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-rw-r--r--utils/synutils/maxent-3.0/lbfgs.cpp48
-rw-r--r--utils/synutils/maxent-3.0/lbfgs.h24
-rw-r--r--utils/synutils/maxent-3.0/mathvec.h48
-rw-r--r--utils/synutils/maxent-3.0/maxent.cpp291
-rw-r--r--utils/synutils/maxent-3.0/maxent.h187
-rw-r--r--utils/synutils/maxent-3.0/owlqn.cpp46
-rw-r--r--utils/synutils/maxent-3.0/sgd.cpp147
7 files changed, 396 insertions, 395 deletions
diff --git a/utils/synutils/maxent-3.0/lbfgs.cpp b/utils/synutils/maxent-3.0/lbfgs.cpp
index 9eb04bef..bd26f048 100644
--- a/utils/synutils/maxent-3.0/lbfgs.cpp
+++ b/utils/synutils/maxent-3.0/lbfgs.cpp
@@ -8,20 +8,17 @@
using namespace std;
-const static int M = LBFGS_M;
+const static int M = LBFGS_M;
const static double LINE_SEARCH_ALPHA = 0.1;
-const static double LINE_SEARCH_BETA = 0.5;
+const static double LINE_SEARCH_BETA = 0.5;
// stopping criteria
-int LBFGS_MAX_ITER = 300;
+int LBFGS_MAX_ITER = 300;
const static double MIN_GRAD_NORM = 0.0001;
-
-double
-ME_Model::backtracking_line_search(
- const Vec & x0, const Vec & grad0, const double f0,
- const Vec & dx, Vec & x, Vec & grad1)
-{
+double ME_Model::backtracking_line_search(const Vec& x0, const Vec& grad0,
+ const double f0, const Vec& dx,
+ Vec& x, Vec& grad1) {
double t = 1.0 / LINE_SEARCH_BETA;
double f;
@@ -39,20 +36,23 @@ ME_Model::backtracking_line_search(
// Jorge Nocedal, "Updating Quasi-Newton Matrices With Limited Storage",
// Mathematics of Computation, Vol. 35, No. 151, pp. 773-782, 1980.
//
-Vec
-approximate_Hg(const int iter, const Vec & grad,
- const Vec s[], const Vec y[], const double z[])
-{
+Vec approximate_Hg(const int iter, const Vec& grad, const Vec s[],
+ const Vec y[], const double z[]) {
int offset, bound;
- if (iter <= M) { offset = 0; bound = iter; }
- else { offset = iter - M; bound = M; }
+ if (iter <= M) {
+ offset = 0;
+ bound = iter;
+ } else {
+ offset = iter - M;
+ bound = M;
+ }
Vec q = grad;
double alpha[M], beta[M];
for (int i = bound - 1; i >= 0; i--) {
const int j = (i + offset) % M;
- alpha[i] = z[j] * dot_product(s[j], q);
- q += -alpha[i] * y[j];
+ alpha[i] = z[j] * dot_product(s[j], q);
+ q += -alpha[i] * y[j];
}
if (iter > 0) {
const int j = (iter - 1) % M;
@@ -63,16 +63,14 @@ approximate_Hg(const int iter, const Vec & grad,
}
for (int i = 0; i <= bound - 1; i++) {
const int j = (i + offset) % M;
- beta[i] = z[j] * dot_product(y[j], q);
- q += s[j] * (alpha[i] - beta[i]);
+ beta[i] = z[j] * dot_product(y[j], q);
+ q += s[j] * (alpha[i] - beta[i]);
}
return q;
}
-vector<double>
-ME_Model::perform_LBFGS(const vector<double> & x0)
-{
+vector<double> ME_Model::perform_LBFGS(const vector<double>& x0) {
const size_t dim = x0.size();
Vec x = x0;
@@ -84,10 +82,11 @@ ME_Model::perform_LBFGS(const vector<double> & x0)
for (int iter = 0; iter < LBFGS_MAX_ITER; iter++) {
- fprintf(stderr, "%3d obj(err) = %f (%6.4f)", iter+1, -f, _train_error);
+ fprintf(stderr, "%3d obj(err) = %f (%6.4f)", iter + 1, -f, _train_error);
if (_nheldout > 0) {
const double heldout_logl = heldout_likelihood();
- fprintf(stderr, " heldout_logl(err) = %f (%6.4f)", heldout_logl, _heldout_error);
+ fprintf(stderr, " heldout_logl(err) = %f (%6.4f)", heldout_logl,
+ _heldout_error);
}
fprintf(stderr, "\n");
@@ -107,4 +106,3 @@ ME_Model::perform_LBFGS(const vector<double> & x0)
return x.STLVec();
}
-
diff --git a/utils/synutils/maxent-3.0/lbfgs.h b/utils/synutils/maxent-3.0/lbfgs.h
index 113919a3..ed5cd944 100644
--- a/utils/synutils/maxent-3.0/lbfgs.h
+++ b/utils/synutils/maxent-3.0/lbfgs.h
@@ -1,21 +1,19 @@
#ifndef _LBFGS_H_
#define _LBFGS_H_
