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author | Avneesh Saluja <asaluja@gmail.com> | 2013-03-28 18:28:16 -0700 |
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committer | Avneesh Saluja <asaluja@gmail.com> | 2013-03-28 18:28:16 -0700 |
commit | 3d8d656fa7911524e0e6885647173474524e0784 (patch) | |
tree | 81b1ee2fcb67980376d03f0aa48e42e53abff222 /training/utils/entropy.cc | |
parent | be7f57fdd484e063775d7abf083b9fa4c403b610 (diff) | |
parent | 96fedabebafe7a38a6d5928be8fff767e411d705 (diff) |
fixed conflicts
Diffstat (limited to 'training/utils/entropy.cc')
-rw-r--r-- | training/utils/entropy.cc | 41 |
1 files changed, 41 insertions, 0 deletions
diff --git a/training/utils/entropy.cc b/training/utils/entropy.cc new file mode 100644 index 00000000..4fdbe2be --- /dev/null +++ b/training/utils/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; +} + +} + |