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-rw-r--r--utils/synutils/maxent-3.0/Makefile.am10
-rw-r--r--utils/synutils/maxent-3.0/lbfgs.cpp108
-rw-r--r--utils/synutils/maxent-3.0/lbfgs.h19
-rw-r--r--utils/synutils/maxent-3.0/mathvec.h87
-rw-r--r--utils/synutils/maxent-3.0/maxent.cpp703
-rw-r--r--utils/synutils/maxent-3.0/maxent.h402
-rw-r--r--utils/synutils/maxent-3.0/owlqn.cpp127
-rw-r--r--utils/synutils/maxent-3.0/sgd.cpp193
8 files changed, 0 insertions, 1649 deletions
diff --git a/utils/synutils/maxent-3.0/Makefile.am b/utils/synutils/maxent-3.0/Makefile.am
deleted file mode 100644
index 64bb038c..00000000
--- a/utils/synutils/maxent-3.0/Makefile.am
+++ /dev/null
@@ -1,10 +0,0 @@
-noinst_LIBRARIES = libtsuruoka_maxent.a
-
-libtsuruoka_maxent_a_SOURCES = \
- lbfgs.cpp \
- maxent.cpp \
- owlqn.cpp \
- sgd.cpp
-
-AM_CPPFLAGS = -W -Wall
-
diff --git a/utils/synutils/maxent-3.0/lbfgs.cpp b/utils/synutils/maxent-3.0/lbfgs.cpp
deleted file mode 100644
index bd26f048..00000000
--- a/utils/synutils/maxent-3.0/lbfgs.cpp
+++ /dev/null
@@ -1,108 +0,0 @@
-#include <vector>
-#include <iostream>
-#include <cmath>
-#include <stdio.h>
-#include "mathvec.h"
-#include "lbfgs.h"
-#include "maxent.h"
-
-using namespace std;
-
-const static int M = LBFGS_M;
-const static double LINE_SEARCH_ALPHA = 0.1;
-const static double LINE_SEARCH_BETA = 0.5;
-
-// stopping criteria
-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 t = 1.0 / LINE_SEARCH_BETA;
-
- double f;
- do {
- t *= LINE_SEARCH_BETA;
- x = x0 + t * dx;
- f = FunctionGradient(x.STLVec(), grad1.STLVec());
- // cout << "*";
- } while (f > f0 + LINE_SEARCH_ALPHA * t * dot_product(dx, grad0));
-
- return f;
-}
-
-//
-// 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[]) {
- int offset, bound;
- 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];
- }
- if (iter > 0) {
- const int j = (iter - 1) % M;
- const double gamma = ((1.0 / z[j]) / dot_product(y[j], y[j]));
- // static double gamma;
- // if (gamma == 0) gamma = ((1.0 / z[j]) / dot_product(y[j], y[j]));
- q *= gamma;
- }
- 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]);
- }
-
- return q;
-}
-
-vector<double> ME_Model::perform_LBFGS(const vector<double>& x0) {
- const size_t dim = x0.size();
- Vec x = x0;
-
- Vec grad(dim), dx(dim);
- double f = FunctionGradient(x.STLVec(), grad.STLVec());
-
- Vec s[M], y[M];
- double z[M]; // rho
-
- for (int iter = 0; iter < LBFGS_MAX_ITER; iter++) {
-
- 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, "\n");
-
- if (sqrt(dot_product(grad, grad)) < MIN_GRAD_NORM) break;
-
- dx = -1 * approximate_Hg(iter, grad, s, y, z);
-
- Vec x1(dim), grad1(dim);
- f = backtracking_line_search(x, grad, f, dx, x1, grad1);
-
- s[iter % M] = x1 - x;
- y[iter % M] = grad1 - grad;
- z[iter % M] = 1.0 / dot_product(y[iter % M], s[iter % M]);
- x = x1;
- grad = grad1;
- }
-
- return x.STLVec();
-}
diff --git a/utils/synutils/maxent-3.0/lbfgs.h b/utils/synutils/maxent-3.0/lbfgs.h
deleted file mode 100644
index ed5cd944..00000000
--- a/utils/synutils/maxent-3.0/lbfgs.h
+++ /dev/null
@@ -1,19 +0,0 @@
-#ifndef _LBFGS_H_
-#define _LBFGS_H_
-
-// 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_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;
-
-#endif
diff --git a/utils/synutils/maxent-3.0/mathvec.h b/utils/synutils/maxent-3.0/mathvec.h
deleted file mode 100644