-//template<class FuncGrad>
-//std::vector<double>
-//perform_LBFGS(FuncGrad func_grad, const std::vector<double> & x0);
+// template<class FuncGrad>
+// std::vector<double>
+// perform_LBFGS(FuncGrad func_grad, const std::vector<double> & x0);
-std::vector<double>
-perform_LBFGS(double (*func_grad)(const std::vector<double> &, std::vector<double> &),
- const std::vector<double> & x0);
+std::vector<double> perform_LBFGS(
+ double (*func_grad)(const std::vector<double> &, std::vector<double> &),
+ const std::vector<double> &x0);
+std::vector<double> perform_OWLQN(
+ double (*func_grad)(const std::vector<double> &, std::vector<double> &),
+ const std::vector<double> &x0, const double C);
-std::vector<double>
-perform_OWLQN(double (*func_grad)(const std::vector<double> &, std::vector<double> &),
- const std::vector<double> & x0,
- const double C);
-
-//const int LBFGS_M = 7;
-const int LBFGS_M = 10;
+// const int LBFGS_M = 7;
+const int LBFGS_M = 10;
#endif
diff --git a/utils/synutils/maxent-3.0/mathvec.h b/utils/synutils/maxent-3.0/mathvec.h
index 4ec82797..f8c60e5d 100644
--- a/utils/synutils/maxent-3.0/mathvec.h
+++ b/utils/synutils/maxent-3.0/mathvec.h
@@ -5,41 +5,40 @@
#include <iostream>
#include <cassert>
-class Vec
-{
-private:
+class Vec {
+ private:
std::vector<double> _v;
-public:
+
+ public:
Vec(const size_t n = 0, const double val = 0) { _v.resize(n, val); }
- Vec(const std::vector<double> & v) : _v(v) {}
- const std::vector<double> & STLVec() const { return _v; }
- std::vector<double> & STLVec() { return _v; }
+ Vec(const std::vector<double>& v) : _v(v) {}
+ const std::vector<double>& STLVec() const { return _v; }
+ std::vector<double>& STLVec() { return _v; }
size_t Size() const { return _v.size(); }
- double & operator[](int i) { return _v[i]; }
- const double & operator[](int i) const { return _v[i]; }
- Vec & operator+=(const Vec & b) {
+ double& operator[](int i) { return _v[i]; }
+ const double& operator[](int i) const { return _v[i]; }
+ Vec& operator+=(const Vec& b) {
assert(b.Size() == _v.size());
for (size_t i = 0; i < _v.size(); i++) {
_v[i] += b[i];
}
return *this;
}
- Vec & operator*=(const double c) {
+ Vec& operator*=(const double c) {
for (size_t i = 0; i < _v.size(); i++) {
_v[i] *= c;
}
return *this;
}
- void Project(const Vec & y) {
+ void Project(const Vec& y) {
for (size_t i = 0; i < _v.size(); i++) {
// if (sign(_v[i]) != sign(y[i])) _v[i] = 0;
- if (_v[i] * y[i] <=0) _v[i] = 0;
+ if (_v[i] * y[i] <= 0) _v[i] = 0;
}
}
};
-inline double dot_product(const Vec & a, const Vec & b)
-{
+inline double dot_product(const Vec& a, const Vec& b) {
double sum = 0;
for (size_t i = 0; i < a.Size(); i++) {
sum += a[i] * b[i];
@@ -47,8 +46,7 @@ inline double dot_product(const Vec & a, const Vec & b)
return sum;
}
-inline std::ostream & operator<<(std::ostream & s, const Vec & a)
-{
+inline std::ostream& operator<<(std::ostream& s, const Vec& a) {
s << "(";
for (size_t i = 0; i < a.Size(); i++) {
if (i != 0) s << ", ";
@@ -58,8 +56,7 @@ inline std::ostream & operator<<(std::ostream & s, const Vec & a)
return s;
}
-inline const Vec operator+(const Vec & a, const Vec & b)
-{
+inline const Vec operator+(const Vec& a, const Vec& b) {
Vec v(a.Size());
assert(a.Size() == b.Size());
for (size_t i = 0; i < a.Size(); i++) {
@@ -68,8 +65,7 @@ inline const Vec operator+(const Vec & a, const Vec & b)
return v;
}
-inline const Vec operator-(const Vec & a, const Vec & b)
-{
+inline const Vec operator-(const Vec& a, const Vec& b) {
Vec v(a.Size());
assert(a.Size() == b.Size());
for (size_t i = 0; i < a.Size(); i++) {
