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-rw-r--r--utils/synutils/maxent-3.0/maxent.cpp703
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diff --git a/utils/synutils/maxent-3.0/maxent.cpp b/utils/synutils/maxent-3.0/maxent.cpp
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--- a/utils/synutils/maxent-3.0/maxent.cpp
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@@ -1,703 +0,0 @@
-/*
- * $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
- *
- */
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