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#ifndef _CANDIDATE_SET_H_
#define _CANDIDATE_SET_H_
#include <vector>
#include <algorithm>
#include "ns.h"
#include "wordid.h"
#include "sparse_vector.h"
class Hypergraph;
namespace training {
struct Candidate {
Candidate() {}
Candidate(const std::vector<WordID>& e, const SparseVector<double>& fm) :
ewords(e),
fmap(fm) {}
Candidate(const std::vector<WordID>& e,
const SparseVector<double>& fm,
const SegmentEvaluator& se) :
ewords(e),
fmap(fm) {
se.Evaluate(ewords, &eval_feats);
}
void swap(Candidate& other) {
eval_feats.swap(other.eval_feats);
ewords.swap(other.ewords);
fmap.swap(other.fmap);
}
std::vector<WordID> ewords;
SparseVector<double> fmap;
SufficientStats eval_feats;
};
// represents some kind of collection of translation candidates, e.g.
// aggregated k-best lists, sample lists, etc.
class CandidateSet {
public:
CandidateSet() {}
inline size_t size() const { return cs.size(); }
const Candidate& operator[](size_t i) const { return cs[i]; }
void ReadFromFile(const std::string& file);
void WriteToFile(const std::string& file) const;
void AddKBestCandidates(const Hypergraph& hg, size_t kbest_size, const SegmentEvaluator* scorer = NULL);
void AddUniqueKBestCandidates(const Hypergraph& hg, size_t kbest_size, const SegmentEvaluator* scorer = NULL);
// TODO add code to draw k samples
private:
void Dedup();
std::vector<Candidate> cs;
};
}
#endif
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