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#ifndef _DTRAIN_SCORE_NET_INTERFACE_H_
#define _DTRAIN_SCORE_NET_INTERFACE_H_
#include "dtrain.h"
namespace dtrain
{
struct NgramCounts
{
size_t N_;
map<size_t, weight_t> clipped_;
map<size_t, weight_t> sum_;
NgramCounts(const size_t N) : N_(N) { Zero(); }
inline void
operator+=(const NgramCounts& rhs)
{
if (rhs.N_ > N_) Resize(rhs.N_);
for (size_t i = 0; i < N_; i++) {
this->clipped_[i] += rhs.clipped_.find(i)->second;
this->sum_[i] += rhs.sum_.find(i)->second;
}
}
inline const NgramCounts
operator+(const NgramCounts &other) const
{
NgramCounts result = *this;
result += other;
return result;
}
inline void
Add(const size_t count, const size_t ref_count, const size_t i)
{
assert(i < N_);
if (count > ref_count) {
clipped_[i] += ref_count;
} else {
clipped_[i] += count;
}
sum_[i] += count;
}
inline void
Zero()
{
for (size_t i = 0; i < N_; i++) {
clipped_[i] = 0.;
sum_[i] = 0.;
}
}
inline void
Resize(size_t N)
{
if (N == N_) return;
else if (N > N_) {
for (size_t i = N_; i < N; i++) {
clipped_[i] = 0.;
sum_[i] = 0.;
}
} else { // N < N_
for (size_t i = N_-1; i > N-1; i--) {
clipped_.erase(i);
sum_.erase(i);
}
}
N_ = N;
}
};
typedef map<vector<WordID>, size_t> Ngrams;
inline Ngrams
MakeNgrams(const vector<WordID>& s, const size_t N)
{
Ngrams ngrams;
vector<WordID> ng;
for (size_t i = 0; i < s.size(); i++) {
ng.clear();
for (size_t j = i; j < min(i+N, s.size()); j++) {
ng.push_back(s[j]);
ngrams[ng]++;
}
}
return ngrams;
}
inline NgramCounts
MakeNgramCounts(const vector<WordID>& hyp,
const vector<Ngrams>& ref,
const size_t N)
{
Ngrams hyp_ngrams = MakeNgrams(hyp, N);
NgramCounts counts(N);
Ngrams::iterator it, ti;
for (it = hyp_ngrams.begin(); it != hyp_ngrams.end(); it++) {
size_t max_ref_count = 0;
for (auto r: ref) {
ti = r.find(it->first);
if (ti != r.end())
max_ref_count = max(max_ref_count, ti->second);
}
counts.Add(it->second, min(it->second, max_ref_count), it->first.size()-1);
}
return counts;
}
/*
* per-sentence BLEU
* as in "Optimizing for Sentence-Level BLEU+1
* Yields Short Translations"
* (Nakov et al. '12)
*
* [simply add 1 to reference length for calculation of BP]
*
*/
struct PerSentenceBleuScorer
{
const size_t N_;
vector<weight_t> w_;
PerSentenceBleuScorer(size_t n) : N_(n)
{
for (size_t i = 1; i <= N_; i++)
w_.push_back(1.0/N_);
}
inline weight_t
BrevityPenalty(const size_t hl, const size_t rl)
{
if (hl > rl)
return 1;
return exp(1 - (weight_t)rl/hl);
}
inline size_t
BestMatchLength(const size_t hl,
const vector<size_t>& ref_ls)
{
size_t m;
if (ref_ls.size() == 1) {
m = ref_ls.front();
} else {
size_t i = 0, best_idx = 0;
size_t best = numeric_limits<size_t>::max();
for (auto l: ref_ls) {
size_t d = abs(hl-l);
if (d < best) {
best_idx = i;
best = d;
}
i += 1;
}
m = ref_ls[best_idx];
}
return m;
}
weight_t
Score(const vector<WordID>& hyp,
const vector<Ngrams>& ref_ngs,
const vector<size_t>& ref_ls)
{
size_t hl = hyp.size(), rl = 0;
if (hl == 0) return 0.;
rl = BestMatchLength(hl, ref_ls);
if (rl == 0) return 0.;
NgramCounts counts = MakeNgramCounts(hyp, ref_ngs, N_);
size_t M = N_;
vector<weight_t> v = w_;
if (rl < N_) {
M = rl;
for (size_t i = 0; i < M; i++) v[i] = 1/((weight_t)M);
}
weight_t sum = 0, add = 0;
for (size_t i = 0; i < M; i++) {
if (i == 0 && (counts.sum_[i] == 0 || counts.clipped_[i] == 0)) return 0.;
if (i > 0) add = 1;
sum += v[i] * log(((weight_t)counts.clipped_[i] + add)
/ ((counts.sum_[i] + add)));
}
//return BrevityPenalty(hl, rl+1) * exp(sum);
return BrevityPenalty(hl, rl) * exp(sum);
}
};
} // namespace
#endif
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