@@ -78,8 +74,7 @@ inline const Vec operator-(const Vec & a, const Vec & b)
return v;
}
-inline const Vec operator*(const Vec & a, const double c)
-{
+inline const Vec operator*(const Vec& a, const double c) {
Vec v(a.Size());
for (size_t i = 0; i < a.Size(); i++) {
v[i] = a[i] * c;
@@ -87,11 +82,6 @@ inline const Vec operator*(const Vec & a, const double c)
return v;
}
-inline const Vec operator*(const double c, const Vec & a)
-{
- return a * c;
-}
-
-
+inline const Vec operator*(const double c, const Vec& a) { return a * c; }
#endif
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
*
*/
-
diff --git a/utils/synutils/maxent-3.0/maxent.h b/utils/synutils/maxent-3.0/maxent.h
index a4391ead..88a04e25 100644
--- a/utils/synutils/maxent-3.0/maxent.h
+++ b/utils/synutils/maxent-3.0/maxent.h
@@ -15,7 +15,8 @@
#include <cassert>
#include "mathvec.h"
-#define USE_HASH_MAP // if you encounter errors with hash, try commenting out this line. (the program will be a bit slower, though)
+#define USE_HASH_MAP // if you encounter errors with hash, try commenting out
+ // this line. (the program will be a bit slower, though)
#ifdef USE_HASH_MAP
#include <ext/hash_map>
#endif
@@ -23,68 +24,68 @@
//
// data format for each sample for training/testing
//
-struct ME_Sample
-{
-public:
+struct ME_Sample {
+ public:
ME_Sample() : label("") {};
- ME_Sample(const std::string & l) : label(l) {};
- void set_label(const std::string & l) { label = l; }
+ ME_Sample(const std::string& l) : label(l) {};
+ void set_label(const std::string& l) { label = l; }
// to add a binary feature
- void add_feature(const std::string & f) {
- features.push_back(f);
- }
+ void add_feature(const std::string& f) { features.push_back(f); }
// to add a real-valued feature
- void add_feature(const std::string & s, const double d) {
- rvfeatures.push_back(std::pair<std::string, double>(s, d));
+ void add_feature(const std::string& s, const double d) {
+ rvfeatures.push_back(std::pair<std::string, double>(s, d));
}
-public:
+ public:
std::string label;
std::vector<std::string> features;
std::vector<std::pair<std::string, double> > rvfeatures;
// obsolete
- void add_feature(const std::pair<std::string, double> & f) {
- rvfeatures.push_back(f); // real-valued features
+ void add_feature(const std::pair<std::string, double>& f) {
+ rvfeatures.push_back(f); // real-valued features
}
};
-
//
// for those who want to use load_from_array()
//
-typedef struct ME_Model_Data
-{
- char * label;
- char * feature;
+typedef struct ME_Model_Data {
+ char* label;
+ char* feature;
double weight;
} ME_Model_Data;
-
-class ME_Model
-{
-public:
-
- void add_training_sample(const ME_Sample & s);
+class ME_Model {
+ public:
+ void add_training_sample(const ME_Sample& s);
int train();
- std::vector<double> classify(ME_Sample & s) const;
- bool load_from_file(const std::string & filename);
- bool save_to_file(const std::string & filename, const double th = 0) const;
+ std::vector<double> classify(ME_Sample& s) const;
+ bool load_from_file(const std::string& filename);
+ bool save_to_file(const std::string& filename, const double th = 0) const;
int num_classes() const { return _num_classes; }
std::string get_class_label(int i) const { return _label_bag.Str(i); }
- int get_class_id(const std::string & s) const { return _label_bag.Id(s); }
- void get_features(std::list< std::pair< std::pair<std::string, std::string>, double> > & fl);
- void set_heldout(const int h, const int n = 0) { _nheldout = h; _early_stopping_n = n; };
+ int get_class_id(const std::string& s) const { return _label_bag.Id(s); }
+ void get_features(
