summaryrefslogtreecommitdiff
path: root/utils/synutils/maxent-3.0/maxent.cpp
diff options
context:
space:
mode:
Diffstat (limited to 'utils/synutils/maxent-3.0/maxent.cpp')
-rw-r--r--utils/synutils/maxent-3.0/maxent.cpp291
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
*
*/
-
generated by cgit v1.2.3 (git 2.39.1) at 2024-10-18 14:38:02 +0000