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author | Chris Dyer <cdyer@cs.cmu.edu> | 2012-06-29 18:45:26 -0700 |
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committer | Chris Dyer <cdyer@cs.cmu.edu> | 2012-06-29 18:45:26 -0700 |
commit | 3044d6d1c6d428e8d06c255e3a2d739bcd187679 (patch) | |
tree | 81baa7315011a57ee363f93f0aa3e1e94affe5d1 /training/entropy.cc | |
parent | c84ef9590d11819b7f8441a53b1699a912d949e1 (diff) |
add option for entropy optimization
Diffstat (limited to 'training/entropy.cc')
-rw-r--r-- | training/entropy.cc | 41 |
1 files changed, 41 insertions, 0 deletions
diff --git a/training/entropy.cc b/training/entropy.cc new file mode 100644 index 00000000..4fdbe2be --- /dev/null +++ b/training/entropy.cc @@ -0,0 +1,41 @@ +#include "entropy.h" + +#include "prob.h" +#include "candidate_set.h" + +using namespace std; + +namespace training { + +// see Mann and McCallum "Efficient Computation of Entropy Gradient ..." for +// a mostly clear derivation of: +// g = E[ F(x,y) * log p(y|x) ] + H(y | x) * E[ F(x,y) ] +double CandidateSetEntropy::operator()(const vector<double>& params, + SparseVector<double>* g) const { + prob_t z; + vector<double> dps(cands_.size()); + for (unsigned i = 0; i < cands_.size(); ++i) { + dps[i] = cands_[i].fmap.dot(params); + const prob_t u(dps[i], init_lnx()); + z += u; + } + const double log_z = log(z); + + SparseVector<double> exp_feats; + double entropy = 0; + for (unsigned i = 0; i < cands_.size(); ++i) { + const double log_prob = cands_[i].fmap.dot(params) - log_z; + const double prob = exp(log_prob); + const double e_logprob = prob * log_prob; + entropy -= e_logprob; + if (g) { + (*g) += cands_[i].fmap * e_logprob; + exp_feats += cands_[i].fmap * prob; + } + } + if (g) (*g) += exp_feats * entropy; + return entropy; +} + +} + |