index feb0efdc..8d00ac1d 100644
--- a/utils/synutils/maxent-3.0/maxent.cpp
+++ b/utils/synutils/maxent-3.0/maxent.cpp
@@ -9,14 +9,13 @@
 
 using namespace std;
 
-double
-ME_Model::FunctionGradient(const vector<double> & x, vector<double> & grad)
-{
+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) {
@@ -33,9 +32,7 @@ ME_Model::FunctionGradient(const vector<double> & x, vector<double> & grad)
   return -score;
 }
 
-int
-ME_Model::perform_GIS(int C)
-{
+int ME_Model::perform_GIS(int C) {
   cerr << "C = " << C << endl;
   C = 1;
   cerr << "performing AGIS" << endl;
@@ -43,11 +40,13 @@ ME_Model::perform_GIS(int C)
   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);
+    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);
+      fprintf(stderr, "  heldout_logl(err) = %f (%6.4f)", hlogl,
+              _heldout_error);
     }
     cerr << endl;
 
@@ -71,13 +70,13 @@ ME_Model::perform_GIS(int C)
   return 0;
 }
 
-int
-ME_Model::perform_QUASI_NEWTON()
-{
+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]; }
+  for (int i = 0; i < dim; i++) {
+    x0[i] = _vl[i];
+  }
 
   vector<double> x;
   if (_l1reg > 0) {
@@ -88,34 +87,39 @@ ME_Model::perform_QUASI_NEWTON()
     x = perform_LBFGS(x0);
   }
 
-  for (int i = 0; i < dim; i++) { _vl[i] = x[i]; }
+  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();
+int ME_Model::conditional_probability(const Sample& s,
+                                      std::vector<double>& membp) const {
+  // int num_classes = membp.size();
   double sum = 0;
   int max_label = -1;
   //  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++) {
+  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++) {
+  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
+  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);
@@ -134,33 +138,38 @@ ME_Model::conditional_probability(const Sample & s,
   return max_label;
 }
 
-int
-ME_Model::make_feature_bag(const int cutoff)
-{
+int ME_Model::make_feature_bag(const int cutoff) {
   int max_num_features = 0;
 
-  // count the occurrences of features
+// count the occurrences of features
 #ifdef USE_HASH_MAP
   typedef __gnu_cxx::hash_map<unsigned int, int> map_type;
-#else    
+#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++) {
+    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++) {
+      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++) {
+  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;
@@ -168,7 +177,9 @@ ME_Model::make_feature_bag(const int cutoff)
       //      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++) {
+    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;
@@ -176,71 +187,72 @@ ME_Model::make_feature_bag(const int cutoff)
     }
   }
   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 ME_Model::heldout_likelihood() {
   double logl = 0;
   int ncorrect = 0;
-  for (std::vector<Sample>::const_iterator i = _heldout.begin(); i != _heldout.end(); i++) {
+  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 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++) {
+  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<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 (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++) {
@@ -248,17 +260,17 @@ ME_Model::update_model_expectation()
     }
   }
 
-  //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());
+  // 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)
-{
+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);
@@ -267,39 +279,41 @@ ME_Model::train(const vector<ME_Sample> & vms)
   return train();
 }
 
-void
-ME_Model::add_training_sample(const ME_Sample & mes)
-{
+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++) {
+  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));
+  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;;
+    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++){
+  //  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()
-{
+int ME_Model::train() {
   if (_l1reg > 0 && _l2reg > 0) {
-    cerr << "error: L1 and L2 regularizers cannot be used simultaneously." << endl;
+    cerr << "error: L1 and L2 regularizers cannot be used simultaneously."
+         << endl;
     return 0;
   }
   if (_vs.size() == 0) {
@@ -307,20 +321,22 @@ ME_Model::train()
     return 0;
   }
   if (_nheldout >= (int)_vs.size()) {
-    cerr << "error: too much heldout data. no training data is available." << endl;
+    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++) {
+  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++) {
@@ -332,7 +348,7 @@ ME_Model::train()
     }
     cerr << "done" << endl;
   }
-  
+
   for (int i = 0; i < _nheldout; i++) {
     _heldout.push_back(_vs.back());
     _vs.pop_back();
@@ -362,25 +378,28 @@ ME_Model::train()
     _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;
+    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 (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;
 
