int FeatureSet::Normalize(Feature& feature)
{
double squareLength = 0;
for (map<int, double>::iterator it = feature.begin(); it != feature.end(); it++)
{
squareLength += (it->second) * (it->second);
}
double length = sqrt(squareLength);
for (map<int, double>::iterator it = feature.begin(); it != feature.end(); it++)
{
it->second /= length;
}
return 0;
}
void ObjectClassificator::addFeature(Feature& feature)
{
newObject(feature);
for (FeatureMap::const_iterator i = feature.begin(); i != feature.end();
++i)
{
addFeature(i->first, i->second);
}
}
int FeatureSet::SaveFeature(Feature& feature, const char* const fileName)
{
FILE* outFile = fopen(fileName, "wb");
int rtn = Write(outFile, (int)feature.size());
CHECK_RTN(rtn);
for (Feature::iterator it = feature.begin(); it != feature.end(); ++it)
{
rtn = Write(outFile, it->first);
CHECK_RTN(rtn);
rtn = Write(outFile, it->second);
CHECK_RTN(rtn);
}
fclose(outFile);
return 0;
}
// Z = XY; X:n1*d1 Y:d1*K
void matrix_mul( double** Z, vector<Feature*>* X, double* Y, int K){
Feature* fea;
Feature::iterator it;
#pragma omp parallel for
for(int i=0;i<X->size();i++){
fea = X->at(i);
for(int k=0;k<K;k++)
Z[i][k] = 0.0;
for(it=fea->begin();it!=fea->end();it++){
for(int k=0;k<K;k++)
Z[i][k] += it->second* Y[it->first*K + k];
}
}
}
