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#ifndef _DTRAIN_UPDATE_H_
#define _DTRAIN_UPDATE_H_
namespace dtrain
{
bool
_cmp(ScoredHyp a, ScoredHyp b)
{
return a.gold > b.gold;
}
/*
* multipartite ranking
* sort (descending) by bleu
* compare top X (hi) to middle Y (med) and low X (lo)
* cmp middle Y to low X
*/
inline size_t
CollectUpdates(vector<ScoredHyp>* s,
SparseVector<weight_t>& updates,
float margin=0.)
{
size_t num_up = 0;
size_t sz = s->size();
if (sz < 2) return 0;
sort(s->begin(), s->end(), _cmp);
size_t sep = round(sz*0.1);
size_t sep_hi = sep;
if (sz > 4) {
while (sep_hi<sz && (*s)[sep_hi-1].gold==(*s)[sep_hi].gold)
++sep_hi;
}
else sep_hi = 1;
for (size_t i = 0; i < sep_hi; i++) {
for (size_t j = sep_hi; j < sz; j++) {
if (((*s)[i].model-(*s)[j].model) > margin
|| (*s)[i].gold == (*s)[j].gold)
continue;
updates += (*s)[i].f-(*s)[j].f;
num_up++;
}
}
size_t sep_lo = sz-sep;
while (sep_lo>=sep_hi && (*s)[sep_lo].gold==(*s)[sep_lo+1].gold)
--sep_lo;
for (size_t i = sep_hi; i < sep_lo; i++) {
for (size_t j = sep_lo; j < sz; j++) {
if (((*s)[i].model-(*s)[j].model) > margin
|| (*s)[i].gold == (*s)[j].gold)
continue;
updates += (*s)[i].f-(*s)[j].f;
num_up++;
}
}
return num_up;
}
} // namespace
#endif
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