//ADEED_BY_TS
bool operator==(const FeatureStats& f1, const FeatureStats& f2) {
size_t size = f1.size();
if (size != f2.size())
return false;
for (size_t k=0; k < size; k++) {
if (f1.get(k) != f2.get(k))
return false;
}
return true;
}
double Point::operator*(const FeatureStats& F)const{
ncall++;//to track performance
double prod=0.0;
if(OptimizeAll())
for (unsigned i=0; i<size(); i++)
prod+= operator[](i)*F.get(i);
else{
for (unsigned i=0; i<size(); i++)
prod+= operator[](i)*F.get(optindices[i]);
for(map<unsigned,float >::iterator it=fixedweights.begin();it!=fixedweights.end();it++)
prod+=it->second*F.get(it->first);
}
return prod;
}
void Data::outputSample( ostream &out, const FeatureStats &f1, const FeatureStats &f2 )
{
// difference in score in regular features
for(unsigned int j=0; j<f1.size(); j++)
if (abs(f1.get(j)-f2.get(j)) > 0.00001)
out << " F" << j << " " << (f1.get(j)-f2.get(j));
if (!hasSparseFeatures())
return;
out << " ";
// sparse features
const SparseVector &s1 = f1.getSparse();
const SparseVector &s2 = f2.getSparse();
SparseVector diff = s1 - s2;
diff.write(out);
}
