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#ifndef _DTRAIN_KBESTGET_H_
#define _DTRAIN_KBESTGET_H_
#include "kbest.h" // cdec
#include "verbose.h"
#include "viterbi.h"
#include "ff_register.h"
#include "decoder.h"
#include "weights.h"
using namespace std;
namespace dtrain
{
typedef double score_t;
struct ScoredHyp
{
vector<WordID> w;
SparseVector<double> f;
score_t model;
score_t score;
unsigned rank;
};
struct LocalScorer
{
unsigned N_;
vector<score_t> w_;
virtual score_t
Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned rank)=0;
void Reset() {} // only for approx bleu
inline void
Init(unsigned N, vector<score_t> weights)
{
assert(N > 0);
N_ = N;
if (weights.empty()) for (unsigned i = 0; i < N_; i++) w_.push_back(1./N_);
else w_ = weights;
}
inline score_t
brevity_penaly(const unsigned hyp_len, const unsigned ref_len)
{
if (hyp_len > ref_len) return 1;
return exp(1 - (score_t)ref_len/hyp_len);
}
};
struct HypSampler : public DecoderObserver
{
LocalScorer* scorer_;
vector<WordID>* ref_;
virtual vector<ScoredHyp>* GetSamples()=0;
inline void SetScorer(LocalScorer* scorer) { scorer_ = scorer; }
inline void SetRef(vector<WordID>& ref) { ref_ = &ref; }
};
///////////////////////////////////////////////////////////////////////////////
struct KBestGetter : public HypSampler
{
const unsigned k_;
const string filter_type_;
vector<ScoredHyp> s_;
KBestGetter(const unsigned 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 (unsigned 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);
h.rank = i;
h.score = scorer_->Score(h.w, *ref_, i);
s_.push_back(h);
}
}
void
KBestNoFilter(const Hypergraph& forest)
{
s_.clear();
KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(forest, k_);
for (unsigned 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);
h.rank = i;
h.score = scorer_->Score(h.w, *ref_, i);
s_.push_back(h);
}
}
};
} // namespace
#endif
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