AttributeList
OGR_Feature::getAttributes() const
{
AttributeTable attrs;
if ( !store_attrs_loaded )
{
const_cast<OGR_Feature*>(this)->loadAttributes();
}
// accumulate the attrs from the store:
for( AttributeTable::const_iterator i = store_attrs.begin(); i != store_attrs.end(); i++ )
{
attrs[ (*i).first ] = (*i).second;
}
// finally add in the user attrs (overwriting the store attrs if necessary)
for( AttributeTable::const_iterator i = getUserAttrs().begin(); i != getUserAttrs().end() ; i++ )
attrs[ (*i).first ] = (*i).second;
// shove it all into a list
AttributeList result;
for( AttributeTable::const_iterator i = attrs.begin(); i != attrs.end(); i++ )
result.push_back( (*i).second );
return result;
}
/// Creates the attribute tables, and in the process creates a count Matrix (cAbove).
/// A dataset consisting of m input variables has m attribute tables.
/// [attribute | class/value | rid ]
void RFTrainer::createAttributeTables(Data<RealVector> const& dataset, AttributeTables& tables){
std::size_t elements = dataset.numberOfElements();
//Each entry in the outer vector is an attribute table
AttributeTable table;
RFAttribute a;
//For each column
for(std::size_t j=0; j<m_inputDimension; j++){
table.clear();
//For each row
for(std::size_t i=0; i<elements; i++){
//Store Attribute value, class and rid
a.value = dataset.element(i)[j];
a.id = i;
table.push_back(a);
}
std::sort(table.begin(), table.end(), tableSort);
//Store this attributes attribute table
tables.push_back(table);
}
}