1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
|
#ifndef _DTRAIN_KSAMPLER_H_
#define _DTRAIN_KSAMPLER_H_
#include "hg_sampler.h" // cdec
#include "kbestget.h"
#include "score.h"
namespace dtrain
{
bool
cmp_hyp_by_model_d(ScoredHyp a, ScoredHyp b)
{
return a.model > b.model;
}
struct KSampler : public HypSampler
{
const unsigned k_;
vector<ScoredHyp> s_;
MT19937* prng_;
score_t (*scorer)(NgramCounts&, const unsigned, const unsigned, unsigned, vector<score_t>);
unsigned src_len_;
explicit KSampler(const unsigned k, MT19937* prng) :
k_(k), prng_(prng) {}
virtual void
NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg)
{
src_len_ = smeta.GetSourceLength();
ScoredSamples(*hg);
}
vector<ScoredHyp>* GetSamples() { return &s_; }
void ScoredSamples(const Hypergraph& forest) {
s_.clear(); sz_ = f_count_ = 0;
std::vector<HypergraphSampler::Hypothesis> samples;
HypergraphSampler::sample_hypotheses(forest, k_, prng_, &samples);
for (unsigned i = 0; i < k_; ++i) {
ScoredHyp h;
h.w = samples[i].words;
h.f = samples[i].fmap;
h.model = log(samples[i].model_score);
h.rank = i;
h.score = scorer_->Score(h.w, *ref_, i, src_len_);
s_.push_back(h);
sz_++;
f_count_ += h.f.size();
}
sort(s_.begin(), s_.end(), cmp_hyp_by_model_d);
for (unsigned i = 0; i < s_.size(); i++) s_[i].rank = i;
}
};
} // namespace
#endif
|
