Import synutils

1 files changed, 698 insertions, 0 deletions

diff --git a/utils/synutils/maxent-3.0/maxent.cpp b/utils/synutils/maxent-3.0/maxent.cpp
new file mode 100644
index 00000000..feb0efdc
--- /dev/null
+++ b/utils/synutils/maxent-3.0/maxent.cpp
@@ -0,0 +1,698 @@
+/*
+ * $Id: maxent.cpp,v 1.1.1.1 2007/05/15 08:30:35 kyoshida Exp $
+ */
+
+#include "maxent.h"
+#include <cmath>
+#include <cstdio>
+#include "lbfgs.h"
+
+using namespace std;
+
+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 = -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++) {
+      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;
+  }
+  assert(max_label >= 0);
+  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 __gnu_cxx::hash_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;
+}
+
+
+/*
+ * $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
+ *
+ */
+