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authorWu, Ke <wuke@cs.umd.edu>2014-12-11 18:52:08 -0500
committerWu, Ke <wuke@cs.umd.edu>2014-12-11 18:52:08 -0500
commit09e9a08a69401a47b9494312fa073db86b50cccf (patch)
tree3b9f786775554574efebd9c75bde349c565cfe4b /utils/maxent.cpp
parent2b30e44c7845c0e9fccbf2fc0ea535a0c5322d60 (diff)
parent8fb14af5dfb8032e2fb4fe6e94ed983baf39de0b (diff)
Merge branch 'const_reorder_no_state' into const_reorder_2
Diffstat (limited to 'utils/maxent.cpp')
-rw-r--r--utils/maxent.cpp702
1 files changed, 702 insertions, 0 deletions
diff --git a/utils/maxent.cpp b/utils/maxent.cpp
new file mode 100644
index 00000000..0f49ee9d
--- /dev/null
+++ b/utils/maxent.cpp
@@ -0,0 +1,702 @@
+/*
+ * $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 = 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;
+}
+
+/*
+ * $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
+ *
+ */