+ std::list<std::pair<std::pair<std::string, std::string>, double> >& fl);
+ void set_heldout(const int h, const int n = 0) {
+ _nheldout = h;
+ _early_stopping_n = n;
+ };
void use_l1_regularizer(const double v) { _l1reg = v; }
void use_l2_regularizer(const double v) { _l2reg = v; }
void use_SGD(int iter = 30, double eta0 = 1, double alpha = 0.85) {
_optimization_method = SGD;
- SGD_ITER = iter; SGD_ETA0 = eta0; SGD_ALPHA = alpha;
+ SGD_ITER = iter;
+ SGD_ETA0 = eta0;
+ SGD_ALPHA = alpha;
}
bool load_from_array(const ME_Model_Data data[]);
- void set_reference_model(const ME_Model & ref_model) { _ref_modelp = &ref_model; };
+ void set_reference_model(const ME_Model& ref_model) {
+ _ref_modelp = &ref_model;
+ };
void clear();
ME_Model() {
@@ -95,13 +96,16 @@ public:
_optimization_method = LBFGS;
}
-public:
+ public:
// obsolete. just for downward compatibility
- int train(const std::vector<ME_Sample> & train);
-
-private:
-
- enum OPTIMIZATION_METHOD { LBFGS, OWLQN, SGD } _optimization_method;
+ int train(const std::vector<ME_Sample>& train);
+
+ private:
+ enum OPTIMIZATION_METHOD {
+ LBFGS,
+ OWLQN,
+ SGD
+ } _optimization_method;
// OWLQN and SGD are available only for L1-regularization
int SGD_ITER;
@@ -109,13 +113,13 @@ private:
double SGD_ALPHA;
double _l1reg, _l2reg;
-
+
struct Sample {
int label;
std::vector<int> positive_features;
std::vector<std::pair<int, double> > rvfeatures;
- std::vector<double> ref_pd; // reference probability distribution
- bool operator<(const Sample & x) const {
+ std::vector<double> ref_pd; // reference probability distribution
+ bool operator<(const Sample& x) const {
for (unsigned int i = 0; i < positive_features.size(); i++) {
if (i >= x.positive_features.size()) return false;
int v0 = positive_features[i];
@@ -127,10 +131,11 @@ private:
}
};
- struct ME_Feature
- {
- enum { MAX_LABEL_TYPES = 255 };
-
+ struct ME_Feature {
+ enum {
+ MAX_LABEL_TYPES = 255
+ };
+
// ME_Feature(const int l, const int f) : _body((l << 24) + f) {
// assert(l >= 0 && l < 256);
// assert(f >= 0 && f <= 0xffffff);
@@ -144,20 +149,20 @@ private:
int label() const { return _body & 0xff; }
int feature() const { return _body >> 8; }
unsigned int body() const { return _body; }
- private:
+
+ private:
unsigned int _body;
};
- struct ME_FeatureBag
- {
+ struct ME_FeatureBag {
#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 mef2id;
std::vector<ME_Feature> id2mef;
- int Put(const ME_Feature & i) {
+ int Put(const ME_Feature& i) {
map_type::const_iterator j = mef2id.find(i.body());
if (j == mef2id.end()) {
int id = id2mef.size();
@@ -167,7 +172,7 @@ private:
}
return j->second;
}
- int Id(const ME_Feature & i) const {
+ int Id(const ME_Feature& i) const {
map_type::const_iterator j = mef2id.find(i.body());
if (j == mef2id.end()) {
return -1;
@@ -178,17 +183,14 @@ private:
assert(id >= 0 && id < (int)id2mef.size());
return id2mef[id];
}
- int Size() const {
- return id2mef.size();
- }
+ int Size() const { return id2mef.size(); }
void Clear() {
mef2id.clear();
id2mef.clear();
}
};
- struct hashfun_str
- {
+ struct hashfun_str {
size_t operator()(const std::string& s) const {
assert(sizeof(int) == 4 && sizeof(char) == 1);
const int* p = reinterpret_cast<const int*>(s.c_str());
@@ -196,7 +198,7 @@ private:
int n = s.size() / 4;
for (int i = 0; i < n; i++, p++) {
// v ^= *p;
- v ^= *p << (4 * (i % 2)); // note) 0 <= char < 128
+ v ^= *p << (4 * (i % 2)); // note) 0 <= char < 128
}
int m = s.size() % 4;
for (int i = 0; i < m; i++) {
@@ -206,17 +208,16 @@ private:
}
};
- struct MiniStringBag
- {
+ struct MiniStringBag {
