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#include "candidate_set.h"
#ifdef HAVE_CXX11
# include <unordered_set>
#else
# include <tr1/unordered_set>
namespace std { using std::tr1::unordered_set; }
#endif
#include <boost/functional/hash.hpp>
#include "verbose.h"
#include "ns.h"
#include "filelib.h"
#include "wordid.h"
#include "tdict.h"
#include "hg.h"
#include "kbest.h"
#include "viterbi.h"
using namespace std;
namespace training {
struct ApproxVectorHasher {
static const size_t MASK = 0xFFFFFFFFull;
union UType {
double f; // leave as double
size_t i;
};
static inline double round(const double x) {
UType t;
t.f = x;
size_t r = t.i & MASK;
if ((r << 1) > MASK)
t.i += MASK - r + 1;
else
t.i &= (1ull - MASK);
return t.f;
}
size_t operator()(const SparseVector<double>& x) const {
size_t h = 0x573915839;
for (SparseVector<double>::const_iterator it = x.begin(); it != x.end(); ++it) {
UType t;
t.f = it->second;
if (t.f) {
size_t z = (t.i >> 32);
boost::hash_combine(h, it->first);
boost::hash_combine(h, z);
}
}
return h;
}
};
struct ApproxVectorEquals {
bool operator()(const SparseVector<double>& a, const SparseVector<double>& b) const {
SparseVector<double>::const_iterator bit = b.begin();
for (SparseVector<double>::const_iterator ait = a.begin(); ait != a.end(); ++ait) {
if (bit == b.end() ||
ait->first != bit->first ||
ApproxVectorHasher::round(ait->second) != ApproxVectorHasher::round(bit->second))
return false;
++bit;
}
if (bit != b.end()) return false;
return true;
}
};
struct CandidateCompare {
bool operator()(const Candidate& a, const Candidate& b) const {
ApproxVectorEquals eq;
return (a.ewords == b.ewords && eq(a.fmap,b.fmap));
}
};
struct CandidateHasher {
size_t operator()(const Candidate& x) const {
boost::hash<vector<WordID> > hhasher;
ApproxVectorHasher vhasher;
size_t ha = hhasher(x.ewords);
boost::hash_combine(ha, vhasher(x.fmap));
return ha;
}
};
static void ParseSparseVector(string& line, size_t cur, SparseVector<double>* out) {
SparseVector<double>& x = *out;
size_t last_start = cur;
size_t last_comma = string::npos;
while(cur <= line.size()) {
if (line[cur] == ' ' || cur == line.size()) {
if (!(cur > last_start && last_comma != string::npos && cur > last_comma)) {
cerr << "[ERROR] " << line << endl << " position = " << cur << endl;
exit(1);
}
const int fid = FD::Convert(line.substr(last_start, last_comma - last_start));
if (cur < line.size()) line[cur] = 0;
const double val = strtod(&line[last_comma + 1], NULL);
x.set_value(fid, val);
last_comma = string::npos;
last_start = cur+1;
} else {
if (line[cur] == '=')
last_comma = cur;
}
++cur;
}
}
void CandidateSet::WriteToFile(const string& file) const {
WriteFile wf(file);
ostream& out = *wf.stream();
out.precision(10);
string ss;
for (unsigned i = 0; i < cs.size(); ++i) {
out << TD::GetString(cs[i].ewords) << endl;
out << cs[i].fmap << endl;
cs[i].eval_feats.Encode(&ss);
out << ss << endl;
}
}
void CandidateSet::ReadFromFile(const string& file) {
if(!SILENT) cerr << "Reading candidates from " << file << endl;
ReadFile rf(file);
istream& in = *rf.stream();
string cand;
string feats;
string ss;
while(getline(in, cand)) {
getline(in, feats);
getline(in, ss);
assert(in);
cs.push_back(Candidate());
TD::ConvertSentence(cand, &cs.back().ewords);
ParseSparseVector(feats, 0, &cs.back().fmap);
cs.back().eval_feats = SufficientStats(ss);
}
if(!SILENT) cerr << " read " << cs.size() << " candidates\n";
}
void CandidateSet::Dedup() {
if(!SILENT) cerr << "Dedup in=" << cs.size();
unordered_set<Candidate, CandidateHasher, CandidateCompare> u;
while(cs.size() > 0) {
u.insert(cs.back());
cs.pop_back();
}
unordered_set<Candidate, CandidateHasher, CandidateCompare>::iterator it = u.begin();
while (it != u.end()) {
cs.push_back(*it);
it = u.erase(it);
}
if(!SILENT) cerr << " out=" << cs.size() << endl;
}
void CandidateSet::AddKBestCandidates(const Hypergraph& hg, size_t kbest_size, const SegmentEvaluator* scorer) {
KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(hg, kbest_size);
for (unsigned i = 0; i < kbest_size; ++i) {
const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
kbest.LazyKthBest(hg.nodes_.size() - 1, i);
if (!d) break;
cs.push_back(Candidate(d->yield, d->feature_values));
if (scorer)
scorer->Evaluate(d->yield, &cs.back().eval_feats);
}
Dedup();
}
}
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