index f8c60e5d..00000000
--- a/utils/synutils/maxent-3.0/mathvec.h
+++ /dev/null
@@ -1,87 +0,0 @@
-#ifndef _MATH_VECTOR_H_
-#define _MATH_VECTOR_H_
-
-#include <vector>
-#include <iostream>
-#include <cassert>
-
-class Vec {
- private:
- std::vector<double> _v;
-
- 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; }
- 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) {
- 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) {
- for (size_t i = 0; i < _v.size(); i++) {
- _v[i] *= c;
- }
- return *this;
- }
- 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;
- }
- }
-};
-
-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];
- }
- return sum;
-}
-
-inline std::ostream& operator<<(std::ostream& s, const Vec& a) {
- s << "(";
- for (size_t i = 0; i < a.Size(); i++) {
- if (i != 0) s << ", ";
- s << a[i];
- }
- s << ")";
- return s;
-}
-
-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++) {
- v[i] = a[i] + b[i];
- }
- return v;
-}
-
-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++) {
- v[i] = a[i] - b[i];
- }
- return v;
-}
-
-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;
- }
- return v;
-}
-
-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
deleted file mode 100644
index 8d00ac1d..00000000
--- a/utils/synutils/maxent-3.0/maxent.cpp
+++ /dev/null
@@ -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
- *
- */
diff --git a/utils/synutils/maxent-3.0/maxent.h b/utils/synutils/maxent-3.0/maxent.h
deleted file mode 100644
index 88a04e25..00000000
--- a/utils/synutils/maxent-3.0/maxent.h
+++ /dev/null
@@ -1,402 +0,0 @@
-/*
- * $Id: maxent.h,v 1.1.1.1 2007/05/15 08:30:35 kyoshida Exp $
- */
-
-#ifndef __MAXENT_H_
-#define __MAXENT_H_
-
-#include <string>
-#include <vector>
-#include <list>
-#include <map>
-#include <algorithm>
-#include <iostream>
-#include <string>
-#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)
-#ifdef USE_HASH_MAP
-#include <ext/hash_map>
-#endif
-
-//
-// data format for each sample for training/testing
-//
-struct ME_Sample {
- public:
- ME_Sample() : label("") {};
- 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); }
-
- // 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));
- }
-
- 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
- }
-};
-
-//
-// for those who want to use load_from_array()
-//
-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);
- 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;
- 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;
- };
- 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;
- }
- bool load_from_array(const ME_Model_Data data[]);
- void set_reference_model(const ME_Model& ref_model) {
- _ref_modelp = &ref_model;
- };
- void clear();
-
- ME_Model() {
- _l1reg = _l2reg = 0;
- _nheldout = 0;
- _early_stopping_n = 0;
- _ref_modelp = NULL;
- _optimization_method = LBFGS;
- }
-
- public:
- // obsolete. just for downward compatibility
- 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;
- double SGD_ETA0;
- 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 {
- for (unsigned int i = 0; i < positive_features.size(); i++) {
- if (i >= x.positive_features.size()) return false;
- int v0 = positive_features[i];
- int v1 = x.positive_features[i];
- if (v0 < v1) return true;
- if (v0 > v1) return false;
- }
- return false;
- }
- };
-
- 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);
- // };