#ifdef USE_HASH_MAP
typedef __gnu_cxx::hash_map<std::string, int, hashfun_str> map_type;
-#else
+#else
typedef std::map<std::string, int> map_type;
#endif
int _size;
map_type str2id;
MiniStringBag() : _size(0) {}
- int Put(const std::string & i) {
+ int Put(const std::string& i) {
map_type::const_iterator j = str2id.find(i);
if (j == str2id.end()) {
int id = _size;
@@ -226,21 +227,23 @@ private:
}
return j->second;
}
- int Id(const std::string & i) const {
+ int Id(const std::string& i) const {
map_type::const_iterator j = str2id.find(i);
- if (j == str2id.end()) return -1;
+ if (j == str2id.end()) return -1;
return j->second;
}
int Size() const { return _size; }
- void Clear() { str2id.clear(); _size = 0; }
+ void Clear() {
+ str2id.clear();
+ _size = 0;
+ }
map_type::const_iterator begin() const { return str2id.begin(); }
- map_type::const_iterator end() const { return str2id.end(); }
+ map_type::const_iterator end() const { return str2id.end(); }
};
- struct StringBag : public MiniStringBag
- {
+ struct StringBag : public MiniStringBag {
std::vector<std::string> id2str;
- int Put(const std::string & i) {
+ int Put(const std::string& i) {
map_type::const_iterator j = str2id.find(i);
if (j == str2id.end()) {
int id = id2str.size();
@@ -261,7 +264,7 @@ private:
}
};
- std::vector<Sample> _vs; // vector of training_samples
+ std::vector<Sample> _vs; // vector of training_samples
StringBag _label_bag;
MiniStringBag _featurename_bag;
std::vector<double> _vl; // vector of lambda
@@ -269,42 +272,46 @@ private:
int _num_classes;
std::vector<double> _vee; // empirical expectation
std::vector<double> _vme; // empirical expectation
- std::vector< std::vector< int > > _feature2mef;
- std::vector< Sample > _heldout;
- double _train_error; // current error rate on the training data
- double _heldout_error; // current error rate on the heldout data
+ std::vector<std::vector<int> > _feature2mef;
+ std::vector<Sample> _heldout;
+ double _train_error; // current error rate on the training data
+ double _heldout_error; // current error rate on the heldout data
int _nheldout;
int _early_stopping_n;
std::vector<double> _vhlogl;
- const ME_Model * _ref_modelp;
+ const ME_Model* _ref_modelp;
double heldout_likelihood();
- int conditional_probability(const Sample & nbs, std::vector<double> & membp) const;
+ int conditional_probability(const Sample& nbs,
+ std::vector<double>& membp) const;
int make_feature_bag(const int cutoff);
- int classify(const Sample & nbs, std::vector<double> & membp) const;
+ int classify(const Sample& nbs, std::vector<double>& membp) const;
double update_model_expectation();
int perform_QUASI_NEWTON();
int perform_SGD();
int perform_GIS(int C);
- std::vector<double> perform_LBFGS(const std::vector<double> & x0);
- std::vector<double> perform_OWLQN(const std::vector<double> & x0, const double C);
- double backtracking_line_search(const Vec & x0, const Vec & grad0, const double f0, const Vec & dx, Vec & x, Vec & grad1);
- double regularized_func_grad(const double C, const Vec & x, Vec & grad);
- double constrained_line_search(double C, const Vec & x0, const Vec & grad0, const double f0, const Vec & dx, Vec & x, Vec & grad1);
-
-
- void set_ref_dist(Sample & s) const;
+ std::vector<double> perform_LBFGS(const std::vector<double>& x0);
+ std::vector<double> perform_OWLQN(const std::vector<double>& x0,
+ const double C);
+ double backtracking_line_search(const Vec& x0, const Vec& grad0,
+ const double f0, const Vec& dx, Vec& x,
+ Vec& grad1);
+ double regularized_func_grad(const double C, const Vec& x, Vec& grad);
