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authorWu, Ke <wuke@cs.umd.edu>2014-10-07 18:44:05 -0400
committerWu, Ke <wuke@cs.umd.edu>2014-10-07 18:44:05 -0400
commit8c26c195213805face566a6407597ba2a871a122 (patch)
tree378301ff345bf465f407f1447ad5fe126b3cd47c /utils/synutils/maxent-3.0/sgd.cpp
parent6c7bf8cf49db88ca47e5b08aa449032995736854 (diff)
Move synutils under utils
Diffstat (limited to 'utils/synutils/maxent-3.0/sgd.cpp')
-rw-r--r--utils/synutils/maxent-3.0/sgd.cpp193
1 files changed, 0 insertions, 193 deletions
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;
-}