@@ -399,13 +418,12 @@ ME_Model::train()
   return 0;
 }
 
-void
-ME_Model::get_features(list< pair< pair<string, string>, double> > & fl)
-{
+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]));
+  //    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++) {
@@ -414,14 +432,12 @@ ME_Model::get_features(list< pair< pair<string, string>, double> > & fl)
       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]) );
+      fl.push_back(make_pair(make_pair(label, history), _vl[id]));
     }
   }
 }
 
-void
-ME_Model::clear()
-{
+void ME_Model::clear() {
   _vl.clear();
   _label_bag.Clear();
   _featurename_bag.Clear();
@@ -433,10 +449,8 @@ ME_Model::clear()
   _heldout.clear();
 }
 
-bool
-ME_Model::load_from_file(const string & filename)
-{
-  FILE * fp = fopen(filename.c_str(), "r");
+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;
@@ -447,22 +461,22 @@ ME_Model::load_from_file(const string & filename)
   _featurename_bag.Clear();
   _fb.Clear();
   char buf[1024];
-  while(fgets(buf, 1024, fp)) {
+  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) );
+    string featurename = line.substr(t1 + 1, t2 - (t1 + 1));
     float lambda;
-    string w = line.substr(t2+1);
+    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();
@@ -472,9 +486,7 @@ ME_Model::load_from_file(const string & filename)
   return true;
 }
 
-void
-ME_Model::init_feature2mef()
-{
+void ME_Model::init_feature2mef() {
   _feature2mef.clear();
   for (int i = 0; i < _featurename_bag.Size(); i++) {
     vector<int> vi;
@@ -486,9 +498,7 @@ ME_Model::init_feature2mef()
   }
 }
 
-bool
-ME_Model::load_from_array(const ME_Model_Data data[])
-{
+bool ME_Model::load_from_array(const ME_Model_Data data[]) {
   _vl.clear();
   for (int i = 0;; i++) {
     if (string(data[i].label) == "///") break;
@@ -500,14 +510,12 @@ ME_Model::load_from_array(const ME_Model_Data data[])
   _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");
+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;
@@ -516,7 +524,8 @@ ME_Model::save_to_file(const string & filename, const double th) const
   //  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]);
+  //    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++) {
@@ -525,8 +534,8 @@ ME_Model::save_to_file(const string & filename, const double th) const
       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
+      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]);
     }
   }
@@ -536,9 +545,7 @@ ME_Model::save_to_file(const string & filename, const double th) const
   return true;
 }
 
-void
-ME_Model::set_ref_dist(Sample & s) const
-{
+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++) {
@@ -548,14 +555,12 @@ ME_Model::set_ref_dist(Sample & s) const
     if (id_ref != -1) {
       v[i] = v0[id_ref];
     }
-    if (v[i] == 0) v[i] = 0.001; // to avoid -inf logl
+    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
-{
+
+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);
@@ -563,22 +568,24 @@ ME_Model::classify(const Sample & nbs, vector<double> & membp) const
   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]; }
+    if (membp[i] > max) {
+      max_label = i;
+      max = membp[i];
+    }
   }
   //  cout << endl;
   return max_label;
 }
 
-vector<double>
-ME_Model::classify(ME_Sample & mes) const
-{
+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++) {
+  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);
+    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++) {
+  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));
@@ -595,7 +602,6 @@ ME_Model::classify(ME_Sample & mes) const
   return vp;
 }
 
-
 /*
  * $Log: maxent.cpp,v $
  * Revision 1.1.1.1  2007/05/15 08:30:35  kyoshida
@@ -695,4 +701,3 @@ ME_Model::classify(ME_Sample & mes) const
  * remove some comments
  *
  */
-