+ double constrained_line_search(double C, const Vec& x0, const Vec& grad0,
+ const double f0, const Vec& dx, Vec& x,
+ Vec& grad1);
+
+ void set_ref_dist(Sample& s) const;
void init_feature2mef();
- double FunctionGradient(const std::vector<double> & x, std::vector<double> & grad);
- static double FunctionGradientWrapper(const std::vector<double> & x, std::vector<double> & grad);
-
+ double FunctionGradient(const std::vector<double>& x,
+ std::vector<double>& grad);
+ static double FunctionGradientWrapper(const std::vector<double>& x,
+ std::vector<double>& grad);
};
-
#endif
-
/*
* $Log: maxent.h,v $
* Revision 1.1.1.1 2007/05/15 08:30:35 kyoshida
diff --git a/utils/synutils/maxent-3.0/owlqn.cpp b/utils/synutils/maxent-3.0/owlqn.cpp
index 7b2cea7d..c3a0f0da 100644
--- a/utils/synutils/maxent-3.0/owlqn.cpp
+++ b/utils/synutils/maxent-3.0/owlqn.cpp
@@ -8,29 +8,24 @@
using namespace std;
-const static int M = LBFGS_M;
+const static int M = LBFGS_M;
const static double LINE_SEARCH_ALPHA = 0.1;
-const static double LINE_SEARCH_BETA = 0.5;
+const static double LINE_SEARCH_BETA = 0.5;
// stopping criteria
-int OWLQN_MAX_ITER = 300;
+int OWLQN_MAX_ITER = 300;
const static double MIN_GRAD_NORM = 0.0001;
+Vec approximate_Hg(const int iter, const Vec& grad, const Vec s[],
+ const Vec y[], const double z[]);
-Vec approximate_Hg(const int iter, const Vec & grad,
- const Vec s[], const Vec y[], const double z[]);
-
-
-inline int sign(double x)
-{
+inline int sign(double x) {
if (x > 0) return 1;
if (x < 0) return -1;
return 0;
};
-static Vec
-pseudo_gradient(const Vec & x, const Vec & grad0, const double C)
-{
+static Vec pseudo_gradient(const Vec& x, const Vec& grad0, const double C) {
Vec grad = grad0;
for (size_t i = 0; i < x.Size(); i++) {
if (x[i] != 0) {
@@ -53,9 +48,8 @@ pseudo_gradient(const Vec & x, const Vec & grad0, const double C)
return grad;
}
-double
-ME_Model::regularized_func_grad(const double C, const Vec & x, Vec & grad)
-{
+double ME_Model::regularized_func_grad(const double C, const Vec& x,
+ Vec& grad) {
double f = FunctionGradient(x.STLVec(), grad.STLVec());
for (size_t i = 0; i < x.Size(); i++) {
f += C * fabs(x[i]);
@@ -64,11 +58,9 @@ ME_Model::regularized_func_grad(const double C, const Vec & x, Vec & grad)
return f;
}
-double
-ME_Model::constrained_line_search(double C,
- const Vec & x0, const Vec & grad0, const double f0,
- const Vec & dx, Vec & x, Vec & grad1)
-{
+double ME_Model::constrained_line_search(double C, const Vec& x0,
+ const Vec& grad0, const double f0,
+ const Vec& dx, Vec& x, Vec& grad1) {
// compute the orthant to explore
Vec orthant = x0;
for (size_t i = 0; i < orthant.Size(); i++) {
@@ -93,9 +85,8 @@ ME_Model::constrained_line_search(double C,
return f;
}
-vector<double>
-ME_Model::perform_OWLQN(const vector<double> & x0, const double C)
-{
+vector<double> ME_Model::perform_OWLQN(const vector<double>& x0,
+ const double C) {
const size_t dim = x0.size();
Vec x = x0;
@@ -108,18 +99,18 @@ ME_Model::perform_OWLQN(const vector<double> & x0, const double C)
for (int iter = 0; iter < OWLQN_MAX_ITER; iter++) {
Vec pg = pseudo_gradient(x, grad, C);
- fprintf(stderr, "%3d obj(err) = %f (%6.4f)", iter+1, -f, _train_error);
+ fprintf(stderr, "%3d obj(err) = %f (%6.4f)", iter + 1, -f, _train_error);
if (_nheldout > 0) {
const double heldout_logl = heldout_likelihood();
- fprintf(stderr, " heldout_logl(err) = %f (%6.4f)", heldout_logl, _heldout_error);
+ fprintf(stderr, " heldout_logl(err) = %f (%6.4f)", heldout_logl,
+ _heldout_error);
}
fprintf(stderr, "\n");
if (sqrt(dot_product(pg, pg)) < MIN_GRAD_NORM) break;
