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#ifndef _DTRAIN_KBESTGET_H_
#define _DTRAIN_KBESTGET_H_
#include "kbest.h"
namespace dtrain
{
struct ScoredHyp
{
vector<WordID> w;
SparseVector<double> f;
score_t model;
score_t score;
};
struct HypSampler : public DecoderObserver
{
virtual vector<ScoredHyp>* GetSamples() {}
};
struct KBestGetter : public HypSampler
{
const size_t k_;
const string filter_type_;
vector<ScoredHyp> s_;
KBestGetter(const size_t k, const string filter_type) :
k_(k), filter_type_(filter_type) {}
virtual void
NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg)
{
KBest(*hg);
}
vector<ScoredHyp>* GetSamples() { return &s_; }
void
KBest(const Hypergraph& forest)
{
if (filter_type_ == "unique") {
KBestUnique(forest);
} else if (filter_type_ == "no") {
KBestNoFilter(forest);
}
}
void
KBestUnique(const Hypergraph& forest)
{
s_.clear();
KBest::KBestDerivations<vector<WordID>, ESentenceTraversal, KBest::FilterUnique, prob_t, EdgeProb> kbest(forest, k_);
for (size_t i = 0; i < k_; ++i) {
const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal, KBest::FilterUnique, prob_t, EdgeProb>::Derivation* d =
kbest.LazyKthBest(forest.nodes_.size() - 1, i);
if (!d) break;
ScoredHyp h;
h.w = d->yield;
h.f = d->feature_values;
h.model = log(d->score);
s_.push_back(h);
}
}
void
KBestNoFilter(const Hypergraph& forest)
{
s_.clear();
KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(forest, k_);
for (size_t i = 0; i < k_; ++i) {
const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
kbest.LazyKthBest(forest.nodes_.size() - 1, i);
if (!d) break;
ScoredHyp h;
h.w = d->yield;
h.f = d->feature_values;
h.model = log(d->score);
s_.push_back(h);
}
}
};
} // namespace
#endif
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