- // int label() const { return _body >> 24; }
- // int feature() const { return _body & 0xffffff; }
- ME_Feature(const int l, const int f) : _body((f << 8) + l) {
- assert(l >= 0 && l <= MAX_LABEL_TYPES);
- assert(f >= 0 && f <= 0xffffff);
- };
- int label() const { return _body & 0xff; }
- int feature() const { return _body >> 8; }
- unsigned int body() const { return _body; }
-
- private:
- unsigned int _body;
- };
-
- struct ME_FeatureBag {
-#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 mef2id;
- std::vector<ME_Feature> id2mef;
- int Put(const ME_Feature& i) {
- map_type::const_iterator j = mef2id.find(i.body());
- if (j == mef2id.end()) {
- int id = id2mef.size();
- id2mef.push_back(i);
- mef2id[i.body()] = id;
- return id;
- }
- return j->second;
- }
- int Id(const ME_Feature& i) const {
- map_type::const_iterator j = mef2id.find(i.body());
- if (j == mef2id.end()) {
- return -1;
- }
- return j->second;
- }
- ME_Feature Feature(int id) const {
- assert(id >= 0 && id < (int)id2mef.size());
- return id2mef[id];
- }
- int Size() const { return id2mef.size(); }
- void Clear() {
- mef2id.clear();
- id2mef.clear();
- }
- };
-
- 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());
- size_t v = 0;
- int n = s.size() / 4;
- for (int i = 0; i < n; i++, p++) {
- // v ^= *p;
- v ^= *p << (4 * (i % 2)); // note) 0 <= char < 128
- }
- int m = s.size() % 4;
- for (int i = 0; i < m; i++) {
- v ^= s[4 * n + i] << (i * 8);
- }
- return v;
- }
- };
-
- struct MiniStringBag {
-#ifdef USE_HASH_MAP
- typedef __gnu_cxx::hash_map<std::string, int, hashfun_str> map_type;
-#else
- typedef std::map<std::string, int> map_type;
-#endif
- int _size;
- map_type str2id;
- MiniStringBag() : _size(0) {}
- int Put(const std::string& i) {
- map_type::const_iterator j = str2id.find(i);
- if (j == str2id.end()) {
- int id = _size;
- _size++;
- str2id[i] = id;
- return id;
- }
- return j->second;
- }
- int Id(const std::string& i) const {
- map_type::const_iterator j = str2id.find(i);
- if (j == str2id.end()) return -1;
- return j->second;
- }
- int Size() const { return _size; }
- void Clear() {
- str2id.clear();
- _size = 0;
- }
- map_type::const_iterator begin() const { return str2id.begin(); }
- map_type::const_iterator end() const { return str2id.end(); }
- };
-
- struct StringBag : public MiniStringBag {
- std::vector<std::string> id2str;
- int Put(const std::string& i) {
- map_type::const_iterator j = str2id.find(i);
- if (j == str2id.end()) {
- int id = id2str.size();
- id2str.push_back(i);
- str2id[i] = id;
- return id;
- }
- return j->second;
- }
- std::string Str(const int id) const {
- assert(id >= 0 && id < (int)id2str.size());
- return id2str[id];
- }
- int Size() const { return id2str.size(); }
- void Clear() {
- str2id.clear();
- id2str.clear();
- }
- };
-
- std::vector<Sample> _vs; // vector of training_samples
- StringBag _label_bag;
- MiniStringBag _featurename_bag;
- std::vector<double> _vl; // vector of lambda
- ME_FeatureBag _fb;
- 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
- int _nheldout;
- int _early_stopping_n;
- std::vector<double> _vhlogl;
- const ME_Model* _ref_modelp;
-
- double heldout_likelihood();
- 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;
- 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;
- 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);
-};
-
-#endif
-
-/*
