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Diffstat (limited to 'utils/synutils/maxent-3.0/sgd.cpp')
| -rw-r--r-- | utils/synutils/maxent-3.0/sgd.cpp | 193 |
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; -} |