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#ifndef _DTRAIN_KBESTGET_H_
#define _DTRAIN_KBESTGET_H_
#include "kbest.h"
namespace dtrain
{
/*
* KBestList
*
*/
struct KBestList {
vector<SparseVector<double> > feats;
vector<vector<WordID> > sents;
vector<double> model_scores;
vector<double> scores;
size_t GetSize() { return sents.size(); }
};
/*
* KBestGetter
*
*/
struct KBestGetter : public DecoderObserver
{
const size_t k_;
const string filter_type;
KBestList kb;
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 );
}
KBestList* GetKBest() { return &kb; }
void
KBest( const Hypergraph& forest )
{
if ( filter_type == "unique" ) {
KBestUnique( forest );
} else if ( filter_type == "no" ) {
KBestNoFilter( forest );
}
}
void
KBestUnique( const Hypergraph& forest )
{
kb.sents.clear();
kb.feats.clear();
kb.model_scores.clear();
kb.scores.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;
kb.sents.push_back( d->yield);
kb.feats.push_back( d->feature_values );
kb.model_scores.push_back( d->score );
}
}
void
KBestNoFilter( const Hypergraph& forest )
{
kb.sents.clear();
kb.feats.clear();
kb.model_scores.clear();
kb.scores.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;
kb.sents.push_back( d->yield);
kb.feats.push_back( d->feature_values );
kb.model_scores.push_back( d->score );
}
}
};
} // namespace
#endif
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