- * $Log: maxent.h,v $
- * Revision 1.1.1.1 2007/05/15 08:30:35 kyoshida
- * stepp tagger, by Okanohara and Tsuruoka
- *
- * Revision 1.24 2006/08/21 17:30:38 tsuruoka
- * use MAX_LABEL_TYPES
- *
- * Revision 1.23 2006/07/25 13:19:53 tsuruoka
- * sort _vs[]
- *
- * Revision 1.22 2006/07/18 11:13:15 tsuruoka
- * modify comments
- *
- * Revision 1.21 2006/07/18 10:02:15 tsuruoka
- * remove sample2feature[]
- * speed up conditional_probability()
- *
- * Revision 1.20 2006/07/18 05:10:51 tsuruoka
- * add ref_dist
- *
- * Revision 1.19 2005/12/23 10:33:02 tsuruoka
- * support real-valued features
- *
- * Revision 1.18 2005/12/23 09:15:29 tsuruoka
- * modify _train to reduce memory consumption
- *
- * Revision 1.17 2005/10/28 13:02:34 tsuruoka
- * set_heldout(): add default value
- * Feature()
- *
- * Revision 1.16 2005/09/12 13:51:16 tsuruoka
- * Sample: list -> vector
- *
- * Revision 1.15 2005/09/12 13:27:10 tsuruoka
- * add add_training_sample()
- *
- * Revision 1.14 2005/04/27 11:22:27 tsuruoka
- * bugfix
- * ME_Sample: list -> vector
- *
- * Revision 1.13 2005/04/27 10:20:19 tsuruoka
- * MiniStringBag -> StringBag
- *
- * Revision 1.12 2005/04/27 10:00:42 tsuruoka
- * remove tmpfb
- *
- * Revision 1.11 2005/04/26 14:25:53 tsuruoka
- * add MiniStringBag, USE_HASH_MAP
- *
- * Revision 1.10 2004/10/04 05:50:25 tsuruoka
- * add Clear()
- *
- * Revision 1.9 2004/08/09 12:27:21 tsuruoka
- * change messages
- *
- * Revision 1.8 2004/08/04 13:55:19 tsuruoka
- * modify _sample2feature
- *
- * Revision 1.7 2004/07/29 05:51:13 tsuruoka
- * remove modeldata.h
- *
- * Revision 1.6 2004/07/28 13:42:58 tsuruoka
- * add AGIS
- *
- * Revision 1.5 2004/07/28 05:54:14 tsuruoka
- * get_class_name() -> get_class_label()
- * ME_Feature: bugfix
- *
- * Revision 1.4 2004/07/27 16:58:47 tsuruoka
- * modify the interface of classify()
- *
- * Revision 1.3 2004/07/26 17:23:46 tsuruoka
- * _sample2feature: list -> vector
- *
- * Revision 1.2 2004/07/26 15:49:23 tsuruoka
- * modify ME_Feature
- *
- * Revision 1.1 2004/07/26 13:10:55 tsuruoka
- * add files
- *
- * Revision 1.18 2004/07/22 08:34:45 tsuruoka
- * modify _sample2feature[]
- *
- * Revision 1.17 2004/07/21 16:33:01 tsuruoka
- * remove some comments
- *
- */
diff --git a/utils/synutils/maxent-3.0/owlqn.cpp b/utils/synutils/maxent-3.0/owlqn.cpp
deleted file mode 100644
index c3a0f0da..00000000
--- a/utils/synutils/maxent-3.0/owlqn.cpp
+++ /dev/null
@@ -1,127 +0,0 @@
-#include <vector>
-#include <iostream>
-#include <cmath>
-#include <stdio.h>
-#include "mathvec.h"
-#include "lbfgs.h"
-#include "maxent.h"
-
-using namespace std;
-
-const static int M = LBFGS_M;
-const static double LINE_SEARCH_ALPHA = 0.1;
-const static double LINE_SEARCH_BETA = 0.5;
-
-// stopping criteria
-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[]);
-
-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) {
- Vec grad = grad0;
- for (size_t i = 0; i < x.Size(); i++) {
- if (x[i] != 0) {
- grad[i] += C * sign(x[i]);
- continue;
- }
- const double gm = grad0[i] - C;
- if (gm > 0) {
- grad[i] = gm;
- continue;
- }
- const double gp = grad0[i] + C;
- if (gp < 0) {
- grad[i] = gp;
- continue;
- }
- grad[i] = 0;
- }
-
- return 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]);
- }
-
- 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) {
- // compute the orthant to explore
- Vec orthant = x0;
