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#include "risk.h"
#include "prob.h"
#include "candidate_set.h"
#include "ns.h"
using namespace std;
namespace training {
// g = \sum_e p(e|f) * loss(e) * (phi(e,f) - E[phi(e,f)])
double CandidateSetRisk::operator()(const vector<double>& params,
SparseVector<double>* g) const {
prob_t z;
for (unsigned i = 0; i < cands_.size(); ++i) {
const prob_t u(cands_[i].fmap.dot(params), init_lnx());
z += u;
}
const double log_z = log(z);
SparseVector<double> exp_feats;
if (g) {
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);
exp_feats += cands_[i].fmap * prob;
}
}
double risk = 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 cost = metric_.IsErrorMetric() ? metric_.ComputeScore(cands_[i].eval_feats)
: 1.0 - metric_.ComputeScore(cands_[i].eval_feats);
const double r = prob * cost;
risk += r;
if (g) (*g) += (cands_[i].fmap - exp_feats) * r;
}
return risk;
}
}
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