dx = -1 * approximate_Hg(iter, pg, s, y, z);
- if (dot_product(dx, pg) >= 0)
- dx.Project(-1 * pg);
+ if (dot_product(dx, pg) >= 0) dx.Project(-1 * pg);
Vec x1(dim), grad1(dim);
f = constrained_line_search(C, x, pg, f, dx, x1, grad1);
@@ -134,4 +125,3 @@ ME_Model::perform_OWLQN(const vector<double> & x0, const double C)
return x.STLVec();
}
-
diff --git a/utils/synutils/maxent-3.0/sgd.cpp b/utils/synutils/maxent-3.0/sgd.cpp
index 6d28c23f..8613edca 100644
--- a/utils/synutils/maxent-3.0/sgd.cpp
+++ b/utils/synutils/maxent-3.0/sgd.cpp
@@ -4,23 +4,20 @@
using namespace std;
-//const double SGD_ETA0 = 1;
-//const double SGD_ITER = 30;
-//const double SGD_ALPHA = 0.85;
-
+// const double SGD_ETA0 = 1;
+// const double SGD_ITER = 30;
+// const double SGD_ALPHA = 0.85;
//#define FOLOS_NAIVE
//#define FOLOS_LAZY
#define SGD_CP
-inline void
-apply_l1_penalty(const int i, const double u,
- vector<double> & _vl, vector<double> & q)
-{
- double & w = _vl[i];
+inline void apply_l1_penalty(const int i, const double u, vector<double>& _vl,
+ vector<double>& q) {
+ double& w = _vl[i];
const double z = w;
- double & qi = q[i];
- if (w > 0) {
+ double& qi = q[i];
+ if (w > 0) {
w = max(0.0, w - (u + qi));
} else if (w < 0) {
w = min(0.0, w + (u - qi));
@@ -28,31 +25,29 @@ apply_l1_penalty(const int i, const double u,
qi += w - z;
}
-static double
-l1norm(const vector<double>& v)
-{
+static double l1norm(const vector<double>& v) {
double sum = 0;
for (size_t i = 0; i < v.size(); i++) sum += abs(v[i]);
return sum;
}
-inline void
-update_folos_lazy(const int iter_sample,
- const int k, vector<double> & _vl, const vector<double> & sum_eta,
- vector<int> & last_updated)
-{
+inline void update_folos_lazy(const int iter_sample, const int k,
+ vector<double>& _vl,
+ const vector<double>& sum_eta,
+ vector<int>& last_updated) {
const double penalty = sum_eta[iter_sample] - sum_eta[last_updated[k]];
- double & x = _vl[k];
- if (x > 0) x = max(0.0, x - penalty);
- else x = min(0.0, x + penalty);
+ double& x = _vl[k];
+ if (x > 0)
+ x = max(0.0, x - penalty);
+ else
+ x = min(0.0, x + penalty);
last_updated[k] = iter_sample;
}
-int
-ME_Model::perform_SGD()
-{
+int ME_Model::perform_SGD() {
if (_l2reg > 0) {
- cerr << "error: L2 regularization is currently not supported in SGD mode." << endl;
+ cerr << "error: L2 regularization is currently not supported in SGD mode."
+ << endl;
exit(1);
}
@@ -85,95 +80,113 @@ ME_Model::perform_SGD()
double logl = 0;
int ncorrect = 0, ntotal = 0;
for (size_t i = 0; i < _vs.size(); i++, ntotal++, iter_sample++) {
- const Sample & s = _vs[ri[i]];
+ const Sample& s = _vs[ri[i]];
#ifdef FOLOS_LAZY
- 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++) {
- update_folos_lazy(iter_sample, *k, _vl, sum_eta, last_updated);
- }
+ 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++) {
+ update_folos_lazy(iter_sample, *k, _vl, sum_eta, last_updated);
+ }
}
#endif
vector<double> membp(_num_classes);
const int max_label = conditional_probability(s, membp);
- const double eta = eta0 * pow(SGD_ALPHA, (double)iter_sample / _vs.size()); // exponential decay
- // const double eta = eta0 / (1.0 + (double)iter_sample / _vs.size());
+ const double eta =
+ eta0 * pow(SGD_ALPHA,
+ (double)iter_sample / _vs.size()); // exponential decay
+ // const double eta = eta0 / (1.0 + (double)iter_sample /
+ // _vs.size());
- // if (iter_sample % _vs.size() == 0) cerr << "eta = " << eta << endl;
+ // if (iter_sample % _vs.size() == 0) cerr << "eta = " << eta <<
+ // endl;
u += eta * l1param;
sum_eta.push_back(sum_eta.back() + eta * l1param);
-
+
logl += log(membp[s.label]);