- for (size_t i = 0; i < orthant.Size(); i++) {
- if (orthant[i] == 0) orthant[i] = -grad0[i];
- }
-
- double t = 1.0 / LINE_SEARCH_BETA;
-
- double f;
- do {
- t *= LINE_SEARCH_BETA;
- x = x0 + t * dx;
- x.Project(orthant);
- // for (size_t i = 0; i < x.Size(); i++) {
- // if (x0[i] != 0 && sign(x[i]) != sign(x0[i])) x[i] = 0;
- // }
-
- f = regularized_func_grad(C, x, grad1);
- // cout << "*";
- } while (f > f0 + LINE_SEARCH_ALPHA * dot_product(x - x0, grad0));
-
- return f;
-}
-
-vector<double> ME_Model::perform_OWLQN(const vector<double>& x0,
- const double C) {
- const size_t dim = x0.size();
- Vec x = x0;
-
- Vec grad(dim), dx(dim);
- double f = regularized_func_grad(C, x, grad);
-
- Vec s[M], y[M];
- double z[M]; // rho
-
- 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);
- if (_nheldout > 0) {
- const double heldout_logl = heldout_likelihood();
- 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);
-
- Vec x1(dim), grad1(dim);
- f = constrained_line_search(C, x, pg, f, dx, x1, grad1);
-
- s[iter % M] = x1 - x;
- y[iter % M] = grad1 - grad;
- z[iter % M] = 1.0 / dot_product(y[iter % M], s[iter % M]);
-
- x = x1;
- grad = grad1;
- }
-
- return x.STLVec();
-}
diff --git a/utils/synutils/maxent-3.0/sgd.cpp b/utils/synutils/maxent-3.0/sgd.cpp
deleted file mode 100644
index 8613edca..00000000
--- a/utils/synutils/maxent-3.0/sgd.cpp
+++ /dev/null
@@ -1,193 +0,0 @@
-#include "maxent.h"
-#include <cmath>
-#include <stdio.h>
-
-using namespace std;
-
-// 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];
- const double z = w;
- 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));
- }
- qi += w - z;
-}
-
-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) {
- 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);
- last_updated[k] = iter_sample;
-}
-
-int ME_Model::perform_SGD() {
- if (_l2reg > 0) {
- cerr << "error: L2 regularization is currently not supported in SGD mode."
- << endl;
- exit(1);
- }
-
- cerr << "performing SGD" << endl;
-
- const double l1param = _l1reg;
-
- const int d = _fb.Size();
-
- vector<int> ri(_vs.size());
- for (size_t i = 0; i < ri.size(); i++) ri[i] = i;
-
- vector<double> grad(d);
- int iter_sample = 0;
- const double eta0 = SGD_ETA0;
-
- // cerr << "l1param = " << l1param << endl;
- cerr << "eta0 = " << eta0 << " alpha = " << SGD_ALPHA << endl;
-
- double u = 0;
- vector<double> q(d, 0);
- vector<int> last_updated(d, 0);
- vector<double> sum_eta;
- sum_eta.push_back(0);
-
- for (int iter = 0; iter < SGD_ITER; iter++) {
-
- random_shuffle(ri.begin(), ri.end());
-
- 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]];
-
-#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);
- }
- }
-#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());
-
- // 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;
-#ifdef SGD_CP
- 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;
-#ifdef SGD_CP
- 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);
- }
-#endif
- }
- logl /= _vs.size();
-// 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);
- }
-#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 << "
- // ";
- 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;
- }
- // 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);
-
- 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, "\n");
- }
-
- return 0;
-}
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