if (max_label == s.label) ncorrect++;
// binary features
- 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++) {
- const double me = membp[_fb.Feature(*k).label()];
- const double ee = (_fb.Feature(*k).label() == s.label ? 1.0 : 0);
- const double grad = (me - ee);
- _vl[*k] -= eta * grad;
+ 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++) {
+ const double me = membp[_fb.Feature(*k).label()];
+ const double ee = (_fb.Feature(*k).label() == s.label ? 1.0 : 0);
+ const double grad = (me - ee);
+ _vl[*k] -= eta * grad;
#ifdef SGD_CP
- apply_l1_penalty(*k, u, _vl, q);
+ apply_l1_penalty(*k, u, _vl, q);
#endif
- }
+ }
}
// real-valued features
- 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++) {
- const double me = membp[_fb.Feature(*k).label()];
- const double ee = (_fb.Feature(*k).label() == s.label ? 1.0 : 0);
- const double grad = (me - ee) * j->second;
- _vl[*k] -= eta * grad;
+ 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++) {
+ const double me = membp[_fb.Feature(*k).label()];
+ const double ee = (_fb.Feature(*k).label() == s.label ? 1.0 : 0);
+ const double grad = (me - ee) * j->second;
+ _vl[*k] -= eta * grad;
#ifdef SGD_CP
- apply_l1_penalty(*k, u, _vl, q);
+ apply_l1_penalty(*k, u, _vl, q);
#endif
- }
+ }
}
#ifdef FOLOS_NAIVE
for (size_t j = 0; j < d; j++) {
- double & x = _vl[j];
- if (x > 0) x = max(0.0, x - eta * l1param);
- else x = min(0.0, x + eta * l1param);
+ double& x = _vl[j];
+ if (x > 0)
+ x = max(0.0, x - eta * l1param);
+ else
+ x = min(0.0, x + eta * l1param);
}
#endif
-
}
logl /= _vs.size();
- // fprintf(stderr, "%4d logl = %8.3f acc = %6.4f ", iter, logl, (double)ncorrect / ntotal);
+// fprintf(stderr, "%4d logl = %8.3f acc = %6.4f ", iter, logl,
+// (double)ncorrect / ntotal);
#ifdef FOLOS_LAZY
if (l1param > 0) {
for (size_t j = 0; j < d; j++)
- update_folos_lazy(iter_sample, j, _vl, sum_eta, last_updated);
+ update_folos_lazy(iter_sample, j, _vl, sum_eta, last_updated);
}
#endif
double f = logl;
if (l1param > 0) {
- const double l1 = l1norm(_vl); // this is not accurate when lazy update is used
- // cerr << "f0 = " << update_model_expectation() - l1param * l1 << " ";
+ const double l1 =
+ l1norm(_vl); // this is not accurate when lazy update is used
+ // cerr << "f0 = " << update_model_expectation() - l1param * l1 << "
+ // ";
f -= l1param * l1;
int nonzero = 0;
- for (int j = 0; j < d; j++) if (_vl[j] != 0) nonzero++;
- // cerr << " f = " << f << " l1 = " << l1 << " nonzero_features = " << nonzero << endl;
+ for (int j = 0; j < d; j++)
+ if (_vl[j] != 0) nonzero++;
+ // cerr << " f = " << f << " l1 = " << l1 << " nonzero_features = "
+ // << nonzero << endl;
}
- // fprintf(stderr, "%4d obj = %7.3f acc = %6.4f", iter+1, f, (double)ncorrect/ntotal);
+ // fprintf(stderr, "%4d obj = %7.3f acc = %6.4f", iter+1, f,
+ // (double)ncorrect/ntotal);
// fprintf(stderr, "%4d obj = %f", iter+1, f);
- fprintf(stderr, "%3d obj(err) = %f (%6.4f)", iter+1, f, 1 - (double)ncorrect/ntotal);
+ fprintf(stderr, "%3d obj(err) = %f (%6.4f)", iter + 1, f,
+ 1 - (double)ncorrect / ntotal);
if (_nheldout > 0) {
double heldout_logl = heldout_likelihood();
- // fprintf(stderr, " heldout_logl = %f acc = %6.4f\n", heldout_logl, 1 - _heldout_error);
- fprintf(stderr, " heldout_logl(err) = %f (%6.4f)", heldout_logl, _heldout_error);
+ // fprintf(stderr, " heldout_logl = %f acc = %6.4f\n",
+ // heldout_logl, 1 - _heldout_error);
+ fprintf(stderr, " heldout_logl(err) = %f (%6.4f)", heldout_logl,
+ _heldout_error);
}
fprintf(stderr, "\n");
-
-
}
return 0;
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